esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Shape Finding Method for the Main Cable of
## 3 Cases Study
### Case 3: 3D Spatial Main Cables of a Self-Anchored Suspension Bridge  
Another case is the Yongiong Grand Bridge in Korea, whose shape finding was investigated by many pioneering studies [16; 22; 26]. The Yongjong Grand Bridge is the first highway and railway self-anchored suspension bridge with three dimensional main cables. The total length of the bridge is 550 m with a 300-m main span and two 125-m side spans. The sag in middle span is 60 m. \(T_{yi}\) has a uniform value of 3.048 KN. The cross-sectional area of the main cable is 0.1355 m\({}^{2}\). The \(y\) and \(z\) coordinate values of the IP point are \(y_{IP}=114.573\) m and \(z_{IP}=1.50\) m, respectively by setting the origin of the Eulerian coordinate system at the midpoint of the bridge with zero elevation. The main cable is divided into 24 segments by 23 hangers. The coordinates of the connection points between hangers and the beam, i.e., \(y_{di}\) and \(z_{di}\) are estimated from the data given by [PERSON] et al. [16] and [PERSON] et al. [26], as listed in Table 2. The IP point is located at \(x=125.0\) m, and the mid-point of the main span is at \(x=275\) m.  
The iteration process for the Yongjong Grand Bridge using present method is illustrated in Figure 16. Both the outer-layer and inner-layer iterations meet the tolerance requirement in five steps. The solved horizontal component of the cable axial force is 48,149.1 kN, which is close to the value, 49,541 kN given by [PERSON] et al. [16] with the relative error of 2.89 %. The solution of the cable shape is compared with the results obtained by [PERSON] et al. [16] and [PERSON] et al. [26] in Table 3. As can be seen, the relative differences in \(y\)-direction between the present study and [PERSON] et al. [16] are less than 0.027%. In \(z\)-direction, the absolute values of the relative difference are less than 0.340%. The location of the IP was further studied by [PERSON] et al. [26], which is different from the coordinates used in the present study and [PERSON] et al. [16]. The maximum absolute value of the relative difference between present study and [PERSON] et al. [26] in \(y\) and \(z\) directions are 0.041% and 0.524%, respectively, which are slightly larger than the errors between the present study and [PERSON] et al. [16]. However, the present method is proved to be accurate enough to find a reasonable cable shape of the suspension bridge for engineering applications.  
Figure 15: Static displacement of the final 3D spatial main cable achieved by the FEM: (**a**) \(y\)-direction; (**b**) \(z\)-direction.
## Materials and Methods
### Canopy Multispectral Image Acquisition  
During the growth period of the canola, multispectral images were obtained using the Rededge-mx multi-spectrometer (MicaSense, Inc., Seattle, WA, USA), which was mounted on a DJI Inspire 2 UAV remote sensing platform (DJI Inc., Shenzhen, China) (Figure 3). The spectral images were captured approximately 7-10 days after sowing, and the UAV remote sensing operation was carried out from 11 am to 1 pm, specifically in sunny and windless weather conditions. During each flight, five images with a resolution of 1280 \(\times\) 960 pixels were obtained, corresponding to the blue (475 nm), green (560 nm), red (668 nm), red edge (717 nm) and near-infrared (840 nm) bands. During each flight operation, images of a reference plate were synchronously captured for radiation calibration. The flight height during the operation was set at 20 m, with an along- and cross-track overlap of 75%. The camera lens was pointed in the downward direction, capturing multispectral images with a ground resolution of 1.4 cm at a field of view of 47.2\({}^{\circ}\). After each flight, the raw images were automatically processed using Pix4 Dmapper software, including camera optimizing, photo aligning, radiometric correction and orthomosaic building. Finally, image reflectance data for each pixel throughout crop growth seasons were generated. For each plot, the average band reflectance was extracted using the selected region of interest (ROI) that excluded two rows of canola from the boundary area to avoid interference from plot edge effects, where crops grew more vigorously due to better lighting, ventilation and population competition.  
Figure 2: (**a**) The layout of the experimental plot for various sowing dates and densities. Blue letters indicate the sowing date levels and red letters indicate the sowing density levels; (**b**) the schematic diagram of the canola planting with three sowing densities.
## 3 Results and Discussion
### Communication Overhead
#### 3.2.3 Cipher Text  
Figure 11 shows the comparison of cipher text size for different access. In the MCP-ABE-BG approach, the cipher text size is linearly increased due to the increased private key size for attributes. However, in the proposed MCP-ABE-BG, the cipher text size is nine times the minimum of the MCP-ABE-BG approach and its size is 4% of the minimum of the LIBAC approach. This shows that the proposed approach requires less storage space in the cloud for saving protected electronic health records compared to other approaches.  
Figure 10: Private key size comparisons.  
Figure 9: Public key size comparisons.
# Leveraging LLMs for Efficient Topic Reviews
## 3 Framework
### Core Components
#### 3.1.2 Dimensional Reduction  
Dimensionality reduction is a critical step in the proposed framework, aimed at simplifying high-dimensional embeddings to facilitate efficient clustering and further analysis. The framework utilizes Uniform Manifold Approximation and Projection (UMAP) [45] as the default technique due to its effectiveness in preserving both local and global structures within reduced dimensions. UMAP excels at maintaining the integrity of these structures, which is essential for ensuring accurate clustering of topics, as it helps retain the nuanced relationships between data points that might be lost with other dimensionality reduction techniques.  
UMAP operates by constructing a high-dimensional graph representation of the data and then optimizing a low-dimensional graph that is as structurally similar as possible to the original. This method allows UMAP to capture both local data point relationships (i.e., how close data points are to their neighbors) and global data structures (i.e., how clusters of data are related to one another). This dual capability makes UMAP particularly effective for applications where understanding the global and local relationships within data is crucial, such as topic modeling in large text corpora.  
One of UMAP's key characteristics is its computational efficiency, particularly when embedding in dimensions higher than two. Unlike t-distributed stochastic neighbor embedding (t-SNE) [46], which requires global normalization and the use of spatial trees that scale poorly with dimensionality, UMAP avoids these computational bottlenecks. UMAP's optimization process is significantly faster, making it more scalable for large datasets and suitable for real-time applications. This efficiency is not only beneficial for visualization purposes but is also critical for machine learning tasks such as clustering or anomaly detection, where high-dimensional data needs to be managed effectively without compromising on the quality of the resulting embeddings.  
Additionally, UMAP's flexibility allows it to adapt to various data structures better than other techniques like LargeVis [47], especially on non-local scales where it excels. This makes UMAP a versatile tool in the framework, capable of providing high-quality, low-dimensional representations that are crucial for subsequent clustering and analysis tasks. The framework further enhances its adaptability by allowing the incorporation of other dimensionality reduction techniques to meet the specific needs of different analyses. This flexibility ensures that the framework can handle a wide range of datasets and analytical requirements.
## 2 Materials and Methods
### Data Collection Software  
In this phase, data collection from milk samples was conducted using the AS7265x multi-spectral sensor and the Arduino Nano 33 BLE microcontroller. The data collection process begins with the setup of the sensor and microcontroller, followed by the collection and recording of data.  
Initialization and sensor setup: The Arduino Nano 33 BLE is first configured to initiate and ensure the proper functioning of the AS7265x sensor, which measures light at various wavelengths to collect spectral data. The sensor is connected to the Arduino via an I2C connection and receives commands necessary for data collection.  
Data collection: The process commences with the sensor reading spectral data from milk samples. Each wavelength is measured, and the data are recorded by the microcontroller. These data are later processed to provide information on the chemical components of the milk samples.  
Data recording: The collected data are locally stored on the Arduino. This step ensures the accuracy and integrity of data that will later be analyzed and used as input for the model.  
Throughout the data collection process, the system operates continuously, saving data after each measurement.  
This software process is critical for ensuring the accuracy and consistency of the data. The coordinated operation between the sensor and the microcontroller supports precise data collection and recording, creating reliable datasets for subsequent analyses.
## 4 Results
### Forest Aboveground Biomass Map  
#### 4.2.1 Landsat-Based AGB of 2008  
The eight-day interval LAI maps with 30 m spatial resolution for years from 2009 to 2012 were generated as important input parameters to drive BEPS model. The statistical relationships between the forest field-based LAI and SAVI from Landsat ETM+ images of 2012 and 2013 were strong for all forest types including needle leaf forest (R\({}^{2}\) = 0.72, \(n\) = 24, \(p\) < 0.01), broadleaf forest (R\({}^{2}\) = 0.57, \(n\) = 21, \(p\) < 0.01), and mixed forests (R\({}^{2}\) = 0.58, \(n\) = 29, \(p\) < 0.01). The LAI for 2012 in the study area varied from 0.1 to 4.80 with average value and standard deviation of 1.60 (m\({}^{2}\)/m\({}^{2}\)) and 0.62 (m\({}^{2}\)/m\({}^{2}\)), respectively.  
* After assuming that the empirical relationship between the field-based LAI and AGB was universal with the same remote sensor, sampling season and study area in different years, we generated the forest AGB map of 2008 based on the statistical model built using the forest field data collected in the summer of 2012 and 2013.
* As shown in Table 3, the field-based LAI was used as independent variable to build the statistical model with field-based forest AGB at forest plot level to produce Landsat-based forest AGB map. The needle leaf forest had the best correlation coefficient (R\({}^{2}\) = 0.72, \(n\) = 24, \(p\) < 0.01), and the Landsat TM/ETM+ image-based LAI of broadleaf forest and mixed forests explained 42% (\(n\) = 21, \(p\) < 0.05) and 57 % (\(n\) = 29, \(p\) < 0.01) of variations in field-based forest AGB, respectively. Finally, the forest AGB map of 2008 based on the developed model was generated and served as the baseline data to parameterize the ecological process-based model to simulate the annual AGB variations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Forest Type** & **Sample No.** & **Statistical Models** & **R\({}^{2}\)** & \(p\) <** \\ \hline
**Needle leaf** & 24 & \(AGB=2.0508\times LAI^{1.2724}\) & 0.72 & 0.01 \\
**Broadleaf** & 21 & \(AGB=3.0717\times LAI^{0.8872}\) & 0.42 & 0.05 \\
**Mixed** & 29 & \(AGB=3.7461\times LAI^{0.6965}\) & 0.57 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The statistical models for forest field-based forest AGB and LAI for different forest types.  
Figure 3: Landcover map for 2012.
## 3 Experimental Validation
### Results Comparison and Analysis  
According to all the temperature measurement results obtained from experiment, the point \(\mathrm{P_{m}}\) on \(\mathrm{M_{3}}\) has been identified as the maximum temperature point on the EMU surface. The 100 K temperature rise of the EMU takes 57 min. In the numerical simulation, the temperature distribution of the magnet surface is also monitored. The maximum temperature region appears near each EMU's four upper-corner surfaces, which is consistent with the experimental results.  
The temperature rises of \(\mathrm{P_{m}}\) from both the numerical method and the experimental results are shown in Table 2. The results given by the numerical simulations agree well with the experimental data. At the initial heating process, the temperature from the numerical results rises slightly faster than the experimental results. This is possibly caused by the following reasons: (1) A simplified model of the electromagnet coil system is used in the numerical simulation, while the actual heat source distribution of the coil may not be completely homogeneous and is likely to have a greater temperature near the core area. This actual configuration may result in a lower temperature on the magnet surface. (2) The numerical simulation assumes zero contact thermal resistance so that the heat from the heating coil is conducted to the EMU surface in a faster rate than the experiment. The difference between the experimental data and the numerical results from medium meshes is 2.1%. Considering the accuracy and precision of the numerical calculation and the measurement error in experiments, it can be concluded that the numerical results agree well with the experimental results.  
As for the mesh sensitivity study, the medium and fine meshes reflect different levels of refinement on the coarse mesh. The grid numbers of each set of meshes is shown in Table 2. By comparing the temperature rise curve of three sets of mesh to the experimental results, it is found that all mesh levels can match well with the experimental data. The difference between each level of mesh is trivial. Considering that the grid number of the coarse mesh is not sufficient to capture the flow and temperature distribution in the fluid domain, the medium mesh is selected as the discretization scheme of the fluid domain. From the experimental validation and the grid independence test, it can be concluded that the domain discretization parameter and numerical methodology used in this study are sufficient to yield accurate results.
# A Compact Multi-Identity Fully Homomorphic Encryption Scheme Without Fresh Ciphertexts
## 2 Preliminaries
### Lattice  
A lattice in the Euclidean space is a set of points arranged in a regular pattern, and the coordinates of these points can be represented by a set of integer-coefficient vectors in the space, referred to as the basis vectors of the lattice. The specific definition is as follows:  
Definition 6 (Lattice): Let \(v_{1},v_{2},\ldots,v_{n}\) be \(n\) linearly independent vectors in \(\mathbb{R}^{m}\), let \(\mathbf{V}=\{v_{1},v_{2},\ldots,v_{n}\}\in\mathbb{R}^{m\times n}\). The lattice \(\Lambda(\mathbf{V})\) generated by the vectors in \(\mathbf{V}\) is defined as follows:  
\[\Lambda(\mathbf{V})=\left\{\sum_{i}^{n}\mathbf{x}_{i}\mathbf{v}_{i}\in\mathbb{Z}\right\}\]  
where \(\{v_{1},v_{2},\ldots,v_{n}\}\) are a set of basis vectors for the lattice \(\Lambda(\mathbf{V})\), and a lattice can have multiple sets of bases. \(m\) represents the dimension of the lattice, and \(n\) is the rank of the lattice, with \(m\geqslant n\). When \(m\geqslant n\), the lattice \(\Lambda(\mathbf{V})\) is referred to as a full-rank lattice.  
Definition 7 (\(q\)-ary Lattice): Given \(\mathbf{V}\in\mathbb{Z}_{q}^{m\times n}\), a prime \(q\), and \(m,n\in\mathbb{Z}\). A \(q\)-ary lattice is defined as follows: For some \(\mathbf{x}\in\mathbb{Z}_{q}^{n}\)  
\[\Lambda_{q}(\mathbf{V})=\left\{\mathbf{t}\in\mathbb{Z}_{q}^{n}:\mathbf{t}=\mathbf{V}^{ \mathbf{s}}\cdot\mathbf{x}\text{ mod }q\right\}\]  
**Definition 8** (Integral Lattice).: \[\Lambda_{q}^{\perp}(\mathbf{V})=\left\{\mathbf{t}\in\mathbb{Z}_{q}^{n}:\mathbf{t}=\mathbf{ V}\cdot\mathbf{t}=0\;\text{mod}\;q\right\}\]  
_If all the elements of the vectors in the lattice \(\Lambda\) are integers, then \(\Lambda\) is called an integral lattice. Let \(q\) be a prime number and \(\mathbf{V}\in\mathbb{Z}_{q}^{m\times n}\). The \(q\)-ary integral lattice is defined as follows: For some \(\mathbf{x}\in\mathbb{Z}_{q}^{n}\)_  
\[\Lambda_{q}(\mathbf{V})=\left\{\mathbf{t}\in\mathbb{Z}_{q}^{n}:\mathbf{t}=\mathbf{V} \cdot\mathbf{x}\;\text{mod}\;q\right\}\]  
\[\Lambda_{q}^{\perp}(\mathbf{V})=\left\{\mathbf{t}\in\mathbb{Z}_{q}^{n}:\mathbf{t}= \mathbf{V}\cdot\mathbf{t}=0\;\text{mod}\;q\right\}\]
## 2 Results
### LPS-Neutralizing Activities  
The capacity of Pro-peptides to neutralize LPS was evaluated using limbulus amoebcyte lysate (LAL) assays. As shown in Figure 5B, all Pro-peptides significantly interacted with LPS in a concentration-dependent manner. At 50 uM, Pro9-3, Pro10-1, and Pro10-1D peptides notably inhibited the activation of LPS by 93.6%, 58.6%, and 58.45%, respectively, when compared to the positive control polymyxin B (67.4%), with Pro9-3 showing much greater affinity for LPS.  
Visualization of the Effects of Pro-Peptides on Morphology of E. coli by Scanning Electron Microscopy  
We next investigated the interaction between Pro-peptides and bacteria by field emission scanning electron microscopy (FE-SEM). Membrane damage induced by peptide treatment was clearly visualized, as shown in Figure 5C. _E. coli_ cells treated with a 1\(\times\) MIC of the peptides for 4 h were wrinkled, whereas the control cells without peptide treatment had a normal oval shape. _E. coli_ cells treated with 2 \(\times\) MIC of all peptides were even more crumpled and severely contracted. These results proved that the Pro-peptides effectively permeabilized the membrane to cause severe damage to _E. coli_.  
Figure 5: Antibacterial mechanism of peptides: (**A**) Dose–response curves of calcein leakage from egg yolk L-\(\alpha\)-phosphatidylethanolamine (EYPE)/egg yolk L-\(\alpha\)-phosphatidylglycerol (EYPG) (7:3, w/w) large unilamellar vesicles (LUVs) (induced by the peptides) were constructed. The concentration-dependent dye release indicates the membrane-permeabilizing ability of the peptides. Melittin was used as a control. (**B**) Lipopolysaccharideacharide (LPS)-neutralizing capacity of the peptides and polymyxin B (positive control): the percentage of inhibition signifies the LPS-neutralizing abilities of peptides. (**C**) Scanning electron micrographs of _E. coli_ (KCTC 1682) treated with peptides at 1\(\times\) and 2 \(\times\) of the minimum inhibitory concentrations (MICs) for 4 h: ultrastructural changes to the outer membrane of _E. coli_ indicate the membrane damage caused by the peptides. Scale bar, 100 um. Data are expressed as the mean \(\pm\) SEM of three independent experiments and are statistically significant at \({}^{*}p<\) 0.05, \({}^{**}p<\) 0.01, and \({}^{***}p<\) 0.001; ns, not significant (by _one-way ANOVA_, _[PERSON]’s post hoc test_).
# Meander Structure Analysis Techniques Using Artificial Neural Networks
## 3 Results
### Results Obtained with Time Delay Neural Network  
The prediction results for \(t_{\rm d}\), \(S_{11}\), and \(S_{21}\) were carried out with all 74 structures' test datasets. The average MAE with SD values for the predicted \(t_{\rm d}\), \(S_{11}\), and \(S_{21}\) characteristics when the size of the delay buffer \(N_{\rm d}\) = 8 are presented in Table 2. For clarity of the results, to avoid a huge overlap in the curves, only the prediction results for MS2 with different numbers of delay buffers are provided graphically (Figure 7). The chosen size of the delay buffer \(N_{\rm d}\) was 1, 2, 4, or 8.  
The prediction results for \(t_{\rm d}\) for MS2 which were obtained with the (6-9-16-1) structure for the TDNN network are presented in Figure 7a. The MAE varied according to the chosen size \(N_{\rm d}\) of the delay buffer. The TDNN worked as the FFNN network did with the size \(N_{\rm d}\) = 1, and the MAE was equal to \(2.64\pm 2.35\) ps. The delay buffer \(N_{\rm d}\) = 2 allowed reducing the MAE to \(1.91\pm 1.73\) ps. Here, \(N_{\rm d}\) = 4 until \(1.4\pm 1.25\) ps for the MAE, and \(N_{\rm d}\) = 8 until \(0.9\pm 0.64\) ps for the MAE. For example, the maximum peak error value of \(t_{\rm d}\) changed from 12.4 ps to 3.8 ps when the delay buffer size \(N_{\rm d}\) increased from 1 to 8, respectively.  
The same effect was observed when predicting the \(S_{21}\) characteristic for MS2 (Figure 7b). The MAE of \(S_{21}\) with a delay buffer \(N_{\rm d}\) = 1 was equal to \(0.59\pm 0.68\) dB. The larger delay buffer allowed reducing the MAE for \(N_{\rm d}\) = 2-to \(0.22\pm 0.31\) dB, \(N_{\rm d}\) = 4-to \(0.16\pm 0.19\) dB, and \(N_{\rm d}\) = 8-to \(0.11\pm 0.12\) dB. The maximum peak error value of \(S_{21}\) decreased from 4.8 dB to 1 dB, with \(N_{\rm d}\) increasing from 1 to 8.  
The effect of the size of the delay buffer \(N_{\rm d}\) was more noticeable when predicting \(S_{11}\) for MS2 (Figure 7c). The MAE varied as follows: \(N_{\rm d}\) = 1-\(2.47\pm 2.13\) dB; \(N_{\rm d}\) = 2-\(2.24\pm 2.09\) dB; \(N_{\rm d}\) = 4-\(2.10\pm 2.01\) dB; and \(N_{\rm d}\) = 8-\(1.63\pm 1.71\) dB. The maximum peak error value of \(S_{11}\) decreased from 10.97 dB to 9.07 dB, with \(N_{\rm d}\) increasing from 1 to 8.  
The summarized prediction results of the TDNN for all 74 unique structures are presented in Table 2 for when the size of the delay buffer was equal to \(N_{\text{d}}=8\).
# Six Sigma-Based Frequency Response Analysis for Power Transformer Winding Deformation
## 2 Materials and Methods  
FRA prognoses faults based on comparability and can identify faults which could not be identified by other methods on account of its heightened sensitivity. In the FRA technique, a voltage signal is introduced into the primary terminal of the transformer windings which is practical for a sweeping frequency. In FRA evaluation, typically, a swept sine wave is applied with respect to the ground, i.e., the unit's steel tank. The impedance attached to the secondary terminal of the transformer is utilized to measure the response signature. The measured voltage response reflects the transformer's impedance variations and structural integrity across different frequencies which could also be a current response at the secondary winding terminal or any other grounded terminal. The proposed work utilizes sweep FRA. The FRA signature response is expressed as follows in Equation (1) [41].  
\[\text{Frequency response}(\text{dB})=20\log_{10}\frac{V(f)_{out}}{V(f)_{in}} \tag{1}\]  
where  
\(Frequency\)\(response\)--FRA signature response  
\(V(f)_{out}\)--FRA response voltage  
\(V(f)_{in}\)--FRA exciting voltage FRA, which is widely adopted as an unfailing diagnostic technique for internal mechanical deformities of the transformer, is not able to interpret the measured signature response continuously as it depends on human expertise. Furthermore, the precision of the traditional FRA signature in tracking down incipient mechanical faults is very little. To redress these concerns, a novel technique, i.e., FRA6\(\sigma\) is proposed for the interpretation of FRA results.  
The proposed FRA6\(\sigma\) methodology follows the define-measure-analyze-improve-control (DMAIC) framework for structured diagnostic enhancement. The define phase is addressed in Section 2.1, where the problem of winding deformity is contextualized and framed. The measure phase is conducted in Section 2.1.1 through baseline FRA data collection. The analyze phase is detailed in Sections 2.1.2 and 2.1.3, where statistical control limits and visualize deviations are computed using control charts. The improve phase is implemented in Section 2.1.4 through frequency-region-based fault classification. The control phase is applied in Section 2.1.5, using Cp and Cpk indices to evaluate process stability and transformer condition. Finally, the verify phase is demonstrated in Section 3 (Results) through three case studies validating the FRA6\(\sigma\) framework's performance in real diagnostic scenarios.