esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Recyclability of Photocatalyst  
The recyclability is among the factors which make the photocatalyst ideal for practical application. So, the reusability of pure CdS and P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanocomposite 1-5 has been tested by photo-degrading of Rh-B for four cycles under visible light. The catalyst particles were centrifuged and dried at 80 \({}^{\circ}\)C after each cycle. The catalytic stability is shown in Figure 7. It represents Rh-B degradation with respect to each cycle, the repeated photodegradation shows that CdS is unstable and that there is loss of photocatalytic activity after two cycles of repeated experiments over pure CdS, as shown in Figure 7a, as CdS is photo-sensitive and will expose to photo-corrosion after dye photo-degradation. Figure 7b shows the repeated photodegradation over P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanocomposite with P25: Cd ratio of 1:5, the catalysts maintain high photocatalytic activity after several times of being used. However, we can say that decorated Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanoflowers by P25 show consistent photocatalytic activity even after four cycles and enhance the transfer of electrons, which would increase the photocatalytic activity of the nanocomposite. As a powerful oxidizing agent, the \({}^{\bullet}\)OH radical may not be generated by single CdS nanocrystals because it will not oxidize OH groups to \({}^{\bullet}\)OH radicals due to its valence band potential and it will suffer photo-corrosion with releasing cadmium cations.  
Figure 6: Photo-degradation efficiency of RhB (100 mL, 30 mg/L) over 80 mg of P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanocomposites 1-5 with P25: Cd ratio of 1:5, under visible light with the presence of different scavengers.  
To further investigate the redox cycles that are involved in the degradation of Rh-B by P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S with P25:Cd ratio of 1:5, XPS analysis of the sample after the 4 th cycle of reaction is carried out, which provides spectroscopic evidences regarding the redox cycles. Figure 8a shows the XPS survey scans of the fresh and used P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S with P25:Cd ratio of 1:5. The Cd 3d core level spectrum in Figure 8b indicates that there are two peaks in used P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S with P25:Cd ratio of 1:5, which are similar to the fresh one in Figure 4b. The Ti 2p core level spectrum in Figure 8c and Zn 2p core level spectrum in Figure 8d indicate that there are two chemical species in the used P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S with P25:Cd ratio of 1:5, which are also similar to fresh one in Figures 4d and 4f, respectively. The results indicate that the catalyst has high stability after four cycles.  
Figure 7: Repeated experiments of photocatalytic degradation of RhB (100 mL, 30 mg/L) over (**a**) pure CdS and (**b**) P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanocomposites 1-5 under visible light irradiation.
## 3 Suichang Gold Mine Shrinkage Method to Filling Method Case
### Economic Benefits of Transformation
Therefore, Suitable Gold Mine is one of the many successful cases of the transfer from the shrinkage method to the backfilling method, and the successful transformation of the Suichang Gold Mine shrinkage method not only improves its own technical indicators and economic benefits, but also provides a feasible transformation plan for other small and medium-sized mines to adopt the filling method. To sum up, we can make the following summary of the transformation of mining methods in Suichang Gold Mine:  
1. Before the transformation, Suichang Gold Mine had extensively utilized the shrinkage method for ore recovery, leading to the creation of numerous underground goals. These goals pose significant safety risks to the nearby residential areas and the National Mine Park. Furthermore, the mine's tailings pond is nearing its designed capacity. If the shrinkage method continues to be employed, the substantial amount of tailings generated will continue to accumulate in the tailings pond. In accordance with China's \"Opinions on Further Strengthening Work Safety,\" the mine is at risk of an imminent shutdown. 2. Before the transformation, the use of pneumatic rock drills and electric rakes in production resulted in low mining efficiency, which was only able to reach a production capacity of 30 t/d. The primary dilution rate of mining was as high as 40%, while there was a secondary dilution rate of up to 5%. 3. After the transformation, the risk of surface subsidence caused by goats generated by ore recovery by the shrinkage method was eliminated, the pressure of tailings storage in the tailings pond was relieved, and the production life cycle of the mine was extended. 4. After the transformation, the number of operators was reduced by 70% due to the use of mechanized equipment. The dilution rate was reduced from 40% to 10%, the recovery rate increased from 95% to 98%, and the daily production capacity increased from 30 t/d to 110 t/d. The use of the upward horizontal layered filling method in the test area of +180 m to +260 m in the middle of the V-1 orebody of Suichang Gold Mine can bring the expected economic benefits of 18,151,000 RMB to the mine.
# Detecting and Mitigating Attacks on GPS Devices
## 5 Detection Techniques and Their Comparison
### GPS jamming Detection
#### 5.1.1 Signal Statistics-Based Methods for Jamming Detection
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of Approach** & **Underlying Concept** & **Strengths** & **Key Open Research Questions** \\ \hline Signal & Monitor received signal & Simple to implement, & How will these approaches work in \\ Statistics-based & attributes and attribute changes & based on easily observable & complex propagation \\ Statistics-based & in statistical properties to & parameters & environments? \\  & jammer & & \\  & Utilize antenna array to & Ability to jointly detect & Can antenna arrays be made \\ Antenna-based & measure aspects of signal to & and mitigate interference & sufficiently simple to be viable for \\  & signals and jammer signals & & small platforms? \\  & Fuse attributes of GPS signal, & Among the best & Can ML models be sufficiently \\ Learning-based & jammer signal, and GPS receiver & performing approaches in & optimized to run on small \\  & into predictive ML model & open literature & platforms with limited \\  & & & computational capability? \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Summary of common approaches for GPS jamming detection. window statistics of the L1 C/No was used to declare the presence of a jammer. As in the case with [69], it is unclear how this approach will perform in dynamic and complex multipath and signal shadowing environments, where C/No will be rapidly fluctuating regardless of the presence of a jammer signal.
## 3 Model Parameter Identification and Comparison
### Model Parameter Identification Using PSO
Then, the following relationship exists between particle velocity and position update during the iteration process:  
\[\mathbf{V}_{id}^{k+1}=\omega\mathbf{V}_{id}^{k}+c_{1}r_{1}\left(\mathbf{P}_{id }^{k}-X_{id}^{k}\right)+c_{2}r_{2}\left(\mathbf{P}_{gd}^{k}-\mathbf{X}_{id}^{k }\right), \tag{10}\]  
\[\mathbf{X}_{id}^{k+1}=\mathbf{X}_{id}^{k}+\mathbf{V}_{id}^{k+1}, \tag{11}\]  
where \(\omega\) is the inertia weight; \(d\) = 1, 2,..., D; \(i\) = 1, 2,..., \(n\); \(k\) is the current iteration number; \(c\)1 and \(c\)2 are acceleration factors, and \(r\)1 and \(r\)2 are random numbers subject to uniform distribution within [0,1]. The calculation flow of PSO is shown in Table 3. Similar to other evolution-based algorithms, PSO is a random search algorithm. However, PSO preliminarily conducts a search based on its own velocity in order to avoid complex genetic operations; hence, it is a very efficient optimization algorithm. To improve the accuracy of the ECM in the entire SOC range, a subarea parameter-identification method is adopted in this study. The basic principles of this method are as follows: The entire SOC range (0-100%) is divided into 10 areas, and the PSO algorithm is then used to identify the model parameters in each area. Ten sets of model parameters are thereby obtained to form model parameters for the entire SOC area. For SOC estimation, the model parameters are selected based on the corresponding area of the SOC.
# Hyperspectral Imaging for the Detection of Bitter Almonds in Sweet Almond Batches
## 2 Materials and Methods
### Study of the Variability and Development of Classification Models  
Firstly, the structure and variability of the population were studied using the CENTER algorithm [18]. This algorithm performs a principal component analysis (PCA) and calculates the global Mahalanobis distance (GH) of each sample to the centre of the population in a new n-dimensional space, which enables the sorting of the samples according to their GH distance. This algorithm was applied using Standard Normal Variate (SNV) and De-trending (DT) as mathematical pre-treatments for scatter correction [19], together with the 1,5,5,1 Norris derivative treatment [20]. The CENTER algorithm was individually applied to the sets of sweet, bitter almonds and mixtures; those samples that displayed a GH > 4 were studied as potential outliers.  
In the case of the sweet and bitter almonds datasets, after removing the spectral outliers, the structured selection of the training and validation sets was carried out following the procedure proposed by [PERSON] and [PERSON] [21]. The training set was made up of 70% of the samples and the remaining 30% constituted the validation set.  
Partial least squares-discriminant analysis (PLS-DA) was used to develop classification models of almonds by bitterness, differentiating between sweet and bitter almonds. Venetian blinds for cross-validation (10 splits) were applied and a maximum number of 16 PLS terms was considered. Normalisation and detrend were used as spectral pre-processing treatments for scatter correction [19] and the first and second derivatives treatments were also tested.  
The performance of the discriminant models was assessed in terms of the sensitivity, specificity and non-error rate (NER). To maximise the success of the models, the optimal threshold value obtained from the ROC curves was considered. To test the robustness of the model, it was externally validated using the validation set.  
Next, global classification models were developed using the totality of the sweet and bitter samples available. For this purpose, the training and validation sets for the sweet and bitter almonds were merged and new models were developed following the procedure mentioned above. Once the best global classification model was selected, the total of mixtures (N = 84) was used to validate the model using the mean spectrum of each sample extracted from the ROI.  
To determine which wavelengths were the main responsibles of the discrimination between sweet and bitter almonds, VIP scores obtained from the global PLS-DA model were used to select the most representative bands. Then, new models were developed to differentiate between sweet and bitter almonds, following the same procedure and using only the selected bands.
## 4 The GSA in Probabilistic Evaluation of ALSC
### GSA for PRPF Based on BASPCE  
GSA is introduced into the PRPF to analyze the global sensitivity of the power of renewable energy power stations on the system's LSC. Specifically, the \(K\)-\(ST\) index in Section 4.1.2 is calculated, and based on the magnitude of the \(K\)-\(ST\), the importance of renewable energy power stations can be ranked. The BASPCE model is combined with the computational strategy outlined in Section 4.1.3, as shown in Figure 2. The detailed procedure is as follows:  
**Procedure 2: GSA for PRPF Based on BASPCE**  
1. Read the parameters of random inputs, including the dimension \(m\), distribution types, and parameters;  
2. Generate a \(m\times N_{\text{MCS}}\) matrix \(\mathbf{Z}_{\text{A}}\) and a \(m\times N_{\text{MCS}}\) matrix \(\mathbf{Z}_{\text{B}}\) (correlated standard Gaussian);  
3. Execute sampling matrix \(\mathbf{Z}_{\text{A}}\) through the BASPCE model and obtain the output \(\lambda_{\text{A}}\);  
4. According to (21)-(22) and \(\mathbf{Z}_{\text{B}}\), generate the conditional sampling matrix \(\overline{\mathbf{M}_{i}}\) for the \(i\)-th input random variables;  
5. Execute sampling matrix \(\overline{\mathbf{M}_{i}}\) through the BASPCE model and obtain the output \(\lambda_{\text{B}i}\);  
6. According to \(\lambda_{\Delta}\) and \(\lambda_{\text{B}i}\) and (19), calculate the \(K\)-\(ST_{i}\) index for the \(i\)-th input random variables;  
7. Repeat steps 4 to 6, traversing \(i\) from 1 to \(N_{\text{RE}}\) (number of renewable energy power stations) to calculate the \(K\)-\(ST\) index for all renewable energy power stations.
## 3 Design of Maritime Data Security Management Platform
### Data Layer
Besides, navigation data can be obtained through satellites, observation stations, multifunctional buoys, the shipborne Acoustic Doppler Current Profiler (ADCP), meteorological instruments, cameras, radar, sonar and other equipment. 2. Data conversion. In the process of the collection, processing, exchange, distribution, service and application of maritime data, a great deal of data exchange is needed. When the data exchange is required to be automated or unattended (or with minimal human intervention), the data exchanged must be predefined and structured so that they can be understood and processed unambiguously by either side or multilateral exchanges. The data interchange format covers the sharing requirements of certain types of data exchanged in maritime data-sharing activities. It aims at ensuring an unambiguous understanding and automatic processing of data exchanged by all parties in bilateral or multilateral data exchange. The data interchange format is important content for the exchange and sharing of various complex heterogeneous data. Therefore, a simple and universal Extensible Markup Language (XML) file is used to describe the structure of the data interchange format. 3. Data fusion. This platform has set up several thematic databases, including the basic geographic information database, hydrometeorological environment database, ship information database, maritime security database and user rights database. According to the special needs of users, it can extract the required information from a specific database in real-time and form a new dataset for users. 4. Log management. This function is for the convenience of viewing and managing the operation record of users when using the platform. In the event of platform problems or human error, the problems can be found timely and solved quickly according to the log records. 5. Acquisition state. This function is mainly used to display the configured data source information.
# Reliability-Aware Cooperative Node Sleeping and Clustering in Duty-Cycled Sensors Networks
## 2 Related Works
### Cooperative Communications  
In reliable and energy-efficient routing (REER) [17], due to the cooperative multi-hop mesh structure [23; 24], the relaying candidates are assigned during the multi-hop mesh route discovery and establishment procedure. In terms of algorithm design complexity and network operations [25; 26], the pre-assigned scheme is chosen to be the simplest approach for relaying candidate selection. However, the pre-assigned relaying candidate selection scheme cannot deal with dynamic wireless channel variation. Furthermore, the relaying candidate selection is constructed fully independently of the data flow and incurs significant communication overhead when network status becomes better. By comparison, the RCR-selection is more adaptive in dynamic network environments [9; 27].
# Combined MvdXML and Semantic Technologies for Green Construction Code Checking
## 4 Methodology of Green Construction Code Checking
### MudXML Technology for Code Checking  
Model view definitions (MVDs) are encoded in a format called mvdXML, which defines allowable values at particular attributes of particular data types [47]. For example, an MVD may require a wall providing a fire rating, a classification according to Table 22 of OmniClass, and information required for structural analysis, such as the elastic modulus of materials. In simple cases, mvdXML may define a single attribute on a single data type, while more complex cases may consist of graphs of objects and collections which can be defined by semantic technology. To check the rules in mvdXML, in general, four steps are needed: (1) generating IFC models using BIM-related software and model information in IFC and/or MVD expressed in Excel; (2) transforming Excel to mvdXML form using an mvdXML rule transformer by data preprocessing and inserting the information which is missing, evolving, or new in the mvdXML using IfcDoc. The IfcDoc tool is used to generate documentation of the IFC specification itself, the baseline MVD ConceptTemplates, and concepts that are part of the IFC specification. It, therefore, allows quickly expanding the generic MVD concepts to cover the specific requirements and business rules that are introduced by a set of exchange requirements; (3) evaluating and comparing IFC and mvdXML files [45]; (4) reading the output of the BCF generator using BIM software.  
A checker was developed based on the open standard mvdXML as the format for structuring checking rules and the BIM Collaboration Format (BCF) to issue reports of the checking process. Two BIM operational standards, i.e., mvdXML and BCF were used for green construction code checking [46].
## 4 Case Study
### Discussions  
The charge/discharge of the battery, natural gas storage, and heat storage are affected by the fluctuation of diversified load demands, peak-valley time-of-use power price, and peak-valley time-of-use heat price. Therefore, the advantages and characteristics of various kinds of energy storage systems can be comprehensively employed by complementing each other, which increases the stability of the EH operation as well as the interaction and coordination of various energies. At the same time, the coordination of different kinds of energy storage devices helps to locally promote the consumption of renewable energy, because this energy consumed in EH does not need to be purchased in this paper.  
Figure 11: Fuel cost of day-ahead optimal operation for IES.  
Figure 10: Change in the pipeline’s temperature in the district heating system.  
In addition, compared with the traditional electric power supply, gas supply, heat supply, and cold supply, the energy conversion equipment such as the _P2G_, _P2H_, and _CHP_ units, provides more scheduling schemes for the dispatching center, which can, in turn, offer an optimal steady state operation scheme for the EH at each time period. The flexible cooperation of various types of energy conversion equipment also increases the security of operation for industrial development zone, because when any individual energy conversion equipment fails, the energy required by the end user is not affected due to multiple supply channels. The operation mode of EHs can be changed and adjusted flexibly according to the optimal allocation proportion coefficient. At the same time, the optimal allocation proportion coefficient provides guidance for equipment capacity selection, equipment operation time, energy conversion equipment combination mode and so on.  
Furthermore, in a certain region, the coordinated operation of different energy systems through the master dispatching center in order to dispatch _CHP_ plant, traditional thermal generating unit and natural gas source powers can ensure the most economically operated fuel cost for IES. Simulation results show that, on the one hand, the electricity transmission system has good stability in a day-ahead optimal operation, and on the other hand, the pressure of each node in the natural gas system fluctuates up and down in a reasonable and safe interval, which further indicates that optimal scheduling means that the natural gas system in a stable operation state. Meanwhile, the pipeline's temperature fluctuation is small under a day-ahead optimal operation, which also indicates that the district heating system has good stability. It has been proven that the proposed day-ahead hierarchical steady state optimal operation for IES is feasible, effective, and reasonable.
## 5 Consistency, Stability, and Convergence  
In this section, we discuss the consistency, the stability, and the convergence of the implicit finite difference scheme. **Consistency. ** According to Equations (6)-(8), the local truncation error of the difference scheme (9) is  
\[R_{j}^{n} = \frac{1}{\Gamma(2-\alpha)}\sum_{k=1}^{n}T_{n,k}(u(x_{j},t^{k})-u (x_{j},t^{k-1})) \tag{14}\] \[+K_{1}\frac{u(x_{j+1},t^{n})-u(x_{j-1},t^{n})}{2\Delta x}-K_{2} \frac{u(x_{j+1},t^{n})-2u(x_{j},t^{n})+u(x_{j-1},t^{n})}{\Delta x^{2}}-f(x_{j},t^{n})\] \[= \frac{1}{\Gamma(2-\alpha)}\sum_{k=1}^{n}T_{n,k}(u(x_{j},t^{k})-u (x_{j},t^{k-1}))-\frac{\partial^{\alpha}}{\partial t^{\alpha}}u(x_{j},t^{n})\] \[+K_{1}\left[\frac{u(x_{j+1},t^{n})-u(x_{j-1},t^{n})}{2\Delta x}- \frac{\partial}{\partial x}u(x_{j},t^{n})\right]\] \[-K_{2}\left[\frac{u(x_{j+1},t^{n})-2u(x_{j},t^{n})+u(x_{j-1},t^{n })}{\Delta x^{2}}-\frac{\partial^{2}}{\partial x^{2}}u(x_{j},t^{n})\right]\] \[= O(N^{\alpha-2})+K_{1}O(\Delta x^{2})+K_{2}O(\Delta x^{2})=O(N^{ \alpha-2}+\Delta x^{2}).\]  
The implicit finite difference scheme defined by (9) or (11) is consistent with model (1) of order \(O(N^{\alpha-2}+\Delta x^{2})\). **Stability. ** For the stability analysis of the implicit finite difference scheme Equation (11) was rewritten as  
\[(-K1-K2)\ U_{j-1}^{n}+(1+2\ K2)U_{j}^{n}+(K1-K2)\ U_{j+1}^{n}\] \[=(\Delta t^{n})^{\alpha}\sum_{k=0}^{n-1}(T_{n,k+1}-T_{n,k})U_{j}^{ k}+F_{j}^{n},\qquad 1\leq n\leq N,\qquad 1\leq j\leq J-1.\]Let \(\bar{U}_{j}^{n}\) be another approximate solution of the difference scheme (11), and let  
\[\rho_{j}^{n}=U_{j}^{n}-\bar{U}_{j}^{n},\qquad 0\leq j\leq J,\qquad 1\leq n\leq N,\]  
be the corresponding round-off error. We let  
\[\rho^{n}=(\rho_{0}^{n},\rho_{1}^{n},\cdots,\rho_{J}^{n})^{T},\]  
and we considered the infinity norm  
\[+K_{1}\frac{u(x_{j+1},t^{n})-u(x_{j-1},t^{n})}{2\Delta x}-K_{2} \frac{u(x_{j+1},t^{n})-2u(x_{j},t^{n})+u(x_{j-1},t^{n})}{\Delta x^{2}}-f(x_{j},t^{n})\]0  
The round-off error satisfied the following round-off equations  
\[+K_{1}\frac{u(x_{j+1},t^{n})-u(x_{j-1},t^{n})}{2\Delta x}-K_{2} \frac{u(x_{j+1},t^{n})-2u(x_{j},t^{n})+u(x_{j-1},t^{n})}{\Delta x^{2}}-f(x_{j},t^{n})\]1 \[+K_{1}\frac{u(x_{j+1},t^{n})-u(x_{j-1},t^{n})}{2\Delta x}-K_{2} \frac{u(x_{j+1},t^{n})-2u(x_{j},t^{n})+u(x_{j-1},t^{n})}{\Delta x^{2}}-f(x_{j},t^{n})\]2  
In order to check whether the finite difference scheme was stable, we studied how the size of the round-off error, \(\rho^{n}\), evolved over time.