esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 ML Model Development
### Feedforward Neural Networks (FFNN)  
Recently, artificial neural networks (ANN) have been widely used in remote sensing data. The fundamental structure of ANN consists of input layer, hidden layer(s), and output layer. There are variations of this structure for different objectives, such as recurrent neural networks, long short-term memory network, generative adversarial network, etc. The biggest advantage of using ANN is that is is able to learn on nonlinear and complex relationships. Furthermore, ANN does not require any restrictions on the input variables, such as multicollinearity, and distribution of predictor variables [47]. Here, we implement one of the most basic structures of ANN, a feedforward neural networks (FFNN). FFNN is a type of ANN with the connection between the nodes does not form a cycle or a loop, and only structured with input layer, hidden layer, and output layer. In this study, FFNN with three hidden layers is used. Batch size is set to be 256, with 2000 epochs. Batch size is selected with a gridsearch algorithm. Five different structures with different number and size of hidden layer were tested, and the best performing structure was selected. Although tested structures did not show significant difference in performance, it should be noted that, with more detailed tuning, FFNN has a room to improve.
## 5 Attack Assumptions and Implementation
### PLC Variables Decompilation  
To construct the model \(\mathcal{M}\), we need to decompile the bytecode-level control logic of PLC into variables. We perform this decompilation in two steps: (1) Convert the bytecode-level control logic to an intermediate set of constraints \(\mathcal{C}\) on input, local, output, and timer variables. (2) Translate \(\mathcal{C}\) to \(\mathcal{M}\) with the NuSMV model checker [35].  
For step 1, the PLC code conforms to the IEC 61131-3, which defines the standard of PLC language [36]. According to the PLC code's control flow graph (CFG), we can obtain the constraints \(\mathcal{C}\) via the symbolic execution of the bytecode. For example, in the temperature regulator system, we assume the symbolic accumulator as \(\alpha\), and the symbolic accumulation of the refrigeration is shown in Table 1.  
\[\mathcal{C}\leftarrow\mathcal{C}\cup\{y_{2}\gets u_{4}\wedge(u_{1}\lor y _{2})\} \tag{8}\]  
For step 2, we will translate the set of constraints from step 1 into a control logic model, which the NuSMV model checker can evaluate. NuSMV takes definitions of labeled transition systems with states consisting of state variables. We declare state variables in \(\mathcal{M}\) with the Var : boolean expression. Variables are initialized with init\((\cdot)\) expression and updated with next\((\cdot)\) expression. A boolean variable can be translated to \(\top\), \(\bot\), another expression or a nondeterministic assignment \(\{\top,\bot\}\) by the model checker.  
To construct the NuSMV model \(\mathcal{M}\) from the constraints \(\mathcal{C}\), we have defined three translation rules as shown in Table 2. Input variables are initialized to \(\bot\) and updated to nondeterministic assignment \(\{\top,\bot\}\). Output and local variables are initialized to \(\bot\) and updated according to the input control logic expression \(\alpha\). Timer variables are different from others, which require an extra bit of states. A PLC timer has a starting state \(t_{s}\) and an expiration state \(t_{e}\), both of which are initialized to \(\bot\). The starting state \(t_{s}\) is updated to \(\top\) when the input expression \(\alpha=\top\) continuously in the timer's preset time duration. Furthermore, the process includes several other steps, including preprocessing bytecode to rewrite vendor-specific instructions. Full details can be found in [37].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Desc.** & **Bytecode** & **Accumulator \(\alpha\)** & **Stack** \\ \hline - & - & \(\top\) & - \\ And \(u_{4}\) & A \(u_{4}\) & \(u_{4}\) & - \\ Nested And & A ( & \(\top\) & \(u_{4}\) : \(\wedge\) \\ And \(u_{1}\) & A \(u_{1}\) & \(u_{1}\) & \(u_{4}\) : \(\wedge\) \\ Or \(y_{2}\) & O \(y_{2}\) & \(u_{1}\lor y_{2}\) & \(u_{4}\) :\(\wedge\) \\ Pop stack & ) & \(u_{4}\wedge(u_{1}\lor y_{2})\) & - \\ Store \(\alpha\) to \(y_{2}\) & =\(y_{2}\) & \(\top\) & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example accumulation of a constraint.
# Power Control and Link Selection for Wireless Relay Networks with Hybrid Energy Sources
## 2 System Model
### Energy Model  
In order to simplify the energy harvesting model, the harvesting process is thought to be accomplished at the beginning of each time slot [18]. Then, discrete energy model is adopted to describe the process of energy harvesting with \(E_{H}^{i}\) energy packets arriving at each time slot [19], which obeys the Poisson distribution with mean \(\lambda\)[20]. And \(\lambda\) represents the intensity of energy harvesting. In addition, each packet contains the energy of \(E_{e}\). In the initial time slot, the energy stored in the battery is the original energy plus the harvested energy. While in the slot of \(i>1\), the energy consumed by the relay should be subtracted. Therefore, battery level in the \(i\)-th slot is expressed as:  
\[E(i)=\left\{\begin{array}{cc}E_{0}+E_{1H}^{i}E_{e}&i=1\\ E(i)-C_{R}^{i-1}+E_{H}^{i}E_{e}&i>1\end{array}\right., \tag{5}\]  
where \(E_{0}\) is the initial energy, and \(C_{R}^{i-1}\) is energy consumed by the relay in the last slot, which is given by:  
\[\text{C}_{R}^{i-1}=\text{I}_{SD}^{i-1}\text{T}p_{SD}^{i-1}+\text{I}_{SRD}^{i-1 }\frac{T}{2}\text{P}_{SRD}^{i-1}. \tag{6}\]  
Since the battery is of the limited size, the following energy constraint should be satisfied:  
\[E(i)\leq E_{\text{max}}, \tag{7}\]  
where \(E_{\text{max}}\) is the maximum capacity of the battery. The energy consumed by the relay should be no more than the energy of the battery, that is \(C_{R}^{i}\leq E(i)\).
## 6 Example Verification  
To validate the rationality of the theoretical formulas derived in this study, the soil was assumed to be a homogeneous clay layer, modeled using the Mohr-Coulomb elastoplastic constitutive model. Soil nails were simulated as steel bars with a diameter of 25 mm, represented by solid elements. The soil nails were arranged in a plum blossom pattern with a spacing of 2 m \(\times\) 2 m, and a total of four layers of soil nails were arranged. Surface-to-surface contact was adopted between the soil nails and the soil; among them, hard contact was used for the normal direction, and Mohr-Coulomb friction contact was used for the tangential direction, with a friction coefficient of 0.4. The numerical model had dimensions of 80 m (length) \(\times\) 4 m (width) \(\times\) 12 m (height). Figures 13 and 14 show the geometric model and the mesh model of the numerical computation, respectively. For different working conditions, the slope angles and internal friction angles were set to 70\({}^{\circ}\), 80\({}^{\circ}\), and 90\({}^{\circ}\) and 10\({}^{\circ}\), 20\({}^{\circ}\), 30\({}^{\circ}\), and 40\({}^{\circ}\), respectively. Other model parameters are detailed in Table 4. The model boundaries were defined as follows: the bottom was fully fixed, the top surface was a free boundary, and the displacement boundary on one side in the z-direction was set to zero.  
Figure 14: Numerical computation grid.  
Figure 12: \(L/H\) and \(h/H\) curves in the form of the rupture surface under different working conditions: (**a**) curves of \(L/H\) for varying internal friction angles and slope angles and (**b**) curves of \(h/H\) for varying internal friction angles and slope angles.  
Figure 13: Numerical geometric model.  
Through the use of ABAQUS (version 2020) finite element software, a comparison between numerical calculations and theoretical analysis was made, as shown in Figure 15. In Figure 15, it can be observed that the results from both methods are largely consistent, indicating that the failure surface calculation formula derived from the soil nail reinforcement theory is reliable and reasonable.
# Contactless Fingerprint Recognition Using Deep Learning--A Systematic Review
## 3 Contactless Fingerprint Capturing Methods  
### 2D Contactless Fingerprint Capturing Methods  
During a contactless fingerprint capture, one or multiple fingers are presented to an optical device like a camera or lens. These devices can be (a) prototype hardware designs developed by researchers or (b) general-purpose devices customized to meet the unique needs for contactless fingerprint recognition [48].  
Smartphone-based image acquisition is one of the widely available techniques to capture 2D contactless finger photos [4]. The NIST [17] published a document to assess contactless fingerprint capturing methods; the document provides proper instructions for contactless fingerprint capturing devices. It describes the smartphone's uniform light lighting, backdrop segmentation, and motion reduction during capture. Multi-finger capturing techniques can also be used with smartphones [49]. The advantage of multi-finger capture is the efficiency since feature extraction of all five fingers can occur from one single image [50]. To evaluate the performance of Nokia N95 and HTC Desire mobile phone for this, 1320 fingerprints were captured. A flashlight-enabled phone was used for appropriate illumination and to cover the entire finger area. However, the image quality was reported to be poor, as a flashlight performs well only in a dark setting [22]. To improve image quality and to reduce camera noise, dark environments might play a very important role. Auto-focus and maintenance of a standard distance from hand to phone may also be useful strategies. Figure 4 shows the identical distance and illumination from hand to phone.  
Figure 5 illustrates the impressions of a fingerprint taken with a contact-based fingerprint device (Figure 5a) and the corresponding finger image acquired using a non-contact device (Galaxy S8). (Figure 5b). The contact-based fingerprint can directly be used for finding feature such as: ridge, valley, delta cores, minutiae etc., whereas the corresponding contactless fingerprint image would need additional processing.  
Figure 4: Homogeneous distance illumination with auto focus capturing by smartphone.  
A digital camera is another tool to capture contactless 2D finger photos. The main feature of this system is white-color- and LED-color-based image sensors [10]. A three-camera-based system with blue LED is much more comfortable than a white LED for the user to acquire fingerprints. The charge-coupled camera device emits a green LED, and there is a stepper motor with a mirror that can capture five fingers at a time, making it convenient [11].
# Parametric Analysis of Compliant End Face Gas Film Seals Considering Slip Flow Effects
## 2 Analytical Model
### Validation of the Validity of the Computational Program  
The parameters in the aforementioned table are applied to calculate the gas film opening force, and the calculation results of this paper are compared and analyzed with the literature so as to verify the correctness of the calculation theory and program writing in this paper. When the slip flow effect is not considered, the parameters of the literature [43] are selected for the calculation model, such as the validation model I in Table 4; when the slip flow effect is considered, the parameters of the literature [31] are selected for the calculation model, such as the validation model II in Table 4. The comparison results are shown in Figure 5.  
Figure 4: Mesh-independent phase verification.  
Figure 5: Computational model validation [31; 43].  
As can be seen from Figure 5, the calculation results of this paper are all smaller than those in the comparable literature. Specifically, without considering the slip flow, the relative error between the calculated gas film opening force in this paper and the results of the Gabriel test [43] is 11.41%, which may be due to the assumptions of isothermal and neglecting inertial force in the calculation process of this paper, resulting in a certain error with these test data. In the case of considering the slip flow, the relative error between the calculation results of this paper and the results of the literature [31] is 3.27%. Taken together, the calculation errors in both cases are within the permissible range, indicating that the theoretical method and program writing in this paper have a certain degree of computational reliability.
## 5 Experiments
### Experimental Results
#### 5.2.4 Comparison of the Four Local Searchers  
As mentioned above, the AMMA controls among others the rate of application of each local searcher or meme by adaptively adjusting its probability to be chosen for application. This includes that poor performing LS may be switched off completely. Figure 7 shows the distribution of averaged LS probabilities recorded at the end of each AMMA run. The dominance of the LS-RS is striking and it corresponds to the findings reported in [49]. The LS-RAS shows a nice performance especially for the all-improvement case. It must be borne in mind that the LS-RAS frequently requires two evaluations and, hence, has to yield often twice the improvement rate of the other LS to be competitive.  
The poor results of the two left shifting LS are explained as follows. Shifting the grid jobs of some application jobs means to schedule them either earlier than before (left shift) or later (right shift). In both cases, the removed grid jobs cause a gap in the schedule, which is filled up by other grid jobs starting earlier now, and an insertion of the moved grid jobs. Both are disturbances in the previous structure of the schedule, but their extents vary as shown in Figure 8. In case of a left shift, there is an insertion, which pushes some grid jobs of the insertion area to the right, _i.e._, they are scheduled later. This may be compensated to some extent by the gap area of the schedule. Hence, the main area of alteration is located left of the gap area. The right shift again produces a gap causing an earlier scheduling until the insertion area is reached, where the shifted grid jobs will frequently find space to fit in. In this case, the area of alteration is right of the gap and occurs later in the scheduling sequence. Therefore, the disturbance induced by a right shift can be assumed to be smaller than by a left shift. We think that this has an impact when applied to the pre-optimized part of the old schedule included in the seeds with which the replanning run was started. It will also have an impact on descendants of these seeds. As an analysis of the seeds shows that some results of the replanning heuristics frequently are of better quality than the other seeds, we can assume that the larger disturbance of the left shift is responsible for its weaker performance. Some experimental runs with an ASMA using the two left shifting LS suggest that this may be the case, but further experiments are required to confirm this.  
Figure 7: Averaged final LS probabilities for all benchmark classes. For a better visibility, the sequence of best- and all-improvement (cf. Section 4.4) is inverted for LS-RAS. The values of each row sum up to 100% for each improvement type. The front row shows the mean of the four benchmark classes. The coloring of the benchmarks and the sum will be maintained in the subsequent Figures 9 and 10, while their sequence is changed for a better visibility of the columns.
## 6 Results and Discussion
Table 8 briefly shows the obtained performance indices from the ANN-BP and ANN-PSO models in the prediction of slip. All the performance values validate the superior performance of the ANN-PSO model. Figure 10 demonstrates the results of the ANN-BP and ANN-PSO models in the prediction of the load. Figure 10a illustrates the training phase of the ANN-BP model. Performance indices of the model in this phase have been determined as \(r\) = 0.955, \(R^{2}\) = 0.912, and \(RMSE\) = 16.747 (kN). Figure 10b represents the testing phase of the ANN-BP model with performance parameters of \(r\) = 0.950, \(R^{2}\) = 0.902, and \(RMSE\) = 17.109 (kN). High values of \(r\) and \(R^{2}\) and a low value of \(RMSE\) imply the excellent performance of the ANN-BP model. Figure 10c shows the training phase of the ANN-PSO model; the performance indices are \(r\) = 0.972, \(R^{2}\) = 0.944, and \(RMSE\) = 13.342 (kN). As can be realized, the ANN-PSO model has shown better performance in the training phase in comparison with that of the ANN-BP model. Figure 10d also demonstrates the testing phase of the ANN-PSO model with performance indices of \(r\) = 0.970, \(R^{2}\) = 0.940, and \(RMSE\) = 13.342 (kN). The testing phase has also improved with respect to that of the ANN-BP model. Most importantly, the close values of performance indices in the training and testing phases confirm the high reliablity of the models. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Slip**} \\ \cline{2-7}  & \multicolumn{3}{c}{**Training**} & \multicolumn{3}{c}{**Testing**} \\ \cline{2-7}  & \(r\) & \(R^{2}\) & \(RMSE\) (mm) & \(r\) & \(R^{2}\) & \(RMSE\) (mm) \\ \hline ANN-PSO & 0.908 & 0.824 & 1.708 & 0.865 & 0.750 & 2.069 \\ ANN-BP & 0.889 & 0.791 & 1.863 & 0.804 & 0.646 & 2.569 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance indices of the ANN models in the prediction of slip. Figure 9: Slip prediction in the testing phase: (**a**) ANN-BP model; (**b**) ANN-PSO model. The capability of the models in the prediction of each test sample is shown in Figure 11. Highly close prediction of the models and better performance of the ANN-PSO model is obvious in this figure. A summary of the obtained performance values from the ANN-BP and the ANN-PSO models in the load prediction of channel shear connectors has been also illustrated in Table 9. The superior performance of the ANN-PSO model can be seen in this table. Figure 10: Comparison of the predicted and measured load: (**a**) Training phase of the ANN-BP model (**b**); Testing phase of the ANN-BP model; (**c**) Training phase of the ANN-PSO model; (**d**) Testing phase of the ANN-PSO.
# Performance Analysis of the Shore-to-Reef Atmospheric Continuous-Variable Quantum Key Distribution
###### Abstract  
The effects of sea salt concluded in oceanic atmosphere are ubiquitous in practical wireless optical links. Here a shore-to-reef atmospheric continuous-variable quantum key distribution (CVQKD) model is established on the basis of Mie scattering theory, with the aim to characterize the complex case of beam propagation in the atmosphere caused by sea salt particles. The effects on performance of shore-to-reef atmospheric CVQKD under the sea salt particles and relative humidity are also studied. Simulation results show that the increase of particle radius and relative humidity will lead to the degeneration of secret key rate. Extending the channel distance also reduces the secret key rate. This paper provides a basis for the establishment of practical shore-to-reef atmospheric CVQKD model. The research of this paper also gives momentous reference for the study of optical communication channel models with other suspended particles over the ocean.  
Keywords:sea salt particles; relative humidity; atmospheric CVQKD; secret key rate +
Footnote †: journal: Applied Sciences
# Data Matrix Based Low Cost Autonomous Detection of Medicine Packages
## 3 Experiments and Results
The remaining codes had slight defects, such as being torn (as illustrated in Figure 11) or their color being too grey. In order to evaluate the illumination interference, a test was performed with different light conditions. The first test was executed turning the embedded illumination off. In this case (with a natural illuminance of 150 Lux), the extracting capacity was decreased to 18%, as presented in Figure 12 where from eleven matrix codes, only two of them were extracted. Figure 11: Medicine packages: (**a**) to be scanned; and (**b**) torn code paper is not detected by the algorithm. Figure 10: Medicine packages scanned: (**a**) covered by reflector plastic; and (**b**) rounded bootles. More tests were executed to evaluate the interference of the lighting. Table 1 summarizes the obtained ratios for data matrix extractions with different lightings. The first three tests were evaluated with both LEDS panels off for an Illuminance of 5, 90 and 150 Lux. The proposed system is very dependent on the illumination conditions. As it can be observed, one LED panel is enough but brings some reflexes that are reduced with two LED panels assembled as previously addressed. In these cases (one and two LED panels on), the ratio of detection for six image samples were 100% even when changing the external illumination from 0 to 1000 Lux. In order to improve the work, there are two main points that might be pointed out (to solve a limitation of the prototype), namely increasing the size of the acquired image, and a more powerful computational performance could be included to speed up the detection of matrix codes.
## 3 Methodology and Accuracy Assessment
### Accuracy Assessment  
Several metrics were proposed to assess the accuracy of Pan-sharpened images that use the precise, high-resolution MS image as a reference image. In this research, the first seven metrics provided in Table 2 were used for the spectral quality assessment, while the later four metrics were used for spatial quality assessment of the results. Although metrics provide important quantitative insights about the algorithm performance, qualitative assessment of the color preservation quality and spatial improvements in object representation is required. Thus, results obtained from the Pan sharpening algorithms were also evaluated with visual inspection.
# A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
## 3 Results  
### Descriptive Statistics  
The corn yield data for all counties in the study area (2006-2020) range from 3.698 to 14.048 MT/ha with a mean value of 9.63 MT/ha and a standard deviation of 1.971 MT/ha (Table 2). The statistical distribution of the yield data is slightly negatively skewed. Table 2 also includes the descriptive statistics of the seven variables that are highly correlated with crop yield: GPP, NDVI, EVI, precipitation, mean dew point temperature (TD), maximum VPD, and minimum VPD. The GPP values range from 0.033 to 0.091 kg C/m\({}^{2}\) with a mean of 0.072. The NDVI values during the research period range from 0.23 to 0.54 with a mean of 0.39. Figure 3 depicts the statistical distribution of corn yield in each state.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Yield** & **GPP** & **NDVI** & **EVI** & **Precipitation** (mm)** & **Mean TD** (\({}^{\circ}\)C)** & **Max VPD** (hPa)** & **Min VPD** (hPa)** \\ \hline
**Minimum** & 3.698 & 0.033 & 0.23 & 0.33 & 1.54 & 6.31 & 11.98 & 0.34 \\ \hline
**Maximum** & 14.048 & 0.091 & 0.54 & 0.76 & 4.91 & 18.95 & 33.28 & 3.45 \\ \hline
**Mean** & 9.6376 & 0.072 & 0.39 & 0.53 & 3.35 & 12.53 & 18.73 & 1.28 \\ \hline
**SD** & 1.971 & 0.010 & 0.05 & 0.06 & 0.60 & 2.40 & 3.39 & 0.39 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The descriptive statistics of the seven features related to corn yield.