esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Raw Materials  
In this study, DSPB was prepared using GBFS as the cementitious material, HCDA as the activator and cured component, SS as the activator, and tap water as the mixing water. The HCDA used in the experiment was provided by Hunan Zhuzhou Glass Factory, with the appearance of yellowish-white powder and a BET specific surface area of 4.830 m\({}^{2}\)/g. The GBFS used was from Hunan Xiangtan Iron and Steel Group, with the appearance of grayish-white powder and a BET specific surface area of 0.958 m\({}^{2}\)/g. The activators were Na\({}_{2}\)O-9 SiO\({}_{2}\) particles (AR) with a purity of \(>\)99% and an SiO\({}_{2}\)/Na\({}_{2}\)O molar ratio of 3.37. PCE powder was used as a water-reducing agent, with a water reduction of 25%. The chemical composition of the raw material was determined by X-ray fluorescence spectroscopy (XRF) analysis, as shown in Table 1. Figure 1 shows the analytical results of the X-ray diffraction (XRD) test of the raw material.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Materials**} & \multicolumn{8}{c}{**Content of Each Ingredient (wt \%)**} \\ \cline{2-11}  & **MgO** & **Al\({}_{2}\)O\({}_{3}\)** & **SiO\({}_{2}\)** & **P\({}_{2}\)O\({}_{5}\)** & **SO\({}_{3}\)** & **K\({}_{2}\)O** & **CaO** & **TiO\({}_{2}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **I** & **Other** \\ \hline HCDA & 0.903 & 0.240 & 0.505 & 0.011 & 12.063 & 0.043 & 85.231 & 0.027 & 0.260 & 0.148 & 3.622 \\ GBFS & 6.123 & 13.299 & 28.852 & 0.014 & 2.689 & 0.469 & 45.810 & 0.978 & 0.654 & 0.040 & 0.440 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of raw materials.  
Based on the activity analysis, the sum of active CaO and MgO content in the GBPS was 51.930%, and the alkalinity coefficient was 1.58, whose value is greater than 1, indicating that the GBFS belonged to alkaline slag, and the cementing performance was better than that of acidic slag. Its quality coefficient was 2.25, with the value reflecting the ratio between the active components and the low active and inactive components in the slag. The larger the value, the higher the activity, so GBFS had high hydration activity [44]. The HCDA contained 85.231% CaO and had a high pH value, which is alkaline.
# Wind Turbine Anomaly Detection Using Mahalanobis Distance and SCADA Alarm Data
## 3 Results and Discussion
This could imply using the MD approach in a condition monitoring system that uses data extensively. Comparison with the SCADA alarm data shows that parameters used in the generation of the predictive models affect how the MD approach detects the faulty behaviors of the wind turbine. Different components of the predictive models could detect abnormalities corresponding to the wind turbine component concerned. This is important in the construction of the predictive models to be used in the condition monitoring system. The results also show that the MD approach was able to detect faulty behaviors at a high percentage. This is important in considering the MD, a data-intensive approach for fault detection in wind turbines. A higher percentage of fault detection using a data-driven approach could be sustainable in the long run [30]. Although the MD measure can be effective in detecting abnormalities in wind turbines, certain defects and challenges can still occur while using this method. One sophisticated challenge and defect is integrating the MD measure into the available CMS of wind turbines. Having a data-intensive method may create problems in maintaining the performance of the system incorporated into the wind turbine. Another possible defect of the method is its reliance on powerful computing software, which may affect the performance of the CMS in the wind turbines. Although this defect can be addressed with improvements in technology, further studies need to be developed to cope with this situation. There are also uncertainties in fault detection using the proposed method. Aside from SCADA network problem where data can be erratic or not at all available, missing SCADA data will affect calculations of times of fault occurrences in the MD approach. This can result in uncertainty in the method if not addressed properly. An uncertainty model presented in [31] is applied in the proposed approach. This model is presented in Equation (3), as follows:  
\[B=\frac{X_{true}}{X_{pred}} \tag{3}\]  
where \(B\) is the model uncertainty, \(X_{true}\) is the true value (SCADA alarm value) and \(X_{pred}\) is the predicted value (MD distance). The calculated model uncertainty was averaged for instances of failures detected, and the results show a final uncertainty of 0.965.
## 4 Derivation of Performance Indices and Stability Conditions
### Stability Condition  
To apply the \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) filter to real systems, it must be stable. Hence, stability conditions are now derived. As shown in Equations (A19) and (A20) of Appendix B, the characteristic polynomial of the \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) filter is \(f_{\alpha\beta\eta\theta}(z)=z^{2}+(\alpha+\beta+\theta-2)z+\alpha\theta-\eta \beta-\alpha-\theta+1\). Applying Jury's stability test [32] to \(f_{\alpha\beta\eta\theta}(z)\), we obtain stability conditions:  
\[(1-\eta)\beta+\alpha\theta>0\quad\text{and}\quad 4-2\alpha-\beta-2\theta+ \alpha\theta-\eta\beta>0\quad\text{and}\quad|\alpha\theta-\eta\beta-\alpha- \theta+1|<1. \tag{29}\]
# Energy-Aware 3D Path Planning by Autonomous Ground Vehicle in Wireless Sensor Networks
## 7 Conclusions  
Because environmental monitoring is becoming more and more important, research is currently focused on longer ranges and broader exploratory ranges. Here, we present an efficient path planning technique that charges the wireless sensor network (WSN) using an autonomous ground vehicle with a limited battery pack, and we theoretically analyse the total energy consumption of the network. We tackle the problem from the perspective of charging because the WSN has a finite amount of energy. Using a large number of AGVs is an efficient method of charging the WSN to extend the exploration network. Furthermore, the exploration range and charging efficiency can be significantly increased by selecting suitable diving locations and designing a path that takes the node's location and data flow into consideration.  
Automated ground vehicles (AGVs) can have their data collection problems resolved using the Nearest Neighbour-based approach, the Grey Wolf Optimizer-based approach, and the Genetic Algorithm-based approach. Simulations demonstrate that the AGV route planning system finds a better solution and converges more quickly than previous algorithms by utilising a Nearest Neighbour-based technique.  
The Obstacle-Avoided Nearest Neighbour (OANN)-based solution for the 3D TSP problem is under a physical limitation or obstacle where visiting node \(i\) just after node \(i-1\) has an extreme distance cost, so it is impossible to visit. Even in this case, Obstacle-Avoided Nearest Neighbour (OANN)-based approach achieves almost the same performance (just a 2.13% longer path for the 50-node scenario and 0.11% longer path for the 100-node scenario) as the performance of Nearest Neighbour (OANN)-based approach in a no-limitation case.  
In the future, we can tackle the energy-aware path planning problem by considering different models for the limitations occurring between some sensor pairs. As a future work, we may tackle the energy-aware path planning problem by considering imperfect accuracy to hit the sensor nodes.  
This the research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The author declares no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AGV & Autonomous Ground Vehicle \\ WSN & Wireless Sensor Network \\ IoT & Internet of Things \\ GA & Genetic Algorithm \\ GWO & Grey Wolf Optimizer algorithm \\ NN & Nearest Neighbor algorithm \\ OANN & Obstacle-Avoided Nearest Neighbor algorithm \\ \end{tabular}
## 3 Proposal of Bridge Maintenance Prioritization Model
### Criticality Score Evaluation Criteria  
When determining maintenance priorities for bridges, it is essential to have indicators that can quantitatively assess social value, criticality, and risk. To quantitatively determine the social importance and risk of bridges, factors such as traffic volume and the locational conditions of bridges are considered. In this study, the Annual Average Daily Traffic (AADT) was applied as a criterion for assessing the importance of bridges. The AADT is regarded as one of the key indicators for determining the efficiency and safety of road and traffic systems, providing the most accurate measure of the number of vehicles and pedestrians crossing a bridge. Additionally, the study comprehensively considered the impact of bridges on surrounding areas by using open data sources such as Google Street View (GSV) and OpenStreetMap (OSM) to evaluate secondary damage factors, including the effects on rivers, bridges, roads, and commercial areas located below the bridge in the event of a collapse.  
To calculate the criticality score, the AADT was categorized into six criteria, and secondary damage factor indices were also applied, with scores assigned based on each criterion. This study adjusted the evaluation criteria and corresponding score ranges based on the perspectives of bridge management organizations. Table 4 presents the evaluation criteria and score allocation for each criticality factor. The criticality score is calculated using Equation (3).  
\[\text{Criticality Score}=(\text{AADT Score}+\text{Secondary Damage}) \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
**Risk Factor** & **Content** & **Score** & **Impact Factor** \\ \hline \multirow{4}{*}{Age} & Age \(<10\) & 0.06 & \\ \cline{2-4}  & \(10\leq\text{Age}<20\) & 0.19 & \\ \cline{2-4}  & \(20\leq\text{Age}<30\) & 0.31 & \\ \cline{2-4}  & \(30\leq\text{Age}\) & 0.44 & \\ \hline \multirow{4}{*}{Design load} & DB-24 & 0.06 & \\ \cline{2-4}  & DB-18 & 0.19 & \\ \cline{2-4}  & DB-13.5 & 0.31 & \\ \cline{2-4}  & Unclear & 0.44 & \\ \hline \multirow{2}{*}{Seismic design status} & Y & 0.17 & \\ \cline{2-4}  & N & 0.83 & \\ \hline \multirow{4}{*}{Safety grade} & A & 0.04 & \\ \cline{2-4}  & B & 0.12 & \\ \cline{2-4}  & C & 0.19 & \\ \cline{2-4}  & D & 0.27 & \\ \cline{2-4}  & E & 0.38 & \\ \hline \end{tabular}
\end{table}
Table 3: Scores by risk factor.
## 5 Discussion
### Limitations  
During model testing, we identified several failure cases. To facilitate analysis, we visualized these results using a fixed node layout, as shown in Figure 19. Some bubble diagrams exhibited abnormal link patterns, such as nodes lacking door links, having excessive door links, or displaying an excessive number of global links. These results suggest that the model's learning of both global link count features and individual node degree features remains insufficient. Developing an attention mechanism that integrates both global and local link count features may help address this issue.  
Additionally, the proposed method has certain limitations. Due to dataset constraints, it can only generate topologies aligned with Asian residential floor plans and cannot create bubble diagrams for other regions or different building types, such as apartments or public buildings. Moreover, since the model is designed for bubble diagrams without three-dimensional spatial information, it cannot handle space topologies with vertical coupling relationships, such as multi-story villas.
## 4 Experiments and Discussion
### Performance Analysis of the Resampling Strategy
The red plot is the detection probability of signal acquisition with the resampling strategy, and the blue one is that of the conventional acquisition algorithm (without the resampling strategy). \begin{table}
\begin{tabular}{c} **Time Cost** \\ **(s)** \\ \end{tabular} \\ \hline
1 & 53.00 & \
\begin{tabular}{c} (\(6.8\times 10^{11}\), \\ \(1.3\times 10^{12}\)) \\ \end{tabular} & 2486 & 5.97 &
\begin{tabular}{c} (\(6.7\times 10^{10}\), \\ \(1.3\times 10^{11}\)) \\ \end{tabular} & 246.8 \\
2 & 53.00 & \
\begin{tabular}{c} (\(6.8\times 10^{11}\), \\ \(1.3\times 10^{12}\)) \\ \end{tabular} & 2467 & 5.97 &
\begin{tabular}{c} (\(6.7\times 10^{10}\), \\ \(1.3\times 10^{11}\)) \\ \end{tabular} & 244.9 \\
3 & 79.25 & \
\begin{tabular}{c} (\(1.1\times 10^{12}\), \\ \(2.0\times 10^{12}\)) \\ \end{tabular} & 3455 & 6.13 &
\begin{tabular}{c} (\(6.9\times 10^{10}\), \\ \(1.3\times 10^{11}\)) \\ \end{tabular} & 268.5 \\
4 & 4.00 & \
\begin{tabular}{c} (\(4.4\times 10^{10}\), \\ \(8.2\times 10^{11}\)) \\ \end{tabular} & 109.5 & 2.36 &
\begin{tabular}{c} (\(2.5\times 10^{10}\), \\ \(4.7\times 10^{11}\)) \\ \end{tabular} & 70.2 \\ \hline \end{tabular}
\end{table} 
Table 3: Sampling frequency, computation, and time cost of signal acquisition without/with the resampling strategy for the received broadband signals of different experimental datasets.
## 2 Related Work  
### Machine-Learning Based Demand Forecasting  
As companies continue to develop, the level of demand for accuracy in predicting product demand only increases. There are factors that further complicate the relationship between them in an environment where the size of the company expands and consumer demand changes due to changes in personal preferences. Therefore, we need to choose a more suitable prediction methodology to solve these problems. The inventory demand prediction problem is the time series prediction problem. Several attempts have been made to predict market demand based on machine learning. In general, research on market-demand forecasts is actively under way in the data-rich power market. Data-based market-demand forecasting requires enormous amounts of data. The power market, which automatically collects data based on sensors, can make various attempts for prediction. In [15, 16], the authors applied the deep-learning model to the prediction of the power load and showed that it works quite well. [PERSON] et al. [17] predicted multivariate power-market scheduling based on deep running. Predictions of not only demand but also solar-power generation were done in various studies, and it was possible to find significant prediction results for complex natural phenomena [18, 19, 20]. There have been many attempts to predict demand by means of machine learning for other items besides the electric-power market. [PERSON]. [21] proposed a technique for accurately predicting rice demand using RNN and LSTM techniques. In addition, because big data is collected in various areas, research on prediction models using machine-learning techniques is being done for predicting demand for water resources in urban areas [22], electric-charge demand for electric vehicles [23], and crude-oil demand [24]. In order to predict the demand of fashion companies, the development of algorithms from a data-driven perspective was studied by using machine learning techniques and identifying important predictors. Many of these studies show that demand forecasting is very difficult.
## 5 Discussion
### Recommendations for the Effective and Responsible Use of IoES in Emergency Management  
Based on the discussion of the potential impact, challenges, risks, and future developments of IoES in emergency management and response, the following recommendations can be made for the effective and responsible use of IoES:  
* Conduct thorough risk assessments and privacy impact assessments before deploying IoES to identify potential risks and develop strategies to mitigate them.
* Involve all stakeholders, including emergency responders, government agencies, community members, and technology providers, in the design and development of IoES to ensure that the system meets their needs and addresses their concerns.
* Develop interoperable standards and protocols that enable seamless integration of different IoES components and facilitate data sharing and analysis across different agencies and organizations.
* Ensure that the collection, use, and sharing of data through IoES comply with applicable laws, regulations, and ethical standards, and protect the privacy and security of individuals.
* Provide adequate training and resources to emergency responders and other users of IoES to ensure they can effectively use and interpret the data generated by the system.
* Regularly evaluate the performance and effectiveness of IoES and make necessary improvements to ensure that it meets the evolving needs of emergency management and response.
* Foster public trust and engagement by communicating the benefits, risks, and limitations of IoES to the public and soliciting their feedback and participation in the design, development, and evaluation of the system.  
By following these recommendations, IoES can be effectively and responsibly used to enhance emergency management and response, improve public safety, and save lives.
## 2 Materials and Methods
### Datasets
#### 2.1.4 TCCON Ground-Based Network  
TCCON is a network of ground-based Fourier Transform Spectrometers recording direct solar spectra in the near-infrared spectral region. From these spectra, accurate and precise column-averaged abundances of CO\({}_{2}\), CH\({}_{4}\), N\({}_{2}\)O, HF, CO, H\({}_{2}\)O, and HDO are retrieved and reported. The TCCON data have become the primary ground-based data source for systematic calibration of atmospheric CO\({}_{2}\) column concentration data acquired by GHG observing satellites and can provide high-precision CO\({}_{2}\) measurements to validate the accuracy of remote sensing products [35]. We collected the GGG2020 dataset of the Hefei site and Xianghe site (Table 1), which are located in the study area, from the TCCON data archive website ([[https://tccondata.org/](https://tccondata.org/)]([https://tccondata.org/](https://tccondata.org/)) (accessed on 9 November 2023)).
# Measurement of Contrast and Spatial Resolution for the Photothermal Imaging Method
## 2 Image Quality Analysis Theory for Photothermal Imaging
### Spatial Resolution  
The spatial resolution also presents a quantified value of image quality. The spatial resolution can be viewed as the ability of a measuring system to accurately distinguish two different objects at a certain distance from each other [15; 17; 18].  
The determination of spatial resolution begins with the LSF. The LSF can be viewed as a response of the imaging system to line impulses (a linear boundary of the internal structure). Hence, the spatial resolution is determined from the form and size of the LSF [13; 15; 16]. Many studies have clarified that the spatial resolution can be quantified through the full width half maximum (FWHM) of the LSF of the imaging system [15; 22].
# Spectral Coexistence of QoS-Constrained and IoT Traffic in Satellite Systems
## 3 Asymptotic Analysis
### Overlay Allocation
#### 3.2.2 Non-Ergodic Case
Leaning on this result, and plugging the binomial distribution of \(W\) into (14), \(\mathsf{R}_{\text{b}}\) can be finally computed for any value of \(\lambda\) as follows:  
\[\mathsf{R}_{\text{b}} =\frac{1}{\mathsf{M}}\log_{2}\Bigl{(}1+\frac{\rho_{\text{b}}}{ \sigma^{2}}\Bigr{)}\sum_{w=0}^{\mathsf{M}}w\binom{\mathsf{M}}{w}\Bigl{(} \lambda e^{-\lambda}\Bigr{)}^{w}\Bigl{(}1-\lambda e^{-\lambda}\Bigr{)}^{\mathsf{ M}-w}\times \tag{18}\] \[\times\Biggl{(}1-\mathbb{P}\Biggl{\{}\sum_{\ell,U_{\ell}\
eq 1}C_{ \text{a},\ell}<\mathsf{R}_{\text{a}}\mathsf{M}-w\log_{2}\biggl{(}1+\frac{\rho_{ \text{a}}}{\sigma^{2}+\rho_{\text{b}}}\biggr{)}\Bigm{|}W=w\Biggr{\}}\Biggr{)}.\]The presented approach provides thus the sought pairs of sustainable rates. As discussed, the maximum sustainable arrival intensity \(\lambda_{m}\) for \(\mathscr{F}_{\text{b}}\) can be computed from (13) for any target QoS requirements (\(\text{R}_{\text{a}},\text{P}_{\text{out}}\)). In turn, mMTC traffic can be operated effectively at any channel load lower than \(\lambda^{*}=\min\{1,\lambda_{m}\}\), with the corresponding aggregate rate \(\text{R}_{\text{b}}\) obtained by evaluating (18) for \(\lambda=\lambda^{*}\).
# ARIBC: Online Reporting Based on Identity-Based Cryptography
## 3 Structure of the ARIBC  
### ARIBC Entities  
As shown in Figure 1, the ARIBC's interactive entities are reporters, authorities, and possibly departments (or individual investigators) within authorities.  
In Figure 1, authority X hosts an instance of ARIBC. A number of reporters, including Reporter A and Reporter B have registered with Authority X and can communicate securely with it. Department A of Authority Y also hosts an instance of ARIBC, with which a number of reporters, including Reporters B and C have registered. Note that a reporter may have registered with more than one instances of ARIBC. Both instances of ARIBC offer to their registered reporters confidential communication, authentication and integrity services. Note also that reporters registered with either instance of the ARIBC can securely communicate with each other as well; however, such communication cannot be in their capacity as reporters of a particular incident, because no entity other than each reporter him/herself and the authority to which the report has been submitted has the knowledge to associate an incident with a reporter of the incident.
## 2 Materials and Methods
### CIE Daylight Model and CCT Determination for a Daylight Spectral Reconstruction
#### 2.3.2 Determination of the CCT from Sensor Readouts  
According to CIE definition [128], the indication of a CCT is only valid for white light sources whose chromaticity coordinates \((u_{r}^{\prime},2/3v_{r}^{\prime})\) differ less than:  
\[\Delta C=\sqrt{(u_{r}^{\prime}-u_{\text{p}}^{\prime})^{2}+\frac{4}{9}\cdot(v_ {r}^{\prime}-v_{\text{p}}^{\prime})^{2}}=5\cdot 10^{-2} \tag{15}\]  
from the chromaticities \((u_{p}^{\prime},2/3v_{\text{p}}^{\prime})\) of a black body radiator of equivalent temperature. Note that the latter is determined by dropping a perpendicular from the respective light source's chromaticity coordinates to the Planckian locus as plotted in the CIE 1960 uniform chromaticity scale diagram [129, 130].  
Thus, the CCT of an arbitrary white light source can be calculated by finding the minimal distance between the light source's chromaticity coordinates and the Planckian locus by means of solving a nonlinear optimization problem and, therefore, finding the temperature corresponding to the Planckian radiator that causes the most similar color impression. In this work, the method proposed by [PERSON] et al. [131] was applied to solve the optimization problem. As it is based on [PERSON]'s method of optimization [132], it by definition only converges locally so that a good initial guess of the optimization's starting point is necessary to find the absolute minimum. This initial guess of CCT\({}_{0}\) is obtained by applying [PERSON]'s CCT approximation method to the transformed color sensor readouts \(X_{\text{SS}}Y_{\text{SS}}Z_{\text{SS}}\). The corresponding equations read:  
\[\text{CCT}_{0}=449\cdot\eta^{3}+3535\cdot\eta^{2}+6823.3\cdot\eta+5520.33, \tag{16}\]  
\[\eta=\frac{x_{\text{SS}}-0.3320}{0.1858-y_{\text{SS}}}, \tag{17}\]  
where:  
\[\begin{split} x_{\text{SS}}&=\frac{X_{\text{SS}}} {X_{\text{SS}}+Y_{\text{SS}}+Z_{\text{SS}}},\\ y_{\text{SS}}&=\frac{Y_{\text{SS}}}{X_{\text{SS}} +Y_{\text{SS}}+Z_{\text{SS}}},\end{split} \tag{18}\]  
Figure 8: Basic functions \(S_{0}(\lambda)\), \(S_{1}(\lambda)\), and \(S_{2}(\lambda)\) of the CIE daylight model after [128].  
give the chromaticity coordinates in the CIE 1931 chromaticity diagram [124]. The transformed sensor readouts are then converted to CIE 1960 uniform chromaticity scale coordinates \((u^{\prime}_{\text{SS}},2/3v^{\prime}_{\text{SS}})\) by applying Equation (7). Using \(CCT_{0}\) as the starting point of the optimization, applying the method of [PERSON] et al. yields the color temperature of a Planckian radiator whose chromaticities are closest to the chromaticity coordinates of the test light source under consideration. The derived color temperature eventually defines the light source's CCT.