esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Models Overview
### Research Route  
The research process was divided into four main parts: data collection, data preprocessing, spectral data analysis, and model building and evaluation. The research process is summarized in Figure 4. Firstly, soil samples were collected from the research area for determination of the SOM content via chemical methods, and the spectral data are determined via a hyperspectral camera. Secondly, the acquired spectral data were processed using ENVI5.3 software to standardize and correct them, eliminating excessive high and low bands that were present in the data. The data were then preprocessed using SG smoothing for denoising and spectral differential transforms. Subsequently, the preprocessed data were analyzed to determine the SOM content, spectral curve characteristics, and correlation between variables. The SCARS algorithm was then employed to select the feature bands from the data. Finally, five different algorithms were utilized to establish SOM inversion models, and these models were evaluated using metrics such as R\({}^{2}\) to assess their performance. In the research, the implementation of differential transformations, SCARS, and the five models were all based on the MATLAB 2020 platform. The experimental environment was as follows: Intel(R) CoreTM i5-9400 CPU @ 2.90 GH8 GB RAM, 64 bit Windows 10, and MatlabR2020a. The SCARS algorithm was implemented using the carspls Toolbox, while the SVR algorithm was implemented using the libSVM Toolbox. The MLR, PLSR, RF, and MPARF algorithms all make use of the Statistics and Machine Learning Toolbox.  
Figure 4: Summary map of research route.
# Characteristics of Collimators Based on the Large-Mode-Area CMCF for Coupling Laser Beam
## 4 Conclusions  
In this research, a homemade single-mode collimator based on the large-mode-area CMCF was fabricated and demonstrated for spatial coupling of the laser. The coupling loss in off-axis, angular, and axial aspects of this collimator was theoretically analyzed and laboratory measured. Due to the special features of CMCFs, the overall performance of this homemade collimator was significantly improved. Of these, the average deviation of the angular orientation, when the laser beam was coupled to this collimator, was enhanced by 20%. It greatly reduced the difficulty of the spatial coupling of laser beams and improved the quality of optical signal transmission. This homemade collimator with good alignment performance can provide a basis for next-generation optical communication.  
Conceptualization, methodology, X.Z. and X.L.; validation, Z.B. and X.L.; formal analysis, X.Z. and X.L.; investigation, X.Z. and X.L.; writing--original draft preparation, Z.G. and T.Y.; writing--review and editing, X.Z., Z.B. and X.L.; visualization, X.Z. and T.Y.; supervision, X.L. and S.L.; project administration, X.L.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers 61905293, 61774175 and the Young Teachers Research Ability Enhancement Project @ Minzu University of China, grant number 2021 QNPY11.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Misalignment** & \(\eta\) & **MCF** & **SMF** & **Improvement** \\ \hline Off-axis X/\(\mu\)m & **1 dB** & 60.3 & 88.2 & \(-\)31.6\% \\ Angular \(\theta/^{\circ}\) & **1 dB** & 0.078 & 0.065 & 20\% \\ Axial Z/mm & **0.2 dB** & 27 & 16 & \(-\)68.75\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table of geometric deviations caused by misalignment measured in lab.  
Figure 14: Coupling loss of (**a**) off-axis deviation; (**b**) angular deviation; (**c**) axial deviation.
## 4 Discussion
### Sensitivity Analysis of Wind Effect on Green Tide Distribution  
The effect of wind on the green tide distribution is composed of two components, one of which are the currents induced by winds and the other is the direct momentum transported from winds by friction (windage or leeway) [35; 41]. In previous studies, windage was a crucial factor for the modeling of macroalgae migration, in which the algae were mostly floating on the sea surface [35; 37; 38; 39; 41; 59]. In this research, the green macroalgae were suspended throughout the water column, which is distinct from the green tides in the Yellow Sea and the golden tides in the Atlantic Ocean. Therefore, the macroalgae particles are placed randomly vertically, and the depth-averaged currents are redistributed vertically using the theoretical velocity profile to move the particles. As concluded in Section 4.1, the effect of the wind is the major factor controlling the migration of the green tide distribution, and thus the sensitivity analysis of the effect of wind is carried out in this section to investigate the quantitative coverage of the green tide distribution under winds with different magnitudes and directions.  
Figure 10 shows the wind rose based on the wind data during the third quarter of 2021 in Jinmeng Bay, when the green tide happened. The winds with a direction from ENE to SSW accounted for 60% of all the counted records, and those with a magnitude below 6 m/s accounted for 96% of all the counted records. Hence, the sensitivity analysis of the effect of wind was carried out for a one-month computation covering the whole green tide event, with wind magnitudes of 2 m/s, 4 m/s, and 6 m/s, respectively, and wind directions of ENE, ESE, SSE, and SSW, respectively; the results of the coverage of green macroalgae trajectories under the winds with different magnitudes and directions are shown in Figure 11. It can be seen that the coverage of green macroalgae trajectories under all wind directions (ENE, ESE, SSE, and SSW) increases with the augmentation of wind magnitude, and that the maximum coverage is 30.73 km\({}^{2}\) under 6 m/s wind in ENE. When the wind direction varies clockwise from ENE to SSW, the coverage of green macroalgae trajectories increases first and then decreases under all wind magnitude (2 m/s, 4 m/s, and 6 m/s), and the alongshore extent under 6 m/s wind in ENE is the largest, passing over the Jinshanzui headland.  
Figure 9: (**a**–**h**) Current fields with or without winds and structures in the moments of maximum ebb and flood; (**i**–**l**) Trajectories of green macroalgae with or without winds and structures. The areas surrounded by dotted red lines represent the coverage of the green macroalgae trajectories.
# Strategic Portfolio Optimization Using Simulated, Digital, and Quantum Annealing
## 4 Annealing Methods and the QUBO Model
### Digital Annealing  
Digital annealing and the corresponding hardware the Fujitsu Digital Annealing Unit (DAU) are designed to solve fully connected quadratic unconstrained binary optimization (QUBO) problems. It is implemented on application-specific CMOS hardware and currently solves problems of up to 8192 variables [28]. The DAU's algorithm is based on SA, but differs from it in two main ways: first, it uses a parallel-trial scheme in which each Monte Carlo step considers a flip of all variables (separately), in parallel. If at least one flip is accepted, one of the accepted flips is chosen uniformly at random and it is applied. The advantage of the parallel-trial scheme is that it can boost the acceptance probability, because the likelihood of accepting a flip out of \(N\) flips is typically much larger than the likelihood of flipping a particular variable. Parallel rejection algorithms on GPU (see [29; 30]) are examples of similar efforts in the literature to address the low acceptance probability problem in Monte Carlo methods [28]. Finally, digital annealing employs an escape mechanism called dynamic offset, such that if no flip was accepted, the subsequent acceptance probabilities are artificially increased by subtracting a positive value from the difference in energy associated with a proposed move. This can help the algorithm to surmount short, narrow barriers [28]. For more details on digital annealing, see [28; 31].
## 3 Results
### Cell Viability and Apoptotic Body Formation Staining  
Rhodamine-B was used to visualize the cell morphology while Hoechst 33342 dye was used to visualize DNA in the nucleus. The obtained results show that after 24 h of exposure, the cell morphology was changed only in 50 \(\upmu\)M of Les-4368 and 50 \(\upmu\)M of Les  
Figure 6: Effects of the increasing concentrations (1 nM-100 \(\upmu\)M) of Les-4368 (**A**,**C**) and Les-4370 (**B**,**D) on caspase-3 activity in the BJ cell line (**A**,**B**) and the CACO-2 cell line (**C**,**D) after treatment for 24 h and 48 h. Data are expressed as means (\(n=6\)) with standard deviation (SD); data marked with *, **, and *** are statistically different at \(p<0.05\), \(p<0.01\), and \(p<0.001\), respectively, compared to the control.  
In case of CACO-2 cell line, after 24 h of exposure, the cell morphology was changed only after treatment with 50 uM of Les-4368 and 50 uM of Les-4370 concentrations, as well as in the positive control group (STS) (Figure 8). Our data showed that after 24 h of exposure, the CACO-2 cell line to Les-4368 in 1 uM concentration, the DNA condensation was not observed. On the other hand, the cells exposed to 10 and 50 uM of Les-4368 were characterized by a DNA fragmentation and condensation, which was similar to the STS-treated cells (Figure 8). Similarly to the BJ cell line, the CACO-2 cells treated with any concentration of Les-4370 did not cause any changes in DNA condensation or fragmentation (Figure 8).  
Figure 8: Fluorescence imaging with Hoechst 33342 (H33342, blue) and Rhodamine-B (red) staining of the CACO-2 line cells after the exposure to 1, 10, or 50 uM of Les-4368 and 1, 10, or 50 uM of Les-4370, 1 uM of staurosporine (STS) was used as positive control and negative control cells with vehicle after 24 h treatment. Dashed squares indicate the regions used for the zoom. The additional magnification shows a representative cell nucleus. The 100\(\times\) magnification was used.
# Experimental Study on Engineering Properties of Cemented Soil with High Water Content
## 3 Test Results
### Stress-Strain Curve
According to Figure 6, the brittleness index decreased rapidly with the confining pressure when the confining pressure increased from 100 kPa to 400 kPa. For the cemented soil with 18% cement content, the brittleness index decreased from 0.664 to 0.529 as the confining pressure increased to 400 kPa from 100 kPa. It can also be seen in Figure 6 that the brittleness index almost remained stable as the confining pressure grew to 1600 kPa from 400 kPa. For the cemented soil with 18% cement content, the brittleness index was in the range 0.515-0.529 as the confining pressure grew to 1600 kPa from 400 kPa. Furthermore, the brittleness index of the cemented soil could also increase significantly with the cement content. The brittleness index of the cemented soil with a cement content of 6%, 12%, and 18% was 0.405, 0.602, and 0.664, respectively, as the confining pressure reached about 100 kPa. The brittleness index of cemented soil with an 18% content was 64% and 10.3% higher than that of cemented soil with 6% and 12% cement content, respectively.
###### Abstract  
Air overpressure (AOp) is an undesirable environmental effect of blasting. To date, a variety of empirical equations have been developed to forecast this phenomenon and prevent its negative impacts with accuracy. However, the accuracy of these methods is not sufficient. In addition, they are resource-consuming. This study employed support vector regression (SVR) optimized with the grasshopper optimizer (GO) algorithm to forecast AOp resulting from blasting. Additionally, a novel input selection technique, the Boruta algorithm (BFS), was applied. A new algorithm, the SVR-GA-BFS\({}_{7}\), was developed by combining the models mentioned above. The findings showed that the SVR-GO-BFS\({}_{7}\) model was the best technique (R\({}^{2}\) = 0.983, RMSE = 1.332). The superiority of this model means that using the seven most important inputs was enough to forecast the AOp in the present investigation. Furthermore, the performance of SVR-GO-BFS\({}_{7}\) was compared with various machine learning techniques, and the model outperformed the base models. The GO was compared with some other optimization techniques, and the superiority of this algorithm over the others was confirmed. Therefore, the suggested method presents a framework for accurate AOp prediction that supports the resource-saving forecasting methods.  
blasting; airblast; input selection; hybrid SVR model; prediction Article
# Feature Fusion Graph Consecutive-Attention Network for Skeleton-Based Tennis Action Recognition
## 5 Materials and Methods
### Grad-CAM  
To generate a Grad-CAM visualization, a specific target class is selected to interpret the model's decision. A Grad-CAM applies the gradients of the class score (the output for the selected class) with respect to the feature maps of a chosen convolutional layer, often the last convolutional layer, during a backward pass. The derivative of the class score with respect to these feature maps indicates how the final prediction changes if the feature map values are adjusted, thus revealing each feature map's contribution to the output. The gradients are then globally averaged (pooled) across the height and width of the feature maps, producing a weight for each feature map. These weights form the basis for creating a coarse localization map, as in Equation (15) [40]:  
\[\alpha_{i}=\frac{1}{N}\sum_{k}\sum_{j}\frac{\partial y^{c}}{\partial A^{i}_{kj}} \tag{15}\]  
In Equation (15), \(\alpha_{i}\) indicates the weight for feature map \(i\). \(N\) is the number of pixels in the map, \(\partial y^{c}\) represents the score value for \(c\) class, and \(\partial A^{i}_{kj}\) denotes the activation of the \(i\)-th feature map at location \((k,j)\). A weight is added for each feature map that represents its importance in the context of the target class. Each feature map is multiplied by its respective weight, and the weighted feature maps are summed to obtain a single two-dimensional heatmap, as in Equation (16) [40]:  
\[\text{Grad}-\text{CAM}=ReLU\Bigg{(}\sum_{i}\alpha_{i}A^{k}\Bigg{)} \tag{16}\]  
A _ReLU_ activation function is used to emphasize positive contributions, generating a heatmap that highlights key regions for the target class. This heatmap can be normalized to map its values to the range \([0,\ 1]\), enhancing its visualization. Afterward, the heatmap is resized to align with the dimensions of the original input image. Finally, it is superimposed onto the original image, often using a color map to represent areas of greater significance.
## 5 Conclusions and Perspectives  
The concept of the process intensification strategy has been significantly implemented in the desalination sector where large, heavy, and energy intensive technologies have been replaced with membrane based technologies with low energy consumption, small footprints, and low environmental consequences. On the basis of process intensification metrics, the membrane operations clearly surpass their conventional thermal counterparts. Relatively less explored membrane based operations including MD/MCr, PRO, RED, etc. are emerging with demonstrated potential to increase water recovery factor, to achieve minerals recovery and renewable energy production, thus breaking the traditional bounds and applications of membrane technology in desalination. Interesting developments of these operations are appearing at commercial scales and mega desalination projects including MEDINA, Seahero, Mega-ton, and the Global MVP project have witnessed the interest of practical applications of these processes. The reshaping of the desalination industry can be foreseen due to the implementation of these processes, and it can be realistically expected that the desalination industry will turn into the energy and raw material sector in the future, with freshwater as a useful byproduct. The promises of new processes, however, are strongly related with successfully overcoming several challenges including the development of better membranes for PRO, RED, and MD/MCr, better control of internal concentration polarization and biofouling, better spacer design, improvement in efficiency of energy recovery devices, improved module design, etc. In remote areas with poor infrastructure, the implementation of renewable energy sources in membrane based desalination is expected to gain more attention. MD is expected to emerge as a strong contester of RO in this field, particularly in the regions with abundant sources of solar and geothermal energy.  
[PERSON] performed the simulation analysis and wrote the manuscript. [PERSON] supervised the work and revised the manuscript. [PERSON] initiated the work.  
The authors declare no conflict of interest.
## 5 Results and Discussion
### Year-Long Results
#### 5.1.1 Self-Consumption and Self-Sufficiency  
The results show that reaching 100% is very hard, even with oversized storage, as is the case with UPACs A and B that reach 99% and 93% of SC, respectively, in the greedy control strategy. Naturally, the same is true for SS, where the most pragmatic case is UPAC F. In this case, even with over-sized storage (9.6 kWh), the SS is 99%, for a 66% SC in the greedy battery operation.  
Concerning the 2-TOU control strategy, the results show a considerable decrease in the levels of SC and SS across the six UPACs. However, the reasons for this effect differ between smaller and larger UPACs. In the case of the smaller installations (A, B and C), due to the pre-charge operation and low morning consumption, it is very likely to observe a decrease in the SC in the morning since excess production can no longer be stored. Note that, in Table 10, for pre-charge SOC below 90%, the SC increases. In contrast, when using the greedy strategy, the batteries are fully discharged most of the time (Table 11). Hence, early morning excess production is stored.  
As for larger UPACs (D, E and F), not being able to discharge the battery during off-peak periods implies that the storage is still charged in the morning. Hence, it is not able to accommodate all the excess production. For example, UPAC D tends to start the day with a SOC around 35%.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Values** \\ \hline Frequency of the simulation steps & 1/600 Hz \\ Control strategies & Greedy; Greedy + Pre-Charge Heuristic \\ Billing parameters & Single-rate; 2-TOU \\ Feed-in Tariff & 0 Euros per kWh \\ Inverter rated power & see Table 4 \\ Inverter efficiency & Dynamic \\ Battery nominal capacity & see Table 4 \\ Initial SOC & 50\% \\ SOC Limits & SOC\({}_{\text{min}}\): 10\%; SOC\({}_{\text{max}}\): 90\% \\ Initial SOH & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Simulation parameters and respective values.
# A Novel Approach to Enhance Crude Oil Recovery Ratio Using Selected Bacterial Species
## 2 Materials and Methods
### Identification of Isolates by 165 rDNA Analysis  
DNA was extracted from the bacterial colonies according to the protocol described by Sigma's GenElute Bacterial Genomic Kit. Genomic DNA from hydrocarbon degraders were amplified by polymerase chain reaction (PCR) using the specific bacterial primers 27 f and 1525 r. These are universal primers that bind at the conserved 5 and 3\({}^{\prime}\) ends of 16S rDNA of the eubacteria. PCR amplification was performed as described by [14]. All reagents used were from Bioline, (London, UK). PCR was performed with a Biometra Thermal Cycler (Jena, Germany). In order to check the sizes of the PCR products, 4 mL of the PCR reactions were analyzed by 1.0% agarose gel electrophoresis in TAE (Tris acetic acid EDTA buffer, 1% \(w/v\); 30 min at 100 V, 0.5\(\times\) TBE), stained with ethidium bromide, and visualized under UV light using image analysis software (Bio-Rad Gel Doc, Flour-S multi-imager, Hertfordshire, UK), according to [15]. The size of the amplified fragments was estimated by comparing with a 100 bp molecular size marker.  
The PCR products were cleaned up using an ExoSAP-IT PCR clean-up kit, the protocol for which consists of a single pipetting step (enzyme mixture addition), a 15 min incubation at 37 \({}^{\circ}\)C followed by a further 15 min incubation at 80 \({}^{\circ}\)C for enzyme inactivation, and then the PCR for products were sequenced by Geneius Laboratories Ltd. (based on Newcastle University campus, UK) using Sanger sequencing based on a big dye terminator using an ABI 3730 xl DNA sequence. Sequences were viewed using MEGA software version 4.0. A contiguous sequence using forward and reverse sequences was made, and the contiguous sequences were submitted into BLAST for comparison with known databases.
## 4 Proposed Scheme
### Illustrative Example
The final bit plane of 64 bits will be generated by concatenating 27 bits from the clinical report (secret message) with the bit sequence obtained from the modified Elias gamma encoding procedure. This will be converted to a 2D array of size \(8\times 8\) and considered the modified bit plane. This process will be continued for a few numbers of the most significant bit planes. Note that the RLE compression can create some vacant spaces only when there is a high correlation between the adjacent bits. If we see the bit planes in the least significant positions, they will be highly random, and we cannot achieve compression over there with RLE. Therefore, those planes should remain the same in the final stego image. Once we perform compression and data hiding on a few of the most significant selected bit planes, then we will combine all those planes along with the unmodified least significant planes to obtain the final image \(E^{\prime}\). Note that \(E^{\prime}\) will not have any natural image characteristics since we completely modified the most significant planes. The image \(E^{\prime}\) we will encrypt using a secure image encryption method using the encryption key \(K\) before it is transmitted through the network. On the receiver side, the receiver will decrypt the image using the decryption key \(K\). Further, the decoding process will be conducted on the bit planes. Note that the receiver should know the number of bit planes used for the RDH process. The modified Elias gamma decoding and run-length decoding will be applied on all the most significant bit planes that are used for RDH. During the decoding process, we will extract a few secret message bits. The decoding process will be stopped once we get the \(R.C\) number of bits which will be bits of the original bit plane.
# Performance Evaluation of LoRa Communications in Harsh Industrial Environments
## 4 Hamming Error-Correcting Code, IIR and FIR Filters
### Filtering Systems
#### 4.2.1 IIR Filter  
The infinite impulse response filter used in our simulations is described by the difference equation represented by Equation (17), where \(x\) represents the value of the input signal and y represents the value of the filter's output signal [42].  
\[y(n)=\sum\
olimits_{k=0}^{N}a_{k}.x[n-k]-\sum\
olimits_{k=1}^{M}b_{k}.y[n-k], \tag{17}\]  
The output signal samples \(y(n)\) are generated by exciting the system with the input samples \(x(n)\), and the associated coefficients are \(a_{k}\) and \(b_{k}\). The relationship indicates that the current output of the IIR filter depends on both the previous output and the current input of the system, as illustrated in Figure 5.