esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Laser Galvanometer Scanning System Accuracy Using Ball-Bar Standard
## 4 Conclusions  
The results obtained within the presented research indicate that Laser Galvanometer Scanning Systems are capable of performing simple coordinate measurements referenced to a CAD model. After reviewing the standards available in the laboratory, two of them were selected and compared: a calibration board as commonly used for adjusting this type of system and a ball-bar type standard as a new approach to system accuracy. No significant difference was observed between the acquisition of the 2D markers and the ball-bar reference spheres. Following the recommendations of ISO 10360, several measurements were carried out to determine the accuracy and repeatability of the laser projection system. An accuracy of MPE(E0) = \(\pm\)0.5 mm was estimated. The maximum result range of R\({}_{\text{max}}\) = 0.117 mm was observed for a length of 2999.249 mm of the XZ1 axis.  
On the basis of the conducted research, it was concluded that the methods of testing the accuracy of coordinate measuring systems coming from the field of coordinate metrology could be applied to assess the accuracy of galvanometric laser scanner-based systems for laser projection of the virtual templates.  
The accuracy of the tested system was estimated only at a certain distance (4.6 m) from the system. To comprehensively assess the accuracy of the entire workspace, further studies shall be carried out. The development of a dedicated reference sphere-based standard is under discussion.  
The proposed method can be used as an interim accuracy check. However, in the course of further research, there is still a need to develop a method to estimate the measurement uncertainty. Estimating the measurement uncertainty using laser projectors can be complex and challenging because performing measurements differ from coordinating measuring systems techniques.  
Furthermore, note that the tested device cannot perform measurements on the principles of coordinate measuring systems. The results obtained strongly depend on the selection of alignment points between the CAD model and the physical object. Moreover, the tested system only compares the detected features to the CAD model and cannot measure geometric deviations as understood by ISO 1101 [15].  
Conceptualization and measurements, K.K. and W.H.; methodology, K.K. and M.J.; validation, K.K. and M.J.; formal analysis, M.K.; investigation, K.K.; resources, M.J. and K.K.; data curation, K.K.; writing--original draft preparation, K.K. and M.J.; writing--review and editing, W.H. and M.K.; visualization, M.J.; supervision, J.A.S. All authors have read and agreed to the published version of the manuscript.  
Figure 11: Error characteristic for all the tested axes combined.  
Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author. Conflicts of Interest: The authors declare no conflict of interest.
# Natural Rubber/Styrene-Butadiene Rubber Blend Composites Potentially Applied in Damping Bearings
## 3 Result and Discussion
### Mechanical Properties and Thermal Aging Properties  
Figure 2a demonstrates the tearing strength of the NR/SBR-x composites, and it can be seen that the tearing strength of the composites increases with the increase in SBR content. Figure 2b shows that, before aging, the hardness of NR/SBR-x composites does not vary much with the SBR content, which may be related to the relatively high loading of carbon black (30 phr of N330 and 50 phr of N774) [39]. After aging, the hardness of all composites increases due to the increase in crosslinking degree. The change values for the hardness gradually become smaller with the increase in SBR content. This may be due to the fact that SBR contains fewer unsaturated double bonds than NR, resulting in a smaller increase in the cross-linking degree of the aged composites, which is favorable for the maintenance of the vibration damping properties of the bearings during service.  
Figure 1: (**a**) G\({}^{\prime}\)-strain and (**b**) tan delta–strain curves of uncured NR/SBR-x compounds.  
As demonstrated in Figure 2c, the tensile strength of the NR/SBR-x composites increases slightly with the increase in SBR content. As the tensile strength of pure NR (12-27 MPa) [40] is usually higher than that of pure SBR (1-2 MPa) [41], our results suggest that the filling of carbon black may strengthen the SBR phase more than the NR phase in the composites, which is likely due to the fact that carbon black tends to be distributed more in the SBR phase [42]. After aging, the rate of decrease in the tensile strength of NR/SBR-x composites increases gradually with the increase in SBR content. Since SBR is slightly more stable than NR under thermal oxidative aging, as mentioned above, such results may be tentatively attributed to the possible formation of defects between the NR and SBR phases. As will be shown below (Figure 5b), when the proportion of SBR is more than 10%, the phase separation between NR and SBR appears. However, this needs to be investigated in more detail in the future. As shown in Figure 2d, the elongation at the break of NR/SBR-x composites does not vary much with the SBR content before and after aging, which suggests that the incorporation of SBR does not affect the ductility of the composites.
# Next-Generation Dual Transceiver FSO Communication System for High-Speed Trains in Neon Smart City
## 2 Related Works
Further, [PERSON] and [PERSON] introduced a solution known as radio over fiber as an antenna extender (RADIATE) to deliver high-speed internet services to HSTs through cellular networks. RADIATE employs a fiber-based antenna system installed on the train's roof to extend coverage and facilitate smooth handovers between BSs [22]. [PERSON] et al. [23] evaluated how laser divergence angles affect the performance of G2T FSO communications. They suggested the employment of a wide beam to alleviate the complexity of the ATP system in FSO transceivers. This approach helps in mitigating the adverse effects caused by train vibrations by utilizing a recommended range of divergence angles for the wide beam. [PERSON] et al. [24] utilized a dual transceiver scheme that was implemented in the G2T communications system. In this arrangement, each BS was equipped with two transceivers capable of orienting in different directions. The study considered only the presence of additive white gaussian noise (AWGN) in the communication channel. A study is conducted in [25] to analyze the performance of G2T FSO links in the context of tropical climate. The objective was to specifically examine rain attenuation and explore techniques to mitigate its effects. Moreover, they proposed an alternative approach to enhance the FSO link model for G2T communications. Their proposal involved implementing multiple transmitters at each BS [26]. [PERSON] et al. [27] put forward a coverage model for G2T communications utilizing FSO technology. The model adopts a sectorized multi-beam approach and incorporates a smaller divergence angle to minimize geometric losses. Additionally, the system considers atmospheric attenuation to ensure accurate performance evaluation. In [28], the authors introduced a system that tackles challenges related to precise beam tracking and time synchronization between the transmitter and receiver. They introduced an innovative approach for sequence blind detection, utilizing multiple samplers at the receiver to detect on-off keying (OOK) signals. [PERSON] et al. [29] explored the use of relayed FSO for G2T communications. Their objective was to address network layer handover challenges by examining the performance of optical amplify and forward and optical-electronical-optical regenerate and forward schemes. [PERSON] et al. [30] proposed an RIS-assisted FSO communication system for HSTs; the proposed system achieved a higher data rate for both fixed-oriented reflection and dynamic-oriented reflection scenarios of RIS.
## 2 Radio Propagation Measurement Method
### Directivity Evaluation Considering the UAV  
To evaluate the effect of the UAV on the amplitude radiation pattern of the antenna when the transmitting antenna is mounted on the UAV, the directivity of the transmitting antenna was measured in an anechoic chamber. To assess the impact of building reflections on propagation, the directivity in the \(zy\)-plane of the used half-wave dipole antenna was measured. Figure 5 shows the amplitude radiation pattern of the antenna for a horizontal installation, while Figure 6 shows that for a vertical installation. Figures 6a and 7a show the photograph of the measurement setup, while Figures 6b and 7b show the measured radiation pattern in the \(zy\)-plane. The solid and dashed lines represent the radiation patterns with and without the UAV, respectively. The blue and red lines represent the \(E_{\theta}\) and \(E_{\phi}\) components, respectively.  
Figure 5: Directivity of the transmitting antenna in horizontal installation: **(a)** photograph of the UAV with the transmitting antenna and **(b)** amplitude radiation pattern in the \(zy\)-plane.  
As can be seen from Figure 5, comparing the measured radiation pattern with and without the UAV, the \(E_{\theta}\) and \(E_{\phi}\) components are generally the same. In addition, the \(E_{\phi}\) component is omnidirectional in the \(zy\)-plane, so communication is possible both in the air and the ground directions, i.e., the \(z\)-axis.  
As shown in Figure 6, the radiation pattern with the UAV matches that without the UAV. However, communication in the air and the ground directions is not possible because the radiation pattern of the \(E_{\theta}\) component is a figure-8 shape. From the measurement results shown in Figures 5 and 6, the UAV used for the measurements did not affect the directivity of the transmitting antenna.
## 4 AMCTD Protocol and Active Attacks  
Various scenarios of active attacks with variations of attacker nodes were implemented in the AMCTD protocol. Scenarios include a combination of 4, 8 and 12 attacker nodes. Different combinations of attacker nodes were utilized for the performance assessment of AMCTD in different scenarios having different numbers of attacker nodes. In UWSNs, randomly chosen sensor nodes were set up as attacker nodes. These attacker nodes had more leftover energy than the other nodes in the UWSNs environment, which increased the likelihood that one of them would be chosen as a carrier node. If an attacker node is chosen as the carrier node, the data will not be sent to the sink node and will be dropped. The data will be transmitted to the sink if the real sensor node is chosen as a carrier node. In UWSNs environment, sensor node communication is multi-hop, meaning that if an attacker node is engaged during the data transmission among the source and sink node, the data will be lost. Performance will suffer if an attacker node is chosen as a carrier node. The AMCTD protocol will perform much worse the more attacker nodes it encounters. Figure 3 depicts the steps taken to implement an active attack using the AMCTD protocol.  
Figure 2: (**a**) Block diagram of AMCTD. (**b**) Data Transmission in AMCTD Protocol.
# Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment
## 4 Our Presented TRP-MC Algorithm
### Coverage Optimization Using PSO
\(\alpha\) and \(\beta\) are two weight coefficients, and they satisfy \(\alpha+\beta=1\). **Step 4:** After updating the parameters of the particles, the bounds checking is conducted. If the speed exceeds the restriction as Formula (15) describes, we set it as the boundary value. If the location of the particle exceeds the boundary of the sensor field as Formula (16) describes, we set it as the boundary value. **Step 5:** Next, the algorithm returns to step 2 and iterates to the maximal number of iterations. Ultimately, we achieve the optimal solution for the SPs selection as \(G_{best}\) shows. One sample after executing PSO is shown in Figure 8. As Figure 8 shows, most of the sensors are covered by only one SP and only a few sensors are covered by multiple SPs. Inevitably, very few sensors may not be covered by any SPs especially when they are outliers. For those uncovered sensors, we could only introduce multi-hop transmission for data delivering. Once a sensor finds that it is not covered by any SP, it will choose the closest relay node which is covered by the nearby SP. Although multi-hop communication still exists in the network, it has a slight effect on the performance of the network because only a few sensors will use it.
# Cross-Technology Interference-Aware Rate Adaptation in Time-Triggered Wireless Local Area Networks
## 4 Experiment Setup  
To evaluate the CARA framework, we constructed a dataset for training the CTI identification model and developed a simulation-based experimental environment for evaluation. The performance of the CTI identification model is assessed based on the power estimation accuracy (mean absolute error, MAE) for each CTI signal type within the detected spectrum. Since the size of the STFT image affects the accuracy, we also evaluate performance based on varying image sizes.  
When designing the model, it is essential to consider the computing performance of the target system (in this experiment, the identification needs to be performed within a 1 ms processing time requirement). Therefore, in addition to accuracy performance, we also analyze the model's floating point operations (FLOPs) and the number of parameters required to meet these performance constraints.  
The performance evaluation of the interference mitigation algorithms integrated with the CARA framework (M-CARA and P-CARA) is conducted in terms of packet error rate (PER), goodput, and latency. The proposed algorithms are compared with the measurement-based algorithm (M-Base) and the packet error-based algorithm (P-Base). The M-Base algorithm employs an explicit feedback approach where the receiver estimates channel conditions by measuring the SNR, selects the appropriate data rate based on these measurements, and communicates the selected MCS value to the transmitter via MCS feedback messages to perform rate control [12]. In contrast, the P-Base algorithm relies on the transmitter selecting an appropriate data rate based on observed packet errors using ACK messages. In this paper, we adopt automatic rate fallback (ARF) [13] as the representative P-Base algorithm.
## 4 Results  
### Groundwater Monitoring  
The fluctuations in groundwater level in both boreholes show similar dynamics. The amplitudes of the fluctuations as well as the depth to the water table are significantly higher in borehole PP-12A, which was located in the upper part of the landslide and covered by a thick layer of coarse-grained gravel (Figure 2). Large fluctuations in groundwater levels in this well indicate the dominant influence of the vigorous flow of groundwater from the upper parts of the landslide recharge area on the groundwater dynamics in this part of the landslide. The constant groundwater temperature indicates deeper and longer groundwater flow paths below the depth at which atmospheric temperature variations and solar radiation exert a clear influence.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline  & \begin{tabular}{l} **Injection** \\ **Location** \\ \end{tabular} & \begin{tabular}{l} **Z** \\ **(m a.s.l.)** \\ \end{tabular} & \begin{tabular}{l} **Date** \\ **(m a.s.l.)** \\ \end{tabular} & \
\begin{tabular}{l} **Tracer** \\ **(kg)** \\ \end{tabular} &
\begin{tabular}{l} **Tracer Mass** \\ **Sampling Points** \\ \end{tabular} \\ \hline
1 & Ui-1 & 1270 & 17 April 2020 & NaCl & 50 &
\begin{tabular}{l} Ui-6, Ui-7, PP-8, PP-4, \\ Urbas stream \\ \end{tabular} \\ \hline
2 & I-1 & 1368 & 14 May 2020 & Uranine & 2 & Ui-1, Urbas stream \\ \hline
3 & Ui-1 & 1270 & 1 October 2020 & Uranine & 0.9 & Ui-4, Ui-6, Ui-1, PP-4 \\ \hline
4 & PP-12A & 1288 & 1 October 2020 & NaCl & 100 &
\begin{tabular}{l} Ui-4, Ui-6, Urbas stream, \\ PP-8 \\ \end{tabular} \\ \hline
5 & I-2 & 1330 & 16 May 2022 & Uranine & 1 & Ui-1, Ui-4, Urbas stream \\ \hline \end{tabular}
\end{table}
Table 1: Main characteristics of tracer experiments.  
Figure 2: Water-level (**above**) and temperature (**below**) fluctuations in piezometers PP-4 and PP-12A.  
The smaller fluctuations in the groundwater level and the bigger temperature changes observed in borehole PP-4 indicate shallower groundwater flow in this part of the landslide, which consists of clastic layers of low permeability. Temperature fluctuations in the range of 1.2 \({}^{\circ}\)C follow annual cycles with a delay of about half a year compared to fluctuations in air temperature.
## 2 Materials and Methods
### Model Evaluation  
The models' performance was evaluated using the following metrics: accuracy, precision, recall, and F1-score, defined as follows:  
1. Accuracy score Accuracy quantifies the proportion of correctly classified instances out of the total number of instances. It is determined by dividing the sum of true positives (TPs) and true negatives (TNs) by the total number of all classification outcomes, including true positives (TPs), true negatives (TNs), false positives (FPs), and false negatives (FNs) [46]. \[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\] (6)  
In this formula, \(TP\) represents the number of true positives, \(TN\) represents the number of true negatives, \(FP\) represents the number of false positives, and \(FN\) represents the number of false negatives.
2. Precision Precision evaluates the proportion of positive predictions that are correct. It is calculated by dividing the number of true positives by the total number of predicted positive instances (TP + FP) [46]. \[Precision=\frac{TP}{TP+FP}\] (7) Here, \(TP\) represents the number of true positives, and \(FP\) represents the number of false positives.
3. Recall (sensitivity) Recall, also referred to as sensitivity or the true positive rate, measures the proportion of actual positive cases that are correctly identified by the model. It is determined as the ratio of the number of true positives to the sum of true positives and false negatives [46]. \[Recall=\frac{TP}{TP+FN}\] (8) In this context, \(TP\) represents the number of true positives, and \(FN\) represents the number of false negatives.
4. F1-score The F1-score combines precision and recall into a single metric by computing their harmonic mean, providing a balance between the two. It is determined as follows [46]:  
\[\text{F1-score}=2\times\frac{Precision\times Recall}{Precision\times Recall} \tag{9}\]In this formula, both precision and recall are considered to provide a balanced evaluation of the model's performance.
# Optimal Interplanetary Transfer of Solar Wind Ion Focusing Thruster-Based Spacecraft
## 5 Conclusions  
This paper has analyzed the performance of a SWIFT-based spacecraft in a heliocentric and two-dimensional mission scenario. In particular, an improved version of the thrust vector mathematical model has been discussed with the aim of obtaining a set of analytical equations useful for a preliminary trajectory analysis. The optimal control law has been obtained with an indirect approach, and the corresponding two-point boundary value problem has been solved using a hybrid numerical technique. The numerical results show that the typical propulsive performance of a SWIFT-based spacecraft, in a simplified interplanetary trajectory, provides a minimum flight time of some years for a mission towards Mars (or Venus) without ephemeris constraints. These numbers, which are comparable to more conventional propellantless systems such as solar sails or E-sails, prevents the use of this propulsion concept from a manned interplanetary mission, as expected. However, the intrinsic characteristic of a SWIFT propeller of providing a continuous thrust for a long period of time allows this propulsion system to be an interesting option in advanced high-energy missions such as the generation of artificial equilibrium points or displaced non-Keplerian orbits. These two latter applications are the focus of our current research and represent the natural extension of this work.  
Conceptualization, A.A.Q.; methodology, A.A.Q.; software, A.A.Q. and L.N.; writing--original draft preparation, A.A.Q. and L.N.; writing--review and editing, G.M. and M.B. All authors have read and agreed to the published version of the manuscript.  
This work is partly supported by the University of Pisa, Progetti di Ricerca di Ateneo (Grant no. PRA_2022_1).  
Not applicable.  
Figure 13: Time variation of the spacecraft states in an Earth–Venus transfer. Black circle (or square) refers to the start (or arrival) point.
# Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns
## 7 Experiments and Results
### Finding the Best Number of Clusters and Their Corresponding Models
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multicolumn{4}{c}{**KMB**} & \multicolumn{4}{c}{**Best \# of Clusters**} & \multicolumn{4}{c}{**Distribution of Classes Defects:Non-Defects**} \\ \cline{2-9}  & **1C** & **3C** & **5C** & **7C** & **9C** & **1** & **1** & **1** \\ \hline cm1 & 0.518 & 0.608 & 0.723 & 0.744 & **0.845** & 9 & 7.79:23, 12.887.2, 14.385.7, 22.277.8, 50.50, 0.100, 0.100, 66.7:33.3, 13.886.2 \\ jml & 0.615 & 0.622 & **0.631** & 0.625 & 0.621 & 5 & 16.683.4, 23.276.8, 49.650.4, 12.287.8, 100.0 \\ kcl & 0.628 & 0.594 & **0.722** & 0.625 & 0.592 & 5 & 17.88.82, 69.430.6, 50.50, 158.4, 44.255.8 \\ kc3 & **0.685** & 0.546 & 0.441 & 0.423 & 0.464 & 1 & 12.678.7 \\ mcl & 0.385 & 0.559 & 0.340 & 0.680 & **0.716** & 9 & 5.694.4, 0.100, 3.596.5, 5.100, 2.6697.4, 2.98, 0.100, 0.100, 50.50 \\ mv1 & **0.593** & 0.519 & 0.532 & 0.517 & 0.526 & 1 & 7.9921. \\ pcl & 0.556 & **0.603** & 0.548 & 0.414 & 0.453 & 3 & 13.586.5, 11.788.83, 2.397.7 \\ pcl & 0.658 & 0.575 & **0.897** & 0.658 & 0.837 & 5 & 0.100, 0.899.2, 6.339.7, 0.100, 54.94.6 \\ pcl & 0.653 & **0.633** & 0.549 & 0.565 & 0.534 & 0.538 & 1 & 11.988.1 \\ pcl & 0.654 & 0.606 & **0.691** & 0.471 & 0.234 & 5 & 5.394.7, 5.8942, 9.890.2, 12.187.9, 31.468.6 \\ pcl & **0.671** & 0.641 & 0.617 & 0.653 & 0.559 & 1 & & 26.173.9 \\ \hline ant-1.7 & 0.574 & 0.460 & 0.427 & 0.486 & **0.639** & 9 & 2.897.2, 43.156.9, 5.894.2, 12.787.8, 36.713.3, 13.585, 16.78.33, 13.18:86.9, 40.60 \\ camel-1.16 & **0.619** & 0.588 & 0.611 & 0.540 & 0.562 & 1 & 17.365.84 \\ ivy-2.0 & 0.589 & **0.671** & 0.633 & 0.630 & 0.564 & 3 & 13.88.63, 6.694, 7.192.9 \\ jedit-4.3 & **0.826** & 0.458 & 0.458 & 0.633 & 0.650 & 1 & 0.899.2 \\ jogd-1.2 & 0.773 & 0.500 & 0.874 & 0.975 & **0.898** & 9 & 80.20, 93.16.9, 1000, 80.713. & 305.00, 1000, 1000, 1000, 1000 \\ lucone-2.4 & 0.734 & **0.822** & 0.720 & 0.592 & 0.548 & 3 & 51.94.84, 40.459.6, 67.83.2 \\ pcl-3.0 & **0.817** & 0.653 & 0.526 & 0.551 & 0.567 & 1 & 6.023.98 \\ tonnet-0.6 & 0.640 & 0.666 & **0.726** & 0.346 & 0.513 & 5 & 37.63, 8.6914.2, 3.277.7, 5.494.6, 9.690.4 \\ salan-2.6 & **0.756** & 0.635 & 0.723 & 0.710 & 0.644 & 1 & 45.954.1 \\ xerces-1.4 & 0.843 & 0.864 & 0.843 & **0.886** & 0.759 & 7 & 89.3:10.7, 50.50, 92.17.9, 48.351.7, 90.59.5, 78.621.4, 100.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: G-mean results of two-folds cross-validation for the proposed approach based on the basic classifiers.