esa-sceva/satcom-chunk-collection huggingface dataset:
# AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models
## 5 Challenges and Future Directions
### Challenges
#### 5.1.5 Multi-UAV Collaboration Problems  
In a multi-UAV system, if each UAV is equipped with LLMs to achieve autonomous decision-making, respecting communication and cluster collaboration among them is still difficult. Each UAV must share its real-time status, environmental data, and threat information over a fast, secure communication network. However, differences in data formats, communication protocols, and interface standards across devices [219] can easily lead to compatibility issues and data distortion. In addition, under sudden security threats,multi-UAV clusters need to respond quickly and coherently to adjust routes and mission assignments to ensure overall system stability [220]. These actions require strong autonomous decision-making skills while addressing hardware variations and communication latency to ensure safety and security.
## 4 Development of Optical Sensing Techniques for Red Meat Quality Prediction
### Hyperspectral Imaging (HSI)  
Spectroscopy plays a crucial role in determining the chemical and physical properties of food by analysing the overtone and combination vibrations present in the visible (Vis) and near-infrared (NIR) spectral regions [205]. However, imaging techniques are highly effective in determining the size, shape, texture, and colour properties of food [206].  
HSI is the process of capturing a scene in a high number of discrete, contiguous spectral bands so that the entire reflectance spectra of the region being scanned can be captured [207]. HSI is beneficial in conditions where various quality aspects must be taken into account since it combines the chemical selectivity of spectroscopy with the power of image visualisation [208]. This is a result of HSI's ability to provide a more thorough description of the constituent content and distribution in any type of heterogeneous samples [209].  
By combining spatial and spectral details together, hyperspectral imaging has proven to be a promising technology for meat quality evaluation [10]. The capturing of a large amount of detailed information across a wide range of wavelengths enables hyperspectral imaging to provide comprehensive and fine-grained data on various quality parameters of meat. Currently, HSI is applied to assess various quality characteristics of red meat, including the microbial spoilage, chemical composition, technological and sensory traits [210].
###### Abstract  
The effects of doping antimony oxides (Sb\({}_{2}\)O\({}_{3}\)/Sb\({}_{2}\)O\({}_{5}\)) on the ferroelectric/piezoelectric and energy-harvesting properties of 0.99 Pb(Zr\({}_{0.53}\)Ti\({}_{0.47}\))O\({}_{3}\)-0.01 BiYO\({}_{3}\) (PZT-BY) have been studied. The feasibility of doping Sb\({}_{2}\)O\({}_{3}\) and Sb\({}_{2}\)O\({}_{5}\) into the PZT-BY ceramics has also been compared by considering factors such as sintering condition, grain size, density, and electrical properties etc. This work discusses a detailed experimental observation using Sb\({}_{2}\)O\({}_{3}\), because Sb\({}_{2}\)O\({}_{5}\) is relatively expensive and does not follow the stoichiometric reaction mechanism when doped in PZT-BY. The Sb\({}_{2}\)O\({}_{3}\)-doped specimens were well sintered by oxygen-rich sintering and reached a maximum density of 99.1% of the theoretical value. X-ray diffraction (XRD) analysis showed a complete solid solution for all the specimens. Scanning electron microscope (SEM) observation revealed that the addition of Sb\({}_{2}\)O\({}_{3}\) inhibits grain growth, and exhibits a denser and finer microstructure. The 0.1 moles of Sb\({}_{2}\)O\({}_{3}\)-doped ceramic shows a sharp decrease in the dielectric constant (\(\varepsilon_{33}\)T = 690), while the piezoelectric charge constant (\(d_{33}\)) and electromechanical coupling factor (\(k_{\rm p}\)) maintained high values of 350 pC/N and 66.0% respectively. The relatively higher value of \(d_{33}\) and lower \(\varepsilon_{33}\)T of the 0.99 Pb(Zr\({}_{0.53}\)Ti\({}_{0.47}\))O\({}_{3}\)-0.01 Bi(Y\({}_{0.9}\)Sb\({}_{0.1}\))O\({}_{3}\) ceramic resulted in an optimum value of piezoelectric voltage constant (\(g_{33}\) = 57.4 \(\times\) 10\({}^{-3}\) Vm/N) and a high figure of merit (\(d_{33}\)\(\times\)\(g_{33}\) = 20075 \(\times\) 10\({}^{-15}\) m\({}^{2}\)/N). These values are high compared to recently reported works. Therefore, Sb\({}_{2}\)O\({}_{3}\)-doped PZT-BY ceramic could be a promising candidate material for the future study of power-harvesting devices.**  
**Keywords: calcination; piezoelectric ceramics; dielectric properties; energy-harvesting**
## 1 Introduction
This TCP-induced enhancement of harmonics has been earlier realized through the generation of 400-nm radiation in a separate channel with further mixing with 800-nm radiation in gases, as well as through direct generation of second harmonic in thin barium borate (BBO) crystals followed by focusing two co-propagating beams in gases [20; 21]. The following studies of TCP in LPPs [22; 23] have also shown the advantages of the TCP in LPP, in comparison with the SCP, using both 800 nm and 1300 nm fundamental sources and their second harmonic. The installation of the nonlinear crystal inside the vacuum chamber on the path of focused radiation allows excluding the spatial walk-off effect and the influence of the dispersion of window and focusing lens [24]. The temporal and spatial overlaps between the two beams become sufficient for the interaction of two waves in the extended plasma plume, provided that the thickness of BBO crystal is chosen properly and optimized from various points of view. In this paper, we analyze different schemes, allowing the modifications of harmonic spectra at the conditions of QPM in silver, indium, and gold multi-jet plasmas using single- and two-color femtosecond pumps at the peak intensities of \(5\times 10^{14}\) and \(1.4\times 10^{14}\) W cm\({}^{-2}\), for 800 and 1320 nm pumps, respectively. We report the role of the number of coherent zones, sizes of plasma jets, plasma formation conditions, and the characteristics of fundamental radiation on the HHG efficiency at QPM conditions in different ranges of XUV. We demonstrate the TCP-induced QPM at different regimes of laser-plasma interaction. We also present the theoretical approach describing the observed peculiarities of HHG spectra modulation based on the model described in Ref. [18], and compare our calculations with the experimental data.
# Optimization of Multi-Mission CubeSat Constellations with a Multi-Objective Genetic Algorithm
## 2 Mission Design
### Target Areas  
Two target areas are defined to evaluate mission success for the global and regional mission objectives. The study uses a 6\({}^{\circ}\) grid in latitude and longitude for global coverage. This results in 1148 target points evenly distributed throughout the world, forming the target area shown in Figure 1. The global coverage analysis is calculated using these grid points.  
The East African region is selected as the regional disaster monitoring target area, as it is often affected by natural disasters related to climate change [16]. Because the region has limited disaster management and monitoring systems, it will benefit from the development of such systems to mitigate the impact of natural disasters. This study proposes disaster monitoring centers to report the data through a CubeSat constellation link. It will increase the speed of data transmission from rural and remote areas to a central disaster management facility, resulting in a faster response time to act on the disaster. Figure 2 shows the distribution of 60 data collection centers across the regional target area, composed of 11 countries. The data transmitted from these centers will contain climate and emergency reports. Each data transmission center is assumed to have an elevation angle of 30\({}^{\circ}\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Subsystem** & **Description** \\ \hline \multirow{2}{*}{Structure} & Mass: 24 kg \\  & Size: 226 mm \(\times\) 226 mm \(\times\) 340 mm \\ \hline ADCS & High performance up to 0.01\({}^{\circ}\) pointing accuracy \\ \hline Power & 90 Wh \\ \hline \multirow{2}{*}{Communication} & S-Band: 1 Mbps \\  & X-Band: 150 Mbps \\ \hline \multirow{2}{*}{Payload} & EOC: Conic sensor FOV angle of 30\({}^{\circ}\) \\  & UHF receiver: COTS dipole antenna at 436 MHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: High-level 12U CubeSat system specifications.  
Figure 1: Distribution of global target area with 6\({}^{\circ}\) grid in latitude and longitude.
## 2 Materials and Methods
### CMA-ES Implementation for HPO
With \(10^{-3}\) being the default value used in the Adam optimizer [62], a range around this value was used since too large a number might cause the network to oscillate in the search space, while too low a number might take too long an execution time. 6. Number of training images ([10; 30]): This refers to the number of annotated image pairs used to train the network. The maximum number of training images was set to 30 as it includes approximately two respiratory cycles, which were considered sufficient for the network to learn any respiration-induced tumor motion. Since using a smaller number of training images reduces the amount of labor for manual contouring, 10 was set as the minimum to test whether satisfactory performance can be achieved. The overall HPO and training process is shown in Figure 1, which was executed for each patient case for patient-specific HPO. Starting with the first HPO iteration, 10 hyperparameter sets (solutions) were sampled using CMA-ES. The size of solution population for CMA-ES was maintained at 10 for faster execution time for all patient cases. Each solution was used to construct a modified U-Net, which was subsequently trained. The number of epochs for the training process of each solution was determined by an early stopping method. This method was implemented to prevent our networks from overfitting to the training images, rather than generalizing to all imaging data (training, validation, and testing images). The early stopping point (optimal number of epochs) was found for each solution by monitoring the exponential moving average (EMA) of validation accuracy, defined as follows:  
\[EMA_{i}=\left\{ \begin{array}{ll}A_{i},&i=1\\ \alpha A_{i}+(1-\alpha)*EMA_{i-1},&i>1\end{array} \right. \tag{1}\]  
where \(A_{i}\) is the validation accuracy (measured by DC on validation images) at epoch \(i\), and \(\alpha\) is the smoothing factor between 0 and 1. When the current EMA was less than the previous EMA for 12 consecutive epochs, which is an empirically determined number indicating no further improvement, training was stopped, and the weights and biases at the start of this fall were saved. A demonstrative sample result of the training accuracy and EMA of validation accuracy over epoch number for patient #9 (P9) is shown in Figure 10 in Appendix A. HPO was terminated after 10 iterations for the entire solution population, which resulted in a validation accuracy of \(\sim\)0.9 for all patient cases.
## 1 Introduction
### Related Works  
To find feasible collision-free paths, the path planning strategy incorporates environmental data, vehicle location, and driving behavior. According to intelligence or flexibility, there are two categories of path planning for autonomous vehicles: rule-based and search-based.  
Rule-based path planning strategies are widely used in autonomous driving systems [4] and the planning results are strictly constrained by the planning process, i.e., if the inputs are unchanged, the output path remains unchanged [5,6]. The driving path can be obtained using the curve-fitting-based planning strategies through fitting functions. It is a fundamental rule-based planning strategy. The common path fitting techniques are B-spline curves, quintic polynomials, and cubic polynomials [7]. Sampling-based path planning strategies are more flexible and adaptable than fitting-based strategies. The expectation maximum (EM) planner [8] and Lattice planner [9] are applied to the vehicle and have been shown to be efficient. The path planning problem can also be transformed into minimum reachable set problems in node matrices [10], enabling global planning approaches such as the Dijkstra algorithm. The outcomes of the rule-based path planning strategies are strictly constrained, and quantitatively evaluated, and controllable. However, this pre-defined planning process is not flexible or intelligent enough, and might not be appropriate for narrow scenarios or scenarios with multiple obstacles.  
Search-based path planning strategies are novel for autonomous driving systems that are capable of adapting to changing scenarios. The outcomes of the planning were more flexible and adaptable. Search-based strategies can be divided into numerical optimization-based strategies and graph search-based strategies. (1) Numerical solution-based strategies are a class of optimization algorithms. Artificial potential field strategies [11], quadratic programming (QP) strategies, model predictive control (MPC) strategies [12], and elastic band strategies are typical numerical optimization-based strategies. These strategies combine the discrete environment model, multiple constraints, and output reference paths that satisfy different preferences. However, the calibration of the discrete environment model that incorporates many constraints is challenging, and the optimization procedure requires a significant amount of computer work. (2) Path planning strategies based on graph searching algorithms are known as \"graph search-based strategies\", which are improved over the motion planning approaches of the robots and have strong scenario adaptability and flexibility. The A-Star (A*) [13], rapidly exploring random tree (RRT) [14] are typical graph search-based strategies that use a random searching process to produce flexible, adaptable, and variable planning outcomes. However, it is difficult to guarantee that the suggested courses will be easy to navigate and adhere to the continuity requirements.
# Simulating Ecological Functions of Vegetation Using a Dynamic Vegetation Model
## 3 Results
### Spatiotemporal Variations of Vegetation Ecological Functions  
Compared with 2001-2020, the CS is a downward trend under the SSP119 scenario and on the rise under the SSP585 scenario during 2021-2100 (Figure 4a), while the SC and WC during 2021-2100 fluctuate considerably, with an overall upward trend 2021-2100 (Figure 4b,c).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Vegetation** & \multicolumn{4}{c}{**SSP119 Scenario**} & \multicolumn{4}{c}{**SSP585 Scenario**} \\ \cline{2-9}
**Type** & **Forest** & **Shrub** & **Grass** & **Bare Land** & **Forest** & **Shrub** & **Grass** & **Bare Land** \\ \hline Forest & 92 & - & 8 & - & \textgreater{}99 & - & \textless{}1 & - \\ Shrub & 2 & 92 & 6 & - & 3 & 97 & \textless{}1 & - \\ Grass & 1 & - & 99 & \textless{}1 & 3 & \textless{}1 & 97 & - \\ Bare Land & - & - & 76 & 24 & \textless{}1 & - & \textgreater{}99 & \textless{}1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Proportion (%) of vegetation type conversion area in the total area of the Loess Plateau from 2001–2020 to 2081–2100.  
During 2081-2100, the CS decreased from the southeastern region to the northwestern region, with the average CS being 8.78-14.20 tC-ha\({}^{-1}\) (Figure S2(a1,a2), Table S3). Compared with 2001-2020, the average CS during the 2081-2100 period was projected to decrease by 0.32 tC-ha\({}^{-1}\) under the SSP119 scenario and increase 5.14 tC-ha\({}^{-1}\) under the SSP585 scenario. The regions predicted to experience a decrease were mainly distributed in the central and southeastern regions of the LP (Figure 5(a2,a3), Table 3). During 2081-2100, the SC decreased from the southeastern region to the northwestern region, with the average SC being 251.68-297.70 t-ha\({}^{-1}\) (Figure S2(b1,b2), Table S3). Compared with 2001\(-\)2020, the average SC during the 2081-2100 period was projected to increase by 89.55-135.57 t-ha\({}^{-1}\). The regions of future increase were distributed everywhere except the western region (Figure 5(b2,b3), Table 3). During 2081-2100, the WC decreased from the southeast to the northwest, with the average WC being 117.67-134.39 mm (Figure S2(c1,c2), Table S3). Compared with 2001-2020, the average WC was projected to increase by 29.58-46.3 mm during 2081-2100. The regions of the decrease were mainly concentrated in the Qilian, Wushaoling, Xinglong, and Liupan Mountains (Figure 5(c2,c3), Table 3), and those of future increase were mainly distributed in the other regions of the LP. These results imply that future climate change will have a large impact on the ecological functions of vegetation in the LP.  
Figure 3: Conversions of vegetation types from 2001–2020 to 2081–2100.  
Figure 4: Change in the annual ecological functions of the Loess Plateau from 2001–2020 to 2021–2100. (**a**) Carbon stock function, CS; (**b**) soil conservation function, SC; and (**c**) water conservation function, WC.
# Low-Power LoRa Signal-Based Outdoor Positioning Using Fingerprint Algorithm
## 2 Related Work
### RSSI Proximity and Path-Loss-Model-Based Positioning  
As shown in Figure 3, the link budget is the sum of signal power gains and losses from the transmitter to the receiver through a medium such as free air space, cable, waveguide, or optical fiber. The RSSI value is a measure of the received signal power [14].  
The RSSI proximity method requires the location of gateways to be known in advance. The location of an end-device can then be roughly calculated by examining whether it is within the service area. The location can be calculated more accurately by using the RSSI data and path loss model together.  
[PERSON] et al. experimentally measured the RSSI signal intensity over the distance in an indoor environment and investigated the relationship between the distribution and distance of RSSI values in non-line-of-sight and line-of-sight conditions using a path loss model [8]. [PERSON] et al. studied gateway selection strategies for a LoRa-signal-based path loss model in an outdoor environment [7]. They found that positioning errors rapidly increased because of noise from gateways. To reduce these errors, they proposed a method for selecting optimal gateways based on k-means clustering.
## 4 Conclusions  
A novel photonic approach to generate microwave waveforms using a Dpol-DDMZM has been studied theoretically and demonstrated experimentally in this paper. No additional assistant optical spectral processing was needed in our scheme. In the full duty cycle three types of pulse trains with 8 GHz repetition rates were successfully generated. The RMSEs between the generated sawtooth, triangular waveform and square waveform are 0.190, 0.168 and 0.135, respectively. The scheme has simple structure and strong universality, and has a good performance in practical engineering.  
Figure 6: Experiment results. (**a**) Electrical power spectra and (**b**) waveforms of the generated sawtooth waveform with repetition rates of 8 GHz. (Blue curve in the figure is the measured result, and the red curve is the theoretical result).  
Figure 7: Experiment results. (**a**) Electrical power spectra and (**b**) waveforms of the generated triangular pulse trains with repetition rates of 8 GHz. (**c**) Electrical power spectra and (**d**) waveforms of the generated square waveform with repetition rates of 8 GHz. (Blue curve in the figure is the measured result, and the red curve is the theoretical result).  
**Author Contributions:** [PERSON] and [PERSON] designed and set up the experiment; [PERSON] carried out the measurements under the supervision of [PERSON], [PERSON] and [PERSON]; [PERSON], [PERSON] and [PERSON] provided the theoretical support for modelling the experiment. [PERSON] and [PERSON] wrote the manuscript, and all authors suggested improvements. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key Research and Development Project (2019 YFB 2203201); National Natural Science Foundation of China (61805032, 62075026, and 61875028); Program for Leading Talents in Scientific and Technological Innovation of \"Xingliao Plan\" (XLYC2002111); Program for Innovative Talents in Universities of Liaoning Province (LR2019017), and Fundamental Research Funds for the Central Universities (DUT 21 RC(3) 085).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Deep Learning for Medical Image Cryptography: A Comprehensive Review
## 4 Deep Learning for Medical Image Cryptography
### Key Generation in Encryption Algorithms for Medical Image Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ref.** & **Year** & **Organ** & \begin{tabular}{c} **Image** \\ **Modality** \\ \end{tabular} & **Datasets** & **Model** & **Metrics** & \
\begin{tabular}{c} **Encryption/Security** \\ **Algorithm** \\ \end{tabular} &
\begin{tabular}{c} **Compression** \\ **Algorithm** \\ \end{tabular} \\ \hline
[110] & 2021 & Chest & X-ray & \begin{tabular}{c} Chest X-ray images \\ (pneumonia) [127] \\ \end{tabular} & \begin{tabular}{c} Deep feed-forward \\ artificial neural \\ network \\ \end{tabular} & \begin{tabular}{c} PSNR, Encryption \\ Time, Compression \\ Ratio \\ \end{tabular} & \
\begin{tabular}{c} Synorr certifateless \\ signcryption \\ \end{tabular} &
\begin{tabular}{c} Levenshtein entropy \\ encoding technique \\ \end{tabular} \\
[140] & 2022 & Chest & X-ray & \begin{tabular}{c} Shenzhen \\ dataset [143] \\ COVID-8B, \\ COVID-8A [90] \\ \end{tabular} & \begin{tabular}{c} EfficientNetV2 \\ ResNet18 and \\ ResNet34 \\ \end{tabular} & \begin{tabular}{c} PSNR, SSIM, and \\ MS-SSIM, Accuracy \\ Accuracy \\ \end{tabular} & \
\begin{tabular}{c} Perceptual \\ encryption algorithm \\ \end{tabular} &
\begin{tabular}{c} JPEG compression \\ algorithm \\ \end{tabular} \\
[141] & 2022 & Chest & X-ray & \begin{tabular}{c} CNN-ray \\ COVID-8A [90] \\ \end{tabular} & \begin{tabular}{c} ResNet18 and \\ ResNet34 \\ \end{tabular} & \begin{tabular}{c} Accuracy \\ Accuracy \\ \end{tabular} & \
\begin{tabular}{c} Federated learning \\ \end{tabular} &
\begin{tabular}{c} KD \\ \end{tabular} \\
[142] & 2019 & Brain & MRI & NA & ANN & \
\begin{tabular}{c} PSNR, UIQ, SNR and \\ correlation factor \\ \end{tabular} & ANN &
\begin{tabular}{c} Peeeevoice linear \\ chaotic map \\ (PWLCM) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: List of encryption or security algorithms with compression mechanism for deep learning-based medical image analysis.