esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methodology
### Black Hole Detection  
The malicious network participates in a variety of harmful acts that reduce the network's efficiency [29]. Then, the malicious node transmits route response (RREP) information to the source node after the source node receives the route request (RREQ) most quickly to the node that is being attacked without considering the real route from the point of origin. The false route response is regularly transmitted to the source node by the blackhole station. When a malicious node maintains data packets before deleting the remainder of the network, then the network's performance drops.  
Figure 3 illustrates that the network consists of 10 nodes connected by a dynamic topology, with a source node and a destination node. The route request (RREQ) for the shortest path to the destination is broadcast by the source node when it needs to transfer data packets to the destination node. When a black hole or malicious node, for instance Node 4, receives the RREQ message, it offers the immediate false route response to the source node. For instance, Node 1 delivers the data packets through that routing after receiving the RREP message. Data packets are received by Node 4, which then drops them from the network, causing the network to break down. Thus, the proposed study presents a new dense convolutional bidirectional gated network (SA_DCBiCNet) model based on twin attention for detecting black-hole nodes. Then, the twin-attention model can simultaneously concentrate on local and global aspects of the input. The term 'twin aspect' describes the capacity of the model to handle, inside a single attention layer, two distinct features of the input data: the general structure and the smaller details. As a result, the model can potentially show unclear signs of black holes by capturing the intricate interactions between various data points. From the dense layers, it can identify spatial information such as temperature disparities or brightness patterns that could point to the existence of a black hole. The term 'dense' describes the utilization of numerous convolutional layers to increase the capacity for feature extraction. Then, bidirectional gated network layers identify dependency relationships and temporal changes are carried out by analyzing the retrieved characteristics across the data, both forward and backward in time. This is important in order to spot minute alterations or signatures that could point to the activity of a black hole. The network generates a prediction about the possibility of a black hole present at a given place in the data based on the extracted features and their temporal correlations.  
Figure 3: Black-hole detection.
## 5 Channel Selection Scheme Based on Estimation of Power and COR  
In the channel selection scheme based on the estimation of the signal power, CCI power, and COR, the packet error rate based on the bit error rate [46] is adopted, and is provided as:  
\[\beta(N_{SF},\gamma_{k})=\{1-0.5\times\] \[Q(1.28\cdot\sqrt{\Gamma\cdot 2^{N_{SF}}}-1.28\cdot\sqrt{N_{SF}}+0.4 )\}^{N}, \tag{14}\]  
where \(N_{pkt}\) is the number of chirp-modulated symbols per packet and \(\gamma_{k}\) is the signal to noise plus interference power ratio (SINR) in the \(k\)th channel. We define \(\gamma_{k,I}\) and \(\gamma_{k,o}\) as the SINR of the \(k\)th channel with and without the occurrence of the CCI, respectively. The occurrence of the CCI is equal to the COR of the \(k\)th channel, \(\rho_{k}\). Therefore, the PDR of the \(k\)th channel is given as  
\[U_{k}=(1-\rho_{k})\beta(N_{SF},\gamma_{k,o})+\rho_{k}\beta(N_{SF},\gamma_{k,I}). \tag{15}\]  
The accessing channel is determined by maximizing the PDR; thus, it is constructed as  
\[k^{\star}=\arg_{k}\max U_{k} \tag{16}\]  
Note that the PDR derived by Equation (15) is decided by the signal power, the interference power, the noise power, and the COR. The estimation errors directly cause the estimation errors of PDR, resulting in the failure of the channel selection with the maximal actual PDR. As a result, the access opportunities of the vacant channel become lower and the packet collision due to the simultaneous access between the sensor of LoRa and the SC system occurs more frequently. The estimation accuracies of the signal power, the interference power, the noise power, and the COR decide the PDR performance.
# An Efficient Encoding Algorithm Using Local Path on Huffman Encoding Algorithm for Compression
## 3 Materials and Methods
### Image Compression Measurement  
The file obtained as a result of compression is called a compressed file. This file is used to restore the image to the original. The compression percentage is the data that shows how the original file is compressed. The process used to obtain the compression percentage is shown in Equation (1). The compression percentage (CP) will be used in all comparisons during this study.  
\[\mathit{Compression\ Percentage}=100\left(1-\ \frac{\mathit{Compressed\ file\ size\ (bytes)}}{\mathit{Original\ file\ size\ (bytes)}}\ \right) \tag{1}\]  
Another term used in this field is the number of bits per pixel (NoBPP). The method of calculating this value for an image with N \(\times\) N dimensions is shown in Equation (2).  
\[\mathit{Number\ of\ Bits\ Per\ Pixel}=\ \frac{\mathit{Number\ of\ bits}}{\mathit{ Number\ of\ pixels}} \tag{2}\]  
The aim of this study was to develop an effective encoding algorithm suitable for all compression algorithms. The Huffman encoding algorithm and arithmetic coding algorithm are frequently used coding algorithms in the kernel of compression algorithms. Here, a new encoding algorithm was developed that used the advantages of the Huffman encoding algorithm, and eliminated the gaps in the arithmetic coding algorithm. The proposed encoding algorithm used a modified tree structure from the Huffman encoding algorithm. After this modification, a group of processes was applied to the image, then, a higher compression ratio and a lower number of bits per pixel were achieved. Since this new coding algorithm addressed deficiencies in the other coding algorithms, it bridged the gap in the literature.  
Figure 7: Decompression stages.
# Fivadmi: A Framework for In-Vehicle Anomaly Detection by Monitoring and Isolation +
## 4 Implementation of FIVADMI
### Side Channel Attack Monitor  
HexPADS [62; 78] is an open-source implementation that uses Perf and covers detections for a variety of side channel attacks including cache-based attacks. HexPADS uses cache hits, cache misses and page faults as HPC attributes; these attributes can be accessed through \"Windows Performance Monitor\" [79]. We adopted the algorithm of HexPADs (Figure 12) to detect Cache Side Channel attacks and used Microsoft Excel (version 2016) to apply the formulas on the readings. Determining the thresholds for the side channel detection algorithm in our environment was done through studying the changes in the values of the monitored HPC attributes when a side channel attack is performed. To study this change, we recorded the values of these attributes for two cases: the first with an ECU is running and the second with the same ECU running in parallel with a script that performs a side channel attack. For each case, we collected the data for 300 min equally spread over 15 iterations. For the attack, we used an open-source project that performs a simple Flush + Reload, a cache-based side channel attack [80]. The approach includes two phases: a profiling phase to locate a memory address that is the most engaged during the execution of the victim application, followed with an exploitation phase the targets that memory address to perform the Flush + Reload [81]. The project includes an attacker application, and the executable of a Virtual ECU is the victim application. The algorithm in Figure 12 shows our side channel detection algorithm, adopted from HexPADS, with the threshold obtained from studying the cache activity in our environment.  
Looking at the figure, the algorithm relies on threshold values of hardware performance counters that give early indications of side channel attacks. The phase of identifying these thresholds enables the algorithm to adapt to different operating systems for the ECUs, leading to increasing the applicability of the proposed approach to autonomous vehicles using chips from a variety of OEM. This algorithm is effective and lightweight, enabling its deployment on ECUs that have very limited computational resources. The simplicity of the rule-based approach makes it suitable for state-of-the-art ECUs where the on-chip memory is limited to 5 MB as indicated by a list compiled by NXP [82].  
Figure 11: Overview of the Open Enclave Architecture.  
Figure 12: The Algorithm for Cache Side Channel Detection.
# The Influence of Immersive Virtual Reality Systems on Online Social Application
## 3 Developing RVM-IVRSA
### Voice Communication  
Voice data transfers require use of microphones in HTC VIVE's HMD to collect users' voice data and create a WAVE file to store it. We use DirectX API DirectSound class to complete the task of voice acquisition. The collected voice data are transmitted to other clients through TCP socket. When other clients receive a voice file, they need to write acquired data to buffer and call function, which is \"GetViocdeData ()\" [28; 29; 30]. Real-time voice communications allow users to communicate more promptly. At the same time, user's tone, accent, and idiom are also included in the audio signal, which can more comprehensively show the user's mood and personality.
# No-Reference Blurred Image Quality Assessment by Structural Similarity Index
## 3 The Proposed IQA Metric
### Parameter Setting
#### 4.3.2 Patch Quantity  
Since we partitioned an image into \(P\times P\) patches when calculating NSSIM, the size and amount of the patches have impact on processing time. In Table 5, we list the running time taken (in seconds) to compute each quality on an image of resolution 768 \(\times\) 512 and 24-bit deep color from LIVE II dataset on a 2.6 GHz single-core PC with 4 GB of RAM. When \(P=16\), each patch is 48 \(\times\) 32 \(\times\) 3 and it takes average 2.4373 seconds to evaluate, which is the least running time. Thus, we set \(P=16\) in the experiments in this paper.
## 8 Conclusions  
This study has significantly deepened the understanding of how human uncertainty impacts resource management within 5G-enabled CPSS. By applying the MMtQHU model to the Ingoldstadt traffic scenario, the variability in driver behavior and its consequent effects on network performance, particularly during periods of high congestion, were effectively analyzed.  
One of the pivotal findings is the inadequacy of traditional frequency allocation methods in fully mitigating the negative impacts of human-induced variability. This limitation underscores the pressing need for the development of more advanced, adaptive strategies, potentially integrating emerging technologies such as artificial intelligence and machine learning, to enhance the responsiveness and resilience of 5G networks in dynamic, human-influenced environments.  
The insights derived from our simulations have substantial implications for real-world applications. By accurately modeling complex urban environments, the MMtQHU model provides a robust framework for predicting and addressing the challenges posed by human-driven behaviors in both traffic and network systems. These results lay a critical foundation for future implementations, where the knowledge gained can inform decision-making processes, optimize resource management, and ultimately enhance the performance and resilience of urban traffic and communication networks.  
To further advance this line of research, it is recommended to validate the MMtQHU model across diverse urban environments to improve its generalizability and applicability. Additionally, incorporating real-time data streams could substantially enhance the model's accuracy in reflecting actual conditions, enabling more effective management of human uncertainty and optimization of network performance. These efforts provide a solid foundation for future research aimed at strengthening the resilience and efficiency of 5G networks in the face of unpredictable human variability, guiding the evolution of adaptive, human-aware mechanisms in CPSS.  
Original draft preparation, N.A.M.; supervision, J.P., G.S.-B. and J.R.d.A.-A.; review and editing, J.P., G.S.-B. and J.R.d.A.-A. All authors have read and agreed to the published version of the manuscript.  
This publication is part of the Spanish I+D+i project TRAINER-A (ref. PID2020-118011 GB-C21) funded by MCIN/ AEI/10.13039/501100011033 and by the aid granted by the Sinfoni project of the Cooperative University of Colombia.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:
## 4 Discussions
### Sensitivity of Parameter Selection  
The method performance is more sensitive to some parameters (e.g., the resolution of voxel filtering, the number of populations and number of iterations of the ICS algorithm, etc.). Different parameter choices may lead to significant variations in computational complexity and alignment accuracy, making it difficult to maintain consistent performance in all scenes. To solve this problem, adaptive parameter tuning methods such as the Genetic Algorithm (GA) or Bayesian Optimization (BO) can be used to automatically select the optimal parameters based on specific data. Identifying key parameters and performing targeted optimization also helps to improve the robustness of the algorithm.
## 3 Preliminaries
### Data-Forwarding Phase
#### 4.2.2 Data Forwarding  
Once the packet is received, the selected forwarder determines whether it is a potential forwarder node using the attached FR value. Hence, after making the forwarding decision, the forwarder node replaces the FR value with its own FR value and other necessary information regarding the packet header before forwarding the packet.
## 6 Overcoming Barriers to Integration
### Regulatory Barriers  
The regulatory environment is another significant barrier, often rooted in outdated classifications and standards that fail to accommodate the innovative use of secondary materials [286]. For example, the classification of industrial by-products, like slags and tailings, as waste imposes strict handling and disposal regulations, increasing compliance costs and discouraging their reuse in construction [287]. There is a pressing need for the modernization of regulatory frameworks, which should recognize secondary materials as valuable resources rather than mere waste. By establishing comprehensive guidelines and standards, such as defining precise minimum quality requirements and acceptable levels of contamination, governments can significantly facilitate their broader adoption. This role is crucial in refining these frameworks to mirror the advancements in technology and adapt to the evolving market needs. Furthermore, policy adjustments that reclassify certain industrial by-products from waste to resources could lead to substantial reductions in compliance costs, thereby incentivizing companies to explore their use. In addition,the introduction of quality standards specifically designed for secondary materials could bridge the gap caused by market hesitancy, ensuring that these materials comply with the necessary performance and safety standards required for construction applications.  
Some jurisdictions have already developed progressive strategies in this direction. For instance, Germany and the Netherlands have applied flexible interpretations of the EU Waste Framework Directive (2008/98/EC), enabling industrial by-products to achieve \"end-of-waste\" status when quality thresholds are met. Similarly, the Italian case of the \"End of Waste Decree\" for aggregates has helped streamline the valorization of certain mineral residues. These examples highlight how updated regulatory instruments can unlock SRM potential by providing legal certainty and market access. Another relevant challenge is the fragmentation of regulatory criteria across countries, which can hinder the circulation and transnational use of SRMs. Improving harmonization, or at least mutual recognition of quality standards, would strengthen regional markets and facilitate the use of SRMs in large-scale public projects or cross-border supply chains.
## 3 The Sensor Integration
### Kalman Filter Update
Kalman filter formulation: In the prediction step of the Kalman filter, only the error covariance matrix is propagated:  
\[\mathbf{P}_{k}^{-}=\mathbf{\Phi}_{k}\mathbf{P}_{k-1}^{+}\mathbf{\Phi}_{k}^{T }+\mathbf{G}_{k}\mathbf{Q}_{k-1}\mathbf{G}_{k}^{T} \tag{37}\]  
Therein, the matrices \(\mathbf{\Phi}_{k}\) and \(\mathbf{G}_{k}\) are the discretizations of the matrices \(\mathbf{F}\) and \(\mathbf{G}\) at the time \(k\). The matrix \(\mathbf{Q}_{k-1}\) includes the discrete time noise. The filter update is computed using the standard Kalman filter equations:  
\[\mathbf{K}_{k} =\mathbf{P}_{k}^{-}\mathbf{H}_{k}^{T}\left(\mathbf{H}_{k}\mathbf{ P}_{k}^{-}\mathbf{H}_{k}^{T}+\mathbf{R}_{k}\right)^{-1} \tag{38}\] \[\mathbf{P}_{k}^{+} =\left(\mathbf{I}-\mathbf{K}_{k}\mathbf{H}_{k}\right)\mathbf{P}_ {k}^{-}\] (39) \[\delta\mathbf{x} =\mathbf{K}_{k}\left(\mathbf{z}_{k}-\mathbf{y}_{k}\right) \tag{40}\]where \(\mathbf{K}_{k}\) is the Kalman gain matrix, \(\mathbf{R}_{k}\) is the measurement noise matrix, \(\mathbf{H}_{k}\) is the measurement matrix, \(\mathbf{z}_{k}\) is the observation vector and \(\mathbf{y}_{k}\) is the vector of the predicted observations, which are based on the results of the SDA. For the update of the full state vector, the error state vector is simply added to the SDA-derived state vector. For the attitude update, Equations (25) and (26) have to be applied using \(\delta\psi\) instead of \(\Delta\sigma\).
## 3 Results and Discussion
### Visualization of the Performance of ML and DNN for This Problem
This deficiency of the dataset can be overcome with extended experimental data or data augmentation techniques to increase the generalization capacity of the model and improve the prediction performance. When the data splitting was 70% training and 30% testing, the test performance of the XGBoost algorithm was 0.8810 according to the \(R^{2}\) metric and 27.3317 according to the RMSE metric. As for 80% training/20% data splitting, the test performance of the XGBoost algorithm was 0.9299 for the \(R^{2}\) metric and 15.8628 for RMSE metric. It can be seen that the XGBoost algorithm made better predictions than the RF algorithm. Furthermore, the test performance of DNN was 0.8846 according to the \(R^{2}\) metric and 26.9236 according to the RMSE metric for 70% training/30% test data splitting. As for 80% training/20% data splitting, the performance of the DNN was 0.9377 according to the \(R^{2}\) metric and 14.3248 according to the RMSE metric in Figure 10. It can be said that the DNN made better predictions than both the RF algorithm and the XGBoost algorithm.
# Review of Advanced Implementation of Doppler Backscattering Method in Globus-M
## 3 Application Results
### Filaments
This is because of their smaller radial size and deeper location inside the separatrix, which makes them different from ELM filaments whose greater elongation reaches the separatrix where the Langmuir probe was situated. The last type is filaments induced by neoclassical tearing modes. This type of filament was observed in Ohmic H-mode discharges with rather high densities \(\mathrm{n_{e}}\) = 2-3.5 \(\times\) 10\({}^{19}\) m\({}^{-3}\)[59]. The main properties are similar to inter-ELM filaments (see Table 6). Such filaments occur in a certain phase of MHD oscillations, namely, when the magnetic island is located on the low field side of the torus (opposite the antenna), where the plasma pressure gradient increases and conditions are suitable for the development of the peeling-ballooning mode. Their location is demonstrated in the reconstruction in Figure 5. It was constructed using the DBS measurements of the filament velocity and recorded time intervals between adjacent bursts, which allowed one to determine the distance between a series of filaments. Their localization in both the poloidal and toroidal directions along with the direction of the movement is clearly highlighted. Full-wave modeling was carried out to obtain information on the behavior of the DBS signal during the transition from linear to nonlinear backscattering from filaments [63; 64; 65]. First, modeling showed that similar BQFs are formed in case of scattering from filament in both linear or nonlinear regimes if the filament size does not significantly exceed the characteristic value of \(\pi/\mathrm{k_{\perp}}\). In such an event, the Doppler shift can be determined by the expression, which is also valid in the Born approximation in slab geometry. Secondly, with an increase in the amplitude and size of the filament, the BQF deforms, forming the double-maximum BQF.
# Structure-Aware Scheduling Methods for Scientific Workflows in Cloud
## 4 Algorithmic Solutions
### Hierarchy-Based Approach
#### 4.2.1 Partitioning  
Through partitioning, the groups that do not depend on the tasks of other groups can begin their execution immediately. As between-group dependency cannot be eliminated, each obtained group in the partitioning step is assigned a dependency value. In this context, a group with dependency can be assigned to VMs once all groups with higher dependency values are mapped to the assigned VMs. Algorithm 3 illustrates the partitioning steps.  
Beginning from entry-level tasks, a group is initialized to contain the first task (line 7). The group under consideration is expanded to include the descendant tasks of all tasks assigned to the group. Once a group cannot be expanded any further, the tasks assigned to that group are excluded from consideration in a subsequent step. Subsequently, the following group is initialized to contain the available entry-level tasks. The dependency value of each group (\(g_{d}\)) is assigned sequentially such that the first constructed group has the highest dependency (one). Once all groups have been constructed, group merging is triggered to examine the obtained groups. An obtained group that spans a single workflow level is merged with another. Merging is important as relatively small groups have small VMs shares, and merging is expected to improve scheduling efficiency. A group with dependency value \(n\) is merged with the latest previously constructed group (dependency value \(n\)-1). Partitioning results in a set of task groups \(g_{1},g_{2},g_{3},...\) in which each group is associated with a dependency value (\(g_{d}\)).  
```
Data:\(GW\): workflow graph Result:\(G\): set of group
1\(i\gets 1\)
2\(L\leftarrow getEntryLevel(GW)\)
3\(t\leftarrow getTasks(GW)\)
4 whilet not emptyto do
5\(g_{i}\leftarrow\emptyset\)
6\(temp\gets getAvilableTask(L)\)
7 add\(temp\) to\(g_{i}\)
8\(D\gets getAllDecentant(temp,GW)\)
9 add\(D\) to\(g_{i}\)
10 add\(g_{i}\) to \(G\)
11\(i\gets i+1\)
12 Remove\(temp\) &\(D\) from \(t\)
13 end while
14 for\(g_{i}\in G\)do
15 if\(numberOfLevel(g_{i})==1\)then
16\(g_{i-1}\gets merge(g_{i},g_{i-1})\)
17 end if
18
19 end while return\(G\)
```  
**Algorithm 3**Partitioning