esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Types of Flexible Wearable Antennas
### Monopole Antennas
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Refs.** & **Antenna Type** & **Dielectric Constant** & **Dimeasions of Wavelength (O)** & **Frequency (GHz)** & **Wavelength (mm)** & **Gain (dB)** & **Return Loss (dB)** & **Material** & **Application** & **Features** \\ \hline
[7] & Planar antenna & 1.7 & 0.2 \(\times\) 0.2 & 2.5-20 & 12-120 & 3.17 & \(-\)27 & **Tensile substrate** & UWB & Road coverage \\ \hline
[8] & Patch Antenna & 1.6 & 0.12 \(\times\) 0.13 & 2.4 \(\times\) 0.25 & 120-125 & 1.24 & \(-\)36 & **FR4 substrate** & SIM band & Body-centric environments \\ \hline
[9] & Patch Antenna & 1.67 & 0.1 \(\times\) 0.093 & 2-4 & 75-150 & N/A & \(-\)31 & RT/Dureid & **Breat sensor** & Highly sensor for breast tissue \\ \hline
[10] & Loop Antenna & 1.3 & 0.016 \(\times\) 0.15 & 2.45 & 122.45 & 1.86 & \(-\)34 & Polymer substrate & **Breats Monitoring** & **Compet design for monitoring** \\ \hline
[11] & Dual-fund Antenna & 3 & 0.136 \(\times\) 0.146 & 2.45 and 5.8 & 51.72 & 3.74 & \(-\)39 & **Tensile substrate** & **WBAN band operation** \\ \hline
[12] & Dipole Antenna & 1.44 & 0.86 \(\times\) 0.9 & 2.45 & 122.45 & N/A & \(-\)13.6 & **Conductive failure** & **Windens** & **Large area design** \\ \hline
[13] & Monopole Antenna & 1.44 & 0.33 \(\times\) 0.33 & 1.0 \(\times\) 0.50 & 60-300 & N/A & \(-\)26 & **Tensile substrate** & **Bone health** & **Deeper tissue** \\ \hline
[14] & Patch Antenna & 3.5 & 0.08 \(\times\) 0.08 & 2.45 & 122.45 & N/A & \(-\)30 & **Facible polymer** & **Medical** & **Specific position-of-care** [1]** \\ \hline
[15] & Planar & 1.28 & 0.09 \(\times\) 0.09 & 2.45-5.8 & 51.72-122 & 1.68 & \(-\)26 and \(-\)19 & **Tensile substrate** & **Medical** & **Data transmission in healthcare** \\ \hline
[16] & Microstrip Antenna & 1.8 & 0.82 \(\times\) 0.73 & 2.45 & 122.45 & 4.79 & \(-\)34 & **Facible polymer** & **WLAN** & **Wearable WLAN systems** \\ \hline
[47] & Monopole Antenna & 1.65 & 0.7 \(\times\) 0.47 & 55.5-7 & 42.8-54.54 & 4.6 & N/A & **Facible substrate** & **X-band** & **X-band** \\ \hline
[18] & Patch Antenna & 3 & 0.216 \(\times\) 0.216 & 2.45 & 122.45 & 4.36 & N/A & **Tensile substrate** & **Kave bone monitoring** & **Modeling with minimal discomfort** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison table of wearable antennas for health monitoring systems.
# Integrated Magnetic Management of Stored Angular Momentum in Autonomous Attitude Control Systems
###### Abstract
The discussion describes the system architecture and the attitude control laws integrated with magnetic angular momentum management. Specifically, the capability of the autonomous system to maintain the internal angular momentum far from the saturation and far from the zero-crossing levels is highlighted. The performance of an example attitude control system with four reaction wheels and three magnetic torquers is presented and discussed, with the simulation results at model-in-the-loop (MIL) level. Finally, the same integration approach is applied to a spacecraft's velocity dumping controller based on reaction wheels. In this way, the integrated overall controller is capable of dissipating the initial angular rates of the spacecraft by exploiting momentum exchange actuators without reaching the saturation speed. The latter method, despite being more complex than classical \"b-dot\" control [10], allows a faster and more precise detumbling of the spacecraft after it is injected in orbit.
## 3 Calculation Method of Risk Factor Coupling Coefficient
### Calculation Formula of Risk Factor Coupling Coefficient  
Based on the above basic assumptions, the calculation formula of risk factor coupling amplification factor can be deduced. \(\beta\) represents the coupling coefficient, \(C\) is the risk event, \(\Gamma_{1}\) and \(\Gamma_{2}\) is the risk factor.  
\[\beta(\Gamma_{1},\Gamma_{2})=\frac{P(C|\Gamma_{1}\Gamma_{2})-max\big{\{}P\big{(} C|\Gamma_{1}\overline{\Gamma_{2}}\big{)},P\big{(}C|\overline{\Gamma_{1}}\, \Gamma_{2}\big{)}\big{\}}}{P(C|\Gamma_{1}\Gamma_{2})} \tag{16}\]  
Its meaning is:  
1. When the coupling coefficient is 1, it means that the coupling effect is great, so that \(\Gamma\)1 and \(\Gamma\)2. When it happens together, \(C\) almost becomes an inevitable event.  
When the coupling coefficient is 0, it represents no coupling effect, \(\Gamma\)1 and \(\Gamma\)2 co-occurrence or single occurrence has the same impact on \(C\).
## 3 Results and Discussion
### Characterization of Films Processed at Pilot Scale
#### 3.2.2 Colorimetric Analysis  
To evaluate the effect of the presence of \(\beta\)-carotene in the pilot-scale developed films, colorimetric analysis was carried out, since colour changes are also employed in developing functional packaging materials with indicator function. Significant differences in colour were observed as the result of the \(\beta\)-carotene addition in ML samples (\(p<0.05\)), as can be observed in Table 1.  
In this sense, neat ML showed high \(L^{*}\) value, with a significant decrease because of the presence of the additive. This indicated a significant darkening of these films, as was also observed for the LDPE_CAR samples. Some authors have also reported a similar effect when adding antioxidant additives to polymer matrices [18; 20; 31]. Significant differences in \(a^{*}\) and \(b^{*}\) values were also found in films containing \(\beta\)-carotene in comparison with control samples (\(p<0.05\)) resulting in a slightly amber colour. ML/LDPE_CAR presented higher \(b^{*}\) value than ML. However, in contrast to LDPE_CAR films, \(a^{*}\) value was lower. It could be explained because of different thickness between two films with \(\beta\)-carotene [20].  
Similar colour changes were reported for polypropylene stabilised with hydroxyty-rosol as antioxidant, contributing to strengthening the colour of the obtained films [28; 32].
## 1 Applying a Specific Warm-Up on Basketball Performance: The Basket-Up Approach  
[PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1,  
[PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 3,4,*C,  
[PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1,  
[PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1,  
[PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1,  
[PERSON] 1, [PERSON] 1 and [PERSON] 1  
1 Alqueria Lab, Valencia Basket Club, 46013 Valencia, Spain; [PERSON]DDRESS] (J.C.-S.); [PERSON]_ADDRESS] (I.M.-C.); [PERSON]_ADDRESS] (V.P.-P.); [EMAIL_ADDRESS] (I.P.-M.); [PERSON]DRESS] (J.G.-R.); [PERSON]ADDRESS] (R.P.-S.); [PERSON]DRESS] (E.M.-H.); [PERSON]IL_ADDRESS] (B.M.-G.); [PERSON]ADDRESS] (S.C.-C.); [EMAIL_ADDRESS] (V.S.-A.); [PERSON]L_ADDRESS] (G.T.-S.); [PERSON]DDRESS] (P.A.-A.); [PERSON]DDRESS] (J.B.-M.); [PERSON]DRESS] (J.M.-O.); [PERSON]DRESS] (A.V.-G.); [PERSON]ADDRESS] (A.G.-M.); [PERSON]RESS] (J.G.-L.); [PERSON]SS] (M.E.-R.); [PERSON]ESS] (C.-P.); [PERSON]ADDRESS] (B.R.-L.);
2 Research Group in Prevention and Health in Exercise and Sport (PHES), Department of Physical Education and Sports, University of Valencia, 46010 Valencia, Spain; [EMAIL_ADDRESS]
3 Biomechanics & Physiology in Sports Research Group (BIOCAPS), Faculty of Health Sciences, Universidad Europega de Valencia, 46010 Valencia, Spain
4 Department of Physiotherapy, University of Valencia, Calle Gasco Oliag 5, 46010 Valencia, Spain  
* Corresponding: [EMAIL_ADDRESS]  
This research aims to evaluate whether a basketball-specific warm-up (Basket-Up) provides immediate benefits in terms of the essential physical performance requirements of basketball and to compare these effects to those obtained from FIFA 11+. In total, 95 highly trained basketball athletes (49 women, 46 men), aged between 13 and 17, were randomly divided to either perform the FIFA 11+ program (\(n=48\)) or the Basket-Up program (\(n=47\)). Immediate evaluation encompassed the assessment of vertical jump (countermovement jump), agility (Lane Agility Test), and 20 m sprint. A two-way mixed analysis of variance was conducted with time (preintervention and postintervention), sex (men and women), and training group (FIFA 11+ and Basket-Up) as the within- and between-participant factors, respectively. Agility (\(p<0.001\), \(\mathrm{F}=66.759\)) and jumping (\(p<0.001\), \(\mathrm{F}=78.062\)) outcomes exhibited significant differences between pre- and postintervention values in both groups. Basket-Up agility values were significantly higher than those from FIFA 11+ (\(p=0.001\), \(\mathrm{F}=12.998\)). The implementation of a specific basketball warm-up program (Basket-Up) appears to be both safe and effective in enhancing immediate athletic performance among highly trained young basketball athletes. Moreover, the effects of this program are comparable to those obtained by a nonspecific basketball warm-up (FIFA 11+) in terms of sprint and jumping performance but demonstrate superiority in agility outcomes, favoring the Basket-Up program.  
warm-up; injury prevention; functional evaluation; basketball
## 3 Results  
A total of 196 injuries were observed from 242 cases because of injury recidivism or different injuries observed in the same football player during the season. Figure 1 depicts the mean number of injuries per football player and repetition rate in different age categories.  
It can be observed that the average number of injuries in each age category increased with age, as well as the number of injuries per player, with greater emphasis from U-14 A upward. With regard to the location of injuries, sixteen different parts of the body were associated with injuries, although five of which accounted for the highest majority of incidence (162 injuries), namely the ankle (19), foot (10), hip (22), knee (27) and thigh (74). All the other locations of injuries were associated with less than 10 incidences during the football season, specifically abdominal, arm, back, face, head, leg, shoulder, thorax, wrist, and \"others\".  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Age Categories** & **Number of Players** \\ \hline U-10 A & 14 \\ U-10 B & 15 \\ U-12 A & 25 \\ U-12 B & 14 \\ U-14 A & 24 \\ U-14 B & 24 \\ U-16 A & 25 \\ U-16 B & 24 \\ U-18 A & 20 \\ U-18 B & 22 \\ U-18 C & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Age categories and the respective number of football players.  
Considering the correlational analysis of the selected variables, a negative relationship was observed between the sporting context and the number of injuries sustained (_Rho_ = \(-0.203;\)\(p\) = 0.002), and a positive relationship between the category and the number of injuries was identified (_Rho_ = \(0.488;\)\(p\) < 0.001). In contrast, no relationship was observed between the month and the number of injuries (_Rho_ = \(-0.024;\)\(p\) = 0.707). Figure 2 shows the trend of injuries in all age categories throughout the entire football season.  
Regarding the context, 118 injuries were sustained during training (62.8%) while 70 occurred in competitive moments (37.2%), the remaining cases were associated with accidents outside football. Table 2 shows the descriptive and comparative values related to the analysed sport context (training or competition). Significant differences were obtained depending on the sporting context, with a greater number of injuries associated with training.  
Figure 1: Mean number of injuries per player and repetition rate in different age categories. The bars and x represent the average number of injuries per player in the respective football age category and the dots are the repetition rate of injuries per athlete in the age category.  
Figure 2: Trend of injury occurrence throughout the football season in all age categories.  
Concerning the type and number of injuries sustained by the youth football players during the season, it was observed that most injuries were muscular (101), followed by articular injuries (49), both combining 150 injuries in a total of 196.
## 2 Materials and Methods
### Data Acquisition
#### 2.2.2 Line Scan Thermography (LST)  
Figure 2b shows the experimental setup used for LST. The robot carries a thermographic head (Visiooimage inc. industrial line scan) holding an uncooled microbolometer camera (FLIR, 7.5-14 \(\upmu\)m, NedT < 30 mK, 640 \(\times\) 480 pixels, lens focal length = 13.1 mm) and a heater. All three porosity samples together with the 4 impacted samples were inspected at once using different scanning speeds (5, 10, 25, 50, 125, and 250 mm/s). The scanning speed has an impact on the amount of energy that is delivered to the samples and hence on the maximum depth at which a defect can be detected. On one hand, if not enough energy is provided, e.g., at very high scanning speeds, the maximum depth that can be reached will be limited. On the other hand, longer heating, e.g., at very low scanning speeds, may worsen defect resolution due to excessive heating. It was determined that the optimal scanning speed is around 25 mm/s in this specific case, although it depends mainly on the depth of the defects. At scanning speeds much slower or faster than this, defects appear with lesser contrast. The camera was positioned at 267 mm from the sample and the heater at 10 mm from the sample surface with a power of 500 W.  
Figure 2: Experimental setups: (**a**) pulsed thermography (in transmission mode); and (**b**) line scan thermography (in reflection mode).
# A Review of Ghost Imaging via Sparsity Constraints
###### Abstract  
Different from conventional imaging methods, which are based on the first-order field correlation, ghost imaging (GI) obtains the image information through high-order mutual-correlation of light fields from two paths with an object appearing in only one path. As a new optical imaging technology, GI not only provides us new capabilities beyond the conventional imaging methods, but also gives out a new viewpoint of imaging physical mechanism. It may be applied to many potential applications, such as remote sensing, snap-shot spectral imaging, thermal X-ray diffraction imaging and imaging through scattering media. In this paper, we reviewed mainly our research work of ghost imaging via sparsity constraints (GISC) and discussed the application and theory prospect of GISC concisely.  
imaging systems; ghost imaging; optimal estimation; coherence theory +
Footnote †: journal: Applied Physics Letters
## 2 Numerical Method
### Model Validation  
To verify the accuracy of the method used in this study, a verification model is built with same operational conditions and then compared with the numerical cases [12; 50] and experimental results [46] reported in the literature, as shown in Figure 3. Specifically, the domain size is 0.065 mm \(\times\) 0.065 mm \(\times\) 0.072 mm. Each cylindrical pillar has a diameter of 3.2 \(\upmu\)m and a height of 20 \(\upmu\)m. The pitch between two pillars is 9 \(\upmu\)m. Moreover, the diameter of the droplet is 43.7 \(\upmu\)m, with an initial velocity of 1.68 m s\({}^{-1}\). The surface tension and contact angle are set as 0.0683 N m\({}^{-1}\) and 110\({}^{\circ}\)[46], respectively. The environment is ambient temperature and pressure, no heat transfer is involved in the impact process, and the wall remains stationary. The results indicate that the current model aligns well with the experimental data, especially after 48 \(\upmu\)s, where the adhesion behavior at the bottom of the droplet more closely matches the experimental outcomes. During the mesh independence validation, four kinds of grid density are compared, with total grids of 735,260, 1,155,424, 1,970,183, and 3,488,424, respectively. Figure 4 shows the variation in the contact area during the impact process with different numbers of meshes. Considering the balance between computational accuracy and resource consumption, the model with 1,970,183 meshes is selected for this study.  
Figure 4: Mesh independence verification with the contact area during the droplet impact process.  
Figure 3: Validation of droplet shape evolution during the spreading and recoiling phases on micro-pillar surfaces compared to the results in the literature. (**a**) The 3D simulation results from this study; (**b**) results of [PERSON] et al. [50]; (**c**) results of [PERSON] and [PERSON] [12]; and (**d**) results of [PERSON] and [PERSON] [46].
## 6 Analysis of Orbit Determination Performance
### Analysis with Measurement Errors--Regional Network  
In this Section, we analyze the OD performance with simulated range measurements affected by errors and biases, as described in Section 4.  
Table 3 gathers the corresponding OD RMSE and SiSRE\({}_{\text{orb}}\) values for arc lengths set at 1 h, 2 h and 3 h, and different combinations of simulated noise and biases. In general, for the three intervals considered, a longer observation period provides better OD results, from an m-level on 1 h arcs to a dm-level on 3 h arcs, in absence of observation biases. When adding observation biases, SiSRE increases by 40-80% with respect to a bias-free scenario, highlighting the importance of proper calibration of the terminals performing range measurements. In contrast to the analysis without noise (Section 6.1), we note that the use of a longer fitting interval has the effect of averaging the observation noise and bias, thus reducing the OD error.  
Figure 3a shows the average error inside the estimated orbital arc for the 2 h scenario affected by random biases. It can be seen that the errors increase on the extremes of the fitting interval leading to a reduced accuracy of the extrapolated orbit outside the interval. This is demonstrated in Figure 3b, where total orbit extrapolation error reaches 40 m for a 1 h prediction.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Orbital arc** \\ **Length** \\ \end{tabular} & \begin{tabular}{c} **Radial RMSE** \\ **(cm)** \\ \end{tabular} & \begin{tabular}{c} **Along-tr. RMSE** \\ **(cm)** \\ \end{tabular} & \
\begin{tabular}{c} **Cross-tr. RMSE** \\ **(cm)** \\ \end{tabular} &
\begin{tabular}{c} **SiSRE\({}_{\text{orb}}\)** \\ **(cm)** \\ \end{tabular} \\ \hline \multicolumn{5}{c}{**(a) Three Ground Stations**} \\ \hline
1 h & 1.23 & 2.49 & 3.46 & 1.33 \\ \hline
2 h & 2.89 & 19.31 & 23.80 & 4.84 \\ \hline
3 h & 7.13 & 35.00 & 39.92 & 9.42 \\ \hline \multicolumn{5}{c}{**(b) Four Ground Stations**} \\ \hline
1 h & 0.98 & 3.27 & 4.74 & 1.21 \\ \hline
2 h & 2.11 & 16.95 & 20.52 & 3.99 \\ \hline
3 h & 5.26 & 25.17 & 28.81 & 7.11 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average OD RMSE for 24 MEO satellites linked to a network of three or four ground stations. No noise or biases are added to the observations.
## 4 Results
### System Optimisation
#### 4.2.1 Increase in Capacity  
Figure 15 shows the respective fill levels of the HST of the four models during a working cycle. As expected, charging the same amount of heat with the SSalt max, Hitec, and Carbonate models leads to a significantly lower fill level than with the Andasol 2 model, because this work used a significantly greater temperature difference, which is presumably with the help of a specific comparable heat capacity, and should be explained using the following formula:  
\[Q=m\cdot cp\cdot(T_{2}-T_{1}) \tag{8}\]  
The fill levels of the charging state and the associated increase in storage capacity compared to the Andasol 2 model can be seen in Table 1.  
Figure 14: Simulated pump utilisation of the two pressure pumps in the charging lines.  
Figure 15: Simulated fill levels of the four models during a working cycle.  
It must be taken into account that these values are theoretical values that can only be approximated in practice, because, for example, it is only possible with a great deal of effort to design a storage system that approaches the melting and decomposition temperature of the storage medium up to 20 \({}^{\circ}\)C as in the simulations. Particularly when carbonate salt is used, the assumption of an adiabatic storage system is associated with a significant error due to the high temperature level, which must be taken into account in practice. Furthermore, the collector principle must be changed regarding system optimisation for the Andasol 2 test facility, because the PT collectors do not allow temperatures above 400 \({}^{\circ}\)C due to the mandatory use of thermal oil as HTF.
# Robust DOA Estimation Using Multi-Scale Fusion Network with Attention Mask
## 5 Experiments and Discussion
### Dataset  
The training dataset is simulated using spherical microphone array impulse response (SMIR) [50]. The SNR is set in the range of [0 dB, 30 dB], and reasonably covers the entire SNR range. Similarly, the RT60 is randomly selected in the range of [0.2 s, 1 s]. Room size is set to room 1 (6 m \(\times\) 8 m \(\times\) 3 m). The position of the array and microphones is random within the room, and the following conditions are met. The center of the array is at least 1 m from the light source, at least 0.5 m from the walls, floor, and ceiling, and the center of the sound source is at least 0.5 m from the walls, floor, and ceiling. Pure speech signals were selected from the Librispeech development corpus [51] randomly for VAD processing [52]. The dataset of two sound sources is generated in the same way as single source, with the pure acoustic signal changed from one to two.  
The test dataset is divided into two parts: simulated and real. The previous part is generated in the same way as the training set. In addition, room size room 2 (7 m \(\times\) 10 m \(\times\) 3 m) was added to test the robustness of the frame in different room environments. The other part is derived from the LOCATA dataset for the IEEE-AASP Sound Source Localization and Tracking Challenges [53]. The LOCATA dataset provides an objective benchmark for state-of-the-art algorithms for sound source localization and tracking. It contains records from a range of real-world scenarios, including single and multiple sources, as well as ground-based real-world data for source and sensor location information. We selected data from Task 1 and Task 2 as a publicly available real-world dataset for testing.  
The STFT of the recorded signals is calculated considering a 512-point Hanning window with 50% overlap between the adjacent frames and a 512-point fast Fourier transform. The sampling frequency is 16 kHz and the mode strength frequency range is chosen from 500 to 3875 Hz to avoid aliasing.