esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment Results and Discussion
### Quantitative Data Analysis
#### 5.2.2 Skipped Tasks  
Figure 10 shows a pie chart presenting the count and percentage of participants who skipped each transformation task. The task that was skipped the most was the Rotation (TG) task (26 times, i.e., 24.07% of participants skipped it). The next task is the Scaling (TG) task, which was skipped 22 times (20.37% of participants skipped it). The Translation (IC) task was skipped 8 times (7.41% of participants skipped it), the Translation (TG) task was skipped 7 times (6.48% of participants skipped it), and the Rotation (IC) task was skipped 4 times (3.7% of participants skipped it). The Scaling (IC) task was skipped only twice.  
From the results, it can be concluded that there is a higher proportion of skipped tasks in pairs of related tasks Scaling TG-IC and Rotation TG-IC in those tasks that implement touch gestures compared to those that implement indirect controls, while for the related tasks Translation TG-IC, there is an equal number of skipped tasks regardless of the interaction mechanism.
# Predictive Power Management for Wind Powered Wireless Sensor Node
## 2 Motivation and Related Work
### Transmission Power Control  
TPC is an efficient technique of power management in a wireless communication system. It needs interactive information between the sender and receiver. By periodically probing the channel condition (RSSI or PER), the sender can track the wireless link quality and select an efficient and effective transmission power.  
[PERSON] et al. investigated the weighted-sum distortion minimization problem in transmitting two correlated Gaussian sources over Gaussian channels using two EH-nodes. They developed an off-line and an online power control policies to optimize the transmission power of two nodes [17]. [PERSON] et al. considered an EH-node equipped with a piezoelectric vibration energy harvester, which is excited by a series of periodic base acceleration impulses. They maximized the bits of data transmitted from the node over a fixed period through the control of transmission power and transducer current [18].  
In addition to the above literatures, there are still some deep studies focused on the TPC problem [19; 20]. Nevertheless, due to the dynamic voltage of supercapacitor, a voltage regulator is needed to supply stable voltage to the wireless sensor node. The power dissipation of this regulator should also be taken into consideration when choosing the optimal working point. Therefore, it is unclear whether the TPC technique can still achieve the best energy efficiency.
# Hybrid Path Planning for Efficient Data Collection in UAV-Aided WSNs for Emergency Applications
## 5 Performance Evaluation  
In this section, the proposed HPP algorithm's performance is evaluated and compared with conventional PRM and ABC algorithms. In our performance study, all the path planning algorithms under evaluation are simulated using the MATLAB R2020a simulator.
## 3 The Proposed Model
### Image Pre-Processing  
In the first phase, the presented AOTL-CDA3S technique eliminates the existing noise. The WAF was intended to pre-process to suppress noise and enhance spatial domain features effectively [22]. The filter \(W^{\eta}\) was defined by the matrix, whereby \(\eta\) represents the odd number. Each component value of matrix is determined by the distance between the center of matrix and the present place, as illustrated below. The center of matrix was defined by \(w_{(\eta+1)/2,(\eta+1)/2}=2/\eta^{2}\). The proposed filter continues to the edges; however, the filter suppresses the speckle noise associated with other filters, such as the mean filter, and maintain the continuity of an image.  
\[w_{i}j=\frac{1}{\eta^{2}\sqrt{\left(\frac{\eta+1}{2}-i\right)^{2}+\left(\frac {\eta+1}{2}-j\right)^{2}}};i=1,2,\ \eta;j=1,2\ \eta; \tag{1}\]  
In Equation (1), \(I_{1}\), \(I_{2}\in R^{N_{r}\times N_{C}}\), the convolution? of each image using \(W^{\eta}\) is accomplished to acquire 2 images, \(I_{1}^{w}(\eta)=I_{1}*W^{\eta}\) and \(I_{2}^{w}(\eta)=I_{2}*W^{\eta}\), while the \(*\) indicates the 2D convolution function.
## 3 Challenges and Related Recommendation
### Future Research Challenges
#### 3.2.4 Digital Twins Networks  
The implementation of 6G's all-digital concept relies heavily on digital twin modern technology. One positive aspect is that new digital twin applications, such as digital twin body area network [46] and digital twin city [125], will be made possible by the 6G network,which will be an improvement over 5G in terms of performance. By contrast, a more protected, productive, smart, and visually represented 6G network can be realized more quickly with the help of digital twin innovation applied to communication networks. This is achieved via the use of real-time linking and interactivity between the two networks.  
Significant network necessities, such as holo-graphic system virtual-real communication visualization, entire life cycle administration, and real-time closed-loop management [126], are inherent to the network virtual twin concept but also provide a number of challenges when trying to put it into practice. Initially, the 6G network will have extensive network components interconnected through intricate network architectures. Consequently, modelling a genuine actual network in real time presents a significant problem.  
The wireless route digital twin is an essential component of the system digital twin. A precise, real-time scenario-adaptive channel framework must effectively define the transmission setting in immediate terms and forecast upcoming alterations in wireless channels. Furthermore, due to the discrepancies in the practical execution and functionalities of hardware from various manufacturers inside the system, it is imperative to focus on the suitability of network machinery during data gathering, handling, and modelling [127]. Moreover, the extensive 6G network presents significant issues with gathering information, retrieval, administration, and analysis. It is essential to investigate the computational construction of complex network structure links in extensive systems [127].
# GNSS Spoofing Identification and Smoothing Localization Method for GNSS/Visual SLAM System
## 3 Time-Localization Verification and Smoothing Localization Method  
In the back-end of the system, the time verification and localization verification of GNSS/Visual SLAM are first performed respectively to identify GNSS spoofing. Then, the smoothing localization method is performed to optimize the cumulative error of the localization results.
## 2 Route Generation Methodology
### Higher-Level Path Generation: Genetic Algorithm (GA)
#### 2.3.2 Chromosome Structure  
For this research, the GA was used to assign vehicles to the desired sub-areas. The objective of the optimization was to minimize the total travel distance required to scan the entire area. Therefore, the chromosome structure should reflect the sub-areas, the vehicle assigned to the sub-area, the corresponding sub-area's starting location, and the order of operation (if one vehicle is assigned to multiple areas). Several constraints are applied to the chromosomes to ensure that a feasible solution can be generated. The length of a chromosome is equal to 3 times the number of available areas (in accordance with the chromosome structure, which follows the sub-area, starting point, and vehicle assignment), where each sub-area should appear once, and each vehicle should appear at least once. For each sub-area, the starting point can be selected out of a set of candidate starting locations, each with their own unique path created by the lower-level path generation. Additionally, the vehicle assigned to each area can be selected out of a set of available vehicles. The chromosome structure is visualized in Figure 5.
## 2 Related Works
### Time-Series Data Usage in Smart Agriculture
Effective data management is essential for real-time decision making and for extracting valuable information from spatial and temporal data generated by agricultural sensors. [PERSON] et al. introduced GeoFIS, an open-source decision-support tool designed for precision agriculture. GeoFIS focuses on managing spatial data to support decision making, although it lacks a temporal and semantic management framework to capture the most essential details of agricultural data [13]. [PERSON] et al. proposed a secure storage mechanism for managing large volumes of agricultural spatio-temporal data using blockchain technology. However, they acknowledged the high computational requirements that limit its usefulness in real-time agricultural scenarios [14]. [PERSON] et al. offered an alternative by presenting an open agricultural spatio-temporal data warehouse designed to handle spatial and temporal attributes using MobilityDB, an extension of PostgreSQL. Their system, while promising, showed limitations, with response times exceeding the maximum threshold for practical real-time applications [15]. Lastly, [PERSON] et al. introduced the SEMAP framework, designed for the spatio-semantic management of agricultural environments. Although it provides powerful spatial descriptions, its complexity makes real-time management difficult. In contrast, the data model proposed in this paper is designed to meet the specific needs of precision agriculture by simplifying spatial characterization while adding temporal and semantic dimensions [16].
## 2 Joint Sparseness on MR/PET
It was observed that the use of structured joint sparsity led to an increase of 1.5 to 2.5 dB in PET PSNR under sparse conditions, sharper anatomical boundaries in both MR and PET images, and a significant reduction in noise and artifacts. Additionally, the joint sparsity constraint enhanced the consistency and complementarity of structural features between modalities, enabling more accurate fusion and interpretation. These improvements were particularly pronounced at lower sampling rates, demonstrating the framework's robustness and efficacy in accelerated imaging scenarios, thus validating its potential for clinical applications requiring rapid yet high-quality MR-PET imaging. In summary, the structured joint sparsity framework introduced here enables stronger coupling of MRI and PET reconstructions by promoting shared low-rank and sparse structures [27; 31]. This results in improved spatial alignment, enhanced image quality, and greater resilience to artifacts in highly sparse acquisitions. The method complements and  
Figure 1: Joint sparse sampling and reconstruction framework for MR/PET imaging. extends the traditional voxel-wise sparsity approaches and is fully compatible with the GAN-based super-resolution strategy employed throughout this work, see Figure 2.
## 5 Discussion
### Implications for NDT of CFRP Structures  
These findings have practical implications for nondestructive testing of CFRP structures, particularly in aerospace and transport sectors where detection of subsurface damage is critical. LVT, combined with RPCA or PCA-based image enhancement, offers a low-power method, capable of revealing BVID with high contrast, especially in thicker and higher impact energy laminates. The ability to isolate defect signals using RPCA and PCA also reduces reliance on high excitation energy or complex thermal stimulation, offering safer and more energy efficient alternatives to conventional methods.
# Supplement Use Among Athletes: Insights from Gyms in Sarajevo
###### Abstract  
This study aimed to examine the habits, attitudes, and consumption patterns of food supplements (FS) among gym users in Sarajevo, Bosnia and Herzegovina, with respect to gender, age, and training experience. Understanding these behaviors is essential given the rising global trend in FS use, often without professional guidance, which can have implications for individual health and public awareness. By investigating local patterns, this study seeks to provide valuable insights into the motivations and risks associated with supplement consumption, contributing to evidence-based recommendations and policy-making in the field of fitness and nutrition. Methods: Cross-sectional data were collected from various fitness centers using an online questionnaire composed of 21 questions divided into three segments. A total of 614 participants (mean \(\pm\) SD: 32.77 \(\pm\) 9.82 years), including both male (67.8%) and female (32.2%) gym users, took part in the study. Results: Participants predominantly belonged to the 36-45 age group, with 70.0% (\(p<\) 0.001) reporting more than one year of regular gym attendance, and 40.4% indicating regular FS consumption (\(p<\) 0.05). Supplements were mainly used to support faster recovery (29.1%, \(p<\) 0.05) and muscle growth (25%, \(p<\) 0.05). Magnesium was the most commonly used supplement, equally favored across genders and age groups. Creatine, fish oil capsules, and multivitamins were also frequently consumed (\(p<\) 0.05). Self-prescription emerged as the predominant method of FS use (40.3%, \(p<\) 0.05), followed by advice from gym coaches (21.8%), with only 5.6% of participants receiving guidance from a nutritionist. Conclusions: The findings highlight a societal trend toward self-directed FS use, often influenced by informal sources, underscoring the importance of further research and the development of targeted, evidence-based educational strategies. Males reported higher FS consumption, with younger users more focused on muscle gain and older individuals prioritizing health maintenance. The reliance on non-expert recommendations raises concerns about the safety and effectiveness of FS usage in the fitness community.  
dietary supplements; sport supplements; non-professional athletes; gym-goers
# Data Independent Acquisition Based Bi-Directional Deep Networks for Biometric ECG Authentication
## 2 Related Work
### Non-Fiducial Methods
#### 2.2.3 Long Short-Term Memory (LSTM)  
Long short-term memory is a type of RNN model designed to prevent the output of a neural network from either exploding or decaying (long-term dependency) as it passes through the feedback loops for a given input [PERSON] and others (2014). Such feedback loops in RNNs allow the network to be better at pattern recognition compared to other neural networks. Due to their ability to learn long-term dependency, LSTMs are applicable to numerous long sequence learning problems such as language modeling and machine translation, and many other related tasks. LSTM models are designed by applying memory cells with several gates in a hidden layer, as shown in Figure 3a. The hidden layer blocks with the LSTM cell unit, and the three functions of gate controllers are formulated as follows:  
Figure 2: A sample of 7-layer CNN model classified digits for digitized pixel greyscale input images in image processing.  
Forget gate \(f_{t}\) decides which part of long-term state \(c_{t}\) should be omitted.
* Input gate \(i_{t}\) controls which part should be added to long-term state.
* Output gate \(g_{t}\) determines which part of \(c_{t}\) should be read and outputs to \(h_{t}\) and \(o_{t}\).  
The following equations facilitate the calculation of the long-term and short-term states of the cell and the output of each layer in the time step.  
\[f_{t}=\sigma(W_{x,f}^{T}x_{t}+W_{h,f}^{T}h_{t-1}+b_{f}) \tag{3}\]  
\[i_{t}=\sigma(W_{x,i}^{T}x_{t}+W_{h,i}^{T}h_{t-1}+b_{i}) \tag{4}\]  
\[o_{t}=\sigma(W_{x,o}^{T}x_{t}+W_{h,o}^{T}h_{t-1}+b_{o}) \tag{5}\]  
\[g_{t}=tanh(W_{x,g}^{T}x_{t}+W_{h,g}^{T}h_{t-1}+b_{g}) \tag{6}\]  
\[c_{t}=f_{t}\otimes c_{t-1}+i_{i}\otimes\delta_{t} \tag{7}\]  
\[o_{t},h_{t}=g_{t}\otimes tanh(c_{t}) \tag{8}\]  
where \(W_{x,f}\), \(W_{x,i}\), \(W_{x,o}\), \(W_{x,g}\) denote the weight parameters for the connected input vector, \(W_{h,f}\), \(W_{h,i}\), \(W_{h,o}\), \(W_{h,g}\) denote the weight parameters of the short-term state of the previous time step, and \(b_{f}\), \(b_{i}\), \(b_{o}\), and \(b_{g}\) are the biases.
###### Abstract
The abovementioned guidelines [19; 20] distinguish between static monitoring, aimed at the measurement of slowly varying parameters, such as strain, stress, deformation, tilt, and displacement, and dynamic monitoring, used to measure the structural response during the occurrence of phenomena exhibiting rapid variation over time, such as seismic events, wind, traffic, or other dynamic actions. In the literature, different classifications of monitoring techniques can be found depending on several criteria. First, they can be categorized into global and local methods. Global methods, such as vibration-based monitoring techniques, can detect the occurrence of damage that affects the whole structure, but cannot precisely locate the damage. In contrast, local methods detect damage through inspection and investigation at the component level. These two approaches are complementary: global methods can alert for the presence of damage and roughly define its location, while local methods are more precise in localizing and providing information about the damage [22]. Monitoring strategies can then be distinguished as continuous or periodic, depending on the frequency of measurements. The first approach considers continuous acquisition and analysis of data to differentiate and filter out changes caused by exogenous factors [23]. The second approach includes general inspections, such as geometric measures by photogrammetry, that can be executed periodically to improve the knowledge level and reduce uncertainties [24]. Finally, depending on the type and configuration of sensing and data acquisition systems, SHM techniques can be classified as traditional (standard) or innovative (smart) [25]. This paper provides a comprehensive state-of-the-art review of SHM and the management of CHS, focusing on innovative technologies. A description of conventional SHM techniques, subdivided into static and dynamic techniques, follows this introduction (Section 2). Section 3 illustrates the innovative SHM technologies, including smart-sensing, imaging and computer-based methods. Finally, data management strategies for managing the large amount of data collected by new monitoring systems are presented in Section 4.
###### Abstract  
We propose a detection system incorporating a weighted voting mechanism that reflects the vote's reliability based on the accuracy of each detector's examination, which overcomes the problem of cooperative detection. Collaborative malware detection is an effective strategy against zero-day attacks compared to one using only a single detector because the strategy might pick up attacks that a single detector overlooked. However, cooperative detection is still ineffective if most anti-virus engines lack sufficient intelligence to detect zero-day malware. Most collaborative methods rely on majority voting, which prioritizes the quantity of votes rather than the quality of those votes. Therefore, our study investigated the zero-day malware detection accuracy of the collaborative system that optimally rates their weight of votes based on their malware categories of expertise of each anti-virus engine. We implemented the prototype system with the VirusTotal API and evaluated the system using real malware registered in MalwareBazaar. To evaluate the effectiveness of zero-day malware detection, we measured recall using the inspection results on the same day the malware was registered in the MalwareBazaar repository. Through experiments, we confirmed that the proposed system can suppress the false negatives of uniformly weighted voting and improve detection accuracy against new types of malware.  
malware detection; collaborative security; VirusTotal; MalwareBazaar
## 2 Results and Discussion
### Ethylbenzene Oxidation  
We determined the effect of the developed five novel SCILL/SILP systems PS-NHPI@CoCl\({}_{2}\)@[emim][OcOSO\({}_{3}\)] on the oxidation of ethylbenzene with dioxygen at 80 \({}^{\circ}\)C, and compared it with the activity of the systems containing (i) PS-NHPI in combination with separately added CoCl\({}_{2}\) (PS-NHPI/CoCl\({}_{2}\)), and (ii) PS-NHPI in combination with separately added CoCl\({}_{2}\) and [emim][OcOSO\({}_{3}\)] (PS-NHPI/CoCl\({}_{2}\)/[emim][OcOSO\({}_{3}\)]) (Table 3). The blank experiment was also performed. CoCl\({}_{2}\) has limited solubility in ethylbenzene, which reduces the extent to which it leaches from the catalyst surface and dissolves in the reaction mixture. In the NHPI-catalyzed hydrocarbon oxidation reactions, Co(II) compounds participate in the generation of the PINO radical but also accelerate hydroperoxide decomposition. The oxidation reactions (carried out by all catalytic systems) resulted in the production of a mixture of unreacted ethylbenzene, ethylbenzene hydroperoxide (EBOOH), acetophenone (AP), and 1-phenylethanol (PEOH), as shown in Scheme 3. All of the obtained heterogeneous PS-NHPI catalysts showed activity in the oxidation of ethylbenzene with dioxygen in the presence of CoCl\({}_{2}\) addition (Table 3, entries 5-9). Compared with the Co(II)-catalyzed reaction (Table 3, entry 4), the presence of PS-NHPI led to about a twofold increase in conversion (\(\alpha\)). Among the catalysts immobilized on carriers with 1% DVB (Table 3, entries 6-8), the highest TON (8.2) was obtained with PS-NHPI-4. It was further determined that heterogeneous NHPI activity is influenced by the size of the catalyst grains, which vary as follows: PS-NHPI-2 (149-297 and 20-170 \(\upmu\)m) \(>\) PS-NHPI-3 (74-149 \(\upmu\)m) \(>\) PS-NHPI-4 (37-74 \(\upmu\)m). As supported by the reactivity results, smaller grains likely provide a larger contact surface area between the catalyst active centers and the reactant. SEM images (Figures S2 and S7) showed that, during the synthesis of the PS-NHPI-2 catalyst, the PS-Cl-2 carrier (grain sizes: 149-297 \(\upmu\)m) was partially mechanically degraded to irregularly-shaped grains with sizes of approx. 20-170 \(\upmu\)m. However, the degradation probably did not increase the availability of the catalyst active sites, since higher ethylbenzene conversions were not recorded.