esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Application Background  
The Jinchuan mine is the fourth largest nickel mine in the world, located in Jinchang city, Gansu Province, China. Due to the roadway characteristics of high in situ stress and the low strength of the surrounding rock in the Jinchuan mine, the combined support method of \"cable bolt + shotcrete + steel mesh\" was mostly used to maintain the stability of the roadway. However, the reinforcement effect of the combined support method was not satisfactory (see Figure 1). The field survey results revealed that the cracks of the shotcrete layer were the main form of failure of the roadway support. The reasons for roadway deformation or support failure are more complicated, but from the perspective of support, the reason was that the task of shotcrete at the Jinchuan mine was still carried out by using the traditional dry-mix shotcrete technique. The dry-mix shotcrete is nonuniformly mixed during spraying followed by poor compaction, low average strength, inhomogeneous concrete strength distribution, etc., and then it is difficult to provide sufficient support resistance to prevent the surrounding rock from displacing and deforming. Consequently, the Jinchuan mine has gradually begun to promote the use of wet-mix shotcrete technology, since wet-mix shotcrete overcomes the problems mentioned above, suffered by dry-mix shotcrete.  
For the Jinchuan mine, the in situ stress varies according to the depth, and the rock quality varies from roadway to roadway, so it is necessary to use suitable wet-mix shotcrete for precise support of the roadway under different conditions, which can achieve the purpose of reducing the support cost while ensuring the stability of the roadway. Hence, the Jinchuan mine applied C20 wet-mix shotcrete support in the shallow 1000 m middle roadway project, C25 wet-mix shotcrete concrete support in the 850 m middle roadway project, and C30 wet-mix shotcrete support in the deep 640 m middle roadway project. Thus, in the process of promoting the use of wet-mix shotcrete, the most important issue was how to efficiently and accurately obtain the optimal mix proportion of wet-mix shotcrete under the designed conditions.  
To address such shortcomings, this article proposed a new ANN-PSO model and used the mix proportion optimization of wet-mix shotcrete of the Jinchuan mine as an example in an attempt to optimize the mix proportion of wet mix concrete intelligently, accurately and economically.
# Analysis of Blasting Efficiency Using Crack-Inducing Holes and Pre-Splitting Method in Blasting
## 3 Experimental Results and Analysis
### Overbreak Measurement Results and Analysis  
Table 5 shows the data measuring the overbreak after blasting. To compare the overbreak amounts, the analysis was conducted based on the sections where combined blasting occurred (measured from 3 to 11). The maximum overbreak amount was measured based on the vertical distance from the over-exavated point to the planned excavation line, and the overbreak cross-sectional area was calculated as a two-dimensional area between the excavation line and the overbreak line.  
The maximum overbreak amount during combined blasting was 0.458 m, while the maximum overbreak amount for general blasting was 0.558 m, indicating a reduction of approximately 17.9%. Upon comparing the overbreak cross-sectional areas, the average overbreak cross-sectional area during combined blasting was calculated to be 3.378 m\({}^{2}\)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification** & \multicolumn{2}{c}{**General Blasting**} & **Combined Blasting** \\ \cline{2-3}  & **1 st Blasting** & **2 nd Blasting** & **3 rd Blasting** \\ \hline Overbreak measurement & 3/2 & 1/12 & 2/12 \\ Overbreak measurement & 1/3 & 1/13 & 1/13 \\ Overbreak Measurement & 1/3 & 1/13 & 1/13 \\ Graph & 1/3 & 1/13 & 1/13 \\ Overbreak cross-sectional area & 1/3 & 1  
while the average overbreak cross-sectional area for general blasting was 4.235 m\({}^{2}\), showing a reduction of approximately 20.2%.  
Overall, based on Point 7 (Tunnel Center Line), the left side of the tunnel shows less overbreak than the right side, indicating better rock conditions. In general blasting, there is a significant difference in overbreak between the left and right sides of the tunnel, whereas the variation is less pronounced in combined blasting. The overbreak on the left side, where the rock conditions are better, does not show a significant difference between general and combined blasting. On the other hand, the overbreak on the relatively poorer right side is significantly reduced during combined blasting. Therefore, it can be inferred that combined blasting can achieve a smoother excavation surface and reduce the amount of overbreak regardless of rock condition variations.  
The small amount of overbreak observed during combined blasting occurred because, when the outer pre-splitting holes were detonated, a tensile stress wave was generated behind the compressive wave from the blast hole wall, leading to the formation of cracks. The concentration of tensile stress around the crack induction holes induced crack propagation, resulting in the formation of a smooth fracture surface.
# HFDF-EffNetV2: A Lightweight, Noise-Robust Model for Fault Diagnosis in Rolling Bearings
## 2 Materials and Methods
### EfficientNetV2  
In the field of FD, accuracy is a crucial metric; therefore, while pursuing a lightweight design, it is essential to ensure high diagnostic accuracy, especially in the context of complex tasks [32; 33; 34].  
EfficientNetV2 is a lightweight model introduced by [PERSON] et al. [35] in 2021 that incorporates the Fused-MBConv and MBConv layers and is available in several versions, including B0-B3, S, and M. The model employs a progressive learning strategy that adaptively adjusts the regularization technique and the input image size, accelerating the training process while maintaining high accuracy. Through neural-architecture search, EfficientNetV2 optimizes three key parameters: input-image resolution, network depth, and channel width. Among the different versions, the M version, with depth and width scaling factors of 1.8 and 1.4, respectively, is suited for high-resolution scenarios, achieving a classification accuracy of 85.1% on the ImageNet dataset. In contrast, the S version, with depth and width scaling factors of 1.2 and 1.1, respectively, focuses more on balancing the number of parameters with accuracy, making it more suitable for lower-resolution applications.  
EfficientNetV2-S is structured into seven stages; this is one MBConv stage fewer than the M version, resulting in a significant reduction in the number of parameters. As shown in the Table 1, stage zero consists of a standard convolutional layer. As illustrated in Figure 3a, stages one to three employ the Fused-MBConv layer, which utilizes \(3\times 3\) convolution for channel upscaling, followed by \(1\times 1\) convolution for downscaling. In contrast, stages four to six utilize the MBConv layer, as shown in Figure 3b. It begins by introducing features into a higher-dimensional space through \(1\times 1\) convolution, and this step is followed by feature extraction with a \(3\times 3\) depthwise convolution. Subsequently, the SE Attention mechanism is applied to capture significant channelwise features. Finally, a \(1\times 1\) convolution maps the processed features back to the input space. The final stage includes a \(1\times 1\) convolutional layer, an AP layer, and a fully connected (FC) layer. This streamlined architecture contributes to its reduced parameter count while maintaining efficient performance.  
Figure 3: **(a)** The Fused-MBConv structure and **(b)** The MBConv structure.
# Energy Efficient Access Point Placement for Distributed Massive MIMO
###### Abstract
The approach effectively captures the obstacles in the environment and radiation pattern of the arrays, taking into account the possible broadside direction of the arrays at different potential AP locations. * Based on an RTS, provided by Huawei Technologies (Gothenburg, Sweden), we perform a numerical validation of the graph-based approach and the channel models that have been introduced in the literature, considering different signal coverage levels and antenna types. We demonstrate that it is essential to take into account the radiation pattern of the arrays and environment information regarding the possible user locations, AP locations, and radio wave propagation. The rest of the paper is structured as follows. Section 2 details the considered D-mMIMO system and formally describes the problem. Section 3 presents different ways  
Figure 1: The access point (AP) placement optimization problem includes two parts: problem modeling (the blocks within the dashed line) and solver development. This paper focuses on the problem modeling part. We discuss different setups for the distributed massive multiple-input multiple-output (D-mMIMO) system and different ways for modeling the environment. Four approaches for calculating the channel gain at each user location have been considered: a ray-tracing simulator (RTS)-based model and a Euclidean distance-based model, both of which are from the literature, and two graph-based models which are proposed in this paper. to model the channel gain, including our proposed graph-based approach as well as others mentioned in the literature. Section 4 discusses the simulation results and Section 5 concludes the paper. Notation: An upper case letter in the default math font, e.g., \(V\), represents a constant. An upper case letter in mathcal font, e.g., \(\mathcal{R}\), is a set. Indices are denoted only using subscripts in lower case, e.g., \(l\) in \(x_{l}\). Vectors are denoted by lower case letters in bold, e.g., \(\mathbf{x}\). The complex conjugate is denoted as \((.)^{*}\). The 2-norm of a vector is denoted as \(\|.\|\). The integer part of a value is denoted by \(\lfloor.\rfloor\). The total number of elements in a set, e.g., \(\mathcal{L}\), is denoted as \(|\mathcal{L}|\). The imaginary unit, \(\sqrt{-1}\), is represented by \(j\), and statistical expectation is denoted by \(\mathbb{E}\{.\}\).
# A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation
## 1 Introduction
Secondly, a composite indicator of feature correlation is constructed through the correlation analysis method, which is used to control the input features, from which the most correlated features are selected as inputs to the model to ensure that the critical information can still be captured in the case of sparse data. Finally, the trajectory restoration model is constructed based on the sequence to sequence (Seq2 Seq) network, and the vessel trajectory restoration is achieved by training the network to improve the data accuracy. This method has the potential to solve the problem of sparse data and provides new ideas for the field of vessel trajectory restoration.
## 4 Simulation Results and Discussion  
### Simulation Description  
The comparative simulations of the proposed control scheme are evaluated in comparison with passive suspension, traditional backstepping, and ESO backstepping to verify the effectiveness of the control design in this study. The passenger comfort of the vehicle suspension is evaluated by the human body's sensitivity to acceleration. Moreover, the ride comfort is considered by the peak and the root mean square (RMS) values of sprung mass acceleration. For this evaluation, a filter is proposed in [52] to find the RMS acceleration during the simulation process.  
\[W(s)=\frac{80.03s^{2}+989s+0.02108}{s^{3}+78.92s^{2}+2412s+5614} \tag{98}\]  
Furthermore, the simulation results of RSD and RTF are analyzed to prove two objectives of driving safety and suspension deflection. The main parameters of pneumatic active suspension are listed in Table 1.  
The sin road profile is chosen for these simulations \(z_{\tau}=0.02\sin(4\pi t)\). The initial condition of the system states are set by \(x_{1}(0)=0.05\;(m)\), \(x_{2}(0)=x_{3}(0)=x_{4}(0)=0\;(m)\), and \(x_{5}(0)=0.5\times 10^{5}\;(Pa)\).  
The prescribed performance constraints are given by \(\rho_{0}=0.058\), \(\rho_{\infty}=0.0029\), \(\varphi=1.5\) and design parameters \(\underline{\kappa}=0.98\), \(\overline{\kappa}=0.98\). The bandwidth of the ESO system is chosen by \(\omega_{0}=150\). Moreover, the constant gains of the adaptive estimation law of RBFNNs are selected as \(\Lambda_{h}=50\), \(n=21\), and \(\delta_{h}=30\). The comparison of the control parameters is given in Table 2.
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 6 Applications of AMI in Heathcare
### Evaluation Procedure for the Case Studies  
This section briefly introduces the validation procedures used for the smart transdermal drug delivery system and the system for automatic identification and localization of colorectal cancer lesions. Ideally, the performance of these systems should be validated in clinical settings; however, empirical validation methods, which are discussed here, are highly essential as well.  
The performance of the smart transdermal drug delivery system can be evaluated based on the correct identification of abnormalities and the mean absolute error between the AI-predicted dosage and the actual dosage required by the patient. The correctness of identification can be statistically measured using accuracy, sensitivity, specificity, precision, and F1-score. In this context, True Positive (\(TP\)) refers to instances where the AI correctly identifies an abnormal condition, False Positive (\(FP\)) to those when the AI mistakenly identifies a patient as abnormal when they are actually normal, False Negative (\(FN\)) to cases where the AI fails to identify an abnormal condition and incorrectly labels it as normal, and True Negative (\(TN\)) as when the AI correctly identifies a normal condition. Additionally, analyzing the transmission time and prediction time separately is essential for assessing the system's efficiency. The statistical formulas are as follows.  
\[\text{Accuracy} = \frac{TP+TN}{TP+FP+FN+TN} \tag{1}\] \[\text{Sensitivity /Recall} = \frac{TP}{TP+FN}\] (2) \[\text{Specificity} = \frac{TN}{TN+FP}\] (3) \[\text{Precision} = \frac{TP}{TP+FP}\] (4) \[\text{F1-Score} = \frac{2 TP}{2 TP+FP+FN} \tag{5}\]  
Additionally, the mean absolute error (MAE) can be used to measure the deviation between the AI-predicted dosage and the actual dosage required by the patient. The actual dosage, denoted as \(x_{i}\), refers to the dosage that is actually required by the patient based on their medical condition, while the predicted dosage, denoted as \(y_{i}\), is the dosage predicted by the AI system:  
\[\text{MAE}=\sum_{i=1}^{n}|x_{i}-y_{i}| \tag{6}\]  
where \(n\) represents the total number of dosage predictions made. Similar statistical measures, namely, the accuracy, sensitivity, specificity, precision, and F1-score, can be used for both slice-level and patient-level analyses in the automatic identification and localization of colorectal cancer lesions.  
Figure 8: Pictorial framework of automatic identification and localization of colorectal cancer lesions.
## 3 Results and Discussion
### Results of the Analysis of P91  
The evaluation of the 3 MA-X8 features in relation to the number of load cycles is given in Figures 3a and 4a for P91. These figures demonstrate that neither the LCF nor the HCF load induce any systematic change in the material's magnetic properties (exemplarily illustrated by \(DZmax\)). The fatigue tests performed at the highest temperature (650 \({}^{\circ}\)C) as well as the lowest temperature (600 \({}^{\circ}\)C) did not cause a change in the mechanical properties until the cyclic saturation phase or just until the beginning of cyclic softening. Note that the cyclic softening was most pronounced at intermediate temperatures (635 \({}^{\circ}\)C), as a more pronounced increase in \(\varepsilon_{u,p\_inter}\). was evident at both loading types, which, however, was only accompanied by a weak decrease in _HV10_.  
\begin{table}
\begin{tabular}{c} \hline
**Linear Models** \\ \hline Linear regression \\ Partial least squares regression \\ Ridge regression \\ Huber regression \\ Bayesian ridge regression \\ \hline \end{tabular}
\end{table}
Table 3: List of models used in this study.  
In both the 3 MA-X8 and the mechanical measurements, no systematic changes were detected regarding all loading temperatures for the P91 material. Only for a temperature of 635 \({}^{\circ}\)C, an increase in \(\varepsilon_{u,p\text{-}inter.}\) and a decrease in _HV10_, which was accompanied with a decrease in _DZmax_, were observed between 300 and 2000 cycles. Moreover, _DZmax_ decreased at 600 \({}^{\circ}\)C in this fatigue life interval, which, however, did not correlate with changes in mechanical properties. In summary, for most conditions, there were no pronounced changes in micromagnetic and mechanical measures, disabling a reasonable correlation analysis.  
The absence of variations suggests that the material did not undergo any significant macroscopic changes. According to the SEM analysis, the density of precipitates, which could affect the magnetic material behavior, did not change during the fatigue loading [13]. Additional examinations indicated that the grain structure and grain size also did not change at a large scale. The cyclic softening observed at the specimens fatigued at 635 \({}^{\circ}\)C was primarily associated with recovery processes, attributed to a reduction in dislocation density. However, based on the changes in micromagnetic properties obtained, it can be assumed that the microstructural changes induced in P91 are too weak to be measured by 3 MA-X8. Therefore, the prediction of the mechanical properties and the microstructural features (e.g., precipitation density) based on 3 MA-X8 measurements using ML methods is not possible.  
Figure 4: Comparison of **(a)** the 3 MA-X8 feature _DZmax_ with **(b)** the mechanical measures, both as a function of the number of HCF cycles.  
Figure 3: Comparison of **(a)** the 3 MA-X8 feature _DZmax_ with **(b)** the mechanical measures, both as a function of the number of LCF cycles.
# A Cyclic Graywater Treatment Model for Sustainable Wastewater Management Applied in a Small Scale
## 3 Results
### Effect of Irrigation with Graywater on Quinoa
#### 3.2.2 Effect on Quinoa  
The results show a significant decrease in the average diameter of quinoa plants irrigated with graywater (GWTP1 and GWTP2) compared to the control (Figure 5). The average diameters were 0.58, 0.50, and 0.36 for the control, GWTP1, and GWTP2, respectively. The average plant lengths were 48.73, 44.41, and 33.07, and root lengths were 15.73, 14.46, and 10.09 for the control, GWTP1, and GWTP2, respectively. Graywater irrigation caused a 26% and 30% decrease in aerial and root lengths, with a significant decrease in GWTP2.  
Irrigation with graywater significantly impacted the fresh (FB) and dry biomass (DB) of quinoa organs. The control plants exhibited the highest fresh biomass compared to those irrigated with treated graywater (GWTP1 and GWTP2) (Figure 6). For fresh biomass, reductions of approximately 32% in roots, 55% in stems, 37% in leaves, and 27% in seeds were observed in quinoa plants irrigated with treated graywater. Comparable reductions of 33%, 45%,62%, and 32%, respectively, were recorded for the corresponding dry biomass when comparing irrigation with RGW to treated graywater.  
The results (Figure 7) show that irrigation with TGW significantly increased Na+ levels: 37% in roots, 3% in stems, 16% in leaves, and 26% in seeds. Potassium was more abundant in leaves, with a notable increase in K+ concentrations, especially with GWTP2: 19% in roots, 66% in stems, 37% in leaves, and 50% in seeds. In addition, irrigation with treated graywater resulted in increased phosphorus (P) concentrations, with a 20% increase in roots, 32% in stems, 50% in leaves, and 64% in seeds at GWTP2. Regarding nitrogen (N), the highest concentrations were found in the roots and seeds. Irrigation with treated graywater led to a significant increase in N content across quinoa organs, with a 63% increase in roots, 53% in stems, 21% in leaves, and 32% in seeds. The results also show that irrigation with treated graywater significantly enhanced the accumulation of calcium (Ca2+) and magnesium (Mg2+) in quinoa. Calcium is primarily concentrated in roots, leaves, and stems, with a decrease in seeds. TGW treatment increased Ca2+ levels by 9% in roots, 38% in stems, 17% in leaves, and 18% in seeds. Magnesium content was also elevated, with increases of 11% in roots, 74% in stems, 41% in leaves, and 76% in seeds. These findings highlight the positive effect of irrigation with treated graywater on mineral uptake in quinoa.  
Figure 6: The variation in (**a**) fresh and (**b**) dry biomass of the different quinoa organs (roots, stems, leaves, and seeds).
## 2 Method Introduction and Data Processing
### Standardized Processing  
The Sigmoid function is used as the activation function of the logistic regression model. If the distribution range of the sample data is very wide, the model can easily directly fall into the saturation area, resulting in the disappearance of the gradient. Hence, it is necessary to standardize the data in advance. The processing method often uses Z-score standardization and min-max standardization.
# Efficient Knowledge Distillation for Brain Tumor Segmentation
## 3 Methods
### Coordinate Distillation  
Inspired by [37], we recommended adding location information to the channel attention (AT) [33], prompting the attention graph to capture remote dependencies with accurate location information, which is especially important for spatial structure capture in segmentation tasks. Based on the works of channel attention transfer, we assumed that a neuron's absolute value indicated its importance. Specifically, given the input \(E\), we summed the channel dimension along the horizontal \(w\) and vertical \(h\) coordinates, so the output could be described as follows.  
\[\varphi^{h}(\mathrm{E})=\sum_{i=1}^{c}\left|E_{i}^{h}\right|^{2} \tag{2}\]  
\[\varphi^{w}(\mathrm{E})=\sum_{i=1}^{c}\left|E_{i}^{w}\right|^{2} \tag{3}\]  
where \(c\) is the number of channels. Next, we multiplied the output \(\varphi^{w}\) and \(\varphi^{h}\) to obtain the final coordinate attention map \(y\).  
\[y=\sigma(\varphi^{w}(\mathrm{E}))\times\sigma(\varphi^{h}(\mathrm{E})) \tag{4}\]  
where \(\sigma(\cdot)\) is the _Sigmoid_ operation. This training was calculated by the following loss.  
\[L_{CD}=\sum_{(i,j)\in M}\left\|\frac{y_{i}^{s}}{\left\|y_{i}^{s}\right\|_{1}}- \frac{y_{j}^{t}}{\left\|y_{j}^{t}\right\|_{1}}\right\|_{1} \tag{5}\]  
Figure 1: The proposed knowledge distillation architecture. The orange box represents the teacher network, and the blue box represents the student network. They obtained the same image as input. The green box between the two networks, from left to right, is the coordinate distillation (CD) module and the knowledge distillation (KD) module.  
where \(M\) is the set of index pairs of all possible positions with the same embedding size, \((i,j)\) is the samples in \(M\). The operation \(\left\|\cdot\right\|_{1}\) is the \(l_{1}\) normalization.