esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
This paper presents a SWCC parameter calculation framework based on Bayesian landslide monitoring data theory. This method makes full use of prior information and limited monitoring data to determine the posterior information of SWCC model parameters. It characterizes the uncertainty of the SWCC model and is of great significance for reliability analysis. The influences of the dataset size, prior distribution type, and time-varying monitoring data on the result of posterior distribution were studied. Finally, the uncertainty of the SWCC model parameters was characterized. Taking the water content monitoring data of the large-scale landslide model test as an example, the effectiveness of this method is explained. The main conclusions from this study are as follows:  
1. This paper proposes a Bayesian-based analysis method that can take the parameters' uncertainty into account, and is different from conventional deterministic analysis methods. The feasibility of the Bayesian updating method in the monitoring data of a large landslide model was demonstrated. Through the Bayesian updating, the posterior value ranges of the SWCC parameters will be narrowed compared to the prior ones.
2. The Bayesian updating method has good application prospects in landslide monitoring. When the prior distribution is lognormal, the coefficient of variation of the posterior distribution is smaller than that of the normal distribution. The SWCC parameter \(a\) tends to increase when the posterior distribution changes with time.
3. In the process of Bayesian updating, a numerical simulation based in a FE model was used. Alternatively, a ANN surrogate model was also trained based on the FE model. This method can greatly improve the efficiency of Bayesian model updating--its accuracy being acceptable.  
Conceptualization, C.F. and M.B. ([PERSON]); data collection, C.F., X.L. and T.H.; methodology, C.F., M.B. ([PERSON]), and B.T.; software, C.F. and M.B. ([PERSON]); validation, C.F., B.X., and B.T.; data curation, C.F.; writing--original draft preparation, C.F.; writing--review and editing, S.B.; project administration, B.T.; funding acquisition, B.T. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Key Research and Development Program of China (2017 YFC1501100), and the Research Fund for Excellent Dissertation of China Three Gorges University (2019 SSPY005).  
Thanks to the National Research Council and Three Gorges University for their support.  
The authors declare no conflict of interest.
# Architecting an Agent-Based Fault Diagnosis Engine for IEC 61499 Industrial Cyber-Physical Systems
## 4 Evaluating the Architecture of the Engine
### Iso 4.2.2 Performance Efficiency  
Efficiency for the engine is defined in terms of how well it uses the resources of the platform it is deployed on. QAS 2 in he SAD requires that the agents must be able to operate faster than the FB application. While the diagnostic point only has to mediate the flow of data to and from the engine, the agent also has to analyze the data streams from multiple DPs and execute diagnostic strategies preemptively. QAS 5 requires the engine to be able to be run natively on the same hardware as the FB application. However, that is unlikely to be a viable option in most cases. Separate and redundant fault diagnostic engines that operate reliably while other systems are failing are the norm in avionic and aerospace control systems [36; 80]. Where large ICPS are being diagnosed, the engine must provide sufficient resources to ensure that the agents can perform all tasks within the required time window.
## 2 Exposure Regulations at mmWave Frequencies  
The guidelines prescribed by the Federal Communications Commission (FCC) encompass specific limits for MPE concerning electric and magnetic field strength, as well as power density for transmitting nodes operating within the range of 300 kHz to 100 GHz [34]. Additionally, the Institute of Electrical and Electronics Engineers (IEEE) has developed standards governing exposure to NIR. These standards explicate the allowable duration and quantity of exposure for individuals in both controlled and uncontrolled areas. These restrictions are outlined in the IEEE C95.1-2005 [8]. Table 1 provides a summary of the NIR exposure limits based on averaging time, in accordance with the IEEE standards. Contrasting this, the International Commission on Non-Ionizing Radiation Protection (IC-NIRP) standard sets restricted levels for power density at frequencies ranging from 10 to 300 GHz. For the general public, the power density restriction is set at 10 W/m\({}^{2}\), while for the occupational group, it is set at 50 W/m\({}^{2}\) (Table 5 in [9]).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Frequency** & **Power Density (Controlled Envir.)** & **Power Density (Uncontrolled Envir.)** \\
**(GHz)** & **“Upper Tier” in W/m\({}^{2}\)** & **“Lower Tier” in W/m\({}^{2}\)** \\ \hline
3 to 30 & 100 in avg. time = 19.63/f\({}^{1.079}\) min & 10 in avg. time = 150/f min \\ \hline
30 to 100 & 100 in avg. time = 2.524/f\({}^{0.476}\) min & 10 in avg. time = 25.24/f\({}^{0.476}\) min \\ \hline \hline \end{tabular}
\end{table}
Table 1: MPE limits—average PD for uncontrolled and controlled areas with averaging time.  
Moreover, the IEEE standard allows for a relaxation of MPE power density when considering exposure to any part of the human body. In controlled environments, the power density must not extend 200(f/3)\({}^{1/5}\) W/m\({}^{2}\) for 3 to 96 GHz and 400 W/m\({}^{2}\) for the frequency band of 96 to 300 GHz. In the case of uncontrolled environments, the standard limits the power density level below 18.56(f)\({}^{0.669}\) W/m\({}^{2}\) for the 3 to 30 GHz band and 200 W/m\({}^{2}\) for the 30 to 300 GHz band [7; 8].
## 4 V2H and V2G
### Prospects
#### 4.4.1 Integration with Smart Electricity Grids  
Smart grids could avoid blind decisions not matching the reality via real-time monitoring technology, and better coordinate the service demands of grid operators, different generation sources, and end-users, minimizing operating costs and environmental damages while maximizing system efficiency and flexibility [76; 77]. The storage of surplus energy and demand-driven dispatch across time scales will be essential aspects of grid intelligence; thus, the demand for battery devices capable of storing local energy or online energy will continuously expand. Given the appreciable development prospects of EVs, onboard batteries are expected to play a significant role in providing essential energy storage capabilities for grid intelligence [78].
# Inverse Design of Distributed Bragg Reflectors Using Deep Learning
## 3 Results and Discussion
### Tandem Model Performance
However, examining a number of spectra from the training and test datasets, it is apparent that there are many similarly shaped curves, often with shallow peaks and troughs. This brings into question the ability of the model to predict more diverse spectra. To simulate the model being used, a number of arbitrary curves were used as input spectra. These were Gaussian-shaped curves with a range of peak-heights, positions, and widths. The results of this were mixed, with some targets being met well, and others performing poorly. This is unsurprising, as the model is only able to predict within the constraints of the patterns observed in the training data, which in turn are constrained by material and structural properties. This meant that relatively shallow spectra, such as that shown in Figure 7, were predicted well with accuracies upwards of 85/90%, but that spectra including high-intensity narrow peaks had significantly lower accuracies. This was verified by making predictions on a number of shallow spectra only, where it was seen that the model was able to predict all targets to above 74% accuracy, with many above 94%, where the larger errors were a result of the low reflectance values as opposed to greater absolute errors. This strongly suggests that the observed limitations are due to the choice of material structure, rather than inaccuracies within the model. Exploring more complex material arrangements would likely increase the range of spectra that could be produced without needing to alter the core model architecture. There is also the potential to adjust the two materials used, where differences in refractive index could create different responses. Figure 6: Model output (solid lines) compared with TMM calculations using the model predictions (dashed lines) for two sample structures with material depths [35.6 52.7 36.9 22.1 46.1 72.0] and [47.1 25.3 23.8 22.6 51.8 68.4] nm, respectively. The strong similarities indicate good agreement between the analytical and numerical methods. The case should also be considered where a DBR spectrum is desired, for which there is no possible solution given photon absorption within the chosen materials. Training data are produced using the TMM, which takes n and k values into consideration, and therefore should account for the effect of absorption where applicable. When designing a DBR, training data should be provided that cover a range of wavelengths, including those which would not produce viable DBRs. This will prevent the model from inferring responses based on only a few training cases. It follows that if the user requests a spectrum that is outside the capabilities of the material in use, then the model will fail to produce a reflector design which performs satisfactorily. Whether a spectrum is satisfactory or not is up to the user, where this can be assessed by comparing the tandem model output to the target spectrum. Where a spectrum is not feasible, it can be assumed that an alternative material pair is needed.
# Characterization of Natural Gypsum Materials and Their Composites for Building Applications
## 3 Results and Discussion
### Thermal Properties  
The thermal properties of natural rocks and powders showed a significant difference between the materials sampled from the two Moroccan sites (Table 5). The difference in thermal conductivity of powders (about 30%) is probably due to their mineralogy; they exhibit a smaller difference in thermal conductivity when compared to rocks (about 20%). The porous nature of the materials and the water content affect specific heat, density, and thermal conductivity [29]. Lower values of thermal conductivity were obtained for the powder when compared to the rock, due to the porous structure of gypsums. In addition, there is a substantial relationship between thermal conductivity and density [30; 31]: thermal conductivity decreases when also density and water content decrease, with the lowest value obtained for the SG powder being (0.1254 W/mK). The thermal conductivities of the Moroccan gypsum samples were lower when compared to the ones from Tunisia-Meknassi gypsum plaster (0.22-0.27 W/mK) [6], especially for SG, which could be used as insulation material [32].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Gypsum** & \(\rho_{\rm{R}}\) [kg/m\({}^{3}\)] & \(\rho_{\rm{P}}\) [kg/m\({}^{3}\)] & **P [\%]** & **W\({}_{\rm{c},\rm{P}}\) [\%]** \\ \hline AG & 2384 & 2617 & 8.90 & 2.52 \\ SG & 2219 & 2437 & 8.94 & 2.98 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Density and porosity of Moroccan Agadir (AG) and Safi (SG) samples of rock and powder.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**\%** & **SO3** & **CaO** & **SiO2** & **MgO** & **Al2O3** & **SrO** & **Fe** & **K2O** & **Na2O** & **I** & **C** \\ \hline AG & 46.6 & 28.8 & 1.69 & 0.83 & 0.77 & 0.250 & 0.175 & 0.187 & 0.10 & 0.024 & 5.59 \\ SG & 48.9 & 29.8 & 0.446 & 0.446 & 0.086 & 0.128 & - & - & - & 0.0215 & 5.58 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Chemical composition of gypsum powders.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Gypsum** & \(\rho_{\rm{R}}\) [kg/m\({}^{3}\)] & \(\rho_{\rm{P}}\) [kg/m\({}^{3}\)] & **P [\%]** & **W\({}_{\rm{c},\rm{P}}\) [\%]** \\ \hline AG & 2384 & 2617 & 8.90 & 2.52 \\ SG & 2219 & 2437 & 8.94 & 2.98 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Thermal characteristics of AG and SG rock and powder samples.  
A difference between the two samples was also noticed in terms of thermal diffusivity (0.147 mm\({}^{2}\)/s and 0.123 mm\({}^{2}\)/s for AG and SG, respectively). This is an important result in terms of thermal insulation, since a good insulating material must not only have a low thermal conductivity, but also be able to delay the heat transfer.  
Thermal effusivity is also an interesting performance, describing the ability of a material to quickly dissipate heat from its surface as soon as ambient temperature decreases. AG gypsum has a higher thermal effusivity (460.8 Ws\({}^{1/2}\)/m\({}^{2}\)k) than SG gypsum (357.6 Ws\({}^{1/2}\)/m\({}^{2}\)k), so it is more suitable to retain heat for a longer time.
# Intelligent Deep-Q-Network-Based Energy Management for an Isolated Microgrid
## 2 Mathematical Models of the System Components  
This section describes the mathematical models of the system components, which are used to calculate their power generation and consumption. In this HRES, solar and wind energy are the primary energy resources. Short-term energy storage technologies have the ability to store and discharge energy for minutes or hours after being charged. In contrast, long-term energy storage can extend the storage time between charging and discharging to weeks or seasons [35]. In HRES, FCs can be used as a long-term energy storage option [4]. However, the slow dynamics of fuel cells and their degradation due to frequent start up and shut down cycles are a major disadvantage. Hence, batteries are also needed to create a hybrid system in which they take care of the power deficit and act as a short-term energy storage medium [36]. Batteries can provide or absorb large power gradients in short time. However, due to their short lifetime, high self-discharge rate, sensitivity to environment conditions, and limited storage capacity, batteries are not suitable for long-term solution.
# A Solution for Predicting the Timespan Needed for Grinding Roller Bearing Rings
## 2 Manufacturing Process for Bearing Rings
### Grinding Operations  
Presently, grinding processes are widely used industry [16]. Grinding is one of the most important and common processes [17] for the production of components and parts with high precision and quality [18]. Consequently, the accuracy of this process is very important in order to ensure the final quality of the product. Current data collection and inspection technologies allow for data to be collected online along the process chain, and can significantly increase quality control and can result in improvements in current dynamic and modifiable environments [19]. The real challenge facing companies is the problem of synthesizing highly heterogeneous data to gain an in-depth understanding of the correlations between the variables throughout the stages of a multi-stage system; the goal being the generation of zero defects at the process level and zero defects at the system level through the proactive control of the process.  
In a manufacturing process, different processing phases for grinding can be identified. These phases are performed successively considering the allowance material and the values of the technical parameters to be obtained after each phase. The main phases were synthetized as the rough grinding phase, the fine grinding phase [20], and the superfinishing grinding phase. In case of a bearing ring, the process phases are performed according to the designed role of the surface. For example, in the case of an inner ring, the last stage is fine grinding for the surface of the inner diameter. In the case of an outer ring, fine grinding is the last phase for the outer ring surface. Meanwhile, for both types of rings, in the case of raceway surfaces, the last stage is superfinishing due to the designed surface texture, which is defined in terms of surface roughness, waviness, lay, and flaws, and is concerned with the geometric irregularities of the surface [21] and profile. The grinding process is one of the most important processing methods widely used in the manufacture of bearings [22]. The control and the optimization of this process could have a great impact on the final results, such as the bearings' clearances, and subsequently on the function of the bearing as a part of a machine or other system. A multi-objective optimization method for reducing the grinding time and material removal rate by applying the genetic algorithm NSGA-II and the Pareto set was proposed in [22].  
A new evolutionary computation technique, namely particle swarm optimization, which optimizes grinding process parameters (such as wheel speed, workpiece speed, the depth of dressing, and the lead of dressing) and that will minimize the unit production cost and time with the finest possible surface finish, was developed by the authors of the study presented in [23].
# Non-Gaussian Pseudolinear Kalman Filtering-Based Target Motion Analysis with State Constraints
## 4 PL-MMSE Kalman Filter with Nonlinear State Constraints
### Linear Approximation  
Use the Taylor series to expand (39) at \(\hat{x}\) as  
\[h(x)-q =h(\hat{x})+h^{{}^{\prime}}(\hat{x})^{T}(x-\hat{x}+\frac{1}{2!}(x -\hat{x})^{T}h^{{}^{\prime\prime}}(\hat{x})(x-\hat{x})+\cdots-q\] \[=0 \tag{40}\]  
\begin{table}
\begin{tabular}{l} \hline \hline
1. Initialization \\ \(\hat{x}_{0}=\hat{x}_{0}\) \\ \(P_{0}=E\big{[}(x_{0}-\hat{x}_{0})(x_{0}-\hat{x}_{0})^{T}\big{]}\) \\ \hline
2. Predict \\ \(\hat{x}_{k|k-1}=F_{k-1}\hat{x}_{k-1}\) \\ \(P_{k|k-1}=F_{k-1}P_{k-1|k-1}F_{k-1}^{T}+DQ_{k}D^{T}\) \\ \hline
3. Filter Gain \\ \(K_{1}=\sum_{j=1}^{n}\lambda_{j}e^{\frac{-q^{2}}{2}}P_{k|k-1}(H_{k}^{T})^{T}\) \\ \(K_{2}=H_{k}P_{k|k-1}H_{k}^{T}+\sum_{i=1}^{n}\rho_{i}e^{\frac{-2q^{2}_{i-1}}{2} }[P_{k|k-1}(1,1)+P_{k|k-1}(2,2)]+R_{k}\) \\ \(K_{k}=K_{1}K_{2}^{-1}\) \\ \hline
4. Update \\ \(\hat{x}_{k}=\hat{x}_{k|k-1}+K_{k}(z_{k}-H_{k}\hat{x}_{k|k-1})\) \\ \(P_{k|k}=P_{k|k-1}-K_{k}K_{1}^{T}\) \\ \hline
5. When the linear constraint is \(Gx_{k}=g\), \\ \(\hat{x}_{k}=\hat{x}_{k|k}-W^{-1}G^{T}(GW^{-1}G^{T})^{-1}(G\hat{x}_{k|k}-g)\) \\ \hline \hline
6. \(k=k+1\), go to 2. \\ \hline \hline \end{tabular}
\end{table}
Table 2: PL-MMSE with Linear Constraints Algorithm.  
where \(h^{\prime}(\cdot)\) denotes the Jacobian matrix of \(h(\cdot)\) and \(h^{\prime\prime}(\cdot)\) is the Hessian matrix of \(h(\cdot)\). Using only the first-order term to approximate the nonlinear state constraint leads to  
\[h^{\prime}(\hat{x})^{T}x\approx q-h(\hat{x})+h^{\prime}(\hat{x})^{T}\hat{x}. \tag{41}\]  
Through observation, (41) has a similar structure with (24) where \(G\) of (24) is replaced with \(h^{\prime}(\hat{x})^{T}\) in (41) and \(g\) with \(g-h(\hat{x})+h^{\prime}(\hat{x})^{T}\hat{x}\). After applying the estimation projection method, the constrained estimator for the linear approximation method becomes  
\[\tilde{x}_{k}=\hat{x}_{k|k}-(h^{\prime}(\hat{x}_{k|k}))^{T}(h^{\prime}(\hat{x} _{k|k})(h^{\prime}(\hat{x}_{k|k}))^{T})^{-1}(h(\hat{x}_{k|k})-g). \tag{42}\]
## 3 Modeling of Backup Systems  
Incorporating backup systems into a HEMS is vital for ensuring a dependable and uninterrupted power supply. This study focuses on three primary backup systems: the HSS, BESS, and the EV. These systems support the main RESs, such as PV and WTs, by providing additional power during periods of low generation or high demand. The HSS stores surplus energy as hydrogen, which can later be transformed back into electricity, offering a long-term storage solution. The BESS provides immediate power balancing, crucial for addressing short-term fluctuations in energy supply and demand. The EV functions as both a mode of transportation and a mobile energy storage unit, charging during times of surplus energy and discharging during peak demand. Integrating these backup systems enhances the effectiveness and dependability of the HEMS, ensuring energy security and optimizing the use of RESs. This section details each backup system, covering their components, operational characteristics, and integration strategies within the HEMS.
## 2 Related Work
### Pre-Processed CSI Signals  
[PERSON] et al. [39] uses PCA and achieves more than 97% detection accuracy with a localization error of 1.22 m and 1.39 m in a lab and meeting room environment using SVM. WFID [40] also applies PCA to uniquely identify humans from groups of nine and six people, and achieves an accuracy of 91.9% and 93.1% respectively with SVM. R-DEHM [41] achieves 94% accuracy in detecting human movements with an average error rate of 8% for duration estimation by implementing a back propagation neural network (BPNN) algorithm on principal components of CSI. R-TTWD [42] applies the majority vote algorithm on PCA based features to identify the presence and absence of human movement using one class SVM with true positive >99%. FallDeFi [43], a fall detection system achieves an accuracy of 93% and close to 80% in a pre-trained and new environment using PCA and SVM. WiFind [44] uses PCA based Hilbert-Hung Transform (HHT) to extract features for detecting the fatigue level of drivers and achieves accuracy of 89.6% and 73.9% for a single driver and multi-passenger, respectively, with one class SVM. Danger-pose detection system [45], detects a danger pose in the bathroom environment with an average accuracy of 96.23% using SVM as an anomaly detection algorithm. BodyScan [46] computes PSD from PCA features to recognize activities and detect breathing rates with 72.5% and 97.4% accuracy, respectively.