esa-sceva/satcom-chunk-collection huggingface dataset:
# Generation and Characterization of Novel Bioactive Peptides from Fish and Beef Hydrolysates
###### Abstract  
Bioactive peptides were successfully produced from fish (_Gadidae_) and beef skeletal muscles after being hydrolyzed for 8 h with pepsin. Subsequently, they were purified using a Sep-Pak C18 cartridge and reversed-phase high-performance liquid chromatography (RP-HPLC). The molecular weights of pure fish and beef peptides were determined to be 2364.4 and 3771.8, respectively. According to Edman degradation, the fish peptide was composed of 21 amino acid residues (F21), while the beef peptide was composed of 34 amino acid residues (B34). F21 and B34 displayed angiotensin-converting enzyme inhibitory activity with a half maximal inhibitory concentration (IC\({}_{50}\)) values of 7.3 \(\mu\)g/mL and 5.8 \(\mu\)g/mL, respectively. F21 exhibited antioxidant activity with an IC\({}_{50}\) value of 389.9 \(\mu\)g/mL, whereas B34 exhibited no antioxidant activity. Moreover, F21 and B34 displayed antimicrobial effects against a wide spectrum of food-borne pathogens and spoilage bacteria. Bioactive peptides derived from muscle proteins are a promising strategy for the production of functional food materials and safe food preservatives.  
Bioactive peptides; muscle sources; pepsin; angiotensin-converting enzyme; antioxidant; antimicrobial peptides +
Footnote †: journal: Applied Sciences
# SecureVision: An Open-Source User-Customizable Image Encryption Program
## Materials and Methods
### Shuffling of Pixel Values  
The pixel values would remain unchanged if only the pixel positions of an image were shuffled (see Figure 5). In other words, the histograms of the original and encrypted image would look almost identical, as shown in Figure 5. This was undesirable for image encryption, as an unauthorized user could reconstruct the original image using the data from the histogram. To avoid this, the histogram of the encrypted image should either be very different from that of the original image or in ideal cases, be uniform and flat.  
Figure 5: Original and encrypted images with their respective histograms showing the distribution of greyscale values of pixels. The \(x\)-axis in the histograms represents greyscale values ranging from 0 (black) to 255 (white).  
In the present version of our program, we shuffled the pixel values through a cosine function that generated pixel values which were then summed with the pixel values in the encrypted image. This was mathematically represented as:  
\[\mathbf{P}(i,j)=255\times\left|A\times\cos(k)\right|\,k=1,2,\dots,w\times h\, \tag{3}\]  
where \(\mathbf{P}(i,j)\) was an array with the same size as the encrypted image pixel array and \(A\) was a user-defined variable that controlled the amplitude of the cosine function. The absolute value of the product of the cosine function with \(A\) was then multiplied by 255 (i.e., the largest pixel value in a greyscale image). In fact, as the present computer code is open-source, users can experiment with different functions and variables to obtain their desired level of encryption. We examined histograms of encrypted images for three different \(A\) values, as shown in Figure 6. It could be noticed that the histograms were no longer similar to the histogram of the original image shown in Figure 5. Therefore, the proposed pixel value shuffling method worked satisfactorily.
## 6 Computational Results and Discussion
### Direct Deliveries
#### 6.1.2 Solving VRP Instances for All Customers under Consideration  
The savings algorithm performs better than the TSP approach (presented in the previous section) with predefined areas and also better than the sweep algorithm for almost all demand scenarios, mainly due to larger areas without residents, which have to be traveled through on more tours using the sweep algorithms than for the savings algorithm (Table 3).  
For all scenarios, the VKT are also about \(23\%\) less for the savings algorithm when using the OSM data compared to randomly generated data sets. For the sweep algorithm, only a reduction in VKT of \(10\) to \(15\%\) can be observed.
# Detection and Attribution of Greening and Land Degradation of Dryland Areas in China and America
## 2 Materials and Methods
### Desertification Quantification  
We apply a non-parametric pixel-by-pixel trend method based on Theil-Sen slope estimator and [PERSON]'s \(\rho\) significance test to generate the ensemble members. The vegetation change is the difference between the NDVI\({}_{m}\) in 1982 and 2021. We define the expected NDVI\({}_{m}\) value differences between the start and end of time series as \(\Delta\) NDVI\({}_{m}\).
## 2 Theoretical Background of Proposed Method
### Arma  
The basic principle of ARMA [12] is as follows:  
1. In a practical application, a high-order differential equation can be used to describe the relationship between an incentive and response for the \(N\) degrees of freedom linear system in a discrete time domain, which is composed of a series of time series in different times; namely, the ARMA time-series model in Equation (2), this equation can be used to represent specific relations between \(x_{t}\) (response data sequence) and \(x_{t-k}\) (history value). \[\sum_{k=0}^{2N}a_{k}x_{t-k}=\sum_{k=0}^{2N}b_{k}f_{t-k}\] (2) where the left side of the equation is the AR model; namely, the autoregressive difference polynomial. The right side of the equation is the MA model; namely, the sliding average difference polynomial. \(2N\) is the order of the autoregressive and sliding mean models. \(a_{k}\) is the autoregressive coefficient of the system to be identified, \(b_{k}\) is the sliding mean coefficient to be identified, and \(f_{t}\) is excited by white noise. When \(k\) = 0, \(a_{0}\) and \(b_{0}\) are regarded as 1. 2. According to the generalized Yule-Walker equation (Equation (3)), the least squares solution (Equation (4)) of the system can be obtained using the pseudo-inverse method, and the autoregressive coefficient \(a_{k}\) (\(k\) = 1, 2,..., \(2N\)) can be obtained. \[\left[R\right]_{(L-M)\times M\left\{a\right\}_{M\times 1}}=\left[R^{\prime} \right]_{(L-M)\times 1}\] (3) \[\left\{a\right\}=\left(\left[R\right]^{T}\left[R\right]^{-1}\right)\left( \left[R\right]^{T}\left\{R^{\prime}\right\}\right)\] (4) where \(R\) is the autocorrelation function of \(x_{t}\). 3. The sliding average model coefficient \(b_{k}\) (1, 2,..., \(2N\)) can be obtained by using the nonlinear equations (Equation (5)). \[\left\{\begin{array}{l}b_{0}^{2}+b_{1}^{2}+\cdots b_{M}^{2}=c_{0}\\ b_{0}b_{1}+\cdots+b_{M-1}b_{M}=c_{1}\\ \vdots\\ b_{0}b_{M}=c_{M}\end{array}\right.\] (5) 4. \(a_{k}\) and \(b_{k}\) can be obtained from Equations (4) and (5), and the modal parameters of the system can be calculated through the transfer function expression of the ARMA model. The transfer function of the ARMA model is shown in Equation (6): \[H_{z}=\frac{\sum_{k=0}^{2N}b_{k}z^{-k}}{\sum_{k=0}^{2N}a_{k}z^{-k}}\] (6)  
Using a high-order algebraic equation to solve the poles of the transfer function, and the relationship between the modal frequency \(\omega_{k}\) and damping ratio \(\xi_{k}\) of the system is shown in Equation (7):  
\[z_{k}=e^{skM}=e^{(-\xi_{k}\omega_{k}+\omega_{k}\sqrt{1-\xi_{k}^{2}})\Delta t} \tag{7}\](5) Modal frequency \(\omega_{k}\) and damping ratio \(\xi_{k}\) obtained using Equation (7), as shown in Equation (8):  
\[\left\{\begin{array}{c}\omega_{k}=|R_{k}|/\Delta t,R_{z}=\ln z_{k}=s_{k}\Delta t \\ \xi_{k}=\sqrt{\frac{1}{1+\left|Im(R_{k})/R_{c}(R_{k})\right|^{2}}}\end{array}\right. \tag{8}\]
# Convolutional Sparse Modular Fusion Algorithm for Non-Rigid Registration of Visible-Infrared Images
## 4 Modular Training Strategy and Loss Functions
### Codec Training Stage Loss Functions  
To ensure that the attention convolutional sparse encoder and decoder function correctly, the image passes through encoding and decoding processes to produce a reconstructed image \(\hat{I}^{n}\) that closely matches the original image \(I^{n}\). The reconstruction loss is defined as follows:  
\[L_{RB} =L_{2}+\mu_{1}L_{SSIM}, \tag{10}\] \[L_{2} =\|I^{n}-\hat{I}^{n}\|_{2}^{2},\] (11) \[L_{SSIM} =1-SSIM(I^{n},\hat{I}^{n}), \tag{12}\]  
where \(L_{2}\) and \(L_{SSIM}\) ensure the overall and structural similarity between the images, respectively. Moreover, \(SSIM(X,Y)\) is the Structural Similarity Index [25] loss function, and \(\mu_{1}\) is a tuning parameter.  
Figure 4: The overall framework and processing pipeline of registration module.
## 3 Results and Discussion
### Influence of Nugget Size  
Lastly, the influence of diameter of the spot weld on the fatigue life of the spot-welded structure was analyzed. For this purpose, this size was changed from 3.5 mm to 5.5 mm with an increment of 0.5 mm. Figure 6 presents the obtained \(N_{f}\) values, where they are 47,369; 76,128; 212,620; 724,440; and \(10^{6}\) cycles, respectively. For the last one, the service life of the welded structure was around 1.73 \(\times\)\(10^{6}\) cycles, but in this study, a fatigue life greater than 10\({}^{6}\) cycles was considered as the run-out. It should be mentioned that \(N_{f}\) represents the number of cycles when the crack length reaches to the nugget diameter. For instance, for \(d\) = 3.5 mm, the crack length reached to 3.5 mm at 47,369 cycles. When the nugget diameter was increased from 3.5 mm to 4.0 mm, the change in \(N_{f}\) was not so significant (approx. 60% increase). However, a dramatic increase in \(N_{f}\) was observed for larger \(d\) values. For instance, the \(N_{f}\) increased by 2.8 and 3.40 times, when \(d\) increased from 4.0 mm to 4.5 mm and from 4.5 mm to 5.0 mm, respectively. Here, we concluded that the size of the spot weld affected the fatigue performance of the structure but at different levels for different size ranges of the spot weld. When the average crack expansion rates were compared for different \(d\) values (see Table 3), the crack propagated at a rate of 6.33 \(\times\)\(10^{-8}\) mm/cycle for \(d\) = 3.5 mm, more than 10 times faster than the structure with \(d\) = 5.0 mm. For smaller nugget size, the crack expanded very fast and, hence, the fatigue life of the welded structure was much shorter. This is in line with the finding in [8], where the smaller nugget diameter led to shorter fatigue life on the basis of notch effect, where higher strains and stresses evolved around the spot-welding point.  
Figure 5: Numerically obtained number of cycles to failure (\(N_{f}\)) for the spot-welded structures with different load ratios.
# Numerical Simulation Study on the Performance of Buried Pipelines under the Action of Faults
## 2 Finite Element Model of Pipe-Soil System
### Determination of Boundary Conditions  
In actual engineering, long-distance pipelines are usually buried in the ground below 0.8-2.5 m, which can be approximated as a semi-infinite domain. By removing a sufficiently large amount of soil including pipelines from the semi-infinite domain and applying reasonable boundary conditions that comply with actual engineering geological conditions, the true working state of buried pipelines can be simulated more realistically. Based on the above premises, this study imposes boundary conditions on the finite element model as shown in Figure 2.  
1. Assuming that the soil on both sides of the fault is in direct contact along the fault displacement surface, without considering the existence of the fault displacement zone, the mutual friction between the soil on both sides is considered through the definition of mutual contact as mentioned earlier, without imposing additional constraints;
2. No constraints are imposed on the surface of the soil on both sides of the fault to simulate the free ground in actual engineering;
3. Before applying displacement loads, fixed constraints are applied to the bottom of the soil on both sides of the fault, while the lateral displacement of the soil is limited in its normal direction. During the quasi-static analysis process of applying displacement loads, the non-staggered soil maintains the aforementioned constraints, moves the soil to release the constraints, and applies displacement loads to simulate the fault displacement process.  
In this study, the fault misalignment displacement \(\delta\) is estimated by the empirical statistical formula for engineering a design for ground shaking in the Chinese region:  
\[\text{lg}\delta=-3.019+0.4646\cdot M, \tag{1}\]  
where \(M\) is the earthquake magnitude.  
According to the distribution of seismic zones along the Second West-East Gas Pipeline Project (China), the minimum magnitude is taken as 4.5 and the maximum magnitude is taken as 7.5, and the fault displacement obtained from the above formula is from 0.12 m to 2.92 m.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Nominal** & **Nominal Strain** & **True Stress/MPa** & **True Strain** & **Plastic Strain** \\
640 & 0.06226321 & 680 & 0.060401736 & 0.05711744 \\
650 & 0.08009188 & 702 & 0.077046112 & 0.073654519 \\
660 & 0.1028639 & 728 & 0.097910342 & 0.094393964 \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 2: Constraint conditions of soil model: (**a**) At the bottom; (**b**) on the side.
## 6 Discussion and Future Considerations
### Physical Impairments  
With regards to the physical layer, in some cases, it is assumed that the IoT device should have a LOS view towards a UAV. This probability is assumed to depend on several factors, such as device location, environment, UAV location, and elevation angle. Both NLOS and LOS should be considered in practice since the presence of obstacles cannot be entirely avoided [62]. An arbitrary A2G link can be determined to be LOS or NLOS based on a 3D map containing environmental information. Since these maps are sophisticated, they can be used to obtain accurate information [62]. ML approaches with low complexity can be used to provide an efficient prediction of LOS probability for a pair of UAVs and ground user locations [95]. Additionally, it is assumed that the UAV does not necessarily have additional information about the exact location, height, and the number of obstacles as well as the channel conditions. For this reason, the randomness associated with LOS and NLOS should be considered in designing the system [1].  
Deploying a UAV with a free space channel model is inaccurate in practice and may lead to a degradation in performance due to blockage [8]. Many studies have not considered the impact of blockages on AoI performance. In urban environments with high-rise buildings and dense settings, the signal propagation between the UAV and IoT nodes could be severely affected by blockages. This makes it difficult for UAVs to collect data in a timely fashion, thus resulting in high AoI due to the low transmission rate. Mitigating the effect of blockage is, therefore essential to improve wireless channel quality between the UAV and ground IoT nodes [103].  
A Re-configurable Intelligent Surface (RIS) can potentially improve the transmission quality between transmitter and receiver devices. It consists of a controller and several low-cost passive elements. Each element has the potential to reflect incident signal by controlling the phase shift, thus changing the reflected signal propagation collaboratively. UAV and RIS (based on the amount of published research in this area) play a significant role in improving IoT data transmission to the cloud or base station [108]. Several factors affect the channel link between UAVs and sensor nodes. These include the number of elements in the RIS, the position of the RIS, and meta-surface material. These impact the channel link performance in UAV-assisted RIS channel modeling [108], and hence all these could be considered in future works. Optimization of UAV trajectory placement, RIS phase shift, mobility, and base station power allocation contribute to improving the system's performance [108]. Similarly, robust models should be proposed so that the UAV does not need to be re-trained once it is deployed in a different environment (building distribution, RIS location, IoT device location). Therefore, generalized approaches with more generalized policies are desired [103].
## 3 Methodology
### Modulation Order Identification Using Circle Radius Ratio  
Each point in an MQAM constellation diagram is surrounded by circles of varying diameters, which are determined by their distance from the constellation center. Different orders of QAM signals exhibit characteristic ranges of circle diameter values. This property allows us to utilize circle radii within the constellation map for the purpose of MQAM signal classification and identification. In the absence of noise interference, a noise-free 16 QAM's constellation map displays a point with the highest amplitude corresponding to a circle with radius \(r_{max}\), while the point with the lowest amplitude corresponds to a circle with radius \(r_{min}\), as shown in Figure 2. In a standard constellation plot, the ratio of the maximum to minimum circle radius is defined as \(R_{b}=\frac{r_{max}}{r_{min}}\).  
After applying the clustering algorithm to obtain the reconstructed signal's constellation diagram, we calculate and sort the distances between the cluster center and the starting point in descending order. The top \(W\) distances are averaged to determine the maximum circle radius, while the bottom \(W\) distances are averaged to determine the minimum circle radius. The \(R_{b}\) values of different MQAM modulation signals are categorized into distinct ranges based on predefined standard values. The categorization is as follows:  
\[\text{MQAM}=\left\{\begin{array}{lcl}4 QAM&:&0.0<R_{b}\leq 1.6\\ 8 QAM&:&1.6<R_{b}\leq 2.6\\ 16 QAM&:&2.6<R_{b}\leq 3.5\\ 32 QAM&:&3.5<R_{b}\leq 5.6\\ 64 QAM&:&5.6<R_{b}\leq 8.1\,.\end{array}\right. \tag{11}\]  
Noise can lead to inaccuracies in the initial determination of cluster centers through adaptive subtractive clustering based on the signal-to-noise ratio. The update of semi-supervised fuzzy mean clustering affects the positions of cluster centers but not the number of cluster centers. Consequently, the number of cluster centers in the reconstructed constellation map may not correspond to the actual number of modulation points. By employing the radius method on different circles of the constellation map, we can calculate radius values, mitigating the impact of an incorrect number of cluster centers. This approach does not affect the final recognition result and enhances the classification accuracy.
## 1 Introduction
In this study, the expected state-action-reward-state-action (SARSA)-based RL approach is applied to obtain a more robust solution to WPG forecast fluctuation. Moreover, multi-dimensional data clustering that considers the WPG forecasting of each unit is combined to enhance the proposed strategy. A simulation study using real datasets recorded by the National Renewable Energy Laboratory (NREL) project of U.S. demonstrates that the proposed strategy provides near-optimal performance. The main contribution of this study is summarized as follows:  
1. This study proposes a VESS operation strategy based on an expected state-action-reward-state-action (SARSA)-based RL approach that is a more robust solution for WPG forecast fluctuation. To the best of our knowledge, this is the first work to apply the RL approach for the VESS operation;
2. The proposed strategy is also combined with multi-dimensional data clustering for enhancing the policy learning performance of the RL approach;
3. Effect of VESS and clustering is carefully discussed, and the usage case of the proposed strategy is suggested. The rest of this paper is organized as follows. In Section 2, the system description, including forecasting uncertainty model and VESS system, and the VESS operation problem formulation is described, and in Section 3, the design method of the proposed RL-based VESS operation strategy is discussed. In Section 4, measurement studies using real WPG profiles applied to the proposed strategy are demonstrated, and in Section 5, a conclusion of the study is presented.
## 3 Results and Discussion
### Speed Sensor Calibration Test  
Accurate measurement of the forward speed of the cultivating fertilizer applicator was related to the calculation and control accuracy of the fertilizer output of the variable formula fertilization system, and the speed test work was required. The Zhongji Miele 1304 cultivating fertilizer applicator kept driving at a constant speed within the range of 3-6 km/h and recorded the driving time of its passing through the calibration interval (100 \(\times\) 5 m). After the conversion of Equation (2), the calculation formula of slip rate was:  
\[\varepsilon=\frac{2500 Tnv-19\pi N}{19\pi N} \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Fertilizer**} & \multirow{2}{*}{**Test Count**} & \multicolumn{4}{c}{**Fertilizer Speed/(r/min)**} \\ \cline{3-5}  & & **10** & **30** & **50** \\ \hline \multirow{5}{*}{N} & 1 & 53.7 & 45.2 & 43.1 \\ \cline{2-5}  & 2 & 52.2 & 45.4 & 41.0 \\ \cline{2-5}  & 3 & 51.2 & 50.1 & 41.2 \\ \cline{2-5}  & Average value & 52.4 & 46.9 & 41.8 \\ \cline{2-5}  & Standard deviation & 1.3 & 2.8 & 1.2 \\ \hline \multirow{5}{*}{Fertilizer discharge (g/r)} & 1 & 61.2 & 57.5 & 55.6 \\ \cline{2-5}  & 2 & 66.8 & 63.7 & 55.1 \\ \cline{2-5}  & 3 & 65.0 & 59.8 & 56.7 \\ \cline{2-5}  & Average value & 64.3 & 60.3 & 55.8 \\ \cline{2-5}  & Standard deviation & 2.9 & 3.1 & 0.8 \\ \hline \multirow{5}{*}{K} & 1 & 85.9 & 78.1 & 78.2 \\ \cline{2-5}  & 2 & 85.1 & 83.5 & 79.5 \\ \cline{1-1} \cline{2-5}  & 3 & 87.0 & 86.6 & 80.4 \\ \cline{1-1} \cline{2-5}  & Average value & 86.0 & 82.7 & 79.4 \\ \cline{1-1} \cline{2-5}  & Standard deviation & 1.0 & 4.3 & 1.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fertilizer discharge measurement test results.  
In this study, 6% of the forward speed of the field fertilizer applicator was selected as the forward slip rate of the implement. In order to check the accuracy of the speed measurement, the test was repeated 3 times and the average value was taken. It used the ratio of the advance distance to the advance time as the actual speed. The detection speed was calculated by Equation (2), and the test results are shown in Table 3. In the experiment, the accuracy of speed measurement was above 98%, which met the requirements of variable formula fertilization speed measurement experiment.