esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Bayesian Techniques--MCMC and Particle Filter  
The Markov Chain Monte Carlo method and the Sampling Importance Resampling particle filter, used to estimate parameters and state variables, were applied using Matlab(r) software, version R2022b. In this study, three distinct models were used, each incorporating a different formulation for the specific speed of cell growth in each one of them. In Equations (2)-(4), each replacement represents a different ODE system to be evaluated; for model 1, Equations (2), (5)-(7), for model 2, Equations (3), (5)-(7), and for model 3, Equations (4)-(7).  
Based on the evaluated models, three different parameter vectors were used for each model: \(\mathbf{P_{1}}\), \(\mathbf{P_{2}}\), and \(\mathbf{P_{3}}\), represented in Equations (9)-(11) for models 1, 2, and 3, respectively. The vector of status variables was represented as \(\mathbf{Y^{VE}}\), according to Equation (12).  
\[\mathbf{P_{1}}=[k_{1},k_{2},k_{3},k_{4},k_{5},n,m,q,p,Y_{cell},\mu^{\max}] \tag{9}\]  
\[\mathbf{P_{2}}=[k_{1},k_{2},k_{3},k_{4},k_{s},n,m,q,p,Y_{cell},\mu^{\max},k_{p}] \tag{10}\]  
\[\mathbf{P_{3}}=[k_{1},k_{2},k_{3},k_{4},k_{s},n,m,q,p,Y_{cell},\mu^{\max},k_{s _{i}}] \tag{11}\]  
\[\mathbf{Y^{VE}}=\left[U,cell,\mathrm{Fe}^{2+},\mathrm{Fe}^{3+}\right] \tag{12}\]  
In the Bayesian approach, information about the parameters that appear in the problem formulation, available before measurements and coded according to a probability distribution, can be considered, along with information from experimental measurements, to improve the statistical inference process. Therefore, solving the inverse problem by the Bayesian method means obtaining the posterior probability density [26].  
The information obtained by the experimental measurements is combined with that assumed for the parameters before carrying out the measurements through the mechanism known as [PERSON] theorem [27; 28; 29]:  
\[\pi_{posterior}(\mathbf{P})=\pi(\mathbf{P}|\mathbf{Y^{exp}})=\frac{\pi_{prior }(\mathbf{P})\pi(\mathbf{Y^{exp}}|\mathbf{P})}{\pi(\mathbf{Y^{exp}})} \tag{13}\]  
where \(\pi_{posterior}(\mathbf{P})\) is the posterior probability distribution, \(\pi_{prior}(\mathbf{P})\) is the prior probability distribution, \(\pi(\mathbf{Y^{exp}}|\mathbf{P})\) is the likelihood function, and \(\pi(\mathbf{Y^{exp}})\) is the marginal probability distribution of the measurements, which represents a normalization constant.  
There are situations in which the corresponding probability distributions presented in Equation (13) cannot be obtained analytically. In these cases, there is a need to use simulation techniques that use samples of \(\pi(\mathbf{P}|\mathbf{Y^{exp}})\) to obtain information about \(P\). Among these techniques, the Markov Chain Monte Carlo method stands out when a posteriori calculation involves parameter estimates, and the Particle Filter when estimates of status variables are obtained.
## 4 Experiments and Analysis
### Ablation Experiment
#### 4.5.5 Model Efficiency Evaluation  
To analyze the segmentation efficiency of different networks, we compared the computational consumption of all networks. Table 6 shows the comparison results, from which it can be seen that the number of parameters in PSANet is about 46.51 M, and the memory requirement is about 177 MB, only higher than SegNet and UNet++, indicating that PSANet is relatively lightweight in terms of memory usage. In addition, PSANet outperforms MAFF-HRNet, SegFormer, and Deeplabv3 plus in terms of Flops, inference time, and training time, indicating that PSANet can complete segmentation tasks more quickly while ensuring lower memory consumption.  
Figure 11: Evaluation metrics obtained using different loss functions in (**a**) Anhui Province and (**b**) Jiangsu Province.  
Figure 10: Attention heatmaps at the intermediate stage of FRM (**a**) using SAM only, (**b**) using CAM only, and (**c**) using CBAM.
## 2 Materials and Methods
### Experimental Methods
#### 2.2.3 Wavelet Analysis  
Wavelet analysis is an effective means of signal processing. Wavelets are uneven and asymmetrical waveforms of adequately limited duration with an average value of zero. Wavelet analysis breaks up the mother wavelet signal into a shifted and scaled version. The typical wavelet transformation of a sine wave is presented in Figure 2.  
Figure 2: Wavelet transformation of a sine wave.  
In Fourier analysis, the signals are decomposed into different sine waves. Therefore, an irregular wavelet performs better than the steady sine wave for rapidly changing signals as it can provide better information about specific and appropriate locations. Wavelet analysis can also show any discontinuity, breakdown, trend, noise, coefficient, etc., of signals [48]. In this study, wavelet analysis was conducted using the MATLAB toolbox. There are two types of wavelet analyses, namely, discrete wavelet transform and continuous wavelet transform. There are various subgroups of these two types of wavelet transforms.
# Path Optimization Using Metaheuristic Techniques for a Surveillance Robot
## 1 Introduction
The obtained optimized trajectories outperform the initial suboptimal trajectories, showing a significant reduction in travel time and an increase in coverage percentage when the entire target space cannot be covered with the specified parameters. The fitness function used here is compared with that of other previous works and provides better results. * Three evolutionary comparison techniques are compared. The results show that, although all are efficient in this CCPP problem, PS gives superior performance in simple and medium-complexity environments. However, the PSO method excels in very complex environments. The organization of this paper is as follows. A brief state of the art is presented in Section 2. The case of use, including the experimental setup, is detailed in Section 3. The results obtained with the three proposed heuristic optimization approaches are presented and compared in Section 4. The strengths and weaknesses of each method are identified in terms of coverage, mission duration, and event detection efficiency. The paper ends with the conclusions and directions for future work.
# A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming
## 5 Uplink Allocation Algorithm  
The uplink allocation problem has been modeled as a potential game and we need to solve it for the correlated equilibrium. Generally, the process of solving Nash equilibrium is often a process of seeking the optimal solution by continuous iteration. Common iterative algorithms include Gauss-Seidel, Jacobi iterations, etc. The Jacobi iteration method is an earlier and simple algorithm, and its convergence speed is slow. Gauss-Seidel iteration is an improvement of the Jacobi algorithm. Although it has improved the convergence speed compared with the former one, it is still not suitable for real-time transmission service. In this paper, there is both competition and partial cooperation in the UAV cluster. In order to achieve a state of correlated equilibrium with a relatively fast convergence rate, the regret matching algorithm in [54] is adopted to design a distributed self-learning algorithm for the game model. The general idea of this algorithm is that the probability of a UAV changing its strategy is proportional to the regret degree of the UAV not choosing other strategies in the past. The implementation steps are as follows:  
**(1) Initialization:**  
Each player can choose the minimum rate as the first strategy from the strategy space \(\{R\_space\}\) at the initial time \(t=1\). Thus, we provide each UAV with a small initial video encoding rate at this step. In fact, the initial strategy can be any value in the rate range of the strategy space.  
**(2) Iterative Update Process:**  
_Strategy Update:_  
When time \(t\geq 2\), each player calculates the utility of the current strategy \(r_{i}\) and the one of another strategy \(r^{\prime}_{i}\), and gets the average difference between the two utilities:  
\[L^{t}_{i}(r_{i},r^{\prime}_{i})=\frac{\lambda}{t}L^{\lambda}_{i}(r_{i},r^{ \prime}_{i})+\frac{1}{t}[u^{t}_{i}(r^{\prime}_{i},r_{-i})-u^{t}_{i}(r_{i},r_{ -i})], \tag{10}\]  
where \(\lambda\) denotes for the time before \(t\), and that means \(\lambda<t\). Then, \(R^{t}_{i}(r_{i},r^{\prime}_{i})=\max\{L^{t}_{i}(r_{i},r^{\prime}_{i}),0\}\) and it is a measure of \"regretting\".  
_Strategy Decision:_  
Assuming that at time \(t\), the \(i\)-th player chooses the strategy \(r_{i}\), then at time \(t+1\), the player will reconsider the strategy. The basis for its choice of strategy will follow the probability distribution:  
\[\left\{\begin{array}{l}\pi^{t+1}_{i}(r^{\prime}_{i})=\frac{1}{\mu}R^{t}_{i} (r_{i},r^{\prime}_{i})\qquad\forall r^{\prime}_{i}\
eq r_{i},\\ \pi^{t+1}_{i}(r_{i})=1-\sum\limits_{r^{\prime}_{i}\
eq r_{i}}\pi^{t+1}_{i}(r^ {\prime}_{i}),\end{array}\right. \tag{11}\]  
where \(\mu>0\), and it is big enough. According to this distribution, we can select the strategy with higher probability for player \(i\) in the strategy space \(\{R\_space\}\). After several iterations, the selected results will not be changed and the algorithm will converge. If each UAV follows the above distributed algorithm to update its strategy, then the whole potential game eventually converges to the correlated equilibria.
# Roles of D-Amino Acids on the Bioactivity of Host Defense Peptides
## 3 Synthetic d-AA-Containing HDPs  
Chemical synthesis gives HDP designers a greater degree of flexibility not found in nature. Virtually any imaginable sequence combination of d-AAs could be envisioned and further subjected to experimental validation. Synthetic d-AA-containing HDPs were found to be effective against the same wide spectrum of pathogens as their all-l counterparts. In many cases, the d-AA-containing HDPs exhibit considerably better selective toxicity than that of their all-l enantiomeric counterpart, as in the case of cancer [44; 45], bacteria [1; 46], parasites [4] and fungus [3]. The selectivity index of d-AA-containing HDPs is capable of reaching values as high as 400 [9]. Additionally, the serum stability and resistance to proteolytic enzymes had been shown to improve significantly [10; 47] upon d-AA incorporation. Furthermore, it can also be effective in addressing potentially toxic immunogenicity, which is one hurdle facing the clinical application of HDPs [11]. Thus, the incorporation of d-AAs into peptides have been shown to improve their pharmacokinetic properties while in some cases may allow peptides to be administered systemically with great effect [13]. Selected examples of synthetic d-AA-containing HDPs are shown in Table S1. To date, the vast majority of d-AA-containing synthetic HDPs are linear. Particularly, the \(\alpha\)-helical structure is by far the most common secondary structure found in HDPs, although there are only a few HDPs that have been found so far with secondary structures other than the \(\alpha\)-helix such as \(\beta\)-sheets [9] or \(\beta\)-hairpins [48].
## 1 Introduction
The electrical conductivity and ionic transport numbers of SZY film deposited on NiO-YSZ (YSZ--yttria-stabilized zirconia) substrate, which was supposed to operate as a supporting anode, were determined, and the applicability of thin strontium zirconate based electrolyte in combination with Ni-cermet supporting anode in SOFCs was evaluated. SZY films were fabricated via a multi-step chemical solution deposition (CSD) technique [15; 29; 30]. The most significant advantages of this technique are the simplicity of complex oxide film deposition, moderate temperature synthesis and the convenience of film deposition on substrates with different shapes, which enables the fabrication of SOFCs with planar and tubular configurations. The obtained films were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX). The conductivity of the samples was studied using impedance spectroscopy (IS). The transport numbers of the thin Ni-cermet supported SZY membrane were determined with the help of a gas concentration cell and compared to those of the bulk SZY sample. Open circuit voltage (OCV), IS and current-voltage (CV) measurements were carried out on the concentration cells with the film and bulk SZY membranes. To the best of our knowledge, this is the first study of the electrical conductivity and transport numbers in the Ni-cermet-supported SZY membrane. Furthermore, it was shown that a relationship between the electrolyte conductivity and the oxygen partial pressure can be derived from the data on the effective conductivity and transport numbers of oxygen ions in the membrane operating in oxygen activity gradients, and hence the oxygen-ion transport number in the electrolyte can be evaluated in a wide range of \(p\)O\({}_{2}\).
# Quality Assessment of Components of Wheat Seed Using Different Classifications Models
## 2 Materials and Methods
### Imaging  
An industrial digital camera (DFK72 AUC02) with a CMOS sensor type was used for imaging. Unlike the prevalent cameras and camcorders, the horizontal and vertical pixels in this camera are equal, a value of 2.2 \(\upmu\)m, with horizontal and vertical resolutions of 2592 and 1944, respectively. To prevent image distortion in imaging margins, a Japanese telecentric lens (MP1614_mp2, focal length 16 mm, Type 2/3 model, Coputar Company) was attached. The camera was installed at a distance of 30 cm from the panel on which seeds were placed. Local calibration of the camera was performed using three coins with a specific area. The relationship between the number of pixels and measurements of the real sample was determined. Circular daylight fluorescent lamp [13] with a power of 22 watts and a conical chamber were used for lighting. To standardize the light conditions, the inner wall of the lighting chamber was painted white and then coated with magnesium oxide [14]. Between the lens of the camera and the circular lamp, a metal wall was placed to receive the reflected light from the grain samples. A photo resistor sensor controlled the stability of the light intensity. The camera was connected to a portable computer via a USB cable (shown in Figure 2). Owing to the high applicability and functionality of MATLAB, all ofthe programs and algorithms of the camera connection, image processing, modelling, and evaluation of classifying methods were analysed by MATLAB version 2015a. The programs were run in Toshiba TECRA_A9 series portable computer. The computer was equipped with an Intel dual core processor of 2.20 GHZ (Intel-R Core TM 2 Duo CPU), 1 GB RAM, and Windows XP sp3 operating system.  
Figure 1: (**a**) Sound white wheat, (**b**) small white wheat, (**c**) barley, (**d**) rye, (**e**) red wheat, (**f**) broken white wheat, and (**g**) shrunken white wheat.  
A black Steinbach cardboard (code 08) was used as the panel on which the grains were to be placed. Cardboard stencils were prepared with 30 holes of exactly the size of wheat grains. After putting each sample with 30 grains on the stencil over the black panel, the stencil was removed. The separated grains were put in the rail drawer, and by moving the drawer inside the lighting chamber, the grains were exposed to the camera's field of view. By running the graphical user interface program, the image of the sample was observed on the monitor, and by pressing the record button, the image of the grain was saved in the computer in RGB mode and five-megapixel resolution. In this way, 700 images (100 images of each grain variety) were obtained. Each image was composed of 30 grains.
## 4 Consumption and Use of Date Seeds
### Other Food Uses  
The possibility of using date seed powder as a partial substitute partly for wheat flour has been investigated in several studies. Date seed flour contains more fat and fiber than wheat flour, which affects several sensory attributes of baked goods, such as flat bread or cookie dough. These attributes directly relate to the acceptance or rejection of the final product. Mixing the flour for sourdough production with pulverized date seeds at a ratio of one-tenth helps to diminish bread staling and increases the cell wall thickness. No significant differences in sensory attributes were detected when comparing \"Barbari\" flatbread with and without date seed powder [33]. Substituting 1-3% of bread dough with date seed fiber has a limited effect on the end product. Compared to the standard bread, the volume of the baked bread was lower, but the stability was higher, and the bread color and crumb were slightly darker and redder [83]. Date seeds contain a high amount of flavonoids and total polyphenic content. By using a mixture of date seed powder and wheat flour for the cookie dough, the final product showed an increase in flavonoids and polyphenolics compared to cookies made with wheat flour alone. This increase in antioxidant activity enhances the quality of the cookies [84].  
Besides date seed powder, extracts, fatty acids, and oil can also be produced from date seeds [28]. The use of date seed oil was first described at the beginning of the 20 th century [27]. Despite the fact that date seeds contain only a small amount of oil, the industry should be further pursued due to the high quantity available at little to no cost [40]. Date seed oil contains a high amount of oleic acid, which has a beneficial effect on the stability of the oil [1].  
A recent study investigated the performance of date seed protein concentrate. The study found that high-intensity ultrasound treatment improved the functional properties of the protein concentrate, such as solubility, making it an interesting protein extract for the food industry [25].  
Adding roasted date seed powder to ice cream increases its total fiber content. This functional by-product can also enhance the nutritional value of other dairy products by increasing their fiber intake and antioxidant effect. It also gives the final product a unique appearance due to the reddish color contained in the seeds [10].
## 2 Materials and Methods
### Satellite Imagery Processing Sentinel-2 Data (Test Data 1)  
Materials used in this study primarily contained Sentinel-2 multispectral bands captured on 13 September 2021, processed by ESA (Test Data 1), and obtained using Copernicus Sci-Hub [25]. The Sentinel-2 product was characterised by granules indicative of a specific geographical location. Each granule comprised thirteen unique spectral bands, categorised into three distinct ground resolution levels: 10 m, 20 m, and 60 m. The 10 m bands were: visible Blue (B), Green (G), Red (R), and Near InfraRed (NIR); 20 m bands were Vegetation Red Edge bands, Narrow NIR, and two Short Wave InfraRed (SWIR) bands; and 60 m bands were Coastal Aerosol, Water, Vapour and SWIR Cirrus bands (Table 1) [26].  
Data preparation included several sub-procedures that were executed on Sentinel-2 bands.  
After selecting the AOI, corresponding Sentinel-2 Level-2A data from Copernicus Sci-Hub [25] representing bottom-of-atmosphere reflectance in cartographic geometry was downloaded. Each tile covered 100 \(\times\) 100 km\({}^{2}\) in extent [26]. Furthermore, all 20 m bands (Table 1) were resampled to the spatial resolution of 10 m. The enhancement method that enabled this possibility utilised in scientific research [27; 28; 29] employed the Sen2 Res plugin [30] in the SNAP [31] software. The Sen2 Res tool employed a sophisticated super-resolution technique to merge a band of lower resolution into one of higher resolution while ensuring that the reflectance value remained unaltered. This technique was particularly significant for its ability to delve into the geometric detail information shared among adjacent pixel contents in both the low- and high-resolution bands [30]. The Sen2 Res tool, through  
\begin{table}
\begin{tabular}{c l c c} \hline \hline
**Band** & **Label** & **GSD Resolution (m)** & **Wavelength (nm)** \\ \hline B02 & Blue & 10 & 457–522 \\ B03 & Green & 10 & 542–577 \\ B04 & Red & 10 & 647–682 \\ B05 & Red-edge 1 & 20 & 697–712 \\ B06 & Red-edge 2 & 20 & 732–747 \\ B07 & Red-edge & 20 & 773–793 \\ B08 & Near-infrared (NIR) & 10 & 784–899 \\ B8A & Near-infrared narrow (NIRn) & 20 & 855–875 \\ B10 & Shortwave infrared/Cirrus & 60 & 1360–1390 \\ B11 & Shortwave infrared 1 (SWIR1) & 20 & 1565–1655 \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of Sentinel-2 bands used for classification as Test Data 1. This table is created using the data provided in Sentinel-2 MSI User Guide [26] document.  
Figure 3: Location of the research area. Created using © OpenStreetMap contributors open data [23].  
its super-resolution method, achieved its dual objectives by ensuring the uniformity of reflectance values among adjacent pixels in the lower-resolution band and preserving the geometric details of sub-pixel components. This resulted in an enhanced resolution of satellite imagery, with essential details and reflectance values accurately maintained [27; 28; 29; 30].  
The next step in data preparation included clipping ten Sentinel-2 bands (B02-B08A, B10, and B11) using the AOI polygon in QGIS v.3.28 software [32].
## 4 Deep Reinforcement Learning-Based Approach for Solving the Problem
### Distributed Deep Reinforcement Learning-Based Algorithm  
It is vital to highlight that distributed deep q-learning is an extended version of the deep Q-learning algorithm that incorporates a series set of deep neural networks (DNNs) capable of parallel processing and thereby deriving the most appropriate solution efficiently [45]. This section presents a distributed deep reinforcement learning-based strategy that is presented to roughly decrease the total reward values shown in Equation (11).  
The architecture of our proposed distributed deep reinforcement learning-based algorithm is shown in Figure 3, in which a \(B\) number of DNNs are used with a shared and fixed-size replay memory \(M\). In addition, the application task's requirements are entered as input (i.e., system state) and the most appropriate offloading decision is obtained as output (i.e., offloading decision). More specifically, the system is provided by the state \(s_{t}\) and then each DNN can generate an offloading action \(y_{t}^{b}\) regarding \(f_{y_{t}^{b}}:s_{t}\to y_{t}^{b}\), where \(b\in B\) denotes as the index of DNN and \(f_{u_{t}^{b}}\) represents the \(b^{th}\) DNN with the weight value \(w_{t}^{b}\). Moreover, from all the generated actions, the action with the least reward value can be selected as output regarding \(y_{t}^{*}=arg\min_{b\in B}Q(s_{t},y_{t}^{b})\) and then stored as transition experience \((s_{t},y_{t}^{*})\) in replay memory \(M\). Subsequently, according to the stored transitions, the DNNs can be trained and then updated via choosing a random sample of data from memory \(M\).  
In Algorithm 2, the procedure to derive the near-optimum task offloading solution is outlined. This procedure is as follows. First, we have \(B\) number of DNNs which are initialized with different random values of weights \(w_{1}^{b}\), and the replay memory is assigned with an empty finite-size \(S\). Then, at time \(t\), the application task's requirements (i.e., \(\alpha\), \(\beta\) and \(\Gamma\)) is given as the input of state \(s_{t}\). Afterward, each DNN obtains the system state \(s_{t}\) and generates a set of \(B\) actions according to \(f_{w_{t}^{b}}:s_{t}\to y_{t}^{b}\), in which \(b\in B\) denotes the index of DNN and \(f_{w_{t}^{b}}\) represents the \(b^{th}\) DNN with the weight value \(w_{t}^{b}\). Subsequently, based on the reward function \(arg\min_{b\in B}Q(s_{t},y_{t}^{b})\), the action with the least reward \(y^{*}\) is chosen and then stored with the state \(s_{t}\) as transition value \(