esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Materials  
As illustrated in Figure 1, the experimental photovoltaic (PV) system installed at Istanbul University-Cerrahpasa Avclar Campus comprises a number of components. To-match brand 100 Watt mono-crystalline and polycrystalline PV panels were utilized in the experimental setup, and the technical specifications of these panels are detailed in Table 1. The experimental system was designed to compare the power generation performance of PV panels with different surface conditions (clean and dusty). The clean PV panels were subjected to regular cleaning, whilst the uncleaned PV panels were allowed to accumulate dust on their surfaces. This grouping was created to more comprehensively analyze the effects of environmental factors on PV panels.  
A Davis brand meteorological system was integrated into the system, allowing for the measurement and recording of environmental factors affecting the PV panels. The meteorological system and the PV panel test system operated in parallel for each measurement, providing real-time monitoring of the data presented in Figure 2.  
Figure 1: Data Acquisition System outdoor test bench [14].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Module Name** & **TT100-36 PM** & **TT100-36P** \\ \hline Cell Type & Monocrystalline & Polycrystalline \\ \hline Max. Power (Pmax) & 100 W & 100 W \\ \hline Max. Power Voltage (Vmp) & 20.60 V & 19.4 V \\ \hline Max. Power Current (Imp) & 4.86 A & 5.16 A \\ \hline Open Circuit Voltage (Voc) & 24.05 V & 22.9 V \\ \hline Short Circuit Current (Isc) & 5.13 A & 5.52 A \\ \hline Number of Cells & 36 & 36 \\ \hline Dimensions & 680 \(\times\) 790 \(\times\) 20 mm & 674 \(\times\) 944 \(\times\) 20 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Electrical characteristics of PV modules at STC [15,16].  
Figure 2: Weather station instant data screen image.  
The Data Acquisition System (DAS) at the center of the system automatically measured and recorded the electrical parameters of the PV panels such as current, voltage, and power throughout the day using a GW Instek programmable electronic load. In order to evaluate the effect of environmental factors on energy production, a dust sensor (Dust Sensor) was used to measure the dust accumulation on the surface of the PV panels and a Kipp & Zonen pyranometer to determine the intensity of solar radiation. This system provides a comprehensive data collection and monitoring infrastructure to compare the power generation performance levels of PV panels under clean and dusty surface conditions. The experimental setup provides a critical basis for understanding the performance of PV panels under different environmental conditions and for using this data in energy prediction models.  
Figure 2 illustrates the instantaneous measurement of meteorological data, including wind direction, temperature, wind speed, precipitation, pressure, solar radiation, humidity, and UV values, at a designated meteorological station. The interval for data measurement and recording can be modified and synchronized with the parameters utilized for electrical measurement of the photovoltaic (PV) panels.
## 1 Introduction
Finally, simulation results indicate that the error performance of the proposed scheme outperforms the conventional WDM-VLC model without considering the channel crosstalk problem. Meanwhile, compared with the fixed chromaticity coordinate constraint, the proposed scheme with the quadrangle range chromaticity constraint behaves more energy efficiently. Moreover, the effect of the proposed scheme under different correlated color temperatures (CCTs) is also discussed. To sum up, the performance of the proposed optimal power allocation scheme can reduce energy consumption while satisfying both the illumination and communication requirements. The reminder of this paper is organized as the following sections: Section 2 provides the model of the multi-color LED-based VLC system considering channel crosstalk. In Section 3, the optimal power allocation scheme is proposed to maximize energy efficiency via collaboratively satisfying luminance, chromaticity constraints, and SINR requirements. In Section 4, simulations are carried out to show that the proposed scheme can achieve significant improvement for error performance and energy consumption. Finally, conclusions are made in Section 5.
## 4 Calculation of the Bending Bearing Capacity
### Calculation Formula of Bearing Capacity  
In the bending reinforcement of the reinforced concrete beam, in order to make the structure have sufficient ductility, it is necessary to ensure that the stressed reinforcement of the original beam reaches the yield state before failure. The above test results show that the failure state of the RC beam reinforced by the thin layer of epoxy mortar is the yield of the original beam until the thin layer reinforced by the new epoxy mortar is broken. The concrete in the compression area is not crushed. Figure 17 shows the strain distribution and the calculation of the bearing capacity. When the concrete in the compression zone is not crushed, it can be divided into two cases: \(0<\varepsilon_{\text{c}}<\varepsilon_{\text{c0}}\) and \(\varepsilon_{\text{c0}}<\varepsilon_{\text{c}}<\varepsilon_{\text{c0}}\), corresponding to the following two calculation formulae: (1) When \(0<\varepsilon_{\text{c}}<\varepsilon_{\text{c0}}\), the resultant force of the concrete compressive stress is as follows:  
\[F_{c}=\int_{0}^{x_{\text{c}}}f_{\text{c}}[1-(1-\frac{\varepsilon_{\text{c}}}{ \varepsilon_{\text{c0}}}\cdot\frac{y}{x_{\text{c}}})^{2}]bdy=f_{\text{c}}bx_{ \text{c}}\frac{\varepsilon_{\text{c}}}{\varepsilon_{\text{c0}}}(1-\frac{ \varepsilon_{\text{c}}}{3\varepsilon_{\text{c0}}}) \tag{4}\]  
Let \(\beta_{c}=\frac{\varepsilon_{\text{c}}}{\varepsilon_{\text{c0}}}\), then  
\[F_{c}=f_{\text{c}}bx_{\text{c}}\beta_{c}(1-\frac{\beta_{c}}{3}) \tag{5}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material Parameter** & \(\sigma_{\text{max}}\)/MPa & \(\varepsilon_{\text{1}}\)/10\({}^{-6}\) & \(\varepsilon_{\text{2}}\)/10\({}^{-6}\) & \(\varepsilon_{\text{3}}\)/10\({}^{-6}\) \\ \hline value & 19.40 & 1650 & 4600 & 10,100 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Values of epoxy mortar material parameters. Figure 16: Epoxy mortar constitutive model curve. Figure 17: Strain distribution and bearing capacity calculation diagram of the strengthened beam section: \(h\), \(t_{\text{EM}}\), and \(h_{0}\)—the section height of the original beam, the thickness of the reinforcement layer, and the distance from the top of the beam to the center of the tension bar; \(A_{\text{s}}\) and \(A_{\text{EM}}\)—the section area of the tensile steel bar and the section area of the reinforcement layer; \(x_{\text{c}}\)—the height of the concrete compression zone; and \(F_{\text{s}}\) and \(F_{\text{EM}}\)—the resultant force of the tensile longitudinal bar and reinforcing layer. where \(\beta_{\rm c}\) is the proportional coefficient of the compressive strain of the concrete in the compression zone; \(\varepsilon_{\rm c}\) is the compressive strain value of the concrete compression zone; and \(\varepsilon_{\rm c0}\) is the compressive strain value corresponding to the peak stress of the concrete.
# Voxel-Based Navigation: A Systematic Review of Techniques, Applications, and Challenges
## 4 Key Techniques for Voxel-Based Navigation
### Voxel-Based Modeling
#### 4.1.2 Outdoor Environment  
Unlike indoor environments, outdoor scene modeling involves larger spatial areas and more diverse data sources. Ref. [30] provided a set of algorithms that can generate voxels from point clouds, curves, or surface objects, obtaining voxel connectivity based on topological voxelization methods [82], which helps in voxelized modeling of 3D city models. An example of voxelization of a surface is shown in Figure 6. Ref. [38] proposed a voxelization technique that combines BIM and GIS, achieving three-dimensional modeling and management of geological information, meeting the needs of different scales. Ref. [40] proposed a voxel modeling and visualization framework for large-scale urban environments, effectively reducing memory consumption and supporting large urban models' generation, storage, and real-time rendering.  
The regular cubic grid structure of voxel models exhibits extremely high efficiency in operations such as spatial queries and neighborhood searches, making them particularly  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Techniques** & **Application** & **Example** \\ \hline \multirow{3}{*}{Voxel storage and management} & \multirow{3}{*}{Static grid} & [PERSON] et al. [66], [PERSON] et al. [67], [PERSON] et al. [68], \\  & & [PERSON] et al. [69], [PERSON] et al. [70], \\  & & [PERSON] et al. [71], [PERSON] et al. [72] \\  & & [PERSON] et al. [73], [PERSON] and [PERSON] [74], \\  & & [PERSON] [75], [PERSON] [76], [PERSON] et al. [77], \\  & & [PERSON] et al. [78], [PERSON] et al. [79] \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._suitable for applications in complex indoor navigation and robotic path planning. Moreover, voxel models support three-dimensional topological analysis, clearly illustrating the adjacency and connectivity of different parts of the space, making them highly applicable in path-planning scenarios.
## 4 Results
### Development Insights and Technical Achievements
#### Implementation of ML Algorithms for Fall Detection  
Multiple machine learning classifiers are implemented and evaluated for fall detection using Python 3.11, each with its own configuration and hyperparameters. To further optimize the performance of these models, a grid search approach is applied, systematically exploring combinations of hyperparameters to identify the optimal settings for each classifier. This process ensures that the models are fine-tuned beyond their default configurations, allowing for a more rigorous and comprehensive evaluation of their performance.  
* The first model utilized is a DT classifier. The splitting criterion is set to Gini (criterion = \"gini\"), used to evaluate the quality of splits at each node. The splitter is configured to select the best possible split (splitter = \"best\"). There is no pre-defined maximum depth (max_depth = None), allowing the tree to grow until all leaves are pure or the node contains fewer than the minimum samples required to split (min_samples_split = 2), and the minimum number of samples for a leaf node is 1 (min_samples_leaf = 1). After training the model on the dataset, predictions are made, and the performance is evaluated using the accuracy metric to assess the model's effectiveness.
* The second classifier is an RF classifier, an ensemble technique that constructs multiple decision trees and aggregates their predictions. Each model is trained on a bootstrap sample of the data (bootstrap = True), with a total of 100 trees being generated (n_estimators = 100). Gini is again used as the splitting criterion (criterion = \"gini\"), and no limit is imposed on the depth of each tree (max_depth = None). The model samples a subset of features at each split, with the number of features determined by the square root of the total features (max_features = \"auto\", which defaults to sqrt (n_features) for classification).
* SVM is implemented next, utilizing the radial basis function (RBF) kernel to capture nonlinear relationships in the feature space. The regularization parameter C is set to 1, which controls the trade-off between maximizing the margin and minimizing classification errors. The gamma parameter, controlling the influence of each individual data point, is set to \"scale\", meaning it is automatically computed based on the number of features and the variance of the input data.
* Lastly, the XGBoost Classifier is implemented, a gradient boosting algorithm known for its computational efficiency and performance. The model uses logistic regression as the objective function for binary classification (objective = \"binary:logistic\"). The number of boosting rounds is set to 100 (n_estimators = 100), and the learning rate, which controls the contribution of each tree, is set to 0.3 (learning_rate = 0.3). The maximum depth of each decision tree in the boosting process is limited to 6 (max_depth = 6). XGBoost also automatically handles missing values and uses multiclass log loss (mlogloss) as the evaluation metric during training.
## 3 Experimental Results and Discussions
### Experimental Settings  
The experiments were conducted on a computer with an Intel(R) Core(TM) i5-13600 KF CPU, with 64 GB of RAM and an Nvidia GeForce RTX 3080 Ti GPU. The distortion space parameters were set to \(D=8\), \(P=20\), and \(C=12\). For the purpose of comparison, we employed ResNet-12 as the backbone network and initialized the similarity function with pretraining on the UCM, AID, and NWPU datasets.  
Before training, all image pixels were resized to 256 \(\times\) 256. The hyperparameters \(\tau\) and \(\gamma\) were set to 50 and 5, respectively. The initial learning rate was 0.001, the batch size was set to 32, and the number of epochs was set to 800. We utilized stochastic gradient descent (SGD) for optimization during both the pretraining and metric learning phases. During the first 200 epochs, only the original \(A\) branch was trained, and in the subsequent 600 epochs, the \(A\) and \(B\) branches were jointly trained. The learning rate was decayed by a factor of 0.5 every 100 epochs. Additionally, the traversal order within the distortion space was \(D\), \(P\), and \(C\), with each parameter exploring its range from small to large. The reported classification accuracy results in all experiments are the averages of the accuracy results from 100 randomly sampled subsets from the test set, with a 95% confidence interval.
## 3 Design of the SOE
### SOE Geometry Optimization
Further simulative analysis, illustrated later in Section 4.1, was used in order to discuss the fine-tuning of the SOE-receiver gap to achieve either higher concentration or better homogeneity, while keeping a relatively high CAP value. 7. The control parameter of the algorithm is \(FS\). The algorithm convergence can be changed by resetting a percentile threshold, \(T_{s}\), that tests the reduction in \(FS\) for consecutive \(r_{j}\) iterations and terminates the process when that reduction is less than the assigned threshold. Analytically, the termination happens if \[\left|\frac{FS_{j}-FS_{j-1}}{FS_{j-1}}\times 100\%\right|<T_{s}\] (33)  
Figure 4: Schematic for the optimization algorithm of the SOE parameters. An algorithmic optimization in MATLAB(r) will iterate \(r\) and optimize the resulting parameters, outputting the smallest SOE radius that can achieve the highest optical efficiency at the design incidence angle within the narrowest receiver size at a balanced distance below the SOE. The choice of the optimum SOE-receiver spacing, \(SR_{opt}\), is for the CAP value to be at a maximum.
# Uncertainty in Visual Generative AI
## 1 Introduction
Future work with generative AI models needs to focus on eliminating unintentional biases or misrepresentations that have been the issue with previous versions. We propose the concept of \"uncertainty\" to measure where visual GAI is certain or uncertain regarding \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Model Family** & **Model Name** & **Release Date** & **Source(s)** \\ \hline \multirow{8}{*}{Language models} & \multirow{8}{*}{OpenAI Generative Pre-Trained (GPT)} & GPT-1 & June2018 & [14; 15] \\  & & GPT-2 & November 2019 & [16] \\  & & GPT-3 & May 2020 & [17] \\  & & GPT-3.5 & March 2022 & [1] \\  & & GPT-4 & March 2023 & [10; 18] \\  & & LoMDA & May 2021 & [19] \\  & & Applications (LaMDA) & LaMDA 2 & May 2022 & [20] \\  & & Google Pathways Language Model (PaLM) & PaLM & March 2023 & [21; 22] \\  & & PaLM 2 & May 2023 & [23; 24] \\  & & Meta Large Language Model Meta AI (LLaMA) & LLaMA & February 2023 & [25; 26] \\  & & Inflection-1 & June 2023 & [27] \\ \hline \multirow{8}{*}{Image models} & \multirow{8}{*}{OpenAI GLIDE} & GLIDE & December 2021 & [28] \\  & & DALL-E & February 2021 & [29; 30] \\  & & DALL-E 2 & April 2022 & [31; 32] \\  & & DALL-E 3 & October 2023 & [33; 34] \\ \cline{1-1}  & & Craiyon 1 & July 2021 & [35; 36; 37] \\ \cline{1-1}  & & Midjourney & Midjourney & February 2022 & [38] \\ \cline{1-1}  & & Stability AI & Stable Diffusion & August 2022 & [39; 40] \\ \cline{1-1}  & & Imagen & May 2022 & [41] \\ \cline{1-1}  & & Parti & June 2022 & [42] \\ \hline \hline \end{tabular}  
* Craiyon was formerly known as DALL-E Mini until its name was changed in June 2022 at the request of OpenAI.  
\end{table} 
Table 1: Generative AI models (modified from [13]). its inputs and outputs. Areas where GAI is uncertain are subject to more chaotic, stochastic results that can lead to unideal results related to the sensitive issues described earlier. To address these issues, we created three research questions:  
1. How can GAI uncertainty be quantified? 2. How should GAI uncertainty be evaluated? 3. What text-to-image and image-to-text model combination performs best? To answer these questions, we start with background on visual GAI, image quality assessment, and text evaluation methods. In Section 3, we describe the methodology used first in agnostic terms and then with details specific to this study. We propose a pipeline to compare the textual inputs and outputs of an image-to-text GAI algorithm, with the differences between the inputs and outputs representing GAI \"uncertainty\". The results are presented and discussed in Section 4, and then the paper wraps up with conclusions and future work in Section 5.
# Improving the Robustness of Model Compression by On-Manifold Adversarial Training
## 3 Background
### Adversarial Training
#### 3.3.3 Trades  
TRADES [15] is a more recent robust training method against adversarial attacks. It considers the trade-off between the robustness against adversarial examples and prediction accuracy on natural samples. The basic idea of this approach is to decompose the robustness error into the natural classification error and the boundary error. The objective consists of the natural loss term for empirical risk minimization and regularization for robustness against adversarial examples. Here, the regularization term tends to push the decision boundary of the classifier away from the inputs by minimizing the distance between the prediction of the natural example \(f(x)\) and the prediction of the adversarial example \(f(x^{\prime})\).  
\[\min_{w}\mathbb{E}_{(x,y)}\bigg{[}\ell(f(x;w),y)+\beta\max_{\delta:\|\delta \|_{\infty}\leq\epsilon}\ell\big{(}f(x;w),f(x^{\prime};w)\big{)}\bigg{]},\ \ x^{\prime}=x+\delta, \tag{4}\]  
where \(\beta>0\) is a balancing parameter, As in PGD-AT, TRADES generates the worst-case perturbation \(\delta\) with PGD.
###### Abstract  
The severe floor heave in gob-side entry retaining is the major restriction factor of the wide application of pillarless mining thin coal seams. Reinforcement and stress-relief floor heave control methods are the most promising. However, in practice, floor restoration is widely used. Therefore, floor heave control technology in gob-side entry retaining needs to be improved. This study proposes anti-shear pile technology to control floor heave in gob-side entry retaining. The research was mainly carried out by numerical simulation. It was found that the transformation of high vertical stresses in the entry floor underneath the filling wall and coal seam body into horizontal stresses starts the floor heave process. The vertical dilatancy of rocks under the roadway span and their subsequent unloading lead to the delamination of the floor strata and uplift of the entry contour. In this paper, the best pile installation scheme was found. It is a 2 pile 5+2 scheme with the installation of two piles, each 2 m long. After that, it was shown that filling piles are more than 3.3 times cheaper than comparable analogs, and pile installation is less labor-intensive. The implementation of the proposed floor heave control method leads to a reduction in heaving by 2.47 times.  
gob-side entry retaining; pillarless mining; floor heave control; mine roadway stability; thin coal seam mining; steel pile; filling pile; anti-shear pile technology
# Numerical Analysis of the Beam-Column Resistance Compared to Methods by European Standards
## 3 Verification According to European Standards
### Resistance of Beam-Columns with RHS 200/100/10 Cross-Section
#### 3.2.1 Axial Compression and Major-Axis Bending Laterally Restrained  
The purpose of these loading conditions was to study the flexural buckling behaviour about the major axis (\(y\)-\(y\)). Therefore, to prevent possible lateral deflections, the lateral restraints were also applied at the centre of top and bottom flanges along the whole member, as shown in Figure 32.  
The interaction curves extracted from the analytical and numerical results are presented on the Figures 33-37.  
Figure 34: Interaction curves for laterally restrained beam-columns with RHS cross-section.  
Figure 35: Interaction curves for laterally restrained beam-columns with RHS cross-section.  
Figure 36: Interaction curves for laterally restrained beam-columns with RHS cross-section.  
In the case of pin ended beam-columns, the curves according to Method 1 and 2 of Eurocode 3 were found to be in a good agreement with the results from the numerical simulations. The differences seem to be larger only for lower slenderness ratios, but they seem to be smaller than in the case of the IPE cross-section. Moreover, the interaction curves of these two methods were mostly on the safe side, the only exception being for Method 2 in the case of a member subjected to concentrated force in the middle of the span and low values of axial force. The higher deviations of these methods compared to FEM simulations can be seen in the case of pinned-fixed beam-columns.  
It can be seen that the accuracy of interaction curves obtained by the method of Eurocode 9 was significantly lower. The results were on the unsafe side for slenderness ratios \(\overline{\lambda}_{y}=0.5\) and \(1.0\), whereas for the slenderness ratios \(\overline{\lambda}_{z}=1.5\) and \(2.0\) the results were noticeably lower. However, the interaction curves obtained by this method had the same behaviour as the curves obtained by numerical analysis.