esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting Electromagnetic Performance Under Wrinkling in Thin-Film Phased Arrays
## 2 Wrinkling of Step-Stiffness Structure and Dual-Domain Displacement Mod
### Dual-Domain Displacement-Driven Function  
The out-of-plane deformation characteristics of the upper film and out-of-plane deformation characteristics of the upper film, and the deformation behavior of the step-stiffness thin-film structure is jointly influenced by both the film's and the patch's stiffness [27]. Thus, deformation is composed of two parts--one is the patch area and the other is the membrane area. The deformation of the patch area mainly shows bending deformation. The deformation of the membrane area is of the Fourier type. So, extracting ABAQUS simulation result data (as shown in Figures 4 and 5) with a typical patch area (as shown in Figure 6), MATLAB 2019b can be used for complex function construction and fitting in a two-region domain.  
Figure 5: Displacement contour of the thin film with a 5 \(\times\) 3 catenary array (**Left**: upper membrane; **Right**: lower membrane; **Up**: under equivalent tension force of 0.5 N; **Below**: under equivalent tension force of 2.1 N).  
Figure 6: Possible deformation modes.  
This treatment is consistent with existing research findings, namely, that the material stiffness of the film and patch regions influences the structure's deformation morphology.  
\[f(x,y)=cfz1{\sum_{i,j}^{n}x^{i}y^{j}}+cfz{\sum_{k,l}^{n}\cos(kx)\sin(kx)}+\cos( ly)\sin(ly) \tag{17}\]  
Different material parameters lead to different forms of out-of-plane deformation of stiffness step films, but the overall picture can be abstracted into two variables--film and patch stiffness. Therefore, in this study, a two-domain displacement model is adopted to approximate characterize the deformation of the stiffness-step thin-film shaped surface.  
The antenna unit deformation patterns corresponding to the 20 th-order fitting results are given in Figure 7 The center patch region deformation and the outer film region wrinkle deformation were independently controlled using two parameters. Using this function, antenna deformation could be approximate estimated. In the following sections, electromechanical coupling modeling of the antenna is performed based on this function to analyze the electrical properties of unit and phased array antennas.
## 7 Simulation and Evaluation
### Performance Metrics
#### 7.1.4 Scalability  
Scalability examines the protocol's ability to handle increasing network sizes without significant performance degradation.  
MMS-DSR demonstrates good scalability performance, as illustrated in Figure 15. The Normalized Performance Index decreases from 0.95 with 10 vehicles/km\({}^{2}\) to 0.63  
Figure 14: Routing overhead comparison in function of vehicle speed.  
Figure 13: Routing overhead comparison in function of vehicle density.  
with 100 vehicles/km\({}^{2}\) and from 0.95 at 10 km/h to 0.63 at 100 km/h. This decline is less pronounced compared to SOL-DSR, Enhanced OLSR, and DSR. MMS-DSR's machine learning-enhanced predictive capabilities and efficient routing strategies allow it to manage increasing network sizes effectively, ensuring consistent performance across various vehicle densities and speeds. This robustness is crucial for maintaining reliable communication in densely populated urban VANET environments.  
In high-density scenarios, MMS-DSR's predictive modeling using Long Short-Term Memory networks allows it to anticipate and adapt to changes in vehicle movements and network conditions dynamically. This capability ensures that even as vehicle density increases, MMS-DSR can maintain high performance with minimal degradation. The MMS-DSR approach benefits from its multi-metric scoring engine, which dynamically adjusts the weights of various routing metrics based on current network conditions. This adaptability is particularly beneficial in urban environments where vehicle densities and speeds can fluctuate rapidly. By exploiting predictive models, MMS-DSR can preemptively adjust routes to avoid congestion, ensuring that data transmission remains smooth and efficient. In addition, the protocol's efficiency is further enhanced by its reduced reliance on frequent route updates. Unlike traditional protocols that may rely on constant GPS data, MMS-DSR predicts vehicle movements and adjusts routes accordingly. This approach reduces the need for continuous control packet transmissions, lowering overhead and preserving network resources.
# Assessment of Hongtu-1 Multi-Static X-Band SAR Constellation Interferometry
## 5 Conclusions  
Investigating multi-static single-pass interferometric data acquired by HT1 confirmed the high potential of such an X-band constellation, above all to map height changes and update DEMs, but not only for these purposes. Thanks to the simultaneous acquisition of the data, there is no temporal decorrelation, no deformation phase and almost no atmospheric path delay phase--which facilitates processing and makes the results more accurate. Furthermore, retrieving landcover information and forest information such as biomass or forest heights based on the multi-static coherence is an additional promising use of the data.  
Conceptualization, all authors; methodology, all authors; software, C.M. and U.W.; validation, U.W. and C.M.; formal analysis, U.W. and O.F.; investigation, all authors; writing--original draft preparation, U.W.; writing--review and editing, O.F. and C.M.; visualization, U.W. and C.M.; supervision, U.W.; project administration, U.W.; funding acquisition, all authors. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementRestrictions apply to the availability of these data. The HT1 SLC data were obtained from PIESAT Information Technology Co., Ltd., YiYuan A-1-5, Xingshikou Road, Haidian District, Beijing, China, 100195.  
HT1 original data copyright PIESAT Information Technology Co., Ltd., YiYuan A-1-5, Xingshikou Road, Haidian District, Beijing, China, 100195. We thank the PIESAT team for making the data available for our analysis.  
Author [PERSON], [PERSON] and [PERSON] were employed by the company Gamma Remote Sensing AG. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Adaptive and Blind Audio Watermarking Algorithm Based on Chaotic Encryption in Hybrid Domain
## 2 Watermarking Algorithm in Hybrid Domain
### The Pretreatment to the Watermark Picture  
It is assumed that the binary watermark to be embedded is a binary picture with two dimensions \(L_{1}\times L_{2}\), and it can be defined as:  
\[W_{1}=\{w_{1}(u,v),1\leq u\leq L_{1},1\leq v\leq L_{2}\} \tag{1}\]  
where \(w_{1}(u,v)\in\{0,1\}\) is the pixel value of this binary watermark. One dimensional binary stream is obtained after dimensionality reduction of the watermark picture.  
\[W_{2}=\{w_{2}(q),1\leq q\leq L\} \tag{2}\]  
where \(L=L_{1}\times L_{2}\), and \(w_{2}(q)\in\{0,1\}\). Using the logistic system shown in formula (3) to generate chaotic binary sequence \(c(q)\):  
\[x_{q+1}=\alpha x_{q}(1-x_{q}) \tag{3}\]  
\[c(q)=\begin{cases}1&x_{q}\geq\delta\\ 0&x_{q}<\delta\end{cases} \tag{4}\]  
where \(0<x_{q}<1\), \(1\leq q\leq L\), and \(\delta\) is the threshold. When \(3.5699456\leq\alpha\leq 4\), the system is located in a chaotic state.  
Chaotic encryption is applied to \(W_{2}\) to increase the security of this algorithm. The encrypted watermark is obtained from \(w_{2}(q)\) and \(c(q)\) by an XOR operation according to formula (5).  
\[w_{3}(q)=w_{2}(q)\oplus c(q) \tag{5}\]  
\[W_{3}=\{w_{3}(q),1\leq q\leq L\} \tag{6}\]  
Figure 1: General block diagram of audio watermarking algorithm.  
Modulate \(w_{3}(q)\) into a bipolar string according to Formula (7):  
\[w(q)=\begin{cases}1&w_{3}(q)=1\\ -1&w_{3}(q)=0\end{cases} \tag{7}\]  
Using \(Ch(x_{1},\alpha,\delta)\) as a secret key to extract the watermark, only users who hold this key can extract the watermark correctly.
# Evaluation of the Temperature and Time in Centrifugation-Assisted Freeze Concentration
## 2 Materials and Methods
### Predicting the Values of the Response Variables with Regression Models  
The results of the factorial design were evaluated through multiple linear regression [27; 28], random forest regression [29; 30] and Gaussian processes [31; 32]. The goal was to evaluate how different variables that characterize the process predict the values of the response variables (concentrate, efficiency, and solute). To implement this evaluation, a number of machine learning algorithms were used for the regression. Machine learning algorithms are methods that use data to create models that can be used for prediction.  
The multiple linear regression method is the simplest among the algorithms used, and it models the response variable as a linear combination of the predictors. Multiple linear regression serves as a baseline for the comparison of machine learning algorithms. The random forest model is an example of an ensemble method that combines the predictions of several base estimators. In this case, the base estimators corresponded to decision trees that were learned from random subsets of the data. Random forest models can be an appropriate approach to problems with dependencies amongpredictors, and evaluating whether these dependencies can improve the quality of the predictions is an important question. Finally, Gaussian processes are very powerful models which, by using Gaussian distributions, are able to exploit the interactions among predictors. Furthermore, they can model noisy and stochastic problems, and thus, given the same set of parameters, the target variable can have different values. This is the case for scenarios such as the one considered in this paper, in which for the same setting of the temperature, time and amount of the raw material, the modelled quantity can exhibit variations in different trials due to other factors.  
The quality of the predictions was evaluated using the mean squared error (MSE) between the predicted and target value for each data point. If the predictions were perfect, i.e., the predicted value is equal to the real measurement, then the error would be zero. We also computed the R2 statistic that serves as another metric to evaluate the error. R2 is inversely related to the MSE. The three types of models were also compared to each other to determine which model produced the most accurate predictions.  
Two different scenarios for the predictor variables were considered. In the first scenario, only two variables were used to predict the target variables: temperature and time. These factors have been reported in previous work as the two most influential variables for the process. In the second scenario, a third predictor variable, namely, the amount of raw material, was added to evaluate whether adding this third variable improved the prediction.
# Impact of Soil on the Penetration of Prefabricated Spherical Fragments and Its Protective Effect
## 1 Introduction
In order to gain a deeper understanding of the mechanism of projectile penetration into soil and select appropriate calculation models, [PERSON] [7] proposed the Spherical Cavity Expansion Theory, which considers the interface effects of different media for soil concrete composite media, and established a penetration model for projectile vertical penetration into soil concrete composite media. [PERSON] et al. [8] also analyzed the dynamic characteristics and motion laws of projectiles during penetration based on the Spherical Cavity Expansion Theory. On this basis, in order to study the effects of soil moisture content and dry density on projectile penetration resistance, [PERSON] et al. [9] introduced these two conditions to modify the soil yield condition and completed the derivation of the Dynamic Spherical Cavity Expansion Theory. In order to further obtain the attenuation laws of different penetators in soil, [PERSON] et al. [10] conducted impact compression performance experiments on soil materials with different parameters, obtained the stress and strain curves of the soil, analyzed the dynamic response characteristics of the materials based on their different characteristics, and obtained the influence of each parameter on the dynamic response. In addition, many scholars have conducted extensive experiments and numerical simulations for different weapons and protective structures [11; 12; 13; 14; 15; 16; 17], proposed various numerical simulation calculation methods and parameter optimization methods, and analyzed various projectile penetration parameters. These scholars have proposed a variety of numerical simulation and parameter optimization methods and analyzed a variety of projectile penetration parameters. Based on these research results, there are many valuable references for the experimental design and application of numerical simulations in this field of study. In order to obtain the characteristics and projectile parameters of prefabricated spherical fragments penetrating into soil, experiments with 6 mm- and 11 mm-diameter prefabricated fragments penetrating into soil were carried out in this paper. The purpose was to obtain a more accurate numerical calculation model of fragments penetrating into soil. Based on the relevant literature on the penetration of fast-moving fragments, the material model suitable for this experiment was selected and compared with the experimental results [18; 19; 20]. In the second part, the target protection and parameters are analyzed and calculated based on these model parameters. In the third part, the fragment penetration effect under various initial conditions is studied. Combined with the actual protection structure, the protection parameters and characteristics of the soil medium are analyzed, which provides a reference for the design of relevant protection parameters.
# Prototyping of Environmental Kit for Georeferenced Transient Outdoor Comfort Assessment
## 2 Objectives and Process  
This study reports on the development of an outdoor comfort assessment tool based on Arduino [19] prototyping with the available environmental sensors. The process begins with the physical connections of the sensors to the microcontroller unit and software coding. In the following phase, the developed tool is tested parallel to its industrialized counterpart. To test the transient application of the device, outdoor thermal comfort maps are demonstrated with the measured data by walking through urban spaces. The final phase concludes with a discussion on short-term and long-term improvements to the device.  
The programming platform is the Arduino integrated development environment (IDE), which uses C and C++ languages. The data is compared with the measurements from branded environmental data loggers. Subsequently, the tool is used to collect georeferenced data in Munich, Germany. For the evaluation, the data is mapped using Grasshopper3D to visualize distinctive microclimates and pedestrian thermal comfort using the universal thermal climate index (UTCI) [20; 21; 22].  
The incentive is to equip architects and urban planners with a tool that can be easily assembled and modified according to the user's needs. In future, multiple hubs can be established within urban communities to retrieve sensible data. The device may also be expended as a weather station.
## 3 Results and Discussion
### Isotherms of Fluoride Adsorption on WA  
Equilibrium experiments were conducted to determine the maximum fluoride adsorption capacity of WA. The experiments were performed using fluoride solutions with different concentrations (from 50 to 1000 mg/L), and the resulting data, along with model fits, are depicted in Figure 2. The Langmuir model (\(R^{2}\) = 0.972) proved to be more suitable than the Freundlich model (\(R^{2}\) = 0.949) for explaining fluoride removal by WA, as detailed in Table 2. The maximum adsorption capacity measured in the batch experiment was 157.34 mg/g, which closely matched the Langmuir model calculation of 156.78 mg/g. These results indicate that fluoride adsorbs onto the WA surface as a uniform monolayer and does not interact with previously adsorbed fluoride molecules [12; 40]. Moreover, the maximum adsorption capacity of WA was comparable to that of other adsorbents reported in earlier studies [41; 42; 43]. Out of 328 adsorbents investigated for fluoride removal (with capacities ranging up to 600 mg/g and a median value of 19.8 mg/g, based on batch experiments), the WA used in this research exhibited a notably high capacity, ranking 34 th (Table S2). Contrary to some high-capacity adsorbents that rely on costly chemical synthesis, WA, a by-product of wood combustion, presents a sustainable and environmentally friendly alternative for environmental remediation, aligning with the principles of green chemistry. In addition, the correlation coefficient for the Temkin adsorption isotherm (Figure 2), which considers the interaction between the adsorbate and the adsorbent by assuming a linear decrease in the heat of adsorption as the adsorbent coverage increases, was notably high (\(\geq\)0.985, Table 2) [44; 45]. The heat of fluoride adsorption onto wood ash, as calculated using the Temkin model, was determined to be 96.3 J/mol.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Observed \(q_{e}\) (mg/g)**} & \multicolumn{2}{c}{**PFO Model**} & \multicolumn{2}{c}{**PSO Model**} & \multicolumn{3}{c}{**Elovich Model**} & \multicolumn{2}{c}{**IPD Model**} \\ \cline{2-13}  & \(q_{e}\) (mg/g) & \(k_{1}\) (1/h) & \(R^{2}\) & \(q_{e}\) (mg/g) & \(k_{2}\) (g/mg/h) & \(R^{2}\) & \(\alpha\) (mg/g/min) & \(\beta\) (g/mg) & \(R^{2}\) & \(ki\) (mg/g)-h\({}^{1/2}\) & \(R^{2}\) \\ \hline \multirow{2}{*}{115.99} & \multirow{2}{*}{110.82} & \multirow{2}{*}{7.70} & \multirow{2}{*}{0.989} & \multirow{2}{*}{113.50} & \multirow{2}{*}{0.181} & \multirow{2}{*}{0.997} & \multirow{2}{*}{\(7.1\times 10^{11}\)} & \multirow{2}{*}{0.247} & \multirow{2}{*}{0.998} & \multirow{2}{*}{2.505} & \multirow{2}{*}{0.983} \\ \cline{5-5} \cline{8-13}  & & & & & & & & & & & 1.828 & 0.896 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Kinetic model parameters derived by fitting the experimental data (PFO, pseudo-first-order model; PSO, pseudo-second-order model; IPD, intra-particle diffusion).  
Figure 2: Fluoride adsorption capacity of WA at equilibrium and its comparison with adsorption isotherm fittings.
## 4 Experimental Results
### Compression of ResNet-18 and ResNet-152 Using Tensor Decomposition Methods
#### 4.2.1 Compression of ResNet-18  
For ResNet-18, we evaluated three compressed variants:  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Model** & **Parameters** & **Compression** & **Model Size** & **CIFAR-10** & **Tiny ImageNet** & **Inference** \\  & **(mln.)** & **ratio** & **(MBs)** & **Accuracy** & **Accuracy** & **Time (s)** \\ \hline
**ResNet-18** & & & & & & \\ Base Model & 11.2 & - & 44.7 & 93.00\% & 58.2\% & 0.6 \\ Tucker-CP & 0.92 & \(\times\)12.2 & 7.91 & 89.4\% & 55.3\% & 0.8 \\ CP-Tucker & 0.89 & \(\times\)12.5 & 7.6 & 89.2\% & 54.4\% & 0.8 \\ CP-CP & 0.77 & \(\times\)14.5 & 6.9 & 88.9\% & 54.1\% & 0.8 \\ \hline
**ResNet-152** & & & & & & \\ Base Model & 58 & - & 233.5 & 96.2\% & 69.2\% & 5.1 \\ Tucker-CP & 23 & \(\times\)2.5 & 93.3 & 95.1\% & 62.4\% & 5.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of compressed and uncompressed versions of ResNet-18 and ResNet-152 models.  
* ResNet-18 (Tucker-CP): Tucker decomposition was applied to all layers in the third block, and CP decomposition to all layers in the fourth block.
* ResNet-18 (CP-Tucker): CP decomposition was applied to all layers in the third block, and Tucker decomposition to all layers in the fourth block.
* ResNet-18 (CP-CP): CP decomposition was applied to all layers in both the third and fourth blocks.  
The compression ratios achieved were \(\times\)12.2, \(\times\)12.5, and \(\times\)14.5, reducing the number of parameters from 11.2 million in the original model to 0.92, 0.89, and 0.77 million, respectively. This parameter reduction corresponded to decreases in model size, with the compressed models occupying 7.91, 7.6, and 6.9 MB compared to 44.7 MB in the uncompressed version.  
Despite significant compression, the accuracy drop on CIFAR-10 was minimal. For instance, with a compression ratio of \(\times\)12.2, the accuracy decreased from 93.06% for the original model to 89.4%. Even for the variant with the most aggressive compression (\(\times\)14.5), the accuracy remained at 88%, demonstrating the robustness of the compression approach.  
On the Tiny ImageNet dataset, the base ResNet-18 model achieved an accuracy of 58.2%, while the model compressed by a factor of \(\times\)12.2 achieved an accuracy of 55.3%.
## 4 Application of Fe-MOF in Advanced Oxidation Processes
### Ozone-Based AOP  
Ozonation allows the oxidation of organic pollutants in water through the use of ozone, allowing the generation of the hydroxyl radical [208,209]. In addition, this process, as shown in Figure 6, can also be combined with other oxidants and examples of this combination can be ozone with hydrogen peroxide or ultraviolet radiation [208]. As with the previous processes explained above, metal ions (Ni, Cu, Fe or Mn) and metal oxides (copper oxide, manganese dioxide, titanium dioxide or iron oxide) can be used to accelerate the degradation reaction [210]. At present, the mechanisms of this process are complex and complicated to understand [211]. However, it can be assumed that the following chemical reaction (Equation (30)) occurs that allows the generation of the hydroxyl radicals by using a transition metal catalyst [212].  
\[O_{3}+Me^{+n}+H_{2}O\to OH^{\bullet}+MeOH^{+n}+O_{2} \tag{30}\]  
A drawback of this type of AOP is that ozone has a short half-life and this means that the costs associated with full-scale water treatment of these processes are expensive. Furthermore, regarding the use of Fe-MOF in ozone-based AOPs, few papers have been reported between 2018 and 2022. However, the use of MOFs as a catalyst can overcome its limitations.  
Among the studies reported in the literature, the study of [PERSON] et al. [213] in which Fe\({}_{3}\)O\({}_{4}\)@Ce-UiO-66 was synthesized by the combination of Fe\({}_{3}\)O\({}_{4}\) nanoparticles with the MOF, Ce-UiO-66 is highlighted. This catalyst system showed a rapid and total elimination of acetaminophen in only 10 min. Furthermore, this catalyst has high catalytic efficiency in successive cycles with a loss of activity of less than 10% on the acetaminophen degradation, making it a promising catalyst. [PERSON] et al. [214] used Co\({}_{3}\)O\({}_{4}\)-C@FeOOH, which is a derivative of ZIF-67, as a catalyst to remove the drug norfloxacin, showing an excellent catalyst activity for the ozonation process achieving the complete degradation after 7 min, keeping this good activity during several cycles of use. [PERSON] et al. [215] evaluated the heterogeneous ozonation using MOF MIL-88A(Fe) to eliminate a phenolic compound, 4-nitrophenol. The results of this process were also promising with degradation levels higher than 90% in 30 min. It is outstanding that the elimination of this compound remains practically the same after four consecutive cycles, which is indicative of the high stability of this catalyst.  
In summary, although few studies have used Fe-based MOFs as ozonation catalysts, their use makes this heterogeneous process a very promising technology.