esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Risk Assessments  
The modelled parameters of the Ulsan faults were again employed for quantitative risk scenarios on the industrial parks centered around UPC based on two different approaches, i.e., Coulomb Failure Function (CFF) estimation and PSHA methods. At first, to quantitatively evaluate the future fault failure risk in the target zones, we modelled the coulomb stress on the lineament for the Ulsan central city area and for the industrial parks (Figure 10). Since the stress during earthquake is transferred by propagation along surrounding faults and segments [(70; 71)] as well as regional dispatch, CFF estimation on the lineament close to the target area implies the risks of the time of future potential earthquakes. From our observations, we now know there is a discontinuity between the Ulsan fault units and the deformation units of the central city area, which may represent the boundary of sedimentary plains. However, we also propose the existences of a second boundary between sedimentary plains and reclaimed grounds where the industrial parks are mostly located. We defined two lineaments (L1 and L2, represented as boundaries 1 and 2 in Figure 10) along with those boundaries between Unit 2 and 4 and calculated the CFF employing the modelled Ulsan fault line parameters. As demonstrated in Figure 10, it was found that many of the heavy industrial parks, and especially UPC, are in highly stressed areas. It should be noted the locations of chemical agent leakages for 2017-2018 are along with the western discontinuity, L1. Although this observation cannot be taken as direct evidence of the leakages being induced by surface deformation, we can infer that the eastern side of UPC most likely belongs to a unit that is susceptible to tectonic deformation and consequent industrial disaster at the time of earthquakes. Even considering the design margins of pipelines and facilities, such stress fields could result in dangerous situations upon the occurrence of even relatively insignificant earthquakes. In addition, half the total industrial park area has suffered 1-cm level surface deformation in both the horizontal and vertical directions, perhaps due to migration of the Ulsan fault. According to the classification of fault activation by [PERSON] [(72)], such a deformation rate corresponds to the A class (10-1 m/Ka), which can cause major earthquake damage; thus, it is an area of high risk for future potential seismic activities. It is not easy task to estimate the magnitude and consequent damage of future earthquakes as there remains ongoing debate about the relationship among slip rates, dimensions of fault lines, and magnitudes of potential earthquakes [(73)]. Because the locations of potential seismic sources are not localized
## 4 Results and Discussions  
The RVS methodologies based on FEMA P-154, Indian, and Turkish RVS methods have been implemented using the post-earthquake data and then the results were compared with the actual damage occurred to the structures during the Bingol earthquake. MCDM methods can be validated for multiple earthquake datasets, as each dataset may have variations in the actual damage state of structures. Thus, discovering appropriate MCDM methods and achieving accurate results can help display the damage index. The pie chart in Figure 2 demonstrates the post-earthquake data have divided into 5 proportions according to the actual state of damage. The collected data had been written in terms of words, so to make it easier for interpretation, they have been converted to the numeric form described in the previous section.  
Results achieved from various RVS methods have a different scale than the actual damage scale of the data that ranges between 1 to 5. Further, for comparing the results from RVS methodologies to the actual damage, the scale has been converted into 1-5.  
Among MCDM methods, a better approach would preferred as per the requirement of the user. In this paper, these requirements were called similarities according to [PERSON] et al. [40], where similarity 1 shows the number of buildings indicating exact damage of RVS methodologies to the actual damage. Similarity 2, illustrates the number of buildings through indicating exact damage with the actual damage and number of building with the one-step severe damage. Similarity 3, presents exact damage with the actual damage and number of building with the one-step lighter damage. Whereas, Similarity 4, express the number of buildings in RVS methodologies with exact damage and buildings with any severe damage state. The last similarity indicates the number of buildings in similarity 4 and the number of buildings with one step lighter damage. More detailed information and description of the similarities can be find by [PERSON] et al. [40].  
In conclusion, similarity 1 is quite identical, and in real scenarios, results may not always be so identical. In the case of a detailed analysis, the priority is safety, then similarity 4 is considered irrespective of time and cost. Further, similarity 2 and 3 gives the damage pretty close to the actual damage. Similarity 5 is performed for the structure that is out of range or slightly affected by the real loss. In general, it becomes riskier when an inappropriate method has been selected for an improper grade. For example, a highly damaged structure has been estimated as slight damage. Therefore,  
Figure 2: Each damage state categorised for the selected earthquake data.  
the method with the Turkish code would be more preferable among three RVS methodologies and the one performed with similarities, as Turkish RVS give closer results with the Indian RVS.  
The ranking should be specified for all parameters, which are required to perform MCDM methods as shown in Table 15[39]. The average rank resulting from three RVS methods are considered to determine the final ranking.
## 3 Performance Analysis of SR-ODBR-DCSK System
### System Energy Efficiency Analysis  
In the SR-ODBR-DCSK system, the length of the reference signal is \(R\), and the length of the information signal is \(\beta\) in the current symbol cycle. Assuming that the number of bits sent by each symbol is \(S+1\), \(E_{\text{ref}}\) and \(E_{\text{data}}\) are used to represent the reference energy of each symbol and the symbol energy of each symbol, respectively. Then the symbol energy of the transmitted signal is:  
\[E_{\text{$S-\text{SR-ODBR-DCSK}$}}=E_{\text{ref}}+E_{\text{data}} \tag{10}\]  
In the SR-ODBR-DCSK system, since the two groups of chaotic signals are generated by the Walsh function in one cycle, the semi-periodic chaotic signals are mutually inverse. Because of special orthogonal relation, the energy of the reference signal and data signal can be calculated, respectively.  
\[E_{\text{ref}}=\sum\limits_{i=0}^{R-1}x_{i,k}^{2} \tag{11}\]  
\[\begin{array}{l}E_{\text{data}}=\sum\limits_{i=1}^{R-1}\sum\limits_{p=1}^{P }\left(\frac{1}{\sqrt{2}}b_{2k}x_{i,p,k}+\frac{1}{\sqrt{2}}b_{2k+1}x_{i,p,k}^ {\prime}\right)^{2}\\ =P\sum\limits_{i=0}^{R-1}x_{i,k}^{2}\end{array} \tag{12}\]  
Among them, \(P=\frac{\beta}{R}\).  
The bit energy of the system in a symbol period is  
\[E_{b}=\frac{E_{\text{$S-\text{SR-ODBR-DCSK}$}}}{S+1}=\frac{((1+P)}{(1+S)}\sum \limits_{i=0}^{R-1}x_{i,k}^{2} \tag{13}\]  
According to the definition of data-energy-to-bit-energy ratio (DBR), the DBR of the SR-ODBR-DCSK system can be calculated as  
\[\text{DBR}_{\text{SR-ODBR-DCSK}}=\frac{E_{\text{data}}}{E_{\text{$B-\text{ SR-ODBR-DCSK}$}}}=\frac{P((1+S)}{1+P} \tag{14}\]  
Next, the energy efficiency of the DCSK system is analyzed. The energy of the transmitted signal of the DCSK system is  
\[E_{\text{$S-\text{DCSK}$}}=E_{\text{ref}}+E_{\text{data}}=2\sum\limits_{i=0} ^{\beta-1}x_{i,k}^{2} \tag{15}\]The bit energy of the system in a symbol period is  
\[E_{b-\text{DCSK}}=\frac{E_{s-\text{DCSK}}}{s+1}=\frac{2}{s+1}x_{i-k}^{2} \tag{16}\]  
The DBR of the DCSK system can be calculated as  
\[\text{DBR}_{\text{DCSK}}=\frac{E_{\text{data}}}{E_{b-\text{DCSK}}}=\frac{1}{2}(s +1) \tag{17}\]  
It can be seen from Equations (14) and (17) that when \(P\) is greater than 1, the energy efficiency of the SR-ODBR-DCSK system is better than that of the DCSK system.
## 4 Results
### Precision of the Navigation Solutions
#### 4.4.2 Tests Results Using Weak Galileo E1b/c Signals  
The accuracy of the navigation solutions was evaluated using weak signals. These measurements yielded variable outcomes. The test results shown below are an example of the performance that could be achieved.  
The \(C/N_{0}\) of the received Galileo E1b/c signals was decreased to the levels shown in Table 21 using the variable attenuator. The receiver processed the received signals and obtained the navigation solutions using four observations. The 2D precision results are presented in Table 22, and the 3D precision results are provided in Table 23.  
The precision of navigation solutions can significantly decrease when tracking weak GNSS signals, and the quality of these solutions becomes highly variable, while the results in Tables 22 and 23 are achievable, errors can increase depending on satellite geometry and signal strength. Implementing algorithms to enhance the accuracy of PVT solution when tracking severely degraded signals is a focus for future work.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Measure** & **Results [m]** &
\begin{tabular}{c} **Confidence Region** \\ **Probability** \\ \end{tabular} \\ \hline
3D 99\% SAS & 18.0 & 99\% \\
3D 90\% SAS & 13.4 & 90\% \\
3D MRSE & 11.4 & 61\% \\
3D SEP & 8.9 & 50\% \\ \hline \hline \end{tabular}
\end{table}
Table 20: The 3D precision results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Satellite** & **C/N0** **in dB-Hz** \\ \hline E3 & 20 dB-Hz \\ E8 & 23 dB-Hz \\ E13 & 22 dB-Hz \\ E15 & 20 dB-Hz \\ \hline \hline \end{tabular}
\end{table}
Table 21: Galileo satellites tracked and \(C/N_{0}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Measure** & **Results [m]** &
\begin{tabular}{c} **Confidence Region** \\ **Probability** \\ \end{tabular} \\ \hline
3D 99\% SAS & 18.0 & 99\% \\
3D 90\% SAS & 13.4 & 90\% \\
3D MRSE & 11.4 & 61\% \\
3D SEP & 8.9 & 50\% \\ \hline \hline \end{tabular}
\end{table}
Table 23: The 3D precision results using weak Galileo E1b/c signals.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Measure** & **Results [m]** &
\begin{tabular}{c} **Confidence Region** \\ **Probability** \\ \end{tabular} \\ \hline
3D 99\% SAS & 18.0 & 99\% \\
3D 90\% SAS & 13.4 & 90\% \\
3D MRSE & 11.4 & 61\% \\
3D SEP & 8.9 & 50\% \\ \hline \hline \end{tabular}
\end{table}
Table 20: The 3D precision results.
## 2 Definition and Methods of Analysis
### SA-AS-TRP Algorithm
```
1: Initialize the reference angular steps \(\Delta\theta_{\text{ref}}\) and \(\Delta\phi_{\text{ref}}\) according to the entire device size and the highest frequency of testing;
2: Initialize a vector with the sampling steps of interest, \(\Delta\mathbf{\Phi}_{1\times N}=[\Delta\phi_{1},\Delta\phi_{2},...,\Delta\phi_{n}]\), where \(N\) is the number of sampling steps;
3: Initialize a random vector of HPBWs with a size of \(1\times 10,000\), where the ranges of values for HPBWs are \([10^{\circ},50^{\circ}]\) and \([2^{\circ},10^{\circ}]\);
4:for\(n=1\) to \(N\)do
5: Sampling step: \(\Delta\phi=\Delta\phi_{n}\), \(\Delta\theta=\Delta\phi/2\);
6:for\(i=1\) to 10,000 do
7: HPBW = HPBWs(\(i\));
8: Calculate \(\text{TRP}_{\text{target}}(\text{HPBW},\Delta\phi_{\text{ref}},\Delta\phi_{ \text{ref}})\);
9: Calculate \(\Delta\text{TRP}_{\text{grid}}(\text{HPBW},\Delta\theta,\Delta\phi)\), \(\Delta\text{TRP}_{\text{two-cut}}(\text{HPBW},\Delta\theta,\Delta\phi)\), \(\Delta\text{TRP}_{\text{three-cut}}(\text{HPBW},\Delta\theta,\Delta\phi)\);
10:endfor
11: Calculate the STD of 10,000 values for each of TRP method to obtain the STD of \(\Delta\text{TRP}_{\text{grid}}(\Delta\theta,\Delta\phi)\), \(\Delta\text{TRP}_{\text{two-cut}}(\Delta\theta,\Delta\phi)\), and \(\Delta\text{TRP}_{\text{three-cut}}(\Delta\theta,\Delta\phi)\);
12:endfor
13: Determine the appropriate sampling step according to the STD of the full spherical grid method;
14: Output the optimal sampling steps \(\Delta\theta\) and \(\Delta\phi\). ```  
**Algorithm 1**Statistical analysis algorithm for the effect of angular sampling on the TRP (SA-AS-TRP)
# Optimization of DC Energy Storage in Tokamak Poloidal Coils
## 3 Need for Energy Storage in Tokamak PF Coils
### A Specific Use of the Ragone Plot  
However, the Ragone plot is often misinterpreted. To introduce the analysis well, it is important to stress that an energy storage system can work also outside its zone, but, in this case, it would be oversized either in terms of power or in terms of energy. Nevertheless, it could be suitable in terms of the number of cycles, costs, or other aspects.  
The duration to be considered is not only that of the scenario, but a shorter equivalent value could be adopted due to several factors. The most relevant is the type of load (dissipative or inductive) and the capability to recover the energy from the load and from the charger by supporting a huge number of fast charge/discharge cycles [43]. In this case, the equivalent duration is approximately the time between two positive power peaks.  
On the other hand, the SC voltage cannot be considered constant during the operations, affecting both the coordinates of the Ragone plot. This always must be included in the optimization, even if it complicates the calculations due to the variable voltage requirements (see Figure 2).  
Therefore, while the scenario is nominally 100 s for the DTT plasma and about 160 s for the DTT PF coils, the maximum interval between positive power peaks is in the interval \(100\div 140\) s in Figure 3, resulting in about 40 s. An input charger with a rating of a few megawatts can further reduce this interval by contributing to the power required by the scenario and thus limiting the SC voltage discharge.  
For these reasons, the use of DC storage and SCs would be appropriate for tokamak PF systems. Another paper will develop this discussion and will extend it also for TF, CS, VS, RC, DIV, EFC, and ELM coils and their operations.
## 1 Introduction  
Supply chain management (SCM) is used to plan operations and design SC systems able to meet customer demand within the due date and at the lowest cost. Such a target can be achieved with an efficient SC management that attempts to avoid errors and measure uncertainties, as well as through the timely and precise monitoring of the inventory at the warehouses [1].  
In this context, the value chain pattern [2] allows for the optimization of the SC stages by focusing on the benefits for customers and on certain added-value processes that they are willing to pay for. Nevertheless, value is subjectively perceived and dependent on context [3]. First, products or services may have not the same values worldwide, and furthermore, such values are not constant over time. Secondly, value occurs when needs are met through the provision of products, resources, or services. This is reasonably true for some agri-food products (e.g., fresh fruits, dairy) featured by low added value from suppliers' perspectives.  
This study evaluated the problem of SC management in the fresh chestnuts industry with a twofold objective. The first was to explore the drivers of choice for the adoption of RFID technologies in food SC management and how these can be applied toward an enhanced control of chestnut storage/distribution operations. The second objective was to appraise the practical impact of this technology on SC management within the food supply chains. With regard to the undertaken methodology, the framework adopted in the study is the VCOR model, used to unpack the potential application of RFID within this model.  
The paper is organised as follows. Section 2 presents a review of the literature focused on the value chain and RFID in SC, while in Section 3, research questions are formulated. Section 4 describes the chestnuts supply chain of the case study and its configuration.  
Section 5 relates technical details of RFID deployments, while in Section 6, the operations observed are detailed. Section 7 presents the results of the study, while Section 8 discusses the managerial implications and concludes the work.
###### Abstract  
Preoperative, non-invasive, and accurate identification of the pathological subtypes of pulmonary ground glass nodules (GGNs) play an important role in the precise selection of clinical surgical operations and individualized treatment plans. Efforts have been made for the classification of pathological subtypes of GGNs, but most existing methods focus on benign or malignant diagnosis of GGNs by means of a one-time computed tomography image (CTI), which fails to capture the nodule development based on follow-up CTI. In this paper, a novel method for subtype classification based on follow-up CTIs is presented as a viable option to the existing one-time CTI-based approach. A total of 383 follow-up CTIs with GGNs from 146 patients was collected and retrospectively labeled via posterior surgical pathology. Feature extraction is performed individually to the follow-up CTIs. The extracted feature differences were represented as a vector, which was then used to construct a set of vectors for all the patients. Finally, a subspace K-nearest neighbor classifier was built to predict the pathological subtypes of GGNs. Experimental validation confirmed the efficacy of the new method over the existing method. Results showed that the accuracy of the new method could reach 72.5%, while the existing methods had an upper bound of 67.5% accuracy. Subsequent three-category comparison experiments were also performed to demonstrate that the new method could increase the accuracy up to 21.33% compared to the existing methods that use one-time CTI.  
radiomics; subtypes classification; ground glass nodules +
Footnote †: journal: Applied  
10.3390/app122010587
###### Abstract  
Grassland ecosystems are increasingly threatened by pressures from climate change and intensified human activity, especially in the arid region of Central Asia. A comprehensive understanding of the ecological environment changes is crucial for humans to implement environmental protection measures to adapt to climate change and alleviate the contradiction between humans and land. In this study, fractional vegetation coverage (FVC), leaf area index (LAI), gross primary productivity of vegetation (GPP), land surface temperature (LST), and wetness (WET) were retrieved from Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite remote sensing products in 2008 and 2018. Principal component analysis (PCA) was used to establish the MODIS data-based ecological index (MODEL) in the study area, and the spatial differentiation characteristics and driving mechanism of ecological quality in the last ten years were explored. The results showed that: (1) FVC, GPP, LAI, and WET had positive effects on the ecological environment, while LST had a negative impact on the ecological environment. FVC and GPP were more significant than other indicators. (2) The MODEL showed a spatial pattern of \"excellent in the north and poor in the south\" and changed from north to south in the study area. (3) From 2008 to 2018, the average MODEL of Fuyun County increased from 0.292 to 0.303, indicating that the ecological quality in Fuyun County became better overall. The improved areas were mainly located in the summer pastures at higher elevations. In comparison, the deteriorated areas were concentrated in the spring and autumn pastures and winter pastures at lower elevations. The areas where the ecological environment had obviously improved and degraded were distributed along the banks of the Irtysh River and the Ulungur River. (4) With the increase in precipitation and the decrease in grazing pressure, the MODEL of summer pasture was improved. The deterioration of ecological environment quality in spring and autumn pastures and winter pastures was related to the excessive grazing pressure. The more significant changes in the MODEL on both sides of the river were associated with implementing the herdsmen settlement project. On the one hand, the implementation of newly settled villages increased the area of construction land on both sides of the river, which led to the deterioration of ecological quality; on the other hand, due to the increase in cropland land and the planting of artificial grasses along the river, the ecological quality was improved. The study offers significant information for managers to make more targeted ecological restoration efforts in ecologically fragile areas.  
ecological quality; mountain-basin system; MODIS data products; spatio-temporal changes; driving mechanism +
Footnote †: journal: International Journal of and Public Health
## 2 Materials and Methods
### Loss Function  
When aiming to train a convolutional neural network model that accurately captures the extent of divergence between the model's predictions and the factual data, the loss function proves to be of utmost significance. It is formulated as a function of the prediction segmentation error. This error metric has the ability to be backpropagated to the preceding layers of the neural network. Through this backpropagation process, it enables the update and optimization of the weights within the network, thereby facilitating the model's learning and improvement. The Cross-Entropy Loss (CEL) acts as a highly useful tool for assessing the performance of classification or segmentation models. It is designed to quantitatively measure the disparity between the real probability distribution state of the data and the probability distribution predicted by the model. By providing a numerical value that represents this difference, this function evaluates each pixel by considering the output of the softmax layer as a pixel classification problem; the lower the value of cross-entropy, the better the model prediction. For the binary classification problem, the cross-entropy loss function can be expressed as a binary cross-entropy (BCE) loss function as follows:  
\[Loss_{BCE}(T,P)=-\frac{1}{N}\sum_{n=1}^{N}[T_{n}\cdot\log(P_{n})+(1-T_{n})\cdot \log(1-P_{n})]. \tag{1}\]  
Previously, the Dice Coefficient (DC) was mainly applied as a metric for assessing the quality of segmentation results. In recent times, it has demonstrated remarkable performance when utilized as a loss function throughout the training of models. \(T_{n}\) represents the true label of the n-th sample. \(P_{n}\) represents the probability value that the model predicts the n-th sample as the positive class. DC measures the extent of overlap  
Figure 4: Position attention module.  
between the labeled image and the predicted image within the range [0, 1]; when a DC value reaches 1, it signifies a complete overlap. DC can be defined as follows:  
\[DC(T,P)=2.\frac{\sum_{n=1}^{N}(T_{n}\times P_{n})}{\sum_{n=1}^{N}(T_{n}+P_{n})}, \tag{2}\]  
\[Loss_{DC}(T,P)=1-DC(T,P). \tag{3}\]  
In this paper, a combination of cross-entropy loss function and DC is employed for the training of the network model in order to facilitate the solution of the sample imbalance problem and prevent the model from overfitting.