esa-sceva/satcom-chunk-collection huggingface dataset:
# NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication Networks
## 4 Problem Solution
### MDQN-Based Trajectory Design and Power Control  
After fixing the subchannel assignment, problem (25a) transforms into  
\[\max_{H,I,A}R \tag{33a}\] \[s.t.h_{\min}\leq h_{u}(t)\leq h_{\max}\] (33b) \[x_{\min}\leq x_{u}(t)\leq x_{\max}\] (33c) \[y_{\min}\leq y_{u}(t)\leq y_{\max}\] (33d) \[P^{u}_{i}\geq 0,P^{u}_{i,D_{j}}\geq 0\] (33e) \[P^{u}_{i}+\sum_{D_{j}\in D_{u}}I^{u}_{i,DD_{j}}P^{u}_{i,D_{j}}\leq P\] (33f) \[S^{u}_{i,D_{j}}\geq 0\] (33g) \[r^{u}_{i}\geq r^{u,thr}_{i,DD_{j,u}}\] (33h) \[r^{u}_{i,DD_{j,u}}\geq r^{u,thr}_{i,DD_{j,u}}\] (33i) \[a^{u}_{i,DD_{j,u}}\geq 0\] (33j) \[a^{u}_{i,DD_{j,1}}+a^{u}_{i,DR_{j,2}}\leq 1 \tag{33k}\]  
Similarly, it can be seen that problem (33a) is still a non-convex problem, and traditional optimization algorithms such as exhaustive search, the branch-and-pricing method, and linear programming may be affected by dimensional catastrophe when dealing with optimization problems, leading to problems such as high computational cost and huge search space. Furthermore, intelligent algorithms such as genetic algorithms, particle swarm algorithms, and annealing simulation algorithms, despite being suitable for solving complex optimization problems, suffer from the problem of falling into local optima. Therefore, in order to be able to obtain the optimal solution of the optimization problem, deep reinforcement learning is used in this section to solve the problem. Compared with traditional reinforcement learning algorithms, the DQN is able to deal with high-dimensional and continuous-state-space situations by using a deep neural network to approximate the Q-value function. The DQN constructs an experience playback pool by continuously interacting with the dynamic communication environment, which contains information related to the states, actions, rewards, and the next state obtained by the intelligent body's interaction with the environment. Through the approximation ability of neural networks, the DQN is able to learn more complex state-action mapping relationships and gradually improve the strategy to maximize long-term rewards during the training process. In order to solve the non-linear non-convex optimization problem of trajectory design and power control, a Multi-Agent Deep Q Network algorithm is designed in this section, which is divided into two steps: (1) MDP model and (2) MDQN algorithm.
## 7 Simulation Results
### Network's Reliability and Lifetime  
Computer simulations were used to evaluate the network stability and lifetime across 5000 rounds using values from Table 1 and the base station located in the middle of the network. To check the network stability and lifetime, the following indicators of performance were evaluated: _FSD_ is the network's stability period, which is defined by the number of rounds before the first sensor node dies; _LSD_ is the network's instability period, which is defined by the number of rounds until the final sensor node dies or the period between _FSD_ and _LSD_; and _PSA_ is the number of rounds until 90% of the sensor nodes remain alive. The charts show the total number of living sensor nodes in a round, including those with energy greater than 0, and show the total number of dead nodes in a round. The simulation results are shown in Figure 15, and Table 5 shows the network's stability and lifetime performance metrics.  
According to the computer simulation and implementation, the proposed algorithm decreased the overall energy consumption by 14.1% in LEACH-KNN, 15.6% in SEP-KNN, 18% in TEEN-KNN, and 22.7% in DEC-KNN. The total distance between CHS was compared between the original and modified protocols; the total distance in the new approach will be decreased by 14.4% in LEACH-KNN, 34.81% in SEP-KNN, 19% in TEEN-KNN, and 25% in DEC-KNN. The proposed algorithm improves network connectivity by reducing link distance between cluster heads and the base station: 49.1% in LEACH-KNN, 66.3% in SEP-KNN, 73.7% in TEEN-KNN, and 65.9% in DEC-KNN. The average distance between CHS and the base station will also be reduced by 4.5% in LEACH-KNN, 2.5% in SEP-KNN,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Protocol Name**} & **Original** & **KNN-Modification** & **Improvement for** \\  & **(0)** & **(0)** & **Protocols with KNN** \\ \hline LEACH & 50.022 & 42.455 & Less 15.1\% \\ SEP & 48.534 & 41.985 & Less 13.4\% \\ TEEN & 47.677 & 39.027 & Less 18.1\% \\ DEC & 44.980 & 34.005 & Less 24.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Total energy consumption between sensors in the original and modified approach.  
Figure 14: Average energy consumption per node in the original and modified protocols.  
26.3% in TEEN-KNN, and 3.3% in DEC-KNN. Additionally, the new method significantly increased the network's lifetime and stability.  
Figure 15: Live nodes and dead nodes per round; (**a,b**) LEACH and LEACH-KNN, (**c,d**) SEP and SEP-KNN, (**e,f**) TEEN and TEEN-KNN, (**g,h**) DEC and DEC-KNN.
# A Survey on LoRaWAN Architecture, Protocol and Technologies
## 2 LoRa & LoRaWAN Technologies
### Energy  
LPWANs built on the communication range and energy efficiency, this has been investigated among other narrowband technologies. LoRaWAN Class A devices are designed to be the most energy efficient modes of the protocol. Since DL procedure is initiated by successful UL transmissions, end-nodes don't require power during sleep mode for network operations. Class A devices intended to send a few packets (i.e., 3 packets per day), LoRa transceivers are not active frequently. Despite efficient protocol design, energy consumption highly depended on the hardware implementations.  
Figure 20: GPS-free location architecture.  
Semtech SX1272/76 transceivers are the first market-ready chips that have been actively used LoRa end-node manufacturers. According to the datasheets, SX1276 has 100 mW constant RF output at +20 dBm and can have high sensitivity down to \(-148\) dBm [8]
## 2 Materials and Methods  
### Experiments  
#### 2.1.1 Instrumentation  
Experimental DCS measurements were obtained using a home-built system that employed a stabilized diode laser centered at 785 nm and coherence length of nearly 3 m (I0785 SH0100B-TH-L; Innovative Photonic Solutions, NJ, USA) as the source. The laser was coupled into one end of an SMA terminated fiber (diameter 200 \(\upmu\)m, NA 0.22) of a fiber optical probe. The sampling end of the probe consisted of a detector fiber (diameter 8.2 \(\upmu\)m) and was custom-fabricated (Gulf Photonics, Oldsmar, FL, USA) with SDS of 0.15 cm. The distal end of the detector fiber was optically coupled to a single photon counting module (ID100; ID Quantique, Geneva, Switzerland), which in turn was electronically connected to a digital correlation board (LS Instruments, Fribourg, Switzerland). The correlator was controlled by a computer and recorded DCS scans as normalized intensity autocorrelation functions, \(g_{2}(\tau)\).
## 6 Future Work
### Internet of Things  
IoT is giving rise to new challenging and exciting applications, such as smart energy management. As the number of connected devices increases exponentially, with each exchanging data for a wide variety of applications such as smart transportation, e-health, and smart homes, the requirement for efficient use of the spectrum becomes paramount. BA relaying can enhance spectral efficiency while maintaining desirable throughput and low outage. One of the key requirements for IoT applications is flexibility in transmission, which is also the attribute of BA relaying. Thus, BA relay selection can be considered as an important enabling technique for the emerging IoT age.
# Human Activity Recognition Based on Deep Learning Regardless of Sensor Orientation
## 4 Experimental Setup, Results, and Analysis
### Stability Testing  
We trained the following models and data groups separately using EarlyStopping, with the patience set to 5. Here, \"epoch\" represents the number of training rounds required to converge with the patience set to 5. Remarkable outcomes were achieved by ResNet, which facilitated the training of highly intricate networks while simultaneously maintaining superior accuracy and quicker convergence speed compared to shallower networks [49].  
Initially, the unaltered ResNet-34 architecture was chosen for the task. However, upon further observation it was determined that its effectiveness was unsatisfactory, as illustrated in Figure 9. The accuracy reached only 90.64%, with a loss of 0.3189. This discrepancy was attributed to substantial training oscillations, leading to challenging convergence that deviated from our expectations.  
In comparison, as illustrated in Figure 10, the accuracy of the optimized ResNet-34 surged to 96.08%, with a loss of 0.1379. This modified configuration facilitated smoother convergence, aligning with our anticipated results. Next, we amalgamated data from all angles and applied the Madgwick algorithm to our dataset. This refinement resulted in an even flatter training curve, which propelled the validation accuracy to an impressive 96.65% and reduced the loss to 0.1475, as shown in Figure 11. This outcome highlights that the challenge posed by device angle change can be significantly mitigated through the synergistic fusion of accelerometer and gyroscope data facilitated by the Madgwick Algorithm. The confusion matrix is shown in Figure 12.  
Figure 9: The accuracy (**left**) and loss (**right**) curve of ResNet-34.
# Solar Extreme and Far Ultraviolet Radiation Modeling for Aeronomic Calculations
## 5 Conclusions  
1. As a result of comparing the data of the last long-term (23-24 solar cycles) measurements in the TIMED and SORCE projects of the spectra of the extreme (EUV, 10-105 nm) and far (FUV, 115-242 nm) ultraviolet radiation of the Sun as a star, it was revealed that the temporal emission variations were closely related to changes in the emission intensity in the L\(\alpha\) line (\(\lambda\) = 121.6 nm). The relationship between the intensities in individual spectral lines and intervals with the intensity L\(\alpha\) is close to the quadratic for the EUV region and to the linear one for the FUV. The deviations of the observed values of fluxes from these dependences do not exceed a few percent at any levels of solar activity for 23-24 cycles.
2. The use of the regularities obtained makes it possible to develop the EUVT and FUVT spectrum models, which allow calculating the spectrum in the range of 10-105 nm 115-242 nm for any level of solar activity with an accuracy of several percent. Both models use a single input parameter, the flux of solar radiation quanta in the Lyman-alpha line. Measurements of its intensity have been carried out on satellites for several decades and can now be carried out using simple photometers.  
Figure 9: Variability of the FUV spectrum depending on the wavelength. The dotted line is for extreme daily values, the solid line is for extreme N\({}_{\rm L\alpha}\) values, averaged over 3 solar revolutions.  
3. The use of the models makes it possible to calculate the fluxes of solar ultraviolet radiation for any levels of solar activity, as well as for any past periods of time, using both the data of current measurements of the flux in the L\(\alpha\) line and the archived data. In the absence of direct measurements, the flux in this line can be reconstructed using archived data on traditional solar indices, i.e., sunspot numbers W, radio flux F10.7 at a wavelength of 10.7 cm, or data on the ionospheric E-layer critical frequencies, as it was proposed by [PERSON] [6].  
Conceptualization, A.A.N.; methodology, A.A.N.; investigation, A.A.N., T.V.K.; data curation A.A.N. and V.V.K.; software, A.A.N.; validation, T.V.K. and V.V.K.; writing--original draft preparation, A.A.N., T.V.K. and V.V.K.; writing--review and editing, A.A.N. and V.V.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Publicly available datasets were analyzed in this study. This data can be found here: [[https://lasp.colorado.edu/lisird/](https://lasp.colorado.edu/lisird/)]([https://lasp.colorado.edu/lisird/](https://lasp.colorado.edu/lisird/)) (accessed on 2 March 2021).  
The authors express their deep gratitude to the team of the Laboratory of Atmospheric and Space Physics (LASP) at the University of Colorado for providing measurements of the extreme ultraviolet radiation of the Sun.  
The authors declare no conflict of interest.
# The Status of Space Weather Infrastructure and Research in Africa
## 2 Distribution of Space Weather Infrastructure in Africa
### Space Weather Monitoring
#### 2.1.2 Magnetometers in Africa
AMBER stations in Africa are Adigrat (Ethiopia), Medea (Algeria), Yaounde (Cameroon), Tumeb (Namibia), Abuja (Nigeria), Conakry (Guinea), and Abidjan (Ivory Coast). (b) Magnetic Data Acquisition System (MAGDAS) stations in Africa [32; 33; 34; 35; 36; 37; 38]  
The principal investigator (PI) of the project which installed these magnetometers was Prof. [PERSON] (deceased) of the International Center for Space Weather Science and Education (ICSWSE), Kyushu University, Japan. ICSWSE, Kyushu University, is one of the few research institutes/ universities conducting research and education in space weather in the world. The MAGDAS network has over seventy fluxgate magnetometers distributed across the world, with 14 of them installed in Africa. The MADGAS data have been used by several researchers in Africa for space weather research, but quite a number of these magnetometers are no longer operational. The African countries and their respective universities hosting the magnetometers are listed in Table 7.  
Most of these facilities are hosted by departments of physics and a few by the electrical engineering departments in the respective universities. Ensuring the continuity of operations of the magnetometers has been a challenge since the funding stream from Japan dried up after the demise of the PI. (c) INTERMAGNET INTERMAGNET is a global network of observatories dedicated to monitoring geomagnetic field variations across the world. There are INTERMAGNET magnetometers in five countries in Africa, namely Ethiopia, Madagascar, South Africa, Algeria, and Senegal. Details of the four INTERMAGNET observatories and other magnetometers in Southern Africa can be found in [16; 39; 40; 41; 42]. At each of the four INTERMAGNET observatories in Southern Africa, there are several types of magnetometers, including Overhauser Scalar Magnetometer GSM-90, 3-axis Fluxgate Magnetometers DMI FGE, and 3-axis Fluxgate Magnetometers LEMI025.
## 3 Results and Discussion
### Further Characterization of the Selected Catalyst
#### 3.1.2 FTIR Analysis  
FTIR spectrometry supplements X-ray diffraction. Absorption bands at 985 cm\({}^{-1}\) and 3704 cm\({}^{-1}\) are consistent with XRD, indicating the presence of kaolinite in clay. In contrast, the hectorite spectrum shows in addition to the main OH stretching band near 3704 cm\({}^{-1}\) an absorption near 3617 cm\({}^{-1}\), assigned to H-O-H stretching vibrations of water molecules weakly hydrogen bonded to the Si-O surface. The band at 1627 cm\({}^{-1}\) is attributable to the flexion of the H-OH bonds of the structural water molecules, and that at 794 cm\({}^{-1}\) and 689 cm\({}^{-1}\) to the bending vibrations of the Al-Mg-OH and Al-Fe-OH groups, indicating the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pos. [\({}^{\circ}\)2 Th.]** & **Height [cts]** & **FWHM [\({}^{\circ}\)2 Th.]** & **d-spacing [Å]** & **Rel. Int. [\%]** \\ \hline  
\end{tabular}
\end{table}
Table 1: Data of XRD patterns of raw clay RC.  
Figure 2: XRD patterns of raw clay RC.  
presence of a smectite. The OH bending region of dioctahedral smectites often provides valuable information on the composition of the octahedral sheets. Two peaks at 985 cm\({}^{-1}\) (Al\({}_{2}\)OH) and 917 cm\({}^{-1}\) (Fe\({}_{2}\)OH) reflect partial substitution of octahedral Al by Mg the smectite clay. The presence of the 3617 cm\({}^{-1}\), 917 cm\({}^{-1}\) and 985 cm\({}^{-1}\) bands indicates that the smectite is dioctahedral. The band around 794 cm\({}^{-1}\) is due to the presence of illite. They confirm the presence of smectite, kaolinite and illite in the studied clay, as indicated by the DRX analysis [33]. The relevant spectrum is showed in Figure 3.
# PD Detection and Analysis Triggered by Metal Protrusion in GIS Through Various Methods
## 3 Results
### Results of Protrusion at Enclosure  
We installed a metal protrusion with the length of 12 mm on the enclosure. The pulse method did not detect the signal, but UHF and ultrasonic method both clearly detected the PD signal.  
Figures 19 and 20 show the PRPD patterns of protrusion on the enclosure detected by the ultrasonic and UHF methods. Comparing Figures 19 and 20, it can be seen that the PRPD patterns detected by the UHF and ultrasonic method are similar. At the beginning, PD only occurs at the positive half-cycle of AC voltage, as is shown in Figures 19a and 20a. Figures 19b and 20b show that PD begins to occur at the negative half-cycle of the voltage as the voltage increases. Figures 19c-e and 20c-e show that PD amplitude increases and PD phase is widened as the voltage further increases. At this point, the maximum amplitude of the positive half-cycle of the voltage is higher than that of the negative half-cycle. Finally, the maximum amplitude of the negative half-cycle of voltage is higher than that of the positive half-cycle, as is shown in Figures 19f-i and 20f-h.  
The PDIV of the protrusion on the enclosure is 149.78 kV, which is clearly higher than that of the conductor.  
Figure 21 shows the variation curve of maximum discharge amplitude under different voltages detected by the UHF and ultrasonic methods. It can be seen that the amplitude of the protrusion on the enclosure is clearly lower than that of the conductor.  
Comparing the results of protrusion at enclosure and conductor, the PDIV of enclosure is higher than that of conductor. In addition, PD of protrusion at conductor firstly occurs at the negative half-cycle of the voltage, which is contrary to the results of enclosure.  
Figure 21: Discharge amplitude vs. voltage. (**a**) Ultrasonic; (**b**) UHF.  
Figure 20: PRPD pattern for a 12 mm protrusion on the enclosure detected by the UHF method: (**a**) 150 kV; (**b**) 160 kV; (**c**) 165 kV; (**d**) 185 kV; (**e**) 205 kV; (**f**) 226 kV; (**g**) 245 kV; (**h**) 255 kV.
# JANNet: A U-Shape Network with Jump Attention for Semantic Segmentation of Road Scenes
## 4 Conclusions  
Segmentation technology of traffic road images, as an important part of autonomous driving technology, plays a significant role in new energy vehicles and new travel modes and has received extensive attention from major car companies in various countries with good prospects. Although the traditional image segmentation technology can extract the target to a certain extent, it cannot meet the requirements of autonomous driving scenarios due to the need for manual feature extraction, poor generalization ability of the model, and the inability of the clustering algorithm to label the pixel points with semantic information. In addition, with the rapid development of computer hardware and software level, artificial intelligence technology has been updated rapidly in recent years, and deep learning semantic segmentation technology has become the mainstream of image segmentation technology. In this paper, starting from the actual application scenario, combining the characteristics of continuous images obtained in the automatic interpretation process and targeting at the mining of contextual information in continuous images, the basic technical principles of  
Figure 15: Details of the segmentation results for each network on the Cityscape dataset.  
deep learning semantic segmentation, JAM attention mechanism, and ConvLSTM network structure are integrated to systematically study and design the JAUNet semantic segmentation network model. Compared with other existing methods, the proposed method in this paper performs more outstandingly on mIoU evaluation. Compared with the common UNet, this paper improves its jump connection part to make up for the lack of processing of low-dimensional features, so that the upsampling process can better figure-restore the image features and the model ground mIoU evaluation index can reach 63.7% and 73.2%, respectively. The experimental results on the CamVid dataset and the Cityscapes dataset show that this model has high average detection accuracy.  
Conceptualization, Z.F. and K.L.; Methodology, Z.F. and K.L.; Software, Z.F., K.L., J.H. and F.Y.; Validation, Z.F., K.L. and Q.Z.; Formal analysis, Z.F. and K.L.; Investigation, Z.F., K.L. and F.Y.; Resources, Z.F., K.L. and Q.Z.; Data curation, Z.F., K.L. and J.H.; Writing--original draft, Z.F. and J.H.; Writing--review & editing, Z.F., K.L. and F.Y.; Visualization, Z.F., K.L. and J.H.; Supervision, Z.F., K.L. and Q.Z.; Project administration, Z.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data and the code of this study are available from the corresponding author upon request ([EMAIL_ADDRESS]).  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Blending Degree of the Virgin and RAP Binders in RAM
Furthermore, the findings discussed herein can be used for the development of a more targeted design and production process of RAM to improve the pavement performance of RAM. Considering that different types of bitumen have an effect on the blending degree of virgin and RAP binders, other types of bitumen would be used to develop the same experimental program. Polymer-modified asphalt binders, such as SBS-modified binder, tire-rubber modified binder, aging modified binder, and so on, are more complex in comparison with the neat binder, also the blending degree is more variable than the pure asphalt binder. Since the viscosity of SBS-modified binder is higher than base bitumen, if SBS-modified binder is utilized in this experimental program, the blending degree of virgin and RAP binders will decrease, the viscosity of bitumen has a significant effect on the blending degree [40; 41; 42; 43]. Additional research will be conducted to explore the influences of different bitumen contents and mixing parameters on the blending degree of virgin and RAP binders in RAM, thereby increasing the proportion of RAP in RAM. Figure 8: Percentages of virgin bitumen at different depths of RAP in the recycled asphalt mixtures (RAM): (a) RAP specimen 1; (b) RAP specimen 2; and (c) RAP specimen 3.
# Brain Implantable End-Fire Antenna with Enhanced Gain and Bandwidth
## 3 Antenna Prototype Fabrication and Measurement
### Fabrication of Brain-Tissue-Emulating Phantom  
The radiation performance of an implantable antenna is highly affected by its surrounding environment. Hence, it is important to test the antenna inside a human tissue-mimicking phantom exhibiting similar electromagnetic material properties (i.e., \(\epsilon_{\mathrm{r}}\) and tan\(\delta\)) of the actual environment--the brain for this study. A liquid phantom is often used to test implantable antennas [25; 26]. However, such a homogeneous phantom is insufficient to represent the complex brain environment consisting of multiple layers with different material properties. We fabricated seven different semi-solid tissue-emulating layers following recipes provided in [12]. Figure 7 shows the fabricated seven layers and their stack-up. The material properties of each layer were measured by an open-ended coaxial probe [27] and compared to the known values listed in Table 1 for validation. The size of the stack-up was 10 cm \(\times\) 10 cm \(\times\) 7.2 cm.