esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Sift  
The similarity between the original and forged images in each language can be measured to identify the differences in the characteristics of these languages. The SIFT (Scale-Invariant Feature Transform) algorithm can find feature points in images while maintaining robustness to changes in scale, rotation, and illumination [25]. Using this algorithm, feature points can be detected, and matching distances can be calculated to measure the similarity between the original and forged images in each language. The method for detecting feature points in images using the SIFT algorithm is described below [26]:  
1. Scale-space Extreme Detection: The first step of the SIFT algorithm is to use the Difference of Gaussians (DoG) function to find points of interest at various scales and locations within an image. The DoG detects stable feature points at different scales, ensuring invariance to changes in size and orientation.
2. Keypoint Localization: to precisely determine the location and scale of candidate keypoints, a detailed model is applied at each location to select stable keypoints.
3. Orientation Assignment: For each keypoint location, one or more orientations are assigned based on the local image gradient directions. All subsequent operations are performed on image data transformed relative to the assigned orientation, scale, and location, ensuring invariance to these transformations.
4. Keypoint Descriptor Generation: At the chosen scale, local image gradients around each keypoint are measured and converted into feature vectors, ensuring strong invariance to local shape distortions and changes in illumination.
## 3 Results and Discussion
### Key Odor Contributors to Shaken Black Tea Infusion
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Compounds**} & \multicolumn{3}{c}{**FL vs. S1**} & \multicolumn{3}{c}{**S1 vs. S2**} & \multicolumn{3}{c}{**S2 vs. S3**} & \multicolumn{3}{c}{**S3 vs. F**} & \multicolumn{3}{c}{**F vs. D**} \\ \cline{2-13}  & & **Log\({}_{2}\)FC** & **VIP** & **Type** & **Log\({}_{2}\)FC** & **VIP** & **Type** & **Log\({}_{2}\)FC** & **VIP** & **Type** & **Log\({}_{2}\)FC** & **VIP** & **Type** & **Log\({}_{2}\)FC** & **VIP** & **Type** \\ \hline
**Alcohols (13)** & & & & & & & & & & & & & & \\
2 & 1-Penten-3-olol & & 1.05 & 2.02 & Up & & & & & & 1.15 & 1.72 & Up & & & \\
4 & (2)-2-Penten-1-ol & & & & & & & & & 1.70 & 2.22 & Up & & & \\
5 & (2)-3-Alexenol & 2.06 & 3.08 & Up & & & & & & & & & -1.06 & 1.24 & Down \\
6 & 1-Hexanol & & & & & & & & & & & & & -1.40 & 1.75 & Down \\
7 & (E)-2-Hexen-1-ol & & & & & & & & & & & & & -1.09 & Down & 1.03 & 1.30 & Up \\
9 & 1-Octa-ol & & & & & & & & & & & & -1.75 & 2.03 & Down & & \\
10 & 2-Ethyl-1-hexanol & & & & & & & & & & & 2.90 & 3.21 & Up & & \\
11 & Benzyl alcohol & & & & & & & & & & & & & & \\
14 & Phenylethyl alcohol & & & & & & & & & 1.40 & 2.60 & Up & & & \\
16 & (E,Z)-3-6-Nonadenier-1-ol & & & & & & & & & & & & 1.53 & 1.31 & Up \\
22 & Linanol & & & & & & & & & & 1.51 & 1.48 & Up & & \\
23 & Linanol oxide (Pyanoid) & & & & & & & & & & & 1.79 & 1.61 & Down & & \\
28 & Nerchild & & & & & & & & & & & & & \\
**Aldehydes (14)** & & & & & & & & & & & 2.85 & 1.46 & Up & & \\
32 & 2-Methylproposal & & & & & & & & & & & & & 1.86 & 3.17 & Up & 1.97 & 4.46 & Up \\
34 & 2-Methylbutanol & & & & & & & & & & & & 1.54 & 1.70 & Down & 3.26 & 2.52 & Up \\
36 & Pernal & & & & & & & & & & & & 1.02 & 1.09 & Up & \\
37 & (E)-2-Pententen & & & & & & & & & & & & 1.73 & 1.79 & Up & -1.04 & 1.06 & Down \\
48 & (E,Z)-2-Pedederal & & & & & & & & & & & 1.42 & 3.75 & Up & 1.39 & 4.18 & Up \\
52 & Benzaldehyde & & & & & & & & & & & & 1.42 & 2.33 & Down & 1.12 & 1.51 & Up \\
54 & Octanol & & & & & & & & & & & & 1.13 & 3.77 & Up & & \\
56 & (E,Z)-4-Heptafenal & & & & & & & & & & & & & 1.22 & 3.11 & Up \\
57 & Benzenedialdehyde & & & & & & & & & & & & & 1.22 & 3.11 & Up \\
64 & (E,Z)-2-6-Nonaden & & & & & & & & & & & & & & & 1.03 & 1.30 & Up \\
67 & Decanal & & & & & & & & & & & & & 1.31 & 1.27 & Up \\
77 & (Z)-3-7-Dimethyl-2-6-codathedral; (Z)-Cital & & & & & & & & & & & & & 1.27 & 1.19 & Up & & \\
78 & (E)-3-7-Dimethyl-2-6-codathedral; Geranial & & & & & & & & & & & & 1.31 & 1.96 & Up & & \\
**Alkanes (1)** & & & & & & & & & & & & & & & \\
79 & 1,1-Dimethyl-cyclopropane & & & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A total of 61 differential volatile compounds during the manufacturing of shaken black tea.
# Oil Press-Cakes and Meals Valorization through Circular Economy Approaches: A Review
## 5 Possible Ways to Capitalize the by-Products Obtained from the Oil Industry
### Substrate for Functional Ingredient Production
#### 5.2.4 Biosurfactants  
Surfactants are amphipatic molecules (derivate from petroleum compounds) composed of a hydrophilic and hydrophobic group that decrease interfacial and surface tension with water/hydrophobic systems. When synthesized by different microorganisms they are called biosurfactants with numerous advantages over the synthetic ones: low toxicity and good biodegradability, biocompatibility, chemical diversity, and stability. Based on chemical composition they are divided into glycolipids, lipopolysaccharide, lipopolysaccharide, and oligosaccharides. Examples include rhamnpids, trehalolipids, sophorolipids, mannosylerythnitol lipids. Due to these properties they can be used in the environmental sector (bioremediation of pesticides, oil, and heavy metals, enhanced oil recovery, antimicrobial agents and biopesticides), petrochemicals, cosmetics, food, agriculture, and pharmaceutics [167; 168; 169].  
Sophorolipids, microbial biosurfactants used in detergents and cosmetics formulation, were synthesized by _Starmerella bombicola_ growth on residual sunflower cake through SSF. Their effectiveness is equivalent to Triton X-100, that is a considerate environmental pollutant [170].  
[PERSON] et al. [171] cultivated _Lactobacillus delbrueckii_ and _Pseudomonas aeruginosa_ on peanut oil cake for the production of biosurfactants (glycolipids and lipopeptides) with potential use in biodegradation of crude oil and in bioremediation of hydrocarbon pollution environments. Another example of biosurfactant (rhammolipid) utilized for this purpose was synthesized from _Ochroachrum anthropi_ cultivated on palm oil decanter cake [168; 172].  
In the pharmaceutical and food industries surfactants from _Enterobacter_ sp. MS16 cultivated on sunflower oil cake can be utilized as fungal inhibitor [95]. Antibacterial and antifungal activities were shown by lipopeptide from _Brevibacterium aureus MSA13_ cultivated on olive/safflower oil cake.  
Glycolipids obtained from _Lactobacillus delbrueckii_ on peanut cake also has good foaming and emulsion capacity and for this reason, can be used in emulsion food [22].  
Lipopeptides are biosurfactants isolated from _Bacillus pseudomycoides_ OR1 cultivated on groundnut oilcake and can be used for their antibacterial activity against foodborne, pathogens. At 50 \(\upmu\)g/mL they inhibit _Escherichia coli_, _Staphylococcus aureus_ and _Klebsiella pneumoniae_. They bind to the bacterial surface, modifying the lipid organization by changing fatty acids and thus preventing cellular processes [173].
## 4 DC Fault Current Analysis of Hybrid MMC Considering FRT Control
### Overall Calculation Flowchart  
The flowchart of the presented fault current calculation method is shown in Figure 11.  
Under the implementation of the DC current FRT strategy in the hybrid MMC, the fault current derivation comprises the following three stages: (1) Pre-Saturation Stage: prior to \(M_{dc}\) attaining the saturation limit of the limiter, the fault current is computed using Equations (11), (13) or (15). (2) Saturation Stage: upon \(M_{dc}\) reaching the limiter's saturation boundary, Equation (25) governs the current calculation. (3) Post-Saturation Stage: following the restoration of \(M_{dc}\) within operational limits, the DC current is solvedthrough Equation (26). For hybrid MMCs operating in DC voltage control mode, the calculation of fault current is divided into the following four stages: (1) Fault Inception Stage: subsequent to DC fault occurrence, Equations (18), (20), or (22) yield the fault current. (2) Limiter Activation Stage: if \(M_{dc}\) attains the limiter's saturation threshold, Equation (25) applies. (3) Control Mode Transition Stage: if the hybrid MMC switches to the current control mode or experiences outer-loop controller saturation, Equations (11), (13), or (15) are utilized, where \(l_{dcrf}\) is the predefined setpoint value during control mode transition or is the outer control loop limiter boundary during outer-loop saturation. (4) Post-Saturation Stage: after fault current decay to a specified threshold and \(M_{dc}\) and \(l_{dcrf}\)'s return within operational limits, Equation (26) is employed. The parameters \(M_{dc}\) and \(l_{dcrf}\) are derived from Equations (4) and (17), respectively.
# A Data Sharing Scheme for GDPR-Compliance Based on Consortium Blockchain
## 3 Scheme Design
### On-Chain Privacy  
Identity Verification: Identity verification mechanisms are of great importance since they are directly in association with the security and privacy of data [23]. In the proposed scheme, asymmetrically encrypted public keys or public key hashes are used to uniquely identify individuals in the blockchain, providing it with pseudo-anonymity. Key pairs are generated through \(KeyGen\).  
zk-SNARK: Let \(C:F^{n}\times F^{h}\to F^{l}\) be an arithmetic circuit, and \(R_{C}=\{(\overrightarrow{x},\overrightarrow{w})\}\subseteq F^{n}\times F^{h}\) be the corresponding circuit satisfaction relation, where \(\overrightarrow{x}\in F^{n}\) is called the statement and \(\overrightarrow{x}\in F^{h}\) is the witness. A zk-SNARK for circuit satisfiability is a triple of polynomial-time algorithms \((ZKPKeyGenerator,ZKPGenerator,\ ZKPVerifier)\). They will be defined in Section 4.5. A zk-SNARK satisfies the necessary properties including completeness, soundness, and perfect zero-knowledge:  
* **Completeness:** Given \((\overrightarrow{x},\overrightarrow{w})\), the prover \(P\) can produce a proof \(\pi\), and thus the verifier \(V\) accepts \((\overrightarrow{x},\pi)\) with probability 1.
* **Soundness:** No Probabilistic Polynomial Time (PPT) adversary can generate a proof \(\pi\) for \(\overrightarrow{x}\) that fools the verifier \(V\) to accept \((\overrightarrow{x},\pi)\).
* **Perfect Zero-knowledge**: There exists a (randomized) polynomial simulator \(S\). Therefore, for any \(\overrightarrow{x},S(x)\) generates a proof that is computationally indistinguishable from an honestly generated one.
## 5 Selenium-Containing Polysaccharides
### Polysaccharide Encapsulated Nanoselenium  
Zero-valent selenium (0 oxidation state) in the form of selenium nanoparticles (SeNPs) has attracted research attention thanks to its high bioavailability, lower toxicity--it is lower than that of inorganic and organic forms of selenium (e.g., SeNPs have seven-fold lower toxicity than sodium selenite), and controlled Se release [282; 283; 284; 285]. As a result, SeNPs carry a lower risk of excess Se supplementation.  
According to the IUPAC definition, a nanoparticle is a particle of any shape with dimensions in the \(1\times 10^{-3}\) to \(1\times 10^{-1}\) micron range [286]. This wide range of nanoparticle sizes results in differences in their properties, with smaller particles showing a greater cellular intake and activity [287; 288; 289]. Hence, [PERSON] et al., (1997) observed that in vitro absorption of 0.1 um particles of SeNPs was found to be 2.5 and six times higher when compared to 1 and 10 um particles, respectively [288]. Thus, when considering preparation of the potential drugs and dietary supplements, an appropriate particle size should be chosen. However, selenium in the form of nanoparticles is unstable and easily transformed into an inactive form [289], which necessitates the use of stabilizing agents in SeNPs solutions. These solutions may be stabilized and modified by various polymers, such as chitosan [290; 291] and other polysaccharides [292; 293; 44; 294; 295; 296; 297], proteins [293; 294], or their combinations [298; 299; 300].  
The stabilizing agents, such as polysaccharides, may play a dual role in the SeNPs formulations, as they can also improve the bioavailability and biological activity due to the synergistic effect of SeNPs and polysaccharides [301]. Polysaccharide-stabilized SeNPs may be obtained by chemical methods, e.g., by reduction of selenites in the solution containing polysaccharides [302], or by biological and biotechnological methods--extraction from the plant, fungal, algal, or microbial material grown in media enriched in selenium compounds, preferably sodium selenite [303; 304]. The reduction of selenium to elemental nanoselenium in organisms is thought to be a form of detoxification and elimination of selenium excess, where part of the reduced elemental selenium becomes linked to the polysaccharides in the cell wall of the cultured organism [39].  
Polysaccharide-encapsulated nanoselenium may have a wide range of biomedical applications. Its exerts antimicrobial activity [305], anticancer effects [306; 307], enhanced hypoglycemic effect [308], protection against diabetic nephropathy [309], antiinflammatory effect in arthritis [310], and antioxidant effects [311]; enhances wound healing effects [312] and anti-diabetic activity [44]; and improves fetal growth and hair follicle development [313].
# Improved JPS Path Optimization for Mobile Robots Based on Angle-Propagation Theta* Algorithm
###### Abstract  
The Jump Point Search (JPS) algorithm ignores the possibility of any-angle walking, so the paths found by the JPS algorithm under the discrete grid map still have a gap with the real paths. To address the above problems, this paper improves the path optimization strategy of the JPS algorithm by combining the viewable angle of the Angle-Propagation Theta* (AP Theta*) algorithm, and it proposes the AP-JPS algorithm based on an any-angle pathfinding strategy. First, based on the JPS algorithm, this paper proposes a vision triangle judgment method to optimize the generated path by selecting the successor search point. Secondly, the idea of the node viewable angle in the AP Theta* algorithm is introduced to modify the line of sight (LOS) reachability detection between two nodes. Finally, the paths are optimized using a seventh-order polynomial based on minimum snap, so that the AP-JPS algorithm generates paths that better match the actual robot motion. The feasibility and effectiveness of this method are proved by simulation experiments and comparison with other algorithms. The results show that the path planning algorithm in this paper obtains paths with good smoothness in environments with different obstacle densities and different map sizes. In the algorithm comparison experiments, it can be seen that the AP-JPS algorithm reduces the path by 1.61-4.68% and the total turning angle of the path by 58.71-84.67% compared with the JPS algorithm. The AP-JPS algorithm reduces the computing time by 98.59-99.22% compared with the AP-Theta* algorithm.  
* 2022
## 2 Simulation Scenario and Models
### Noise Models  
The delay waveform is impacted by two noises which are thermal and speckle noise as  
\[|Y(\tau)|^{2}=|Y_{0}(\tau)|^{2}\epsilon(\tau)+n(\tau) \tag{3}\]  
where \(\epsilon(\tau)\) is speckle noise which caused by coherence of scattered signals from multiple scattering units. Many models of speckle noise have been proposed. In [PERSON] (2016), speckled measurements were simulated assuming that the amplitude of the reflected signals follows a Rice distribution. In Synthetic Aperture Radar (SAR) system, the K-distribution Elfouhaily (2016) or unit-mean exponential distribution Sorensen (2016) were used to model the speckle noise. For GNSS-R, [PERSON] et al. [PERSON] et al. (2016) performed a detailed analysis on signal fading statistics using the UK-DMC data and found that the scattered GNSS signals from ocean followed the exponential probability. Figure 2a which is the distribution of scattered GNSS signals for UK-TDS-1 presents the same phenomenon that a exponential function could well fit the distribution of real data received from spaceborne configuration. Overall, in this paper, the speckle noise is assumed as an unit-mean exponential distribution. Figure 2b shows the distribution of simulated GNSS-R correlation power using scattering and speckle noise models above, from which it is clear that the simulated results also approximately is subject to the exponential distribution. In in-phase and quadrature branch, the thermal noises are assumed to be Gaussian distribution as  
\[n_{I}\sim N(0,\sigma^{2}) \tag{4}\]  
\[n_{Q}\sim N(0,\sigma^{2}) \tag{5}\]  
where \(\sigma^{2}\) is the noise power. \((n_{I}^{2}+n_{Q}^{2})/\sigma^{2}\) takes chi-squared distribution \(\chi(2)\). Therefore, the additive noise in Equation (3) could be modeled as  
\[n(\tau)=\sigma^{2}\cdot\chi(2) \tag{6}\]  
Figure 2: Exponential distribution of (**a**) reflected GNSS signals for UK-TDS-1 satellite and (**b**) simulated data using scattering and noise models. The dotted lines are the best least squares fit using exponential function.
# A Cellular Automata-Based Crossover Operator for Binary Chromosome Population Genetic Algorithms
## 6 Experimental Analysis for _Cgacell_ Crossover Operator in Specific Tasks  
**Experiment 1 (Comparing _Cgacell_ crossover with other crossover techniques)**  
Experimental performance outcomes of the algorithm (_CGACell-GA_) were established for the problem of classifying classes of face images, corresponding to multiple individuals, from the free Yale Face database. The Yale database contains 165 images of faces corresponding to 15 people with 11 images, with each representing certain facial expressions or configurations (_glasses_, _happy_, _left light_, _right light_, _sad_, _sleepy_, _surprised_, _wink_, _center light_, _no glasses_ and _normal_). The experimental results were achieved by the stability of the correctness percentages in the classification of image classes from the Yale image database by using a GA with different crossover methods (proposed crossover, classical GA, permutation-based, heuristic methods, and hybrid methods).  
_Proposed Crossover--Heuristic and Hybrid Methods_  
* CGACell ECA crossover.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Aspect** & **Single-Point Crossover** & **CGACell ECA (1D)** & **CGACell kD CA (General)** \\ \hline Exploration Capability & Moderate & High (fine-grained variation) & High (spatially diverse recombination) \\ \hline Parallelism Potential & Very high & Extremely high & High \\ \hline Rule Control & None & ECA rule selectable (e.g., 90, 110) & CA rule + topology configurable \\ \hline Adaptability & General-purpose & Suitable for local dynamics & Highly adaptable to structured search \\ \hline Chromosome Format & Binary or real & Binary only & Binary (extendable to real-valued CA) \\ \hline Use Case Suitability & Simple problems, global exchange & Local gene interactions, diversity preservation & Complex, spatial, multimodal optimization \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Operator** & **Strengths** & **Weaknesses** \\ \hline Single-Point Crossover & \(\bullet\) & Simple and fast & \(\bullet\) & Poor locality preservation \\
## 3 Methodology
### Data Preprocessing
#### 3.2.1 Data Cleaning  
The datasets used in this study contain redundant and incomplete data, which can potentially impact the subsequent classification results. Therefore, prioritizing data cleaning is crucial. Before performing data cleaning, it is necessary to identify instances of redundant data and missing values. Redundant data refers to instances that contain duplicate information, whereas missing values indicate instances with incomplete or undefined data. These issues can potentially lead to biased classification model performance. In order to mitigate these issues, we eliminate duplicate data and fill the missing values with the median of the corresponding feature. As a measure of central tendency, because the median can accurately represent the distribution and statistical characteristics of features, this ensures data integrity and consistency.
# Design and Development of a Flexible 3D-Printed Endoscopic Grasping Instrument
## 3 Results
### Measurement of the Bending Force  
The maximum bending force was measured 30 times using both the instrument and the gastroscope-forceps combination. The mean bending force achieved by the instrument was 7.85 N (SD \(\pm\) 0.53), while the gastroscope-forceps combination achieved a mean force of 7.68 N (SD \(\pm\) 0.3). Although it was 3D-printed, the instrument demonstrated comparable bending force to its commonly used counterpart (\(p\) = 0.56).
# Indoor Localization for Augmented Reality Devices Using BIM, Point Clouds, and Template Matching
## 3 Methodology
### Template Matching  
With the inverse distance map \(d_{i,j}\) and the rasterized point cloud \(g_{i,j}\) of the floor plan obtained, we try to find the best positional match for the point cloud in the BIM model. For the template matching, we perform a convolution to find the best match for the localization. The convolution is defined as follows:  
\[(g*\hat{d})_{x,y}=\sum_{i}\sum_{j}g_{x-i,y-j}\cdot\hat{d}_{i,j}. \tag{4}\]  
The convolution is performed for every rotational candidate. The maximum value of \((g*\hat{d})\) over all rotational candidates yields the localized position \(x_{\max},y_{\max}\) and the rotation \(\phi_{\max}\), which is the most likely transformation \(T\) of the point cloud.
## 2 Forest Fire Detection by the Satellite data in China  
### The NOAA Satellite  
Forest fires detection and monitoring in China have been achieved using a variety of space-borne systems/sensors in last three decades. In the early 1980s, the NOAA/AVHRR satellite images were used to detect the fire hot spots by thresholding of three infrared channels, including Ch. 3 (3.55-3.93 \(\upmu\)m), Ch. 4 (10.5-11.3 \(\upmu\)m) and Ch. 5 (11.5-12.5 \(\upmu\)m), and combined BT (bright temperature) and NDVI (Normalized Difference Vegetation Index) were used for monitoring burned areas. Figure 1 represents the detected fire spot and some extensive burned areas in the Daxing'anling forest area by using NOAA/AVHRR satellite data through study by the National Satellite Meteorological Center, China [38].  
Figure 1 shows the Daxing'anling forest fire hot-spot and burnt area detection by using NOAA/AVHRR satellite data during 05/06/1987 and 02/06/1987. The multi-false-color composite method with AVHRR channels 3 (3.55-3.93 \(\upmu\)m, R), 2 (0.725-1.1 \(\upmu\)m, G) and 1 (0.55-0.9 \(\upmu\)m, B) was used to detect the outbreak of the forest fire in 1987/05/6 (Figure 1a); the spreading of the forest fire in 1987/05/8 (Figure 1b); and burnt area after the extinguishing fire in 1987/06/2 (Figure 1c), respectively. In the multi-false-color composite images, the red, dark red, green, and gray colors stand for active fire, burnt area, vegetation and smoke, respectively. The yellow line is border between China and Russia.