esa-sceva/satcom-chunk-collection huggingface dataset:
# Reinforcement Learning for Efficient Drone-Assisted Vehicle Routing
## 5 Computational Studies
### Training and Evaluation Configurations
\begin{table}
\begin{tabular}{r r r r r r r r r r r} \hline \hline  & & \multicolumn{3}{c}{\(|\mathcal{K}|=\mathbf{1},|\mathcal{D}|=\mathbf{2}\)} & \multicolumn{3}{c}{\(|\mathcal{K}|=\mathbf{2},|\mathcal{D}|=\mathbf{1}\)} & \multicolumn{3}{c}{\(|\mathcal{K}|=\mathbf{2},|\mathcal{D}|=\mathbf{2}\)} \\ \cline{3-10}  & **Methods** & **Costs** & **Gaps, \%** & **Time, s** & **Costs** & **Gaps, \%** & **Time, s** & **Costs** & **Gaps, \%** & **Time, s** \\ \hline \multirow{5}{*}{\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{ }}}}}}}}}}}}}}\) & Solver & 158.53 & 0.51 & 175.53 & 149.53 & 1.10 & 601.33 & 125.55 & 1.09 & 481.62 \\  & Solver Lazy & 158.53 & 0.51 & 76.97 & 147.60 & 0.00 & 148.52 & 123.9 & 0.00 & 161.35 \\  & Heuristic & 158.53 & 0.51 & 154.55 & 150.32 & 1.94 & 61.84 & 125.5 & 1.06 & 65.06 \\  & RL greedy & 161.69 & 2.38 & 0.02 & 148.68 & 0.90 & 0.03 & 126.98 & 2.25 & 0.21 \\  & RL sampling & 160.23 & 1.60 & 0.87 & 148.32 & 0.59 & 1.31 & 126.87 & 2.14 & 4.00 \\  & Ensemble RL & 159.95 & 1.45 & 6.02 & 147.60 & 0.00 & 9.27 & 126.87 & 2.14 & 28.18 \\ \hline \multirow{5}{*}{\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{ \mathbf{ }}}}}}}}}}}}\)} & Solver & 196.48 & 11.25 & 601.29 & 193.75 & 16.42 & 601.52 & 171.78 & 30.89 & 601.69 \\  & Solver Lazy & 195.26 & 10.78 & 556.72 & 200.91 & 20.42 & 600.67 & 176.63 & 34.64 & 602.05 \\  & Heuristic & 178.93 & 1.42 & 308.29 & 167.59 & 0.82 & 63.68 & 131.36 & 0.23 & 99.1 \\  & RL greedy & 185.42 & 4.74 & 0.01 & 168.40 & 1.22 & 0.40 & 136.06 & 3.58 & 0.03 \\  & RL sampling & 180.26 & 1.89 & 0.43 & 167.57 & 0.71 & 1.43 & 132.40 & 0.92 & 2.06 \\  & Ensemble RL & 177.01 & 0.28 & 3.05 & 167.56 & 0.70 & 9.38 & 134.20 & 2.23 & 14.65 \\ \hline \multirow{5}{*}{\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{   \mathbf{  }}}}}}}}}}}}\)} & RL greedy & 243.06 & 3.86 & 0.02 & 219.02 & 6.91 & 0.02 & 185.88 & 8.17 & 0.32 \\  & RL sampling & 234.45 & 0.28 & 1.31 & 209.65 & 2.12 & 4.24 & 174.88 & 1.71 & 8.01 \\ \cline{1-1}  & Ensemble RL & 234.09 & 0.12 & 8.94 & 205.67 & 0.15 & 29.42 & 172.64 & 0.39 & 55.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The VRPD results with Infinite Flying Range of Drones. Averages of 10 problem instances. ‘Cost’ refers to the average cost value. ‘Gap’ is the mean relative difference to the cost of the best algorithm for each instance. ‘Time’ is the average solution time of the algorithm for a single instance.
# A Novel IEEE 802.15.4e DSME MAC for Wireless Sensor Networks
###### Abstract  
IEEE 802.15.4e standard proposes Deterministic and Synchronous Multichannel Extension (DSME) mode for wireless sensor networks (WSNs) to support industrial, commercial and health care applications. In this paper, a new channel access scheme and beacon scheduling schemes are designed for the IEEE 802.15.4e enabled WSNs in star topology to reduce the network discovery time and energy consumption. In addition, a new dynamic guaranteed retransmission slot allocation scheme is designed for devices with the failure Guaranteed Time Slot (GTS) transmission to reduce the retransmission delay. To evaluate our schemes, analytical models are designed to analyze the performance of WSNs in terms of reliability, delay, throughput and energy consumption. Our schemes are validated with simulation and analytical results and are observed that simulation results well match with the analytical one. The evaluated results of our designed schemes can improve the reliability, throughput, delay, and energy consumptions significantly.  
wireless sensor networks; IEEE 802.15.4e; performance analysis; DSME; star topology +
Footnote †: journal: Sensors
# Citrus Pest Identification Model Based on Improved ShuffleNet
###### Abstract  
To address the current issues of complex structures and low accuracies in citrus pest identification models, a lightweight pest identification model was proposed. First, a parameterized linear rectification function was introduced to avoid neuronal death. Second, the model's attention to pest characteristics was improved by incorporating an improved mixed attention mechanism. Subsequently, the network structure of the original model was adjusted to reduce architectural complexity. Finally, by employing transfer learning, an SCHNet model was developed. The experimental results indicated that the proposed model achieved an accuracy rate of 94.48% with a compact size of 3.84 MB. Compared to the original ShuffleNet V2 network, the SCHNet model showed a 3.12% accuracy improvement while reducing the model size by 22.7%. The SCHNet model exhibited an excellent classification performance for citrus pest identification, enabling the accurate identification of citrus pests.  
citrus; lightweight; parametric linear rectification; attention mechanism; transfer learning +
Footnote †: journal: Applied
## 4 Testing of Manufactured Device  
In order to test and validate the structures made by FDM technology, but also to compare the results obtained by simulation with the results of the experimental model, a physical system was developed that simulated the functioning of the proposed mechanisms for the given application. The experimental stand proposed in the paper presents the possibility of fixing the tested part in a support that allows its displacement, especially on the X axis and the rotation around the Z axis of the compliant mechanism. The testing rig was specially made for the tests and is not part of any patent or technical solution. Its purpose was to determine if the mechanism could be used for a reasonable number of times, less than a couple hundred, without it breaking or losing its mechanical characteristics, and testing was continuously performed.  
However, the part was not fixed on any axis; the assembly was made with high tolerances. At each of the moving ends of the mechanism, end-of-travel sensors were  
Figure 8: Working parameters used for compliant mechanism.  
mounted, as shown, for clarity, as mechanical microswitches in the diagram in Figure 9, but optical sensors in the stand were made. Actuation was conducted with the help of two linear pneumatic motors with double action (Figure 10), dimensioned so as to produce a force of, at most, 35 N at the working pressure (3 bar). The two motors were mounted in opposition on the same Y axis of the system, in the direction of the red arrows in Figure 9. The movement of the two motors simultaneously occurred, according to the proposed control algorithm.  
The control and monitoring of the system was conducted with the help of an electronic system based on an ESP microcontroller, together with the signal adaptation electronics necessary for the interface with the distributors and limit switches used.  
Figure 11 shows a schematic diagram of the algorithm implemented for control.  
Figure 10: Pneumatic actuation schematics.  
Figure 9: Principle schematics of testing.  
Figure 12a shows the complete scheme of the system made for testing the parts. A simple PC-level application was developed to control the application. This opens a bidirectional TCP port and allows for the initiation of commands and the reception of data from the limit switches mounted on the construction. The advance and withdrawal strokes along with the signals from the end-of-travel sensors were counted at the controller level and transmitted to the PC.  
Figure 12b shows an overview of the test stand made for testing this solution and obtained preliminary results to confirm those obtained by the simulation. It contains the elements presented above and works according to those described.  
As expected, the system poorly performs for both ABS and PLA, with minimal elasticity of the parts, and they fail after only a few uses. TPU parts, however, perform generally better, as tested, with no obvious issues to the mechanism after a few tries.
# Study on the Vertical Stability of Drilling Wellbore under Optimized Constraints
## 2 Theoretical Methodology
### Derivation of Equations for Critical Depth  
#### 2.3.1 Necessary Condition  
It has been seen from Figure 3 that the structure is in an unstable state when \(m\leq 0\), which means that the points appear inside the bifurcation points set B. For Equation (22), when \(\Delta=0\), it has two identical roots out of three roots, which is the critical state for the system to mutate. When \(\Delta>0\), it has only one real root and two complex roots, at which time we consider the system to be stable. When \(\Delta<0\), it has three real roots, at which time the system will exceed the critical state and then mutate. Using the discriminant \(\Delta\), we can also determine whether the system is stable or not. From Equations (27), (28) and (32), it can be determined that the destabilization condition of the wellbore structure is  
\[\Delta=4m^{3}+27n^{2}=4\bigg{(}\frac{A_{2}}{\sqrt{A_{4}}}\bigg{)}^{3}\leq 0 \tag{33}\]  
from which it can be determined that  
\[A_{2}\leq 0 \tag{34}\]  
By substituting Equation (24) into Equation (34), we can obtain  
\[H\geq\sqrt[3]{\frac{41 EI\pi^{4}}{(10\pi^{2}-32)q_{c}+64q_{w}\sin^{2}\frac{\pi H _{w}}{2H}\sin^{2}\frac{\pi H_{w}}{H}}} \tag{35}\]  
Equation (35) is a necessary condition for wellbore structure instability. The results show that vertical wellbore structure instability will occur as long as the depth of the wellbore is within its range. In addition, when Equation (35) obtains an equal sign, it is the critical condition for shaft wall structure instability, so the critical depth of shaft wall instability is  
\[H_{cr}=\sqrt[3]{\frac{41 EI\pi^{4}}{(10\pi^{2}-32)q_{c}+64q_{w}\sin^{2}\frac{ \pi H_{w}}{2H}\sin^{2}\frac{\pi H_{w}}{H}}} \tag{36}\]
## 4 Discussion
Hence, silver (monovalent) would thus be used in place of calcium (bivalent) to reduce the dissolving of the glass because silver ions had greater electronegativity than calcium ions. Silica ions release rates were assessed as a sign of glass dissolving to further support the idea that the addition of silver has reduced glass dissolution. The results showed that as the silver concentration in the glasses grew, silica release rates from their surfaces dropped [89]. For this situation, Ag may reduce the quantity and nature of the glass groups [88], which would slow down the synthesis of HA and result in a reduction in bioactivity. This was likewise the case with cerium ions, as shown Figure 5d,e, where most Ce particles were not visible on the BG surface. The MTT assay test was the final investigation for this study. Silver may be dangerous if discharged in large amounts, thus it is important to carefully monitor how much is injected. The potential of ion-exchange treatments to surface functionalize glasses while maintaining the bulk structure and primary characteristics of MBG is well recognized [34]. According to a study by [PERSON] et al., when the Ag-doping concentration increases from 0.04 to 197 \(\mu\)mL\({}^{-1}\), the antibacterial rate improves from 63% to 99% but cell viability reduces from 97% to 62% [90]. Furthermore, [PERSON] et al. also developed in vitro testing on osteoblasts and fibroblasts, and their study revealed no harmful effects from the inclusion of 0.75 and 1 wt% Ag\({}_{2}\)O; however, 2 wt% Ag\({}_{2}\)O was toxic [91]. When we investigated, all materials were not toxic at a concentration of 100 \(\mu\)g mL\({}^{-1}\), but 5.75 mol% Ag-doped MBG was toxic to MT3 Ca-E1 stobalobal cells after 72 h; therefore, we had to utilize co-doped Ag-Ce MBG, which led to 100% death for _E. coli_ bacteria and had no toxic effects on MT3C3-E1 osteoblasts cells, as shown Figure 11.  
As the result of this study, co-doped MBGs could offer superior benefits, such as enhanced bioactivity and antibacterial activity. This makes them more effective compared to doped or un-doped MBGs. Hence, co-doped Ag and Ce MBG should be utilized to decrease the issue of Ag toxicity in MBGs and enhance Ce's bacterial killing effectiveness and bioactivity, that is why we examined Ag-Ce co-doped MBG. In our contemplation of the forthcoming period, the subsequent stages of investigation will encompass the exploration of their associations with varied cell classifications, the elucidation of their operational processes in opposition to additional drug-resistant bacteria, the enhancement of doping concentrations for refined biocompatibility, and the evaluation of their performance within a living organism (in vivo performance), thus establishing the foundation for their potential clinical applications.
## 2 Materials and Methods
### STARFM Model Description  
The theoretical basis for STARFM, as outlined by [PERSON] et al. [28], was based on the premise that MODIS and Landsat surface reflectances are comparable, with small biases. Neglecting any small bias, the surface reflectance of a heterogeneous course pixel at date t (\(C_{t}\)), is equal to the sum of each of the finer resolution homogenous pixel (\(F\)) times the percent of the area which the fine resolution pixel covers (\(A\)) at each location \(i\), written as:  
\[C_{t}=\sum(F_{t}^{i}\times A_{t}^{i}) \tag{1}\]  
For a MODIS image that has been super sampled to the equivalent fine resolution and boundary of a Landsat image with the same coordinate system, the homogenous MODIS course pixel (\(M\)) from the same location (\(x_{i},y_{i}\)) and time (\(t_{0}\)) of the Landsat fine pixel (\(L\)) is equal to the Landsat pixel plus the difference between the observed MODIS and observed Landsat values, written as [28]:  
\[L\big{(}x_{i},y_{j},t_{0}\big{)}=M\big{(}x_{i},y_{j},t_{0}\big{)}+\varepsilon _{0} \tag{2}\]  
As explained by [PERSON] et al. [28], in an ideal situation, ground coverage and system errors for any given pixel does not change from a base date (\(t_{0}\)), when both coarse and fine resolution values are known for a pixel as described above, to the prediction date (\(t_{k}\)), when course resolution values are known but fine resolution values are unknown and need to be predicted. In the situation where there is no change, which is often not satisfied, \(\varepsilon_{0}=\varepsilon_{k}\) and:  
\[L\big{(}x_{i},y_{j},t_{k}\big{)}=M\big{(}x_{i},y_{j},t_{k}\big{)}+L\big{(}x_{ i},y_{j},t_{0}\big{)}-M\big{(}x_{i},y_{j},t_{0}\big{)} \tag{3}\]  
To incorporate additional information from neighboring pixels, a central fine resolution pixel can be computed with the weighting function [28]:  
\[L(x_{w/2},y_{w/2},t_{k})=\sum_{i=1}^{w}\sum_{j=1}^{w}\sum_{k=1}^{u}W_{ijk} \times\big{(}M\big{(}x_{i},y_{j},t_{k}\big{)}+L\big{(}x_{i},y_{j},t_{0}\big{)} -M\big{(}x_{i},y_{j},t_{0}\big{)}\big{)} \tag{4}\]  
where \(w\) is the search window size and \(L(x_{w/2},y_{w/2},t_{k})\) is the pixel that is in the center of this search window. \(W_{ijk}\) is a weight that determines how much each neighboring pixel contributes to the predicted reflectance of the central pixel. This weight is higher for neighboring pixel locations where the difference between the fine and course pixel of the same time is smaller, the difference between the coarse pixels at the base and prediction date are smaller, and the neighboring pixel is closer in distance to the central pixel. Within the search window, the algorithm uses spectrally similar pixels that are cloud free [28].
## 2 Materials and Methods
### Biospeckle Cross-Correlation Method  
The cross-correlation analysis method was used to evaluate the microorganisms' swimming ability under different concentrations of heavy metal pollution. In this method, the correlation coefficient \(\rho(t_{i})\) between the initial frame \(I(t_{0};x,y)\) being fixed as a reference frame and the frame \(I(t_{i};x,y)\) at the time \(t_{i}\) of the recorded frame of biospeckle video was calculated as given by Equation (1):  
\[\rho(t_{i})=\frac{\sum_{x,y}\left\{I(t_{i};x,y)-\langle I(x,y)\rangle\right\} \{I(t_{0};x,y)-\langle I(x,y)\rangle\}}{\sqrt{\sum_{x,y}\left\{I(t_{0};x,y)- \langle I(x,y)\rangle\right\}^{2}\sum_{x,y}\{I(t_{0};x,y)-\langle I(x,y) \rangle\}^{2}}} \tag{1}\]  
where \(I(t_{i};x,y)\) is the intensity at coordinate (x, y) at time \(t_{i}\) and \(\langle I(x,y)\rangle\) denoted time average of the intensity given by Equation (2):  
\[\langle I(x,y)\rangle=\frac{1}{N}{\sum}_{i=1}^{N}I(t;x,y) \tag{2}\]  
where N is the total number of frames.  
Next, in this study, the correlation time, denoted as \(\tau_{c}\), served as a quantitative measure of the swimming ability of microorganisms. The concept of correlation time is rooted in the analysis of biospeckle patterns generated by the movement of microorganisms. These speckle patterns fluctuate over time as the microorganisms move, and the rate of these  
Figure 2: A schematic diagram of the experimental system consisting of M1\(\sim\)M2 mirrors, L1\(\sim\)L2 lenses, a polarizer, and a CCD camera. The parallel beam irradiated a sample cell (cell gap: 1 mm) containing microorganisms.  
fluctuations provides insights into their motility. The correlation time \(\tau_{\text{c}}\) was defined as the time at which the cross-correlation coefficient falls to \(\frac{1}{\epsilon}\) of its initial value.  
The correlation time is inversely related to the speed of movement of the microorganisms. A larger correlation time indicates slower movement of the microorganisms, which could imply that they are experiencing stress, reduced motility, or poor health. Conversely, a smaller correlation time indicates swifter movement of the microorganisms, suggesting that they are healthier and more active. By measuring the correlation time, it is possible to observe the swimming ability of microorganisms or, indirectly, their overall health and physiological state. Finally, the correlation time for both microorganisms was plotted as a function of varying concentrations of Fe and Zn.
## 2 Materials and Methods
### Evaluation of Simulation Accuracy  
In this study, three statistical indicators were chosen to evaluate the modification of the simulation accuracy of the Biome-BGC model before and after the optimization of the parameters of the PEST method, which are the coefficient of determination (\(R^{2}\)), the root mean square error (RMSE), and the mean absolute error (MAE). It can be represented mathematically as [52; 53]:  
\[R^{2}=\left[\frac{\sum_{i=1}^{n}\left(S_{i}-\overline{S}\right)\left(O_{i}- \overline{O}\right)}{\sqrt{\sum_{i=1}^{n}\left(S_{i}-\overline{S}\right)^{2} \left(O_{i}-\overline{O}\right)^{2}}}\right]^{2} \tag{4}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}\left(S_{i}-O_{i}\right)^{2}}} \tag{5}\]  
\[\text{MAE}=\frac{1}{n}{\sum_{i=1}^{n}}{\left|S_{i}-O_{i}\right|} \tag{6}\]  
where \(S_{i}\) is the simulated value of the model on the \(i\)th day; \(O_{i}\) is the observed value of the flux on the \(i\)th day; \(\overline{S}\) and \(\overline{O}\) are the average values of the simulated and observed values, respectively; and \(n\) is the total number of days of the model simulation.  
We used \(R^{2}\) to evaluate the degree of fit of the Biome-BGC model and the RMSE and MAE to evaluate the deviance between the simulated values of the Biome-BGC model and the observed fluxes. \(R^{2}\) was obtained by linear regression between the simulated values of the model and the observed fluxes, reflecting the ability of the simulated values to match the variation in the observed values. It is concerned with the closeness of the change trends of these two curves, emphasizing whether the changes of simulated and observed values are in the same direction. The closer the value to 1 indicates the better the fit of the simulation results to the measured results. The RMSE was used to evaluate the difference between the simulated and measured values. It penalizes extremely poor predictions and amplifies the difference between simulated and observed values with large deviations. It is more sensitive to abnormal values in the simulations, where smaller values indicate less discrepancy between simulated and measured results. The MAE is the average of the absolute errors between simulated and observed values, and its contribution per error to the MAE is proportional to the absolute value of the error, reflecting the actual error between each set of simulated and observed values. In contrast to the RMSE, which involves squaring the errors, some relatively large errors will cause the RMSE to increase more than the MAE.
# Influence of Degree of Saturation on Soil-Pile Interactions for Piles in Expansive Soils
## 3 Laboratory Testing and Results
### Index Properties  
To classify the expansive soil, basic index property tests were conducted per ASTM standards. These included specific gravity (\(Gs\)), liquid limit (%LL), plastic limit (%PL), plasticity index (%PI), soil classification, grain size distribution, and the standard Proctor compaction test. The results of the index property testing were summarized in Table 1.  
The soil specimens were compacted to 95% of the maximum dry density (MDD), considering both the wet and dry sides of the optimum water content. Table 2 summarizes the conditions of the soil specimens prepared for direct shear tests, pile uplift model tests, and constant-volume swelling tests in the study.  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Test** & **Standards** & **Value/Description** \\ \hline
1 & Soil & - &
\begin{tabular}{c} Artificial Clay \\ (sand: sodium bentonite = 45:55) \\ \end{tabular} \\
2 & Specific Gravity (Gs) & ASTM & 2.61 \\
3 & Liquid Limit (\%) & D854-10 [33] & 2.61 \\
4 & Plastic Limit (\%) & ASTM & 315 \\
5 & Plastic Index (\%) & D4318-10 [34] & 268 \\  & Grain Size Distribution & & - \\  & \% Gravel & & 0 \\  & \%Coarse Sand & & 3.2 \\
6 & \%Medium Sand & USCS & 27.2 \\  & \% Fine Sand & & 14 \\  & \%Stilt and clay & & 55.6 \\
7 & Soil Classification & USCS & CH \\
8 & Color & - & Brown \\
9 & Maximum Dry Density & ASTM & 14.95 \\  & (kN/m\({}^{3}\)) & D698-12 [35] & \\
10 & Optimum Water Content (\%) & ASTM & 24 \\ \hline \end{tabular}
\end{table}
Table 1: Physical properties of Expansive soil.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Degree of Saturation** & **Dry Unit Weight** & **Water Content (\%)** &
\begin{tabular}{c} **Total Unit Weight** \\ **(kN/m\({}^{3}\))** \\ \end{tabular} \\
**(\%)** & **(kN/m\({}^{3}\))** & **(kN/m\({}^{3}\))** \\ \hline
60 & 14.20 & 18.45 & 16.82 \\
70 & 14.20 & 21.53 & 17.26 \\
80 & 14.20 & 24.61 & 17.70 \\
90 & 14.20 & 27.68 & 18.13 \\
100 & 14.20 & 30.76 & 18.57 \\ \hline \end{tabular}
\end{table}
Table 2: Soil specimen properties at different degrees of saturation.