esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results  
We carried out a series of experiments approximately corresponding to the system theoretically considered above: a thin beam resting on a thin elastic layer. However, the equivalence of a thin layer to an elastic foundation could not be guaranteed due to the properties of the elastomer used; thus, the aim of experiment was to _qualitatively_ illustrate the theoretical findings.  
We used a rectangular steel plate with linear dimensions of 85 mm \(\times\) 20 mm and a thickness of 0.3 mm which was placed on a square sheet of TANAC CRG N3005 (TANAC Co., Ltd., Gifu, Japan) elastomer (TANAC Co., Ltd., Gifu, Japan) with a thickness of \(h\) = 5 mm and horizontal dimensions of 100 mm \(\times\) 100 mm. Earlier, in [24], when analyzing a large amount of experimental data, the elastic parameters of the CRG N3005 (TANAC Co., Ltd., Gifu, Japan) material were experimentally determined (elastic modulus \(E\) = 0.324 MPa and Poisson's ratio \(\
u\) = 0.48).  
The steel plate was pressed, using a cylindrical indenter (length \(L\) = 20 mm and diameter \(D\) = 10 mm), into the elastomer to a maximum depth of \(d\) = 0.4 mm. A schema of the experimental setup and a photograph of the contact region are shown in Figure 5.  
Figure 4: Non-dimensional dependence of the vertical displacement of the beam as a function of the coordinate (20) for adhesive contact (see Figure 3). The shape on an infinite beam adhering to an elastic foundation under action of a concentrated force at the point \(x=0\).  
Figure 5: (**Left panel**) Schema of the experiment. (**Right panel**) A photograph of the contact region, viewed from the side.  
After achieving the maximum indentation depth of \(d=0.4\) mm, the indenter reversed the direction of motion and moved to the initial level \(d=0\) mm, i.e., the contact was completely unloaded. In the loading and unloading phases, the indenter speed was \(v=5\) um/s. In [25], it was shown that at such speed, the adhesive contact can be considered as quasi-static, meaning that there are no viscoelastic effects.  
After the first series of experiments, an additional experiment was conducted in which a softer material, CRG N0505 (TANAC Co., Ltd., Gifu, Japan), was used. For the softer elastomer, indentation was carried out to a larger depth, \(d=1.3\) mm, to achieve the same normal force as in the case of using the CRG N3005 material (TANAC Co., Ltd., Gifu, Japan). Both materials are polystyrene-type gels with good adhesive properties. The indenter was shifted in the normal direction using a high-precision linear stage PL 511.24 AD00 (Physik Instrumente (PI) GmbH & Co. KG, Karlsruhe, Germany); normal force was measured using an ME K3D40 three-axis force sensor (ME-Messysteme GmbH, Hennigsdorf, Germany). The contact area was observed from the side using a Ximea MQ022 CG-CM digital color camera (XIMEA GmbH, Munster, Germany) with a FUJINON HF16 SA-1, 2/3\" lens (Fujifilm, Tokyo, Japan). A more detailed description of the experimental setup can be found in our previous work [26].
## 2 Materials and Methods
### Field Operation and Data Collection
The flying height (relative to take-off elevation) was maintained at approximately 58 m and 106 m for the Sillon de Talbert and La Palue field sites, traducing to a GSD of 0.014 m and 0.026 m, respectively. To satisfy local drone regulations active at the sites, five flights, from five different take-off and landing spots, were necessary at La Palue, whereas two flights were enough to cover the breach at Sillon de Talbert. Flight pattern was designed to include an overlap between each image block (cf. Figure 2) while ensuring that flight duration remained below the 20-min mark. Before performing the flights, camera settings were tentatively adjusted to the environmental conditions. Additional topographic measurements were carried out at La Palue using RTK-GNSS (Figure 2a) to serve as vertical ground truths for the evaluation of photogrammetry. To make for an efficient survey covering as much of the beach length as possible, a GNSS rover was securely mounted onto a bike, using a pannier rack above the rear wheel. The rover was mounted with the antenna pointing downwards (vertically) and aligned with the rear wheel axis. The approximate height of the antenna center above the ground was measured, using a tape ruler, to be \(\sim\)0.60 m. Determination of the exact height was completed using a geodetic mark, with the bike positioned atop and loaded to replicate survey conditions. The height of the antenna center was used to reference the data into the same coordinate system as other surveys presented above. Point sampling along the beach using the \"bike GNSS\" was carried out in dynamic mode at 1 Hz. The survey was limited to the intertidal zone close to the low-tide water line with packed sand and where photogrammetric quality was expected to be lower due to the difficulty of deploying GCPs and more generally because of the presence of water, the latter making for more challenging tie point detection. Surveying was done by riding the bike alongshore (cf. Figure 2a) to limit as much as possible introducing a pitch angle between the GNSS antenna and the vertical. Roll minimization was made possible by a bubble level mounted onto the bike handlebar. Strategies to account for pitch and roll-related errors on point coordinates were implemented and will be presented in Section 2.5.
## 4 Experimental Validation
### Discussion  
This paper addresses the issue of polishing the complex curved surfaces of marble by proposing a robotic polishing solution based on the SG flexible tool, achieving high-quality polishing of intricate marble surfaces. This study establishes a material removal function model for the polishing process, optimizes the polishing trajectory spacing, and develops a relationship model between the optimal polishing trajectory spacing and curvature for different curved surfaces. Experimental validation demonstrates that this solution not only significantly reduces surface roughness but also greatly enhances surface gloss, with good consistency in surface quality after polishing. Compared to traditional manual polishing, robotic flexible polishing not only improves production efficiency and reduces potential health hazards to workers but also significantly enhances the final quality and added value of the products. This research not only provides an effective solution for the automated polishing of complex curved marble surfaces but also offers new insights into the intelligent development of the stone processing industry. In the future, with continuous advancements in robotic technology and polishing processes, it is believed that this field will witness more innovations and breakthroughs.  
Figure 22: SG flexible tool after robotic polishing.
## 3 Results
### Effect of Sintering
#### 3.2.2 Reduction in Sensing Element Resistance Variation  
In addition to the substantial reduction in the initial resistance of the printed sample, sintering also affects the resistance stability, an effect of high importance for the sensing element. In more detail, it was noticed that samples subjected to longer heat treatments were more stable during static (strain-free) I-V characterization (characterization performed by recording the developed electrical potential (V) vs. the applied current intensity (I). That is, a significantly decreased variation in their resistance value was recorded when no strain was applied. The deduced standard deviation \(\sigma_{\text{AR}}\) for the sample resistance is presented in Table 1; as we can notice, a tenfold decrease on \(\sigma_{\text{AR}}\) is recorded after sintering for 20 h. We should also note that the Temperature Coefficient of Resistance (TCR) for a CNT-enriched thermoplastic composite has been reported in the literature to have a value as high as \(-1.28\times 10^{-2}/^{\circ}\)C [32]. Therefore, even a small temperature increase due to self-heating can potentially result in a significant variation in its resistance, affecting the sensing element's operation. With this in mind, the variation in the resistance of the sample was recorded (by calculating \(\sigma_{\text{AR}}\)) for different values of constant current applied during static characterization. The recorded standard deviation of resistance is presented in Figure 8. As can be seen, applying a current higher than 3 mA leads to approximately a threefold increase of \(\sigma_{\text{AR}}\); this can be attributed to self-heating that modifies the conductive pathways within the sample combined with the resistance change due to the material's high TCR. Therefore, to ensure accurate and consistent results, a current of 0.1 mA was utilized in all measurements performed.  
Figure 7: Effect of thermal sintering on the electrical resistance of the 3D printed piezoresistor; a 44% decrease in the initial value is noticed.
# Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks
## 2 System Model
### Air-to-Ground and Device-to-Device Channels  
The channel between UFBS and its associated GMTs is characterized as an A2G channel. To conduct performance analysis, the channel complex coefficient for each GMT\({}_{I}\)\((1\leq l\leq N)\) is denoted as \(h_{I}^{u}\), and follows the complex Gaussian distribution with zero mean and unit variance \(\sim\mathcal{CN}(0,1)\). Additionally, the path loss attenuation of the UFBS signal is modeled using the elevation angle-based path loss model [25] in an urban environment, and is represented as follows:  
\[\mathrm{PL}_{I}^{u}(h,r_{l})=\mathrm{FSL}_{I}+\eta_{\mathrm{LoS}}P_{\mathrm{LoS }}(h,r_{l})+\eta_{\mathrm{NLoS}}(1-P_{\mathrm{LoS}}(h,r_{l})), \tag{1}\]  
where \(\mathrm{FSL}_{I}\) is the free space pathloss given by \(\mathrm{FSL}_{I}=20\log\left(\frac{4\pi d_{I}F_{u}}{c}\right)\), \(d_{I}\) is the transmission distance between UFBS and each GMT\({}_{I}\)\((1\leq l\leq N)\), and \(c\) is the speed of light. In addition, the \(\eta_{\mathrm{LoS}}\) and \(\eta_{\mathrm{NLoS}}\) coefficients reflect the extra losses for LoS and Non-LoS (NLoS) air-to-ground transmission links, and they depend on the propagation environment. Moreover, \(P_{\mathrm{LoS}}\) denotes the probability of the LoS component between the UFBS and each GMT\({}_{1}\) and is modelled as a function of the altitude \(h\) of the UFBS and the 2D Euclidean distance \(r_{l}\) between the UFBS and each GMT\({}_{1}\). Hence, \(P_{\mathrm{LoS}}\) can be expressed as follows [30]:  
\[P_{\mathrm{LoS}}(h,r_{l})=\frac{1}{1+a\exp(-b(\arctan(\frac{h}{\eta_{l}})-a))}, \tag{2}\]  
where \(a\), \(b\) are parameters determined by the propagation environment. Regarding the D2D link between the strong GMT\({}_{i}\) and weak GMT\({}_{j}\) of each pair \(k\)\((1\leq k\leq K)\) the multipath fading is modeled by the complex Gaussian distribution with zero mean and unit variance \(\sim\mathcal{CN}(0,1)\). The complex channel coefficient for the D2D link is denoted as \(h_{I}^{u}\). Moreover  
Figure 1: System model.  
the path loss model for the D2D communication of each pair \(k\) (\(1\leq k\leq K\)), used from [27], is as follows:  
\[\mathrm{PL}_{k}^{d}(d_{k}^{e})=157+\log_{10}(d_{k}^{e}), \tag{3}\]  
where \(d_{k}^{e}\) is the distance in km between the strong GMT\({}_{i}\) and the weak GMT\({}_{j}\) of each pair \(k\) (\(1\leq k\leq K\)). Furthermore, the A2G and the D2D links under consideration are assumed to be degraded by additive white Gaussian noise (AWGN), which is statistically modeled by the normal distribution \(\sim\mathcal{N}\left(0,\sigma_{q}^{2}\right)\) with \(q=\{u,d\}\). The noise power of the A2G and D2D receivers are given by \(N_{u}=k_{B}T_{u}B_{u}\) and \(N_{d}=k_{B}T_{d}B_{d}\), respectively; where \(k_{B}\) is the Boltzmann constant, and \(T_{u}\), \(T_{d}\) are the A2G and D2D receiver system noise temperatures, respectively. Therefore, the corresponding noise variances for each receiver type are \(\sigma_{u}=\sqrt{N_{u}}\) and \(\sigma_{d}=\sqrt{N_{d}}\).
###### Abstract  
Cisplatin, a widely used chemotherapeutic agent, is highly effective in treating various cancers, including ovarian and lung cancers, but it often causes ovarian tissue damage and impairs reproductive health. Exosomes derived from mesenchymal stem cells are believed to possess reparative effects on such damage, as suggested by previous studies. This study aims to evaluate the reparative effects of cisplatin and exosome treatments on ovarian tissue damage through the analysis of histopathological images and machine learning (ML)-based classification techniques. Five experimental groups were examined: Control, cisplatin-treated (Cis), exosome-treated (Exo), exosome-before-cisplatin (ExoCis), and cisplatin-before-exosome (CisExo). A set of 177 Local Binary Pattern (LBP) features were extracted from histopathological images, followed by feature selection using Lasso regression. Classification was performed using ML algorithms, including decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and Artificial Neural Network (ANN). The CisExo group exhibited the most homogeneous texture, suggesting effective tissue recovery, whereas the ExoCis group demonstrated greater heterogeneity, possibly indicating incomplete recovery. KNN and ANN classifiers achieved the highest accuracy, particularly in comparisons between the Control and CisExo groups, reaching an accuracy of 87%. The highest classification accuracy was observed for the Control vs. Cis groups (approximately 91%), reflecting distinct features, whereas the Control vs. Exo groups demonstrated lower accuracy (around 68%) due to feature similarity. Exosome treatments, particularly when administered post-cisplatin, significantly improve ovarian tissue recovery. This study highlights the potential of ML-based classification as a robust tool for evaluating therapeutic outcomes. Additionally, it underscores the promise of exosome therapy in mitigating chemotherapy-induced ovarian damage and preserving reproductive health. Further research is warranted to validate these findings and optimize treatment protocols.  
cisplatin; exosome; local binary pattern; k-nearest neighbors; support vector machine; artificial neural network; decision tree  
Article
# Improved Q Network Auto-Scaling in Microservice Architecture
## 2 Literature Review: Related Work
### Reinforcement Learning  
Reinforcement Learning (RL) refers to a collection of trials and errors in which agents are trained to interact with their environment and make good decisions by being given positive or negative feedback in the form of rewards for each action. The widely used RL algorithm is Q-learning without a model. Q-learning is a stepwise learning that can train the approximation of the optimal behavior value. The approximation of the \(Q\) value specifies the cumulative reward that can be expected when the agent starts with state (\(s\)), takes action (\(a\)), and then forever acts on the optimal policy afterward. By observing the actual reward in each iteration, the optimization of the Q-function is performed step by step.  
RL describes how much newly observed information redefines old information, along with discount factors used to balance present and future rewards. Since RL is a trial-and-error method, the agent must choose between exploring new measures and using the current best option during training [9]. With a certain probability, the agent selects the task that is anticipated to maximize the expected return based on the optimal policy; i.e., the task with the highest \(Q\) value starting with \(s\).
# Health Monitoring of Civil Infrastructures by Subspace System Identification Method:
## Vibration-Based Damage Detection (VDD)
### Time-Domain (TD) Methods  
TD techniques rely on the fact that the vibrational properties of structures can be captured through the time-history response of a dynamic system. Hence, the extracted response in a healthy state is different from that of one that is in a damaged state. Figure 2 shows the schematic architecture of TD methods that were used for the identification of dynamic systems. Different numerical techniques, for example, FFT, SVD, least squares (LS), QR decomposition, Eigen-vector decomposition (EVD) [56], and statistical methods, were used to develop these algorithms. Observer/Kalman filter identification (OKID), NExT, and random decrement (RD) are the most common TD methods for extracting the FRF when there is no access to the input data. The input signal can be estimated using an auto-correlation or cross-correlation function [57].  
The TD techniques in the health monitoring of civil structures can be classified into three widely known categories, which include [26]:  
1. NExT methods;
2. ARMA methods; and,
3. SSI methods.  
These methods are adopted for the identification of system parameters in civil engineering structures by introducing output-only extensions. NExT and SSI-COV methods are generally indirect where the SSI-DATA and ARMA methods are direct in nature, as can be seen in Figure 2. The TD methods are more appropriate for continuous monitoring compared to modal analysis methods due to direct use of response signals. Furthermore, the information extracted using TD methods is more complete than that of FD methods, particularly when a large number of modes or a large frequency range exist. A further explanation of the TD class of the identification methods are provided in the following subsections.
# Object Detection in Autonomous Driving Scenarios Based on an Improved Faster-RCNN
## 2 Related Work  
### Object Detection Algorithm Based on Deep Learning  
At this stage, object detection is divided into one-stage and two-stage algorithms. The detectors with the highest detection accuracy at present are two-stage detectors. The most representative two-stage detector is R-CNN and its variants. R-CNN uses the selective search algorithm to extract suitable candidate proposals in the image, converts them to a standard size, and sends them to the convolutional network for feature extraction; finally, it uses a support vector machine (SVM) for classification. [PERSON] proposed Fast-RCNN to address the shortcomings of R-CNN, such as its slow detection speed and large amount of calculation it requires. Fast-RCNN performs softmax classification and candidate proposal regression on the feature vectors extracted from the region of interest (ROI), achieving a unified implementation of feature extraction and classification tasks. [PERSON] et al. proposed the Faster-RCNN algorithm, which uses region proposal networks (RPNs) instead of selective search to recommend candidate proposals; this greatly improves the quality of candidate proposals, reduces the amount of calculation, and achieves end-to-end training classification. [PERSON] et al. proposed Mask-RCNN and made two improvements based on previous studies, using ROI Align and Mask branches to achieve more accurate instance segmentation while reducing the running time. [PERSON] et al. proposed Cascade-RCNN to solve the problem of low sample quality for RPNs. [PERSON] et al. proposed Libra-RCNN to solve the three problems of sample imbalance, feature imbalance, and detection imbalance, which improved the detection efficiency.  
A one-stage detector converts classification and positioning problems into regression problems and performs end-to-end detection [17]. The detection speed is greatly improved, but the accuracy is reduced. SSD incorporates the concept of anchors in Faster-RCNN, proposes a default box generation method, and uses different-scale feature maps to detect objects of different sizes, thereby improving the speed and accuracy of the algorithm. The YOLO series algorithm greatly improves the detection speed. YOLOv3 uses ResNet's [18] remaining block structure to solve the gradient dispersion and gradient explosion problems caused by the deepening of the network hierarchy; it also uses FPN [19] to fuse multiscale feature maps to improve detection accuracy.
# Zero-Shot Recognition Enhancement by Distance-Weighted Contextual Inference
## 3 Method
### Contextual Inference
#### 3.4.2 Distance-Weighted Calibration for Multiple Surrounding Objects  
Associations for all surrounding objects are eventually synthesized to derive the contextual inference to calibrate the results of the visual inference. Using the intuition that objects near a target object offer better context than objects farther away, the context inference applies a distance-weighted average of associations rather than a simple average. We assume that the bounding boxes, \(b=(x,y,w,h)\) of the objects needed to calculate distance are given, where \((x,y)\) is the center point of a bounding box and \(w\) and \(h\) denotes its width and height, respectively.  
However, we do not simply use the Euclidean distance between center points. In Figure 2, the Euclidean distances between the two objects in panels (a,b) are the same, but the actual relative distances in panel (b) are much smaller. By generalizing all the related cases, we define a distance calculation equation that relaxes the Euclidean distance with the size of two objects.  
\[d=\left\{\begin{aligned} \frac{\sqrt{\tilde{x}^{2}+\tilde{y}^{2}}}{ \tilde{x}+\tilde{y}}\cdot\tilde{w}+\frac{\tilde{y}}{\tilde{x}+\tilde{y}}\cdot \tilde{h}&&,\quad\text{if }\tilde{x}\
eq 0\text{ or }\tilde{y}\
eq 0\\ 0,&&\quad\text{else}\end{aligned}\right. \tag{8}\]  
where \(\tilde{x}=|x_{1}-x_{2}|\) and \(\tilde{w}=\frac{w_{1}+w_{2}}{2}\). A distance is calculated when center points are not exactly the same, and it is fixed to 0 otherwise. When calculating the distance, the application of the Euclidean distance is adjusted to the average width and height of two objects, where the width and height are referenced using the ratio of the x-axis and y-axis intervals between the objects.  
As previously explained, because the context of close objects should be exploited more, the reciprocal of the distance is considered as a weight. By weighted-averaging all obtained associations,the set of \(S(e,si)\), and the corresponding weights are combined to derive the contextual score for an unseen class label as follows,  
\[H(e,SI(i))=\frac{\sum_{j}^{N^{\text{noobj}}}r_{j}\cdot S(e,si_{j})}{ \sum_{j}^{N^{\text{noobj}}}r_{j}}, \tag{9}\]  
where \(r=1/(d+\epsilon)\) and \(\epsilon\) is a smoothing factor whose value is fixed at 0.001 in the experiments.