esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Performance Evaluation
### Experiment 1: Bottleneck Scenario  
In the context of computer networks, a bottleneck refers to a situation where the data flow experiences degradation, either in terms of delays or packet loss [35]. It occurs when the system delivers more data than the existing capacity of the network. Identifying network bottlenecks can prevent traffic congestion and significantly improve network performance.  
The network's performance and potential bottlenecks were analyzed through comprehensive testing on all three topologies. We performed the tests for various payload sizes by varying the bitrates, and the results showed that in Topology 1, at all payload sizes (i.e., 500-5000 bytes), without packet loss communication is observed with bitrates lower than 100 kbps, which can be seen in Figure 10. While for Topology 2, where the child node communicates with the leader node through a router, this level drops to 40 kbps, as shown in Figure 11. It indicates that the network's performance is highly dependent on the network topology; direct communication between two routers experiences less packet loss and can be more suitable for certain scenarios. This experiment helps to understand the network's performance and highlights the need for proper network design according to the requirements of the scenario.  
Figure 11: Bottleneck for Topology 2.  
Figure 10: Bottleneck for Topology 1.  
Similarly, this experiment was repeated for Topology 3 for two different scenarios. In the first scenario, child-to-leader communication is observed, where all three nodes are sending data to the leader node, making the network congested. Figure 12 shows that in such a congested environment the bottleneck drops to 10 kbps. This drop occurs because due to high congestion and high bitrate, the packet loss will be more frequent. Similarly, on the same topology, another communication is also observed where child-to-child communication is observed. In this scenario, the communication takes place over three hops, as shown in Figure 13. It is worth noting that the bottleneck occurred at 30 kbps. In this particular scenario, the network environment was not congested as other nodes did not participate in any communication. It is evident that the bottleneck condition occurs beyond that of the congested communication scenario, despite the two-hop communication. However, it still exhibited lower performance compared to the two-hop communication scenario employed in Topology 2.
## 3 Basic Statistical Analysis
### Correlation Analysis
#### 3.1.1 Spearman's Rank Correlation Coefficient (Spearman's Rho)  
[PERSON]'s rank correlation coefficient (Spearman's rho) is a nonparametric statistic that measures the strength and direction of the association between two ranked variables by assessing how well a monotone function can describe the relationship between the rankings [34]. It is denoted by \(\rho\) and ranges from \(+1\) to \(-1\), where \(+1\) indicates a perfectly increasing monotonic relationship, \(-1\) indicates a decreasing relationship, and \(0\) indicates no monotonic relationship [35]. Spearman's rho converts raw data to ranks, calculates differences in paired ranks, squares these rank differences, and bases the coefficient on the covariance of these squared rank differences. Because it uses ranks rather than raw values and makes minimal distributional assumptions, [PERSON]'s correlation can be useful with non-normal data or ordinal variables and is often used when [PERSON]'s correlation can be misleading [36]. For each monitoring point, assign ranks to displacement values and stability reduction factors separately, from the smallest to the largest. If ties exist, assign the average rank. The general formula for [PERSON]'s rho is in Equation (2).  
\[\rho=1-\frac{6\sum\mathrm{d}_{\mathrm{i}}^{2}}{n(n^{2}-1)} \tag{2}\]  
where  
\(\mathrm{d}_{\mathrm{i}}=\mathrm{For}\) each pair, calculate the difference between the ranks of the displacement and the stability reduction factor  
\(n=\mathrm{number}\) of observations (or data points) at a monitoring point  
\(\rho=\mathrm{incorporating}\) the rank differences to assess the correlation.
# MIMO Channel Estimation in an SDR Platform for Evaluation of D&F Relay Nodes
## 4 Channel Estimation for the Core Network: Link eNB-to-Relay Node
### Interpolation Methods
#### 4.1.3 BNNI Method  
In this subsection, one of the simplest interpolation methods is explained. As in the prior subsections, the Nearest-Neighbor interpolation (NNI) technique is the first step to determine the BNNI method, in which interpolation in the frequency domain is first performed, while another interpolation is carried out later; in this case, in the time domain. All estimated data symbols using the NNI method are obtained by convolving \(\mathbf{A}_{c_{d}^{i}s_{d}^{i}}^{ij}\) with \(\mathbf{B}_{c_{d}^{i}s_{d}^{i}}^{ij}\), which can be calculated, according to [31; 35], as  
\[\hat{\mathbf{H}}_{c_{d}^{i}s_{d}^{i}}^{ij}=\sum_{(c_{p},s_{p}^{i})=-\infty}^{ \infty}\mathbf{B}_{(c_{d}^{i}s_{d}^{i})-(c_{p}^{i}s_{p}^{i})}^{ij}\mathbf{A}_{ c_{p}^{i}s_{p}^{i}}^{ij}, \tag{8}\]  
with  
\[\mathbf{B}_{c_{d}^{i}s_{d}^{i}}^{ij}=\begin{cases}1,&0\leq(c_{d}^{i},s_{d}^{i}) \leq Z\\ 0,&\text{otherwise}.\end{cases}, \tag{9}\]\[\mathbf{A}_{c_{d}^{i},s_{d}^{i}}^{i,j}=\begin{cases}\hat{\mathbf{H}}_{c_{p}^{i},s_{ p}^{i}}^{i,j},&(c_{d}^{i},s_{d}^{i})=0:Z:Z(P-1)\\ 0,&otherwise.\end{cases}, \tag{10}\]  
where \(Z\) denotes the zeros inserted between sequential samples of the pilot symbols matrix \(\hat{\mathbf{H}}_{c_{p}^{i},s_{p}^{i}}^{i,j}\) and \(P\) is the total number of pilot symbols.
## 2 Materials and Methods
### Creation of a PCA-Based Statistical Shape Model
Each shape \(G_{i,\text{CN}}\) was rotated to be aligned to \(G_{\text{CN,avg}}\): the rotation matrix \(Q\) was obtained from the singular value decomposition of the product of shape \(G_{i,\text{CN}}\) by the average shape calculated in the previous step:  
\[[U_{i},V_{i}]=SVD\left(G_{\text{CN,avg}}^{T}\times G_{i,\text{CN}}\right) \tag{11}\]  
\[Q_{i}=U_{i}\times V_{i}t \tag{12}\]\[G_{i,\text{CNR}}=G_{i,\text{CN}}\times Q_{i} \tag{13}\]  
New rotated matrices \(G_{i,\text{CNR}}\) were therefore obtained. The distances between each shape \(G_{i,\text{CNR}}\) and the average shape \(G_{\text{CN,avg}}\) were summed over all nodes and all shapes, obtaining:  
\[D=\sum\limits_{i=1}^{M}\sum\limits_{j=1}^{N}\sqrt{\left[\left(x_{i,j}\right)_{ \text{CNR}}-\left(\overline{x_{j}}\right)_{\text{CN}}\right]^{2}+\left[\left(y _{i,j}\right)_{\text{CNR}}-\left(\overline{y_{j}}\right)_{\text{CN}}\right]^{ 2}+\left[\left(z_{i,j}\right)_{\text{CNR}}-\left(\overline{z}\right)_{\text{ CN}}\right]^{2}} \tag{14}\]  
If the distance value was greater than a threshold value, a new average shape was computed, and the process started again from point 2. The iterative process ended when the sum of squared distances \(D\) had converged (deviations smaller than 0.001 mm\({}^{2}\)). Eventually, a set of centred and aligned geometries, sharing the same norm, was obtained; they will be called \(G^{\prime}_{i}\) (\(i=1\dots M\)), in the following paragraphs. PCA could be applied on this set of geometries in order to obtain the variability model. The calculation of PCs required, first of all, that matrices \(G_{i,\text{CNR}}\) and \(G_{\text{CNR,avg}}\) were reshaped. These were organized in one single column vector, being composed of 3N elements, named \(V_{i,\text{CNR}}\). and \(V_{\text{CNR,avg}}\) respectively. Next, a new matrix \(M_{\text{CNR}}\) was built, whose \(i\)th column was made of (V\({}_{i,\text{CNR}}\)-V\({}_{\text{CNR,avg}}\)); \(M_{\text{CNR}}\) size was therefore \(N\times M\). Finally, \(PC_{i}\) sets were calculated as the eigenvectors of the covariance matrix \(COV\) given by:  
\[COV=\text{M}_{\text{CNR}}\times\text{M}_{\text{CNR}}\prime \tag{15}\]  
PCs satisfy the following properties:  
Whatever couple of eigenvectors \(PC_{i}\), \(PC_{j}\) are orthogonal: in other words, the following equations holds for \(i=1\dots M\); \(j=1\dots M\); \(i\
eq j\):  
\[\overline{x_{i}}=\sum_{j=1}^{N}\frac{x_{i,j}}{N};\overline{y_{i}}=\sum_{j=1}^{ N}\frac{y_{i,j}}{N};\overline{z_{i}}=\sum_{j=1}^{N}\frac{z_{i,j}}{N} \tag{7}\]0  
\(PC_{i}\), \(PC_{j}\) are sorted according to the respective eigenvalue \(\lambda_{i}\) which represents the variability among vectors of \(M_{\text{CNR}}\), captured by the \(i\)th \(PC\).
## 5 Simulation Results and Analysis  
In this section, we present the performance evaluation of the proposed FISRSA-S RBMSA algorithm within a dynamic traffic scenario. We use PyCharm as a dedicated Python-integrated development environment for the implementation. The simulations are performed on a PC with a 3.2 GHz Intel Core i9 and 16 GB RAM. In our previous work [26], we have confirmed that by allocating primary and backup paths to different bands, it alleviates physical layer impairments in the network, thereby ensuring the OSNR of signals while guaranteeing the survivability of services. Building upon this, our current study additionally takes into account the spectrum fragmentation issue during the resource allocation process. In comparing algorithmic fragmentation metric schemes, we choose the classical entropy-based fragmentation metric (EFM) [21]. Furthermore, we compare the impact of DDP [9] and SBPP [10] algorithms on network resource utilization. To further validate the efficacy of the algorithm, we also introduce a comparative algorithm where the primary path is allocated to the C band and the backup path is allocated to the L band.  
Our analysis focuses on two distinct network topologies: the NSFNET network [28] and the COST239 network [29]. All links within these networks are bidirectional and constructed using SSMF. As depicted in Figure 2, the numerical value adjacent to each link represents its length. A single FS serves as a guard band, separating individual requests [30]. The total FS of the C band is 358, while the L band is 558 [31]. It is assumed that the arrival process \(\lambda\) adheres to the Poisson process, and the holding time \(\mu\) of each request follows a negative exponential distribution [32]. The range of the data rate for each request is 20-100 Gb/s, which follows uniform distribution. For each request, we compute \(K=3\) primary paths, and for each primary path, \(K_{b}=3\) backup paths (disjoint from the primary path) are determined. Additionally, other pertinent physical parameters are concisely summarized in paper [33].
## 3 Results
### Screening for Energy Consumption
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**EACTORS** & **CODE** & **-Boller** & **-Heat Pump** & **+Boller** & **+Heat Pump** \\ \hline
**Automation for water heating** & FC1 & no & yes & \\ \hline
**Category of automatic control** & FC2 & **A** & & **D** \\ \hline
**Number of ceiling fan (summer)** & F3 & 0 & 5 & \\
**Type of heating systems** & FC4 & **boiler** & & **Airecooled hip.** \\ \hline
**Energy source for heating systems (only for degree** & FC5 & Oil & & Electricity \\ \hline
**Power of heating systems (only for degree** & F6 & **47.2** & **15.5** & **67.2** & **16.5** \\
**of efficiency)** & **Energy efficiency ratio of heating systems** & F7 & 0.59 & 3.3 & 0.84 & 3.7 \\
**Location of distribution network of heating systems** & **FC8** & **Outdoor** & & **Indoor** \\
**efficiency of distribution network of** & P9 & 0.86 & 0.96 & \\
**heating systems** & **F10** & **0.83** & **1** \\
**Efficacy of terminal units of heating systems** & FC11 & Air-cooled & water-cooled \\
**Type of cooling systems (only for degree of** & F12 & 4 & 8 \\
**efficiency)** & **Energy efficiency ratio of cooling systems** & F13 & 1.5 & 3.72 \\
**Efficacy of terminal units of cooling systems** & F14 & **0.9** & **0.96** \\
**Solar water heater** & FC15 & Yes & No \\
**Power of water heating systems (only for degree of efficiency)** & F16 & **0.64** & **1.27** \\
**Energy efficiency ratio of water heating systems** & F17 & 0.85 & 1 \\
**Recirculation of distribution network** & **FC18** & **No** & Yes \\
**Efficiency of distribution network of water** & F19 & 0.64 & 0.92 & \\
**Efficiency of the hot water storage container** & F20 & **0.93** & 0.98 & \\
**Type of auxiliary units for hot water** & FC21 & No & 0.04 & circulator \\
**Type of solar panels** & **FC22** & **Simple** & Vacuum & - \\ Utilization rate of solar water heating** & F23 & 0.34 & 0.38 & - \\
**Surface area of solar panels** & **E24** & **2** & 5 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Controlling factors and their settings for the system design screening of the examined apartment unit. \begin{table}
\begin{tabular}{c c c c} \hline
**Run** & **EC (kWh/m\({}^{2}\))** & **IC (EUR, \(\Theta\))** & **JR** \\ \hline
**1** & 104.0 & 10.003 & 0.5633 \\ \hline
**2** & 163.4 & 9233 & 0.5897 \\
**3** & 149.2 & **7641** & 0.4402 \\
**4** & 105.7 & 16,815 & 0.7587 \\
**5** & 137.2 & 13,613 & 0.6364 \\
**6** & 132.7 & 20,302 & 0.9904 \\
**7** & 223.7 & 10,733 & 10.091 \\
**8** & 204.1 & 3743 & 0.8363 \\
**9** & 197.7 & 13,028 & 0.9503 \\
**10** & 139.7 & 7894 & 0.3834 \\
**11** & 123.2 & **15,432** & 0.6970 \\
**12** & 146.3 & 20,975 & 1.0606 \\
**13** & 158.7 & 9892 & 0.5798 \\
**14** & 192.4 & 14,820 & 0.9791 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The responses of the energy consumption (EC), intervention costs (IC), and their joint response (JR). Figure 8: MDAC for energy consumption (EC) units in the abscissa and ordinate axis are in kWh/m\({}^{2}\).
## 5 Simulation Results
### Acceleration-Level Algorithm Results
#### 5.2.2 Disturbance Rejection Results  
Figure 14 shows the response of the system to an impulsive force on the end-effector after a linear horizontal trajectory when controlled through the acceleration-level algorithm. The impulse is triangular, with a time duration of 0.2 s and a maximum value of 12 N. In this case, the controller shows more robustness compared to the velocity-level one: the error drops under 0.1 mm just 0.3 s after the impulse.  
Figure 14: System response in the case of an impulsive force acting on the manipulator end-effector at the end of a linear trajectory (\(t=T_{path}\)). The impulse is acting in the negative x direction. The system is controlled by a closed-loop acceleration-level inverse kinematics algorithm (\(K_{P}=100\), \(K_{D}=20\)). The inverse kinematics is solved using the maximum positive variation in all the inertial parameters, see Table 5. (**left**) Task-space error 2-norm, \(\|e(t)\|\) (in blue), as a function of time. From the instant \(t=T_{path}\), indicated with the black vertical line, the end-effector tries to remain still at the trajectory final point. The orange line indicates the impulsive force, \(F_{EE}\), on the end-effector. (**right**) Graphical output of the simulator in the vertical (x–z) plane; the blue star represents the UAM CoG.  
Figure 13: Three-DOF system response in the case of wind disturbance and sensors noise. The system is controlled by a closed-loop acceleration-level inverse kinematics algorithm (\(K_{P}=100\), \(K_{D}=20\)). The inverse kinematics is solved using the maximum positive variation in all the inertial parameters, see Table 5. (**left**) Task-space error 2-norm, \(\|e(t)\|\), as a function of time. From the instant \(t=T_{path}\), indicated with the black vertical line, the end-effector tries to remain still at the trajectory final point. (**right**) Graphical output of the simulator in the vertical (x–z) plane; the blue star represents the UAM CoG.  
Similarly to the velocity-level solution, the algorithm is also tested for lower peak values of the impulse obtaining lower errors and faster rejection dynamics. On the other hand, higher impact forces cause a large UAV horizontal translation, which in turn causes the arm to reach singular configurations and the algorithm to fail.
## 4 In-Network Computing via ICN
### Security  
By shifting computation from centralized, trusted environments to distributed nodes within the network, INC introduces critical security and privacy challenges. Without adequate mechanisms to ensure trust in these entities, malicious nodes could tamper with results, disrupt services, or misuse sensitive data. Ensuring the integrity of computation results is essential for maintaining trust, particularly in scenarios where errors or tampering could lead to severe consequences, such as in healthcare or autonomous systems. Moreover, INC systems must resist malicious attacks, including replay and DoS, while holding nodes accountable for their actions through mechanisms like provenance tracking and tamper-proof auditing. Privacy concerns are also significant, as INC often processes personal or sensitive information at distributed nodes, necessitating encryption and access control to prevent unauthorized access or data leakage.  
Security concerns of INC via ICN, with a special focus on the NFN architecture, are discussed in [69]. The authors propose the use of provenance records, which document the computation process and allow consumers to verify the correctness and authenticity of computation results without needing to re-execute the function themselves. Provenance metadata includes the computing entity's identity, input data and function signatures, the produced result, and a statement covering all elements. This creates transparency in NFN, mitigating the risks of relying on potentially untrustworthy nodes. In terms of verification, the provenance system enables the identification of malicious nodes by allowing users to recompute and validate intermediate steps. Furthermore, provenance recordsestablish accountability by acting as indisputable proofs of a node's behavior. This not only aids in detecting malicious actors but also protects honest nodes from false accusations.  
The ShieldDINC framework in [7] focuses on data confidentiality and integrates Homomorphic Encryption (HE) in ICN to enable secure computations on encrypted data. ShieldDINC performs computations directly on encrypted data using the Brakerski-Fan-Vercauteren HE scheme, ensuring that sensitive information remains confidential. This approach not only prevents data leakage but also enables secure result sharing by caching computed results at intermediate routers. To ensure data integrity and authenticity, computed results are signed by the computing nodes and can be verified by the requesting entities.
###### Abstract  
The present study investigates four materials, namely quartz, marble, quartzite and metasandstone and aims to establish correlations, with the use of simple and multiple regression analysis, between their properties and breakage rate parameters. The material properties considered in this study derived from the application of destructive and non-destructive tests and include P-wave velocity (\(V_{p}\)), Schmidt rebound value (\(R_{L}\)), uniaxial compressive strength (\(UCS\)) and tangent modulus of elasticity (\(E_{l}\)), while the breakage rate parameters determined from batch grinding tests, include breakage rate \(S_{i}\), maximum breakage rate \(S_{m}\), \(\alpha_{T}\) and \(\alpha\), and optimum particle size \(x_{m}\). The results indicate that the properties of all materials examined show very good correlation and can be used to predict \(S_{i}\) or \(\alpha_{T}\). Furthermore, parameter \(\alpha\) is well correlated with \(V_{p}\), \(R_{L}\) and \(E_{t}\) using inverse exponential functions, while \(S_{m}\) is strongly correlated with \(R_{L}\) and \(UCS\). Overall, it is deduced that multiple regression analysis involving two independent variables is a reliable approach and can be used to identify correlations between properties and breakage rate parameters for quartz, quartzite and metasandstone, which are silica rich materials. The only exception shown is the determination of \(x_{m}\) for marble.  
Keywords:comminution; grinding; material properties; population balance model; breakage rate parameters +
Footnote †: journal: Applied Sciences
# Trend Prediction of Event Popularity from Microblogs
## 4 Experiment
### Results of Popularity Trend Prediction  
This paper takes the popularity of microblog events as input and the changed state, measured by box plot algorithm, as output to build Bi-LSTM network model. For the evaluation of predictive performance, the paper compares our proposal with three existing methods, including BP neural network (BPNN) [16], Particle Swarm Optimization Based Support Vector Machine model (PSO-SVM) [29] and Long Short Term Memory network (LSTM) [18]. Meanwhile, the model effectiveness is measured in terms of the metric \(P_{micro}\), \(P_{weight}\) and \(F_{weight}\).  
The experimental results as shown in Figures 7 and 8 show that our model achieves better results compared with other algorithms in the task of popularity trend prediction. Especially, compared with the basic LSTM network, the proposed model in the paper is characterized by component extraction on the basis of LSTM to achieve the effect of denoising. In addition, considering the influence of future information on event popularity, the model forms a bidirectional LSTM, so it has better performance than the basic LSTM model.  
Figure 6: Comparison of popularity time series before and after denoising.  
Figure 7: Results of popularity trend prediction on dataset 1.
# Patient Mobility Support for Indoor Non-Directed Optical Body Area Networks
## 2 Proposed System Overview  
The proposed OBAN system architecture is shown in Figure 1. The OBAN system consists of sensors on various parts of the body for monitoring physiological signals. All the sensor nodes are directly connected to the optical coordinator via wires. The optical coordinator is composed of a LED which transmits the data via an optical link. The optical coordinator is fixed at both the wrist and the shoulder of the patient body. The PD is installed on the ceiling to receive the data.  
Figure 2 illustrates the block diagram of the proposed system. We used the ECG as one of the biomedical signals for transmission. The detected ECG signal was sampled, quantized, and converted to binary data using an analog to digital (A/D) converter. The binary data is intensity modulated and then transmitted over the optical link using a single white LED as shown in Figure 2. At the receiver, a PD was utilized to convert the light intensity values of the received signal into a corresponding electrical signal. An adaptive threshold was used to retrieve the information more precisely [15]. Finally, the recovered data was evaluated by the medical advisor.