esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Conclusions and Future Work  
This research contributes empirical insights to the fields of sentiment analysis and data science. It involves a comparison of various conventional categorization techniques to assess their accuracy. Notably, there is a scarcity of studies focused on sentiment analysis using Twitter data. Prior research predominantly examined tweets at the word level, disregarding word order. However, this study employs document vectors (Word2 vec) to analyze tweets at the phrase level, taking word order into account. By incorporating the RF algorithm, a robust classifier was developed, yielding an AUC value of 0.96 and an average precision score of 0.96.  
The high accuracy levels of these classifiers hold promise for investigating user satisfaction within the realm of sentiment analysis. However, the primary limitation lies in the relatively small sample size used to train the model, indicating potential for further enhancement. Strengthening the model and improving categorization accuracy can be achieved by augmenting the number of tweets utilized in the analysis.  
Conceptualization, M.P., U.M.M. and R.R.; methodology M.P., R.R., S.S.T. and S.B.; software, R.R.; validation, M.P., U.M.M., R.R., S.S.T. and S.B.; formal analysis, M.P.; investigation, R.R.; resources, U.M.M.; data curation, S.S.T. and S.B.; writing--original draft preparation, M.P.; writing--review and editing, M.P., U.M.M., R.R., S.S.T. and S.B.; visualization, M.P. and S.S.T.; supervision, R.R., U.M.M., and S.S.T.; project administration, M.P., R.R., S.S.T. and S.B.; funding acquisition, U.M.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability Statement: The data that supports the findings of this paper are available upon reasonable request from the first author. Conflicts of Interest: The authors declare no conflict of interest.
###### Abstract
To the best of the authors' knowledge, none of the previous research has examined how a human-robot interaction torque estimation method for an LLRR, usable for lower limb isotonic exercises, may be used as biofeedback to favor isotonicity, despite the BSIPs uncertainties. Therefore, this paper extends the ID-based and NDO-based methods reported by [PERSON] et al. [12]. We propose the Identified ID-based method (IID) and the Identified NDO-based method (INDO). The following statements emphasize this study's significant contributions:  
* We defined a list of 18 requirements of a human-robot torque estimation method for an LLRR, usable for isotonic. We defined the requirements with a literature review and a survey-based methodology. * This paper proposes two human-robot interaction torque estimation methods, the IID and INDO. Both methods must cope with two challenging issues: (a) BSIPs uncertainties exist in the subject model, and (b) no force or additional sensors are to be used. Both methods do not require a physiotherapist to make an exact measurement of the BSIPs of the patient's limbs, but uses approximate values computed in terms of total height and body weight. Finally, the methods would take the data from the first iterations to reduce the sensitivity to BSIPs uncertainties. * Our proposal avoids the bidirectional coupling between identifier, estimator, and controller to guarantee convergence in each stage separately. For this purpose, the robot performs a persistent excitation trajectory to identify the parameters. The optimized trajectory may be used for any given patient regardless of their BSIPs. Then, we turn off the parameter identifier in a second phase, and the robot executes the rehabilitation therapy movements, estimating the torque exerted by the subject. The organization of the paper is as follows. Section 2 describes the requirements for the torque estimation method. Sections 3 and 4 describe the model of the system and the torque interaction method reported by [PERSON] et al. [12], respectively. Section 5 presents two methods to estimate the human-robot interaction torque despite uncertainties in the BSIPs. Section 6 shows the effectiveness of the IID and INDO by performing simulations on a 3-DOF LLRR called Nukawa, see Appendix A for a detailed explanation of the LLRR. Finally, some conclusions and future work are presented in Section 7.
# Three-Line Microstrip Array for Whole-Body MRI System at 7 T
###### Abstract  
This paper proposes the use of a triple-line microstrip array for transmitting a magnetic field (\(\,\mathrm{I}\,\mathrm{B}_{1}{}^{+}\,\mathrm{I}\,\)) into the whole body for magnetic resonance applications at ultra-high field strength, such as 7 T. We explored some technologies that can potentially be applied for whole-body 7 T magnetic resonance imaging, as there is ongoing research on this topic. The triple-line microstrip transmission line (t-MTL) array consists of 32 channels. Each channel has a t-MTL, comprising a main conductor line and two adjacent coupled lines. The adjacent lines are not connected directly to the source. This configuration resulted in increased intensity and a centered \(\,\mathrm{I}\,\mathrm{B}_{1}{}^{+}\,\mathrm{I}\,\)-field. We compared the proposed structure and some reference radiofrequency (RF) transmitters, such as a patch antenna, using a magnet bore as a waveguide and a whole-body birdcage coil. We evaluated the performance of the t-MTL using cylindrical phantoms. We computed the \(\,\mathrm{I}\,\mathrm{B}_{1}{}^{+}\,\mathrm{I}\,\)-field from each RF transmitter inside a 3D human model containing more than 200 tissues. We compared their uniformity and field intensity and proposed a t-MTL array that yielded better performance. The proposed design also showed a lower specific absorption rate compared with a patch antenna.  
MRI; three-line microstrip array; RF transmitter; full-body MRI +
Footnote †: journal: Applied Sciences
## 2 Ratcheting Experiments
### Uniaxial Experiments
#### 2.2.2. Uniaxial Ratcheting Experiments  
We performed the uniaxial ratcheting experiments with a EUM - 25K20 tension - torsion fatigue testing machine (CARE Measure & Control Ltd. Co., Tianjin, China). The triangular fluctuating load was applied to the round bar specimen. The load was controlled with a load rate of 312 MPa/s. The ratcheting strain was measured by an Epsilon cryogenic temperature extensometer (Epsilon Tech. Comp., Jackson, WY 83001, USA). The volume fraction of martensite was determined by a ferrite measuring instrument, FMP30 (HELMUT FISCHER GMBH Co., Hunenberg, Germany). The loading conditions of the ratcheting experiments are shown in Table 2.  
Figure 3 shows the stress-strain response of materials under different loading conditions. The strains accumulated in the direction of the mean stress and the accumulating rates decreased continuously with the increasing cyclic number. In order to quantitatively study the law of plastic strain accumulation under cyclic loading, in this paper, ratcheting strain is defined as:  
\[\varepsilon_{\text{r}}=\frac{\varepsilon_{\text{max}}+\varepsilon_{\text{min }}}{2} \tag{1}\]  
where \(\varepsilon_{max}\) and \(\varepsilon_{min}\) are the maximum and minimum plastic engineering strain in a cycle.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Mean Stress** \\ \(\sigma_{\text{m}}\)**/MPa** \\ \end{tabular} } & \multicolumn{2}{c}{**Amplitude Stress \(\sigma_{\text{a}}\)/MPa**} \\ \cline{2-4}  & **190** & **215** & **240** & **290** \\ \hline
240 & & & \\
290 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ratcheting experiments scheme.  
Figure 3: _Cont._  
Figure 2: Uniaxial tension curve.  
The ratcheting strains for various loading conditions are shown in Figure 4. The ratcheting strain of materials accumulated rapidly in the first few cycles, and with the increase of cycles, the ratcheting deformation rate gradually showed a slow trend. As can be seen from Figure 4, when the mean stress remained unchanged, the ratcheting strain increased with the increase of amplitude stress.
# A Semantic Model in the Context of Maintenance: A Predictive Maintenance Case Study
## 3 State of the Art on Ontologies  
### Ontology-Based Knowledge Representation and Relevant Efforts for Maintenance  
Ontology engineering presents methods and principles that are used when building ontologies [28]. This is an important field of study, as ontologies have a crucial role in knowledge management: they enable knowledge sharing and knowledge processing between domain experts and users [29]. Hence, ontologies enable a common understanding of any domain that is communicated across application systems and among people [30]. There have been many endeavors to address the issue of knowledge management and representation in the maintenance domain. In this regard, [PERSON] et al. (2017) proposed a knowledge-management mechanism for system failures in automobiles in the form of an ontological model to improve maintenance processes in the automotive industry [31]. [PERSON] et al. (2017) mapped decision-making and knowledge-management processes through an ontology and highlighted the collaboration of various factors in managing the lifecycle of a machine tool [32]. [PERSON] (2020) proposed and implemented an ontology-based approach for achieving semantic interoperability across several systems in a collaborative manufacturing environment [33]. This system permits knowledge management and effective decision-making in automotive troubleshooting. The most recent work, by [PERSON] et al. (2021), focused on prognostics and health management on the shop-floor level, utilizing ontologies to effectively integrate product-related and process-related data [34]. This facilitates a joint decision-making mechanism for maintenance and production.
## 4 Performance Evaluation  
This section presents tools, metrics, and mythology used in the evaluation of GeoQoE-Vanet protocol. The VANETs simulation scenario used is an urban scenario generated by OpenStreetMap [54] and SUMO mobility traffic simulator [55]. The evaluation is made under NS-2.35 network simulator [56] and Evalvid tool [57].  
For the simulation scenario, an area of Ouargla city is extracted from Open Street Maps (OSM) as shown in Figure 6, then SUMO is used to generate the mobility of vehicles for simulation scenarios as exposed in Figure 7. Thereafter, the mobility files are created for use in NS simulator.  
EvalVid is a tool for evaluating the quality of transmitted video over a communication network [58]. The video stream used in the simulations is a YUV video sequence [59] with a resolution of \(352\times 288\) pixels in standard interface format (CIF). The main parameters are summarized in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Environment & Urban \\ Simulation area & 1000 \(\times\) 1000 m \\ Simulation time & 200 s \\ Number of nodes & 10, 20, 30, 40, and 45 \\ Max speed & 22 m/s \\ Propagation model & Two-Ray-Ground \\ MAC, PHY layer protocol & IEEE 802.11p \\ Transmission range & 250 m \\ Video frame size & 352 \(\times\) 288 pixels (CIF format) \\ Video & Akiyo (300 frames, 11 s) \\ Background traffic & CBRtraffic \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.  
Figure 6: Simulated roadmap area from Open Street Maps (OSM).  
Figure 7: Simulated zone imported in SUMO.  
* Peak signal to noise ratio: (PSNR) [41], is the most popular and widely accepted quality metric. It compares frame by frame the quality of the video received with the original one.
* Structural similarity: (SSIM) [42], is frame-to-frame video quality metric. It is based on structural information, luminance and contrast. The values of SSIM are comprised between 0 and 1, where a higher SSIM value means better video quality.  
The MSU video quality measurement tool [60] was used to measure the PSNR and the SSIM. It compares the quality of the received video with the original video. To obtain the MOS of received video, the mapping of PSNR to MOS given in Table 4 was used.  
In the simulation of each scenario, fifteen (\(15\)) runs (replications) are performed and then the mean value is used as a result. For each replication, the source vehicle, the destination vehicle and the time when streaming starts are randomly chosen.  
The \(k_{1}\) and \(k_{2}\) values have been chosen by trial and error until the authors found the suitable \(k_{1}\) and \(k_{2}\) values that produce the highest PSNR. The more appropriate disincentive values (\(k_{1}=k_{2}=1\)) that gives best PSNR results are used later in the simulations. The performance evaluation metrics used to measure the performance of the proposed protocol are PSNR, SSIM, frame loss, and end-to-end delay. The performance of GeoQoE-Vanet protocol is compared with GPSR [13] and GPSR-2P [14].
###### Abstract  
With the rapid development of industrialization and urbanization, air pollution is becoming increasingly serious. Accurate prediction of PM\({}_{2.5}\) concentration is of great significance to environmental protection and public health. Our study takes Nanning urban area, which has unique geographical, climatic and pollution source characteristics, as the object. Based on the dual-time resolution raster data of the China High-resolution and High-quality PM\({}_{2.5}\) Dataset (CHAP) from 2012 to 2023, the PM\({}_{2.5}\) concentration prediction study is carried out using SARIMA, Prophet and LightGBM models. The study systematically compares the performance of each model from the spatial and temporal dimensions using indicators such as mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R\({}^{2}\)). The results show that the LightGBM model has a strong ability to mine complex nonlinear relationships, but its stability is poor. The Prophet model has obvious advantages in dealing with seasonality and trend of time series, but it lacks adaptability to complex changes. The SARIMA model is based on time series prediction theory and performs well in some scenarios, but has limitations in dealing with non-stationary data and spatial heterogeneity. Our research provides a multi-dimensional model performance reference for subsequent PM\({}_{2.5}\) concentration predictions, helps researchers select models reasonably according to different scenarios and needs, provides new ideas for analyzing concentration change patterns, and promotes the development of related research in the field of environmental science.  
machine learning; PM\({}_{2.5}\); prediction; Nanning urban area; China
## 4 Discussion
### Velocity  
As emphasized in previous research [36; 53; 54], conducting studies within appropriate competitive and developmental contexts is essential for ensuring methodological rigor, supporting athlete progression, and safeguarding physical integrity. This study aimed to identify speed intensity zones during competition for male U14 basketball players. Five speed intervals were defined: \(<\)5.42 km/h, 5.42-10.19 km/h, 10.20-14.63 km/h, 14.64-18.59 km/h, and \(>\)18.59 km/h. Analysis revealed that 63.6% of all speed actions occurred below 5.42 km/h (n = 3,730,195), while 85.54% were below 10.19 km/h and 96.13% were below 14.63 km/h, indicating that the vast majority of actions were performed at low to moderate intensities. Only 3.28% of actions were classified as high-intensity running, and just 0.5% exceeded 18.5 km/h, with 34,755 sprint actions recorded above 20 km/h--demonstrating that U14 players are capable of reaching high-speed thresholds.  
Comparative studies have proposed different speed zones based on context [18; 49], competition level [17; 50], gender [14; 29], age [12; 50], and playing position [11]. Among 25 studies analyzing speed-based intensity zones, eight used five-zone models with mean thresholds as follows: Zone 1 (0.0-4.6 \(\pm\) 1.8 km/h), Zone 2 (4.7-9.4 \(\pm\) 3.4 km/h), Zone 3 (9.5-14.5 \(\pm\) 4.5 km/h), Zone 4 (14.6-19.7 \(\pm\) 5.6 km/h), and Zone 5 (\(>\)19.7 \(\pm\) 5.6 km/h) [22]. The present findings show that U14 players operate at the upper limits of these ranges. Notably, the sprint speed cluster for U14 players (20.05 km/h) exceeds that of ACB senior players (19.35 km/h) [11]. The sprint cluster center was 20.05 km/h, which exceeds the sprint threshold of 20 km/h and is higher than values reported for senior professional players in previous studies. This aligns with the literature suggesting that elite adult players cover less distance at moderate speeds and perform fewer high-intensity actions [1; 17; 18], likely due to more efficient technical-tactical execution. In contrast, younger players exhibit more frequent transitions and turnovers, leading to a higher incidence of fast-break scenarios.  
Although the speed profiles of U14 players fall within the general ranges reported in the literature, these findings are specific to this age group and should not be generalized across gender, age, or competitive level. Each population requires targeted investigation to inform training prescriptions and ensure appropriate workload management aligned with the demands of competition. These findings indicate that while most movement during U14 basketball competition occurs at submaximal speeds, players are capable of reaching high sprint velocities when required. The low frequency of high-speed actions may reflect the intermittent nature of the sport, tactical constraints, or developmental characteristics of this age group. Nonetheless, the ability to reach elite-level sprint speeds suggests a strong physical potential that can be further developed through targeted training interventions.
# Hazard Assessment for a Glacier Lake Outburst Flood in the Mo Chu River Basin, Bhutan
###### Abstract
The most widely used commercial 2-D flood routing tool is FLO 2D, it has already been used for GLOF simulations e.g., in the Central Caucasus [15]. Another example is BASEMENT, the freeware was developed at the ETH Zurich and proved its ability to model glacier lake outbursts in Patagonia [16] and Nepal [17]. Probably the most widely used software for 2-D GLOF routing is HEC-RAS [18], a package developed by the US Army Corps of Engineers. By applying two-dimensional, hydrodynamic modelling of inundation depth and flow velocity, we aim to simulate both timing and spatial extent of flood waves by assuming several scenarios for a case study in Bhutan. This kind of research is an important basis for risk assessment, the design of mitigation measures, spatial planning, and installation of early warning systems.
# Data Downloaded via Parachute from a NASA Super-Pressure Balloon
## 4 Results  
The DRS system comprised both hardware and software. Hardware worked reliably throughout the mission in three of the four capsules. They were power cycled every 15 min for 40 days and booted successfully every time. Data were gradually copied to every DRS as they were acquired and later (after a mean delay of 20 days at altitude plus two days exposed in Argentina) recovered with no bit errors in 219 GB of files. Curiously, one of the 5920 files accumulated 10 bit errors when being copied from the telescope's camera data store to the central data store, but none was further corrupted on either the central data store or the DRS capsules.  
The internal batteries in the DRS capsules became very cold at altitude and provided insufficient voltage to power the Iridium transceiver until they warmed up after landing. This meant the true descent trajectories were not recorded, which would have been interesting to compare to our model trajectories. However, the batteries did warm up and worked successfully, even from an exposed, high altitude location on Earth, while covered in snow. The capsules reported their location and were found.  
One of the four DRS capsules failed to respond after launch. Following the destruction of the telescope to which it remained attached, it is impossible to determine the cause. We speculate that a cable or connector might have been dislodged by the launch shock, but we also note that the DRS in question used a brand of SD card that had displayed erratic behaviour during testing. Either way, only 75% hardware reliability calls for additional testing before future use to reduce or improve single points of failure.  
The software we developed to predict the trajectory of a DRS descending through the Earth's atmosphere worked very well, with accurate predictions and estimates of uncertainty. This was essential to drop the DRS to a safe but accessible landing site. It would also work as backup information to find a DRS that failed to respond after release (although the reliability of our hardware other than during launch meant that this was not needed).  
Analysis of the astronomical data collected by SUPERBIT and the DRS capsules will be presented in forthcoming papers.  
Figure 7: Landing sites of the DRS capsules in Argentina. (**a**) General terrain and the search and rescue team from the governor’s office of Santa Cruz Province. The bright-orange parachutes of DRS1 (**b**) and DRS2 (**c**) were visible from a distance. The white foam shell and release crown were finally also visible; the foam helped to insulate and waterproof DRS2 when it landed on snow.
# Camera-Based Continuous Heart and Respiration Rate Monitoring in the ICU
## 3 Results
### Heart Rate Extraction
#### 3.1.3 RGB and NIR Combination  
In Table 3, we report the performance of the heart rate measurements using the combination strategy of RGB and NIR measurements shown in Figure 3. We chose the motion threshold to be 0.008 such that the overall agreement of the remaining segments would be 90%. The patients where NIR-based measurements were used to contribute to the coverage of the camera-based heart rate extraction have been highlighted in the table. We observe that for a number of patients, where NIR measurements were not used, the coverage was still near 100%, meaning that RGB-based measurements could cover monitoring throughout patient recording. This indicates that in those cases, the ICU room did not become dark enough for the NIR camera to become relevant. Furthermore, we note that there is a clear difference in the monitoring quality of the postoperative patients as compared to the emergency patients, where for the postoperative patients, the overall agreement was 91%, while for the emergency patients, it was only 62.6%. Overall, the agreement of all patients was 81.5%, with an MAE of 6.52 BPM and a coverage of 81.9%.  
Furthermore, in the analysis of the largest time gaps between the heart rate measurements of each patient, we divided the patients into three groups. First, for the patients where NIR contributed to the coverage, we observe that the time gap was at most 46 min. Second, for patients where NIR was not required, as the RGB face ROI brightness never became lower than 25, we observe very low time gaps of at most a few minutes. Finally, for the remaining patients where NIR could contribute to coverage but could not be used due to the reasons outlined in Section 3.1.2, the largest time gap was simply the longest interval where insufficient room lighting impeded heart rate extraction with the RGB camera.  
Figure 7: Performance of the NIR-based heart rate measurement plotted against the motion threshold.