esa-sceva/satcom-chunk-collection huggingface dataset:
# The Role of Data-Driven Methodologies in Weather Index Insurance
## 3 Theoretical Framework
### Performance Metrics  
ANNs for classification tasks usually generate two types of predictions. One is a continuous prediction, usually in the form of a probability output. Another is a discrete prediction, which generates a class. In practical applications, categorical predictions are usual as they ease decision-making. However, probability outputs for continuous predictions  
Figure 1: Artificial neural network of one single layer. Source: authors.  
are useful when assessing the model's performance and reliability according to a known class [29].  
One common method to assess the performance of a classification model is through a confusion matrix. This method relies on a table which is a summary of the number of correct and incorrect predictions made by a classifier. The diagonal elements show the observations for which the class is predicted correctly, whereas the rest of the elements show the two types of errors that are present in classification problems; particularly false negatives and false positives (Figure 2).  
Despite the use of a confusion matrix to assess a model's performance in a classification ANN, when there are thousands of configurations, it is necessary to use a univariate metric to assess the performance of the ANN model. If one of the classes is considered as one of interest, it is possible to use metrics such as precision, sensibility, and specificity to summarize part of the information in the confusion matrix [28].  
\[Sensibility=\frac{TP}{TP+FN} \tag{3}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{4}\]  
\[Precision=\frac{TP}{TP+FP} \tag{5}\]  
There are other metrics to assess other components of the matrix such as the accuracy and F1 score. The accuracy metric measures the relationship between the observed and predicted classes, and it is easy to interpret. However, there are some disadvantages in its use. For example, it is incapable of distinguishing between the type of errors (false positive or false negative), and it can be unreliable when there are unbalanced classes [29].  
\[Accuracy=\frac{TP+FP}{TN+FN+TP+FP} \tag{6}\]  
On the other hand, the F1 score measures the harmonic media between precision and sensibility [24].  
\[F1\;score=2\cdot\frac{Precision\cdot Sensitivity}{Precision+Sensitivity} \tag{7}\]  
Another way to measure the model's performance is through probability prediction which can potentially provide more information than the simple value of the class [29].  
The curve \"receiving operator characteristic\" or ROC, is a chart that uses the rate of true positives (sensibility) against the rate of false positives (1-specificity) for the range of probabilities. A perfect model would have a specificity and sensibility of 100% for all the probability ranges, and the area under the curve (AUC) ROC would be 1. On the other hand, there is the PS curve, which is obtained by plotting precision against sensibility. In this case, the AUC could be used to measure the model's performance [28].
## 4 Discussion
### The Impact of LF EMF on Humans  
The cases described in Section 3 were assessed for compliance with various EMF limit standards and regulations applicable in selected countries. The obtained results are presented in Table 7. It can be observed that the current version of standard ICNIRP is less restrictive in the context of the maximum permissible values of H than the older one. For this reason, all of the investigated cases obey limitations imposed by ICNIRP 2010. Nevertheless, it should be mentioned that there was only one case (warehouse hall near the 110 kV double-track overhead lines) when the requirements of ICNIRP 1998 [18] and 1999/519/EC standards were not met. Local legal regulations proved to be more demanding in terms of permissible exposure to EMFs. For example, the limit for H in Belgium is one order of magnitude smaller than the value defined by the ICNIRP 2010 standard, which is why most of the investigated cases are not in line with these requirements. In the case of Italy, the failure to meet local EMF requirements concerns in most cases the lowest limits of H, which apply only in selected provinces. The situation is similar in Switzerland, but the restrictions imposed mainly apply to new installations and objects of \"sensitive use\". Polish regulations are distinguished by a very low permissible E value, which is equal to 1 kV/m for residential areas, and a reduced permissible value of B = 75 \(\mu\)T compared to standard ICNIRP 2010. The above restrictions mean that the construction of residential buildings under high-voltage lines or in their immediate vicinity is significantly limited. The restrictions on the maximum allowable value of E in Japan and China are also more restrictive than those defined by the ICNIRP 2010 standard, which is why (especially in the first case) some of the facilities considered did not meet their requirements. Among the cases analyzed, the most problematic in terms of compliance  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & & & & & & & & \\ \hline E [kV/m] & 5 & 5 & 5 & 5 & 5 & 5 & 5-10 1 & 5 & 1/10 2 & 5 & 5 & 5 & 5 & 5 & 1–12 1 & 3 & 4 & 5 \\ \hline B [\(\mu\)T] & 100 & 200 & 100 & 100 & 100 & 10 & 3-100 1 & 75 & 100 & 1/100 3 & 100 & 100 & 100 & - & 200 & 100 & 100 \\ \hline H [A/m] & 80 & 160 & 80 & 80 & 80 & 80 & 8 & 2.4–80 1 & 60 & 80 & 0.8–80 3 & 80 & 80 & 80 & - & 160 & 80 & 80 \\ \hline \hline \end{tabular}  
* Differences between provinces/states. 2
* Residential buildings/other buildings. 3 New installations and objects of “sensitive use”/basic limit.  
\end{table}
Table 6: The reference limit values for 50 Hz EMF following different standards and local regulations.  
with the above-mentioned requirements was the case of the warehouse hall near the 110 kV double-track overhead lines.
# Calculation Method of Earth Pressure Considering Wall Displacement and Axial Stress Variations
## 2 Theoretical Analysis
### Earth Pressure under Different Displacement Modes  
In previous studies, [PERSON] [4] classified the displacement modes of retaining walls into three categories: translational mode (T), rotation about a point above the top (RTT), and rotation about a point below the wall base (RBT), as illustrated in Figure 6. The parameter \(\lambda\) is used to determine the location of the rotation center.  
Figure 5: The relationship diagram between \(\varphi\) and \(\mu\).  
Based on the distribution characteristics of the wall displacement under different displacement modes, the formulas for the earth pressure behind the wall in each mode are given below.  
In the T mode, the soil displacement and strain are uniform at different depths, which means that the strain distribution function along the depth \(z\) can be represented as \(\varepsilon_{x}(z)=\varepsilon_{x}\). Based on this, \(\sigma_{x}\) and \(F_{\Sigma x}\) are determined by Equations (29) and (30).  
\[\sigma_{x}=K_{0}\sigma_{z}+\frac{\left(1-R_{f}S\right)^{2}E_{i}\varepsilon_{x} }{1-\mu^{2}} \tag{29}\]  
\[F_{\Sigma x}=\frac{1}{2}K_{0}\gamma h^{2}+\int_{0}^{h}\frac{\left(1-R_{f}S \right)^{2}E_{i}\varepsilon_{x}}{1-\mu^{2}}\mathrm{d}z \tag{30}\]  
In the RBT mode, the displacement and strain of the soil are largest at the top of the wall, while they are zero at the center of rotation. Based on this, \(\varepsilon_{x}(z)=\varepsilon_{x}(\lambda H+H-z)/\left(\lambda H+H\right)\), \(\sigma_{x}\) and \(F_{\Sigma x}\) are determined by Equations (31) and (32).  
\[\sigma_{x}=K_{0}\sigma_{z}+\frac{\left(1-R_{f}S\right)^{2}E_{i}(\lambda H+H-z )\varepsilon_{x}}{(1-\mu^{2})(\lambda H+H)} \tag{31}\]  
\[F_{\Sigma x}=\frac{1}{2}K_{0}\gamma h^{2}+\int_{0}^{h}\frac{\left(1-R_{f}S \right)^{2}E_{i}(\lambda H+H-z)\varepsilon_{x}}{(1-\mu^{2})(\lambda H+H)} \mathrm{d}z \tag{32}\]  
In the RTT mode, the soil experiences its maximum displacement and strain at the bottom of the wall, while the displacement and strain at the center of rotation is zero. Based on this, \(\varepsilon_{x}(z)=\varepsilon_{x}(\lambda H+Z)/(\lambda H+H)\), \(\sigma_{x}\) and \(F_{\Sigma x}\) are determined by Equations (33) and (34).  
\[\sigma_{x}=K_{0}\sigma_{z}+\frac{\left(1-R_{f}S\right)^{2}E_{i}(\lambda H+z) \varepsilon_{x}}{(1-\mu^{2})(\lambda H+H)} \tag{33}\]  
\[F_{\Sigma x}=\frac{1}{2}K_{0}\gamma h^{2}+\int_{0}^{h}\frac{\left(1-R_{f}S \right)^{2}E_{i}(\lambda H+z)\varepsilon_{x}}{(1-\mu^{2})(\lambda H+H)}\mathrm{ d}z \tag{34}\]  
Figure 6: Diagram of each displacement mode.
## 2 Signal Model and Problem Statement
### Existing Phase Compensation Techniques  
To solve the above problem, a kind of phase compensation technique was developed in [14; 15]. In these works, the local carrier replica is assumed to be aligned with \(s(t)\), i.e., \(\delta f=0\) and \(\delta\varphi=0\), and the correlation noise is ignored. Thus, the discriminator output can be obtained as \(\delta\tilde{\varphi}=\arctan(Q/I)=\psi\).  
The phase bias estimation is performed by directly using the discriminator output as the phase bias estimate, given by  
\[\hat{\psi}=\delta\hat{\varphi} \tag{9}\]  
Then, the phase compensation is performed by adjusting the NCO using the phase bias estimate to obtain the modified local carrier replicas as  
\[\hat{i}_{\text{carr}}(t) =\cos(2\pi(f_{\text{IF}}+\hat{f}_{d})t+\hat{\varphi}+\hat{\psi}) \tag{10}\] \[\hat{q}_{\text{carr}}(t) =-\sin(2\pi(f_{\text{IF}}+\hat{f}_{\text{d}})t+\hat{\varphi}+\hat {\psi})\]  
Therefore, if \(\hat{\psi}=\psi\), the phase bias disappears from the I and Q correlation signals in Equation (7). Thus, the carrier phase estimate \(\hat{\varphi}\) is not affected.  
There are two problems that hinder the use of this type of phase compensation technique in practical applications. (1) The existing phase compensation technique lacks phase bias detection. In the existing phase compensation technique, the correlation noise is ignored and it is considered that if \(\hat{\psi}\
eq 0\), then the phase bias appears. However, in practice, the correlation noise is unavoidable, which makes it difficult to determine the phase bias appearance time. (2) The existing phase compensation technique has poor phase bias estimation. This is because the discriminator output is directly used as the phase bias estimate, and it cannot mitigate the random noise and gross errors. This problem is especially severe if the satellite signal is not strong enough.
# A Blockchain-Based Secure Inter-Hospital EMR Sharing System
## 2 Preliminary and Security Requirements
### Security Requirements
#### 2.2.1 Mutual Authentication  
The information receiver must be capable of confirming the legal identity of the information's sender during the message transmission process. Therefore, each party must be able to confirm the legal identity of the other party in an EMR sharing system environment. If any two parties can confirm one another's legal identity, then the proposed system achieves mutual authentication.
# Improved Anti-Collision Algorithm for the Application on Intelligent Warehouse
## 2 Related Work
Although most of the above algorithms minimize collision and idle slots, the recognition efficiency is mostly between 0.5 and 0.55, and there is room for improvement. The efficiency of the MQT algorithm can reach 0.55 to 0.6 when m = 2. Although the bigger M is, the higher recognition efficiency is. However, due to the exponential growth of mapping strings, the amount of data transmission will also be greatly increased. Therefore, it is necessary to design an anti-collision strategy with higher recognition efficiency and less data transmission.
# Type Hierarchy Enhanced Event Detection without Triggers
## 3 Methodology
### Instance Encoding
#### 3.3.1 Word/Entity/Pos Embeddings  
Word embeddings have been shown to be able to capture the complex semantic regularities of words from massive unlabeled data [33]. In our work, we use the Skipgram [34] (accessed on 1 January 2023) model to learn word embeddings in New York Times Corpus(NYT) [[https://catalog.ldc.upenn.edu/LDC2008T19](https://catalog.ldc.upenn.edu/LDC2008T19)]([https://catalog.ldc.upenn.edu/LDC2008T19](https://catalog.ldc.upenn.edu/LDC2008T19)). The reason why we chose NYT is that it is large and diverse; moreover, its data domain is similar than the ACE2005 dataset, which is helpful [35]. As for entity tags and pos tags, we randomly initialize embedding tables for them. Then, every word token and the tag will convert to a low-dimensional dense vector. Finally, the input token embedding will consist of three parts: (1) word embeddings, where the dimension is _dw_; (2) entity embeddings, where the dimension is _de_; and (3) pos embeddings, where the dimension is _dp_.  
Figure 3: The architecture overview of THEED. It consists of three components. Component 1: Instance encoding. Component 2: Type hierarchy concept module. Component 3: Binary encoding. (The type hierarchy concept module only indicates one example. Due to space constraints, confidence is not marked on the lines.)
## 3 Results and Discussion
### Shelf-Life Study
#### 3.1.3 Chemical Indices--Lipid Oxidation  
Thiobarbituric acid reactive substance (TBAR) values remained stable during the 49 days of storage time at refrigeration (5 \({}^{\circ}\)C), and all measurements showed a level below 1.0 mg of malonaldehyde (MDA)/kg of the sample, a behavior attributed to the anaerobic conditions in the vacuum-sealed packaging of the bacon slices. Moreover, no significant differences were observed among the samples, whether enriched or not with the extracts (RSE and RSME samples). It has been demonstrated that pro-oxidants have little effect on oxidation during storage when oxygen is absent [42]. The average initial TBAR values on day 0 of storage were 0.49 \(\pm\) 0.02, 0.50 \(\pm\) 0.02, 0.47 \(\pm\) 0.01 and 0.51 \(\pm\) 0.02 mg MDA/kg of bacon for the CNT, RN, RSE and RSE-RSME samples, respectively, and after 49 d of storage, the average values were 0.51 \(\pm\) 0.02, 0.53 \(\pm\) 0.03, 0.51 \(\pm\) 0.02 and 0.53 \(\pm\) 0.02 mg MDA/kg of the sample, respectively, as shown in Figure 4. Similar results were obtained at the other two storage temperatures (10 and 15 \({}^{\circ}\)C), with low TBAR values and insignificant differences among the samples. Similar results were reported by other researchers, where TBAR values did not exceed 1.0 mg of malonaldehyde/kg of the sample until day 126 of storage [43]. If the storage period was extended, an increase in TBA values would be expected, as reported by previous studies on vacuum-packaged bacon [44]; however, the microbiological spoilage of the samples was much more rapid in comparison to autooxidation, rendering the samples rejectable before their deterioration due to oxidation.
## 5 Plasma Precursors
### Nitrogen  
Similar to oxygen plasma, nitrogen plasma generates reactive species that convert organic contaminants into smaller, volatile molecules. However, unlike oxygen plasma, nitrogen plasma does not significantly promote the oxidation of materials such as copper or silver. This makes nitrogen plasma particularly suitable for cleaning lead frames with these surfaces. Furthermore, nitrogen plasma cleaning is effective against contaminants that are resistant to both oxidation and reduction processes [70]. A higher activation energy is required for nitrogen plasma compared with oxygen plasma, primarily because the strong triple bond in the diatomic nitrogen molecule (N\(\equiv\)N) requires more energy to dissociate [78].  
Table 16 presents the grading of the parameters relevant to nitrogen plasmas to outline nitrogen's suitability for use in cleaning plasmas.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Summary** & **Parametric Score** \\ \hline Removal of Organic & Nitrogen plasmas react with organic contaminants; however, in & \\ Contaminants & general, nitrogen plasmas are weaker in reduction than & \\ Through Chemical & hydrogen-based plasmas. Nitrogen plasmas also create toxic & \\ Reduction and & substances such as cyanates and nitrous oxides when they react with & \\ Oxidation & organic contaminants. & 5/10 \\ \hline  & Crucially, nitrogen plasma is not an effective reducing agent for typical & \\  & metal oxides found on lead frames. It does not readily react with CuO & \\  & or Ag\({}_{2}\)O in a way that removes the oxygen and leaves clean metal. Its & \\  & bond energy (N\(\equiv\)N) is very high, and the resulting radicals do not & \\  & have the right chemistry to efficiently strip oxygen from metal oxides & \\ Removal of Oxides & like hydrogen does. Nitrogen plasmas create nitrides with metals, & \\ Through Chemical & thereby nitriding surfaces and changing their physical properties. & \\ Reduction & Moreover, as stated before, the volatile oxides of nitrogen are toxic and & \\  & challenging to handle. On the other hand, the nitride surfaces are & \\  & effectively passivated, which means that they are protected from & \\  & future oxidation. The literature has indicated the evaluation of the use & \\  & of nitrogen as a follow up with other gases, such as hydrogen, to form & \\  & a passivation layer and also improve the surface roughness created by & \\  & the precursor hydrogen plasma process [117]. & \\ \hline \hline \end{tabular}
\end{table}
Table 16: Grading of parameters for nitrogen plasmas.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Summary** & **Parametric Score** \\ \hline Plasma Safety: Handling & Emissions from the plasma process should be also safe and & \\ Precursors and Emissions Risks & would not need scrubber systems for disposal. This is & \\  & especially true if the gas volumes are small, as is expected & \\  & from a non-equilibrium plasma cleaning process. & 8/10 \\ \hline \hline \end{tabular}
\end{table}
Table 15: _Cont._Nitrogen plasmas have also been used to improve the hydrophilicity of surfaces [118].
## 2 Cross-Correlation Function-Based Flow Estimation  
CCF flow estimation relies on small fluctuations in the measured signal due to normal process variation [21]. Temperature measurements are commonly used for CCF flow estimation because temperature sensors are reliable, economical, and widely used in industry, and temperature flows display normal local variation. A general schematic of a piping system where CCF flow estimation can be performed is shown in Figure 1, and examples of relevant measured signals are shown in Figure 2.  
Figure 1: A general schematic of a hardware installation for CCF flow estimation [21].  
The two signals, \(x(t)\) and \(y(t)\), measure a common perturbation with a time delay, \(t_{d}\), between them. This transport time can be estimated by performing the CCF on the two signals, \(x(t)\) and \(y(t)\), as shown in Equation (1):  
\[R_{xy}(\tau)=\frac{1}{T}\int_{0}^{T}x(t)y(t+\tau)dt \tag{1}\]  
where \(\tau\) is the delay between the two signals. When \(R_{xy}(\tau)\) is at a maximum, \(\tau\) is equal to the transport time \(t_{d}\). The target mass flow rate \(\dot{m}\) is calculated by the system geometry, the known distance \(d\) between the two sensors, and the time delay \(t_{d}\):  
\[\dot{m}=\frac{\rho dA}{t_{d}} \tag{2}\]  
where \(\rho\) is the flow density and \(A\) is the cross-sectional area.  
The accuracy of CCF-based flow estimation is primarily driven by the magnitude and persistence of the process variations, which may be small relative to the measurement noise. In previous work, periodic injection of coolant to increase the process variation signal was explored in an experimental facility [19; 21]. This work focuses on the development of a CFD model to investigate the relationship between the sensors' locations, the rate of fluid injection, and the accuracy of CCF flow estimation.
# A Deep Learning Approach with Feature Derivation and Selection for Overdue Repayment Forecasting
## 4 Experiments
### Result Analysis  
The comparison results of AD-BLSTM with baseline methods are listed in Table 2. Our proposed AD-BLSTM model can be seen to outperform other methods in all evaluation indicators, especially in recall, AUC and KS values. AD-BLSTM achieves 0.59 recall value on the basis of an accuracy of over 0.85, indicating that 59% of overdue repayment behaviors can be predicted accurately. The AUC and KS values of AD-BLSTM suggest that it is capable of distinguishing positive and negative samples and thus represents a stronger classifier compared with other methods.  
Traditional methods regard repayment records as a single input instead of a sequence, processing data into a fixed vector via feature engineering and training by general machine learning methods or neural networks, which has a similar procedure to the LR, XGBoost, FM and DNN baselines. By observing these baseline results, we can conclude that even the procedure of feature engineering has been improved by our proposed feature derivation approach, and although strong classifier algorithms such as XGBoost and FM have been utilized, the recall, AUC and KS still fluctuate to an unsatisfying level.  
The training pipeline for the two memory network baselines, Mul-BiRNN and Mul-BiLSTM, shows large difference with the above four baseline methods. Repayment records are regarded as a time sequence instead of a non-sequential vector. As the recurrent network can extract sequential information effectively, the two baseline methods perform much better than traditional methods. Therefore, sequential modeling is essential in the context of our research.
# A Remote Sensing Image Target Detection Algorithm Based on Improved YOLOv8
## 5 Conclusions  
The remote sensing target detection task is characterized by high detection difficulty due to basic features such as a large proportion of image background information, dense targets, and a large number of small targets. To address the above problems, this paper proposes an improved YOLOv8 remote sensing image target detection algorithm based on the YOLOv8 target detection algorithm by adding a detection layer for small target feature  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **P/\%** & **R/\%** & **[EMAIL_ADDRESS]\%** \\ \hline SSD & 79.8 & 76.4 & 79.3 \\ YOLOv5 & 84.2 & 74.4 & 80.1 \\ YOLOv7 [26] & 81.5 & 74.5 & 79.3 \\ YOLOX [27] & 84.4 & 75.7 & 80.6 \\ YOLOv8 & 84.0 & 76.6 & 81.4 \\
**Our algorithm** & **84.5** & **77.3** & **82.7** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparisons with different detection algorithms.  
Figure 11: Comparison of detection results between the original YOLOv8 and the improved YOLOv8. The detection results of YOLOv8 are shown on the top, and the results of our algorithm are shown on the bottom.  
information extraction to its multi-scale feature fusion network structure; introducing the EMA attention mechanism in the C2f module; and, finally, using Wise-IoU to replace the CIoU loss function in the original network. The ablation and control experiments are designed based on the DOTA dataset, and the experimental results show that the detection effect of the algorithm proposed in this paper is better than that of the original algorithm and other typical target detection algorithms, and it achieves improved detection performance for remote sensing targets. However, due to the addition of various new modules, the computational volume and complexity of the algorithm increase slightly. In the future, we plan to consider introducing more advanced convolution modules into the model to reduce the number of parameters and improve the detection speed of the model.  
Conceptualization, H.W.; Methodology, H.W.; Software, H.W.; Validation, H.Y., H.C., J.W. and X.Z.; Resources, H.Y.; Data curation, H.W.; Writing--original draft, H.W.; Writing--review & editing, H.Y. and H.C.; Visualization, H.W.; Supervision, H.Y., H.C., J.W., X.Z. and Y.X. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.