esa-sceva/satcom-chunk-collection huggingface dataset:
# GPU-Accelerated Eclipse-Aware Routing for SpaceWire-Based OBC in Low-Earth-Orbit Satellite Networks
## 2 Problem Description
### Overall Structure of the Algorithm and the Need for Parallelization  
In this paper, a BFS-based KSP algorithm is combined with EAR to implement an efficient routing method for LEO satellite networks. The overall flow of the implemented algorithm is illustrated in Figure 1 and consists of three main stages: graph initialization, KSP search, and energy consumption calculation.  
First, to perform routing, the algorithm initializes a graph that represents the connectivity of the satellite network. The input data is stored in a text file (.txt) format and includes the total number of satellites, the source node, the destination node, the number of paths to search (K), and inter-satellite connection information. Based on this data, the algorithm updates the node and edge information to construct the graph, preparing it for the subsequent KSP search.  
In the next step, the algorithm performs the KSP search based on BFS to find K shortest paths. Starting from the source node, the BFS search expands along the paths, and each candidate path is managed as a list. When a path reaches the destination node, it is sorted  
Figure 1: Block diagram of a conventional Eclipse-Aware Routing algorithm.  
based on the path cost, and the top K paths are selected. Finally, the K candidate paths are returned, and the optimal path is determined through energy consumption analysis.  
Finally, to select the most energy-efficient path among the K shortest paths, the algorithm analyzes the energy consumption of each satellite, as previously described. First, the eclipseMatrix file is loaded to determine whether each satellite is in an eclipse state at a given time. Then, the expected energy consumption of each satellite is calculated based on the energy required for transmission, reception, and baseline operations. In particular, the battery depletion is predicted by considering the duration of the eclipse. The algorithm evaluates the maximum energy consumption for each path and ultimately selects the path that minimizes energy usage.  
The performance evaluation results of applying this algorithm to an OBC simulating a LEO satellite network environment are presented in Figure 2. To analyze the execution time of the code, the number of satellites was set to 150, and the number of shortest paths to search (K) was set to 15 for profiling. The analysis revealed that the KSP search process accounted for 92.53% of the total execution time. In contrast, the graph initialization and energy consumption calculation processes incurred relatively lower computational costs.  
Therefore, this paper identifies the KSP search process as the primary bottleneck determining the overall algorithm's performance. To optimize this process, a GPU-based parallelization approach is proposed. By leveraging the parallel computing capabilities of the GPU, tasks such as path expansion and candidate path management can be executed more efficiently. This optimization enhances the path search speed compared to conventional computation methods.
# Automatic Detection System of Deteriorated PV Modules Using Drone with Thermal Camera
## 3 System Configuration
### Thermal Image Processing
#### 3.4.1 ROI Segmentation  
Given image \(I_{RGB}\), the PV modules are extracted from the image by using color thresholding approach similar to the one used in Section 3.2. The image is converted to HSV color space which separates the color information from the intensity information. After color space conversion, color thresholding is applied with a lower threshold limit as (80, 65, 65) and an upper threshold limit as (140, 255, 255). The resulting image \(I_{ROI}\) is smoothed and inputted to canny edge detection algorithm [19]. Contours detected in the edge image \(I_{e}\) are filtered based on area of the contours. Contours below a certain threshold area are removed, which results in binary image containing the borders of the PV modules. The boundaries/borders are flood filled to create a mask \(M_{RGB}\) for input to the feature detection algorithm.
## 5 Simulation Analysis
### Simulation Parameters  
The simulation parameters are shown in Table 2. The bandwidth setting for satellite nodes is adapted from the reference [20]. This paper uses 15 Mbps as the message service transmission rate. According to ZTE's white paper '5G Video Uplink Transmission Applications and Challenges' published in 2021, the transmission rate for high-definition video is generally between 8 and 15 Mbps. This paper uses 8 Mbps as the video service transmission rate. In [21], it is mentioned that the satellite data uplink rate in emergency scenarios is 13.8 Mbps. In this paper, 15 Mbps is adopted as the transmission rate for message services. Additionally, since the standard rate used for telephony voice communication in G.711 is 64 kbps [22], this paper also uses 64 kbps for the voice service transmission rate. Since all data must be forwarded by the cluster heads, the cluster heads are prone to be overwhelmed. To avoid a queue overflow at the cluster head, the number of member nodes in a cluster should not be too large. Therefore, without loss of generality, in this paper the maximum number of cluster member nodes is set to 4.
# The Deep Deterministic Policy Gradient Algorithm Based on RIS Technology in a Coal Mine Tunnel
## 3 Deep Reinforcement Learning Based Optimization  
### Overview of the DDPG Algorithm  
To cope with the limitations of traditional reinforcement learning methods in continuous action spaces, the DDPG algorithm combines the expressive capabilities of deep learning with the optimization strategies of policy gradient methods, achieving policy optimization through the actor-critic structure. The framework of the DDPG algorithm is shown in Figure 3. The actor and critic training networks are used to learn to select and evaluate actions, respectively. The actor target network provides stable target strategies for the actor training network, while the critic target network provides stable Q-value for the critic training network and is updated synchronously regularly.  
The action value function \(Q_{\pi}\) evaluates the expected cumulative reward obtained by taking action \(a_{t}\) in state \(s_{t}\). The Bellman expectation equation for the Q-value under policy \(\pi\) is expressed as follows:  
\[Q_{\pi}(s_{t},a_{t})=\mathbb{E}[R_{t+1}+\gamma\cdot\sum_{a^{\prime}}\pi\big{(} a^{\prime}|s_{t+1}\big{)}Q_{\pi}\big{(}s_{t+1},a^{\prime}\big{)}|S_{t}=s_{t},A_{t}=a _{t}] \tag{20}\]  
where \(R_{t+1}\) is the reward obtained after taking action \(a_{t}\) in the state \(s_{t}\), \(s_{t+1}\) is the next state, \(a^{\prime}\) is the possible action to be taken in the next action, and \(\pi(a^{\prime}|s_{t+1})\) is the probability of the policy to take the action \(a^{\prime}\) in the next state.  
The optimal policy is determined by maximizing the Q-value:  
\[\pi^{*}=\underset{\pi}{\text{argmax}}Q_{\pi}(s_{t},a_{t}) \tag{21}\]  
To directly evaluate the current state and action without considering the influence of policy \(\pi\), the optimal action value function \(Q^{*}(s_{t},a_{t})\) is introduced. For the optimal Q-value function, the Bellman optimality equation is expressed as follows:  
\[Q^{*}(s_{t},a_{t})=\mathbb{E}[R_{t+1}+\gamma\cdot\max_{a^{\prime}}Q^{*}\big{(} s_{t+1},a^{\prime}\big{)}|S_{t}=s_{t},A_{t}=a_{t}] \tag{22}\]  
The \(Q^{*}(s_{t},a_{t})\) function is updated as follows:  
\[Q^{*}(s_{t},a_{t})\leftarrow(1-\alpha)Q^{*}(s_{t},a_{t})+\alpha\cdot\big{(}R_ {t+1}+\gamma\cdot\max_{a^{\prime}}Q(s_{t+1},a^{\prime})\big{)} \tag{23}\]  
where \(\alpha\) is the learning rate of the \(Q^{*}(s_{t},a_{t})\) function update.
###### Abstract  
The increased use of Industrial Wireless Sensor Networks (IWSN) in a variety of different applications, including those that involve critical infrastructure, has meant that adequately protecting these systems has become a necessity. These cyber-physical systems improve the monitoring and control features of these systems but also introduce several security challenges. Intrusion detection is a convenient second line of defence in case of the failure of normal network security protocols. Anomaly detection is a branch of intrusion detection that is resource friendly and provides broader detection generality making it ideal for IWSN applications. These schemes can be used to detect abnormal changes in the environment where IWSNs are deployed. This paper presents a literature survey of the work done in the field in recent years focusing primarily on machine learning techniques. Major research gaps regarding the practical feasibility of these schemes are also identified from surveyed work and critical water infrastructure is discussed as a use case.  
industrial informatics; industrial sensor network; cyber-physical systems; critical infrastructure; water monitoring +
Footnote †: journal: Sensors
## 3 Results and Discussion
Damaging underestimations are likely to be due to real conditions of low attitude and direct UV exposure (for example, within the setup of AtOx fluence and UV ray intensity adopted, the potential AtOx erosion may effectively be up to 50% greater); on the contrary, an overstated aging will be wrongly expected if the ground-simulated erosion rates (commonly evaluated by means of normal AtOx irradiation tests) are applied to long-term, in-flight stages with high attitude conditions. This can be of key importance for spacecraft C/C-based TPS design, because the crucial functionality of these components in re-entry missions requires an accurate safety limit assessment. For instance, supposing that the tested C/C material is analyzed to be employed as the exposed layer of re-entry systems, a reliable failure alert may be set off when the component is damaged by LEO agents at 30% of its thickness: a simple evaluation of erosion depth rate (\(\upmu\)m/s) is made possible by the experimental results reported, stating that a mechanical breakdown is expected to occur after less than two months within the operative conditions of aligned AtOx/UV irradiance, while the same structure is believed to withstand LEO agents for more than one year by addressing particular spacecraft design and mission dimensioning. In any case, the most recent findings establish that commercial C/C materials cannot be considered as fully suitable for long-term LEO re-entry missions without specific surface antioxidative reinforcement: it has been confirmed that advanced coating treatments are necessary in order to provide modified C/C bulk materials with improved AtOx resistance (e.g., \(E_{Y}<10^{-24/-25}\) cm\({}^{3}\)/n.at. ), as has been previously reported elsewhere [14,23].
## 3 Results and Discussion
### Interfacial Structure  
The structural feature is described by the RDD of BNNS/EP composites that is defined as the average per-atom densities in slices with a specified thickness along a specified axis. The RDD is computed by using the fix/ave/chunk command in LAMMPS [61]. The interfacial position and thickness are characterized by analyzing RDD. The RDD along the z-axis of four kinds of composites was computed, in which the slice thickness was chosen as 0.6 A, as shown in Figure 6.  
From the RDD curves of BNNS/EP composites, the high density of about 4.0 g/cm\({}^{3}\) is at 40 A at the position of BNNS. According to the RDD of pure EP, the average density is 1.12 g/cm\({}^{3}\), which is consistent with the characteristics of epoxy-based amorphous materials. The mass density near BNNS is significantly higher than that of pure EP, which can explain the existence of interface at nanocomposites, which is formed under the action of Van der Waals long-range attraction on the surface of the BNNS. Furthermore, the interfacial thicknesses of BNNS/EP, 4 FBNNS/EP, and 8 FBNNS/EP are about 8 A, 9.5 A, and 10.5 A, respectively, as shown in Figure 6. Besides, the interface region of BNNS/EP is composed of three regions, namely compact region, buffer region, and normal region. The mass density of the compact region is 15-25% higher than that of the normal region. The mass density of the buffer region decreases with the distance from the BNNS increasing, but the mass density is still higher than that of the normal region. The mass density of the normal region is the same as that  
Figure 5: Models for nanocomposites with vacuum layer: (**a**) EP, (**b**) BNNS/EP, (**c**) 4 FBNNS/EP, (**d**) 8 FBNNS/EP.  
of pure EP. Considering the interphase density may be related to the nanomaterials-matrix interaction, interface interaction strength is analyzed in the next section to further explain this phenomenon.
# Homogeneous Data Normalization and Deep Learning: A Case Study in Human Activity Classification
###### Abstract  
One class of applications for human activity recognition methods is found in mobile devices for monitoring older adults and people with special needs. Recently, many studies were performed to create intelligent methods for the recognition of human activities. However, the different mobile devices in the market acquire the data from sensors at different frequencies. This paper focuses on implementing four data normalization techniques, i.e., MaxAbsScaler, MinMaxScaler, RobustScaler, and Z-Score. Subsequently, we evaluate the impact of the normalization algorithms with deep neural networks (DNN) for the classification of the human activities. The impact of the data normalization was counterintuitive, resulting in a degradation of performance. Namely, when using the accelerometer data, the accuracy dropped from about 79% to only 53% for the best normalization approach. Similarly, for the gyroscope data, the accuracy without normalization was about 81.5%, whereas with the best normalization, it was only 60%. It can be concluded that data normalization techniques are not helpful in classification problems with homogeneous data.  
h _future internet_ +
Footnote †: journal: Computer Science and Engineering  
_Future Internet_**2020**, _12_, 194; doi:10.3390/fi12110194
## 3 Results and Discussion
### SG4 Induced Activation of Caspase-9 and Caspase-3 in HT-29 Cells  
There are two fundamental pathways, the extrinsic pathway (death receptor pathway) and the intrinsic pathway (the mitochondria pathway), in cell apoptosis [34]. The intrinsic pathway may operate as a consecutive process of loss of mitochondrial transmembrane potential, release of cytochrome c into the cytoplasm, formation of a complex composed of cytochrome c, the cytoplasmic protein Apaf-1, and pro-caspase-9 (known as an apoptosome), which induces the activation of caspase-3. The effector caspases may cleave various proteins leading to the various morphological and biochemical processes involved in apoptosis [35,36]. Here, the activation of caspase-9 and -3 in cells was measured by a flow cytometry. As shown in Figure 4a,b, when HT-29 cells were treated with 200 ug/mL SG4 for 48 h, the high fluorescent cell population in the caspase-9 group significantly increased from 5.30\(\%\pm\) 0.14\(\%\) (control) to 16.3\(\%\pm\) 0.8\(\%\) (\(p\) < 0.001), indicating an increase of active caspase-9. Moreover, as shown in Figure 4c,d, when HT-29 cells were treated with 200 ug/mL SG4 for 48 h, the high fluorescent cell population in the caspase-3 group significantly increased from 5.43\(\%\pm\) 0.33\(\%\) (control) to 17.1\(\%\pm\) 0.5\(\%\)  
Figure 3: The MMP and cytochrome c analyses of HT-29 cells treated with 200 ug/mL SG4 for 48 h: (**a**) In the histograms, \(x\) axis and \(y\) axis denote TMRE content and cell counts, respectively. The percentage of cells with mean channel fluorescence values above the marker border is indicated in each histogram; (**b**) the percentage of cells with high fluorescence of TMRE is presented as determined for (**a**). (**c**) In the histograms, \(x\) axis and \(y\) axis denote cytochrome c content and cell counts, respectively. The percentage of cells with mean channel fluorescence values above the marker border is indicated in each histogram; (**d**) the percentage of cells with high fluorescence of cytochrome c is presented as determined for (**c**). The experiments were performed in triplicate. ***\(p\) < 0.001.  
(\(p<0.001\)), indicating an increase of active caspase-3. These results clearly show that SG4 induced activation of caspase-9 and -3 in HT-29 cells.
## 5 Statistical Analysis
### Realiability Analysis  
The reliability test is about whether a measurement is free from random error. Evaluation of reliability is determined by the proportion of systemic variation in a scale. Where measurements are repeated several times. Therefore, if the reliability measurement relation is strong, the scale yields consistent outcomes and is thus accurate.  
To perform reliability analysis for our collected data we calculated the Cronbach coefficient. Cronbach's alpha is a measure of internal consistency and shows how closely related a set of items are as a group [38]. The value of the Cronbach coefficient varies from 0 to 1, [PERSON]'s alpha at \(\alpha<0.5\) is unacceptable, at \(0.5\leq\alpha<0.6\) is poor, at \(0.6\leq\alpha<0.7\) is questionable, at \(0.7\leq\alpha<0.8\) is acceptable, at \(0.8\leq\alpha<0.9\) is Good, at \(0.9\leq\alpha\) is Excellent [38]. Mathematically,  
\[\alpha=\frac{N\bar{c}}{\bar{v}+(N-1)\bar{c}} \tag{2}\]  
where \(N\) is the number of items in the group, \(\bar{c}\) is the average covariance between paired items, and \(\bar{v}\) is the average variance. Table 3 reports the Cronbach's coefficients calculated for Agile methodology use, person-job fit, and project success. From this table, it can be noticed that the internal consistency of the project's success is excellently reliable (Cronbach value of 0.852), that of Agile methodology is acceptably reliable ([PERSON] value of 0.742), and for person-job fit, the internal consistency is unreliable ([PERSON] value of 0.569).
# Reviews Based on the Reconfigurable Intelligent Surface Technical Issues
## 2 Technical Considerations for RIS
### RIS Standardization
According to the report, RIS is a key post-IMT-2020 wireless technology that can dynamically control the wireless environment and is mentioned as an important element for the physical layer of next-generation networks [31]. For 3 GPP, the focus is currently on launching 5G Phase 2 with Release 18, as shown in Figure 10, and it is expected that RIS-related standardization after 2023 will start with SI on use cases, deployment scenarios, and channel models in Release 19, followed by WI after that SI is completed [32]. Since the first proposal was submitted by ZTE Corp. in March 2021 [33], many companies (mainly network operators) are interested in the RIS as it will be a key component in 5G-Advanced networks. As a completely new technology, one of the most important tasks to standardize RIS will be channel modeling.
# Calculation of Main Circuit Steady-State Parameters for Capacitor Commutated Converter System
## 3 Calculation Model and Method of the Main Circuit Parameters
### Determination of the Capacitance Value
#### 3.1.1 Voltage Waveform of Commutation Capacitors  
The voltage waveforms of the commutation capacitors for a six-pulse CCC are shown in Figure 3. The three-phase capacitor voltages \(u_{\text{cat}}\), \(u_{\text{cb}}\) and \(u_{\text{cc}}\) present periodic changes. To further explore the voltage change in the capacitor in the commutation process and the non-commutation process, the commutation capacitor voltage waveforms of phase A in Figure 3 is partially enlarged, as shown in Figure 4.  
Figure 4: Partially enlarged voltage waveform of the commutation capacitor in phase A.  
Figure 3: Voltage waveforms of the commutation capacitors.  
Figure 2: Diagram of a six-pulse CCC equivalent model at the inverter side.  
In Figure 4, curve ab represents the commutation process from valve 2 to valve 4, and the corresponding capacitor voltage drops from \(U_{\rm c}\) to \(U_{\rm c}-\Delta U_{1}\). Straight line bc indicates that only valve 4 is on at the common anode. The commutation capacitor of phase A discharges from \(U_{\rm c}-\Delta U_{1}\) to 0, and reversely charges from 0 to \(\Delta U_{2}-U_{\rm c}\). Curve cd represents the commutation process from valve 4 to valve 6, and the corresponding capacitor voltage drops from \(\Delta U_{2}-U_{\rm c}\) to \(-U_{\rm c}\). \(\Delta U_{1}\) and \(\Delta U_{2}\) represent the voltage change in the commutation capacitor connected in series with the phase entering the commutation and the phase exiting the commutation during the commutation process. \(\Delta U_{1}\) and \(\Delta U_{2}\) can be calculated as follows:  
\[\Delta U_{1}=\left|\frac{1}{\omega C}\right\rangle_{\alpha}^{\alpha+\mu}i_{k} d\theta\Bigg{|} \tag{1}\]  
\[\Delta U_{2}=\left|\frac{1}{\omega C}\right\rangle_{\alpha}^{\alpha+\mu}[(-1) ^{k+1}I_{\rm d}-i_{k}]d\theta\Bigg{|} \tag{2}\]  
where \(i_{k}\) is the commutation current of the \(k\)-th commutation process, \(\alpha\) is the firing angle and \(\mu\) is the overlap angle.  
The peak capacitor voltage \(U_{\rm c}\) satisfies:  
\[2U_{\rm c}=\Delta U_{1}+(\frac{2\pi}{3}-\mu)\frac{I_{\rm d}}{\omega C}+\Delta U _{2} \tag{3}\]
## 2 Materials and Methods
### Unsupervised Clustering and Validation  
The dimensional reduction analysis provides a set of restricted dimensions which can suitably model the subjects' heterogeneity. Using these dimensions and the related clinical features, we adopt a clustering procedure to characterize the similarity of the patients enrolled in this study. To this aim, we evaluated the pairwise distances between the patients in the embedding space. Then, we used the unweighted pair group method with arithmetic mean (UPGMA) [23] to build a dendrogram. This procedure combines iteratively patients in groups according to growing distance scales among them, as defined by  
\[d_{A,B}=\frac{1}{|A||B|}\sum_{xx,A}\sum_{y\
eq B}d(x,y)\]  
followed by the next patients' group distance weighted by the cardinality of the previous groups \(d_{A\cup B,C}=(|A|d_{A,C}+|B|d_{B,C})/(|A|+|B|)\). In this way we define communities of patients typical of each distance scale. The distance is computed by considering the feature in a Euclidean reference system. The choice of the cutting level in the dendrogram is implemented according to internal validations, which reward cluster compactness and separation, while to measure how much the yielded partition matches with the studied medical variables we used the external validation. In particular, the decision tree nature of molecular subtypes allows us to improve explainability [10]. To evaluate the robustness of the cluster analysis, several metrics were investigated, such as total within sum of squares, average silhouette width, Dunn index and adjusted Rand index [22; 24; 25] (Appendix C). The limited cardinality of the considered data set imposes a required quantification of uncertainty that we implement by means of a bootstrap procedure [26]. Bootstrap consists in sampling data with replacement in order to obtain replica of the original data which statistically follow the same distribution. To ensure a higher fidelity of this procedure, we replicate the original data set by adopting a sampling ratio equal to the 80% of the available instances. This ratio ensures that the balance of considered labels is preserved. This procedure is implemented 1000 times, in each trial evaluating the three internal validations and the ARI external validation for both molecular subtypes and therapy labels. Internal and external validation metrics were implemented by means of R packages Clustering (version 1.7.2), factoextra (version 1.0.7) and mclust (version 5.4.7).