esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The substrate temperature during the sputtering of In\({}_{\text{x}}\)S\({}_{\text{y}}\) buffer layers significantly influences the performance of CIGS thin-film solar cells. The best power conversion efficiencies in the as-grown state were obtained at a temperature plateau around 200 \({}^{\circ}\)C with the best value of 15.3% compared to the reference cell with solution-grown CdS buffer with 17.1% efficiency. The difference in performance compared to cells with buffers deposited below 200 \({}^{\circ}\)C is mainly due to a reduced \(V_{\text{OC}}\). The chemical composition and homogeneity of In\({}_{\text{x}}\)S\({}_{\text{y}}\) changes from stoichiometric and homogenous In\({}_{\text{2}}\)S\({}_{\text{3}}\) for layers deposited RT to inhomogenous and more sulfur-rich and indium-deficient composition for higher temperatures in the range of 220-240 \({}^{\circ}\)C. In addition, there is a pronounced copper depletion at the CIGS absorber surface and a sodium accumulation in the In\({}_{\text{x}}\)S\({}_{\text{y}}\) buffer and at the CIGS/buffer interface for higher temperatures compared to In\({}_{\text{x}}\)S\({}_{\text{y}}\) layers deposited at RT. These two features could explain the higher \(V_{\text{OC}}\) values of cells with In\({}_{\text{x}}\)S\({}_{\text{y}}\) buffers grown at higher substrate temperatures compared to RT. However, In\({}_{\text{x}}\)S\({}_{\text{y}}\) layers with a non-ideal bandgap energy of 2.0-2.1 eV (indirect) induce parasitic losses and a limited \(J_{\text{SC}}\). In future research, appropriate quantities of sodium and/or oxygen could be intentionally incorporated in the In\({}_{\text{x}}\)S\({}_{\text{y}}\) layer and--with very thin In\({}_{\text{x}}\)S\({}_{\text{y}}\) layers, in combination with an optimized high-resistive layer--the parasitic absorption could be further reduced and the \(J_{\text{SC}}\) increased.  
Conceptualization, D.H. and W.W.; validation, D.H., W.H., R.M. and W.W.; investigation, D.H., W.H., R.M., and W.W.; writing--original draft preparation, W.W.; writing--review and editing, D.H., W.H., R.M., and W.W.; visualization, D.H.; project administration, W.W.; funding acquisition, W.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the German Federal Ministry for Economic Affairs and Energy (BMWi) within the \"EFFCIS\" project under contract number 0324076A.  
We thank the CIGS team at ZSW for solar cell preparation, especially [PERSON] for CIGS deposition.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Description of Different Deployed VPN Solutions
### IPsec Ikev2 EAP Road Warriors Scenario  
The following EAP connection definition allows multiple Windows clients to connect to the strongSwan VPN gateway via any EAP method over IKEv2. Cypher suites aes256-sha256-modp2048 for IKE and aes256-sha1-modp2048 for ESP are the strongest proposals; the Windows client is able to offer unless PowerShell is used. These proposals are not explicitly configured here to accept stronger algorithms proposed by other clients via strongSwan's default proposals.  
Figure 6: Comparison of the average Throughput in IKEv1-L2 TP Road Warriors scenario.  
Figure 8 shows comparison of the average throughput and standard deviation in IKEv2-EAP Road Warriors scenario, wired. As seen from the figure, Windows 10 as an Ikev2-EAP client turns out to be the best performing, while Mikrotik and Raspberry Pi are almost equivalent. The virtual machine running the demo version of Mikrotik is locked to Ethernet 100 Mbps. Using IKEv2 with certificates, the performance of OpenWRT (client) and the Raspberry Pi 2 are noticeably close. As for the wireless clients, the Android, this time, turns out to be slightly better performing than iPhone 11 and iPhone 8.
## 3 Results  
### Model Validation  
As shown in Figure 3a,c, the mean (standard deviation) of the longitudinal bending stiffness of the sole's forefoot at 7.5 mm/s was 7.65 (0.15) N/mm. At 10.0 mm/s, it was 7.67 (0.09) N/mm; at 12.5 mm/s, it was 7.74 (0.09) N/mm; and at 15.0 mm/s, it was 7.73 (0.08) N/mm. The 10 sets of data obtained at each rate met the assumption of homogeneity of variances, allowing one-way ANOVA to be performed. The results indicated no significant differences in stiffness among the rates tested (\(p>0.05\)).  
Figure 3b,d present the error rates between the simulated stiffness values at different rates and the experimentally measured stiffness values. The simulated stiffness at 7.5 mm/s was 7.35 N/mm, with an error rate of 3.92%. At 10 mm/s, the simulated stiffness was 7.11 N/mm, with an error rate of 7.30%. At 12.5 mm/s, the simulated stiffness was 8.41 N/mm, with an error rate of 8.66%. At 15 mm/s, the simulated stiffness was 7.45 N/mm, with an error rate of 3.62%. Due to simplifications in the model, as well as minor fluctuations in error during the fitting of the linear regression equation, the simulated value at 12.5 mm/s is slightly higher than the measured value. However, the overall error rates between the simulated and measured values were less than 10%, confirming the validity of the \"three-point bending fixture-sole\" model used in this study.  
Figure 2: (**a**) Carbon fiber composite laminate: stacked structure (left) and polar properties (right). Below: stacking direction (purple vector) and layup direction (yellow vector). (**b**) Carbon plate model: top view (top), side view of carbon plate three-point bending FEA simulation (middle, 200 mm radius, 1 mm thickness), and coupled plate-sole structure three-point bending FEA simulation (bottom).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Epoxy Carbon Woven (230 GPa) Wet** \\ \hline Density (kg/m\({}^{3}\)) & 1451 \\ Young’s Modulus X direction (MPa) & 59,160 \\ Young’s Modulus Y direction (MPa) & 59,160 \\ Young’s Modulus Z direction (MPa) & 7500 \\ Poisson’s Ratio XY & 0.04 \\ Poisson’s Ratio YZ & 0.3 \\ Poisson’s Ratio XZ & 0.3 \\ Shear Modulus XY (MPa) & 3300 \\ Shear Modulus YZ (MPa) & 2700 \\ Shear Modulus XZ (MPa) & 2700 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Material properties of epoxy carbon woven (230 GPa) wet.
# Laser Absorption Sensing Systems: Challenges, Modeling, and Design Optimization
## 3 Fundamentals of Different LAS Measurement Schemes
### Recovery of the Absorbance Profile Based On Higher-Order Harmonic Signals  
While previous WMS approaches use the harmonic signals directly, a phasor decomposition method has been proposed to recover the absorbance profile from the first harmonic on the basis of RAM [60, 61]. However, these earlier attempts only worked well with small modulation indices (\(m<0.2\)) when the first-order harmonic shape is close to the first derivative of the absorbance profile. [PERSON] et al. employed additional higher odd harmonics (3 rd, 5 th,... ) to enhance the recovery accuracy with large modulation indices [62]. Figure 6 shows the flow chart of the recovery and data reduction process. It is worth noting that while considering more harmonic components improves the model recovery accuracy, these high-order terms usually have low SNRs and are difficult to detect in practical measurements.  
Figure 5: Flow chart of a calibration-free WMS scheme with scanned-wavelength laser characterization.
## 3 Numerical Simulation
### Rock Mass Conditions for the 3D Numerical Analyses  
Rock mass classification, which qualitatively and quantitatively indicates the condition of a rock mass, is currently acknowledged as a mandatory adjunct when evaluating rock mass environments for engineering purposes. Although well-known classification systems have been applied in various engineering projects such as mining, slopes, dam foundations, and tunnel-boring machines (TBMs), they have been developed, refined, and updated for tunnel support design [13]. Numerous rock mass classifications have been developed, such as the RQD, RMR, Q-system, and GSI. In this study, RMR was adopted as a way of expressing the rock mass condition. The RMR classification was first proposed by [PERSON] [14], and it has since been modified several times. Its features and structures have also been modified significantly [15; 16; 17].  
The change in the LDP curve according to the rock mass conditions was analyzed via numerical analyses, and the rock mass conditions were defined by dividing the RMR value in 5-20 intervals. Numerical modeling numbers were classified into 35 cases from A-1 to G-6, including the initial stress conditions. Table 1 lists the rock mass properties and the initial stresses of the rock mass for the numerical analyses.  
The rock mass properties obtained from the Korean railway construction site (Figure 6) were used as input values for the numerical analysis. The Songjeong Tunnel, which features an extension of 10 km, is located at this construction site. The Ulsan and Ocheon faults are in the area where the Songjeong railway tunnel is planned; these are large faults that extend for approximately 50 km and intersect with the Yeonil  
Figure 5: Numerical model for calculating the LDP.  
50-m-wide brittle layer. The distribution of rock quality on the project site is generally poor; mudstone and andesite are noted at the starting point, whereas most of the remaining section features granite distributed along the tunnel alignment.
## 5 Case Study
### Effectiveness Analysis of the Designed Algorithm  
In this section, the effectiveness of the algorithm is evaluated. The performances of the ego vehicle in simulation testing are shown in Table 6. With the proposed motion planning scheme, the ego vehicle maintains mobility instead of coming to an infinite stop in an unanticipated scenario. After the obstructed delay time reaches the congestion threshold, the compliance-oriented potential fields are truncated based on PVCI to transform into the violation cost potential fields. In the primary objective of ensuring safety, obstacle avoidance is accomplished. According to the profile report of the simulation, the optimization problem can be resolved within approximately 4.8 ms when the motion planning does not involve potential fields. However, the resolution time increases to about 6 ms when potential fields are integrated into the optimization problem. Therefore, the proposed potential fields do not impose a significant additional computational burden. The preceding matrix computations in the MPC module exhibited an average computation time of 0.37 ms. The MPC meets strict real-time requirements, especially in emergency situations.
## 3 Discussion and Results
### Field Results on GFRP Piles, Paramteric Study
\[P_{AX}=P_{C}+P_{\text{GFRP}} \tag{1}\]  
\[P_{C}=A_{C}f_{cc} \tag{2}\]  
\[P_{\text{GFRP}}=A_{f}E_{f}\varepsilon_{ff}^{\prime}. \tag{3}\]The reduction of the hoop stiffness is shown by the decrease of the \(f_{cx}\) in Equation (2). Therefore, the \(P_{AX}\) is reduced in Equation (1). \(P_{\rmGFRP}\) is increased by increasing the longitudinal stiffness of the tube. For small variations in the thickness the rate of decrease of the \(P_{\rm C}\) is more than the increasing rate of the \(P_{\rm GFRP}\). As a result, the ultimate axial compression load capacity, \(P_{AX}\), is decreased. However, for large thickness values, the \(P_{AX}\) becomes greater due to the increasing rate of the \(P_{\rm GFRP}\). The decline in the rate of hoop stiffness does not considerably affect the \(P_{AX}\) for large thickness values. For the median values of the thicknesses, the reducing amount of the \(P_{\rm C}\) happens at the same rate as the \(P_{\rm GFRP}\) increment and consequently, the change rate of the \(P_{AX}\) is almost constant, as is presented in the Zone I of Figure 6 for the GFRP tube with 7.5 mm thickness (D/t = 33). For the larger wall thicknesses, D/t within 33 and 17, interaction curves for various structures of GFRP tubes have no intersection. For the smaller thickness values, D/t between 33 and 50, the interaction curves have points of intersection. These intersection points affect the effectiveness of the design procedure for various eccentricity values. For instance, as shown in Figure 6, the curve attributed to the 6:1 ratio and with a relatively large eccentricity \(e_{A}\), is tangent to the other two curves and acts as the envelope of the family curves. Therefore, the 6:1 ratio is the most preferable fiber percentage for the design procedure compared to 1:6 and 1:1 ratios. On the other hand, the interaction curve related to the 1:6 ratio and with relatively small eccentricity \(e_{\rm C}\), is the envelope curve, compared to the other two curves. Therefore, the 1:6 is the most desired fiver proportion for the small eccentricity values. For the median eccentric values, \(e_{B}\), all the three curves attributed to 1:6, 1:1, and 6:1 have comparable efficiency to be selected for the design procedure. As a result, the ultimate axial and bending capacities of the piles are directed by the combination of GFRP fiber percentages and the tube thickness values for different ranges of eccentrics.
# From Phase Space to Non-Equilibrium Dynamics: Exploring [PERSON]'s Theorem and Its Implications
## 3 Out-of-Equilibrium Dynamics
### Non-Negativity and Constraints of the Extended Entropy \(\overline{S}\)
#### 3.6.4 Thermodynamic Consistency  
In equilibrium (\(U^{(a)}_{\text{mce}}=0\), \(\mathbf{a}=\mathbf{b}=0\)), \(\overline{S}\) reduces to the Gibbs entropy:  
\[S=\sum_{a}S^{(a)}\Bigg{(}E^{(a)}-\frac{(p^{(a)})^{2}}{2m^{(a)}}-\frac{(J^{(a)} )^{2}}{2I^{(a)}}-q^{(a)}V^{(a)}+q^{(a)}(\mathbf{A}^{(a)}\cdot\mathbf{v}^{(a)} )\Bigg{)}, \tag{17}\]  
which is non-negative. For non-equilibrium systems, the additional terms refine microstate selection while maintaining \(X^{(a)}\geq 0\) through energy constraints, ensuring thermodynamic consistency.  
The extended entropy \(\overline{S}\) is non-negative by construction when the following is true:* **Energy constraints** are satisfied: \(E^{(a)}\geq\) [mechanical energy terms];
* **Lagrange multipliers \(\mathbf{a},\mathbf{b}\)** are derived from variational principles to enforce conservation laws;
* **Bounds on linear terms** are respected via inequalities tied to kinetic/angular energies.  
This framework generalizes equilibrium thermodynamics to non-equilibrium systems while preserving the core property \(S\geq 0\).
###### Abstract  
Vehicles are now rapidly transitioning from a conventional torsion bar suspension to an in-arm suspension unit (ISU), reflecting the growing industrial demand for more compact, high-performance systems. Although the ISU system can adapt well to rough terrain, the side forces produced when the piston moves can affect its reliability. Current models built on ideal gas assumptions fail to describe the complex nonlinear behavior of nitrogen under extreme pressure and temperature variations. This study incorporates the Beattie-Bridgeman real-gas equation into a dynamic force-displacement model to overcome this limitation. Furthermore, a bi-objective optimization strategy was devised that simultaneously minimizes the side forces and enhances acceleration stability across diverse environmental conditions. The optimized design based on a metamodel and a hybrid metaheuristic algorithm resulted in an 81.4% reduction in peak lateral forces and a 53.3% improvement in acceleration robustness, which marks a significant increase in suspension system durability. These findings not only advance ISU design methodologies but also offer viable solutions to existing reliability challenges.  
Keywords:in-arm suspension unit; robust multi-objective optimization; hydropneumatic suspension; nonlinear damper; metamodeling; hybrid metaheuristic algorithm +
Footnote †: journal: Applied Sciences
## 4 WiFi 6/6E: IEEE 802.11 ax  
Since the introduction of the legacy IEEE 802.11 standard, WiFi has experienced a significant growth in data rates over the years. Starting with 2 Mbps in 1997, the IEEE 802.11 standard has come a long way, with IEEE 802.11 ac (WiFi 5) reaching 6.93 Gbps in 2013. Due to higher data rates supported by the latest WiFi technologies, WiFi has also been considered for outdoor deployment in conjunction with other wireless networks [33]. The development of these WiFi standards has primarily been focused on improving data rates using higher-order modulation and coding schemes, multi-input multi-output (MIMO) techniques, and wider channel allocations [34; 35]. However, as discussed earlier, 6G applications require guaranteed latency and reliability for the uninterrupted delivery of services. To support these QoS requirements of emerging applications, the latest WiFi 6/6E/7 and 60 GHz WiFi standardization have proposed a number of features [36]. In this section, we discuss these developments of WiFi 6/6E, WiFi 7, and 60 GHz WiFi, and investigate how  
Figure 2: Positioning of the communication technologies in the electromagnetic spectrum.  
they align with the 6G ecosystem. For this purpose, we start our discussion with WiFi 6 in this section, and continue with WiFi 7 and 60 GHz WiFi in Sections 5 and 6, respectively.  
In 2019, IEEE 802.11 ax was introduced to address the inherent challenges associated with legacy IEEE 802.11 wireless technologies and to enhance the network performances and connectivity. In this section, we discuss such challenges and the proposed techniques in IEEE 802.11 ax in detail.
## 3 Experimental Results
### Mitigation of Noise Effects: Simulation Tests
In order to give a quantitative evaluation of the improvement effect allowed by the filtering technique proposed in this paper, we adopted the performance index proposed in [21]:  
\[PI_{I_{h_{k}}}=\frac{\sum_{j=0}^{N_{h}}\left[h_{k}[j]-h_{k}[j]\right]^{2}}{\sum_{j =0}^{N_{h}}\left[h_{k}[j]\right]^{2}}\qquad k=1,\ \ldots,\ N_{H} \tag{12}\] \[PI_{3}=\frac{1}{N_{h_{l}}}\sum_{k=1}^{N_{H}}\left(PI_{I_{h_{k}}} \right)=\frac{1}{N_{h_{l}}}\sum_{k=1}^{N_{H}}\left(\frac{\sum_{j=0}^{N_{h}} \left[h_{k}[j]-h_{k}[j]\right]^{2}}{\sum_{j=0}^{N_{h}}\left[h_{k}[j]\right]^{2 }}\right)\]  
where \(h_{k}[j]\) is the \(k-th\) reference kernel and \(\hat{h}_{k}[j]\) is its estimate, \(N_{s}\) is the number of samples of both the actual and estimated kernels, and \(N_{H}\) is the order of the Hammerstein model. The values of the indices calculated both before and after filtering for the two cases of signal to noise ratio equal to 10 [dB] and 5 [dB] are shown in Tables 2 and 3. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(SNR\) = 10 dB & \(PI_{I_{h_{1}}}\) & \(PI_{I_{h_{2}}}\) & \(PI_{I_{h_{3}}}\) & \(PI_{I_{h_{4}}}\) & \(PI_{I_{3}}\) \\ \hline pre filter & \(-7.37\) & \(-0.61\) & \(-0.58\) & \(2.92\) & \(-0.12\) \\ filtered & \(-14.74\) & \(-8.91\) & \(-10.76\) & \(-4.13\) & \(-8.00\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Values [dB] of the performance indexes defined by (12): case of \(\text{SNR}\) = 10 [dB]. Figure 5: Estimated \(h_{k}(t)\) kernels in the case \(SNR_{1}=10\) [dB] (blue lines) and reference kernels (red lines). Plots (**A**–**D**): \(h_{1}(t),\ h_{2}(t),\ h_{3}(t),\ h_{4}(t)\) estimates before lowpass filtering and reference. Plots (**E**–**H**): \(h_{1}(t),\ h_{2}(t),\ h_{3}(t),\ h_{4}(t)\) estimates after lowpass filtering and reference. Figure 6: Estimated \(h_{k}(t)\) kernels in the case \(SNR_{1}=5\) [dB] (blue lines) and reference kernels (red lines). Plots (**A**–**D**): \(h_{1}(t),\ h_{2}(t),\ h_{3}(t),\ h_{4}(t)\) estimates before lowpass filtering and reference. Plots (**E**–**H**): \(h_{1}(t),\ h_{2}(t),\ h_{3}(t),\ h_{4}(t)\) estimates after lowpass filtering and reference. It is evident from the values shown in Tables 2 and 3 that both in the less noisy case and in the noisier one, the action of filtering is such as to significantly reduce the index value, and, therefore, improve the signal to noise ratio obtained.
###### Abstract  
We report an approach to incorporate Ge into Cu\({}_{2}\)ZnSnSe\({}_{4}\) using GeSe vapor during the selenization step of alloyed metallic precursors. The vapor incorporation slowly begins at \(T\approx 480\)\({}^{\circ}\)C and peaks at 530 \({}^{\circ}\)C, resulting in a Ge-based composition shift inside the previously formed kesterite layer. We initially observe the formation of a Ge-rich surface layer that merges into a homogeneous distribution of the incorporated element during the further dwelling stage of the annealing. This approach is very versatile and could be used in many similar fabrication processes for incorporating Ge into CZTSe-absorber layers. Because the vapor-based composition shift in the layer happens after the formation of the absorber film towards the end of the fabrication process, most process parameters and the precursor structure may not need any significant re-optimization. The careful integration of this step could help to reduce Sn-related deep defects and accompanying \(V_{\text{OC}}\) losses. The best CZTSSe-power-conversion efficiency obtained in this series is 10.4 % (with \(E_{G}=1.22\) eV, \(FF=54\%\), \(J_{\text{SC}}=36\) mA/cm\({}^{2}\), \(V_{\text{OC}}=540\) mV, \(V_{\text{OC}df/SQ}=417\) mV). These results demonstrate the potential of this approach for Ge incorporation into kesterite absorbers.  
CZTGSe; kesterite solar cell; vapor-phase incorporation; accelerated aging study; stability +
Footnote †: journal: Applied  
doi:10.3390/app12031376  
Academic Editor: [PERSON]