Search Results (110)

To date, the ordinal pattern-based method has been applied to problems in natural and social sciences. We report, for the first time to our knowledge, an attempt to apply this methodology to a topic in the humanities. Specifically, in an effort to investigate the applicability of the methodology in analyzing the quality of texts that are translated into a language preserving the so-called vowel harmony, computed results are presented for the metrics of divergence between the back-translated and the original texts. As a specific language we focus on Japanese, and as metrics the Hellinger distance as well as the chi-square statistic are employed. Here, the former is a typical information-theoretical measure that can be quantified in natural unit, nat for short, while the latter is useful for performing a non-parametric testing of a null hypothesis with a significance level. The methods are applied to three cases: a Japanese novel along with a translated version available, the Preamble to the Constitution of Japan, and seventeen translations of an opening paragraph of a famous American detective story, which include thirteen human and four machine translations using DeepL and Google Translate. Numerical results aptly show unexpectedly high scores of the machine translations, but it still might be too soon to speculate on their unconditional potentialities. Both our attempt and results are not only novel but are also expected to make a contribution toward an interdisciplinary study between physics and linguistics. Full article

Satellite–terrestrial integrated communication systems (STICSs) are envisioned to provide ubiquitous, seamless connectivity in next-generation (6G) wireless communication networks for massive-scale Internet of Things (IoT) deployments. This global coverage extends beyond densely populated areas to remote regions (e.g., polar zones, open oceans, deserts) and disaster-prone areas, supporting diverse IoT applications, including remote sensing, smart cities, intelligent agriculture/forestry, environmental monitoring, and emergency reporting. Random access signals, which constitute the initial transmission from access IoT devices to base station for unscheduled transmissions or network entry in terrestrial networks (TNs), encounter significant challenges in STICSs due to inherent satellite characteristics: wide coverage, large-scale access, substantial round-trip delay, and high carrier frequency offset (CFO). Consequently, conventional TN preamble designs based on Zadoff–Chu (ZC) sequences, as used in 4G LTE and 5G NR systems, are unsuitable for direct deployment in 6G STICSs. This paper first analyzes the challenges in adapting terrestrial designs to STICSs. It then proposes a CFO-resistant preamble design specifically tailored for STICSs and details its detection procedure. Furthermore, a dedicated root set selection algorithm for the proposed preambles is presented, generating an expanded pool of random access signals to meet the demands of increasing IoT device access. The developed analytical framework provides a foundation for performance analysis of random access signals in 6G STICSs. Full article

Background/Objectives: Adolescence is a sensitive developmental window shaped by both vulnerabilities and adaptive potential. From an evolutionary standpoint, mental health difficulties in this period may represent functional responses to environmental stressors rather than mere dysfunctions. Despite increasing interest, integrative models capturing the dynamic interplay of risk and protective factors in adolescent mental health remain limited. This study presents a holistic, multi-level framework grounded in ecological and evolutionary theories to improve understanding and intervention strategies. Methods: A two-round Delphi method was used to develop and validate the framework. Twelve experts in adolescent mental health evaluated a preliminary draft derived from the literature. In Round 1, 12 items were rated across five criteria (YES/NO format), with feedback provided when consensus thresholds were not met. Revisions were made using consensus index scores. In Round 2, the revised draft was assessed across eight broader dimensions. A consensus threshold of 0.75 was used in both rounds. Results: Twelve out of thirteen experts (92%) agreed to join the panel. Round 1 item scores ranged from 0.72 to 0.85, with an average consensus index of 0.78. In Round 2, ratings improved significantly, ranging from 0.82 to 1.0, with an average of 0.95. The Steering Committee incorporated expert feedback by refining the structure, deepening content, updating sources, and clarifying key components. Conclusions: The final framework allows for the clustering of indicators across macro-, medium-, and micro-level domains. It offers a robust foundation for future research and the development of targeted, evolutionarily informed mental health interventions for adolescents. Full article

Optical intrabody wireless nanosensor networks (OiWNSNs) enable groundbreaking biomedical applications via optical nanocommunication within biological tissues. Synchronization is critical for accurate data recovery in these energy- and size-constrained nanonetworks. In this study, we investigate timing synchronization in a highly dispersive and noisy intravascular optical channel, particularly under an on–off keying preamble comprising Gaussian optical pulses. We proposed a synchronization scheme based on the repetitive transmission of a preamble and noncoherent detection using continuous-time moving average filters with multiple integrator windows. The simulation results reveal that increasing the communication distance degrades the synchronization performance. To counter this degradation, we can increase the number of preamble repetitions, which markedly improves the system reliability and timing accuracy due to the averaging gain, although the performance saturates due to the dispersion floor inherent in the blood channel. Moreover, we found that low-resolution nanoreceivers with fewer integrators outperform high-resolution designs in dispersive environments, as they mitigate the energy-splitting problem due to pulse broadening. This tradeoff between temporal resolution and robustness highlights the importance of channel-aware receiver design. Overall, this study provides key insights into the physical layer design of OiWNSNs and offers practical guidelines for achieving robust synchronization under realistic biological conditions. Full article

With the rapid development of the Internet of Things and 5G communication technologies, the demand for the random access of a massive number of user equipment in burst scenarios has significantly increased. Traditional fixed-frame-length mechanisms, due to their inability to dynamically adapt to fluctuations in access traffic, are prone to exacerbating channel resource competition, increasing the probability of preamble collisions, and significantly elevating access delays, thereby constraining the system performance of large-scale machine-type communications. To address these issues, this paper proposes a dynamic frame length optimization algorithm based on Q-learning. By leveraging reinforcement learning algorithms to autonomously perceive access traffic characteristics, this algorithm can dynamically adjust frame length parameters without relying on estimates of the number of user equipment. It optimizes the frame length to improve random access performance, reduces collisions among user equipment competing for preambles, and enhances the utilization ratio of preamble resources. Full article

In the field of the Internet of Things (IoT), technical solutions that enable information transmission with minimal energy consumption are of particular interest. Common solutions frequently used in the field of radio frequency identification (RFID) involve utilizing electromagnetic waves to power tags and employing backscattering for communication. Detecting the received signal in a coherent manner enables increased reliability in tag reading. This paper proposes a method for coherent signal detection in a bistatic backscatter communication system (BBCS), which includes coarse carrier frequency offset (CFO) from preamble and fine phase correction from data symbols. The proposed method outperforms the detection approach based on maximum likelihood estimation (MLE) of CFO from the preamble, particularly in scenarios with higher CFO values. The proposed detection method is well suited for implementation in software-defined radios, particularly in low-cost devices characterized by less stable oscillators. It is also shown that a preamble of six symbols is sufficient to perform a coarse CFO estimation. Since the analyzed system is equivalent to binary frequency-shift keying (FSK) modulation, the performance of FSK is presented as the theoretical upper bound in the results. Full article

Water scarcity in Southern Europe, driven by climate conditions and water-intensive land use, is promoting water reuse adoption. Water reuse regulations are emerging, but little is known about integrating spatial concerns into their contents. This study examines how spatial issues are addressed within water reuse regulations adopted by the European Union (EU), Portugal, and Spain. Through a comparative content analysis, this research explores the inclusion and distribution of key terms related to water drivers, spatial concepts, and land use types within key sections, preamble, objectives, permitting, risk assessment, monitoring, and governance. The findings show that Portugal and Spain exhibit poorer integration of water scarcity compared to the EU, and Portugal does not address it in its objectives. In contrast, broad spatial terms are more prominent in Portugal, while Spain emphasises conservation and environmental areas more. Spatial terms are distributed differently across sections, reflecting different regulatory approaches. Surprisingly, none of the regulations link to plans. They mention risk management plans and, occasionally, circular economy and river basin management plans. Agriculture and urban activities dominate, although Portugal emphasises industry and green areas. This study highlights the need for more spatially informed water reuse regulations. Full article

In mobile dynamic random access memory (DRAM) receivers, the data strobe complement (DQS_c) and data strobe true (DQS_t) signals must be maintained at high and low voltage levels in the write data strobe off (WDQS_OFF) mode. Therefore, we developed a voltage regulation circuit to optimize the differential voltage signals of DQS_c and DQS_t, ensuring a high voltage level above 0.9 V and a low voltage level below 0.3 V. Experimental results showed that the circuit stably maintained DQS_c above 0.9 V and DQS_t below 0.3 V before the write preamble time (tWPRE) and in WDQS_OFF mode. This configuration effectively prevents unintended activation in the mobile DRAM DQS input receiver. Full article

Applications of wireless sensor networks have significantly increased in the modern era. These networks operate on a limited power supply in the form of batteries, which are normally difficult to replace on a frequent basis. In wireless sensor networks, sensor nodes alternate between sleep and active states to conserve energy through different methods. Duty cycling is among the most commonly used methods. However, it suffers from problems like unnecessary idle listening, extra energy consumption, and packet drop rate. A Deep Reinforcement Learning-based B-MAC protocol called (RL-BMAC) has been proposed to address this issue. The proposed protocol deploys a deep reinforcement learning agent with fixed hyperparameters to optimize the duty cycling of the nodes. The reinforcement learning agent monitors essential parameters such as energy level, packet drop rate, neighboring nodes’ status, and preamble sampling. The agent stores the information as a representative state and adjusts the duty cycling of all nodes. The performance of RL-BMAC is compared to that of conventional B-MAC through extensive simulations. The results obtained from the simulations indicate that RL-BMAC outperforms B-MAC in terms of throughput by 58.5%, packet drop rate by 44.8%, energy efficiency by 35%, and latency by 26.93% Full article

While the market for massive machine type communications (mMTC) is evolving at an unprecedented pace, the standardization bodies, including 3GPP, are lagging behind with standardization of truly 5G-grade cellular Internet-of-Things (CIoT) systems. As an intermediate solution, an early data transmission mechanisms encapsulating the data into the preambles has been recently proposed for 4G/5G Narrowband IoT (NB-IoT) technology. This mechanism is also expected to become a part of future CIoT systems. The aim of this paper is to propose a model for CIoT systems with and without early transmission functionality and assess the optimal distribution of resources at the random access and data transmission phases. To this end, the developed model captures both phases explicitly as well as different traffic composition in downlink and uplink directions. Our numerical results demonstrate that the use of early transmission functionality allows one to drastically decrease the delay of uplink packets by up to 20–40%, even in presence of downlink traffic sharing the same set of resources. However, it also affects the optimal share of resources allocated for random access and data transmission phases. As a result, the optimal performance of 5G mMTC technologies with or without early transmission mode can only be attained if the dynamic resource allocation is implemented. Full article

Radio Frequency Fingerprint Identification (RFFI) is a promising device authentication technique that utilizes inherent hardware flaws in transmitters to achieve device identification, thus effectively maintaining the security of the Internet of Things (IoT). However, time-varying channels degrade accuracy due to factors like device aging and environmental changes. To address this, we propose an RFFI method integrating Zero-Forcing (ZF) equalization and KAN-ResNet. Firstly, the Wi-Fi preamble signals under the IEEE 802.11 standard are Zero-Forcing equalized, so as to effectively reduce the interference of time-varying channels on RFFI. We then design a novel residual network, KAN-ResNet, which adds a KAN module on top of the traditional fully connected layer. The module combines the B-spline basis function and the traditional activation function Sigmoid Linear Unit (SiLU) to realize the nonlinear mapping of the complex function, which enhance the classification ability of the network for RFF features. In addition, to improve the generalization of the model, the grid of B-splines is dynamically updated and L1 regularization is introduced. Experiments show that on datasets collected 20 days apart, our method achieves 99.4% accuracy, reducing the error rate from 6.3% to 0.6%, outperforming existing models. Full article

Mobile devices, such as smartphones and laptops, face energy consumption challenges due to battery limitations, with Wi-Fi being one of the major sources of energy consumption in these devices. The IEEE 802.11 standard addresses this issue with Power Saving Mode (PSM), which reduces power consumption but increases latency. To mitigate this latency, Adaptive-PSM (A-PSM) dynamically switches between PSM and Constantly Awake Mode (CAM); however, the associated Idle Listening (IL) process still results in high energy consumption. Various strategies have been proposed to optimize IL time; however, Medium Access Control (MAC)-level contention and network delays limit their effectiveness. To overcome these limitations, we propose MILD (Minimizing Idle Listening energy consumption via Down-clocking), a novel scheme that reduces energy consumption without compromising throughput. MILD introduces specialized preambles for Packet Arrival Detection (PAD) and Device Address Recognition (DAR), allowing the client to operate in a down-clocked state during IL and switch to full clocking only when necessary. Experimental results demonstrate that MILD reduces energy consumption by up to 23.6% while maintaining a minimal throughput loss of 12.5%, outperforming existing schemes. Full article

This paper presents algorithms to estimate the signal-to-noise ratio (SNR) in the time domain and frequency domain that employ a modified Constant Amplitude Zero Autocorrelation (CAZAC) synchronization preamble, denoted as CAZAC-TD and CAZAC-FD SNR estimators, respectively. These SNR estimators are invoked in a space–time block coding (STBC)-assisted multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. These SNR estimators are compared to the benchmark frequency domain preamble-based SNR estimator referred to as the Milan-FD SNR estimator when used in a non-adaptive $2\times 2$ STBC-assisted MIMO-OFDM system. The performance of the CAZAC-TD and CAZAC-FD SNR estimators is further investigated in the non-adaptive $4\times 4$ STBC-assisted MIMO-OFDM system, which shows improved bit error rate (BER) and normalized mean square error (NMSE) performance. It is evident that the non-adaptive $2\times 2$ and $4\times 4$ STBC-assisted MIMO-OFDM systems that invoke the CAZAC-TD SNR estimator exhibit superior performance and approach closer to the normalized Cramer–Rao bound (NCRB). Subsequently, the CAZAC-TD SNR estimator is invoked in an adaptive modulation scheme for a $2\times 2$ STBC-assisted MIMO-OFDM system employing M-PSK, denoted as the AM-CAZAC-TD-MIMO system. The AM-CAZAC-TD-MIMO system outperformed the non-adaptive STBC-assisted MIMO-OFDM system using 8-PSK by about 2 dB at BER = ${10}^{-4}$. Moreover, the AM-CAZAC-TD-MIMO system demonstrated an SNR gain of about 4 dB when compared with an adaptive single-input single-output (SISO)-OFDM system with M-PSK. Therefore, it was shown that the spatial diversity of the MIMO-OFDM system is key for the AM-CAZAC-TD-MIMO system’s improved performance. Full article

With the development of the Internet of Things, cloud networking, and 4K/8K high-definition video, global internet traffic has seen a dramatic increase. This surge in traffic has placed higher demands on the performance of optical networks, featuring higher data rates, lower latency, and lower cost. The passive optical network (PON) is a representative scenario of optical access networks. Issues such as burst-mode detection in upstream PON scenarios, flexible rate allocation in downstream scenarios, and the simplification of hardware complexity at the optical network unit (ONU) side have attracted considerable attention. Compared to intensity modulation/direct detection (IM/DD), a recently proposed coherent PON incorporates a local oscillator laser at the receiver, enabling superior receiver sensitivity, spectrally efficient modulation, linear optical field recovery, and flexible channel selection. These features significantly enhance the flexibility and data rates of PON systems. This paper provides a comprehensive review of the development of coherent PONs, particularly in aspects of preamble design for burst-mode detection in upstream scenarios, the design of flexible rate PONs in downstream scenarios, and solutions for reducing hardware complexity at the ONU side. Full article

The growing demand for high-speed data transfer and ultralow latency in wireless networks-on-chips (WiNoC) has spurred exploration into innovative communication paradigms. Recent advancements highlight the potential of the terahertz (THz) band, a largely untapped frequency range, for enabling ultrafast tera-bit-per-second links in chip multiprocessors. However, the ultrashort duration of THz pulses, often in the femtosecond range, makes synchronization a critical challenge, as even minor timing errors can cause significant data loss. This study introduces a preamble-aided noncoherent synchronization scheme for time-of-arrival (ToA) estimation in pulse-based WiNoC communication operating in the THz band (0.02–0.8 THz). The scheme transmits the preamble, a known sequence of THz pulses, at the beginning of each symbol, allowing the energy-detection receiver to collect and analyze the energy of the preamble across multiple integrators. The integrator with maximum energy output is then used to estimate the symbol’s ToA. A preamble design based on maximum pulse energy constraints is also presented. Performance evaluations demonstrate a synchronization probability exceeding $0.98$ for distances under 10 mm at a signal-to-noise ratio of 20 dB, with a normalized mean squared error below ${10}^{-2}$. This scheme enhances synchronization reliability, supporting energy-efficient, high-performance WiNoCs for future multicore systems. Full article