esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Evolution Analysis of Coal Fracture Damage Process Based on Digital Image Processing
## 4 Results and Discussion  
In this study, the uniaxial compression test of coal rocks filled with calcite vein is carried out by using RFPA2D-DIP software, and the following beneficial results are obtained:  
1. When the calcite vein has low dip angle (\(0^{\circ}\), \(15^{\circ}\) and \(30^{\circ}\)), the internal stress of coal rocks is mainly concentrated in the calcite vein under uniaxial compression, which is uniform and has high compressive strength. When \(\alpha\) = \(0^{\circ}\), the maximum compressive strength of coal rocks is 40.39 MPa. The final failure mode of coal rocks is complex, showing tensile failure and accompanied by more shear failure. In the process of uniaxial compression test, the input energy and accumulated elastic energy are larger,  
Figure 11: Relationship between loading stress, AE account, Accumulative AE and AEenergy and step number of coal under low dip angle.  
Figure 12: Relationship between loading stress, AE account, Accumulative AE and AEenergy and step number of coal under high dip angle.  
the elastic energy index and impact energy index of coal rocks are larger and the energy released during failure is larger.
2. When the calcite vein has a high dip angle (45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\) and 90\({}^{\circ}\)), the internal stress of coal rocks is mainly concentrated in the calcite vein under uniaxial compression, but the distribution is uneven and the compressive strength is low. When \(\alpha=60^{\circ}\), the compressive strength is the lowest, 23.45 MPa. The final failure mode of coal rocks is single, showing tensile failure accompanied by a small amount of shear failure. In the process of uniaxial compression test, the input energy and accumulated elastic energy are small, the elastic energy index and impact energy index of coal rocks are relatively small and the energy released during failure is low.  
It can be seen from the above test results that a calcite vein has a significant impact on the mechanical properties, failure mode and energy evolution characteristics of coal. In the mining process, by artificially changing the angle between mining direction and calcite vein, the mechanical properties, failure mode and energy evolution characteristics of coal seam under the influence of mining can be changed, which can be used to control energy release disasters in the mining process and optimize mining technology. According to the test and analysis results, under high dip angles (45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\) and 90\({}^{\circ}\)), the coal compressive strength and energy release are low, and the mining is relatively safe.
## 3 UAV Platform for Multi-Sensor Photogrammetry Aerial Mapping  
Nowadays, technology and sensor minimization enable the development of the multi-sensor custom-made UAVs (unmanned aerial vehicles) for multi-sensor aerial mapping. As already mentioned, UAVs can be applied to acquiring spatial data about the condition of buildings [59], cultural heritage [56], but also for acquiring spatial data of larger areas for risk assessment of damage detection and mapping, e.g., after earthquakes [57; 58]. These platforms are equipped with the newest sensors for in-flight position determination. These sensors include GNSS (Global Navigation Satellite System), RTK (real-time kinematic), and IMU (inertial measurement unit) systems that allow positioning in centimeter accuracy in real time. Today, significant importance in the development of the UAV platform was devoted to developing new technologies for stabilization and orientation of the multi-sensor system [62; 63; 64]. The UAV platforms for multi-sensor aerial mapping can be made based on the open-source or commercial flight controllers and technologies and are able to carry out mapping missions autonomously [65; 66; 67]. For high-accuracy photogrammetric measurement of the safety
## 1 Introduction
[PERSON] [27] proposed a rock damage model based on the linear energy dissipation law, including the compression energy dissipation coefficient, and verified it by combining the experimental results of four typical brittle rocks under conventional uniaxial compression and single cycle loading unloading uniaxial compression. Significant advancements have been made in the study of plastic behavior during the process of rock damage and failure [8; 28]. [PERSON] [29] incorporated the interaction between damage caused by the plastic deformation of rocks and the effects of void nucleation, growth, and agglomeration into the constitutive model, and conducted numerical studies to elucidate the influence of initial porosity and its evolution on macroscopic mechanical properties of rocks. [PERSON] [30; 31] employed a non-associated plastic flow rule to describe the plastic deformation of rocks and a damage-plasticity constitutive model was developed to account for the hardening and softening characteristics exhibited by rocks during the loading process. [PERSON] [32] investigated the impact of confining pressure and plastic strain on the post-peak strength of brittle hard rocks, proposing an enhanced Hoek-Brown model that incorporates the determination of post-peak plastic strain. [PERSON] [33] introduced a plastic-damage model for rocks based on the generalized plastic shear strain, providing a mathematical description of rock damage evolution during various loading and unloading processes. Despite significant progress in studying the energy mechanisms and plastic behavior in rocks, previous research has primarily treated them as independent research topics, neglecting the comprehensive consideration of energy dissipation and plastic growth during rock damage [34; 35]. The lack of research on the correlation between energy dissipation and plastic growth has resulted in an unclear relationship between rock dissipated energy and plastic strain evolution [36; 37]. To address this issue, this study aims to explore the correlation between energy dissipation and plastic growth during rock damage. By analyzing the energy evolution characteristics and plastic strain evolution characteristics during rock damage separately, the evolution relationship between energy dissipation and plastic growth is established. Based on this, methods for calculating input energy, elastic energy, and dissipated energy during rock damage are proposed and verified using experimental results. A modified rock damage model is proposed for the development of numerical simulation programs based on the correlation between energy dissipation and plastic growth. The surrounding rock damage and vertical stress evolution process of the Yimaqianqiu Coal Mine were simulated, and the risk of inducing rockburst was analyzed.
# Visual MAV Tracker with Adaptive Search Region
## 2 Model
### Fully Convolutional Neural Network  
The proposed algorithm detects the object in the SR image, scaled to a \(100\times 100\) resolution. The FCNN is responsible for predicting a bounding box in the SR; the bounding box can be characterized by four variables, \(x_{r}\), \(y_{r}\), \(w_{r}\), \(h_{r}\), where \(x_{r}\) and \(y_{r}\) are the offsets from the top left in the scaled SR and \(w_{r}\) and \(h_{r}\) represent the width and height of the object in the scaled SR, respectively. FCNN is composed of 15 convolutional layers and five max pooling layers, as listed in Table 1. It utilizes 3 \(\times\) 3 convolutional filters based on VGG [15] and Darknet [11; 12; 13]. The number of convolutional filters is increased over the layers. Zero paddings were applied to convolutional layers to enhance the tracking performance when  
Figure 2: SR shrinkage caused by truncation. The blue box represents the SR, and the yellow box represents the tracking result calculated with a fully convolutional neural network (FCNN). (**a–d**) show sequential frames.  
the object was close to the SR edge. All layer blocks (except the final layer) include batch normalization and LeakyReLu.  
The images used for training FCNN were prepared by cropping the SR from the full image because FCNN tracks the object in the SR. The cropped regions for training the FCNN were randomly selected to induce robustness to variations in the size and location of the object in the image. Specifically, the ratio of the cropped region scale to the object size was randomly selected from a value between 1.2 and 1.7. The location of the cropped region is determined such that a 90% or higher portion of the target is contained in the cropped region. In addition, image rotation and color variation were also conducted to facilitate data augmentation.  
The resulting model was trained using the following loss function.  
\[L=\left(x-\hat{x}\right)^{2}+\left(y-\hat{y}\right)^{2}+\left(w-\hat{w}\right) ^{2}+\left(h-\hat{h}\right)^{2}, \tag{2}\]  
where \(x\), \(y\), \(w\), and \(h\) represent the estimated location and size, and \(\hat{x}\), \(\hat{y}\), \(\hat{w}\), and \(\hat{h}\) are the true location and size. The Adam optimizer was used for training. The model was trained for 1000 epochs with a learning rate of \(10^{-4}\). The model was implemented using the PyTorch 1.8.0 framework with Compute Unified Device Architecture (CUDA) 11.1. GeForce RTX 3090 GPU and Xeon Silver 4215R 3.20 GHz CPU were used for training and testing.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Number of CNN Filters** & **Size/Stride** & **Output Size** \\ \hline Convolutional layer & 16 & \(3\times 3/1\) & 100 \(\times\) 100 \\ \hline Convolutional layer & 32 & \(3\times 3/1\) & 100 \(\times\) 100 \\ \hline Convolutional layer & 16 & \(3\times 3/1\) & 100 \(\times\) 100 \\ \hline Convolutional layer & 32 & \(3
# Evaluation of Multi-Source High-Resolution Remote Sensing Image Fusion in Aquaculture Areas
## 2 Materials and Methods
### Raff Culture Zone Fusion Evaluation
#### 3.2.3 WV-2 Fusion Evaluation  
Figure 10 shows the fusion results of WV-2 for a floating raft culture area. Table 7 gives the quantitative evaluation results for WV-2 fusion data.  
The standard deviation, information entropy, and average gradient in each band of the fused images (Figure 10) are higher than in the original multispectral data, indicating that each image contains more information after fusion. The standard deviation in the B1 and B2 bands of the PCA results is higher than with other fusion modalities. The standard deviation in the B3 and B4 bands of the GS results is also higher, indicating increased grayscale contrast. The information entropy, average gradient, and correlation coefficient in each band of the PCA fusion results are higher than in other fusion images, indicating the PCA data contain diverse and well-distributed information. These images exhibit higher definition and the best fusion effects.  
The fused images exhibit varying degrees of spectral distortion, deviation, and information loss. The spectral distortion and the deviation index of each band are the smallest in the NNDiffuse results. The spectrum remains unaltered, which is consistent with a visual inspection. Gray mean values in the B1, B2, and B3 bands of the NNDiffuse results are closest to the original multispectral data. Although the PCA results exhibit a little larger degree of spectrum distortion, PCA is more suitable for WV-2 because the information retention of PCA is higher than the NNDiffuse images, which is more important to information extraction.
## 4 Result
### The Impacts of the Residual Zenith Tropospheric Delay on the SPP Solution
To further evaluate the SPP accuracies and stabilities influenced by different ZTD models at different altitudes, the globally distributed stations were divided into groups along to their station heights (orthometric height) and RMSs of the vertical SPP biases by using these models were calculated within each altitude region. Considering that ZTD has a large number of stations within 500 m, and the variation is large within 500 m [42], the group of station heights were subdivided into seven groups. The station numbers at different altitudes regions are shown in Table 6 and RMSs of the vertical SPP biases by using the nine models at different altitudes regions are shown in Figure 11.  
Table 6 shows that nearly 75% of these stations are below 500 m and stations with height above 2000 m are rare. Figure 11 shows that RMSs of the vertical SPP biases by using the nine models generally show significant downward trends as the heights increase except for the saas+GPT2 and VMF3 models. The saas+GPT2 model performs best on stations with heights between 500-1000 m, and significantly worst on stations above 2000 m. The VMF3 performs equally at different height regions. The results coincide with the fact that ray tracing used for producing VMF3 products considers changes with height. In general, the vertical SPP solutions would suffer more significantly influence by the uncorrected ZTD at lower stations.
# Estimate of Passive Time Reversal Communication Performance in Shallow Water
## 4 Results
### Channel Characteristics at the Experimental Sites  
Figures 3 and 4 show the measured CIRs and their corresponding eigenrays at a receiver depth of 15 m for the nominal source-receiver ranges of 100 m (top), 500 m (middle) and 1 km (bottom) at sites 1 and 2, respectively. The arrival time of each CIR was aligned based on the first arrival, and its timing was normalized to that of the peak of the first arrival. The eigenrays were calculated using a ray-based acoustic propagation model BELLHOP [25] using the sound speed profiles shown in Figure 1b,c.  
Figure 3: Channel intensity impulse responses measured at a source depth of 32 m and a receiver depth of 15 m and their eigenray tracing outputs for source-receiver ranges of (**a**) 115 m; (**b**) 500 m and (**c**) 1023 m at site 1. D, B, and S in eigenray tracing outputs indicate direct, bottom and sea surface paths. The combinations of S and B mean multi-bounce paths. For example, SB is surface-bottom path. The seabed-grazing angle is noted for the B path.  
Figure 2: Block diagram of the single-input multiple-output (SIMO) passive time reversal communication system using four vertically separated receiver array. TR: time reversal; PLL: phase lock loop; LPF: low-pass filter; BER: bit error rate; SNR: signal-to-noise ratio.  
The CIRs of the two different sites have very different characteristics; for example, the CIRs at site 1 are less reverberant, whereas those at site 2 are highly reverberant, having strong multipaths following the first arrival. This difference is mainly attributed to the difference in the bottom loss as mentioned in Section 3. At site 1 (where the bottom is considered an acoustically slow bottom and the angle of intromission was estimated to be about 18.2\({}^{\circ}\)), most energy incident on the bottom was transmitted into the sediment, resulting in a large bottom loss. See Reference [23] for a detailed discussion of the channel characteristics of site 1. In contrast, the bottom of site 2 is considered to be a relatively fast bottom, and the critical grazing angle was estimated to be about 24\({}^{\circ}\). The rays incident on the bottom interface at grazing angles less than 24\({}^{\circ}\) were totally reflected into the water medium, producing strong multipaths. As shown in Figure 4, strong multipaths persist for even 50 symbols in time (one symbol duration is 1 ms). Thus, one can anticipate that site 2 is in a severe ISI environment when compared to site 1.
# Plant-Derived Phytobiotics as Emerging Alternatives to Antibiotics Against Foodborne Pathogens
## 3 Mechanisms of Action of Phytobiotics Against Foodborne Pathogens
### Induction of Oxidative Stress  
Pytobiotics exert their antibacterial effects, in part, by targeting molecular pathways associated with oxidative stress in microbial cells. They may act directly by inducing oxidative stress through the generation of reactive oxygen species (ROS). For example, leaf extract from _Litsea salicifolia_ has been shown to trigger ROS production in bacterial cells, leading to oxidative damage and cell death [149]. A similar oxidative mechanism is observed for hop-derived phytochemicals, which induce oxidative damage in bacterial cells, contributing to their antimicrobial activity [150]. One of the primary molecular targets of phytobiotics is catalase--an enzyme responsible for decomposing hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) into water and oxygen, thereby protecting bacterial cells from the toxic effects of ROS [151; 152]. Inhibition of catalase by phytobiotics results in the accumulation of hydrogen peroxide and other ROS within bacterial cells, leading to lipid peroxidation, protein misfolding, and DNA damage. For instance, astragalain, a flavonoid glycoside extracted from _Gymnocarpipium dryopteris_, has been shown to inhibit catalase activity [153].
## 3 Proposed Method
### Cognitive Emotion Recognition Using Active Learning Based on Complexity and Uncertainty  
Most emotion recognition methods are supervised learning methods. However, this kind of method not only needs a large number of samples, but the samples usually also do not have enough labels. Labeling the training samples not only requires the participation of a large number of experts, but it also takes a lot of time [63]. It seriously affects the training efficiency and feasibility of emotion recognition methods. Therefore, a method that can fully utilize a small sample of labeled data and unlabeled data to achieve efficient and accurate cognitive emotion recognition is proposed in this section.  
Active learning is a machine learning strategy that can actively select the most valuable data and query its labels, thereby reducing the cost of labeling.  
The key aspect of active learning is the sample selection criteria [64], for example, whether the sample represents the overall input mode of typical unlabeled data, the difference between an instance and the labeled data in diverse scenarios, etc. However, most active learning algorithms only use one selection criterion, which may limit their performance [65].  
This paper proposed a dual standard active learning method based on uncertainty and complexity. Uncertainty was used to describe the confusion of the samples, and complexity was used to measure the difference between local and global samples.
## 2 Materials and Methods
### Preparation of Extract for Cell Viability Assay  
Extracts were prepared according to ISO 10,993 standards: Biological evaluation of medical devices; Part 5: Tests for in vitro cytotoxicity; and Part 12: Biological evaluation of medical devices, sample preparation and reference materials (ISO 10993-5:2009 and ISO/IEC 17025:2005). Briefly, MTZ-loaded matrices were manufactured as described, with the difference that this process took place directly in the wells of a 24-well plate (Nunc, Roskilde, Denmark) in a laminar chamber to maintain aseptic conditions. Next, the dressings were immersed in 2 mL of sterile DMEM medium and incubated for 24-48 h at 37 \({}^{\circ}\)C/5% CO\({}_{2}\) (Binder, Tuttlingen, Germany). After incubation, the extracts were used in a cytotoxicity assay.  
Cytotoxicity Assay of MTZ-Loaded Matrices Toward Fibroblast and Osteoblast Cell Cultures  
The evaluation of potential cytotoxic effect displayed by the analysed formulations was tested using fibroblast and osteoblast reference cell cultures [27; 28; 29] (L929 and U2-OS, respectively) (ATCC, Manassas, VA, USA). Cells were incubated in a cell culture flask (Nunc, Roskilde, Denmark) at 37 \({}^{\circ}\)C/5% CO\({}_{2}\) in DMEM medium supplemented with 10% bovine serum (Sigma-Aldrich, Hamburg, Germany). When the cultures reached confluence, the cells were detached from the flask surface using trypsin/EDTA. Next, the suspended cells were centrifuged (400\(\times\)\(g\) for 5 min); the cells were then resuspended in fresh DMEM medium. Cells were then counted using a Brucker chamber, and 100 \(\mu\)L of 10\({}^{5}\) cfu/mL of cell suspension was plated in a well of a 24-well plate. The cells were incubated for 24 h at 37 \({}^{\circ}\)C/5% CO\({}_{2}\). Next, the medium was removed, and previously prepared extracts (please refer to the preparation of extract section of this manuscript) were introduced to the cells. The samples were then incubated for the 24 h at 37 \({}^{\circ}\)C/5% CO\({}_{2}\) conditions. After incubation, the medium was removed and 100 \(\mu\)L of Neutral red (NR) dye (40 \(\mu\)g/mL; Sigma-Aldrich, Hamburg, Germany) was introduced to the wells of the plate. The cells were incubated with NR for 2 h at 37 \({}^{\circ}\)C. After incubation, the dye was removed and the wells were rinsed with PBS (Sigma Aldrich, Hamburg, Germany) and left to dry at room temperature. Subsequently, 150 \(\mu\)L of a destain solution (50% ethanol 96%, 49% deionized water, 1% glacial acetic acid; POCH) was introduced to each well. The plate was vigorously shaken in a microtiter plate shaker for 30 min until NR was extracted from the cells and formed a homogenous solution. Next, the value of NR absorbance was measured spectrometrically using a microplate reader (Multi-scan GO, Thermo Fisher Scientific, Waltham, MA, USA) at 540-nm wavelength. The absorbance value of the cells not treated with extracts was considered 100% of potential cellular growth (positive control).
## 2 Materials and Methods
### Point Source Tracking and Motion Compensation  
The overall workflow of the reconstruction process is divided into five steps (see Figure 1):The reconstruction process is divided into five distinct steps, which are described below:  
1. **LOR Centroid:** To address motion-related issues, we track the rat's movement during the scan. The centroid position of all LORs is calculated every 50 milliseconds, representing the movement center;
2. **Quick-Movement Subtraction**: Rapid movement is identified using the \(v_{max}\) parameter, derived from periods with minimal centroid variation. Such periods are indicative of minimal rat motion. Removing these high-movement intervals helps reduce motion artifacts, vital for small animal studies;
3. **Obtaining transformations**: We use a reference image from the most stable part of the scan. The acquisition is divided into 12.5 ms frames, reconstructed with low iterations while considering rapid movement removal. Rigid transformation matrices are derived by comparing point source locations with the reference;
4. **Non-Precise transformations subtraction**: To assess the quality of our transformations, we calculate a discrepancy measure, \(\chi^{2}_{fr}\), for each frame: \[\chi^{2}_{fr}=\frac{\sum_{s}^{N}(p_{s}^{ref}-T(p_{s}^{fr}))^{2}}{N}\] (1) In this equation, \(N\) represents the total number of point sources, \(p_{s}^{ref}\) is the position of source \(s\) in the reference image, and \(T(p^{fr}s)\) is the position of source \(s\) in a specific frame \(fr\) after applying the transformation \(T\). Frames with \(\chi^{2}\) values below a set limit (\(\chi^{2}max\)) are retained, as rigid transformations may not fully account for the rat's flexible skin, ensuring more accurate image reconstruction;
5. **Final reconstruction**: With the transformation parameters obtained for all frames, we proceed with the reconstruction process. Each event within a frame is transformed based on its corresponding transformation, adjusting scanner positions. As the scanner position changes during reconstruction, we need to adapt the standard Expec  
Figure 1: Workflow of the reconstruction process with motion tracking and compensation.  
tation Maximization Maximum Likelihood (EMML) algorithm to ensure accurate reconstruction. We modify sensitivity corrections \(a_{ij}\) as follows:  
\[a_{ij}=\frac{1}{T_{acq}}\int_{0}^{T_{acq}}T(t)a_{ij}dt \tag{2}\]  
where \(T_{acq}\) is the total acquisition time and \(T(t)\) represents the transformation at each time point; it should be noted that voxel \(i^{\prime}\) in \(a_{ij}\) may not correspond to the same voxel \(i\) after applying \(T(t)\).