esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Underwater Image-Enhancement Experiments and Result Analysis
### Experimental Setup and Assessment Indicators  
To validate the proposed algorithm for underwater-image enhancement, we performed experiments on real underwater structural-inspection photographs and evaluated the performance with several complementary metrics.  
(1) Comparison methods. The WWSF approach developed in this study was benchmarked against six widely used enhancement techniques: UDCP, CLAHE, relative global histogram stretching (RGBS), unsupervised color correction method (UCM), minimal color loss and locally adaptive contrast enhancement (MLLE), and weighted wavelet visual perception fusion (WWPF) [24; 27; 31; 32; 33].  
(2) Image data. High-quality inspection imagery of hydraulic structures is scarce because acquiring it requires extensive time and specialized equipment [34; 35]. We therefore used an underwater inspection dataset recorded at a controlling sluice gate in Anhui Province, China.  
(3) Evaluation metrics. Guided by the review of [PERSON] et al., three objective indices were selected: the average gradient (AG), information entropy (EN), and underwater image quality measure (UIQM) [36; 37; 38; 39]. The AG quantifies image sharpness--the larger the AG, the clearer the image--and is calculated as follows:  
\[AG=\frac{\sum\limits_{i=1}^{M-1}\sum\limits_{j=1}^{N-1}\sqrt{(dx(i,j)^{2})+(dy (i,j)^{2})}}{(M-1)(N-1)} \tag{13}\]  
where \(dx(i,j)\) and \(dy(i,j)\) represent the gradient components of the image in the horizontal and vertical directions, respectively, and \(M,N\) represent the number of rows and the number of columns in the image.  
EN measures the randomness of the gray-level distribution. A larger EN value indicates a more heterogeneous histogram--useful for judging quality improvements in scenarios such as complex-texture enhancement. It is defined as follows:  
\[Entropy=-\sum\limits_{i=0}^{L-1}p_{i}\log_{2}(p_{i}) \tag{14}\]  
where \(p_{i}\) denotes the probability of occurrence of each gray value, and \(L\) is the level of gray level division of the image.  
The UIQM is a reference-free index designed specifically for evaluating the visual quality of underwater photographs. The UIQM blends three perceptual attributes--color fidelity (UICM), sharpness (UISM), and contrast (UIConM)--and produces a single score via a weighted sum:  
\[UIQM=\omega_{1}\text{-}UICM+\omega_{2}\text{-}UISM+\omega_{3}\text{-}UI\text{ Con}M \tag{15}\]  
where \(\omega_{1},\omega_{2},\omega_{3}\) are the weighting factors. Commonly used general values of these factors are \(\omega_{1}\) = 0.0282, \(\omega_{2}\) = 0.2953, and \(\omega_{3}\) = 0.3753.  
(4) Experimental environment. All experiments were executed on Windows 11 with Python 3.10 and accelerated by an NVIDIA RTX 3050 Ti GPU.
## 4 Experimental Evaluation of the GAGAN Model
### Visual Comparison of GAGAN and DCGAN Models  
Alongside the numerical analysis, we conducted a detailed examination of the visual outputs generated by both DCGAN and GAGAN, presented in Figures 10 and 11, respectively, to further illustrate the qualitative differences in image quality despite comparable training parameters. The images produced by GAGAN consistently demonstrate superior detail, diversity, and realism. Specifically, the GAGAN outputs feature more defined textures and sharper edges, contributing to a more lifelike appearance across generated images. These qualities indicate that the genetic optimization applied to the discriminator in GAGAN enhances the generator's ability to produce higher-quality, more realistic outputs.  
In contrast, the images generated by DCGAN tend to be less varied, and often display visible artifacts or inconsistencies, suggesting limitations in capturing complex details and achieving output stability. This is particularly evident when comparing similar image classes between the models, where GAGAN-generated images present a broader range of facial expressions and structural variety, while DCGAN's outputs are more prone to repetitive patterns and lack finer stylistic elements. The reduced artifacts and increased diversity in GAGAN's outputs highlight the success of the genetic algorithm in addressing common GAN challenges, such as mode collapse and artifact formation, by promoting greater diversity within the generator's outputs.  
The qualitative differences observed between the two models reinforce the quantitative findings, suggesting that the evolution of discriminator weights through genetic algorithms provides significant advantages for image realism and diversity. This synergy between numerical and visual analyses underscores GAGAN's ability to outperform DCGAN by achieving a balance of detail, diversity, and stability, ultimately resulting in synthetic images that are both visually convincing and varied.  
Figure 11: Generated images with GAGAN.  
Figure 10: Generated images with DCGAN.
## 3 System Model
### Energy Model  
The proposed work makes use of the popular energy model described in [37], which makes use of both the free space (d\({}^{2}\) power loss) and the multipath propagation (d\({}^{4}\) power loss) model depending on the distance between the communicating entities.  
If the distance between the sender and the receiver is less than a threshold d\({}_{o}\), then the free space model is utilised; otherwise, the multipath model is utilised. If \(E_{dec}\) is the amount of energy dissipated by the radio to run the transmitter or receiver circuitry per bit,\(\varepsilon_{\textit{fs}}\) and \(\varepsilon_{\textit{mp}}\) are the energy required for amplification in the free space and multipath model, respectively. The energy required by the radio to transmit a \(k\)-bit message over a distance \(d\) is given by Equation (1) as:  
\[E_{Tx}(k,d)=\left\{\begin{array}{ll}k*E_{\textit{elec}}+k*\varepsilon_{fs}*d^{ 2},&d<d_{o}\\ k*E_{\textit{elec}}+k*\varepsilon_{mp}*d^{4},&d\geq d_{o}\end{array}\right. \tag{1}\]  
To receive the \(k\)-bit message, the radio expends \(E_{Rx}\) energy, as given by Equation (2).  
\[E_{Rx}(k)=k*E_{\textit{elec}} \tag{2}\]  
The given parameter \(E_{\textit{elec}}\) depends on numerous factors, such as digital coding, modulation, filtering and spreading of the signal, while the amplification energy \(\varepsilon_{\textit{fs}}\) and \(\varepsilon_{\textit{mp}}\) is subject to the distance between the sender and the receiver and to the acceptable bit rate error.  
For simulation purposes, the parameters are set to: \(E_{\textit{elec}}\) = 50 nj/bit, \(\varepsilon_{\textit{fs}}\) = 10 pj/bit/m\({}^{2}\), \(\varepsilon_{\textit{mp}}\) = 0.0013 pj/bit/m\({}^{4}\) and \(E_{\textit{DA}}\) = 5 nj/bit/signal. Threshold \(d_{o}\) is calculated as \(d_{0}=\sqrt{\frac{\varepsilon_{\textit{fs}}}{\varepsilon_{\textit{mp}}}}\).
###### Abstract  
Deoxynivalenol (DON) is one of the major trichothecene mycotoxins commonly found in grains, in particular barley. This study focused on the reduction of DON concentration on barley samples using atmospheric cold plasma (ACP) treatment. The effects of moisture content, post-treatment storage, and relative humidity of air on DON degradation on barley were evaluated. Additionally, the germination and the quality parameters of barley, including protein content, \(\beta\)-glucan, and moisture content, were evaluated. The results showed that ACP treatment for 6 and 10 min reduced DON concentration by 48.9% and 54.4%, respectively. No significant differences were observed in the DON degradation levels by increasing the moisture content of barley from 9.5 to 15.7 g water/100 g sample and relative humidity of air from 12 to 60%. Steeping of barley grains without subsequent drying prior to ACP treatment significantly increased the degradation rate of DON by ACP due to the presence of water on the grain surface. No significant differences were observed for the tested quality parameters of barley in comparison with control samples. This study shows that ACP may offer an effective DON reduction in barley without affecting the quality attributes. However, ACP treatment parameters should be optimized to achieve a better DON reduction efficacy.  
deoxynivalenol; mycotoxins; atmospheric cold plasma; degradation; quality; grain; barley
## 4 Experimental Results
### Robot Control Power Consumption Analysis  
The gait trajectory of the WalkON Suit was determined considering the pilot's anthropometric data and the physical conditions of the joints, including the contracture angle. The actuators were controlled to follow position trajectories introduced in the previous work [29]. In walk mode, the pilot can move one step forward by making an action command in walk mode. WE remind that walk mode includes two different actions, swing and stance. The swing period was pre-determined in the control algorithm, and the stance period was controlled by the user. In this experiment, the swing period was set to one second.  
Figure 12 shows the motor torque data of each joint with respect to the gait cycle. The thick continuous lines show the averaged values for ten cycles. The terms LH, LK, RH and RK refer to the left hip, the left knee, the right hip and the right knee, respectively. Figure 12a-d shows the experimental results when the left leg moved forward (i.e., the left leg was in a swing phase and the right leg was in a stance phase). Up to one second, distinguished by the red vertical lines, the left leg swung in the air (see Figure 12a,b), and the right leg supported the whole body weight, generating a propulsion force to move forward (see Figure 12c,d). After one second, the robot was waiting for the next action command from the pilot. The same analyses apply to Figure 12a,d, which were when the right leg moved forward.  
Figure 12 also shows the difference between left and right legs when performing the same actions. Figure 12c,e shows the hip joint torque for supporting the body weight and moving the whole body forward, and Figure 12d,f represents the knee joint torque in the same condition. In both cases of the hip and knee joints, the left leg required larger torques than the right leg. The root-mean-squared (RMS) values for the actuation torque for the left and right hip joints in a stance phase were 103.39 Nm and 95.27 Nm, respectively. This may be because of the difference in the leg length of the pilot.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Output Interface Method** & **Average Step Time (s)** \\ \hline Crutch display only & 2.48 \\ Proposed UI & 1.78 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Step time of difference interfaces.  
Figure 11: Walking speed result for each trial. The step length of each step was set as 30 cm, and the step time was controlled by the pilot’s intention.
# Unexpected-Behavior Detection Using TopK Rankings for Cybersecurity
## 2 Intrusion Detection Systems
### Anomaly-Based Detection  
Anomaly-based detection is the process of comparing definitions of what is considered normal (i.e., profiles) against observed events in order to identify significant deviations. The profiles are built by monitoring the characteristics of typical activity over a period of time [7]. An advantage of this methodology is its ability to find new forms of attacks, but normally it has high rates of false positives. Figure 3 depicts a generic diagram of this detection methodology.  
Anomaly-based IDS is considered to be one of the foremost research areas in network security [9]. The detection methods and the selection of the system or network features to be monitored are two open issues [8].  
Many research works have been developed around the classification of network traffic using different machine-learning techniques, with the aim of achieving 100% intrusion detection and a low rate of false positives. Two classes of machine learning techniques are used: (a) single and (b) hybrid classifiers. Examples of single machine-learning classifiers include the following: support vector machines, self-organizing maps, neural networks, and k-nearest neighbors. Hybrid machine-learning classifiers have the purpose of acquiring a superior probable accuracy for intrusion detection; these classifiers combine many machine-learning techniques to improve their performance [9].
## 3 Results and Discussion
### Forecast of Wind Speed  
In this case, the weather information to predict is the wind speed in km/h, using as input parameters wind speed (km/h).  
Due to the number of samples relating to wind parameter is similar to the number of samples available of temperature, the optimal size of the sliding window is 1. The results are shown in Table 8 and Figure 10.  
When the prediction of the wind speed is done only using wind speed as input data, the best results are achieved by means of SVM with polynomial kernel. The system reaches good results in both places, getting an average of MSE smaller than 1 km/h.
# Mitigating DDoS Attacks in LEO Satellite Networks Through Bottleneck Minimize Routing
## 6 Conclusions  
This paper addresses DDoS attacks in LSN by introducing a K-Bottleneck Minimize routing algorithm. First, we leverage the ICARUS framework to measure each link's bottleneck occurrence frequency and map it into a nonlinear risk weight. Under the constraint that the end-to-end latency does not exceed the equivalent fiber-optic delay threshold, Dijkstra iteration weighted by bottleneck weights is used to generate up to k edge-disjoint paths by pruning ISL to avoid high-risk links.  
Simulation on the Starlink S1 topology demonstrates that, relative to the classical K-SP approach, K-Bottleneck Minimize raises the average attacker cost by 13.1% and the median cost by 16.6%; against the K-DG algorithm, the corresponding gains are 21.6% and 20.3%. In terms of MaxUp, our method delivers a 48.5% increase in mean detection probability and a 45.4% uplift in the median compared to K-SP. Against K-DG, our method increases the mean MaxUp by 30.7% and the median by 56.5%. This proves that our algorithm can effectively improve the cost and detectability of attackers. This greatly increases the probability of attackers being detected.  
The total computation times for K-SP, K-DG, and our K-Bottleneck Minimize algorithms are 3297 s, 724 s, and 2613 s, respectively. While K-DG remains the fastest, completing in roughly 4.5 times faster than K-SP. Our K-Bottleneck Minimize approach still achieves a reduction in routing-calculation time compared to K-SP and incurs an overhead relative to K-DG. Compared with the most common K-SP algorithm, our routing calculation time is still reduced, which shows that our algorithm still has room for application in large-scale satellite scenarios.  
Our future work will focus on reducing time overhead by streamlining path pruning and using multi-objective optimization to balance latency and bandwidth and integrate continuous monitoring with load-aware controls. These refinements aim to deliver more efficient adaptive routing and further bolster DDoS resilience in LEO satellite systems.  
Conceptualization, F.M. and Y.Z. ([PERSON]); methodology, F.M. and Y.Z. ([PERSON]); software, F.M.; formal analysis, J.Y.; investigation, A.C. and R.L.; writing--original draft preparation, F.M.; writing--review and editing, F.M. and X.Y.; supervision, X.Y.; funding acquisition, X.Y. and Y.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 62206019.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# Spectrum and Energy Efficiency of Uplink Massive MIMO System with D2D Underlay
## Appendix B Proof of Theorem 2  
Substitute (11) and (10) into (3), we have  
\[\mathcal{R}_{ck} =\int_{0}^{\infty}\left[\log_{2}\left(\xi_{ck}+I_{ck}\right)-\log _{2}\left(\sigma^{2}+I_{ck}\right)\right]f(I_{ck})\mathrm{d}I_{ck}\] \[=\frac{l(a_{ck},b_{ck},\xi_{ck})-l(a_{ck},b_{ck},\sigma^{2})}{(b_{ ck})^{a_{ck}}\,\Gamma(a_{ck})\ln(2)}, \tag{11}\]  
where \(l(t_{1},t_{2},t_{3})=\int_{0}^{\infty}x^{(t_{1}-1)}e^{-\frac{\pi}{2}}\ln\left( t_{3}+x\right)\mathrm{d}x\). The integrals \(l(a_{ck},b_{ck},\xi_{ck})\) and \(l(a_{ck},b_{ck},\sigma^{2})\) cannot be solved according to the ([18] (Equation (4.222.8)) because \(a_{ck}\) and \(b_{ck}\) are positive but not necessarily integers. However, \(l(a_{ck},b_{ck},\xi_{ck})\) can be derived as follows in closed-form using the property of [PERSON]'s G-function,  
\[l(a_{ck},b_{ck},\xi_{ck}) =\int_{0}^{\infty}x^{(a_{ck}-1)}e^{-\frac{\pi}{b_{ck}^{2}}}\ln \left(\xi_{ck}\right)\mathrm{d}x+\int_{0}^{\infty}x^{(a_{ck}-1)}e^{-\frac{\pi }{b_{ck}^{2}}}\ln\left(1+x/\xi_{ck}\right)\mathrm{d}x\] \[\stackrel{{(a)}}{{=}}\ln\left(\xi_{ck}\right)(b_{ck} )^{a_{ck}}\,\Gamma\left(a_{ck}\right)+\int_{0}^{\infty}x^{(a_{ck}-1)}e^{- \frac{\pi}{b_{ck}^{2}}}\,G_{2,2}^{1/2}\left(\left.\frac{x}{\xi_{ck}}\right| \begin{array}{c}1,1\\ 1,0\end{array}\right)\mathrm{d}x\] \[\stackrel{{(b)}}{{=}}\left(b_{ck}\right)^{a_{ck}} \left[\mathcal{J}\left(a_{ck},b_{ck}/\xi_{ck}\right)+\ln\left(\xi_{ck}\right) \Gamma(a_{ck})\right]. \tag{12}\]  
Step (a) is obtained by representing \(\ln(1+x/\xi_{ck})\) as the [PERSON]'s G-function, and step (b) is derived according to ([25] Equation (44)). Similarly to (12), we can derive \(l(a_{ck},b_{ck},\sigma^{2})\), and concludes the proof.
## 1 Microgravity in Human Health
### Microgravity and Bone Remodelling  
Besides inflammatory response, bone homeostasis represents the main issue in Space-related pathophysiology, due to the fact that prolonged Space flights lead to decreased bone deposition and osteoporosis [50].  
A dynamic bone remodelling results from the balance between deposition and resorption of hydroxyapatite mineral matrix, regulated by bone-resident osteoblasts and osteoclasts, respectively. This balance is regulated both remotely through the parathormone/calcytonine axis, which modulates osteoblasts and osteoclasts, and locally through osteocytes embedded in the mineral matrix that act as mechanosensors, translating the compressive forces exerted by gravity on the tissue into biological and hormone-like signals that participate in bone homeostasis [51].  
In particular, continuous mechanical stimulation is required for proper osteocyte function, as demonstrated by the fact that lack of mobility (achieved through surgical intervention on animal models) and authentic microgravity both induce osteocyte apoptosis and the recruitment of osteoclasts that resorb bone matrix [52; 53; 54; 55]. Microgravity can also directly hinder bone mineralization, by inhibiting activation and differentiation of osteoblasts from their progenitors [56; 57; 58; 59], as well as by directly inducing osteoclastogenesis [58; 60]. To date, the molecular mechanisms behind this process have not yet been fully disclosed, however it is thought that cytoskeletal proteins can sense gravity, the lack of which triggers detrimental signals in bone cells. An involvement of bioactive lipids in these processes is quite expected, because all cells involved in bone homeostasis can produce and/or respond to eicosanoids [61; 62], endocannabinoids [63; 64] and glycerophospholipids [65]. Yet, investigations into lipid signalling in bone remodelling are still scarce.
# An Overview of Self-Heating Phenomena and Theory Related to Damping and Fatigue of Metals
## 3 The Thermodynamic Framework
### Damping  
[PERSON] [9] developed a unified approach using thermographic measurements to characterize both damping and HCF. Damping is due to anelastic dislocation motion that creates a lag in phase between the oscillating stress and strain [49; 50]. The specific damping capacity is defined as [9]:  
\[\psi=\frac{\Delta W}{U}=4\pi\zeta,\ U=\frac{\Delta\sigma^{2}}{2E} \tag{20}\]  
where \(\Delta W\) is the hysteresis energy, \(U\) is the elastic energy during the cycle, \(E\) is [PERSON]'s modulus, and \(\zeta\) is the damping ratio. The specific damping capacity can also be measured independently using vibration methods [51]. Thus, Equation (20) provides an independent means to assess the hysteresis energy \(\Delta W\) and vice versa, for damping. Furthermore, the measure of damping can also be used for non-destructive damage detection [52; 53].  
The stabilized temperature increment is related to the hysteresis energy by [9]:  
\[\Delta T_{AS}=\frac{N_{AS}\Delta W}{\rho c_{p}} \tag{21}\]  
where \(N_{AS}\) is the cycle to the stabilized temperature (the cycle number of Phase I)  
To correlate with the fatigue life, [PERSON] further proposed the following relationship [9]:  
\[\Delta W_{TOT}=\Delta W\cdot N_{f} \tag{22}\]  
where \(\Delta W_{TOT}\) is the total energy of fracture, \(N_{f}\) is the number of cycles to fatigue fracture. Here, \(\Delta W_{TOT}\) is assumed to be constant for the fatigue process.  
[PERSON] observed that \(\Delta T_{AS}\) was nearly zero when the applied stress is below a certain value, \(\sigma_{0}\); and above it,  
\[\Delta T_{AS}=\frac{N_{AS}\psi}{2\rho c_{p}}\Big{(}\Delta\sigma^{2}-\sigma_{0}^ {2}\Big{)} \tag{23}\]  
This relationship was experimentally verified on AISI304 steel and AA6082 aluminum alloy welded joints, and \(\sigma_{0}\) was found to correspond to the fatigue endurance limit. But, to match the experimental data, i.e., the S-N curve (the Basquin equation), \(\Delta\sigma^{m}N_{f}=C\), [PERSON] assumed that \(\Delta T_{AS}=A\Delta\sigma^{m}\), and found \(m=6.3\) for AA6082 by fitting the S-N curve from the conventional fatigue testing [9]. Apparently, an inconsistency exists between the stress dependence of \(\Delta T_{AS}\) in Equation (23) as observed by IR thermography and the S-N.