esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### \(Ieee\)-14 Bus System  
In this case study, the partitioned \(IEEE\)-14 bus system is used and illustrated in Figure 1. The multi-area non-linear state estimation model presented previously is applied and, upon convergence, the final estimates of the states are shown in Figures 2 and 3. As shown, in the \"Area Number\" axis, area number 5 is the power flow solution while the different bars associated with a bus across areas indicate the specific bus is shared with the corresponding areas. The results show that the converged solution are much close to the power flow solution.  
To show the performance of the presented model, the error metric \(e_{k}=\left\|x_{k}-x_{pf}\right\|^{2}/N_{k}\) in log scale is utilized. A 100 Monte Carlo simulation is generated and the average error curves for each area is shown in Figure 4. From the results, one can see that the presented model is able to achieve a per area mean error smaller than \(10^{-2}\) within a few more iterations.  
Figure 3: Estimated state (magnitude) on the \(IEEE\)-14 bus system 4 regions.  
Figure 2: Estimated state (angles) on the \(IEEE\)-14 bus system 4 regions.  
which indicates that no gross error is present. Hence, the estimated states are optimal and the measurements error are minimal.  
To further analyze the robustness of the presented model, the noise level in the measurements is varied from 0 to 1.4% of the measurements' standard deviation (sd). Test result is presented in Figure 7. This figure highlights the effect of noise level in the  
Figure 5: Per area mean of _l(x)_ on the \(IEEE\)-14 bus system 4 regions.  
Figure 6: Per area mean of _l(x)_ on the \(IEEE\)-14 bus system 4 regions with standard deviation.  
measurements associated with each area on the performance index \(J(x)\). Each area has different \(\chi^{2}\) threshold because of different measurement set across areas. The results show that even with noise level above 1%, the performance index \(J(x)\) per area is smaller than the threshold value, which reduces the false positive alarms.
## 3 Results and Analysis
### Spectral Indicators and Estimation Models Based on the Original Spectrum
#### 3.4.2 Model Construction and Accuracy Test  
Using the determined vegetation index as an independent variable, the peanut leaf SPAD values were linearly regressed to construct an estimation model for the leaf chlorophyll (Figure 7). The linear regression models based on the different indices were as follows: NDSI (R\({}_{520}\), R\({}_{528}\)), R\({}^{2}\) (0.66), RMSE (2.002); ISI (R\({}_{748}\), R\({}_{561}\)), R\({}^{2}\) (0.65), RMSE (2.032); DSI (R\({}_{758}\), R\({}_{602}\)), R\({}^{2}\) (0.68), RMSE (1.94); SASI (R\({}_{753}\), R\({}_{624}\)), R\({}^{2}\) (0.68), RMSE (1.88). These results showed that the linear model can achieve good prediction accuracy (R\({}^{2}\)\(>\)0.65, RMSE\(<\)2.04). By comparing R\({}^{2}\) and RMSE, the optimal single variable estimation model for each vegetation index was obtained.  
Figure 6: Pictures from **A** to **D** are contour maps of the coefficients of determination of the linear relationship between the vegetation indices NDSI, ISI, DSI, and SASI and the chlorophyll content of peanut leaves, respectively. (left) 10-nm sampling interval (right) 1-nm sampling interval.  
Figure 7: _Cont._
# A Multi-Customer Vehicle Scheduling Optimization Method for Coal Intelligent Loading System
## 2 Analysis of Coal Loading Operations
### Loading Operation Process  
The loading operation process incorporates almost all tasks undertaken by coal enterprises upon receipt of customer orders. These tasks involve loading coal onto vehicles according to particular specifications and subsequently delivering the coal to various customer locations. Key processes include order reception, coal blending and storage, loading operation notification, vehicle entry to the yard for skin removal and queuing, loading, weighing, quality inspection, and vehicle departure to destinations, as shown in Figure 1.  
Figure 1: Flow chart of coal loading operation.  
The coal loading operation process unfolds in the following manner: 1 Upon receipt of customer orders, the enterprise obtains detailed specifications from each customer regarding coal type, quantity, and delivery schedule. 2 Notify the coal preparation plant to meticulously blend the coal based on order details and store it in the loading bunker. 3 The designated personnel dispatch the loading operation notification to the customer and inform other staff members to ready themselves for the loading process. 4 Throughout the loading procedure, each customer's vehicle proceeds to the electronic weighbridge along the specified route for skin removal before queuing up beneath the loading bunker for the loading task. 5 The customer's vehicle undergoes the loading operation at the loading bunker. 6 Conduct quality inspections on the vehicle weight and coal to ensure that the quality standards specified in customer orders are met. Subsequently, the vehicle proceeds along the designated route towards the customer-specified destination.
# Deep-Learning Multiscale Digital Holographic Intensity and Phase Reconstruction
## 2 Principles and Methods  
### Principles of Digital Holography  
Digital holography is a technique that uses the principle of interference to record and reconstruct the diffracted wavefront from an object as interference fringes [21; 22]. A beam of light is split into two parts by a beam splitter. One part is the reference beam, which is directed to a photodetector, while the other is reflected from the object and serves as the object beam. These two beams interfere with each other, and the charge-coupled device (CCD) records the information from the interference fringes. This information contains the object beam's intensity and phase information [23]. By reconstructing the hologram, the intensity and phase information of the object wave can be obtained [24; 25].  
The digital hologram system model is shown in Figure 1, where the \(x_{0}\)-\(y_{0}\) plane is the object plane, the \(x\)-\(y\) plane is the hologram plane and \(Z\) is the recording distance. The off-axis holographic object wave interferes with the reference beam at a certain angle. The interference between \(R\) (reference beam) and \(O\) (object beam)  
on the right-hand side of the equation represent the intensity values of the object and reference waves, respectively: \(O^{*}(x,y)R(x,y)\) represents the twin image, which is the conjugate field of the object wave; \(O(x,y)R^{*}(x,y)\) represents the real image, corresponding to the field of the object wave. These four components together form the hologram of the measured object. Hologram reconstruction is the process of recovering intensity \(o(x,y)\) and phase \(\varphi(x,y)\) from \(I\).
# Exploration of Multi-Scale Reconstruction Framework in Dam Deformation Prediction
## 2 Materials and Methods
### Research and Design  
#### 2.7.1 Project Overview  
Taking a roller compacted concrete gravity dam as an example, which is located in Fujian Province, China, the design flood level is 633.0 m. The elevation of the foundation surface is 562.0 m, and the maximum dam height is 73.4 m. The horizontal displacement  
Figure 4: Flow chart of the proposed model.  
monitoring of the concrete dam crest adopts the tension line method, and a total of 11 measuring points are arranged, of which 9 working measuring points are located on the top of each dam section; 2 check base points are located on the left and right ends of the tension line. The deformation monitoring system of the dam includes horizontal displacement and vertical displacement. This article mainly models and analyzes the horizontal displacement of the dam crest. Taking into account the unevenness and local differences of the dam deformation, the monitoring data of the middle measuring point EX5 and the left bank measuring point EX2 of the fourth dam section are taken for analysis, as shown in Figure 5. The deformed data obtained by processing the outliers of these two sets of data are shown in Figure 6. It can be seen that the data a highly volatile and EX5 shows periodicity on the whole, but EX2 looks messy and has no rules to follow. The numbers of deformation monitoring data of the two measuring points are 739 and 417, respectively. The division results of their training set, validation set and prediction set are shown in Table 1.
# Security Risk Analysis of LoRaWAN and Future Directions
## 5 Security Threat and Risk Assessment of LoRaWAN v1.1
### LoRaWAN v1.1 Threat Catalog
#### 5.2.7 RF Jamming Attack  
In the past, it has been shown that by using low cost and commodity hardware jamming of the RF signals is possible [27]. RF jamming attacks in wireless networks lead do Denial-of-Service (DoS), which are generally easy to detect. However, selective RF jamming attacks are harder to detect and very harmful for wireless communications as it is not trivial to avoid. Therefore, in LoRaWAN, it is possible to jam reception of the signals at a gateway or a node by using RF jamming. This could open-up some advanced attacks as it is described in the Replay attack (refer to Section 5.2.11), which partially relies on the ability to perform selective RF jamming.
## 6 Feasibility Analysis Considering Slow Charging at Destinations  
The increase in slow charger coverage in the public parking lot will effectively reduce the fast charging demand, and the increase in EV ownership will, in turn, boost the fast charging demand. Therefore, in this study, based on the siting result of charging stations, we focus on the impact of the coverage of the slow charger and the growth of EV ownership on the fast charging demand. The time for slow charging of the EVs is subject to the probability density function in Equation (24) [23], and the MCS is applied to generate the slow charging time at each destination.  
\[f_{3}(t_{slow})=\frac{\omega}{\eta P_{slow}t_{slow}\sigma_{3}\sqrt{2\pi}}\exp(- \frac{\left(\ln(\frac{\eta P_{slow}t_{slow}}{\omega})-\mu_{3}\right)^{2}}{2 \sigma_{3}^{2}}), \tag{24}\]  
where \(\omega\) indicates the power consumption of EV per 100 km. \(\eta\) represents the charging efficiency. \(P_{slow}\) is the slow charger rated power. \(t_{slow}\) is the slow charging time and \(\mu_{3}\) denotes the expected slow charging time. \(\sigma_{3}\) is the standard deviation, \(\mu_{3}=3.5\), \(\sigma_{3}=0.8\).  
The probability of slow charging at the destination reflects the coverage of slow chargers in the public parking lot, considering it's difficult to determine whether a user can be charged at the destination based on the coverage of slow chargers, while it is certain that a relationship exists between the coverage of slow chargers and the probability of slow charging. The slow charging time at destination \(i\) is recorded as \(t_{slow,i}\) and the added travel range \(d_{ei}\) is shown in Equation (25). The maximum travel range of EVs will be updated according to Equation (26) and we continue with the trip feasibility analysis described in Section 3.3.  
\[d_{e,i}=\min(\frac{t_{slow,i}P_{slow,i}}{P_{ev}}R,d_{oi}), \tag{25}\]  
\[R=R+d_{e,i}, \tag{26}\]  
where \(d_{oi}\) indicates the mileage that has been traveled from the previous charging station to the newly arrived destination. \(t_{slow,i}P_{slow,i}\) represents the energy recharged at destination \(i\) through slow charging and Equation (25) turns the energy recharged into the added driving range.  
Figure 12 explains that the charging choice and analysis of route feasibility depend on whether we consider slow charging at the destination. Figure 12a shows the situation when slow charging is not considered while Figure 12b is for the situation when slow charging is considered. From Figure 12b, we can see that the time for slowing charging at destination D\({}_{2}\) is \(t_{slow}\). The added travel range \(d_{e}\) after charging and the maximum travel range \(R\) could be obtained via Equations (25) and (26). With the optimization method and analysis approach proposed in Section 3, we know that the user could successfully reach destination D\({}_{3}\) and the trip would be a success. While in the situation Figure 12a  
without considering slow charging, the user cannot reach destination D3 directly and would have to charge at charging station C4 to continue with the journey.
## 5 Comparison and Discussion  
### Comparison of Evaluation Methods with Test Results  
Table 6 shows a comparison of the experimental results and the flexural strength evaluated using the \(f_{ps}\) approximation method and the strain-compatibility method. Firstly, in the case of the non-corroded PC beam (RB5), the flexural strength was evaluated to be 346.48 kN using the strain compatibility method, which was 4.39% lower than the nominal flexural strength. On the other hand, the \(f_{ps}\) approximation resulted in a flexural strength of 363.66 kN, which was 0.30% higher than the nominal flexural strength. As mentioned earlier, RB5 facing the situation support went slightly out of place, so the evaluation methods were compared to the nominal flexural strength. Secondly, in the cases of specimens, CB2, CB3, and CB4, there were no significant differences in global behavior. These results were caused by the low flexural moment at the location of corrosion--thus, both evaluation methods showed the same flexural strength with RB5. Thirdly, in the case of CB1, which had corrosion in the middle of the span, the flexural strength using the strain compatibility method was 290.26 kN, a 0.14% difference. The \(f_{ps}\) approximation method resulted in a flexural strength of 299.22 kN, which was 3.23% higher than the test result.
## 4 Results and Discussion
### Influence of Satellite Distribution on the Detection Rate
[21] determined the impacts of the SRB at the GPS L2 and L5 frequencies but not at the L1 frequency during the SRB event on 6 September 2017. Beyond their work, our analysis indicates that this SRB event may have an impact at the L1 frequency. The impact of this SRB event on the \(C/N_{0}\) at the L1 frequency is difficult to observe with the naked eye but can be reflected by the experimental results of the proposed method. As shown in Figure 11, the detection rate is much higher in the ranges above 100 SFU than at 0-100 SFU. Although the number of samples for 6-7 kSFU, 8-9 kSFU, 9-10 kSFU, and \(\geq\)10 kSFU is merely 1, the number of samples in other ranges is sufficient. The detection rate of 0-100 SFU is close to 0%, while the number of samples in this range accounts for almost half of all samples. Most of the predicted samples are in flux density ranges above 100 SFU, and this phenomenon does not seem to be an accident.
## 3 Problem Formalization and Taxonomy
### Simulation  
Large-scale multi-entity simulation is a computationally intensive task. An entity is either passive, like a volume in the finite element methods (FEM) used for physical and mechanical simulations, or active, either as a simple state-based Turing machine (a state-based finite state machine (FSM)) in a cell of a cellular automata (CA) system or a more complex agent in a multi-agent system (MAS). FEM and CA cells present short-range interaction leading to static short-range (local) data dependencies (and data exchange), whereas agents can offer short- and long-range interaction up to global data dependencies and global communication. That is, FEM/CA cells have a fixed set of neighboring elements, whereas agents have dynamic sets of interacting agents. This is important for parallelization and the distribution of simulations.  
Control-path parallelism can be efficiently applied to multi-entity simulation if there is either a regular geometrical ordering of the elements or if it is possible to partition the geometrical cell space \(\Sigma\) in mostly independent sub-partitions and regions \(\{\sigma_{1},\,\sigma_{2},\,\ldots\,\}\)ideally with a 1:1 mapping of partitions on worker processes [8]. Both FEM and CA worlds can be partitioned into independent areas. MAS simulations can be partitioned in the same way, based on agent context and with geospatial constraints limiting the communication (mostly) of agents to the bounds of a geospatial region, e.g., the social interaction of agents is limited to the current spatial context (building spaces are independent of street areas, and so on).  
Each sub-partition \(\sigma_{1}\) containing a number \(n\) of entities can primarily use private memory for the state variables of the entities. Only boundary entities close to another sub-partition/region require shared memory or message-based communication, implementing distributed memory. The first case study in this paper will discuss a sliced memory architecture with shared and non-shared memory segments.  
The distributed simulation requires message-passing that produces a significant overhead and can affect scaling up and speeding up. A hybrid distributed-parallel approach can be used as a good trade-off, i.e., parallel simulation on the same host platform using shared memory, with distribution on multiple host platforms (clustering).
# Recent Advances in Spectroscopy Technology for Trace Analysis of Persistent Organic Pollutants
###### Abstract  
Persistent organic pollutants (POPs) have attracted significant attention because of their bioaccumulation, persistence, and toxicity. As anthropogenic products, POPs mainly contain polychlorinated biphenyls (PCBs), organochlorine pesticides (OPs), and polycyclic aromatic hydrocarbons (PAHs), and they pose a great threat to human health and the environment. To deal with these toxic contaminants, many different kinds of strategies for sensitively detecting POPs have been developed, such as high performance liquid chromatography (HPLC), surface enhanced Raman spectroscopy (SERS), and fluorescence. This paper mainly summarized the achievements of spectroscopy technologies, which generally consist of SERS, surface plasmon resonance (SPR), and fluorescence, in the detection of low-concentration POPs in different matrices. In addition, a retrospective summary is made on several critical considerations, such as sensitivity, specificity and reproducibility of these spectroscopy technologies in practical applications. Finally, some current challenges and future outlooks for these spectroscopy technologies are provided in regards to environmental analysis.  
Keywords:spectroscopy technology; surface enhanced Raman spectroscopy; surface plasmon resonance; fluorescence; persistent organic pollutants +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Polystyrene Nanoparticle Synthesis
#### 3.2.2 Influence of A Change in the Nucleation Mechanism on Nanoparticle Size
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **Oligoglycid** & **Surfmer** & **Average** & **PDI a** & **Average** & **Size** & **Zeta** \\  & **(n =)** & **Monomer** & **Diameter** & **(DLS)** & **Diameter b** & **Distribution c** & **Potential** \\  & **ratio** & **(DLS) (nm)** & **(SEM) (nm)** & **(SEM)** & **(mV)** \\ \hline
**3a** & **2a** (6) & 1:1984 & 247 & 0.015 & 235 & 0.029 & –37.7 \\
**3b** & **2a** (6) & 1:545 & 173 & 0.004 & 160 & 0.027 & –46.9 \\
**3c** & **2a** (6) & 1:283 & 163 & 0.009 & 151 & 0.031 & –44.6 \\
**3d** & **2a** (6) & 1:137 & 137 & 0.010 & 126 & 0.033 & –45.9 \\
**3e** & **2a** (6) & 1:842 & 195 & 0.009 & & & \\
**3f** & **2a** (6) & 1:1984 & 243 & 0.012 & & & \\
**3g** & **2a** (6) & 1:3968 & 369 & 0.031 & & & \\
**4a** & **2b** (10) & 1:840 & 387 & 0.012 & 369 & 0.024 & –42.3 \\
**4b** & **2b** (10) & 1:500 & 273 & 0.014 & 261 & 0.021 & –46.5 \\
**4c** & **2b** (10) & 1:250 & 206 & 0.018 & 199 & 0.033 & –44.3 \\
**5** & **2c** (20) & 1:500 & 405 & 0.037 & 389 & 0.041 & –45.5 \\
**6** & - & - & 365 & 0.18 & 352 & 0.22 & –46.3 \\ \hline \hline \end{tabular}  
* The polydispersity index (PDI) describes the size distribution calculated from DLS measurements. b Average diameter calculated from SEM images using at least 200 particles. c Size distribution calculated from SEM images by dividing the standard deviation by average size of at least 200 particles.  
\end{table} 
Table 3: Synthesized polystyrene nanoparticles.
# Radar-Based Target Tracking Using Deep Learning Approaches with Unscented Kalman Filter
## 4 Conclusions  
Combining an unscented Kalman filter with various deep learning models represents a viable approach for accurately predicting dynamic systems in multiple applications. This approach is beneficial when a data-driven approach is required to estimate the covariance matrix. It was possible to achieve forecasts of target states that were extremely accurate and reliable by integrating the strengths of UKF and deep learning models. When it comes to nonlinear state estimation, UKF offers a framework that is both robust and adaptable. At the same time, deep learning models can learn from enormous datasets and produce accurate predictions. Among deep learning models, CNN and GRU models have been observed to outperform LSTM and MLP models in terms of accuracy and reliability. Specifically, CNN and GRU models estimated a one-meter-per-second speed less than LSTM and MLP models at the 100 th second. It is possible to impart the improved performance of CNN and GRU models to their capacity to process sequential input and maintain long-term dependencies. Additionally, these models are well-suited for this task because of their ability to handle sequential data. In contrast to MLP models, often utilized for more generic classification tasks, CNN models are built specifically for image processing.  
Conceptualization, U.P., S.K.R., B.O.L.J., H.M.R., S.A. and W.P.; writing--original draft preparation, U.P., S.K.R., B.O.L.J., H.M.R., S.A. and W.P.; writing--review and editing, U.P., S.K.R., B.O.L.J., H.M.R., S.A. and W.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The datasets generated during and analyzed during the current study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.