esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiparameter Fusion Decision Routing Algorithm for Energy-Constrained Wireless Sensor Networks
## 4 Simulation Results and Analysis
### Comparison Algorithms  
In this section, three classical routing algorithms, MRE, MTEC, and MPEC, as well as two novel algorithms, the Energy Cost-Based energy Routing algorithm (ECBR) [38] and the Energy-Balanced Routing Protocol based on Forward-Aware Factor (FAF-EBRP) [39], are compared with our proposed algorithm. Moreover, we analyze the impact of the node number on NL, AE, PLR, and AH, and the results prove that the MPFDR algorithm enhances network lifetime. In Figures 4 and 5, NL, AE, PLR, and AH under different numbers of nodes are given. It can be seen that the MPFDR algorithm has the lowest PLR and outperforms the other algorithms in congestion control capability. Meanwhile, although NL is lower than that of MRE, and AE and AH are slightly higher than those of MPEC, the other performance indicators of the MPFDR algorithm are superior to those of the two algorithms. Therefore, the proposed MPFDR algorithm has apparent advantages.  
Figure 3: (**a**) Impacts of \(\alpha\) on packet loss rate (PLR); (**b**) Impacts of \(\alpha\) on average hops (AH).  
Figure 2: (**a**) Impacts of \(\alpha\) on network lifetime (NL); (**b**) Impacts of \(\alpha\) on average energy (AE).
# Optical OFDM for SiPM-Based Underwater Optical Wireless Communication Links
## 3 General Assumptions
### Modeling Received Optical Power  
Consider the Lambertian model for the LED radiation pattern \(P_{t}\), which is given by [21]  
\[P_{t}=P_{\mathrm{Tx}}\frac{m+1}{2\pi}\cos^{m}(\theta),\quad\theta\in[0,\pi/2], \tag{1}\]  
where \(\theta\) is the angle of irradiance of the LED, \(m\) is the Lambertian order and \(P_{\mathrm{Tx}}\) is the transmitted optical power. Ignoring system losses, the received power \(P_{\mathrm{Rx}}\) on the PD is then given by [6]:  
\[P_{\mathrm{Rx}}=P_{\mathrm{Tx}}\exp(-KZ)\,\frac{A}{Z^{2}}. \tag{2}\]  
Here, \(Z\) represents the distance between the Tx and the Rx, \(K\) is the beam diffuse attenuation coefficient (which depends on the wavelength and water turbidity), and \(A\) denotes the effective active area of the PD. Note that the approximate exponential attenuation, which is considered here for the sake of simplicity, is valid for the case of using a diffuse light source and in low-turbidity waters [22]. Nevertheless, this does not incur any loss of generality for the presented study.
## 2 System Description
### Synchronization Signal  
In LTE-MTC, PSS, and SSS sequences are two synchronization signals that introduce 504 distinct CIDs. The CIDs are divided into 168 unique GIDs, which are indexed as \(N_{ID}^{(1)}\in\{0,1,\cdots,167\}\). Each group contains three SIDs in a cell, which are indexed as \(N_{ID}^{(2)}\in\{0,1,2\}\). A physical layer CID can thus be represented uniquely by \(N_{ID}=N_{ID}^{(2)}+3N_{ID}^{(1)}\). To camp on a cell successfully, MTC UE must acquire the integer-valued information \(N_{ID}^{(1)}\) and \(N_{ID}^{(2)}\) using the SSS and PSS sequences, respectively. In the frequency domain, the PSS and SSS sequences are transmitted twice per 10 ms radio frame in the central six resource blocks (RBs). Let \(N_{p}\) denote the number of the subcarriers of PSS or SSS excluding null subcarriers. The synchronization signals use \(N_{p}=62\) subcarriers in total, with \(N_{p}/2\) subcarriers assigned on each side of the DC subcarrier. The PSS is on the basis of the frequency-domain length-63 Zadoff-Chu (ZC) sequence with zero punctuation at the DC subcarrier component [20]. Three PSS sequences with the root indices \(\{z_{0},z_{1},z_{2}\}=\{25,29,34\}\) are selected to carry the SID information  
\[P_{i}(k)=\begin{cases}e^{-j\pi z_{i}(k^{2}+992)/63-j\pi z_{i}k},\ k\in\mathcal{ P},\\ 0,\ \text{otherwise},\end{cases} \tag{1}\]  
where \(\mathcal{P}=\{k|-N_{p}/2\leq k\leq N_{p}/2,k\
eq 0\}\). As can be seen from Equation (1), \(P_{i}(k)=P_{i}(-k)\) for \(k\in\mathcal{P}\) and all \(i\)'s.  
The main purpose of the SSS is to recognize the GID and to find the starting position of an LTE frame. The SSS sequence forms binary phase-shift keying (BPSK) signal \(S(k)\), which is derived from two length-31 scrambled and cyclically shifted binary sequences [1]. The position of the SSS in LTE frame depends on the duplex mode. In FDD mode, as depicted in Figure 1, the PSS is present in the last symbol of the first slot of subframe of 0 and 5, whereas the SSS is located just prior to the PSS.
# Recovery Motion Analysis for False Ceiling Inspection Robot
## 3 Materials and Methods
### Mathematical Formulation: Jump-Start Motion  
For FalconX, the main method of recovery is to position the rotary wheels perpendicular to the robot's length and ground surface, thereby letting the robot move using a 3-point wheeled contact with the ground surface. When moving off from a stationary position, the robot experiences a jump-start that jerks the back edge of the robot with the support wheels upwards. The pose the FalconX robot has to take before self-righting is seen in Figure 6a. This is achieved by having the rotary wheels contact the ground after the robot has been flipped over.  
The motion for jump-starting the robot is crucial in getting the FalconX robot into the proper position for self-righting using a vertical surface. This can be modelled using a force diagram of the flipped robot, as seen in Figure 7. The main forces to be modelled would be those acting on the support wheel and those from the rotary wheels. The torque from the rotary wheels has to overcome the robot's inertia to provide the upwards jerk for the robot to assume the required position for self-righting.  
\[T+mgw^{\prime}-fh_{2}=0 \tag{1}\] \[w^{\prime}=w_{2}\cos P\] (2) \[h^{\prime}=\sqrt{{w_{2}}^{2}+{h_{2}}^{2}}\sin(\arctan\frac{h_{ 2}}{w_{2}}+P)\] (3) \[T_{lim}=fr \tag{4}\]  
Figure 6: (a) State of toppled robot before conducting self-righting procedure. (b) Robot jump-starts from stationary position and contacts wall to perform self-righting.  
Assume that after the robot reaches its flipped position, it keeps moving without further self-rotation. The torque balance equation on the robot is shown in Equation (1). Equations (2) and (3) are to calculate the horizontal and vertical distances between the contact point on the ground and the centre of gravity. Equation (4) is to calculate the torque limit to make the robot flip. These equations are based on static equilibrium and constant friction assumptions.  
Solving the equations for \(f\) (the friction from the ground surface) and \(T\) (torque required to jump-start the robot to encounter the vertical surface for self-righting) with values \(m=0.576\) kg, \(g=9.81\), \(h_{2}=4.5\) cm, \(w_{2}=6.5\) cm, and angle \(P=25^{\circ}\) (i.e., the angle required for the support wheel to rotate from its existing ground contact position to that required for contacting the wall for self-righting) results in the following.  
From the calculations based on the mathematical model, the amount of torque needed by the rotary wheel was calculated to be 0.2435 Nm for this FalconX iteration as seen in Table 1. The robot's motors would have to provide this amount of torque to the rotary wheel for the robot of this design and mass to achieve the intended self-righting pose. The robot could then self-right after achieving the required position. This is described in the next section and subsequently validated in the Results section.
# Targeting Antigen-Presenting Cells in Multiple Sclerosis Treatment
## 3 Dendritic Cells
Migration of myelin-specific CD4+ T-cells across the BBB, a crucial step in the pathogenesis of MS, is suggested to be an antigen-specific process. It has been proposed that this process is mediated by brain endothelium. [PERSON] et al. reported that in inflammatory conditions, brain endothelial cells (BECs) act as a non-professional APCs able to process and present myelin-derived antigens complexed with MHC class II. Such complexes stimulated myelin-reactive Th1 and Th17 2D2 cells to transmigration through endothelium. Blocking of interactions between myelin/MHC II complexes and reactive T-cells abrogated the trafficking of the latter across the BBB [11]. It has been reported that BECs express MHC class I, but the MHC class II is present in very low levels in physiological conditions. Induction of the inflammatory process activates BECs and results in increased expression of MHC II, CD40, and ICOSL and, subsequently, enhances their ability to induce T-cells proliferation in vitro [76]. Additionally, it was shown that myelin enters the endosomal/lysosomal pathway after internalization by BECs, which was autonomous from their activation status [77]. This is opposite to what is observed for professional APCs such as DCs, where internalization of antigens takes place in an immature state of these cells [78; 79]. Overall, these results pointed out that brain endothelium is an active and important contributor to the pathogenesis of MS.  
Astrocytes are the most abundant cells in the CNS and they can act as a non-professional APCs modulating the activity of autoreactive T-cells by myelin antigen presentation and secretion of pro- or anti-inflammatory cytokines. The role of astrocytes as APCs has remained controversial. There are reports that have shown that astrocytes express low levels of MHC class II molecules constitutively, and their expression may be upregulated during inflammatory reactions [80; 81]. The expression of co-stimulatory molecules, such as CD40, CD80, and CD86, is also upregulated during astrocytes activation [81; 82; 83]. However, studies utilizing human fetal astrocytes in vitro failed to detect the expression of co-stimulatory molecules, even after stimulation [84]. Functional studies have shown that stimulated murine astrocytes moderately activate CD4+ and CD8+ T-cells, whereas cytokine-treated human astrocytes did not induce proliferation of encephalitogenic T-cells [80]. These may reflect differences between species or artifacts associated with astrocytic cell cultures, which may be contaminated by microglia. It has also been reported that activated astrocytes efficiently presented MBP, PLP, and MOG epitopes [85; 86; 87]. Yet, they were incapable of processing and presenting native myelin peptides. Thus, the astrocytes potential for antigen presentation needs more detailed studies to elucidate its role for the development and progression of MS.
## 7 RNN-Based Optimization of Mirror Placement for Maximizing SNR in ADRs
### RNN Methodology  
The optimization of mirror placement in an indoor VLC environment requires a robust approach capable of capturing the temporal dependencies inherent in dynamic communication scenarios. RNNs are well suited for this task, as they excel in processing sequential data by maintaining an internal state that evolves with time. Using the temporal information from the signal powers received at multiple photodiodes, the RNN dynamically adjusts the positions of the mirrors, whether planar or convex, to maximize the SNR at the ADRs.  
This methodology utilizes the received signal powers as input to the RNN, which processes them through a series of hidden states to learn the optimal mirror positions over time. The RNN supports both the MRC and SBC schemes, ensuring adaptability to various signal processing requirements. Here, we detail the mathematical model underlying the RNN, including its inputs, hidden state dynamics, outputs, and the specific loss function used to train the network for the effective optimization of mirror placement.
## 3 Experimental Study
### Experimental Results and Discussion
#### 3.2.1 Prediction Performance Comparison of Different Neural Network Architectures
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Methods** & **RMSE** & **Score** \\ \hline k-NN (without difference feature) & 19.2 \(\pm\) 0.4 & 1180.6 \(\pm\) 360.7 \\ k-NN (with difference feature) & 20.9 \(\pm\) 0.2 & 825.2 \(\pm\) 22.8 \\ \hline RF (without difference feature) & 21.5 \(\pm\) 0.2 & 2447.9 \(\pm\) 383.2 \\ RF (with difference feature) & 19.5 \(\pm\) 0.2 & 830.2 \(\pm\) 105.2 \\ \hline GBM (without difference feature) & 19.2 \(\pm\) 0.3 & 1454.7 \(\pm\) 395.6 \\ GBM (with difference feature) & 18.3 \(\pm\) 0.5 & 714.2 \(\pm\) 195.9 \\ \hline GMDH (without difference feature) & 18.0 \(\pm\) 0.2 & 651.5 \(\pm\) 90.5 \\ GMDH (with difference feature) & 17.7 \(\pm\) 0.2 & 542.1 \(\pm\) 70.4 \\ \hline LSTM (without difference feature) & 17.8 \(\pm\) 0.7 & 2476.3 \(\pm\) 1607.1 \\ LSTM (with difference feature) & 17.0 \(\pm\) 1.3 & 1176.5 \(\pm\) 1368.6 \\ \hline
1D-CNN (without difference feature) & 17.2 \(\pm\) 0.2 & 716.1 \(\pm\) 129.8 \\
1D-CNN (with difference feature) & **15.5 \(\pm\) 0.5** & 498.9 \(\pm\) 170 \\ \hline
1D-CNN-LSTM (without difference feature) & 19.1 \(\pm\) 2.1 & 1680.9 \(\pm\) 1284.1 \\
1D-CNN-LSTM (with difference feature) (proposed) & 16.1 \(\pm\) 0.5 & **437.2 \(\pm\) 126.4** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of RUL prediction performances on the test set with and without the difference feature.
## 4 Results
### Error Metric  
For each of the three evaluated weeks, the mean absolute error (MAE) and root mean square error (RMSE) are determined (see Section 3.5). The results are presented in Table 3. For an additional three weeks, the error metrics can be found in Appendix A.  
Comparing methods II and III, method III in general performs better. This method is based on the historical data, which can result in situations in which the opposite holds and the accuracy of method III is lower than the accuracy of method II. This is in particular the case for the first week of July, using curtailment option 2. Figure 14 shows that the first few days of July have a lower PV output. As these days are used to generate the baseline, this directly influences the result. In case of curtailment option 2b, this effect is increased due to flexibility activations being incorporated in the historical dataset.  
It can be observed that overall, the performance of the new baselining method IV is better than that of methods I, II and III. Furthermore, the impact of the user dilemma on methods III and IV can be observed in curtailment option 2b. For these two methods, the error increases slightly when flexibility activation has occurred in the measurements of the historical days used to generate the baselines. This was to be expected, but the effect is relatively small, especially for method IV.  
Overall it can be observed that the accuracy in weeks with volatile PV irradiance, like the first week of August, is significantly lower. This is because the historical data on which some of the baselines are based are strongly affected by this volatility. For curtailment option 1, the accuracy seems to decrease less during volatile weather, which can be explained by the lower amounts of flexibility activation.  
The accuracy's dependency on the actual weather situation shows one of the trade-offs a DSO must be prepared to make. When implementing baselining methods that are simple and transparent, there situations during which the performance of those baselines is lower are inevitable. This is not necessarily problematic, as the expectation is that the majority of flexibility activations will be during peak PV production weeks. Should a DSO often need flexibility in weeks with volatile PV output, alternative, more complex baselining methods might be required.
# Matrix Method of Defect Analysis for Structures with Areas of Considerable Stiffness Differences
## 2 Materials and Methods  
### Formulation of Two-Dimensional Multi-Area Finite Elements. Theoretical Assumptions  
The use of shape functions enables a matrix definition of a finite element. In structural modeling applications, finite elements are implemented to represent material characteristics, assuming a linear relationship between strain and stress. Therefore, the basic FEM equations leading to the determination of the finite element stiffness matrix \(\mathbf{K_{e}}\) have the following form:  
\[\mathbf{q}=col\left\{u_{i},\;v_{i},\;u_{j},v_{j},\;\ldots\right\} \tag{1}\]  
\[\Omega(\varepsilon)=\mathbf{N_{e}}\mathbf{\cdot q} \tag{2}\]  
\[\mathbf{B_{e}}=\frac{\partial}{\partial V}\Omega(\varepsilon)=\mathbf{L} \mathbf{\cdot N_{e}}\mathbf{\cdot q} \tag{3}\]\[\mathbf{K_{e}}=\int\limits_{V}\mathbf{B_{e}^{T}}\mathbf{D_{e}}\cdot\mathbf{B_{e}} \cdot\mathrm{d}V\ \ \ \text{or}\ \ \ \mathbf{K_{e}}=\int\limits_{\xi}\mathbf{B_{e}^{T}}\mathbf{D_{e}}\cdot\mathbf{B_{e}} \cdot\mathbf{J}(\xi)\cdot\mathrm{d}\xi \tag{4}\]  
where \(\mathbf{L}\)--matrix of differential operators,  
\(\mathbf{N_{e}}\)--shape function matrix,  
\(\mathbf{B_{e}}\)--strain matrix,  
\(\mathbf{D_{e}}\)--material characteristic matrix,  
\(\mathbf{J}(\xi)\)--Jacobi matrix,  
\(V\)--volume of the element.  
Determination of the stiffness matrix of the finite element described by Equation (4) can be performed as follows:  
* explicit, i.e., by analytical integration of the expression (4); in this way, the so-called explicit forms of the stiffness matrix are obtained in the form of formulas; explicit forms of the stiffness matrix are the most effective form of element matrix in computational analyses; however, difficulties arise related to their obtaining limit applications to the simplest elements of regular shapes (bar elements, two-dimensional rectangular elements, etc.);
* implicit, i.e., by numerical integration of the expression (4); in this way the stiffness matrix is obtained directly in numerical form; the Gauss-Legendre formula is often used and allows changing the integral expression into a summation expression; for example, for a function of one variable:  
\[\int\limits_{-1}^{+1}f(\xi)d\xi\cong\sum\limits_{i=1}^{n}w_{i}\cdot f(\xi_{i}) \tag{5}\]  
where \(n\) is the number of integration points (so-called Gauss points), and \(w_{i}\) and \(f(\xi_{i})\) denote the weight and the value of the integrand function at the \(i\)-th integration point, respectively.
# A Usable Encryption Solution for File-Based Geospatial Data within a Database File System
## 6 Discussion
### GIS Security Model and Security Analysis  
This section presents a GIS security model based on the proposed security solution for spatial files within a DBFS and discusses each aspect. Our approach aims to address the security vulnerabilities with spatial files within enterprise GIS data _\"silos\"_. We first propose a _central storage repository_ that can store, manage, and control access to spatial files with different formats. A DBFS offers features that are necessary for the future of GIS file management and security that are not provided by traditional file systems. ECMSDK, for instance, provides consolidation for these spatial files into a centralised platform and includes different protocol services commonly used in GIS for accessing and using these files. Essentially, since it is built on top of a multi-purpose database, the underlying database access control mechanism is leveraged by the DBFS providing increased security for files.  
The GIS security model is based on the concepts derived from the popular SIS model for security measures [82]. Protecting geospatial data within a DBFS requires a combination of _Technical_ and _Administrative security_. Administrative security includes any security measures that focus on managing people and their behaviour. In contrast, technical controls focus on technology. They consist of _IT security_, which pertains to ensuring that computer systems are able to carry out their tasks, and _Physical security_ which pertains to controlling access to physical objects and spaces. This includes mechanisms designed to deter unauthorized access to rooms, equipment, documents, and other items. These security measures in this GIS security model would be similar to those employed in enterprise IT systems. The most important characteristic in our study is to ensure the confidentiality of spatial data. For these reasons, here we focus mainly on IT security measures and later highlight the extra emphasis placed on Administrative security.  
**IT security** focuses on ensuring these four aspects: _Availability_: providing continual access to data and systems; _Confidentiality_: protecting the privacy of data; _Integrity_: ensuring that data have not been modified; and _Accountability_: holding people responsible for their actions with data and systems.
###### Abstract  
An unsupervised k-means/k-means++ clustering algorithm was implemented on daily images of standardized anomalies of brightness temperature (T\({}_{\text{b}}\)) derived from the Geostationary Operational Environmental Satellite (GOES)-13 infrared data for the period 1 December 2010 to 30 November 2016. The goal was to decompose each individual T\({}_{\text{b}}\) image into four clusters that captures the characteristics of different cloud regimes. The extracted clusters were ordered by their mean value in an ascending fashion so that the lower the cluster order, the higher the clouds they represent. A linear regression between temperature and height with temperature used as the predictor was conducted to estimate cloud top heights (CTHs) from the T\({}_{\text{b}}\) values. The analysis of the results was performed in two different ways: sample dates and seasonal features. Cluster 1 is the less dominant one, representing clouds with the highest tops and variabilities. Cluster 4 is the most dominant one and represents a cloud regime that spans the lowest 2 km of the troposphere. Clusters 2 and 3 are entangled in the sense that both have their CTHs spanning the middle troposphere. Correlations between the monthly time series of the number of pixels in each cluster and of the entropy with several circulation indices are also introduced. Additionally, a fractal-related analysis was carried out on cluster 1 in order to resolve cirrus and cumulonimbus.  
Keywords:brightness temperature; cloud cover; cloud regimes; clustering; GOES IR imagery; kmeans; kmeans++; South America +
Footnote †: journal: Accepted: 17 July 2020; Published: 14 September 2020