esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Intelligent Recognition Method of Ground Penetrating Radar Images Based on SAHI
## 1 Introduction
Slicing-aided hyper inference (SAHI) is a recently developed technique for small object detection on high-resolution images, which can be integrated with various types of object detection methods. It achieves further enhancement of small object detection by automatically overlapping and segmenting original images during inference, significantly improving small object detection capabilities [15]. [PERSON] et al. [16] proposed an improved object detection algorithm, YOLOX_w, based on YOLOX-X, which was developed for UAV aerial images with complex backgrounds and a large number of small targets. They preprocessed images using SAHI to slice them according to set overlap rates, allowing small targets to occupy larger pixel areas on slices. Combining SAHI with data augmentation strategies effectively enhances small object detection performance in UAV aerial images. [PERSON] et al. [17] proposed a deep learning model-based, automatic detection method for submarine pipelines and introduced SAHI to solve the challenge of detecting small targets with inconspicuous features in large-size, low-resolution images. [PERSON] et al. [18] utilized the VisDrone-DET dataset to combine the real-time detection and recognition model RT-DETR-X with the SAHI methodology, which was used to significantly improve the model detection accuracy, especially for small targets. Currently, there is no case of SAHI being used in GPR image recognition. In a GPR image, the structural loose distress only occupies a small portion of the pixels in the original image. After using SAHI, the original image is sliced according to the set size and overlap rate, so that the structural loose distress can occupy more pixels in the slice, and has a relatively greater visibility compared to the original image, which can avoid the leakage of detection to a large extent. The result is a larger visibility compared to the original image, which largely avoids missed detections. In this paper, we first intelligently expand the GPR images of structural loose distresses based on DCGAN. Then we use YOLOv5 to detect the structural loose distresses in the GPR images. In the training stage, SAHI is used for data preprocessing, which slices the original image into the input size of the model to eliminate the loss of the target details of the image in the compression process. This allows the model to better learn distress features. In the inference stage, SAHI is combined for detection. Through experiments, it is verified that adding DCGAN-generated images and using SAHI in the training and inference stages can significantly improve the recognition accuracy of structural loose distress.
# A Communication Framework for Image Transmission through LPWAN Technology
## 3 Proposed Framework
### Stage II: Compressive Sensing
Another approach is to use a sensing matrix with algorithms such as Iterative Hard Thresholding (IHT), Orthogonal Matching Pursuit (OMP), Gradient Projection for Sparse Reconstruction (GPSR), or Two-Step Iterative Shrinkage/Thresholding (Twist) [90; 96; 97; 98]. Previous research evaluated these algorithms, and the results enable us to select Twist as the best output in our case. We can reduce the computational process, data payload, processing time, and energy consumption with the help of CS.
# On Self-Interference Cancellation and Non-Idealities Suppression in Full-Duplex Radio Transceivers
## 2 Proposed System Model and Techniques
### Sinr  
Figure 10a shows SINR after the implementation of analog SI cancellation techniques. Kautz and GALL filters are better than CSI techniques, because for Kautz and GALL filters, although separate antennae are used, no antenna cancellation techniques are required. Figure 10b shows the SINR before digital signal processing and after implementing the digital SI cancellation technique, i.e., the Kalman filter. By applying the Kalman filter on the digital side, the system SINR improved to a significant value, i.e., approximately equal to \(SINR_{Ideal}\), which falls because of the presence of the residual SI signal.
## 3 Results and Discussion
### Cytotoxic Analysis of ZnO-NPs  
The extracts from most parts of the plant are known to contain substantial amounts of metabolites that are responsible for stabilizing, reducing, and capping efficacy [59]. It is with this regard that \"green\" or biosynthesized NPs are more stable with higher activity compared to conventionally synthesized NPs [60]. Here, the green and conventional ZnO-NPs were investigated for their ability to prevent the proliferation of cancer cells. The results of the assay are presented in Table 2, which showed that the cell viability of both HEK 293 and HeLa cells decreased with increasing concentrations of both studied ZnO-NPs (Figure 7). However, the standard used (5-Fluorouracil) showed higher anti-tumor activity compared to both ZnO-NPs on both cell lines. At higher concentration (25 to 100 \(\mathrm{\SIUnitSymbolMicro g}\)/mL), the green ZnO-NPs lead to lower cell viability on both cell lines compared to the standard and conventional ZnO-NPs (Figure 7a,b). As a result, the green ZnO-NPs showed a lower  
Figure 5: UV-visible absorption spectra (**a**), and Tauc plot (**b**) of biosynthesized ZnO-NPs using \(C\). _dcinense_ fruits extract.  
Figure 6: UV-visible absorption spectra (**a**), and Tauc plot (**b**) of ZnO-NPs synthesized without \(C\). _dcinense_ fruits extract.  
concentration of the substance that led to half-maximal response (IC\({}_{50}\)) on both cell lines compared to conventional ZnO-NPs. These results suggest that the _C. chinense_ mediated ZnO-NPs can effectively suppress the growth of studied cancer cells. Similar findings have been reported in literature [61; 62; 63], whereby green synthesized ZnO-NPs were proven to be effective in fighting cancer cells. Moreover, the study by [PERSON] et al. [63], observed the up-regulated apoptotic gene expression, which confirmed the occurrence of apoptosis. This suggests that the biosynthesized ZnO-NPs might induce the apoptosis of cancer cells through decreased intracellular ROS. Furthermore, ZnO-NPs has been reported to have the capacity to dissolve inside lysosome and donate Zn\({}^{2+}\) ions [64]. These ions are involved in scavenging the ROS [65].
## 3 Results and Discussion
### Load-Temperature Coupled Stress Analysis  
#### 3.2.1 Single-Axis Load-Temperature Coupling Stress  
The single-axis load-temperature coupling stress variation rule is shown in Figure 12. It can be seen that the location of the maximum temperature-load coupling stress under single-axis loading occurs directly below the wheel load. As the depth of the road surface increases, the magnitude of the change in coupled stresses becomes smaller and less affected by temperature stresses.  
A sub-high stress peak occurs in the upper layer at 20 cm from the edge of the wheel load with time variations. A comparison of the peaks of the second-highest stresses at different times of the day reveals that they are mainly related to road surface temperature stresses. The second-highest stress is largely absent in the winter when the temperature stress is low, and this stress occurs mainly in the high-temperature period between 12:00 and 16:00. The secondary high-stress peaks disappear with increasing depth. Outside of 30 cm from the edge of the wheel load, the time-dependent coupled stress action curves show different patterns. By comparing the coupling stress change curve and the temperature stress change curve, it is found that this change rule is caused by the temperature stress. When the temperature stress is manifested as compressive stress, the coupling stress curve at this location shows a gradual decrease, and vice versa, a decrease followed by an increase. It was also found that the temperature-load coupling stresses decreased the fastest from ~8:00-12:00 h under these three seasons.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Structural Level**} & \multicolumn{3}{c}{**Maximum Tensile Stress (MPa)**} \\ \cline{2-4}  & **Fall** & **Winter** & **Spring** \\ \hline Top surface course & 0.250 & 0.816 & 0.212 \\ Intermediate layer & 0.081 & 0.500 & 0.073 \\ Under surface layer & 0.066 & 0.178 & 0.060 \\ Base & 0.053 & 0.047 & 0.046 \\ Subgrade & 0.019 & 0.021 & 0.017 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Maximum tensile stress of each structure of pavement.
# End-Effectors Developed for Citrus and Other Spherical Crops
## 2 Materials and Methods
### Clamping Mechanism Design
#### 2.2.4 Analysis of Stresses on Citrus Fruits  
When the end-effector carries out the picking operation, the first gripping finger will grasp the fruit. There are six force points between the three flexible fingers and the citrus skin; the mathematical model of citrus and its force analysis is shown in Figure 3.  
The contact type between citrus and gripping fingers can be considered as point contact with friction, and when the gripping fingers tighten on the citrus fruit at this time, the critical condition that the citrus can be held stably without slipping is that the gravity  
Figure 3: Schematic diagram of the forces on citrus. Note: \(\mathrm{f_{i}}\) is the static friction force in the vertical direction at the i-th contact point, \(\mathrm{N_{i}}\)\(\mathrm{N_{i}}\) is the positive pressure at the ith contact point, \(\mathrm{N_{i}}\) i = 1,2,3,4; \(\mathrm{mg}\) is the gravity of the citrus fruit, \(\mathrm{N}\).  
Figure 2: Diagram of citrus picking at different sizes and diameters. (**a**) Large-diameter citrus picking map; (**b**) Small-diameter citrus picking map.  
of the fruit is equal to the maximum static friction between the fruit and the fingers [43]. The pressure between the citrus skin and the end-effector is set to N. Since the end-effector finger is centrally symmetric, the pressure between the clamping finger and the citrus skin is equal, i.e.,  
\[N_{1}=N_{2}=N_{3}=N_{4}=N_{5}=N_{6} \tag{1}\]  
When the end-effector gripping finger holds the citrus steadily, the force analysis is as follows:  
\[N_{\text{min}}=\min\limits_{i=1}^{6}f_{i}=mg \tag{2}\] \[N_{\text{min}}\geq\frac{m_{\text{max}}g}{6\mu}\] \[f_{i}=\mu N_{i}\]  
where \(m_{\text{max}}\) is the maximum mass of the citrus fruit, kg;  
\(g\) is the acceleration of gravity, taken as 9.8 m/s\({}^{2}\);  
\(\mu\) is the coefficient of friction between the citrus skin and the flexible finger contact material.  
Applying a certain cushioning material on the surface of the pressure sensor on the three gripping fingers [44] can increase the friction coefficient and improve the surface softness of the fingers, which, in turn, can reduce the required output positive pressure an, at the same time, can reduce the mechanical damage that may occur during the gripping process, which is conducive to achieving a soft grip. Different materials for soft gripping fingers can cause different damage to citrus. To determine the best cushioning material, the friction coefficient between the citrus peel and ABS material was 0.64 based on the friction characteristics of citrus peel in Section 2 and considering that the biological yield point of citrus is located between 10 and 20 mm of displacement [45], and the rupture point is located about 10 mm after the biological yield point. Therefore, ABS (3D printing material) was chosen as the flexible gripping finger material.
## 1 Introduction  
Meat and meat products are perishable due to their chemical composition. Their shelf life is limited by the development of undesirable microflora, enzyme activity or oxidation processes [1]. The growth of undesirable microflora leads to a decrease in sensory quality of food and, particularly, its safety. Proliferating bacteria can cause foodborne infection and intoxication. Therefore, it is crucial to reduce the possibility of the above-mentioned health hazard occurrence in food [2]. The health safety of food, including meat products, can be ensured by the use of functional additives, proper processing and packaging, and maintaining refrigerated storage conditions [1]. In the meat industry, chemical food additives are used to control specific microorganisms. Many chemicals show potential as food preservatives, but only a few are approved for use in food production. According to Regulation (EC) No 1333/2008, food \"preservatives are substances which prolong the shelf-life of foods by protecting them against deterioration caused by micro-organisms and/or which protect against growth of pathogenic microorganisms\" [3]. They should be used at the lowest possible dose (less than 0.2%) to achieve the intended effect [4]. The amount that may be used and the residue that may be left in food products are strictly defined by food law, e.g., Commission Regulation (EU) No 1129/2011 [5]. Commission Regulation (EU) No 1129/2011 permits the use of only a few chemical additives to meat preservation, e.g., nitrites (potassium and sodium nitrite), nitrates (potassium and sodium nitrates), sodium ascorbate, and sorbic acid. These substances may only be added to a specific category of meat products; thus, it is important to define it correctly. For instance, sorbic acid can be added at quantum satis to surface treatment of dried meat product (category: non-heat-treated processed meats), but only in strictly defined quantities (1000 mg/kg) to pate (category: heat-treated processed meats). Despite legal restrictions, some consumers are wary of chemical food additives and are more interested in using natural substances derived from plants and plant materials to preserve and improve meat quality [6]. Food producers and technologists are trying to meet those expectations by creating new recipes of meat products based on natural ingredients [4]. Essential oils or extracts derived from plants, such as oregano [7], clove [8], sage [9], marjoram [10], garlic [11] are used in meat systems as natural preservatives. Therefore, this study provides the information on the effect of commercial functional additives containing spice extracts on microbiological safety and sensory quality of homogenized meat products.
## 3 Epicardial Adipose Tissue
### Anatomy and Functions of Epicardial Adipose Tissue  
The intrathoracic adipose tissue surrounding the heart can be divided into epicardial adipose tissue (EAT) and pericardial fat (PF). The PF is located between the pericardial visceral and parietal layers; it derives from the primitive thoracic mesenchyme and is supplied by branches of the internal thoracic artery [93]. On the other hand, EAT is placed between the pericardial visceral layer and the myocardial muscle covering about 80% of the cardiac surface; it originates from the splanchnopleuric mesoderm and is vascularized by branches of coronary arteries. Lastly, the portion of the EAT immediately contiguous with the adventitial layer of coronary arteries is called pericoronary adipose tissue (PCAT) [94].  
The EAT is microscopically composed of different cells, including adipocytes, resident monocytes, immune cells, and nerve cells [95]. Interestingly, no muscle fascia between EAT and myocardium is present, allowing direct communication between these two organs through paracrine and vasocrine mechanisms [93]. Physiologically, EAT plays a protective role for the heart. It acts as a local energy store and provides free fatty acids to the myocardial tissue in times of high demand [96]. It also releases adipokines with anti-inflammatory, antioxidant, and vasodilator functions (adiponectin and adrenomedullin) [97]. In addition, the EAT offers mechanical support preventing coronary artery torsion during cardiac contraction, exerts a thermoregulatory function [96] and acts as an immune organ [98].  
However, in pathological conditions, EAT becomes pro-arrhythmogenic and pro-atherogenic. Indeed, in patients who have CAD, EAT shows pro-inflammatory features with an increased concentration of inflammatory M1 macrophages than anti-inflammatory M2 macrophages [99], higher production of interleukin-6 (IL-6), interleukin-1 (IL-1), and tumor necrosis factor-alpha (TNF-a) associated with reduced production of adiponectin and adrenomedullin, and an elevated concentration of reactive oxygen species (ROS) [100].  
The mechanisms of EAT dysfunction might translate into atherosclerotic plaque development are not entirely known. However, it has been proposed that macrophages, together with T- and B-lymphocytes, are triggered by the Toll-like receptor (TLR) binding that stimulates nuclear factor-kB (NF-kB) and JUNN-terminal kinase (JNK) [101; 102]. The activation of these two molecules leads to increased interleukin production, such as IL-6, facilitating endothelial cell permeability and monocyte adhesion [103]. The entire process is powered by the increased ROS levels that further favor endothelial dysfunction and interleukin secretion [100].
## 2 Methodology and Related Works
### Methodology
#### 2.1.2 Origin-Destination (OD) Matrices  
Mobility is usually described using OD matrices, which indicate the number of trips between relevant locations in a city [8]. Each trip can have multiple legs, if a passenger makes intermediate stops and transfers between vehicles to get to their final destination. Thus, when building OD matrices, the destination of a trip is considered as the final destination of the sequence of legs, where a passenger is assumed to go to perform an activity. Different divisions can be used to analyze mobility at a finer (e.g., specific locations, bus stops) or coarser grain (e.g., municipalities, neighborhoods). OD matrices can be built for specific periods of time to characterize mobility in different days (e.g., working days vs. weekends) or times of the day (e.g., peak vs. non-peak hours).  
Traditionally, OD matrices are generated using information from mobility surveys. Unless performed regularly, surveys offer a partial and outdated view of mobility patterns. Additionally, in large cities, where mobility analysis requires detailed ramification and time disaggregation, surveys demand very large sample sizes to compute results with statistical significance. As a consequence, surveys are usually a very expensive mean to characterize urban mobility. Thus, there is a growing interest in using data analysis to estimate OD matrices from available sources of urban data.  
Automatic Fare Collection (AFC) systems automate ticketing of a public transportation network. Most AFC systems are comprised of fare media, read/write devices for these media, networks for communication, and back-office systems. Contactless smart cards are the de facto fare media in AFC systems. [PERSON] et al. [9] provided a thorough review on the use of smart cards in public transportation systems. Different alternatives to estimate OD matrices using AFC data are reviewed next. The trip-chaining method used in this work is described in Section 4.
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 3 Proposed Methods
### Region Filling
#### 3.4.2 Outline of the Region-Filling Algorithm  
The algorithm begins to fill the area with drone coverage, specifically, circles, from the origin towards the other corner. This is achieved by filling each \(x\)-directional and \(y\)-directional inner layer at each iteration until the whole region is covered. The algorithm considers the following attributes of a drone object \(d\):  
1. \((x,y)\)--\(x\) and \(y\) coordinates of the position of a drone
2. \(r\)--radius of the cone-shaped network coverage
3. \(h\)--height of the cone-shaped network coverage
4. \(b\)--an indication of the base station; a value of 0 means the base station of the drone is at (0, 0) and 1 means the base station of the drone is at (m, n)
5. \(e\)--leftover energy of the drone when the drone reaches its assigned location starting from its base station
6. \(h_{ref}\)--the maximum value among the heights of reference points associated with each drone, which is needed in order to cope with the terrain of the affected region
7. \(north\), \(south\), \(west\), \(east\)--the northern, southern, western, and eastern neighbouring drones of the network drone  
We use \(h_{ref}\) to denote the height of the reference point of a drone. There are many reference points associated with each drone object. These points can be the corner points  
Figure 4: The base stations are located at diagonally opposite corners.  
where a circle intersects other circles, the \(x\)-axis, or the \(y\)-axis; thus, \(h_{ref}\) is the maximum value of the heights of all reference points of a particular drone. A view of reference points and \(h_{ref}\) is shown in Figure 5.  
The algorithm fills the rectangular region layer by layer, starting from the base station at (0,0) and moving towards the one at (\(m\), \(n\)) while assigning each drone to the nearest base station. We follow the approach of filling the circles along the \(x\)-axis first, followed by the \(y\)-axis. The algorithm maintains a few more variables, which are provided below:  
1. \(D\)--an ordered collection of drone objects (the output of the algorithm)
2. \(X_{ref}\)--a reference \(x\)-coordinate for \(y\)-directional filling
3. \(Y_{ref}\)--a reference \(y\)-coordinate for \(x\)-directional filling  
An outline of the method is shown in Figure 6.