esa-sceva/satcom-chunk-collection huggingface dataset:
# An Effective Scheduling Algorithm for Coverage Control in Underwater Acoustic Sensor Network
## 2 Preliminaries
### Sensing Coverage Model  
The perceptual coverage model is used to describe the node's perception of the surrounding waters [17]. Assuming there is a node \(s_{i}(x_{i},y_{i},z_{i})\) in the three-dimensional space, its Euclidean distance to any spatial event \(e_{j}(x_{j},y_{j},z_{j})\) is \(d(s_{i},e_{j})\). The binary detection model determines that the probability that event \(e_{j}\) is covered by node \(s_{i}\) is  
\[\gamma_{ji}=\begin{cases}1&d\left(s_{i},e_{j}\right)=\sqrt{\left(x_{i}-x_{j} \right)^{2}+\left(y_{i}-y_{j}\right)^{2}+\left(z_{i}-z_{j}\right)^{2}}<r_{s}\\ 0&\text{otherwise}\end{cases} \tag{1}\]  
Equation (1) shows that, in monitoring area \(A\), for \(\forall e_{j}\in A\), if the Euclidean distance between the event \(e_{i}\) and the node \(s_{i}\) is smaller than the sensor radius \(r_{s}\), then it can be covered, and otherwise it is not covered. Assume that there are \(k\) nodes \(s_{1}\), \(s_{2}\), \(\cdots\), \(s_{k}\), and their coverage rates for event \(e_{i}\) are \(\gamma_{i1}\), \(\gamma_{i2}\), \(\cdots\), \(\gamma_{ik}\) respectively. Then, the coverage of event \(e_{i}\) in the network is  
Figure 1: Three-dimensional underwater acoustic sensor network structure.  
\[\gamma_{A}\left(e_{i}\right)=1-\prod_{n=1}^{k}\left(1-\gamma_{in}\right) \tag{2}\]
## 2 Study Area, Data and Methods  
### Study Area  
This study was carried out in Northeast China (NEC), comprised of Heilongjiang, Jilin, Liaoning and parts of Inner Mongolia provinces (Figure 1). This region is \(115\times 10^{4}\) km\({}^{2}\) in area, accounting for 15% of the national territorial area of China. Latitude and longitude for the NEC range from 115\({}^{\circ}\)E-135\({}^{\circ}\)E and 38\({}^{\circ}\)-56\({}^{\circ}\)N, respectively. Its terrain is primarily composed of three parts: mountainous areas, hills, and plains (Figure 1a). The mountainous areas are mainly distributed across the northern and eastern parts. The central and northeast sections are dominated by the Songnen Plain and the Sanjiang Plain, respectively. The elevation of the NEC ranges from 0 to 2667 m, with lower elevations found in the northeast, south, and west, and higher elevations in the north, east, and southeast (Figure 1a). Much of the region belongs to a temperate zone characterized by a continental monsoon with four distinct seasons (cold in the winter and warm in the summer). Annual precipitation decreases from east to west, varying from 400 to 800 mm; a similar trend exists for relative humidity. NEC has a high proportion of vegetation cover, including temperate forests, grasslands and agriculture (Figure 1b). The coniferous forest is distributed in the northern mountain area (Daxing'anling), while broad-leaved forests are located in the eastern and southern parts. The central and eastern plains are covered by croplands.
# Body Area Networks in Healthcare: A Brief State of the Art
## 3 Physical Layer Requirements
### Capacitive Sensors
#### 3.3.3 Electrical Sensors  
Electrical sensors are sensitive to the electrical resistance of the skin and also measure alterations of the capacitance or the conductivity at the surface of the skin. These types of sensors requires good electrical contact with the skin to be able to produce reliable measurements. Electrical sensors use two types of contacts, wet and dry electrodes. Wet electrodes consist of a solid conductive pad and an electrolyte gel that decreases the impedance of the skin. In contrast, dry electrodes are applied in direct contact with the skin. Dry electrodes can also be non-contact dry electrodes that have an insulating layer that separates the skin from the electrode. These electrodes eliminate irritation, allergic reactions and prevent from short circuits.  
In a quite recent approach, the authors developed a paper-based electrical sensor that monitors respiration [28]. The authors achieve a low-cost single-use respiration monitoring system that is used for routine and at-home monitoring. The sensor detects the humidity of the paper using the hygroscopic character. Then, it converts the changes in humidity into electrical signals. These signals are transmitted to an electronic device to be processed. When the user breathes out, the amount of water in the sensor increases, augmenting its ionic conductivity. In contrast, when the user breathes in, the level of water decreases. This device is less accurate than sensors used in laboratories but is portable and can be used outdoors.  
There are other suitable applications for electrical sensors. For example, they can be used in medical applications that monitor the skin (sweating, hydration level, etc.). However, other new applications are appearing, since these sensors are suitable for augmented reality as feedback interfaces.
# An Overview of Intelligent Transportation Systems in Europe
## 5 Key Features of ITSs
### Challenges and Obstacles in the Implementation of ITSs in Europe
#### 5.2.5 Information Security and Privacy in ITSs  
Ensuring the security of user information has become a priority in Europe, as Intelligent Transport Systems rely heavily on real-time data exchange and connectivity. The General Data Protection Regulation (GDPR) is essential for setting standards for data privacy in information and technology systems (ITSs). Projects such as DataVaults and CyberSec4 Europe aim to create secure frameworks for managing mobility data. In addition, the European Commission supports initiatives focused on cybersecurity by design, ensuring that ITS platforms include encryption, authentication protocols and intrusion detection systems to prevent data breaches and malicious attacks [71].
## 5 Industrial Applications of CFD Simulations in Food Technology
### Optimization of Production Processes Using CFD
Energy Equation (Equation (21)):  
\[\rho C_{p}\left(\frac{\partial T}{\partial t}+v\cdot\
abla T\right)=\
abla \cdot(k\
abla T)+Q \tag{21}\]  
where \(C_{p}\) is specific heat, \(T\) is temperature, \(k\) is thermal conductivity, and \(Q\) represents internal heat generation. By solving these equations, CFD models allow for the prediction of temperature and velocity profiles, identifying areas with undesirable thermal gradients or airflow inefficiencies. These data can then be used to guide the optimization of production processes, improving design and operation in various ways. For example, by simulating different scenarios, engineers can identify the best operational conditions, such as flow rates, heating profiles, or equipment configurations, that result in reduced energy consumption, improved product uniformity, and minimized waste. In conclusion, CFD serves as a powerful tool in the optimization of food production processes, offering both a detailed understanding of thermal dynamics and a platform for testing various process configurations before implementation. By applying CFD, food manufacturers can not only improve the efficiency of their processes but also enhance the quality and consistency of their products, ensuring better resource utilization and lower operational costs [14; 145]. Application of CFD in Equipment Design and Development for the Food Industry (For Example, Ovens, Dryers, Freezers)  
CFD simulations are integral in the design and optimization of food processing equipment, such as ovens, dryers, and freezers, where thermal processes play a pivotal role in product quality. In ovens, for example, CFD can be employed to simulate the heat distribution within the cooking chamber. This is crucial for ensuring uniform baking, preventing overcooking or undercooking, and optimizing energy consumption. Similarly, in dryers, CFD helps in modeling the airflow and moisture removal rate, which can be adjusted to achieve the desired texture and preservation of nutrients in food products [96]. For thermal treatment equipment, such as pasteurizers and sterilizers, CFD is often used to model the convective heat transfer between hot water or steam and the food product. By resolving the fluid flow and thermal fields, engineers can fine-tune the temperature distribution to ensure that the product undergoes sufficient heat treatment while maintaining its quality and safety [146]. To capture these phenomena, models for forced convection (when air or fluids are actively circulated), natural convection (driven by temperature gradients), and heat conduction within solids are employed. In addition, phase change models (for drying or freezing processes) are crucial, as they account for the latent heat during phase transitions, such as water evaporation or ice crystallization [147].
## 1 Introduction
The induced polarization was transverse (i.e., perpendicular to the plane of the sample) and the magnetization was longitudinal (directed along the longest side of the sample) when the structure was not bent (cf. Figure 1). Such an arrangement is known as the L-T configuration. The corresponding ME voltage coefficient carried the indices 31 (\(\alpha_{\text{E31}}\)) and is denoted simply as \(\alpha\) in the following work. In traditional ME layered heterostructures this is the most interesting arrangement for applications because the ME voltage coefficient \(\alpha_{\text{E33}}\) is diminished with respect to an external magnetic field due to a demagnetizing field [1]. Frequency and field dependencies of the generated ME voltage were obtained experimentally. In particular, we reported the lowest, to the best of our knowledge, resonance operation frequencies of ME heterostructures between \(\approx\)20 and \(\approx\)50 Hz. It is important that our design did not rely only on thin films. Furthermore, in studied heterostructures, both ferromagnetic and PE phases are composite materials. An MAE can be considered as a composite with (0-3) connectivity, while PEP can be considered as a composite with (2-2) connectivity [22]. Figure 1: Sketch of the fabricated ME heterostructure and the orientation of the applied magnetic field.
## 3 Results  
### Article Selection  
A flow diagram, according to the PRISMA 2020 guidelines, is illustrated in Figure 1. A total of 1996 articles were initially identified using the search strategy, comprising 1069 in PubMed, 858 in Web of Science, and 69 in Embase. After 485 duplicates were removed and 415 records were identified as ineligible by automatic tools, 1096 records were screened by title and abstract, resulting in the selection of 19 articles. The full text of these articles was assessed, and eight of these articles were excluded for at least one of the following reasons: bone defect <2 cm (n = 4), not a large animal model (n = 2), no treatment of a bone defect (n = 1), and histological analysis was not performed (n = 1). In total, 11 studies were included in the final qualitative synthesis.  
Figure 1: PRISMA flow diagram. Search strategy and selection process of the included studies. * selection by a human according to inclusion and exclusion criteria.
# Best-Suited Communication Technology for Maritime Signaling Facilities: A Literature Review
## 3 Results and Discussion  
This paper relies on data from the WOS, SCOPUS, and GOOGLE SCHOLAR databases, among others. The analytical literature review was performed using the criteria in Table 1 over 21 years (from 2003 to 2023) and yielded 214 relevant scientific papers, as shown in Figure 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & TI = (((”safety of navigation”) OR (“navigation safety”) OR (“maritime signalling facilities”) OR (“buoy”)) AND ((“communication”) OR (“gsm”) OR (“l OR a”) OR (“ais”) OR (“LTE”) OR (“GPRS”) OR (“wireless”) OR (“iot”) OR (“industry 4.0”) OR (“SENS OR S”) OR (“IOTA”) OR (“fusion solution”) OR (“remote monit OR ing”))) \\ \hline  & TITLE ((“safety of navigation” OR “navigation safety” OR “ maritime signalling facilities” OR  “buoy”) AND (“communication” OR “gsm” OR “lora” OR “ais” OR “te” OR “gprs” OR “wireless” OR “remote monitoring” OR “iot” OR “industry 4.0” OR “sensors” OR “iot4” OR “fusion solution”)) and pubyear \  
The flowchart from Figure 2 for searching and filtering papers can be divided into three stages. The first stage is \"document identification\", where initial analysis is performed using a set of entry criteria for searching the WOS, SCOPUS, and GOOGLE SCHOLAR databases, among others (Table 1). In addition to the aforementioned databases, our search included the following sources: Hrcak, websites of reputable maritime organizations, and citation searching.  
In the second stage, named \"Document screening\", a set of exclusion rules is applied to derive a filtered number of papers for stage three (Figure 2). The last stage, \"Document included\", represents the selected papers analyzed in the paper.
# A Telepresence Wheelchair with 360-Degree Vision Using WebRTC
## 2 Methods
### Telepresence Wheelchair with 360-Degree Vision Based on WebRTC
\[Throughput=\frac{L}{\frac{d}{V}+\frac{L}{B}} \tag{3}\]  
The TCP-friendly rate control (TFRC) bandwidth estimation model is used for bandwidth estimation algorithm, which relies on the p-value (the packet loss rate) [17]. \[T_{TFRC}=\frac{s}{RTT\sqrt{\frac{2p}{3}}+t_{RTC}\left(\sqrt{\frac{3\frac{3p}{ 8}}}\right)p(1+32p^{2})} \tag{4}\]  
where \(s\) represents the average size of received packet size per second, \(RTT\) is round-trip-time and \(t_{RTO}\) is the value of the retransmission timer. \(T_{TFRC}\) does not represent the actual WebRTC real-time video output rate, but it represents an upper bound for it. To deliver the video with improved quality, we adopted a Google congestion control (GCC) algorithm [13]. The sender-side uses the packet loss rate to estimate the sending rate (\(As\)) given by  
\[As(i)=\left\{ \begin{array}{l}\max\{S(i),As(i-1)(1-0.5\,p)\,\,\,p\,>\,\,0.1\\ As(i-1)\,\,\,\,\,0.02\,\,<\,\,p\,\,<\,\,0.1\\ 1.05(As(i-1)+1\text{kbps})\,\,\,p\,\,<\,\,0.02\end{array} \right. \tag{5}\]  
Figure 2: A 360-degree view telepresence using web real-time communication architecture. where \(As(i)\) is the sender available bandwidth estimate at time \(i\), \(S(i)\) is the throughput, and \(p\) is the packet loss rate. The receiver-side estimates the receiving rate given by  
\[Ar(i)=\left\{ \begin{array}{ll}\eta Ar(i-1)&\text{Increase}\\ Ar(i-1)&\text{Hold}\\ \alpha R(i)&\text{Decrease}\end{array} \right. \tag{6}\]  
where \(Ar(i-1)\) denotes the receiving rate estimate at time \((i-1)\), \(\eta\) is the receiving rate increase constant. \(R(i)\) is the current incoming rate, and \(\alpha\) represents the incoming rate decrease factor.
## 4 Experimental Results and Analysis
The dataset consists of multi-source images with 11 original RGB image pairs selected from Google Earth. Seven pairs contain seasonal changes with an image size of 4725 \(\times\) 2700. The other four pairs contain seasonal changes with a size of 1900 \(\times\) 1000. They were cropped into 10,000 pairs with the image size set to 256 \(\times\) 256 for training and 3000 pairs for testing, as shown in Figure 11.  
Because KOMPSAT-3 and CDD have different image characteristics and information, this study trained the specific dataset for generating a model. Hence, individual models were generated for KOMPSAT-3 and CDD and separate analyses on these datasets are presented. We used two metrics to measure the detection accuracy, namely, \(F_{1}\)-score and kappa coefficient (KC), to assess the effectiveness of the proposed and conventional architectures. The \(F_{1}\)-score can be interpreted as a weighted average of the precision and recall, the best score of \(F_{1}\)-score is 1.0, and the worst score is zero. In addition, KC is widely used to measure binary change detection because it is more informative for imbalanced data. The score of KC is interpreted in Table 1[30; 31]. To assess the effectiveness of the proposed method, several conventional architectures (specifically, U-Net [23], ATTUNet [24] and  
Figure 11: Samples of change detection dataset (CDD) image pairs. Figure 9: Test area 3 (3232 \(\times\) 2206): (**left**) Image P, acquired in March 2014; (**middle**) Image C, acquired in October 2015; (**right**) Ground truth. Figure 10: Test area 4 (3232 \(\times\) 2206): (**left**) Image P, acquired in March 2014; (**middle**) Image C, acquired in October 2015; (**right**) Ground truth. Modified-UNet++ [22]) were trained with the same hyperparameter settings and dataset as the proposed architecture and evaluated on the test datasets.
## 4 Results  
### Experimental Results  
The Macbert-BiLSTM hybrid model proposed in this study performs well in the Chinese spelling error correction task. On the test set, the model achieves ***93.37%** accuracy, ***93.25% recall, and 93.25%** F1 value (micro-average). The training/validation curves show stable model convergence and a low risk of overfitting, thanks to the progressive parameter unfreezing strategy and the cosine learning rate scheduling mechanism.As shown in Figures 4 and 5, these are the line charts of the loss values, accuracy rates, regression values, and F1 values of our training model.  
As seen in the above graph, the proposed hierarchical fusion architecture (MacBERT-BiLSTM) achieves a state-of-the-art 93.37% better performance on the official partitioned test set of SIGHAN 2015 (data fine-tuning and supplementation) (\(n\) = 49,997 homophonic word pairs). The outstanding F1 score of 93.25% highlights the model's effectiveness for the specific task of homophone disambiguation/correction evaluated here. This level of performance is a prerequisite for its deployment in high-demand applications such as Chinese clinical natural language processing systems.  
Figure 4: Experimental loss and accuracy folds.  
Figure 5: Recall and F1 values.  
The convergence dynamics depicted across the four evaluation metrics reveal three fundamental characteristics of the training process. First, the model exhibits rapid feature assimilation, evidenced by the steep decline in training loss (blue curve) from an initial value of 0.14 to near-zero levels within the first 10 epochs, accompanied by synchronous surges in validation accuracy (green), recall (purple), and F1 score (cyan) from \(\approx\)0.88 to \(>\)0.91 during this phase. This accelerated learning trajectory indicates efficient capture of discriminative features for homophone disambiguation. Second, asymptotic stability emerges beyond epoch 15, where all validation metrics plateau within a remarkably tight band of \(\pm\)0.01 fluctuation (e.g., accuracy stabilizes at 0.93 \(\pm\) 0.005 after epoch 20), while validation loss (red) maintains equilibrium at 0.60 \(\pm\) 0.02. Such high smoothness with minimal stochastic oscillations suggests robust generalization and algorithmic stability. Crucially, the persistent \(\approx\) 0.40 gap between train and validation losses after epoch 10 - without divergent tendencies--confirms optimal regularization, as the model avoids overfitting while achieving near-perfect training loss (0.02 at epoch 50). The synchronous saturation of precision-oriented (accuracy), recall-sensitive, and balanced (F1) metrics at identical asymptotic values (0.93) further demonstrates task-aligned equilibrium, implying that no performance trade-offs were incurred during convergence. These collective behaviors not only validate the training protocol's efficacy but also suggest operational advantages: the 15-epoch stabilization threshold enables early stopping with guaranteed model integrity, while the sub-0.01 post-convergence variability ensures deployment reliability for real-world applications.
# Knowledge Distillation-Based GPS Spoofing Detection for Small UAV
## 7 Conclusions  
In this paper, we propose a novel approach to implementing a lightweight detection model for UAV systems so that GPS spoofing attacks can be detected from a long distance. With LSTM, we proposed a novel detection in ground control stations, and then we transferred it into the control system of a UAV with knowledge distillation in a compact size. The experimental results show that our lightweight detection model detects GPS spoofing attacks efficiently. Due to the resource restrictions of compact platforms, the platform has strict requirements for implementation, like communication efficiency. In addition, the detection results need to be delivered to the ground so that the ground response team can eliminate the attacks. In future research, we will implement the model onto compact platforms that can be integrated into a UAV system.  
Conceptualization, Y.R., J.W. and H.S.; Methodology, J.W. and B.J.; Software, Y.R., J.W. and Y.L.; Validation, W.T., J.W., Y.L. and B.J.; Formal analysis, Y.R., W.T., Y.L. and H.W.; Investigation, R.D.R. and H.W.; Resources, H.W. and H.S.; Supervision, H.S.. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** IEEE DataPort [39].  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Cluster Head Selection
### Self-Organized Schemes
#### 2.1.2 Adaptive Weight-Based Parameters Schemes  
In clustering algorithms, CHs as the local coordinator of the clusters play the key role in network performance. Therefore, from a local perspective, the lifespan and performance of a part of the network, which is coordinated by a CH, is quite related to the accessibility and performance of its CH. On a network scale, the relative position and even distribution of CHs according to the node density in the network field are important features that greatly affect the network throughputs. Although the selection of the CH in adaptive weight-based schemes [13; 14; 15; 16; 17; 18; 19; 20; 21; 22] is still distributed among the sensor nodes, the procedure is not based on sheer probabilistic solutions anymore, and rather node resources and other determinant factors are weighed up in selection of the optimum CHs. In this respect, we classify these schemes in accordance with the factors that are considered to improve the stochastic CH selection schemes as energy expenditure, density dispersion, sensing coverage and regional selectivity.