esa-sceva/satcom-chunk-collection huggingface dataset:
# The Design and Implementation of a Novel Open Source Massive Deployment System
## 2 Related Works
### File System Imaging Method  
Many solutions are available for the file system imaging method. The Ghost [23], which was commercialized in 1985, is one of the most famous examples of commercial proprietary software focusing on MS DOS (Disk Operating System) and Windows deployment. The enterprise version of Ghost supports the multicast function. Therefore, Ghost is suitable for massive deployment. True Image [24] is another commercial proprietary software provided by Acronis. Two types of deployment methods are available in True Image: online/offline files backup and file system imaging. In addition, in previous years, many open source programs have been developed to fulfill the function of system deployment using the file system imaging method. The Partimage [25], which was developed in 2001, supports the Linux and MS Windows system deployment. The development of Partimage was terminated in 2008 due to the development of another open source system deployment software, the FSArchiver [26], by the same author. FSArchiver provides some new features that Partimage lacks. Redo Backup and Recovery [27] is open source file system imaging software with a GUI; the software can be executed from a bootable Linux CD or a USB flash drive. The open source OpenGnsys (Open Genesis) [13] system provides the functions that are required to deploy, manage, and maintain computers. OpenGnSys has been used in the computer lab in the Department of Electronic Engineering, Computer Systems, and Automatics at the University of Huelva (UHU) in Spain to improve teaching and learning in the classroom. FOG [28] is an open source network computer cloning and management solution that provides a web interface. Microsoft also provides proprietary technology, Windows Deployment Services (WDS) [29], for the automated installation of Windows operating systems. The WDS is especially suitable for Windows Vista, Windows Server 2008, and later versions of Windows. With WDS, system administrators can deploy Windows systems to new computers using network-based installations. Apple Software Restore (ASR) [30] is the system image software that is provided by Apple Computer. ASR can be used for the massive deployment of Macintosh computers using two deployment methods: file-based and block-based modes. Normally, the block-based mode is faster due to the transactions that are not going through the OS file system. Frisbee [31], or Emulab [32; 33], is an open source system that was developed at the University of Utah. Frisbee aims to save and deploy entire disk images and has been used in the Utah Emulab testbed. Frisbee manages thousands of disk images for users. OpenStack [9] is open source software that is dedicated for the IaaS in cloud computing. OpenStack supports bare-metal, virtual machine (VM), and container-based hosts [34]. The integrated program in OpenStack for bare-metal machines is OpenStack Ironic [9; 35], and it provides bare-metal provisioning for the Linux system.
## 3 The LBP-FFT-CNNs Model
### Data Preprocessing
#### 3.3.3 FFT  
FFT [20] converts texture information from the spatial domain to the frequency domain, emphasizing distinct frequency components of texture. It provides additional discriminative features for LBP-CNNs by capturing these subtle differences. Furthermore, combining LBP-based spatial patterns with frequency characteristics significantly enhances the network's ability to recognize unique patterns, and even spatial texture differences are minimal or nearly indistinguishable. FFT also effectively filters out high-frequency noise or irrelevant low-frequency components, focusing on the most informative frequency bands. This capability strengthens the robustness of LBP-CNNs against variations in lighting, shadows, or environmental conditions that often affect texture representation and make distinguishing similar textures more challenging. Suppose the FFT transforms a spatial domain texture \(f(x,y)\) into its frequency domain representation \(F(u,v)\). The FFT of this sequence is defined as follows:  
\[F(u,v)=\sum_{x=0}^{M-1}\sum_{y=0}^{N-1}f(x,y)e^{-j2\pi(\frac{\mu}{M}+\frac{vy}{N})} \tag{7}\]  
where \((x,y)\) and \((u,v)\) represent spatial coordinates and frequency coordinates, respectively. \(M\) and \(N\) are dimensions of the texture image. \(j\) is the imaging unit. This transform decomposes texture into its frequency components where magnitude \(|F(u,v)|\) represents the strength of a frequency and phase. Here, \(arg(F(u,v))\) encodes spatial alignment. Obstacles with similar textures in the spatial domain may differ in their frequency domain representations. FFT highlights these differences as follows:  
\[\Delta F=|F_{1}(u,v)|-|F_{2}(u,v)| \tag{8}\]  
where \(F_{1}(u,v)\) and \(F_{2}(u,v)\) are the frequency domain representations of two textures. Even if spatial patterns are similar, \(\Delta F\
eq 0\) due to variations in high- or low-frequency components. Combining spatial features \(f(x,y)\) (captured by LBP) with frequency features \(F(u,v)\) augments the feature space as follows:  
\[Combined\ Feature=\alpha LBP(f(x,y))+\beta\times F(u,v) \tag{9}\]  
where \(\alpha,\beta\) act as weights to balance spatial and frequency contributions. This dual domain feature improves the discriminative power of LBP-CNNs. The FFT allows filtering of noise by zeroing out specific frequency bands. For instance, it applies a low-pass filter to filter high-frequency noise:  
\[F_{filtered}(u,v)=\begin{cases}F(u,v),\ \sqrt{u^{2}+v^{2}}\leq CF\\ \ \ \ \ \ \ \ \ \ \ \ \ 0,\ otherwise\end{cases} \tag{10}\]  
where \(CF\) is the cutoff frequency. This operation enhances the signal-to-noise ratio, making the textures more distinguishable under varying conditions. The combined spatial and frequency features are fed into the CNNs, which learns discriminative mappings \(\phi\) during training:  
\[\hat{y}=\emptyset(LBP(f(x,y)),F(u,v);\theta) \tag{11}\]  
\(\hat{y}\) is the predicted label of obstacle, and \(\theta\) is the trainable parameters of the CNN.
# Optimal Control Approach to [PERSON]'s Problem and Gibbs' Method
## 3 Extended Lambert's Problem
### [PERSON]'s Problem under J2 Perturbation  
If J2 perturbation is taken into account, the potential energy term in Equation (4) should be modified as follows [8]:  
\[U=-\frac{\mu}{\sqrt{\overrightarrow{r}\cdot\overrightarrow{r}}}-\frac{\mu J _{2}R^{2}}{2\sqrt{\overrightarrow{r}\cdot\overrightarrow{r}}^{3}}\Big{(}1- \frac{3z^{2}}{\overrightarrow{r}\cdot\overrightarrow{r}}\Big{)} \tag{21}\]  
where \(R\) is the mean equatorial radius of the Earth, \(J_{2}\) is the second zonal harmonic coefficient for the Earth, and \(z\) is the Z-axis component of the position vector in the Earth Centered Inertial (ECI) frame.  
Then, the [PERSON]'s problem under \(J_{2}\) perturbation can be reformulated to an optimal control problem given as:  
\[arg\underset{\overrightarrow{v}}{min}\int_{t_{1}}^{t_{2}}\frac{1}{2} \overrightarrow{v}\cdot\overrightarrow{v}+\frac{\mu}{\sqrt{\overrightarrow{r }\cdot\overrightarrow{r}}}+\frac{\mu J_{2}R^{2}}{2\sqrt{\overrightarrow{r} \cdot\overrightarrow{r}}^{3}}\Big{(}1-\frac{3z^{2}}{\overrightarrow{r} \cdot\overrightarrow{r}}\Big{)}\,dt \tag{22}\]  
\[\overset{\cdot}{\overrightarrow{r}}=\overrightarrow{v}\]  
\[\overrightarrow{r}(t_{1})=\overrightarrow{r}_{1},\quad\overrightarrow{r}(t_ {2})=\overrightarrow{r}_{2}\]  
\[t_{1},t_{2}\text{ \emph{given}}\]
## 2 Methodology
### Chlorine Residual Analysis  
Chlorine is injected into the WDN at the desalination plants and is used as a secondary disinfection. Adequate chlorine concentrations along with a maintained pressure are essential to keep the water free of contaminants. As pipe walls break, the disturbances caused by pressure result in contaminant intrusion and reduced chlorine residual and water quality. The SEWA network contains 46 water quality monitoring points distributed throughout the 13 zones. Various water quality parameters (temperature, pH, chlorine) are often monitored to determine the status of the WDN, with residual chlorine being the most critical water quality indicator [41]. Any drops in chlorine levels below 0.2 mg/L would  
Figure 1: Water distribution network of Sharjah.  
be possible indicators of leaks in the network [42]. In this study, the chlorine residual of 0.2 mg/L was used as the threshold for leak detection.
# Detecting Jamming in Smart Grid Communications via Deep Learning
## 6 Performance Evaluation
### Image Size  
An important parameter that affects the performance of the classifier is the size of the images considered as the input to the classifier itself. In this section, we investigate the impact of image size on the performance of the classifier. In particular, we only focus on _Scenario 2_, i.e., jamming detection via autoencoders without prior knowledge of jammed signals. We do not apply our analysis to _Scenario 1_ since changing the input size for CNN involves significant changes in the input layers of the network, thus making the comparison unfair between the cases considered.  
Figure 13: The SNRs with RJP = 1 at different distances regarding the jammer (\(\mathcal{E}\)) and receiver (\(\mathcal{B}\)). Transmitter (\(\mathcal{A}\)) and receiver (\(\mathcal{B}\)) have been set to 30 m.  
Figure 15 shows the accuracy of the autoencoder as a function of distance \(\mathcal{E}\)-\(\mathcal{B}\) while considering different image sizes. We observe that image size affects the performance of the classifier: larger images (\(224\times 224\)) allow for the detection of the jammer at longer distances, i.e., 68 m with respect to 66 m when considering images of size \(128\times 128\). Our analysis also considers the training time as a function of the image size, as shown in Figure 15. While performances are slightly affected by the size of the image (about 6%), the training time increases from about 37.7 s to 112 s (about +300%). Thus, system administrators should consider the trade-off between performance (accuracy) and training time when choosing the image size to be used at deployment time.
# Influence of Earthquakes on Landslide Susceptibility in a Seismic Prone Catchment in Central Asia
## 4 Discussion
### Relationship between Landslide Metrics and Environmental Variables
However, most forest areas cover the high- to mid-streams in the Mailuu-Suu catchment, where a high susceptibility level was identified. The spatial distribution of soil types was also in accordance with LSI; therefore, a clear correlation between LSI and soil type was indicated in this study. Slight differences in such correlations were observed between VOL and LSI. However, it is necessary to note that the explanatory power of LSI (79.06-87.85% under the first four earthquake scenarios) was significantly higher than that of VOL (only 11.07-16.21% under the first four earthquake scenarios). Therefore, the relationship analysis between LSI and the environmental variables is more meaningful than that between VOL and the environmental variables from a statistical point of view.
## 3 Results and Discussion
### Physical Activation Process Test Results
As presented above, 28 d SEM images showed (Figures 12a, 13a and 14a) that as hydration proceeded, C-S-H gels and rod-like AFt (AFm) increased, intertwining and distributing in the system to form a backbone, which optimized the structure of the hardened slurry, resulting in a structural densification that was significantly better than that at the age of 3 days. EDS images showed (Figures 12b, 13b and 14b) that as the specific surface area increased, the resulting Ca-Si ratios of 1.78%, 1.87%, and 1.94% decreased from 3 days, while the aluminium-silicon ratios of 0.32%, 0.33%, and 0.35% increased from 3 days. Due to the increase in silica aluminate in the hydration products, the active substances of the admixture consumed some of the Ca(OH)\({}_{2}\) crystals, which fully utilized the volcanic ash effect, accelerated the hydration process, further filled the pores, and improved the denseness of the material. This combination of low calcium-to-silica and high aluminium-to-silica ratios endowed the hydration products with stronger gelling properties, lower porosity, and reduced permeability, thus improving the overall density and erosion resistance of the material [55]. Combining the analysis of macroscopic mechanical properties and microscopic hydration products, the volcanic rock powder demonstrated a significant reaction potential within three days. It rapidly reacted with Ca(OH)\({}_{2}\) in cement to promptly produce hydration products, including C-S-H and C-A-S-H. Nonetheless, the initial hydration products of the volcanic rock powder were limited and insufficient to considerably enhance concrete strength. With time, the accumulation of hydration products, along with an optimized pore structure, facilitated a continuous increase in strength, thereby enhancing the compactness and mechanical properties of the concrete. Mechanical activation's intervention not only regulated the particle size distribution of the additions and promoted material lattice recon  
Figure 4:2 SEM (**a**) and EDS (**b**) patterns of volcanic rock powder with a specific surface area of 640 (m\({}^{2}\)/kg) at 28 days. struction, but it also augmented the content of amorphous silica and alumina, effectively reinforcing the material [56]. Experimentation revealed that volcanic rock powder with a specific surface area of 735 m\({}^{2}\)/kg exhibited an activity index 4% higher compared to that at 480 m\({}^{2}\)/kg, emphasizing the rapid reaction rate and pronounced volcanic ash effect of finer particulate volcanic rock powder. This was beneficial for optimizing the interfacial transition zone, reducing defects [57; 58; 59], and augmenting the material's toughness. Nevertheless, the mineral composition of the volcanic rock powder, particle re-agglomeration phenomena, and potential passivation effects following the saturation of abrasion's impact somewhat mitigated its contributions to strength enhancement and activity promotion. For optimal cost-effectiveness, establishing the optimal fineness range for volcanic rock powder at 560-640 m\({}^{2}\)/kg is advised.
## 3 Analyze Results and Discussion
### Comparison of Analytical Results with Field Observations Reported by [PERSON] et al. [30]  
[PERSON] et al. [30] conducted a comprehensive field investigation across the 11 most affected provinces, documenting the distribution of damage levels observed in residual reinforced concrete buildings. The analytical results obtained from the current study are com-pared with the findings of Ref. [30] in Table 9. When the damage classifications reported in their study are grouped into two main categories--(1) collapsed, heavily damaged, and moderately damaged, and (2) no damage and slightly damaged--and compared with the analytical results obtained in the presented study, it becomes evident that the Turkish building stock did not meet the expected seismic performance levels during this earthquake, especially for buildings up to six stories. In fact, a performance gap of nearly 100% is observed in some cases.  
For three-story buildings, the analytical model estimated 94.24% of buildings to experience no or slight damage, compared to the 90.73% observed in the field. For four- to six-story buildings, the calculated rate of no or slight damage was 89.02%, while field observations indicated a rate of 81.17%. For moderately to heavily damaged structures, the calculated damage ratio was 5.76% for three-story buildings and 10.98% for 4-5-6-story buildings. In contrast, the observed field study reported significantly higher values, with rates of 9.27% for 1-2-3-story structures and 18.83% for 4-5-6-story structures. The comparison between the analytically calculated moderate-to-collapse damage ratios and those observed in the field for these buildings raises serious concerns regarding their code compliance and structural adequacy.  
However, for buildings with seven or more stories, a reverse trend is noted. While the analytical model predicted a higher level of damage (16.63% moderate to complete damage), field observations reported only 14.85%. This discrepancy is likely due to the exclusion of shear walls from the analytical models used in this study, previously discussed as a limitation for this group of structures. The omission of shear walls, which are known to significantly enhance lateral strength and deformation capacity in taller RC  
Figure 18: Fragility curves for new and old buildings.  
buildings, likely resulted in more conservative (i.e., worse) analytical outcomes compared to real-world observations.
## 3 Results
### Visual Quality Evaluation
In SRR 1:6, the same-sensor FVI results are very similar in color, texture and shape. The roads, rivers, built-up, forest areas in FVI are very similar to RVI. For the images acquired at the same time by the same sensor, most algorithms for pan-sharpening provide very good results, i.e., they retain the high spatial resolution of the panchromatic image and the spectral information from the multispectral image (single-sensor, single-date fusion) [2]. For multi-sensor fusion in SRR 1:6, EVI shows the river area well but not the built-up areas. Moreover, GNDVI shows that some fusion algorithms have good results for edge detection like PC but not good for built-up areas. Ehlers does not have a good result for road detection. However, the GS method has a better visual quality than the other methods for these features (Table 8). Some VIs (ARVI, NDVI and OSAVI) have a very similar visual quality with their corresponding RVI in single-sensor fusion with SRR 1:8. However, other VIs (EVI, GNDVI and SAVI) have a better quality for all features in multi-sensor fusion with SRR 1:8. Same-sensor fusion still has a good visual quality in SRR 1:10 like in SRR 1:6 and SRR 1:8. However, in multi-sensor fusion, for GF4 PAN(50 m) with MODIS MS (500 m) fusion, the visual quality is degraded. In EVI and OSAVI, the size and boundary of the river are not properly mapped and other details are also very poor. Thus, we made other multi-sensor fusion by resampling the Landsat-8 OLI image. NDVI and SAVI have a better visual quality for built-up, road, river and forest areas. In SRR 1:12, the same-sensor FVI has a good visual result. In SRR1:12, for multi-sensor fusion, GF2 PAN (4 m) with GF4 MS (50 m), the quality slightly improved compared to SRR 1:10. EVI and OSAVI showed a better visual quality for the GS and PC methods (Table 8).
# Characteristics of Removal of Lead, Cadmium and Chromium from Soil Using Biosorbent and Biochar
## 4 Conclusions  
The conducted studies demonstrated that the use of aspen wood sawdust and biochar made from this type of wood is effective in removing heavy metals from soil. Experiments on the removal of lead, cadmium, and chromium ions were carried out in two scenarios: one with a single metal and another with three metals. The impact of process time and sorbent concentration in the soil on the removal capacities of Pb(II), Cd(II), and Cr(VI) was investigated, and the results were compared with those from commercial activated carbon. The removal capacities of heavy metal ions from the soil are strongly correlated with the type of material and the metal ion being removed. Activated carbon exhibited the highest removal capacities for all metal ions, while aspen wood sawdust had the lowest capacities. The highest removal efficiencies for individual metal ions were achieved for lead ions at a sorbent concentration of 10%, with removal rates of 46% for sawdust, 50% for biochar, and 71% for activated carbon. The highest sorption capacities were achieved for sorbent concentrations of 2%, with values of 20.1 mg/g, 22.3 mg/g, and 23.2 mg/g, respectively. Studies in the three-metal scenario showed that all metals were removed from the soil, but the obtained values were lower compared to the single-metal scenarios. In this case, the removal efficiencies for biochar were 23.4% (Pb), 10.9% (Cd), and 9.7% (Cr). The efficiency of removing heavy metal ions from the soil increased with process time and the concentration of the used sorbent. Statistical analysis revealed that statistically significant parameters for the process were the type of material used, its concentration in the soil, process time, and the type of metal being removed. The use of sorbents, including biochar, could be a potential method for immobilizing heavy metals in soil and preventing their movement into the food chain. Despite some limitations compared to activated carbon, biochar is an attractive and sustainable alternative for heavy metal removal due to its low cost, versatility, and ecological benefits.  
Figure 8: Approximation profile with the desirability function of the metal removal process from the soil for the variables: heavy metal content in the soil and heavy metal removal efficiency ((**A**)—Series I and II, (**B**)—multi-component system).  
**Author Contributions:** Conceptualization, P.S.; Methodology, J.C.; Validation, P.S. and J.C.; Investigation, P.S., A.Z., P.R. and J.C.; Writing--original draft, P.S. and A.Z.; Visualization, P.S.; Supervision, P.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Preparation of Freeze-Dried Porous Chitosan Microspheres for the Removal of Hexavalent Chromium
## 3 Results  
### Porous Chitosan Microspheres Preparation  
Preparation of the porous chitosan microspheres has two main processes, obtaining the chitosan hydrogel beads and freezing-lyophilization of the beads, presented in Figure 1. Chitosan, as a high polymer material, can be dissolved in acidic solutions but not in neutral and alkaline solutions, which creates the opportunity for the preparation of the chitosan hydrogel beads. Chitosan acid solutions were added dropwise into an alkaline NaOH solution using a syringe. Because of acid-base neutralization, the chitosan droplets remain round without dissolution. After being hardened in alkaline solution, chitosan hydrogel beads with microsphere shapes were obtained. In this procedure, the diameters of the chitosan hydrogel beads mainly depended on the size of the syringe needle. Thus, in this work, two syringe needles of different sizes were used.  
The porous chitosan microspheres were prepared by first freezing the chitosan hydrogel beads and then drying them. When the chitosan hydrogel, a mixture of the chitosan molecule chain and water, was placed at a low temperature under the crystallization point of water, the chitosan chains were squeezed into the gaps of the ice crystals. At that time, non-toxic ice crystals played the role of a pore-forming agent while chitosan was the backbone. After the sublimation of ice crystals, porous chitosan microspheres with abundant pores replaced the ice crystals in the microspheres were successfully prepared. Interestingly, the factors that could affect the shape, quantity, crystallization degree, and growth velocity of the solvent crystals included solvent concentrations and types, the freezing temperature and methods, the drying method, and time, which could influence the formation of the obtained porous materials of various morphologies [24; 25; 26; 27; 28]. In this  
Figure 1: The generation process of the porous chitosan microspheres.  
study, the chitosan concentration, freezing methods, and drying time were the focus for the preparation of porous chitosan microspheres.