esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Evolution of Research Themes and Technological Focus in VRU Wearable Safety Solutions
#### 5.1.2 The Paradigm Shift from Passive Sensing to Active Prediction  
The functional paradigm of VRU safety wearable technology has undergone a fundamental transformation from passive sensing to active prediction, an evolution clearly demonstrated in the progression from the \"#1 obstacle avoidance\" to the \"#3 cyclist safety\" clusters. Research has experienced three distinct phases: the early passive detection phase, represented by the basic obstacle recognition systems of [PERSON] et al. [61] and [PERSON] et al. [80]; the intermediate technical optimization phase, where systems like those by [PERSON] et al. [68] and [PERSON] et al. [69] improved sensing precision but remained limited to reactive responses; and the critical turning point (2017-2020), when research began integrating contextual awareness and predictive capabilities, with [PERSON] et al. [41] achieving differentiated perception of dangerous scenarios, while the [40] 4-8 warning window of [PERSON] et al. marked a key breakthrough from reactive responses to proactive prediction. This paradigm shift has expanded the safety protection model from post-event response to pre-event prevention, redefining the role of wearable technology in VRU safety. Recent systematic reviews in related domains consistently validate this technological evolution pattern, demonstrating parallel transformations from basic reactive safety equipment to intelligent predictive systems across pedestrian safety IoT frameworks [84], cyclist safety technologies [85], and visually impaired navigation devices [86].
## 2 Maximum Dynamic Shear Modulus Tests
### Testing Apparatus and Process  
The bender element measurement system installed in the GCTS HCA-300 static and dynamic triaxial instrument is used for the test. The bender element includes two piezoelectric ceramic sheets and a central copper stiffening layer. During the test, two bender elements, excitation and receiving, are installed at both ends of the soil sample. The excitation element produces horizontal vibration under the excitation of a specific pulse voltage. In addition, the shear wave reaches the receiving element following the sample propagation. A weak electrical signal is generated at this time, and the oscilloscope can calculate the propagation time (\(t\)) of the shear wave. A series of sinusoid signals from 1 to 40 kHz was used as the excitation, and the received signals corresponding to these excitation frequencies were examined to better identify the \(t\). The 10 kHz excitation signal consistently yielded a clear arrival of the shear wave for floodplain soft soils. This is in good agreement with the observation of [PERSON] and [PERSON] [27] and [PERSON] et al. [28]. The height of the soil sample minus the length of the bender element deep into the soil body can be utilized to calculate the shear wave propagation distance and thus determine the shear wave velocity. The equation for calculating the soil shear wave velocity (\(Vs\)) using the indoor bender element test is:  
\[V_{\mathrm{s}}=L_{0}/t \tag{1}\]  
where \(L_{0}=L-L_{\mathrm{b}}\), \(L\) is the height of the sample; \(L_{\mathrm{b}}\) is the length of the bender element deep into the soil; \(t\) is the propagation time.  
Calculating the shear wave velocity (\(V_{\mathrm{s}}\)) and the maximum dynamic shear modulus \(G_{0}\) requires accurately determining the shear wave's arrival time. Different scholars have proposed various analytical methods for identifying the shear wave arrival time. The widely used methods are the time-domain initial wave, peak-to-peak, and intercorrelation methods. [PERSON] et al. [29], [PERSON] et al. [30], and [PERSON] et al. [31] indicated that the shear wave velocity propagation time \(t\) could be determined easily and accurately using the time-domain initial wave method. In this experiment, a sinusoidal pulse frequency of 10 kHz was applied, and the shear wave always arrived clearly and effectively under the excitation frequency of 10 kHz. The time domain initial wave approach is employed to determine the arrival time of shear wave velocity, i.e., depending on the first turning point A of the received signal as the arrival point of the shear wave. Figure 3 depicts a typical bender element test received signal diagram. After determining \(V_{\mathrm{s}}\) for each sample, the following equation is utilized to calculate \(G_{\mathrm{max}}\) according to elastic theory:  
\[G_{\mathrm{max}}=\rho V_{\mathrm{s}}^{2} \tag{2}\]  
Figure 3: Typical time histories of output signals from bender element tests.
# Alkali-silica Reaction Elimination Potential of High-Performance Concrete Containing Glass Powder
## 2 Materials and Methods  
### Materials  
The tested samples were GP and reference silica flour (SF), which were prepared in the laboratory (each 100 g). A total of four types of glass powder are used in this project and were selected based on the need to recycle, reuse, or use them up:  
* container waste glass = CWG,
* secondary raw material from glass jewelry production = SGJ,
* secondary raw material from glass production = SGP,
* glass from used photovoltaic panels = GPP.  
The finest fraction of glass is used as a SF replacement to create high-performance concrete and verify the chemical properties, ASR expansion, and structures regarding the mechanical and durability properties from previous research. The replacement ratio was 50% and 100%. Therefore, 9 mixtures were made and tested:  
* reference high-performance concrete mixture = REF HPC,
* high-performance concrete containing 50% replacement of silica flour with container waste glass = CWG HPC 50,
* high-performance concrete containing 100% replacement of silica flour with container waste glass = CWG HPC 100,
* high-performance concrete containing 50% replacement of silica flour with secondary raw material from glass jewelry production = SGJ HPC 50,
* high-performance concrete containing 100% replacement of silica flour with secondary raw material from glass production = SGP HPC 50,
* high-performance concrete containing 50% replacement of silica flour with glass from used photovoltaic panels = GPP HPC 50,
* high-performance concrete containing 100% replacement of silica flour with glass from used photovoltaic panels = GPP HPC 100,  
The concrete mixes were designed on the basis of previous research on the basic properties, particle size distribution, and evaluated results. The mixes that were used in this research are summarized in Table 1. Samples of size 25 mm \(\times\) 25 mm \(\times\) 285 mm were made according to the standards, always in a set of three pieces. For testing the chemical and ecotoxicological properties, 27 cubes (3 cubes of each mixture with the dimensions 50 mm \(\times\) 50 mm \(\times\) 50 mm) were prepared. Accompanying samples were made to verify the constancy of mechanical properties compared to early research [26].
# Utilizing Multi-Dimensional MmWave MIMO Channel Features for Location Verification
## 2 Problem Formulation and System Model
### Communication Model  
The baseband signal received by [PERSON] at time \(t\) is written as  
\[\mathbf{r}(t)=\mathbf{G}_{RF}^{H}\text{vec}(\mathbf{H}_{XB}(t))s(t)+\mathbf{n} (t), \tag{8}\]  
where  
* \(s(t)\) is the transmitted signal at time \(t\). The signal power is \(p=\mathbb{E}\{|s(t)|^{2}\}\).
* \(\mathbf{H}_{XB}(t)\) denotes the channel matrix between transmitter \(X\) (either [PERSON] or [PERSON]) and receiver [PERSON].
* \(\mathbf{n}(t)\) is a zero-mean complex additive white Gaussian noise (AWGN), i.e., \(\mathbf{n}(t)\sim\mathcal{CN}(0,\sigma_{n}^{2}\mathbf{I})\).
## 3 Methodology
### Application of Approximate Dynamic Programming (ADP)
The old plan is discarded, and a new optimization is performed. This constant cycle of re-planning based on real-world feedback allows the system to continuously correct its course, effectively managing uncertainty without needing to model the full probability distribution of demand. 3. Rolling-Horizon Implementation  
The implementation of ADP follows a rolling-horizon approach, which is particularly effective for managing operations in dynamic environments like warehouses. The optimization model is performed over discrete time periods such as daily. The decisions are made at the beginning of each period based on the system's current state using demand predictions as inputs. At the end of each period, the state information is updated based on the actual demand fulfilled and replenishment actions taken. This updated state information is then used to make decisions in the next period. A feedback loop is incorporated whereby the outcomes of previous decisions inform updates of the value function approximations, refining the decision-making process over time. To effectively apply ADP, certain assumptions are made about the operational context, including but not limited to the predictability of demand variations, the reliability of supply chain logistics, and the stability of replenishment lead times. These assumptions help simplify the model while ensuring it remains robust enough to handle real-world complexities. This detailed application of the ADP model with certainty equivalence seeks to optimize internal replenishment processes by effectively handling uncertainties in demand and supply, thus enhancing operational efficiency and reducing costs in the spare parts warehouse setting.
# Determining Thresholds for Optimal Adaptive Discrete Cosine Transformation
## 2 Materials and Methods  
### Earlier Adaptive DCT  
Research on adaptive discrete cosine transformation dates back to the 1980s. The earliest implementations of the algorithm achieved bitrates as low as 0.5 bits per pixel while maintaining the compression practically losslessly [21; 22]. The early developments were primarily dedicated not to the splitting of the images into fragments of variable size but to the encoding.  
In video compression, there are developments dating back to the 20 th century as well. For example, adding variable length to three-dimensional discrete cosine transform (3D DCT) blocks, implementing temporal adaptivity, has allowed the achievement of an astonishing 0.1 bit per pixel rate on average for a video without a significant loss in quality (bottom right frame in Figure 1) [23].  
More recent implementations of 3D adaptive DCT claim to be superior to common compression methods. It is also said to reduce the complexity of the calculations, allowing them to be implemented easily in hardware [24]. The lack of temporal dimension efficiency of regular 3D DCT is compensated for by adaptivity, such as presented in Figure 2, and is described as surpassing MPEG in both bitrate and efficiency. Mentions of thresholds being used in adaptive fragmenting quadtree building are first found in the abovementioned work.  
However, methods to calculate the adaptive fragmenting thresholds have never been described thoroughly. Our work aims to provide the foundation for implementing adaptivity techniques in compression methods by describing how to obtain the threshold values.  
Figure 1: Examples of video compression using 3D DCT, comparing the original video frame and various compression techniques, with variable-temporal-length 3D DCT implementations in the bottom row, clearly showing its superiority.
# Assessment and Categorization of Biological Effects and
## 5 Assessment of Effects and AS Due to Exposure to RF-EMF
### SR and MA Applications
The fourth SR [118] evaluated the effect of RF-EMF exposure on cognitive performance in human experimental studies by assessing associations between short-term RF-EMF exposure and cognitive performance. The assessment techniques used allow the grouping of cognitive performance tests into the following cognitive domains: orientation and attention, perception, memory, verbal functions and language skills, construction and motor performance, concept formation and reasoning and executive functions. * SR on Cancers and Oxidative Stress [119, 120, 121]  
The first SR [119] aimed to assess the literature regarding the possibility that RF fields can induce cellular oxidative stress. Such stress is accompanied by an amplified creation of oxidizing species or a substantial decrease in the ability of antioxidant defenses. The consequences of oxidative stress depend on the magnitude of these deviations. Since a cell is capable of obstructing slight disturbances and returning to its initial condition, but medium oxidation can generate apoptosis, while more strong stresses can produce necrosis. Such a disorder can cause different diseases and can be caused by different external exposures. In the SR, the authors presented a protocol of experimental studies on exposure to the fields and biomarkers of oxidative stress. The authors assessed the relationships between RF-EMF exposure and oxidative stress through in vivo and in vitro laboratory studies managed to evaluate the exposure effects on valid markers of oxidative stress compared to a null or impostor exposure. The second SR [120] used observational studies to assess the effect of exposure to RF fields on cancer risk in the general and working population. Thus, to assess the quality and strength of evidence provided by these human studies for a causal association between RF-EMF exposure and the risk of different cancers. The third SR assessed the effects of RF-EMF on cancer in laboratory animal studies [121]. * SR on Male Fertility and Pregnancy Outcomes [122,123]  
The first SR [122] aimed to assess the effects of RF-EMF exposure on male fertility and pregnancy outcomes by an SR of experimental studies, conducted according to international guidelines. The evidence is organized into three strands: (1) studies evaluating the impact of RF-EMF on the male reproductive system of experimental mammals; (2) studies evaluating the impact of RF-EMF on human sperm exposed in vitro; (3) studies evaluating the impact of RF-EMF on unwanted pregnancies, birth outcomes and delayed effects in experimental mammals exposed in utero. The second SR [123] examined the effect of RF-EMF exposure on male fertility and adverse pregnancy outcomes in human observational studies with meta-analysis.
# Effective Multi-Frame Optical Detection Algorithm for GEO Space Objects
## 3 Methodology  
The flowchart of the proposed algorithm is represented in Figure 4. To summarize, the algorithm mainly consists of two parts. The 2D object detector PP-YOLOv2 primarily focuses on searching potential candidate target among all the frames according to the appearance features. Then, the tracker based on CFS is deployed to filtrate true targets and supplement misdetected objects.  
Figure 3: Another example image sequence: (**a–e**) five frame images within a sequence that contain six objects; (**f**) the source positions are shown by the cross symbols in different colors.
## 4 Simulations Results and Discussion with Performance Evaluation
### Simulation Results
#### 4.3.2 Packets Delay  
Delay is the most important parameter to be considered in UWSNs for communication because sometimes a small delay in a network collapse the entire system. Delay in packets is mainly influenced by the channel used in UWSNs and the distance from the source to destination. Therefore, to tackle this important problem, two routing protocols are proposed. Delay of the previously discussed protocols is shown in Figure 10. Delay in FLMPC-One is higher than FLMPC-Two because FLMPC-One 2-hop neighbors information leads to a high number of active nodes. Meanwhile, delay of SPF-Three is greater than all aforementioned protocols. The reason behind this delay is that SPF-Three finds the shortest path from source to destination. It collects 3-hop information as FLMPC-Two, which results in the unique path with unintentionally packets collision. Due to the above-mentioned reasons, the number of data packets are unable to reach the destination because of massive collisions( the ratio of packet drop is high). Therefore, SPF-Three retransmits the data packets using the shortest route with minimum EC. However, they exhibit delay but provides reliable data delivery. In BFS-SPF-Three, the delay is less than the SPF-Three and higher than existing protocols because of binary tree generation. In addition, BFS-SPF-Three returns all shortest possible routes from source to the destination by exploiting the binary tree breadth wise and by performing opportunistic routing. Opportunistic routing in BFS-SPF-Three selects the route with minimum distance and provides reliable packet delivery. The farthest nodes selection causes the delay in packets delivery and enhances the propagation time.  
It is evident that as the layers increases, the active nodes increase which results in an end-to-end delay. At layer 6, the minimum delay is 0.82 s and 0.63 s in BFS-SPF-Three. In existing protocols, a total end-to-end delay is 240.6178 ms and 220.1483 ms while in proposed protocols end-to-end delay is 390.4561 ms and 351.0972 ms.  
Figure 9: Number of Active Nodes per Layer.
# Formulation Strategies to Improve Oral Bioavailability of Ellagic Acid
## 3 EA Dietary Assumption  
In foods and beverages, EA is present in several forms, such as unmodified, glycosylated and/or acetylated or inside the structure of hydrolysable ET polymers, usually esterified with glucose moieties. However, only a small fraction of free EA is absorbed in the stomach, since ET are resistant to acidic pH. ET hydrolysis occurs in the small intestine, leading to the release of EA, which can be absorbed mainly by passive diffusion, although an in vitro experiment on Caco-2 cells monolayer model suggested the involvement of a protein-mediated transport [39]. Once reached the systemic circulation, EA undergoes a massive first pass effect, being transformed in methyl esters, dimethyl esters or glucuronides, detectable in human plasma from 1 to 5 h after ET ingestion [40]. Meanwhile, unabsorbed ET and EA fractions are mostly converted to a family of metabolites called urolithins by colon microflora. Urolithins contain a common structure of dibenzopyranone and are more lipophilic, showing higher absorption rate across colon epithelium compared to EA, thus resulting 25-80 times more bioavailable [41]. Naturally, there is huge variability in microbial metabolism of EA among individuals depending on differences in gut microbiota composition. According to the metabotype (M-0, M-A and M-B), humans may produce no urolithins, highly active urolithins or less active urolithin, so that EA consumption may not exert equal health benefits in all subjects [42; 43; 44].  
EA oral bioavailability was studied in human subjects after 180 mL of pomegranate juice consumption. In this study, the maximum EA blood concentration (Cmax) was 32 ng/mL which was rapidly metabolized in the next 4 h, confirming the extremely poor intestinal absorption [41]. Other pharmacokinetic studies supported the hypothesis that higher EA-to-ET ratio in the oral dose corresponds to higher EA plasma concentrations, but, intriguingly, no enhancement in bioavailability was observed increasing the dose of free EA up to 524 mg, suggesting that a saturation condition in the absorption process was achieved [45]. In addition to these unfavorable conditions, it must be considered that polyphenols are not consumed in sufficient quantities among population in Western Europe and developing countries, because of inadequate fruit and vegetable intake, though an increased awareness of the benefits of a balanced diet is fostering the assumption of dietary supplements [46].
## 4 Proposed Methodology  
### General Structure of the Proposed Methodology  
1. Dataset collection The dataset used in this study is the WSN-DS dataset obtained from Prince Sultan University, Saudi Arabia. This dataset contains instances of various types of attacks in WSNs, making it suitable for our classification task.
2. Data Splitting  
Eightly percent of the dataset was reserved for training purposes, while the remaining twenty percent was used for testing. The holdout method was used to divide the data in this way so that the proposed model could be trained on a large enough portion of the data while retaining a separate set for evaluation. The robustness of the suggested optimization-based classifier (LOA-Cb-C) was evaluated on the mentioned dataset consisting of 374,661 samples. Table 2 displays the breakdown of the total sample size.
3. Model Selection and Optimization  
We chose the Cb-C for its effectiveness in handling categorical features and its ability to handle imbalanced datasets. The hyperparameters of the Cb-C were optimized using the LOA. This algorithm is known for its efficiency in finding optimal hyperparameters, which can considerably increase the classifier's performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Year** & **Author Names** & **Model Used** & **Dataset** & **Accuracy** & \
\begin{tabular}{c} **Computational** \\ **Efficiency** \\ \end{tabular} &
\begin{tabular}{c} **Energy** \\ **Efficiency** \\ \end{tabular} \\ \hline
2022 & [PERSON]; [PERSON] [25] &
\begin{tabular}{c} Optimized \\ DL-based IDS \\ \end{tabular} & Edge IoT & 93\% & Medium & High \\ \hline
2021 & [PERSON]; [PERSON] [20] & \
\begin{tabular}{c} ML for Big Data \\ Analytics \\ \end{tabular} &
\begin{tabular}{c} VANET \\ \end{tabular} & 95\% & High & Moderate \\ \hline
2021 & [PERSON], [PERSON]; [PERSON], [PERSON][21] & DL-IDS &
\begin{tabular}{c} MQTT-Enabled \\ IoT \\ \end{tabular} & 96\% & High & Moderate \\ \hline
2021 & [PERSON]; [PERSON][22] &
\begin{tabular}{c} Multi-Layer ML \\ IDS \\ \end{tabular} & WSN & 93\% & Moderate & High \\ \hline
2021 & [PERSON]; [PERSON] [26] &
\begin{tabular}{c} Lightweight \\ IDS \\ \end{tabular} & WSN & 94\% & Moderate & High \\ \hline
2024 & [PERSON] et al. & LOA-Cb-C & WSN-DS & 99.66\% &
\begin{tabular}{c} Low \\ (Optimized) \\ \end{tabular} & High \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._* Training at the Cb-C for its effectiveness in handling categorical features nd Prediction  
The optimized Cb-C was trained on the training dataset to learn the patterns in the data related to diverse attacks. After training, the model was applied to the testing dataset for prediction purposes. Several metrics were utilized to evaluate the model's performance, including accuracy, precision, recall, and F1-score.
## 3 Results and Discussion  
The experiments to assess the effectiveness of the proposed method are divided into the different steps of the process. First, the accuracy of the segmentation algorithm is shown. Then, the results of the selection of the most effective color features are presented. Next, the training of the classifier and the classification results are discussed. Last, the computational efficiency of the algorithm is evaluated.
# Self-Reporting Technique-Based Clinical-Trial Service Platform for Real-Time Arrhythmia Detection
## 3 Results
### Design of Case Study  
After implementing the entire platform, a case study was designed to evaluate its clinical feasibility in a multicenter setting. To evaluate secure data acquisition and transmission throughout the platform, it was decided that the subject of the trial would collect and transmit the ECG data using the VP-100. SNUH and the Kyungpook National University Hospital (KNUH) were to recruit 50 healthy male participants (SNUH: 40, KNUH: 10). All the participants would be given a written consent form before the procedure. After signing the consent form, a VP-100 and mobile phone pair with a long-term evolution universal subscriber identity module in it would be allocated to each participant. Once the VP-100 is placed on the chest of the participant and the Bluetooth communication is connected between the device and mobile app, ECG data would be collected for six hours continuously. During data acquisition, participants would be asked to report their physical conditions and to call medical staff when a communication failure or any adverse effect occurs. The study protocol complies with the Declaration of Helsinki and Good Clinical Practice. Currently, this study is registered on ClinivalTrials.gov (Registration code: NCT05182684).