esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
## 3 Routing Protocols for UWSNs
### MFPR
#### 3.1.2. Location-Based Routing without Mobility Consideration
EGBLOAD adaptively regulates the transmission power according to the energy level of the forwarder, traffic load on the forwarder, and distance of the sender from the forwarder. Energy grade (EG) w/o corona helps in even distribution of traffic load, and in reducing congestion at forwarders by dividing data packets into three sizes i.e., large, medium, and small. Depth adjustment (DA) w/o corona aims to avoid voids by adjusting the depth of the forwarder, without affecting its connection with existing neighbors. Moreover, the proposed scheme achieves balanced load distribution by considering residual energy while choosing forwarders.
## 5 On the Robustness of the Proposed Feedback Power Control Strategies
### Noisy Feedback Channels  
Referring to the feedback power control commands (given by binary sequences) described in Section 3., we assume that each bit of the \"up/down\" binary sequence can be \"flipped\" with probability \(P_{\rm e-fb}\). In other words, the noisy feedback links are modeled as binary symmetric channels (BSCs). Therefore, the source nodes receive power control commands which may differ from those sent by the AP. For instance, suppose that a +5 dB power control command, coded as the binary sequence  
Figure 9: Average BER, as a function of the average SNR at the sources, in a scenario with non-orthogonal links, considering: (a) DD LDPC-coded schemes and (b) SCCCed schemes. Two possible values for \(\epsilon\) are considered: (i) 0.1 and (ii) 0.3.  
\"+1+1+1+1+1,\" is transmitted by the AP to a source and two bits are flipped by the feedback channel, so that the command received by the source is \"+1-1+1+1-1.\" In this case, the received power control command is interpreted (simply by following the up/down commands) as +1 dB.  
In Figure 11, the average BER is shown, as a function of the average SNR at the sources, in a scenario with noisy feedback channels, considering: (a) DD LDPC-coded schemes and (b) SCCCed schemes. Two possible values for \(P_{\rm e-fb}\) are considered: (i) 0.02 and (ii) 0.1. First, one can observe that for all values of \(P_{\rm e-fb}\) the unbalanced SNR power control strategy allows to obtain better performance than the balanced SNR power control strategy. In particular, in the waterfall BER region the unbalanced SNR power control strategy guarantees a noticeable performance gain with respect to the balanced SNR power control strategy. This gain reduces for large values of the SNR and, in the LDPC-coded scenario, the curves tend to overlap. This is due to the fact that the LDPC code does not effectively exploit the source correlation in the JCD algorithm. Moreover, as expected, the higher is the probability of error in the feedback channels, the worse is the performance. In particular, this effect is more pronounced for SCCCed schemes than for LDPC-coded schemes, since the SCCC better exploits the source correlation.  
In Figure 12, the outage probability is shown, as a function of the average SNR at the sources, in a scenario with noisy feedback channels, considering the same schemes of Figure 11. The same considerations carried out on the basis of the average BER performance still hold and, therefore, the proposed optimized unbalanced SNR power control strategy is effective for all values of \(P_{\rm fb}\). Moreover, the LDPC code and the SCCC tend to behave similarly.
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 11 Transition Metal Dichalcogenides
Moreover, TMDs exhibit strong absorption across a broad spectrum of light, including visible light. This property enables the efficient generation of electron-hole pairs for photocatalytic activity. Many TMDs, such as MoS\({}_{2}\) and WS\({}_{2}\), are also chemically stable under ambient conditions and resistant to oxidation. This makes them durable in environmental applications, including prolonged exposure to polluted air and light irradiation. Recent advances in synthesis methods, such as chemical vapor deposition (CVD) and hydrothermal techniques, allow for relatively scalable and controllable production of TMDs, making them more accessible for large-scale applications than other competitors. TMDs often suffer from rapid recombination of photogenerated electron-hole pairs, which can limit their photocatalytic efficiency. Strategies like doping or forming heterojunctions are often required to mitigate this issue, but this adds additional complications to the process. Moreover, the efficiency of TMDs as photocatalytic materials relies mainly on their structure, and obtaining defect-free monolayers can be challenging, particularly for large-scale productions. Moreover, certain TMDs, particularly in their 2D form, can degrade under prolonged light irradiation or in reactive environments. For instance, photoinduced oxidation or sulfide leaching may occur in materials like MoS\({}_{2}\), reducing their long-term stability. Moreover, potentially toxic metals like Mo or W can be leached into the environment following TMDs' degradation.
# Feature Optimization for Gait Phase Estimation with a Genetic Algorithm and Bayesian Optimization
## 4 Results
### Estimation Error of the Heuristic Model  
A feature set and LTW are empirically determined in the heuristic model. Five feature sets were selected, as shown in Table 3. Feature set 1 is a set comprising 12 TD features; feature sets 2 and 3 consist of TD features extracted from the angle and angular velocity, respectively; feature sets 4 and 5 are randomly selected from the possible TD feature set. When training the MLP with feature sets 1-5, the LTW value was set to 300 ms, as suggested in a previous study [22].  
The MLP was trained using a heuristically determined feature set and LTW. Seventy percent of the time-series data were used for training. The test error was computed using the remaining 30% of the data. The MLP was trained using five different initial values for weights and biases. The mean and standard deviations of the five errors were obtained and are presented in Tables 4 and 5. For walking, the error ranges from 1.1% to 1.6% in most cases, as presented in Table 4. The error is relatively large when feature set 3 is used,  
\begin{table}
\begin{tabular}{c c} \hline Feature set 1 & \(\theta_{\text{min}}\), \(\theta_{\text{max}}\), \(\mu_{\theta}\), \(\theta_{(t_{f})}\), \(\theta_{\text{max}}\), \(\theta_{\text{min}}\), \(\mu_{\hat{g_{r}}}\), \(\sigma_{\hat{g_{i}}}\), \(\hat{\theta}_{(t_{f})}\) \\ Feature set 2 & \(\theta_{\text{min}}\), \(\theta_{\text{max}}\), \(\mu_{\theta}\), \(\sigma_{\theta}\), \(\theta_{(t_{f})}\), \(\theta_{(t_{f})}\) \\ Feature set 3 & \(\theta_{\text{max}}\), \(\theta_{\text{min}}\), \(\mu_{\hat{g_{r}}}\), \(\sigma_{\hat{g_{i}}}\), \(\hat{\theta}_{(t_{f})}\) \\ Feature set 4 & \(\theta_{\text{max}}\), \(\theta_{\text{min}}\), \(\sigma_{\theta}\), \(\theta_{\text{max}}\), \(\theta_{\text{min}}\)\(\sigma_{\hat{g_{i}}}\) \\ Feature set 5 & \(\mu_{\theta}\), \(\theta_{(t_{i})}\), \(\theta_{(t_{f})}\), \(\mu_{\hat{g_{i}}}\), \(\hat{\theta}_{(t_{i})}\) \\ \hline \end{tabular}
\end{table}
Table 3: Feature sets for comparison.  
suggesting that angle information is essential for gait phase estimation. For running, the error is approximately 2%, which is relatively larger than that for walking, as shown in Table 5.  
To quantitatively compare the performance of the heuristic model and the proposed optimizer, one of the five heuristic feature sets was selected and used for comparison. For walking, feature set 1 provides the smallest or second smallest error. Therefore, this set was used for the comparison. Similarly, feature set 2 was chosen as the representative heuristic set for running.
## 5 Discussion and Implications
### Cross-Cutting Challenges and Gaps  
Despite advances in ML and GenAI for learning analytics (LA), several structural limitations persist that hinder their educational effectiveness.  
A key cross-cutting gap lies in the limited grounding of LA systems--particularly those powered by GenAI--in established learning theories. For instance, several tools in the corpus provided personalized feedback using large language models, such as the explainable feedback dashboard by [PERSON] et al. [91] and the fine-tuned GPT-4 system for hint revision explored by [PERSON] et al. [115]. While both systems offer scalability and automation, they rarely aim to develop students' feedback literacy--a construct encompassing the capacity to interpret, act on, and benefit from feedback [136; 137]. Moreover, the feedback remains largely unidirectional, with little scaffolding for student agency or dialogic interaction [138].  
Similarly, only a subset of studies explicitly aligned with the dimensions of self-regulated learning (SRL), such as metacognitive monitoring, goal-setting, and reflection [139; 140]. For example, while [PERSON] et al. [100] focused on evaluating the quality of GPT-generated feedback for open-ended writing tasks, they did not examine how such feedback supports SRL processes or learner engagement. In contrast, [PERSON] et al. [98] integrated GenAI to deliver adaptive scaffolds based on real-time analytics of SRL processes, showing improved metacognitive strategies--yet still reported variability in learner compliance and limited generalizability.  
These gaps suggest that, while ML and GenAI can technically model learner behavior, their educational impact remains limited without deliberate integration into pedagogical frameworks, such as SRL, feedback literacy, and formative assessment [141]. Additionally, many ML and GenAI models remain opaque, functioning as black boxes that impede interpretability and educator trust. While explainable AI (XAI) techniques, such as RoBERTa with LIME for peer feedback classification [123], have shown promise, they are still underused.  
Educators are rarely involved as co-designers, resulting in systems poorly aligned with classroom realities. GenAI implementations are often tested in artificial or highly controlled environments. Even when benefits like improved instructional design are observed [124], few studies assess long-term impact in authentic educational settings.  
Geographic disparities also persist. Research and deployment efforts are concentrated in high-income regions, limiting global representativeness and inclusivity. Latin America and other underrepresented regions remain largely absent from the discourse [129].  
Finally, multimodal and real-time data, such as emotional, behavioral, or biometric streams, are rarely integrated into adaptive feedback loops, which constrains the responsiveness and depth of learner modeling.  
Table 1 summarizes these cross-cutting challenges and outlines the recommended research directions to inform the development of pedagogically grounded, explainable, and context-sensitive LA systems.
# Tire Defect Detection via 3D Laser Scanning Technology
## 4 Conclusions  
Existing methods mainly rely on X-ray images to detect tire defects with clear visual quality and, therefore, are insensitive to certain types of tire defects in the manufacturing process, especially challenging defect types with low visibility. Moreover, existing methods apply semantic segmentation approaches to locate defects. Therefore, slight false pixel predictions significantly impact the results of defect defection, hampering their deployment in actual production lines. To this end, we proposed a new tire defect detection pipeline by introducing 3D laser scanning technology into the inspection. We first captured scanned 3D tire surface data presented as 3D point clouds, which can effectively capture accurate depth distribution on the 3D surfaces. Our method proposes a novel normal-based rendering strategy for capturing accurate depth information and a 2D detector based on deformable attention for the accurate detection of tire defects.  
Under standard experimental settings, our detector achieved 0.356, 0.409, and 0.691 mAP on RGB, depth, and rendered images, respectively. Compared with state-of-the-art 2D detection methods, our detector showed superior performance. The experimental results shed light on the application of tire defect detection in actual production lines. However, there are a few limitations, mainly in the following two aspects:  
1. The performance of the model was constrained by the scale of training data. However, collecting a large number of data in a short time is hard because of the time-consuming annotation process. While our novel data augmentation eased the lack of training data, synthetic data are not as good as real data.
2. The normal-based rendering process consisted of reading point cloud data, normal estimation, and 3D-to-2D projection. Because of the large scale of tire point clouds (more than \(10^{8}\) points) and high dependency on the CPU, the first two steps of the rendering strategy consumed most of the time, which was more than 90% of the total time in detecting one tire.  
Figure 10: Illustration of failure cases. Failure cases can be classified as missing and wrong detection cases.  
Therefore, in future endeavors, we aim to focus on (1) collecting more real defect samples data and devising more robust data augmentation methods; and (2) reducing the time consumption of normal-based rendering process with optimized mechanisms (e.g., GPU-based normal estimation and parallel data reading).  
Conceptualization, L.Z.; methodology, L.Z.; validation, X.X.; resources, H.L.; data curation, X.X.; writing--original draft preparation, L.Z.; writing--review and editing, J.L.; supervision, J.L.; project administration, H.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 62293504, 62293500, and in part by the National Key Research and Development Plan of China under Grant 2022 YFC2403103.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Simulation and Discussion  
In this section, we will evaluate the performance of PTC algorithm by simulation. To highlight the outstanding qualities of the PTC algorithm, we combine the PTC with the FTC algorithm and compare the performance of FTC algorithm with the PTC-FTC algorithm. The simulation results shown in [6] have demonstrated that the FTC algorithm is highly efficient in controlling the network topology.
# Forward Link Optimization for the Design of VHTS Satellite Networks
## 3 Fundamentals and Analytical Models
### Proposed Analytical Model for the CINR\({}_{b}\)  
This paper proposes an analytical model of the average \(CINR\) per beam \(CINR_{b}\) (in dB) as in (16). This model depends on the number of beams (\(B\)), the \(G\)/\(T\) (dB/K) of the user terminals, and the annual availability (\(P\)). The \(a_{ij}(P)\) are the fitting terms due to the link availability obtained from the rain attenuation in the service area.  
\[CINR_{b}\left(B,\frac{G}{T},P\right)=\sum\
olimits_{i=0}^{4}\sum\
olimits_{j=0}^{k }a_{ij}(P)\;B^{i}\!\left(\frac{G}{T}\right)^{j} \tag{16}\]  
where  
\[k=\left\{\begin{array}{rl}3,\;i=0\\ 4-i,\;0<i\leq 4\end{array}\right. \tag{17}\]  
Equation (16) has been derived from multiple simulations evaluating different scenarios and different technical requirements. In these simulations, the \(CINR_{b}\) is obtained. A total of 14,000 different combinations of the number of beams, user \(G/T\), and link availability for each scenario have been evaluated to obtain \(a_{ij}\).  
The values of the index \(i\) go from 0 to 4, whereas the range of index \(j\) depends on \(i\), and it must be fulfilled that \(i+j\leq 5\) to obtain the best fitting of \(CINR_{b}\). This condition is represented by \(k\) in Equation (17), which limits the range of the index \(j\).  
An example of the application of (16) is shown in Figure 1 where the average \(CINR_{b}\) in Central Asia is represented for a link availability of 99.5%: blue dots represent the values obtained with the traditional link budget (9) and the surface is the fitting derived from Equation (16). The graph shows that the variation of the \(CINR_{b}\) does not behave monotonically with the number of beams.
## 1 Introduction
In the RF based model, we used a select set of descriptive hand-engineered features of the AE while in our CNN based models raw AE signals are used as input for training the CNN prediction models. In our related study [19], we developed a deep learning method to monitor the respective degradation stage of the SiC\({}_{\text{f}}\)-SiC\({}_{\text{m}}\) material using the generated AE signals. While our past work led to accurate classification of an AE event into one of three stages, namely, elastic stretching, matrix cracking or fiber cracking, the RUS prediction of an AE event would be more informative and challenging. The predicted RUS of an AE event would yield more insight to the overall degradation process of the SiC\({}_{\text{f}}\)-SiC\({}_{\text{m}}\) material since it would indicate the intensity of the deterioration. Even though previous studies did use those aforementioned supervised learning methods on AEs, their application to predict the RUS of SiC\({}_{\text{f}}\)-SiC\({}_{\text{m}}\) material is yet to be adapted [19; 20], which is done in this work. Figure 1: Open end burst test setting on the SiC\({}_{\text{f}}\)-SiC\({}_{\text{m}}\) tubes. (**a**) Schema of the setting for the burst test. (**b**) Real experimental setup of the burst test. As the first implementation of RUS prediction for SiC\({}_{\text{f}}\)-SiC\({}_{\text{m}}\) materials, our proposed methods particularly addressed the problem of reusability of tested materials by removing the need to completely rupture the tubes of SiC\({}_{\text{f}}\)-SiC\({}_{\text{m}}\). Furthermore, due to our approach to train and evaluate the models on nine separate sets of AEs from nine experiments, our results reflect the robustness and reliability of our RUS prediction algorithms. The contributions of this study include the following:  
1. We use expert-engineered features of AE signals to train a RF model so as to learn the patterns of the generated AE sound events from SiC\({}_{\text{f}}\)-SiC\({}_{\text{m}}\) materials for RUS prediction during their degradation. 2. We employ the raw AE signals to train two types of end-to-end deep CNN models, thus extracting high-level features from AE events, to achieve better RUS prediction performance and ensure better monitoring of the deterioration process of the test tubes of SiC\({}_{\text{f}}\)-SiC\({}_{\text{m}}\). 3. We provide a comprehensive analysis of the models' performance at predicting the RUS of SiC\({}_{\text{f}}\)-SiC\({}_{\text{m}}\) materials based on nine different experiments leading.
## 4 Discussion
### Impact of Intermittency  
To our knowledge, this is the first study which examines the effect of various exercise intermittency levels, including acceleration/deceleration patterns, on oxidative stress biomarkers. Interestingly, the current results show that various intermittencies and accelerations/decelerations patterns of the HIIE do not significantly influence the biochemical markers of the oxidative stress levels evaluated. These results are in agreement with previous studies, which also found that short bouts of HIIE did not influence oxidative stress  
Figure 2: **(A,B)** Day-to-day variation in baseline lipid hydroperoxides for individual participants (n = 9) following 6 visits to the laboratory **(A)**. Diurnal variations in individual participants (n = 9) during control session **(B)**. Blood samples were analysed at baseline, 1 h, 2 h, and 24 h post-exercise to examine lipid hydroperoxides. Each dashed line represents an individual participant (P1–P9).  
levels [36]. [PERSON] et al. [37], in particular, used a submaximal intensity, similar to the present study, with results also indicating an absence of oxidative stress (8-Oxo-dG and malondialdehydedehyde) at 1 h and 24 h post exercise, suggesting that higher intensity protocols may be required to elicit oxidative stress. Consequently, based on the current results, submaximal or maximal HIIE--including also glyometric exercise, since glyometric muscle contraction is produced due to acceleration/deceleration patterns--does not negatively impact oxidative stress levels or improve antioxidant status.
# NMDA Receptor Model of Antipsychotic Drug-Induced Hypofrontality
## 3 Expression of NMDA Receptor Subunits in the Rat Brain after Antipsychotic Treatment
### Thalamus, Hypothalamus, and Neocortex
The activity of the corticolimbothalamic circuit can be disturbed due to antipsychotic drugs inducing a change of expression of NMDA receptors’ (NMDAR) subunits. A decrease of the number and activity of NMDARs can decrease the glutamatergic activity in the hippocampus and the thalamus and disinhibit glutamatergic stimulation from the thalamus to the prefrontal cortex, sustaining the hypofrontality. In rat parietal cortex, we observed only a non-significant increase of NR1 NMDAR subunits. The result supports the findings of [PERSON] et al. (1993), who reported that after three-week administration of haloperidol, the expression of NR1 subunits was increased [106]. As a probable mechanism, the blockade of D2 receptors was proposed. [PERSON] et al. (2003) showed that haloperidol and clozapine given for two weeks decrease NR1 and NR2B subunits in rat frontal cortex [75]. In turn, clozapine administered for six months decreases the number of NR1 subunits in the dorsolateral prefrontal cortex. In another study, both haloperidol and clozapine decrease expression of NR2A in rat prefrontal cortex [76]. The discrepancies can result from the fact that different cortex areas were studied and the changes of expression of NMDAR subunits indicate a brain regional specificity of antipsychotic drugs' influence on NMDAR composition [19].