esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep-Reinforcement-Learning-Based Joint Energy Replenishment and Data Collection Scheme for WRSN
## 3 System Model and Problem Formulation
### System Model
#### 3.1.2 Sensor Node Model  
The network deploys \(k\) sensor nodes with fixed and known positions. Consider the set of nodes in the network as \(S_{x}\triangleq\{s_{1},s_{2},s_{3},\ldots,s_{k}\}\). The CH nodes are denoted as \(ch_{i}\triangleq\{ch_{1},ch_{2},ch_{3},\ldots,ch_{m}\}\), and the CM nodes are denoted as \(cm_{j}\triangleq\{cm_{1},cm_{2},cm_{3},\ldots,cm_{h}\}\). Assuming that node batteries can be charged within a very short time compared with the data collection time by the UAV, we can neglect the charging time. Regarding the energy consumption of nodes in sleep mode, since this portion of energy consumption is very small and can be considered negligible, we have not included it as a significant influencing factor, and therefore, it has not been incorporated into the node energy consumption. Nodes only consume energy when receiving and transmitting data. The data buffer length of the CH node \(ch_{i}\) at \(t\) is denoted as \(D_{ch_{i}}(t)=[0,Q_{max}]\). Based on the literature [43], assuming that, at any time \(t\), the energy consumption of the CM node \(cm_{j}\) sending a unit data packet (where the unit data packet size \(f\) is 1024 kb) to the CH node \(ch_{i}\) can be given as follows:  
\[E_{j}^{cm}(t)=e_{t}\sum_{j=1}^{h}f_{j,i} \tag{1}\]  
where \(e_{t}\) indicates the energy consumption of transmitting unit data, the energy consumption of the CH node \(ch_{i}\) can be divided into two parts: reception energy and transmission energy:  
Figure 1: Network model of the UAV-WRSN.  
\[E_{i}^{ch}(t)=\sum_{j=1}^{h}f_{j,i}\sum_{i=1}^{m}e_{r}+E_{ch_{i}}^{t}(t) \tag{2}\]  
where \(e_{r}\) indicates the energy consumption of receiving unit data, and the remaining energy of the CM node \(cm_{j}\) and the CH node \(ch_{i}\) at \(t\) can be respectively defined as follows:  
\[RE_{j}^{cm}(t)=E_{j}^{cm}(t-1)-E_{j}^{cm}(t) \tag{3}\]  
\[RE_{i}^{ch}(t)=E_{i}^{ch}(t-1)-E_{i}^{ch}(t) \tag{4}\]
## 3 State-Constrained Sub-Optimal Tracking Controller
### Model Modification for Input Smoothing  
The input generated by the proposed controller may have severe vibration that is not found in numerical solutions. To mitigate this, we propose a modification of the plant model including a low pass filter before the input as shown in Figure 2.  
The state equation of the modified plant model is  
\[\frac{d}{dt}\!\left[\begin{array}{c}\mathbf{x}\\ \mathbf{u}\end{array}\right]=\left[\begin{array}{cc}\mathbf{A}&\mathbf{B}\\ 0&-\beta\mathbf{I}\end{array}\right]\left[\begin{array}{c}\mathbf{x}\\ \mathbf{u}\end{array}\right]+\left[\begin{array}{c}0\\ \beta\mathbf{I}\end{array}\right]\overline{\mathbf{u}}, \tag{45}\]  
\[\mathbf{y}=\left[\begin{array}{cc}\mathbf{C}_{y}&0\end{array}\right]\left[ \begin{array}{c}\mathbf{x}\\ \mathbf{u}\end{array}\right]. \tag{46}\]  
Then, the problem (7) should be changed as follows:  
\[\begin{array}{c}\underset{\mathbf{e},\mathbf{u},\overline{\mathbf{u}}}{ \text{minimize}}\ J=\frac{1}{2}\mathsf{f}_{0}^{\mathsf{f}_{f}}\!\left(\left[ \begin{array}{cc}\mathbf{e}\\ \mathbf{u}\end{array}\right]^{T}\!\left[\begin{array}{cc}\mathbf{Q}&0\\ 0&\mathbf{R}\end{array}\right]\left[\begin{array}{c}\mathbf{e}\\ \mathbf{u}\end{array}\right]+\overline{\mathbf{u}}^{T}\overline{\mathbf{R}} \overline{\mathbf{u}}\right)\!dt\\ \text{subject to}\ \left\{\begin{array}{c}\left[\begin{array}{cc}\mathbf{A}& \mathbf{B}\\ 0&-\beta\mathbf{I}\end{array}\right]\left[\begin{array}{c}\mathbf{x}\\ \mathbf{u}\end{array}\right]+\left[\begin{array}{c}0\\ \beta\mathbf{I}\end{array}\right]\overline{\mathbf{u}}-\left[\begin{array}{c} \dot{\mathbf{x}}\\ \dot{\mathbf{u}}\end{array}\right]=0\\ \left[\begin{array}{cc}\mathbf{C}_{h}&0\end{array}\right]\left[\begin{array}[] {c}\mathbf{x}\\ \mathbf{u}\end{array}\right]+\mathbf{w}\leq 0\end{array},\right.\end{array} \tag{47}\]  
where \(\overline{\mathbf{R}}\) is the weight for \(\overline{\mathbf{u}}\). Since this problem can be solved by using the same methods described in Sections 2 and 3, we omit a detailed description of the solution.
# Clustering of Heart Failure Phenotypes in Johannesburg Using Unsupervised Machine Learning
## 3 Results
### Cluster Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Clinical Parameters** & \begin{tabular}{c} **All Patients** \\ **(_n_ = 636)** \\ \end{tabular} & \begin{tabular}{c} **HFrEF** \\ **(_n_ = 409)** \\ \end{tabular} & \
\begin{tabular}{c} **HFmEF** \\ **(_n_ = 91)** \\ \end{tabular} &
\begin{tabular}{c} **HfpEF** \\ **(_n_ = 136)** \\ \end{tabular} & \(p\)**-Value** \\ \hline Peripheral oedema & 436 (68.6) & 282 (64.7) & 58 (13.3) & 96 (22.0) & 0.5298 \\ S3 & 324 (50.9) & 227 (70.1) & 32 (9.9) & 65 (20.1) & 0.0015 \\ JVP elevated & 493(77.5) & 326(66.1) & 69(14.0) & 98(19.9) & 0.1653 \\ Pulse & 100 \(\pm\) 24.3 & 102 \(\pm\) 25.1 & 101 \(\pm\) 16.8 & 94 \(\pm\) 25.1 & 0.1324 \\ Systolic BP, mmHg & 128 \(\pm\) 30.1 & 125 \(\pm\) 28.8 & 136 \(\pm\) 35.7 & 135 \(\pm\) 27.5 & 0.0002 \\ Diastolic BP, mmHg & 82 \(\pm\) 19.7 & 83 \(\pm\) 20.4 & 85 \(\pm\) 20.5 & 78 \(\pm\) 16.1 & 0.0318 \\ Laboratory tests & & & & & \\ Haemoglobin, g/dL & 12.9 \(\pm\) 2.5 & 13.3 \(\pm\) 2.4 & 12.4 \(\pm\) 2.9 & 12.5 \(\pm\) 2.8 & 0.0016 \\ Sodium, mmol/L & 139 \(\pm\) 5.4 & 139 \(\pm\) 5.5 & 138 \(\pm\) 5.1 & 140 \(\pm\) 5.2 & 0.1415 \\ Potassium, mmol/L & 4.4 \(\pm\) 0.7 & 4.4 \(\pm\) 0.7 & 4.3 \(\pm\) 0.9 & 4.4 \(\pm\) 0.8 & 0.5181 \\ Urea, mmol/L, IQR & 8.5 (6.0–12.5) & 12.1 \(\pm\) 16.9 & 16.5 \(\pm\) 43.3 & 13.2 \(\pm\) 20.8 & 0.2692 \\ eGFR, mL/min, IQR & 66.1 & 66.7 \(\pm\) 27.8 & 73.5 \(\pm\) 35.5 & 63.7 \(\pm\) 37.3 & 0.4604 \\ Echocardiography & & & & \\ LVIDd, cm & 5.6 \(\pm\) 1.1 & 6.0 \(\pm\) 0.9 & 5.3 \(\pm\) 1.1 & 4.5 \(\pm\) 1.1 & \textless{}0.001 \\ LVIDs, cm & 4.6 \(\pm\) 1.3 & 5.2 \(\pm\) 1.0 & 4.3 \(\pm\) 0.8 & 3.1 \(\pm\) 1.3 & \textless{}0.001 \\ \hline \hline \end{tabular} Data are presented as mean \(\pm\) standard deviation for continuous features with a normal distribution, and median and interquartile ranges (IQR) (p25–p75) when the distribution is skewed. Categorical data are represented as absolute numbers and percentages. BP = blood pressure; eGFR = estimated glomerular filtration rate using the modification of diet in renal disease (MDRD) formula; HFFEF = heart failure with reduced ejection fraction (LVEF < 40%); HFmEF = heart failure with midrange ejection fraction (LVEF = 40–49%); HFPEF = heart failure with preserved ejection fraction (LVEF \(\geq\) 50%); JVP = jugular venous pressure; LVEF = left ventricular ejection fraction; LVIDd = left ventricular internal diameter in end-disatole; LVIDs = left ventricular internal diameter in end-systolic; S3 = third heart sound.  
\end{table} 
Table 2: _Cont._
###### Abstract  
Low power wide area network (LPWAN) is among the fastest growing networks in Internet of Things (IoT) technologies. Owing to varieties of outstanding features which include long range communication and low power consumption, LPWANs are fast becoming the most widely deployed connectivity standards in IoT domain. However, this promising network are exposed to various security and privacy threats and challenges. For reliable connectivity within networks, the security and privacy challenges need to be effectively addressed with proper mitigation protocol in place. In this paper, a comprehensive review on the security feature of LPWAN is presented. The paper mainly focuses on analyzing LPWAN's key cybersecurity architecture and it present a significant emphasis on how the LPWAN is highly attractive to intruders and attackers. This paper aims at summarizing recent research works on key LPWAN security challenges such as replay attack, denial-of-service attack, worm hole attack, and eavesdropping attack, the effect of the attacks, and most importantly the various approaches proposed in the literature for the attacks' mitigation. The paper concludes by highlighting major research gaps and future directions for the successful deployment of LPWAN.  
attacks; CIA triad; Internet of Things (IoT); low power wide area network; security; sigfox
# Detection of Threats to Farm Animals Using Deep Learning Models: A Comparative Study
## 3 Materials and Methods  
This study employed advanced image processing algorithms to differentiate wild and farm animals in diverse environments. These algorithms--YOLO v8, YOLO NAS, and Fast R-CNN--represent the forefront of machine learning techniques in object detection tasks. Each algorithm was chosen for its unique strengths in handling the complexities of animal detection in natural and controlled settings.
# Efficient Multi-Objective Simulation Metamodeling for Researchers
## 3 Experimentation
### Simulation Models
#### 3.1.1 Student Services Simulation  
This model represents a trio of student service centers that face a stochastic distribution of incoming students in each location. These students require different types of service: one general service and two specific services. The two objectives being examined are the total labor costs of hiring three different classes of employees corresponding to service types (specialists can handle their specific specialist enquiries as well as general enquiries, while non-specialists can only handle general enquiries) and the total time cost, faced by students on average.  
The student traveling, queuing and service processes correspond to a classic discrete event simulation with stochastic elements, while the simulation model's main agent-based features are the students, using a \"smart app\" that gives students information about queue length and average waiting times, allowing them the decision to select between different centers. A student using the smart app will select the service center with the lowest expected time cost to them, considering not only the expected queuing times but also travel time, which depends on the distance of the student from a service center as well as the student's travel speed.  
Students start in one of three locations, with each location being close to one service center (5 distance units away) and far from the other two service centers (15 distance units away). The distribution of student starting locations is not uniform each day, but instead one service center will see half of the students starting near it, and the other two service centers will see one quarter of the students starting near each of them. Students have stochastic travel speeds, with a mean of one distance unit per minute. Even with the smart app, students do not know the exact time they will be queuing at the time they start, but this is estimated by multiplying a moving average of waiting times for their enquiry type by the queue length for their enquiry type at the time they make their decision. The expected service time after traveling and queuing is the same across all centers for any given student enquiry type (which they are aware of).
###### Abstract  
The depth of water is of great significance to the safe navigation of unmanned surface vehicles (USV)in shallow waters, such as islands and reefs. How to consider the influence of depth on the safety of USV navigation and path planning is relatively rare. Under the condition of ocean disturbance, the hydrodynamic characteristics of unmanned surface vehicles will affect its draft and depth safety. In this paper, the hydrodynamic model of unmanned surface vehicles is analyzed, and a water depth risk level A* algorithm (WDRLA*) is proposed. According to the depth point of the electronic navigation chart (ENC), the gridding depth can be obtained by spline function interpolation. The WDRLA* algorithm is applied to plan the path, which takes hydrodynamic characteristics and navigation errors into account. It is compared with the traditional A* shortest path and safest path. The simulation results show that the WDRLA* algorithm can reduce the depth hazard of the shortest path and ensure the safety of navigation.  
unmanned surface vehicles; path planning; hydrodynamics; electronic navigation chart; numerical simulation; water depth risk Article
## 3 Results
### Computerized Tests
#### 3.1.2 Results of Test 2  
The number of correct responses and the RT were statistically analyzed in two distinct ways: by spatial frequency and by the contrast level of the stimulus.  
For spatial frequency, the results were divided into six variables: 1.5, 3, 6, 12, and 18 cycles/2.52\({}^{\circ}\). For contrast, the results were divided into eight levels in descending order of contrast (C1, C2...C8), ranging from 0.99 PsychoPy contrast units at the first level to 0.14 units at the last level, with steps of approximately 0.11.  
For the number of correct responses by spatial frequency (Figure 5A), no statistically significant differences (\(p\leq 0.05\)) were found when comparing blue ambient lighting with white or red lighting. However, when red ambient lighting was compared with white ambient lighting, better results were observed with the former, with statistically significant results only for frequencies of 3 and 6 cycles/2.52\({}^{\circ}\) (\(p=0.037\) and \(p=0.030\), respectively).  
Figure 5: The comparison of the correct answers (**A**) and reaction times (**B**) in each spatial frequency (in cycles/2.52\({}^{\circ}\)) of Test 2 for each type of lighting. Each \(p\) value is represented on the bars, where * and bold letters indicate statistically significant differences (\(p\leq 0.05\)).  
Analyzing the RT by spatial frequency (Figure 5B), blue lighting resulted in slower RTs at frequencies of 1.5 and 18 cycles/2.52\({}^{\circ}\), with statistically significant differences compared with the other two ambient conditions. However, at a frequency of 6 cycles/2.52\({}^{\circ}\), a significantly faster RT (\(p\) = 0.041) was observed with blue light than with white light. Between white and red lighting, statistically significant differences were found only at a frequency of 6 cycles/2.52\({}^{\circ}\) (\(p\) = 0.012), with a shorter RT observed under red lighting.  
An analysis of the results based on the stimulus contrast revealed that although there were multiple significant differences (\(p\leq\) 0.05) among the three ambient conditions, no overall condition consistently produced better or worse results in terms of the number of correct responses (Figure 6A), or RT (Figure 6B).  
However, a general trend of increasing RT was observed as the contrast between the stripes of the stimulus decreased, except for contrast level C1.
## 2 Materials and Methods
### Mucoadhesive Evaluation of Unloaded and DIC- or DIEP-Loaded NPs  
Unloaded EUD particles (EUD3A) and loaded with DIC or DIEP (EUD3A_DIC4 and EUD3A_DIEP3, respectively) were evaluated for their mucoadhesive properties. An in vitro method based on the evaluation of two parameters (zeta potential and turbidimetry) was used to assess the mucoadhesive properties of EUD3A, EUD3A_DIC4, and EUD3A_DIEP3.  
Briefly, mucin (0.1%, \(w/v\)) was suspended in SNF (2.192 g NaCl, 0.145 g CaCl\({}_{2}\) and 0.745 g KCl in 250 mL of double distilled water; pH 5) and stirred overnight to allow its complete dispersion. The interaction between EUD3A, EUD3A_DIC4 and EUD3A_DIEP3 and mucin was determined by mixing equal volumes of mucin dispersion and each system for 15 min at 25 \({}^{\circ}\)C. After 0, 1 and 24 h of incubation at 37 \({}^{\circ}\)C, turbidimetric and zeta potential measurements were performed by comparing the absorbances at 650 nm by UV-Vis spectrophotometry and the difference in zeta potential values, respectively, of the pure suspension and each dispersion with mucin.
# Malware Detection Issues, Challenges, and Future Directions: A Survey
## 5 The Taxonomy of Feature Extraction and Representation Methods
### Feature Extraction Methods
#### 5.1.6 Iterative-Based Extractor  
Since the final objective of each developed malware detection and classification model is to achieve satisfactory accuracy when classifying the test data, this method focuses on the features by which the detection accuracy is extremely improved. The challenge when an iterative-based extractor method is used is how to identify the initial feature set. The obvious solution to this challenge is that the whole features that appear in almost all the samples of one class have to be included. Therefore, performing extensive experiments and analyzing the results, along with including and excluding parts of the initial feature set, in order to identify the optimal feature set by which it achieves the highest performance. This method entails conducting a large number of experiments, followed by analyzing the obtained results to highlight the features that are associated with the highest detection accuracy. The authors of [84] extracted the typical feature set from the analysis reports based on the series of experiments that were conducted to identify the effective features relying on the improvements that have been achieved by each additional feature in every experiment. Even though this method ensures the efficiency of the extracted feature set because those extracted features are associated with the highest achieved detection accuracy, it is time- and effort-consuming.
## 7 Results and Discussion
### Comparative Analysis with Baseline Architectures  
To contextualize Edge-FLGuard's trade-offs, we compared it with two baseline strategies, as follows:  
* A centralized model trained on global data in the cloud;
* A local-only baseline, with models trained and deployed independently.  
Table 12 captures the contrast across key dimensions. The centralized system, although achieving a slightly higher F1-score (0.92), suffered from high inference latency (45.6 ms) and full exposure of raw data to the cloud. On the other hand, local-only systems, while fast and privacy-preserving, delivered the weakest performance (F1-score of 0.79). Edge-FLGuard offered a best-of-both-worlds tradeoff: strong accuracy (F1 = 0.91), low latency (13.1 ms), and full privacy compliance, as no raw data ever left the edge.
# MIM_SLAM: A Multi-Level ICP Matching Method for Mobile Robot in Large-Scale and Sparse Scenes
## 2 Algorithm Overview  
Figure 1 shows the overall MIM_SLAM algorithm framework. The MIM_SLAM algorithm simplifies the SLAM problem into the data association and the incremental pose graph optimization. Firstly, the data association problem is solved by the multi-level ICP matching method which includes matching the time-neighbor frame, matching the current frame with the map, and matching the current frame with an area-neighbor keyframe. Then the uncertainty of the estimated pose is described via the Fisher information matrix. After the above steps, this algorithm can obtain the transformation matrix \(T_{ij}\) and covariance matrix \(\Sigma_{ij}\) between pose \(x_{i}\) and pose \(x_{j}\), which can be saved in the pose graph. Finally, the incremental QR decomposition method [14] is used to optimize the pose graph. In the next section, the MIM_SLAM algorithm will be described in detail.
## 3 Results
### Dataset Uncertainty and Reliability Analysis  
To rigorously evaluate the robustness and predictive reliability of the proposed model, a comprehensive uncertainty analysis was conducted. Beyond overall accuracy metrics, it is crucial to understand the model's confidence in its predictions, both globally and for individual classes. This analysis was performed on the independent test set, which comprises 424 samples distributed across six distinct land quality classes.  
We first assessed the confidence interval for the model's overall accuracy using two standard methods: the Wilson Score Interval and the Bootstrap Confidence Interval. The results are presented in Figure 9(a). The Wilson Score Interval, which is well-suited for binomial proportions like accuracy, indicates that the model achieves an overall accuracy of approximately 80.0% with a remarkably tight confidence interval. This suggests a high degree of stability and reliability in the model's aggregate performance. In contrast, the Bootstrap CI, while centered at a lower accuracy of approximately 70.0%, displays a significantly wider range. The large variance in the bootstrap estimate underscores the potential for instability in resampling-based evaluations, reinforcing the conclusion that the model's true accuracy is more reliably represented by the stable and high-performing Wilson Score.  
To gain deeper insight into the model's behavior, we performed a class-wise accuracy analysis, with results and sample counts for each class shown in Figure 9(b). This analysis reveals a strong correlation between predictive accuracy and the number of samples available for each class, highlighting the impact of the inherent data imbalance. The key findings showed that the model performs exceptionally well on classes with substantial representation, such as Class 3 (\(n=136\)), Class 2 (\(n=86\)), and Class 5 (\(n=60\)), which achieved high accuracies (approx. 85%, 81%, and 80%, respectively) with narrow confidence intervals. For classes with moderate sample sizes like Class 1 (\(n=26\)) and Class 4 (\(n=111\)), the model maintained good performance (approx. 78% and 74%) with slightly wider confidence intervals, indicating reliable predictive capability. Conversely, the model's primary limitation was exposed in the prediction of Class 6. With only \(n=5\) samples, its accuracy plummeted to approximately 40% with an extremely wide confidence interval, a result directly attributable to the scarcity of data.  
Figure 10: (a) Confidence interval comparison; (b) class-wise accuracy with confidence intervals.
# In-Orbit Results and Attitude Analysis of the SNUGLITE CubeSatellite
## 2 Materials and Methods
### Attitude System of SNUGLITE
#### 2.3.1 Attitude System Configuration
Therefore, sensing and actuating of the magnetic field are performed alternately every other second. Algorithms of the attitude system are implemented in the onboard computer, a 32-bit AVR32 processor. In the SNUGLITE CubeSat, the attitude, the command, and the data handling processes are conducted on the same computer in real-time. In this paper, we describe the attitude estimation by using an EKF in an LQG-based attitude system. The derivation and detailed description of the attitude control algorithm are given in [17].