esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Potential Source Contribution Factor Analysis (PSCF)  
The Potential Source Contribution Function (PSCF) analysis method utilizes backward trajectories to determine both the geographical origin and spatial dispersion patterns of pollution sources [57,58]. The Ambient Air Quality Standard (GB 3095-2012) [59] specifies the classification of ambient air functional areas, standard classification, pollutant items, averaging time and concentration limits, monitoring methods, validity regulations of data statistics, implementation and supervision, etc. This standard applies to the evaluation and management of ambient air quality. In this study, the study area was discretized into grids of 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) in accordance with the \"Ambient Air Quality Standard\" (GB 3095-2012). To ensure analytical consistency across different seasons, the daily average secondary standard value, denoted as \(\rho\) (PM\({}_{2.5}\)) = 75 \(\mu\)g\(\cdot\)m\({}^{-3}\), was adopted for both spring and summer, as well as the autumn and winter seasons. Trajectories yielding pollutant concentrations surpassing this threshold were classified as pollution trajectories. The value of the Potential Source Contribution Function (PSCF) predominantly hinges on the count of pollution trajectories (\(m_{ij}\)) transiting a specific grid (\(i,j\)). Computed as the ratio of all trajectories (\(n_{ij}\)) traversing said grid, the PSCF value signifies the likelihood of air masses traversing this grid influencing the air quality at the designated point of study. The pertinent formula is delineated in Equation (2).  
\[PSCF_{ij}=m_{ij}/n_{ij} \tag{2}\]  
Given that the Potential Source Contribution Function (PSCF) operates on a conditional probability framework, instances where the count of distant grid trajectory endpoints (\(n_{ij}\)) is relatively scant can introduce significant errors in the computed results. To mitigate this issue and enhance precision, a weighting coefficient, denoted as W45, is incorporated. Consequently, the adjusted potential source contribution factor (WPSCF) is formulated as WPSCF = \(W_{\bar{ij}}\)\(\times\)\(PSCF_{\bar{ij}}\). The specific definition of the \(W_{\bar{ij}}\) function is delineated in Equation (3).  
\[W_{ij}=\left\{\begin{array}{ll}1.00&80<n_{ij}\\ 0.72&20<n_{ij}<80\\ 0.42&10<n_{ij}<20\\ 0.05&n_{ij}<10\\ \end{array}\right. \tag{3}\]
# Compact Bistatic Iterative Passive Radar Based on Terrestrial Digital Video Broadcasting Signals
## 3 Proposal Description
### Channel Estimation  
During the idle period, i.e., the time in which there is no target within the scenario, the received DVB-T signal is processed at each antenna. As indicated earlier, not all the available subcarriers are transmitting information. Depending on the configuration, there are some subcarriers reserved for the transmission of pilots (known signals at both sides to estimate the channel or synchronize the frame). Using the pilot structure of the signal (see Figure 6[27] or [28], channel estimation is performed using LS in the frequency-domain. As shown in this figure (the pilot pattern PP1 in DVB-T2 is the same), in the frame structure, at sub-carrier 0, there is always a pilot (a known signal) throughout the whole transmission frame, denoted as the edge pilot (in DVB-T2, it is denoted as the closing frame symbol and it is the same); other scattered pilots throughout the frame swipe their position during frame transmission in order to cover the whole bandwidth (in DVB-T2, there are eight different pilot patterns). In general, the number of pilots per OFDM symbol depends on the FFT size (2 K, 8 K, or even 4 K, 16 K, or 32 K for DVB-T2) and configuration, and the percentage varies from the standard and the FFT size.  
Afterwards, the time-domain version is calculated by applying the inverse fast Fourier transform (IFFT) to the estimated channel to obtain \(\mathbf{\hat{h_{i}}}\). These calculations are continuously being estimated and used as reference. This time-domain estimation is similar to the power-delay profile of the system wherein the taps (reflections) of the channel can be identified. Using these taps (and the time), the different reflections can be observed.  
Figure 5: Error on the three coordinates for different \(\Delta_{z}\) when \(\Delta_{0}=0\). \(\Delta_{z}\) in meters.  
The LS channel estimation summarized above can be mathematically described as follows. Let \(\mathbf{a^{i}}\in\mathbb{C}^{1\times L}\) be the received time-domain signal at the \(i\)-th antenna and \(\mathbf{A^{i}}\in\mathbb{C}^{1\times L}\) be its frequency-domain signal. Within this \(\mathbf{A^{i}}\), the values at pilot positions \(\mathcal{K}\) are denoted as \(\mathbf{P}_{\mathcal{K}}\). The channel at pilot positions can be estimated using zero-forcing [32]:  
\[\mathbf{\hat{H}^{i}_{\mathcal{K}}}=\frac{\mathbf{A^{i}_{\mathcal{K}}}}{\mathbf{ P}_{\mathcal{K}}} \tag{14}\]  
Since only the channel is estimated at the pilot position, an interpolation process is required. In this case, simple low-pass filtering is used. Zeros are inserted in the data positions (\(\
otin\mathcal{K}\)) before the resulting new vector goes through a low-pass filter, meaning that known values stay equal and unknown values (zeros) are interpolated between unchanged data such that the mean-square error between the interpolated points and their ideal values are minimized [33]. Finally, the time-domain version of the estimated channel is calculated alongside its IFFT (see Figure 7).
## 3 Results
### Comparison of Results with a Theoretical Model  
For the comparison of extracted experimental natural frequencies, a uniform composite cantilever beam of 24 mm width was first modeled mathematically, and the results were then used for the  
Figure 11: Damping ratio plots for all specimen groups and frequency modes.  
Figure 10: Natural frequency plots for all specimen groups and frequency modes. Mode 1 (), mode 2 (), mode 3 (), and mode 4 ().  
finite element modeling using Ansys 13. Figure 12 shows a comparison of the experimental natural frequencies for all the specimens with the value calculated by the finite element method. As it can be seen, there is a very good correspondence between the values, especially for Groups 3 and 4, while the calculation predicts slightly lower values (between 2 and 4%) for the high frequency modes of Groups 1 and 2.  
As an example of validity of the selected model, Table 5 shows a comparison of the modeled frequency with experimental and FEM results for sample 3 of Groups 1 and 2, and sample 6 of Group 3, i.e., samples with W = 24 mm with and without central holes. Similarly, Table 6 shows the results calculated for the cantilever plate (W = 48 mm) and compared with sample 7 of Groups 1 and 2 (W = 48 mm with and without central hole).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Mode**} & \multicolumn{6}{c}{**Natural Frequency (Hz)**} \\ \cline{2-9}  & \multicolumn{2}{c}{**W = 24 mm**} & \multicolumn{2}{c}{**W = 24 mm**} & \multicolumn{2}{c}{**W = 24 mm**} & \multirow{3}{*}{**Model**} \\  & \multicolumn{2}{c}{**No Hole**} & \multicolumn{2}{c}{**D = 4 mm**} & \multicolumn{2}{c}{**D = 8 mm**} & \\ \cline{2-9}  & **Exp. 1** & **FEM 2** & **Exp. 1** & **FEM 2** & **Exp. 1** & **FEM 2** & **SEM 2** \\ \hline
1 & 31.9 & 31.1 & 31.5 & 31.0 & 30.4 & 30.7 & 36.6 \\
2 & 244.6 & 235.9 & 244.0 & 235.4 & 233.2 & 234.2 & 229.3 \\
3 & 688.0 & 658.0 & 686.5 & 656.9 & 665.6 & 656.7 & 641.5 \\
4 & 1275.7 & 1259.9 & 1303.0 & 1256.3 & 1264.0 & 1257.4 & 1254.2 \\ \hline \hline \end{tabular}  
* Exp.: experimental. 2 FEM: calculated by Ansys. 3 Model: calculated for a cantilever beam.  
\end{table}
Table 5: Comparison of the natural frequency of a uniform composite rectangular cantilever beam (W = 24 mm) with experimental and FEM results for samples of the same width.  
Figure 12: Comparison of experimental (closed symbols, solid line) and analytical (open symbols, dotted line) frequencies for all the specimens.  
The correlated mode pairs (CMPs) were plotted to check the degree of correlation between the two sets of results for a single specimen in each group (sample 3), as shown in Figure 13. The two sets of data show a high degree of correlation, since the points lie near or close to the straight line of slope equal to 1.
# R-PreNet: Deraining Network Based on Image Background Prior
## 3 Proposed Work
### Loss Function  
Given a clean single channel image \(I\) and a noisy image \(K\) of size \(m\times n\), the mean square error (MSE) is defined as:  
\[\text{MSE}=\frac{1}{mn}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}\ [I(i,\ j)-K(i,\ j)]^{2} \tag{12}\]  
On this basis, PSNR (dB) is defined as:  
\[\text{PSNR}=10\text{log}_{10}\Bigg{(}\frac{MAX_{I}^{2}}{\text{MSE}}\Bigg{)} \tag{13}\]  
where \(MAX_{I}^{2}\) is the maximum possible pixel value of the image. If each pixel is represented by 8 bits of binary, then it is 255. In general, if the pixel value is represented by \(B\)-bit binary, \(MAX_{I}^{2}=2^{B}-1\).  
If it is a color image, there are usually three ways to calculate it:  
1. Calculate the PSNR of the RGB image's three channels separately and then take the average value.  
2. Calculate the MSE of the RGB image's three channels, then divide by 3.  
3. Convert the image to YCbCr format, and then only calculate the PSNR of the Y component, which is the brightness component.  
Among them, the second and third methods are more common. This algorithm uses the second method.  
The peak signal-to-noise ratio (PSNR) is an objective measure of image distortion or noise level. The larger the PSNR value between two images, the more similar it is. The general benchmark is 30 dB, and the deterioration of images below 30 dB is more obvious.  
SSIM also describes the similarity of two images, and the formula is measured based on three comparisons between samples x and y: luminance, contrast and structure.  
\[\text{l}(x,y)=\frac{2\mu_{x}\mu_{y}+c_{1}}{\mu_{x}^{2}+\mu_{y}^{2}+c_{1}} \tag{14}\]  
\[\text  
\(c_{1}=\left(k_{1}L\right)^{2}\) and \(c_{2}=\left(k_{2}L\right)^{2}\) are two constants to avoid division by zero, and \(L\) is the range of pixel values.  
\(k_{1}=0.01\) and \(k_{2}=0.03\) are the default values.  
Then:  
\[\text{SSIM(x,y)}=\left[l(x,y)^{\alpha}c(x,y)^{\beta}s(x,y)^{\gamma}\right] \tag{17}\]  
During each calculation, an N \(\times\) M window is taken from the image, and then the window is constantly sliding for calculation. Finally, the average value is taken as the global SSIM.  
SSIM specifies the MSSIM of the returned image. This is also a floating-point number between zero and one (the higher the better).  
A negative SSIM loss [38] is adopted as the objective function. For a model with T stages, there are T outputs,\(x^{1}\), \(x^{2}\), \(\ldots\), \(x^{\text{T}}\), with supervision applied only to the final output \(x^{\text{T}}\). The negative SSIM loss is:  
\[L=-\text{SSIM}\left(x^{\text{T}},\ x^{gt}\right) \tag{18}\]  
where \(x^{gt}\) is the corresponding ground-truth clean image.
## 1 Introduction
Their noise influence coefficient was related to the grayscale of the original image, and after this process, the radiation was corrected by the exponential function. [PERSON] et al. [23] proposed an object-oriented automatic shadow detection method and a shadow compensation method by region matching based on Bag-of-Words. The \(I\) component of the shadow pixels is primarily corrected by the matched region pairs. Then, the final compensation result is heightened by the overall mean and variance of the shadow and non-shadow regions. These models generally involve many parameters; and the accuracy of the parameter values is relevant to the quality of the compensation results. However, most of the values are determined by manual expertise; and a need, therefore, exists for a way to automatically calculate suitable parameter values in shadow compensation. All in all, the above methods cannot compensate for the information to the degree that it is the same as the non-shadow area. In addition, they are not self-adaptive enough to change the model parameters to gain the most suitable restoration, and the object's color often deviates from the original color. Therefore, an appropriate compensation model that can improve the brightness and contrast effects similar to the non-shadow regions would be helpful. The original Wallis filter is usually used for image dodging to recover an uneven color. It typically can complement the lost color with an image contrast extension coefficient and a brightness coefficient for targeted adjusting. Yet, it cannot be used in shadow compensation directly. Since the LCC model is useful in shadow compensation, the study presented in this paper designed a compensation model based on the Wallis filter and the LCC model. Additionally, an automatic parameter calculation strategy and a final compensation combination with local information based on the designed model are discussed in detail.
## 2 Materials and Methods
### Antiviral Activity Test of USeNPs
#### 2.5.2 Virus Propagation and Antiviral Activity Evaluation  
The cytopathogenic strain of hepatitis A virus (HAV HM175) was propagated and tested using Vero cells as a control [40]. The Spearman-Karber method was used to calculate the tissue culture infectious dose (TCID\({}_{50}\)) using eight wells for each concentration and 20 \(\mu\)L of inoculum in each well to numerate the infectious viruses [41]. A cytopathic effect inhibition assay was conducted for antiviral screening at the Regional Center for Mycology and Biotechnology (RCMB, Al-Azhar University, Cairo, Egypt). The MTT technique demonstrated the precise suppression of a biological process, specifically the cytopathic effect in sensitive mammalian cells [42,43]. Briefly, 96-well microtiter plates with monolayers of Vero cells (2 \(\times\) 10\({}^{5}\) cells/mL) attached to the bottom of the wells were incubated for 24 h at 37 \({}^{\circ}\)C in a humidified incubator with 5% CO\({}_{2}\). After being rinsed with fresh DMEM and inoculated with doses of the virus equal to 104 \(\mu\)L, the cultures treated with the USeNPs were concentrated on fresh maintenance medium and incubated for 48 h at 37 \({}^{\circ}\)C. Infection controls and an untreated Vero cell control were generated without USeNPs. Six wells were used for each concentration of the substance under investigation. Antiviral activity was estimated by comparing the inhibition of the virus effect with the control, and the level of USeNPs protection provided to the cells was also estimated. Each of the three independent experiments that were evaluated included four replicates of each treatment. Amantadine was used as a reference drug. Following the incubation period, MTT assay was used to estimate the viability of the cells as was previously discussed in the cytotoxicity section [36]. The rate of the viral inhibition was estimated as follows: [(A - B)/(C - B)] \(\AA\) 100%, where A, B, and C indicate the absorbance of the USeNPs with virus-infected cells, the virus control absorbance, and the cell control absorbance, respectively.
## Materials and Methods
### Histopathology and Scoring  
Right knees were dissected and fixed in 4% paraformaldehyde at 4 \({}^{\circ}\)C overnight. Decalcification with Decal StatTM (StatLab, Baltimore, MD, USA) and Formical-2000 TM (StatLab, Baltimore, MD, USA) was carried out on coronally bisected joints. Following processing and paraffin embedding, knees were frontally sectioned at 6 \(\upmu\)m. Surface decalcification with Decal StatTM was used as required. The centre of the joint was analyzed by staining 3-5 serial sections, 200 \(\upmu\)m apart, stained in 0.04% Toluidine Blue for cartilage and bone histopathology or with Hematoxylin and Eosin (H&E) for synovial histopathology analysis. Slides were randomized and blinded for scoring by 2 observers.  
Toluidine Blue stained sections were analyzed using the Osteoarthritis Research Society International (OARSI) rat histopathologic system [28; 29]. Cartilage degeneration, subchondral bone damage, and osteophytes were graded in four knee quadrants--Medial Femoral Condyle (MFC), Medial Tibial Plateau (MTP), Lateral Femoral Condyle (LFC), and Lateral Tibial Plateau (LTP). For each animal, a score per parameter was assigned based on the summed score for the slides, where there is a maximum score of 75 for total cartilage damage, 25 for subchondral bone damage, and 20 for osteophyte scores.  
H&E-stained sections were analyzed using a six parameter synovial scoring system, assessing synovial lining thickness, sub-synovial infiltration, fibrin deposition, vascularization, fibrosis, and perivascular edema [30]. Scoring was assessed separately in the medial and lateral parapatellar, superior, and inferior compartments (6 compartments total). Each synovial histopathology parameter was assigned a score of 0 (none) to 3 (severe). Final scores for each synovial histopathology parameter were calculated for each animal by summation of the mean score (3-5 slides per knee) from each of the 6 joint compartments, producing a maximum score of 18.
## 4 Bayesian Optimization of Autoencoder Hyperparameters  
The structure of the autoencoder model is not fixed. Since a dataset may include multiple unordered multi-categorical variables with varying numbers of categories, the number of hidden layers and nodes in the autoencoder adapts according to the dataset's size and the required level of data compression. Therefore, parameters such as the number of layers and nodes in the hidden layer of the autoencoder are the key influencing factors that affect the performance of the autoencoder and the effect of data dimensionality reduction.  
Bayesian optimization is suitable for black-box optimization problems where the target function is complex and has no analytical expression and the computational cost is high. The core idea is to use a probabilistic agent model (e.g., Gaussian process or random forest) to approximate the objective function and use this model to select the next evaluation point so that the objective function can converge to the optimal value as soon as possible, which is a kind of optimization method based on a priori information. There are two core components in this process: the probabilistic model and the acquisition strategy.  
[PERSON] theorem is shown in Equation (27).  
\[p(y|x)=\frac{p(x|y)p(y)}{p(x)} \tag{27}\]  
This paper uses the TPE (Tree-structured Parzen Estimator) algorithm in the Optuna framework to specify the implementation of the Bayesian optimization technique. The TPE is a tree-structured Bayesian optimization method. In the TPE, two density functions are used to define \(p(x|y)\).  
\[p(x|y)=\begin{cases}l(x),y<y^{*}\\ g(x),y\geq y^{*}\end{cases} \tag{28}\]  
The TPE uses Kernel Density Estimation (KDE) to model the probability distribution of objective function values. Specifically, the TPE divides the objective function value into two components: a probability distribution \(l(x)\) for the better objective value and a probability distribution \(g(x)\) for the worse objective value.  
The TPE uses Expected Improvement (EI) as a collection function.  
\[EI_{y^{*}}(x)=\int_{-\infty}^{+\infty}max(y^{*}-y,0)p_{M}(y|x)dy=\frac{\int_{- \infty}^{y^{*}}(y^{*}-y,0)p(y)dy}{\gamma+(1-\gamma)\frac{g(x)}{l(x)}} \tag{29}\]  
This leads to Equation (30).  
\[EI_{y^{*}}(x)\propto\left(\gamma+(1-\gamma)\frac{g(x)}{l(x)}\right)^{-1} \tag{30}\]  
The value of EI is proportional to \(\left(\gamma+(1-\gamma)\frac{g(x)}{l(x)}\right)^{-1}\) and depends on the ratio of the two probabilities, so it is necessary to find the \(x\) that makes the ratio \(\frac{l(x)}{g(x)}\) maximal and, in each iteration, return the maximum EI.
## 4 Experimental Results and Discussion
### Effectiveness of the Proposed Method
#### 4.2.2 Two-Class and Three-Class RF(CSI)  
Another way to verify the effectiveness of the proposed method is to perform a direct two-class relief extraction, i.e., the RF classifier was trained with two classes, relief and background background. The comparison of the classification results between the two-class and three-class RF(CSI) is presented in Table 4. The performance of the three-class RF is higher than the two-class RF in all metrics except precision. Based on this result, the three-class RF(CSI) is more reliable in the extraction of reliefs when compared to the two-class RF(CSI).
## 2 Materials and Methods
### Sample Extraction and Clean Up  
After sampling, the filter was cut in two symmetrical halves. The first half was used for PAHs analysis, while the second half was used for the determination of PCDD/Fs. The procedure, carried out for PAHs analysis, was fully described in our previous study [36]. In general, the filters were spiked with the mix of internal standards (phenanthrene D10 and perylene D12), and then were extracted with dichloromethane in an ultrasonic bath (Ultrasonic LC 130H, Elma, Germany). The extraction procedure was performed in triplicate. The obtained extracts were combined and concentrated in volume using a rotary evaporator (Rotavapor R-210, Buchi, Switzerland) at 28 \({}^{\circ}\)C nearly to 2-3 mL. Then, a solvent change step was performed with the addition of hexane. Subsequently a purification step was followed. A 30 cm \(\times\) 1 cm i.d. glass column chromatography was used. The column was packed with anhydrous sodium sulphate and silica gel, and was activated with hexane before the sample was placed on the top of the column. The clean-up was performed using initially hexane and then a mixture of CH\({}_{2}\)Cl\({}_{2}\): n-hexane 3:2. This eluted fraction was collected and finally evaporated under a flow of nitrogen.  
The analysis of PCDD/Fs and PCBs was performed according to EPA T0-9A protocol. Quartz filters were extracted overnight with toluene in a soxholt apparatus. Prior to extraction, samples were spiked with 0.1 ng \({}^{13}\)C\({}_{12}\) labeled PCDD/Fs and non-ortho PCBs (n.o.-PCBs), and 1.0 ng mono-ortho PCBs (m.o.-PCBs) and indPCBs as quantitative standards. The extract was subjected to clean-up. Active carbon Carbosphere or FU4652 was used for the separation of PCDD/Fs and n.o.-PCBs, in two different fractions, with toluene as elution solvent. Further clean-up of fractions was performed by column chromatography with basic alumina and 44% H\({}_{2}\)SO\({}_{4}\)-silicagel eluted with different mixtures of hexane:dicloromethane. After evaporation, the eluate containing the PCDD/Fs was re-dissolved in n-nonane containing 2 ng mL\({}^{-1}\) of injection standard \({}^{13}\)C\({}_{12}\) 1,2,3,4-TCDD, while that containing the n.o. PCBs was re-dissolved in n-nonane containing 2 ng mL\({}^{-1}\)\({}^{13}\)C\({}_{12}\) PCB80. For mono-ortho and indicator PCBs, 10% of the fraction obtained from Soxhlet extraction was dissolved in hexane and brought onto a column chromatography filled with 10 g of 44% H\({}_{2}\)SO\({}_{4}\)-silica. Further clean-up was performed by column chromatography with basic alumina and 44% H\({}_{2}\)SO\({}_{4}\)-silica and elution with hexane:dicloromethane. The eluate was evaporated to dryness and re-dissolved in n-nonane containing 20 ng mL\({}^{-1}\) of injection standard (\({}^{13}\)C\({}_{12}\) PCB 80). A detailed description of the whole clean-up procedure has been given elsewhere [7].