esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### STPA-SafeSec  
There is a growing recognition that Safety and Security should be considered holistically when analysing/designing CPSs such as industrial control systems [16], automated vehicles [17; 18; 19], and intelligent transport systems [20; 21; 22].  
STPA analyses the system mainly from Safety perspective while STPA-Sec--Security perspective. As an effort to treat safety and security equally, [PERSON] et al. combines STPA and STPA-Sec into a concise approach called STPA-SafeSec [23]. To complement the STPA's generic control loop diagram (shown in Figure 1), STPA-SafeSec introduces a generic component layer diagram encompassing the physical components (e.g., network device and connection) that typically exist in any control loop. In addition, it suggests the likelihoods of certain security threats violating availability /integrity can be exploited on particular system components; these threats serve as additional causal factors, besides those included in Figure 1, that the analyst should consider in identifying the S&S constraints. The constraints are then mapped to the system components using a graph, as illustrated in Figure 2, to highlight which constraints may be violated at each component. The greater the number/criticality of constraints potentially violated at a component, the greater the urgency to perform in-depth security analysis on that component. By leveraging on the graph and the component layer diagram, the analysts might see which S&S constraints are in fact more crucial for addressing the hazards, thereby determining the most cost-effective set of constraints/solutions.  
However, it is unclear how STPA-SafeSec defines those Availability/Integrity threats and even the likelihoods of exploiting them. Obviously, it does not address Confidentiality--one of the three key Security properties besides Availability and Integrity. Please note that, in contrast with STPA-SafeSec, the proposed S&S analysis methodology (see Section 3) employs STRIDE model [24] and thus covers all three key Security properties--i.e., Confidentiality, Integrity, and Availability.  
Furthermore, the inter-dependency between S&S constraints (aka requirements), which STPA-SafeSec considers, seems not falling into any of these three categories (discussed in [25, 26]), namely Conditional Dependency, Conflict, and Reinforcement. There is a consensus that the conflicts between requirements should be identified and resolved as early as possible especially during system development [27, 28, 29, 30, 31, 32]. Identifying or resolving conflicts between requirements can be challenging [33]. There seems to be a paucity of literature on how to identify conflicts between Safety and Security; [PERSON] et al. [32] suggest that both safety analysts and security analysts should work together; [PERSON] et al. [26] utilize a modeling formalism called BDMP (Boolean logic Driven Markov Processes). The latter the conflicts get spotted, the greater the cost/effort to resolve them [33].
## 2 Materials and Methods
### Data Sources and Reprocessing
#### 2.1.4 Meteorological Datasets  
The reanalysis meteorological datasets from 2003 to 2016 were obtained from the ERA-Interim global reanalysis data ([[https://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/](https://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/)]([https://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/](https://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/)), accessed on 13 December 2020). Here, we selected 2 m temperature, total precipitation and photosynthetically active radiation (PAR) from ERA-5. All of these variables were obtained from the surface-level fields of this reanalysis at 0.125\({}^{\circ}\) for a daily interval. The potential evapotranspiration (PET) was downloaded from the gridded Climatic Research Unit (CRU) datasets ([[https://catalogue.ceda.ac.uk/uuid/10d3e3640f004](https://catalogue.ceda.ac.uk/uuid/10d3e3640f004)]([https://catalogue.ceda.ac.uk/uuid/10d3e3640f004](https://catalogue.ceda.ac.uk/uuid/10d3e3640f004)) c578403419 aac167d82, accessed on 27 January 2021). The original climate datasets from 2003 to 2016 were aggregated to a monthly time scale at a 0.05\({}^{\circ}\) spatial resolution to calculate climatic limitation indices.
## 4 Hypothesis Testing Results  
Hypothesis testing was conducted using PLS-SEM with 5000 bootstrapped resamples to ensure robustness in our estimates, as detailed in Table 6. The results indicate that perceived intimacy (PAI) significantly and positively influences both parasocial interaction (PSI) (\(\beta\) = 0.172, \(p\) < 0.01) and trust (TRS) (\(\beta\) = 0.390, \(p\) < 0.001). Similarly, perceived morality (PAM) is positively associated with PSI (\(\beta\) = 0.159, \(p\) < 0.01) and TRS (\(\beta\) = 0.149, \(p\) < 0.01). In contrast, information privacy risk (IPR) negatively influences both PSI (\(\beta\) = \(-\)0.129, \(p\) < 0.05) and TRS (\(\beta\) = \(-\)0.190, \(p\) < 0.01). Notably, perceived dependency (PAD) has a significant negative association with PSI (\(\beta\) = \(-\)0.134, \(p\) < 0.01), but its relationship with TRS (\(\beta\) = \(-\)0.044, \(p\) > 0.05) is not significant, demonstrating that no significant relationship was found between PAD and TRS. Moreover, TRS is positively associated with PSI (\(\beta\) = 0.265, \(p\) < 0.001). Both PSI (\(\beta\) = 0.305, \(p\) < 0.001) and TRS (\(\beta\) = 0.332, \(p\) < 0.001) are positively associated with the continuance intention to use (CUI). Therefore, as shown in Table 6, all hypotheses from H1 to H11 were supported, except for H7, which was not statistically significant. Furthermore, the mediation effect of PSI and TRS on the relationship between the dimensions of MOCCA's features and CUI was examined. We used the bootstrapping procedure of SmartPLS to test the indirect effect, as we cannot assume a direct relationship between these structures based on the SOR framework [110]. As shown in Table 6, PSI significantly mediates the effects of PAI (\(\beta\) = 0.052, \(p\) < 0.05), PAM (\(\beta\) = 0.049, \(p\) < 0.05), PAD (\(\beta\) = \(-\)0.041, \(p\) < 0.05), and IPR (\(\beta\) = \(-\)0.039, \(p\) < 0.05) on CUI. Similarly, TRS mediates the impact of PAI (\(\beta\) = 0.129, \(p\) < 0.001), PAM (\(\beta\) = 0.049, \(p\) < 0.05), and IPR (\(\beta\) = \(-\)0.063, \(p\) < 0.01) on CUI. However, TRS does not mediate the effect of PAD on CUI (\(\beta\) = \(-\)0.014, \(p\) > 0.05).
# Performance Evaluation Method for Intelligent Computing Components for Space Applications
## 3 Evaluation Method
### High-Performance Computing Benchmark
#### 3.3.4 Matrix Multiplication Benchmark (GEMM)  
Matrix multiplication is applied in many fields. In space exploration and astronomy, matrix multiplication is used to describe the motion of celestial bodies and analyze their orbits. Through matrix multiplication, the position, velocity, and orbital parameters of celestial bodies can be calculated for planetary orbit analysis, spacecraft navigation, and celestial dynamics research. The study in [25] documents the linear algebra benchmark performance of space processors using a 1024 \(\times\) 1024 matrix.  
The GEMM benchmark kernel is designed for matrix multiplication based on the Basic Linear Algebra Subroutines (BLAS) library and has been simplified. It is an optimized implementation of GPU matrix multiplication (GEMM) that is compatible with a wider range of devices. The GEMM benchmark tests the multiple applications of matrix multiplication; it calculates \(C=\alpha\cdot A\cdot B+\beta\cdot C\), where \(A,B,C\in R^{n\times n}\) and \(\alpha,\beta\in R\). The floating-point operations of this benchmark are calculated as \(2\cdot n^{3}\).  
The GPU implements GEMM by dividing the output matrix into tiles and then assigning small tiles to thread blocks. When calling cuBLAS with specific GEMM dimensions, the internal heuristic methods of cuBLAS can choose the tiling option expected to perform the best. The general tiling outer product method for matrix multiplication is shown in Figure 2.
# Bilateral Matching Decision Model and Calculation of High-Speed Railway Train Crew Members
###### Abstract  
To meet the preference demands of the crew members of high-speed railway trains while forming a crew team, and to automate the compilation of adaptable crew member schemes, a bilateral matching decision method for crew members is proposed based on complete preference order information. This method first describes the mutual selection process between the chief stewards and stewards of a high-speed railway train as a one-to-many bilateral matching decision process between the chief stewards and stewards Subsequently, by constructing a virtual train chief stewards, the original one-to-many bilateral matching relationship between the chief stewards and stewards is transformed into a one-to-one bilateral matching relationship between the virtual chief stewards for modeling. Then, a dual-objective integer programming model is established with the minimum sum of preference order values as the objective. Finally, an optimization solver is used to calculate the problem under different scales, and a genetic algorithm is designed for large-scale scenarios. The analysis results of numerical examples show that the model and algorithm of train crew members based on bilateral matching decisions can meet the actual requirements of the crew department and have good application value.  
Keywords:railway transportation; train crew matching; one-to-many bilateral matching; crew scheduling; genetic algorithm +
Footnote †: journal: applied sciences
## 2 The Continuum Model for EIT
### The Complete Electrode Model  
In the laboratory, we do not know the current density nor the potential at every point on the surface \(\partial\Omega\). Rather, \(N\) electrodes are attached to the boundary and current flows through these electrodes. Thus, there will be no current density in the surface normal direction at the gaps between the electrodes. In other words, if \(E_{n}\) is the \(n\)-th electrode, then for \(\mathbf{x}\in\partial\Omega\) and \(\mathbf{x}\
otin E_{n}\), we have  
\[\sigma\frac{\partial\phi}{\partial\mathbf{n}}=j=0. \tag{4}\]  
While the current passing through the electrodes is given by  
\[\int_{S_{n}}\sigma\frac{\partial\phi}{\partial\mathbf{n}}ds=I_{n} \tag{5}\]  
where \(I_{n}\) is the current passing through the \(n\)-th electrode. Due to charge conservation, we must also have  
\[\sum_{n}I_{n}=0. \tag{6}\]  
Typically, between the surface and the electrode, there is a contact impedance layer, so that if the voltage on the \(n\)-th electrode is \(\Phi_{n}\), the potential on the surface will be less than \(\Phi_{n}\), since there will be a voltage drop across this layer. Therefore, we use the mixed or Rubin boundary condition  
\[\phi=\Phi_{n}-z_{n}\sigma\frac{\partial\phi}{\partial\mathbf{n}} \tag{7}\]  
where \(z_{n}\) is the contact impedance for the \(n\)-th electrode. It is usually assumed that this does not vary on \(E_{n}\). This boundary condition is only satisfied at points \(x\in E_{n}\). Finally, by the choice of a ground, we can write  
\[\sum_{n}\Phi_{n}=0. \tag{8}\]  
Equations (4)-(8) along with the differential Equation (1) have a unique solution [30], and are known as the complete electrode model. The full electrode model mimics what happens when taking measurements for EIT imaging much better than the continuum model.
# The Effect of Inhaled Corticosteroid Therapy on Periodontal Status in Patients with Asthma
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multirow{2}{*}{**Median**} & \multicolumn{2}{c}{**Central Sextants**} & \multicolumn{3}{c}{**Lateral Sextants**} & \multirow{2}{*}{**-*} \\  & & **Quartile** & & & **Quartile** & **Quartile** & **-* \\  & **25\%** & **75\%** & & **Median** & **25\%** & **75\%** & **(\(\alpha=0.05\)), \(p=\)** \\ \hline Baseline & & & & & & \\ CAL & 0.438 & 0.087 & 1.403 & 1.408 & 0.637 & 2.042 & 0.000109 \\ PD & 2.500 & 2.315 & 2.771 & 2.656 & 2.374 & 2.953 & 0.000408 \\ After 2 weeks & & & & & & \\ CAL & 0.438 & 0.099 & 1.403 & 1.406 & 0.637 & 2.061 & 0.000030 \\ PD & 2.336 & 2.208 & 2.669 & 2.386 & 2.236 & 2.849 & 0.008766 \\ After 3 & & & & & & \\ months & 0.467 & 0.073 & 1.410 & 1.440 & 0.628 & 2.134 & 0.000030 \\ CAL & & & & & & \\ PD & 2.469 & 2.198 & 2.642 & 2.546 & 2.299 & 2.866 & 0.003619 \\ After 6 & & & & & & \\ months & 0.462 & 0.073 & 1.432 & 1.482 & 0.623 & 2.157 & 0.000025 \\ CAL & & & & & & \\ PD & 2.271 & 2.094 & 2.514 & 2.573 & 2.290 & 2.703 & 0.000004 \\ \hline \end{tabular}  
* Wilcoxon test.  
\end{table} 
Table 2: Comparison of clinical indicators between central and lateral sextants in group I (PERIO-ASTHMA). Periodontal parameters were also compared between groups for four timepoints--on the study day, two weeks, three months, and after six months. Detailed results are presented in the Supplementary Materials (Tables S1-S11). Table 3 shows comparison of clinical indicators between central and lateral sextants in group II. There were significant differences between central and lateral sextants for CAL, PD parameters in BE, after 2 weeks for CAL and PD, after 3 months for CAL and PD and for PI, and PD, CAL and PI after 6 months. Table 4 shows comparison of clinical indicators between central and lateral sextants in group III. There were significant differences between central and lateral sextants for CAL, PD parameters in BE, after 2 weeks for CAL, BOP, and PD, after 3 months for CAL and PD and for PI, and PD, CAL and PI after 6 months. Table 5 shows the intergroup comparison between periodontal indices for the whole mouth. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \multirow{2}{*}{**Median**} & \multicolumn{2}{c}{**Central Sextants**} & \multicolumn{4}{c}{**Lateral Sextants**} & \multirow{2}{*}{**u**} \\  & & **Quartile** & & **Median** & & **Quartile** & **75\%** & **(\(\alpha\) = 0.05), \(p\) =** \\ \hline Baseline & \multirow{2}{*}{0.000} & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.999999 \\ CAL & & 1.823 & 1.573 & 2.000 & 1.981 & 1.680 & 2.085 & 0.00001 \\ \hline After 2 weeks & \multirow{2}{*}{0.000} & 0.000 & 0.000 & 0.000 & 0.000 & 0.161 & 0.001782 \\ CAL & & 1.740 & 1.583 & 1.956 & 1.886 & 1.781 & 2.049 & 0.000008 \\ \hline After 3 & \multirow{2}{*}{0.000} & 0.000 & 0.000 & 0.000 & 0.000 & 0.179 & 0.001782 \\ \cline{1-1} \cline{6-10} \cline
## 2 The Contribution of the Present Study  
In this paper, we proposed a deep learning approach based on LED detection and data decoding for a MIMO C-OOK scheme to improve the data rate and decrease the BER in a harsh environment that considers long-range and mobility effects. Our scheme presents several advantages as outlined below.  
* Support for frame rate variations: Frame rate variation significantly impacts the OCC system, causing packet losses on the receiver side. Most people tend to ensure that a camera frame rate remains constant regardless of its specifications (e.g., 30 or 1000 fps). Depending on the technological parameters of different cameras, synchronizing transmitters and receivers can be difficult. Then, the sequence number (SN) is used to improve the system's performance by identifying whether the camera frame rate is higher than the transmitter's package rate.
* The discovery of lost packets: To detect the missing packet, we compared two SNs in two consecutive pictures when the length of the SN exceeded a specified value to discover each missing packet collected by the camera.
* Data-merging algorithm: We presented this process for each data sequence in our experiment by deploying the sequence number in each packet to detect the exact sequence of packets.  
* Improved data rate: By applying deep learning, many LEDs could be detected with high accuracy by considering long-range and mobile environments compared with RoI algorithms.
* Mobility support: The C-OOK scheme based on the rolling-shutter effect, which was more sensitive to the mobility effect than RoI algorithms, highlighted issues for detecting multiple LEDs compared with CNN. When using a rolling-shutter camera, the LEDs were displayed in an image as black and white strips; the number of LEDs could not be clearly detected when using RoI algorithms. Accordingly, we proposed a neural network for improving the performance of the OCC system.
* The BER was reduced compared with that using the conventional decoder approach. By collecting data from several cases (using different distances, mobility, and from several cameras), the dataset for a deep learning neural network decoder could achieve good performance compared with using the conventional decoder method. A comparison between the conventional decoder method and our proposed technique based on deep learning is shown in Section 4.
# A Comprehensive Review of Mechanisms, Predictive Techniques, and Control Strategies of Rockburst
## 5 Rockburst Prediction Methods
### Energy Methods  
The stored strain energy in a rock mass fluctuates during excavation and its release during subsurface excavation can predict rockbursts. Rock mass energy analysis can determine the nature and severity of rockbursts. [PERSON]'s study [76] was one of the first to show the relationship between energy changes in rock mass and rockburst mechanisms. The elastic energy stored in the rock is a major factor in calculating the intensity of rockburst. [PERSON] described the comprehensive stress-strain curve as the elastic strain energy index [123] in Equation (3):  
\[R=\frac{W_{E}}{W_{P}} \tag{3}\]  
where \(W_{E}\) is the elastic strain energy saved before rock failure and \(W_{P}\) is the plastic strain energy consumed after rock failure. The elastic strain energy index (\(W_{E}\)) and brittleness index modified (\(BIM\)) are commonly used indices. \(BIM\) is an equation that calculates the energy available for severe rupture, with a higher value indicating less energy (Figure 11). [PERSON] [155] developed a risk rating for rockbursts based on the \(BIM\) value, as illustrated in Table 7.  
The BIM equation is written in Equation (4), as follows:  
\[BIM=\frac{A_{2}}{A_{1}} \tag{4}\]  
where \(A_{2}\) is the area under the loading curve and \(A_{1}\) is the area under the line corresponding to the rock's elastic modulus (E). When the \(BIM\) increases, more energy is dissipated during loading, resulting in less energy available for severe rupture.  
[PERSON] [156] was the first to propose using the radiated energy of rockburst as a measurement tool. The relationship between the radiated energy and the intensity of rockbursts was explored, leading to radiated energy being recommended as an evaluation  
\begin{table}
\begin{tabular}{c c} \hline \hline
**BIM** & **Elastic Strain Energy** \\ \hline Between 1.00 and 1.20 & High \\ Between 1.20 and 1.50 & Moderate \\ More than 1.50 & Low \\ \hline \hline \end{tabular}
\end{table}
Table 7: BIM indicative values and risk of violent rupture [155].  
Figure 11: BIM determination using uniaxial compression testing (Modified after ref. [155]).  
index for rockburst intensity classification. [PERSON] [157] later analyzed seismic energy at a hydropower station in China to create a new seismic energy index. They studied the features, size, and laws of the radiated energy and their relationship to the intensity of rockburst. The energy-size-scaling (ESS) criterion was originally introduced by [PERSON] [49] as a measure of available energy during the transition from static resistance to dynamic resistance. However, not all positive ESS scenarios resulted in seismic occurrences, potentially due to data inaccuracies or stress involved, as noted by [PERSON] [158] and [PERSON] [49].
# Efficient Opportunistic Routing Protocol for Sensor Network in Emergency Applications
## 3 Proposed Opportunistic Routing Protocol
### Form Density-Based Clusters  
Initially, each node calculates its weight, defined by Equation (3). If the weight of a node \(x\) is close to zero, the minimum period of connectivity (MPC) of that node to all its neighbors is close to global \(MPC\) (\(MPC_{g}\)). That means the node \(x\) is more stable and a good candidate to become CH. Once all opportunistic nodes or sensors calculate their weight based on Equations (2)-(4), they broadcast their weight. The node \(x\) with the minimum weight will elect itself as a CH and broadcasts a \"hello\" message to let other nodes know that \(x\) is a CH. Other nodes will join to a cluster whose CH is closer to the node. Thus, clusters of the network are constructed. Algorithm 1 presents the pseudocode to select CH, cluster members and border nodes of a cluster \(C\).  
```
1:\(N\)\(\leftarrow\) GetNeighbors(\(x\)) //find number of neighbors of node \(x\)
2:weight \(\leftarrow\)GetWeight(\(x\)) //find weight of node \(x\) and all other nodes using Equations (2)-(4)
3:if\(|N|\geq minNode\)
4: Select \(x\) as a core point //i.e., a good candidate to be a CH
5:if weight(\(x\)) \(\leq\) weight (\(y\)), \(\forall\)\(y\)\(\in\)\(N\)
6: select \(x\) as CH of cluster C
7:\(minNode=|N|\) // minNode is set to the number of neighbors of node \(x\)
8:endif
9:else
10: select \(x\) as border point // if weight of x is greater than all its neighboring nodes
11:endif
12:for each \(y\)\(\in\)\(N\)do
13:if\(y\) is not CH nor member of any cluster
14:if\((MPC_{x,y}\geq MPC_{g})\)
15:\(C\)\(\leftarrow\)\(\{y\}\); include \(y\) in the cluster \(C\) //\(y\) is in cluster C if its MPC greater than global MPC
16:else
17: select node \(y\) as a noise to \(C\)
18:endif
19:endif
20: return CH
21: Repeat steps 1-20 until all clusters are formed
22:endif
```  
**Algorithm 1****(cluster formation)**  
In Algorithm 1, each node \(x\) calculates its weight and number of neighboring nodes and broadcasts this to its neighboring nodes (presented in lines 1-2 of Algorithm 1). Then, the weight of the node \(x\) and all its nodes are compared. The node that has the lowest weight becomes the cluster head (CH) of cluster \(C\) (presented in lines 3-8 of the Algorithm 1). All other nodes \(y\) who are neighboring nodes of but are not selected as the CH or member of any cluster will compare \(MPC_{x,y}\) with global \(MPC_{g}\). If the \(MPC_{x,y}\) of the node \(y\) is greater than or equal to \(MPC_{g}\), node \(y\) is included as a member node of cluster \(C\) (presented in lines 12-15 of the Algorithm 1). This process continues until all nodes become the CH or member node of a cluster or noise of a cluster.
# A Lightweight Small Target Detection Algorithm for UAV Platforms
## 1 Introduction
The output of this layer is connected to the new small target detection head, effectively enhancing the small target detection capability. Meanwhile, by analyzing the characteristics of target detection from the UAV viewpoint, it is determined that the detection capability of the target detection layer with deeper semantic information is redundant. Therefore, the deepest layer in the neck feature pyramid and the large target detection head in the head are removed to reduce the depth of the network and the number of parameters, achieving a lightweight network. Based on the above improvements, a lightweight small target detection algorithm, RSG-YOLO, is proposed in this paper. This algorithm is capable of accurately capturing the position and category information of small targets while having a low number of parameters and high inference speed, making it effectively deployable on UAV platforms.