esa-sceva/satcom-chunk-collection huggingface dataset:
# Fabrication of Random Microlens Array for Laser Beam Homogenization with High Efficiency
## 2 Beam Homogenization Model of rMLA  
The working principle of beam homogenization based on the random microlens array (rMLA) is shown in Figure 1a. The condition of interference among the beamlets is broken, and the influence of interference on beam homogenization is suppressed by the rMLA, as the phases of the beamlets are randomly distributed due to the random apertures, focal lengths and arrangements. According to this principle, the preparation of the two types of rMLAs have been proposed in this paper: one is the honeycomb rMLA, as shown in Figure 1b, which can be used to produce a circular homogenizedlight spot; and the other is the rectangular rMLA, as shown in Figure 1c, which can be used to produce a rectangular homogenized light spot. The structural parameters of sub lenses of the two types of rMLAs are shown in Figure 1d, including aperture \(D_{i}\) and sag height \(h_{i}\). As can be seen from Figure 1d, the geometric relationship between the radius of curvature \(R_{i}\) and the sag height \(h_{i}\) can be obtained, expressed by Equation (1). According to the relationship between the radius of curvature and the focal length, the focal length (\(f_{i}\)) can be calculated by Equation (2), where \(n\) is the refractive index. Therefore, the divergence angle of the beam passing through the sub lenses can be calculated by Equation (3). Meanwhile, the divergence angle \(\theta_{i}\) of the entire light beam after passing through the rMLA is formed by the superposition of randomly distributed sub lenses, and is determined by the divergence angle of the sub lenses with a higher frequency of occurrence.  
\[R_{i}=\frac{D_{i}^{2}+4h_{i}^{2}}{8h_{i}} \tag{1}\]  
\[f_{i}=-\frac{R_{i}}{n-1}=-\frac{D_{i}^{2}+4h_{i}^{2}}{8(n-1)h_{i}} \tag{2}\]  
\[\theta_{i}=tan^{-1}\frac{D_{i}}{2\big{|}f_{i}\big{|}} \tag{3}\]
# Water Quality Changes in Cement-Lined Water Pipe Networks
## 3 Results
### Analysis of Test Results Obtained Over a Few-Hour Stagnation of Water in the Setup  
The second series involved examination of water stagnant in the setup at time intervals of 1, 2, 4, 8, 16 and 32 h. The sample used consisted of water found in the setup after about 11 months of its operation. The tests were intended to show changes in water quality over a short-term contact of water (used in the water supply practice) with the cement coating covering the internal surface of the conduits. Results of physico-chemical analyses of water samples are presented in Table 4.  
Analyses of individual water samples show changes in water quality after prolonged contact with cement-mortar lining. The rate of these changes was very slow and they were related to penetration of cement components found in the coating into the water; this mainly concerned calcium (aluminium content remained very low and rather stable). Reactions between water and cement led to increased turbidity, pH and gradual reduction of carbon dioxide content. As far as provisions concerning water intended for human consumption are concerned, water turbidity had excessively high values.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Hour** & \(\mathbf{I_{L}}\) & \(\mathbf{I_{R}}\) & \(\mathbf{I_{AWWA}}\) \\ \hline
0 & 0.33 & 6.87 & 12.64 \\
1 & 0.32 & 6.88 & 12.64 \\
2 & 0.41 & 6.69 & 12.56 \\
4 & 0.47 & 6.63 & 12.64 \\
8 & 0.48 & 6.62 & 12.68 \\
16 & 0.48 & 6.62 & 12.71 \\
32 & 0.56 & 6.49 & 12.73 \\ water stable for & 0 & 6.25 \(\div\) 6.75 & \(>\)12 \\ \hline \end{tabular}
\end{table}
Table 4: Corrosive properties of the water during stagnation period in the experimental set-up (series II).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Week** & \(\mathbf{I_{L}}\) & \(\mathbf{I_{R}}\) & \(\mathbf{I_{AWWA}}\) \\ \hline
0 & \(-\)0.04 & 7.14 & 12.09 \\
1 & 0.08 & 7.02 & 12.20 \\
2 & 0.31 & 7.04 & 12.67 \\
4 & 0.54 & 6.71 & 12.89 \\
5 & 0.81 & 6.49 & 13.13 \\
6 & 0.81 & 6.64 & 13.21 \\
7 & 0.92 & 6.48 & 13.39 \\
8 & 0.76 & 6.74 & 13.15 \\ water stable for & 0 & 6.25 \(\div\) 6.75 & \(>\)12 \\ \hline \end{tabular}
\end{table}
Table 3: Corrosive properties of the water during many weeks’ stagnation in the experimental set-up (series I).  
Changes in the physico-chemical indicators of water quality were accompanied by changes in the values of stability indices. The increased Langelier Saturation Index and decreased Ryznar Stability Index reflected increased water capacity to precipitate calcium carbonate, whereas increased AWWA index--safe use of the applied coating in case of contact with the examined water.
# An Analysis of MPTCP Congestion Control
## 7 MPTCP Congestion Control
### Fairness Perspectives  
There are different perspectives on \"fairness\" in the Internet. We define MPTCP fairness from the two perspectives of flow level fairness and network level fairness. Coupling the CC of subflows makes it possible to achieve the fairness when competing with TCP at the flow level. The existing algorithms assume that all paths share the same bottleneck and deliberately ensure that the window does not grow faster than TCP would across all paths. The obvious inference is that it is _possible_ that each separate path could be slightly less aggressive in increasing _cvond_. This has the potential to result in reduced throughput when competing with TCP NewReno. The ultimate result is that if an algorithm assumes that all paths share a bottleneck, coupled CC may be limited in its ability to achieve network level fairness.  
We expect this issue to be reasonably common. For instance, phone/tablet/IoT devices are generally connected via both LAN/Wi-Fi and/or LoRa/4G/5G interfaces. As such, a multi-homed user typically has access to multiple paths by paying different network providers. Similarly, a wireless network provider might permit users to use multiple Access Points (APs) using MPTCP.  
An MPTCP user should expect to obtain the same bandwidth from each AP/network provider as a regular TCP flow. Otherwise, additional payment and privilege are lost. Consider a scenario where an MPTCP connection uses two separate links to transfer data, with one link being shared with a single TCP connection and the second link having no other traffic. Both subflows should be able to fairly compete for bandwidth on their own link regardless of the other link conditions. In this way, the first subflow should gain approximately same amount of bandwidth as the TCP connection while the second subflow attains all of the available bandwidth.  
To reach network level fairness, when paths are separated, each MPTCP subflow should perform similar to a single TCP connection. The shared path should not affect other MPTCP subflows ability to obtain bandwidth over their own paths. Otherwise, the MPTCP application will unfairly attain less bandwidth from one path due to conditions on an alternate-unshared-path.
## 3 Results and Discussion
### Morphology  
The laser confocal microscope images of the cross-sectional morphologies of the initial MRE samples and after the fatigue tests are shown in Figure 2. Initially, the CIPs were aligned and embedded in NR matrix with some of the agglomerations in the magnetic field direction. However, in the samples subjected to fatigue, most of the particles were separated from the surrounding NR matrix, as can be seen in the dark area around the particles in Figure 2c-f. Different morphologies were observed based on the presence or absence of a magnetic field. Figure 2c shows that the separation regions are mainly arranged in the shear strain direction. Meanwhile, most of the CIPs are dissociated from the surrounding NR matrix in this direction. However, when shear fatigue was applied  
Figure 1: (**a**) Photos of the modified electromechanical fatigue system equipped with an electromagnet. (**b**) Maximum recorded load during cyclic shear fatigue of the NR and anisotropic MRE samples.  
under a magnetic field, separation occurred in the magnetic field direction, as shown in Figure 2e,f. Although the CIPs are separated from the surrounding NR matrix by the shear strain, interparticle forces between the magnetised CIPs maintained the physical bond with the NR matrix. However, the shear strain continuously separated the magnetised CIPs and NR matrix, along with the interparticle forces generated the migration regions by shifting the CIPs, as shown in Figure 2f. Therefore, the separation regions were observed in the shear strain and magnetic field directions as in zig-zag arrows. Some CIPs exhibited agglomerated morphologies in the direction of the interparticle forces. In addition, the magnetised CIPs on the surface of the MRE can be attracted to and lost to an external electromagnet when separated from the NR matrix.  
Figure 2: Microscope images with (**a**) 20\(\times\) and (**b**) 100\(\times\) magnification of the initial MRE cross-section; and (**c**) 20\(\times\) and (**d**) 100\(\times\) magnification of the MRE cross-section after the cyclic shear fatigue in the zero field; and (**e**) 20\(\times\) and (**f**) 100\(\times\) magnification of the MRE cross-section after 500,000 shear fatigue cycles under magnetic field. The bright parts represent the magnetic particles embedded in the NR. The white arrows represent the direction of separation or migration regions.
## 6 System Evaluation
### Throughput  
First, for the open throughput, it was noticed that both this study and [PERSON] et al.'s study showed a decrease in the transactions per second in all rounds. Our study excelled over [PERSON] et al.'s study, with slight differences in the TPS in the first three rounds and at two seconds in the fourth round and fifth round. The overall TPS for our study was between approximately 35 and 37 TPS, while for [PERSON] et al.'s study it was between 31 and 32 TPS. Figure 26 illustrates a summary of the open throughput.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Performance Environment and Specifications** \\ \hline No. & This Study & [PERSON] et al. (2022) [73] \\ \hline \multirow{4}{*}{Configuration} & AWS EC2 (2.2 \(\times\) large) with an & Intel Xeon®, 2.6 GHz with a 12 core \\  & 8 core CPU, 32 GB RAM, and & CPU, 16 GB RAM, 500 GB disk space, \\  & running Ubuntu 22.04 LTS & and running Ubuntu 18.04 LTS \\ \hline Algorithm & RAFT & RAFT \\ \hline Originations & 4 Organizations & 2 Organizations \\ \hline Peers & 1 Peer each & 1 Peer each \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance environment and specifications.  
Figure 24: Latency query transactions [73].  
For the query throughput, the results show a large difference between this thesis and [PERSON] et al.'s study. It was approximately higher for the open transactions; however, in this study, the throughput of the query transactions was higher than almost all transactions sent if the first three rounds had no delay, and it decreased in the fourth and fifth rounds with 354.3 TPS and 356.1, respectively. Figure 27 illustrates a summary of the query throughput.  
Table 4 illustrates an overview of the different performances (i.e., throughput and latency) under various evaluation environments and systems [73,75]. The comparison consisted of 1000 transactions, utilizing Hyperledger Caliper and Hyperledger Fabric, and considered the query and open transaction functions.  
Figure 26: Summary of the open throughput [73].  
Figure 25: Latency open transactions [73].
# Lithium-Ion Battery Capacity Prediction with GA-Optimized CNN, RNN, and BP
## 2 Overview and Dataset Information  
The general diagram of the proposed work is shown in Figure 1. In summary, the flow comprises five steps: feature extraction, data processing stage, parameter optimization, capacity prediction, and performance evaluation.  
Step 1: In the first step of the study, four different battery characteristics are extracted from the dataset obtained through experiments conducted by NASA in a laboratory setting. Subsequently, a Pearson correlation analysis is performed to determine the linear relationship between these features and battery capacity.  
Step 2: In this step, the features and battery capacity slated for use undergo normalization within the range of 0-1. This normalization process is performed using the range method. After normalization, the relevant data for the three different training sets are separated into training and test datasets.  
Step 3: In this step, the genetic algorithm is used to the relevant parameters of the CNN, BP, and RNN algorithms for capacity prediction. During this process, the genetic algorithm selects the parameters that lead to the lowest Root Mean Square Error (RMSE) values. The appropriate parameters are determined separately for each different training set, resulting in optimal parameter sets being identified.  
Step 4: Using the training data determined in the second step and the optimal parameters identified in the third step, capacity prediction is conducted using three distinct algorithms: Convolutional Neural Network (CNN), Backpropagation (BP), and Recurrent Neural Network (RNN) algorithms. For each algorithm, five separate capacity predictions are generated by training different networks five times each. Subsequently, these five predictions are averaged. The resulting average predictions undergo denormalization in preparation for the performance analysis.  
Step 5: Five different indicators are used to check and evaluate the accuracy and reliability of the predictions.
# Study on Dominant Frequency Attenuation of Blasting Vibration for Ultra-Small-Spacing Tunnel
## 1 Introduction
In open-pit mines blast, [PERSON] [28] compared the differences in the attenuation laws of three types of dominant frequencies caused by different blast charge structures. [PERSON] [29] analyzed the frequency range of ground vibrations in detail in terms of the instantaneous frequency and by time-frequency analysis. [PERSON] [30] and [PERSON] [31] compared the frequency characteristics for the blast-induced vibration velocity signal at different delay sequences from a millisecond (ms) delay blast. These studies on the propagation law of blast vibration waves in open-pit mines provide useful guidance for the analysis of tunnelling blast vibration waves. Furthermore, Fourier variation is the most common method to analyze the characteristics of seismic waves [32], and these studies also provide a reference to analyzing blast vibration waves. Based on the above investigations, there is still a lack of research on blast vibration frequency distribution the tunnels with a small clearance; and a theoretical study on the prediction and distribution law of the dominant frequencies of blast vibrations still needs a large amount of engineering data for validation, particularly for the two-track tunnel that the distance between two tunnels less than a tunnel span. In this study, a series of blast vibration tests were conducted on the background of a two-track roadway tunnel in which the clearance between the two tunnels is ranged from 9.4 m to 4.0 m. The blast vibration waves on the structure of the preceding tunnel, with completed primary support and secondary lining, were recorded, and analyzed; and the differences in the dominant frequency distributions on the primary support and lining were compared. Thus, the differences in the spectrum analysis for complete waveform and the dominant frequency of different delay sequences were studied. Moreover, the relationship between the frequency determined by the different methods was analyzed.
## 3 Results
### Pickering Emulsion Formation and Polymerization
We believe that an indication that this mechanism is at play is that the surface of these particles is covered by a compact monolayer of NPs, which adsorbed and self-assembled at the interface during emulsion formation prior to polymerization. This hypothesis stood at the base of developing a new method, called NanoTraPPED, for measuring the contact angle and surface energy of nanoparticles [35]. The clear mechanistic picture in Pickering emulsion polymerization is further complicated by other factors, such as the water solubility of the monomers, which can drive the migration of the nucleation _loci_ from the emulsion droplet into the water phase. Some other studies [45] link the mechanism of nucleation to the solubility of the initiator, for example, when the initiator is soluble in the water phase such as ammonium persulfate (APS) homogeneous nucleation mechanism, probably around the nanoparticle stabilizers present in the water phase, resulting in polymer particles that are loosely covered with nanoparticle stabilizers. We believe this is also the case for NP-SH with MM and NP-CN with MM, for  
Figure 1: SEM images of PMM microspheres at different magnifications: (**a**) 200\(\times\), (**b**) 500\(\times\), (**c**) 800\(\times\), and (**d**) 2500\(\times\), resulted from the polymerization of Pickering emulsions that varies in size and curvature depending on the surface energy of the nanoparticles. In (**d**), the hexagonal close-packed lattice of the self-assembled monolayer of NPs can be seen. which Pickering emulsions could be generated, but no polymer microspheres could be obtained, suggesting that the polymerization _loci_ are not in the monomer oil colloidosome, but have shifted into the water phase. This way, nano and microparticles have formed from the nuclei in the water phase, consuming the monomers in the colloidosomes, and can be easily identified in the SEM images, because they bear no NPs, Figures S6 and S7. The NPs used initially in stabilizing the colloidosomes in the emulsion formation adsorb due to mechanical energy input, but do not spontaneously adsorb at the newly formed polymer particle surfaces. The same hypothesis was supported by [PERSON] et al. [46], who consider that the emulsion stabilizing particles do not adsorb at the interface of the nucleating particles, but that their surface wettability is initially modified by the adsorption of the oligomers and thereafter these are capable of stabilizing the growing polymer particles. Thus, when measuring the diameters of the microspheres, we did not consider those that were not covered by a monolayer of NPs, i.e., those formed by the competing mechanism of water nucleation.
## 3 Simulation Results
### Channel Measurements
#### 3.1.1 Scenario 1: Corridor  
In this section, we focus on the corridor environment, where the measurements were conducted. The transmitter was stationed at the designated position, and the receiver commenced measurement from a distance of 1.0 m away from the transmitter. Subsequently, the receiver moved along the central axis of the corridor, with measurements taken at intervals of 0.113 m. This meticulous approach ensured a continuous line-of-sight (LoS) path between the transmitter and receiver, essential for accurate data collection.  
Situated on the fifth floor, the corridor is characterized by reinforced concrete walls on one side, while the other side features metal rods installed along its length. These structural elements contribute to the unique channel characteristics observed within this environment.  
Following data collection, the vector network analyzer (VNA) assessed the channels in the frequency domain, recording the corresponding S21 values. These values were then utilized to construct the channel frequency response (CFR). Subsequently, the channel impulse response (CIR) was derived using inverse fast Fourier transform (IFFT) processing.  
From the CIR data, various metrics were computed, including the power delay profile (PDP), mean excess delay (MED), and root mean square (RMS) delay spread. These metrics offer valuable insights into the channel's characteristics and behavior within the corridor environment, contributing to a comprehensive understanding of its propagation properties.  
Figure 5 describes the S21 magnitude versus frequent, CFR, CIR, and averaged PDP correspondingly.  
Figure 4: Experiment setup for measuring channel.
## 4 Case Study
### System Failure Prognostics
#### 4.2.1 System Health Prediction Based on Degradation
The system failure time predicted by this method was about 5262 h, and the deviation from the real value was small.
## 3 Principle of Least Squares Support Vector Machine (LSSVM)  
The Support Vector Machine (SVM) algorithm is a statistical learning theory developed for situations with limited samples based on the principle of minimizing structural risk. However, the traditional SVM approach requires solving a quadratic programming problem, which is often slow and suffers from the curse of dimensionality, resulting in high computational complexity.  
In contrast, the LSSVM [40] differs from the standard SVM in that the loss function of the optimization objective is represented by the Euclidean norm of the error. It converts the inequality constraints in SVM into equality constraints, thereby transforming the training process from solving a quadratic programming problem to solving a system of linear equations. This transformation simplifies the computational complexity and improves the convergence speed.  
Consider a training dataset (\(\mathbf{x}_{i}\), \(\mathbf{y}_{i}\)), \(i=\)1, 2, \(\ldots\), \(m\), where \(\mathbf{x}_{i}\) is the \(i\)-th input vector, \(\mathbf{y}_{i}\) is the corresponding output data, and m represents the number of samples. By mapping the input vectors to a high-dimensional feature space, it is possible to achieve linear separability of the samples in the linear space. Let \(\varphi(\mathbf{x})\) represent the mapped feature vector of \(\mathbf{x}\). Consequently, the model corresponding to the hyperplane dividing the feature space can be expressed as:  
\[f(\mathbf{x})=\mathbf{\omega}^{T}\varphi(\mathbf{x})+b, \tag{4}\]  
where \(\mathbf{\omega}^{T}=(\omega_{1},\omega_{2},\ldots,\omega_{n})\) is the normal vector that determines the direction of the hyperplane, and b is the displacement term, a scalar constant that determines the distance between the hyperplane and the origin.  
According to the principle of Structural Risk Minimization (SRM) [41], the optimization problem is transformed into:  
\[\begin{array}{l}\min F(\mathbf{\omega},b,\mathbf{e})=\frac{1}{2}\mathbf{\omega}^{T}\mathbf{ \omega}+\frac{1}{2}C\sum_{i=1}^{m}\varepsilon_{i}^{2}\\ \text{s.t.}\ y_{i}=\mathbf{\omega}^{T}\varphi(\mathbf{x})+b+e_{i},\ \ i=1,2,\ldots,m, \end{array} \tag{5}\]  
Figure 7: Influence of the perforation compaction damage on the productivity ratio.  
where \(\mathbf{e}=[e_{1},e_{1},\ldots,e_{m}]\) is the deviation vector reflecting the regression error between the actual and predicted values, and C is the regularization parameter used to control the penalty for samples exceeding the calculation error, which is manually set.  
By introducing the Lagrange function and solving according to the Karush-Kuhn-Tucker (KKT) conditions, the regression function model of the LS-SVM can be obtained:  
\[y(\mathbf{x})=\sum_{i=1}^{m}\alpha_{i}K(\mathbf{x}_{i},\mathbf{x})+b, \tag{6}\]  
where \(\alpha_{i}\) is the Lagrange multiplier, subject to the constraint \(0\leq\alpha_{i}\leq C\); \(K(\cdot,\cdot)\) is the kernel function, mainly used for inner product operations on sample data.  
Therefore, when using LSSVM to establish a regression model, it is crucial to appropriately select the regularization parameter C and the kernel function parameters.