esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy and Distance Based Multi-Objective Red Fox Optimization Algorithm in Wireless Sensor Network
## 6 Results and Discussion
### Performance of Energy Consumption  
The comparison of the suggested ED-MORFO and correlated devices, such as the existing MCH-EOR [24] and existing RDSAOA-EECP [25], in the case of energy consumption, is shown in Figure 3. Figure 3 shows that when compared to the following conventional approaches, the suggested ED-MORFO consumes less energy. In the ED-MORFO technique, a smaller number of nodes are twisted in packet progression. Furthermore, more energy is saved, as nodes with the highest optimality factor continue to be important in progressing data packets; in contrast, the traditional methods require an extra node when dispatching the same data packet; as a result, sophisticated energy is consumed in the existing MCH-EOR [24] and existing RDSAOA-EECP [25] methods. The analysis of energy consumption performance is shown in Table 2. It shows that the performance of the proposed ED-MORFO varies from 0.06 to 0.46, while the existing MCH-EOR [24] achieves 0.09 to 0.58, and RDSAOA-EECP [25] achieves 0.11 to 0.62.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Transmission power & 0.660 W \\ Receiving power & 0.4 W \\ Packet size & 512 bytes \\ Number of nodes & 200 \\ Initial energy & 14.0 J \\ Antenna model & Omni antenna \\ Data transfer rate & 250 (kb/s) \\ Network size & 1000 \(\times\) 1000 m \\ Simulation time & 100 s \\ Traffic type & CBR \\ Transmission rate & 50 to 250 \(\mathrm{Kb/s}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation specification.  
Figure 3: Performance of energy consumption for proposed EDMORFO and Existing MCH-EOR [24], RDSAOA-EECP [25].
## 5 Effect of Optical Fiber Dispersion
It can be observed that the graph of 40 GHz is different from 20 GHz and 60 GHz; the 40 GHz graph is more linear than the others with big difference in amplitude power. In the graph, the 40 GHz level is above the other signals, 20 GHz and 60 GHz, with a power difference of 28 dBm at 10 km distance. Based on this graph, we can conclude that the DSB-OCS system is compatible more with a 40 GHz mm-wave signal than the others. The linearity for such a system can be enhanced using various techniques, for instance, ref. [31] used the direct digital predistortion technique to improve the laser  
Figure 7: DSB-OCS electrical output spectra at different optical fiber lengths. (**a**) 0 km; (**b**) 10 km; (**c**) 20 km; (**d**) 30 km; (**e**) 40 km. chirp and fiber dispersion in long-haul RoF links. However, the process of converting the signal from the digital form to analog form was found to induce distortion in the signal [32]. Additionally, it is possible to avoid a dispersion penalty using single-sideband (SSB) modulation and DSB in the system [33], or using a tunable dispersion-compensation module [34].
# Low-Grade Clay as an Alkali-Activated Material
## 4 Results and Discussion
### NMR Analysis
They also stated that Al (VI) coordination converts into Al (IV) coordination at 673 \({}^{\circ}\)K. As stated by [PERSON] et al. (2011), the loss of local hydroxyl groups increases with increasing temperature, and this loss of local hydroxyl groups causes structural disorder of the material, which leads to the migration of Al (VI) coordination into Al (IV) coordination [63]. In the clay pre-treated at 750 \({}^{\circ}\)C, there was no change in the Al coordination; the whole Al (VI) coordination was converted into Al (IV) coordination during clay pre-treatment. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Clay (\%)**} & \multicolumn{2}{c}{**Geopolymer**} & \multicolumn{2}{c}{**Geopolymer**} \\  & & & **Mortar 10\% (\%)** & **Mortar 15\% (\%)** \\ \cline{2-7}  & **Al (VI)** & **Al (IV)** & **Al (VI)** & **Al (IV)** & **Al (VI)** \\ \hline Clay without pre-treatment & 61.6 & 38.4 & 45.5 & 54.5 & 43.8 & 56.2 \\ Clay pre-treated at 120 \({}^{\circ}\)C & 67.6 & 32.4 & 38.5 & 61.5 & 28 & 72 \\ Clay pre-treated at 750 \({}^{\circ}\)C & 0 & 100 & 0 & 100 & 0 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Relative area and coordination of Al (VI) and Al (IV) of clay and alkali-activated mortar at 28 days (AM 1.0, dosages of 10% and 15%). The NMR data indicate that the conversion of aluminum from octahedral to tetrahedral coordination as well as the quantity of aluminum in the tetrahedral coordination influence the mechanical performance. The data indicate that the more aluminum ions in Al (IV) coordination in the reactive crystalline phases, the more significant the role that dissolution of aluminum plays in the ultimate strength achieved during activation.
## 2 The Smart Rainfall System (SRS)
### Satellite-Earth Link Model
Thanks to the semi-empirical model ITU-R P.838-3 [19], the specific attenuation can be related with the rainfall intensity \(\mathcal{R}\) [mm/h] as  
\[\gamma_{rain}=\beta\mathcal{R}^{\alpha} \tag{6}\]  
The quantities \(\alpha\) and \(\beta\) can be computed as follows,  
\[\beta=\frac{1}{2}[\beta_{H}+\beta_{V}+(\beta_{H}-\beta_{V})\mathrm{cos}^{2}( \vartheta)\mathrm{cos}(2\tau)] \tag{7}\]\[\alpha=\frac{1}{2\beta}[\beta_{H}\alpha_{H}+\beta_{V}\alpha_{V}+(\beta_{H}\alpha_{ H}-\beta_{V}\alpha_{V})\text{cos}^{2}(\vartheta)\text{cos}(2\tau)] \tag{8}\]  
where \(\tau\) evaluates to 0, \(\pi/2\) or \(\pi/4\) when the polarization is horizontal, vertical, or circular, respectively. The terms \(\beta_{H}\), \(\beta_{V}\), \(\alpha_{H}\), and \(\alpha_{V}\) are frequency-dependent; their values can be found in [19] for the working frequency \(f=12\) GHz considered in the following. Given a measurement of \(\gamma_{rain}\), the relation in Equation (6) can be inverted to get an estimate of \(\mathcal{R}\). However, if higher local accuracy is needed, more sophisticated models can also be adopted (see, e.g., in [20; 21; 22; 23; 24]).
## 2 Materials and Methods  
### S. cerevisiae Strains Origin and Sampling  
A total of 206 _S. cerevisiae_ isolates, belonging to the microbial collection of the Dipartimento di Scienze della Vita e dell'Ambiente (DiSVA), Universita Politecnica delle Marche (Italy), were previously isolated from two different wineries, located 20 km away from each other. The first cell (winery 1) was in the town of Cupramontana (coordinates: 43.45939, 13.12515), the second cellar (winery 2) was in Montecarotto (coordinates: 43.532023, 13.075781) (Figure 1), and both in the winemaking area of Verdicchio DOC Castelli di Jesi Marche region, Italy. Ninety-nine _S. cerevisiae_ strains came from winery 1, while 107 strains came from winery 2. These strains were collected during a two-year sampling campaign.  
Figure 1: Geolocalization of winery 1 (Cupramontana) and winery 2 (Montecarotto) and both are in Marche Region, Italy.  
In winery 1, 43 isolates were collected in the year 2016 and 56 isolates in the year 2019. The yeasts were isolated from grapes, after 7/15 days of auto-enrichment, during _\"pie de cuvve\"_, tank fermentation, winery equipment and winery environment. Regarding winery 2, 80 isolates were collected in the year 2021 and 27 isolates in the year 2022. The isolates came from the winery work equipment and environment, fermentation tanks and during _\"pie de cuvve\"_.  
The sampling campaign was carried out by collecting about 1 Kg of undamaged ripe grape bunches in sterile plastic bags, and 50 mL of fermenting must in sterile tubes from _\"pie de cuvve\"_ must fermentation samples. Winery equipment and environment samples were performed using sterile swabs streaked on random surfaces (10 cm\({}^{2}\)). All the samples were stored on ice during sampling and transported to the laboratory as soon as possible for their processing and _S. cerevisiae_ isolation and identification [8].  
The wineries did not use commercial starter strains.
# PNMAVis: Visual Analysis Tool of Protein Normal Mode for Understanding Cavity Dynamics
## 3 Related Work
### Normal Mode Analysis  
Normal modes classify all available motions that a system can undergo, given its 3D structure, and they are essentially large, non-uniform vector fields (at discrete locations) [10]. Normal mode is an evaluation of system interaction. In the context of biology, normal patterns represent all of the internal dynamics of proteins. [PERSON] et al. [11] showed that the correlation determined from the GNM (Gaussian network model, which is a minimalist NMA model used to study biomolecules) and MD simulation is similar. Compared with the method of using MD simulation to calculate the similarity, NMA takes less time and is more convenient to use. In recent years, NMA has become an important tool for studying biomolecules. This technique was first utilized because of its relatively modest computational demands, but even now, when more complex calculations are routinely performed, NMA has remained of value because of the type of information that it can provide. In order to transform from one functional conformation to another, a protein will deform from its normal mode space according to a specific subset of modes. [PERSON] et al. [12] naturally reproduces the collective functional motions of biological macromolecules by using NMA. In order to link the intrinsic dynamics of proteins with their functions, domain experts usually use protein conformations obtained by various experiments. For example, [PERSON] and [PERSON] [13] evaluated the transition of normal mode between two known functional states. NMA is also used to capture the inherent flexibility of proteins. [PERSON] et al. [10] proposed the inference of relevant protein function from single structure dynamics through protein tunnel analysis while considering normal mode combinations spanning the whole normal mode space. At present, the study of allosteric sites based on protein dynamics has attracted much attention. [PERSON] [14] uses elastic net for the NMA calculation of the network model, and studies the influence of sites on protein movement characteristics. Based on this, our work uses the protein dynamics information provided by the normal mode to study the relationship between key amino acids and cavity-lining amino acids on the cavity, and analyzes the reasons for the dynamic changes of the cavity.
# From the Martian Surface to Its Low Orbit in a Reusable Single-Stage Vehicle--[PERSON]
## 3 Results
### Vehicle Subsystems
#### Abort Propulsive System  
From the different simulations carried out earlier, it has been determined that the abort engines must provide an acceleration of 79 m/s\({}^{2}\). This corresponds to 8 g\({}_{\text{Earth}}\) for a duration of about 3 seconds. While this could injure one or more crew member, such acceleration is non-lethal [18]. This acceleration is lower than the 11 g\({}_{\text{Earth}}\) of abort with Orion [17]. After multiple iterations, the capsule mass was computed to be 14,200 kg. Hence, the abort engines must provide 1065 kN of thrust. To lower the footprint of the abort engines, they are placed two by two around the capsule, as seen in Figure 9. They are also angled at 20\({}^{\circ}\), so that their exhaust does not interfere with the frame of the capsule. Because of this angle, each abort engine shall provide 189 kN of thrust.  
The combustion chamber and nozzle of these abort engines has been sized, and their performance simulated using the Rocket Propulsion Analysis software ([PERSON], Germany) [19]. Using Hydrazine and Nitrogen Peroxide, these engines each of a mass of 119 kg, have a vacuum \(I_{sp}\) of 259.6 s, a mass flow rate of 74.4 kg/s, and each provide the required 189 kN of thrust.  
To be able to fulfil the most demanding abort modes, as well as land propulsively, the capsule will thus have 2336 kg of propellant, from which 64% is the hydrogen peroxide, and the remainder is the hydrazine.  
Finally, the abort engines use a pressure-fed cycle, using Helium at 30 bar, and the propellant is self-igniting. These design choices both make the abort engines more reliable.
## 4 Discussion
### Limitations  
The results obtained in this study must be interpreted in the context of the limitations they present. These results can only be applied to HM obtained from a certain profile of participants as described above. The conclusions are valid only for one microorganism (_E. coli_) and under the specified conditions. Milk inoculation with _E. coli_ was carried out in a specified amount (10\({}^{4}\) CFU/mL), and the growth of this microorganism could be different according to different inoculation doses or without inoculation. Moreover, this study was carried out with mature HM; therefore, we cannot draw conclusions about the evolution of _E. coli_ growth in transition milk or colostrum. Finally, only four PIF brands were evaluated, and differences were found between them. Microorganism growth in other brands could be different.
## 2 Materials and Methods
### LiDAR Processing and Derived Structural Parameters  
The LiDAR-based forest structural parameters were derived from canopy height models (CHM) generated with a ground sampling distance of 1 m. The CHM was calculated by subtracting the Digital Elevation Model (DEM, last \"ground\" returns) from the Digital Surface Model (DSM, first \"canopy\" return), both derived from the raw LiDAR point clouds data as per the method outlined in [69]. A height threshold of 1 m was applied to all the CHM products, to exclude possible noise from non-woody vegetation (e.g., grass). The following structural metrics were calculated: Mean tree height (H\({}_{\text{mean}}\)) and canopy cover (CC). The height threshold mentioned above implies that the structural metrics are calculated from the woody vegetation component including shrubs and trees, but we will refer to tree structural metric for simplicity. These structural parameters were computed as follows:  
1. Tree height is defined as the vertical distance from the base of the tree to its treetop [71]. It plays a key role in forest ecosystem studies, for instance for predicting species richness and species distribution models [72; 73] or for assessing fire severity and modelling fire escape mechanisms [74; 75]. It contributes to estimate variables such as canopy volume and biomass [76; 77]. The mean tree height (H\({}_{\text{mean}}\)) parameter was calculated as the average of the CHM pixels excluding the non-tree pixels (<1 m) within each MISR-HR 275 m pixel. This is a useful measure especially in even-aged forests. There was a strong relationship between tree height estimated by the CAO CHMs and field measurements (R\({}^{2}\) = 0.93, \(p\)-value < 0.001 and standard error of 0.73 m), as described in [PERSON] et al. [78].
2. Canopy cover (CC) is defined as the percentage area of a MISR-HR 275 m pixel covered by the vertical projection of tree crowns. As a simple 2D structural measure, CC is a key descriptor of ecosystems and is useful for monitoring vegetation changes, for instance habitat connectivity and fragmentation [79; 80; 81]. This parameter was estimated by calculating the percentage of CHM pixels with a height above 1 m relative to the total number of LiDAR pixels included in a 275 m MISR pixel. A strong relationship between CAO LiDAR-derived CC and field measurements was previously demonstrated for the KNP dataset (R\({}^{2}\) = 0.79, Root Mean Square Error (RMSE) of 12.4%) [17].
# Method of Suppressing Rayleigh Waves Based on the Technology of Time-Domain Differential Detection
## 4 Engineering Validation of the Differential Detection Method  
The effectiveness and application feasibility of the time-domain differential detection method are validated through various typical field tests.
## 4 Temperature Field Prediction Model Application
### Simulation  
When the speed of the motorized spindle was 10,000 rpm, the isotherm was obtained from the temperature field prediction model to optimize the heat transfer coefficient as shown in Figure 8. Figure 8,b show that the isotherms changed significantly with increase in the number of iterations of the heat transfer coefficient from 25 to 50. However, there was a slight change for iterations 75 to 100 as shown in Figure 8,d. This implies that the results of the optimization of the heat transfer coefficient of the motorized spindle based on the genetic algorithm were convergent.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Speed** & \multicolumn{6}{c}{**Initial Value (W/(m\({}^{\circ}\)C))**} & \multicolumn{6}{c}{**Optimized (W/(m\({}^{\circ}\)C))**} \\ \cline{2-10} **(rpm)** & \(\mathbf{h_{1}}\) & \(\mathbf{h_{2}}\) & \(\mathbf{h_{3}}\) & \(\mathbf{h_{4}}\) & \(\mathbf{h_{5}}\) & \(\mathbf{h_{1}}\)* & \(\mathbf{h_{2}}\)* & \(\mathbf{h_{3}}\)* & \(\mathbf{h_{4}}\)* & \(\mathbf{h_{5}}\)* \\ \hline
10,000 & 9.7 & 146.8 & 121.4 & 71.4 & 190.0 & 20.0 & 188.4 & 188.2 & 127.7 & 500.3 \\
15,000 & 9.7 & 178.4 & 142.5 & 109.1 & 223.9 & 10.8 & 171.3 & 223.8 & 134.9 & 331.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The heat transfer coefficients.  
Figure 8: Isotherm of the motorized spindle at 10,000 rpm. (**a**) 25 iterations, (**b**) 50 iterations, (**c**) 75 iterations, (**d**) 100 iterations.  
Similarly, Figure 9 shows the isotherm of the motorized spindle at 15,000 rpm. Figure 9a shows the isotherm for the initial values of the heat transfer coefficient and Figure 9b the isotherm when the coefficient was optimized over 100 iterations. Comparing Figure 9a,b, the effect of the heat transfer coefficient on the temperature field of the motorized spindle appeared to be significant.
# SatGuard: Satellite Networks Penetration Testing and Vulnerability Risk Assessment Methods
###### Abstract  
Satellite networks face escalating cybersecurity threats from evolving attack vectors and systemic complexities. This paper proposes SatGuard, a novel framework integrating a three-dimensional penetration testing methodology and a nonlinear risk assessment mechanism tailored for satellite security. To address limitations of conventional tools in handling satellite-specific vulnerabilities, SatGuard employs large language models (LLMs) like GPT-4 and DeepSeek-R1. By leveraging their contextual reasoning and code-generation abilities, SatGuard enables semi-automated vulnerability analysis and exploitation. Validated in a simulated ground station environment, the framework achieved a 73.3% success rate (22/30 attempts) across critical ports, with an average of 5.5 human interactions per test. By bridging AI-driven automation with satellite-specific risk modeling, SatGuard advances cybersecurity for next-generation space infrastructure through scalable, ethically aligned solutions.  
satellite network; penetration testing; cybersecurity; risk assessment; LLMs; prompt engineering
# Metaheuristics and Support Vector Data Description for Fault Detection in Industrial Processes
## 5 Conclusions and Future Work  
In this paper, a methodology for fault detection in multivariate processes is presented. This proposal is able to work in industrial processes and machines which are recently operated. It can deal with little data about faults by the implementation of a one class classifier (SVDD). Such algorithm is optimized during its training stage, that is, its hyper-parameters are tuned using a recent metaheuristic based on the behavior of spotted hyenas (SHO). In order to evaluate its computational performance, SHO has been compared with three other metaheuristics (KH, SSA y PSO) that have been successfully implemented for solving several engineering problems. The approaches were tested using data taking from a plastic injecting machine in the automotive industry. The results after 30 runs show that SHO reaches higher values in the F1 score metric, that is, it shows higher performance for fault detection in considerably shorter computing time with respect to the rest of the considered approaches. Furthermore, a non-parametric statistical analysis was performed to prove the statistical significance of the superior performance of SHO. The Kruskal-Wallis test and the post hoc analysis show that there is a significant statistical difference between SHO and the other metaheuristics. Moreover, SHO-SVDD was tested for generalization performance using 5-fold and 10-fold cross validation. The obtained F1 score mean values were 0.9701 and 0.9714, respectively, which are very close to the F1 score mean value of 0.9702 obtained by the proposed analysis. Considering this, it is concluded that the SHO-SVDD method presents a good generalization to classify this data set. Based on this, the SHO-SVDD approach seems to be the best to carry out fault detection for this particular industrial application. As future work, some feature selection approaches will be implemented to obtain a subset of variables that allows for performing a fault diagnosis in order to trace back operational issues or other problems in the process.  
Conceptualization, J.A.N.-A. and I.D.G.-C.; Data curation, E.O.R.-F.; Formal analysis, E.O.R.-F.; Investigation, J.A.N.-A., I.D.G.-C. and V.A.-G.; Methodology, J.A.N.-A. and I.D.G.-C.; Project administration, J.A.N.-A., I.D.G.-C., V.A.-G. and E.O.R.-F.; Resources, J.A.N.-A., I.D.G.-C., V.A.-G. and E.O.R.-F.; Software, J.A.N.-A. and I.D.G.-C.; Supervision, J.A.N.-A., I.D.G.-C. and E.O.R.-F.; Validation, J.A.N.-A. and I.D.G.-C.; Visualization, J.A.N.-A. and V.A.-G.; Writing--original draft, J.A.N.-A., I.D.G.-C., V.A.-G. and E.O.R.-F.; Writing--review & editing, V.A.-G. and E.O.R.-F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Autonomous University of Coahuila Grant No. C01-2019-49, and the APC was funded by the Autonomous University of Coahuila.  
The authors declare no conflict of interest.  
Figure 5: Computational times Tukey test.
## 2 Materials and Methods
### VO Localization  
Since the Bayesian classifier is robust against retinal lesion in the images of the training set, it is chosen for retinal vessel origin detection. Pixels are classified into two classes, vessel origin (VO) and non-vessel origin (NVO). The pixel with the maximal average likelihood ratio of _a posteriori_ probabilities is chosen as the vessel origin. This is described in detail in the following.  
Based on the four features mentioned in the previous subsections, each pixel is associated with a multivariate feature vector x. The multivariate Gaussian density function is used to model the conditional densities [20], which are given as:  
\[p(\textbf{x}\mid C_{VO}) = \frac{1}{(2\pi)^{2}\big{|}\Sigma_{VO}\big{|}^{1/2}}\times\exp[- \frac{1}{2}(\textbf{x}-\textbf{\mu}_{VO})^{t}\Sigma_{VO}^{-1}(\textbf{x}- \textbf{\mu}_{VO})] \tag{6}\] \[p(\textbf{x}\mid C_{NVO}) = \frac{1}{(2\pi)^{2}\big{|}\Sigma_{NVO}\big{|}^{1/2}}\times\exp[- \frac{1}{2}(\textbf{x}-\textbf{\mu}_{NVO})^{t}\Sigma_{NVO}^{-1}(\textbf{x}- \textbf{\mu}_{NVO})] \tag{7}\]  
Where \(\Sigma_{VO}\) and \(\Sigma_{NVO}\) are covariance matrices, and \(\mu_{VO}\) and \(\mu_{NVO}\) are the population mean vectors generated from the training data set for classes VO and NVO, respectively.  
On the basis of the Bayes rule, the well-known likelihood ratio of _a posteriori_ probabilities is defined as follows [8]:  
\[R_{llh}\big{(}\textbf{x}\big{)}=\frac{p(C_{IO}\mid\textbf{x})}{p(C_{NVO}\mid \textbf{x})}=\frac{p(\textbf{x}\mid C_{VO})\cdot p(C_{VO})}{p(\textbf{x}\mid C _{NVO})\cdot p(C_{NVO})} \tag{8}\]  
Where \(p(C_{VO})\) and \(p(C_{NVO})\) denote the _a priori_ probabilities of VO and of NVO, respectively. Accordingly, the likelihood ratio function R\({}_{llh}\) builds a confidence map, denoted as \(F_{C}\). The VO location (_h,k_) is defined as the point (_x,y_) having the maximal average confidence value. This is presented as the following:  
\[(h,k)=\arg\max_{(x,y)}\big{(}\textit{w}\ast F_{C}\big{(}x,y\big{)}\big{)} \tag{9}\]  
Where \(w\) is an \(r\times r\) mean filter, and symbol \" * \" represents the convolution product. Here, \(r\) is the radius of optic disc.