esa-sceva/satcom-chunk-collection huggingface dataset:
# Vibroacoustic Optimization Study for the Volute Casing of a Centrifugal Fan
## 5 Vibroacoustic Optimization Strategy
### Multi-Objective Optimization Procedure  
In fact, the parameters of the vibrational sound power and total mass on the volute surface are somewhat contradictory. Thus, a multi-objective optimization method is needed to obtain the optimal volute thickness combination. The definition of the multi-objective optimization mathematical model is presented as follows:  
The objective function, \(W_{s}\), \(M_{T}\) (minimize):  
\[W_{s}=\sum_{j=1}^{N_{c}}W_{j}=\frac{1}{2}Re(\sum_{j=1}^{N_{c}}\frac{1}{2}\int _{S_{j}}p_{j}v_{n}^{*}dS),\text{ vibrational sound radiated power of volute surface, }W: \tag{32}\]  
\[M_{T}=S_{FT}\cdot FT+S_{ST}\cdot ST+S_{BT}\cdot BT,\text{ (total mass, kg)} \tag{33}\]  
Variables:  
[FT, ST, BT] (34)  
Constrained conditions:  
\[\begin{array}{l}4\leq\text{FT}\leq 10\\ 4\leq\text{ST}\leq 10\\ 4\leq\text{BT}\leq 10\end{array} \tag{35}\]  
Figure 23 presents a flow chart of multi-objective optimization. The multi-objective genetic algorithm termed NSGA_2 was adopted to solve the multi-objective optimization with the optimal value obtained by taking the single-objective optimization of specific mass constraints as the initial values. The global search iterates 4800 steps, and the Pareto frontier solutions are marked. A solution satisfying the requirement is selected as the initial value of MISQP for mixed integer quadratic programming; then, the final solution satisfying the engineering requirement is obtained by iterating 15 steps again. Finally, the vibroacoustic estimation method mentioned in Section 4.3 is used to verify the precision of the optimal solution. The multi-objective optimization results will be discussed in the following sections.
## 4 Materials and Methods
### Pre-Processing of Multispectral Imagery  
To conduct a comprehensive remote sensing analysis of the near-surface structures at Hermopolis, WorldView-3 (WV-3) imagery was utilized. Originally launched in August 2014, WV-3 is an American Earth observation satellite that is part of the DigitalGlobe, Inc satellite constellation. The images captured by this satellite consist of one panchromatic band (450-800 nm) and eight multispectral channels: red, red edge, coastal, blue, green, yellow, near-IR1, and near-IR2 (400-1040 nm). The WV-3 data used for the archaeological site was acquired by the satellite on 20 June 2017 and was 100% cloud-free. The image was obtained as a Level-2A product and was, therefore, geometrically corrected (orthorectified) using the rational polynomial coefficient (RPC) model. The availability of the panchromatic channel, in conjunction with the multispectral bands, provides the ability to create VHR multispectral pan-sharpened imagery. The orthorectified scene was pan-sharpened by means of the widely used Gram-Schmidt Pan Sharpening algorithm using the nearest neighbor resampling method [39]. This sharpening algorithm relies primarily on the higher spatial resolution panchromatic data to sharpen the multispectral data's pixel resolution. Through this process, the WorldView-3's spatial resolution improved by approximately four-fold, from 1.24 m to 31 cm. The resulting pan-sharpened image held critical information that improved the overall performance and accuracy of subsequent image analysis algorithms [6; 40].  
Before starting to manipulate the imagery, the Forward Minimum Noise Fraction (MNF) transform, a widely used pre-processing step, was first applied to the multispectral image in order to denoise the data. The isolation of noise was performed using the filtration of the eigenvalues and associated images. The resulting product included a cascading principal component analysis (PCA) for the eight WV-3 spectral bands, thus allowing for the identification of the \"noisiest\" bands (possessing the least amount of useable data) [41]. In addition to denoising, the MNF process can also help in lessening the computational requirements necessary for later image processing algorithms [42]. Through this MNF process, it was found that PCA bands 1 through 4 (the coastal, blue, green, and yellow bands, respectively) contained the least noise and the most significant information for the above-mentioned WV-3 image. These four MNF components were retained as the input variables for use in the subsequent classification.
## 5 Discussion
### Hydrochemical Characteristics and Evolution of Spring Water
#### 5.1.2 Isotopic Tracing of Recharge and Flow Depth  
The \(\delta^{18}\)O-\(\delta\)D relationship shows that local meteoric water plots below the Global Meteoric Water Line (GMWL) likely reflect evaporative enrichment due to the region's arid climate and continental interior location. Atmospheric moisture is primarily derived from westerly circulation with significant contributions from local evaporative recycling [58]. Most groundwater samples plot to the left of both the global and local meteoric lines, suggesting recharge primarily from meteoric precipitation with potential inputs from glacial meltwater or fracture-zone derived deep groundwater [57]. The \(\delta\)-values of hot springs S13 and S14 fall closer to the local meteoric line, indicating deeper circulation and isotopic homogenization due to prolonged subsurface residence and geothermal heating. In contrast, the shallower cold springs exhibit more depleted isotope values consistent with rapid recharge and minimal interaction. Isotope values increase with decreasing elevation (Figure 3), likely due to altitude-driven isotope fractionation. Oxygen drift is accounted for, and groundwater recharge elevation (H) is determined using \(\delta\)D-elevation relationships.  
\[\text{H}=(\delta G+\delta\text{P})/\text{k}+\text{h}\]  
where \(\delta\)G represents the \(\delta\)D value of the sampling point, \(\delta\)P is the \(\delta\)D value of local precipitation, k is the atmospheric \(\delta\)D gradient (\(-0.25\%\)/100 m), and h is the elevation of springs (m). The isotope-weighted mean \(\delta\)D of Hetian precipitation (\(-41\%\)) was used [57]. Calculated recharge elevations (1435-4033 m, avg. 2514 m) align with expected glacial meltwater origins, confirming elevation effects on isotope composition.
# Region Pixel Voting Network (RPVNet) for 6D Pose Estimation from Monocular Image
## 2 Materials and Methods
### Model of the Problem
#### 2.1.1 Pose of an Object  
The pose of an object refers to its spatial position and attitude angle. A fixed coordinate system, commonly called the world coordinate system (WCS), is required to describe pose. If a three-dimensional Cartesian coordinate system called object coordinate system (OCS) is artificially fixed on the object, then the object's pose is defined as follows:  
**Definition 1**.: _If WCS coincides with OCS after applying a virtual rotation \(\mathbf{R}\) and translation \(\mathbf{t}\), then \(\left[\mathbf{R}_{w}^{o}|\mathbf{t}_{w}^{o}\right]\) is called the pose of the object's in WCS._  
The superscript of \(\mathbf{R}\) and \(\mathbf{t}\) represents the target coordinate system and the subscript represent the source coordinate system.  
However, in practice, the pose of an object is not calculated according to Definition 1 because it is not possible to actually fix a coordinate system on the object. Instead, a Computer Aided Design (CAD) or point cloud [32] model of the same size as the object is virtually fixed at the origin of another virtual coordinate called \(Model\). Then, the object's pose in \(Model\) is defined as:  
**Definition 2**.: _For any point \(P\) on the object, its coordinates in \(Model\) is \(P_{o}^{model}\), if \(P_{m}\) is its corresponding point on the model in \(Model\), and satisfy \(P_{o}^{model}=\mathbf{R}_{\mathbf{m}}^{o}\cdot P_{m}+\mathbf{T}_{\mathbf{m}}^{o}\), then \(\left[\begin{array}{c|c}\mathbf{R}_{\mathbf{m}}^{o}&\mathbf{T}_{\mathbf{m}}^{o}\end{array}\right]\) is the object's pose in \(Model\)._  
Note that \(P_{o}\) represents the coordinates of P in OCS, and \(P_{o}^{model}\) represents the coordinates of P in \(Model\). Since the model is exactly equal to the object, \(P_{o}\) is equal to \(P_{m}\).
# Adaptive Algorithm for Fast 3D Characterization of Magnetic Sensors
## 3 Results
### 3D Estimation and Adaptation Times  
Lastly, the algorithm was tested for the intended full 3D directivity estimation. We chose a slightly lower resolution of \(3^{\circ}\) in azimuth and elevation as well as a higher tolerance with regards to the target system distance \(\zeta_{\text{suff}}\). We also changed the axis of rotational symmetry to the x-axis, so that the direction of the excitation field looped around the sensor's sensitive axis.  
Due to the added dimension, a visualization of only a single frequency bin around 598 Hz is shown in Figure 12. This can be seen as an expansion of the polar plot for the 2D experiment, where the radial distance of a scatter point to the origin corresponds to the estimated magnitude of the sensor at the shown frequency with a field excitation from the radial direction from the origin. In addition, the color in this figure maps the time taken for adaptation to the individual points of estimation. It is visible that only the estimations around the insensitive area show up red with higher adaptation times.  
The overall 3D estimation run took around 2435.1 s or 40.59 min. With \(N_{\varphi}N_{\theta}=7200\) points, this results in an average of under 0.34 s per direction. This speedup is related to the higher tolerance of the target threshold, but could also be caused partly by the higher overall points with generally faster adaptation. Not only the average adaptation time is of interest but also the times for an individual direction. A visualization of these adaptation times is shown in a position-index-based and histogram form in Figure 13.  
Here, we can once more see the increase in adaptation times around the sensor's insensitive direction because of the low amount of information available to our algorithm due to the bad SNR of the sensor response. When the sensor signal power becomes low enough, the fallback condition (32) is triggered, which leads to minimal adaptation times for these directions (middle). The histogram gathers the individual adaptation times into bins of small ranges, showing a strong dominance of sub-second times. Still, from the spread  
Figure 12: Scatter visualization of the estimation results of a 3D characterization, shown for an exemplary frequency bin in the sensitive range of the sensor at 598 Hz. Each individual point represents a single estimation position, with the radius to the center showing the strength of the magnitude. The color mapping encodes the time taken for adaptation at each field direction before switching to the next direction of interest.  
over the directions, a trend is visible for a higher frequency of slightly longer adaptation times towards the start and the end of the experiment. These correspond to the sensitive areas of the sensor under investigation and were expected to have a higher SNR and thus a better adaptation behavior.
# Fine-Tuning Large Language Models for Kazakh Text Simplification
## 5 Human Evaluation  
To complement automatic metrics, we conducted a human evaluation study to assess the fluency, meaning preservation, and simplicity of simplified Kazakh texts. We selected two sets of 30 sentence pairs: one generated by our fine-tuned KazSim model (LLaMA-3.3-70B) and another from the test set outputs of GPT-4o-mini. Each sentence pair includes the original complex sentence and its simplified counterpart.  
Figure 5: Comparison of original complex sentences and simplified outputs generated by KazSim Using English and Kazakh prompts.  
Three native Kazakh speakers participated in the evaluation. Among them, two are trained computational linguists familiar with natural language processing techniques, while the third is a native Kazakh speaker without formal training in linguistics, which was included to represent the perspective of general users. Each evaluator rated the sentence pairs independently on a 5-point Likert scale across three dimensions:  
* Fluency: How natural and grammatically correct the simplified sentence sounds.
* Meaning preservation: How accurately the simplified sentence retains the meaning of the original.
* Simplicity: How much simpler and easier to understand the output is compared to the original.  
Table 10 shows the average scores given to the KazSim outputs. The model achieved high marks in all three criteria, particularly in simplicity, with an average score of 4.42, followed by meaning preservation (4.09) and fluency (4.08). These results suggest that KazSim is capable of generating simplified sentences that are not only easier to understand but also preserve the original meaning and are grammatically correct.  
For comparison, Table 11 presents the evaluation of GPT-4o-Mini outputs. While the outputs were generally fluent and preserved meaning to a reasonable degree, they scored lower across all criteria, especially in simplicity. Interestingly, although KazSim was trained on data generated by GPT-4o-mini, its outputs were consistently rated higher across all dimensions. This suggests that fine-tuning with aligned sentence pairs and supervised learning significantly improves quality beyond what the original generator can achieve. This demonstrates the value of model alignment and supervised fine-tuning, even when training data comes from the same underlying source. It also indicates that LLaMA-3.3-70B, once fine-tuned, adapts well to Kazakh stylistic and syntactic nuances.  
Figure 6 lists the seven Kazakh sentence pairs with the highest average quality scores, showing each original and simplified version (with English translations).  
\begin{table}
\begin{tabular}{l c c c} \hline
**Evaluator** & **Fluency** & **Meaning Preservation** & **Simplicity** \\ \hline Evaluator 1 & 3.50 & 3.67 & 3.93 \\ Evaluator 2 & 4.43 & 4.27 & 4.77 \\ Evaluator 3 & 4.30 & 4.33 & 4.57 \\ \hline Average & 4.08 & 4.09 & 4.42 \\ \hline \end{tabular}
\end{table}
Table 10: Per-evaluator average ratings for KazSim’s simplified texts across fluency, meaning preservation, and simplicity.
## 2 Methods
### Branchformer Block
#### 2.2.2 Local Extractor Branch  
The local extractor branch is implemented through the Convolutional Gated Multi-Layer Perceptron (cgMLP) module, which is capable of effectively learning powerful  
Figure 1: Architecture of the proposed B-transformer model and its components. Its core component, the Branchformer-block, consists of two parallel branches, where the attention-based branch captures the global context and the convolution-based branch captures the local context. Finally, the merge module combines information from both branches.  
representations from sequences. The input features are first normalized by a LayerNorm, followed by a projection of these features into a higher dimensional space using a linear layer, while the \(GELU\) activation function [36] is utilized to enhance the nonlinear representation. Subsequently, a Convolutional Spatial Gating Unit (CSGU) is employed to finely capture local features. This mechanism splits the high-dimensional features in two, applies LayerNorm and depthwise convolution to one half, and then performs element-wise multiplication with the other half. Finally, the output of the CSGU is adjusted back to the original feature space dimension through a linear layer and treated with dropout to enhance the model's generalization ability.  
\[P=GELU(LayerNorm(X)\cdot W) \tag{2}\]  
\[[C,D]=Split(P) \tag{3}\]  
\[Q=C\odot DwConv(LN(D)) \tag{4}\]  
\[Y_{local}=Dropout(Q\cdot H) \tag{5}\]  
With \(P\in\mathbb{R}^{T*d}\) and \(Q\in\mathbb{R}^{T*d/2}\) representing the output features from \(GELU\) and CSGU, respectively. The split features of M are denoted by \(C,D\in\mathbb{R}^{T*d/2}\). The operation \(\odot\) signifies an element-wise multiplication, and the output from the local feature extraction branch is denoted by \(Y_{local}\in\mathbb{R}^{T*d}\).
## 3 Results
### Study Area 1--Colli Alti e Bassi Fault (CAB)
#### 3.1.2 Structural and Kinematics Analysis of the CAB  
During the fieldwork, several attitudes of the CAB were measured as a dip and dip azimuth, using either a traditional compass-clinometer and a Fieldmove-equipped Apple iPad. After data collection, the whole dataset was managed in a georeferenced digital database.  
Subsequently, we extracted attitude and kinematic data from the virtual outcrop reconstructed through remotely acquired images (see Section 2.3.4)  
A comparison between the three different attitude datasets is shown in Figure 9.  
The hand-compass collected dataset shows that on six measurements on the fault plane, the trend of dip-azimuth is variable between N220 and N260, with a mean resultant plane orientation of dip-azimuth at N237 and dip-angle of 68 degrees (Figure 9a). In the same points, the measurements with the Fieldmove app show a dip-azimuth trend on a fault plane, comprised between N220 and N260, with a mean resultant plane orientation of dip-azimuth at N235 and dip-angle of 70 degrees (Figure 9b). However, the results obtained from virtual outcrop, clearly with a huge number of data (77 points measurements), shows a main dip-azimuth trend between N220 and N240 and only a few data comprised between N240 and N260; also in this last case, mean dip-azimuth of the fault plane orientation shows a trend of N233 and dip-angle of 70 degrees (Figure 9c).  
In order to compare the measures of the striation trend collected through three different used tools (Figure 9d-f and Table 1), a number of six data were also acquired using hand-compass and Fieldmove integrated compass. In both cases, the dispersion of data varies in the range between N180 and N240, except for the huge data extracted from the virtual outcrop that shows an attitude between N180 and  
Figure 9: Comparison between the fault attitude and striations trends measured through high-resolution sampling on the CAB using traditional compass-clinometer (**a,d**), Fieldmove App on a digital device (**b,e**), and virtual outcrop reconstructed from drone-acquired imagery (**c,f**). The upper plots (**a–c**) shows the fault dip-azimuth; the lower plots (**d–f**) shows the trend of associated striae; for each rose diagram, the mean value resulting from the statistical analysis of the dataset is reported in the bottom-right rectangle Rose diagrams were extracted through Move 2019 software and redrawn through Corel Draw X4 software.  
N220 and the mean direction dipping toward N207, well comparable with data acquired with the previously described tools.  
As regards the determination of fault attitude data, the good degree of mutual correlability between the differently acquired data is evident and encouraging. In fact, they mostly agree in providing an average fault attitude of- a 235/70 (dip-notation value, compare rose diagrams Figure 9a-c,d-f and Table 1). From such a point of view, the remote sensing survey is advantageous, considering the large number of data collected over a very short time in comparison with both the traditional and digital field mapping techniques.
## 7 Current Challenges and Future Research Directions
### High Cost of Implementation  
Machine learning-based LDDoS detection methods used multiple traffic engineering parameters for the current state of the network. These methods are implemented at the control level of the SDN architecture. A central controller responsible for policy-making can serve a limited number of new flow requests to manage OpenFlow switches. As a result, the controller must put forth a lot of effort to handle network traffic efficiently. However, this extra overhead by the detection methods for computation for multiple parameters can harm the controller's performance. Therefore, researchers must apply an appropriate approach to reduce the number of parameters used in LDDoS detection methods.
## 4 Discussion
### Effect of Methomyl on Sex Steroid Hormone Synthesis Substrates
The liver is the main site of CHO synthesis in the body, as well as an important organ to maintain CHO demand and the stability of blood CHO content [24]. Therefore, the significant decrease in serum CHO content in this study indicates that its synthesis in the liver is blocked to some extent. Meanwhile, CHO, which is used as a steroid hormone synthesis substrate in teleost fish, is mainly exogenous CHO absorbed from plasma [25]. Under the action of lipoproteins, CHO is transported from the synthesis or absorption site to the functional site through blood circulation. The sex hormone synthesis pathway is initiated only when CHO is transported into the inner membrane of mitochondria [18]. Transmembrane transport of CHO from the mitochondrial outer membrane to the intima is a rate-limiting step in sex hormone synthesis [26]. The CHO transport process in fish is completed by high-density lipoprotein (HDL) [27], and StAR is an important CHO transporter involved in CHO metabolism. Therefore, damage to the StAR protein could lead to obstruction of the CHO transport pathway [18]. The simultaneous study of our experiment showed that methyl had no significant (\(p>0.05\)) effect on the expression of StAR mRNA in the testes at low concentrations (0.2 and 2 \(\mu\)g L\({}^{-1}\)) but significantly (\(p<0.05\)) inhibited its expression at high concentrations (20 and 200 \(\mu\)g L\({}^{-1}\)) [5]. Consequently, the decreased CHO content in the testes induced by methyl at high concentrations (20 and 200 \(\mu\)g L\({}^{-1}\)) was due to both the obstruction of CHO synthesis and transport pathways. CHO, PREG, and PROG are the primary precursors of sex steroid hormones. The synthesis of steroid hormones and their primary precursors is an important and critical factor in the physiological development of organisms, including growth, development, and reproduction [28]. Therefore, the decrease in CHO, PREG and PROG levels affects the synthesis of sex steroid hormones and ultimately affects the reproductive development of fish [18].
## 3 Theoretical Development
### Streaming Potential Coupling Coefficient
#### 3.4.3 Streaming Potential Coupling Coefficient  
At steady state, the total electrical current through the REV is zero and one obtains  
\[I_{s}+I_{c}=0. \tag{35}\]  
Combining Equations (31), (34) and (35) yields  
\[\Delta V=\frac{\epsilon_{r}\epsilon_{0}\zeta\Delta P}{\eta\left[\sigma_{w}+ \frac{(1+2\beta)\Sigma_{s}}{\beta w_{max}}\frac{2-D_{f}}{\Gamma-D_{f}}\frac{1 -\alpha^{1-D_{f}}}{1-\alpha^{2-D_{f}}}\right]}. \tag{36}\]  
Consequently, the SPCC is obtained as  
\[C_{S}=\frac{\Delta V}{\Delta P}=\frac{\epsilon_{r}\epsilon_{0}\zeta}{\eta \left[\sigma_{w}+\frac{(1+2\beta)\Sigma_{s}}{\beta w_{max}}\frac{2-D_{f}}{ 1-D_{f}}\frac{1-\alpha^{1-D_{f}}}{1-\alpha^{2-D_{f}}}\right]}. \tag{37}\]  
Equation (37) indicates that the SPCC for fractured media under fully saturated conditions is related to the zeta potential, fluid properties, specific surface conductance at the solid water interface and microstructural properties of the media (\(D_{f}\), \(\alpha\), \(w_{max}\) and \(\beta\)). When \(\Sigma_{s}=0\), Equation (37) reduces to the HS equation as shown by Equation (2). It is indicated that for negligible surface conductivity, the SPCC is independent of fracture size distribution and geometrically shaped pore structures.  
Comparing Equations (3) and (37), the characteristic length scale \(\Lambda\) (m) is obtained for fractured media as  
\[\Lambda=w_{max}\frac{2\beta(1-D_{f})(1-\alpha^{2-D_{f}})}{(1+2\beta)(2-D_{f}) (1-\alpha^{1-D_{f}})}. \tag{38}\]It should be noted that [PERSON] et al. (2020) [56] and [PERSON] et al. (2020) [96] also obtained expressions for the characteristic length scale in porous media.