esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Visual Tracking via Patch Descriptor and Structural Local Sparse Representation
## 3 Patch Descriptor and Structural Local Sparse Representation
### Patch Descriptor  
Although the patch-based local sparse representation with the alignment-pooling process in [9] can capture both partial information and spatial information, it does not consider the different status among these patches, and this may influence the tracking performance when the appearance of patches  
Figure 1: Target region division. (**a**) Sampling patches; (**b**) sampling sub-patches; (**c**) a patch distribution map.  
of the target varies inconsistently. To address this issue, we assigned patch descriptors for different patches according to reconstruction errors of sub-patches using sparse representation.  
The detailed analysis of target region division was given in Section 3.1. The dictionary base of each patch, obtained by clustering all the sub-patches in the patch at the same positions of the template, is \(\mathbf{P}=[p_{1},p_{2},\ldots,p_{\text{K}}]\in\mathbb{R}^{2\times\text{K}}\), where z is the feature dimension of each dictionary, and K is the number of dictionaries. Let \(\mathbf{S}=[\mathbf{s}_{1},\mathbf{s}_{2},\ldots,\mathbf{s}_{\text{L}}]\) denote the vectorized sub-patches of each patch of a target candidate, where \(\mathbf{s}_{i}\in\mathbb{R}^{x\times 1}\) is the \(i^{\text{th}}\) sub-patch, and L is the number of sub-patch. For each patch, when sparse coefficients of sub-patches in different patch were calculated, we used different dictionary base respectively, and its calculation formula was written as following:  
\[\min_{x_{i}}\lVert\mathbf{s}_{i}-\mathbf{P}x_{i}\rVert_{2}^{2}+\gamma\lVert x_{i} \rVert_{1},\text{ s.t.}x_{i>0} \tag{3}\]  
where \(x_{i}\in\mathbb{R}^{\text{K}\times 1}\) is the sparse coefficient of \(i^{\text{th}}\) sub-patch in each patch, and \(\gamma\) is the regularized parameter. The reconstruction error of all the sub-patches in each patch can be calculated by the formula, \(\varepsilon_{i}=\lVert\mathbf{s}_{i}-\mathbf{P}x_{i}\rVert_{2}^{2}\), where \(\varepsilon_{i}\) is the reconstruction error of the \(i^{\text{th}}\) sub-patch in each patch. Let \(\delta_{i}\) denote a factor of noise of the corresponding \(i^{\text{th}}\) sub-patch. It is defined by \(\delta_{i}=\left\{\begin{array}{cc}0,&\varepsilon_{i}<\varepsilon_{0}\\ 1,&\text{otherwise}\end{array}\right.\), where \(\varepsilon_{0}\) is a threshold (\(\varepsilon_{0}=0.04\) in our work). Each element of the patch descriptor \(\rho=[\rho_{1},\rho_{2},\ldots,\rho_{N}]\in\mathbb{R}^{N\times 1}\) was defined as follows:  
\[\rho_{j}=1-\sum_{i=1}^{L}\delta_{i}/L,\ j=1,2,\ldots,N \tag{4}\]  
where \(\rho_{j}\) is the \(j^{\text{th}}\) patch descriptor representing the degree of noise pollution of the patch, and \(L\) is the number of sub-patch. The smaller the value of \(\rho_{j}\) is, the more serious the patch will be corrupted.
## 3 Effect of UV-C on the Mechanical Properties of Potato

Table 1: _Cont._was reported that reduced weight could occur at the epicticular wax morphology of the pretreated tuber, while transpiration and respiration also contributed to the enhanced weight loss. Preliminary work on the mechanical property of pre-injured potato was conducted on different varieties of potato tuber at a wavelength of 253.7 nm, a power density of 80-100 \(\upmu\)W\(\cdot\)cm\({}^{-1}\), and exposure time of 5, 10, and 15 min and five months of storage. The result led to a smaller weight loss of samples exposed to UV-C for 10 and 15 min compared to control samples [95]. The author suggested the use of UV-C in combination with conveyors and a multi-spaced irradiator to potentially reduce weight loss caused as a result of mechanical injury during post-harvest handling. Conversely, in other research work, the effect of UV-C irradiation in the presence of fluorescence light and darkness on the weight loss of two potato species (_Agata_ and _Monalisa_) was investigated at 0, 2.3, 6.9, 11.5, or 34.5 kJ\(\cdot\)m\({}^{-2}\) dose, 3.83 mW\(\cdot\)cm\({}^{-2}\) flow density and 254 nm. the author found higher weight loss than the control sample for tubers stored under fluorescent light and UV-C in darkness and they related this result with the early-stage development of sprouting as this requires dry matter from the tuber [96]. It has been found that the UV-C dose in the range of 5-20 kJ\(\cdot\)m\({}^{-2}\) is able to suppress the sprout propagation [97]. Research on a comparative study between sodium acid sulfate and UV-C treatment on the storage quality of fresh-cut potato was conducted. Firmness as part of the textural analysis was tested after exposing the potato for 3 min from 0 to 25 days of storage duration at 4 \({}^{\circ}\)C. Firmness was found to be stable in the entire duration of storage for UV-C treatment. However, it was indicated that, in the combined treated sample with UV-C and Sodium acid sulfate, the firmness was higher than the control sample in the later storage duration [98]. A study reporting on the effect of UV-C light on a sprout length depicted that those potatoes treated with 13.6 kJ\(\cdot\)m\({}^{-2}\) resulted in a shorter length of sprouts compared to other UV-C doses and untreated tubers [99]. The dimensional growth of potato sprouts caused because of UV-C exposure was reported to be unknown. However, it was speculated to be a result of physical change or related to its tubular biochemistry [97]. A similar study was conducted on the impact of UV-C on the deformation of potato tuber (variety _Vieta_, _Lord_ and _Owacja_) at varied UV-C doses (69.4, 86.3, and 171.9 \(\upmu\)W\(\cdot\)cm\({}^{-2}\)), exposure time (1, 10, and 30 min. ), and irradiator height (40, 70 and 100 cm). Consequently, it was confirmed that UV-C induced a minor percentage of deformation of tubers, as compared to the control combination [100].
# A Comparative Study of Deep Learning Models for Dental Segmentation in Panoramic Radiograph
## 3 Materials and Methods
### Segmentation Models
#### 3.3.4 Nano-NET  
The Nano-Net model was proposed in the paper by [22], and its objective is to perform accurate segmentation in real time. As a result, Nano-Net uses the encoder-decoder model (see Figure 4), but in the encoder a pre-trained model is used so that the convergence of the model is faster. The pre-trained model used in Nano-Net was MobileNetV2 [23]. The decoder, in turn, is formed by three blocks. In the paper by [22], three configurations of Nano-Net are proposed (Nano-Net-A, Nano-Net-B and Nano-Net-C); the difference between the models is the number of feature channels used, where A is 32, 64 and 128; B is 32, 64 and 96; and C is 16, 24 and 32. In the present paper, Nano-Net-A was used because it was the model that obtained the best results. For more details about Nano-Net, see the paper by [22].  
Figure 3: DoubleU-Net architecture (adapted from [21]).
# Performance Analysis of Cooperative Non-Orthogonal Multiple Access in Visible Light Communication
## 3 BER Analytical Expression
### Cooperative PD-NOMA  
#### 3.2.1 Single Relay  
In the case of a single relay (one branch, two users), as shown in Figure 6, errors at \(U_{1}\) occur either when \(U_{2}\) receives in error the transmitted signal through the LED-\(U_{2}\) link, and \(U_{1}\) receives correctly the transmitted signal through \(U_{2}\)-\(U_{1}\) link; or when LED-\(U_{2}\) transmitted signal is received correctly, and \(U_{2}\)-\(U_{1}\) transmitted signal is received in error. Hence, the LED-\(U_{2}\)-\(U_{1}\) link has error probability given by:  
\[P_{\epsilon_{\text{LED}}-\upsilon_{2}-\upsilon_{1}}=\left[1-P_{\epsilon_{U_{ 2}}-\upsilon_{1}}\right]P_{\epsilon_{\text{LED}}-\upsilon_{2}}+\left[1-P_{ \epsilon_{\text{LED}}-\upsilon_{2}}\right]P_{\epsilon_{U_{2}}-\upsilon_{1}} \tag{28}\]  
where \(P_{\epsilon_{\text{LED}}-\upsilon_{2}}\) and \(P_{\epsilon_{U_{2}}-\upsilon_{1}}\) are the error probabilities of LED-\(U_{2}\) link and \(U_{2}\)-\(U_{1}\) link, respectively.  
Figure 5: Signal constellation for three users.  
Figure 6: Cooperative PD-NOMA system model for two users.  
After combining the received signals from the direct transmission and \(U_{1}\) using MRC, the probability of error of \(U_{1}\) is expressed in consistence with the presented model as in [20] by:  
\[P_{\varepsilon_{1}}=\left[1-P_{\alpha_{\text{LED}}-u_{1}}\right]Q\left(\frac{ \sqrt{2}(SNR_{\text{LED}}-u_{1}+SNR_{\alpha})}{\sqrt{SNR_{\text{LED}}-u_{1}+ \frac{SNR_{\alpha}t^{2}}{SNR_{\text{LED}}-u_{1}}}}\right)+P_{\alpha_{\text{LED} }-u_{2}}\,Q\left(\frac{\sqrt{2}(SNR_{\text{LED}}-u_{1}-SNR_{\alpha})}{\sqrt{ SNR_{\text{LED}}-u_{1}+\frac{SNR_{\alpha}t^{2}}{SNR_{\text{LED}}-u_{1}}}}\right). \tag{29}\]  
The equivalent SNR for the link LED-\(U_{2}\)-\(U_{1}\) is represented as in [13] by  
\[SNR_{\alpha t}=\frac{SNR_{\text{LED}}-u_{2}SNR_{\text{2}}-u_{1}}{SNR_{\text{ LED}}-u_{2}+SNR_{\text{U}_{2}}-u_{1}+1} \tag{30}\]  
where  
\[SNR_{\text{LED}}-u_{2}=\ \rho_{k}(h_{2}a_{2})^{2} \tag{31}\]  
and  
\[SNR_{\text{U}_{2}}-u_{1}=\ \rho_{k}(g_{2,1}q_{2,1})^{2}. \tag{32}\]  
Substituting Equations (30)-(32) in Equation (29), we get the probability of error of \(U_{1}\). However, we calculate the probability of error of \(U_{2}\) using Equation (25).
###### Abstract  
The monitoring of conveyor belt deviation based on computer vision is the research topic of this paper. A belt conveyor system equipped with cameras and a laser generator is used as the test apparatus. Two kinds of conveyor condition monitoring algorithms are developed, applied and compared in this paper. Method 1 is to extract the edges of the conveyor belt based on the methods of edge detection and line fitting, and an analysis method is proposed to quickly determine whether the conveyor belt is off course. This method simplifies the information processing process after edge line extraction and realizes the fast and effective state detection of the conveyor belt. A conveyor belt deviation detection system was built in Method 2 with the aid of laser technology and the Labview platform. The result of deviation detection was more accurate and reliable with the addition of the laser line. It is observed that both methods can perform well for belt deviation detection, especially Method 2 that resulted in higher reliability and accuracy with faster processing speed. It provides new ideas for ensuring the safe and efficient operation of the conveyor.  
Keywords:belt conveyor; running deviation; computer vision +
Footnote †: journal: Applied Sciences
# RCS and Aero Surfaces Control Allocation Research on RLV's Re-Entry Phase
## 1 Introduction  
At the beginning of the unpowered re-entry phase, the aero surface (AS) could not meet the attitude control system's requirements of the reusable launch vehicle (RLV), due to the thin air and low dynamic pressure [1], which led the reaction control system (RCS) to be the dominant control actuator. With the increase of dynamic pressure, the control efficiency of AS rises, while the control efficiency of RCS, in contrast, drops. Therefore, the actuators were switched in the order of RCS only, RCS and AS controlled together, and AS only [2]. In order to maintain the stable and accurate attitude control, the control allocation algorithm of RCS and AS should be studied [3,4].  
At present, there are mainly five ways to deal with the hybrid control problem of RCS and AS [5]: (1) flight command requirements based allocation [6], such as the proportional allocation method, which has a simple logic, and good tracking performance, as proved by the simulation; (2) control variables based allocation [7], such as the daisy-chaining allocation method [8]; (3) sampling control, which has the advantage of dealing with the actuators' discrete control output and maintaining the stability of the control system; (4) multi-input control, in which the allocation is designed into the controller design part; (5) intelligent control, which refers to dealing the hybrid control problem with the fuzzy control [9], neural network, genetic algorithm [10], and other intelligent methods [11]. Each method has its pros and cons. For example, the intelligent control has good performance, but is hard to evaluate the stability [12]. Even though, the hybrid control technology has made some progressuntil now [13], for the most part, RCS is normally assumed to be continuous [14], and the stability analysis of the whole control system is seldom studied [15].  
This paper mainly focuses on the RCS and aero surface hybrid control allocation research on the RLV's re-entry phase. The dynamic model of RCS, AS, and RLV, which forms the basis of the RLV re-entry phase, has been discussed in Section 2. In the next section, the proportional allocation scheme, based on dynamic pressure, daisy-chaining allocation scheme based on allocation errors, and a multi-object-based optimal allocation scheme, are presented, to deal with the hybrid control allocation problem, in turn. Then, the stability of the control system has been analyzed on the basis of the Lyapunov theory. Finally, Section 4 verifies the feasibility of the aforementioned allocation schemes by simulations, following their contrastive analysis.
# Survey on Unmanned Aerial Vehicle for Mars Exploration: Deployment Use Case
## 4 Simulation Results and Discussion
### Sensors Block
#### 4.2.4 Pressure Sensor  
The pressure sensor is used to sense pressure, which will further work in calculating altitude. As the UAV flies higher in altitude, the pressure of the air falls slightly. The pressure sensor uses this trivial change in pressure to guesstimate how the elevation of the UAV changes.  
UAV will be deployed as a payload on Mars' surface by a lander on the surface of Mars. Satellite communication is used to communicate between the lander, UAV, and Ground Control System (GCS). The Ka band (uplink: 34.2 to 34.7 GHz; downlink: 34.2 to 34.7 GHz) communicates between the UAV and the GCS. Figure 5 shows the UAV prototype graph for the flight on Mars' surface. From the graph, it is observed that the UAV reaches a maximum altitude of 2200 m. The UAV then stabilised and hovered at an altitude of 380 m for around 1 min and 40 s. The rotors do not need to work as hard on Mars' surface to counteract its effort since its gravity is only one-third that of the Earth, making it easier for them to work.  
Since the air density of Mars' surface is lower, the altitude is increased. Therefore, we may conclude from the above findings that it is possible to successfully deploy UAVs on Mars' surface. However, other space exploration methods would be hampered by UAV's problems. Figure 6 shows the acceleration graph for the UAV flight  
Figure 5: Altitude graph for a UAV flight on the surface of Mars.  
The acceleration graph is used to investigate the acceleration of the UAV prototype flight on Mars' surface. The graph shows that the UAV prototype is initially at rest and that after acquiring a velocity of 8 m/s at 40 s, the UAV prototype becomes stable at an acceleration of 320 m/s\({}^{2}\) and begins to hover. The acceleration is calculated through a fundamental formula: Acceleration = velocity/time.  
In addition, the data is organised into blocks, with each block containing one or more transactions. Each new block in a cryptographic chain binds to all the blocks before it so that tampering is nearly impossible. A consensus process validates and agrees on all transactions within the blocks, ensuring that each transaction is accurate and suitable. As a result, there was almost no chance of significant security threats for this Mars flight. Figure 7 shows the graph of the Euler's angles and behaviour of various sensors inbuilt in the UAV, such as pressure sensor, camera, Inertial Measurement Unit (IMU) sensor, and an ultrasound sensor.
# New Approach for Brain Tumor Segmentation Based on Gabor Convolution and Attention Mechanism
## 2 Materials and Methods  
When performing the task of brain tumor segmentation on 3D MRI images, it is usually necessary to slice the images, which may lead to the waste of some useful slice information. Although existing segmentation algorithms for multi-feature fusion are able to utilize the 3D data of medical images to a certain extent, these algorithms usually compensate for the lost information by fusing the results of the segmented results instead of actively utilizing the slicing information of the 3D data of the medical images in the segmentation process. Therefore, in order to utilize the contextual information more effectively, a 3D multimodal MRI brain tumor image segmentation network called Ga-U-Net is proposed in this study. Its network structure is shown in Figure 1. The network as a whole adopts the 3D U-Net encoder-decoder architecture.  
Firstly, in order to improve the accuracy of the features obtained from brain tumors and edema regions, especially the information regarding fine tumors, a Gabor convolution kernel is added to the original encoder part to form the GA module of the proposed architecture, which firstly performs a convolution operation on the input image, and since the Gabor convolution kernel, as a kind of visual sensory field, possesses a superior ability of spatial and frequency feature extraction, it is expected to effectively identify and segment regions such as brain tumors and edema through its sensitivity analysis of texture so as to enhance the feature extraction effect. After that, its convolved result is added with the ordinary convolution result of the original network and then sent to the next layer through the down-sampling operation, and the next layer carries out the same operation until the last layer. In the decoder part, the up-sampling operation has linear interpolation, transposed convolution, inverse pooling, etc. Here, the transposed convolution method is chosen, and finally the Softmax activation function is used to map the multi-channel features to the corresponding tumor region to get the final segmentation result.
# Life Cycle Carbon Assessment of Mortars with Carbonated and Non-Carbonated Recycled Aggregates
## 4 Life Cycle Impact Assessment
[12, 27] investigated the environmental performance of mortars with RAs. The authors verified that the environmental impacts of using RAs are strongly related to their transport distance. The transportation plays a crucial role in this context and can significantly limit the use of recycled aggregates, especially in countries with extensive territories. [PERSON] and [PERSON] [27] also found that the transport distance and the amount of RAs incorporated are the main aspects regarding the environmental performance of cementitious products with RAs. The authors stated that the use of RAs can present better results in GWP if the transportation distance is up to 22 km. Another matter to consider is the cement used; [PERSON] et al. [7] highlighted that the GWP of cementitious materials is more affected by the type of cement used than by the type of aggregates (natural or recycled). Nevertheless, the authors analysed the environmental impacts of concrete with natural and recycled coarse aggregates from cradle-to-gate and the results showed that the use of coarse recycled concrete aggregates reduced the GWP up to 18% when compared to the natural ones. In this study, it was possible to conclude that the aggregate treatment and the additional transportation distances travelled by these aggregates were more than compensated by carbonation benefits. The mortars' carbon footprints were considerably lower than the products that are currently being commercialized. This research has found that using carbonated recycled aggregates instead of non-carbonated recycled aggregates results in a lower environmental impact. This finding can be seen as a solution to mitigate the environmental impact associated with long transportation distances.
## 2 Materials and Methods  
### Cultivation of Drosophila melanogaster and Escherichia coli  
_Wolbachia_-free _D. melanogaster_ Canton S flies with a conventional gut microbiome (CONV) were reared on an autoclaved yeast-glucose diet (10/10 food) containing 10% D-glucose, 10% inactivated brewer's yeast and 1.2% agar to which 0.04% phosphoric acid and 0.42% propionic acid were added as preservatives. For germ-free or so-called axenic (AX) and axenic flies that were associated with a single bacterial strain of choice (so-called gnotobiotic flies, see below), the preservative acids were omitted from the 10/10 food. For oviposition plates, a few spoons of frozen, concentrated grape juice were added to a deep purple color. For feeding on liquid food, the fly food was adjusted to 5% D-glucose and 10% yeast extract without any agar (5/10 food). Flies were maintained at 25 \({}^{\circ}\)C with a 12 h light/12 h dark cycle and transferred to fresh food every two weeks unless stated otherwise. All fly handling occurred gently by swiftly flipping bottles by tapping or using brushes and tweezers while flies were sedated using CO2 sorting pads or via cold treatment (15 min on 0-4 \({}^{\circ}\)C cold ice). Flies were homogenized via grinding with plastic pestles or in a FastPrep24 bead homogenizer using lysis matrix D in sterile phosphate-buffered saline (PBS).  
The _E. coli_ strains that were used in this study are listed in Table 1. Unless stated otherwise, _E. coli_ was grown on lysogeny broth (LB; 1% ryptone, 1% NaCl, 0.5% yeast extract) supplemented with 1.5% agar for solid plates. Liquid cultures were incubated at 30 \({}^{\circ}\)C while shaking orbitally at 200 rpm unless stated otherwise. When _E. coli_ strains were associated with flies, I always used axenic flies that were devoid of their endogenous microbiome. For confirming the absence of endogenous microbial inhabitants in _Drosophila_, I plated homogenized flies on agar plates of modified De Man, Rogosa and Sharpe medium (mMRS; 1.25% vegetable pettone, 0.75% yeast extract, 2% D-glucose, 0.5% sodium acetate, 0.2% potassium dibasic trihydrate, 0.2% ammonium citrate dibasic, 0.02% magnesium sulfate, 0.005% manganese sulfate hydrate, 1.2% agar).  
Antibiotics were added to the cultures, LB plates, 10/10 or 5/10 food for example when assessing the sensitivity of the development of _D. melanogaster_ towards antibiotics or when necessary to select for specific bacterial strains or plasmids: ampicillin (AP) at 100 \(\mathrm{\SIUnitSymbolMicro g}\) mL\({}^{-1}\) and kanamycin (KM) at 40 \(\mathrm{\SIUnitSymbolMicro g}\) mL\({}^{-1}\).