esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation of Design Provisions
\begin{table}
\begin{tabular}{c l c c c c} \hline \hline \multicolumn{1}{c}{**Codes**} & \multicolumn{1}{c}{**Statistics**} & \multicolumn{1}{c}{**Monolithic**} & \multicolumn{1}{c}{**Monolithic**} & \multicolumn{1}{c}{**Roughened**} & \multicolumn{1}{c}{**Smooth**} \\ \hline \multirow{4}{*}{_ACI 318-19_} & Average & 2.94 & 1.86 & 2.86 & 2.54 \\  & Maximum & 8.86 & 3.42 & 9.18 & 5.34 \\  & Minimum & 1.70 & 1.13 & 1.80 & 1.07 \\  & STD & 1.28 & 0.42 & 1.03 & 0.99 \\  & COV (\%) & 43.39 & 22.49 & 35.99 & 39.13 \\ \hline \multirow{4}{*}{_Eurocode 2_} & Average & & & 1.62 & 1.46 \\  & Maximum & & & 3.60 & 2.57 \\  & Minimum & n/a & n/a & 1.11 & 0.74 \\  & STD & & & 0.44 & 0.50 \\  & COV (\%) & & & 27.08 & 33.99 \\ \hline \multirow{4}{*}{_Fib MC 2010_} & Average & & & 2.09 & 1.60 \\  & Maximum & & & 4.48 & 2.73 \\  & Minimum & n/a & n/a & 1.25 & 0.77 \\  & STD & & & 0.51 & 0.52 \\  & COV (\%) & & & 24.49 & 32.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._  
Figure 9: Bar charts for statistical analysis of design provisions depending on the interface conditions. there were many unconservative cases. The _ACI 318-19_ is secure to calculate for monolithic precracked specimens because all ratios \(v_{test}/v_{cal}\) are greater than 1.0. Intentionally roughened cold joint: Values of \(v_{test}/v_{cal}\) summarized in Table 8 indicate that the _AASHTO LRFD_ and _Eurocode 2_ were the most accurate because the mean value is closest to 1.0 and it is stable due to low COV values, while the _CSA-S6_, _PCI_, and _Fib MC 2010_ provided more conservative predictions for interface shear resistance (mean \(v_{test}/v_{cal}\) of 2.44, 2.08, and 2.09, respectively). The _ACI 318-19_ provided the most conservative and scattered estimates as their mean and COV for \(v_{test}/v_{cal}\) were 2.86 and 35.99%, respectively. Cold joint that is not roughened: The _AASHTO LRFD_, _Eurocode 2_, and _Fib MC 2010_ were the most accurate and consistent due to the lowest mean and COV values for \(v_{test}/v_{cal}\), but there were many unconservative cases. The _CSA-S6_, _PCI_, and _ACI 318-19_ are secure to calculate for cold joint specimens that are not roughened because all ratios \(v_{test}/v_{cal}\) are greater than 1.0.
# Satellite IoT Edge Intelligent Computing: A Research on Architecture
## 3 Satellite IoT Edge Intelligent Computing Architecture
### Distributed Intelligent Computing Architecture in Satellite IoT Edge Computing
#### 3.2.2 Training-Inference-Isolated Satellite IoT Edge Intelligent Computing Architecture
When there is an inference task, the satellite can select local processing or offload the task to nearby satellites for distributed collaborative reasoning according to the amount of computation required by the task and its own computing power. In general, reasoning on the satellite IoT side can greatly reduce the amount of data that satellites transmit to the ground. At the same time, it saves a lot of valuable network bandwidth resources. This method is feasible and meaningful. With the development of hardware technology, embedded devices on satellites usually have higher computing power and can meet the computational requirements of common neural network inference. We give an intuitive explanation in Section 4. While meeting the computing needs, satellites also need to consider the issue of energy consumption. In general, the energy consumption of satellites is mainly reflected in the energy consumption of communication and the energy consumption of task processing. Task processing generates a lot of energy consumption. When the inference task is offloaded to the satellite IoT edge node, the satellite IoT edge node faces pressure in terms of computation and energy consumption. Of course, in order to alleviate the pressure on the satellite IoT edge nodes caused by large computing tasks (such as large inference networks such as VGG-16 and WRN), we can choose to put large computing tasks on the satellite IoT cloud nodes. In particular, large computing tasks that do not have timeliness requirements can also be offloaded to the ground data center. In short, the intelligence of satellite IoT edge nodes provides the possibility of real-time processing of edge-side data.
# Metal Artifact Reduction in X-ray CT via Ray Profile Correction
## 2 Methods  
In the following, we use spine surgery as an example but our method applies in any scenario where a prior patient scan is available. In fact, it even applies in single-scan scenarios when metal is only present in a restricted region of the object.  
Our method requires two CT datasets (or one that has a sufficient portion free of metal). One dataset is an artifact-free prior CT scan taken before the (spine) surgery. The other is obtained during the surgery, containing metal artifacts due to the implanted pedicle screws. Although the two CT scans are taken from the same patient, they might be obtained in different conditions (e.g., patient's pose, X-ray dosage amount, field-of-view, etc.). Therefore, it is necessary to register one volume to the other before applying the proposed ray profile correction method. The volume registration is the process of aligning two or more volumes of the same scene. This process involves designating one image as the reference volume, also called the fixed volume, and applying geometric transformations or local displacements to the other volumes so that they align with the reference. This registration step is described in Section 2.1. Ray profile correction is also required to know which parts of a ray path belong to metal objects, and which ones do not. For this, we extract the implanted pedicle screws from the uncorrected CT volume (the volume suffering from metal artifacts). This metal localization step is described in Section 2.2. In the following, we call a set of sample points along a ray the _ray profile_. A line integral is then computed as the weighted sum of all sample points of a ray profile. In addition, we define the regions where the corresponding rays pass through metal objects as _metal shadow_. The projection values under the metal shadow are unreliable because of beam-hardening, photon starvation, and so on, and they will result in metal artifacts. Our goal is to compute surrogate values in the metal shadow regions by correcting the corresponding ray profiles using the aligned prior CT volume and geometric information of the implanted metal (here the pedicle screws). This new correction scheme is explained in Section 2.3. Finally, the corrected metal shadow is combined smoothly with the original CT projection data, as described in Section 2.4. The overall process is illustrated in Figure 1.
# Federated Learning-Based Security Attack Detection for Multi-Controller Software-Defined Networks
## 3 The Proposed Methodology
### Model Training and Attack Classification
#### 3.4.1 GCNN-GRU-Based Model Training
In particular, the proposed system builds the local model through the learning parameters of the GCNN-GRU model. By providing the constructed feature matrix and adjacency matrix of the input network traffic dataset as the input, the GCNN model precisely learns the spatial relationship in the SDN that facilitates the accurate recognition of attack behaviors with the aid of the GRU model. The proposed GCNN-GRU model comprises the graph convolution layers, max pooling layer, flatten layer, and GRU layer. Initially, the graph convolution layers are responsible for capturing the neighborhood information from the nodes in terms of the spatial relationships in the graph network. In subsequence, the max pooling layer compresses the feature representation with highly informative features, preventing overfitting as well as reducing the dimensionality. Also, the pooled feature representation is provided as the input to flatten layer to transform the 2D output of the GCNN model into 1D input for the GRU model. Finally, the GRU layer learns the spatially aggregated features in sequential patterns to capture the temporal relationships using the gating mechanism, resulting in normal and malicious traffic with the integration of a fully connected layer. Here, n number of local models are generated and updated to the global server through main controllers, facilitating the global model generation. Algorithm 1 explains the local model generation of FedSec using GCNN-GRU. ```
1:Input: Local datasets of different network-centric nodes
2:Output: n number of LM generation for FL aggregation
3:for each client do
4: Construct local dataset in its area
5: Create a graph with Feature matrix and adjacent matrix
6:for all the pairs of nodes do
7: Compute edge weight using Equation (8)
8:if EW = 1 minimum 3 criteria for a pair of nodes then
9: Assign weight as 1 in adjacency matrix
10:else
11: Assign weight as 0 in adjacency matrix
12:endif
13:endfor
14: Initialize the GCNN-GRU training process as the local model for each client
15:for each GCNN-GRU do
16: Implement convolution, max pooling, flatten layer, and GRU layer
17: Learn the spatial and temporal relationships from enriched NSL-KDD dataset
18:endfor
19:endfor
20:for all the clients do
21: Feed the 'n' number of local models' parameters to global server for aggregation
22:endfor
```  
**Algorithm 1** **Local Modeling using GCNN-GRU**
## 2 Materials and Methods
### Methods
#### 2.2.4 Mathematical Models  
Equilibrium Studies  
An adsorption isotherm represents an equilibrium relationship at a given temperature between the amount of adsorbate in the liquid phase and that on the adsorbent surface. In this paper, we analyze the fit of the equilibrium experimental data of MPB adsorption onto AC and AOS to six adsorption isotherm models ([PERSON], Freundlich, Elovich, Temkin, Jovanovic and Dubinin-Radushkevich) to determine which model best describes those processes (Table 1). Kinetic Studies  
adsorption kinetics describes the rate of retention of an adsorbate in a fluid environment on the surface of the adsorbent, determining the time required to reach equilibrium. Four kinetic models ([PERSON] pseudo-first order, [PERSON] pseudo-second order, [PERSON] and Avrami) were used to determine the one that best describes methylparaben adsorption onto activated carbon and activated olive stones (Table 2). adsorption Mechanism  
The above kinetic models are not able to identify the diffusion mechanisms and the rate-controlling step that affect the adsorption process. Any solid-liquid sorption process is characterized by the following two mass transfer processes:  
1. The adsorbate molecules diffuse from the bulk solution to the external surface of the adsorbent (boundary layer diffusion);
# Concatenated Coding for GNSS Signals in Urban Environments
###### Abstract  
This work investigated concatenated coding schemes for Global Navigation Satellite System (GNSS) signals in order to increase their error correction capability in urban environments. In particular, a serial concatenated code that combines an outer Reed-Solomon (RS) code with an inner low-density parity-check (LDPC) code was designed, and the performance was investigated over the land mobile satellite (LMS) channel for characterizing multipath and shadow fading in urban environments. The performance of the proposed concatenated coding scheme was compared to that of a B-CNAV1 message, in which two interleaved 64-ary LDPC codes were employed. The simulation results demonstrate that the proposed concatenated code can obtain a similar error correction performance to the two interleaved 64-ary LDPC codes in both the additive white Gaussian noise (AWGN) and LMS channels at a lower complexity level.  
concatenated code; GNSS signals; urban environments; LMS channel +
Footnote †: journal: Applied  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# A Chaotic Compressive Sensing Based Data Transmission Method for Sensors within BBNs
## 7 Security Analysis
### Pixel Distribution Analysis  
Statistical analysis attacks mean that attackers try to obtain the secret key by analyzing the statistical rules or patterns divulged from encrypted signals and their relevant original signals. In the Internet or wireless network environment, attackers might easily listen to the network flows to capture ciphertexts, which contain signal \(Y_{c}\) of the proposed method. These malicious or just curious attackers might sum up the statistical laws and patterns revealed by the resource they have obtained and manage to extract the transformation  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Name** & **Parameter Type** & **Value Range** & **Key Space** \\ \hline \(b\) & double-precision & \((0,1)\) & \(K_{b}\approx 1\times 10^{16}\) \\  & floating point & \((0,1)\) & \(K_{z}\approx 1\times 10^{16}\) \\ \(z_{0}^{{}^{\prime}}\) & floating point & \((0,1)\) & \(K_{z}\approx 1\times 10^{16}\) \\ \(n_{0}\) & positive integer & \([100,199]\) & \(K_{n}=100\) \\ \(d\) & positive integer & \([1,10]\) & \(K_{d}=10\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Secret key space analysis.  
Figure 6: The first row shows the reconstruction results using the right initial value. The second row shows the reconstruction results using the initial value that is modified \(10^{-15}\). The third row shows the reconstruction results using the initial value that is modified \(10^{-16}\). The fourth row shows the reconstruction results using the initial value that is modified \(10^{-17}\).  
relationships between plaintexts and ciphertexts, so as to analyze the encryption scheme. Figure 7 reports histograms of pixel distribution that are calculated based on pixels from red, green, and blue channels of original RGB images and histograms that are calculated based on pixels from red, green, and blue channels of signal \(Y_{c}\), which may be transmitted through open or insecure channels.  
Figure 7 implies that the pixel distribution of the original images leaks obvious statistical laws and patterns, whereas the histograms generated based on signal \(Y_{c}\) clearly show that the pixels in such signals are distributed uniformly. It is noticeable that the histograms of pixel distribution of the original images and of signal \(Y_{c}\) have no internal connections to each other. As is well known, the more uniform the pixel distribution is, the less statistical information the signal reveals, and the more secure the encryption scheme is.
## 3 Results and Discussion
### Environmental Implications  
It is essential to propose targeted measures for controlling and remediating soil heavy metal pollution in the vicinity of these enterprises. Due to the presence of very strong  
Figure 11: Spatial distribution maps of the _HI_ and _TLCR_. (In the figure, (**a**) represents _HI_, and (**b**) represents _TLCR_).  
ecological risk areas in the central-western and central-southern regions of the study area (Figure 5), along with the distribution of point sources of high concentrations of Hg and As, this study posits that the soil heavy metal contamination in the study area is not related to agricultural sources. Nevertheless, it is still recommended that the concentrations of heavy metals in food crops produced in these very strong ecological risk areas be monitored to ensure food safety.  
To address factor 1, implementing a comprehensive emissions control strategy for heavy metals, alongside a pollution discharge permit system, is essential for regulating emissions from key industrial sectors. The government should establish relevant regulatory frameworks that encompass the entire policy implementation process before, during, and after to prevent occurrences of \"retaliatory pollution\" [67]. For factor 2, promoting clean coal technology is crucial for reducing emissions of mercury and other heavy metals. Gradually phasing out outdated coal-fired facilities while encouraging a transition to clean energy sources is necessary [68; 69]. Regarding factor 3, establishing centralized facilities for the collection, treatment, and disposal of solid and hazardous materials in industrial parks is vital. These measures will help prevent the leakage and spread of heavy metal pollution into the soil. In areas affected by contamination, strict controls and the development of new remediation technologies are essential. This includes researching effective management systems to enhance remediation capabilities and effectiveness [70]. Finally, to address factor 4, it is crucial to implement measures such as raising vehicle emission standards to ensure compliance with stricter exhaust requirements. Given the close link between heavy metal pollution and human activities, enhancing public awareness and environmental consciousness is also essential.  
Furthermore, establishing and refining a heavy metal pollution monitoring network across the city is vital for the regular assessment of heavy metal concentrations in soil, water, and air. Special attention should be directed towards the two red zones identified as having very strong ecological risk (Figure 5). Conducting environmental risk assessments in these areas will facilitate the timely identification of heavy metal pollution issues and provide early warning systems to mitigate potential impacts.
# Advances in Permeation of Solutes into Hair: Influencing Factors and Theoretical Models
## 4 Permeation Models of Solutes in Hair
### Porous Media Diffusion Model  
Inspired by the catalytic action of enzymes in chemical reactions and the adsorption of zeolites, [PERSON] et al. [28] assumed that the hair is porous and the pores are continuously distributed on the hair surface. In their model, the size and shape of the solute are described by the longest edge of the smallest cross-section that can enclose the parallelepiped of the molecule (\(L_{\text{D}}\)), and the effects of the solute charge characteristics on the permeation and absorption characteristics are considered. The permeation distances of anionic, cationic, and neutral dyes in hair are calculated based on \(L_{\text{D}}\) using Boltzmann sigmoidal theory, and the critical sizes of anionic, cationic, and neutral dyes entering hair are approximately 1.2-1.3, 1.4, and 0.95 nm, respectively.  
The model considers the influence of the size, shape, and charge characteristics of the solute on its penetrating properties in hair, and it calculates the permeation distance of the solute in hair. However, due to the limited experimental data of cationic and neutral dyes, the equations for the permeation distances of the two in hair cannot be determined. In addition, the model does not consider the hair composition, and it also does not simulate the dynamic absorption process of the solute in hair.
## 4 Preliminaries  
### Network Model for Code Dissemination  
We assume that the underlying code dissemination scheme is Deluge. Additionally, we assume that the code dissemination scheme employs a dynamic packet size control scheme which adjusts a per-hop packet size based on the link quality in order to enhance energy efficiency such as DPLC, ECD, and Plena. Note that our schemes can be integrated with any kind of Deluge-based code dissemination schemes where the packet size is dynamically adjusted in each hop. For example, the scheme in [23] can be employed to obtain an optimal packet size depending on PER which is estimated by the medium access control layer acknowledgements. Although both the medium access control and the message authentication code are abbreviated as MAC, we only use MAC as the message authentication code to avoid confusion in the paper. Finally, it is important to note that our work can be applied to code dissemination with the fixed packet size as well as the dynamic packet size.  
A BS, which is a source in our work, is assumed to be responsible for securely disseminating a code image to all sensor nodes in the WSN over multihop communications and always trustworthy. Each node then authenticates the code image by verifying the authentication tokens. All cryptographic primitives, including hash functions, MACs and digital signatures, are assumed to be secure. We assume that an attacker's goal is to inject a malicious code into the WSN in order to get the information of interest or disable the WSN. Furthermore, the attacker is assumed to be able to compromise sensor nodes physically, but not infinitely.
## 2 Materials and Methods
### Deep Learning Model for Evaluating Weather Disaster Emergency Response Capacity
#### 2.2.1 Forward Calculation  
The weighted adaptive focus loss function formulated in this paper is depicted in Equation (8). As a function reliant on the output value of the ability evaluation model, it necessitates the application of the neural network's forward propagation to ascertain the loss function's value. The forward propagation of ability information entails the input of the institution \(a_{i}\)'s ability feature data into the model. Through the utilization of each hidden layer's linear mapping matrix and non-linear activation function, a successive feature mapping is performed, culminating in the prediction of the institution's ability level at the output layer. The loss function calculation doesn't impinge on the deep learning forward propagation process; hence, this paper's model forward calculation aligns with that of the deep neural network model.  
Suppose the model comprises 'Layers' network layers, where \(C_{l}\), \(W_{l}\), and \(b_{l}\), and \(g_{l}\), \(z_{l}\), and \(r_{l}\) represent the number of neurons, the weight matrix, and the bias vector in the \(l^{\text{th}}\) layer, as well as the activation function, the input to the activation function, and the output at the \(l^{th}\) layer (with \(l\) ranging from \(1\) to \(M\)), respectively. The attributes such as capability characteristics, the true capability level, and the predicted capability level of the \(i^{\text{th}}\) weather disaster emergency agency in area \(i\) are represented by \(a_{i},y_{i}\), and \(p_{i}\), respectively, where \(i\) falls within the range of \(\{1,2,\ldots,N\}\).  
The forward propagation operates under the following principles:  
1. \(a_{i}\) serves as the input to the deep neural network. The first layer's activation function input, \(z_{1,i}\), is determined through the first hidden layer as elucidated in Equation (9): \[z_{1,i}=W_{1}a_{i}+b_{l}\] (9)
2. \(z_{1,i}\) is activated, undergoing a non-linear transformation as delineated in Equation (10): \[r_{1,i}=g_{1}(z_{1,i})\] (10)
3. Following calculations at the \(M-1\) layer, the output \(r_{M-1,i}\) corresponding to \(a_{i}\) is acquired. The predicted value \(p_{i}\) is then computed as \[z_{M,i}=W_{M}r_{M-1,i}+b_{2}\] (11)  
\[p_{i}=g_{M}(z_{M,i}) \tag{12}\]  
Ability evaluation constitutes a multi-classification issue, typically utilizing the Soft-max function as the output layer's activation function \(g_{M}\). This is represented as  
\[p_{i}=\frac{e^{z_{Mi}}}{\sum_{j=1}^{n_{IM}}e^{z_{j}}} \tag{13}\]  
where \(z_{Mi}\) denotes the \(M^{\text{th}}\) hidden layer's output value for the \(i^{\text{th}}\) sample, and \(n_{IM}\) indicates the number of nodes at the \(M^{\text{th}}\) layer.