esa-sceva/satcom-chunk-collection huggingface dataset:
# Inhibition of Peroxidation Potential and Protein Oxidative Damage by Metal Mangifera Complexes
## 3 Results
### Inhibition of Peroxidation Potential and Protection against Protein Oxidative Damage  
Table 4 shows the data of the metal-MF complexes effects on the peroxidation potential through the MDA differential lectures and the protection against protein oxidative damage through the determination of CO groups in RBC lysates. Whereas both Zn (II), Complexes I and II, and Se (IV), Complexes II and III, decreased the peroxidation potential, Cu (II) complexes did not influence MDA concentration as compared to MF. Interestingly, Cu (II)-MF complexes showed higher values of MDA concentration than MF. Se (IV), Complex III, had the highest significant inhibition percentage of peroxidation potential as compared to the rest of synthesized metal-MF complexes (see Figure 3), but also it was the most potent inhibitor of protein degradation. Cu (II) and Zn (II)-MF complexes did not improve the protection capacity of MF against protein degradation. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**RT (min)**} & \([\mathrm{M-H}]^{-}\) & \multicolumn{6}{c}{**Elemental Composition**} & \multirow{2}{*}{**Others**} \\ \cline{4-7}  & & **(\(\mu\)/z)** & & & **C** & **O** & **Cu** & **Zn** & **Se** \\ \hline MF & 17.8 \(\pm\) 0.2 & 421 & 55.61 & 40.66 & & & 0.34 \\ \hline Cu 1 & 25.8 \(\pm\) 0.4 & 905 & 48.41 & 36.20 & 0.06 & & 0.11 \\ \hline Cu 2 & 27.5 \(\pm\) 0.4 & 903 & 49.01 & 37,14 & 0.07 & & 1.49 \\ \hline Zn 1 & 26.2 \(\pm\) 0.2 & 907 & 48.51 & 37.20 & & 0.08 & 1.24 \\ \hline Zn 2 & 28.8 \(\pm\) 0.4 & 905 & 48.42 & 37.31 & & 0.07 & 1.00 \\ \hline Zn 3 & 26.4 \(\pm\) 0.3 & 907 & 48.48 & 37.18 & & 0.08 & 1.07 \\ \hline Zn 4 & 28.6 \(\pm\) 0.4 & 905 & 48.43 & 37.33 & & 0.07 & 1.03 \\ \hline Zn 5 & 41.4 \(\pm\) 0.6 & 1388 & 46.88 & 35.97 & & 0.09 & 2.14 \\ \hline Se 1 & 33.5 \(\pm\) 0.5 & 934 & 48.88 & 39.51 & & 0.08 & 3.12 \\ \hline Se 2 & 33.3 \(\pm\) 0.5 & 934 & 48.70 & 39.47 & & 0.08 & 2.96 \\ \hline Se 3 & 44.5 \(\pm\) 0.5 & 1447 & 47.13 & 38.54 & & 0.11 & 3.22 \\ \hline \hline \end{tabular} Legend: RT: Retention time; \([\mathrm{M-H}]^{-}\): molecular ion in negative mode mass spectrometry (\(m/z\)); MF: mangiferin. Sample nomenclature according to preparatory chromatography fractions.  
\end{table} 
Table 2: Elemental analysis data of mangiferin and purified metal–mangiferin complexes by scanning electron microscopy–energy dispersive spectroscopy (SEM–EDS).
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### Smart Metering Application Projects
#### 4.1.4 Edrp  
Another project in UK is the EDRP project, which involved four energy suppliers and aimed at examining if reduced energy consumption can be achieved when end users receive a better feedback about their energy usage. For this purpose, smart meters and real-time displays have been installed. The smart meters normally communicated with the energy supplier via GSM. In some cases, smart meters and real-time displays were hard-wired to a GSM modem transmitting data to the energy supplier or were integrated with a GSM modem. The PLC option has been also examined, at a minor extent within two communities for data transmission from the smart meters to the concentration hub, while GSM has been employed for further connection of the concentrator and the control center [77].
## 3 Simulations
### Influence of the Number of Target Placements  
The diameter of the circular target is 50 mm and Gaussian noise is incorporated into the circular target profile and light stripe used for calibration. The noise level is \(\sigma\) = 0.4 mm. The number of target placements varies between 2, 3, 4, 5, 6, and 7. The relative errors of the calibration data obtained at different placement numbers are depicted in Figure 7, which clearly shows that the calibration accuracy increases as the number of target placements increases. When its value is larger than five, the relative error decreases significantly. Therefore, the calibration accuracy tends to stabilize when the number of target placements reaches a certain threshold. Furthermore, satisfactory results are obtained at a number of target placement greater than five.
## Appendix A The Hermite Transform
### The Discrete Hermite Transform  
The expansion of the image sequence \(I(X,t)\) of Equation (11) requires convolution of the image at time \(t\) with an infinity set of Hermite filters \(D_{m,n-m}\) with \(n=0,\,\ldots,\,\infty,\,m=0,\,\ldots,\,n\), in the discrete case, we limit the number of Hermite coefficients by [38]:  
\[n =0,\,\ldots,\,2\mathcal{N}\] \[m =0,\,\ldots,\,n, \tag{13}\]  
where \(\mathcal{N}+1\) is the size of the Gaussian kernel, thus, the maximum order of the expansion is limited by the size of the discrete Gaussian window. For large values of \(\mathcal{N}\) the discrete Gaussian kernel reduces to the Gaussian window.  
Furthermore, instead of recovering the original image, we obtain an approximation of the original image \(\hat{I}(X,t)\), where the quality of this reconstruction improves by increasing the maximum order of the expansion \(2\mathcal{N}\), i.e., the size of the Gaussian window \(\mathcal{N}+1\)[38]. In terms of the artifacts in the approximated image \(\hat{I}(X,t)\), small values of the Gaussian windows causes \"speckles\", while high values result in Gibbs-phenomenon-like artifacts such as ringing and blur [41].  
Thus, to determine the maximum order or the expansion \(2\mathcal{N}\) and consequently the size of the Gaussian window \(\mathcal{N}+1\), in [41] [PERSON] and [PERSON] determined that using an expansion of the Hermite transform equal to 3, the reconstructed image will contain the largest quantity of AC energy (84%) according to [PERSON]'s theorem. In general, with \(2\mathcal{N}\geq 3\) we can obtain a good reconstruction and with much greater values we will obtain a perfect reconstruction of the image, e.g., \(2\mathcal{N}=8\).
# Detecting SPIT Attacks in VoIP Networks Using Convolutional Autoencoders: A Deep Learning Approach
## 6 Conclusions  
In this paper, a novel anomaly detection model, D1-DCAE, based on a deep one-class denoising convolutional autoencoder, is introduced. This model is intended to learn the underlying normal pattern of data and detect anomalies from the learned pattern. Using two publicly available datasets, INRIA and RIT, the performance of the D1-DCAE model is compared to that of the classical anomaly detection approaches. The proposed D1-DCAE outperforms the classical models in terms of accuracy, F1 score, and AUC. The experimental results have also shown that D1-DCAE achieved a lower false positive rate than the traditional methods. These outcomes indicate that the proposed model can be an effective and efficient solution for anomaly detection tasks in various domains. It is worth noting that one limitation of autoencoders is the potential impact of noise and the presence of anomalies in the training data on the extraction of relevant feature representations. To address this issue, robust deep autoencoders can be employed. Future research directions may involve reducing the number of network traffic features selected to enhance the model's complexity and accuracy. Additionally, the implementation of a fine-grained model could be explored to investigate various VoIP attacks in greater detail.  
Conceptualization, W.N. and O.Y.A.; methodology, O.Y.A.; software, O.Y.A. and W.N.; investigation, W.N. and O.Y.A.; data curation, W.N. and O.Y.A.; writing--original draft preparation, W.N. and O.Y.A.; writing--review and editing, W.N., O.Y.A., K.A. and E.E.; visualization, W.N., O.Y.A. and E.E.; funding acquisition, K.A., E.E. and W.N. All authors have read and agreed to the published version of the manuscript.  
P. Prince Sattam bin Abdulaziz University, project number (PSAU/2023/R/1444).  
The data that support the findings of this work are available on request from the corresponding author.  
This study is supported via funding from Prince Sattam bin Abdulaziz University, project number (PSAU/2023/R/1444).  
The authors declare no conflict of interest.
## 3 Resolution of Degeneracy
### Degeneracy Phenomenon Detection  
In visual-inertial odometer systems with line features, degeneracy occurs during the triangulation process to recover the spatial position of features. As shown in Figure 2, when the camera is moved to a certain position, the projections \(l^{\prime}\) and \(l^{\prime\prime}\) of the line features on two frames are located in the same line with the epipole, the planes \(\pi_{0}\), \(\pi_{1}\) formed by the center of the camera and projected lines coincide with the epipolar planes, and the reconstructed 3D line is located on the DABC plane:  
Figure 2: Illustrative diagram of degeneracy phenomenon of line features.  
The actual position of the line feature is at \(l\), but the triangulation process may reconstruct the line feature at the position of any of the dashed lines in that plane, resulting in a biased position estimate. From the above analysis, it is clear that degeneracy occurs when the point is close to the epipole. Since there are numerous point features on line features, systems containing line features are more susceptible to degeneracy problems compared with systems with point features alone.  
In response to this phenomenon, a degeneracy detection step was added to the system. Calculating the distance between the line feature and epipole can determine whether the feature is degraded or not, and the position of the epipole is calculated as follows:  
\[P_{epipole}={R^{w}_{c_{i+1}}}^{T}(t^{w}_{c_{i}}-t^{w}_{c_{i+1}}) \tag{3}\]  
where \(P_{epipole}\) is the epipole of the \(i\)th camera coordinate system \(c_{i}\) relative to the \(i+1\)th camera coordinate system \(c_{i+1}\); \(R^{w}_{c_{i+1}}\) is the rotation matrix from the \(i+1\)th camera coordinate system to the world coordinate system; and \(t^{w}_{c_{i}}\) and \(t^{w}_{c_{i+1}}\) are the translation matrices from the two camera coordinate systems to the world coordinate system, respectively.
###### Abstract
Therefore, it is necessary to strengthen the network's ability to extract edge feature information. These models do not have a specialized structure to increase the model's ability to learn edge features, which could cause some edge information loss;
4. Finally, various stains in crack images would interfere with the crack feature extraction, causing some crack areas to be ignored. The present methods often fuse output features from different layers or use attention mechanisms to solve this problem. However, as the characteristics of stains vary greatly, these methods cannot effectively solve the issue. In this paper, a pyramid hierarchical convolution module (PHCM) is proposed by us to extract the features of cracks with different sizes, since PHCM contains series convolution kernels with different sizes. Meanwhile, a mixed global attention module (MGAM) was used to fuse global feature information, guiding the model to restore high-resolution features with global information. Furthermore, an edge feature extractor module (EFEM) was designed by us to learn the edge features of cracks through adding edge detection operators like Sobel, Laplacian, and Canny. In addition, a supplementary attention module (SAM) was used to solve the interference caused by stains in crack images. The structure of this article is arranged as follows. Section 2 introduces the related works. Section 3 introduces our proposed methods. The model structure of our proposed PCHNet is also described. Additionally, the MGAM, EFEM, and SAM are illustrated in detail. Furthermore, we introduce the Swish activation function and our data augmentation strategies. Section 4 introduces the crack datasets and some experiments, and an analysis of results is presented. Section 5 provides the conclusion to the whole article.
## 2 Materials and Methods
### Computational Fluid Dynamics Simulation
#### 2.3.2 Computational Setup for CFD Simulation
Figure 2: Computational fluid simulation set-up and resolution-independent study: (**a**) fluid computational domain for flight simulation of the butterfly multi-body model; (**b**) domain structure with lattice mesh on the middle symmetry plane of the butterfly; (**c**) the aerodynamic forces of the butterfly under three-resolution set-ups (\(F_{x}\), horizontal force; \(F_{y}\), vertical force); and (**d**) the average aerodynamic forces under three-resolution set-ups. The aerodynamics model of the butterfly is simulated numerically with the Lattice Boltzmann method (LBM) [33], which is applied with a commercial LBM code: Next Limit Technologies' XFlow. Unlike traditional CFD software, XFlow provides a proprietary fully Lagrangian particle-based kinetic solver, which dramatically reduces the time to calculate for a given level of solution accuracy. The LBM proves to be efficient and reliable for aerodynamic force, torque, and vortex wake structure simulation under various conditions [34; 35; 36]. The kinematic law of the butterfly is adapted to the multi-body model by the kinematic simulation software (ADAMS). Firstly, the three-dimensional model of the butterfly is established in the modeling software Solidworks. Then, the model is imported into ADAMS. In ADAMS, kinematics rules for the rigid body parts are supplemented and then the \".res\" file is generated. Finally, the three-dimensional model and motion rule file of the butterfly are imported into XFlow. The fluid conditions can be set and then carry out simulation data analysis. The spatial resolution of the far field (Resolved scale in XFlow) is set to be the largest level of the lattice in adaptive refinement mode. The smaller the resolved scale is, the more accurate the numerical calculation is. The simulation time also increases with the number of elements being calculated. The resolution of the near wall is set to smallest to refine dynamically moving geometries. The dynamic domain topology will perform octree stratification according to the several resolution settings to get the ideal computational lattice, as shown in Figure 2b. The resolution of the wake is set to the same value as the resolution of the near wall for getting the clear wake flow. In this study, the time step of each flight case is set to 0.001 s, and the resolution scheme is carefully selected, as shown in Table 3. Note that \"s\" represents the initially tested resolution. Figure 2c,d shows the comparison of aerodynamic forces during the four flapping strokes from three resolution set-ups (coarse, medium, and fine). The differences of the average values of the horizontal and vertical aerodynamic forces between the medium-resolution (adopted in this paper) and the fine-resolution are about 2% and 10%, respectively. In XFlow, it is necessary to ensure that the stability parameter of the simulation is low, and the relatively fine domain structure can be obtained. Considering the calculation time and accuracy, the medium-resolution scheme of s/2 is chosen as the simulation condition.
# Surface Correlation-Based Fingerprinting Method Using LTE Signal for Localization in Urban Canyon
## 3 Surface Correlation-based Fingerprinting Method  
### Fingerprinting Database  
Generally, the fingerprinting database consists of the coordinates of RPs and RSS data from surrounding BS at the RP location. As mentioned above, the SC method compares spatial RSS patterns generated during pedestrian movement with the database; thus, it is necessary to generate a reference trajectory matching with the pedestrian's route from the database. Therefore, it utilizes a database of 'link-node' structures to generate the reference trajectories dynamically. The link is generated based on crossroads and consists of a set of RPs as shown in Figure 2. The crosspoint connecting each link is defined as a node. The fingerprinting database can be expressed as follows:  
\[RP_{k}^{l}=[(x,y),[(PCI_{1}:RSS_{1}),\cdots,(PCI_{n}:RSS_{n})]] \tag{1}\]  
where, \(RP_{k}^{l}\) is the \(k\)th RP on the \(l\)th link, (\(x\),\(y\)) is the coordinate of RP, \(PCI_{n}\) is Physical Layer Cell ID (PCI) of \(n\)th BS, and \(RSS_{n}\) is the received signal strength from \(PCI_{n}\), respectively. The reference trajectory, which matches the route of a pedestrian, is generated through a combination of \(RP_{k}^{l}\).  
Figure 2: The link-node structure for fingerprinting database.
# Multi-Source Cooperative Data Collection with a Mobile Sink for the Wireless Sensor Network
## 3 Directed Graphs and Distributed Low-Density Parity-Check Codes for Data Collection  
In this section, distributed low-density parity-check (LDPC) codes are designed based on the multi-source sparse cooperation models above to achieve the diversity gain and coding gain simultaneously. Specifically, the node cooperation models are firstly described using directed graphs.  
Figure 3: Multi-source sparse cooperation model with two-round moving of the sink node.  
Then, the coded cooperation schemes with LDPC codes are devised based on the directed graphs. In the design, the coding schemes are constructed with the expansion from the basic matrix, which can be derived from the cooperative models. In this way, the data of all the sources in the cooperative group \(S=\{s_{1},s_{2},\ldots,s_{N}\}\) are encoded in a fully distributed and concise way and jointly transmitted to the mobile sink.
###### Abstract  
In this paper, we report the synthesis of a multi-template molecularly imprinted polymer (MIP) to target and extract naproxen, ibuprofen, diclofenac, emtricitabine, tenofovir disproxil, and efavirenza from wastewater bodies. A bulk polymerization procedure was used to synthesize the MIP and non-imprinted polymer (NIP). The specific recognition sites for each target were obtained through the removal of the imprinted targeted compounds. The interaction of antiretroviral drugs (ARVs) and non-steroidal anti-inflammatory drugs (NSAIDs) compounds with the MIP was studied under various conditions such as pH, mass, concentration, and time factors. The results demonstrated the optimum conditions were 55 mg of MIP, pH 7.0, a concentration of 5 mg L\({}^{-1}\), and a contact time of 10 min. For every compound studied, the extraction efficiencies for ARVs and NSAIDs in aqueous solutions was >96%. The adsorption capacity for the MIP was >0.91 mg g\({}^{-1}\). Adsorption obeys a second-order rate, and the Freundlich model explains the adsorption isotherm data. This study demonstrated that the synthesized multi-template MIP has huge potential to be employed for the removal of ARVs and NSAIDs from the environment as well as in drug purification or recovery processes.  
multi-template molecularly imprinted polymer (MIP); NSAIDs; ARVs; adsorption; second-order isotherm Article
## 3 Case Study: Generation of the Structural Model  
The selected building concerns a four-story residential building located in Lisbon. The established structural solution is a reinforced concrete frame. The structural BIM model was generated based on the available set of drawings of floor plants, sections cuts and details of the structure (Figure 1):  
* The structure is composed of an orthogonal frame which supports the floor slabs;
* The isolated foundations are 0.60 m high and are connected to each other by beams placed in both directions.
# Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2
## 2 Materials and Methods
### Study Areas and Datasets
#### 2.1.4 Reference and Validation Samples  
For both test areas, we randomly generated points that were well spatially distributed across the study areas, covering most parcels with an identifiable land cover type. These were further classified according to the CropScape layer for 2017, which is temporally overlapping with our Sentinel-2 time  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Description** & **Equation** & **Range** \\ \hline NDVI & Normalized Difference Vegetation Index & \(\left(\text{B8}-\text{B4}\right)/\left(\text{B8}+\text{B4}\right)\) & \(\left[-1,1\right]\) \\ GNDVI & Green Normalized Difference & \(\left(\text{B8}-\text{B3}\right)/\left(\text{B8}+\text{B3}\right)\) & \(\left[-1,1\right]\) \\ VDRE & Normalized Difference Red-Edge & \(\left(\text{B6}-\text{B5}\right)/\left(\text{B6}+\text{B5}\right)\) & \(\left[-1,1\right]\) \\ SAVI & Soil Adjusted Vegetation Index & \(\left(\text{B8}-\text{B4}\right)/\left(\text{B8}+\text{B4}+0.5\right))\times \left(1+0.5\right)\) & \(\left[-1,1\right]\) \\ NDWI & Normalized Difference Water Index & \(\left(\text{B8A}-\text{B11}\right)/\left(\text{B8A}+\text{B11}\right)\) & \(\left[-1,1\right]\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vegetation indices we used for crop mapping, their equation and range of values. Sentinel-2 bands used are the green (B3, 0.560 μm), red (B4, 0.665 μm), red-edge 1 (B5, 0.705 μm), red-edge 2 (B6, 0.740 μm), near-infrared (B8, 0.842 μm), narrow near-infrared (B8A, 0.865 μm), and SWIR (B11, 1.610 μm).  
Figure 3: Workflow of our object-based dynamic time warping (DTW) classifications using multiple vegetation indices extracted from Sentinel-2 SITS, as shown in Table 1.  
series. A temporal pattern for each class was obtained, but additional refinement was necessary to remove the inconsistent patterns and was done through visual interpretation and by analyzing the temporal pattern of NDVI. The good performance of a DTW classification comes from good quality reference samples [18]. DTW performs well also with a low number of samples, as demonstrated in [26], as long as their temporal patterns are representative for each class. Therefore, to avoid smoothing a reference temporal pattern by selecting multiple heterogeneous samples for the same class, we selected only 3 reference samples per class to generate the temporal patterns needed as input for the DTW classification, avoiding selecting samples that were contaminated with clouds or clouds shadow. For single-band DTW, we used the NDVI to generate the patterns, while for multi-band DTW we used the temporal patterns from all five vegetation indices (Table 1). By using a clean reference pattern for a class, DTW will be able to deal with the intra-class heterogeneity of patterns and match them well. The rest of the samples were used for validation and their number varied in accordance with class representativeness within each test area, i.e., the area of the class occupied in the CropScape (Table 2).