esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Modeling
### Simulation Results and Analysis
#### 4.5.2 Effect of the Position of the Vibration Isolation Trench  
Figure 16 shows the variations of PGA and PGV with the measurement distance for the isolation trench located at different positions. The results revealed that the isolation trench in all positions has a significant isolation effect on the soil immediately behind it. For instance, in the vertical direction of PGA, the model of the isolation trench located 6 m away from the impact point of the soil produces the lowest vibration response for the soil located at 7 m away. Similarly, the model of the isolation trench located 8 m away produces the lowest vibration response for the soil located at 9 m away, while the model of the isolation trench located 10 m away produces the lowest vibration response for the soil located at 11 m away or beyond. The same trend is observed for other directions of PGA and PGV, indicating that the closer the isolation trench is to the soil that needs vibration reduction, the better the vibration reduction effect. Therefore, in practical engineering, to achieve the best isolation effect of the isolation trench, it is recommended to increase the distance between the impact point and the isolation trench and reduce the distance between the vibration source and the isolation trench. Thus, in the case discussed in this paper, the isolation trench should be placed as close to the Yangtze River dike as possible while ensuring the stability of the dike structure.
## 2 Materials and Methods
### Sample Selection and Inclusion Criteria
* Group B: a 0.33% acidified sodium fluoride gel (MEDICAL(r), Treviso, Italy) was used and applied in contact with the enamel for 120 s.
* Group O: irradiation procedure with a 300-micrometer fiber of a soft touch diode laser (Creation Medical Laser 810 nm, 5 W; Ennebi Elettronica S.r.l. Novedrate--CO; Italy) with a power of 0.2 Watt in continuous mode for 120 s was administered. The tip of the fiber was kept at a maximum distance of about 1-2 mm from the enamel of the areas test by moving it in a circular direction to cover the entire surface of interest (low level laser therapy: LLLT). * Group X: the procedure used involved a combined action with the application of a 0.33% acidic sodium fluoride gel held in contact with the enamel surface for 120 s and, at the same time, the area test was irradiated with a soft touch diode laser (Creation Medical Laser 5 W) with the same modalities and times as in Group O. The first clinician, who had already been employed in the selection of the specimens, was again summoned to subject the selected teeth to an erosive treatment based on an acid solution. Indeed, after the completion of pretreatments, all the specimens were rinsed with distilled water for 2 min and, after waiting for about 30 min, they were immersed in a solution of hydrofluoric acid, with a concentration of 1 molar for 30 min, and renewed for each tooth. At the end of the exposure period, all teeth were retrieved from the acid solution and rinsed thoroughly with distilled water for 2 min, and then viewed again with SEM vision by a fourth researcher. The images coming from four surfaces of each tooth were examined after exposure to the acid solution to evaluate the protective effect provided by each of the procedures used for each single study group. The quantification of the data collected on SEM was carried out using energy dispersion spectrometry (EDS) equipped with a spectrometer consisting of a detector, capable of separating the characteristic rays, and a counter that measures the distribution of the intensity of the X-rays produced by the electron beam on the sample. In this way, the properties of the surfaces could be quantified using the gray values in relation to the total area and compared with the gray values for the same surface before the study. Figures 2-4 show some phases of our research. Figure 1: (**a**) Example of specimen numbering and labeling of axial surfaces. (**b**) Preparation of specimen for SEM vision. Figure 3: Example of a test area after exposure to the acidic solution and superimposition of a grid used to measure morphological characteristics and quantify surface properties. Figure 2: SEM images of each test area for each study sample after immersion in the acid solution. Group A (**a**), Group B (**b**), Group O (**c**), and Group X (**d**).
## 3 Results
### Model Comparison and Selection  
As mentioned before, a significance test was performed to determine which of the models performs best. The results of the significance tests are shown in Table 7 for the striking technique and Table 8 for the target object classification. The results for the striking technique classification models show that there is only a significant difference between the models of the RF and SVC as well as the SVC and MLP models with a significance level of \(\alpha=0.05\). Regarding the results of the significance test for the target object classification models, slightly more models seem to be significantly different with the significance level of \(\alpha=0.05\). In particular, the following models differ: RF and SVC, RF and kNN, and RF and MLP. Relating these results to the results of the test set, it is evident that those models that have a small difference in accuracy also seem not to be significantly different. Combining the insights of all results, the conclusion is that the kNN, MLP, and SVC models all produce similarly good results. Thus, to make a final decision for the final models, the best accuracy result for the striking technique and strike target object was taken into account. Hence, the SVC model was selected for both classification tasks as the final model.
# Artificial Intelligence Approach for Early Detection of Brain Tumors Using MRI Images
## 3 Results  
Figure 3 shows our multi-level thresholding segmentation method on a database of T2-weighted normal brain MR images.  
The photos in Figure 3 were examined using the automatic multilayer threshold and show that the tumor is well separated from the other objects in the image. The color representation of the objects shows that in the first column of images Figure 3A, the red is primarily representing the tumor. In the second column of images, yellow is primarily representing the tumor; in the third column of images, the orange color represents the segmented tumor. Finally, in the fourth column of images, the orange represents the suspected tumor in Figure 3D.  
Figure 4 shows a color-segmented image in image Figure 4C with three levels and four colors. The HSO thresholds method is used for the segmentation of the original image. The image has four colors: dark blue for the background, sky blue for brain tissue, yellow for the tumor's surrounding area, and red for the tumor's core.  
Figure 4: (**A**) shows the original MRI scans utilized in segmentation, and (**B**) shows the histogram of the original image with the three levels selected. (**C**) Display the segmented image as a color-coded image. (**D**) Display the red-colored pixels in the image (**C**). (**E**) shows the result after morphological processing. (**F**) show the final result after connected component analysis.  
Figure 3: Color segmentation findings using the multilevel threshold method applied to three grey levels of images. (**A**) the red is primarily representing the tumor. (**B**) yellow is primarily representing the tumor; (**C**) the orange color represents the segmented tumor. (**D**) the orange represents the suspected tumor.  
The tumor is subsequently extracted using the component connect approach after the second step of morphological operations.  
The four performance indicators were calculated (accuracy, precision, sensitivity, dice) to compare the result to the grand truth (GT), two of which are displayed in Figure 5 below.  
The green-colored pixels in Figure 5 are those chosen by the proposed approach and not found in the GT. The purple pixels represent missing areas that the proposed approach did not detect.
## 3 Haar Wavelet Method and Laplace Haar Wavelet Method
### Laplace Haar Wavelet Method (LHWM)  
In this section, we summarize the methodology of Laplace Haar wavelet method to the two-dimensional integro-differential Equation (7), where we consider in this approach the more general case, for \(n-1<\alpha<n\) and initial conditions  
\[u(x,0)=u_{0}(x),\frac{\partial}{\partial t}u(x,0)=u_{1}(x),...,\frac{\partial^{ n-1}}{\partial t^{n-1}}u(x,0)=u_{n-1}(x)\]  
which can be reached in the following steps.  
**Step 1**. Determine the level of resolution \(J\).  
**Step 2**. Applying the Laplace transform to Equation (7), use Lemma 1, Corollary 1, and use the initial conditions \(u_{0}(x)=u(x,0)\), \(u_{1}(x)=\frac{\partial}{\partial t}u(x,0)\),..., \(u_{n-1}(x)=\frac{\partial^{n-1}}{\partial t^{n-1}}u(x,0)\), one gets:  
\[s^{\alpha}U(x,s)-\sum_{k=0}^{n-1}s^{\alpha-k-1}u_{k}(x)=F(x,s)+\int_{0}^{1} \int_{0}^{1}u(y,w)K(x,s,y,w)dwdy,\]  
equivalently,  
\[U(x,s)=\frac{1}{s^{\alpha}}\left(F(x,s)+\sum_{k=0}^{n-1}s^{\alpha-k-1}u_{k}(x) \right)+\int_{0}^{1}\int_{0}^{1}u(y,w)\frac{K(x,s,y,w)}{s^{\alpha}}dwdy,\]  
**Step 3**. Divide by \(s^{\alpha}\) and take inverse Laplace transform  
\[u(x,t)=g(x,t)+\int_{0}^{1}\int_{0}^{1}u(y,w)R(x,t,y,w)dwdy, \tag{10}\]  
where \(g(x,t)=\mathcal{L}^{-1}[s^{-\alpha}F(x,s)+\sum_{k=0}^{n-1}s^{\alpha-k-1}u_{k}( x)]\) and \(R(x,t,y,w)=\mathcal{L}^{-1}[s^{-\alpha}K(x,s,w,y)]\).  
**Step 4**. Use the Haar wavelet truncated series \(u(x,t)\approx\sum_{i=1}^{2J+1}\sum_{j=1}^{2J+1}a_{ij}h_{i}(x)h_{j}(t)\) to discretize Equation (10):  
\[\sum_{i=1}^{2J+1}\sum_{j=1}^{2J+1}a_{ij}h_{i}(x)h_{j}(t)=g(x,t)+\sum_{j=1}^{2 J+1}\sum_{i=1}^{2J+1}a_{ij}\int_{0}^{1}\int_{0}^{1}h_{i}(y)h_{j}(w)R(x,t,y,w)dwdy.\]  
**Step 5**. Use the nodes \((x_{m},t_{n})\), \(m,n=1,2,3,...,2^{J+1}\) to construct a \(2^{J+1}\times 2^{J+1}\) system  
\[\sum_{i=1}^{2J+1}\sum_{j=1}^{2J+1}a_{ij}h_{i}(x_{m})h_{j}(t_{n})=g(x_{m},t_{n} )+\sum_{j=1}^{2J+1}\sum_{i=1}^{2J+1}a_{ij}\int_{0}^{1}\int_{0}^{1}h_{i}(y)h_{j }(w)R(x_{m},t_{n},y,w)dwdy.\]  
Then, solve this system to obtain the coefficients \(a_{ij}\).
# Reliable Visual Exploration System with Fault Tolerance Structure
## 2 Fault-Tolerant Visual Robot Navigation
### Fault Tolerance within an Exploration System  
Within such a framework mentioned above, for achieving fault tolerance, a series of techniques are implemented to overcome the fault during visual exploration:  
1. For fault recovery, by building multiple submaps, the visual mapping is strengthened in terms of graph maintainability.  
2. By a higher frame rate with a parallel framework to perform VLM, the difficulty of data association for visual tracking is reduced.  
3. Active controller is used for keeping a submap building all the time, which increases the strength by tracking recovering in terms of hardware.  
4. Mapping completeness of explored space is conducted in order to improve the ability of re-localization based on the built map.  
Within the visual exploration system, fault is defined as the failure of visual tracking and mapping; fault prevention is realized by parallel design in VLM and completeness evaluation in termination condition; fault recovery for maintaining mapping is realized by a submap-based back-end and active controller. The achievement of fault tolerance is summarized in Table 1.  
The evaluations of each module in Section 6 are designed according to the solutions shown in Table 1, including the mapping maintenance and frame rate improvement in VLM, proper driving and obstacle avoidance without localization of active controller and re-localization enhancement by mapping completeness evaluation using the termination condition.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fault Tolerance** & **Module** & **Solution** \\ \hline Fault Recovery & VLM & Submap-based back-end, maintaining mapping within one of the submaps \\ Fault Recovery & A-C & All-time driving of mobile robot, keeping image capturing \\ Fault Prevention & VLM & Parallel constraint building, higher frame rate for easier data association \\ Fault Prevention & T-C & Completeness evaluation, rich environment information for localization \\ \hline \hline \end{tabular}
\end{table}
Table 1: Achievement of fault tolerance in all the modules within our proposed system, where VLM is the visual localization and mapping module, A-C is the Active Controller and T-C is the Termination Condition.  
Figure 1: Structure of our visual exploration system, three modules are contained: visual localization and mapping (VLM), active controller and termination condition.
## 1 Introduction
In summary, the proposed automatic-vehicle-detection-module (AVDM) approach successfully provides an efficient and reliable route by avoiding congestion and automatically detecting vehicles, as shown in Figure 1.  
Figure 1: Efficient and reliable path-detection process. The main contributions of this study can be summarized as follows:  
* The heterogeneous algorithm is introduced, which combines the best aspects of two state-of-the-art algorithms (PT and ACO). ACO delivers an efficient and dependable approach using local and global search features. Furthermore, ACO incorporates real-time probabilistic and flexible properties. On the other hand, ACO has problems with convergence speed and accuracy when dealing with enormous amounts of data. To address the shortcomings of ACO, PT provides support for network scalability and vehicle mobility. The analytical model of PT is based on two separate parameters: packet-generation rate and pheromone sensitivity for single and multiple networks. Before delivering packets on each connection, the packet-generation rate minimizes congestion and pheromone sensitivity defines the link capacity. As a result, energy efficiency is improved. * The novel congestion-avoidance model is intended to save fuel, minimize carbon emissions, and protect the environment. It is possible to achieve this by estimating the average speed of vehicle movement, which is affected by vehicle density. As a result, throughput is improved and energy usage is decreased. * The automated-vehicle-detection module detects the presence and movement of vehicles in a given region by going through many phases (image preprocessing, contour detection, contour matching, and blob detection). As a consequence, the accuracy of automated detection is improved compared to other state-of-the-art methods. The remainder of the paper is organized as follows: Section 2 presents the salient features of current approaches in related work. Section 3 provides the details of the proposed heterogeneous algorithm for efficient-path detection. Section 4 presents congestion-avoidance and vehicle-detection modules of ACO-PT for vehicles. Section 5 presents the experimental setup, including the results. Section 6 discusses the results, and finally, Section 7 concludes the entire paper with future work.
# DFDT: An End-to-End DeepFake Detection Framework Using Vision Transformer
## 3 Methodology
### Patch Extraction & Embedding  
All mandatory preprocessing steps, including face localization, patch extraction, and patch/positional embeddings, are described in this section.  
**Preprocessing & Face Localization.** In general image manipulation tasks, the forgeries might be scattered across regions of the image, however, in deepfake generation techniques manipulations are mainly limited to facial areas and the background area is left intact. Therefore, having trained a model based on only face area would not only reduce computational complexity but also would improve the model performance due to background noise reduction [49]. Therefore, first, 20% of frames are extracted for each input video in consecutive order, and then facial landmarks are extracted leveraging the RetinaFace [50], a state-of-the-art face detection method. Finally, the facial area on each frame is cropped, resized, and aligned using the calculated landmarks [51].  
**Patch & Embedding Extraction.** Before analyzing images using transformer models, they need to be converted into a \(D\)-dimensional sequence of smaller patch embeddings. While non-overlapping patch extraction methods harm the neighboring local structures, the overlapping image splitting approach, where two adjacent patches share an area, helps preserve and learn the neighboring information of the local area much better. In other words, in the overlapping patch extraction approach, every given image with a resolution of \((H,W)\) and \(C\) channels, \(I\in\mathbb{R}^{H\times W\times C}\), is dissected into \(N\) smaller image patches with a resolution of \((P,P)\) and \(C\) channels, \(I_{p}\in\mathbb{R}^{P\times P\times C}\), using a sliding window of stride \(S\). Each adjacent patch shares an area of size \(P\times(P-S)\).  
\[N=N_{H}*N_{W}=\left\lfloor\frac{H-P+S}{S}\right\rfloor*\left\lfloor\frac{W-P+S} {S}\right\rfloor \tag{1}\]  
The resulting patches are then flattened and projected into a latent \(D\)-dimensional embedding space \(\mathbf{E}\in\mathbb{R}^{N\times D}\). Furthermore, to maintain positional information of each patch, patch embeddings are integrated with position embeddings \(\mathbf{E}_{pos}\in\mathbb{R}^{(N+1)\times D}\). The  
Figure 1: An overview of the proposed end-to-end transformer-based deepfake detection framework. DFDT is developed leveraging the unique characteristics of transformer models on learning hidden traces of perturbations from local image features and the global relationship of pixels at different forgery scales. Patch extraction & embedding, a multi-stream transformer block, attention-based patch selection, followed by a multi-scale classifier are the main components of the DFDT.  
resulting sequence of embedded patches, \(\mathbf{z}\), serves as an effective input sequence for the transformer blocks [24].  
\[\mathbf{z}=[I_{\text{cls}}\,;E]+E_{\text{pos}},\quad E\in\mathbb{R}^{N\times D},E _{\text{pos}}\in R^{(N+1)\times D} \tag{2}\]
# Silver Nanoparticles-Loaded Exfoliated Graphite and Its Anti-Bacterial Performance
## 2 Materials and Methods
### Structural Characterization  
The crystalline structure of Ag nanoparticles were characterized by X-ray diffraction (XRD, Rigaku D/max 2500 V, Target = Cu, Step width = 0.01, operating at an acceleration voltage of 40 kV). FT-IR of the EG and Ag-EG was performed by Bruker FT-IR spectrophotometer. Mixed EG and KBr were ground to powder, then compressed into thin slices. FTIR spectra were recorded with 4 cm\({}^{-1}\) resolution over the range 500-4000 cm\({}^{-1}\) using a Vertex 70 Fourier transform infrared spectrometer. Nitrogen adsorption-desorption measurement was performed to investigate the specific surface area (SSA) using a BEL-SORP-II (BEL Japan, Inc., Tokyo, Japan) system at 77 k, the SSA were calculated using the Bruauer-Emmett-Teller (BET) equation. The pore size distribution of EG and Ag-EG was measured by mercury intrusion porosimetry (Micromerities, AutoPore IV 9500, from pressure 0.20 to 60,000.00 psia). The morphology of exfoliated graphite was observed by scanning electron microscopy (SEM, MERLIN Compact, Carl Zeiss, Jena, Germany), the samples were coated with a layer of sputtered gold using a vacuum sputter to provide electrical conductivity before SEM observation. At the same time, Energy Dispersive Spectrometer (EDS) was used to examine the nanoparticles loaded on the surface of EG. High-resolution transmission electron microscope (HR-TEM) images were obtained on a JEOL JEM-2100 F. Samples for HRTEM imaging were prepared by placing a drop of the solution sample (Ag-EG was suspended in ethyl alcohol and treated through ultrasonication for 2 h) onto a carbon-coated Cu grid, dried in air and loaded into the electron microscopic chamber. The Ag content was obtained by ICP-AES (VISTA-MPX, VARIN). The samples for ICP-AES were prepared as below: certain amount of Ag-EG and the mixture of HNO\({}_{3}\) and HClO\({}_{4}\) solution were put into digestion high-pressure tank, the nitrolysis process accomplished in an oven, then a metered volume of solution was obtained to be tested.
# Regression of Likelihood Probability for Time-Varying MIMO Systems with One-Bit ADCs
## 4 Proposed Solution
### Linear Regression  
This subsection introduces a linear regression approach to obtain the model parameter \(\mathbf{\Theta}_{n}\). However, solving the regression for individual time index \(n\) increases the complexity of both learning and applying \(\mathbf{\Theta}_{n}\). To address this issue, learning and applications are considered on a per-data-block basis. Subsequently, the optimization problem in (18) is re-formulated as follows:  
\[\mathbf{\Theta}_{d}^{\star}=\underset{\mathbf{\Theta}_{d}}{\text{argmin}}\sum_{n\in \mathcal{N}_{d}}\sum_{r\in\mathcal{R}}\sum_{k\in\mathcal{K}}\|p_{r,k}[n]-\hat{ p}_{r,k}[n](\mathbf{\Theta}_{d})\|^{2}, \tag{19}\]  
where \(\mathbf{\Theta}_{d}==[\theta_{d}^{0},\theta_{d}^{1},\theta_{d}^{2}]^{T}\). Linear regression is a well-known technique for solving the optimization problem in (19). To achieve this outcome, the matrices \(\mathbf{X}_{d}\in\mathbb{R}^{N_{d}\times 3}\) and \(\mathbf{y}_{d}\in\mathbb{R}^{N_{d}\times 1}\) are, respectively, defined as  
\[\mathbf{\Phi}_{d}=\left[\begin{array}{cc}1&p_{r,k}^{\text{channel}}[(d-1)N_{d}+1 ]&p_{r,k}^{\text{sample}}[(d-1)N_{d}+1]\\ 1&p_{r,k}^{\text{channel}}[(d-1)N_{d}+2]&p_{r,k}^{\text{sample}}[(d-1)N_{d}+2] \\ \vdots&\vdots&\vdots\\ 1&p_{r,k}^{\text{channel}}[dN_{d}]&p_{r,k}^{\text{sample}}[dN_{d}]\end{array} \right],\mathbf{y}_{d}=\left[\begin{array}{c}p_{r,k}[(d-1)N_{d}+1]\\ p_{r,k}[(d-1)N_{d}+2]\\ \vdots\\ p_{r,k}[dN_{d}]\end{array}\right]. \tag{20}\]  
Then, the optimal parameter \(\mathbf{\Theta}_{d}^{\star}\) of (19) can be derived as follows:  
\[\mathbf{\Theta}_{d}^{\star}=(\mathbf{\Phi}_{d}^{T}\mathbf{\Phi}_{d})^{-1}\mathbf{\Phi}_{d}^{T }\mathbf{y}_{d}. \tag{21}\]  
The optimal parameters in (21) are suboptimal because they are obtained from a limited number of samples. Using the optimal parameters, the proposed probability at the end of the \(d\)-th data block is expressed as follows:  
\[\hat{p}_{r,k}^{\text{pro}}(\mathbf{\Theta}_{d}^{\star})=(\theta_{d}^{0})^{\star}+ (\theta_{d}^{1})^{\star}p_{r,k}^{\text{channel}}[dN_{d}]+(\theta_{d}^{2})^{ \star}p_{r,k}^{\text{sample}}[dN_{d}]. \tag{22}\]
## 1 Introduction
And, thus, monolithic integration of a Si/SiGe THz detector and the necessary CMOS readout circuitry on a single chip may be feasible. The paper is organized as follows: Section 2 is devoted to materials and Methods, the Si/SiGe MODFET transistor is presented and its geometry and epilayer description are detailed, finally, a description of the set up used in the THz characterization is given. Section 3 presents the experimental and simulation results of an analysis of the device response under CW excitation at 0.15 and 0.30 THz. The study focuses on the differences of the photovoltaic response when the device receives front (the one commonly used) and back THz illumination. 3D FDTD (Finite-difference time-domain) simulations are performed to explain the results obtained in measurements at 0.3 THz.