esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Correlation Analysis  
In the case of the ethanol extracts, very high positive correlations were found between the content of total polyphenols and the ability to neutralize the DPPH radical (r = 0.99), the ability to neutralize the ABTS\({}^{\bullet+}\) radical (r = 1.00), and the ability to chelate iron ions Fe2+ (r = 0.98) and reduce power (r = 0.99). Similar correlations were found between phenolic acids and flavonoids and all methods used for the determination of antioxidant capacity (r > 0.95) (Table 5). Figure 4: Xanthine oxidase -XO (**a**), lipoxygenase--LOX (**b**), and cyclooxygenase-2—COX-2 (**c**) inhibitory activity of EtOH, PBS, and DIG extracts from control and fortified wafers. C—wafers without raspberry pomace (control), RP-10–RP-75—wafers with 10% to 75% raspberry pomace addition, respectively. All values are mean \(\pm\) standard deviation for triplicate experiments. Means with different small letters show significant differences between samples with different % of supplementation (red—EtOH, orange—PBS, green—DIG) at \(p\leq 0.05\). Values denoted by different capital letters are statistically different within the same sample but between the different types of extract (\(p<0.05\)). Lower correlation coefficients between the ability to neutralize the DPPH radical and the content of phenolic compounds (r = 0.50), phenolic acids (r = 0.63), flavonoids (r = 0.72), and anthocyanins (r = 0.64) were obtained for the PBS extracts. A relatively high positive correlation was demonstrated between the concentration of phenolic acids and the Fe\({}^{2+}\) chelating ability (r = 0.87) and between the content of flavonoids and chelating properties (r = 0.79). All correlation coefficients were very high in the analysis of the in vitro digested extracts (r > 0.92). The coefficients reached lower values only in the case of the relationship between the power reduction and the content of phenolic acids, flavonoids, and anthocyanins: r = 0.89, r = 0.84, and r = 0.75, respectively. In turn, the correlation between the content of phenolic compounds and potential anti-inflammatory properties varied. A negative correlation was found between the polyphenol content and the IC50 values determined for COX-2 inhibition by all types of extracts. Pearson's correlation coefficients were lower than \(-\)0.72 for the ethanol extracts, while very high coefficients (\(-\)0.96 to \(-\)0.99) were found for the buffer extracts and those obtained after simulated digestion. Regarding the potential anti-inflammatory properties, the inhibitory activity of the extracts against XO, LOX, and COX-2 was expressed as the IC50 value; hence, all correlation coefficients were negative. This means that the higher the content of the tested compounds, the lower the IC50 value, i.e., the stronger the inhibitory properties. A very high correlation was found between the content of all analyzed groups of phenolic compounds and the ability to inhibit LOX by the ethanol extracts (all r values > \(-\)0.9). Lower coefficients were
## 3 Case Study
### Survey of the Existing Building  
The survey of control points was carried out using total station and laser scanner. The main aim of the system of control points was to obtain a three-dimensional digital model of the geo-referenced survey in absolute UTM coordinates, located so they can be recorded by laser scanning. Figure 4 shows the control points determined for the building and its surroundings.  
To survey the building, the exterior facades were scanned along with the tower's roof. The survey covered the dimensions of the facades, balconies, window openings and visible installations. A Leica RTC 360 scanner was used. This massive data acquisition system provides very high geometric accuracy and is very useful in buildings with complex geometries or great heights. The scanning was completed with 360\({}^{\circ}\) panoramic images. The overlapping of the panoramic images on the point cloud made it possible to analyse the building off-site to visualise damages and make very precise measurements, avoiding the need for repeated visits to the building.  
The clouds were recorded and geo-located in Leica's Register software. A total of 52 stations were realised, with 83 links. The point cloud was processed by importing it in pts software to Recap; after processing, it was re-exported in rcp format so it could connect with the Revit model for modelling of the building's current phase, linking all the information in this model so the respective state of conservation could be assessed. The location of the building's scan points and the point cloud can be seen in Figure 5.  
Figure 4: (**a**) Control points on the ground, roof and north and west facades; (**b**) control points on the ground, roof and north and east facades; (**c**) control points on the ground, roof and south and east facades.
# A Systematic Review of Defensive and Offensive Cybersecurity with Machine Learning
## 4 Discussion
### Uncertainty of Information  
The increasing occurrence of cyberattacks worldwide has resulted in the misuse or loss of information assets, thereby, increasing organizations' expenses [43]. Over the years, intrusion detection systems have been used for the protection of networks and computer systems. To detect cyberattacks, most of the present intrusion detection systems depend on low-level raw network data. A current practice is to employ knowledge-based intrusion detection systems which store cyberattack related information as well as the corresponding vulnerabilities. Also, this stored information is used for guiding the process of predicting attacks. A major challenge knowledge-based IDS face is the inability to predict attacks due to the lack of contextual information or the uncertainty of information. Contextual information involves not only information about the configuration on the target systems and their vulnerabilities but any important pre-condition that must exist to achieve a successful attack. Also, contextual information includes probable semantic relationships between the targeted locations and the activities of the attackers at the time of the activities. Machine learning and probabilistic approaches have been employed in many studies to tackle common uncertainty challenges. However, many authors discovered that these approaches use models that users cannot understand but fuzzy logic approaches model uncertainty in a user-friendly form.
## 2 The ProbPlot via IR-AVPCA Technique for Bearing Fault Detection and Diagnosis (BFDD)
### AVPCA-Base Image Recognition
#### 2.2.2 Fault Recognition  
For fault recognition, the AVPCA [4] is used to project the ProbPlot features (images) generated from the collected vibration signals under healthy and faulty conditions in both constant and variable speed environments. First, it generates the training bases of the space (i.e., training vectors), which correspond to the eigenvectors, which are ordered according to the largest variance of the generated ProbPlot images, in which they are described by its eigenface coefficients called the weights.  
In features (images) recognition, after generating the features and computing the eigenfaces, different decisions can be achieved according to the desired target of the different applications. Consequently, the recognition can be the obtaining of the labels of individuals, i.e., the identification can be about deciding if the person has already been seen or not, i.e., the recognition of individuals; finally, the task of the recognition can be about assigning a face or a fault to a certain class, which is known as categorization.  
Only the task of categorization is considered in this paper, in which the features in the training set are projected into the face space and stored in memory as AVPCA bases to form the training databases that contain the Healthy base vector \(H_{b}{}^{I}\) and the Faulty base matrix \(F_{b}{}^{m-1}\). Once a new feature (image) is presented as an unknown input to our system, it will be projected onto the face space and generate the test database that contain the Data-test vector \(T_{b}{}^{new}\). Then, our proposed algorithm computes its error distance from all stored training databases. The smallest distance gives the desired class. This information is used to detect the presence or absence of the fault and then diagnose the different bearing faults (ORF, IRF, and BBF).
## 3 Results and Discussion
### Physicochemical and Electrical Properties of PVAc-Bioadditive IFR Adhesive Blends
#### 3.3.1 Shear Strength Test
A similar study on PVC modified with Mg(OH)\({}_{2}\)- lignin showed an increase in the stiffness and thermal stability of the polymer with higher concentrations of the additive, improving its resistance to thermal decomposition compared to pure PVC [78]. In the evaluation of shear strength under three-cycle conditions, values ranged from 1.55 MPa to 11.70 MPa. The highest strengths occurred in the PVAc-5% F2, PVAc-2.5% F2, and PVAc-2.5% F1 blends, with only PVAc-5% F2 exceeding the strength of the standard adhesive by 32%. These joints also exceeded the values required by ASTM D5751 (3.35 MPa) by 249%, 161%, and 158%, respectively. For wood failure, values ranged from 10% to 82%, with the PVAc-2.5% F2, PVAc-2.5% F1, and PVAc-5% F2 blends having the highest failures (82%, 76%, and 75%). These samples also exceeded the wood failure requirement (50%) of ASTM D5751, with increases of 64%, 52%, and 50%, respectively, but only PVAc-2.5% F2 showed a 4% increase over the standard. In the three-cycle condition, the samples were subjected to immersion in water at room temperature for 4 h, followed by 19 h at 43 \({}^{\circ}\)C, repeating this process three times. PVAc adhesive, a thermoplastic polymer with a low vitreous transition temperature (Tg) near 30 \({}^{\circ}\)C [1], is sensitive to moisture and high temperatures [74]. During cycling, the polymer fluence could have favored a good mechanical anchorage between the adhesive and the wood. The bonds with the bioadditive-modified adhesive (2.5% and 5% in F2) showed higher stiffness and ductility, reaching shear strengths and wood failure close to or higher than the standard adhesive. However, samples with concentrations higher than 7.5% bioadditive (F1 and F2) showed poor performance, with failures outside the limits required by the standard. This negative performance is attributed to the embrittlement of the glue line due to the excessive presence of additive, which generates internal tensions, leading to defects and discontinuity at the interface [59], emphasized by humidity and temperatures higher than the Tg of the adhesive. Consequently, this results in joints with poor mechanical performance.
## 4 Numerical Results and Discussion
### Comparison of Numerical Modeling Using Revised and Original Energy-Based Failure Criteria  
In order to investigate the influence of the revised energy-based failure criteria proposed in Section 2.2, a comparison study is conducted by modeling the DCB delamination using the revised and original energy-based failure criteria. The revised energy-based failure criteria are illustrated in Equation (22). And the original energy-based failure criteria [15] for two-dimensional modeling are  
\[\left|\frac{w_{\mathrm{I}}}{w_{\mathrm{IC}}}\right|\geq 1,\ w_{\mathrm{IC}}= \frac{G_{\mathrm{IC}}}{\frac{4}{3}\delta^{3}},\ w_{\mathrm{I}}=t_{z}(w^{\prime}- w). \tag{31}\]  
The modeling load-displacement curve using revised and original energy-based failure criteria, as well as the experimental results, are shown in Figure 15. The comparison of maximum load using the revised and original energy-based failure criteria and the average experimental results are presented in Table 4.  
Figure 14: PD modeling delamination growth process damage contours on the mid-plane of DCB specimen, between Layer 16 and 17.  
Figure 15: Comparison of load–displacement curve obtained using revised and original bond energy-based failure criteria.  
From Figure 15, it is observed that the modeling load-displacement curve using revised and original energy-based failure criteria overlapped each other before delamination initiation. However, the modeling delamination initiation was delayed using the revised energy-based failure criteria, which agrees more closely with the experimental results. As shown in Table 4, the differences in the maximum load of the modeling load-displacement curve in comparison with the average experimental results was reduced from \(-12.93\%\) to \(-6.96\%\) by using the revised energy-based failure criteria. The above results show that the revised energy-based failure criteria can significantly improve the accuracy of the PD delamination modeling of DCB test.
# Early Age Assessment of a New Course of Irish Fly Ash as a Cement Replacement
## 4 Results  
### Fly Ash Composition  
The XRD patterns of four samples taken from Phase 1 in the ash field are shown in Figure 8. The amount of reactive silica and alumina in the ash is largely responsible for its reactivity, which has been noted by [PERSON] et al. [16], who also studied the ash in Moneypoint. Previous work on fly ash mineralogy has identified up to 316 individual minerals and 188 mineral groups [16; 17], with the most abundant phase being glass, with crystalline compounds such as quartz, mullite, magnetite, melilite, gehlenite, kalsilite, calcium sulphate and alkali sulphate accounting for 5-50% [18].  
As shown in Figure 8, there is significant glass in the fly ash. [PERSON] et al. [16] calculated this to be between 40 and 60%, based on the background area between 15-35\({}^{\circ}\) 2\(\theta\). Here, the glass/amorphous content is calculated to be 81% using a Rietveld analysis, with amounts of quartz, mullite, hematite, calcite and magnetite. This is in broad agreement with previous studies on Moneypoint fly ash [16; 19] and is summarised in Table 5. This also agrees with the previous literature, which reports quartz and mullite to be the two major crystalline phases in low-Ca (CaO \(<\) 8%) fly ash, with a crystalline mineral content in the range of 11-48 wt.% [17]. [PERSON] et al. [16] concluded that the lack of sulphates and  
Figure 8: XRD pattern for fly ash in Phase 1. Significant amorphous content within highlighted area (15–35\({}^{\circ}\) 2\(\theta\)).  
calcium silicates in the Moneypoint fly ash is due to the composition of the subbituminous coal used in the power station, which is low in sulphur and calcium.
# Optimizing Methanol Injection Quantity for Gas Hydrate Inhibition Using Machine Learning Models
## 2 Materials and Methods  
### Data Generation  
The first step in developing the predictive models to optimize methanol injection for hydrate inhibition in gas pipelines was to generate a full pseudo-experimental synthetic dataset to train the machine learning algorithms on. The aforementioned task was performed with the use of Aspen HYSYS V14 software, commonly adopted process simulation software known for its strong capacity for chemical and thermodynamic process modeling [10]. HYSYS has been validated for modeling multicomponent systems across a wide range of operating conditions and has been verified to accurately replicate real-world pipeline conditions, including model systems predicting long-term hydrate formation dynamics [25]. Specifically, and as shown in the study, the five input features were carefully selected to build a model for the methanol injection of the hydrate preventer to control the input quantity of methanol into a lean natural gas pipeline to prevent hydrate formation. The inputs were flow rate, temperature, pressure, gas specific gravity, and water content and the model output was the minimum methanol quantity required to prevent hydrate formation.  
The choice of these characteristics was derived from a theoretical study of their effects on the nucleation and inhibition of hydrates [26]. Temperature is a dominant controller of phase behavior in hydrocarbon-water systems and also hydrate stability, making it a critical factor when accounting for thermal fluctuations in pipeline environments [26]. Hydrate formation conditions and methanol efficacy depend on pressure, a major driver of the gas-water phase equilibrium, which must be included to represent operational variations [6]. Flow rate is vital as it affects methanol distribution and contact efficiency across pipeline segments, mirroring the dynamic flow conditions that impact hydrate risk [4]. Gas specific gravity, indicative of gas composition, directly correlates with hydrate-forming tendencies in lean natural gas systems [27], while water content dictates the potential for hydrate crystallization, serving as a primary driver of inhibition requirements [26]. These features were assigned specific ranges, as outlined in Table 1, to encompass the diverse operational scenarios encountered in gas pipeline systems, ensuring the dataset's relevance to real-world conditions. Aspen HYSYS simulations, using the Peng-Robinson model, spanned 20 different gas compositions with varied feature combinations, yielding 74,000 data points of methanol requirements to prevent hydrate formation and forming a robust training dataset.
# Reversible Data Hiding Scheme in Homomorphic Encrypted Image Based on EC-EG
## 2 Related Knowledge  
### Elliptic Curve  
The image trajectory of elliptic curve is not an ellipse, but a cubic curve on a plane. However, it is a problem that people find when studying the arc length of an ellipse.  
**Definition 1**.: _Elliptic curve is a plane curve determined by the cubic Weierstrass equation:_  
\[y^{2}+axy+by=x^{3}+cx^{2}+dx+e \tag{1}\]  
The points on the curve satisfy the equation. If the coefficient is \(a,b,c,d,e\in F_{p}\), the number of points on the curve is finite, and all the points on the curve plus an artificial infinity point constitute the set \(E(F_{p})\) and the number of points in \(E(F_{p})\) is recorded as \(\#E(F_{p})(p+1-2\sqrt{p}\leq\#E(F_{p})\leq p+1+2\sqrt{p})\).  
When constructing a cryptosystem, the following elliptic curve is mainly used:  
\[y^{2}=x^{3}+ax+b(x,y,a,b\in F_{P}) \tag{2}\]  
Figure 1 shows the distribution of points on the elliptic curve when \(a=4,b=20,p=33331\).  
The set \(E(F_{p})\) of points on the elliptic curve forms an Abel group for the addition defined below:  
1. \(\mathrm{O}+\mathrm{O}=\mathrm{O}\)
2. \(\forall(x,y)\in E(F_{P})\), \((x,y)+\mathrm{O}=(x,y)\)
3. \(\forall(x,y)\in E(F_{P})\), \((x,y)+(x,-y)=\mathrm{O}\)
4. (addition rule)If \((x_{1},y_{1})+(x_{2},y_{2})=(x_{3},y_{3})\) is satisfied when \(x_{1}\
eq x_{2}\), then \[\left\{\begin{array}{l}x_{3}=\lambda^{2}-x_{1}-x_{2}\\ y_{3}=\lambda(x_{1}-x_{3})-y_{1}\end{array}\right.,\lambda=\frac{y_{2}-y_{1}}{ x_{2}-x_{1}}\] (3)
5. (multiple point rule) \(\forall(x_{1},y_{1})\in E(F_{P}),y_{1}\
eq 0\), If \(2(x_{1},y_{1})=(x_{2},y_{2})\) is satisfied, then \[\left\{\begin{array}{l}x_{2}=\lambda^{2}-2x_{1}\\ y_{2}=\lambda(x_{1}-x_{2})-y_{1}\end{array}\right.,\lambda=\frac{3x_{1}^{2}+a}{ 2y_{1}}\] (4)
## 6 Conclusions  
A model predictive priority contouring control is proposed to deal with the problem of multi-robot robust motion planning in this paper. Firstly, the ICBS planner is utilized to obtain the initial guess for model predictive priority contouring control. The low-level planner of the CBS planner is replaced by the hybrid A* planner. Second, double-layer corridors are proposed to deal with chance safety constraints. Static-layer corridors ensure obstacle avoidance and dynamic-layer corridors guarantee inter-robot collision avoidance. The double-layer corridors are applied as safety constraints of model predictive priority contouring control. Third, a prioritization mechanism is devised to improve the computational efficiency of model predictive contouring control. The priority of the robot is assigned based on the percentage of uncompleted tasks. Finally, simulations show the effectiveness and real-time performance of our approach in multi-robot robust motion planning. In future work, the authors plan to extend the proposed method to a maze-like environment. Moreover, interaction with unknown dynamic obstacles will be considered in our future work.  
Conceptualization, L.Y.; methodology, L.Y. and Z.W.; software, Z.W.; validation, L.Y.; formal analysis, Z.W.; investigation, Z.W.; resources, Z.W.; data curation, Z.W.; writing--original draft preparation, Z.W.; writing--review and editing, L.Y.; visualization, Z.W.; supervision, L.Y.; project administration, L.Y.; funding acquisition, L.Y. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (No. 61976224, 61976088).  
Not applicable.  
Figure 11: Comparison between our method and centralized method. (**a**) calculation time per horizon, (**b**) cost.
## 2 Materials and Methods
### Vanadium Complexes  
The vanadium complexes (Figure 1) **V1** (N,N\({}^{\prime}\)-1,2-cyclohexylenebis(3,5-dichlorosilylideneiminato)vanadium dichloride, molecular formula C\({}_{20}\)H\({}_{16}\)Cl\({}_{6}\)N\({}_{2}\)O\({}_{2}\)V), **V2** (N,N\({}^{\prime}\)-1,2-cyclohexylenebis(5-chlorosalicylicideneimmato)vanadium dichloride, molecular formula C\({}_{20}\)H\({}_{18}\)Cl\({}_{4}\)N\({}_{2}\)O\({}_{2}\)V) and **V3** (N,N\({}^{\prime}\)-1,3-propylenebis (salicylicdineeminmato)vanadium dichloride, molecular formula C\({}_{17}\)H\({}_{16}\)Cl\({}_{2}\)N\({}_{2}\)O\({}_{2}\)V) were prepared according to the procedure described in the literature [40; 41; 42]. The appropriate ligand was dissolved in dichloromethane,  
Figure 1: Structure of vanadium complexes.  
Figure 3: Chemical structure of lecithin: DPPC (1,2-dipalmitoyl-\(sn\)-glycero-3-phosphocholine) and EYL (L-\(\alpha\)-Lecithin, Azolectin, 3-sn-Phosphatidylcholine, PC, 1,2-Diacyl-sn-glycero-3-phosphocholine).  
Figure 2: Chemical structure of the spin probes TEMPO (2,2,6,6-tetramethylpiperidine-1-oxyl) and 16-DOXYL(2-ethyl-2-(15-methoxy-15-oxopentadecyl)-4,4-dimethyl-3-oxazolidinyloxy) stearic acid methyl ester; the position of the spin-active nitroxyl groups was indicated.  
and then a solution of \(\rm{VCl_{4}}\) in hexane was introduced dropwise at the molar ratio of 1:1 in relation to ligand. The reaction was carried out overnight at room temperature. Then, the reaction mixture was concentrated. The complex was separated by filtration, washed with dichloromethane and hexane, and dried under vacuum. Syntheses were carried out under argon atmosphere. The solvents were dried by standard methods.