esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, based on the target scattering characteristics and the normal mode theory in a shallow water waveguide, a mathematical model of the acoustic intensity under the coupling condition of the target and the environment was deduced. Through simulation and lake tests, the range-frequency interference patterns of the target scattering field in the shallow water waveguide were obtained, and four typical phenomena named the bending, the density, the slope, and the discontinuities were found and explained, as follows:  
* The relationship between the range and the frequency changed linearly, so the shape of the striations followed the shape of the target's trajectory. The propagation path of the sound wave in the system and the position distributions of the source and receiver made the interference striations of the bistatic configuration more complicated than that of the monostatic configuration;
* The number of the modes of the high-frequency part was larger than that of the low-frequency part, so the interference striations of the high-frequency part were denser than those of the low-frequency part;
* There was a constraint relationship between the slope of the interference striations and the range and frequency. If any element between the range and the frequency changed, other elements compensated for it;
* The interference striations were affected by the amplitude of the scattering function. The higher the frequency, the more severe the changes in the amplitude of the scattering function with the incident and reflected angles. The interference striations in the acoustic intensity appeared discontinuous due to the change in the amplitude of the scattering function.  
In conclusion, the interference striations of the target scattering field in the shallow water waveguide were closely related to the configuration of the sonar system, the target scattering function, the frequency range, the target motion trajectory, etc. An in-depth analysis of the mechanisms causing these interference phenomena is of great value to target detection and recognition, signal parameter estimation, and target tracking.  
Conceptualization, T.S. and J.F.; methodology, H.Z. and P.Z.; validation, T.S. and H.Z.; formal analysis, Y.G.; writing--original draft preparation, T.S. and H.Z.; writing--review and editing, H.Z.; funding acquisition, T.S., P.Z. and Y.G. All authors have read and agreed to the published version of the manuscript. This work was supported by the Natural Science Foundation of China (No. 11974084), Zhejiang Provincial Natural Science Foundation of China (No. LZ20F010002), and the funding from the Extension Fund from Underwater Test and Control Technology Key Laboratory (No. YS24071802).  
Not applicable.  
This article uses experimental data collected by Shanghai Jiaotong University, and we gratefully acknowledge the teachers and the students.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Deployment and Optimization of Advanced Energy Storage Systems
#### 3.3.3 Lifecycle Cost and Performance Evaluation  
Evaluating the lifecycle costs and performance of storage technologies is essential for guiding investment decisions and planning long-term system operation. Studies emphasize the need to account for capital expenditures, operation and maintenance costs, performance degradation over time, and end-of-life disposal requirements [77; 78]. Analyses indicate that although BESSs currently involve higher initial costs, technological developments are gradually improving their economic competitiveness [12; 14].  
Hydrogen-based storage systems, despite requiring considerable initial investments, are being examined for their potential in long-duration storage applications and integration with sector coupling strategies [19; 20]. PHS remains a cost-competitive option for large-scale energy storage over extended periods [16; 36]. However, incorporating lifecycle performance indicators into investment evaluations continues to be a complex task, highlighting the need for consistent and standardized assessment methodologies [22; 34].
# Implications of GNSS-Inferred Tropopause Altitude Associated with Terrestrial Gamma-ray Flashes
## 2 Data and Methodology
The combined observations from the TGF-detecting satellite and GNSS-RO are illustrated in Figure 1a. An example FERMI GBM-detected TGF event (blue square) at 0916:58.3906 UTC on 7 June 2016 in the vicinity of a thundercloud region with a 230-K NCEP/CPC merged infrared (IR) brightness temperature [41] is shown in Figure 1c, with the near-simultaneous observations from GNSS-RO at 0916 UTC (red circle) and radiosonde at 1200 UTC (green diamond). The GNSS-RO temperature profile where the tropopause is identified near the altitude of 18 km is shown in Figure 1b. The coincident temperature profile and tropopause altitude from Kunming radiosonde station (66 km west of the lightning location) are superimposed, showing a tropopause at 18 km. This example shows that the tropopause remains unchanged within 300 km and 3 h.
## 3 Antenna Simulation and Measurement  
### Simulation and Measurement Results  
It is well known that the return loss is a critical indicator for evaluating the impedance matching between an antenna or transmission line and its load. Ideally, one wants the impedance of the antenna or transmission line to be perfectly matched with the impedance of its load. This maximises the transmission efficiency of the power and reduces the energy losses. If there is an impedance mismatch, more energy will be reflected back, leading to decreased efficiency as it returns in the form of reflected waves. The current distribution diagram for the Vivaldi antenna at different frequencies is shown in Figure 15, while Figure 16 shows the overlay of the simulated and measured responses of the Vivaldi antenna. Throughout the entire X-band, the observations indicate that the return loss measured falls below \(-10\) dB. It is worth noting that minor manufacturing tolerances are inevitable. In practical measurements, passive antennas need to be terminated with a 50-ohm load, while active antennas are fed power. The observations reveal that the simulation and measured gain of the Vivaldi antenna exhibit good consistency. However, minor fluctuations at certain frequency points may be attributed to impedance matching or the effects of the GSG connectors used in the measurements. Overall, the measurement results are nearly identical to the simulated ones.  
Figure 14: (**a**) Vivaldi antenna with slots; (**b**) S-parameter simulation with and without slots.  
Figure 15: Current distribution of the antenna at different frequencies: (**a**) at a frequency of 85 GHz; (**b**) at a frequency of 95 GHz; and (**c**) at a frequency of 102 GHz.
## 2 Materials and Methods
### Methods
#### 2.2.2 Calculation of Agricultural Drought Index  
Vegetation Health Index (VHI) combines both the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI), and is used to assess drought stress [50] based on Equation (2).  
\[\text{VHI}=\alpha\text{VCI}+(1-\alpha)\text{TCI} \tag{2}\]  
where \(\alpha\) is a fixed coefficient equal to 0.5, and VCI and TCI were calculated using the following Equations. In these equations, _NDVI_ and _LST_ are Normalized Difference Vegetation Index and Land Surface Temperature, respectively, which were extracted from different bands of MODIS images based on Equations (3) and (4).  
\[\text{VCI}=\frac{NDVI-NDVI_{min}}{NDVI_{max}-NDVI_{min}}*100 \tag{3}\]  
\[\text{TCI}=\frac{LST_{max}-LST_{i}}{LST_{max}-LST_{min}}*100 \tag{4}\]  
VHI values were divided into five categories: very severe drought (10\(<\)), severe drought (10-20), moderate drought (20-30), mild drought (30-40), and no drought (\(>\)40) [50]. VHI with a resolution of 4 km and a weekly time scale was selected from the Blended Vegetation Health Product (Blended-VHP). It is widely used to supervise and identify agricultural droughs [50; 51; 52; 53]. Blended-VHP products were derived from the satellite images of the Visible Infrared Imaging Radiometer Suite (VIIRS) (since 2013) and the Advanced Very High-Resolution Radiometer (AVHRR) (1981-2012). In the current study, arithmetic mean was used to obtain annual VHI.
###### Abstract  
Satellite communication systems are increasingly facing serious environmental challenges such as malicious jamming, monitoring, and intercepting. As a current development of artificial intelligence, intelligent jammers with learning ability can effectively perceive the surrounding spectrum environment to dynamically change their jamming strategies. As a result, the current mainstream satellite communication anti-jamming technology based on wide interval high-speed frequency hopping is unable to deal with this problem effectively. In this work, we focus on anti-jamming problems in the satellite communication domain, and reformulate the power allocation problem under two kinds of confrontation scenarios as one-shot and repeated games model. Specifically, for the problem of multi-channel power allocation under a one-shot confrontation scenario, we firstly model the problem of allocating limited power resource between communication parties and a jammer on multi-channel based on a BG (Blotto Game) model. Secondly, a DO-SINR (Double Oracle-Signal to Interference plus Noise Ratio) algorithm is designed to approximate the Nash equilibrium of the game between two parties. Experiments show that the DO-SINR algorithm can effectively obtain the approximate Nash equilibrium of the game. For the problem of multi-channel power allocation under a repeated confrontation scenario, we firstly transform the problem into an online shortest path problem with a graph structure to make the problem solving process more intuitive, and then design the Exp3-U (Exp3-Uniform) algorithm which utilizes the graph structure to solve the multi-channel power allocation problem. Experiments show that our algorithm can minimize the expected regret of communication parties during online confrontation, while maintaining good operating efficiency. The two power allocation problems constructed in this paper are common problem formed in confrontation scenarios. Our research and analysis can simulate some actual confrontation scenarios of the satellite communication power allocation, which can be used to improve the adaptability of satellite communication systems in complex environments.  
anti-jamming; one-shot game; approximate Nash equilibrium; repeated games; online learning; expected regret; adaptability +
Footnote †: journal: 2022
## 3 Problem Formulation  
As mentioned above, the purpose of this study is to design a coverage precedence routing algorithm for mission-critical applications. The primary goals of a mission-critical network is to prevent the sensed data from being routed through sparsely populated areas covered by a small number of sensor nodes, and to maximize the network lifetime under full coverage. To accomplish these goals in the cluster-based WSNs, the problem here can be formulated as a root selection problem. Generally, the _sink node_ (or known as _base station_) is assumed to be deployed at any location inside or outside of the monitoring area, and a root node is chosen to collect all sensed data and then transmit it to the base station. According to the general radio transmission model, transmitting a packet through a long path consumes great energy. The idea to achieve these tasks is that the nodes in the sparsely populated areas are less chosen to be root nodes (or as packet routers). Before we introduce the proposed algorithm, the mathematical models for network and coverage are defined in the following subsections.
## 4 Performance of KF Used for Parameter Estimation in RTHS
### Parameter Study of KF
#### 4.3.1 Effect of Initial Guess \(\mathbf{x}_{0}\)  
The time delay of the control plant compensated by the proposed adaptive delay scheme was calculated and presented in Table 4. The table shows that even though the initial guess \(\mathbf{x}_{0}\) varied from \(-30\%\) to \(30\%\), the calculated time delay of the compensated system remained almost unchanged. Moreover, with the increase in the stop frequency of the swept input signal, the calculated time delay varied over a limited range. For all of the 39 simulations, almost the entire calculated time delay was 0.1 ms, of which the maximum value was 0.2 ms. This indicated that the proposed compensation method is effective and robust.  
Figure 5: Evolutions of the estimated system model parameter.  
The _RMSE_s between displacement command and measurement are shown in Figure 6. The figure shows that as the stop frequency increases, the _RMSE_ increases, and the _RMSE_ of the highest stop frequency has the greatest value. Meanwhile, the _RMSE_ decreases first and then increases as \(W_{\text{f}}\) increases. Despite this, the maximum value of _RMSE_ is less than 5.0%, indicating that the proposed method is still effective with a different initial guess \(\mathbf{x}_{0}\).  
The convergence values for the discrete model are shown in Figure 7. For comparison convenience, the true values and an error threshold of 1% are also given in the figure. The figures show that the converged values remain almost unchanged with an increase in factor \(W_{\text{f}}\). However, if focusing on one of the specifically prescribed displacements, e.g., a stop frequency of 10 Hz, one can find that the converged values increase with an increase in the factor \(W_{\text{f}}\). Furthermore, the relative error between the converged value and the true value is within 1%, indicating that although there are notable errors in the initial value, the parameters of the control plant can also be significantly improved using KF.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{\(W_{\text{f}}\)} & \multicolumn{2}{c}{**Stop Frequency of the Swept Signal**} \\  & **10 Hz** & **20 Hz** & **30 Hz** \\ \hline
0.7 & 0.2 & 0.2 & 0.2 \\
0.75 & 0.2 & 0.2 & 0.2 \\
0.8 & 0.2 & 0.2 & 0.2 \\
0.85 & 0.2 & 0.2 & 0.2 \\
0.9 & 0.2 & 0.2 & 0.2 \\
0.95 & 0.2 & 0.2 & 0.2 \\
1 & 0.2 & 0.2 & 0.1 \\
1.05 & 0.1 & 0.1 & 0.1 \\
1.1 & 0.1 & 0.1 & 0.1 \\
1.15 & 0.1 & 0.1 & 0.1 \\
1.2 & 0.1 & 0.1 & 0.1 \\
1.25 & 0.1 & 0.1 & 0.1 \\
1.3 & 0.1 & 0.1 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Calculated time-delay.  
Figure 6: RMSEs for different situations.  
Figure 7: Effects of initial guess on the convergence values of system model parameters.
# Position-Based Formation Control Scheme for Crowds Using Short Range Distance (SRD)
## 3 Proposed Algorithms
### Setup for User-Specific Environment  
#### 3.2.1 Crowds, Groups, and Agents  
In this article, a group is defined as a subset of the entire crowd. We consider a group as a set of agents who share the same purposes of movement. One of the purposes is to form a formation, and the other one is to move to a final destination together. That is, the final goal of a group is to move to a certain destination while maintaining formation.  
Mathematically, suppose that the entire crowd \(G\) has a total of \(n\) agents. We divide \(G\) into \(m\) groups. In this case, the relationships between the crowd, agents, and groups are represented in Equation (1). Through the establishment of these relationships, we can have various groups and formations.  
\[\begin{gathered} G=\{x_{1},\ x_{2},\ \dots,\ x_{n}\}\\ G_{a}\subseteq G(a=1,\ 2,\ \dots,\ m)\\ G_{a}\ \cap\ G_{b}=\varnothing\ \ where\ \forall\ a,\ b\in\{1,\ 2,\ \dots,\ m\}\\ G=\cup\ G_{a}\ (a=1,\ 2,\ \dots,\ m)\\ \end{gathered} \tag{1}\]  
where \(x_{i}(i\in 1,\ 2,\ \dots,\ n)\) is an agent and \(G\) is the complete set of agents. \(G_{a}\) is a subset of \(G\), which we use in the sense of a group. Figure 4 is a graphical representation of this.
## 3 Related Works  
There is no comprehensive review of both channel capacity and SE enhancement techniques together in 5G networks and Beyond. However, some papers explore channel capacity enhancement techniques, while some SE improvement techniques are in subtopics. To deliver ultra-fast data speed by using increased spectral efficiency in 5G networks various authors have proposed different techniques including NOMA [36; 37; 38], OFDM [39; 40], D2D communications [41; 42], mmWave-SCN [23], MIMO [43], by enhancing modulation. As in [2], the authors mentioned that integration of NOMA with mmWave technology can also be a solution by the multiplexing of NOMA but they discussed it conceptually while the algorithmic designs to realize NOMA and the implementation methods are still a myth. Similarly, the authors in [44] declared mMIMO an efficient method for improving SE but were unable to explain how it will mitigate the challenges. [PERSON] et al. [45] also discussed key enabling techniques for 5G and considered mMIMO can significantly increase the SE but the results will not fully achieve the predicted requirements of 5G-PPP and IMT-2020, especially higher data rate of 10 Gb/s along with linked densities of 100 k-1 \(M\) devices/km\({}^{2}\). They also provide the solution to this problem by using super high and extremely high-frequency bands which will obvious drawback of giant propagation loss. A brief comparative overview of the existing surveys on 5G key enabling techniques has been investigated in Table 2.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Attribute** & **5G** & **Beyond 5G** \\ \hline Energy and SE & 10\(\times\) (bps/Hz/m\({}^{2}\)/Joule) & 100\(\times\) (bps/Hz/m\({}^{2}\)/Joule) \\ Data Rate & 1 Giga bits/sond & 100 Giga bits/sond \\ End-to-end Delay & 5 (ms) & 1 (ms) \\ Processing Delay & 100 (ns) & 10 (ns) \\ Spectrum & - MmWave & - Mm Wave \\  & - Sub-6 GHz & - Sub-6 GHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 2: 5G, connecting the community.
# High-Quality Text-to-Image Generation Using High-Detail Feature-Preserving Network
###### Abstract  
Multistage text-to-image generation algorithms have shown remarkable success. However, the images produced often lack detail and suffer from feature loss. This is because these methods mainly focus on extracting features from images and text, using only conventional residual blocks for post-extraction feature processing. This results in the loss of features, greatly reducing the quality of the generated images and necessitating more resources for feature calculation, which will severely limit the use and application of optical devices such as cameras and smartphones. To address these issues, the novel High-Detail Feature-Preserving Network (HDFpNet) is proposed to effectively generate high-quality, near-realistic images from text descriptions. The initial text-to-image generation (iT2 IG) module is used to generate initial feature maps to avoid feature loss. Next, the fast excitation-and-squeeze feature extraction (FESFE) module is proposed to recursively generate high-detail and feature-preserving images with lower computational costs through three steps: channel excitation (CE), fast feature extraction (FFE), and channel squeeze (CS). Finally, the channel attention (CA) mechanism further enriches the feature details. Compared with the state of the art, experimental results obtained on the CUB-Bird and MS-COCO datasets demonstrate that the proposed HDFpNet achieves better performance and visual presentation, especially regarding high-detail images and feature preservation.  
Keywords:generative adversarial network; text-to-image generation; high detail; feature preservation +
Footnote †: journal: Applied sciences
## 3 System Model  
Reliable and stable data transmission with low energy consumption and the least temperature-rise of each sensor node is significant in WBAN. One of the primary goals of this research is to intelligently operate single-hop and multihop transmission to enhance the network efficiency in domains of energy and temperature. In our proposed model, the sink is planted in the middle of the human body, as shown in Figure 1.  
WBAN is a heterogeneous network, the deployment of nodes in/on the body is application dependent; therefore, in our scheme, we are taking 9 or 12 implanted sensor nodes. The nodes at a distance of one-hop from the sink can communicate directly with the sink while distant nodes carry out their communication using intermediate nodes (multihop). We have taken some assumptions in our proposed routing protocol listed below:  
* Every node in the network are fixed and implanted
* The sink node's responsibility is to acquired data from all the neighbouring sensor nodes. The software and hardware resources are sufficient with a steady energy supply.
* The transmission range and power are static for every sensor.
* The sink node is the final destination of each sensor node.
* Each sensor node is generated by the fixed size of the packet.
* The multihop path is used to forward the data to the destination.
* Optimum path selection is made by considering two constraints of WBAN, i.e., temperature- rise and energy consumption of a node from source to destination.  
Energy consumption and temperature-rise issues are addressed in our approach by selecting the forwarding node with the minimum temperature and maximum energy. Moreover, the node having a temperature more than the threshold value of both energy and temperature is also avoided.
## 4 Evaluation Metrics
### E2E Delay  
The time needed for a packet to travel from the source to the destination across a network is known as the E2E delay or one-way delay [57]. Its formula is given as follows:  
\[E2E=\frac{\sum A_{t}\cdot S_{t}}{\sum N_{L}}, \tag{5}\]  
where \(A_{t}\) represents the arrival time of a packet, \(S_{t}\) represents the sent time of a packet, and \(N_{L}\) represents the number of links traversed by each packet.
# Multi-Objective Decision-Making Meets Dynamic Shortest Path: Challenges and Prospects +
###### Abstract
Recently, we performed a first incursion on the so-called Multi-Objective Dynamic Shortest Path (MODSP) problem addressing the MODM bias to compose the cost of crossing edges in a DSP problem [3]. A MODSP problem is subject to unpredictable changes on the graph in real-time, and the edge costs comprise more than one objective. Since the MODSP handles DSP and MODM problems combined, an algorithm capable of tackling multiple objectives and avoiding recomputing the entire solution from scratch after an update is strategic. In this article, we delved into the multi-objective dynamic shortest path problem by introducing its formal definition grounded on the DSP and MODM literature. In particular, we devised the MODSP as a unified view of these areas and elaborate on this new problem class in terms of its challenges, as well as its impact and prospects for the area. The main contributions of this work can be enumerated as: (i) a brief review of the DSP, MODM, and MOSP problems and the gaps in their methods to solve the MODSP problem; (ii) a complete formalization of the MODSP problem class and its relationship to the DSP, MOSP, SP, and MODM problem classes through a novel taxonomy; (iii) examples of different real-world applications as MODSP problem candidates. The MODSP problem's delimitation is formally presented for the first time with the intention of turning attention to this problem class. To promote knowledge on the topic, we initially present an overview of the DSP in Section 2, MODM in Section 3, the MOSP in Section 4, preparing the reader for the formalization of the MODSP problem in Section 5. After formalizing the MODSP problem, Section 6 presents real-world application examples for the MODSP problem, followed by a discussion of the challenges and open questions in Section 7. Finally, we give the final considerations regarding this article in Section 8.