esa-sceva/satcom-chunk-collection huggingface dataset:
# Salient Semantic Segmentation Based on RGB-D Camera for Robot Semantic Mapping
## 4 Experiments
### The Performance in the Testing Set Data
#### 4.2.4 Salient Semantic Segmentation in RGB-D People Dataset  
In this part, we used the indoor RGB-D people dataset [66; 67], an indoor life scene, to verify the practical usability of the proposed algorithm.  
Semantic Annotation  
We recognized salient objects in RGB images based on the work above, as shown in Figure 8.  
(1) Firstly, we transformed the walking person RGB-D image's RGB part into a grayscale image.  
(2) Secondly, we took the grayscale image as the input image of the trained neural network to obtain the semantic information and predicted bounding boxes.  
(3) Thirdly, we obtained the salient objects' semantic annotation information in the RGB image by projecting the semantic annotations with bounding boxes from the greyscale image to the same coordinate position in the RGB image under the same coordinate system.  
As shown in Figure 9, we put the grayscale and RGB images into the same coordinate system and ensured that the two pictures were the same size. To achieve the salient semantic segmentation of the RGB image at the pixel level, we adopted a method by setting the pixel values of the RGB image's two walking persons to any three-channel consistent values. This method completely differs from the pixel-level semantic segmentation based on neural network architecture.  
Figure 8: We project the semantic information with bounding boxes in the grayscale image into the color image under the same coordinates.  
The realization of the salient semantic segmentation is achieved by the following steps.  
(1) Ensure that the size of the grayscale image containing the salient objects with semantic information is consistent with the corresponding RGB image.  
(2) The two images are placed in the same coordinate system. The X-axis and Y-axis represent the width and height of the images, respectively. The image sizes in Figure 8 are both 480 \(\times\) 640.  
(3) The RGB three-channel value can be set into any value at the place where the RGB image's coordinates are the same as the grayscale image's gray values' coordinates of more than 200. As shown in Figure 9, we set the values of the salient objects' three channels in the RGB image to 255, 255, and 255.  
The experimental results show that the proposed algorithm depends on the imaging effect of the salient object. When the imaging of the salient gray value images is clear and without occlusion, the imaging quality, as shown in Figure 9, the average correct predicted confidence is 0.925.
## 2 Related Works  
Several studies have demonstrated that device-to-device communications can improve data rate [14], transfer rate, and spectral efficiency [15] of cellular networks. However, a major challenge is the potential increase in interference, which could have detrimental effects on network performance. To address this issue, numerous research efforts have been focused on developing effective resource allocation schemes. In their recent work, [PERSON] et al. [16] compiled various resource allocation methods from the literature aimed at minimizing interference by efficiently allocating resources. In [17], a compilation of regression techniques contributing to the computational model was presented.  
In [18], the authors propose a resource allocation scheme based on deep reinforcement learning to maximize the effective communication throughput of D2D peers in a cellular communication system. The proposed scheme involves designing the state, action, and reward functions for the learning process. Using these functions, each D2D pair selects the appropriate channel resource and transmits power iteratively through observations, resulting in an optimized resource allocation strategy.  
In [19], a theory-based selection method of graphs and a resource allocation algorithm for D2D devices in cellular communication systems are proposed. These methods aim to improve network performance by reducing the frequency range or increasing total flow.  
The algorithm selects D2D pairs based on the centrality concept of graph theory, and a modified resource allocation algorithm for full-duplex environments is used to reduce the frequency band range.  
There are also studies in the literature that have explored algorithms inspired by animal behaviors to optimize resource allocation in D2D communication systems. One such study is presented in the article by [PERSON] et al. [20], where a pigeon swarm-based resource allocation algorithm is proposed with a \"one to one\" correspondence. By fitting the problem to this optimization algorithm and adjusting the parameters, the convergence speed is accelerated, interference is reduced, and system performance is optimized.
# A Comprehensive Review of Micro UAV Charging Techniques
## 8 Potential Challenges in UAV Charging Techniques
### Route Planning  
By leveraging computer vision, edge computing and machine learning algorithms, collision-free and short trajectories can be adopted to substantially reduce energy consumption. Collaborative routing mechanisms can be used for UAV recharging scheduling. In [131], the authors discussed UAVs for data collection with wireless charging. In the proposed system, UAVs can hover over a static wireless charger which support wireless charging of UAVs whenever UAVs lack in energy. Such systems can support efficient charging, data collection and enhanced throughput. Similarly, by discretizing the UAV trajectories into spatio-temporal segments, efficient charge scheduling methods can be adopted to reduce energy consumption.
## 4 Methodology  
In this article, two types of data were preprocessed, including orthorectification, radiometric calibration, atmospheric correction, cropping, and image fusion. Then, seven spectral processing methods were comprehensively applied to OLI and WV-2 images to enhance the color differences between various lithologies. The advantage of high spatial resolution was fully utilized to establish the main lithological interpretation signs. By establishing training samples, effective supervised classification of OLI images was performed using the support vector machine algorithm, and the \"de-interference anomaly principal component thresholding technology\" was used to extract abnormal information. Finally, remote-sensing images were comprehensively evaluated and verified in the field. The image-processing operations were completed using ENVI 5.6 and ArcGIS pro3.0 software, as shown in the technical flowchart in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Satellite** & **Band** & **Range/um** & **Resolution/m** & **Swath Width/km** & **Altitude/km** & **Launch Date** \\ \hline \multirow{10}{*}{Landsat8} & B1 Coastal & 0.43–0.45 & & & & \\  & B2 Blue & 0.45–0.51 & & & & \\  & B3 Green & 0.53–0.59 & & & & \\  & B4 Red & 0.64–0.67 & 30 & & & \\  & B5 NIR & 0.85–0.88 & & & & \\  & B6 SWIR 1 & 1.57–1.65 & & 185 & 705 & February 2013 \\  & B7 SWIR 2 & 2.11–2.29 & & & & \\  & B8 Pan & 0.50–0.68 & 15 & & & \\  & B9 Cirrus & 1.36–1.38 & 30 & & & \\  & B10 TIR 1 & 10.6–11.19 & 100 & & & \\  & B11 TIR 2 & 11.5–12.51 & & & & \\ \hline \multirow{10}{*}{WorldView-2} & B1 Coastal & 400–450 & & & & \\  & B2 Blue & 450–510 & & & & \\  & B3 Green & 510–580 & & & & \\  & B4 Yellow & 585–625 & & & & \\  & B5 Red & 630–690 & 1.84 & 16.4 & 770 & October 2009 \\  & B6 Red edge & 705–745 & & & & \\  & B7 NIR 1 & 770–895 & & & & \\  & B8 NIR 2 & 860–1040 & & & & \\  & Pan & 450–1040 & 0.46 & & & \\ \hline \end{tabular} NIR = visible near infrared, SWIR = short-wave infrared, and TIR = thermal infrared.  
\end{table}
Table 1: Characteristics of Landsat8 and WorldView-2 data.
# Research on Ultra-Low-Frequency Communication Based on the Rotating Shutter Antenna
###### Abstract  
This paper proposes a rotating shutter antenna that can directly generate 2 FSK signals in the ULF band and it is expected to be used as the transmitter for magnetic induction (MI) underground communication systems. The antenna was modeled using ANSYS Maxwell and the magnetic field distribution was simulated. The results show that the interaction between the high-permeability shutter and the mutual cancellation of magnets decreased the transmitting magnetic moment of the antenna. A prototype antenna was manufactured and the time and frequency properties of the measured B\({}_{2}\) field were the same as the simulated results, while the magnitude of the measured signal was larger. The propagation characteristics of the antenna in air-soil-rock were simulated using FEKO and the results show that the signal strength was greater than 1 fT at a depth of 450 m from the antenna whose magnetic moment as 1 Am\({}^{2}\). The relationship between different magnetic components and azimuth could be used to enhance the signal strength. The formula of the B\({}_{2}\) field was derived using the measured magnitude versus distance and the path loss was also analyzed. Finally, the 2 FSK modulation property of the antenna was verified by indoor communication experiments with a code rate of 12.5 bps in the ULF band.  
ultra-low-frequency communication; mechanical antenna; layered media; rotating shutter antenna +
Footnote †: journal: Electronics
## 4 Land-Streamer vs. Conventional Data
### First-Arrival Traveltimes  
The first arrival traveltimes for both conventional and land-streamer data sets are picked and compared to one another. To compare both data sets together we subtracted the conventional picks from the land-streamer picks, then presented the difference in the histogram shown in Figure 6. The two sets of traveltime picks agree with one another, most of the picks (82%) are almost identical with a difference of less than \(\pm\)0.5 ms, however,  
Figure 4: The frequency-offset spectrum of both conventional (red) and land-streamer (blue) data sets.  
Figure 5: The frequency-wavenumber spectrum of (**a**) conventional and (**b**) land-streamer data sets.  
there are some picks (18%) with an error within about \(\pm\)4 ms (Figure 6), which is smaller than T/10 for the 22 Hz data (T = 45 ms) and T/14 for the 17 Hz data (T = 58 ms), where T is the dominant period of the source wavelet. This is much smaller than the accepted error margin of T/4. This close agreement suggests that the associated traveltime tomograms should be similar.
## 2 Measurement Methodology
### Measurement Setup  
Measurements are performed in an indoor residential environment, i.e., a living room with an area of 40 m\({}^{2}\), shown in Figure 3. We compare results of a LOS link with a distance of 4.7 m between the two antennas, to results of two NLOS links. The distance between the two antennas is 4.7 m for link NLOS1, and 3.4 m for link NLOS2. For both links, a wall obstructs the direct path, and communication is established through a reflecting path.  
The goal of the measurements is to determine the link quality of a high-throughput wireless link for residential scenarios, such as high-throughput video streaming of high-resolution video and virtual reality. In realistic scenarios, the access point will be mounted close to the ceiling to decrease the number of obstacles in a cluttered environment. Therefore, we put the TX antenna at a height of 1.9 m. As the receiver represents a client device such as a notebook or tablet, the receiver is placed at a lower height of 1 m. Both antennas are tilted so that they face each other.  
For the PL measurements, we use an omnidirectional TX antenna, and a full angular scan is performed with the directional RX antenna. The TG nodes, used for PL measurements as well as PER and throughput tests, are directed towards each other, i.e., along the direction of the obstructed path. As only reflections that are within an azimuth range of 90\({}^{\circ}\) of the obstructed path are captured, we also perform measurements rotating the TX or RX node of the TG sounder. For the throughput measurements, we also measure the effect of human body blockage by obstructing the LOS and NLOS links during the measurement by a male person with a height of 175 cm and a weight of 70 kg.
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPP  
Third Generation Partnership Project  
5G  
6G  
6 th Generation  
AAS  
American Astronautical Society  
AI  
Artificial Intelligence  
AIAA  
American Institute of Aeronautics and Astronautics  
AoI  
Area of Interest  
API  
Application Programming Interface  
APS  
Acquisition Planning System  
APSI  
Advanced Planning and Scheduling Initiative  
AR  
Attach Request  
ASPEN  
Automated Planning/Scheduling Environment  
AV  
Authentication Vectors  
AVSI  
Authentication Vectors and Subscriber Information  
BS  
Business Support Systems  
CCSDS  
Consulative Committee for Space Data Systems  
CDL  
Context Download  
CH  
Construction Heuristics  
CGR  
Contact Graph Routing  
CMS  
Constellation Management System  
CN  
Core Network  
COTS  
Commercially Off-The-Shelf  
CSP  
Constrained Satisfaction Programming  
CUL  
Context Upload  
D-SpaCPlanS  
Distributed Spacecraft Coordination Planning and Scheduling  
DADSSP  
Data Acquisition and Download Scheduling Satellite Problem  
DAI  
Distributed Artificial Intelligence  
DB  
Database  
DoD  
Depth of Discharge  
DRL  
Deep Reinforcement Learning  
DTN  
Delay-Tolerant Network  
DSN  
Deep Space Network  
eNB  
evolved Node-B  
EO  
Earth Observation  
ESA  
European Space Agency  
EUROPA  
Extensible Universal Remote Operations Planning Architecture  
FIFO  
First In First Out  
FoV  
Field of View  
GA  
Genetic Algorithm  
GAS  
Ground-Air-Space  
GEO  
Geosynchronous Equatorial Orbit  
GMAT  
General Mission Analysis Tool  
gNB  
Next Generation Node-B  
GPS  
Global Positioning System  
GS  
Ground Station
# Low-Carbon Operation Strategy of Park-Level Integrated Energy System with Firefly Algorithm
## 3 Mathematical Model of Low-Carbon Integrated Energy Systems
### Mathematical Model of Energy Storage Devices  
Energy storage devices contribute to smoothing peak demand, filling energy valleys, and enhancing overall energy utilization efficiency. The energy storage devices mentioned in this article include electrical energy storage and thermal energy storage, with their respective mathematical models outlined below.  
In an ideal scenario, the charging and discharging processes of electrical energy storage can be represented by the following equation:  
\[E_{ES}(t)=(1-\eta_{ES})E_{ES}(t-1)+(P_{ESC}\eta_{ESC}-P_{ESD}/\eta_{ ESD})\Delta t \tag{7}\]  
\[0\leq P_{ESC}\leq\alpha_{ESC}P_{ESC\ max} \tag{8}\]  
\[0\leq P_{ESD}\leq\alpha_{ESD}P_{ESD\ max} \tag{9}\]  
\[E_{ES\ min}\leq E_{ES}(t)\leq E_{ES\ max} \tag{10}\]  
In this equation, \(\alpha_{ESC}\) and \(\alpha_{ESD}\) are in Boolean algebra, which equals a parameter Boolscean operator applicable to either 0 or 1. \(E_{ES}(t)\) and \(E_{ES}(t-1)\) respectively denote the electrical energy stored by the electrochemical energy storage system at time \(t\) and \(t-1\), kW; \(\Delta t\) represents the time difference between \(t\) and \(t-1\); \(P_{ESC}\) and \(P_{ESD}\) signify the charging power and discharging power of the electrochemical energy storage system, kW; \(\eta_{ES}\), \(\eta_{ESC}\), and \(\eta_{ESD}\) represent the self-discharge rate (i.e., energy loss rate), charging efficiency, and discharging efficiency of the electrochemical energy storage system; and \(\alpha_{ESC}\) and \(\alpha_{ESD}\) are integers with a value of 0 or 1. \(\alpha_{ESC}+\alpha_{ESD}\leq 1\) ensures that the electrochemical energy storage system cannot simultaneously charge and discharge. \(\mathrm{P_{ESCmax}}\) and \(\mathrm{P_{ESDmax}}\) respectively denote the rated charging power and rated discharging power of the electrochemical energy storage system, kW and \(E_{ES\ min}\) and \(E_{ES\ max}\) represent the energy storage lower limit and upper limit of the electrochemical energy storage system, kW.
# Review of Structural Health Monitoring Techniques in Pipeline and Wind Turbine Industries
## 3 Health Monitoring in Corrosion
### Distributed OFS for Chemical Sensing
For compatibility with OFS, there are a number of pH-sensing materials, including localized surface plasmon resonance (LSPR) Au or Ag nanoparticles (NP) integrated composites (Figure 13a) [124; 125; 126], organic dyes [127; 128; 129; 130; 131; 132], fluorescent molecules [133; 134; 135; 136; 137], polymers [138; 139; 140; 141], pH-sensitive hydrogel [142; 143; 144; 145], etc. The pH value of the solution independent of the material embedded in the matrix is observed to be more integrated with the silica matrix coating [146; 124]. For more information regarding the sensitive pH products and optical fiber pH sensors (Figure 14), refer to these references [147; 148; 145]. To measure the emergence of water, ion intensity, and temperature, graphene oxide coatings and polymers have been tested, and at the same time, with no covering by measuring phase transition in all ways (Figure 13b), a multi-sensor OFS has been designed that can  
Figure 11: The figure shows the internal and external look of an evanescent field-based semiconductor Reprinted with permission from [112] Copyright 2014 Elsevier. also help in monitoring the internal corrosion within natural gas pipelines [107]. In the case of acid gas detection, salinity tracking, gas-sensitive coatings, or gas absorption sheets are also used in OFS, which are based on the changes in the refractive index using tapered optical fibers [118, 149, 150, 151]. MOFs that can absorb CO\({}_{2}\) have shown rapid, reversible, and promising results for monitoring CO\({}_{2}\). Along with the employment of pH, indicators are a must in case of CO\({}_{2}\) monitoring, as it can reduce solution pH value. Some examples of OFS chemical sensing layers for corrosivity control are mentioned in Table 2.
## 3 Result and Discussion
### Mechanical Properties  
#### 3.3.1 Consequence of Fibre Content  
The average tension, bending, and impact behaviours of the sisal/flax-based hybrid nanocomposite with 0, 5, 10, and 15% fibre content are shown in Figure 8a,b. The mechanical characteristics of hybrid composites improve with increasing fibre loading up to 10% but then decline to 15%. When compared to a pure epoxy composite, adding 5% sisal or flax to the epoxy composite increases its strength. The 5 wt.% fibre-based composites showed lower strength when compared to others. This is because compared to other fibre content, the 5 wt.% content composites have poor adhesion to the matrix. Therefore, the stress transfer rate is low in 5 wt.% fibre composites. Consequently, matrix-high regions occurred in the composites through a deprived fibre-content link [37]. The fibre reinforcement was pulled out of the matrices when overloaded in this state. It demonstrates that the sisal/flax reinforcing effect of 5% in the epoxy is inadequate to tolerate the applied stress. The material's overall performance rises as the sisal/flax content increases from 5% to 10%. At 10% fibre-reinforced composite, extreme ductile strength is 59.94 MPa, bending is 149.52 MPa, and impact is 37.0 KJ/m\({}^{2}\). This is primarily due to the formation of good  
Figure 7: Chromatogram of the samples of neem leaves extract.  
bonding between the fibres and the matrix, which occurred due to the natural-fibre pre-treatment (NaOH Treatment). It reduced the void content in the composite by presenting the additional short fibres and confirming adequate load distribution throughout the layers. Figure 9 shows the mean value of the tensile strength of various input parameters.  
When 15 wt.% fibre loading was used, the mechanical strength of the composite was reduced. The cause of this might be that the matrix volume % is insufficient to ensure excellent bonding, resulting in poor wettability between the fibres and the matrix and the formation of a fracture in the matrix. It is clear from the data that the 10 wt.% fibre-loaded composite has better mechanical characteristics than the other combinations. The most likely cause is that as fibre content increases, the applied stress is adequately communicated to the fibres via the matrix. It contributes to the ability to bear tensile solid, flexural, and impact loads. The fibre concentration of 10% is better for optimum load distribution in the composites.  
Figure 8: Average value of Mechanical Properties (a) wt.% of Sisal Fibre; (b) wt.% of Flav Fibre.  
Figure 9: Mean Values of Tensile Behaviour of Hybrid Composites with various input parameters.
# Research on Hybrid Vibration Sensor for Measuring Downhole Drilling Tool Vibrational Frequencies
## 3 Experiments
### Stability Experiments  
As shown in Figure 12, the effects of downhole high temperature, high humidity, and vibration period on HDV-TENG were explored. In the experiment of temperature and humidity changes, the output voltage and peak power of TENG (f = 10 Hz), PENG (f = 1k Hz), and EMG (f = 1k Hz) at 300 K and 30% relative humidity (RH) were used as reference values. Calibration was carried out by using the attenuation rate:  
\[Attenuation\ rate=\frac{p2-p1}{p1}\times 100\%\]  
Among them, \(p2\): corresponding output voltage and peak power under different temperatures and humidity, \(p1\): corresponding output voltage and peak power under 300 K, 30% RH; the measurement point is selected from the maximum rate of change measured for output voltage and peak power at different temperatures and humidity.  
Due to the high-temperature and humidity conditions encountered by the sensor in downhole environments, we conducted tests on the output voltage of the sensor within the temperature range of 300 K to 440 K and the humidity range of 30% RH to 90% RH. From Figure 12a,c, it can be observed that with increasing temperature and relative humidity, the output voltage of the sensor decreases. However, when the temperature is below 440 K or the relative humidity is below 90%, the attenuation rate of the output voltage is less than 10%. Frequency measurement is calculated based on the number of pulse waves per unit time. As long as there is an output voltage, it can be detected through HDV-TENG and output pulse waves. The change rate of the output voltage is less than 10%, indicating that the measurement performance of HDV-TENG is very little affected by temperature and humidity variations. At the same time, the influence of temperature and humidity changes on power generation performance was explored. From Figure 12b,d, it can be seen that EMG is most affected by temperature and humidity, followed by PENG, and TENG has the least influence, but the peak power change rates of the three are at different temperatures. All are less than 25% and are less than 20% under different humidity, which can realize the power supply for the sensor itself. In addition, the effect of the vibration cycle on the output voltage of HDV-TENG was explored. As shown in Figure 12e,f, the  
Figure 11: Schematic diagram of sensor lighting LED lamp. (**a**) Schematic diagram of the rectifier circuit; (**b**) the HDV-TENG lights up 8 LED bulbs.  
output voltage of the PENG unit remained stable under 10\({}^{6}\) vibration cycles, while the output voltages of the TENG unit and EMG unit have decreased to some extent; the reduced values are still obvious, proving that HDV-TENG has a long working life. In conclusion, HDV-TENG has a high tolerance and good stability and can be applied to complex underground environments.