esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation and Experimental Measurements  
The proposed isomorphic circuits of the VM NPFPs and VM QSOs were simulated with Cadence OrCAD PSpice simulation software version 16.6 to confirm the theory and implemented by three off-the-shelf LT1228 ICs, two grounded capacitors and three resistors to verify the performance of the isomorphic circuits. The isomorphic circuits adopted a DC bias of \(\pm\)5 V for the voltage supplies, and the voltage output was measured in the time domain with a Tektronix DPO 2048B oscilloscope, the voltage output was measured in the frequency domain with a Keysight E5061B-3L5 network analyzer, and the voltage output in the frequency spectrum was measured with a Keysight-Agilent N9000A CXA signal analyzer. The measurement photos of the isomorphic circuits are shown in Figures 4 and 5.
## 4 Proposed Method  
Conventionally, in impedance spectroscopy, the values of the parameter of an equivalent circuit after parameter extraction are represented versus the SoH. The ECM parameter showing the highest dependence on SoH is chosen for its assessment. Due to the random development of the side reactions in the cells related to the operation parameters, the evolution of the different elements of the cell gives each cell a unique development. As discussed in the previous section, the mechanisms inside a cell are still the same, but they influence the electrical properties of the cells differently due to the unique SEI layer characteristics gained during the first cycles.  
The same behavior can be observed for the charge transfer, as fewer electrons would still be active in the process [17]. In this case, the resistance of charge transfer, or the resistance inferred by trapped electrons in SEI, is one of the most prominent for SoH estimation and is mainly used in the literature. However, it is not a generalized solution, which could be applied to all the cells, even from the same manufacturer.  
Using a ratio of the electrons involved in the charge transfer and comparing them to the electrons stalled in the SEI layer, it is possible to infer the SoH pragmatically. Unfortunately, accessing these parameters is not directly feasible. However, an image of the electrons can be provided from the charge transfer and SEI resistances following an appropriate ECM. Therefore, following a data fitting, the resistances can infer the electrons' images, as shown in Equation (4). Using the ECM provided in the next section and enforcing a current,and assuming that the current at low frequencies passes through the resistances only, the approximation Equation (4) becomes valid:  
\[\eta_{\text{ct/SEI}}=\frac{\text{R}_{\text{ct}}}{\text{R}_{\text{ct}}+\text{R}_{ \text{SEI}}}=\frac{\text{n}_{\text{ct}}\alpha_{\text{SEI}}}{\text{n}_{\text{ SEI}}\alpha_{\text{ct}}+\text{n}_{\text{ct}}\alpha_{\text{SEI}}}\propto\frac{ \text{n}_{\text{ct}}}{\text{n}_{\text{SEI}}+\text{n}_{\text{ct}}} \tag{4}\]  
Experimental investigations in the following section have the aim to validate this theoretical and chemical-physical-based hypothesis. Therefore, it is worth investigating if this method depends on individual cell parameters, which would require a priori experimental measurements and calibrations.
## 2 System Overview and Analysis
### Equivalent Linear Model Set
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Value** & **Unit** \\ \hline Supply pressure \(p_{\mathrm{s}}\) & 18–21 & MPa \\ Piston effective area \(A_{\mathrm{L}}\) & 24.63 & cm\({}^{2}\) \\ Total control volume \(V_{\mathrm{L}}\) & 4.5 & cm\({}^{3}\) \\ Equivalent load mass \(m_{\mathrm{L}}\) & 8–12 & kg \\ Fluid bulk modulus \(\beta_{\mathrm{e}}\) & 700–1100 & MPa \\ Viscous damping coefficient \(B_{\mathrm{L}}\) & 800 & N\(\cdot\) s/m \\ Force sensor stiffness \(K_{\mathrm{s}}\) & 100 & kN/mm \\ Valve flow gain \(K_{\mathrm{q}}\) & 2.92–3.85 & m\({}^{2}\)/s \\ Total flow-pressure coefficient \(K_{\mathrm{tm}}\) & \(7\times 10^{-2}\) –\(7.5\times 10^{-2}\) & cm\({}^{5}\)/(N\(\cdot\) s) \\ Valve gain \(K_{\mathrm{v}}\) & 0.3/20 & mm/mA \\ Valve natural frequency \(\omega_{\mathrm{v}}\) & 200 & Hz \\ Valve damping ratio \(\zeta_{\mathrm{v}}\) & 0.7 & - \\ Feedback gain \(K_{\mathrm{a}}\) & \(4\times 10^{-4}\) & mA/N \\ \hline \end{tabular}
\end{table} 
Table 1: Parameters of the loading system. represents the natural angular frequency of the hydraulic cylinder; \(\xi_{\rm h}\) can be calculated, but the symbolic representation is difficult to obtain. After some calculation, the loading system \(P(s)\) can be represented as an equivalent linear model set \(P_{0}(s)\) having four uncertain parameters as  
\[P(s)\in P_{0}(s)=\left\{\frac{K_{\rm op}G_{\rm v}(s)}{\left(\frac{s}{\omega_{ \rm L}}+1\right)\left(\frac{s^{2}}{\omega_{\rm h}^{2}}+\frac{2\xi_{\rm h}}{ \omega_{\rm h}}s+1\right)}\right\} \tag{9}\]  
where \(\omega_{\rm L}\in[31.62,46.03]\); \(\omega_{\rm h}\in[3387,4464]\); \(\xi_{\rm h}\in[0.012,0.015]\); and \(K_{\rm op}\in[9.56\times 10^{8},1.354\times 10^{9}]\). In light of the statistics of the hydraulic servo system, the damping ratio of the hydraulic servo system is generally more than 0.1, and therefore we take \(\xi_{\rm h}=0.1\). From the frequency characteristics of the equivalent model set in Figure 4, the dynamic characteristics of the loading system can be described by the linearized equivalent model set, but this equivalent method introduces a significant amount of dynamic uncertainty in the low-frequency region, which is smaller in the actual plant.
# Speckle Noise Reduction in Digital Holography Using a DMD and Multi-Hologram Resampling
## 3 Optical Speckle Reduction
### Plane Wave Illumination  
The Lee hologram to generate a tilted plane wave is a set of binary linear fringes, which can be generated with the functions \(\phi(x,y)=0\) and \(A(x,y)=1\). The orientation of the fringes and their spatial frequency control the tilting of this plane wave. To establish the available range of tilt angles that can be used in our set-up, we considered that the angle of illumination produced by the DMD is related to the light and DMD pattern by [26]  
\[\theta_{X}=sin^{-1}(\frac{\lambda}{\Lambda_{X}})\approx\frac{\lambda}{\Lambda_ {X}}, \tag{7}\]  
where \(\lambda\) is the light wavelength, \(\Lambda_{X}\) is the period of DMD pattern in the X direction, and \(\theta_{X}\) is the corresponding angle of the plane wave measured with respect to the optical axis. We implemented the Fourier filtering based on two positive lenses \(f_{1}\) and \(f_{2}\), and in this case, the angle in which a ray leaves the lens \(f_{2}\) depends on the angle in which it enters the first \(f_{1}\) by (under paraxial approximation)  
\[\theta_{X}^{\prime}\approx\frac{f_{1}}{f_{2}}\theta_{X}, \tag{8}\]  
where \(\theta_{X}^{\prime}\) is the output angle and \(\theta_{X}\) is the input angle, both measured with respect to the optical axis.  
Under these considerations, we have control over the angle of the plane wave that illuminates the object by simply changing the orientation and period \(\Lambda\) of the binary pattern on the DMD, since the other parameters \((f_{1},f_{2},\lambda)\) can be considered as exclusively experimental factors and they are fixed for practical reasons. An example of a DMD pattern used to generate a plane wave in our experiments is displayed in Figure 3a, with experimental values \(f_{1}=250\) mm, \(f_{2}=500\) mm, \(\lambda=473\) nm. We generated plane waves with an angular variation inside a range of \(0.1\deg\) for a total of \(h=20\) recorded holograms.
# Fisher Discriminative Sparse Representation Based on DBN for Fault Diagnosis of Complex System
###### Abstract  
Fault detection and diagnosis in the chemical industry is a challenging task due to the large number of measured variables and complex interactions among them. To solve this problem, a new fault diagnosis method named Fisher discriminative sparse representation (FDSR), based on deep belief network (DBN), is proposed in this paper. We used DBN to extract the features of all faulty and normal modes. Features extracted by the DBN were used to learn subdictionaries, then the overcomplete dictionary was constructed by cascading all subdictionaries in order, and each dictionary atom corresponded to class labels. The Fisher discrimination criterion (FDC) was applied to the dictionary learning to ensure smaller within-class scatter but greater between-class scatter. The quadratic programming method was applied to estimate the sparse coefficients simultaneously class by class. Therefore, both the reconstruction error and sparse coefficients were discriminative, so that the reconstruction error after sparse coding can be used for pattern classification. An experiment performed on the Tennessee Eastman (TE) process indicated that compared with the traditional monitoring methods, the FDSR based on DBN produced more interpretable results and achieved superior performance for feature extraction and classification in the field of complex system fault diagnosis. T-distributed stochastic neighbor embedding (t-SNE) appropriately visualized the performance of the proposed method and produced reliable fault diagnosis results.  
Keywords:fault diagnosis; Fisher discriminant analysis; sparse representation; deep belief network; complex system; Tennessee Eastman process +
Footnote †: journal: Applied Sciences
# Synthetic Fog Generation Using High-Performance Dehazing Networks for Surveillance Applications
## 4 Experiments and Results Analysis
### Visual Comparison of Real and Synthetic Fog  
To evaluate the visual realism of the synthetic fog generated by our method, we compare it with real-world fog and other baseline fog generation methods, as shown in Figure 8. The figure presents two real images (a clear image and a foggy image captured under natural fog conditions) alongside synthetic fog samples produced using four different approaches: simple fog, depth-aware fog, Dark Channel Prior, and our proposed GridNet-based method.  
Among these, the synthetic fog generated by our model exhibits a more natural fog distribution and density that closely resembles the characteristics of real fog. The fog appears more spatially consistent and visually plausible, especially in its interaction with background structures and atmospheric depth. In contrast, the fog produced by the baseline methods tends to be either too sparse or uniformly spread, failing to capture the heterogeneous and complex nature of real fog. These observations visually support the superiority of our method in generating realistic fog, which is critical for training effective dehazing networks in surveillance scenarios.  
Figure 8: Comparison of real-world clear and foggy images with synthetic fog samples generated by different fog-generation methods.
## 5 Discussion
DEMs possess the capability to represent surface topography with varying levels of detail, where higher-resolution DEMs (e.g., 10 m) capture finer terrain features, producing more detailed slope aspect, gradient, and flow direction data that closely align with actual ground conditions. However, this increased resolution often leads to excessively fragmented slope units, complicating computational analysis. Conversely, lower-resolution DEMs (e.g., 90 m) generate coarser terrain representations but may yield appropriately sized slope units that meet practical needs. To address this trade-off, systematic investigations should be conducted to evaluate SUD-CGHP-derived slope units across multiple resolutions (e.g., 90 m, 30 m, and 10 m). Such studies would analyze how resolution affects both the geometric coherence of slope units and their internal mechanical and hydrological properties, providing critical insights for optimizing method selection in different application scenarios. Furthermore, this research could advance our understanding of scale-dependent effects in terrain analysis, particularly regarding the balance between spatial detail and computational feasibility. The application adaptability of this methodology across diverse geographic types constitutes another critical research dimension. In the study area of Yanglousi Town, where hills dominate and plains occupy minor proportions, SUD-CGHP demonstrates commendable performance in classifying plain and hilly terrains. Nevertheless, its effectiveness remains constrained under other geomorphic conditions such as mountainous and plateau regions. Future research will extend the methodological application to these complex terrains by systematically evaluating slope unit behavior and deriving optimized implementation protocols across varied geomorphic environments.
# Effect of Three Chlorhexidine-Based Mouthwashes on Human Gingival Fibroblastsets: An In Vitro Study
## 3 Results
### Apoptosis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Grouping** & **Comparator** & **Comparator** & **\(p\)-Value** & **\(p\)-Value** & **\(p\)-Value** \\
**Variable** & **1** & **2** & **(day 0)** & **(day 3)** & **(day 6)** \\ \hline \multirow{3}{*}{CHX 0.1} & 30 s & 60 s & - & - & - \\  & 30 s & 120 s & - & - & - \\  & 60 s & 120 s & - & - & - \\ \multirow{3}{*}{CHX 0.2} & 30 s & 60 s & - & - & - \\  & 60 s & 120 s & - & - & - \\ \multirow{3}{*}{CHX + CPC} & 30 s & 60 s & - & - & - \\  & 30 s & 120 s & - & - & - \\  & 60 s & 120 s & - & - & - \\ \multirow{3}{*}{NaCl} & 30 s & 60 s & 1.000 & 0.648 & 1.000 \\  & 30 s & 120 s & 0.002 ** & 0.006 ** & 0.004 ** \\  & 60 s & 120 s & 0.011 * & 0.183 & 0.009 ** \\ \multirow{3}{*}{30 s} & CHX 0.1 & CHX 0.2 & 0.813 & 1.000 & 0.623 \\  & CHX 0.1 & CHX + CPC & 1.000 & 1.000 & 1.000 \\  & CHX 0.1 & NaCl & 0.000 *** & 0.016 * & 0.065 \\  & CHX 0.2 & CHX + CPC & 1.000 & 1.000 & 1.000 \\  & CHX 0.2 & NaCl & 0.048 * & 0.004 ** & 0.000 *** \\  & CHX + CPC & NaCl & 0.004 ** & 0.001 ** & 0.003 ** \\ \multirow{3}{*}{120 s} & CHX 0.1 & CHX 0.2 & 1.000 & 1.000 & 0.291 \\  & CHX 0.1 & CHX + CPC & 1.000 & 1.000 & 0.444 \\  & CHX 0.1 & NaCl & 0.007 ** & 0.023 * & 0.185 \\ \multirow{3}{*}{60 s} & CHX 0.2 & CHX + CPC & 1.000 & 1.000 & 1.000 \\  & CHX 0.2 & NaCl & 0.028 * & 0.002 ** & 0.000 *** \\  & CHX + CPC & NaCl & 0.000 *** & 0.001 ** & 0.000 *** \\ \hline \multirow{3}{*}{120 s} & CHX 0.1 & CHX 0.2 & 1.000 & 1.000 & 1.000 \\  & CHX 0.1 & CHX + CPC & 0.248 & 1.000 & 1.000 \\ \multirow{3}{*}{120 s} & CHX 0.1 & NaCl & 0.032 * & 0.024 * & 0.004 ** \\  & CHX 0.2 & CHX + CPC & 0.167 & 1.000 & 1.000 \\ \multirow{3}{*}{120 s} & CHX 0.2 & NaCl & 0.052 & 0.001 ** & 0.005 ** \\  & CHX + CPC & NaCl & 0.000 *** & 0.002 ** & 0.003 ** \\ \hline \hline \end{tabular}  
* \(p<0.05\), ** \(p<0.01\), *** \(p<0.001\).  
\end{table} 
Table 3: Apoptosis. A multiple Kruskal–Wallis test was performed to compare the groups at 0, 3 and 6 days. In case of significance, a post hoc test was performed, and the adjusted \(p\)-values from the post hoc test are reported here. Figure 4: Overview of apoptosis of HGFs after exposure to test and control mouthwashes (for 30 s, 60 s and 120 s) measured in CPS at day 0, 3 and 6.
## 2 Theoretical Background and Concept
### Critiques of Technology Acceptance Models
Considering the above, UTAUT2 was selected as the theoretical base for this study, where the moderating role of personal innovativeness and experience factors and their influence on consumers' acceptance toward smart metering technology are investigated. It is worth mentioning that the UTAUT2 requires further modifications in order to suit the smart metering context as shown in our proposed model (see Figure 1). Figure 1: Research model.
# Multi-Field Models of Fiber Reinforced Concrete for Structural Applications
## 4 Electro-Mechanical Analyses with Applications to Self-Sensing  
### Background  
With respect to the electrical properties of fiber concrete, conductivity (or resistivity) of the composite material is commonly of interest. Changes in composite resistance due to applied loading can be measured and used for self-sensing of structural concrete [12]. Variations in voltage along the periphery of fibrous sensing skins have been used to produce conductivity maps and characterize damage within the enclosed region of interest [68]. Such self-sensing capabilities are potentially useful for the condition assessment of structural concrete.  
Three behavioral regimes have been used to characterize composite conductivity. At low fiber contents, the fibers are electrically isolated from one another within the lowly conductive cement-based matrix. In this condition, conductivity of the composite increases with increasing fiber content, but at a relatively low rate. Effective medium theories have been developed to describe the dependence of composite conductivity on the amount, aspect ratio, conductivity, and orientation distribution of the fibers [14]. By increasing the amount of fibers, the number of direct contacts (or near contacts) between fibers also increases. Within the second regime, dramatic improvements in composite conductivity result from the establishment of those longer conductive pathways and, ultimately, percolation clusters across the region of interest [11; 12]. In the third regime, composite conductivity continues to increase with increasing fiber content, but at a lower rate after achieving a sufficient degree of percolation. The simulations presented herein mainly involve the first behavioral regime, due to the relatively low fiber dosages and low aspect ratios considered.
# Haystack Fires in Australia: Causes and Considerations for Preventative Management
## 6 Introduction of 'Smart Agriculture' for Haystack Management  
### Definition  
Smart agriculture incorporates recent technologies such as a Global Positioning System (GPS), the Internet of Things (IoT), Big Data, Cloud Computing, and Artificial Intelligence (AI) along with sensors and actuators into agricultural systems (Figure 5).  
Smart agriculture utilises sensing nodes placed in target areas, such as broadacre crops, pastures, greenhouses and irrigation channels, which can collect data of interest in real-time. Common data types collected include temperature, humidity, light, pressure, gas concentration, moisture content, electrical conductivity and imagery. Generally, collected data is aggregated into a cloud-based system for processing and delivered to the end user in the appropriate format. These data may also be used in a feedback loop to provide automation of actuators based on algorithms and AI (Figure 6).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fire Stage** & **Combustion Product/s** & **Types of Sensors** \\ \hline Development, ignition, & Invisible smoke aerosols and & Gas sensors and aspirating \\ pyrolysis phase & fire gases & systems \\ Smouldering fire & Visible smoke and aerosols & Gas sensors, optical smoke \\  & & detectors and heat detectors \\ Open fire & Heavy smoke and heat & Optical smoke detectors and \\  & development & heat detectors \\ \hline \hline \end{tabular}
\end{table}
Table 7: Sensors based on the development stages of fire.
## 2 Hybrid System and Techno-Economic Analysis Method
### Energy Dispatch Principle of the Hybrid System  
The load following strategy is considered for dispatching energy. In this strategy, the fuel cell only operates to meet critical load demand, which PV and grid cannot meet. The backup power from the fuel cell does not handle electrolyzer. The power needed for feed electrolyzer is supplied by excess PV power. Furthermore, the PV power will be sold to the grid when it greater than the demand for hydrogen production. The dispatched energy for the following load is widely used due to the benefit such as reduce fuel consumption belonged to lower generator size [72]. This strategy has advantages in terms of of environmental aspects, and curtailment generator size in hybrid renewable energy/distributed generation (DG), which allows the generator to run for meet critical load but not further tasks such as supply batteries or electrolyzer [73].
## 2 Materials and Methods
### Crack Segmentation and Quantification Method  
Before quantizing cracks, the U-Net++ network [33] is used for fine crack image segmentation. U-Net++ is a deep learning image segmentation network based on a prototype of U-Net. It is optimized by introducing nested jump connections and a deep supervision mechanism. It maintains U-Net's architecture, comprising an encoder and a decoder. The encoder features a 3 \(\times\) 3 convolutional layer, a subsequent batch normalization layer, and a ReLU activation function for high-level semantic feature extraction. To reduce computational complexity, the encoder utilizes a 2 \(\times\) 2 max pooling layer for downsampling. The decoder's design is distinguished by its nested and dense jump connections, enabling the encoder's outputs to connect not only to the corresponding decoder layer but also to all  
Figure 5: Camera pinhole model.  
preceding decoder layers. The decoder upsamples the feature maps, matching the original image's resolution, to produce precise pixel-level segmentation labels.  
The above designs create a dense feature transfer network, facilitating full-scale feature utilization and enhancing the fusion of low- and high-level features. U-Net++ introduces a predictive output module at the encoder's final layer to bolster model robustness and minimize over-segmentation risk. This module pre-determines the presence of target objects across the entire image region, thus reducing non-target region mis-segmentation. Furthermore, U-Net++ adopts an intensely supervised strategy with a custom composite loss function, which offers multi-scale training supervision to improve segmentation performance.  
After image segmentation, potential edge contours are identified through a gradient strength and orientation analysis. A non-maximum suppression technique refines the edges, and a dual-thresholding approach distinguishes solid and weak edges, ensuring continuity and clarity. The distance transform algorithm calculates each pixel's proximity to the nearest background pixel for extracted crack edges. This transform indicates the distance to the crack's central region in crack detection and is equal to half the crack width. The crack width, as depicted in Figure 6, the shooting distance, and the binocular camera parameters can be input into Equation (12) to accurately calculate the actual crack width.
# Two-Level Sharing and Extraction of Sensing Information with Hybrid V2X Communications
## 3 System Functionality and Motivation
### Problem and Motivation  
The collection and extraction of distributed sensing information from vehicles must confront the problem of network resource consumption. The collection of sensing data from individual vehicles that is sent to the BS/AP incurs a high amount of traffic load, especially when the number of vehicles is large, as illustrated in Figure 1. This causes the scalability problem of extended sensing-perception functionality.  
In this paper, we address the problem of how sensing information can be extracted in an efficient manner to, at the edge server, avoid high traffic and alleviate the resource consumption caused by data collection and extraction. Furthermore, we attempt to examine the functionality of combining the local sharing of sensing information among vehicles and the global extraction of sensing information from the edge server in order to extend the sensing-perception capability of both vehicles and edge servers.  
Our target is to enable an extended sensing-perception function ability with a low consumption of wireless resources and a low traffic load at the edge server in data collection and extraction. We aim to design a set of local cooperation among vehicles to enable the local extraction of sensing information and reduce the global reports of sensing data. In order to improve the system scalability and reduce the traffic load of sensing-perception at the edge server, the desired method is to extract the object sensing information, for example, a min RSSI, with only a few reports from the vehicle to the BS/AP. To enable such a target, a set of cooperation steps are desired to locally extract the object sensing information, before the vehicle starts to report to the edge server.