esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Silane Treatment Technology
After the metal substrate is put into the silane hydrolysate, the activated Si-OH and hydroxylated substrate surface (i.e., Me-OH group) are rapidly combined in the form of hydrogen bond, so as to adsorb on the metal surface, as shown in formula (5). At the same time, there are still a large number of active Si-OH groups in the solution [81,82]. Adjacent Si-OH is prone to intermolecular dehydration and condensation reaction, forming an outer silane membrane with a Si-O-Si interpenetrating network structure, as shown in formula (6). \[Si-OH+Me-OH\to Si-O\cdots H_{2}O\cdots Me \tag{5}\]  
\[Si-OH+Si-OH\to Si-O-Si+H_{2}O \tag{6}\]  
(3) Silane molecules are solidified into films. After adsorption, silane molecules are solidified on the surface of the substrate. In this process, due to the instability of the hydrogen bond, \(Si-O\cdots H_{2}O\cdots Me\) groups lose one molecular water and form stable _Si-O-Me_ covalent bonds, as shown in formula (7). The curing and film forming need to be heated at a certain temperature. Different silane curing processes need different temperatures. Generally, the curing temperature should be greater than 90 \({}^{\circ}\)C. During the curing process, a dehydration reaction will occur between hydrogen bonds, forming _Si-O-Me_ bonds at the interface where the surface metal participates in bonding, which can significantly improve the bonding strength between the metal matrix and silane. At the same time, the remaining silanol hydroxyl groups adsorbed on the surface of the matrix that do not participate in bonding at the interface will dehydrate and condense with each other to form _Si-O-Si_ bonds, thus forming a layer of cross-linked mesh three-dimensional silane film on the metal surface. Generally, one end of the silane coupling agent is hydrolyzed with three silanol hydroxyl groups, but the energy required for bonding with the metal substrate surface is different. When one silanol hydroxyl group is bonded, the other two will no longer bond, or dehydrate and condense with the silanol hydroxyl groups in adjacent molecules, or finally exist in the silane film in a free state at one end. \[Si-O\cdots H_{2}O\cdots Me\to Si-O-Me+H_{2}O \tag{7}\]  
The complete impregnation method and electrodeposition method are the main methods for preparing silane membranes on metal surfaces. Under the action of hydrogen bonding, the silane solution will quickly adsorb to the metal surface after contact with the metal, compared to mature chromate passivation that requires a longer period of dipping, and silanes take much less time to complete adsorption on metal surfaces. Generally, the surface modification of the metal surface with silane has the following advantages: simple process, non-toxic, non-polluting, a wide range of applications, low cost, and the anticorrosion effect is better than the traditional phosphating, and passivation process. The silane-treated metal surface can also enhance the adhesion between the metal matrix and organic coating.
## 4 Experiment and Analysis
### Building Heating-and-Cooling Dataset  
The building heating-and-cooling dataset is a dataset [41; 42] used for energy efficiency forecasting created by Xifara. It consists of eight input variables and two output variables and has a data size of \(768\times 10\). Relative compaction, surface area, wall area, roof area, total height, direction, glazing area and distribution of the glazing area are input variables. The heating and cooling loads are the output factors, but this analysis uses only the heating load. To conduct the experiment, the building heating dataset was equally divided into learning and verification sets and data values were normalized to between 0 and 1.
## 6 Discussion
### Comparison of Satellite and UAV Results and Future Developments  
Both case studies' burn severity was analyzed via the computation of spectral indices, RBR and NDVI. Despite the two indices having different mathematical structure and temporal parameters, they both analyzed land cover status after wildfires. Satellite-based data has shown the advantages of modern sensing platforms, short revisit time, great areal coverage, and medium-high resolution, allowing for the investigation of extensive phenomena as wildfires and ecosystem recovery. The RBR analysis highlighted a stark contrast in the severity classes coexisting in a small area. The presence of clashing classes confining with each other could be addressed to the spatial extent of pixels. In contrast, UAV-computed NDVI data efficiently represented the transition, even shortly extended, occurring among different severity classes. The ability to represent this phenomenon could be addressed to the ultra-high UAV data resolution. This discrepancy could be addressed to scale factor, where, depending on the observation scale, processes which appear homogeneous at a local scale may become heterogeneous at a large scale [98; 99; 100]. The scale effect is widely known to be a problematic phenomenon in the remote sensing of environment and needs specific calibration to reduce its effect on the analysis [101; 102; 103]. As land surface is very heterogeneous, upscaling data validation (from large scale to local scale) is more relevant than down-scaling (from local scale to large scale) because of the generalization performed to scale data up [104]. In this optic, the proposed methodology theoretically analyzed the possible benefit of drone acquired data for satellite product detail enhancement. Indeed, the UAV acquisition pointed out its versatility in acquiring almost ground level data. This capability, combined with the modularity of the drone sensor bay to host multiple sensors, opens new possibilities to adopt the UAV platform as a satellite validation tool. Furthermore, as multispectral and hyperspectral satellites provide limited ground spatial resolution and suffer from atmospheric interactions phenomena, if correctly scaled, the synergy with UAV-sensed data could create a measurement-validation multiplatform procedure.
# Learning Airfoil Flow Field Representation via Geometric Attention Neural Field
## 2 Methodology
### Point Cloud Input  
In the preprocessing stage of numerical simulation, mesh generation is a critical step, aimed at appropriately discretizing the space. For the problem of airfoils, this process involves generating mesh nodes that closely conform to the shape of the geometry, defining the geometric boundaries for numerical simulation. Starting from this boundary, the mesh extends throughout the entire physical domain, creating a comprehensive spatial discretization representation.  
CFD results are typically described using grid structures. However, for our implementation, we do not require grid-structured inputs; instead, we only need point cloud inputs, where any randomly sampled subset of the point cloud can serve as input.  
To assist the neural network in distinguishing geometric boundaries, additional geometric information is required. Each mesh point includes the distance to the airfoil geometry. By incorporating the distance and normal vectors of mesh points relative to the geometry as input features, the neural network can more accurately learn and understand the behavior patterns of fluids around geometric structures. The distance field provides essential information about the shape and size of the geometry, while the normal vectors reveal the directional characteristics of fluid interactions with the airfoil. This combina  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Definition** \\ \hline \(\Omega\) & Euclidean space dimensions \\ \(d_{a}\) & The number of a domain’s dimensions \\ \(c\) & The number of neural network input/output channels \\ \(a\) & Input space function \\ \(u\) & Solution space function \\ \(\hat{u}\) & Approximate solution space function \\ \(p\) & Mesh points cloud in the input space \\ \(p_{geo}\) & Airfoil boundary points in the input space \\ \(\hat{\mathcal{H}}\) & Latent Representation Space. \\ \(z\) & Latent Representation. \\ \(g\) & Geometric latent Representation. \\ \(\Gamma\) & Geometric shape in the input space \\ \hline \hline \end{tabular}
\end{table}
Table 1: Definitions of basic symbols.  
tion of geometric attributes enables the predictive model to exhibit greater accuracy and adaptability when dealing with wall boundary conditions and fluid behaviors.  
Based on the acquired geometric information, for each mesh point in the flow field, we construct a vector composed of seven dimension features \((pos_{x},pos_{y},V^{x}_{inf},V^{y}_{inf},d,n_{x},n_{y})\), which specifically includes the following information:  
* Position coordinates _pos_: provide the model with the specific location of each point in space.
* Angle of attack (AoA) and free-stream velocity \(V_{inf}\): these two parameters are key initial conditions in flow field analysis.
* Signed distance \(d\): Represents the directed distance from each point to the nearest geometric surface.
* Normal vectors \(n\): for points with a signed distance of 0, the normal vector is perpendicular to the surface of the airfoil. However, for volume flow field points, a zero vector is assigned.
## 3 Results and Discussion
### Physical Properties
#### 3.1.4 Browning Index (BI)  
The browning index was performed in order to indicate the non-enzymatic alterations in browning colour of the samples [32]. In the case of analysed snacks, the lowest values were noted for control samples, regardless of the way if their obtainment (frying, microwaves). In general, snacks gained after frying had almost 1.8-fold higher browning index than products obtained after application of microwaves, regardless of the storage time (Table 2). This points out a strong influence of the processing on non-enzymatic changes in snacks. When the type of fruit powders was considered, the value of browning index was lower when the chokeberry and blackcurrant powders was added to fried and microwaved snacks when compared to control sample (no fruit powder addition). Taking the above into consideration, the application of natural fruit-based powders might prevent the formation of browning compounds. Storage also had an impact on browning index values. In general, browning index values increased, on average, about 10% after 5 weeks of storage and the increase was linear within that time.  
A strong positive correlation between browning index (BI) and \(a^{*}\) and \(b^{*}\) values (r = 0.85 and r = 0.85, respectively) was noted that pointed a possible formation of Maillard reaction and caramelisation products in microwave irradiated and fried samples. This was confirmed by positive correlation between BI and \(C^{*}\) indicating the strength of processing (\(r\) = 0.87). No correlation was indicated between BI and \(L^{*}\).
# Triangular Sierpinski Microwave Band-Stop Resonators for K-Band Filtering
## 5 Design of the 26 GHz Single Resonators  
The design of the triangles to have a 26 GHz band-stop response presents additional problems in the prediction of the frequency of resonance, as demonstrated in Figure 11, where three mirrored triangles in a CPW configuration were simulated imposing a rescaled edge length, with the same arrangement of the 20 GHz resonators, but smaller size of the triangle. In this case, the longitudinal size of the ground plane remained constant, only changing the length of the central line for the CPW, according to the difference between the edge lengths of the big and the small triangles, as clarified in Figure 12.  
It is worth noting that the expected frequency of resonance coming out of the simulations is significantly shifted from the wanted one based on the rescaling procedure and, in this case, the basic configurations C0 and C1 look less promising than C2 and C3. Furthermore, the spectrum of the small resonators appears richer than that of the big ones, and some excited modes are close to each other, with poor selectivity compared to the performance of the big resonators. The modes are excited at approximately 24 GHz and approximately 27 GHz. They are both suitable for K-Band operation, but of course, they need to be properly centered depending on system requirements. On the other hand, exhibiting modes having at least a \(-\)20 dB peak is a promising result for dual-band applications. The mixed configuration, involving the simulated structures C2 and C3, will be considered later in the discussion on the experimental results for the 24 GHz filtering capability. The conclusion is that higher frequencies, even if in our case they are not so different between them (20 GHz vs. 26 GHz), might be treated more carefully, including in the design of the optimization of all the geometrical parameters to enhance the mode matching and the filter selectivity. From this point of view, the CPW configuration might be re-designed, also accounting for the boundary ground planes surrounding the resonator.  
Figure 10: Expected response of the hexagonal resonators, simulated by Microwave Office. The S\({}_{21}\) parameter is plotted in the dB scale, and the internal complexity is given for each structure with the definition “hex” to indicate that the resonators C0, C1, C2, and C3 were organized in a hexagonal arrangement.  
Figure 11: Simulated response of the transmission parameter S21 in dB scale for the small resonators, C0, C1, C2, and C3.  
Figure 12: Comparison between the footprint of the big and the small triangular resonators, originally minded for 20 GHz and 26 GHz operations. The side lengths of the triangles are 6 mm and 4 mm, respectively.
## 2 Study Area and Data
### Data and Data Processing
#### 2.2.2 Green Tide Extraction  
The normalized difference vegetation index (NDVI), which is calculated using Formula (1) based on the unique spectral characteristics of green algae in the red and near-infrared bands, is widely applied in the extraction of green tides [2; 4; 6; 7; 8; 12; 42; 43; 44]. The optical satellite images used in green tide monitoring all have red-light bands and near-infrared bands. Therefore, this paper applies the NDVI threshold method to extract green tide information. From April to July every year, the Yellow Sea is cloudy and foggy [45; 46], which seriously affects the monitoring of green tide. Therefore, we designed a green tide information extraction process combining visual and automatic processes to reduce the influence of cloud and sea fog on green tide extraction (see Figure 2). First, a standard  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Satellite/Sensor** & **Date** & **Object** & **Stage** \\ \hline HY-1C/CZI & 10 May 2021 & Process analysis, comparative analysis & Emergence \\ \hline HY-1D/CZI & 17 May 2021 & \begin{tabular}{c} Algorithm development, application, \\ validation, comparative analysis, and \\ process analysis \\ \end{tabular} & Development \\ \hline HY-1C/CZI & 25 May 2021 & Process analysis & Development \\ \hline HY-1D/CZI & 6 June 2021 & \begin{tabular}{c} Algorithm application, validation, \\ comparative analysis, and process \\ analysis \\ \end{tabular} & Explosion \\ \hline HY-1C/CZI & 9 July 2021 & \
\begin{tabular}{c} Algorithm application, validation, \\ comparative analysis, and process \\ analysis \\ \end{tabular} & Explosion \\ \hline HY-1C/CZI & 18 July 2021 & Process analysis & Decline \\ \hline HY-1C/CZI & 5 August 2021 &
\begin{tabular}{c} Algorithm application, validation, \\ comparative analysis, and process \\ analysis \\ \end{tabular} & Decline \\ \hline HY-1C/CZI & 25 August 2021 & Process analysis & Extinction \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information on satellite images used for green tide monitoring in this paper.  
false-color image combined with NDVI is used to visually identify the green tide area, which can effectively eliminate interference factors such as clouds and fog. Next, the NDVI threshold method is used to extract the green tide in the green tide area. Under normal conditions, the NDVI threshold is 0 and can be slightly adjusted regarding the extraction effect of green tide.  
\[\text{NDVI}=(\text{Rnir}-\text{Rred})/(\text{Rnir}+\text{Rred}) \tag{1}\]  
where Rnir and Rred denote the reflectance in near-infrared and red wavebands, respectively.
## 3 Results and Discussion
### Identification of Minerals through FTIR Analysis
Many researchers have studied similar patterns [43; 44; 45]. An additional peak in the ranges of 2919-2928, 2851-2854, 2004, 1984-1998, and 1878-1879 cm\({}^{-1}\) indicates C-H stretching vibrations caused by a weaker organic compound [46; 47; 48; 49]. The prominent iron minerals hematite and magnetite were detected, with 531 and 576 cm\({}^{-1}\) spectra in the Pratima Garh sample. Their presence is possibly due to the replacement of Fe\({}^{2+}\) and Fe\({}^{3+}\)[50]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**Frequency with Relative Intensity (cm\({}^{-1}\))**} \\ \hline \hline \multicolumn{2}{c}{**Assigned**} & \multicolumn{3}{c}{**Sample No with Absorption Frequency and Relative Intensity (cm\({}^{-1}\))**} & \multicolumn{2}{c}{**Reference**} \\ \cline{2-7}
**Minerology/** & **Mouramora-1** & **Mouramora-2** & **Mouramora-3** & **Padumpukhuri** & **Pratima Garh** & **Rukmini Garh** & \\ \hline \hline \multirow{8}{*}{Quartz} & 1870 VW & - & - & - & 1876 VW & 1873W \\  & - & - & - & - & 1619 VW & \\  & - & 795S & 795S & - & - & - \\ \cline{1-1}  & 778S & 778S & 778S & 776M & 778 SSp & [15; 28; 51; 52; 53] \\ \cline{1-1}  & 693S & 693M & 694S & 692M & 693M & 693S & \\ \cline{1-1}  & 460S & 461S & - & - & 463S & - \\ \cline{1-1}  & - & - & 457S & 458S & - & 458S & \\ \cline{1-1} Microcline & - & - & - & - & 1050S & - & ([54]) \\ \cline{1-1} Orthoclase & - & - & - & - & 642W & - & ([28]) \\ \cline{1-1}  & - & - & - & - & 725W & - & [16] \\ \cline{1-1}  & - & 3851 VW & - & - & 3851 VW & - & \\ \cline{1-1}  & - & 3746 VW & - & - & 3745W & - & \\ \cline{1-1}  & - & 3671 VW & - & - & - & - & \\ \cline{1-1}  & - & - & - & - & - & 3496 WBr & \\ \cline{1-1}  & - & - & - & 3475W & - & - & \\ \cline{1-1}  & group & 3468W & - & 3460W & - & 3462M & - & \\ \cline{1-1}  & 1080S & 108S & 1081S & 1084 SBr & - & 1081 SBr & \\ \cline{1-1}  & - & 3647 VW & - & - & - & - & \\ \cline{1-1}  & Chlorite & - & 3565 VW & - & - & - & - & [56] \\ \cline{1-1}  & Palygorskite & 1623W & 1630W & 1625W & 1629W & 1629W & - & [28] \\ \cline{1-1}  & Carbonate & - & - & - & - & - & 1788W & ([28; 42]) \\ \cline{1-1}  & Minerals & - & - & 1384W & 1381 VW & - & - & \\ \cline{1-1}  & Magnetic & - & - & - & - & 576W & - & [57] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: FTIR spectra wave numbers with assigned mineralogy of the studied brick samples.
## 4 Cellular Network Reconstruction and Validation
### Measurements Validation  
When inserting new measurements in the cellular network database, it must be taken into account that each possible data source may use specific units or formats for storing the captured parameters; since our main goal is dealing with heterogeneous data sources, we adopted specific conventions that have to be followed in the database. As an example, for the signal strength there are different measurement units: relative indexes such as received signal strength indicator (RSSI) [28], or instead, row values expressed in absolute numbers representing power in decibels relative to a milliwatt (dBm). We chose to rely on the latter and convert the measurements accordingly.  
In addition, as measurements come with errors (see, e.g., the vertical line of observations depicted in Figure 9b), it is necessary to take all possible actions to guarantee the quality of the data ultimately stored in the database. Thus, an appropriate a set of constraints has been defined to validate every new measurement before its actual insertion. A common case is when an observation contains one or more values out of range, which could be a valid reason to entirely discard it. Domain ranges for each attribute and technology are summarized in Table 2. Other anomalies that can be easily detected are the cases where some attributes are incorrectly set; for instance, where \(\textit{rnc}=\textit{ci}\) (in OpenCellID, also \(=\textit{cell}\)) or where some identifier have NULL values. Furthermore, the GNSS position allows us to add more complex constraints to the incoming data. For example, a measurement characterized by the presence of GNSS data is kept only if it has at least three visible satellites, avoiding poor readings that could reduce the overall quality of the database. Another interesting check is to verify whether the GNSS location of an observation is contained  
Figure 9: Observations spatially grouped by hexagons. Color = number of observations per hexagon.  
within the borders of the country corresponding to the associated _mcc_. Specifically, country borders were considered with a 20 km buffer to retain cases where the radio signal can be received barely outside of them. In this regard, consider Figure 10, reporting a situation in which this last check is not carried out, and as a result, an observation referring to the French _mcc_ is considered valid although being very far from its boundaries, generating exaggerated coverage for the related cell (orange polygon).  
Although in this work we applied the previously described conditions only to serving cell observations, a subset of them may also be applied to neighbor cells.
# Comprehensive Study of IoT Vulnerabilities and Countermeasures
## 2 Preliminary Materials
### Commonly Used Protocols
#### 2.7.10 Narrowband Internet of Things (NB-IoT)  
NB-IoT is a low-cost, low-power, and low-data-rate cellular technology built from LTE functions; therefore, it uses the same infrastructure as cellular networks, which makes it a scalable and reliable technology that can be deployed in a variety of locations. It has a range of up to 15 km in rural areas and 1-5 km in urban areas, a data rate up to 250 kbps, and a 200 KHz bandwidth [21; 42].
# Classification of Virtual Harassment on Social Networks Using Ensemble Learning Techniques
## 4 Results
### Machine Learning Algorithms
#### 4.2.3 Logistics Regression  
We can manage classes by using logistic regression, which is a non-linear extension of linear regression. This is accomplished by categorizing predictions according to a probability threshold. Logistic regression is frequently utilized in practical applications, including multi-label classification difficulties and estimating creditworthiness across a range of categories. The Naive Bayes classifier, which applies Bayes' Theorem and produces a larger bias but lower variance, can be replaced with logistic regression.  
* We know the equation of the straight line can be written as: \[y=b_{0}+b_{1}x_{1}+b_{2}x_{2}+b_{3}x_{3}+\cdots+b_{n}x_{n}\] (5)
* In Logistic Regression \(y\) can be between 0 and 1 only, so we divide the above equation by \((1-y)\): \[\frac{y}{1-y};0\ for\ y=0,and\ infinity\ for\ y=1\] (6)
* We need a range between \(-\)[infinity] to \(+\)[infinity], then, if we take the logarithm of the equation, it will become: \[log\bigg{[}\frac{y}{1-y}\bigg{]}=b_{0}+b_{1}x_{1}+b_{2}x_{2}+b_{3}x_{3}+\cdots +b_{n}x_{n}\] (7) The above equation is the final equation for Logistic Regression.  
The sigmoid function is an S-shaped curve that can acquire any real-valued number and map it onto a value between the range of 0 and 1, but never exactly those limits.  
\[1/\left(1+e^{\cdot}-value\right) \tag{8}\]  
where \(e\) is equal to the base of the natural logarithm (Euler's number) and \(value\) is equal to the actual numerical value to be transformed [38].