esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Final Considerations
### Limitations  
Outdated Database. Although the SECTRA 2014 Origin-Destination Survey remains the only dataset that jointly provides georeferenced trip origins and destinations, detailed waiting times and traveller socio-economic attributes for the Greater Valparaiso Metropolitan Area, the time span of more than ten years means that our findings should be regarded as a historical baseline. Socio-economic change, the COVID-19 pandemic and subsequent operational adjustments may have reshaped mobility patterns. Nevertheless, official documents released between 2020 and 2024 suggest that the physical layout of the three key modes--Limache-Puerto commuter rail, the trunk bus corridor and the historic funicular network--has undergone only minor modifications [42, 43, 44]. Accordingly, any projection of our results to the present should be made with appropriate caution, acknowledging potential shifts in demand and operation.  
Network Structure Representation. It is important to acknowledge that the present spatial analysis of aggregated waiting times does not explicitly incorporate the structural characteristics of the transit network, such as route connectivity, headways, and overlapping service corridors. The survey-based nature of the SECTRA 2014 dataset provides user-reported waiting times aggregated at the commune and macrozone level, which reflect experiential delays across all served routes but do not permit disaggregation by specific route frequency or multi-route overlap. Consequently, zones with differing numbers of overlapping routes and service headways may exhibit apparent differences in waiting times that partly stem from the aggregation process rather than inherent spatial autocorrelation. While detailed transit schedule and route topology data were not available for the study period, the overall spatial patterns remain robust given the high positive Moran's I value and the clustering results derived from user-experienced waiting times. Future research should integrate transit network attributes--such as route graphs and headway-weighted spatial weights matrices--to more precisely account for network structure and to mitigate potential aggregation bias.  
Lack of Subjective Perception. The absence of qualitative data or satisfaction metrics constrains a deeper understanding of the real impact of waiting times. Future studies could incorporate surveys or urban neuroscience methods [15] to gain insights into users' psychological responses.  
Transferability. While the results offer valuable guidance for comparable contexts, replicating the methodology with more recent data and in other urban regions would strengthen external validity.
# Dense Small Object Detection Based on an Improved YOLOv7 Model
## 3 Proposed Method
### More Focus on Small Object Detection Head  
Small objects have the disadvantage of having fewer pixels, and in scenarios where they are densely packed, a higher-resolution detection layer can help differentiate between closely spaced small objects, reducing false positives and improving detection accuracy. The P2 layer corresponds to an earlier stage in the network with higher spatial resolution. This means that the features at this level are more detailed, which is crucial for detecting small objects that might be missed at lower resolutions. YOLOv7 models use multiple detection layers (P3, P4, and P5) to handle objects of various sizes. By adding a P2 head, we enhance this multi-scale detection capability, specifically targeting very small objects.  
In the domain of small target image detection, the landscape often teems with a multitude of diminutive objects. Each layer in YOLO corresponds to a different resolution of the input image, and the P5 layer, being deeper, represents a downsampled version of the image (typically 1/32 of the original size). For small objects, this downsampling is too severe, and crucial details might be lost. By removing the P5 layer, the model can allocate more resources and attention to the higher-resolution P2, P3, and P4 layers, which are  
Figure 1: Proposed network structure. We introduced the P2 detection header and removed the P5 detection layer, and the SPD Conv was used to replace the original downsampling convolution. In addition, C3R2N replaced an ELAN_1. The network structure of C3R2N and SPD Conv are described in the following text.  
more relevant for detecting small objects. The P5 layer increases the computational load without significantly contributing to the detection of small objects. Removing this layer can make it more efficient without sacrificing performance for small object detection. These adjustments culminated in a remarkable 69% reduction in network parameters.
## 3 Results and Discussion  
### Sample Characterization  
#### 3.1.1 Phase Analysis  
Figure 1 presents a typical XRD spectrum of the synthesized MgB4O?:Zn glass. The amorphous nature of the synthesized sample is confirmed through the absence of sharp peaks and the presence of broad humps. The broad humps between 24\({}^{\circ}\) to 36\({}^{\circ}\) and 37\({}^{\circ}\) to 48\({}^{\circ}\) could be attributed to MgO and BO, respectively [26]. The two broad peaks in the XRD spectra result from short-range orders and confirm the amorphous nature of the synthesized samples [30]. The results demonstrate that MgB4O?:Zn glass could be successfully prepared using the melt-quenching method.
## 3 Experiments
### Experimental Results and Analysis
#### 3.3.2 Path Planning Efficiency (PEF)  
By setting target points with different distance ranges, the path length data for the BBCN model were recorded, and the PEF was calculated. The path planning efficiency of the BBCN model is shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline
**Obstacle Count** &
\begin{tabular}{c} **Electromagnetic** \\ **Interference Strength** \\ \end{tabular} & **GPE (cm, mean \(\pm\) std)** \\ \hline
5 & No Interference & 4.8 \(\pm\) 0.3 \\
10 & Moderate Interference & 5.6 \(\pm\) 0.4 \\
15 & High Interference & 6.2 \(\pm\) 0.5 \\ \hline \end{tabular}
\end{table}
Table 1: Positioning error performance of BBCN model.  
\begin{table}
\begin{tabular}{c c} \hline
**Model** & **GPE Under High Interference (cm, mean \(\pm\) std)** \\ \hline BBCN Model & 6.2 \(\pm\) 0.5 \\ GNSS Model & 40.2 \(\pm\) 4.1 \\ Vision-SLAM Model & 18.5 \(\pm\) 2.3 \\ GNSS-Vision & 11.1 \(\pm\) 1.2 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of GPE between the BBCN model and other models under high interference.  
The BBCN model maintains a high path planning efficiency close to 1 across different distances and complex environments, indicating that the model generates near-optimal paths. Even in environments with dense obstacles, the efficiency remains below 1.05, demonstrating high adaptability.
# Self-Sustainable Biomedical Devices Powered by RF Energy: A Review
## 3 RF Energy Harvesting Application in Medical Devices
### RF Energy Harvesting for Implantable Medical Devices
#### 3.2.4 Discussion
In the far-field WPT method, the misalignment sensitivity is lower, and the power transfer distance between the power source Tx and the Rx implant is extended to a few meters, which is more convenient for patients. Furthermore, this method can be combined with other RFEH technologies to power the IMDs [206]. However, the PTE of this method is low, compared with the near-field method, due to the loss of the human tissues. Moreover, the SAR regulation limits of this method also reduce their power density. In conclusion, the midfield power transfer method, which permits focusing on the energy hotspot and overcome the shortcomings of the near-field and far-field methods seems to be the best benchmarked option. Far-field methods are mostly considered when the mobility of patients is the priority at the expense of the efficiency and power level of the link. **Safety Regulations**: Specific conditions must be fulfilled to design a safe and efficient RFEH system for IMDs. The safety regulations of ICNIRP and IEEE involve general exposure only; the potential field produced by IMDs is described as an untested case [207]. Therefore, special regulations are necessary with a precise assessment of SAR, currents, and E-field in the human body to offer sufficient protection for patients [208]. Both electric and magnetic fields pose a risk of exposure in IMDs that operate with different frequency ranges. In near-field applications, SAR can be used to assess the electrical fields in tissue depending on eddy current, which is the main contributor [209]. When it comes to the high-frequency range, a considerable improvement of local absorption in the skin can be evaluated with standing wave effects [210]. Moreover, inaccurate tissue modeling may increase the difficulties of performing an accurate risk assessment. The complexity of the human body demands various structural and material in vitro model designs. For tissue, which is considered nonmagnetic [211], the conductivity and permittivity properties [212] should be emphasized. Different properties influence tissue absorption and electric field distribution, ultimately affecting the SAR [213; 214]. **Packaging Materials**: The packaging material for IMDs is an important factor to assure biosafety. The recommended metals for IMDs could have negative consequences once implanted. To avoid these undesirable consequences, the entire system must be encapsulated in biocompatible material [215; 216]. The encapsulation materials used were consistent with the original design of IMDs, such as NuSil and PDMS [181]. However, different encapsulations might have an impact on PTE. In terms of material considerations, metamaterials might boost the electromagnetic field while maintaining the mass augmentation under control [217].
## 3 Results and Discussion
### The Geodynamic Model Examples
#### 3.2.3 Model of Rock Failure near the Borehole
\begin{array}{l}k_{11}^{22}=\sqrt{\frac{\sigma_{22}}{\sigma_{11}}}=\sqrt{(\frac{ \Delta\rho_{1}}{\Delta\rho}(\frac{1}{\alpha_{1}\frac{\Delta\rho_{1}}{\Delta\rho} +\sigma_{0}\frac{\Delta\rho_{0}}{\Delta\rho}})+\frac{\Delta\rho_{0}}{\Delta\rho} \frac{1}{\sigma_{0}})(\frac{\Delta\rho_{1}}{\frac{1}{\sigma_{1}}\frac{\Delta \sigma_{1}}{\Delta\rho}+\frac{1}{\sigma_{0}}\frac{\Delta\rho_{0}}{\Delta\rho} \sigma_{0})};\\ k_{11}^{33}=\sqrt{\frac{\sigma_{31}}{\sigma_{11}}}=\sqrt{(\frac{\Delta\rho_{1}}{ \Delta\rho}(\sigma_{1}\frac{\Delta\rho_{1}}{\Delta\rho}+\sigma_{0}\frac{\Delta \rho_{0}}{\Delta\rho})+\frac{\Delta\rho_{0}}{\Delta\rho}\sigma_{0})(\frac{ \Delta\rho_{1}}{\Delta\rho}\frac{1}{\sigma_{1}}\frac{\Delta\sigma_{1}}{\Delta \rho}+\frac{\Delta\rho_{0}}{\Delta\rho}+\frac{\Delta\rho_{0}}{\Delta\rho} \sigma_{0})}.\end{array}  \tag{1}\]  
Thus, as a result of the destruction of the rock space near the well, the initially isotropic and homogeneous geoelectric media with electric conductivity is transformed into a uniformly anisotropic one with an electric conductivity tensor of 1.14. The macroscale anisotropy tensor of the obtained azimuthal anisotropic model characterizes the irreversible deformations of rock near the workings of the mine. As a numerical example, let  
\[ \begin{array}{l}\Delta\rho_{0}=2,\ \Delta\phi_{1}=1,\ \Delta\phi=3,\\ \Delta\rho_{0}=0.08,\ \Delta\rho_{1}=0.04,\ \Delta\rho=0.12,\\ \sigma_{0}=0.01,\ \sigma_{1}=0.1.\end{array} \]  
The numerical values of the macroscale anisotropy tensor will then be equal to  
\[ \begin{array}{l}\sigma_{11}=0.0125;\\ \sigma_{22}=0.012;\ k_{11}^{22}\cong 1;\\ \sigma_{33}=0.027;\ k_{11}^{33}=1.4.\end{array} \]  
Thus, with the destruction of the rock near the well, the macroscale anisotropy tensor of electric conductivity changes significantly. In this case, the anisotropy coefficient increases by a significant (measurable) value, namely a factor of 1.4.
## 2 Background and Motivation  
The WSNs have revolutionized the way we sense and interact with our environment. WSNs allow for data collection from different areas, such as smart cities, health monitoring, and environmental conditions by deploying a network of small-scale battery-powered sensor nodes. Regardless of this, a significant obstruction remains because these sensor nodes lack an appropriate source of energy [56]. Traditional WSNs operate on battery power, which is both expensive and bad for the environment as they need frequent replacement. The major advantage of EH technologies is that they provide a continuous and perpetual power supply for sensor nodes, harvesting renewable ambient energy from the environment (e.g., solar energy, thermal energy, kinetic energy, and RF energy). This grants WSNs a longer life and less need for battery replacement, reduces price costs, and is more environmentally friendly.  
* Solar Energy: This energy is derived from sunlight and the solar panels are used to convert it into electrical energy.
* Thermal Energy: Manufactured from temperature via thermoelectric generators that transform warmth electricity into electric power.
* Kinetic Energy: generated through vibrations or motion, and piezoelectric materials are employed to transmute in an electric power.
* RF Energy: RF harvesting modules are used to harvest energy from radio frequency signals.  
These energy sources are combined with WSNs by linking them to the sensor nodes via an Energy Management Module [57], which controls and optimizes the consumption of collected energy. This component ensures that the energy acquired is efficiently utilized for the network functionalities such as data gathering, transmission, and security. The inspiration for creating EH methods in WSNs derives from the desire to set-up self-sustaining networks that can run eternally without human participation [58]. By reducing trust on the standard batteries [59; 60], EH-WSNs give a long-term solution that can assist in a wide range of applications, from environmental monitoring to industrial automation and elegant infrastructure [5; 8; 9].
## 2 Modeling
### Effect of Different Blade Rotation Speeds  
For the jet flow field of the slurry blast device, rotation speed mainly affects the velocity of the rotating blade centrifugal jet and the distribution position of the rust removal, which is an important factor of the rust removal effect. When the impeller speeds are 600, 800, 1000, 1200, 1470 rpm and the particle diameter is 0.4 mm, the average erosion rate and particle velocity under different system pressures are shown in Figure 8.  
Figure 8: Average erosion rate and particle velocity with rotation speed at different inlet pressures.  
Figure 7: Average erosion rate and particle velocity with inlet pressure at different blade rotation speeds.  
Figure 8a shows that the erosion rate increases sharply with the increase in blade rotation speed. When pressure is 12 MPa, the erosion rate at 1470 rpm is approximately 3.6 times that at 600 rpm. The variation trend of abrasive velocity in Figure 8b is consistent with that of the erosion rate in Figure 8a. When pressure is 12 MPa, the particle velocity at 1470 rpm is approximately 3.4 times that at 600 rpm. Compared with the effect of inlet pressure, the variation of rotation speed has more effect on the variation of these two parameters. In this equipment, the rated speed of the motor is 1470 rpm. The higher the rotation speed is, the more energy is consumed. The influences of rotation speed on equipment performance and distribution position of the rust removal should be considered comprehensively.
# Cloud-Based Single-Frequency Snapshot RTK Positioning
## 3 Experiment Setup and Results
### Results and Discussions
#### 3.3.2 Signal Bandwidth Impact  
As shown in Figure 8, the bandwidth change has some impact on the fix rate as well. The green line representing 6.36 MHz of sampling rate is lower than the one for 31.8 MHz by approximately 5% for most baseline distance cases. However, this impact seems much less significant compared to influences of baseline distance. As also shown in Figure 9, the mean LRF value only slightly decreased from 7.2 to 5.8 for short baselines. This change is even less noticeable under longer baseline scenarios.
## 7 Bacterial Cellulose
### BC-Based Nanofibers  
In general, BC nanofiber dressings utilize two main strategies: (1) using BC nanofibrous structure modified by loading with a model drug or antibacterial nanoparticles, and (2) nanofibrous dressing using a combination of BC and another natural or synthetic polymers with or without further chemical or physical modifications [127; 128; 129; 130]. Both these strategies have proven to be highly successful for wound-healing applications and are among the most successful cellulose-based wound-healing applications [127; 129]. Among various techniques for the fabrication of BC nanofiber dressings, electrospinning has proven to be the most cost-effective and reproducible technique [115; 128].  
Impregnation of BC nanofiber dressings with silver nanoparticles has been undoubtedly the most successful preparation strategy over the years due to the perfect complementary combination of BC as an already successful biomaterial for wound-dressing and the proven anti-infection capability of silver nanoparticles [124; 127; 128]. The most influential and pioneering study was conducted by [PERSON] et al. towards preparing nanocomposite nanofibrous BC wound-dressings with embedded silver nanoparticles [124]. They were one of the first groups that showed that continual release of silver nanoparticles under physiological conditions was not high enough to cause the normal human cells damage but was sufficient for prolonged anti-microbial activity. This was followed by a range of studies that addressed the same or similar combinations of silver particles and BC using different fabrication techniques and employing other potential modifications [125; 131; 132; 133].
# Evaluation of the Urban Weather Generator on the City of Toulouse (France)
## 2 Materials and Methods
### Study Site and Associated Data
#### 2.1.3 Meteo-France Data  
Complementary to local station data, Meteo-France, the French meteorological agency, provided us with half-hourly air temperature data, radiative fluxes, and soil temperature measurements acquired at the Meteopole station throughout 2020 [44]. Such data are required, after some processing (averaging, calculation of new variables, etc.) as an input of UWG and these measurements allow us to carry out simulations under realistic conditions, which can therefore be compared to real conditions.
# Electromagnetic Characteristics Analysis of a Tubular Moving Magnet Linear Generator System
## 5 Conclusions  
In this paper, a novel tubular moving magnet linear generator is proposed for the application of free-piston engines. Based on the three-dimensional finite element model, a combined simulation model of the system is created in the software of MATLAB. A prototype is manufactured and tested at  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Items** & **MMLG** & **MCLG** \\ \hline Rated power output & 1.4 kW & 0.5 kw \\ Rated frequency & 50 Hz & 50 Hz \\ Rated current & 15 A & 25 A \\ Rated stroke & 36 mm & 36 mm \\ Mover mass & 2 kg & 1.2 kg \\ Coil resistance & 0.76 \(\Omega\) & 0.18 \(\Omega\) \\ Coil inductance & 12 mH & 0.7 mH \\ EMF coefficient & 30 V/(m/s) & 8.5 V/(m/s) \\ Power mass density & 160 W/kg & 120 W/kg \\ Power volume density & 480 W/L & 190 kW/L \\ Generating efficiency & 91.4\% & 91.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Prototype performance of the linear generator.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Items** & **Power** & **Percentage** \\ \hline Mechanical input & 1.53 kW & 100\% \\ Loss in the copper & 96.4 W & 6.3\% \\ Loss in the magnet & 12.2 W & 0.8\% \\ Loss in the core & 4.6 W & 0.3\% \\ Loss in the shell & 18.4 W & 1.2\% \\ Electrical output & 1.4 kW & 91.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Loss analysis of the linear generator at the rated conditions.  
the frequency of 35 Hz with the generating current of 25 A to validate the model. The electromagnetic characteristics and performances are simulated and analyzed in detail.  
The electrical power output of 1.4 kW with the efficiency of 91.4% is obtained by the designed moving magnet linear generator when it works at the rated working frequency of 50 Hz, the rated stroke of 36 mm, and the rated current amplitude of 15 A. The loss of the linear generator mainly comes from the copper loss. Compared the moving coil, the loss in the moving magnet is greatly reduced, and thus the structure is more stable for avoiding overheating for long-term operation.  
Compared with the reported moving coil linear generator with a similar efficiency, the new single-phase moving magnet linear generator designed in this paper has a more stable structure and higher power density. Compared with the reported three-phase moving magnet linear generator, it has a better controllability to enable free-piston engines working at a high-speed reciprocating motion of up to 50 Hz. It is a more suitable choice for the application of free-piston engines.  
[PERSON] completed the model analysis and finished the manuscript; [PERSON] designed the prototype and provided the guidance; [PERSON] completed the testing; [PERSON] revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (NO. 51875290, 51975297).  
The authors declare no conflict of interest.
# Electronic Beam Steering Metamaterial Antenna with Dual-Tuned Mode of Liquid Crystal Material
## 4 Conclusions  
An electronic beam-steering CRLH metamaterial antenna loaded with nematic LC material on a downlink Ku satellite communication band was presented in this paper. The dual-tuned mode of the LC is proposed through the combination of three substrates with multi-layers of metal etching and double LC layers between them. The bias voltages are loaded on the multi-metal layers, sequentially, to control the electric field on the LC layers and deflect the direction of the LC molecules. Thus, the LC material exhibits four extreme states by appropriate bias voltages, and the permittivity transforms linearly between these states. A CRLH unit cell is designed based on the dual-tuned mode of the LC and CRLH transmission line theory. Moreover, an electronically controlled beam-steering metamaterial antenna is constituted by the CRLH unit cells. The simulation results reveal that the antenna can achieve a 35\({}^{\circ}\) beam steering range, including broadside at 14.4 GHz with 6 dB stable realized gain under the dual-tuned mode. In addition, the antenna realizes fixed-frequency beam steering at any frequency within the 2 GHz band of the Ku downlink band. This paper provided a fire-new approach for the tuning mode of LC and supplied a theoretical basis for the future development of LC microwave devices.  
Writing, S.M., X.-N.L. and Z.-D.L.; methodology, S.M.; software, S.M. and J.-J.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of Liaoning Province of China (Grant No. 2022-BS-216).  
Figure 8: Magnitude of S\({}_{11}\) and S\({}_{21}\) of the electrical beam-steering CRLH metamaterial antenna under dual-tuned mode of LC.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Type** & **Freq. (GHz)** & **Max S\({}_{11}\) (dB)** & **Beam Range (\({}^{\circ}\))** & **Gain (dB)** \\ \hline
[38] & CRLH & 27 & \(-2\) & 40 & - \\ \hline
[39] & Rectangular & 9.7 & \(-7\) & 31 & 7 \\ \hline
[40] & CRLH & 26.7 & \(-8\) & 14 & - \\ \hline
[41] & Half Mode SIW & 15.8 & \(-12.5\) & 22 & 9.8 \\ \hline
[42] & Half Mode SIW & 11 & \(-16\) & 25 & 10 \\ \hline This Work & CRLH & 14.4 & \(-11\) & 35 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with other LC antennas.