esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
For ensuring lifespan enhancement and energy stability in WSNs, the MFA-AOA optimized method was presented in this paper: a combination of the global optimization capabilities of the MFA optimization method with the local optimization ability of AOA. This inclusion of AOA in MFA aids in achieving a balance between exploration and exploitation in the selection of CH for long-term energy stability. Meta-heuristic qualities of MFA and AOA are inherited in this clustering algorithm for locating essential CHs and the best BS placement to improve energy efficiency. In comparison to the existing RDSA, GAPSO, CSEHO, and MFOGA methods, the simulation results of the proposed MFA-AOA-based routing protocol enhanced network lifespan by 9.78%, 11.24%, and 14.25%.  
Furthermore, with different densities of SNs, the proposed MFA-AOA routing protocol reduces energy consumption by 10.22%, 11.26%, and 14.28%, which is better than the existing methods. Furthermore, it increased the median number of living nodes by 12.54%,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Best** & **Worst** & **Mean** & **Median** & **SD** \\ \hline
**Proposed MFA-AOA** & 1.6842 & 2.9245 & 1.8947 & 1.9084 & 0.1610 \\
**RDSA**[34] & 1.9873 & 3.4122 & 2.1115 & 2.1015 & 0.1911 \\
**GAPSO**[35] & 2.2741 & 5.6486 & 2.2340 & 2.2115 & 0.2042 \\
**CSEHO**[36] & 2.3941 & 12.1641 & 2.6163 & 2.6141 & 0.2141 \\
**MFOGA**[37] & 2.5123 & 13.2462 & 3.1211 & 3.024 & 0.3042 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time complexity comparison of proposed MFA-AOA with existing schemes.  
Figure 14: Number of rounds until last node death [34; 35; 36; 37].  
13.63%, and 15.28%, respectively, while maintaining median normalized energy of 9.56%, 11.78%, and 13.76%, which was superior to the existing schemes. Therefore, it has been determined that the proposed MFA-AOA should be used to endorse practical issues as part of the future scope of work. Furthermore, optimization techniques based on self-adaptive approaches may be employed to address the network's energy consumption problem.  
Conceptualization, G.N.; Formal analysis, S.K.; Funding acquisition, R.P.d.P. and M.W.; Methodology, S.K.; Software, G.N.; Supervision, R.P.d.P. and M.W.; Validation, R.P.d.P. and M.W. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge contributions to this project from the Rector of the Silesian University of Technology under a proquality grant no. 09/010/RGJ22/0068.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Proposed Protocol
### Protocol Overview  
We assume that the nodes do not know the hole location in advance. Therefore, before forwarding data, we need some setup phases that help to determine the hole as well as the core polygons and broadcast their information to the surrounding nodes. Our protocol consists of three phases as shown in Figure 1. The first phase is to detect the hole boundaries. For each hole, the boundary nodes determine a simple convex polygon covering the hole. This polygon is known as the _core polygon_ of the hole. In the second phase, the information of the holes and the core polygons is disseminated to the nodes in the network. To reduce the overhead, we do not broadcast full information of all the holes to all nodes in the network. Instead, the broadcast information received by a node depends on the node's location. Intuitively, nodes near a hole will receive the full information of the hole, while nodes staying far from the hole receive only the information of the hole's core polygon. Once the first two phases finish, the nodes use hole information to make the routing decision. Specifically, for every packet, the source node first determines a base path to the destination. This base path is a Euclidean path that bypasses all the core polygons whose information stored in the local memory of the source node. The base path then is magnified using homothetic transformations to obtain the Euclidean routing path which will act as the guideline for the packet. The homothetic centers are chosen randomly to conserve the diversity of the routing paths, while the scale factors are controlled to guarantee the stretch upper bound. If the source node has full information about all the holes, then the packet is forwarded along the Euclidean routing path until reaching the destination. Otherwise, the packet is forwarded along the Euclidean routing path until arriving at an intermediate node which has more detail information about the holes. The intermediate node then redetermines the routing path to the destination and forwards the packet along the new routing path until reaching the destination.  
We use the first two phases as in our previous work [37]. In the following, we first briefly describe the first two phases and then propose a new forwarding algorithm in the third phase.
## 4 Experimental Results
### Performance of Classification Models  
Using the stadium fire risk dataset, through the five-fold cross-validation technique and the stratified five-fold cross-validation technique, the performance of a classification model for predicting the fire risk level of a stadium is established and evaluated. It can be seen from Table 7a,b that all models achieve an accuracy of more than 71% and an accuracy of more than 83%. The Gradient Boosting model built with RFE-Top20 (17 features) achieves the highest accuracy (93.2%) and precision (84.2%) through the K-fold cross-validation technique. Moreover, through the K-fold cross-validation technique, the model built with full features also achieved more than 84.2% accuracy and precision. Furthermore, through the stratified K-fold cross-validation technique, the models built using RFE-Top20 and full features both achieved an accuracy higher than 86.0% in risk prediction with a precision ranging from 68.8% to 81.5%.  
Table 7c,d shows recall and F1-score. Among all models, the Gradient Boosting model with full features and RFE-Top20 achieved the highest recall (84.3%) and F1-score (81.9%) by the k-fold cross-validation technique. On the other hand, in Table 7e, the auroc ranged from 90.1% to 96.2%, where the Gradient Boosting model with selected full features was the optimal model to distinguish extremely high risk, high risk, medium risk, low risk, and not at risk categories. As shown in Table 7f, most of the models obtained a ratio of over 84.0% in the auroc metric, among which the highest auroc (89.8%) was obtained by the Gradient Boosting model with full features under the K-fold cross-validation technique. Overall, the  
Figure 8: Stratified K-Fold Cross-Validation For Six Classifiers.  
Figure 9: Other performance metrics for six classifiers using stratified K-fold cross-validation.  
models developed using full features performed well in distinguishing extremely high risk, high risk, medium risk, low risk, and not at risk classes, with auroc ranging from 90.1% to 96.2% while those developed using RFE-Top20 features models show similar auroc (i.e., range from 88.8% to 95.9%).
## 1 Introduction  
### Background  
As Internet-of-Things (IoT) technology continues to advance, communication signals are increasingly present in our environment. Recent studies have shown that these signals can also be used for sensing purposes [1, 2, 3, 4]. For instance, the Orthogonal Frequency Division Multiplexing (OFDM) WiFi signals with the IEEE 802.11n protocol can be analysed to gain sensing insights into the channel through which they travel and thus gain information by processing the data within it. This information, called the CSI signature, is sensitive to any changes in the channel, from macroscopic changes such as fall detection, occupancy counting, and flame detection [5; 6; 7], to microscopic changes such as human heartbeats and breathing [8; 9]. WiFi sensing is particularly interesting for detecting personal electronic device inputs such as phone passwords [10] and keystrokes [11]. It may be noted that the privacy-preserving mechanisms for WiFi sensing are a bit different from those of the blockchain-based IoT networks, as the nature of signal-based inference poses distinct challenges and mitigation strategies. [12].  
In this paper, we focus on the wireless keystroke recognition [11]. The result shows that typing patterns and sentences can be mined from WiFi, for which the resolution of applications is enough to cover small devices. Previously, a CSI-based low-cost commercial off-the-shelf device (Raspberry Pi) structure was generated which required intricate models and a thorough comprehension of the capabilities and limitations of WiFi-based keystroke recognition. Furthermore, accurate hand location and motion detection are necessary for this application. This paper investigates the efficiency of these IoT-enabled WiFi sensing technologies for keystroke detection using IoT CSI data analytics and embedded software. Our investigation involves building an experimental test bed to quantify the implications of this technology on the security and privacy preservation of sensitive data that may be compromised due to the next-generation sensing capabilities of innovative services enabled through future IoT application support.
# Autonomous Driving Decision-Making Method Based on Spatial-Temporal Fusion Trajectory Prediction
###### Abstract
A Deep Deterministic Policy Gradient (DDPG) algorithm considering state distribution is proposed in the literature [15], which guarantees that the reinforcement learning algorithm still has sufficient exploration ability when the scene distribution varies greatly, and is validated in the lane keeping task. The literature [16] used RL to provide informationally supervised signals for IL learning, which greatly improved the success rate of end-to-end intelligent agent decision-making under the 'guidance' of RL experts. In [17], to ensure that the optimal performance of the RL model performs optimally in complex traffic scenarios, the sensor input images are preprocessed to reduce the state space while guaranteeing better safety and accuracy of the model. Comprehensive research has found that most of the current decision-making methods are based on reinforcement learning, in which the state space is equivalent to the \"per-spective\" of the self-driving vehicle, playing a crucial role in the decision-making process. However, most of the existing methods only focus on the current vehicle state and conduct research on general scenarios such as \"high speed straight ahead\", \"on-ramp convergence\", etc. [18]. Since complex scenarios may not be able to make decisions through conventional methods, it is often too late to react in dynamic scenarios, which leads to limitations in the application of ego-vehicle decision-making capabilities in some complex scenarios. Based on the previous literature, this paper focuses on self-driving vehicle prediction and reinforcement learning decision-making and designs a self-driving decision-making method considering future trajectory prediction through spatial and temporal fusion, aiming to effectively improve decision-making ability under various dynamic driving scenarios.
# A Survey on Energy Drainage Attacks and Countermeasures in Wireless Sensor Networks
## 4 Recent Review Based on State-of-the-Art Works for Energy Drain Attack
### Transport Layer  
Two types of attacks, namely, synchronization floods and desynchronization attacks, cause energy draining by sending unnecessary connection establishment requests or disrupting the already established connection. Compared with other layers, few studies have been published on this topic since 2019.  
First, [PERSON] [82] presented DDoS-PSH-ACK (ACK & PUSH ACK Flood) in WSNs. Through the experimental results and analysis for the DDoS attack in the transport layer, the transmission-session WSN and factor through the DDoS attack were highlighted. The author indicated that the proposed scheme could predict DDoS attacks by monitoring transmission behavior.  
TCP SYN-based DDoS attacks on EKG signals are monitored through WSNs. In 2022, [PERSON] and [PERSON] [83] focused on syn flooding attacks in WSNs. They proposed the detection of syn flooding attacks using power generated while establishing a connection. The efficiency of the mitigation of the syn flooding scheme when identifying an attacker node was demonstrated through simulations. Detection was accomplished by comparing the waiting time for receiving the ACK and the threshold.
# An Overview on Management and Valorisation of Winery Wastes
## 4 Winery Waste for Water Treatment
Pharmaceutical removal by adsorption is one of the most simple and efficient methods [94]. The commercial adsorbents ensure high removal rates, but their high cost represents a drawback; thus, rapid discovery of alternative low-cost and biodegradable adsorbents is needed. One of the residual products that remains after wine consumption is the cork stops. An interesting study proposed the use of this waste for low-cost bio-sorbent preparation [95]. The goal was to compare the adsorption capacities of commercial adsorbents (like activated carbon and synthetic zeolites) with those of bio-sorbents (cork waste) in terms of pharmaceutical (such as fluoxetine) removal from water. The waste-based bio-sorbent exhibited lower maximum adsorption capacity (4.7 mg/g) compared with the commercial adsorbents (233.5 mg/g, 32.1 mg/g, and 21.9 mg/g for active carbon, zeolite 13X, and zeolite 4A, respectively). Although the removal efficiency was lower, the economic feasibility was examined in terms of cost per gram of fluoxetine removed, with commercial adsorbents exhibiting higher costs (6.80 EUR/g, 3.13 EUR/g, and 1.07 EUR/g for zeolite 4A, zeolite 13\(\times\), and activated carbon, respectively) compared to the low-cost bio-sorbent (0.41 EUR/g) [95].
# Adaptive Damping Log-Domain Message-Passing Algorithm for FTN-OTFS in V2X Communications
## 3 Adaptive Damping Log-Domain Message-Passing Algorithm  
The message-passing algorithm exploits the highly sparse and locally coupled structure of the time-varying channel. Instead of relying on cubic-complexity matrix inversion, it performs local probability updates with computational complexity that scales linearly with the frame size, yet still achieves performance close to that of the MAP detector. Specifically, the sparse and locally coupled structure of the time-varying channel is naturally represented as a factor graph. In this framework, the global inference problem is decomposed into a series of local, iterative probability updates between nodes. The message-passing algorithm models the detection process as message exchanges on a factor graph, with variable nodes representing transmitted symbols and observation nodes representing received signals. Due to channel sparsity, each observation node is connected to only a limited number of variable nodes, enabling efficient computation. By iteratively updating and exchanging probability messages between nodes, the algorithm approximates the marginal posterior probabilities of the transmitted symbols. This approach transforms a high-dimensional joint-detection problem into a set of manageable subproblems, achieving efficient and near-optimal detection performance with significantly reduced computational complexity. These characteristics of low complexity and near-optimality make message passing a natural foundation for V2X receivers, which must meet strict requirements for latency and reliability.  
In this context, we propose an adaptive damping log-domain message passing (ADL-MP) detection algorithm, which leverages probabilistic message passing with log-sum-exp (LSE) normalization to effectively prevent exponential overflow and ensure numerical stability. By operating in the log-domain, the algorithm avoids the computational challenges associated with handling extremely large or small values, making it more reliable in practical implementations. Additionally, an adaptive damping factor is introduced to further optimize the performance of the algorithm. The damping factor is designed to accelerate the convergence of the iterative process while simultaneously enhancing the robustness of the algorithm, particularly under challenging or noisy conditions. The combination of log-domain normalization and adaptive damping provides a powerful framework for achieving both stability and efficiency in message-passing computations.
## Data and Methods
### Other Data
#### 2.2.2 OSISAF ICETYPE  
**Data source:** The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI-SAF). The dataset provided four classification types in the Arctic region as follows: 1. No ice or very open ice; 2. Relatively young ice; 3. Ice that survived a summer melt; 4. Ambiguous ice type. The four classification types were assigned from the atmospherically corrected SSMIS brightness temperature and ASCAT backscatter  
Figure 3: Schematic diagram of the Ku Band footprint of the HY-2A/B altimeters.  
Figure 2: Spatial coverage of HY-2A/B in the polar regions in November of 2018: (**a**) Arctic; (**b**) Antarctic.  
values using a Bayesian approach. All of the sea ice types in the Antarctic were set as ambiguous ice. The spatial resolution was 10 km.
## 2 Materials and Methods
### Color Determination  
The color of each tortilla was determined in the CIE L*a*b* system using a Konica Minolta CR-400 trichromatic reflectance colorimeter with Spectra Magic NX, Color Data Software CM-S100w (Konica Minolta Sensing INC., Osaka, Japan). The colorimeter was calibrated using a white tile. The measurements were conducted in six independent repetitions. The color of each tortilla was measured at several points. The L* parameter determines the brightness of the product and ranges from black (0) to white (100). The a* coefficient with values from \(-\)50 to \(+\)50 corresponds to the color from green (\(-\)50) to red (50). The b* coefficient with values from \(-\)50 to \(+\)50 corresponds to a color from blue (\(-\)50) to yellow (50). The total difference or distance between two colors on the CIELAB diagram is known as the Delta E (\(\Delta\)E*). It is a good tool for assessing significant color changes of a given product in relation to the product considered as the standard. \(\Delta\)E* value was calculated by using formula \(\Delta\)E* = \(\sqrt{(\Delta\)L\()^{2}+(\Delta\)a\()^{2}+(\Delta\)b\()^{2}}\). The calculated \(\Delta\)E* value represents the total color difference between the tested tortilla and the control. This value indicates whether or not the color difference is perceptible to the human eye [37].  
\(\Delta\)E* \(<\) 1: color differences are not obvious to the human eye.  
1 \(<\Delta\)E* \(<\) 3: the human eye can detect subtle color differences depending on the specific hue.  
\(\Delta\)E* \(>\) 3: color differences are obvious to the human eye.
# Welding Image Data Augmentation Method Based on LRGAN Model
## 3 Experimental Process
### LRGAN Model Training  
Using the alignment tool of OpenFace, the 1468 images were processed and standardized to a size of 768 \(\times\) 480 pixels. These images were then divided into a training set and a testing set, with 900 images in the training set and 568 images in the testing set. The model was run for 50 epochs, with 2000 iterations per epoch, and the variations in the discriminator and generator loss functions during the image generation process were monitored. To prevent multiple batches from having the same data during iterations, the dataset was randomly shuffled before each training session, which helped accelerate the model's convergence speed. The training set was used as input for the LRGAN model, and the loss function variations during the training process are shown in Figure 11.  
As shown in Figure 11, at the beginning of the training, the generator has a higher initial loss value compared to the discriminator. However, after 15 iterations, both losses begin to decrease, with the discriminator's loss decreasing more significantly. After 40 iterations, the generator's loss starts to slightly increase, but the fluctuation is not large, remaining within the range of 0.3. This indicates that the generator initially learned some features and patterns from the data, which led to a reduction in the loss function. However, as the model continues to train, due to the complexity of the data and the inadequacy of the model structure, the loss function begins to rise. After 90 iterations, both the discriminator and generator loss values drop below 0.5 and remain stable with small fluctuations, indicating that the model is approaching a Nash equilibrium state. This suggests that the original and generated images fed to the discriminator have gradually become more similar, with the discriminator's loss stabilizing and the generated images becoming increasingly realistic.
# A Self-Scrutinized Backoff Mechanism for IEEE 802.11 ax in 5G Unlicensed Networks
## 2 IEEE 802.11 ax Use Cases in Fifth-Generation Radio Access Network on Unlicensed Bands (5G-U)
### Support for Highly Dense Scenarios  
In most of the WLAN configurations, each BS serves only a limited number of WEs. Traditionally, to analyze the best-case scenario, only a single WE per BS is assumed. The worst-case scenario arises when multiple Wi-Fi networks are deployed in the same area as densely deployed networks. Examples of such scenarios are an office floor/building, train stations, or stadiums (as shown in Figure 1d), in which each end WE connects to its own BS but the maximum system throughput is limited due to interference coming from neighboring devices (BSs and WEs). If more WEs were served by each BS or more Wi-Fi networks were present in the same area, the results would be worse due to higher overheads and increased interference. HEW expects to handle the interference due to highly dense network deployments using more intelligent and optimized MAC-RA schemes, such as dynamic sensitivity thresholds for BS and WE traffic differentiation, and adaptive transmit power [5].
## 2 Materials and Methods
### QDTT-OP Evaluation  
In this study, a dithiothreitol (DTT) assay was conducted to analyze the oxidative potential (OP) at a constant temperature (37 \({}^{\circ}\)C) with pH adjustment to 7.4 using 0.2 mM DTT, 2.32 mM DTNB, 100 mM potassium phosphate dibasic, and 500 mM potassium phosphate monobasic. The OP analysis of residual DTT and the reaction product, 5-thio-2-nitrobenzoate (TNB), measured absorbance at 412 nm over time using a microplate absorbance reader (Multiskan SkyHigh, Waltham, MA, USA). The DTT consumption rate (nmol/min), representing the final consumption of DTT, was calculated as the slope of the absorbance decrease (\(\sigma\)ABS) obtained from four absorbance measurements within the reaction time. The background absorbance decrease slope of the blank sample was adjusted, and the DTT consumption rate was normalized to the trapping volume concentration to calculate the dithiothreitol-oxidative potential (DTT-OP) [27; 28]. Detailed information on the DTT assay can be found in [28].  
Furthermore, the DTT analysis process shares similarities with the redox cycling of quinones, which generate reactive oxygen species (ROS) in the human body. For this reason, quinones are used as a metric for PM-related human toxicity, and the DTT assay can measure PM oxidative potential (OP) based on the DTT loss rate. The present study used quinone to normalize the DTT reduction rate to quinone concentration, yielding the quinone dithiothreitol-oxidative potential (QDTT-OP). The consumption of DTT was analyzed through absorbance analysis over a total duration of 40 min, at 10 min intervals, for six quinone concentrations, including blanks (Figure 2). Upon verifying the final calibration curve based on quinone concentration and reduction rate, a high correlation coefficient (r\({}^{2}\) = 0.99) was observed. Consequently, this slope was applied to the study samples to calculate the final QDTT-OP. Detailed descriptions can be found in previous studies [23; 29; 30]. Duplicate and standard quinone concentration analyses were performed every 15 samples to maintain accuracy and precision within 5%.