esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion and Verification
### Fuji C-2
#### 6.2.1 Out-of-Plane Vibration  
As shown in Figure 7, ten mode shapes were obtained in the out-of-plane vibrations up to 12 kHz. We compared the experiment results and the FEM results. \"Manufacturer provided\" indicates the results of FEM calculations using the material constants provided by the manufacturer. However, not all of the parameters were available from the manufacturer, so we had to use parameters measured using RM to make up for the missing \(C_{13}^{E}\), \(\varepsilon_{31}\), \(\varepsilon_{33}\), and \(\varepsilon_{33}^{S}\) in the FEM calculations of the vibration characteristics. Comparisons of the out-of-plane mode shape revealed high accuracy in all modes except Mode 5, which could not be derived using the parameters provided by the manufacturer. The RM errors were all less than 3.765%, thereby indicating that the RM results were all highly accurate. In contrast, only the errors of the results from the parameters provided by the manufacturer in Modes 1, 2, 3 and 6 were less than 10%. The errors in the remaining modes were greater than 10%. The manufacturers provided their piezoelectric material constants by measured in standard specimens; however, the quality control in turns of mass production influenced the difference of material properties. We can thus confirm that this study successfully used RM to obtain reliable material constants for piezoelectric ceramic materials.  
Figure 7: _Cont._
## 2 Materials and Methods
### NEAM Model Coefficients  
To estimate adequate values for NEAM model coefficients, \(\Omega_{A},\Omega_{B},\ \Psi_{A},\Psi_{B}\), input data from a material is needed. For that purpose, sound absorption coefficient, measured at normal incidence, and material thickness, are the only parameters that are required to calculate these coefficients. The proposed NEAM method requires simplicity, making it an appealing alternative for practical application. As there is some similarity between Equations (3), (4), (18) and (19), an initial parameters guess can be based on those from the low-frequency approximation. Afterwards, the simulated absorption coefficient is compared through an iterative optimization algorithm to the measured value in order to fit the equivalent values. Considering that the equivalent coefficients have to describe a comprehensive physical phenomenon, they are constrained for a non-multi-evaluated performance, as shown in Figure 1. Consequently, a simple linear gradient descent method could be used to find the function error minimum which, in this case, is an absolute minimum.
## 2 Data and Method
### Data Processing
#### 2.4.4 Inversion Modeling Method  
Firstly, the band combination with the highest correlation coefficient will be selected to establish the Chla concentration inversion model in the Bohai Sea. The in situ samples are first randomly divided into a modeling group and a testing group [49]. The modeling group data were applied by linear, polynomial and exponential means to obtain curve fit for the above factors and the measured Chla concentration. Finally, the model was tested using in-situ data.  
All calculations were performed in the software ENVI and Python 3.7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**PMS/Gain** & **P1** & **P2** & **P3** & **P4** & **P5** \\ \hline
2,6,4,6,6 & 0.5395 & 1.0028 & 1.0418 & 0.8017 & 0.5655 \\
4,16,12,16,16 & 0.3327 & 0.3803 & 0.3863 & 0.3299 & 0.2343 \\
6,20,16,20,20 & 0.1752 & 0.3531 & 0.2750 & 0.2946 & 0.2038 \\
6,40,30,40,40 & 0.1735 & 0.1375 & 0.1308 & 0.1171 & 0.0818 \\
8,30,20,30,30 & 0.1288 & 0.1887 & 0.2030 & 0.1569 & 0.1084 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters for GF-4.
# Provision of Energy- and Wavelength-Efficient Traffic
## 4 Simulation and Performance Evaluation
### Analysis of Blocking Probability  
As the average wavelength utilization ratio improves, the blocking probability of traffic requests cannot be ignored. Figure 7 presents the blocking probability between the proposed TPTG_MA and the other three algorithms. It can be observed that, with the utilization ratio of wavelength capacity increasing, the blocking probability significantly increases. This is because the wavelength capacity is insufficient to serve the increasing traffic requests. As shown in Figure 7, the blocking probability of the proposed TPTG_MA is lower than that of other algorithms. When the AWNR is 40%, the TPTG_MA can achieve 12%, 9%, and 3% blocking probability reduction compared to the DLG, TPTG, and DLG_GA, respectively. This is owing to the effective principle of flow matching between traffic requests, sub-wavelengths, and wavelengths.
## 4 Experiment Results
### Quantitative Results Summary
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **BACC (\%)** & **SPEC (\%)** & **SENS (\%)** & **AUC** & **Testing Time (s)** \\ \hline AlexNet & \(96.8\pm 0.39\) & \(96.0\pm 1.50\) & \(94.1\pm 1.05\) & \(0.985\pm 0.002\) & 81.01 \\ ResNet-50 & \(96.5\pm 0.51\) & \(97.8\pm 0.34\) & \(95.3\pm 1.07\) & \(0.992\pm 0.003\) & 88.08 \\ DenseNet-201 & \(96.2\pm 0.43\) & \(9 and \(84.2\pm 2.31\) [\(p<0.05\)], respectively). The IMNet \(A\)-\(E\) produces a higher BACC than ResNet-50 and DenseNet-201 (\(87.7\pm 2.46\) [\(p=0.30\)], and \(87.6\pm 2.30\) [\(p=0.21\)]). The highest AUC of \(0.953\pm 0.018\) is achieved with IMNet \(A\)-\(E\) which is significantly higher than the best of benchmark methods, DenseNet-201, (\(0.931\pm 0.019\) [\(p<0.05\)]). Note that these IMNets outperform the large scale models in this application with far less computational cost and computational time. Results in Table 5 also indicate a modest but consistent boost in the performance as we add more SubNets during the IMNS process. Moreover, we compare our IMNets against different current state-of-the-art methods. For malaria application, our IMNet \(A\)-\(D\) has a comparative AUC score of 0.995 compared with the current state-of-the-art methods with lower computational complexity, including [PERSON] et al. (0.993) [63], [PERSON] et al. (0.993) [28], and [PERSON] et al. (0.991) [48]. For DR application, IMNet \(A\)-\(D\) and IMNet \(A\)-\(E\) produced a comparable AUC of 0.995 and relatively lower computational complexity with the following proposed methods, including [PERSON] et al. (0.991) [64], [PERSON] et al. (0.986) [38], and [PERSON] et al. (0.987) [65]. Finally, IMNet \(A\)-\(E\) has a comparative AUC score of 0.953 compared with the following state-of-the-art methods, including [PERSON] et al. (0.920) [66], [PERSON] et al. (0.850) [67], and [PERSON] et al. (0.926) [40]. Figures 10-12 show ROC curves for malaria, DR, and TB, respectively. The ROC curves provide further insight because they illustrate classifier performance for a range of operating points. For clarity, we only show ROC curves for the top five models in each application. For malaria detection, the IMNet \(A\)-\(D\) obtained the best result in terms of AUC and an area of \((0.995\pm 0.001)\). However, IMNet \(A\)-\(E\) obtained a competitive AUC score for both DR and TB with areas of \((0.996\pm 0.001)\) and \((0.949\pm 0.019)\), respectively. Figure 10: Malaria dataset ROC curve for the five best performing networks.
## 3 Optimization with Consideration of the Reliability Criteria
### Objective Functions and Constraints for Market Settlement with Reliability Criteria
#### 3.1.3. Mathematical Model of Coordination Considering the Reliability Constraints  
The limit on the output measured at the PCS was the maintained within the specified range, as stated in Table 3. Thus, by introducing a coefficient representing installation outside the structure, \(\epsilon\):  
\[0\leq PCC_{t}^{S}\leq PCC^{Smax},\quad\forall\;t \tag{30}\]  
\[PCC^{Smax}=PV_{k}^{cap}\cdot\epsilon \tag{31}\]In accordance with the new reliability constraints, the SoC of the BESS must be maintained within the specified range, as stated in Table 3. Thus, by introducing the coefficient representing installation outside the structure, \(\gamma^{o}\):  
\[SoC_{wb}^{min}\leq SoC_{wb,t}\leq SoC_{wb}^{max}\cdot\gamma^{o},\quad\forall\;wb,t \tag{32}\]  
\[SoC_{sb}^{min}\leq SoC_{sb,t}\leq SoC_{sb}^{max}\cdot\gamma^{o},\quad\forall\; sb,t \tag{33}\]
# Frequency Selective Surfaces: A Review
## 5 Future Challenges and Potential Applications of FSSs
Coating of FSS is to be etched carefully to reduce the degradation of thermal insulation and to maintain the aesthetic feature of the window glass. The possibility of getting a strong resonating FSS and perfect transmission response is a challenge due to oxide that is present in the coated layer; however, recently improved performances are reported by careful designing. 9. Active FSS issues  
Active FSSs solve the problem of permanent constituent characteristics in the performance of conventional FSS and potentially control the EM response by dynamically tuning through external excitations. Various issues that are associated with active FSSs require the focus of the designers in this area. Occasionally, degradation of FSS performance is observed which occurs from signal interference by a large-sized biasing network. The fabrication difficulty and increased cost are additional issues linked to such structures. Power consumption has been a major problem in active FSS due to high number of active devices. The number of active elements may considerably reduce by using an active high impedance surface (HIS), on the other hand, a complex structure is formed. 10. FSS for smart cities applications  
Nowadays, wireless mobile communication takes place mostly in the 6 GHz and below frequency bands. A significant issue occurs in the Wifi band with the dense working environment, such as conference venues and Condo buildings. Besides, smart city applications require access at millimeter wave frequencies higher than 24 GHz. Practical challenges for such higher band are poor EM wave propagation through the objects and structure materials. The FSS placed on and inside buildings are specifically designed to improve the reception and transmission of radio frequency signals. Still, smart cities applications require managing these propagation and interference issues to properly perform at these low and higher bands of frequencies. 11. Problem of Transmission losses  
FSS structures have been designed to exhibit lossless transmission response, such as in radome design; nonetheless, they show high transmission losses in real scenarios. Although fundamental theoretical analysis suggests these to be only a fraction, in measurement these losses are quite high on a low-loss substrate. Many sources may account for such losses e.g., Ohmic-losses, dielectric heating losses, radiation losses, and separating all of them is not easy. 12. Future Trends of FSS  
On the basis of current trends, future developments of FSS in coming years can be predicted, since it is obvious that much progressive research is going on. Potential applications of FSS include, but are not limited to:
# Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera
## 2 Methodology
### Semantic Segmentation Architecture  
Various deep learning architectures have been developed by previous researchers to solve different tasks such as image classification, object detection, and semantic segmentation. A semantic segmentation network classifies every single pixel in an image that leads to an image that is segmented by class, which can also be seen as a dense-prediction task. To achieve its goal, semantic segmentation generates a segmentation map as the output that has the same size as the input. Semantic segmentation architecture generally consists of the encoder-decoder network. The encoder usually uses pre-trained classification networks that distinguish classes by performing downsampling, while the decoder performs the upsampling operation for learning dense prediction. To illustrate the training procedure, this paper used two different types of convolutional neural network (CNN) that were designed for semantic segmentation tasks, namely DeepLabv3+ [59] and SegNet [60].
# Sentinel-1 and -2 Based near Real Time Inland Excess Water Mapping for Optimized Water Management
## 2 Materials and Methods
### Data
#### 2.2.3 Auxiliary Data  
The satellite data-based algorithm requires multiple mask files to identify and evaluate inland excess water inundations. Permanent water bodies in the study area, like lakes and rivers, were extracted and stored in a training mask file (reference areas). Based on this mask, reference statistics were calculated that were used by the three algorithms of the workflow to identify pixels with water. The mask was initially derived from vector maps stored in the hydro-geographic database of the General Directorate of Water Management. These maps were manually updated using very high resolution satellite images stored in Google Earth. Only open water was digitized, while avoiding vegetation along the shores and shadows. In total, 1400 ha of training pixels was extracted evenly spread over the study area.  
The presented algorithm only assesses areas where temporary inundations can develop; therefore, built-up areas, anthropogenic land cover like large roads, railroads and large buildings were excluded, using buffers of respectively 10 and 20 m. Also, permanent water bodies were not considered as IEW. This way only areas with nature, agriculture and forest were considered as potential IEW areas. Based on these criteria, areas were extracted from the National High Resolution Layer (nHRL) with a 10 m spatial resolution created by the Lechner Knowledge Center. Furthermore, large-scale vector maps stored in the hydro-geographic database of the General Directorate of Water Management were extracted and used to update the potential IEW areas. In this way, 12% of the study area was excluded for evaluation as IEW area.
# Research Progress of Visual Inspection Technology of Steel Products--A Review
## 6 Analysis
### Literature Analysis  
From the review of detection methods, we can see that a large number of publications over the past 30 years have been related to statistics and filtering methods, as shown in Figure 9.  
From the point of development trends, both statistical methods and filtering methods have shown a significant downward trend since 2010, while the discussion of learning-based methods has steadily improved. This has much to do with the upsurge of deep learning in recent years. Model-based detection methods have always been out of the mainstream.  
A detailed analysis of filtering methods is provided in Figure 10, because these methods have attracted much attention. From the 36 papers collected (relating to space filtering and frequency domains), it can be seen that although the best method of defect detection cannot be determined, it is clear that the joint spatial/frequency analysis methods (i.e., Gabor transform, wavelet transform, and MGA) have increased since 2010, which shows that these methods have been increasingly recognized by a majority of researchers.  
Figure 10: Distribution of filtering detection methods.  
Figure 9: Distribution map detection method.  
In terms of classification methods, supervised classification methods have always dominated compared to unsupervised classification methods. As the knowledge set of defect models is imperfect, the supervised classification method is preferred if prior knowledge is available, since this method can achieve superior results.  
Support vector machines and neural networks based on back propagation (NN-BP) are the mainstream supervised classification methods.  
It can be seen from the Figure 11 that the NN-BP method was a classifier that was commonly discussed in the literature prior to 2010, and that the frequency of discussion of SVMs has increased sharply since 2010.
## 2 Materials and Methods Background
### RES in Conservation Areas  
Buildings under protection and buildings perceived to be of cultural value are specific to the given area and historical period in terms of form. They are legally protected from changes related to their location (urban planning), materials, technologies used during construction (historic structures), and visual look (roofscape).  
Therefore, for the RES design, it is necessary to anticipate not only the lifetime of the equipment or the return on investment but also the potential impacts on the surrounding development and the character of the building and locality. Then, the criteria for the RES integration are mainly related to the aesthetic appearance and possibilities of the territory. Solar energy has great potential.  
For applications in buildings with a cultural value, an integrated installation that fully replaces the structure (e.g., BIPV as roofing and, at the same time, PV) does not damage the structure of the building and does not distort the external shape and structure of the construction is an ideal case. In foreign publications, visibility assessment from starting points (e.g., public spaces, scenic points, panoramas, etc.) is mentioned. BIPV includes all photovoltaic application technologies, particularly thin-film technologies such as CdTe, CIGSe, a-Si te- (a-Si/ uc-Si), and organic PV technologies [58].  
Integrated panels are available in more colours than conventional photovoltaic panels. The panel coloring is problematic in terms of color fastness and application to the layers, but it provides the desirable variety. Coloring affects the reflectance of sunlight and, therefore, the overall performance of the BIPV. Examples of colored BIPV include monocrystalline solar panels in the HJT (HIT) or PERC production variant made by SOLAXESS. Conventional monocrystalline panels are complemented by a colored layer and a top protective ETFE film. In addition, the coloring inevitably affects their performance (see Figure 14) [59].  
In the Czech Republic, systems of (non-)connection to a power grid are used generally:  
* Off-grid-so-called island PV systems. The inverter does not supply power to the grid; it is isolated from the public grid. Consumption of self-generated energy is preferred. In case of a power shortage and connection to the grid, it is possible to switch to mains consumption and recharge the batteries. When wiring, appropriate appliances operating under DC current or having a voltage converter (inverter) installed are advisable to choose [59].
* On-grid. The system is connected to the grid and supplies electricity to the public grid.
* Hybrid. This allows, for example, a zero overflow setting and energy storage in batteries.
# Design and Development of a Management System for Energy Microgrids Using Linear Programming
## 2 Materials and Methods
### Design of Scenarios  
The experiments carried out with the model seek to generate information to validate the model's sensitivity and to understand the impact that some selected parameters have on different management scenarios. For this, the following four research questions are formulated:  
* How sensitive is the model to changes in demand? (Scenario 1)
* How sensitive is the model to changes in the availability of resources? (Scenario 2)
* What impact do the technical characteristics of the battery have? (Scenario 3)
* What is the impact of the penalty on the unserved demand? (Scenario 4)  
For each of these questions, scenarios with the parameters of interest (called factors) will be identified and defined, and different levels will be experimented with.  
Table 1 describes the four scenarios in terms of the parameters of interest in each case and the values that they will take. The experiments of each scenario correspond to a complete factorial design of the different levels for each factor.  
The first scenario seeks to show the capacity of the model to properly manage the changes on demand under different conditions of availability of renewable resources. Therefore, it considers the actual demand, an increase of 25% and a decrease of 25% in the hourly demand, and a special case in which only the interval with the highest demand is increased by 25%. Additionally, the percentage of renewable energy generation varies from 0% to 30% with steps of 5%. The remaining factors are set to their default value according to the recommendations of experts in the field of study. The second scenario explores the model's sensitivity to changes in the availability of renewable energy resources. This is achieved by keeping demand and other parameters constant and making variations in the percentage of renewable energy generation of 5%, starting from 0% and reaching 30%. The third scenario evaluates the impact of the technical characteristics of the battery in meeting the demand and the performance of the microgrid. For this, the demand is left constant, a level of renewable resource capacity is selected where the use of batteries is evidenced, and different levels of the parameters \(U_{B},eb_{max},g_{B}^{max}\), and \(L_{max}\)\(L_{min}\) are tested. Finally, in the fourth scenario, the effect of the penalty to the unmet demand is studied. In this case, different levels of penalty cost are tested for the unmet demand to check the behavior of the latter as said cost increases. These different test values are based on percentages according to the cost of the most expensive generation source, generally diesel generation. In this way, it is possible to know how much the cost of unattended energy should be raised so that the management model minimizes the unmet demand.
## 2 Methods
### Feature Extraction Methods
#### 2.3.1 Configuration of Models Which Used LSTMs  
Proper configuration of ML and DL models requires experiments and studies. In order to configure model parameters in this study, we based them on past research [63] and our experience in the field.  
When LSTMs were used to obtain entity-level feature vectors from embeddings that corresponded to sub-entities, we were able to adopt the following LSTM hyperparameters: the LSTM analyzed the sequence of embeddings from the beginning of the tweet until its end and then in the opposite direction (bidirectional) = true, number of LSTM layers = 2, and size of embedding corresponding to the tweet created by the LSTM (hidden size) = 512. To train the LSTMs, the following parameters were used: initial learning rate = 0.1, minimal learning rate = 0.002, factor at which the learning rate was decreased after training with the given learning rate was ended (anneal factor) = 0.5, and number of epochs without improving the loss on the validation set for which training was continued with the given learning rate (patience) = 20. In addition, the data instances were shuffled before each training epoch. Other parameters were set to the default values proposed in the Flair module.
# Compensation of Measurement Uncertainty in a Remote Fetal Monitor
## 3 Uncertainty Estimation Approach
### System Model
#### 3.1.1 Device Model  
To describe the dynamic behavior from a set of input and output measurements, an auto-regressive and exogenous (ARX) (23) (p. 81) linear model structure is assumed. A scheme for system modeling is shown in Figure 2.  
The ARX structure model is  
\[y_{k}=\phi_{k}^{T}\theta+\xi_{k}, \tag{15}\]  
\(\phi_{k}=[y_{k-1},\ \cdots,\ y_{k-l},\ u_{k},\ \cdots,u_{k-l}]^{T}\) is the regression vector, \(\theta=[a_{1},\cdots,a_{l},b_{0},\cdots,b_{l}]^{T}\) is the parameter vector, while \(l\) is the system degree. Based on Equation (15), the relationship between the input and the output in a compact way can be written in the following vector form.  
\[Y=\Phi\theta+\Xi, \tag{16}\]  
where \(Y=[y_{1},y_{2},\cdots,y_{n}]^{T},\Phi=\left[\Phi_{1}^{T},\Phi_{2}^{T},\cdots, \Phi_{n}^{T}\right]^{T}\), and \(\Xi=[\xi_{1},\xi_{2},\cdots,\xi_{n}]^{T}\).  
With this information and applying the least-squares method (24) (pp. 17-27), a deterministic model is obtained which represents the fetal monitor dynamic, the solution is  
\[\hat{\theta}=\left[\Phi^{T}\Phi\right]^{-1}\Phi^{T}Y. \tag{17}\]  
Finally, the relationship between white noise \(\xi_{k}\) in the system input and additive noise \(r_{k}\) in the system output for the ARX model structure is  
\[\mathrm{R}(z)=\frac{1}{1-A(z)}\Xi(z), \tag{18}\]  
where \(A(z)=\hat{a}_{1}z^{-1}+\cdots+\hat{a}_{l}z^{-l}\).