esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Deep Learning Approach for Improved Detection and Prediction of Brain Stroke
## 6 Proposed Methodology
### Convolutional Neural Networks (CNNs)  
While CNNs are generally connected with picture information, they can be adjusted to work with organized numeric information sources, like patient information, by utilizing 1D convolutional layers [60]. CNNs are magnificent at distinguishing neighborhood examples and conditions inside information, which makes them ideal for recognizing connections between various wellbeing boundaries that may not be promptly clear in crude numeric form [70]. With regards to stroke forecast, CNNs can be utilized to distinguish unpretentious relationships, for example, those between glucose level variances and BMI, in any event, when the information is loud or fragmented. By gaining from these cooperations, CNNs assist with working on the model's capacity to identify complex, non-straight examples in understanding wellbeing metrics [71].  
By treating numerical data as a structured input in grid-like forms, CNNs can also be used for brain stroke diagnosis and prediction [72]. A 2D matrix is used to arrange numerical characteristics, including age, blood pressure, BMI, glucose levels, and lifestyle variables as shown in Figure 5. CNN layers use convolutional filters to identify patterns and spatial correlations among these features [73]. In order to prevent overfitting and preserve important patterns, pooling layers minimize the dimensionality. The retrieved characteristics are processed by fully connected layers to categorize results, including risk level prediction [74]. This method provides precise and accurate predictions by utilizing CNNs' feature extraction ability for structured numerical data [75].  
Figure 4: Basic long short-term memory (LSTM) architecture.  
Figure 5: Basic CNN architecture.
# Advances and Challenges of Corrosion and Topology Detection of Grounding Grid
## 4 Challenges and Opportunities
#### 2.2.4 Small target body of flat steel in grounding grid  
The common cross-sectional area of the grounding grid conductor is very small compared to other geophysical targets. The cross-sectional area of the grounding grid is much smaller than the length of the conductor, which brings great difficulty to the software simulation. For the electromagnetic induction method, because the conductor cross section is small, the eddy current response intensity and the influence range are small; for the ground penetrating radar, the reflection surface is too small, which brings great difficulty for detection.
# Alternative Categorization of Radio Frequency Power Amplifier for Generalized Design Insights
## 4 Generalized Design Approach
### Vertical Flow  
The appropriate hardware, which in turn provides the necessary computer models for simulating, verifying, and ultimately manufacturing the final RFPA design, is referred as vertical flow. These models encompass various components like capacitors, inductors, usually the S-parameter files, and non-linear models of the transistors, enabling a comprehensive and effective design process. While the design of an RFPA shares a common approach, the fabrication/physical realization of it can be carried out in two ways. COTS-based involves the use of readily available Commercial Off-The-Shelf (COTS) components [57; 58]. During setup, discrete electronic components, such as transistors, capacitors, and inductors, are combined to construct the power amplifier. This approach offers flexibility and ease of customization but may involve more manual assembly and tuning. It is a practical choice when specific performance requirements need to be met or when cost-effectiveness is a priority. Chip-based is an integrated implementation that is adopted often in the form of Monolithic Microwave Integrated Circuits (MMICs) or Radio Frequency Integrated Circuits (RFICs) [59]. Here, the entire power amplifier circuit is integrated into a single chip or substrate. This approach streamlines manufacturing, reduces component count, and typically results in smaller form factors. MMICs and RFICs are designed to operate at microwave and radio frequencies, making them well-suited for RFPA applications.
# A Preemptive-Resume Priority MAC Protocol for Efficient BSM Transmission in UAV-Assisted VANETs
## 3 System Model  
In our analysis, we consider a single-directional highway segment \([x_{0},x_{0}+D]\) based on UAVs with a length of D. We need to consider the following situation: As shown in Figure 1, \(x_{0}\) represents the reference point where vehicles arrive. The network consists of N vehicles (index \(\mathrm{K}=\{\mathrm{K}_{1},\ldots\ldots,\mathrm{K}_{\mathrm{N}}\}\)) and M UAVs (index \(\mathrm{U}=\{\mathrm{U}_{1},\ldots\ldots,\mathrm{U}_{\mathrm{M}}\}\)). The UAVs have a communication range that covers the entire road segment, with the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper Number** & **The Markov Chain Modeling** & **Queuing Theory** & **Priority** \\ \hline
[29; 30] & \(\surd\) & \(\times\) & \(\times\) \\
[31] & \(\times\) & \(\times\) & \(\surd\) \\
[32; 33] & \(\times\) & \(\surd\) & \(\times\) \\
[34] & \(\times\) & \(\surd\) & \(\surd\) \\
[35] & \(\surd\) & \(\surd\) & \(\times\) \\
[36; 37; 38] & \(\times\) & \(\times\) & \(\surd\) \\
[39] & \(\times\) & \(\surd\) & \(\surd\) \\
[40; 41] & \(\times\) & \(\times\) & \(\surd\) \\ Our work & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of Other Papers and Our Work. distribution parameter \(1/D\) following a uniform distribution. Given the limited availability of communication resources in UAV and VANET communications, ensuring real-time monitoring of vehicle safety conditions and road conditions becomes a critical challenge. It is essential to transmit BSM information at the shortest possible intervals to meet these requirements. However, this can result in resource constraints and insufficient timeliness, leading to potential safety hazards caused by an overwhelming number of BSMs. Consequently, there is a need to prioritize vehicles and their transmitted data packets. This paper will focus on prioritizing vehicles based on speed ranges and give particular attention to BSM information. The process can be divided into two steps: (1) classifying vehicles and their transmitted information based on their speed and designing a MAC layer protocol to schedule and queue the transmitted information; (2) using the 802.11p protocol, vehicles transmit the required messages through the MAC layer scheduling process and send them to the UAVs via the uplink. The following provides a detailed explanation. For convenience, the significant notations and variables used in the analysis procedure are summarized in Table 2.
## 2 Antitumor Immunity and Tumor Immune Escape
In addition to a constitutive expression in normal peripheral tissues and on most immune cells during the initiation of immune response, PD-L1 is expressed also in the majority of cancer cells. By contrast, PD-L2 expression is selective and limited to APCs (and this explains why PD-L2 exerts a limited role in anticancer immunity). Upon recognition of TAAs presented by MHC on cancer cells, activated T lymphocytes release not only perforin and granzymes, but also interferon-\(\gamma\) (IFN\(\gamma\)) and other cytokines. As a consequence, cancer cells upregulate PD-L1, which through the binding to PD-1 on lymphocytes, leads to a weakening of the T cells' response. The final result is an immune escape or immune tolerance (Figure 1B). Tumor cells can take advantage of these mechanisms in order to avoid immune-mediated destruction, not only by expressing ligands activating the immune checkpoints (intrinsic resistance, elicited by tumor cells), but also by favoring the development of a tolerant TME through the recruitment of non-neoplastic cells expressing these ligands (extrinsic resistance) [27; 28; 29]. ICIs are monoclonal antibodies specifically designed to disrupt these ligand/receptor interactions, removing T cells inhibition and promoting their antitumoral cytotoxic activity (Figure 1C). The administration of anti-CTLA4 antibodies permits the interaction between CD28 and B7-1 (or B7-2) and allows the co-stimulatory signal, thus restoring an effective priming phase of T-lymphocytes. Similarly, antibodies against PD-1/PD-L1 release the brake mechanism provided by the interaction between PD-1/PD-L1 signaling, allowing T lymphocytes to remain active despite the inhibitory molecules expressed by the tumor. Figure 1: Mechanisms of anti-tumor immunity and cancer immune escape. **(A)** In lymph nodes, T lymphocytes are activated upon recognition of tumor-associated antigens presented by the major histocompatibility complex (MHC) expressed on antigen-presenting cells (APCs), in combination with a co-stimulatory signal through the B7-CD28 pathway. These activated T cells, in the tumor microenvironment, recognize tumor-associated antigens expressed on tumor cells and, in turn, attack malignant cells by releasing perforin and granzymes. **(B)** Immune checkpoint molecules (PD-1, PD-L1, CTLA4) induce cancer immune escape. T lymphocytes activation is suppressed by the B-7/CTLA4 pathway in lymph nodes, while the PD-1/PD-L1 pathway mediates immune escape in cancer cells. **(C)** Schematic mechanism of action of immune checkpoint inhibitors anti-PD-1, anti-PD-L1, and anti-CTLA4. Anti-CTLA4 restore T lymphocytes activation in lymph nodes and anti-PD-1/anti-PD-L1 antibodies allow an effective cancer attack. Adapted from [PERSON], 2017 [26].
## 3 Simulation
### Single-Frequency Simulation Result Analysis  
In this simulation, the system sampling frequency was 2400 Hz, the primary noise was a single-frequency noise of 320 Hz, and the adaptive filter length was set at 256. The proposed SFDFBANC algorithm's step size was set at 0.4, the WSMANC algorithm's step size was 0.7, and the SAIMC algorithm's step size was 0.8.  
Figure 7 shows the time-domain error signal curve of the SFDFBANC algorithm. It began to converge at about 7400 sampling numbers, taking about 3 s, and gradually stabilized at about 100,000 sampling points. The error signal spectrums after and before control of the three algorithms are shown and compared in Figure 8. When the three algorithms have achieved a steady state, the SFDFBANC algorithm has similar noise reduction to that of both the SAIMC algorithm and SWMANC algorithm. After control, the noise reduction achieved was 19.8 dB.  
In the simulation, the proposed SFDFBANC algorithm had a step size of 0.5, the WSMANC algorithm's step size was 0.6, and the step value of the SAIMC algorithm was set at 0.7. The MSE curves of the three algorithms are shown in Figure 12. Among the three algorithms, both the WSMANC algorithm and the SAIMC algorithm have equivalent  
Figure 11: Spectra of two different noises (**a**) pink noise; (**b**) white noise.  
Figure 10: The three algorithms’ error signal spectrum.  
convergence speeds; however, the SEDFBANC algorithm has the fastest convergence rate due to adopting the structure of the block LMS algorithm.  
The comparison diagram of the three algorithms' error signal spectra after and before the control is shown in Figure 13. The noise reduction effect of the SEDFBANC algorithm was equivalent to that of both the WSMANC and SAIMC algorithms. After control, the noise reduction was about 12.5 dB.
## 3 Results and Discussion
### Effects of Copper Exposure on Ethanol Yield
[79] reported the use of sweet potato plants for remediation of lead-contaminated soil, followed by use of the tubers for bioethanol production. [PERSON] et al. [80] used napier grass for phytoremediation of Zn, Cd and Cr. They reported that metals generally lowered fermentation efficiency, but Cr had a positive impact on enzyme hydrolysis. Our study is the first report of the use of vetiver grass for the production of bioethanol, after optimization of bioethanol production conditions using RSM technology. Our results show that growing vetiver in Cu-contaminated soil affected lignin production, which had a positive impact on bioethanol yield. It was previously reported that the lignin content of vetiver is lower than other commonly used biofuel crops [16; 17]. This indicated that vetiver biomass would potentially  
Figure 4: Total sugars (v) consumed and ethanol (\(\Delta\)) produced during fermentation of the vetiver hydrolyses. Solid lines represent the experimental group and dashed lines represent the control group. Error bars show standard deviation with n = 6.  
provide higher ethanol yield from fermentation. Our studies show for the first time that growing vetiver in Cu-contaminated soil had a further positive impact on bioethanol yield. Studies are currently in progress in our lab to dissect the impact of Cu on lignocellulosic biomass composition.
## 5 Experimental Results
### Experimental Results for BCC Algorithm
#### 5.2.1 Traffic Generation and Simulation Platform for BCC  
The BCC algorithm was evaluated with a set of traffic flows based on application demands. Real data center traffic for typical query/search type applications like map-reduce and index-search were measured in [43]. Using these measured traffic flows, a Poisson shot-noise based model to synthesize data center traffic was proposed and verified in [42]. According to [42], the new flow arrival time, the flow duration and the injection rate for each application followed a Poisson, Pareto and, Gaussian distribution respectively. The new flow arrival time was generated using a Poisson distribution with an average flow arrival rate. The average flow arrival rate was considered to be 1000 flows/s for the small-sized DCN [43]. In our evaluations, we considered a Gaussian distribution for the injection rate to have a mean of 8.0 Kbps as the base case for the simulation. Application flow duration was generated following an independent Pareto distribution having a minimum duration of 10 microseconds [43] and a mean of 1 s. We then increased the average injection rates on an incremental basis to 8 Mbps, 100 Mbps, 400 Mbps, and 650 Mbps and regenerated new traffic which represented different types of multimedia traffic and repeat the simulations. We used the Network Simulator-3 (NS-3) suite [49] to evaluate the performance of BCC for both wired fat-tree and wireless S2S-WiDCN networks. NS-3 supported the characteristics of wireless propagation as well as network-level communications. This simulation platform was used to evaluate the S2S-WiDCN with and without consolidation and compare it with the fat-tree wired DCN. For the fat-tree based wired data center architecture, we considered 1.0 Gbps links between servers to access switches and 40.0 Gbps upper-layer links. For all the cases, the migration cost for consolidation is not included in the performance analysis. We have compared the performance of the BCC consolidation algorithm for S2S-WiDCN with traditional wired fat-tree based DCN. We considered a small data center consisting of 800 servers arranged in a \(20\times 8\) array of racks as [25]. Each of the racks housed five servers and occupied an area of 0.6 m \(\times\) 0.9 m and is 2 m high. There were 10 racks arranged in a single row and two columns of 8 rows, totaling 160 racks. In our simulations, the racks were assumed to be without any front or back door. In the traditional wired fat-tree based DCN, we considered the same number of servers arranged in same layout as S2S-WiDCN. We considered three hierarchical network layers consisting of 160 access, two aggregate, and two core layer switches, where each rack having an access layer switch.
## 2 Related Works
### Discussion  
The choice of an appropriate deployment method of a camera network depends strongly on the aim of each system. In fact, target based methods are more suitable for monitoring specific objects that may move in the monitored environment, while the goal of landscape based methods is to maximize the coverage area in order to observe the whole scene of interest. The main objective of this paper is to propose a novel method that belongs to the landscape based approach. Our proposed method is also based on the space segmentation using Voronoi Diagram (as the works of [19, 20, 21]). However, as detailed in the following sections, the proposed solution differs from other methods found in the literature in twofundamental aspects: (1) the choice of objects that generates the Voronoi Diagram and (2) The method of assessing the level of coverage.  
On one hand, Voronoi-based methods that are presented in previous work ([19, 20, 21]) use the expected position of the sensors (cameras) to generate the Voronoi diagram. Each Voronoi cell will contain the objects of the environment that may be monitored by the corresponding sensor. However, it will be more judicious to start by analyzing the environment where the sensor deployment will take place, and then propose the optimal position of the sensors based on the result of such analysis. For this purpose, the Voronoi Diagram will be generated based on objects of interest (_i.e._, Buildings) that exist in the monitored environment. Thereafter, cameras will be placed initially on the edge of each Voronoi cell.  
On the other hand, the methods that integrate a model of the area of interest when calculating the coverage use either a very simplistic model ([16, 17, 18]) or a Digital Surface Model (DSM) that represent only the surface of the monitored environment ([19, 21]). This second category allows only assessing the coverage level of the surface that can be seen from above. Nevertheless, it is impossible through these methods to assess the visibility of vertical surfaces that may be very important to monitor such as walls, doors, windows, _etc_. Hence, it is important to combine the visibility assessment based on raster DSM with 3D models that takes into consideration all facets (surface and vertical) of objects in the environment of interest.  
In the following sections, a recall about the main principles of Voronoi diagrams, as well as the coverage calculation are presented. Thereafter, the proposed approach called \"HybVOR\" for deployment of surveillance cameras is detailed.
## 2 Experimental Configuration and Phase Reconstruction
This approach to high-precision phase-shifting holography, without mechanical components, could reduce the vibration influence during holographic phase reconstructions. The object and reference waves can be represented by:  
\[ \begin{array}{l}O(x,y)=o(x,y)\exp[-j\varphi_{o}(x,y)]\\ R(x,y)=r(x,y)\exp[-j\varphi_{r}(x,y)]\end{array}  \tag{3}\]  
respectively, where \(o(x,y)\), \(\varphi_{o}(x,y)\), \(r(x,y)\) and \(\varphi_{r}(x,y)\) are the amplitude and phase of the object and reference waves, respectively. The intensity of the hologram \(I(x,y)\) recorded by the CMOS camera can be expressed as:  
\[ \begin{array}{ll}I(x,y)&=\left|O(x,y)+R(x,y)\right|^{2}\\ &=\left|O(x,y)\right|^{2}+\left|R(x,y)\right|^{2}+2o(x,y)r(x,y)\cos[\varphi_{o }(x,y)-\varphi_{r}(x,y)]\end{array}  \tag{4}\]  
As in conventional double optical path interference, \(I(x,y)\) can be represented as:  
\[I(x,y)=a(x,y)+b(x,y)\cos[\varphi(x,y)] \tag{5}\]  
where \(a(x,y)\) is the hologram background, \(b(x,y)\) is the modulation, and \(\varphi(x,y)\) is the phase difference between the two optically coherent beams. The phase \(\varphi(x,y)\) in Equation (2) can be modified by adding the term \(\Delta\varphi(x,y)\), which assumes values of 0, \(\frac{\pi}{2}\),\(\pi\) and \(\frac{3\pi}{2}\). As such, the corresponding wavelengths become 632.85, 632.85219, 632.85438, and 632.8567 nm according to Formula (2). The inclusion of wavelength tuning then produces four-step phase-shifting holograms denoted by \(I_{1}(x,y)\), \(I_{2}(x,y)\), \(I_{3}(x,y)\) and \(I_{4}(x,y)\). Based on reference [31], the required phase difference can be calculated as:  
\[\varphi(x,y)=arctg\frac{I_{4}(x,y)-I_{2}(x,y)}{I_{1}(x,y)-I_{3}(x,y)} \tag{6}\]  
Figure 3: The calculation of optical path difference. A basal plate was also included to accurately retrieve the object wave front and reconstruct basal holograms. The corresponding phase difference \(\varphi_{b}(x,y)\) can be expressed as:  
\[\varphi_{b}(x,y)=\varphi_{ob}(x,y)-\varphi_{rb}(x,y) \tag{7}\]  
where \(\varphi_{ob}(x,y)\) and \(\varphi_{rb}(x,y)\) are the phases of the object beam and reference beam, respectively. A functional micro-optics sample was placed on the basal plate for testing. In this configuration, the phase difference \(\varphi_{m}(x,y)\) between the two beams could be expressed as:  
\[\varphi_{m}(x,y)=\varphi_{ob}(x,y)+\varphi_{om}(x,y)-\varphi_{rb}(x,y) \tag{8}\]  
where the phase \(\varphi_{om}(x,y)\) introduced in the test component can be calculated using:  
\[\varphi_{om}(x,y)=\varphi_{m}(x,y)-\varphi_{b}(x,y) \tag{9}\]
## 3 Analysis of SR Stability and Excavation Scheme of a Large Underground Cavern Group
### Model Building  
The section where unit 3 is located is selected to establish a two-dimensional model for stability analysis. The geometric dimensions of the model are shown in Figure 1.  
The optimized DDARF program was used to generate a model with a heterogeneous grid, as shown in Figure 2. It can be seen that a relatively dense grid is generated near the caverns to be excavated to ensure the calculation accuracy, and a relatively sparse grid is generated in the peripheral area far from the caverns, which can reduce the calculation load and improve the calculation efficiency. The calculation boundary conditions are: adding a U-shaped steel plate to the bottom and left and right sides of the model; the U-shaped steel plate being fixed with four fixed points for displacement constraints; and the top of the model being the earth's surface without constraint. The calculation convergence condition is that the displacement generated by one step is less than 0.001 of the displacement generated by the previous step, namely convergence [48]. The discontinuous deformation analysis is calculated in the optimized DDARF program. SR classification of the underground cavern group is shown in Figure 3, and the physical and mechanical parameters of various surrounding rocks during calculation are shown in Table 1.  
Figure 1: Geometric dimensions (m) of the model.  
Figure 2: Computational model generated in optimized DDARF.