esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual-Channel Mapping-Gas Column Concentration Inversion Method Based on Multispectral Imaging
###### Abstract  
Infrared multispectral imaging technology can achieve the long-distance, wide-ranging and fast detection of target gas, and has been widely used in the fields of dangerous-gas detection and environmental monitoring. However, due to the difficulty in acquiring background radiation as well as atmospheric disturbance and noise interference in the detection process, the quantitative detection of gas concentration has become a difficult problem to solve. Therefore, this paper proposes an inversion method for gas column concentration based on infrared multispectral imaging technology. Firstly, infrared background radiation images of the non-target gas absorption spectrum band were collected and converted into background radiation images of the target gas absorption spectrum band according to the dual-channel mapping relationship. Then, combined with the gas radiation images of the target gas absorption spectrum band, the column concentration distribution of the gas was obtained by using the measured calibration relationship between absorbance and column concentration. Experiments of gas detection in different environments were carried out, and the column concentration distribution of the target gas was inverted using this method; the results showed that the average relative error of the inversion of the gas column concentration was 4.84%, which enables the quantitative detection of gas column concentration in a complex environment.  
multispectral imaging; dual-channel background radiation mapping; gas column concentration inversion +
Footnote †: journal: Medical Imaging, April. Sci.
## 4 Numerical Results
### Kovasznay Flow  
The Kovasznay flow [45] is a two-dimensional steady flow past a grid of equally spaced parallel strips described by the following non-dimensional exact solution of the incompressible Navier-Stokes equations  
\[\begin{split} p&=p_{0}-\frac{1}{2}\mathrm{e}^{2 \kappa x},\\ \mathbf{u}&=[1-\mathrm{e}^{\kappa x}\cos{(2\pi y)}]\mathbf{i }+\frac{\kappa}{2\pi}\mathrm{e}^{\kappa x}\sin{(2\pi y)}\mathbf{j},\end{split} \tag{26}\]  
where \(p_{0}\in\mathbb{R}\) is an arbitrary constant and the parameter \(\kappa\) depends on the Reynolds number  
\[\kappa=\frac{\mathrm{Re}}{2}-\sqrt{\frac{\mathrm{Re}^{2}}{4}+4\pi^{2}}.\]  
In order to apply this test case for VDI Navier-Stokes equations, the additional solution for the density is required  
\[\rho=1. \tag{27}\]  
The viscosity field \(\mu\) is uniform and is defined as a function of the Reynolds number. The computational domain is the bi-unit square \(\Omega=(-0.5,1.5)\times(0,2)\) and boundary conditions are derived from the analytical solution (26) and (27) at \(\mathrm{Re}=40\).  
Validation of the \(h\)-convergence rates is performed based on an \(h\)-refined quadrilateral elements mesh sequence. At each refinement step, the number of cells is doubled in each Cartesian direction (from \(8\times 8\) to \(256\times 256\)) and we consider the first to fourth-degree DG discretization, i.e., \(k=\{1,2,3,4\}\). As the analytical density field is uniform, see (27), the change of the working variable has only a marginal influence on the numerical solution. Accordingly, only the results obtained with the exponential transformation (12) are reported here. Errors in the \(L^{2}\)-norm and \(h\)-convergence rates are tabulated in Table 1. Numerical results show a convergence rate of \(k+1\) for both the velocity components and the density. The pressure, instead, exhibits an order equal to \(k+1\) only for \(k=1\). For higher degrees, the order reduces to \(k\). This behavior is commonly observed for high-order DG discretization of the incompressible Navier-Stokes equations, see e.g., [46; 47; 48; 49; 50]. In the incompressible and VDI flow models, the unknown pressure plays the role of the Lagrange multiplier with respect to the divergence-free constraint and the incompressibility constraint converges at a rate of \(k\), which is the same rate as the velocity gradients.
## 6 Numerical Experiments
### Impact of User Numbers  
Different user numbers correspond to different market sizes, e.g., thin, medium, and thick markets. In the experiments of this section, the user number and the service provider number in a single domain are assumed to be the same, i.e., \(N=M\). In other words, when the user number increases, the service provider number also increases, so that the supply and demand relationship remains stable.  
The evaluation result of the impact with different user numbers is shown in Figure 6. In a thicker market with more users, all three evaluation metrics of the proposed ascending auction mechanism increase. The performance of the ascending auction mechanism also becomes closer to the optimal trade mechanism, showing that it is asymptotically optimal with an increasing market size. This observation makes our proposed mechanism favorable in practice, with both a theoretical guarantee of economic properties and an asymptotically optimal service provision performance.
# Enhanced Peer-to-Peer Botnet Detection Using Differential Evolution for Optimized Feature Selection
## 4 Experiments
### Evaluation Metrics  
The following evaluation matrices have been used in our experiments:  
Confusion Matrix: A confusion matrix is a very useful tool for evaluating classification models. It is particularly beneficial for handling imbalanced datasets and provides a more comprehensive evaluation than accuracy alone. It offers a comprehensive analysis of a classification algorithm's performance by presenting a detailed summary of true positive, true negative, false positive, and false negative predictions. The key components of a confusion matrix are as follows:  
True Positive (TP): TPs are occurrences that are correctly predicted as positive by the classification model.  
True Negative (TN): TNs are occurrences that are correctly predicted as negative by the classification model.  
False Positive (FP): FPs are occurrences that are incorrectly predicted as positive by the classification model.  
False Negative (FN): FNs are occurrences that are incorrectly predicted as negative by the classification model.  
Accuracy: Accuracy is a fundamental performance metric that measures the overall correctness of predictions made by a classification model. It is the ratio of correctly classified instances, both positive and negative, to the total number of instances in the dataset. It is calculated as Equation (5):  
\[Accuracy=\frac{(TP+TN)}{(TP+TN+FP+FN)} \tag{5}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter Name** & **Optimal Value** \\ \hline
**Estimator** & lda, knn, dt, rf, nb, svm \\
**Voting** & Soft \\
**Weights** & [1; 2; 2; 2; 1; 1] \\ \hline \hline \end{tabular}
\end{table}
Table 16: Key parameters and optimal values of majority voting algorithm for peer-to-peer botnet detection system.  
Precision: Precision is a performance metric that measures the accuracy of the positive predictions made by a classification model. In other words, it is the ratio of true positive instances, among all instances predicted as positive. It is calculated as Equation (6):  
\[Precision=\frac{TP}{(TP+FP)} \tag{6}\]  
Recall (Sensitivity): Recall is also known as sensitivity or the true positive rate. This performance metric measures the ability of a classification model to correctly identify all relevant instances from a dataset. In other words, it is the ratio of true positive instances among all actual positive instances. It is calculated as Equation (7):  
\[Recall=\frac{TP}{(TP+FN)} \tag{7}\]  
F1-Score: The F1-score is a performance metric that combines both precision and recall into a single value. It is the harmonic mean of precision and recall and provides a balanced measure of performance of a classification model. It is particularly useful when there is an uneven class distribution or when the cost of false positives and false negatives needs to be balanced. It is calculated as Equation (8):  
\[F1=2\,*\,\frac{(Precision*Recall)}{(Precision+Recall)} \tag{8}\]
# Utilising Acknowledge for the Trust in Wireless Sensor Networks
###### Abstract  
Wireless Sensor Networks (WSNs) are emerging networks that are being utilized in a variety of applications, such as remote sensing images, military, healthcare, and traffic monitoring. Those critical applications require different levels of security; however, due to the limitation of the sensor networks, security is a challenge where traditional algorithms cannot be used. In addition, sensor networks are considered as the core of the Internet of Things (IoT) and smart cities, where security became one of the most significant problems with IoT and smart cities applications. Therefore, this paper proposes a novel and light trust algorithm to satisfy the security requirements of WSNs. It considers sensor nodes' limitations and cross-layer information for efficient secure routing in WSNs. It proposes a Tow-ACKs Trust (TAT) Routing protocol for secure routing in WSNs. TAT computes the trust values based on direct and indirect observation of the nodes. TAT uses the first-hand and second-hand information from the Data Link and the Transmission Control Protocol layers to modify the trust's value. The suggested TATs' protocols performance is compared to BTRM and Peertrust models in terms of malicious detection ratio, accuracy, average path length, and average energy consumption. The proposed algorithm is compared to BTRM and Peertrust models, the most recent algorithms that proved their efficiency in WSNs. The simulation results indicate that TAT is scalable and provides excellent performance over both BTRM and Peertrust models, even when the number of malicious nodes is high.  
wireless sensor networks; routing; trust; cross-layer; malicious
# Multi-Source Satellite Imagery and Machine Learning
###### Abstract
Our study made the following contributions:
* We have created a complete and representative dataset of different types of geological formations. This dataset is based on the integration of multiple satellite imagery sources. The data were carefully preprocessed to match spatial resolutions and correct for atmospheric biases. This process guarantees consistency and optimal quality for the analysis and classification phase. This multi-source approach maximizes the information available, allowing a more precise and complete exploration of the types of geological formations. * We analyzed three advanced classification algorithms: Random Forest, XGBoost, and Gradient Boosting. These methods were selected for their ability to handle complex and multidimensional data and their robustness to noise. Each algorithm was evaluated using the different parameters extracted from satellite data, allowing us to identify each approach's performances and specificities for classifying the types of geological formations. * A key aspect of our methodology involves merging the highest precision parameters. This data fusion, which incorporated spectral, morphometric, and topographical features, leveraged the information's complementarity. Subsequently, we performed a new classification using the fused data to assess the impact of multi-source integration on the accuracy and efficiency of the algorithms. This approach underscored the critical role of combining diverse data types in enhancing the overall performance of geological classification. The manuscript is structured as follows: Section 2 reviews the state-of-the art on pod maturity and methods based on color spaces. Section 3 describes the materials used and the methodologies adopted as part of our approach. Section 4 presents the experiments conducted, and the results obtained. Section 5 performs an in-depth analysis (ablation) of our study. Finally, Section 6 discusses these results and concludes the study.
# Efficient Inorganic Stabilization Materials for Chromium and Arsenic Pollution in Water and Soil
## 2 Inorganic Stabilization Materials
### Iron/Aluminum Based Materials  
Iron/aluminum-based stabilization materials utilize their high specific surface area and surface functional groups to reduce the toxicity of heavy metals in soil and water. The most typical iron-based material is zero-valent iron (ZVI). Cr(VI) removal was enhanced by [PERSON] et al. [44] through the use of carboxymethyl cellulose (CMC) and zero-valent iron nanoparticle complex (CMC-nFe\({}^{0}\)). Adding CMC can reduce 53% of Cr(VI) on the surface of nFe\({}^{0}\) (Figure 6a). However, it was found that using nFe\({}^{0}\) alone resulted in an agglomeration phenomenon. Iron/aluminum-based materials also include ferrous sulfate (FeSO\({}_{4}\)), ferrous chloride (FeCl\({}_{2}\)), aluminum oxide (Al\({}_{2}\)O\({}_{3}\)), etc., which have unique dispersibility. [PERSON] et al. [45] used FeSO\({}_{4}\).7H\({}_{2}\)O, Fe\({}_{2}\)(SO\({}_{4}\))\({}_{3}\)-6H\({}_{2}\)O, and NaOH as raw materials to prepare  
Figure 5: (**a**) The kinetic curves of As on limonite before and after mechanical activation [40]; (**b**) The occurrence forms of As in soil repaired by limonite before and after mechanical activation [40]; (**c**) FeOOH@C/TiO\({}_{2}\)@Fe\({}_{3}\)O\({}_{4}\) SEM images [41]; (**d**) FeOOH, C/TiO\({}_{2}\)@Fe\({}_{3}\)O\({}_{4}\) and FeCOOH@C/TiO\({}_{2}\)@Fe\({}_{3}\)O\({}_{4}\) adsorption energy [41]; (**e**) The stabilization mechanism of Cr(VI) by ZnO-Ni [42].  
hydroxy sulfate green rust (GR\({}_{\text{SO4}}\)) as a stabilizer to stabilize Cr. It was found that due to the presence of manganese oxides in the soil, Cr(III) can be oxidized again. [PERSON] et al. [46] mixed soil dissolved organic matter (DOM) with three iron-based NPs (nZVI, \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\), and Fe\({}_{3}\)O\({}_{4}\)) to explore the effect of their interactions on Cr adsorption. Fe\({}_{3}\)O\({}_{4}\) adsorbs more strongly with soil DOM. In contrast, nZVI with DOM aging can remove 79.87% of Cr(VI). When aluminum-based materials are brought into contact with water, hydroxides of aluminum tend to be formed. The hydroxide of aluminum is combined with Cr to form insoluble chromium hydroxide precipitates and with arsenic to form insoluble arsenic compounds such as Al\({}_{2}\)(AsO\({}_{4}\))\({}_{3}\). Aluminum sludge, a byproduct from water treatment plants containing amorphous Al(OH)\({}_{3}\) as its primary component, was utilized for arsenic adsorption; the adsorption capacity is about 19 mg/g [47]. [PERSON] et al. [48] ball-milled magnetite to transform irregular particles into spherical shapes (Figure 6b). The successful adsorption of As reduced the exchangeable state of As in the soil by 30.25% (Figure 6c). Hydroxyl/carboxyl groups formed on ball-milled magnetite effectively removed As(III)/As(V) via hydrogen bonding and complexation, while synergistic oxidation with reactive oxygen species converted As(III) to more removable As(V) (Figure 6d).
# A Sustainability Analysis Based on the LCA-Energy-Carbon Emission Approach in the Building System
## 4 Results and Discussion
### LCA-Emergy Analysis
#### 4.1.4 Sensitivity Analysis of LCA-Energy View  
According to the main contributors, the building operation stage and building material stage have the primary impact on the total energy amount for the entire building. Therefore, their sensitivity analysis needs to be considered for accuracy.  
The specific assumptions are as follows:  
**Hypothesis 1 (H1) (Table 4).** At the stage of building operation, six subsystems should be investigated, including environmental inputs, water supply and sewage system treatment facilities, heating and cooling systems, electricity installations, telecommunications system, elevator system, etc. The energy of each subsystem varies by 5% and 10%, and then the amplitude of the final sustainability indicator change will be verified.  
Figure 8: Sustainability changes of three renewal strategies.  
Figure 9: Sustainability index variation.  
**Hypothesis 2 (H2) (Table 5).** At the stage of building material, seven main types of building materials are selected, involving steel, cement, brick, sand, gravel, iron, and wood, etc. (accounting for about 98.7% of the total energy on the stage of building material). Similarly, under the changes of 5% and 10% for each material value, the magnitude of changes in sustainability indicators needs to be displayed.  
Figure 10A shows the sensitivity analysis under hypothesis 1. Based on a 10% change, three sustainable indexes have a consistent float and it is close to a linear trend, which demonstrates the validity of the calculation results. Under the 10% change, EYR (10.32%) has a more distinct difference than ELR (7.8%) and ESI (2.24%). Similar results at a 5% alteration can be obtained from Figure 10B. The difference is that a 5% linearity is worse than a 10% linearity, which illustrates that a 5% variation is more sensitive to the impact of sustainability indicators under Hypothesis 1. For Hypothesis 2, the data validity is also verified at 10% and 5% changes. However, a clear distinction is that a 10% change in the data has a large impact on sustainable indexes, which can be found in Figure 10C,D. It can be concluded that the building operation phase is more sensitive to small changes in data (5% variation), whereas the building materials phase is more sensitive to large changes in data (10% variation). The reason for this result is that the subsystems of the building operation stage are multiple mechanical systems, whose sensitivity to data is significantly higher than that of the building material stage.
# Object-Based Approach for Adaptive Source Coding of Surveillance Video
###### Abstract  
Intelligent analysis of surveillance videos over networks requires high recognition accuracy by analyzing good-quality videos that however introduce significant bandwidth requirement. Degraded video quality because of high object dynamics under wireless video transmission induces more critical issues to the success of smart video surveillance. In this paper, an object-based source coding method is proposed to preserve constant quality of video streaming over wireless networks. The inverse relationship between video quality and object dynamics (i.e., decreasing video quality due to the occurrence of large and fast-moving objects) is characterized statistically as a linear model. A regression algorithm that uses robust M-estimator statistics is proposed to construct the linear model with respect to different bitrates. The linear model is applied to predict the bitrate increment required to enhance video quality. A simulated wireless environment is set up to verify the proposed method under different wireless situations. Experiments with real surveillance videos of a variety of object dynamics are conducted to evaluate the performance of the method. Experimental results demonstrate significant improvement of streaming videos relative to both visual and quantitative aspects.  
m 2019; 9, 2003; doi:10.3390/app9102003
## 7 Simulation Results and Analysis  
To evaluate the feasibility and performance of the RAWC algorithm in QKDON, the simulation was performed on both the six-node network topology (shown in Figure 5) and the NSFNET topology, which consists of 14 nodes and 21 links. This paper studies short-distance QKDON with limited network resources and scenarios where multiple QKD service requests arrive simultaneously. In our work, the quantum channel and the public channel occupy two wavelengths, and each link has the same total number of wavelengths for the data channel. Without special instructions, there are 40 wavelengths on each link. In addition, it is assumed that each QKP stores the same number of quantum keys. In the following simulation, we set the link weighting factors to \(\alpha=0.5,\beta=0.5\). The arrival of QKD service requests follows a Poisson distribution. The holding time of each service request is fixed and the same. When the duration of the services at a certain moment is over, the services at the next moment arrive. The channels are released when the services end their occupation of the data channels. To select more alternate paths, Section 6.2.2 employs the KSP algorithm with \(K=3\).  
This section evaluates the performance of this scheme by comparing different schemes based on the following three metrics: service request success rate (SR), wavelength resource utilization (WRU), and quantum key utilization (QKU). SR refers to the proportion of QKD services that can successfully obtain quantum keys from QKP and can be successfully transmitted to the total services, indirectly reflecting the encryption efficiency of QKP and the ability of the network to guarantee service transmission. WRU and QKU are utilized to describe the usage of network resources of QKDON.
# A Survey on Change Detection and Time Series Analysis with Applications
## 3 Decomposition Methods into Time-Frequency Domain
### Least-Squares Wavelet Analysis (LSWA)  
The LSWA is a natural extension of LSSA that decomposes a time series from the time domain into the time-frequency domain using an appropriate segmentation of the time series [43]. This segmentation is linked to a translating window (rectangular or Gaussian) whose size decreases as frequency increases within a frequency band of interest. The segmentation results in the detection of constituents with amplitude and/or frequency variability over time as well as detection of short-duration signatures buried in the time series by simultaneously removing the constituents of known forms from the time series. The LSWA can statistically analyze any non-stationary and unequally spaced time series without any need for editing of the original time series, which is indeed a breakthrough in signal processing and time series analysis. The flowchart of the LSWA algorithm is illustrated in Figure A5. Note that the locations of the translating windows may also be chosen to be equally spaced, and the translating windows may also be set up in such a way that they do not overlap each other for the sake of faster computation.  
The normalized spectrogram in LSWA shows percentage variances of the spectral peaks corresponding to the time series segments, i.e., it shows how much the time-frequency localized sinusoids contribute to the corresponding residual segment of the time series. Assuming the normality condition, the LSWA normalized spectrogram follows the beta distribution, and so a stochastic confidence level surface is defined to identify the statistically significant peaks in the spectrogram, see Appendix C in [4]. The amplitude spectrogram in LSWA shows the localized signal amplitudes. Selection of the Gaussian window, i.e., the weight matrix whose diagonal elements are the Gaussian values, generates smoother spectral peaks but wider bandwidth in the spectrogram. Simultaneous estimation and removal (suppression) of the constituents of known forms in the spectrogram allows one to search for any hidden signatures in the residual time series.  
Unlike CWT, the least-squares-based methods, such as WWZ, WWA, and LSWA, are more robust in estimating the signal frequency and amplitude. However, they generally have a higher computational cost than CWT. Like LSSA, LSWA can also consider the constituents of known forms and covariance matrices associated with the time series.