esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Interest in hydrogen is rapidly growing due to rising greenhouse gas emissions and the depletion of fossil fuel reserves. Additive manufacturing (AM) is extensively employed to produce high-quality components, with a strong focus on enhancing mechanical properties. The efficiency and cost-effectiveness of AM have further increased interest in its application to manufacturing components capable of withstanding demanding conditions, such as those encountered in hydrogen technology. In this study, 316L stainless steel specimens were fabricated using AM via the selective laser melting (SLM) technique. The specimens then underwent various post-processing heat treatments (PPHT). A subset of these specimens, measuring 50 \(\times\) 50 \(\times\) 3 mm\({}^{3}\), was tested as electrodes in a water electrolysis cell for oxyhydrogen (HHO) gas production. The HHO gas flow rate and electrolyzer efficiency were evaluated at 60 \({}^{\circ}\)C under varying currents. The remaining AM specimens were evaluated for their susceptibility to hydrogen embrittlement under various hydrogen storage conditions, including testing at both room and cryogenic temperatures. Tensile and Charyp impact specimens were fabricated and tested before and after hydrogen charging. The fracture surfaces were analyzed using scanning electron microscopy (SEM) to assess the influence of hydrogen on fracture characteristics. Additionally, as-rolled stainless-steel specimens were examined for comparison with AM and PPHT 316L stainless steel. The primary objective of this study is to determine the most efficient alloy processing condition for optimal performance in each application. Results indicate that PPHT 316L stainless steel exhibits superior performance both as electrodes for HHO gas production and as a material for hydrogen storage vessels, demonstrating high resistance to hydrogen embrittlement.  
316L stainless steel; post-processing heat treatment (PPHT); electrodes; oxyhydrogen (HHO) gas production; hydrogen embrittlement; cryogenic conditions
# A Novel Approach Proposal for Estimation of Ultimate Being Capacity Based on Pile Loading Test Data
## 2 Materials and Methods  
### Study Parameters  
In the study, data obtained from 8 pile loading tests conducted in various provinces of Turkey was used (Table 1). The ground conditions obtained as a result of the drilling work carried out in the regions where the piles were located are given in Table 2. Since the pile loading test is a test performed in a few days at most, the situation it represents for cohesive soils is the undrained condition. For this reason, undrained parameters were taken into account when \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Date/Test** & **Type of** & **Working Type** & \multicolumn{2}{c}{**Section Geometry**} \\ \cline{3-5}
**Pile No** & **Manufacture** & **of Piles** & **Diameter (D)** & **Length (m)** \\ \hline
1 & Bored & Pressure & 1.20 & 36.00 \\
2 & Bored & Pressure & 1.00 & 25.00 \\
3 & Bored & Pull-Out & 0.80 & 18.00 \\
4 & Bored & Pressure & 1.00 & 20.00 \\
5 & Bored & Pressure & 1.00 & 34.00 \\
6 & Bored & Pressure & 0.80 & 26.00 \\
7 & Bored & Pressure & 0.65 & 25.00 \\
8 & Driven & Pull-Out & 0.65 & 30.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: General information about pile loading tests used in the study. determining the mechanical parameters of fine-grained soils. The undrained shear strength resistance of fine-grained soils was determined by field SPT tests. Since it is impossible to take undisturbed samples in coarse-grained soils, the mechanical parameters of coarse-grained (cohesionless) soils were determined by SPT tests conducted in the soil. In order to determine the mechanical parameters of the rock units, the Hoek-Brown failure criteria were changed to Mohr-Coulomb parameters. The end soil on all piles is clay. Piles 1, 2, 3, 4, and 5 are bored piles in fine-grained soils. Pile 7 is a bored pile that reaches failure in fine-grained soils. Pile 8 is a driven pile in fine-grained soil. Pile 6 is the bored pile in the coarse-grained ground. Test piles 3 and 8 are pull-out piles, and others are pressure piles. All piles have a circular cross-section and are friction/adhesion piles. All piles specified within the scope of the study are reinforced concrete. The elasticity module of the piles and therefore, of the concrete, has been determined in accordance with the Turkish Standard, Code Number TS500, Design and Construction Rules of Reinforced Concrete Structures [54]. According to this standard, the modulus of elasticity of concrete is:  
\[E_{p}=3250\cdot\sqrt{f_{ckj}}+14000 \tag{1}\]  
can be calculated with the relation. Here;  
\(E_{p}\): Modulus of elasticity of concrete (pile) (MN/m\({}^{2}\)),  
\(f_{ckj}\): It is the 28-day compressive strength of concrete in MPa (MN/m\({}^{2}\)).
# Social-Aware Peer Selection for Device-to-Device Communications in Dense Small-Cell Networks
## 3 System Description
### Social Closeness Index  
The _SCI_ characterizes the intimacy between the users in terms of the number of common friends in their friend-lists. A larger number of mutual friends between two users implies a stronger _SCI_ between them. As the _SCI_ is based on the similarity between the friends' lists of the two users, which are finite sets, we utilize the well-known Jaccard coefficient method, which compares the elements of two sets (i.e., friends lists), and finds mutual and distinct friends. We define _SCI_ between _Tx_ and _Rx_ as a ratio of their common friends' to the total number of friends in their friends lists, calculated as given below [15]  
\[SCI_{(Tx,Rx)}=\frac{(\Phi_{Tx}\bigcap\Phi_{Rx})}{(\Phi_{Tx}\bigcup\Phi_{Rx})}, \tag{1}\]  
where \(\Phi_{Tx}\) and \(\Phi_{Rx}\) are the friends lists of _Tx_ and _Rx_, respectively.  
Interest Similarity Index  
The _ISI_ determines the degree of interest of the users for similar contents. Users interested in the same contents are more likely to cooperate with each other in D2D communications even if they are not friends of each other. As we consider the interest lists of users as sequences of 1 s and 0 s, the famous method to determine the similarity between such sequences is the weighted cosine similarity. Hence, we use it to determine the _ISI_ between _Tx_ and _Rx_, which is defined as the ratio of their common interest to the root of the product of their individual interests [16] and is given by  
\[ISI_{(Tx,Rx)}\ =\ \frac{\Psi_{Tx,Rx}}{\sqrt{\Psi_{Tx}\cdot\Psi_{Rx}}}, \tag{2}\]  
where \(\Psi_{Tx,Rx}\) represents the mutual interest list while \(\Psi_{Tx}\) and \(\Psi_{Rx}\) represent individual interest lists of _Tx_ and _Rx_, respectively. In case of no friend users in proximity (i.e., _SFI_ \(<\) 0.25), _ISI_ can be used to select a D2D peer as a user could agree to share contents of common interest based on mutual reciprocity.
## 4 Simulations and Results
### Operation of Channel and Physical Modules in a Network Simulator  
A network simulator follows the logic of the protocol stack. This is, a set of modules defines the behavior of a node during the simulation. For a wireless node, the first two modules represent the wireless communication channel and the physical layer, which are the most relevant for the objective of our study.  
Channel module: This is in charge of computing the packet signal attenuation caused by the distance. This attenuation is typically called path loss or large-scale fading. It considers the attenuation caused by the presence of buildings. Then, the module introduces a variability effect in the computed power, which is called small-scale fading or just fading. Fading simulates effects, like reflectionand scattering.  
If the power computed in the reception of a packet is lower than a minimum threshold, then the packet is discarded and it is not processed by the physical simulator's module. This threshold is typically obtained as a small fraction of the Nyquist noise associated with the channel (see Figure 6).  
Physical layer module: When a packet is received by this module, it is checked against the antenna sensitivity. If the power of the incoming packet is lower than the antenna sensitivity, then it is considered interference for other packets received during their duration. Otherwise, the packet is received, and its error probability is evaluated to determine if it was correctly demodulated. Multiple incoming packets with low power are grouped to get the total interference of the channel, as shown in Figure 6. In this work, we employ the analytical packet error model proposed in [14], which considers signal to interference and noise ratio (SINR), channel capacity and packet length to decide if a packet is erroneous. An additional task of the physical module is setting the state of the channel as busy or idle according to the sensed power compared to the antenna sensitivity level.
## 3 Results and Discussion
### Overpotential Measurements  
The overpotentials were estimated from the difference between the voltage intercept on the ordinate at linear extrapolation and the measured open circuit potential. To this end, the linearized Butler-Volmer equation, valid for very low overpotentials, was used. The polarization curves show that overpotentials are modest, even low, i.e., less than 10 mV in any case. One polarization curve obtained at 230 mg dm\({}^{-3}\) initial concentrations of sulfide is presented in Figure 10. The open circuit potential extrapolated from the experimental data is 0.665 V, which is lower than the measured one, i.e., 0.67 V. The overpotential in this case is 0.005 V. The overpotential of 0.0155 V is the highest one observed in the present work.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sulfide Concentration,** & **Supporting** & **Maximum Current** & **Maximum Power** & **Cell** & **Reference** \\
**g dm\({}^{-3}\)** & **Electrolyte** & **Density, A/m\({}^{2}\)** & **Density, W/m\({}^{2}\)** & **Density, W/m\({}^{2}\)** & **Efficiency, \%** & **Reference** \\ \hline
0.241 & 0.27M NaCl & 8 & 2.6 & 30.5 & This work \\
0.320 & 3M NaOH & 16 & 15 & 50 & Ref. [23] \\
70 & Water (?) & 0.075 & 0.025 & 40 & Ref. [24] \\
150 & Water (?) & 0.5 & 0.031 & 30 & Ref. [24] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the measured current and power densities to referent data.
# Solving the Urban Transit Routing Problem Using a Cat Swarm Optimization-Based Algorithm
## 1 Introduction
Our main motivation to create and use a CSO-based algorithm to cope with the UTRP is derived from the fact that although there have been many effective applications of CSO-based algorithms for solving constrained optimization problems (such as timetabling and scheduling problems [18], scheduling workflow applications in cloud systems [19], optimizing wireless sensor network environments [20], clustering problems [21], set-covering problems [22], modeling of plants [23], combinatorial optimization problems [24], etc. ), there is no specific CSO-based application to solve the UTRP, as far as we know. In order to demonstrate the efficiency and effectiveness of the CSO-based algorithm, we compare its performance on the widely known [PERSON]'s Swiss transit network [25] with twelve other approaches presented in the respective literature. These approaches are presented in short in the related work section (see Section 1.1). Furthermore, four additional instances are used that are presented in [13] and on which a heuristic and evolutionary approach algorithm has been applied. The CSO application results are also compared with the results of this algorithm. The structure of the paper is as follows. In Section 1.1 related work is presented, while in Section 1.2, the comparison criteria used to compare the proposed algorithm's performance with other approaches are discussed. Section 2 includes problem description, while Section 3 describes in detail the proposed CSO-based algorithm. In Section 4, computational results are presented and discussed. Finally, Section 5 summarizes conclusions and discusses future work.
## 2 System Description and Modelling
### Battery Energy Storage System (BESS)  
Whenever the total generation from the WT and PV exceeds the load demand, the BESS will switch into the charging mode. On the other hand, whenever the load demand exceeds the total generation, the BESS switches into the discharging state. The BESS state of charge at any given time \(t\) depends on the difference between the system load and the sum of the total renewable generation with the previous state of charge as the reference. The charging power or discharging power, as the case may be, is determined by the difference between the instantaneous load and generation. Hence, the current BESS state of charge at any given time, \(t\) (hour) is given by:  
\[SOC(t)=SOC(t-1)\left(1-dh\right)+\left[P_{b}^{ch}(t)\times\beta_{ec}-\frac{P_{ b}^{ds}(t)}{\beta_{ed}}\right] \tag{3}\]  
where \(SOC(t)\) and \(SOC(t-1)\) is the state of charge of the BESS for the current and previous time period in KWh respectively, \(dh\) is the hourly rate of self-discharge, \(\beta_{ed}\) and \(\beta_{ec}\) is the discharging and charging efficiency of the BESS, respectively. The minimum (\(SOC^{min}\)) and maximum (\(SOC^{max}\)) state of charge of the BESS are a function of the optimal capacity size as expressed:  
\[SOC^{min} =0.2\times S_{b} \tag{4}\] \[SOC^{max} =0.8\times S_{b} \tag{5}\]  
where \(S_{b}\) is the optimal capacity size of the BESS.
## 3 A Community Anomaly Detection Approach Based on Structural/Attribute Information
### Proposed Community Anomaly Detection Approach
#### 3.2.3 Algorithm  
Our algorithm, called the CAnom algorithm, is described as follows. It computes the anomaly score of each node in four steps: (1) determine the node community, (2) calculate its community-structure-based score, (3) select its relevant attributes, and (4) compute its attribute-based score. Finally, all nodes are ranked based on their anomaly score. As parameters, we require the number of attributes to select (N), which determines how many relevant attributes should be selected according to their Laplacian score. In addition,\(\alpha\) in Equation (10) refers to the weight of the structure-based anomaly and the attribute-based anomaly in the anomaly score.  
Firstly, we partition the graph into communities using the Louvain algorithm (line 2). Then, we calculate the eigenvector centrality of each node (line 3). We iterate over each node to compute its connectivity before the community-structure-based score is assigned (line 6). Thereafter, we calculate the Laplacian score for each attribute (line 8), then we select N attributes with the lowest Laplacian score to be the relevant attributes (line 9). Next, we iterate over each community and use the relevant attributes to compare the distance between the node and its community nodes with the mean distance for the community (lines 10-16). Finally, we unify the community-structure-based score and attributes-based score (lines 17-19) and return the anomaly score of the nodes from G (line 20). Our community-based detection algorithm is called CAnom (Algorithm 1), an amalgamation of the words community and anomaly.  
```
0:\(G:(V,E)\), \(A\): Attributes, \(N:\) number of select attributes
0:Ranking of all \(v\in V\)
1:Initialize empty vectors \(str\_anomaly\), \(attr\_anomaly\), \(total\_anomaly\) for all \(v\in V\)
2:C \(\leftarrow\) Partition(G)
3:EC \(\leftarrow\) eigenvector centrality for all \(v\in G\)
4:For each \(v\in G\) do
5: Compute connectivity (v) (Equation (6))
6: Compute StrAnomaly score (v) (Equation (2))
7:End For
8:For all \(a_{r}\in A\) calculate Laplacian score (Equation (6))
9:A\({}^{\prime}\leftarrow\) subset of \(N\) attributes with the lowest Laplacian score
10:For each community \(C_{k}\) in \(C\) do
11:Md \(\leftarrow\) mean values of attributes from A\({}^{\prime}\) in \(C_{k}\) (Equation (9))
12:For each \(v_{i}\) in \(C_{k}\) do
13:\(s_{v_{i}}\leftarrow\) a dictionary containing for each attribute \(a_{r}\) of \(v_{i}\) its anomaly score
14:Compute attrAnomaly[v] (Equation (7))
15:End For
16:End For
17:For each \(v\in G\) do
18:AnomalyS[v] = a strAnomaly[v] + (1 - \(\alpha\)) attrAnomaly[v] (Equation (10))
19:End For
20:Return AnomalyScore
```  
**Algorithm 1**CAnom
# A Dual-Polarized and Broadband Multiple-Antenna System for 5G Cellular Communications
## 8 Conclusions  
This paper introduces a new dual-polarized eight-antenna array design that employs CPW-fed trapezoid slot antennas with L-shaped feedings. The primary goal of this design was to achieve a wide/broad bandwidth from 3.2 to 6 GHz to accommodate the 5G spectra. The presented MIMO design is implemented on a single layer of a smartphone board,  
Figure 19: Beam-steering of the phased array at (**a**) 0, (**b**) 30, and (**c**) 60 degrees.  
Figure 20: Possible placements of the mmWave phased array.  
Figure 18: (**a**) S-parameters and (**b**) gain comparison of the designed phased array.  
utilizing the cost-effective and widely used FR4 substrate material, without compromising its overall performance. The results demonstrate that the proposed smartphone antenna design outperforms recently reported designs in several key aspects. It exhibits superior efficiency results, improved coverage, minimal ECC/TARC parameters, and a notably wider impedance bandwidth. Furthermore, due to its single-layer planar structure without ohmic losses, the design proves to be highly promising for future handheld platforms. The experimental results of the suggested design align well with the simulation data, affirming its reliability and validity. These compelling features make the proposed design an ideal candidate for high data-rate mobile networks in future smartphones. Overall, this dual-polarized/eight-antenna model presents a significant advancement in MIMO smartphone antenna technology, offering enhanced performance and broader applications in the rapidly evolving world of mobile communications. Moreover, a compact mmWave phased array antenna with broad bandwidth is presented, and its properties are discussed, highlighting its potential for future smartphone connectivity.  
Conceptualization, H.J.B., N.O.P. and C.H.S.; Methodology, H.J.B.; Software, H.J.B. and C.H.S.; Validation, H.J.B., N.O.P. and C.H.S.; Investigation, H.J.B. and N.O.P.; Writing--original draft, H.J.B.; Writing--review & editing, N.O.P. and C.H.S.; Supervision, N.O.P. and C.H.S.; Project administration, N.O.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.
## 4 Discussion
### Influence of the Proposed Model's Parameters on Balancing Performance and Clustering Efficiency
#### 4.3.2 Outlier Proportion Parameter  
The outlier proportion parameter \(\alpha\) balances the accuracy of the model's linear optimization against the proportion of outliers and mismatches present in the two sets. Lower \(\alpha\) values ensure that matches are identified only for features that correspond with high accuracy, making this approach particularly effective for problems with minimal displacements. However, for problems involving larger displacements or complex interactions, lower \(\alpha\) values may lead to missed matches, resulting in both false positives and false  
Figure 25: Feature clustering (represented by distinct colors) using DBSCAN (top) and DENC (bottom) using \(\varepsilon=0.250\) m, \(\varepsilon_{\mathrm{x}}=0.220\) m, and \(\varepsilon_{\mathrm{y}}=0.110\) m—case study for segment S5.  
negatives, as defects are classified as newly detected rather than matched to their actual correspondents. Conversely, higher \(\alpha\) values cause the linear optimization to favor farther neighbors, as the solution is now stabilizing for these neighbors at the expense of the true, closer correspondents, which similarly increases false positives and false negatives, thereby reducing the overall performance.  
[PERSON] and [PERSON] [13] proposed a feasible range of \(\alpha\in[0,0.1]\) for the annealing model. This range aligns with the Voronoi model, where performance significantly declines beyond these values, as detailed in Section 3.1.1. On the other hand, \(\alpha\) has a notably weaker influence on the proposed model, as detailed in Sections 3.2.2 and 3.4.2. While performance does decline beyond this range, the model consistently maintains recall above 90.4% for the proposed approach using DENC and 87.4% for the DBSCAN-based alternative. This is primarily because many features are either matched directly (Category 1) or through distance-based filtering (Categories 2 and 3), which results in reduced problem size, distortions, and outliers, thereby enhancing transformation precision and stabilizing the correspondence optimization for changes in or extended ranges of \(\alpha\).
# Remote Low-Cost Differential Isolated Probe for Voltage Measurements
## 3 Probe Test and Validation
### Isolated Voltage Probe as a Remote Autonomous Instrument
#### 3.2.2 Multipoint Measurement with Power Arbitrary Function Generator  
The validation of the multipoint measurement is carried out by replicating the measurement setup of the previous section, replicating it in this case three times. The connectivity diagram is shown in Figure 21.  
As can be seen in the diagram, three probes have been mounted simultaneously, each of them acquiring information from an AC/DC power source GW Instek APS-1102 used as an arbitrary function generator. This information is then published to its own topic, so that the measurements of each probe are clearly identifiable.  
The generator connected to Probe #1 has a peak voltage of 200 V and a harmonic level ratio, excluding the fundamental at 50 Hz, of 5% for the third, 6% for the fifth, 5% for the seventh, and 1.5% for the ninth. Probe #2 has an input voltage of 150 V with a relative harmonic content of 2% and 1.5% for harmonics 17 and 25, respectively. Finally, the source connected to Probe #3 has an output voltage of 100 V, with a relative harmonic content in voltage of 2% for the second harmonic and 5% for the third harmonic.  
The relative harmonic contents in each of the sources correspond to those indicated in the limits of the EN 50160 standard, which are shown in Table 3.  
Figure 21: MQTT connectivity block diagram for multipoint measurement.  
Figure 20: FFT: Theoretical voltage (blue), commercial probe voltage (red), and proposed probe voltage (green).  
As can be seen in Figure 22, the results obtained are similar to those shown with a single probe. However, the present validation allows us to demonstrate empirically that the adopted solution is adequate.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Manufacturer** & **Model** & **Type** & **Max. Voltage** & **BW** & **Cost** \\ \hline Tektronix & P5205 & Differential & 1300 V & 100 MHz & EUR 2000 \\ \hline Chauvin & DP25 & Differential & 1400 V & 25 MHz & EUR 450 \\ \hline This work & Proposed & Isolated and Differential & 600 V & 50 kHz & EUR 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of the probes used for the verification measurements carried out in this work.  
Figure 22: FFT for simultaneous multipoint measurement: (**a**) Probe #1, (**b**) Probe #2, and (**c**) Probe #3.