esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion and Future Directions
### Cluster 1 (Blue): Improving the Efficiency of Intelligent Transportation System Element Management  
The intelligent transportation system (ITS) has played a crucial role in allowing societies to cope with rapid urbanization. It connects all core transportation elements, facilitates information exchange and sharing, and ensures the optimal configuration and efficient use of various elements of transportation. ITS management can help improve transportation efficiency, reduce traffic congestion and environmental pollution, and ensure safety. In light of this, various researchers have become increasingly interested in it [38].  
Future research is mainly concerned with research objects and management methods. According to the study objects, different types of transportation infrastructure are gaining widespread attention, such as bridges, buildings, railroads, roads and streets, urban transportation, and supporting smart power grids. Moreover, as for management methods, future ITS research will focus on traffic control and traffic management, seeking new technology development and collaboration based on the Internet of Vehicles [39].  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Unigram Words** & **Bigram Words** & **Trigram Words** \\ \hline BIM & building information & building information model(ling) \\ construction & digital twin(s) & entire life cycle \\ information & BIM technology & digital twin technology \\ building & life cycle & green space planning \\ design & construction industry & life cycle assessment \\ data & AEC industry & urban green space \\ management & construction management & building column base \\ model & management system & common data environment \\ digital & green building & geographic information system \\ project & information management & indoor environmental quality \\ system(s) & project management & active safety systems \\ research & smart city & ancillary building facilities \\ process & artificial intelligence & architecture engineering construction \\ \hline \hline \end{tabular}
\end{table}
Table 8: Content Analysis in Unigram, Bigram, and Trigram Words.
# Predictive Maintenance for Remanufacturing Based on Hybrid-Driven Remaining Useful Life Prediction
## 2 Proposed Methodology
### Condition Monitoring  
Condition monitoring is regarded as a pivotal component of the proposed predictive maintenance framework. It aims to inspect and evaluate the current state of a machine or its components through the original real-time data collected from different types of sensors (e.g., vibration, noise, temperature, etc.) connected to the machine [39]. The advanced predictive maintenance framework is built on the condition monitoring system, then integrates effective prognostic approaches that can estimate the future condition [40; 41]. In this work, the CM module consists of the following two tasks: feature extraction and performance assessment.
## 4 Discussion
### Limitations and Future Perspectives  
This study presents certain limitations that should be taken into account when interpreting the results. First, the dataset is exclusively limited to offensive possessions during the final moments of games and overtime periods in the 2022/23 men's EuroLeague season. While this allows for a specific analysis of critical situations, it does not encompass other phases of the game that could provide a more comprehensive understanding of performance dynamics. Second, the focus on a single competition restricts the generalizability of the findings to other competitive contexts, such as national leagues or international tournaments. Additionally, this study does not consider external variables such as psychological pressure, players' physical condition, or contextual factors (e.g., injuries, coaching strategies), which might have influenced the results.  
Moreover, the lack of temporal analysis prevents us from understanding how performance varies throughout the season, and how competition level, team rankings, and playing styles affect late-game offensive strategies.  
Future studies could expand this work by exploring different competitions and categories, such as women's leagues or youth divisions, to assess whether the observed patterns hold consistent across other contexts. It would also be valuable to incorporate additional variables, such as physiological or psychological metrics, to better understand the impact of pressure and fatigue on decision-making during critical moments of the game. Lastly, examining how specific defensive strategies influence the success of offensive possessions in high-pressure situations could provide a more balanced view of game dynamics.
## 2 Materials and Methods
### Self-Reported Musculoskeletal Pain, Pain Severity, Functional Limitation, and Perceived Exertion  
The participants in the study were asked to complete a survey regarding job characteristics, the perceived exertion level at work, the presence of pain in six body areas (yes/no) that lasted less and more than three months in the past 12 months, the pain severity experienced in the last seven days, the treatment received to handle pain, and the pain-related functional limitations.  
The self-reported perceived exertion for the last seven work days was quantified using the Borg ratings of perceived exertion (RPE) to provide an idea of the job's physical demands. We considered that self-reported physical demands during the last seven working days would provide us with a reliable estimate of their physical demands considering this group of workers performs very similar tasks daily.  
Pain in six body areas was self-reported as a binary variable (yes/no) using a modified version of the Nordic questionnaire [30]. It was categorized as acute when it lasted between one day to three months and chronic if it lasted more than three months [31]. The pain severity during the last seven days was self-reported using a five-item questionnaire asking for the characteristics of the pain in the low back, neck/shoulder, wrist/forearm, knee, and ankle/feet. Each participant scored the severity (e.g., please rate the severity of the pain in your low back) using a 5-point Likert scale ranging from 1 (none) to 5 (extreme pain) [32].  
The workers detailed the functional limitations by reporting their ability to perform ten daily activities over the past seven days (e.g., the ability to carry a shopping bag). Workers rated the levels of difficulty performing these tasks on a 5-point Likert-type scale, from 1 (\"no difficulty in carrying out the task\") to 5 (\"unable to do a task without help\") [33]. The scores allowed us to identify high difficulty levels in certain activities. We calculated the Cronbach alpha and Loveringer H to assess the internal consistency and scalability of the questionnaire assessing the pain severity and functional limitations.
## 5 Performance Evaluation and Analysis
### Network-Related Performance Measure
#### 5.2.1 Definition of Network Performance Measure  
If transmission delay and processing delay can be ignored, the _Recovery Time_(**R**) can be expressed as a sum of _Link Error Detection Time_(\(T_{e}\)) and \(Re-Searching\ Time(T_{s})\). So it is written as (12):  
\[\text{R}=T_{e}+T_{s} \tag{12}\]  
And _Link Overhead_(\(O\)) is defined as _Load (bit-per-sec) caused by routing_ and satisfies these conditions:  
1. All ants travel only through a single path.
2. There is no loss, so 100% of the response (backward ant) is received.  
These two conditions mean the worst case. In other words, _Link Overhead_(\(O\)) means the upper bound of the actual measured load.
# Research and Analysis of MEMS Switches in Different Frequency Bands
## 3 The Research Status of MEMS Switches in Different Frequencies
### Research Regarding Insertion Loss and Isolation of MEMS Switches in Different Bands
#### MEMS Switches for Submillimeter Applications.  
Table 6 summarizes the performance parameters of MEMS switches for submillimeter applications.  
The highly integrated calibration MEMS switch using silicon micromachining proposed by [PERSON] et al. [PERSON] et al. (2018) achieved a low insertion loss of 0.2 dB and high isolation of better than 25 dB at 500-750 GHz. An electrostatic MEMS switch designed by [PERSON] et al. [PERSON] et al. (2018) showed an insertion loss of 0.7-2.7 dB and a high isolation of 17-25 dB at 500-750 GHz. The CPW transmission lines were suspended on the substrate, and the input part of the signal line was used as a cantilever in which the tip gradually reduced in width in this design. A submillimeter-wave MEMS-reconfigurable phase shifter using MEMS-reconfigurable surfaces designed by [PERSON] et al. [PERSON] et al. (2018) exhibited an insertion loss of better than 3 dB at 500-550 GHz and, attributed by the MEMS-switched surfaces and stubs, was only 0.5-1.5 dB. [PERSON] et al. [PERSON] et al. (2018) designed a single-pole, single-throw MEMS waveguide switch based on a MEMS-reconfigurable surface and achieved an insertion loss of 2.5-3 dB and an isolation of 19-24 dB at 500-750 GHz.
## 5 Discussion and Conclusions
### Conclusions  
Conclusions can be drawn as follows. Firstly, the step-by-step inversion method proposed in this article is as effective as the conventional inversion method in the simple inversion analysis based on the results of forward analysis. The maximum relative error between material parameters calculated by both methods and actual material parameters was less than 1.5%. Secondly, the step-by-step inversion method applied in the inversion analysis during construction periods showed a higher accuracy in vertical compression values compared with the conventional inversion method. The relative error between compression values calculated by step-by-step inversion method and monitoring compression values was 4.6%, which is smaller than that (14.4%) between compression values calculated  
Figure 10: Comparison of compression values of the bottom of the core wall using variable parameter constitutive model.  
by conventional inversion method and monitoring compression values. Thirdly, the trend of material parameter \(K\) was studied in this article and the variable parameter constitutive model was then proposed. The relative error of compression values using the variable parameter constitutive model was 9.74%, which is smaller than that of the conventional inversion (14.4%) and larger than that of the step-by-step inversion (4.6%). Considering that more time is needed using step-by step inversion and adequate calculation accuracy cannot be reached using conventional inversion, the variable parameter constitutive model proposed in this paper might be more suitable for the analysis of a high earth-rockfill dam during construction periods.  
Meanwhile, there are still some limitations in this study. The proposed step-by-step inversion method may have the disadvantage of being time consuming as the material parameters must be inversed at each construction steps. Also, the applicability of the variable parameter constitutive model in three-dimensional conditions or other earth-rockfill dams cannot be ensured due to the lack of data.  
In further study, the feasibility of 3D inversion problems using step-by-step inversion method and the applicability of the variable parameter constitutive model in other earth-rockfill dam projects can still be probed.  
Conceptualization, S.P.; Investigation, H.Z.; Methodology, S.P.; Supervision, T.L.; Validation, S.P.; Writing--original draft, S.P. and H.Z.; Writing--review & editing, S.P., G.S., Z.C. and L.Y. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The study did not report any data.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Plan2 Heat: In-House Developed Software Package
#### 2.1.2 Discretization and Gridding in Plan2 Heat  
Plan2 Heat performs calculations using the FDM within a model that is discretized on a uniform rectangular (tissue) grid. Examples of such grids are shown in Figure 1. Each voxel is assigned appropriate dielectric tissue properties for calculations. Regarding the discretization shown in Figure 1, if an electrode covers the voxel node (black dot; the center of the voxel), then the voxel is modelled as an electrode voxel (grey voxel).  
Furthermore, in this study we assumed two types of grids:  
* Centralized grid (Figure 1A): the grid is chosen such that the center of a voxel coincides with the center of the electrode. In this example, the electrode is discretized by five voxels;
* Shifted grid (Figure 1B): a grid shift of \(\frac{1}{2}\) equilateral voxel size in both x- and y-directions was realized by reducing the grid size by one voxel in both x- and y-directions and centralizing the grid again.
## 2 Materials and Methods  
### X-Y Pedestal System  
X-Y pedestals are generally used for the tracking of LEO satellites. Their capability of tracking the satellite in the zenith pass area is their main advantage.  
The coordinates of a satellite are usually expressed in terms of the elevation and azimuth angles. The aim is to determine the desirable angles in joint space for directing the antenna toward the satellite. The joint angles are entitled \(\theta_{1}\) and \(\theta_{2}\). In fact, the satellite trajectory is expressed by the elevation and azimuth angles at any moment, as illustrated in Figure 1.  
As a result, if the satellite trajectory is expressed by elevation (\(El\)) and azimuth (\(Az\)) angles, the required joint angles could be extracted. For this purpose, the mapping given in (1) is utilized, which maps the elevation and azimuth angles to the joint angles \(\theta_{1}\) and \(\theta_{2}\)[2; 7; 12; 13]. In this expression, \(\theta_{1}\) and \(\theta_{2}\) are \(-Y\) and \(X\) angles, respectively.  
\[\begin{split} sin(Az)cos(El)=sin\theta_{2}\Rightarrow\theta_{2} =sin^{-1}[sin(Az)cos(El)]\\ cos(Az)cos(El)=sin\theta_{1}cos\theta_{2}\\ sin(El)=cos\theta_{1}cos\theta_{2}\\ -\frac{\pi}{2}<\theta_{1},\ \theta_{2}<+\frac{\pi}{2}\end{split} \tag{1}\]  
The Euler-Lagrange method is used to extract the dynamic equations of the pedestal. In this method, the kinetic and potential energies of the system are calculated. The X-Y pedestal is modeled by the following differential equation [2; 7]:  
\[[M(\underline{\theta})]\left[\begin{array}{c}\ddot{\theta}_{1}\\ \ddot{\theta}_{2}\end{array}\right]+\left[C\Big{(}\underline{\theta},\dot{ \underline{\theta}}\Big{)}\right]+\left[G(\underline{\theta})\right]=\left[ \begin{array}{c}\tau_{1}\\ \tau_{2}\end{array}\right] \tag{2}\]  
In (2), \(\tau_{1}\) and \(\tau_{2}\) are the input torques for \(Y\) and \(X\) axes, respectively. According to (2), it is clear that the X-Y pedestal is a Multiple-Input Multiple-Output (MIMO) system, where the interference effect exists as well. For detailed information about the elements of the matrices in (2), see Appendix A and [2; 7].  
Figure 1: Satellite El/Az angles in earth station.
## 3 Design of Experiments
### Single-Beam Test Rig with Local SSDI  
The configuration of the beam used in this experiment is shown in Figure 9 and the parameters are listed in Table 1. The beam is clamped at one end and a piezoelectric patch (referred to as PZT1) is bonded on the beam near the clamped end to connect with the SSDI shunt. Another piezoelectric patch (referred to as PZT2) is collocated on the other side as the sensor to detect displacement for the switching circuit. The poling directions of the two piezo-patches are perpendicular to the beam. An electromagnetic vibrator is set at free end of the beam to excite the beam vibration.  
Figure 8: The implementation of the SSDI circuit: (**a**) the circuit board for the switching control; we also put a resistor and a coin (1 CNY) into the photo to illustrate the size of the board; (**b**) the employed inductor in comparison with the traditional winding coin inductor; (**c**) the schematic plot of the electric board.  
When a displacement extremum is detected in the voltage signal of PZT2, a trigger signal is generated to turn on the electronic switch so that the voltage on the PZT1 is inverted. Vibration tests of the beam with open circuit are also carried out under the same excitation to compare the dynamical characteristics of the system with SSDI shunt. The material parameters of piezoelectric patches are given in Table 2.  
Owing to the smaller internal resistance (around 100 \(\Omega\)) of the new type of inductor and large inductance, the electrical quality factor of the circuit implemented is higher than 10, which produces a large voltage inversion factor (around 0.9). Other facilities used in the experiment are: a signal generator, a power amplifier, a signal acquisition and processing system, and an oscilloscope (Figure 10c).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Length** & **Width** & **Thickness** & **Elastic Modulus** & **Poisson’s** & **Density** \\
**(unit)** & **(mm)** & **(mm)** & **(mm)** & **(Gpa)** & **Ratio** & **(kg/m3)** \\ \hline Value & 200 & 50 & 2 & 70 & 0.33 & 2700 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the aluminum beam.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Parameter** & **Length** & **Width** & **Thickness** & **Elastic** & **Density** & **Force Factor** & **Intrinsic** \\
**(unit)** & **(mm)** & **(mm)** & **(mm)** & **Modulus (GPa)** & **(kg/m3)** & **(N/V)** & **Capacitance (nF)** \\ \hline Value & 43 & 35 & 0.5 & 66 & 7650 & \(1.27\times 10^{-4}\) & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the piezoelectric patch.  
Figure 9: Setup of the single-beam test rig: (**a**) the schematic plot; (**b**) the photo of the test rig, where the red part is the actuator, and the clamping is realized by two bolts.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Patient** & **Gender** & **Age** & **Site** & \begin{tabular}{c} **Joint** \\ **Involvement** \\ \end{tabular} & **Diagnosis** & \begin{tabular}{c} **Surgical** \\ **Margins** \\ \end{tabular} & \begin{tabular}{c} **Revision** \\ **Surgery** \\ **(Months)** \\ \end{tabular} & \
\begin{tabular}{c} **Implant** \\ **Removal** \\ **(Months)** \\ \end{tabular} &
\begin{tabular}{c} **Patient** \\ **Follow Up** \\ **(Months)** \\ \end{tabular} \\ \hline
1 & M & 48 & Tibia & & Myxofibrosarcoma & Marginal & Infection (1) & & 14 \\
2 & M & 76 & Pelvis & Hip & Chondorsarcoma & Wide & Infection (1) & & 2 \\
3 & M & 18 & Pelvis & Hip & Ewing Sarcoma & Wide & Infection (1) & & 76 \\
4 & F & 23 & Pelvis & Hip & Ewing Sarcoma & Wide & & & 69 \\
5 & F & 26 & Femur & & Osteosarcoma & Wide & & & 11 \\
6 & M & 22 & Femur & & Ewing Sarcoma & Wide & Infection (1) & & 33 \\
7 & F & 46 & Astragalus & Ankle & Osteosarcoma & Wide & Arthritis (19) & & 19 \\
8 & M & 56 & Pelvis & Sacro Ilica & Chondorsarcoma & Wide & Infection (1) & Yes & 39 \\
9 & F & 47 & Pelvis & Hip & Chondorsarcoma & Wide & Recurrence (39) & Yes & 44 \\
10 & M & 40 & Scapula & Shoulder & Osteosarcoma ** & Wide & & 3 \\
11 & M & 26 & Pelvis & Sacro Ilica & Ewing Sarcoma & Wide & Screw (1) & & 11 \\
12 & M & 45 & Tibia & Osteosarcoma & Wide & Non union (9) & & 18 \\
13 & M & 55 & Pelvis & Hip & Chondorsarcoma & Wide & Infection (1) & & 1 \\
14 & F & 40 & Pelvis & Hip & Osteoblastoma * & Wide & Recurrence (37) & Yes & 39 \\
15 & M & 38 & Tibia & Ankle & Chondorsarcoma & Wide & & & 16 \\
16 & M & 14 & Pelvis & Sacro Ilica & Ewing Sarcoma & Wide & & & 55 \\
17 & M & 35 & Pelvis & Hip & Chondorsarcoma & Wide & & & 20 \\
18 & M & 28 & Pelvis & Hip & Osteosarcoma & Marginal & & & 48 \\
19 & M & 45 & Pelvis & Hip & Chondorsarcoma & Wide & & & 33 \\
20 & M & 26 & Pelvis & Hip & Ewing Sarcoma & Wide & & & 66 \\
21 & M & 43 & Pelvis & Hip & Osteosarcoma & Wide & & & 8 \\
22 & M & 26 & Pelvis & Hip & Giant Cell Tumor & Wide & & & 28 \\
23 & M & 36 & Pelvis & Hip & Chondorsarcoma & Wide & Recurrence (14) & Yes & 62 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Demographics, surgical details, complications and follow up of 23 3D/PSI custom implants; * benign tumour ** low-grade malignant tumour.
## 4 Experiments and Results
### Comparison to Baseline Models  
In this section, we discuss how our method compares to DGCNN, which is the backbone of our model, to show how much our geometric-centric aware module and attention module help in classifying objects.  
In Figure 5, we can see that our model achieves a higher accuracy than DGCNN. As we can see, our model reaches a higher accuracy within a reasonable number of epochs.  
After showing the extensive evaluation figures and detailed comparison between our model and the DGCNN, we can see that our model performs well by itself, reaching low false negative and false positive rates, and can perform well under any threshold. In addition, compared to other SOTA architecture, our model displays its advantage by incorporating geometric preprocessing: it is more resistant to overfitting and can reach a higher accuracy in the long run by capturing local geometric information.
# Structural Health Monitoring Using Machine Learning and Cumulative Absolute Velocity Features
## 2 Setting of the Computer Experiments
### Data Source
#### 2.1.3 Damage States  
The maximum displacement ductility, \(\mu\), is commonly used to quantify the structural damage.  
\[\mu=\max\left[\frac{d_{si}}{d_{y}}\right], \tag{1}\]  
where \(d_{si}\) is the maximum displacement experienced by the structure during the loading cycle \(i\) and \(d_{y}\) is the yield displacement. If the structure sustains residual displacement, \(d_{r}\), in cycle \(i-1\), the displacement for cycle \(i\) is calculated as \(d_{si}=d-d_{r}\), where \(d\) is the displacement relative to the original \"undeformed\" position. The displacement ductility  
Figure 3: (**a**) Magnitude and (**b**) distance of Set-2 (ground motions from [38]).  
Figure 2: (**a**) Magnitude and (**b**) distance of Set-1 (ground motions from PEER NGA-West2).  
index is based on the peak displacement to represent the damage caused by excessive deformations while neglecting the fatigue effect of cyclic loads. For an undamaged case, \(\mu\) is smaller than 1.0 and the higher its value, the greater the expected damage. In this experiment, the damage states were divided into three categories according to FEMA P-58 [39]: \(1\leq\mu\leq 2\) as minor damage, \(2<\mu\leq 6\) as moderate damage, and \(\mu>6\) as major damage.  
Figure 4 shows the percentage of records producing each damage state for Set-1 and Set-2. For Set-1, the structure remained undamaged for 1215 (71%) records while 308 (18%), 143 (8%), and 45 (3%) records caused minor, moderate, and major damages, respectively. This was representative of a database from real instrumented structures where most of the collected data are attributed to undamaged structures and very few data from severely damaged structures. On the contrary, for Set-2, 5%, 29%, and 66% records caused minor, moderate, and major damages, respectively.