esa-sceva/satcom-chunk-collection huggingface dataset:
# Development on Solid Polymer Electrolytes for Electrochemical Devices
## 3 Basic Solid Polymer Electrolytes
### Strategies to Improve Ionic Conductivity of SPEs
#### 3.2.7 SPEs Containing Other Kind of Additives
Polyaniline (PANI), which is a conducting polymer that is usually used as counter electrode in DSSCs [197; 198], has been incorporated in SPE containing PEO, tetrapy-lammonium iodide (TPAI) and I\({}_{2}\) to trap the large cations ((C\({}_{3}\)H\({}_{7}\))\({}_{4}\)N\({}^{+}\) or simply TPA\({}^{+}\)) so that the anions can contribute more to conduction, which is beneficial in DSSC [199]. The authors also believed that PANI has performed the role of plasticizing agent in decreasing crystallinity of PEO and its melting point based on DSC analysis. Upon PANI addition, the degrees of crystallinity and \(T_{m}\) were reduced to 3.37% and 66.2 \({}^{\circ}\)C from 8.93% and 66.5 \({}^{\circ}\)C [199]. The PANI-added SPE attained higher values of room temperature conductivity (8.61 \(\times\) 10\({}^{-5}\) S cm\({}^{-1}\)) and ionic transference number (0.99) than PANI-free SPE (\(\sigma\) = 6.81 \(\times\) 10\({}^{-5}\) S cm\({}^{-1}\); \(t_{ion}\)= 0.96). The former also gave better DSSC performance (5.01% efficiency) than the latter (efficiency 3.5%) [199]. A special additive, i.e., hydroxypropyl trimethylammonium bis(trifluoromethane) sulfonamide chitosan salt (HACC-TFSI), was synthesized via ion-exchange route, using hydroxypropyl trimethylammonium chloride chitosan salt and LiTFSI before HACC-TFSI, PEO and LiTFSI were mixed in DMF solvent to form SPE via the solution-casting technique [200]. Ionic conductivity values for HACC-TFSI-added PEO electrolyte at 30 and 60 \({}^{\circ}\)C were 1.77 \(\times\) 10\({}^{-5}\) S cm\({}^{-1}\) and 5.01 \(\times\) 10\({}^{-4}\) S cm\({}^{-1}\), respectively [200]. In contrast, conductivities of 3.80 \(\times\) 10\({}^{-6}\) S cm\({}^{-1}\) and 1.90 \(\times\) 10\({}^{-4}\) S cm\({}^{-1}\) were recorded at the same temperature by PEO SPE without HACC-TFSI [200]. Besides better conductivity, Li\({}^{+}\) ion transference number and tensile strength increased by 1.7 and 3.6 times. Moreover, the SPE with HACC-TFSI was found to be resistant against elevated temperature (150 \({}^{\circ}\)C) by retaining its shape after exposure at high temperature for one hour [200]. On the contrary, the electrolyte without HACC-TFSI has melted under similar conditions. LiFePO\({}_{4}\)/Li cell with HACC-TFSI added SPE demonstrated good cyclability and stability over 100 cycles maintaining 73% capacity of its initial capacity of \(\sim\)108 mAh g\({}^{-1}\)[200]. It is worthy to mention that this is considered quite an achievement, since there are not many cells with SPEs that can withstand and operate under such a high temperature.
## 3 Materials and Methods
### Testing Procedures
#### 3.5.4 Production Cost and Energy Use  
Production costs were calculated based on material usage, labor, and energy consumption. Material costs were determined by measuring filament mass (FDM) or resin volume (SLA) and multiplying by the respective unit price. Labor costs included setup, print monitoring, and post-processing time. Energy consumption was measured using the PCE-PA6000 power analyzer and converted into monetary cost using local electricity rates.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameter** & **FDM (PLA)** & **FDM (ABS)** & **SLA (Resin)** \\ \hline Layer Height (mm) & 0.20 & 0.20 & 0.05 \\ Nozzle (or Laser) & & & \\ Temp. & 210 \({}^{\circ}\)C & 230 \({}^{\circ}\)C & N/A\({}^{*}\) \\ Bed Temperature (\({}^{\circ}\)C) & 60 & 100 & Ambient \\ Print Speed (mm/s) & 60 & 60 & Default PreForm \\ Infill Density (\%) & 20 & 20 & N/A (solid) \\ Support Structures & Auto (Cura) & Auto (Cura) & Auto (PreForm) \\ Post-Processing & Optional sanding & Optional smoothing & IPA wash + UV cure \\ \hline \hline \end{tabular}  
* Laser power and resin curing parameters automatically set by PreForm.  
\end{table}
Table 2: Key printing parameters for FDM (PLA/ABS) and SLA resin.
# A Distributed Energy-Throughput Efficient Cross-Layer Framework Using Hybrid Optimization Algorithm
## 3 Energy-Throughput Efficient Cross-Layer Solution Using MPNMRA for WUSNs
### DS-CDMA Design  
In WUSNs, the DS-CDMA system offers several benefits such as low-packet retransmissions and high-channel reuse, which help mitigate the effects of multi-path propagation. By effectively managing multi-path interference, the DS-CDMA system reduces energy consumption and increases network throughput.  
However, when employing the DS-CDMA technique in MI-USNs, it is essential to address the issue of multiuser access interference (MAI) [9]. In the context of MI-USNs, MAI can degrade the signal quality and overall system performance. To overcome the challenges posed by MAI, various interference mitigation techniques can be employed. These techniques aim to reduce the interference caused by other users' signals and enhance the signal quality at the receiver. By effectively managing MAI, the DS-CDMA system can be optimized for MI-USNs, ensuring reliable communication with reduced interference and improved network performance.
## 3 Results and Discussion
### Mizoroki-Heck Cross-Coupling Reaction by the Pd/C Catalyst
of SDS at 100 \({}^{\circ}\)C in 10 h produced the best conversion. Therefore, in order to investigate the substrate scope, an MH cross-coupling reaction was performed under the optimized reaction conditions with several aryl halides and acrylic acid. The results are summarized in Table 2.  
The turnover frequency (TOF) and turnover number (TON) values for the 3% Pd/C catalyst were calculated and are graphically illustrated in Figure 9. It was found that the TOF and TON values for the Suzuki coupling reaction were 118 and 20 h\({}^{-1}\). For the Heck coupling, the TOF and TON values were 1.2 and 12 h\({}^{-1}\). Under the optimized reaction conditions, all the aryl halides (**1a-f**) reacted with the acrylic acid very well, with moderate to good yields (Table 2). The lower yield in the case of 2-bromoanol could be due to the steric hindrance of the ortho-substitution (**2c**, **72%**) of \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sl. No.** & **Aryl Halides** & **1a–f** & **2a–e** & **Isolated Yield** \\ \hline
1 & 4-bromoanisol & 1a & 2a & 89\% \\
2 & 3-bromoanisol & 1b & 2b & 85\% \\
3 & 2-bromoanisol & 1c & 2c & 72\% \\
4 & 4-Iodot toluene & 1d & 2d & 87\% \\
5 & 4-Iodotbenzene & 1e & 2e & 91\% \\
6 & 4-bromobenzene & 1f & 2e & 88\% \\ \hline \hline \end{tabular}
**Reaction Conditions:** 3% Pd/C (10 mg), K3 PO4 (424 mg, 2 mmol), SDS (72 mg, 1 mmol), H2O (4 mL), aryl halide (1.0 mmol) and acrylic acid (1.0 mmol) at 100 \({}^{\circ}\)C for 10 h (isolated yields).  
\end{table} 
Table 2: 3%Pd/C catalyst Mizoroki–Heck reaction of aryl halides with acrylic acid. Figure 8: 3%Pd/C catalyzed Mizoroki–Heck reaction of aryl halides with acrylic acid under optimized conditions. Figure 9: Graphical illustration of the comparative turnover frequency and turnover number (TON) values for the Suzuki and Heck coupling reactions of the 3% Pd/C catalyst. bromoanisol. The highest yields were observed for iodobenzene (**2e, 91%**) and iodotoluene (**2d, 87%**). The reusability of the 3% Pd/C catalysts was tested for Mizoroki-Heck reaction efficiency. The reactions were done in a similar approach to the above-mentioned process. Using a standard \(p\)-bromoanisol substrate and acrylic acid, the residual catalyst was collected by filtration after the completion of the reaction. The catalyst was washed with copious amounts of water to eliminate any residual impurities. It was found that there was a decrease in the percentage of product formed upon repeated use. The product percentage depreciated up to 78% when used up to 3 times (Figure 10).
## 2 Materials and Methods  
### Imaging Setup  
The imaging system used for the investigation of lung tissue dynamics during ventilation is a combination of OCT and IVM using the same beam path in one scanner head. The custom-made OCT setup is described in detail by [PERSON] et al. [7]. Shortly, the system consists of a superluminescence diode (Superlum, Moscow, Russia) centered at 845 nm with a FWHM of 50 nm. As OCT is based on the use of a Michelson interferometer, the emitted near-infrared light is separated into a reference beam and a sample beam. The sample beam is deflected using two galvanometer scanners (Cambridge Technology Inc., Cambridge, MA, USA) to perform a 2D-scan pattern (\(x\) and \(y\)) over the sample. The scattered and reflected light from different sample depths (\(z\)) is collected and superimposed with the reference beam that was reflected on a mirror. This interference signal is spectrally resolved by a diffraction grating and measured by a line-detector (Teledyne DALSA Inc., Bromont, QC, Canada). The depth-dependent information is calculated by a Fast Fourier Transformation of the spectrum and results in a depth profile of backscattered light called an A-scan. Displaying the logarithm of the intensity from several A-scans at different transversal positions on an 8 bit grayscale results in a cross-sectional view called a B-scan. The axial and lateral resolution are 11 \(\upmu\)m and 6 \(\upmu\)m in air, respectively. The line scan rate is 12 kHz, which allows data acquisition of typical cross sections within 29 ms (320 \(\times\) 512 pixels corresponding in this system to 1.28 \(\times\) 2.8 mm (width \(\times\) depth)).  
For IVM, a high-speed video camera (acA2000-340 kc, Basler, Ahrensburg, Germany) in combination with an adjustable liquid lens (EL-10-30, Optotune AG, Dietikon, Swizerland) is used to acquire 2D surface images with a maximum framerate of 340 fps (frames per second) at a full resolution of 2040 \(\times\) 1088 pixels, which corresponds to a field of view of 3 \(\times\) 1.5 mm using a four-fold magnification (refer to Figure 1). Therefore, the IVM beam path is coupled into the same beam path used for OCT by a dichroic mirror. Illumination for IVM is achieved by using a custom-made white light LED ring at the bottom of the scanner head. The electrical lens is driven by a 20 kHz pulse width modulated current signal allowing a focal shift of 5 mm within a 15 ms response time. This effectively reduces the video image acquisition down to a maximum of 66 fps.
###### Abstract  
Quantum cascade lasers subject to tilted optical feedback produce periodic oscillations, quasi-periodic oscillations, and low-frequency oscillations. This work presents the modulation characteristics of period-one (P1) oscillations in a quantum cascade laser with tilted optical feedback. The electrical signal at the oscillation frequency is more than 50 dB higher than the noise level, and the electrical linewidth is less than 2.0 kHz. This electrical linewidth is about four orders of magnitude narrower than the optical linewidth (around 16 MHz) of the free-running laser, which suggests that the optical sidebands induced by the P1 oscillations are highly coherent with the main optical mode. In addition, the modulation depth of the optical signal is found to be in the range of 1% to 3.5%. In addition, it is verified in the simulations that the P1 oscillations induce not only amplitude modulation but also frequency modulation due to the phase-amplitude coupling effect.  
quantum cascade lasers; optical feedback; period-one oscillations; modulation characteristics
## 3 Results and Discussion
### Sensor MgF\({}_{2}\)-Ag  
The angle where the second surface plasmon occurs is 87\({}^{\circ}\). Since it is a difficult angle to achieve experimentally in the incidence of the optical fiber on the MgF\({}_{2}\) layer, we could work in the interval between 87-80\({}^{\circ}\) which is much easier, and the reflectance lines (and therefore the sensitivity) to follow are sufficiently separated. The thickness of the MgF\({}_{2}\) is 81 nm and the thickness of the silver one is 30.2 nm. Figure 2 shows the reflectance curves. We have chosen the compound MgF\({}_{2}\) and the elements Ag because they had refractive indices that make the curves in Figure 2 more widely spaced, therefore they were able to better distinguish between concentrations by increasing the sensitivity with respect to the other substances.  
The reflectance curve vs. the refractive index of the concentration of sucrose is shown in Figure 3.  
The reflectance curves vs. the concentration of sucrose are shown in Figure 4. We have used data from [19].  
As Figures 3 and 4 show the sucrose concentration (or the refractive index of the solution) can be determined by measuring the reflectance.  
The increase in reflectance with the refractive index is a monotonically increasing function although it is not linear and shows that the refractive index increases with the concentration of sucrose.  
The sensitivity of our sensor can be calculated from the change in reflectance per unit of change of refractive index. The sensitivity range is between 1 RIU\({}^{-1}\) and 11.9 RIU\({}^{-1}\), the maximum value corresponding to a 22% sucrose concentration. Thus, when the sucrose concentration diminishes or increases, the sensitivity diminishes.  
Figure 2: Reflectance curves as a function of the angle of incidence of the light in the prism for the range of refractive index values.  
The sensor resolution depends upon the accuracy with which the monitored SPR parameter can be determined by the specific sensing device and as such is limited by sensor-system noise. To calculate the resolution of the sensor, we divided the accuracy of the photodetector by its sensitivity. Considering an accuracy of 0.2% in the signal registered by the photodetector [23; 24], we achieved a resolution of between 1.7 \(\times\) 10\({}^{-4}\) RIU and 1.9 \(\times\) 10\({}^{-3}\) RIU. As we can see, the uncertainty is in the third and fourth decimal places. In the case of the fourth decimal place, the maximum accuracy is taken into account and so the refractive indexes are given with four decimal numbers.
# Dynamic Monitoring of the Water Flowing Fractured Zone during the Mining Process under a River
## 4 Results and Discussion
### Discussion  
The TEM survey revealed the distribution of the aquifers of the 8101 longwall face without mining influence and the water flow mechanisms during the mining process. The work confirmed that the TEM method can effectively detect the underground water distribution [10; 13; 14; 15]. At the same time, the TEM survey results also showed that the WFFZ could connect to the aquifer, and that the height of the WFFZ was over 97.25 m, which was 24-31 times the mining height. This is consistent with the result of [PERSON]'s work [18] and [PERSON]'s work [20], and is much larger than the result of 49.9-67.7 m calculated from the empirical formula [3; 32].  
During the mining process of the 8101 longwall face, the water flow of the aquifer has some interesting characteristics. The 2D numerical and the TEM survey showed that the water quickly flowed down when the WFFZ connected to the overlying aquifer, which is consistent with the literature [29]. As there was no surface water supply, the water level gradually tended to be stable. The TEM survey confirmed the existence of lateral recharge phenomenon in the aquifer [33; 34]; when the water level declined at a position of the aquifer, the lateral seepage occurred to recharge this position, leading to the decrease in the water level of the K\({}_{\text{lzh}}\) unconfined aquifer. In other words, although the damage of WFFZ was local, it affected the entire aquifer. Based on the TEM survey result, the 2D numerical simulation showed that the lateral recharge played an important role in aquifer water seepage [35] and even dominated the water flow in the late stage when the water volume of the aquifer was limited.  
Because the TEM survey was carried out in the dry spell, the water volume was limited and the harm could be neglected. Since the 8101 longwall face is the first longwall face of Selian No.1 coal mine, when mining other longwall faces under this river, it is necessary to consider not only the influence of the corresponding aquifers and WFFZs but also the influence of the goat and the WFFZ of 8101 longwall face. Therefore, it is necessary to take corresponding measures such as cutting off rivers, rock fracture grouting and other measures to ensure mining safety.
## 6 Isolated Empirical Studies on ML-based xApp Vulnerabilities
### Synergistic Attack Analysis  
To capture the interplay between vulnerabilities, we introduce a formal framework for analyzing synergistic attacks, where multiple vulnerabilities are exploited simultaneously. For instance,  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Ref.** & **Holistic (1)** & **ML-Focus (2)** & **Model/Survey (3)** & **Framework/Future (4)** & **Emp. Defenses (5)** & **Isolated ML Attack (6)** \\ \hline [46] & ✓ & & & & ✓ & \\
[51] & & & ✓ & & & \\
[48] & ✓ & ✓ & & & ✓ & \\
[53] & & & ✓ & & ✓ & \\
[27] & ✓ & ✓ & & & & \\
[49] & & ✓ & & & & ✓ \\
[52] & & ✓ & & & & ✓ \\
[47] & & & ✓ & ✓ & ✓ & \\
[50] & & & ✓ & ✓ & ✓ & \\
[54] & ✓ & & & ✓ & ✓ & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification of research papers by focus areas.  
* A data poisoning attack can degrade the accuracy of an ML model, facilitating subsequent model inference attacks;
* A compromised xApp can manipulate resource allocation, enabling denial-of-service (DoS) attacks.  
Mathematical Representation of Synergistic Attacks  
Let \(V=\{v_{1},v_{2},\ldots,v_{n}\}\) denote the set of vulnerabilities in the O-RAN system. Each vulnerability \(v_{i}\) has an associated impact score \(I(v_{i})\), quantifying its severity when exploited independently:  
\[I(v_{i})=w_{1}\cdot S_{\text{conf}}(v_{i})+w_{2}\cdot S_{\text{integ}}(v_{i})+w _{3}\cdot S_{\text{avail}}(v_{i}) \tag{1}\]  
where  
* \(S_{\text{conf}}(v_{i})\): Impact on confidentiality.
* \(S_{\text{integ}}(v_{i})\): Impact on integrity.
* \(S_{\text{avail}}(v_{i})\): Impact on availability.
* \(w_{1},w_{2},w_{3}\): Context-aware weights updated dynamically based on network slice requirements (see Section 4.6).  
When two vulnerabilities \(v_{i}\) and \(v_{j}\) are exploited together, their combined impact is given by  
\[I(v_{i},v_{j})=I(v_{i})+I(v_{j})+C(v_{i},v_{j}) \tag{2}\]  
where the synergy coefficient \(C(v_{i},v_{j})\) captures the additional impact resulting from their interaction:  
\[C(v_{i},v_{j})=\alpha\cdot\min(I(v_{i}),I(v_{j}))+\beta\cdot\delta(v_{i},v_{j }), \tag{3}\]  
with  
* \(\alpha,\beta\): Empirically derived coefficients validated using DAWN dataset attack traces (Table 6);
* \(\delta(v_{i},v_{j})\): Correlation metric, defined as \[\delta(v_{i},v_{j})=\frac{|\text{Common Affected Components}(v_{i},v_{j})|}{| \text{Total Components Affected by }v_{i}\cup v_{j}|}.\] (4)
## 2 Materials and Methods
### Statistical Analysis  
The statistical analysis was performed with the use of IBM SPSS Statistics for Windows (IBM Corp.; Armonk, NY, USA), version 25.0. All measured values were presented as means \(\pm\) standard deviation (SD), with 95% confidence intervals (CI). The level of statistical significance was set at \(p\) < 0.05. Initially, the normality of the data was assessed using the Shapiro-Wilk test. Subsequently, the mean relative concentrations of particular metabolites were compared between the study and the control group. If the obtained data did not have a normal distribution, the Mann-Whitney U test was applied. In cases where a normal distribution was observed, the equality of variances was evaluated with the use of [PERSON]'s test. Student's \(t\)-test was used for the analysis when the homogeneity of variance was observed, and [PERSON]'s \(t\)-test was used when variances were heterogeneous. Furthermore, the relative concentrations of metabolites was compared between the hemispheres with the use of the Wilcoxon signed-rank test, as all data did not have a normal distribution.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Sequence** & **TR [ms]** & **TE [ms]** & **NEX** & **Slice Thickness [mm]** \\ \hline T1 BRAVO & 8.6 & 3.2 & 0.5 & 2 \\ T2 & 4374 & 102 & 1 & 5 \\ T2 CUBE & 2500 & 114.7 & 1 & 1.4 \\ PRESS & 1500 & 35 & 8 & 20 \\ \hline \end{tabular}  
*TE—echo time, TR—repetition time, NEX—number of excitations, PRESS—point-resolved spectroscopy sequence.  
\end{table}
Table 1: Magnetic resonance imaging scan parameters used in the present study.
## 3 Improved Active Disturbance Rejection Control Strategy Design
### The Basis of the Existing Introduction of the Feedforward Compensation Link  
In order to address the impact of the compensation disturbance on the output position error during the dynamic process, a differential feedforward link has been introduced in Figure 6. This link connects the input signal with the position output to enhance the system's dynamic tracking performance concerning the position accuracy and to decrease the positional tracking error. The detailed design of the feedforward compensation is illustrated in Figure 7. In the diagram, \(F_{v}(s)\) is the feedforward compensation function, \(k_{pp}\), \(k_{\text{req}}\) is the proportional gain of the position loop, \(T_{\text{req}}\) is the time constant, \(k_{\text{req}}/T_{\text{req}}s+1\) is the transfer function of the response time of the position loop, and \(1/s\) is the integral link.  
According to the schematic diagram in Figure 3 above, combined with Figures 6 and 7, we can bring the position loop into the schematic diagram to obtain the closed-loop transfer function \(G_{fp}\) in the position loop control system after adding differential feedforward compensation. The specific expression is as follows:  
\[G_{\text{fp}}=\frac{\big{[}F_{v}(s)+k_{\text{pp}}\big{]}k_{\text{req}}}{s \big{(}T_{\text{req}}s+1\big{)}+k_{\text{pp}}k_{\text{req}}} \tag{34}\]  
To achieve the desired tracking performance for the system's position, the closed-loop transfer function of the position loop is set to \(G_{fp}=1\), and the transfer function of the feedforward control is derived accordingly; the details are as follows:  
\[F_{v}(s)=\frac{s\big{(}T_{\text{req}}s+1\big{)}}{k_{\text{req}}}=\frac{1}{k_{ \text{req}}}s+\frac{T_{\text{req}}}{k_{\text{req}}}s^{2} \tag{35}\]  
Figure 6: Block diagram of feedforward compensation.  
Figure 7: Block diagram of differential feedforward compensation.  
In summary, the primary purpose of the structure utilizing feedforward compensation is to enhance the estimation of the observed disturbance signal and to predict future disturbance trends. The estimated disturbance signal is then directly added to the input of the controller as a feedforward compensation signal, allowing for proactive compensation for the disturbance's impact on the system output. Additionally, the control signal generated by the radial basis function (RBF) neural network, the adjusted parameters of the extended state observer (ESO), and the control law of the active disturbance rejection controller are combined to create the final control command. Furthermore, the feedforward compensation signal is incorporated into the control directive to improve the system's resistance to interference and its position-tracking capability.