esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
NAND flash memories are widely used in many electronic devices. These devices face reliability issues because of the densely-populated memory cells [1]. In fact, the 3D method used to manufacture flash memories, discussed in detail by [PERSON] et al. [2], enforces the necessity to have high throughput error correction techniques. Bose-Chaudhuri-Hocquenghem (BCH) codes [3] are the most common error correction mechanisms for flash memory devices and other digital communications like optical networks. The increasing efficiency and throughput of the flash memory systems have drawn researchers to provide highly-efficient BCH decoders. The three major categories of the BCH decoders proposed are Central Processing Units (CPUs), hardware circuits, and Graphical Processing Units (GPUs). [PERSON] proposed an efficient CPU-based implementation in [4], and [PERSON] discussed multi-bit error using Hamming, BCH, and Low-Density Parity Check (LDPC) codes in [5]. Later, [PERSON] et al. proposed a high throughput hardware architecture in [6]. Moreover, [PERSON] discussed different hardware implementation techniques in [7]. [PERSON] et al. [8] proposed a GPU-based BCH decoder; later, we proposed an efficient algorithm for BCH decoders using GPUs in [9]. In addition to the requirement of high throughput, modern BCH decoders are required to support multiple bit error correction across various block sizes, which is the focus of this paper. Technology scaling has renderedthe ability to integrate multiple GPUs within a System On Chip (SOC), which has enabled researchers to use GPU for non-graphical applications. In fact, the term General Purpose Graphical Processing Unit (GPGPU) refers to the application of GPU for nongraphical applications. We use the term GPU instead of GPGPU since these terms are interchangeable in practice. Streaming Multiprocessors (SMs) are the building blocks of these GPUs, which has multiple CPUs within them. Each of the instantiated SMs is capable of handling multiple threads, which are scheduled by a warp scheduler. Therefore, we need an exclusive compiler like the Computer Unified Device Architecture (CUDA) C [10] software to program these SMs. The CUDA software creates the necessary grid of kernel routines, which in turn create the same instruction that operates on a different data path; this technique is referred to as the single instruction multiple data (SIMD) stream. The kernel subroutines are executed across multiple cores and in a multiple thread fashion. The GPU-based BCH decoders [9] are flexible, and they can support multiple BCH block sizes.  
We have organized this paper as follows: Section 2 discusses the background and previous works. Section 3 describes our proposed hybrid method using GPUs and hardware design. Section 4 presents the results observed, and we conclude in Section 5.
## 2 Materials and Methods
### Data Preprocessing  
Sentinel-2 L2A data are radiometrically calibrated, geometrically corrected, and atmospherically corrected products. In this study, the spatial resolution of bands 2, 3, 4, and 8 is 10 m, and that of bands 11 and 12 is 20 m. To keep each band's spatial resolution consistent, we used the nearest neighbor allocation method of the Sentinel Application Platform (SNAP) to resample all the bands to 10 m. The results were output in the IMG storage format, which is supported by The Environment for Visualizing Images (ENVI) [32]. Bands 4, 3, and 2 were synthesized using ENVI 5.3 to form Red-Green-Blue (RGB) images.  
The four images have an average size of about 5000 pixels \(\times\) 5000 pixels. From these images 893 sample images, we randomly cropped a size of 640 pixels \(\times\) 640 pixels. Figure 2  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Waveband** & **Central Wavelength** & **Bandwidth** & **Central Wavelength** & **Bandwidth of** & **Spatial** \\  & **of Sentinel-2A/nm** & **of Sentinel-2A/nm** & **of Sentinel-2B/nm** & **Sentinel-2B/nm** & **Resolution/m** \\ \hline Band 1 & & & & & \\ Coastal aerosols & 443.9 & 27 & 442.3 & 45 & 60 \\ Band 2 & & & & & \\ Blue & & & & & \\ Band 3 & 560 & 45 & 559 & 46 & 10 \\ Green & & & & & \\ Band 4 & 664.5 & 38 & 665 & 39 & 10 \\ Red & & & & & \\ Band 5 & 703.9 & 19 & 703.8 & 20 & 20 \\ Vegetation red edge & & & & & \\ Band 7 Vegetation & & & & & \\ red edge & & & & & \\ Band 8 & & & & & \\ Near infrared & & & & & \\ Band 8A & & & & & \\ Narrow near infrared & & & & & \\ Band 9 & 945 & 26 & 943.2 & 27 & 60 \\ Water vapor & & & & & \\ Band 10 & & & & & \\ Shortwave & 1373.5 & 75 & 1376.9 & 76 & 60 \\ infrared-Scrrus & & & & & \\ Band 11 & & & & & \\ Shortwave & 1613.7 & 143 & 1610.4 & 141 & 20 \\ infrared & & & & & \\ Band 12 & & & & & \\ Shortwave & 2202.4 & 242 & 2185.7 & 238 & 20 \\ infrared & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Waveband parameters of Sentinel-2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Acquisition Time** & **Orbit Number** & **Splice Domain Number** \\ \hline
20201111T022929 & R046 & T52 TCQ \\
20201025T023759 & R089 & T51 TWF \\
20201025T023759 & R089 & T51 TWJ \\
20210426T024539 & R132 & T51 TWJ \\ \hline \hline \end{tabular}
\end{table}
Table 2: Images taken by Sentinel-2B under product grade MSIL2A.  
shows examples of positive samples with smoke and negative samples without smoke. False positives were reduced using the negative samples.
###### Abstract  
The evolution of interconnected systems and the evolving demands in service requirements have led to data centers integrating multiple heterogeneous technologies that must coexist. Consequently, the resource management and the security of the infrastructure are becoming more complex than in traditional scenarios. In this context, technologies such as Software-Defined Networking (SDN) or Network Function Virtualization (NFV) are being embraced as mechanisms that facilitate communication management. The integration of both technologies into a single framework, termed Software-Defined NFV (SDNFV) introduces a multitude of tools for managing the security of the data center's resources. This work delineates the primary characteristics of the evolution of these communication networks and their application to information security and communications within a data center. It presents an illustrative use case demonstrating the application of these next-generation technologies to detect and mitigate a security issue through virtualized network functions deployed in containers.  
NFV; SDN; security threats; detection; mitigation; dockers Article Detection and Mitigation of Security Threats Using Virtualized Network Functions in Software-Defined Networks
## 3 Results and Discussion  
### Results  
#### 3.1.1 Selection and Optical Characterization of CQDs and SQDs  
First, in order to establish an adequate comparative toxicological study, it is important to define and describe the main features of each of the fluorescent quantum dots, separately and in combination.  
Both water-soluble CQDs and SQDs exhibited stable emission behaviors during a long-term irradiation, without any noticeable precipitation at room temperature. The maximum excitation (365 nm and 390 nm for CQDs and SQDs, respectively) and emission (450 nm and 650 nm for CQDs and SQDs, respectively) wavelengths of the two types of nanodots are depicted in Figure 1. The maximum emission of SQDs was independent of excitation (Figure S1 in Supplementary materials), whilst the CQD emission band shifted as a function of the excitation wavelength, as previously reported [25].  
To explore the electron-donor capability of both selected nanodots, CQDs and SQDs were mixed at equimolar concentrations. The photoluminescence curve for SQDs in the presence of CQDs resulted in a significant reduction of their fluorescence intensity when excited at 390 nm (Figure S2), indicating that an electron transfer occurred.
## 3 The LBOP-ARIMA Model
### Model Parameter Selection
#### 3.2.2 Determination of \(p\), and \(q\) Value  
There are typically two steps to choose \(p\) and \(q\). The first step is to determine the approximate range of parameters which can be obtained according to the autocorrelation diagram and the partial correlation diagram of the \(d\)-order difference sequence. When the proper range is determined, the final \(p\) and \(q\) values can be obtained by comparing the modeling accuracy using different parameter combinations. The specific parameter determination method is shown in Table 5. Figures 7 and 8 are the autocorrelation and partial autocorrelation diagrams of the second-order sequence of downlink traffic. It can be seen from Figure 7 that the autocorrelation coefficient truncation is observed after the second-order difference. So, \(q\) can be taken as MA (2), MA (3) or MA (4). Figure 8 shows that the partial autocorrelation coefficient truncation occurs after the eight-order difference,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Type**} & \multirow{2}{*}{**Acceptance Probability (\%)**} \\ \cline{2-5}  & **T** & **1\%** & **5\%** & **10\%** & \\ \hline Original sequence & \(-2.14\) & \(-3.86\) & \(-3.35\) & \(-3.21\) & 32.56 \\ First-order difference sequence & \(-3.75\) & \(-3.86\) & \(-3.35\) & \(-3.21\) & 2.33 \\ Second-order difference sequence & \(-23.68\) & \(-3.86\) & \(-3.35\) & \(-3.21\) & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The ACF verification result.  
Figure 6: The autocorrelation diagram of the bandwidth occupancy rate of link \(l_{j}\).  
which means \(p\) can be taken as AR (2), AR (3), AR (4) or AR (5). The model parameters can be obtained as ARIMA (2, 2, 2)-ARIMA (5, 2, 4).  
According to the obtained parameters range, the final values need to be determined. The Bayesian information Criterion (BIC) is employed here to choose the proper parameter. The BIC is a well-known general approach to model selection that favors more parsimonious models over more complex models. The smaller BIC score means the better parameter selection. The BIC heat map of different model parameters is obtained as shown in Figure 9. It can be seen from Figure 9 that when \(p=2\), \(q=2\), the BIC value is the minimum. Thus, the model ARIMA (2, 2, 2) can be selected.  
Figure 8: The partial autocorrelation diagram of the second-order difference series of downstream traffic.  
Figure 7: The autocorrelation comparison of the original sequence and the difference sequence.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **ACF (Autocorrelation)** & **PACF (Partial Autocorrelation)** \\ \hline AR (\(p\)) & Attenuation tends to 0 (geometric & Truncation after the \(p\)-order \\  & or oscillatory) & Attenuation tends to 0 \\ MA (\(q\)) & Truncation after the \(q\)-order & Attenuation tends to 0 \\ ARMA & Attenuation tends to 0 after the \(q\)-order & Attenuation tends to 0 \\ (\(p,q\)) & (geometric or oscillatory) & after the \(p\)-order \\ \hline \hline \end{tabular}
\end{table}
Table 5: ARIMA (\(p,d,q\)) order determination.
## 3 Results and Discussion
### \(PV\) Power Plants Under Study
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Specifications** & **Basir** & & **Miraflores** & & **Canredondo** \\ \hline
**(kWh/m2)** & \(H_{bh}\) & \(H_{dh}\) & \(H_{bh}\) & \(H_{dh}\) & \(H_{bh}\) & \(H_{dh}\) \\ \hline January & 56.03 & 30.17 & 48.02 & 28.51 & 41.23 & 25.61 \\ February & 64.19 & 37.39 & 64.59 & 34.21 & 51.81 & 31.87 \\ March & 85.76 & 55.02 & 86.08 & 52.56 & 71.22 & 51.80 \\ April & 109.94 & 64.58 & 102.51 & 64.75 & 86.08 & 65.02 \\ May & 152.00 & 68.64 & 141.68 & 71.77 & 120.92 & 75.57 \\ June & 169.40 & 65.56 & 164.27 & 66.70 & 147.19 & 72.93 \\ July & 182.42 & 62.35 & 189.83 & 58.26 & 176.54 & 63.24 \\ August & 162.41 & 57.08 & 167.48 & 53.68 & 153.86 & 57.90 \\ September & 115.55 & 52.42 & 115.99 & 49.69 & 104.50 & 52.09 \\ October & 84.56 & 44.92 & 79.17 & 42.69 & 68.37 & 40.61 \\ November & 54.12 & 33.08 & 47.97 & 31.21 & 36.17 & 30.11 \\ December & 51.14 & 28.37 & 44.36 & 25.50 & 39.62 & 23.13 \\ \hline \end{tabular}
\end{table} 
Table 3: Horizontal surface solar irradiations at the locations under study obtained from the PVGIS database. Several papers have used the cloudiness index to classify sky types as follows [34]: clear, partly cloudy, and cloudy. As will be shown below, \(k_{d}\) influences the choice of the movement limit of a solar tracker. Figure 6 shows the \(k_{d}\) at the locations under study obtained from the \(PVGIS\)-\(SARAH3\) solar irradiation database. In this figure, it can be seen that the Miraflores \(PV\) plant has the lowest \(k_{d}\) and the Canredondo \(PV\) plant has the highest \(k_{d}\). The maximum and minimum values occur in the spring and summer seasons, respectively. It should be noted that the three selected \(PV\) power plants meet the selection criteria (see Tables 2 and 3). The three selected \(PV\) power plants have a movement limit of \(\pm 60\) (\({}^{\circ}\)), which is referred to as the actual scenario. The data shown in Tables 2 and 3 were used to calculate the annual incident solar irradiation on the \(PV\) field for the studied movement limits. Equations (4) and (11) were used for this purpose. Table 4 shows the annual incident solar irradiation on the \(PV\) field obtained at the studied locations.
# An Ensemble Stacked Convolutional Neural Network Model for Environmental Event Sound Recognition
## 3 DS-CNN: Sound Recognition Model Based on CNN and DS Evidence Theory
### Network Architectures
#### 3.1.3 Structure of the RawNet and MelNet  
As shown in parts (c1) and (c2) of Figure 1, the recognition modules of both RawNet and MelNet contain five convolutional layers and a fully connected layer. The architecture and parameters of the network are as follows:  
* L1: The first layer of the network uses 24 filters with a receptive field of (6,6) and stride of (1,1). ReLU is used as the activation function. The small receptive field of (6,6) here is used to learn smaller and more local \"time-frequency\" characteristics of sound segments.
* L2: motivated by the VGG network [39], the second layer uses the same parameters as the first layer, which uses 24 filters with a receptive field of (6,6) and stride of (1,1). At the same time, ReLU is used as the activation function. The nonlinear transformation of the output from previous convolutional layer enables the network to learn high-level features.
* L3: the third layer of the network uses 48 filters with a receptive field of (5,5) and stride of (2,2). This is followed by an ReLU activation function.
* L4: the fourth layer uses 48 filters with a receptive field of (5,5) and stride of (2,2). ReLU is used as the activation function.
* L5: the fourth layer uses 64 filters with a receptive field of (4,4) and stride of (2,2). ReLU is used as the activation function.
* L6: the sixth layer is a fully connected layer with 200 hidden units, followed by a ReLU activation function.
* L7: the output is 10 or 50 units, which is followed by a softmax activation function.  
Differently from the standard CNN configuration, we do not adopt a max-pooling layer after the convolutional layers of the recognition modules in either RawNet or MelNet to keep more information. Instead, a Batch normalization layer is used to accelerate and improve the learning process of deep neural networks. In addition, the Batch normalization layer is followed by a fully connected layer. For the RawNet model, we applied a 0.5 dropout probability for the fully connected layer to prevent overfitting. We used a learning decay scheme to train the models. In order to investigate the effect of the stacked CNN architecture and parameters on classification performance, we conducted extensive experiments in Section 4.3.
# Mitigation of Gravity Segregation by Foam to Enhance Sweep Efficiency
###### Abstract  
Foam-assisted gas injection exhibits promising potential for enhancing sweep efficiency through the amelioration of gravity segregation, particularly within reservoirs characterized by heterogeneity. In this work, the implicit-texture (IT) model featuring two flow regimes is employed to examine the impact of heterogeneity on gravity segregation. The validation of the numerical results for water-gas conj injection and pre-generated foam injection is accomplished through a comparative analysis with analytical solutions. A hypothetical two-layer model with varying permeabilities and thickness ratios is used to examine the impact of foam on gravity segregation. The numerical findings demonstrate satisfactory conformity with analytical solutions in homogeneous reservoirs. A high-permeability top layer in a layered model with a fixed injection rate results in sweep efficiency similar to that of a homogeneous reservoir with each individual permeability. A low-permeability top layer could increase the sweep efficiency, but with severe permeability contrast, the bottom high-permeability layer could impact the displacement process, even with a thin thickness. The sweep efficiency increases with the thickness of the high-permeability top layer and decreases with a thicker low-permeability top layer under fixed injection pressure. The predicted segregation length through a single-layer approximation cannot match the results of the layered models where the permeability contrast is too great or the thickness of two layers is comparable.  
CO2 foam; implicit-texture model; gravity segregation; sweep efficiency; layered reservoirs +
Footnote †: journal: Applied
# A Trust-Based Model for Secure Routing against RPL Attacks in Internet of Things
## 3 Proposed SMTrust Model
### System Model
#### 3.1.1 Topology Creation and Deployment of Attacks  
SMTrust follows the RPL specification RFC 6550 for initialization of the normal routing operation. Similarly, the topology is created according to the default functioning and features of RPL, which is referred to as topology creation in proposed SMTrust model (Figure 2). The attacker nodes are deployed in the network for the considered case studies of Rank and Blackhole attacks. The particular attacker nodes are assumed to be positioned randomly among the legitimate nodes. The attacker process is launched by programming the nodes to act according to the attack type. For the Rank attacker nodes, the node is programmed to advertise the lowest rank to attract the traffic. Whereas for the Blackhole attacker nodes, the nodes are programmed to drop all the received packets.
# Registration of Dental Tomographic Volume Data and Scan Surface Data Using Dynamic Segmentation
## 2 Proposed Method: MICP
### Automatic Registration Process
#### 2.2.2 Dynamic Segmentation  
For a single point on the surface data, a corresponding point in the volume data can be found by intensity profile analysis. If this intensity profile analysis is applied to all points on the surface data, a set of corresponding points for ICP can be obtained. The surface data and volume data are initially positioned. All detected edge points can be visualized as surface data. This surface data is a segmented surface representing the tooth volume data and can be a corresponding point for ICP. In the proposed method, if correspondence is obtained, the conventional ICP step for finding corresponding points becomes unnecessary. Now, points with edge points are set as moving surface data and the segmented points are set as the target surface data. Then, the sum of the distance between the corresponding points is minimized using the singular value decomposition (SVD) method and a rigid transformation matrix can be obtained [36]. The moving surface data is transformed by the obtained transformation matrix in a process which is a single iteration process under the proposed procedure. Even if the data is down-sampled, ICP still uses several thousands of points for which it needs to find correspondences. Repeating SVD iteratively increases the computational costs of ICP. In contrast, the proposed algorithm uses only hundreds of points from the interpolated data and already knows the correspondences. Therefore, the computational costs incurred by using SVD are substantially lower than those of ICP.  
This whole process is performed iteratively just the same as for the conventional iterative closest point algorithm. The procedure of the established registration method for volume data and surface data uses only one segmented surface data. However, the proposed registration method includes the segmentation process within the iteration which is why this segmentation method is termed 'dynamic' segmentation. The segmented surface used as the target surface differs every iteration. For the proposed dynamic segmentation method using intensity profile analysis, the volume data can be used directly as input data. Above all, the dynamic segmentation works automatically without needing any human input. During the iteration process, the edge point on the intensity profile gets closer to the point on the surface.
# A Model and Quantitative Framework for Evaluating Iterative Steganography
## 4 Results
### Discussion  
The results of first experiment, shown in Figure 1, include the plots of the functions \(IIF_{(0,0)}\), \(IIF_{(0,1)}\), \(IIF_{(1,0)}\), \(IIF_{(1,1)}\), and the overall \(IIF\) function for video steganogram No. 1. Analyzing the plots yields the following observations:  
* for video steganogram No. 1, the message was successfully decoded at the 18 th iteration (\(chIteration^{s}\) = 18) with an \(IIF\) value of \(chIIF^{s}\) = 0.828;
* the functions \(IIF_{(0,0)}\), \(IIF_{(1,1)}\) and the overall \(IIF\) function asymptotically converge to a maximum value close to 1.0;
* the values of \(IIF_{(0,1)}\) and \(IIF_{(1,0)}\) carry no significant information, in this example, as they are complementary to the values of \(IIF_{(0,0)}\) and \(IIF_{(1,1)}\);
* the initial value of \(IIF_{(0,0)}\) is notably high due to a property of the decoding function, which assumes that all information bits are initialized to zero at the start of the algorithm.  
Figure 2: Plots of the IIF for video steganogram No. 2 encoded with 5 levels of encoding.  
The results of the second experiment, presented in Figure 2 and Table 2, show that, within the range of studied iterations, information was successfully decoded for the three highest encoding levels of video steganogram No. 2, while for the two lowest encoding levels, the \(IIF\) values remained at noise levels.  
Based on the experiments, two characteristic regions of \(IIF\) values were identified: the noise region and the decoding region. The noise region corresponds to \(IIF\) values in the range of 0.5-0.7. The decoding region corresponds to \(IIF\) values in the range of 0.7-1.0, and it is within this range that the characteristic value \(chIIF^{*}\) is found, depending on the steganographic technique used, particularly on the implementation of the decoding algorithm.  
As described earlier, the properties of the \(IIF\) indicate that, in cases where hidden information can be decoded from the steganogram, the \(IIF\) increases and asymptotically approaches the maximum value specific to the steganogram. The rate of increase in the \(IIF\) is directly correlated with the encoding level, as higher encoding levels result in a greater information increment per iteration. However, if the information gain in successive iterations does not exceed the information loss associated with transitioning between iterations (e.g., due to inter-frame compression mechanisms used by video codecs), the \(IIF\) will remain static, and its values will stay within the noise region.