esa-sceva/satcom-chunk-collection huggingface dataset:
# Carbon Quota Allocation Prediction for Power Grids Using PSO-Optimized Neural Networks
## 3 Materials and Methods
### Status of Carbon Emissions in the Power Sector
#### 3.1.1 CO\({}_{2}\) Emission Measurement in the Power Sector  
Thermal power generation CO\({}_{2}\) emissions  
The IPCC methodology was developed by the Intergovernmental Panel on Climate Change (IPCC) to account for emissions from electricity generation and is the most widely used methodology in the world (IPCC, 2006) (IPCC, 2006). Since power plants are the main source of carbon emissions in the power sector, power generation capacity is used as a quantitative indicator to measure these emissions. The mathematical expression for carbon emission calculation from coal-fired power generation for the period 2021-2023 is as follows:  
\[\mathbf{EM}_{f}=\mathbf{E}_{f\mathbf{r}}\times\mathbf{ECE}\mathbf{F} \tag{1}\]  
where \(\mathbf{EM}_{f}\) represents carbon emissions from thermal power (billion ton), \(\mathbf{E}_{f\mathbf{r}}\) represents thermal power generation (**k**wh), and \(\mathbf{ECE}\) represents carbon emission factors for electricity in each region (**kg**/**k**wh).  
Green energy neutralizes carbon emissions  
Since green energy does not produce carbon dioxide during power generation, it helps reduce some of the emissions associated with thermal power generation. Therefore, this paper uses the electricity generated by green energy, both during and after its integration (collectively referred to as the electricity generated in and after green energy), as the quantitative indicator of green energy generation. Similarly, the carbon emissions from green energy and the corresponding portion of thermal power generation (collectively referred to as the carbon emissions from green energy in and after green energy) are used as the indicators of emissions after the influence of green energy. The green energy neutralization and post-carbon emissions for 2021-2023 are then measured using Equations (2) and (3):  
\[\mathbf{G_{e}}=\left(\mathbf{E}_{f\mathbf{r}}-\mathbf{E}_{g\mathbf{r}}\right) \tag{2}\]  
\[\mathbf{EM}_{g}=\mathbf{G_{e}}\times\mathbf{ECE}\mathbf{F} \tag{3}\]  
where \(\mathbf{G_{e}}\) represents green energy neutralization generation (kWh), and \(\mathbf{E}_{g\mathbf{r}}\) represents renewable energy generation (kWh). \(\mathbf{EM}_{g}\) represents post-neutral green energy (100 million tons).  
Analyses of the past decade based on electricity sector CO\({}_{2}\) emissions measured from 2012 to 2023 show a continuous increase in CO\({}_{2}\) emissions from the electricity sector (Figure 1). However, as carbon emissions are affected by electricity output and CO\({}_{2}\) emission factors, the trend is unstable, rising sharply at times and falling sharply at others. The data for this analysis is sourced from the (2012-2024) China Electricity Statistical Yearbook.  
Figure 1: Carbon emissions trends, 2011–2023.
# Challenges and Opportunities for Conducting Dynamic Risk Assessments in Medical IoT
## 3 Systematic Literature Review
### Analysis of Selected Results
Next, we provide an overview of interesting ideas and concepts extracted from our in-depth results analysis:  
* Risk-related standards (for a list of standards, please refer to Appendix A.3)--ISO 27000, IEC 62304:2006, ISO/IEC 27032:2012, IEC 82304-1:2016, ISO/IEC 8001 (Risk Management of Medical Devices on a Network), IEC/TR 80002-1:2009, ISO/TR 800020-2:2017, IEC/TR 80002-3:2014, ETSI's risk-based security assessment, CCTA CRAMM, EBIOS. * Organisations--MITRE/US, AAMI, TGA, EU regulations, ENISA, OWASP, ETSI. * Other standards--CMMI, CAG. * RA methodologies--ISO 27000, NIST 800-30, OCTAVE and OCTAVE Allegro. * Other methodologies applied to risk--IoTRiskAnalyzer, SecKit, attack graphs. * Threat modelling and techniques--STRIDE, DREAD, TARA, attack/risk trees, MVL, CKC, BDN, DSE. * Catalogues of vulnerabilities--OWASP Top 10 IoT vulnerabilities. * IoT services and features--security (CIA attributes, as explained in Section 4) and safety; device and system interoperability; resilience to attacks and fault-tolerant design; authorisation, authentication, access control; use of real-time location services (RTL) for tracking employees, patients, visitors, and assets; accounting for dynamism and temporality of devices in dynamic settings. * Risk quantification--likelihood, impact, and vulnerability prioritisation. * IoT ecosystems--in-hospital (within a hospital's premises) and near-patient (within patients, wherever they are located, e.g., at home or in other settings). * IoT layers--basic representations encompass three layers, namely, perception, network, and application; however, as previously mentioned, the literature considers extensions such as middleware, business, end-user, processing, and service management, which can drive assessment efforts as each layer presents its own set of weaknesses that sophisticated threat actors can potentially exploit. From a patient's perspective, their smart-based apparatuses may interfere with their medical equipment. For instance, [PERSON] et al. (2018) [PERSON] et al. (2018) studied IoT in smart homes and the future prevalence of ambient assisted living (AAL) technologies to revolutionise remote care and treatments for the elderly. The benefits of such technologies should meet stringent security requirements, but offer a balance in terms of respecting privacy whilst monitoring patients. One cannot dismiss the fact that these novel smart home setups are not immune to cyber-attacks as sophisticated actors may deploy malware or eavesdrop on communications for financial gain or for other motives.
# Hybrid NHPSO-JTVAC-SVM Model to Predict Production Lead Time
## 2 Prediction Model  
### Svm  
For non-linear regression problems, assume the training data \(\{(x_{1},y_{1}),(x_{2},y_{2}),\cdots(x_{p},y_{t})\}\), \(i=1,\,2,\,\ldots,n\), \(x_{i}\subset R^{n}\), \(y_{i}\subset R\), where \(n\) is the total number of training samples. The regression concept of SVM is to determine a non-linear mapping from the input to output and map the data to a high-dimensional feature space, in which the training samples can be regressed through a regression equation \(f(x)\), where \(f(x)\) can be expressed as the following equation [23]:  
\[f(x)=\omega\cdot\varphi(x)+b \tag{1}\]  
where \(w\) represents the weight vector, and \(b\) represents the bias vector.  
The SVM problem can be described as solving the following problem [24; 25]:  
\[\textbf{minimize:}\frac{1}{2}\|\omega\|^{2}+C\sum_{i=1}^{n}(\xi_{i}+\xi_{i}^ {*}) \tag{2}\]  
\[\begin{array}{ll}y_{i}\,-\,(\omega\cdot\varphi(x_{i}\ ))-b\leq\ \varepsilon+\xi_{i}\\ \textbf{subject to:}\,\,(\omega\cdot\varphi(x_{i}\ ))+b-y_{i}\,\leq\ \varepsilon+\xi_{i}^{*}\\ \xi_{i},\xi_{i}^{*}\geq 0,\ i=1,2,3\ldots n.\end{array} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \
\begin{tabular}{c} **SVM Optimization** \\ **Techniques** \\ \end{tabular} & \multicolumn{1}{c}{**Prediction Field**} \\ \hline [PERSON] et al. [17] & & 1. Man-hours in aircraft assembly \\ [PERSON] et al. [18] & PSO & 2. Risk of the expressway project \\ [PERSON] et al. [19] & & 3. Steel corrosion \\ \hline [PERSON] et al. [20] & & 1. Vertical load capacity of driven piles in \\ [PERSON] et al. [21] & GA &
\begin{tabular}{c} cohesionless soils \\ 2. Landslide groundwater levels \\ \end{tabular} \\ \hline [PERSON] et al. [22] & BA & Printed circuit board sales \\ \hline [PERSON] et al. [23] & GOA & Short-term load \\ \hline This paper & NHPSO–JTVAC & Lead time in shipyard’s process \\ \hline \hline \end{tabular}
\end{table}
Table 1: Research literature on SVM optimization techniques.  
value of \(\omega\), the greater the particle's velocity, and the particle will perform a global search with a more significant step size; for smaller values of \(\omega\), the particle tends to perform a more finely local search. To balance global and local search capabilities, \(\omega\) generally assumes a dynamic value. In addition, the linearly decreasing inertia weight (LDIW) strategy is most commonly used to determine the values of \(\omega\)[33].  
\[\omega=\omega_{max}-(\omega_{max}-\omega_{min})\times\frac{Iter}{Iter_{max}} \tag{12}\]  
where \(Iter\) is the current number of iterations, \(Iter_{max}\) is the maximum number of iterations, and \(\omega_{max}\) and \(\omega_{min}\) are maximal/minimal inertia weights, frequently set to 0.9 and 0.4, respectively [34].
## 3 Using ML for Water Activities
### Water Resource Management and Quality Prediction
#### 3.1.2 Flood Forecasting and Hydrological Models  
* **Flood forecasting with TVF-EMD[246]**: A hybrid approach combining time-varying filtering with empirical mode decomposition (TVF-EMD) and ML techniques was employed for flood forecasting. This approach excelled in handling nonstationary time series data.
* **Mekong River water levels[247]**: Support vector regression (SVR) was applied to predict water levels in the Mekong River. SVR achieved a satisfactory mean absolute error, meeting stringent requirements for flood forecasting by the Mekong River Commission.
# A Versatile Resilience Packet Ring Protocol Model for Homogeneous Networks
### Problem Statement and Contribution  
To the best of our knowledge, as can be observed from the literature, the existing methods only cover the network performance in terms of packet loss and data center protection. However, a substantial amount of work is missing, e.g., the core reasons for packet loss, collision, and performance degradation. Furthermore, opportunistic and dynamic spectrum access in Core Route CR-TCP introduces results in the dropping of packets because of node mobility, spectrum mobility, and network congestion. As a result, the congestion window variation has a direct impact on the throughput and performance of CR-TCP.  
To address these problems as mentioned above, our main contribution can be summarized as follows: the existing VRPR model works only on Layer 2, while our proposed model works on both Layer 2 and Layer 3 protocols. In addition, we address the delay and packet loss with the help of different scenarios, and provide maximum bandwidth to the active session, which has not been mentioned or implemented in the literature.
## 5 Inversion Test of an Ideal Model
### Inversion Performance Analysis of the Algorithm  
In this section, the performance of each algorithm when applying a 1D TEM inversion is explored. For this purpose, a five-layer layered model was built and inverted using four algorithms in the following order: the ISSA, SSA, JADE, and SAPSO. The control parameters of the algorithms are detailed in Table 2, and the number of layers of the inversion model was set to 15.  
As the number of inversion model layers is not the same as the number of real model layers, we adopted the discretized mean square error of model fitting (D-MSE) as the evaluation criterion for the inversion results. The D-MSE is calculated as follows:  
\[MSE_{D}=\sum_{i=1}^{K}\left|\log_{10}(\rho_{Ti}/\rho_{Ci})\right|/K \tag{14}\]  
where \(K\) is the total number of discrete values between the surface and the maximum depth boundary (here, we take \(K=300\)), \(\rho_{T}\) is the theoretical resistivity at each depth after the discretization of the test model, and \(\rho_{C}\) is the calculated value of resistivity after discretisation of the inverse model.  
Figure 6 shows the results obtained by taking the median of the values of D-MSE after 11 experimental inversions. At first, in the inverse model comparison graph, although the effects of various algorithms show the changing trend of the real model, it is evident that the curve change of the ISSA closely resembles the real model. For the fitness curve, the convergence speed of the ISSA in the early stage is similar to that of the SSA and SAPSO. Still, going out of the local optimal solution is more accessible because more population diversity is retained in the optimization process. In order to verify the robustness of the algorithm, 5% and 10% Gaussian random noise (the orthogonal response data multiplied by a percentage) was added to the TEM synthetic data, and the results are shown in Table 3. The final results of the ISSA are significantly better than the other algorithms at both no noise and 5% noise, and 5% noise has a lesser effect on the model fitting results. In contrast, after the addition of 10% Gaussian random noise, the ISSA's model fitting decreases, but its accuracy is still higher.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Notation** & **Clarification** & **Value** \\ \hline n & Population size & 100 \\ M & Maximum number of iterations of the algorithm & 50 \\ ST & Safety threshold & 0.8 \\ P & Discoverers as a proportion of population size & 0.2 \\ F0 & Variance factor & 0.4 \\ CR & Cross-factor & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Control parameter settings for ISSA.
## 5 Conclusions  
In this study, we comprehensively developed the digitally encoded signals for the scanning electromagnetic waves of the ground penetrating radar and the electromagnetic wave transmission theory. First, the reflected voltage at the corrosion interface was applied to detect the corroded steel bars in concrete structures. Second, the result was compared with the corrosion potential of the corroded steel bars. Finally, the reflected voltage characteristics of electromagnetic waves between media with different dielectric constants were applied to identify the degree of corrosion of the steel bars. The results also showed that the ground penetrating radar method can directly detect the degree of corrosion of steel bars in concrete without the requirement of damaging the concrete surface and forming a pathway to the steel bars. The key findings of the study are as follows:  
1. The changes of the reflected voltage of the electromagnetic wave of the ground penetrating radar can indicate the changes of the corrosion interface of the concrete and steel reinforcement with different corrosion degrees and different thicknesses of the protective layer.
2. The changes in the reflected voltage of the propagated electromagnetic wave to the corrosion of the steel bar from the ground penetrating radar have greater sensitivity than the detection of the electrochemical corrosion potential. The ground penetrating radar method can detect the degree of corrosion of steel bars in the early stage.
3. The reference standard and reflected voltage analysis technology for determining the degree of corrosion by the reflected voltage of electromagnetic waves can effectively solve the limitation of destroying the concrete surface by electrochemical corrosion potential detection and can determine the degree of corrosion of steel bars more quickly and effectively.  
Conceptualization, C.-W.C.; data curation, C.-W.C.; formal analysis, C.-A.T.; funding acquisition, C.-W.C.; investigation, C.-W.C.; methodology, C.-W.C. and Y.-C.S.; project administration, C.-W.C.; software, C.-A.T.; supervision, C.-W.C.; validation, C.-A.T.; writing--original draft, Y.-C.S.; writing--review and editing, Y.-C.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 23: Steel Bar Corrosion in Plate.
## 3 The Signal Adaptive Iterative Optimization Resampling (SAIOR) Algorithm
### Constructing Anti-Jamming Weights
Then, subtract the estimated LEO satellite signal power spectrum \(Y_{e}(f)\) from the input signal power spectrum \(X(f)\) to obtain the power spectrum \(\hat{X}(f)=X(f)-Y_{e}(f)\) of the PFM interference and noise signal. Perform IFFT on \(\hat{X}(f)\) to obtain the interference and noise signal \(\hat{x}(n)\); Step 3: Perform autocorrelation processing on \(\hat{x}(n)\). According to Equation (15), the modulation period \(T_{m}\) of the PFM interference can be obtained by detecting the peak value of \(|R_{x}(\tau)|\); Step 4: Perform secondary sampling on \(\hat{x}(n)\) based on the modulation period \(T_{m}\) to obtain a set of resampled data. Determine the positions of interference spectral lines in the power spectrum using the interference detection threshold, construct the interference set \(I_{j}\), and establish the anti-jamming weights \(W_{f}\); Step 5: Subsample the original received signal \(x(n)\) at modulation period \(T_{m}\), perform FFT on the resampled data, and obtain the power spectrum \(\widetilde{X}(f)\); Step 6: Apply anti-jamming weighting \(W_{f}\) to \(\widetilde{X}(f)\), \(Y(f)=W_{f}\widetilde{X}(f)\), then transform the weighted spectrum via IFFT to recover the suppressed time-domain signal, \(y(t)=F^{-1}\{Y(f)\}\). ```  
**Algorithm 1**SAIOR Algorithm-Specific Steps
# Cystic Fibrosis: Overview of the Current Development Trends and Innovative Therapeutic Strategies
## 1 Introduction  
Cystic fibrosis (CF) is a recessive genetic disease caused by a mutation in the epithelial chloride channel--cystic fibrosis transmembrane conductance regulator (CFTR). CF is a predominant genetic disorder with a disease severity ranging from mild to life-threatening. The number of CF patients in a population varies depending on ethnicity. Previous reports have revealed that one in every 3000 newborns suffer from CF in Northern Europe, whereas this ratio is one in every 10,000 newborns in Latin America [1,2]. The CFTR gene, one of the ATP-binding cassettes (ABC) on chromosome 7, encodes a cyclic AMP (cAMP)-activated chloride channel [3,4]. CFTR is found in different organs like the pancreas, lungs, and sweat glands, and a gene mutation in CFTR results in different types of CF diseases [5]. Numerous diseases like sinusitis, liver disease, and meconium ileus can be seen inCF patients [2]. The CF mutation database enlisted approximately 2076 mutations in the CFTR [6]. However, not all mutations in the CFTR gene cause CF. In fact, according to the clinical and functional translation of CFTR (CFTR2) website, 23 gene variants of CFTR out of the 432 variants identified in 89,052 CF patients, do not cause CF [7]. Mutation in the CFTR gene either results in fewer CFTR channels or has a negative impact on the functioning of these channels. For instance, mutation in CFTR in the pancreas reduces the functioning of CFTR channels, resulting in the defective functioning of the pancreas [8]. As the CFTR channels on the epithelial cells of the pancreatic duct release bicarbonate (HCO3\({}^{-}\)) to neutralize the pancreatic fluid and maintain it at its optimum pH [9; 10], abnormalities in their function can lead to obstruction of the pancreatic duct [11]. The primary and most common organ affected by CF is the lung. Briefly, in CF, the defective CFTR reduces the release of Cl\({}^{-}\) ions, thereby hyperactivating the epithelial sodium channels (ENaC) and causing the absorption of more Na\({}^{+}\) ions. This results in the dehydration and thickening of the formed mucus, making it a good environment for microbial growth [12; 13]. Current therapeutic strategies for curing lung CF focus on repairing the CFTR mutation, inhibiting ENaC functions, breaking down the thickened mucus using mucolytic medications, and targeting the inhabitant microbes. This review has first examined the pathogenesis of CF. Subsequently, it has covered the current gene-based and non-gene-based therapeutic approaches relevant to pulmonary complications of CF diseases, particularly focusing on the innovative therapeutic strategies that could be implemented in the near future. Finally, the role of different viral and non-viral nanocarriers in the delivery of therapeutics to the lungs has been evaluated.
## 1 Introduction
To address the issue of temporal information loss as the network depth increases, the model also employs an enhanced position encoding mechanism. Furthermore, the partial auto-correlation function is used to assess the correlation between different features, thereby enhancing the model's ability to recognize and understand the temporal dependencies between features. Ultimately, it outputs the probability of elevator faults over a future period, optimizing maintenance strategies and providing early risk assessment and warning. This study contributes significantly in three main areas:  
* We have developed and provided a comprehensive dataset of elevator temporal information, including basic elevator attributes, operational state parameters, historical fault types, etc. It also introduces a feature embedding layer that integrates and learns these features. * We introduce the Transformer model for predicting the time series of elevator operations, surpassing traditional methods that rely on the RNN and LSTM. * Our approach implements an innovative temporal compression mechanism within the Transformer model, effectively reducing feature dimensions and sequence lengths. Italso introduces an enhanced position encoding mechanism and partial autocorrelation function to strengthen the extraction of temporal features.
## 3 The Model Validation
### Stiffness Contrast  
The mesh stiffness with the parameters shown in Table 1 is obtained by adopting the comprehensive analytical model and using the finite element model. More precisely, the mesh stiffness with or without the axial effect is calculated.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No.** & **Items** & \multicolumn{2}{c}{**1\#**} & \multicolumn{2}{c}{**2\#**} \\ \hline
1 & gear & Pinion & Wheel & Pinion & Wheel \\
2 & Normal module \(m_{\text{n}}\) (mm) & 3.5 & 3.5 & 2 & 2 \\
3 & Pressure angle \(\alpha\) (\({}^{\circ}\)) & 20 & 20 & 20 & 20 \\
4 & Number of teeth & 20 & 30 & 20 & 20 \\
5 & Face width \(b\) (mm) & 34 & 34 & 16 & 16 \\
6 & Helix angle \(\beta\) (\({}^{\circ}\)) & 5 & 5 & 30 & 30 \\
7 & Profile shift coefficient & 0.1798 & \(-\)0.1321 & 0.0795 & 0.0795 \\
8 & Addendum coefficient & 1 & 1 & 1 & 1 \\
9 & Tip clearance coefficient & 0.25 & 0.25 & 0.25 & 0.25 \\
10 & hub bore radius \(r_{\text{int}}\) (mm) & 22 & 38 & 15 & 15 \\
11 & Center distance (mm) & 88 & & 46.5 & \\
12 & Contact ratio \(\epsilon_{\alpha}/\epsilon_{\beta}/\epsilon_{\gamma}\) & 1.5684/0.2695/1.8379 & 1.2625/1.2732/2.5357 \\ \hline \end{tabular}
\end{table}
Table 1: The detailed parameters.  
Figure 9: The mesh stiffness of single tooth pair with different number of finite element mesh.  
The results obtained by the analytical model in this paper are consistent with the finite element model. The errors of the mean value, maximum value, and minimum value between the two models do not exceed 5% in a meshing cycle, as shown in Table 2. This indicates that the comprehensive model is reasonable and accurate. Compared with the model in Ref. [24], which does not take the slice coupling, structure coupling, or effect of axial mesh force into account, the mesh stiffness obtained by the analytical method in a multitooth engagement region is smaller. Because the deformation of the fillet-foundation will increase when the coupling effect is considered, the stiffness of the fillet-foundation and the mesh stiffness will decrease. Another phenomenon is that the axial mesh force has a greater influence on the mesh stiffness of gear pair 2 than gear pair 1, because the helical angle of gear pair 2 is large than that of gear pair 1, which increases the axial component of the mesh force and the axial deformation of the fillet-foundation and tooth, so the mesh stiffness is small. The mesh stiffness of gear pair 1 is larger than that of gear pair 2 because gear pair 2 has a small face width. The duration time of the engagement region with fewer teeth is different from that of the multitooth engagement region in a mesh cycle because the contact ratio is different, as shown in Figure 10.  
The finite element model of gear pair 1# to obtain the mesh stiffness with element type C3D8R and element number 73,964 needs about 12 h; in contrast, the analytical method only needs no more than 1 min.