esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Numerical Illustrative Examples
### Numerical Model of a Thin Plate
Therefore, there were six weighting factors in the objective function: three for vertical bending signal residuals (\(w_{1}^{h}\), \(w_{2}^{h}\), \(w_{3}^{h}\)) and the other three for torsional signal residuals (\(w_{1}^{\alpha}\), \(w_{2}^{\alpha}\), \(w_{3}^{\alpha}\)). Figure 6 shows the convergence lines for the six weighting factors for three cases of noise level at a wind velocity of 8 m/s. It is observed that only three iterations are needed for the convergence of weighting factors. In addition, the weighting factors related to torsional signal residuals are generally smaller than those related to vertical bending signal residuals. Please note that the unit of vertical displacement and torsional angle signal is \(m\) and \(rad\), respectively. This is probably because the magnitude of torsional signal residuals is larger than that of vertical bending ones. According to Equation (15), the optimal weighting factors are inversely proportional to the related residuals, so the larger the residuals, the smaller the corresponding weighting factors. Figure 5: Convergence lines for the six benchmark functions with dimension equal to 30. Figure 7: Comparison of identified flutter derivatives and theoretical solutions for the case of 2% noise level. (**a**) \(H^{{}^{\prime}}\)1- \(H^{{}^{\prime}}\)4 (2% noise level); (**b**) \(A^{{}^{\prime}}\)1- \(A^{{}^{\prime}}\)4 (2% noise level). Figure 8: Comparison of identified flutter derivatives and theoretical solutions for the case of 5% noise level. (**a**) \(H^{*}1\)-\(H^{*}4\) (5% noise level); (**b**) \(A^{*}1\)-\(A^{*}4\) (5% noise level). In order to demonstrate the accuracy advantage of this new method in identification of flutter derivatives compared with traditional methods, a comparison study based on the average cumulative square deviations was conducted between the proposed method and MLS method [8]. The average cumulative square deviations were calculated by the following equation:  
\[\Delta=\frac{1}{N_{w}}\frac{1}{N_{s}}\sum_{1}^{i=N_{w}}\sum_{1}^{j=N_{c}}\left( \theta_{ij}^{e}-\theta^{t}\right)^{2}, \tag{25}\]  
Figure 9: Comparison of identified flutter derivatives and theoretical solutions for the case of 10% noise level. (a) \(H^{*}\)1-\(H^{*}\)4 (10% noise level); (b) \(A^{*}\)1-\(A^{*}\)4 (10% noise level).
## 6 Discussion  
### Data Collection and Preprocessing  
The preprocessing of the raw vibration data using a band-pass filter effectively reduces ambient noise and external interferences, and a cleaner signal can show the relevant frequency components associated with bearing faults. The segmentation of this filtered signal into overlapping windows captures local temporal features, and a detailed analysis of the vibration characteristics over time can be implemented. This approach enables the identification of specific patterns and anomalies within defined time frames, and a comprehensive dataset can be provided to keep essential features for model training.
## 2 Materials and Methods
### Theoretical Principle Used for Energy and Exergy Analyses
#### 2.4.2 Exergy Analysis  
The exergy balance equation on account of the second law of thermodynamics can be express as Equation (17) [35], which was adopted to analyze the exergy rate of each of the components of the drying system:  
\[\sum\dot{E}_{xi}-\sum\dot{E}_{x\alpha}=\sum\dot{E}_{x\alpha s} \tag{17}\]  
The chemical exergy of the coal \((\dot{E}_{xfuel})\) used in present works is calculated using Equation (18) [37]:  
\[\dot{E}_{xfuel}=\dot{m}_{fuel}[0.978+\frac{0.267O_{ar}+0.103M_{ar}}{100-(A_{ar }+M_{ar})}]\cdot LHV \tag{18}\]  
In this study, the exergy of the corn and air were calculated using Equations (19)-(21) [36]:  
\[E_{x}=(h-h_{0})-T_{0}(s-s_{0}) \tag{19}\]  
\[h-h_{0}=c_{p}(T-T_{0}) \tag{20}\]  
\[s-s_{0}=c_{p}\ln(\frac{T}{T_{0}})-R\ln(\frac{P}{P_{0}}) \tag{21}\]  
The exergy rate of the drying medium entering and leaving the DC includes chemical exergy (ch) and physical exergy (ph), which can be expressed to Equation (22) [38]:  
\[\sum\dot{x}=\sum\dot{x}^{ph}+\sum\dot{x}^{ch} \tag{22}\]  
According to the previous studies [4; 16; 35], the physical exergy of the inlet and the outlet corn kernels \((\dot{E}_{xc})\) is obtained using Equation (23):  
\[E\dot{x}_{c}^{ph}=C_{c}\dot{m}_{c}\bigg{[}(T_{c}-T_{\infty})-T_{\infty}\ln( \frac{T_{c}}{T_{0}})\bigg{]} \tag{23}\]  
The physical exergy and chemical exergy rates of the drying medium was calculated using Equations (24) and (25) [35]:  
\[E\dot{x}_{a}^{ph}=\dot{m}_{a}\bigg{\{}(C_{a}+\omega C_{\
u})(T_{a}-T_{\infty} )-T_{\infty}\bigg{[}(C_{a}+\omega C_{\
u})\ln(\frac{T_{a}}{T_{\infty}})-(R_{a }+\omega R_{\
u})\ln(\frac{P_{a}}{P_{\infty}})\bigg{]}\bigg{\}} \tag{24}\]  
\[E\dot{x}_{a}^{ch}=\dot{m}_{a}\bigg{\{}T_{\infty}\bigg{[}(R_{a}+\omega R_{\
u} )\ln(\frac{1+1.6078\omega_{0}}{1+1.6078\omega})+1.6078\omega R_{a}\ln(\frac{ \omega}{\omega_{0}})\bigg{]}\bigg{\}} \tag{25}\]  
The exergy used for the water evaporation in the drying process \((\dot{E}_{x\alpha\
u})\) is calculated using Equation (26) [35]:  
\[\dot{E}_{x\alpha\
u}=(1-\frac{T_{\infty}}{T_{c}})\dot{E}_{n\alpha\
u} \tag{26}\]The recover radiant exergy (\(\dot{E}_{x\mathit{fir}}\)) recovered by the radiators can be calculated using Equation (27) [39]:  
\[\dot{E}_{x\mathit{fir}}=A_{\mathit{fir}}\sigma\varepsilon_{r}T_{r}^{4}(1-\frac{T_ {\infty}}{T_{r}}) \tag{27}\]  
Hence, the exergy efficiency of the entire drying system was calculated using Equation (28):  
\[\eta_{\mathit{exw}}=\frac{\dot{E}_{\mathit{exw}}}{\dot{E}_{\mathit{xit}}}= \frac{\dot{E}_{\mathit{exw}}+\dot{W}_{h}}{\dot{E}_{\mathit{xfud}}+\dot{P}_{ \mathit{if}}+\dot{W}_{h}+\dot{P}_{\mathit{dm}}+\dot{P}_{\mathit{ap}}} \tag{28}\]  
Additionally, the thermodynamic constants and the related technical parameters used in the present works are tabulated in Table 3.
## 2 Materials and Methods
### Characterization Methods  
The UV-VIS absorption spectra were recorded with a UV\(-\)VIS absorption spectrometer (U3010, Hitachi, Loveland, CO, USA) by dissolving the CoPcS into DMF with the concentration of 1.0 \(\times\) 10\({}^{-6}\) mol/L. The FT-IR spectrum was characterized by a Spectrum One infrared spectrometer (Perkin Elmer, Waltham, MA, USA) using a KBr presser. X-ray photoelectron spectroscopy (XPS) was obtained on an AXIS Ultra DLD Thermo scientific spectroscopic (Shimadzu, Kyoto, Japan) with the measurement condition of an X-ray source, a monochromatic aluminum target at 1486.6 eV, ion gun current of 3 mA, and electric voltage of 15 kV. The specific surface area based on nitrogen physisorption of the catalyst samples was measured by a JW-BK100C surface area instrument (JWGB, Sci.&Tech., Beijing, China).  
Figure 3: Schematic representation showing the preparation of AC-supported sulfonated cobalt phthalocyanine (AC-CoPcS).
## 3 Results and Discussion
### Analysis of SERS Spectra
(a) The average SERS spectrum of the control group; (b) the average SERS spectrum of the cordycepin-12 h group; (c) spectral difference between (b) and (a). The x-axis is the Raman shift and y-axis is the intensities in arbitrary unit (a.u.). A more detailed comparison among cells from the control group, the cordycepin-12 h group, and the cordycepin-48 h group at important characteristic SERS peaks is shown in Figure 5. It was clear that prominent peak intensity changes at 534, 640, 746, 830, 998, 1145, and 1546 cm\({}^{-1}\) in the late stage of apoptosis were different from those of the early stage of apoptosis. Peaks at 805 and 1438 cm\({}^{-1}\) were shown to encounter continued decreases from the early stage of apoptosis to the late stage of apoptosis, indicating a progressing loss of the RNA content. Figure 4: SERS spectral changes of HeLa cells treated with cordycepin in the late stage of apoptosis. (**a**) The average SERS spectrum of the control group; (**b**) the average SERS spectrum of the cordycepin-48 h group; (**c**): spectral difference between (b) and (**a**). The x-axis is the Raman shift and y-axis is the intensities in arbitrary unit (a.u.). Figure 5: Intensity changes of important SERS characteristic peaks among the experimental groups. At each characteristic peak, the intensity of the control group was set as a base value, 1, and was divided by intensities from the cordycepin-12 h group and the cordycepin-48 h group. The error bars represent the standard deviations from 15 measurements in each group.
## 3 Plant-Based Biopolymers
### Synthesizing Table  
A synthesizing table (Table 5) has been created to summarize the benefits and limitations of plant-based biopolymers as viscosity-modifying admixtures in concrete. This table serves as a database for ongoing research in this area, highlighting the distinct advantages of each biopolymer in enhancing rheological properties. However, it also notes potential drawbacks or side effects. The table aids in selecting the right viscosity-modifying admixture for specific applications and desired performance criteria.  
_Appl. Sci._**2024**, _14_, 4307  
\begin{table}
\begin{tabular}{c c c} \hline
**Biopolymers** & **Advantages** & **Drawbacks** \\ \hline \hline \multicolumn{3}{c}{Cellulose ethers (CEs):} \\ - & Thickening agents. \\ - & Binding agents. \\ - & Enhance the water retention \\ - & capacity. \\ - & Increase the shear-thinning \\ - & behavior and the viscosity. \\ - & Attenuate the forced bleeding and \\ & the leaching. \\ Cellulose & - & When combined with PMS, \\ & significantly increase the viscosity \\ & and the yield stress. \\ & Cellulose filaments (CFs) improve the \\ & stability of SCC. \\ & Cellulose nanofibers (CFFs) raise the \\ & flow time and reduce the spreading \\ & diameter. \\ & Both CF and CFF improve the viscosity \\ & and the yield stress. \\ & Starch: \\ - & Improves the viscosity and yield \\ - & Increases the flow time and \\ & reduces the mortar flow. \\ \multirow{2}{*}{Starch} & Modified starch: \\ - & Improves the adhesion of plaster \\ & to walls. \\ - & Provides the initial strength. \\ - & Enhances the consistency. \\ \multicolumn{3}{c}{Corn starch improves the workability} \\ & and density at 1\% \\ \multicolumn{3}{c}{Pure alginate improves the workability} \\ & and the water retention capacity of SCC. \\ & Sodium alginate reduces the \\ & segregation of SCC and enhances the \\ & viscosity at high dosages. \\ & The gel formed between sodium \\ & alginate and calcium ions leads to an \\ \multirow{2}{*}{Alginate} & increase in both the yield stress and the \\ & viscosity. \\ & Alginate is used to enhance the \\ & durability of concrete: \\ & - & Self-healing agent for concrete \\ & cracks. \\ - & Ion diffusion reducer. \\ - & Internal hardening agent. \\ \hline \end{tabular}
\end{table}
Table 5: Synthesis of the advantages and drawbacks of the plant-based biopolymers.
# Task-Oriented Motion Planning of a Space Manipulator under Multiple Constraints
## 4 Mission Profile Analysis of the Space Manipulator
### Environmental State Characterization  
The task profile analysis layer does not consider the size of the object, but only analyzes the logical relationship before and after the object is operated in the environment. Therefore, the object in the environment is abstracted into the operation interface vector. The end pose of the manipulator is consistent with the object interface vector to operate the object, and the movement of the object is abstracted as the change of the interface vector. Suppose there are one mechanical arm and \(k\) objects in the environment, then the environmental state is represented by \(S\):  
Figure 2: Task-planning system structure of the space manipulator.  
\[S=\left[\begin{array}{cccc}s_{0}&s_{1}&\cdots&s_{k}\\ \end{array}\right]=\left[\begin{array}{cccc}\Theta_{0}&\Theta_{1}&\cdots& \Theta_{k}\\ g_{0}&g_{1}&\cdots&g_{k}\\ \end{array}\right] \tag{1}\] \[= \left[\begin{array}{cccc}x_{0}&x_{1}&\cdots&x_{k}\\ y_{0}&y_{1}&\cdots&y_{k}\\ z_{0}&z_{1}&\cdots&z_{k}\\ \alpha_{0}&\alpha_{1}&\cdots&\alpha_{k}\\ \beta_{0}&\beta_{1}&\cdots&\beta_{k}\\ \gamma_{0}&\gamma_{1}&\cdots&\gamma_{k}\\ g_{0}&g_{1}&\cdots&g_{k}\\ \end{array}\right]\]  
where \(s_{0}\) is the end of the manipulator, and \(s_{1}\cdots s_{k}\) is the object; \(\Theta=\left[\begin{array}{cccc}x&y&z&\alpha&\beta&\gamma\\ \end{array}\right]^{T}\) is the coordinate. For the manipulator, it refers to the end pose coordinate; for the object in the environment, it refers to the operation interface vector, which is generally defined in the capture position of the object; \(g\) indicates whether the manipulator carries an object, \(g=\left[\begin{array}{cccc}g_{0}&g_{1}&\cdots&g_{k}\\ \end{array}\right]\). There are only two possibilities:  
1. \(g=0\) indicates that the manipulator is empty.
2. \(g_{0}=1\), indicates that there is only one item in \(g_{1}\cdots g_{k}\) for which \(g_{i}=1\); for other items, \(g_{i}=0\). It indicates that the manipulator carries the \(i\)-th object. Define the state mask to assist the operation between the state matrices: \[M=\left[\begin{array}{cccc}m_{11}&m_{12}&\cdots&m_{1k}\\ m_{21}&m_{22}&\cdots&m_{2k}\\ m_{31}&m_{32}&\cdots&m_{3k}\\ m_{41}&m_{42}&\cdots&m_{4k}\\ m_{51}&m_{52}&\cdots&m_{5k}\\ m_{61}&m_{62}&\cdots&m_{6k}\\ m_{71}&m_{72}&\cdots&m_{7k}\\ \end{array}\right]\] (2) where \(m_{ij}\in\{0,1\}\).
## 3 The Proposed FedSAGE Framework for Federated DGA Detection
### Unsupervised VAE Clustering for Novelty Detection
#### 3.2.2 Privacy-Preserving Latent Encoding  
Although FedSAGE does not transmit raw inputs, one might ask whether the transmitted VAE latent vectors-\(\mu\) of each client (as shown in Figure 3) could still be reverse-engineered to recover sensitive domain names. FedSAGE assures that the VAE inherently ensures non-reversibility of the latent representation due to the following reasons:  
1. The encoder maps input domain strings \(x\in\mathcal{X}\) to a low-dimensional latent mean vector \(\mu(x)\in\mathbb{R}^{d}\), typically with \(d\ll|x|\). This process is _non-invertible by design_, as multiple inputs are projected to nearby or overlapping regions in latent space. The dimensionality reduction forces the model to compress only the most semantically relevant aspects of the input. This is aligned with the _information bottleneck principle_, which suggests that \(\mu\) retains information that is useful for downstream tasks (e.g., clustering) while discarding instance-specific noise or details that might enable reconstruction.
2. During training, VAE introduces Gaussian noise into the encoding through the reparameterization trick in Equation (1). Even after training, the encoder learns to focus on capturing latent factors of variation, not full input recovery. Therefore, even if one were to recover a valid latent point \(\mu\), it is impossible to deterministically recover \(x\), especially for non-deterministic inputs such as randomized domain names.
3. The decoder network used during training is not deployed or shared in the federated setup. The server only observes isolated latent vectors from independent clients. Even with white-box access, reconstructing a valid decoder without aligned \(\mu\)-\(x\) pairs from each client is infeasible due to a lack of supervision and data pairing.  
Otherwise, prior work in representation learning (e.g., [PERSON] et al. [36]) supports that VAE latent spaces are robust to inversion, particularly when regularized with KL-divergence and trained without reconstruction fidelity as the primary goal. Our encoder is trained with minimal capacity to discourage memorization, further reducing invertibility risk.  
In summary, the latent vector \(\mu\) serves as a _semantic signature_, not a reconstructible representation. While we acknowledge that empirical verification (e.g., decoder attack) can further reinforce this claim, our current use of lossy, many-to-one, stochastic encoding with no decoder availability provides a strong theoretical foundation for its privacy-preserving nature.
# Fracturing Fluids and Their Application in the Republic of Croatia
## 4 Fracturing Fluid Design, Properties and Testing
### Fracturing Fluid Testing  
For the successful implementation of the hydraulic fracturing process, frac fluid testing, water analysis, and proppant analysis are extremely important [45]. Samples of the base fracturing fluid (usually water) should be taken and analyzed. Basic and advanced fracturing fluid testing include linear gel rheology, high-temperature, high-temperature, high pressure (HTHP) rheology, viscosity profiles, breaker profiles, and regained permeability (see Table 1). For example, in the case of using a water-based fluid (a combination of a linear and crosslinked gel) with various gel loadings (depending on the formation parameters) a stability test, variance test, shear test and breaker test must be carried out. Every test (graph) must include the composition (with the name and additive concentrations) of the tested fluid, the name of the instrument with configuration sleeve/bob, the temperature at which the test was performed, water characteristics, linear gel temperature, pH and viscosity at 300 RPM.  
The chemicals used for stimulation operations are extremely sensitive and, as such, require the base water to be of a particular quality. Typically, a water analysis is carried out on the base fluid to determine the minerals and type of bacteria present. Therefore, before the fracturing fluid is prepared, the water for based fluids is sampled and its quality is analyzed. It is necessary to state the water source, sample date, and test temperature. Water quality testing includes chlorides, sulfates, pH, calcium (hardness), nitrates and total dissolved solids (TDS).  
Commonly analyzed parameters, their required values, as well as the quality of the base water (ground water from the company's wells or fresh water from the public supply system) used for preparing fracturing fluids in Croatia are listed in Table 2. Data from the water analysis can be used to select the additives needed to mix the viscous fracturing fluid required to create a wide fracture and to transport the propping agent into the fracture. In addition, samples of the additives used during a treatment and the fracture fluid after all the additives have been added should be taken and saved
## 3 Results  
### Study Selection  
The initial database search across PubMed, Scopus, and WoS yielded 263 articles potentially relevant for the review. Following the removal of duplicates, 160 articles underwent screening. The initial screening of titles and abstracts resulted in the exclusion of 141 articles that did not involve a comparison of analysis between different specialists or AI/software. Subsequently, 19 articles underwent further full-text analysis, during which 2 articles were excluded for not meeting the inclusion criteria. Ultimately, a total of 17 articles were included in the qualitative synthesis of this review. The considerable heterogeneity among the included studies prevents the possibility of conducting a meta-analysis.
# Real-Time Conveyor Belt Deviation Detection Algorithm Based on Multi-Scale Feature Fusion Network
## 3 Experiments
### Datasets  
Aiming for the specific task of conveyor belt detection, it is necessary to extract the edge features of the belt in the image, while ignoring other edge features, such as beams, coal on the conveyor belt and so on. However, the commonly used dataset for edge detection is global edge detection. Therefore, a specific dataset of belt edge is made for the task in this paper. The data that come from the conveyor video are about one hour long and are collected by the camera under the mine; then the OpenCV is used to extract 500 valid images of coal, no-coal and coal being transported, with an average size of \(1000\times 560\) pixels.  
The larger the training dataset, the stronger the generalization of the training model; therefore, the training data are increased by flipping 1500 images vertically and horizontally. In each image, the edge of the belt is traced by a white line with one-pixel width, and the rest is regarded as background filled with black so it can make the label. Then the label is binarized to \(\{0,1\}\) to facilitate the training and testing of subsequent networks. We randomly divide the dataset in to 1200 training images and 300 test images according to the ratio of 4:1. In this paper, the five-fold cross-validation is adopted on training images and tested on the rest of the 300 images. Figure 5 shows the original image and the corresponding label.
# CubeSat-Based Observations of Lunar Ice Water Using a 183 GHz Horn Antenna: Design and Optimization
## 4 Smooth-Walled Horn Antenna Fabrication and Practical Results  
The experimental procedure commenced with the simulation of a corrugated horn antenna. Given the complex geometries associated with the corrugations, such antennas often pose fabrication challenges, particularly at THz frequencies where antenna dimensions are relatively small. To address these fabrication challenges, we employed a smooth-walled algorithm designed to simplify the structure of the corrugated antenna. The algorithm  
Figure 8: 2D pattern Phi and Theta gain.  
Figure 7: Gain comparison of (**a**) corrugated horn antenna and (**b**) smooth-walled horn antenna.  
transformed the corrugated horn antenna into a smooth-walled antenna, maintaining the efficiency of wave propagation while enabling a simpler manufacturing process.