esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Application of the New Charging Structure  
### Charging Structure Optimization Analysis  
Using the sympathetic detonation experiments that we conducted under blasthole constraints, the traditional charging structure was optimized and upgraded based on the explosive critical distances. The new charging structure shown in Figure 6 was thus designed. The new charging structure is divided into three parts. The bottom of the blasthole contains a fortified charge. Two rolls of explosives are charged at this position. Such a quantity is conducive to overcoming the resistance line and clamping effects at the bottom of the blasthole. The middle of the blasthole contains a normal charge. Because the detonating cord contains black sorkin, if charged according to the traditional charging structure, it is equivalent to 50 g black sorkin in each peripheral hole. Since the power of black sorkin is greater than that of the emulsion explosive and the new charging structure removes the detonating cord, it is necessary to charge at least 50 g of extra emulsion explosive per hole when the new charge structure used. To facilitate charging, 1.5 rolls of explosives are charged at this position as the donor charge. This increases the detonation energy and facilitates sympathetic detonation. The blasthole orifice is decreasing charge part, where contains only 1 roll of explosive. In the new charging structure, each blasthole requires 4.5 rolls of explosives. The detonator uses reverse detonation, which is good at fragmenting rock mass at the plugging surface [24]. Meanwhile, it is important to ensure the quality of stemming at the blasthole orifice to ensure that sympathetic detonation of the explosive occurred at decreasing charge part. Moreover, it is important to ensure that the orifice is 65 cm away from the first roll of explosive during charging.  
However, the blasthole length is likely to vary from 4.1 m in practice. From the gap distance tests and sympathetic detonation characteristics, one can see that when the distance between explosives is smaller than the critical distance, the acceptor charge can always be detonated by the donor charge. When the blasthole length is shorter than 4.1 m, the distance between explosives is decreased appropriately during charging. This aids sympathetic detonation. However, the insufficient blasthole length also causes reduction in cyclical footage, resulting in underbreak. When the blasthole length exceeds 4.1 m, we must guarantee that the distances between explosives are accurate, as shown in Figure 5. The following improvements may be made: (1) we can add a roll of explosive to the bottom of the blasthole; (2) the explosives can be placed 4.1 m away from the orifice instead of directly at the bottom of the blasthole; or (3) the quantity of explosives at the orifice can be increased to 1.5 rolls. Statistically, there are few blastholes with lengths greater than 4.1 m, so this situation does not have a large influence on application of this process.
# Low-Cost Area-Efficient FPGA-Based Multi-Functional ECDSA/EdDSA
## 3 Hardware Architecture
### Modular Arithmetic-Logic Unit
#### 3.1.3 Modular Inversion  
In our design, the modular inversion is calculated by using the binary Euclidean algorithm (BEA) [20]. BEA is the traditional method that can calculate the inversion of a number over the prime field. It is independent of the usage of the ECC algorithm. Therefore, it is the most suitable for a multi-functional design. Algorithm 3 reveals the binary Euclidean algorithm. Figure 6 shows the optimized hardware implementation of BEA in our proposed architecture. The BEA memory system includes four registers to store the temporal data during calculation. The datapath contains a modular addition/subtraction and a right shift circuit. The controller determines the updated register and the source of updated data. The controller of the modular inversion module is based on a finite state machine (FSM). Depending on Algorithm 3, the FSM includes the following states: calculate new value of \(u\); calculate new value of \(v\); compare new value of \(u\) and \(v\); update the value of \(u\); update new value of \(v\); calculate new value of \(x\); calculate new value of \(y\); update \(x\); update \(y\); check the condition; and assign the final result. BEA contains three kinds of operation, which are right shift, modular addition, and modular subtraction. Based on our design of FSM, only one modular addition/subtraction module and one shift right module are needed. Finally, after the calculation process is completed, the final result is assigned for y, and the system raises the finish signal to indicate that the process is finished.  
```
0: Prime \(p\) and \(a\in[1,p-1]\)
0:\(a^{-1}\) mod \(p\)
1:\(u\gets a\), \(v\gets p\), \(x\gets 1\), \(y\gets 0\)
2:while\(u\
eq 1\) and \(v\
eq 1\)do
3:while\(u\) mod \(2=0\)do
4:\(u\gets u\gg 1\)
5:if\(x\) mod \(2=0\)then
6:\(x\gets x\gg 1\)
7:else
8:\(x\leftarrow(x+p)\gg 1\)
9:endif
10:endwhile
11:while\(v\) mod \(2=0\)do
12:\(v\gets v\gg 1\)
13:if\(y\) mod \(2=0\)then
14:\(y\gets y\gg 1\)
15:else
16:\(y\leftarrow(y+p)\gg 1\)
17:endif
18:endwhile
19:if\(u\geq v\)then
20:\(u\leftarrow(u-v)\) mod \(p\)
21:\(x\leftarrow(x-y)\) mod \(p\)
22:else
23:\(v\leftarrow(v-u)\) mod \(p\)
24:\(y\leftarrow(y-x)\) mod \(p\)
25:endif
26:endwhile
27:if\(u=1\)then
28:return\(x\)
29:else
30:return\(y\)
31:endif
```  
**Algorithm 3** Binary Euclidean algorithm (BEA).
## 4 Case Study
### Procedure
Step 3: With the conversion model of the anchor position, the ship positions in step 2 were converted to the anchor position of the existing anchoring ship \(\left(x_{1}^{\prime},y_{1}^{\prime}\right)\), \(\left(x_{2}^{\prime},y_{2}^{\prime}\right)\ldots\left(x_{n}^{\prime},y_{n}^{ \prime}\right)\). Step 4: Based on MATLAB, the static parameters of the existing anchoring ship and the improved anchorage circle radius model, the anchorage circle radius of the existing anchoring ship was simulated. Step 5: Based on MATLAB, static parameters of the ship to be anchored and the improved anchorage circle radius model, the anchorage circle radius of the ship to be anchored was simulated. Step 6: Based on the data gathered from steps 3 and 4 and the safety distance model of the ship's anchorage position, the value of the safety distance for the ship to be anchored was calculated. Step 7: Under MATLAB, the Monte-Carlo random algorithm was used to randomly generate 5000 two-dimensional coordinates \(\left(x_{1}^{\prime\prime},y_{1}^{\prime\prime}\right)\), \(\left(x_{2}^{\prime\prime},y_{2}^{\prime\prime}\right)\ldots\left(x_{n}^{ \prime\prime},y_{n}^{\prime\prime}\right)\), so as to simulate the two-dimensional coordinates of the anchor position of the target anchoring ships. Step 8: With the mooring area detection model of the anchoring ships, successive operations upon the data from steps 3 and 7 were performed, and two-dimensional coordinates of the anchor position \((X_{1},Y_{1}),(X_{2},Y_{2})\ldots(X_{n},Y_{n})\) of the ship to be anchored were obtained, which satisfied the safety distance value in step 6. Step 9: The dropping anchor position conversion model was taken in converting the coordinates of the dropping anchor position \(\left(X_{1}^{\prime},Y_{1}^{\prime}\right)\), \(\left(X_{2}^{\prime},Y_{2}^{\prime}\right)\ldots\left(X_{n}^{\prime},Y_{n}^{ \prime}\right)\) of the ship to be anchored. Step 10: The drawing command was invoked to generate the simulation graphics of the work space of the mooring area.
###### Abstract  
This paper presents a 19 ps precision and 170 M samples/s time-to-digital converter (TDC) in FPGA. Through the direct count method and tapped delay line method, the coarse count and fine count can be extracted, respectively. The direct count is realized by the 350 M clock and the tapped delay line is constructed by the CARRY4 block. The ones-counter encoder is used to convert the thermometer code with bubble errors into binary code, which is applicable to all the FPGA chips. This work not only explains the schematic of the ones-counter encoder, but also shows how to configure it. Owing to the inconsistency of delay elements caused by process, bin-by-bin calibration is utilized to improve the differential nonlinearities (DNL) and integral nonlinearities (INL) of the TDC. A novel method was developed to compensate the influence of voltage and temperature. As the delay elements vary with voltage and temperature, a frequency counter is used to extrapolate and compensate its effect on the delay line. All of the above strategies use online calibration and improve the precision and sampling rate of TDC. The experimental results show the least significant bit (LSB) achieves 17.4 ps, the DNL is within [\(-\)0.90, 1.67] LSB, and the INL is in the range of [\(-\)1.90, 3.31] LSB.  
time-to-digital converter; FPGA; tapped delay line; bubbles; online calibration Article
# Development of Prediction Method for Dimensional Stability of 3D-Printed Objects
## 4 Conclusions  
In this study, 3D objects with various support bars and inclination angles were manufactured using the FDM process. The main effect and interaction factor were determined for the 3D-printed object quality utilizing a multiple regression model and ANOVA test. Prediction models were formulated to forecast printing accuracy through the utilization of three distinct machine learning models.  
1. The influences of support spacing, layer thickness, and raster angle were founded to be statistically significant by the Pearson and \(p\)-value analysis, and all parameters (supporting spacing, layer thickness, raster angle, and inclination angle) were assessed to be statistically significant predictors of the dependent variable by multiple linear regression model and ANOVA analysis. The optimal combination entailed a 45\({}^{\circ}\) inclination angle, 5 mm of support spacing, 0.2 mm of layer thickness, and a 45\({}^{\circ}\) raster angle.
2. Results of the R\({}^{2}\) from multiple linear regression, GPR, SVM, and the neural network model (from Python) were 76%, 98%, 93%, and 99%, respectively. MAEs for GPR and SVM were 0.020 mm and 0.034 mm. MAEs for neural network models from Python were 0.0055 mm for supporting cases and 2.1468 x 10\({}^{-5}\) mm for non-supporting cases.
3. The minimum MSDE was obtained at a 45\({}^{\circ}\) raster angle and 45\({}^{\circ}\) inclination angle (when 5 mm of support space with 0.2 mm of layer thickness), according to the neural network model.  
The dimensional deformations of molds, which are caused by thermal deformations such as warpage and heat distortion and can occur during the rapid tooling process, can be predicted. Using machine learning models, it is possible to predict and compare dimensional deformation for various material composites.  
Conceptualization, K.-E.M., J.-W.J., J.S. and S.Y.; methodology, K.-E.M., J.-W.J. and J.S.; investigation, K.-E.M., J.-W.J. and C.K.; writing--original draft preparation, K.-E.M. and C.K.; writing--review and editing, C.K. and S.Y.; project administration, J.S. and S.Y. All authors have read and agreed to the published version of the manuscript.  
This study was conducted with the support of the Korea Institute of Industrial Technology as \"Development of root technology for multi-product flexible production (KITECH EO-23-0008)\".  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Optimal Design of Wearable Micro Thermoelectric Generator Working in a Height-Confined Space
## 2 Experimental
### Governing Equations for Thermoelectric Analysis  
The governing equation of heat flow in a TEG module can be expressed as [38]  
\[\rho C\frac{\partial T}{\partial t}+\
abla\cdot\mathbf{q}=\dot{q}, \tag{2}\]  
where \(C\) is the specific heat capacity, \(\rho\) is the density, \(T\) is absolute temperature, \(\mathbf{q}\) is the heat flux vector, and \(\dot{q}\) is the heat generation rate per unit volume.  
And the continuity equation of electric charge can be written as [38]  
\[\
abla\cdot\left(\mathbf{J}+\frac{\partial\mathbf{D}}{\partial t}\right)=0, \tag{3}\]  
where \(\mathbf{J}\) is the electric current density vector, and \(\mathbf{D}\) is the electric flux density vector.  
Equations (1) and (2) are coupled by the set of thermoelectric constitutive equations as follows [39]:  
\[\mathbf{q}=T[\alpha]\cdot\mathbf{J}-[\kappa]\cdot\
abla T, \tag{4}\]  
\[\mathbf{J}=[\sigma]\cdot(\mathbf{E}-[\alpha]\cdot\
abla T), \tag{5}\]  
Figure 1: (**a**) The structure of wearable TEG in a height-confined space; (**b**) schematic of thermal resistance from skin to environment; (**c**) integrated design of TEG and heat dissipation fin with fixed overall height.  
where \([\alpha]\) is the Seebeck coefficient matrix, \([\kappa]\) is the thermal conductivity matrix, and \([\sigma]\) is the electrical conductivity matrix. The electric field intensity vector \(E\) is irrotational (\(\
abla\times E=0\)), and can be derived from the electric scalar potential:  
\[E=-\
abla\varphi. \tag{6}\]  
The equation for a dielectric medium can be expressed as  
\[D=[\varepsilon]\cdot E, \tag{7}\]  
where \([\varepsilon]\) is the dielectric permittivity matrix.  
Substituting Equations (3) and (4) into Equations (1) and (2) produces a system of coupled equations of thermoelectricity:  
\[\rho C\frac{\partial T}{\partial t}+\
abla\cdot(T[\alpha]\cdot\mathbf{J})-\
abla \cdot([\kappa]\cdot\
abla T)=\dot{q}, \tag{8}\]  
\[\
abla\cdot\left([\varepsilon]\cdot\
abla\frac{\partial\varphi}{\partial t} \right)+\
abla\cdot([\sigma]\cdot[\alpha]\cdot\
abla T)+\
abla\cdot([\sigma] \cdot\
abla\varphi)=0, \tag{9}\]  
where \(\dot{q}\) includes the electric power \(\mathbf{J}\cdot\mathbf{E}\) spent on Joule heating and on work against the Seebeck field \([\alpha]\)\(\
abla T\).  
If the simulation is based on the steady-state model and materials properties of all components are considered to be isotropic, Equations (7) and (8) can be simplified as  
\[\
abla\cdot(T\alpha\mathbf{J})-\
abla\cdot(\kappa\
abla T)=\dot{q}, \tag{10}\]  
\[\
abla\cdot(\sigma\alpha\
abla T)+\
abla\cdot(\sigma\
abla\varphi)=0. \tag{11}\]
# Classifying X-Ray Tube Malfunctions: AI-Powered CT Predictive Maintenance System
## 6 Experiments
### DWT Coefficients as Features  
We empirically tested different mother wavelets--Haar, Daubechies, Symlets, and Coiflets. In pywt, they are referred to as \"haar\", \"dbx\", \"symx\", and \"coifx\", where x is a parameter representing a number of vanishing moments of the wavelet. Haar wavelet does not feature a configurable vanishing moment and hence was only used once. Testing was performed in a similar way as we did with AR model coefficients:  
1. We treated the wavelet shape as a hyperparameter. We selected and tried haar, db1-db10, sym2-sym10, coif1-10
2. For every wavelet, every classifier with default parameters was trained
3. Afterwards, we selected the best wavelet for every model based on the accuracy score on the validation set
4. We tried to optimise the classifier-specific hyperparameters (listed in Section 6.1) to maximise the validation accuracy  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Parameters** & **AR Lag** & **Accuracy/Micro F1-Score** \\ \hline LR & c = 1, penalty = L2 & 7 & 72.2\% \\ \hline SVC & c = 100, gamma = 0.1, rbf kernel & 6 & 71.6\% \\ \hline RF & Estimators = 10,000 criterion = “logloss”/“entropy” Minimal number of samples for leaf = 1 & 3 & 83.4\% \\ \hline DT & criterion = “logloss”/“entropy” Minimal number of samples for leaf = 5 & 1 & 75.4\% \\ \hline GBDT & Estimators = 100 Subsample = 0.75 Depth = 5/7/9, Learning rate = 0.2 & 6 & 76.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance of classifiers when AR coefficients were used as features.  
Finally, we tested the best candidate for every classifier  
Results are visible in Table 2. The performance of LR and SVC was relatively stable. Sixteen different configurations achieved the same performance--all of the combinations of c and gamma for the linear kernel. While the LR had a clear winner, 10 of 12 possible setups achieved the same accuracy.  
The downfall of tree-based methods is rather interesting. We hypothesise that this can be attributed to the feature selection process. It likely means that DWT coefficients alone are not as distinctive as needed for these methods. Even more so, during the initial wavelet selection, rather simple wavelets performed the best.  
To sum up these experiments, DWT coefficients seem more suitable for SVC and LR classifiers, while AR coefficients seem more suitable for tree-based methods. The difference between the best method utilising DWT coefficients and AR coefficients is negligible (0.6%).
## 4 Vehicle State Estimation, Localization, and Sensors  
### The Proposed Algorithms for Vehicle State Estimation, Localization, and Sensors  
The localization method used in this study relies on data fusion of visual-inertial odometry and wheel odometry. Data from the aforementioned sources are sent to an estimator algorithm that uses an extended Kalman filter to provide an almost accurate estimation of vehicle location and heading with reference to the initial point of the car in the odometry frame.
## 5 Simulation Results and Discussion
### PV Jali in Grid-Following Mode, PQ Control, and Irradiance Variation  
As can be seen from the Figure 10 results, our grid had a Jali PV plant that was based on SS9 Mount Kigali. In its operation, it was based on input conductions of temperature and irradiance; therefore the temperature had a range of 25-45 \({}^{\circ}\)C, and the irradiance had a range of 200-1000 W/m\({}^{2}\). Figure 10 shows how the \(V_{dc}\) and \(P_{dc}\) changed depending on temperature and irradiance, and the measured values at 10 s (on-grid) show that active power and reactive power were required for Load 20 and Filter C before working with the grid on SS9 Mount Kigali. By examining the grid, it appeared that there was a power drop, but it was able to support Load 20. Moreover when it was completely off, it can be seen that there was a disturbance in the measurements obtained from all the G1-G6 generators.  
Table 3 shows the simulated power flow results for a solar panel connected to the electric network at various levels of solar irradiance with demand. As demonstrated in  
Figure 10: Variations in Jali PV module parameters with irradiance and temperature.  
Figure 9: The improved prediction method’s diagram layout.  
Table 3, the power generated by the solar panel was related to its solar irradiation. There were several types of testing. The first alternative was that, when the PV system's output power exceeded the power load, the excess electricity was consumed by the utility grid. The second option was that, if the PV system's power generated equaled the power demand, no power would be supplied to the grid. Once the energy generated from the PV plant was inadequate for self-sufficiency, in the third situation, the solar panel and the grid would be shared to provide the load. As shown, employing around 30 \({}^{\circ}\)C with 400 W/m\({}^{2}\) provided us with the optimum option to serve our utility grid based on Mount Kigali SS9.  
Figures 11 and 12 show how the grid behaved at different times. For example, in Figure 11 the PV was removed when there was an example r separated by the grid and operating at 45 degrees and 1000 degrees of irradiance. These changes were observed from 0 to 18 s and affected the active power, reactive power, \(P_{dc}\), and \(V_{dc}\) grids.
## 2 Materials and Methods
### Three-Point Flexural Experiments  
The bending performance of CARALLs was investigated by three-point bending experiments according to ASTM D7264. The specimens used for the tests were 78 mm in length, 13 mm in width, and a span of the support roll 60 mm. The tests were performed using a universal electronic testing machine (TSE105D, Shenzhen Wance Testing Machine Co., LTD) at a displacement rate of 1 mm/min. To obtain reliable results, four parallel specimens were tested for each group and abnormal data were removed before calculating the average values. The loading head and the two supports were equipped with cylindrical contact surfaces with a radius of 5 mm, which were uniformly contacted across the entire width of the specimen. More detailed experimental parameters are shown in Figure 3.  
The flexural strength, maximum strain, and bending modulus of hybrid laminates were calculated as follows:  
\[\sigma_{f}=\frac{3 PL}{2 bh^{2}} \tag{1}\]  
\[\varepsilon_{f}=\frac{6\delta h}{L^{2}} \tag{2}\]  
\[E_{f}=\frac{L^{3}M}{4 bh^{3}} \tag{3}\]  
where \(P\) is the applied load, \(\delta\) is the mid-span deflection, and \(M\) is the secant slope of the load-deflection curve.
# Machine Learning-Based Satellite Routing for SAGIN IoT Networks
###### Abstract  
Due to limited coverage, radio access provided by ground communication systems is not available everywhere on the Earth. It is necessary to develop a new three-dimensional network architecture in a bid to meet various connection requirements. Space-air-ground integrated networks (SAGINs) offer large coverage, but the communication quality of satellites is often compromised by weather conditions. To solve this problem, we propose an extended extreme learning machine (ELM) algorithm in this paper, which can predict the communication attenuation caused by rainy weather to satellite communication links, so as to avoid large path loss caused by bad weather conditions. Firstly, we use Internet of Things (IoT)-enabled sensors to collect weather-related data. Then, the system feeds the data to the extended ELM model to obtain a category prediction for blockage caused by weather. Finally, this information helps the selection of the data transmission link and thus improves the satellite routing performance.  
space-air-ground integrated network; satellite Internet of Things; limit learning machine model +
Footnote †: journal: Electronics  
0202211862. [[https://doi.org/10.3390/electronics11060862](https://doi.org/10.3390/electronics11060862)]([https://doi.org/10.3390/electronics11060862](https://doi.org/10.3390/electronics11060862))