esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Battery Multiplier Performance  
High-power and large-capacity lithium-ion batteries are required in high-performance electronic devices, electric vehicles, and other applications. Therefore, the ability of PU and PU/PAN composite diaphragm-assembled batteries to be used under large multiplier charge and discharge conditions is critical. Battery charging and discharging under large multiplier conditions can cause heat to collect in the cell and reduce the battery life. To characterize the performance of the diaphragm for charging and discharging at large multipliers, corresponding multiplier performance tests were performed. Figure 12 shows the multiplier performance plots of the commercial Celgard 2400 and PU/PAN fiber diaphragms in assembled lithium-ion batteries at a different rate. The discharge capacity of lithium-ion batteries assembled with PU/PAN fiber diaphragms was higher than that of  
Figure 11: Cyclic performance of batteries with commercial Celgard 2400 and PU/PAN fiber diaphragms at 0.2 C rate.  
Figure 10: First discharge curve of batteries using commercial Celgard 2400 and PU/PAN fiber diaphragms at 0.2 C rate.  
the Celgard 2400 diaphragm at 0.2 C, 0.5 C, 1 C, 2 C, and 5 C rates. The lowest discharge capacity was recorded for lithium-ion batteries fitted with a Celgard 2400 diaphragm. This was because PU/PAN fiber diaphragms contained many polar groups that could absorb more electrolytes, and the diaphragm had a low internal resistance and low loss, resulting in a higher discharge capacity.
## 2 Materials and Methods
### The Geological and Metallogenic Setting of the Study Area
#### 2.1.2 The Bou Azzer-El Graara Inlier  
Located in the central part of the Anti-Atlas, the Bou Azzer-El Graara inlier represents an important segment of the Anti-Atlas region for the perception of Pan-African events [20,22]. The Precambrian outcrops there in two contiguous massifs, Bou Azzer and El Graara, following a Variscan uniform structure which runs along the major accident of the Anti-Atlas [23]. This accident subdivides the Precambrian Anti-Atlas terrains into two distinct domains, a cratonic domain to the SW and a recent Pan-African domain to the NE. The Bou Azzer inlier is divided into eight lithological units (Figure 2) having undergone greenschist facies metamorphism linked to a deformation phase dated at 685 Ma [24,25], which in order of descending age are the following:  
* Tachdamt-Bleida Group (NP1-2): attributed to the Upper Tonian-Lower Cryogenian and consisting of sandstone-quartzites and siltstones and formed by platform deposits characterized by alkaline tholeites emplaced in a fissural context. Another group of calc-alkaline basalts generated in an arc and back arc context has been highlighted;  
Figure 1: Localization of the studied area at (A) the West African C ration scale and (B) at the Moroccan Anti-Atlas scale [21].  
* Lower Cryogenian Tichibanine-Ben Lgrad Group (NP2i): consisting of siltstones enclosed in granitoids of the Taghouni massif as well as a mixture of volcano-sedimentary and volcanic series;
* Assif n'Bougmmane-Takroumt plutono-metamorphic complex: consisting of orthogneiss and paragneiss attributed to the Lower Cryogenian (NP2i). These rocks are crosscut by anatectic granitoids attributed to the Upper Cryogenian (NP2s);
* Bou Azzer-El Graara Group (ophiolitic complex): attached to the Upper Cryogenian (658 \(\pm\) 9 Ma), comprising here serpentinized mantle peridotites, microgabbro-dolerite veins, basalts, and a volcano-sedimentary unit;
* Dioritic to granodioritic intrusions at the Cryogenian-Ediacaran boundary (NP2-3): including the Boufrokh and Bou Azzer massifs, as well as the polyphase Taghouni massif;
* Bou Lbarod-Iouraghene Group (NP3i): consisting of Lower Ediacaran ignimbrites and anddesites dated at 625 \(\pm\) 8 Ma;
* Tiddiline Group: attached to the Lower Ediacaran (NP3i, 606 \(\pm\) 4 Ma, Alougoum map) and made up of sandstone and siltstone, with local rhyolite intercalations;
* Ouarzazate Group: attributed to Upper Ediacaran (NP3s, 566 \(\pm\) 4 and 567 \(\pm\) 5 Ma) and consisting essentially of ignimphritic pyroclastic flows of dacitic to rhyolitic composition associated with tuffs and pyroclastic breccias as well as sedimentary volcano-detrital deposits. Its summit is characterized by the appearance of andesitic flows interlayered with ignimphritic flows.
# Functional Safety Networks and Protocols in the Industrial Internet of Things Era
## 4 The Fail Safe over EtherCAT Protocol Implemented on WiFi
### Tests in a Noisier Environment with Additional Transport Layer Protocols
Specifically, as can be seen, there were 25 lost packets (15 more with respect to the previous case) whereas the polling time resulted, on average, about 685 us longer. However, the two additional strategies considered for the transport layer revealed interestingly better results. As can be seen, with TCP, the number of lost packets was dramatically reduced, however, at the expense of an increased polling time. This is clearly due to the quality of service features introduced by the TCP protocol that ensures a more robust data transmission, but requires longer execution times, since the protocol stack is definitely more complex than that of UDP. Even better performance figures are provided by \(UDP_{c}\). In this case, no packets were lost and the polling time was very close to that obtained with UDP. This is because the caching layer introduces only the services necessary to support the FSoE protocol (handling of packet retransmission and duplication), resulting in an efficient stack, with very limited impact on performance. The SFRT for the three different transport layers are shown in Table 5.  
Figure 6: EPDF of the polling time for different transport layers on IEEE802.11. As can be seen, the watchdog timeout has a noticeable impact on the SFNT. Clearly, by decreasing it, the system would result more responsive to communication problems and therefore be able to force the entire system to the safe state much more quickly. However, reducing the watchdog timeout could imply further drawbacks. Indeed, especially when wireless networks are used, the use of a short watchdog timeout might lead to erroneously consider a device as faulty (because it does not answer within the timeout) while, conversely, its polling is simply delayed due to temporary decay of the communication channel status. Thus, the choice of the watchdog timeout is a trade-off between the ability of the system to quickly react to fault situations and the necessity of ensuring the adequate safety performance.
# An Enhanced Photonic Quantum Finite Automaton
## 5 Confidence Amplification: An Enhanced Strategy
Notice the different scaling for the \(y\)-axis. that is, the probability of detect \(n\) photons depends on the average number of detected photons \(\mu\). How this affects the error probability has been thoroughly addressed both theoretically and experimentally in [6]. The second effect that we should consider in order to apply our enhanced strategy is due to the dark counts, namely, the random counts registered by the detector without any incident light on it. Being still related to the detection process, also the dark counts follow a Poissonian distribution, whose mean \(\langle N_{\text{dc}}\rangle\) depends on the particular detector one choose to use. In a typical quantum optics experiment, the dark-count rate ranges from tens to hundreds of photons per second, but this number can be drastically reduced by using coincidence counting techniques [16], up to making this effect negligible. For instance, in the implementation in [6] the dark counts where only \(0.001\%\) of the effective coincidence counts. As the dark counts occurs randomly, we cannot distinguish between a dark count and signal one. Therefore, the probability of detecting \(N\) photon in the \(H\) or \(V\) photodetector for a word \(a^{k}\) is finally given by  
\[P_{k}^{x}(N) =\sum_{n=0}^{+\infty}\sum_{n=0}^{+\infty}\text{Poi}(n;\eta^{x}) \text{Poi}(m;\langle N_{\text{dc}}\rangle)\delta_{n+m,N} \tag{16}\] \[=\text{Poi}(N;\mu_{k}^{x}) \tag{17}\]  
where \(\eta^{H}=\langle N_{\text{c}}\rangle\cos^{2}(k\theta_{m})\) and \(\eta^{V}=\langle N_{\text{c}}\rangle\sin^{2}(k\theta_{m})\), while we have defined the overall mean number of detected photons as  
\[\mu_{k}^{H}=\langle N_{\text{c}}\rangle\cos^{2}(k\theta_{m})+\langle N_{\text {dc}}\rangle\quad\text{and}\quad\mu_{k}^{V}=\langle N_{\text{c}}\rangle\sin^{ 2}(k\theta_{m})+\langle N_{\text{dc}}\rangle. \tag{18}\]  
As we noticed above, the dark count rate is usually very small with respect to the detected count rate of the signal. Therefore, for the \(H\) detector which detects the higher number of photons, see Equation (18), they are relevant only when \(\langle N_{\text{c}}\rangle\sim\langle N_{\text{dc}}\rangle\). On the contrary, for the \(V\) detector, detecting the lower number of photons, their role is fundamental in determining the performance of the photonic automaton, as \(\mu_{m}^{V}=\mu_{\text{dc}}=\langle N_{\text{dc}}\rangle\). This is the main difference between the two strategies: in the first, we need to distinguish between two finite mean numbers of photon \(\mu_{m}^{H}=\langle N_{\text{c}}\rangle+\langle N_{\text{dc}}\rangle\) and \(\mu_{k_{\text{f}}}^{H}\), while in the second case, we need to distinguish between the noise due to dark counts, being \(\mu_{m}^{V}=\langle N_{\text{dc}}\rangle\), and \(\mu_{k_{\text{f}}}^{V}\). However, to assess the performance of second strategy with respect the first one, we need to evaluate the probability of errors in the two cases. Let us first find the threshold values in the two different strategy.
# A Novel UWB Pulse Expander Using an Integrated Microstrip Splitter, Delay Lines, and a Combiner
## 2 Design and Modeling
### Pulse Splitter Selection and Design
The calculations of input pulse (port 1), reflected pulse (port 1), and output pulse (ports 2, 3, 4) energies indicate that using \(R_{\rm iso}=1\)\(\rm M\Omega\) is the most successful, from the standpoint of energy. The total output pulse energy in ports 2, 3, and 4 with \(R_{\rm iso}=1\)\(\rm M\Omega\) was \(0.7631\cdot E_{\rm in}\). At different \(R_{\rm iso}\) values, which were smaller than \(1\)\(\rm M\Omega\), the output pulse energy sum was smaller than in the case of \(R_{\rm iso}=1\)\(\rm M\Omega\). From the standpoint of energy preservation, considering that pulses in output ports 2, 3, and 4 must have similar energy and waveform, strong isolation between these ports is not necessary, while isolation resistors at ports 5 and 6 can be omitted. These resistors dissipate power, and the total output pulse energy is lower. One drawback of the designed pulse splitter is a considerable reflection (see Table 3), the minimization of which would be an interesting topic for future research. As a result of the optimization process, pulse splitter geometric sizes were acquired, as shown in Table 2, in the \"Final value\" column. Size \(W_{0}\) ensures that splitter port impedances are \(Z_{0}=50\)\(\Omega\). Figure 7 shows the transient simulation results when a Gaussian pulse with a duration of 200 ps is applied to the splitter's input without isolation resistors. Its geometric sizes are listed in the column \"Final value\" of Table 2. Pulses in ports 2 and 4 match; however, the amplitude of the pulse in port 3 is slightly lower, and the energy of this pulse is also smaller, as can be seen in Table 3. The energies of these pulses were calculated and are  
Figure 6: Simulated S-parameters of the pulse splitter at \(R_{\rm iso}=100\)\(\Omega\) and \(R_{\rm iso}=1\)\(\rm M\Omega\): (**a**) magnitude of S-parameters of the input pulse, (**b**) phase of S-parameters. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{\(R_{\rm iso}\), \(\rm k\Omega\)} & \multicolumn{5}{c}{**Energy, pJ**} \\ \cline{2-6}  & **Port 1** & **Port 2** & **Port 3** & **Port 4** & **Reflected** \\ \hline
0.1 & 150.198 & 24.098 & 21.508 & 24.056 & 54.004 \\ \hline
1000 & 150.198 & 39.255 & 36.060 & 39.296 & 33.249 \\ \hline \end{tabular}
\end{table} 
Table 3: The calculated energy of input and output pulses of the pulse splitter. Simulation results. shown in Table 3. The time shift between these pulses is minimal. Figure 7 also shows the reflected pulse from port 1, while the energy of the reflected pulse is displayed in Table 3.
## 3 Results  
The algorithm described in the _Materials and Methods_ section and illustrated in Appendix A by Figure A1 was applied and results are provided in Figure 5 for the state variable \(x\). Long (\(\approx\)5,000,000 samples) waveforms produced via Runga-Kutta (MATLAB ODE45) integration of the chaotic Lorenz system with the parameters \(\sigma^{\prime}=\sigma-\Delta\approx 10\), \(\rho^{\prime}=\rho-\Delta\approx 28\) and \(\beta^{\prime}=\beta-\Delta\approx\frac{8}{3}\) with \(\Delta=10^{-6}\), \(dt=0.001\) resulted in a total waveform length of \(5000[s]\). These parameters were selected to avoid prominent periodicities that may not accurately represent the natural density of trajectories. The resulting time series \(x(t)\) was first regularized with respect to the timing of symbolic content to form \(x(\tau)\). The results of processing the basis function extraction of long waveform sample \(x(\tau)\) are given by Figure 5.  
These results are presented in three parts: (i) natural vs. uniform bit statistics (shown in Figure 5a), (ii) extracted and averaged basis functions \(<p(\tau)>\) for three primary regions of natural bit statistics (shown in Figure 5b), and (iii) the extracted and averaged basis functions with natural bit statistics \(<p(\tau)>\), uniform bit statistics \(<\rho(\tau)>\), and the small difference between the two \(<\rho(\tau)>-<p(\tau)>\) (shown in Figure 5c). In Figure 5a, bit statistics for each 8-bit long segment are shown. These statistics are used to establish that the Hamming distance criterion has been met and thus constitute the symbolic content involved with constructing basis function averages. The red histogram in the figure shows the natural density of symbolic content provided by the \(x\) state variable and indicates the proportion of each symbol sequence used to average the basis function \(<p(\tau)>\). The blue histogram shows uniformly distributed bit sequences, where all bit sequences are represented evenly with approximately 200 examples of each. This histogram constitutes the average basis function \(<\rho(\tau)>\).  
In Figure 5b, averaged basis functions in three regions of the red histogram that demonstrate prominently occurring bit sequences from Figure 5a are shown. The first prominent statistical peak (second most likely region) was identified heuristically as the bit sequences 0-2 (decimal) that correspond to the bit sequences [00000000], [00000001], and [00000010] (binary). Similarly, the next region (third most likely) was identified as bit sequences 126-128 and the final region was identified as bit sequences 253-255. Averaged basis function for each region is computed and showed a small, but non-zero difference. In Figure 5c, the resulting averaged basis functions for both natural bit density (solid red) and uniformly distributed bit density (dashed blue) are shown. The difference between these two results is provided below the time series. Finally, Figure 6 shows the results for bit statistics and extracted, averaged basis functions of all three state variables of the chaotic Lorenz system.
# Frequency-Bounded Matching Strategy for Wideband LNA Design Utilising a Relaxed SSNM Approach
## 3 IM-OM Circles and Frequency-Bounded Mismatch Envelope
### IM-OM Circles for a Single Frequency Point
Figure 4b presents a zoomed-out view of the IM and OM circles overlaid with the source and load stability circles. A notable behaviour observed in the IM circles (green) is their reversal of curvature direction after reaching a certain mismatch level. Specifically, in this process, the IM circles begin to reverse when IM \(\approx\) 0.73, even before reaching the IM = 1 stability boundary. This curvature reversal originates from the shift in IM circle centres (e.g., C2, C3, C4, C5) into the right half of the Smith chart, which corresponds to active terminations. In this region, the device sees conditions conducive to instability, and the IM contour ceases to behave monotonically with respect to OM. This behaviour imposes a practical constraint on the maximum usable IM value in wideband amplifier design, especially when avoiding unstable terminations. The IM = 1 and OM = 1 contours also serve as stability boundaries, beyond which the device enters potentially unstable regions and thus is excluded. A linearised visualisation of the IM-OM tangency points discussed above is shown in Figure 5. The horizontal axis traces tangency points from the lower edge (point 1) to the upper edge (point 24) of the Smith chart. Figure 5: IM-OM pair with linear variation of tangency points shown in Figure 4 (moving from bottom (point 2) to top (point 24) of the Smith chart). Point 1 represents an extreme case with OM = 1.1, indicating a highly mismatched and unstable load. Point 2 lies exactly on the OM = 1 circle and corresponds to IM = 0.1. Moving upward, point 5 intersects IM = 0, while point 14 marks OM = 0. At the final tangency point (point 24), the match reaches the IM = 1 circle, and OM again rises to 1.1, mirroring the behaviour observed at the chart's lower edge. In situations where only a single IM or OM value is observed, its alternate counterpart lies outside the unit Smith chart. These out-of-bound values represent either unstable or non-physical terminations and are excluded from practical use.
# Chemometric Classification of Feta Cheese Authenticity via ATR-FTIR Spectroscopy
## 3 Results
### Classification Models
Although freeze-dried samples offered improved classification performance, comparable accuracy levels were also achieved using fresh samples, as shown in the Supplementary Materials. This supports the method's flexibility for rapid screening applications without necessitating extended pretreatment when time constraints exist. PLS-DA score plots for the train set (Figure 3A,C,E) show projections of the first two latent variables derived from models built on raw, first derivative, and second derivative spectra, respectively. The degree of separation between Feta and non-Feta white brined cheese samples improved with spectral preprocessing. In the raw spectra (Figure 3A), moderate class overlap remained evident. First derivative preprocessing (Figure 3C) showed clearer group boundaries, while second derivative preprocessing (Figure 3E) produced the most distinct clustering, with minimal overlap between groups. VIP score profiles (Figure 3B,D,F) consistently highlighted spectral regions important for classification. A complementary classification analysis was also performed using fresh (non-freeze-dried) cheese samples. In these samples, lower overall performance metrics in both PCA-LDA and PLS-DA models were observed. Similarly, when derivative preprocessing was applied, the performance improved, achieving high sensitivity and moderate specificity. Full performance metrics, along with relevant classification figures for the fresh dataset, are provided in the Supplementary Material (Supplementary Tables S1 and S2 and Figures S1 and S2). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Train Set**} & \multicolumn{3}{c}{**Test Set**} \\ \hline
**Preprocessing** & **\# LVs** & **AUC** & **Sensitivity** & **Specificity** & **AUC** & **Sensitivity** & **Specificity** & **Accuracy** \\ \hline
**Raw Spectra** & 8 & 0.992 & 0.970 & 0.929 & 0.874 & 0.846 & 0.818 & 0.833 \\ \hline
**First Derivative** & 3 & 1.000 & 1.000 & 0.964 & 0.923 & 1.000 & 0.727 & 0.875 \\ \hline
**Second Derivative** & 9 & 1.000 & 1.000 & 1.000 & 0.958 & 1.000 & 0.909 & 0.958 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: PLS-DA classification performance metrics for raw, first derivative, and second derivative FTIR spectra.
# Selection of Optimal Path Control Algorithms for Probe Heads Used on Five-Axis Measuring Systems
## 1 Introduction
### Five-Axis Coordinate Measuring System
The steps comprised in the procedures includes: The determination of calibration of ball position in the three-axis mode for the probe directed vertically, with B angle set to \(0^{\circ}\), and then the second time with B angle set to \(180^{\circ}\); the determination of probing parameters, such as probing speed and maximal acceleration (this step is done without standard measurements); and the determination of the interpolated probe map, based on sphere measurements using head rotational movements, performed for different angular orientations of the probe head and for different cross-sections of the standard. Both parts of the qualification process take a considerable amount of time (more than 10 min for the Renishaw PH20 probe head), but they do not have to be repeated, in the case of the probe, until a new probe configuration is needed, and in the case of the head geometrical calibration, until the probe head would be disassembled from the machine quill. The problem of the articulating probing system's accuracy was studied mostly for systems with fixed indexation, retained during probing. Described aspects of this problem include angular positioning repeatability [10], general accuracy [11; 12], and probing system modelling [13]. Fewer studies have been focused on a probing system with continuous indexation, mostly done in the Laboratory of Coordinate Metrology (LCM), among them research has been done about such probing system's hysteresis and the influence of stylus length [14] and the angular position of the probing system on measurement accuracy [15]. Conducted experiments revealed the existence of additional problems worth further examination; one of them is the issue of selecting the appropriate control algorithm for the measurement path for measurements utilizing the rotational movements of the probing system. This problem will be investigated in the following chapters of this paper.