esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Responsiveness and Sustainable Concepts in Cellular Manufacturing Systems
## 3 Review of the Responsiveness Approaches
### Virtual Cellular Manufacturing  
A different approach to handling dynamic conditions is the virtual cellular manufacturing system (VCMS). This approach forms cells including machines that are not physically moved. This type of cell leads to increased movements costs because the machines cannot move closer to the materials. Moreover, the costs related to the reallocation of the machines to support the reconfiguration of the cells are minimized, and the reconfiguration of virtual cells are faster and easier.  
[PERSON] and [PERSON] [21] proposed a multi-agent system to control virtual cellular manufacturing cells in real-time. This coordination mechanism to support scheduling problems reacts to the changing market dynamics, reducing the processing and holding costs.  
[PERSON] and [PERSON] [22] developed a multi-agent system to support the family part formation, virtual cell formation, and scheduling, considering dynamic demand arrivals. The numerical results showed that the proposed model was the most promising in dynamic conditions for the manufacturing system. These approaches proposed can use a multi-agent system to distribute the computational complexity of the problem.  
[PERSON] et al. [23] proposed a stochastic model to assign parts and machine workers to multi-dynamic virtual cells. The approach proposed minimizes the costs of holding, outsourcing, inter-cell material handling, and external transportation, and fixed the costsfor producing each part. The complexity of the mathematical model obtained needs a genetic algorithm to solve the problem.  
[PERSON] and [PERSON] [24] studied the problems of cell formation, cell scheduling, and group layout in the case of virtual cellular manufacturing and classical cellular manufacturing systems. To solve the problem, metaheuristic algorithms were proposed to minimize the cycle time and total handling costs. The numerical results from their experiments demonstrated that virtual cell configurations lead to better solutions than classical cell configurations.  
Virtual cellular systems are a promising approach when combined with multi-agent systems to obtain more robust cellular manufacturing systems.
# Detection of Yunnan Pine Shoot Beetle Stress Using UAV-Based Thermal Imagery and LiDAR
## 2 Materials and Methods
### Thermal Imagery
#### 2.2.3 The Average Temperatures of the Small Plots  
The 50 small plots of 10 m \(\times\) 10 m were subdivided from two larger plots to analyze the effect of the LAI on the relationship between temperature and the SDR. The plot temperature (\(T_{\text{P}}\)) was the production of mixed of thermal radiation from all the ground features in the plot. When calculating the average temperature of a sample plot, if many shrubs are present, the contribution of shrub temperature should be considered because of the lower temperature of shrubs compared with pine-tree crowns. Therefore, LiDAR data were used to reverse LAI of a plot with a large number of shrubs.
## 6 Security Analysis
### Informal Security Analysis
#### 6.1.12. Support Perfect Forward Secrecy  
Following the CK model specified in Section 3.4, an adversary \(\mathcal{A}\) can obtain the \(CS\)'s master key \(S_{k}\). Using \(S_{k}\), \(\mathcal{A}\) seeks to compute the session key \(SK=h(UID_{i}||SI_{j})\oplus h(n_{i1}||n_{i2}||n_{i3})\). However, the values \(UI=h(UID_{i}||k_{i}||S_{k})\) and \(SI_{j}=h(SID_{j}||r_{k})\) are masked using \(k_{i}\) and \(r_{k}\). Thus, \(\mathcal{A}\) cannot compute the session key with \(S_{k}\). Thus, our scheme offers perfect forward secrecy.  
6.1.13. Support Mutual Authentication  
In the authentication phase, \(U_{t_{i}}\), \(IoT_{SL_{j}}\), and the \(CS\) verify the legitimacy of the received messages. The \(CS\) verifies the validity of \(V_{1}\) and \(V_{3}\), and \(IoT_{SL_{j}}\) verifies \(V_{2}\). In addition, \(U_{t_{i}}\) verifies \(V_{4}\). The entities mutually authenticate and establish a shared session key via this mutual verification process. Hence, our scheme ensures mutual authentication.
# Zero-Day Threat Mitigation via Deep Learning in Cloud Environments
## 5 Results
### Visualization of Results  
The training process provided key insights into the model's learning dynamics across epochs. Figure 4 displays the evolution of training and validation loss and accuracy, highlighting the model's convergence behavior and generalization capacity.  
Figure 4 illustrates the convergence behavior of the MVT model during training. The observed decrease in loss, coupled with a steady increase in accuracy, reflects the model's effective learning process in differentiating between benign and malicious files. In addition to this, the evolution of accuracy, loss, and performance metrics for benign (Class 0) and malware (Class 1) samples is presented in Figures 5 and 6, providing a comparative view of the model's behavior across classes during training.  
Figure 5 presents the evolution of precision, recall, and F1-Score for the benign file class throughout the training process. Compared with the malware class, the metrics exhibit greater variability, primarily due to the class imbalance in the dataset. Precision reflects the proportion of true benign predictions among all predicted benign instances, while recall assesses the model's ability to correctly identify all benign samples. The F1-Score provides a balanced measure of these two aspects. The observed fluctuations, particularly during the initial training epochs, indicate early instability, which progressively stabilizes as the learning process adapts, underscoring the impact of class distribution on model performance. Figure 6 shows the results of Class 1 (Malware).  
Figure 4: Training and validation loss and accuracy over epochs.  
Figure 5: Precision, recall, and F1-Score for Class 0 (Benign) over epochs.  
Figure 6 illustrates the evolution of precision, recall, and F1-Score for the malware class across training epochs. After initial fluctuations, the metrics stabilize, indicating consistent and reliable detection performance. The precision reflects the proportion of correctly identified malware among all positive predictions, while recall captures the model's ability to detect all malware instances. The high and stable F1-Score suggests a balanced trade-off between precision and recall, which is critical for minimizing both false positives and false negatives in malware classification.
# A Self-Supervised One-Shot Learning Approach for Seismic Noise Reduction
## 2 Method
### Proposed Masking Process  
The effectiveness of the N2S method is significantly influenced by the choice of masks, which could be a potential limitation of the method. The construction of the masks is performed through a masking procedure. This involves selectively obscuring parts of the image, thereby dividing it into target regions \(J\) and their complementary set \(J^{c}\). The original  
Figure 1: (**a**) Dimensions of independent features; (**b**) independent features.  
approach uses a neural network to implement \(J\)-invariant functions, which are adapted to process and reconstruct the information from \(J^{c}\), the unmasked segments. By tailoring the mask to the specific characteristics of the noisy dataset, considering strategies such as single-pixel masks for zero-mean noise, the signal coherence within \(J^{c}\) can be enhanced while the influence of noise is reduced. This masking process not only augments the ability of the model to infer clean images directly from noisy inputs but also maximizes the self-supervised learning efficiency.  
In this study, two masking processes were proposed to address different noise characteristics. For artificially generated Gaussian noise, a mask that simulates a regular grid was used. This grid is evenly spaced in both the horizontal and vertical directions on the basis of the hyperparameters designated \"h\" and \"w\", both of which are fixed at 10-pixel units.  
The novelty of this approach lies in the fact that the initial position of the grid is randomly determined and modified at each iteration during the training process. This method, therefore, ensures that the model is exposed to different sections of the image during its various training phases, thereby resulting in better generalization during the learning process.  
For data characterized by real noise, which are commonly more prominent in the vertical direction, we were required to rethink the strategy for the construction and application of the masks. An approach that preserves the independence of the noise was adopted, using vertical masks with a size of nx1, fixed at 250 in this study. The choice of masks with a unit vertical width was motivated by the prevalence of horizontal coherence in the signals that is significantly more pronounced than that of the noise. This approach aims to minimize signal removal, an inevitable consequence when opting for masks of greater width.  
The mask is developed by analyzing subtle differences between pixel values in vertical positions within the image. In this process, the mask is applied precisely where the difference between adjacent pixels in the y-axis is small, ensuring the maximum efficacy of the strategy.  
In essence, this approach is implemented similar to that used for Gaussian noise in terms of mask positioning. However, prior analysis of the differences in pixel values allows more precise placement of the mask in the context of seismic noise, providing more appropriate processing for this type of data.
# Retrofitting of Bridge Slabs for Safety Railing Refurbishment in Italy: A State-of-the-Art Review
## 3 Slab Assessment and Retrofitting Approaches
### Slab Retrofit Approaches
#### 3.2.1 Additional Steel Rebars  
As can be seen from Figure 7 (showing the section and plan views of the intervention details), the additional reinforcement in terms of rebars is placed across the external beam to provide the necessary development length, obtaining a total width of intervention equal to W. New rebars are installed only in correspondence with the new barrier post axis to limit the invasiveness of works. This requires a precise cut in the slab concrete cover and the hydodemolition commonly of about 5 cm or until finding the existing rebars. New rebars are anchored in the curb through an inclined branch. Once the main rebars are installed, vertical anchors are realized through vertical holes and epoxy resin. The concrete cuttings are finally filled with high-strength fiber-reinforced grout and a waterproofing layer. Thus, the road pavement is restored to complete the intervention.  
Referring to technique no. 2, the only difference can be found in the fact that additional rebars are distributed throughout the slab length and not only close to the post axis. Therefore, lower-diameter rebars are used, and more cover demolition is necessary. This approach allows neglecting the final position of posts since the slab is uniformly upgraded. However, it is more expensive and impacting on the slab.  
Figure 7: Slab strengthening through additional rebars.
# Depth Segmentation Approach for Egocentric 3D Human Pose Estimation with a Fisheye Camera
## 5 Results
### Ablation Study
#### 5.3.1 Decoder Architecture  
To assess the influence of the decoder architecture, we compared two configurations: a single-decoder architecture and a dual-decoder architecture. The single-decoder architecture regresses both the depth map and segmentation using a single decoder, whereas the dual-decoder architecture uses two separate decoders for depth map and segmentation. The experimental results in Table 3 show that the single-decoder architecture has advantages over the dual-decoder configuration. (i) The single-decoder architecture reduces the overall network size, making it more computationally efficient. (ii) It achieves better performance in terms of MPJPE, showing that the shared representation of depth map and segmentation features improves generalization.
Most of the LULC mapping from large areas has relied on supervised or unsupervised classification algorithms applied to the entire pixel (resolution cell). The use of the entire pixel to discriminate LULC classes in coarse spatial-resolution images (and even in moderate-resolution images) is, to some extent, limited because of the frequent mixture of different spectral classes in a single pixel [25; 26]. Spectral mixture analysis [27], often modeled using a linear spectral mixture model (LSMM), is one of the most common techniques for considering the nature of the obtained mixed spectral data [26]. LSMM converts the digital numbers or reflectance of an image to a few endmembers or fraction images containing proportions of different terrain components, usually vegetation, soil, and shade, at a subpixel level [27]. LSMM has been demonstrated to be a useful technique for interpreting coarse and moderate spatial-resolution remote sensing data by converting spectral information into physical parameters related to the abundance of materials on the ground surface [28; 29]. The LSMM technique has been used for detecting seasonal changes in three LULC classes (open tropical forest, Cerrado grasslands, and soybean plantation) [30] to differentiate the major Cerrado physiognomies [31] and vegetation seasonality [32] and to monitor deforestation and degradation by fire and selective logging [33]. Annual or monthly composite images based on the maximum vegetation fraction values ensure that cloudy and off-nadir pixels have low chances to be selected in the final composites, as stated by [30] and allow better characterization of LULC classes with strong seasonality (e.g., tropical savannas, croplands, and pasturelands), as shown by [34]. Since different LULC classes in Mato Grosso state present distinct annual behavior in terms of the proportion of vegetation, soil, and shade, in theory, the maximum fraction images (and their corresponding standard deviations) have the potential to better discriminate the most representative LULC classes from this region (forestlands, shrulands, grasslands, rain-fed croplands, and pasturelands). In this context, this study aimed to present a new method to map the major LULC classes in Mato Grosso state based on the maximum value proportional fractions of vegetation, soil, and shade derived from the 2015 PROBA-V 100-m images.
# Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data
## 4 Results
### Improvement of the Sentinel-1 Flood Service  
Figure 13a shows the classification result of the S-1 FS (see Section 1) for the validation area in Namibia, which initially only contains the open flood water areas [47]. For the extension and improvement of the S-1 FS, the open flood areas were supplemented by TOW and TFV of the time series approach, in order to detect the entire flood extent. The object-based classification result was used as a supplement, since higher accuracy could be achieved compared to pixel-based classification results.  
An accuracy assessment was carried out for both the classification results of the S-1 FS and the supplemented classification results of the S-1 FS. The classes TFV and TOW, both in the validation data and in the supplemented classification of the S-1 FS, were combined into a single class: flood. This represents the entire flood extent. The modified reference data is shown in Figure 13d. The merging of the classes of TOW and TFV provides a more comprehensive coverage of the flood extent in the reference data compared to the results of the S-1 FS (Figure 13a). Figure 13b shows the classification result of the S-1 FS, which are supplemented by TOW, and Figure 13c represents the S-1 FS classification  
Figure 12: Pixel-based classification result for the entire study area in Namibia (**a**). Object-based classification result for the entire study area in Namibia (**b**).  
result simultaneously supplemented by TOW and TFV of the time series approach. The validation of the S-1 FS and the two supplemented S-1 FS classification results were performed using OA, PA, UA, and Kappa Index (Figure 14). A significant improvement in classification accuracy is observed for PA Flood and UA Dry Land, which are 57.1% and 31.0%, respectively. This improvement is mainly achieved by supplementing the S-1 FS classification results with the TFV surfaces. Thereby, the OA increases by 27.0% and the Kappa coefficient increases from 0.24 to 0.69.
## 4 Discussion
### Limitations and Strengths  
The main limitation of this study is that we analyzed the injury incidence in a single team, which reduces the possibility of generalizing conclusions to a wider context with regard to the specifics of club culture, methodology, and other factors. Also, the study did not include direct parameters of training load, so causality could only be discussed hypothetically. Other organizational shifts, like changing fitness coaches, medical staff, and similar, were not analyzed in the study. However, their influence is acknowledged based on prior literature, and future studies should control for these variables. Finally, the aforementioned problem of underreporting is possible in situations where players do not want to report injuries. However, the use of a single elite team ensured consistency in the collection and recording of data, which certainly contributed to the reliability and validity of the results.  
The most significant strength of this research is its longitudinal design, which spans eight competitive seasons, allowing for systematic monitoring of injuries within the same club environment. Also, an essential strength of the research is the fact that this is the first study to examine both muscle and overall injuries in situations of coaching changes, which offers a more clear and comprehensive understanding of how different types of injuries are influenced by coaching turnovers.
# A Secure Auditable Remote Registry Pattern for IoT Systems
## 4 The Secure Auditable Registry Based On Message-Oriented Middleware (SARMOM) Pattern
### Known Uses  
Currently, there are many systems in a variety of industries using Message-Oriented Middleware, such as Kafka, RabbitMQ and Amazon SQS. Many of these systems must also support auditing processes, which means they also need to use different forms of secure registries, a combination that fits the \(\mathrm{SARMOM}\) pattern proposed in this work.  
Below, we list some known uses of systems that combine the use of a Message-Oriented Middleware and a Secure Registry [40]:  
* Netflix deals with different IoT devices (ranging from set-top boxes to smart TVs) and uses an MOM system (to convey its real-time monitoring and to support its event-processing pipeline) and a secure registry that is audited by independent processes implemented using HIVE [41].
* Uber deals with car-tracking devices and end-users' devices, and it uses MOM as part of the overall infrastructure stack powering various online and near-real-time use cases. Moreover, Uber uses the distributed cluster of Kafka nodes as a registry when performing internal audits and investigations [42]. While the security needs that arise from the requirement to prove the event sequence to external entities are not all present there, the internal auditors still need the most important ones that we have identified for the SAR component, referenced in the implementation section.
* Several banks (e.g., Barclays and Rabobank) connect to devices such as ATMs and use both an MOM and a registry that provides the event recording and the audit functions of our SAR to support several financial processes and services, including those for proving compliance and for fraud tracking. Their combined use also fits the present pattern.
## 7 Conclusions  
In this paper, we presented a comprehensive performance analysis of SSC, SEC, and SECps schemes over Fisher-Snedecor \(\mathcal{F}\) fading channels. We first studied the bivariate Fisher-Snedecor \(\mathcal{F}\) distribution with arbitrary fading parameters, then the mathematical expressions of the statistical characteristics of the output SNR for the above schemes were deduced in various fading scenarios. In particular, certain novel analytical expressions of the statistical properties of the output SNR for the SECps scheme were obtained in terms of the multivariate Fox's H-function. Thirdly, these performance metrics of interest, including the average SNR, AoF, OP, ABEP/ASEP, and average channel capacity, were investigated in detail for SSC, SEC, and SECps schemes under different fading conditions. Fourthly,  
Figure 11: Comparison of the average capacity levels of different switched diversity systems as a function of the switching threshold (\(\gamma_{\tau}\)) over Fisher–Snedecor \(\mathcal{F}\) fading channels with \(m=2\), \(n=5\), and \(\overline{\gamma}_{1}=10\) dB.  
Figure 10: Comparison of the average capacity levels of different switched diversity systems as a function of the average SNR of the first branch (\(\overline{\gamma}_{1}\)) over Fisher–Snedecor \(\mathcal{F}\) fading channels with \(m=2\), \(n=5\), and \(\gamma_{\tau}=6\) dB.  
the numerical and simulation results confirmed the validity of the theoretical expressions under various fading and shadowing scenarios. Finally, the obtained results suggested that the multipath parameter has a greater impact on the performance of SDC systems than the shadowing parameter, the correlation coefficient, or the average SNR. Moreover, the SDC systems can provide switched diversity gains only when the switching threshold is not too large or too small compared to the average SNR. These new results will be meaningful to enhance the system reliability in the design and deployment of future communication applications, including device-to-device, wearable communication, and Internet of Things. In future work, we will apply SDC techniques to design some wireless nodes for industrial Internet of Things applications, as two or more antennas can be installed on these nodes operating at 2.4 GHz or higher.  
W.C., conceptualization, methodology, theoretical analysis, supervision. X.W., theoretical analysis, numerical analysis, writing--original draft preparation. T.M., software, simulation, writing--editing. G.W., numerical analysis, simulation, writing--review, project administration. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China under grant no. 52071349, and in part by the Open Project Fund of Key Laboratory of Mine Disaster Prevention and Control under grant no. SMDPC202102.  
Not applicable.  
The authors declare no conflict of interest.
# An Adaptive Privacy Protection Method for Smart Home Environments Using Supervised Learning
## 3 Assumptions and Requirements
### Requirements of Privacy Protection  
The particularity and sensitivity of the smart home makes it important to consider the privacy, energy efficiency, and latency of the sensor network in the design of privacy protection methods.