esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments Results
### Evaluating Models  
We used different measurement methods: accuracy, precision, recall, f1-score AUC, and ROC are used to evaluate the performance of the proposed model and DL and ML models. Each one is defined as follows:  
Accuracy is calculated as the percentage between the correct predictions and the total number of tweets.  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{TN}+ \text{FN}}. \tag{4}\]  
Precision is calculated as the percentage of positive tweets that are rightly classified from the total number of positive tweets.  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{5}\]  
Recall is calculated as the percentage of positive tweets that are rightly classified from the total number of tweets.  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{6}\]  
F1-score is the weighted average of precision and recall  
\[\text{F1-score}=\frac{2\cdot\text{precision}\cdot\text{recall}}{\text{ precision}+\text{recall}} \tag{7}\]  
where TP stands for the amount of positively predicted sentences that were correctly made, FP for negatively predicted sentences that were incorrectly made, TN for positively anticipated negative sentences that were correctly made, and FN for positively predicted sentences that were correctly made.  
Furthermore, the true positive rate (TPR) and false positive rate (FPR) are plotted against one other at different threshold levels to create the receiver operating characteristic curve (ROC) [65]. Additionally, it maps the classification findings from the most favorable to the least favorable [66]. We also computed the area under the curve (AUC). AUC, where \(s_{p}\) is the number of positive records and \(n_{p}\), \(n_{n}\) are the numbers of positive and negative records, respectively, evaluates how well the model can distinguish between models [66].  
\[\frac{s_{p}-n_{p}+n_{(n+1)/2}}{n_{p}n_{n}} \tag{8}\]
# Prediction System for Overhead Cranes Based on Digital Twin Technology
## 4 Life Prediction for the Bridge Crane Based on Tent-SSA-BP
### BP Optimized by the Chaotic Sparrow Algorithm  
The flow chart of the chaotic sparrow algorithm is shown in Figure 8.  
The steps of the chaotic sparrow algorithm are listed below.  
1. Initialization. According to the input matrix, determine the BP topology and initialize the maximum number of iterations, \(T_{0}\).
2. Set the population size, \(N\); the number of discoverers, \(p_{num}\); the number for reconnaissance and warnings, \(s_{num}\); the dimension of the objective function, \(D\); the upper and lower bounds of the initial value, lb and ub, respectively; the maximum number of iterations, \(T\); and the solution precision, \(t_{0}\).
3. Apply Tent to generate N D-dimensional vectors, \(Z_{i}\), and set them to the value range of the variables through Equation (2), where \(d_{min}\) and \(d_{max}\) denote the minimum and maximum values of the \(d\)-dimensional vector, \(X_{new}^{d}\), respectively. \[X_{new}^{d}=d_{\min}+(d_{\max}-d_{\min})Z_{i}\] (2)Calculate the fitness, \(f_{i}\). Select the best fitness, \(f_{g}\); the worst fitness, \(f_{w}\); and their corresponding positions, \(xb\) and \(xw\), respectively.
* Select the first \(p_{num}\) with the best fitness as the discoverer and the rest as joiners. Then, update the positions of the discoverers and the joiners.
* Randomly select \(s_{num}\) as vigilantes and update their positions.
* Conduct an iteration to calculate the fitness, \(f_{i}\), and average fitness, \(f_{avg}\), of each one.
* If \(f_{i}\geq f_{avg}\), perform chaotic perturbation on the individual. If the performance of the individual is better after the perturbation, replace the previous individual; otherwise, it will remain unchanged. If \(f_{i}<f_{avg}\), use Equation (3) to perform Gaussian mutation on the individual, where x represents the original parameter value, \(N(0,1)\) represents the random number for normal distribution, and mutation(x) denotes the value after Gaussian mutation.  
\[mutation(x)=x(1+N(0,1)) \tag{3}\]
* Update the best position, \(xb\), and its fitness for the entire population, as well as the worst position, \(xw\), and its fitness.
* Judge whether the maximum number of iterations or solution precision was reached. If so, the iteration will terminate, and the optimal parameter results will be output.
* After the optimization process of the Tent-SSA algorithm is completed, the optimal parameters will be output and assigned to the BP network for prediction. Moreover, the coefficient of determination, \(\varepsilon\), will be calculated.  
Figure 8: The flow chart of the chaotic sparrow algorithm.
## 5 Numerical Results
### Radar Detection Performance Analysis
#### 5.1.3 Integration for Weak Targets  
In this subsection, the integration output responses for each method are provided in \(SNR=-15\) dB, with motion parameters identical to those detailed in the preceding section. Figure 7a depicts the integration output response for MTD, Figure 7b for MFI, and Figure 7c illustrates the results of the proposed method. The findings from the simulation studies on weak-target scenarios indicate that the proposed MHI method effectively integrates the energy of weak targets compared to other approaches.  
Figure 6: Single target results for \((SNR=20\) dB). (**a**) MTD results, (**b**) HI integration results, (**c**) proposed method’s result.  
Figure 7: Single target results for \((SNR=-20\) dB). (**a**) MTD result, (**b**) HI integration result, (**c**) proposed method’s result.
## 2 Materials and Methods  
### Damage Assessment Method  
As bridges age, damage occurs, reducing stiffness and increasing structural responses such as deflection and member forces, which, in turn, decreases their load-carrying capacity [10]. The main components of an RC slab bridge are the deck, piers and abutments. The damage to an RC slab bridge is defined by the damage to its individual components. The structural performance of an RC bridge is primarily related to the deck. For example, mid-span flexural cracks are a common type of deck damage. This type of damage can be simulated and analyzed using a structural analysis program. Figure 4 [11] shows the results of a simulation, indicating a linear relationship between the deflection response ratio and the stiffness reduction rate of a bridge.  
The measured deflection response ratio \(\mu_{m}\) and measured stiffness reduction rate \(R_{m}\) indicated in Figure 4 are defined as follows:  
\[\mu_{m}=\frac{\delta_{measured}}{\delta_{analyzed}} \tag{1}\]  
\[R_{m}=\left(\frac{f_{analyzed}^{2}-f_{measured}^{2}}{f_{analyzed}^{2}}\right) \times 100 \tag{2}\]  
where \(\mu_{m}\) is the measured deflection response ratio, \(\delta_{measured}\) and \(\delta_{analyzed}\) are the measured and analyzed deflections, respectively, \(R_{m}\) is the measured stiffness reduction rate, and \(f_{measured}\) and \(f_{analyzed}\) are the measured and analyzed natural frequencies, respectively.  
Figure 4: Correlation between RC bridge displacement response ratio and stiffness reduction rate under (**a**) good behavior and (**b**) poor behavior.  
During damage assessment, the response behavior of the bridge is considered relatively good compared to the damage level if the measured deflection response ratio is smaller than the theoretical deflection response ratio (Figure 4a). In contrast, if the measured deflection response ratio is larger than the theoretical deflection response ratio (Figure 4b), the response behavior is considered poor, necessitating appropriate maintenance measures. This judgment is based on the additional damage affecting structural responses beyond typical deflection due to the general reduction in stiffness. In this paper, we propose a method to evaluate the damage to RC slab bridges using deflection and natural frequency. Figure 5 shows a schematic of the proposed method. This method is not a quantitative damage assessment tool but an approximate evaluation of bridge damage using deflection and natural frequency. This study was conducted by selecting a target bridge, performing structural analysis and field load tests, and ultimately validating the proposed damage assessment method.
# On the Utilization of Reversible Colour Transforms for Lossless 2-D Data Compression
## 5 Conclusions  
The utilization of the reversible transforms in conjunction with BWCA algorithm resulted in greater compression as compared to the benchmark schemes. The YA6 UA6 VA6 RCT achieved the best compression results in general for various size of images and creates a single base for the compression of a variety of data including CFA, EEG and RASTER data. The proposed compression outperforms  
Figure 11: (**a**) Original image [58] (**b**) MTF encoded image (**c**) Histogram of original image and its MTF encoded version.  
the techniques exclusively developed for 2-D EEG, RASTER map and Color Filter Array (CFA) image compression. In the future, the BBWCA is also aimed for the application of DCT and DWT to achieve lossy compression over the data as well. In comparison to the existing schemes, for CFA images the proposed system gives 3.35 Bits Per Pixel (BPPs) compared to 3.95. Similarly, for EEG data, the proposed system achieved a compression ratio of 3.12 and gave promising results for RASTER image compression.  
Conceptualization, M.W., T.-W.U. and A.K. ([PERSON]); methodology, M.W., T.-W.U. and A.K. ([PERSON]); software, M.W. and A.K. ([PERSON]); validation, T.-W.U. and A.K. ([PERSON]); formal analysis, T.-W.U. and A.K. ([PERSON]); investigation, M.W. and A.K. ([PERSON]); resources, T.-W.U. and A.K. ([PERSON]); data curation, M.W. and A.K. ([PERSON]); writing--original draft preparation, M.W. and A.K. ([PERSON]); writing--review and editing, M.W., T.-W.U. and A.K. ([PERSON]); visualization, M.W., T.-W.U. and A.K. ([PERSON]); project administration, T.-W.U.; funding acquisition, T.-W.U. All authors have read and agreed to the published version of the manuscript.  
This work was supported by research fund from Chosun University and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No.2018R1A2B2003774).  
The authors declare no conflict of interest.
# Optimization of Coverage and Capacity Using Smart Antennae
## 4 Evaluation
### Different Numbers of UEs  
This research conducted experiments in a test area with dimensions of 1000 m by 1000 m. The number of base stations (BS) was fixed at 10, while the number of user equipment (UE) items varied from 200, 400, 600, 800, and 1000 to 1200. The experimental parameters were set as shown in Table 4.  
The results of the experiments are presented in Figure 7, which shows that the coverage rate ranged from 50% to 80%. This variation in coverage rate was due to the random distribution of UEs in the test area. Compared with omnidirectional antennas, when the number of users was 200, the majority of users could be covered adequately using omnidirectional antennas due to the lower user count. However, as the user count increased to 1200, simulation results indicated that the proposed approach enhanced coverage by approximately 23.6%. The CCO algorithm for smart antennas proposed in this study effectively enhanced the coverage rate of base stations.  
\begin{table}
\begin{tabular}{c c} \hline \hline _Area_ & **1000 m \(\times\) 1000 m** \\ \hline \(n\) & 10 \\ \(j\) & 5 \\ \(P_{max}\) & 19 dBm \\ \(P_{min}\) & \(-\)30 dBm \\ \(B\) & 20 Mbps \\ \(K\) & 200–1200 \\ \(\gamma\) & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter settings for different numbers of UEs case.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\(S_{i}\)} & \multicolumn{4}{c}{\(X_{j}^{i}\)=1, _Antenna on;\(X_{j}^{i}\)=0, Antenna off_} & \multicolumn{1}{c}{**Covered**} \\ \cline{2-6}  & \(X_{1}^{i}\) & \(X_{2}^{i}\) & \(X_{3}^{i}\) & \(X_{4}^{i}\) & \(X_{5}^{i}\) \\ \hline \(S_{7}\) & 1 & 0 & 0 & 0 & 1 & 5 \\ \(S_{8}\) & 0 & 0 & 0 & 0
## 3 Mechanical Model and Finite Element Analysis
### Establishment of Finite Element (FE) Model  
As shown in Figure 6, the FE simulation model of the thin-walled conical part with variable-section during spinning is established by Unigraphics NX, which includes roller, blank, mandrel and back-plate. Unigraphics NX software is a flexible and powerful integrated solution that delivers the next generation of design, simulation, and manufacturing solutions. The blank is fixed on the top of the mandrel by the back-plate, while the mandrel and back-plate are rotated at a fixed speed; they drive the blank to rotate at the same time. The rollers on both sides are fed according to the preset track, driven passively by the blank, and they apply pressure to the blank. The FE model in STL format was imported into Simufact.Forming, which is an advanced material processing and heat treatment process simulation optimization software. The blank selected in this paper is a circular sheet with a thickness of 2 mm and a diameter of 250 mm. The material in the top area of the blank does not participate in the deformation process, so the model of the blank is simplified as a circular blank with a hole in the center of the top. The volume of the blank becomes smaller, and the number of meshes is reduced, thus shortening the simulation time. The material of the blank used in the simulation is superalloy GH1140. It is defined as elastoplastic and it is divided by a Hexahedral mesh cell. The mandrel, roller and back-plate are both considered as rigid. In order to reduce unnecessary simulation time, the friction heat generated between the roller and blank during the forming process is neglected. The ambient temperature, blank temperature and mandrel temperature are set to 25 \({}^{\circ}\)C. The relative parameters of the simulation are as follows: the rotary speed is 250 r/min, the feed speed is 1.0 mm/r, the friction coefficient is 0.5 and the gap deviation rate is 5%.
# Active Flow Control of a Supercritical Airfoil and Flap with Sweeping Jets
## 2 Experimental Set-Up
### Models  
The selected airfoil in this experiment was the NASA SC(2)-0410, a supercritical airfoil in the form of a two-dimensional straight-wing model made using three-dimensional printing from resinous material; a planform view of the wing is shown in Figure 2. The wing chord length (\(c\)) and span (\(l\)) were 300 mm and 400 mm, respectively, and the flap position was set in the range 70-100% of the chord length. The hinges of the flap were embedded in the model and were controlled using a square-section carbon fiber rod as a rotating shaft. The deflection angle of the flap varied from 0\({}^{\circ}\) to 30\({}^{\circ}\) and was controlled using a fixed-angle deflection plate. In addition, 26 pressure tappings were arranged on the symmetrical plane of the wing: 12 each on the upper and lower surface of the wing, 1 on the leading edge, and 1 on the trailing edge.  
Six sweeping jet actuators were designed according to the literature and uniformly embedded on the wing model with equal spanwise spacing (\(d\) = 50 mm) [23]. The outlets  
Figure 1: Low-speed, low-turbulence wind tunnel (LLW).  
Figure 2: Wing model and layout of the sweeping jet actuators.  
of the actuators were located at 70% of the chord (from the leading edge and tangent to the airfoil surface).  
The actuators were grouped in pairs, with each group sharing a common air pipeline for gas transmission, thus reducing the unevenness of the gas supply. The key internal dimensions of the sweeping jet actuators are shown in Figure 3. As shown in the figure, the length of each actuator is 24 mm, and the width and thickness are 24 mm and 2 mm, respectively. The width at the throat of the actuator is 4 mm, as is the width of the feedback channel. The 10 mm diameter compressed-air inlet was located at the left-hand side of the actuator, and the width of the inlet of the working section of the actuator is 6 mm. For the sweeping jet outlet, the diffusion angle of the actuator is 120\({}^{\circ}\) (with an aspect ratio of 2.0), with a throat area of 8 mm\({}^{2}\).
# Improved Control of Forest Microgrids with Hybrid Complementary Energy Storage
## 5 Simulation Analysis
### Simulation Analysis with Simulated Natural Wind Speed Similar to the Actual One  
To simulate the actual wind speed, the superposition of basic wind, gust wind, and random wind was adopted as wind time series. The basic wind is a constant value, which was 7.5 m/s in this simulation. The gust wind \(v_{\mathrm{z}}\) and the random wind \(v_{\mathrm{n}}\) were obtained through the following Equation.  
\[v_{\mathrm{z}}=0.5V_{\mathrm{zmax}}\bigg{[}1-\cos\frac{2\pi(t-T_{1})}{T_{ \mathrm{g}}}\bigg{]}, \tag{16}\]  
\[v_{\mathrm{n}}=V_{\mathrm{nmax}}\cdot\mathrm{unifrnd}(-1,1)\cdot\cos[2\pi+ \mathrm{unifrnd}(0,2\pi)], \tag{17}\]  
where \(V_{\mathrm{zmax}}\) and \(V_{\mathrm{nmax}}\) represent the maxima of the gust wind and the random wind, respectively; \(T_{1}\) and \(T_{\mathrm{g}}\) are the start time and the cycle of the gust wind, respectively; and unifrnd (a, b) is a function that generates uniformly distributed random numbers with a and b as upper and lower bounds.  
Figure 14: Performance of each unit in the DC microgrid with fluctuating load: (**a**) The solid lines were obtained by using the improved method of hybrid complementary energy storage based on prediction and the dotted lines were obtained utilizing the basic coordinated control; (**b**) The solid lines were obtained by using the improved method based on PI control and the dotted lines were obtained utilizing the basic coordinated control.  
As is shown in Figure 15, the output of wind power presented fluctuating characteristics. When basic coordination control was adopted, DC voltage was disturbed by the fluctuating power. Although the voltage could still be maintained above 0.98 pu, fluctuating voltage will adversely affect voltage-sensitive loads in forest areas, such as parameter detection devices, and reduce the quality of output power. When the hybrid energy storage method proposed in this paper was adopted, under fast predictive control of converter, the wind turbine unit and the energy storage systems provided corresponding power support for the system according to the change rate of DC voltage, with additional power output suppressing the random fluctuation. The proposed method ensured the stability of system power and improved the quality of the DC voltage.
# Venus Magnetotail Long-Term Sensing Using Solar Sails
## 3 Numerical Simulations and Results
#### Effects of the Eclipse Period  
In this last part of the section, we analyze the effect of the presence of an eclipse period on mission performance during the revolution of the solar sail-based spacecraft around Venus. In particular, we use a simplified cylindrical model for the shadow induced by the planet, as schematized in Figure 11, which neglects the presence of penumbra zones [51].  
During the eclipse period, that is, when the spacecraft covers the orange arc near the apocytherion shown in Figure 11, the propulsive acceleration due to the solar sail is zero, and the vehicle's motion is substantially Keplerian. This corresponds to setting \(\bar{a}_{p_{r}}=0\) and \(\bar{a}_{p_{t}}=0\) in the equations of motion when the eclipse condition occurs. In this case, the numerical procedure used to find the required value of the characteristic acceleration yields the results summarized in Figure 12. As expected, the presence of an eclipse period during the flight increases the required value of the characteristic acceleration compared to the ideal case of full illumination during the orbit, for a given pair \(\{\tilde{r}_{p},\tilde{r}_{a}\}\). For example, considering again the characteristics of the science orbit indicated in Ref. [48], one has \(a_{c}\simeq 0.42\,\mathrm{mm/s^{2}}\) when the shadowing effects are considered in the simulation, while the value of the characteristic acceleration is roughly \(0.363\,\mathrm{mm/s^{2}}\) in the ideal (full-illumination) case. In that case, the \(\
u\)-variation of angle \(\omega\) is shown in Figure 13, in which one can observe the presence of a coasting arc (i.e., the horizontal flat segment) near the apoctherion point of the nominal science orbit.  
Figure 11: Schematic concept of the (simplified) cylindrical shadow model, which was used to determine the solar sail’s required performance in the presence of a period of eclipse.  
Figure 10: Dimensionless apocytherion radius \(\tilde{r}_{a}\) as a function of the dimensionless pericytherion radius \(\tilde{r}_{p}\) when the solar sail required characteristic acceleration is \(a_{c}\in\{0.1,0.15,0.2\}\,\mathrm{mm/s^{2}}\).
# An Interventional Surgical Robot Based on Multi-Data Detection
## 4 Data Training and Results
### Experiments and Training Result  
As shown in Figure 11, in order to establish data sets for network training, we used silicone tubes to make vascular models with different curviness and used them to conduct catheter push experiments. During the experiment, except for changes in the angle of the vascular model, the advancing speed and advancing time of the catheter were kept consistent to ensure the stability of the data. Considering the actual vascular environment of the human body, each 15\({}^{\circ}\) bend of the vascular model is classified into one category. The total number of categories is 11. Therefore, the catheter bending angle of 0-150\({}^{\circ}\) can be detected. In order to enrich the data set, experiments under each angle group were automatically controlled by the computer 200 times, and the catheter was rotated randomly before each advance to ensure the diversity of data, avoiding the overfitting of the network caused by less data. We set the push time of each experiment at 15 s and the sampling rate at 3 Hz. Therefore, 45 lengths of sequence data could be collected in a push experiment. A total of 2200 sets of sequence data were collected to train and verify the network under 11 different vascular angles. 70% of all data is used for network training, 15% is used for verification group and another 15% is used as test set to verify network reliability. In network training, the step size was set to 0.001, BatchSize to 32 and the maximum number of iteration rounds to 100. The training gradient diagram is shown in Figure 12. The training results are shown in Table 2.  
Figure 11: (a) 30\({}^{\circ}\) vascular model; (b) 45\({}^{\circ}\) vascular model; (c) 90\({}^{\circ}\) vascular model; (d) 120\({}^{\circ}\) vascular model.  
Figure 10: The workflow of the vascular angle classification network.  
As can be seen from the table, the network after training has a good classification performance for the multi-dimensional sensor data on the slave side of the surgical robot system. The classification accuracy of the test set reached 94.27% through the network, which could well realize the recognition and classification of multi-dimensional sensor data, feedback the bending status information of the catheter to the main end and give surgeons more feedback information to ensure the safety of robotic surgery.