esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Effect of Incorrect Estimation
Different approximations of the gradient have been used: first-order differences, \(I_{X}^{D}\), [66; 73; 74; 77]; Brenner gradient, \(I_{X}^{\mathrm{B}}\), [60; 73; 74; 75]; Gaussian derivative, \(I_{X}^{\mathrm{G}}\); and Tenengrad, \(I_{X}^{T}\), [69; 73; 74; 77; 80; 81; 90]. Different combinations of the components of the gradient have also been looked at: one-dimensional approximations [60; 74; 75; 79]; maximum component approximations [41; 73]; and component sum [66; 69; 72; 74; 77; 78; 80; 81; 90]. Additionally, prior to summation, either the absolute value of the components of the gradient [73; 74] or the squared value of the components of the gradient, [60; 72; 73; 74; 75; 77; 78; 79; 80; 81; 90] can be used. Laplacian-based FQMs, \(\phi^{\mathrm{L}}\), use second-order differentiation to reward higher frequency content in the image and hence reward images of higher quality. The second-order derivative is approximated by convolving a Laplacian kernel (two-dimensional) with the image, where the kernel, \(L\), is given by:  
\[L=\frac{1}{6}\left( \begin{array}{ccc}1&4&1\\ 4&-20&4\\ 1&4&1\end{array} \right) \tag{5}\]  
The Laplacian kernel can also be applied in each direction independently, where the kernels in the \(x\) and \(y\) directions are given by  
\[L_{x}=L_{y}^{T}=\left( \begin{array}{ccc}-1&2&-1\end{array} \right) \tag{6}\]  
Finally, the Laplacian kernel can also be estimated along the diagonals where the kernels along the two diagonals, \(L_{d}\) and \(L_{d}^{\prime}\), are given by  
\[L_{d}=\frac{1}{\sqrt{2}}\left( \begin{array}{ccc}0&0&1\\ 0&-2&0\\ 1&0&0\end{array} \right)\text{, }L_{d}^{\prime}=\frac{1}{\sqrt{2}}\left( \begin{array}{ccc}1&0&0\\ 0&-2&0\\ 0&0&1\end{array} \right) \tag{7}\]  
Statistics-based metrics, \(\phi^{\mathrm{S}}\), analyse the distribution of values of the image or the histogram of the image, such as  
* variance;
* contrast;
* entropy;
* and the central moment. Wavelet-based metrics, \(\phi^{\mathrm{W}}\), use the discrete wavelet transform (DWT) to describe the frequency content of the image and reward images of higher quality. The DWT decomposes the image into three detail sub-bands--\(W_{\mathrm{L}\mathrm{H}}^{1}\), \(W_{\mathrm{H}\mathrm{L}}^{1}\) and \(W_{\mathrm{H}\mathrm{H}}^{1}\)--and the coarse approximation sub-band, \(W_{\mathrm{L}\mathrm{L}}^{1}\). To create higher-level transforms, the coarse approximation sub-band is successively decomposed.
## 3 Results and Discussion
### Drained Behavior of ESR  
The deviator stress-strain curves and volume strain-strain curves of the ESR with 0 and 10% rubber fiber content in the drained tests are shown in Figures 6 and 7, respectively. The tested ESR showed different damage patterns. The middle part of the unreinforced expansive soil (ESR-6) showed a significant uplift, whereas the uplift of the rubber fiber-reinforced expansive soil (ESR-7) was distributed uniformly along the height of the sample. This indicated that the rubber fibers played an important role in transferring the stress and strain inside the sample.  
Figure 5: Pore water pressures of reinforced expansive soil and unreinforced soil under undrained conditions: (**a**) ESR-2, (**b**) ESR-3, (**c**) ESR-4, and (**d**) ESR-5.  
Figure 6: Deviator stress–strain curves of ESR-6 and ESR-7 in the drained tests.  
As anticipated, the deviator stress and shrinkage increased as the rubber fiber content and confining pressure increased. Under equal confining pressure, the strain of the reinforced soil when attaining the maximum deviator stress was significantly higher than that of the unreinforced soil. This indicated that the rubber fiber significantly improved the ductility of the soil. This was in agreement with the study of the effect of plant roots on the mechanical properties of the soil by [PERSON] et al. [31]. With an increase in strain, the deviator stress of ESR-6 attains a peak at 16% strain. After the plastic deformation of ESR-7, the resistance of the fibers was activated. There was no apparent peak deviator stress, which was manifested in the form of strain hardening. At confining pressures of 100, 200, and 300 kPa, the deviator stress of ESR-7 increased from 111.8, 130.6, and 204.3 kPa to 130.4, 177.8, and 210.8 kPa, respectively. This corresponded to increases of 16.6%, 36.1%, and 3.2%, respectively. [PERSON] et al. [32] indicated that this could be owing to frictional interactions between the particles and fibers, as well as an increase in tensile strength provided by the fiber modulus.  
With regard to the volume strain-strain response, the volume strain-strain curves of ESR-6 and ESR-7 exhibit compressive behavior throughout the shear process until the end of the test, as shown in Figure 7. Under equal confining pressure, the volume variations of ESR-6 and ESR-7 were marginally different. ESR-7 exhibited a larger initial shrinkage than ESR-6 and was distributed over a larger strain range. This may have been owing to the compression of the rubber fiber. [PERSON] et al. [33] also observed that fiber-reinforced soil contracts more than unreinforced soil during shearing.
## 2 Results
### Animal Model
#### 2.5.2 Hematological and Biochemical Parameters  
The experimental group supplemented with terpenoids showed a significant increase (\(p<0.05\)) in WBCs (Table 5) in the blood after 8 weeks compared to the control group. Similar changes were also found in the RBC (\(p<0.01\)). In the group that received terpenoids, an increase in WBCs and RBCs was also observed, but no statistical differences were observed. The supplementation used also resulted in HGB and HCT (Table 5). No clear differences were found between the different red cell indices. Conversely, platelets reached the highest concentration in the TERP group, and were slightly lower for SAP, while PLT levels were lowest in the control group. Other studies indicate significantly higher plasma levels of RBC, WBC, monocytes and platelets, and leptin in obese rats [33]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Group**} \\ \cline{2-5}  & **CON** & **SAP** & **TERP** & \(p\)**Value** \\ \hline Initial body weight (g) & 298.86 \(\pm\) 65.89 [1] & 299.29 \(\pm\) 15.24 & 293.71 \(\pm\) 14.02 & 0.282 \\ \hline Final body weight (g) & 521.00 \(\pm\) 44.39 & 512 \(\pm\) 51.91 & 528.14 \(\pm\) 35.18 & 0.346 \\ \hline Feed intake (g/week) & 123 \(\pm\) 8.98 & 118 \(\pm\) 6.62 & 132 \(\pm\) 9.32 & 0.682 \\ \hline Liver weight (g) & 23.10 \(\pm\) 2.01 & 22.73 \(\pm\) 4.19 & 24.06 \(\pm\) 1.77 & 0.534 \\ \hline \hline \end{tabular} \({}^{1}\) Values are mean \(\pm\) SD.  
\end{table} 
Table 4: Body weight, feed intake and liver weight. Figure 1: Weight changes after using saponins and terpenoids over a period of 8 weeks. The greatest changes in lipid parameters were found after terpenoid treatment (Table 6). There was a significant increase (\(p\) < 0.057) in TG concentration in the TERP group. Increases in the concentrations of total cholesterol, LDL and HDL fractions were also evident in the TERP group compared to the control group. However, these changes were not statistically significant. The use of saponins caused a slight decrease in the concentrations of cholesterol and LDL, and HDL. Supplementation with saponins caused a marked increase in TG concentrations compared to the control group. There was an increase in lipolysis in the TERP group, and the statistical differences compared to the SAP group were clear. The serum NEFA concentration was lowest in the SAP group. The administration of terpenoids or saponins to Zucker rats had no effect on AST activity. Genetically obese Zucker (fa/fa) rats overexpression leptin in the stomach. Gastric leptin expression is regulated by feeding conditions in lean but not obese rats. This would indicate the impaired regulation of leptin expression in the stomachs of obese Zucker rats (as in adipose tissue), possibly related to the lack of the leptin receptor ([PERSON], 2018). In a study in mice with an obesity-inducing diet, Yerba extract supplementation was shown to alleviate hyperglycemia and improve insulin sensitivity and plasma lipids ([PERSON], 2018). Zucker rats receiving saponins and terpenoids had reduced serum insulin and glucose levels. This indicates their
# Connectivity Analysis in VANETs with Dynamic Ranges
## 3 Analytical Procedure
### Connection Probability Using Dynamic Communication Ranges  
In this section, we analyse the connectivity probability between two adjacent vehicles communicating with each given a transmitting power, \(P_{\text{c}}\), and known associated distributed communication range, \(R\) as derived in the previous section. To simplify analysis and limit space, the connectivity in the case of big-vehicle shadowing shall be included without derivation since only the constant term \(\Lambda\) is replaced. For a receiving vehicle to communicate with a transmitter, their inter-vehicle distance of separation \(D\) must be less than the communication range. Then the probability of connection, \(P_{\text{c}}=\Pr(D\leq R)\), given in [20; 22] and with \(N\) vehicles within a network area under consideration, the overall connectivity is given by;  
\[P_{\text{c}}^{\text{T}}=\prod_{i=1}^{N-1}P_{\text{c}}=\prod_{i=1}^{N-1}\Pr(D \leq R)=\Big{[}\Pr(D\leq R)\Big{]}^{N-1}, \tag{30}\]  
where \(N-1\) is the number of communication links. Since both \(D\) and \(R\) information is known, it is simple to derive \(P_{\text{c}}^{\text{T}}\) and later give the significance of the result for different road situations.
# Improving Short Term Clock Prediction for BDS-2 Real-Time Precise Point Positioning
## 4 Validation of the Predicted Clocks
### PPP Validation
#### 4.2.1 Static PPP Validation  
Table 5 gives the position accuracy statistics of daily static PPP solutions in east, north and up components based on the IGS coordinates for the eight stations from day 001 to day 100 in 2018, using GBU-P, spectrum analysis, GRNN(ONLY) and improved model, respectively.  
Compared with GBU-P, seven out of eight stations are enhanced using spectrum analysis model in the east component, and further optimized by GRNN(ONLY) model with the largest improvement of 29.3% at ISC station. Similar conclusions can be drawn in the north component for the first three methods. However, five stations perform better in the spectrum analysis and seven stations for GRNN(ONLY) model, compared with GBU-P. Meanwhile, accuracies of six stations' coordinates in the up component have increased with the best of 28.7% at DARW station, using spectrum analysis model. When the GRNN(ONLY) model is used, maximum 38.2% improvement at LHAZ station and minimum 3.9% improvement at ISC station are obtained. In general, GBU-P can obtain an accuracy of decimeter level for most stations, using static PPP, while the sub-decimeter level is achieved for some special components for spectrum analysis and GRNN(ONLY) models.  
When the improved model is employed, accuracies of all stations' coordinates are enhanced further. Compared with GBU-P, the best performance in the east component is obtained at HKSL station, with an improvement of more than 40%. The corresponding improvement in the north component is 41.6% at LHAZ station. For the up component, the largest enhancement is 42.7% for LHAZ station. In all stations, the best improvement is visible at LHAZ station, accuracies are increased by 38.8%, 41.6%, 42.7% in east, north and up component, respectively. Generally, compared with GBU-P, the improved model achieves more than 30.0% improvements on average for each component. Additionally, more than 60% stations obtain sub-decimeter positioning accuracy at the horizontal direction.
# Resilient Modulus--Physical Parameters Relationship of Improved Red Clay by Dynamic Tri-Axial Test
## 2 Material Properties and Testing Method  
### Materials  
The basic properties of the red clay used in the test are shown in Table 1. In order to analyze the influence of stress state, moisture content, compactness, additive type and content on the resilient modulus of red clay, samples prepared at different moisture content (OMC minus 3%, OMC, OMC plus 3%), different compactness of P (91%, 93%, 95%), different additive types (ordinary Portland cement, lime) and content (3%, 6%, 9%) were tested.  
The chemical and mineral compositions of the red clay, obtained from X-ray diffraction measurements, are summarized in Table 2, and Table 3, respectively. It is indicated that the mineral compositions include mainly kaolinite (i.e., Al\({}_{2}\)Si\({}_{2}\)O\({}_{5}\)(OH)\({}_{4}\)), goethite (i.e., FeO(OH)), and diaspore (i.e., SiO\({}_{2}\)). The amount of kaolinite is 60.87%. Therefore, kaolinite dominates the mineral compositions of the red clay.  
Proctor compaction tests were carried out to determine maximum dry densities and optimum moisture contents of the improved red clay samples which are presented in Table 4. Based on the compaction test results, the samples were prepared at the targeted compactness and moisture content.  
The samples were prepared according to China highway geotechnical test code (JTGE40-2007). The samples were prepared as follows: Drying, grinding, mixing with additive, curing, and preparation. Three parallel samples were prepared for each group with different admixtures. The samples were placed in a standard curing room with 25 \(\pm\) 2 \({}^{\circ}\)C for 7 days. For each sample, the difference between the actual and the targeted moisture contents is less than 1%, and that difference is less than 2% for compactness.
# Assessment of Heavy Metal Distributions in Sand Beaches in the Maltese Islands
## 3 Results and Discussion
### Environmental and Health Risk Assessment
#### 3.2.1 Health Risk Assessment
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multicolumn{2}{c}{**Mean Con-**} & \multirow{2}{*}{**HQ\({}_{\text{default}}\)**} & \multirow{2}{*}{**HQ\({}_{\text{modified}}\)**} & \multirow{2}{*}{**CR\({}_{\text{default}}\)**} & \multirow{2}{*}{**CR\({}_{\text{modified}}\)**} & \multirow{2}{*}{**I\({}_{\text{geo}}\)**} & \multirow{2}{*}{**CF**} & \multirow{2}{*}{**E\({}_{\text{i}}\)**} \\  & **(ppm/g)** & & & & & & & & \\ \hline \multirow{2}{*}{Cu} & \multirow{2}{*}{2.549} & \(1.092\times 10^{-3}\) * & \(1.28\times 10^{-4}\) * & N/A & N/A & \(-1.268\) & 0.947 & 85.252 \\  & & \(1.600\times 10^{-2}\) ** & \(1.872\times 10^{-3}\) ** & N/A & N/A & \(-1.268\) & 0.947 & 85.252 \\  & & \(3.680\times 10^{-4}\) * & \(4.310\times 10^{-5}\) * & N/A & N/A & \(-5.962\) & 0.024 & N/A \\  & & \(4.780\times 10^{-4}\) * & \(5.596\times 10^{-5}\) * & N/A & N/A & \(-6.056\) & 0.042 & 0.765 \\  & & \(5.479\times 10^{-3}\) ** & \(6.420\times 10^{-4}\) ** & N/A & N/A & & & \\ Pb & 5.082 & \(1.451\times 10^{-2}\) ** & \(1.700\times 10^{-3}\) ** & \(4.319\times 10^{-7}\) * & \(5.060\times 10^{-8}\) * & \(-1.509\) & 0.793 & 71.600 \\  & & \(4.609\times 10^{-2}\) ** & \(6.300\times 10^{-2}\) ** & \(1.372\times 10^{-6}\) ** & \(1.875\times 10^{-6}\) ** & & & \\ Sr & 186.953 & \(7.950\times 10^{-4}\) * & \(9.320\times 10^{-5}\) * & \(4.274\times 10^{-13}\) ** & \(4.975\times 10^{-14}\) * & \(-2.597\) & 0.282 & N/A \\  & & \(1.825\times 10^{-3}\) ** & \(2.494\times 10^{-3}\) ** & \(1.332\times 10^{-12}\) ** & \(9.743\times 10^{-13}\) ** & \(-2.863\) & 0.236 & 4.404 \\ Zn & 4.474 & \(2.222\times 10^{-4}\) * & \(2.600\times 10^{-5}\) * & N/A & N/A & & & \\  & & \(3.406\times 10^{-3}\) ** & \(3.990\times 10^{-4}\) ** & N/A & & & & \\ HI\({}_{\text{adults}}\) & & & \multicolumn{4}{c}{Default = 0.018; Modified = 0.002} & & \\ HI\({}_{\text{children}}\) & & & \multicolumn{4}{c}{Default = 0.074; Modified = 0.070} & & \\ RI\({}_{\text{adults}}\) & & & \multicolumn{4}{c}{Default = 4.320 \(\times\) 10\({}^{-7}\); Modified = 5.060 \(\times\) 10\({}^{-8}\)} & & & \\ RI\({}_{\text{children}}\) & & & \multicolumn{4}{c}{Default = 1.372 \(\times\) 10\({}^{-6}\); Modified = 1.875 \(\times\) 10\({}^{-6}\)} & & & \\ PERI & & & \multicolumn{4}{c}{162.021} & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results for the Health Risk Assessment and the Potential Ecological Risk Assessment variables. Modified results for HQ and CR values obtained were in the same order as those computed through the default values used across different studies. These modified values were calculated to provide a better approximation of the exposure frequency of persons visiting the studied sites. Such results are tabulated in Table 10.
# Low-Complexity QRD-\(M\) with Path Eliminations in MIMO-OFDM Systems
## 4 Proposed Low-Complexity QRD-\(M\)
\begin{table}
\begin{tabular}{l} \hline \hline
1: \(\mathbf{H}=\mathbf{QR}\), \(\mathbf{Z}=\mathbf{Q}^{H}\mathbf{Y}\). \\
2: \(\mathbf{for}\,k=1:|S|\) \\
3:  Calculate \(\left[E_{N_{i},k}\right]_{N_{i}}\) for the \(k\)-th modulated symbol. \\
4: \(\mathbf{end}\) \\
5: \(\mathbf{for}\,l=N_{i}:-1:1\) \\
6: \(\mathbf{for}\,m=l-1:-1:1\) \\
7:  Select \(T\) paths in an ascending order in \([\mathbf{E}_{l}]\). \\
8: \(\mathbf{for}\,p=1:T\) \\
9: \(\mathbf{for}\,k=1:|S|\) \\
10:  Calculate \(\left[E_{m,k}^{p_{m+1}}\right]_{l}\) for the \(k\)-th modulated symbol. \\
11: \(\mathbf{end}\) \\
12:  Find the smallest value in \(\left[E_{m}^{p_{m+1}}\right]_{l}\) \\
13: \(\mathbf{end}\) \\
14: \(\mathbf{end}\) \\
15: \(\mathbf{\eta}_{l}=\min\{[\mathbf{E}]_{l}\}\). \\
16: \(\mathbf{for}\,k=1:|S|\) \\
17: \(\mathbf{if}\,\left[E_{l,k}\right]_{l}>\mathbf{\eta}_{l}\) \\
18:  Path corresponding to \(\left[E_{l,k}\right]_{l}\) is eliminated. \\
19: \(\mathbf{end}\) \\
20: \(\mathbf{end}\) \\
21: \(|S|M_{l}\) paths are extended to the next layer and \(M\) paths are selected. \\
22: **end** \\
23: **Estimate the \(\mathbf{\hat{X}}_{\text{P}}\) by selecting the path which has the smallest ASED.** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The whole algorithm of the proposed QRD-\(M\). Figure 4: The flow chart of the proposed QRD-\(M\). ASED: accumulated squared Euclidean distance. Table 2 represents the number of complex multiplications for the conventional and the proposed QRD-\(M\). For simple representation, it is assumed that the number of transmit antennas is the same as receive antennas. The number of complex multiplications \(12N_{t}^{3}+4N_{t}^{2}\) is always required for calculating the inverse channel matrix, QR decomposition of the channel and multiplying \(\mathbf{Q}^{H}\) with \(\mathbf{Y}\). Additionally, the number of complex multiplications for the extended decision feedback equalizer (E-DFE) is represented where \(\mathrm{CLLL}_{N_{t}}\) is the number of complex multiplications for complex Lenstra-Lenstra-Lovasz (CLLL) algorithm in a \(N_{t}\times N_{t}\) system [16]. The aim of the inverse channel matrix calculation is to decide the detection order at the receiver so as to minimize the error propagation.
# Customized Convolutional Neural Networks Technology for Machined Product Inspection
## 3 Experimental Method
### Comparison of Results after Training with Common Models  
This paper used MATLAB software to develop convolutional neural networks, selecting several common models, including LeNet, VGG-19, ResNet-34, DarkNet-19, and DarkNet-53 for training, and compared the results. We used the following hyperparameters for each training: the Batch Size was set to 8, the Learning Rate was set to 0.0001, and the Epoch was set to 1. The data set was used for training with each model in sequence. In order to achieve fair experimental results, we used the same hyperparameters and data set to train each model five times. The results of training of the LeNet, VGG19, ResNet34, DarkNet19, and DarkNet53 models five times were averaged to obtain accuracies of 98.45%, 98.93%, 93.68%, 98.64%, and 98.64%, respectively, as shown in Table 1. In Table 2, the prediction times by implementing the LeNet, VGG19, ResNet34, DarkNet19, and DarkNet53 models five times were averaged to 0.391, 0.303, 0.946, 0.329, and 1.167 s, respectively. A note is made here that the first prediction time is always the highest with real implementation. This is caused by the time in deploying the parameters into the GPU. The rest of the consecutive prediction time will be less than the first one naturally.
## 1 Introduction  
Info and Communication Technology (ICT) is currently playing a significant role in increasing the efficiency of production scheduling at industrial entities. This is made possible by integrating multiple software solutions to minimize the makespan and reach optimal resources use. Most of small to mid-sized manufacturing entities plan their production activities according to market demand. The production scheduling on the shop floor level is designed by the production planning staff in a form of static schedule. Disruptions to the planned static schedule such as: machine breakdowns, increased order priority, sudden orders, quality assurance tests failure and order cancellation require constant update to the initial static schedule. These disruptions change the nature of the scheduling task making it a highly dynamic one, and decreases the overall efficiency of the industrial entity due to repeating the scheduling process. Resolving machine breakdowns and sudden orders disruptions is not a straightforward task. Since, machine repair time is unpredictable and forecasting sudden orders arrival needs exhaustive market research.  
Consequently, it is crucial that any automated scheduling tool should handle machine failures and sudden orders efficiently and as quickly as possible.  
The main focus of this work is to propose a simple yet efficient framework that automates dynamic flow-shop scheduling while taking into consideration machine failures and rush orders. The proposed framework is implemented by commercial software tools used for scheduling. To handle machine failures, predictive maintenance parameters are calculated using machine-learning techniques and included in the makespan. A case study is presented for a pharmaceutical company to validate the proposed framework. The paper is organized as follows: literature review about dynamic scheduling and maintenance types is presented in Section 2. The proposed scheduling framework is given in Section 3 with detailed description of the predictive maintenance parameters calculations. A real-life case study of a pharmaceutical factory X production scheduling using the proposed framework is presented in Section 4. Results and discussion are given in Section 5 and finally, the paper is concluded in Section 6.
# KeyNet: An Asymmetric Key-Style Framework for Watermarking Deep Learning Models
## 1 Introduction
### Contributions and Plan of This Article
* We demonstrate the ability of our framework to scale and fingerprint different unique copies of a DL model for different users in the system. The information of a user is combined with the owner's information, the carrier is signed with the combined information, and then a unique pair of a pretrained model along with its corresponding private model is fine-tuned before being delivered to the user. After that, the owner can identify the point of leakage with a small number of queries. * We conduct extensive experiments and evaluate the proposed framework on different DL model architectures. The results show that _KeyNet_ can successfully embed WMs with reliable detection accuracy while preserving the accuracy of the original task. Moreover, it yields a small number of false positives when tested with unmarked models. The remainder of the paper is organized as follows. Section 2 discusses related work. Section 3 describes the attack model to watermarking systems. Section 4 presents our framework in detail. Section 5 describes the experimental setup and reports the results on a variety of data sets. Finally, Section 6 gathers conclusions and proposes several lines of future research.