esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
It was reported that 1 g/L and 4 g/L nano iron concentrations did not cause a significant change in the hydraulic conductivity of the soil, but 10 g/L nano iron concentration decreased the hydraulic conductivity from 6.1 \(\times\) 10\({}^{-6}\) m/s to 2.5 \(\times\) 10\({}^{-7}\) m/s [24]. [PERSON] and [PERSON] investigated the effect of carbon nanofibers (CNF) and multi-walled carbon nanotubes (CNTs) in the same carbon family, which are relatively inexpensive and readily available on the market, on the hydraulic conductivity of clay soils. The hydraulic conductivity of the soil sample (2.16 \(\times\) 10\({}^{-9}\) m/s) decreased with increasing CNT and CNF ratio, and the hydraulic conductivity decreased to 9.46 \(\times\) 10\({}^{-10}\) m/s and 7.44 \(\times\) 10\({}^{-10}\) m/s with the addition of 0.2% CNT and CNF, respectively [25]. [PERSON] and [PERSON] treated a later soil with nano silica, which has superior properties such as large surface area, small particle size, and porous structure, and showed that the permeability coefficient decreased at pressures above 40 kN/m\({}^{2}\)[26]. [PERSON] and [PERSON] investigated the geotechnical properties of fly-ash-doped soils with different nano calcium silicate (NCS) doses (0.2-1%). With the addition of 1% NCS to soil containing 6% fly ash, the permeability decreased from 6.2 \(\times\) 10\({}^{-6}\) cm/s to 4.8 \(\times\) 10\({}^{-6}\) cm/s [27]. In this study, the effects of three different nanomaterials (iron oxide, aluminum oxide, and Oltu clay) at two different rates (1% and 5%) on the permeability and heavy metal removal rate of the compacted kaolin through which the leachate was passed were investigated. The Fe, Mn, Zn, Cu, and Pb removal rates, as well as the permeability of compacted kaolin and compacted kaolin/nanomaterial clay samples, were determined. Identifying the compacted clayey soil sample possessing the lowest permeability and the greatest inhibition of the transmission of heavy metals to the groundwater is beneficial for the design of soil to be used in landfill areas. In addition, the final product obtained by adding natural Oltu clay and a small amount of nanomaterials to kaolin, which is abundant in nature, is a more economical and simple material that is able to compete with the expensive geomembranes used in landfills, but which have tearing problems.
## 1 Introduction
[21]  
Figure 1: Different shapes of canopy trees: olive (**on the left**) and fir (**on the right**) trees. combine WV3 imagery and convolutional neural networks (CNNs) to map Brazil nut trees in the Amazon, demonstrating an effective method for monitoring key species in dense forests. [PERSON] et al. [22] explore how WV3 imagery can estimate mango yield by analyzing tree health and canopy structures, providing insights for precision agriculture. The versatility of WV3 imagery for multispectral classification is further highlighted in various studies. For example, [PERSON] et al. [23] show how bi-temporal WV3 images enhance urban tree species classification. [PERSON] et al. [24] demonstrate the effective use of WV3 imagery with UAV data for assessing macadamia tree crop health. [PERSON] et al. [25] combine texture analysis and WV3 data for improved tree species classification in tropical forests. Similarly, [PERSON] et al. [26] use a combination of spectral and textural features to classify mangrove species. [PERSON] et al. [27] propose a methodology for deriving detailed vegetation indices in olive orchards, offering insights into agricultural monitoring. [PERSON] et al. [28] use WV3 shortwave infrared indices for mapping crop residue and tillage intensity, while [PERSON] et al. [29] further emphasize the utility of WV3 for tree species classification. In this study, a method to estimate the height of olive trees using a monoscopic WV3 satellite image is proposed. The approach is based on a mathematical model that calculates the height of the trees; this is conducted by using the solar position information (available in the satellite image metadata), measuring the shadow length on the orthoimage, and considering the slope and aspect of the ground. To validate the results, we compare the estimated tree heights with altimetric data obtained through drone surveys, providing a reliable ground-truth assessment of the proposed methodology. Methods similar to the one proposed here focus solely on buildings or objects with well-defined geometries, and no standardized tests exist for measuring tree heights. The innovative aspect of the proposed methodology lies in its focus on measuring tree heights (e.g., olive trees) by exploiting the very high resolution of WorldView-3 monoscopic satellite imagery and the metadata related to the Sun's position at the acquisition time. This approach enables rapid and cost-effective measurements compared to other techniques, such as UAV surveys. It allows for the analysis of very large areas (over a few square kilometers) much more efficiently than ground-based or drone measurements and benefits from the wide availability of archived satellite images, which can be purchased at lower prices compared to new acquisitions. Additionally, it facilitates data collection in remote or inaccessible regions, such as areas in developing countries, where other techniques may be limited. Potential applications of the proposed method include using tree height measurements to estimate parameters such as biomass, carbon stock, or canopy volume, contributing to ecological and agricultural studies.
## 5 Discussion
### Vulnerabilities Mitigation
Specific to the NIST SP 800-53, which reviews security and privacy controls for information systems and organizations, the framework mentions the sections regarding information flow enforcement (AC-4), separation of duties (AC-5), the principle of the least privilege (AC-6), personnel screening (PS-3), access agreements (PS-6), boundary protection (SC-7), transmission confidentiality and integrity (SC-8), cryptographic protection (SC-13), covert channel analysis (SC-31), system monitoring (SI-4), and protection from information leakage due to electromagnetic emanation (PE-19). Regarding the latter, the TEMPEST is a valuable specification regarding equipment shielding against non-intentional leakage of radio or electric signals, sounds, and vibrations. As these countermeasures are closely related to the attack vector, we considered the breach_type of the dataset for reviewing them. However, it is essential to note that listing all security measures for mitigating data breach-related vulnerabilities is impractical due to the vast number of attack vectors. \begin{table}
\begin{tabular}{c l} \hline \hline
**Core Function** & **Description** \\ \hline Identify & Help determine the current cybersecurity risk to the organization. \\ Protect & Use safeguards to prevent or reduce cybersecurity risk. \\ Detect & Find and analyze possible cybersecurity attacks and compromises. \\ Respond & Take action regarding a detected cybersecurity incident. \\ Recover & Restore assets and operations that were impacted by a cybersecurity incident. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: NIST CSF core functions. Figure 15: Frequency and average cost of initial attack vectors responsible for data breaches in 2023. Adapted from [58]. Therefore, this section provides general good practices against common attack vectors, not an exhaustive list of available security controls.
# Plant-Derived Bioactive Compounds for Rhabdomyosarcoma Therapy In Vitro: A Systematic Review
## 3 Results  
The results have been presented by describing the mechanism of action of each plant extract or its bioactive compound. The main mechanism of action in RMS cells of all extracts or their isolated compounds was apoptosis, representing 43% of all bioactive compounds of plant origin studied in the present systematic review [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36], followed by 19% with antioxidant activity and cytotoxicity against tumor cells [37; 38; 39; 40; 41; 42; 43; 44; 45]. Up to 9% of extracts or bioactive compounds induce cell death by cell cycle arrest [23; 32; 35; 46], although some of these extracts overlap with another mechanism such as apoptosis. Minor mechanisms are the inhibition of the formation of tumor colonies, representing 3% of the total [47; 48], the blockade of specific transporters important in cell viability, representing 5% of all bioactive compounds studied [36; 49; 50], and another 5% of them acting by inhibiting cell motility [21; 23; 34]. Finally, for 16% of all bioactive compounds studied in the review, the mechanism of action remains unknown [15; 16; 35; 51; 52; 53; 54], as no molecular studies were carried out to reveal it (Figure 2).  
On the one hand, the most commonly used parts of the plant to obtain the bioactive component were the leaves and the whole plant, which stood out in 23% of the studies. In studies that referred to the entire plant, no reference was made to which specific parts were included. Another important group for obtaining the bioactive compound has been the aerial parts of plants. The term 'aerial parts' refers to the set of stems, leaves, flowers, fruits, or seeds. It is noteworthy that the least used plant part was the seed (Figure 3A).  
Figure 3: Graphic representation of (**A**) plant parts and (**B**) solvents that have been used to obtain the functional extracts.  
Figure 2: A graphic representation of the main mechanisms of action of plant extracts or bioactive compounds on RMS cells in vitro.  
On the other hand, regarding the extraction method of the plant extract or bioactive compound, the most commonly used chemical components were methanol (56%), ethanol (25%), and distilled water (15%) (Figure 3B).  
Another important fact to highlight in this first part of the results regarding the relevance of the topic is reflected in the number of articles published in recent years. As previously mentioned, research on plant extracts and their possible use as a complementary treatment to the current treatment for various tumors is booming, especially for the most common cancers, such as colon cancer. Regarding RMS, a progressive increase in the number of investigations has been observed. The first article on this topic dates to 1988, and the next one is from 2003, with no articles existing in this 15 year gap. Since 2002, no publications were registered until 2012, in which we can find two articles. The highest number of publications was reached in 2020 with a total of nine articles; however, this number has decreased to six in 2022 (Figure 4).
## 4 Materials and Methods
### Model Selection and Training
#### 4.2.3 Model Training  
In ML and DL model training, data are fed into the model and continuously adjusted to minimize a loss function. DL uses multiple-layer neural networks along with very large datasets and substantial computing capacity to discover intricate patterns in the data. In this study, 80% of the entire data is used for training and validation. Throughout the training process, several strategies were used to prevent overfitting and help the models generalize appropriately. To improve generalization and avoid relying greatly on certain neurons, dropout layers were added to the neural networks in the DL models (CNN and LSTM). The dataset was carefully split into training, validation, and testing sets to make sure that the models were tested on new data. Traditional ML models such as RF, KNN, and SVM employed built-in regularization methods, and hyperparameter optimization was conducted to achieve a balance between model complexity and generalization performance. Cross-validation was applied when needed, to ensure robust model selection and parameter tuning, maintaining a reliable evaluation framework for practical IoT deployment scenarios.  
Specific stopping criteria were defined for each model type to ensure optimal performance. Early stopping was used for DL models such as CNN and LSTM by monitoring validation accuracy and automatically terminating training when validation performance stopped improving. Traditional ML models implemented algorithm-specific stopping criteria: RF used a predetermined number of estimators, KNN classified using distance-based classification without iterative training, and SVM optimized using convergence tolerance parameters.
# Automatic Defect Detection for Web Offset Printing Based on Machine Vision
## 2 Methodology  
The proposed defect detection method consists of two parts as shown in Figure 1, the first part is template construction, while the second part is defect detection for printed images. For the first part, multiple defect-free samples are collected, and then are aligned to generate template images which include the reference image, a bright image and a dark image. The reference image is obtained by averaging multiple aligned defect-free images, and is used to register the detected image during the process of defect detection. In addition, two tolerance images, named bright image and dark image, are built with respect to the reference image. The bright image and the dark image give the maximum and minimum deviations of a pixel value, and in combination with the reference image serve as template images for further defect detection. For defect detection in the second part, the web offset image is first cut to the same size as the reference image by the algorithm in Section 2.1, then aligned with the reference image and compared with the bright image and the dark image. If a pixel value of the detected image is not in the range of the bright image and dark image, it is identified as a defect pixel. Finally, a Blob analysis algorithm is used to label defect region.
## 4 Review of Existing Methods and Capabilities
### Path Planning
#### Moving Target
Stochastic simulations of realizations of the transition matrix with posterior distribution approximation enable easy re-sampling of the posterior distribution. This method is valuable for searching for moving targets where the models of the target motion are poorly known. [PERSON] and [PERSON] [39; 75] proposed a method of UAS control utilizing POMDPs for tracking moving targets including evasive targets and threat avoidance. The resulting algorithm enabled a vehicle, or multiple vehicles, to be able to track moving targets. The design was robust enough to be able to track an evasive ground vehicle as well as avoid threats, obstacles, and other friendly vehicles while maintaining tracking of the target. Wind compensation and variable speed and altitude capabilities were integrated as well.
# Security Analysis of the Symmetric Cryptosystem Tiny Jambu
## 5 Statistical Analysis of Randomness
### Fips Test 140-2. Requirements of Security for Cryptographic Modules  
FIPS (Federal Information Processing Standard) Publication 140-2 is a U.S. government security standard [22] used to evaluate cryptographic modules. FIPS 140-2 is made up of four statistical tests (e.g., the Monobit Test, the Poker Test, the Runs Test and the Long Runs Test). The proposed TinyJambu keystream sequences passed all the FIPS tests. The average results we obtained are as follows:  
Figure 11: This figure shows the results of the last four DIEHARD tests applied to sequence_1 to sequence_15.  
Figure 10: This figure shows the results of another four DIEHARD tests applied to sequence_1 to sequence_15.  
1. LONG RUNS TEST: passed. No runs of more than 25 equal and consecutive bits appeared.
2. MONOBIT TEST: passed. The test is passed if 9752 < number of ones < 10,275. The obtained result was 9941.
3. POKER TEST: passed. The test is passed if \(2.16<X<46.17\). The obtained result was 12.8192.
4. RUNS TEST: passed. The test is passed if the runs (runs of zeros in red and runs of ones in blue) of length 1-6 are within the specified interval in green in Figure 12. Notice that runs of a short length are more frequent than runs of a long length. For short runs, the number of runs of zeros (gaps) and runs of ones (blocks) must be equal (the second postulate of pseudorandomness of [PERSON] [23]).
## 4 Importance of Electrode Configurations for FECG Measurement
### Current ECG Monitoring Development with Smart Textile Electrodes  
Traditionally, ECG monitoring relies upon adhesive hydrogel electrodes. There are some key challenges that arise with this type of electrode including discomfort to patients due to skin irritation caused by hydrogels [109, 110], difficult positioning when more electrodes are needed, and less accurate signal quality over longer period due to the degradation of hydrogel [105]. Over the past few years, novel concepts of using smart textile electrodes for monitoring ECG electrodes have emerged. Smart textiles-based electrodes are preferred for ECG monitoring as they can be deployed for longer term monitoring easily without any risk of hydrogel degradation and provide enhanced comfort to patients due to no skin irritation [111, 112, 113]. Some examples of current development in smart textile ECG electrodes are summarized in Table 3.  
Figure 10: (**a**) Sewn ECG electrode; (**b**) screen printed ECG electrode. (Reprinted with permission from ref [39] [PERSON], [PERSON] (2009)).  
With the increase in cardiovascular diseases, there has been a growing interest in developing wearable devices, specifically smart textiles for ECG monitoring, that can continuously monitor cardiac activity without causing discomfort to patients [105]. A smart textile-based T-shirt achieved by knitting and embroidering stainless steel yarns on nylon fabric was deployed for long-term ECG monitoring, thus providing no skin irritation to patients, and enhancing the comfort to patients [113, 114]. The authors of [115, 116] used the woven and screen-printed smart textile electrodes for ECG monitoring. The authors of [117] achieved a silver chloride electrode using electroless plating for the ECG monitoring.  
However, the use of smart textile electrodes in FECG and NIFECG has yet to be reported due to the following barriers: (a) the optimized number of electrodes required for fetal heart rate monitoring needs to be investigated, and (b) the signal quality of FECG needs to be improved due to the small amplitude compared with MECG. The deployment of smart textile electrodes in non-invasive fetal electrocardiography (NIFECG) for fetal heart rate (FHR) monitoring will revolutionize the healthcare monitoring in a way that smart textile-based electrodes would provide enhanced comfort to patients and no skin irritation during long-term monitoring.
# MapReduce Algorithm for Location Recommendation by Using Area Skyline Query
## 3 MapReduce-Based Area Skyline  
We first divide a given region A into \(n\times n\) grids on average by using a grid structure. To distinguish the grids, we assign IDs to all the grids from bottom-left \(g_{1,1}\) to top-right \(g_{n,n}\) (see Figure 3). Next, for each row, we calculate the distance of each grid to each type of facilities in the Map function. Then, we calculate the distance for each column in Reduce function. Using the distances for each type of facilities, we retrieve non-dominated grids. To simplify the problem, we assume that the distance between a facility and a grid is approximate to the distance of the center of the two grids when the length of the grid is small enough.
Unlike the direct disturbance feedforward method, by which additional sensors should be equipped on the pedestal to extract disturbance, DOB could acquire the disturbance through the difference between the data of the existing sensors and the model output [21; 22]. Since the FOG's low-frequency signal was weak and susceptible to drift and noise, which resulted in the inaccuracy of the observed low-frequency disturbance, the additional DOB mainly benefited the high-frequency ADA. Hence, the proposed method could make the CCD-based EOB and FOG-based DOB complementary. To verify the effectiveness of the method, a platform consisting of groups of the fast steering mirror system was established, which was the core component of MOTS [14]. Experiments demonstrated that the proposed system had a good TP in low frequency and a strong ADA in a wide band. This paper is organized as follows. Section 2 introduces the physical structure of the MOTS, the basic VPDC method and the common tracking and disturbance feedforward way based on direct measurement. Section 3 analyzes the proposed feedforward method based on the EOB and DOB,which could provide theoretical derivations. Section 4.1 discusses the matter of how to design the EOB controller \(Q_{1}\) to maximize the performance of the EOB under the condition of guaranteed gain and phase margin. Section 4.2 focuses on the design of the DOB controller \(Q_{2}\) and analyzes the promotion of ADA. Section 5 is the experimental part, indicating the detailed improvement of the TP and ADA by the proposed method. Section 6 lists the concluding remarks.
# Preparation and Chemical Protective Clothing Application of PVDF Based Sodium Sulfonate Membrane
## Materials and Methods
### Water Uptake (WU), Ion-Exchange Capacity (IEC), and Linear Swelling Ratio (LSR)  
IEC value was measured by acid-base titration. First, the membranes (0.2 g) were changed to acid form by being immersed in 1 M HCl aqueous solution for 24 h. Secondly, the membranes were washed with DI and weighed by analytical balance. Finally, the membranes were soaked in NaCl aqueous solution (1 M) and equilibrated for 24 h. Hydrogen ions were quantified by titration of 0.1 M NaOH aqueous solution using phenolphthalein as an indicator. The IEC value can be calculated by the mass of the dry membrane in H\({}^{+}\) form (\(W_{\text{dry}}\)) and the amount of NaOH consumed in the titration (\(V_{\text{NaOH}}\times C_{\text{NaOH}}\)) (Equation (1)).  
\[\text{IEC}=\frac{V_{\text{(NaOH)}}\times C_{\text{(NaOH)}}}{W_{\text{dry}}} \tag{1}\]  
WU and LSR of the membranes (10 \(\times\) 30 mm) were calculated by following equations (Equations (2) and (3)) based on the measured data of weight and length of membranes after being immersed in water and equilibrated for 24 h, respectively.  
\[\text{WU}=\frac{W_{\text{wet}}-W_{\text{dry}}}{W_{\text{dry}}}\times 100\% \tag{2}\]  
\[\text{LSR}=\frac{L_{\text{wet}}-L_{\text{dry}}}{L_{\text{dry}}}\times 100\% \tag{3}\]  
where the weight and length of the wet membrane are marked as \(W_{\text{wet}}\) and \(L_{\text{wet}}\), whereas the weight and length of the dry membrane are marked as \(W_{\text{dry}}\) and \(L_{\text{dry}}\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **PVDF (g)** & **SSS (g)** & **DVB (g)** & **AIBN (g)** & **DMF (mL)** \\ \hline M1 & 20 & 0 & 0 & 0 & 200 \\ M2 & 20 & 2.5 & 0.25 & 0.025 & 225 \\ M3 & 20 & 6 & 0.6 & 0.06 & 260 \\ M4 & 20 & 10.4 & 1.04 & 0.104 & 304 \\ M5 & 20 & 16 & 1.6 & 0.16 & 360 \\ M6 & 20 & 24 & 2.4 & 0.24 & 440 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Compositions of membranes with different IECs.