esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Evaluation and Analysis of First-Frame Localization Effects
Focal Loss assigns greater weight to hard-to-classify samples, enabling the model to focus more on challenging instances, thereby improving performance in complex scenarios. The preceding discussion collectively demonstrates that optimizing the RFBNet model not only preserves the accuracy of the initial frame localization model but also reduces its complexity. This indicates that under the same hardware configuration, the optimized model can perform the first-frame localization task of the perforating robot arm more rapidly. Figure 7 presents a comparison image of the effect of the original RFBNet and the improved RFBNet on the first-frame localization model for detecting the perforated robotic arm, which visually demonstrates the effectiveness and efficiency of the improved model. Figure 7a illustrates the detection efficacy of the original RFBNet, whereas Figure 7b depicts the detection efficacy of the initial frame localization model with enhanced RFBNet. The analysis demonstrates that the optimized first-frame localization model of RFBNet produces more precise target frames in locating the perforation machine arm. This establishes a robust foundation for the subsequent processing steps of target tracking.
# Analysis of the New Thomson Scattering Diagnostic System on WEST [PERSON]
## 3 Results  
### Normal Operation Plasma Scenario--Global Analysis  
The results in terms of deformation (total and directional) of the structural steady-state analysis are illustrated in Figure 8.  
Figure 8: (**a**) Total deformation (mm); (**b**) directional deformation in the vertical axis (mm); (**c**) directional deformation in the tangential vacuum vessel direction (mm); (**d**) directional deformation in the radial axis (mm).  
The thermal load due to plasma operation has the greatest impact on the total deformation. Because of cantilever beam, the Earth gravity leads to high vertical deformation. However, the obtained vertical deformation of 1 mm is lower than the maximal allowable value of 6 mm. The radial axis deformation shows the endoscope moving into the vacuum vessel for about 3 mm. This can be taken into consideration in the manufacturing and calibration process. Vacuum force and, mainly, the thermal load are responsible for this phenomenon.  
Figure 9 depicts the results in terms of equivalent von Mises stresses both for primary and secondary loads.  
The primary stress is lower than the allowable value of 111 MPa given in SS2.2.2. For the secondary load case, the maximal stress value of 243 MPa found between the cylinder of the endoscope and the frame is actually a numerical overstress and does not have physical meaning. Furthermore, this stress is still lower than the allowable value of 333 MPa.
###### Abstract
The key contributions are listed below:  
1. A novel, complete routing protocol is proposed that successfully takes advantage of the location estimation process to route the packets in the proper direction;
2. This method considers the buffer congestion state of the neighboring UAVs to avoid congestion and ensure successful data transmission;
3. The LECAR protocol forwards the packets following a single copy unicast fashion. Thus, it highly minimizes the power consumption by excess and unnecessary data transmission;
4. The LECAR protocol successfully achieves a high packet delivery ratio compared with the compared routing protocols, while ensuring the minimum number of copies per packet;
5. Moreover, LECAR consumes a significantly small amount of energy in comparison with the considered DTN-based routing protocols. The rest of the paper is organized as follows. Section 2 summarizes the related work, and Section 3 briefly describes the considered problem scenario. Section 4 details the proposed routing scheme, and Section 5 evaluates the proposed LECAR routing protocol compared to the present routing protocols. Finally, Section 6 concludes the paper.
## 4 SRSI for Temporal Profile Characterization
### Xpw-SRSI  
The XPW effect was the first third-order nonlinear process used in the SRSI method in which a single incident beam is used [31]. Figure 5 presents the principle of XPW-SRSI. In brief, a linear polarized unknown femtosecond laser beam passes through a calcite plate (CP) by which the polarization of a small fraction of the incident pulse becomes perpendicular with a time delay proportional to the thickness of the calcite plate. The incident pulse is then focused onto a BaF\({}_{2}\) crystal to generate an XPW signal also with polarization perpendicular to that of the incident laser beam. Finally, a polarizer (P) separates the polarization-rotated incident pulse and the XPW signal. A spectrometer measures the spectral interference.  
XPW-SRSI has two advantages over other methods: (1) It needs only one input beam, which simplifies its setup and alignment; and (2) the XPW process is a self-phase-matched process, which makes it suitable for broadband spectrum laser pulses. These two advantages have spurred much research in the past few years [40; 41; 42; 54]. A laser pulse as short as 4 fs has been measured successfully using a low-dispersion optical setup [37]. A dynamic range up to 50 dB with a \(\pm\)400 fs window was characterized using a common 25 dB SNR spectrometer [35]. A commercial femtosecond pulse measurement device named Wizzler (FASTLITE, Paris, France) has been used by some groups to characterize laser pulses. Figure 6 shows a reconstructed spectrum and spectral phase of a laser pulse obtained by Wizzler in the laboratory.  
Figure 4: (**a**) Typical improvement in the temporal contrast of the reference pulse (black line) compared to that of the incident pulse (red line) during the SD process. (**b**) Typical spectrum and spectral phase of the reference pulse (red dotted lines) compared to that of the incident pulse (black solid lines) [55].  
Figure 5: Principle of XPW-SRSI. CP is a calcite plate used to generate a delayed test pulse with polarization perpendicular to that of the incident pulse, and P is a polarizer.
## 4 Simulation Results  
### PAPR Performance  
In this section, we analyze and compare the PAPR of our proposed method with other existing methods using the parameters in Table 2. Each input data block is oversampled by inserting L(N\(-1\)) zero between QAM modulated data symbols, providing a more accurate representation of a continuous-time signal. The filter length after the oversampling is equal to KLT, for which K = 4; the oversampling factor L [20] was set to 4 in all the simulations.  
Figure 4: Block diagram of the proposed method.  
Figures 5 and 6 show the PAPR CCDF plots of the pure FBMC/OQAM signal, DFT spreading with the ITSM condition, improved ITSM in [12], and our proposed method for N = 64 and N = 256. The PAPR of the pure FBMC/OQAM signal is the highest, with 10.7 dB and 11.3 dB at a probability of \(10^{-3}\), respectively. For the sub-carrier value N = 64, applying DFT spreading to pure FBMC/OQAM, while satisfying the ITSM phase condition, reduces the PAPR by approximately 1.6 dB, and combining it with the modified PTS leads to a reduction of approximately 3.4 dB and 4 dB for the sub-block values, V = 2 and V = 4, respectively. Similarly, for N = 256, the PAPR reduction is approximately 1.8 dB for DFT spreading, 3.2 dB, and 3.8 dB for V = 2 and V = 4, respectively. The proposed method exhibits better PAPR performance than the improved ITSM [12], with a reduction of approximately 0.4 dB and 0.3 dB. For small sub-block values such as V = 2 and V = 4, the results show an acceptable reduction in PAPR. As seen from Figures 5 and 6, the PAPR reduces significantly when V = 4, with a considerable increase in the computation complexity. For higher sub-block values, the computation complexity increases exponentially. Thus, the implementation becomes more difficult. Increasing the order of QAM symbols will lead to an increase in the PAPR in the presence of the proposed  
From the results, we conclude that the proposed method outperforms the other mentioned methods with a reduction percentage of 29% and 36.5% for the sub-carrier value of N = 64, and 27.4% and 33.6% for the sub-carrier value of N = 256.
# Highway Travel-Time Forecasting with Greenshields Model-Based Cascaded Fuzzy Logic Systems
## 1 Introduction
Adjustments to the rules and inference schemes are made according to the Greenshields model parameters and actual traffic data collected from the segment. This approach leverages the strength of fuzzy logic in handling imprecise and uncertain data, while minimizing the computational overhead. As a result, it produces accurate predictions without requiring extensive data training, ensuring both efficiency and accuracy compared to regression methods. The remainder of this paper is organized as follows: Section 2 presents the traffic data collection methods and schemes used for traffic status prediction. Section 3 details the development of the proposed Greenshields model-based fuzzy logic system for predicting vehicle speeds in both congested and non-congested conditions. It also explores the construction of fuzzy logic subsystems and their integration into cascaded systems for long-range travel-time prediction. Section 4 describes the simulation of the Greenshields model-based fuzzy logic subsystem for vehicle speed prediction, using realistic data collected from specific highway segments. The simulation results are compared with those obtained from regression methods to verify the accuracy and effectiveness of the proposed system. Additionally, the simulation of the cascaded fuzzy logic system for long-range travel-time prediction is illustrated, and the results are analyzed and discussed. Finally, Section 5 summarizes the key contributions and accomplishments of the proposed fuzzy logic system.
## 3 Results and Discussion
### Ultrasonic Laboratory Measurements  
The results of the ultrasonic laboratory tests carried out in the longitudinal direction on a sample of a healthy holm oak of the same age as the investigated one are shown in Table 1.  
The data highlight that in the analyzed samples the elastic properties increase from path to sapwood. The increasing longitudinal velocity values from path to sapwood were related to the progressive improvement in density and elasticity towards sapwood. Higher density values favored the propagation of the ultrasonic signal, as recognized in previous studies [16; 49; 50]. The longitudinal velocity (Vp) values in heartwood and sapwood were quite similar because the holm oak is characterized by a diffuse porosity in the wood [51] that similarly affects the passage of the acoustic signal in the two parts. The signal in the path is attenuated because it is made up of soft and spongy parenchyma cells that make the wood less dense and therefore characterized by poor elastic conditions.
## 4 Experiments
### Experimental Results
#### 4.4.2 Continuous Frame Experiment Comparison
On the contrary, the steep error curve indicates that the  
Figure 5: Comparison of output trajectories of registration algorithm in x, y, and z directions. Figure 6: Comparison of output pose Euler angles of registration algorithm. Figure 8 is a 5-frame point cloud top view selected in the 1,2,3,4 parts of Figure 7. Figure 9 shows the corresponding image data, with an interval of 1 s between every two frames of data. The coordinate values contained in Figure 8 are the x and y coordinates of the current ship in the world coordinate system. It can be seen from the figure that case1 and case2 have obvious turns. In case3 and case4, the ship is closer to the straight line. Table 1 is the mean (\(\mu\)) and standard deviation (\(\sigma\)) of the inter-frame distance of the four-part point cloud in Figure 8, which is the square of the distance between the corresponding points in the two frame point clouds. It can be seen from the data in the table that the \(\mu\) and \(\sigma\) of the point cloud distance in case1 and case2 are large, indicating that the distance between the corresponding  
algorithm are less different. The algorithm with the smallest RPE is FastVGICP, followed by NDTOMP7. FastVGICP achieves optimal values on both ATE and RPE. Through the above analysis, the registration accuracy of each registration algorithm, from high to low, is FastVGICP, FastGICP, PTPLOMPICP, NDTOMP7, and NDT. The two experiments in this paper are different, and the results are not the same. Compared with single-frame registration, continuous-frame registration is a process of multiple registrations, which has a cumulative error and is more complicated. It can be seen from the experimental results that the algorithm with better single-frame registration results is not satisfactory at continuous frames, such as ICP and LMICP. Similarly, algorithms with better continuous-frame registration may not be very prominent on a single frame, such as PTPLICP (PTPLOMP). Experimental results show that ICP and LMICP are suitable for single-frame registration, and FastVGICP and FastGICP are more suitable for continuous-frame registration.
## 7 Numerical Simulation and Results
### Test 2: Measuring the Speed-Up of the Parallel Implementation
As listed in Table 4, the overall algorithm executes in 20.7 s which represents a 33.3\(\times\) speed-up compared to the sequential implementation. This is a real advantage when new paths need to be computed in a short time such as when new sensors or obstacles are discovered.
# Numerical Simulation of Seepage Surface and Analysis of
###### Abstract  
Background: It is adverse for the safety of a tailings dam to use fine-grained tailings as the materials for a high tailings dam because of the low penetration coefficient, the slow consolidating velocity, and the bad physical mechanical property. Furthermore, with the influence of complicated geography conditions, the phreatic line will be increased enormously when encountering special conditions, which directly affect the safe operation of the tailings dam. Methods: In this study, based on the engineering, geological, and hydrogeological conditions and survey results of a tailings dam, a 210 m fine-grained tailings dam located in three gullies was selected and used to simulate the three-dimensional seepage field of a tailings dam under a steady saturated state by using the finite element software MIDAS GTS. The permeability coefficient was inverted, the seepage field of the project under different working conditions was simulated, and the position of the phreatic line was obtained. The controlled position of phreatic lines was determined by combining the seepage field with the stability requirements. Results: Back analysis could accurately reflect the actual permeability coefficient of each partition of tailings dams. Due to the multiple areas of seepage accumulation, large valley corners, and narrowing of the dam axis, the phreatic line of the shoulder region was elevated by 2-3 m compared to the surrounding area and was thereby the most critical region of the tailings dam seepage control. The stability requirements and minimum controlled position of the phreatic line requirements could be met when the controlled position of the phreatic line was 23 m. Conclusion: This study revealed the key areas and reasons why the tailings dam's phreatic line is prone to be uplifted under complicated geography conditions. It was very critical to control the local phreatline by adopting local horizontal seepage drainage measures or radiation wells in the key areas of the tailings dam to ensure the safety of the tailings dam. In addition to strengthening the daily monitoring of the key areas and the exfiltration facilities of the tailings dam, it is recommended to carry out determination tests of the permeability coefficient and particle size at regular intervals. The findings could provide countermeasures for seepage control.  
fine-grained tailings; seepage back analysis; phreatic lines; seepage failure; seepage control Article
# Electrochemical Degradation of Crystal Violet Using Ti/Pt/SnO\({}_{2}\) Electrode
## 3 Results and Discussion
### Influence of Operating Parameters
#### 3.2.3 Influence of Applied Current  
The effect of current density on CV degradation is studied by keeping the other parameters constant (initial CV concentration = 10 mg L\({}^{-1}\) and 50 mg L\({}^{-1}\) and initial pH = 7). The temporal variation of absorbance spectra in UV-vis for each of the current densities are shown in Suppl. Figures S1 and S2.  
Figure 7a, shows the rate of CV degradation (10 mg L\({}^{-1}\)). From these results it can be seen that the color removal increases significantly with increasing applied current density. When the current density increased from 20 mA cm\({}^{-2}\) to 60 mA cm\({}^{-2}\), the decolorization increased from 23% to 100%. On the other hand, with an initial CV concentration of 50 mg L\({}^{-1}\), it was found that when a current density of 50 mA cm\({}^{-2}\) and 60 mA cm\({}^{-2}\) was applied, a total reduction rate of 100% was achieved after 2 h of treatment.  
The plot of CV removal as function of the applied current density for a given electrical charge, Figure 7c, shows that the increase of the applied current until 40 mA cm\({}^{-2}\) causes an increase of the quantity of electrogenerated HClO and also hydroxyl radicals. For a higher current, it is the amount of CV that limits the process.  
Table 1 shows the velocity constants (\(k\)), half reaction times (\(t_{1/2}\)), the correlation coefficients (R\({}^{2}\)). The values of the velocity constants were calculated from the linear regression of the corresponding semi-logarithmic curves according to a pseudo-first order reaction [49].  
\[Ln(\frac{A_{0(590\,\mathrm{nm})}}{A_{t(590\,\mathrm{nm})}})=k\times t \tag{8}\]  
with \(k\): the pseudo-first-order velocity constant (min^-1).  
According to the results of Table 1, it is observed that the degradation of crystal violet dye for both concentrations 50 mg L\({}^{-1}\) and 10 mg L\({}^{-1}\) followed the pseudo-first order kinetics (R\({}^{2}\)\(\geq\) 0.99 in the range of i = 20 to 60 mA cm\({}^{-2}\)).  
Figure 6: (**a**) Diagram of predominance of the chlorinated species [48]; (**b**) reduction of CV as a function of adjusted initial pH ([CV]\({}_{0}\) = 10 mg L\({}^{-1}\), i = 50 mA cm\({}^{-2}\) and temperature = 25 \({}^{\circ}\)C).
## 4 Results and Discussion
### Kinematic Interaction and Incidence Parameter
The measured value of k\({}_{\text{t}}\), measured at low input intensity, was slightly higher than the predicted value for two reasons in this study: (a) the foundation was just placed on the ground surface, and (b) the  
Figure 9: Translational transfer function between FM and FFM for the S1 structure at two levels of peak acceleration of the base motion (PGA\({}_{\text{BM}}\)). foundation's width was relatively small. At low input intensity, \(k_{t}\) estimates for all three structures increased with PGA\({}_{\text{BM}}\) due to the loose contact between the soil and foundation and due to the increase in the wave's inclination because of reflected waves in the ESB. \(k_{t}\) decreased with a further increase in PGA\({}_{\text{BM}}\) because of a decrease in soil stiffness at a higher earthquake intensity [5; 8]. Regardless of the input intensity, the \(k_{t}\) parameter for the S3 structure was larger than those for S2 and S1, which indicates that the \(k_{t}\) is higher for structures with smaller foundation size [8]. A similar trend was also found in numerical simulation results, as shown in Figure 10a. Figure 10b shows the variation of \(k_{t}\) against peak acceleration of the base motion (PGA\({}_{\text{BM}}\)) for S1 structure on the three soil profiles. Similar to the results obtained for the Uni profile shown in Figure 10a, the \(k_{t}\) parameter in layered profiles initially increased with an increase in PGA\({}_{\text{BM}}\) before decreasing due to the reduction in the soil's stiffness beneath the structure. For horizontal response, the higher \(k_{t}\) value indicates a more regressive decrease in transmissibility between FFM and FM\({}_{\text{T}}\). Moreover, the values of \(k_{t}\) for the layered profiles were always higher than those obtained for the Uni profile; this indicates the effects of the soft upper layer on increasing kinematic interaction in SFSI because when propagating in the layer profiles, the seismic wave inclined more and caused the \(k_{t}\) parameter to increase. This effect was not accounted for in previous studies and design  
Figure 10: Translational incoherence parameters defined for (**a**) three structures on Uni profile, (**b**) S1 structure on three profiles, (**c**) S2 structure on three profiles, and (**d**) S3 structure on three profiles. codes [43; 44]. As the thickness of the soft upper layer decreased from 5.2 m to 2.6 m, the values of \(\mathrm{k_{t}}\) further increased, pointing to the existence of a highly inclined seismic wave when the foundation is located near the interface between soil layers.
# Complex Network Analytics for Structural-Functional Decoding of Neural Networks
## 4 Experiment and Result Analysis
### Experimental Setup
#### 4.1.1 Model Architecture  
Base model (SimpleCNN)  
SimpleCNN, as the base model, consists of two convolutional layers and two fully connected layers. Its convolutional layer adopts a \(3\times 3\) kernel with ReLU activation function, and the maximum pooling operation compresses the spatial dimensions step by step and finally outputs the classification results through the fully connected layer. The model does not introduce any regularisation mechanism, which provides a basic reference for subsequent comparisons.  
(2) Dropout augmented model (DropoutCNN)  
DropoutCNN, based on SimpleCNN, inserts a Dropout layer with probability 0.5 after the first fully connected layer, which randomly blocks 50% of neuron connections in the training phase, forcing the network to pass information through redundant paths, and thus alleviating overfitting; the other structures are the same as the base model.  
(3) Batch normalised model (BatchNormCNN)  
BatchNormCNN, on the other hand, adds a batch normalisation layer after each convolutional layer, suppresses the internal covariate bias problem by dynamically normalising the mean and variance of the activation values, and improves the training stability and convergence speed, while other structures are consistent with the base model.