esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### Chi-Square Attack and RS Steganalysis
Then through the mask \(-m\), we can obtain the relative frequency of the regular group denoted as \(R_{-}\) and the relative frequency of the singular group denoted as \(S_{-}\). Let \(p_{e}\) be the embedding rate in \(f\), RS steganalysis can estimate \(p_{e}\) by solving the following equation:  
\[2(d_{1}+d_{0})z^{2}+(d_{-0}-d_{-1}-d_{1}-3d_{0})z+d_{0}-d_{-0}=0, \tag{21}\]  
where  
\[ \begin{array}{l}d_{0}=R_{+}(p_{e}/2)-S_{+}(p_{e}/2),\ d_{1}=R_{+}(1-p_{e}/2) -S_{+}(1-p_{e}/2),\\ d_{-0}=R_{-}(p_{e}/2)-S_{-}(p_{e}/2),\ d_{-1}=R_{-}(1-p_{e}/2)-S_{-}(1-p_{e}/2),\end{array}  \tag{22}\]  
and \(R_{+}(p/2)\) represents \(R_{+}\) of a stego image with \(p\) pixels embedded (i.e., the LSBs of \(p/2\) pixels flipped from a cover image). Thus, we can obtain \(R_{+}(p_{e}/2)\) easily through \(f\); furthermore, by flipping the LSB of each pixel in \(I\) to get \(I^{\prime}\), we can also obtain \(R_{+}(1-p_{e}/2)\) through \(I^{\prime}\). \(S_{+}\), \(S_{-}\), and \(R_{-}\) can be obtained by the similar way. Then, \(p_{e}\) is calculated from the root \(z\) of Equation (20), whose absolute value is smaller, by the following equation:  
\[p_{e}=\frac{z}{z-\frac{1}{2}}. \tag{23}\]  
Two experiments are conducted, one for [PERSON] et al. 's [16] and the proposed methods uses block size \(2\times 2\) and three capacities (23%, 46%, and 69%); the other for [PERSON] et al. 's [17] and the proposed methods uses block size \(1\times 3\) and the same three capacities. In the experiments, the mask \(m\) is defined as \([0\;1\;1\;0]\). Tables 6 and 7 show part of the \(p_{e}\)s of [PERSON] et al. 's, [PERSON] et al. 's, and the proposed methods. Note that both [PERSON] et al. and [PERSON] et al. 's methods embedded message by reordering the original palette, so there are two results for their methods by analyzing the original and the reordering palettes. From these tables, we can see that RS steganalysis cannot estimate \(p_{e}\) accurately. The reason is that these three methods are not LSB-based methods. We can conclude that the three methods can resist RS steganalysis and are undetectable.
## 1 Introduction  
Delirium is a clinical syndrome with core symptoms of inattention and acute cognitive dysfunction that often fluctuate [1]. The incidence of postoperative delirium after spinal surgery is reported to be 4.5-24.3% [2; 3; 4; 5] and has been found to be associated with prolonged hospital stays and increased costs of care, postoperative functional deterioration, and increased mortality [6; 7]. Up to USD 82.4 billion is spent annually on medical costs associated with delirium in the United States alone [8]. Well-known risk factors include older age, duration of surgery, and blood loss during surgery [9]. As global life expectancy is continuously increasing [10], and considering the characteristics of spinal surgery [11; 12; 13]--including a long surgical duration and increased risk of blood loss--the individual, societal, and financial burdens of postoperative delirium after spinal surgery are expected to increase [14].  
Early diagnosis and treatment of delirium can reduce length of stay, in-hospital morbidity, and healthcare costs [6]. A diagnosis of delirium is primarily made by trained psychiatrists based on the Diagnostic and Statistical Manual of Mental Disorders, 5 th Edition (DSM-5). For other medical personnel to detect and evaluate delirium easily, an assessment tool called the Confusion Assessment Method (CAM) has been developed [15], and for the case of intensive care unit (ICU) patients, the Confusion Assessment Method for the ICU (CAM-ICU) has been developed and validated. Although several biomarkershave been studied for an objective diagnostic test for delirium, further research is required to apply them in clinical practice [16; 17].  
Electroencephalography (EEG) has attracted attention as an objective diagnostic tool for delirium [18]. EEG waves are known to correspond to certain mental functions and states. Delta waves are associated with deep sleep stages [19]. Theta waves reflect relaxation, drowsiness, and meditation. Alpha waves reflect relaxation and reduced anxiety [20]. Sensorimotor rhythm (SMR) waves reflect active, busy, or anxious thinking. M-beta waves indicate anxiety and performance [21]. H-beta waves are associated with significant stress, anxiety, and arousal [22]. Gamma waves reflect stress and conscious perception [23; 24]. However, the inconvenience of testing methods using EEG recording devices remains a limitation. To the best of our knowledge, there has not been a study of delirium after spinal surgery based on EEG signals from a compact wearable device. In this study, we aimed to analyze EEG signals from such a device in patients with and without delirium both before and after spinal surgery.
# Green Communication for Wireless Body Area Networks: Energy Aware Link Efficient Routing Approach
## 3 Green Computing for WBANs  
ELR-W protocol aims to dynamically select the best next-hop from each BSN to the BNC based on residual energy, link efficiency, number of hop counts, and distance to the BNC. ELR-W is a multi-hop routing protocol in which each BSN generates data packets and sends it to the neighboring node. The receiving node then forwards these packets to BNC. The best next-hop selection is the main idea in ELR-W. In general, selecting a path with a lower number of hop counts to BNC is an effective approach. However, a path with a greater number of hop counts that uses more energy of intermediate nodes may be considered better for the sake of energy balancing in the network. In this situation, the proposed protocol increases the path cost having nodes with lower energy level. This means that a neighboring node having greater residual energy will be selected over a neighboring node with less residual energy. The distance between nodes and signal strength is also a significant parameter for next-hop selection which directly affects energy consumption. This approach balances the energy utilization among all BSNs which results in more stable and improved network lifetime of WBANs.
###### Abstract  
The development of low earth orbit (LEO) satellites has provided the possibility to improve the accuracy of wide-area global navigation satellite system (GNSS) tomography; however, the existing LEO constellations were not designed to consider the effect on the improvement of the accuracy of GNSS computerized ionospheric tomography (CIT). In this paper, we use simulated observations to reconstruct a global higher-resolution three-dimensional (3D) ionospheric electron density (IED) model with a voxel-based model to explore the combined effects of heterogeneous LEO constellations on CIT. The results are as follows: (1) The number of grids crossed by rays increases after adding LEO satellites at different altitudes, while in the altitude interval of 100-300 km, the gain percentage increases with the number of LEO satellites at all four altitudes (300 km, 500 km, 800 km, and 1000 km). (2) The root mean square (RMS) gain percentages are positive after adding LEO satellite observations at 300 km and 500 km altitudes. Whereas, after adding LEO satellite observations at 800 km and 1000 km altitude, the RMS gain percentages from 100-300 km are negative. (3) From the overall percentage gain, the percentage RMS gain of all six plans exceeds 25%, with planB (96/96/60/30 LEO satellites at 300/500/800/1000 km, respectively) having the smallest percentage RMS gain of 27.31% and planA (192/96/60/30 LEO satellites at 300/500/800/1000 km, respectively) having the largest percentage RMS gain of 32.42%. Considering the LEO satellite launch maintenance cost for the enhancement effect of heterogeneous LEO constellations on CIT, this paper demonstrates that planA can effectively improve the accuracy of the 3D IED model.  
computerized ionospheric tomography (CIT); global navigation satellite system (GNSS); low earth orbit (LEO); ionospheric electron density (IED)
# Centralized vs. Decentralized Cloud Computing in Healthcare
## 7 Results  
The papers discussed in the previous two sections were analyzed according to this study's research questions, presented in the introduction.  
RQ1: What Are the Benefits and Limitations of the Use of Centralized Cloud Computing in Healthcare That Aims to Improve HIE?  
Centralized cloud computing has been developed for the healthcare sphere to improve HIE. It offers several benefits and limitations, as discussed below.
## 3 Working Mechanism
### Full-Wave Simulation  
Summarized, topology 3 has the potential to obtain a wide CP bandwidth if the desired modes 1-6 are effectively excited with proper feeding. In the following, a coaxial probe (inner-pin diameter is 1 mm, outer-shell diameter is 4.1 mm) is used as feeding, and full-wave simulations are carried out to validate the proposed design. The final optimized parameters are given in Figure 1. The simulated reflection coefficient and AR results of the three topologies are shown in Figure 7. The full-wave simulation results agree with the above-analyzed results very well. It is clear that the proposed LPCPA with slit-loadings has significantly improved the performance of the low-profile antennas of topology 1 and topology 2. Its impedance bandwidth (S\({}_{11}<-10\) dB) and CP bandwidth (AR \(<\) 3 dB) are 2.2-2.64 GHz and 2.29-2.53 GHz, respectively.  
The Specific Absorption Rate (SAR) over 10 g of tissue is simulated by CST Studio 2020 according to the IEEE C95.3 standard. For realistic wearing applications, the antenna is placed about 5 mm above the body most of the time due to the insertion of air gaps and mid-layers to prevent scratching, potential allergic reactions, etc. Figure 8 shows the simulation model, which has been validated in [37; 38]. The permittivity of the fat, skin, and muscle is 5.28, 38.01, and 52.73, respectively [39]. The simulated SAR result is 0.0159 W/kg at 2.4 GHz. The simulated SAR value is significantly lower than the European standard threshold of 2 W/kg. This is a benefit of the ground shielding of the proposed design.  
Figure 7: Simulated (**a**) S11 and (**b**) AR evaluation of the antennas.
## 3 The Proposed Coyote Optimization Algorithm
### Improved Coyote Optimization Algorithm
#### 3.2.2 The Second Modification  
In the second modification, we concentrate on the performance of the second new solution generation, which is accomplished by using Equation (19) above. The review on Equation (19) can indicate that the use of \(x_{j,r1,p}^{new}\) and \(x_{j,r2,p}^{new}\) has the same function, because the former is taken from a random solution and the latter is also taken from anther random one among the population. Thus, the conditions of (\(\mu_{4}<Pro_{1}\)) and (\(\mu_{4}<Pro_{1}+Pro_{2}\)) are meaningless, but they result in more computation steps and time-consuming aspect. In addition, Equation (19) uses random variables within lower and upper bounds in the case that the two conditions above are denied completely. Some variables in the new solution are taken from two random solutions, while rest of variables in the new solution are randomly produced. Clearly, the combination is not from certain and believable arguments. Thus,in the second modification, we propose using the following model for finding one new solution for each pack.  
\[Sol_{p}^{new}=Sol_{best,r1}+\mu_{6}(Sol_{best,r2}-Sol_{best,r3})+\mu_{7}(Sol_{ Gbest}-Sol_{best,r4}) \tag{24}\]  
By using the modification, COA can take advantages as follows:  
1. Reduce the calculation of \(Pro_{1}\) and \(Pro_{2}\).
2. Reduce two conditions: \(\mu_{4}<Pro_{1}\) and \(\mu_{4}<Pro_{1}+Pro_{2}\).
3. Reduce random generation of variable \(x_{j}\), in Equation (19).
4. Shorten simulation time due to the reduction of three tasks above.
5. \(N_{pack}\) solutions with high quality are produced: The advantages can enable to reduce the number of iterations, leading to shorter simulation time, but the best optimal solution is still much more effective than that of original COA method.  
The whole computation process of the proposed ICOA approach is given in Figure 3.  
Figure 3: The flowchart of using the proposed ICOA approach for a typical optimization problem.
## 4 Discussion
### Future Outlook and Research Directions  
Future research should prioritise addressing the existing methodological deficiencies, with a particular emphasis on reducing costs, improving process efficiency, and effectively incorporating renewable energy sources in the context of TEAs and LCAs.
# Joint Deployment Optimization of Parallelized SFCs and BVNFs in Multi-Access Edge Computing
## 5 Performance Evaluation
### The Impact of the Number of VNFs on Each SFC on Different Algorithms  
In this section, we vary the number of VNFs on each SFC. Figure 9a shows that the number of parallel VNF instances increases with the VNF number. Since the transmission delay increases with the number of VNFs, the number of parallel VNFs needed to satisfy the delay requirement increases significantly. The reliability enhancement of all feasible sets decreases with the number of VNFs. Thus, the reliability provided by all parallel instances for each VNF in SFC decreases with the VNF number, as shown in Figure 8b. Moreover, to satisfy reliability requirements, the number of BVNFs is increasing.  
Figure 9: **(a)** Throughput when the number of VNFs in SFC changes; **(b)** the number of VNF instances and the VNF reliability of all accepted user requests when the number of VNFs in SFC changes; **(c)** the average reliability provided by one VNF instance, all active VNF instances and all VNF instances of all accepted user requests when the number of VNFs in SFC changes.  
In Figure 9b, we can observe that the difference between VNF reliability and base reliability increases with the VNF number in SRPD. The increase in delay leads to an increase in the number of parallel VNFs, thus decreasing reliability.  
Figure 9c shows that the throughput decreases with the VNF number. When the VNF number increases, the resource demand and transmission delay of user requests increase. However, the resource capacity in the network is fixed, so the throughput will decrease.
## 4 Experimental Results  
In this section, we present the results of time and frequency analysis which bring complementary information over the neural oscillations measured by EEG.  
In terms of participant's mood, the experiment scenario did not produce influences and it remained approximately the same during and after the experiment, stated by each participant at the end of the experiment. Half of the participants stated higher complexity for Nat images as compared to Frac images, one participant categorized Frac images as more complex, and the rest considered equal complexity. As for the difficulty of decision, 5 participants rated Nat images as more difficult to evaluate, one Frac images and 2 as equal difficulty. Moreover, participants stated that in some cases they were unintentionally still thinking over the images complexity, even after the stimulus interval, even though they were requested to only relax in the relaxation period. A quarter of participants associated the natural images content with known objects, even though we have done our best to avoid this fact when we selected the natural images, such as to restrict thinking to the structural form and not to trigger memory.  
Considering signals investigation, it is important to note that the stimulus software and the wireless hardware introduced a constant delay of approximately 150 ms in the signal due to communication between sensors and acquisition system, after the start of the trial (time: 0 ms). Therefore, this delay has to be considered when interpreting the observed responses.  
After the channels and artifacts rejection steps, no channels were rejected and 21 epochs on average were removed with a ratio of 0.65:0.11:0.24 from the Uni, Nat and Frac classes. No artifactual components were detected and removed after ICA, mainly because of less spatial information available due to the small number of channels and also missing pre-frontal and EOG electrodes, which could've captured better the eye movements, for example.
# Metabolic Potential of Halophilic Filamentous Fungi--Current Perspective
## 5 Biomolecules Produced by Halophilic Fungi
### Fungal Halophilic Enzymes  
Enzymes obtained from halophilic filamentous fungi have the potential to find industrial applications under unfavorable conditions such as high salt concentration or presence of organic solvents which would normally inhibit enzymatic reactions [6; 42]. They also maintain high stability and enzymatic activity at low water activity which can be as low as 0.75 [57]. Another feature of halophilic enzymes is their polythermophilicity, and it is common that in addition to salinity conditions, they can also resist various temperatures and a wide range of pH [15; 58]. A characteristic of halophilic fungi is the fact that most of them produce extracellular enzymes, which makes their extraction under industrial conditions easier and more efficient compared to halophilic bacteria. Such enzymes are usually water-soluble and can adjust to lower water activity [59; 60; 61]. There are several specific adaptive mechanisms by which enzymes stand out.  
Halophilic enzymes have more acidic amino acids and a smaller number of hydrophobic residues on the surface compared to hydrophobic residues [51; 62; 63]. That means they have a negative charge due to a lower percentage of small hydrophobic residues and basic amino acids (glycine, alanine, serine, threonine, lysine, and arginine), lower composition of amino acids with bulky hydrophobic side chains (phenylalanine, leucine, and isoleucine) compared to those mentioned above, and higher content of acidic amino acids (aspartic and glutamic acid) [6; 51; 64]. Mostly because of the negatively charged and very acidic amino acids on the surface of the enzymes, halophilic proteins can maintain functional conformation, preserve their solubility, reduce surface hydrophobicity, and prevent aggregation at high salt concentrations [24; 65]. It also means that halophilic enzymes are more likely to form random-coil structures than \(\alpha\)-helices [66].  
Another way developed by halophilic proteins to maintain high solubility in high salt concentration was described as the \"solvation-stabilization model\". The protein is surrounded by a solvation shell composed of a very high local concentration of solvent ions, which allows it to maintain balance by excluding small solutes by the solvation shell, thus avoiding protein precipitation [51; 67]. Hydrated ions interact with acidic amino acids creating a negative surface which leads to the formation of shells of water protecting the protein from low water activity [49]. It allows proteins to remain stable, soluble, and active in very high salt concentrations [68]. In some cases, it also means the need for high salt concentrations, making halophilic proteins unstable in low-salt solutions [66].  
The enzymes produced by halophilic fungi belong mainly to the classes of hydrolases (EC 3) and oxidoreductaseases (EC 1).
# A Research Approach to Port Information Security Link Prediction Based on HWA Algorithm
## 3 Preliminaries
### Multilayer Perceptron  
A multilayer perceptron (MLP) [27] is a basic feed-forward neural network model that consists of multiple fully connected hidden and output layers through multiple fully connected hidden and output layers. In the port information security network in this study, MLP detection is used to identify abnormal situations and take corresponding measures in a timely manner. For the hidden layer \(l\), where \(j\) is the index of the neuron, \(z_{j}^{l}\) is the weighted input of the \(j\) th neuron, and \(a_{j}^{l}\) is the output after the activation function. The calculation of the hidden layer is shown in Equations (5) and (6):  
\[z_{j}^{l}=\sum_{i=1}^{n^{l-1}}w_{ij}^{l}.a_{i}^{l-1}+b_{j}^{l} \tag{5}\]  
\[a_{j}^{l}=f\Big{(}z_{j}^{l}\Big{)} \tag{6}\]  
In Equation (5), \(n^{l-1}\) is the number of neurons in the previous layer, \(w_{ij}^{l}\) is the weight connecting the \(i\) neuron in the \(l-1\) layer and the \(j\) neuron in the \(l\) layer and \(b_{j}^{l}\) is the bias of the \(j\) neuron in the \(l\) layer, and in Equation (6), \(f(\cdot)\) is the activation function. The output layer uses different activation functions according to the type of task, and the calculation equation of the output layer is similar to that of the hidden layer. The gradient of the loss function with respect to the model parameters is computed by the back-propagation algorithm, and then the parameters are updated using gradient descent or its variantsto train the model to minimize the loss function. The parameter updates are shown in Equations (7) and (8):  
\[w_{ij}^{l}=w_{ij}^{l}-\alpha\frac{\partial L}{\partial w_{ij}^{l}} \tag{7}\]  
\[b_{j}^{l}=b_{j}^{l}-\alpha\frac{\partial L}{\partial b_{j}^{L}} \tag{8}\]  
In Equations (7) and (8), \(\alpha\) is the learning rate and \(L\) is the loss function.  
Finally, the MLP evaluates the monitoring performance through the validation set and evaluates the generalization capability using the test set.
# Analysis of Wind-Induced Vibration Response of Transmission Wire Under Crosswind in Canyon Terrain
## 3 Numerical Simulation of Mountainous Terrain and Wire
### Computing Domain and Grid Division  
In this study, the finite element simulation software STAR-CCM+ (2206 Build 17.04.007) [24] was used to establish the numerical simulation model of the traverse in the canyon terrain and perform the fluid-structure interaction calculation. Set the fluid model as Standard Air, the density as 1.29 Kg/m\({}^{3}\), and the dynamic viscosity as 1.855 \(\times\) 10\({}^{-5}\) Pa-s. The calculation domain model and grid division are shown in Figure 3. The overall height of the computing domain was 50 m, the width was 150 m, and the length was 240 m. The fluid region mesh used the cut volume mesh cell generator and the prismatic layer mesh generator. The base mesh size was set to 1 m, the minimum surface size was set to 0.005 m, the maximum mesh cell size was set to 2 m, the number of prismatic layers was set to three, and the total thickness of the prismatic layer was set to 0.01 m.  
\begin{table}
\begin{tabular}{c c} \hline
**Wire Parameter** & **Numerical Value** \\ \hline Outside diameter (mm) & 30 \\ Density (kg/m\({}^{3}\)) & 3172 \\ Weight (kg/km) & 1642 \\ Elastic modulus (GPa) & 63 \\ Rated tensile force (N) & 119,500 \\ \hline \end{tabular}
\end{table}
Table 1: Wire parameters.  
Figure 2: The three-dimensional model of the mountainous terrain and the wire.  
The mesh of wire was divided by the block grid of the source surface, using the patch mesh dividing the end face of wire into 85 parts, and the layer number was 120. The grid was locally encrypted in the area around the wire to meet the accuracy of the fluid-structure interaction simulation. The grid division and local amplification of the wire are shown in Figure 4. There were 3.11 million grid cells in the fluid domain, among which there were 10,200 grid cells of wire.