esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Verification
### Experimental Results and Comparison with Analytical Results  
The coherence function and the frequency-amplitude graph obtained as a result of the modal analysis are shown in Figure 6; the graph was obtained by taking the average of the experimental data. The peaks in the frequency-amplitude graph show the natural frequency values of the system. The frequency resolution was set to 0.125 Hz to obtain the acceleration data precisely. The measurements were repeated by applying an impact to the points on the beam with the impact hammer. Thus, a precise measurement of the natural frequencies of the beam was performed. A coherence function value of 1 means that there was no noise in the measurement. The coherence values of the obtained frequency values were very close to 1, indicating that the measurements were not affected by noise.  
The first five natural frequencies of the beam-end mass system obtained by experimental modal analysis and analytical method are given in Table 4 comparatively. It is seen that the biggest difference between analytical and experimental natural frequencies was 1.86%. This difference becomes smaller at higher frequencies. Experimental results and analytical results do not fully agree with each other due to undesirable disruptive effects, such as air friction, environmental noise, etc. in the experimental study. For this reason, in scientific studies, this difference can be accepted up to 15% depending on the difficulty of the experimental study. In this study, the highest difference was 1.86%. This shows that the  
Figure 6: Coherence function and frequency–amplitude graph of the free–free beam with unequal tip masses.  
frequency equation obtained analytically represents as well as possible the real operating conditions of the free-free beam with unequal end masses.
# Current Trends in Fully Automated On-Line Analytical Techniques for Beverage Analysis
## 4 In-Tube Solid-Phase Microextraction (In-Tube SPME)
### Extraction Modes and Operational Devices  
One of the most important advantages of in-tube SPME devices is the on-line coupling with LC systems, which leads to reduced analysis time, operating costs and sample manipulation, thus improving sensitivity, reproducibility, and precision of the analytical method [62]. Even though in-tube SPME was originally conceived for LC applications, it can also be on-line coupled directly to UV-vis [63], and direct analysis in real time (DART-MS) [64], or off-line with MALDI-TOF [65; 66] detectors. [PERSON] et al. [67] recently provided a comprehensive overview of the strengths and weaknesses of in-tube SPME, showing the possible different couplings and configurations, and new developments made within this technique (Figure 3).  
In-tube SPME can easily be automated and carried out in two extraction modes: Flow-through or draw/eject cycles (Figure 4). The flow-through approach, also known as in-valve mode, uses a capillary column as a loop of an injector valve. The system consists of an auxiliary pump, a capillary extraction column installed in a six-port valve and an HPLC system. The sample introduction could be manual or automated using another auxiliary pump. In the load position (Figure 4a), the aqueous sample passes continuously through the capillary in one direction, occurring the extraction process. Then, the six-port valve is switched to the inject position (Figure 4b) for the desorption process to transfer the analytes from the extraction column to the analytical column to be separated and detected by HPLC [5].  
In the draw/eject configuration, the capillary column is placed between the injection loop and the autosampler syringe. The sample contained in a vial is aspirated and dispensed several times into the SPME device by automated cycles (Figure 4c), where the analytes suffer a partition equilibrium between the matrix and the stationary phase. Afterwards, by changing the six-port valve position, the analytes retained in the capillary are desorbed (Figure 4d). The desorption process can be static or dynamic; the former uses a solvent that passes through the capillary and desorbs the analytes and sent it to the injection loop valve. Dynamic desorption uses the same LC mobile phase to elute the analytes from the capillary to the analytical column [5].  
Figure 4: _Cont._  
Figure 3: Schematic diagram of possible coupling and configurations of in-tube SPME with analytical instruments. Flow through mode 1V = one valve; 2V = two valves. Cyc = Draw/eject cycles mode. ACIT = array capillary in-tube. Syr = Syringe configuration. Adapted from Reference [67]. Reproduced by permission from Elsevier.
# Arrays of Sub-Terahertz Cryogenic Metamaterial
## 3 Metamaterial Matrices of Microwave Filters: Design, Fabrication, and Experimental Studies  
Cryogenic band-pass filters are required for the suppression of background IR radiation by three orders of magnitude to avoid the overheating of bolometers from radiation through the optical window. Such filters should have minimum losses at the signal frequency. Filters can be designed in the form of a periodic metal film frequency-selective surface (FSS). A period of the grid and size of a unit cell determines the resonant wavelength and beam pattern of the grid. The characteristics of the filters can be described by out-of-phase interference in the plane of the filter and in-phase interference normal to the surface of the filter. The distance between the adjacent elements is close to half a wavelength. For band-pass filters, inductive grids with cross-shaped openings are often used to obtain a higher Q-factor compared to square- and circle-shaped openings. An inductive grid is presented by  
Figure 1: Basic frequency-selective surfaces: (**a**) capacitive mesh; (**b**) inductive mesh; and (**c**) combined (resonant) mesh. Grey color—metal film, white—openings.  
electrically connected unit cells, as in Figure 1. In the simplest approximation, the openings in the unit cells can be viewed as diaphragms in the waveguide and described as parallel LC tank circuits inserted in the transmission line with an impedance equal to free space impedance, \(Z_{0}\). Such a description is valid for a single-mode waveguide. For resistive losses in such a circuit, both the resistivity of the metal and skin depth should be taken into account. For good conductors such as copper and gold, this resistance is much lower compared to \(Z_{0}\) at low temperatures, and the quality factor is determined by the ratio \(Z_{0}/\omega_{0}\)L, that is, practically independent of R.
# Improving Deep Mutual Learning via Knowledge Distillation
## 3 Results and Discussion
### Experiment on Cinic-10  
In addition to verifying our proposed method, we use the dataset, namely Cinic-10. The dataset section that we use consists of two parts, namely the training set and the testing set. We use the Resnet20 network, Shufflev2 as a different student and the WRN-40-2 network as the teacher, each of which has an accuracy of Resnet20: 81.42%, Shufflev2: 74.03%, and WRN-40-2: 85.07%. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Network Architecture**} \\ \hline Teacher & WRN-40-2 & Resnet50 & Resnet34 & WRN-40-2 & Resnet50 & Resnet50 \\ Student 1 & Resnet34 & Resnet18 & Resnet18 & WRN-16-2 & Resnet34 & Resnet8x4 \\ Student 2 & Resnet34 & Resnet18 & Resnet18 & WRN-16-2 & Resnet34 & Resnet8x4 \\ \hline \multicolumn{6}{c}{Test Accuracy (\%)} \\ \hline Teacher & 31.06 & 33.26 & 29.96 & 31.06 & 33.26 & 33.26 \\ Student 1 & 29.96 & 29.68 & 29.68 & 28.32 & 29.96 & 29.42 \\ Student 2 & 29.96 & 29.68 & 29.68 & 28.32 & 29.96 & 29.42 \\ \hline DML: [11] & & & & & \\ Student 1 & 30.92 & 32.06 & 32.06 & 28.8 & 30.92 & 30.12 \\ Student 2 & 32.86 & 30.94 & 30.94 & 29.22 & 32.86 & 29.46 \\ \hline FDDML: & & & & & \\ Student 1 & **33.56** & **33.77** & 32.44 & 29.54 & **33.18** & 30.06 \\ Student 2 & 32.62 & 33.66 & 31.94 & 30.06 & 32.76 & 29.9 \\ \hline HDDML: & & & & & \\ Student 1 & 32.3 & 31.52 & 31.78 & **30.8** & 31.52 & 29.7 \\ Student 2 & 33.02 & 33.5 & **32.64** & 29.54 & 32.68 & **31.18** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparing results top-1 accuracy on The TinyImageNet datasets resize with 32-image size. The experiment results in the form of top-1 accuracy are shown in Figure 6. Compared with the results from students for 5 different methods, namely DML, FDDML(ours), HDDML(ours), KD, CRD, and CRD+KD who were trained with two different batch sizes, namely 64 and 128. In Figure 6a, in batch size 64 FDDML achieved the highest accuracy of 84.70% followed by HDDML with an accuracy of 84.54% all obtained by student 1 (S1) while student 2 (S2) FDDML still outperformed the other four methods besides HDDML. While, when using batch size 128 all methods experienced a decrease in accuracy such as FDDML for (S1) was reduced by 0.5%, (S2) was reduced by at least 0.14% as well as DML (S1) decreased by 0.65%, (S2) 0.43%, and KD at least 0.27% while CRD and CRD+KD tend to increase in accuracy but not better than other methods. Then in Figure 6b, when using the ShuffleV2 network as a student, the accuracy of all methods tends to increase when using batch size 128 except for CRD, there is a decrease in accuracy of 0.51%. This section shows that the highest accuracy occurs in FDDML (S2) as much as 86%, then followed by HDDML (S2) with 85.92%.
# PCCAU-Net: A Novel Road Extraction Method Based on Coord Convolution and a DCA Module
## 2 Methods
### Overview of PCCAU-Net  
Designed explicitly to address the intrinsic complexities of high-resolution remote sensing imagery, PCCAU-Net is a deep learning model that features a unique multi-scale, multi-tiered strategy. Traditional deep learning approaches often encounter difficulties when confronted with diverse terrains, such as urban and rural landscapes [38]. In contrast,PCCAU-Net excels in road extraction tasks, executing them with both greater precision and robustness. One of the model's salient features is its Pyramid Pathway Input. This component not only processes imagery at its original resolution but also automatically rescales it across four distinct levels, creating an input feature pyramid. This multi-scale input mechanism enables the model to capture information comprehensively, from micro- to macroscales, meeting the demands for feature extraction in complex landscapes and road structures encountered in high-resolution remote sensing imagery. To augment the model's geospatial perception further, CoordConv convolutional layers are initially applied to the five input images sourced from the pyramid pathway. This ensures an equitable treatment of geospatial information alongside traditional pixel values, affording the model deep insights into geographic positioning at early stages. Additionally, the DCA module furnishes unprecedented feature integration capabilities. At the encoder stage, it effectively amalgamates information from two different pyramid pathways, which is crucial for handling common issues like occlusion, entanglement, and noise in high-resolution imagery. Further, at the decoder stage, the DCA serves as a skip connection, facilitating feature integration between the decoder and its corresponding encoder, and thereby ensuring the full recovery of image details.  
In summary, PCCAU-Net not only surmounts the limitations inherent to traditional methods but also heralds a new era in the processing of high-resolution remote sensing imagery, offering an innovative and exceptionally effective solution. The PCCAU-Net network structure is shown in Figure 1.
## 5 Probabilistic Seismic Risk Assessment Result
### HCLPF Capacity  
The HCLPF of the containment building was calculated based on the median and logarithmic standard deviations of the individual seismic fragility curves. The HCLPF is the ground motion level at which there is a low probability of failure with a high confidence. The low probability of failure is defined as 5% non-exceedance probability of the ground motion level that follows a lognormal distribution with a standard deviation, \(\beta_{R}\). The ground motion level has a median value with a high confidence, that is, 95% exceedance probability subjected to a lognormal distribution with a lognormal standard deviation, \(\beta_{U}\)[1]. The HCLPF is defined by Equation (8). In addition, HCLPF', which is the minimum of HCLPF approximated by 1% probability of failure, was calculated using Equation (9) [1].  
\[\text{HCLPF}=A_{m}e^{-1.65(\beta_{R}+\beta_{U})} \tag{8}\]  
\[\text{HCLPF}^{\prime}=A_{m}e^{-2.33\beta_{C}} \tag{9}\]  
where \(A_{m}\) is the median capacity obtained from the median model; and \(\beta_{R}\), \(\beta_{U}\), and \(\beta_{C}\) are the logarithmic standard deviation due to randomness, the logarithmic standard deviation due to uncertainty, and the combined logarithmic standard deviation, respectively.  
The HCLPF and HCLPF' values, calculated based on the variables presented in Table 5, are summarized in Table 7. Here, the values in the parentheses are normalized to the corresponding values of the UHRS. Compared with UHRS, the HCLPF value for CS1 increases by 9%, while the HCLPF' values for CS2 and CS3 decrease by 11% and 36%, respectively. HCLPF has a tendency similar to that of HCLPF. The difference in HCLPF or HCLPF' for various seismic input models is caused by the difference in the logarithmic standard deviation considering that the median capacities of the UHRS, CS1, and CS2 are similar, as shown in Table 5. As a result, the HCLPF or HCLPF' can be assessed more reasonably by using a CS-based seismic input model, and it is necessary to choose a control frequency predominantly in the inelastic structural behavior to reduce the effect of randomness in the seismic input.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Seismic Input Model** & **HCLPF** & **HCLPF\({}^{\prime}\)** \\ \hline UHRS & 2.442 & 2.378 \\  & (1.00) & (1.00) \\ CS1 & 2.663 & 2.615 \\  & (1.09) & (1.10) \\ CS2 & 2.162 & 2.156 \\  & (0.89) & (0.91) \\ CS3 & 1.569 & 1.128 \\  & (0.64) & (0.47) \\ \hline \hline \end{tabular}
\end{table}
Table 7: HCLPF and HCLPF’ capacities in PGA (parenthesis: normalized with UHRS).
# Electrospun Nanomaterials for Energy Applications: Recent Advances
## 2 Principle of Electrospinning, Experimental Setup, and Production of Fibrous Nanomaterials
Also, manifold are the ambient, instrument, and solution parameters that can be tuned in order to obtain the desired fiber properties (temperature, atmosphere, humidity, needle gauge, needle-tip/collector distance, solution feeding-rate, applied voltage, nature and molecular weight of the polymer(s), polymer mass-ratio in the case of blend, type and relative amount of the solvent, addition of precursors, preformed nano-sized components or sacrificial templating agents and their relative-to-polymer amount, use of conductive additives). At last, a single-step (calculation) or multi-step post-spinning heat treatment (stabilization and carbonization, sometimes followed by activation) upon different atmospheres (air, inert gases, gas mixture, activating gases) can be necessary to finalize the production process of the various fiber typologies (Figure 3). Polymer fibers do not require any treatment [46; 47]; pure (and doped) oxide fibers are obtained by removing the organic component of the precursor(s) via an oxidative process [48; 49; 50; 51], whereas stabilization of the polymer enabling its subsequent processing at higher temperature for graphitization is needed to obtain carbon and composite carbon-based fibers [52; 53; 54; 55; 56; 57; 58]. Activation upon gaseous atmosphere [53] or chemical etching to remove sacrificial templating agents [59] are utilized to enhance the fiber porosity and surface area. The produced ENMs can be in the form of powders (oxide fibers), self-standing non-woven membranes (polymer fibers), or self-standing flexible paper-like membranes (carbon and composite carbon-based fibers). Figure 2: Schematic illustration of the fiber production via electrospinning. For the sake of completeness, it has to be mentioned that, in the latest years, several novel alternative strategies to fabricate nanofibers are emerging, such as plasma-induced synthesis, CO\({}_{2}\) laser supersonic drawing, electro-hydrodynamic direct writing, solution blow spinning, and centrifugal jet spinning. The readers interested in this subject can refer to reference [5]. In the following, an overview of some energy-related applications of ENMs is given. Recent advances in the field of rechargeable alkali metal-ion batteries, redox flow batteries, metal-air batteries, and supercapacitors are illustrated. Also, reactors for water desalination via capacitive deionization (based on energy storage) and for hydrogen production by water splitting, as well as nanogenerators for energy harvesting and textiles for energy saving through personal thermal management, exploiting EMNs, are briefly presented.
## 3 Proposed Intelligent Weighting Factors Design Approach
### Proposed Weighting Factor Tuning Scheme Based on DRL
#### 3.2.3 Comparison of TD3 with Other Reinforcement Learning Algorithms  
To show the efficacy of TD3 in optimizing the weighting factors in the MPC asymmetric decision function, we summarize a comprehensive qualitative analysis from the literature comparing TD3 with other state-of-the-art RL methods, such as deep deterministic policy gradient (DDPG) [37] and soft actor-critic (SAC) [38], in Table 4. TD3 provides a good trade-off between control performance, computational complexity, and stability, which is essential for real-time applications.  
While SAC shows superior robustness and sample efficiency, its computational complexity and memory requirements during training and inference make it less effective for deployment in embedded systems. TD3, on the other hand, addresses the overestimation bias in DDPG and uses delayed policy updates and target smoothing to improve training stability without introducing significant complexity.  
Furthermore, TD3 achieved faster convergence to optimal weights and demonstrated improved transient and steady-state performance compared to DDPG, while being computationally lighter than SAC. These qualities make TD3 particularly suitable for industrial motor control applications.
## 3 Results and Discussion
### Free Charge Carrier Properties of MLG and QFS-MLG
Since the bulk doping in the SI substrate is the same for all three samples leading to a similar positive space charge in the substrate depletion layer, the observed lower free hole density in QFS-MLG with \(T_{\text{Ar}}\) = 1300 \({}^{\circ}\)C indicates the presence of donor surface states. As noted earlier, the buffer layers grown at different \(T_{\text{Ar}}\) on SI substrates exhibit very similar \(sp^{3}\) contents and defect densities (Figure 6). We also confirmed by \(\upmu\)-Raman scattering spectroscopy mapping that no structural changes occur as a result of the intercalation process. Recall that in comparison to lower \(T_{\text{Ar}}\) the buffer layer with \(T_{\text{Ar}}\) = 1300 \({}^{\circ}\)C is incomplete. We speculate that this incomplete buffer layer formation may be the cause of the surface donor states, likely dangling bonds. Interestingly, purging with moist air (RH 45%) for \(\sim\)18 h leads to small increase of free hole density in QFS-MLG with \(T_{\text{Ar}}\) = 800 \({}^{\circ}\)C and \(T_{\text{Ar}}\) = 640 \({}^{\circ}\)C while for \(T_{\text{Ar}}\) = 1300 \({}^{\circ}\)C the hole density remains unchanged. This can be potentially explained by the above-mentioned scenario since the purge with moist air has different effects: for the polarization doped QFS-MLG it leads to chemical acceptor doping of graphene while for the sample with \(T_{\text{Ar}}\) = 1300 \({}^{\circ}\)C it leads to passivation of surface donor states. The two processes will naturally have different dynamics. This proposal is also consistent with the results for prolonged exposure to ambient. The free hole density in QFS-MLG with \(T_{\text{Ar}}\) = 1300 \({}^{\circ}\)C increases to 9.2 \(\times\) 10\({}^{12}\) cm\({}^{-2}\) nearing the intrinsic polarization doping since most (all) surface donor states have been passivated. For \(T_{\text{Ar}}\) = 640 \({}^{\circ}\)C and \(T_{\text{Ar}}\) = 800 \({}^{\circ}\)C the free hole densities increase to 2.3 \(\times\) 10\({}^{13}\) cm\({}^{-2}\) and 1.9 \(\times\) 10\({}^{13}\) cm\({}^{-2}\), respectively, as a result of chemical acceptor doping. In all cases, except for the freshly annealed samples, the largest hole mobility parameters are found for the QFS-MLG with \(T_{\text{Ar}}\) = 1300 \({}^{\circ}\)C. Thisis most likely related to the generally lower free hole density parameters. Note that the free charge mobility (and density) parameters represent average parameters obtained over the entire sample area of 10 mm \(\times\) 10 mm.
# Fault Injection Emulation for Systems in FPGAs: Tools, Techniques and Methodology, a Tutorial
## 4 Radiation Effects on SRAM-Based FPGAs  
To understand the effects of radiation on SRAM-based FPGAs, the abstraction of layers in the device is described, as shown in Figure 5. The two main layers that stand out are:  
* Application layer: includes the logic and memory elements managed by the user's design.
* Configuration layer: includes the logic and memory elements that allow the designer to configure the logic and routing resources in the application layer.  
SRAM-based FPGAs are sensitive to SEUs. Depending on which layer is struck by particles, the effects can produce different consequences:  
* SEUs induced in the Application layer are shown as transient errors that could alter the stored data or the state of the user logic memory elements such as Flip-Flops or BRAMs (ASIC failure model).
* SEUs affecting the Configuration layer produce persistent errors that can be reverted using a reconfiguration process. This kind of error consists in a bit flip which, in case of being an essential bit, may change the design functionality. This can have two consequences:  
* Change a routing element connection or disconnecting internal wires.
* Change a logic element modifying the behavior of a LUT belonging in a CLB.  
SEUs in the configuration layer are the most common type of errors in SRAM-based FPGAs because a high percentage of all the memory elements in the device are SRAM cells [42]. A summary of SEU consequences is presented in Table 1.  
Figure 5: Xilinx FPGA conceptual layers: Application and Configuration layers, extracted from [41].
## 3 Results and Analysis
### Extraction of Landslide Based on Filtering and OTSU  
Image binarization enhances the feature information of an image by comparing the pixel gray values with a threshold value. Pixels less than the set threshold are assigned a value of 0, while those greater than the threshold are assigned a value of 255. This is an image segmentation method, and the resulting image displays two colors: black and white. Commonly used binarization methods include the bimodal method, P parametric method, OTSU, and iterative method.  
The entire processing procedure consists of three main stages: image preprocessing, image morphological processing, and edge detection. Initially, the image undergoes pre-processing, which involves converting the image to grayscale and then to binary. This process is followed by enhancing the image contrast to facilitate easy contour extraction. If the binarization effect is satisfactory, then the binarized image is output, and dynamic threshold testing is conducted until the optimal threshold is determined. Examination of the classified images reveals a significant amount of noise. Subsequently, the median filter method is employed to eliminate the noise, and the OTSU algorithm is ultimately utilized to establish the optimal threshold for generating the binary image by dynamically setting the threshold. The resulting image effectively reduces the presence of most of the noise points. The binary image is shown in Figure 7.  
Figure 6: Results of slope constraint. (**a**) Original classification. (**b**) Extraction of landslide after slope constraint. (**c**) Variance analysis.  
Figure 7: The binarized image.