esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Main Focus Areas  
As shown in Figure 4, ten topics discovered in all repositories were as follows: Medical images analysis, applications for medical practice, medical data reuse, clinical text processing, applications for doctors, medical research, medical education, healthcare management systems, clinical trials, and medical data analysis. Table 5 offers a brief description of each of these topics and their top keywords.  
Analysis of topics generated from the yearly data revealed that new technologies emerged over time. For instance, the open source community has devoted a lot to medical images analysis since the very beginning. It has also been a topic throughout all the years. Top keywords in the topic showed that deep learning and neural networks emerged as popular techniques in 2015. Moreover, we noticed that recently developers have been starting to evaluate blockchain in medical record sharing.  
Figure 3: Partial dependency plots showing the relationships between the predictors (forks, open issues, repository size, owner type, README file size, total contributors, total commits, year of creation) and the number of stars on GitHub repositories.
# A Novel Deep Learning Approach for Machinery Prognostics Based on Time Windows
## 3 Experiment and Analysis
### Experimental Setup
#### 3.2.4 Evaluation Functions  
There are mainly two kinds of evaluation functions for this dataset. One evaluation function gives different weights to the late predictions and early predictions, and the other is that the weights for both conditions are the same.  
The first type of evaluation function [29] is shown in Equation (7), where \(N\) represents the number of aircraft engines in the testing set.  
\[\begin{array}{ll}S=\left\{\begin{array}{ll}\sum\limits_{i=1}^{N}(e^{-\frac {h_{i}}{13}}-1)&h_{i}<0\\ \sum\limits_{i=1}^{N}(e^{\frac{h_{i}}{10}}-1)&h_{i}\geq 0\end{array}\right.. \end{array} \tag{7}\]  
The scoring function is asymmetric, which penalizes late prediction (i.e., the estimated RUL value is larger than the actual RUL value) more heavily than early prediction (i.e., the estimated RUL value  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sub-Dataset** & **FD001** & **FD002** & **FD003** & **FD004** \\ \hline Length of the TW & 10/20/30 & 20 & 30 & 15 \\ Training samples & 19731/18731/17731 & 48,819 & 21,820 & 57,763 \\ Batch size & 760/721/682 & 2220 & 809 & 1992 \\ Epoch number & 40 & 40 & 30 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Training parameters.  
is smaller than the actual RUL value) since late prediction may result in more severe consequences [23]. The second type of evaluation function can be expressed as  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}h_{i}^{2}}. \tag{8}\]  
Root mean square error (RMSE) has the same weight for early predictions and lag predictions.
# Attack Classification Schema for Smart City WSNs
## 3 Intrusion Detection Framework
### Alarms
#### 3.2.2 Alarms Triggered by Correlation Rules  
A correlation rule is a type of rule that is used to group alarms that have some kind of relationship. The new alarm triggered by a correlation rule can be considered more trustworthy than the alarms that it is composed of, since it gets triggered only if several unwanted situations have already triggered some alarms. Therefore, alarms triggered by correlation rules can be considered more critical, not only because they are more reliable, but also because the alarms that it is composed of may come from several services, devices, providers, etc. Moreover, correlation rules allow administrators to reduce the number of alarms that require their attention. For instance, creating a correlation rule to group the alarms by location in a WSN reduces all of the alarms triggered by a source of interference to a single alarm, instead of receiving individual alarms from each of the sensor nodes in the area that receives the interference.  
Moreover, administrators can use correlation rules to implement signatures for known attacks, for which administrators can clearly identify the attack traces. In this way, alarms triggered by these rules will not only be reliable, but they will also straightaway point to a specific attack type. The creation of correlation rules is highly dependent on the WSNs that are deployed together, their configurations and, in general, the specific scenario. Therefore, defining a methodology to create correlation rules falls out of the scope of this article.  
However, general purpose correlation rules can be easily implemented in most scenarios, and as the proof of concept of Section 5 will show, this can significantly improve the detection success. As a general guideline, we recommend that smart city administrators create correlation rules to group the alarms received within a certain time interval triggered by devices sharing certain characteristics, such as a nearby location, the frequency band, the provider, the gateway, etc.  
In this way, once a severe alarm built from a correlation rule is received, administrators can begin to collect more insights about the compromised components, providers, etc. In the next sections, we provide some guidelines to assist administrators to this end.
# Satellite-Based Estimation of Carbon Dioxide Budget in Tropical Peatland Ecosystems
## Materials and Methods
### Data
#### 2.2.2 Flux Tower Observations  
The study area contained three sites: an undrained forest (_UF_), a drained forest (_DF_), and a drained burnt forest (_BF_) on flat terrain within 15 km [25] in Palangkaraya, Kalimantan, Indonesia (Palangkaraya drained forest (PDF) site in AsiaFlux database). The available observation periods are as follows: _DF_ was from 2001, and _UF_ and _BF_ were from 2004. The heights of the flux towers are 35, 50, and 4 m for _UF_, _DF_, and _BF_, respectively. The _DF_ and _BF_ towers were drained and located about 400 and 200 m from the canals [25]. _UF_ is a relatively intact forest site located in a national park along the Sebangau River. The compositions of land cover types in the _UF_, _DF_, and _BF_ based on MODIS land cover types (MCD12Q1) by the international geosphere-biosphere programme (IGBP) scheme were evergreen broadleaf forest (EBF) (in _UF_ and _DF_), woody savannas (WSAV) (in _DF_), and grasslands (GL) and savannas (SAV) (in _BF_). The spatial representativeness of flux tower is usually known as 100-1000 m [38]. CO\({}_{2}\) flux data were generated every 30 min. Each 5-min CO\({}_{2}\) concentration that was recorded with a closed-path CO\({}_{2}\) analyzer (LI7500, Li-Cor Inc., Lincoln, NE, USA) was interpolated for estimating the 30-min CO\({}_{2}\) fluxes. The observed _GWT_ was also used and was observed in 30-min intervals. Flux partitioned \(R_{e}\) and the observed _GWT_ were used for modeling \(R_{e}\). The _GWT_ was used for the validation of the RS-based GWT. The flux partitioned GPP was also used to calibrate parameters in the MODIS GPP algorithm.
# An Accelerated Maximum Flow Algorithm with Prediction Enhancement in Dynamic LEO Networks
## 4 Predictive Acceleration Algorithm for e-TEG Satellite Networks  
This section presents methods for efficiently solving the maximum flow problem in e-TEG networks by using predictive flows and residual network updates. Predictive flows provide an informed starting point for computations, reducing the time needed to reach the optimal solution. Residual network updates enable efficient incorporation of dynamic changes in the network topology and resource constraints. To further enhance the adaptability and efficiency of our approach, we integrate an SDSN framework. By decoupling the data and control planes, SDSNs enable centralized decision-making, which improves routing flexibility and resource management.
## 4 Simulation and Results Analysis
### Discussion  
It can be seen from the experimental results in Sections 4.2-4.4 that the battery SOC estimation algorithm proposed in this paper is accurate and has a short calculation time with good robustness.  
Figure 8: Error between the SOC estimated value and the actual value under BBDST operating conditions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Estimation Algorithms** & **Maximum Absolute Error** & **Time (s)** \\ \hline Ampere-hour & 9.49\% & 5.951 \\ Two RC KF & 3.46\% & 67.326 \\ Fuzzy control & 3.43\% & 69.542 \\ Proposed algorithm & 3.47\% & 55.756 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Computing time (s) and error of various SOC estimation algorithms.  
Figure 7: Difference between the SOC estimated value and the actual value under BBDST working conditions.  
However, there are still problems that need to be solved. The error at the beginning of each measurement will be larger. As the experiment progresses, the error value will gradually decrease and tend to be stable. This is suspected to be caused by the polarization of the battery. When the current passes through the battery, battery potential is deviated from the equilibrium potential, which affects the SOC estimation accuracy of the battery. This proves that there is still room for improvement in the algorithm.  
Two points will be focused in our future work. Temperature plays a key role in battery characteristics. In order to eliminate the effect of temperature on the results, the battery was placed in the thermostat to maintain a constant temperature (25 \({}^{\circ}\)C). However, in electric vehicles, the batteries are not placed in the thermostat and the influence of temperature on the batteries needs to be considered. In the future, it is necessary to further consider the robustness of the SOC estimation algorithm against temperature. Moreover, this paper focused on the performance of the algorithm when the battery was discharged and did not discuss the condition when the battery was charged. In the future, we will study the performance of the estimation algorithm under charge, which may increase the precision with time.
# Fault-Tolerant Resolvability and Extremal Structures of Graphs
## 2 Preliminaries
Note that our definition of a constant/bounded metric/fault-tolerant metric dimension could be different from the one in the literature. In view of Theorem 1, path graphs are a family of graphs with a constant metric dimension. On the other hand, in view of Theorem 2, complete graphs provide a family of graphs possessing a bounded resolvability structure. In a path graph, there exists a unique fault-tolerant metric basis comprising the initial and terminal vertices. Thus, we obtain \(\beta^{\prime}(P_{n})=2\). [PERSON] et al. (2018) showed that the tree \(T\) in Figure 1 has \(\beta(T)=10\) and \(\beta^{\prime}(T)=14\). The set \(P=(1,2,3,4,\ldots,10)\) (resp. \(Q=P\cup\{y,v,r,s\}\)) forms the metric basis (resp. fault-tolerant metric basis) of \(T\). [PERSON] et al. proved the following lemma, which shows an alternative way to trace a fault-tolerant resolving set in a graph. Lemma 1: [PERSON] et al. (2018) _A resolving set \(R\) of graph \(\Gamma\) is fault-tolerant if and only if any arbitrary pair of vertices of \(\Gamma\) is resolved by at least two vertices of \(R\)._  
Proof. : Let \(R\) be a fault-tolerant resolving set of \(G\). Assume contrarily that two vertices \(x\) and \(y\) of \(G\) are resolved by a single vertex \(r\in R\). Then, \(R\setminus\{r\}\) is not a resolving set since both \(x\) and \(y\) have the same codes with respect to \(r\in R\). This raises a contradiction to the assumption that \(R\) is a fault-tolerant resolving set of \(G\). Now, we assume that every pair of vertices of \(G\) is resolved by at least two vertices of \(R\). Then, \(R\setminus\{r\}\) for any \(r\in R\) is a resolving set by definition. This shows the lemma. [PERSON] et al. (2018) showed the following upper bound on \(\beta^{\prime}\) in terms of \(\beta\). Theorem 4: ([PERSON] et al., 2018) _The upper bound \(\beta^{\prime}(\Gamma)\leq\beta(\Gamma)\big{(}1+2\times 5^{\beta(\Gamma)-1} \big{)}\) holds for any arbitrary graph._  
The following result demonstrates that the difference between the two parameters \(\beta\) and \(\beta^{\prime}\) can be increasingly large enough. Theorem 5: [PERSON] et al. (2018) _There always exists a graph \(\Gamma\) for which \(\beta^{\prime}(\Gamma)\geq\beta(\Gamma)+p\) holds for any integer \(p\)._  
From this, we can also note that, with the defining structures of \(\beta\) and \(\beta^{\prime}\), we can have \(\beta^{\prime}(\Gamma)\geq\beta(\Gamma)+1\). In the next section, we discuss graphs which hold equality in this lower bound. **3. Main Results**  
This section contains the main result presented in this paper. Figure 1: The tree example from [PERSON] et al. (2018).
# Optimized Dissolved Oxygen Fuzzy Control for Recombinant _Escherichia coli_ Cultivations
## 2 Theory and Software
### Artificial Neural Networks  
Artificial Neural networks are inspired by biological nervous systems and are composed by single or multiple layers of neurons. A layer includes a combination of weights--in the form of a matrix--that multiplies the input strength. The summation (net input function) of the weighted inputs with the bias form the argument for the transfer function (typically a sigmoid or a linear function) [50; 51]. The main idea of a neural network is that these parameters (weights and biases) can be adjusted (or trained), in the sense that a particular input can lead to a specific target output. Neural networks can be classified into static and dynamic networks. The first (also called feedforward) has no feedback elements and contains no delays. The second group (also called recurrent), in turn, depends not only on the current input of the network but also on previous inputs and outputs. Dynamic networks are generally more powerful than static ones (due to the presence of memory, they can also be trained to learn sequential or time varying patterns) [50; 51].  
Before training, the first step is a division of the loaded dataset into three subsets. The first is the training set, which is used for updating the network parameters (weights and biases). The second subset is the validation set, which is used to measure the network generalization power and halt the training process when the generalization stops being improved (when the error associated with the validation set begins to rise). The third subset is the test set. The neural network architecture choice plays an important role as too few neurons can lead to underfitting. Too many neurons, on the other hand, can increase the power of the network but can also lead to overfitting. During training, the weights and biases are iteratively adjusted to minimize the network performance function (\(F\)), by default, the mean squared error (\(mse\)), presented in Equation (3) [50; 51].  
\[F=mse=\frac{1}{N}\sum_{i=1}^{N}e_{i}^{2}=\frac{1}{N}\sum_{i=1}^{N}(t_{i}-a_{i} )^{2}, \tag{3}\]  
where \(mse\) is the mean squared error between the network outputs \"\(a\)\" and the target outputs \"\(t\)\".  
Any standard numerical optimization algorithm can be used to optimize the performance function. Every time the neural network is trained, a different solution is expected due to the different parameter initialization and the different data subset allocation (for training, validation, and test). To analyze the training results, a regression plot can be created. It shows the relationship between the outputs of the network and the targets [50; 51].
## 5 Conclusions  
This paper proposes a new scheme for the current uniformity of a \(\pm\)550 kV DC GISA using a spiral-stacked core self-generated inductor. The research focuses on the electrical-magnetic-thermal-flow multi-physics simulation of a \(\pm\)550 kV DC GISA and the current uniformity of the spiral structure of the self-generated inductor. A simulation and an  
Figure 16: The current uniformity effects of the spiral structure self-generated inductor. (**a**) is the V-I curve with no inductor. (**b**) is the V-I curve with the 6.4 \(\upmu\)H inductor.  
Figure 17: The influence of the inductor on the three-column current distribution. (**a**) is the current distribution with no inductor. (**b**) is the current distribution with the 6.4 \(\upmu\)H inductor.  
experiment of the miniature model validate the simulation method and model. The spiral-stacked arrester tank's electric field and temperature distribution are obtained. The results of the study are summarized below.  
1. The spiral structure of the \(\pm\)550 kV DC GISA core reduces the length of the core by 56.25%. In the simulation results of the \(\pm\)550 kV DC GISA, the maximum electric field strength is 4.85 kV/cm. The self-generated inductor of the \(\pm\)550 kV DC GISA spiral structure is calculated to be 6.4 uH. The temperature difference of the ZnO varistors under the same condition is 7 \({}^{\circ}\)C.
2. In the experimental results of the \(\pm\)66 kV DC GISA miniature model, in the range of 154-3140 A per column, the current distribution coefficient \(\beta\) of the \(\pm\)66 kV DC GISA prototype is between 1.01 and 1.04. With the increase in current, the inter-column current coefficient \(\beta\) decreases gradually. The current of columns 1, 2, and 3 increases sequentially, and the energy absorbed increases.
3. The current uniformity effect of the 6.4 uH inductor is validated. The results show that compared with the linear structure arrester (which has no inductor), the self-generated inductor of the spiral structure reduces the uneven coefficient of current distribution from 1.06 to 1.02. The current deviation coefficients are reduced by 28-65%. This current uniformity scheme automatically plays a role in the use of the arrester, which relies on the self-generated structure of the arrester for current uniformity. It provides a new direction for improving the current uniformity of multi-column parallel arresters.  
Conceptualization, S.L. ([PERSON]) and L.W.; methodology, S.L. ([PERSON]) and L.W.; writing--original draft preparation, L.W.; writing--review and editing, S.L. ([PERSON]) and S.L. ([PERSON]); supervision, Z.L.; project administration, Y.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China, grant number 2021 YFB2400604.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# A Multi-Fidelity Successive Response Surface Method for Crashworthiness Optimization Problems
## 4 Multi-Fidelity Metamodeling
### Linear Multi-Fidelity Metamodeling  
In this section, we briefly present a common linear multi-fidelity technique used in engineering disciplines to integrate computational models of varying fidelity (accuracy) to make predictions. This autoregressive scheme was proposed by [PERSON] and [PERSON] in 2000 [30], commonly referred to as AR1, and is still widely viewed as a reference point. Although most practical problems involve only two fidelities, we generalize the problem by assuming that we have \(s\) levels of information sources available. Therefore, for a generic level \(t\), we define \(\mathbf{y}_{t}(\mathbf{x}_{t})\) to be the output of a given input \(\mathbf{x}_{t}\in\mathbb{R}^{d}\). We can then, for each level, group inputs and outputs into a generic database \(\mathcal{D}_{t}=\{\mathbf{x}_{t},\mathbf{y}_{t}\}\) by ordering the data by increasing fidelity, i.e., \(t=1,\ldots,s\). In other words, \(\mathbf{y}_{1}\) is the output of the least accurate model, while \(\mathbf{y}_{s}\) is the output of the most accurate model. That being said, we present the autoregressive scheme of [PERSON] and [PERSON] in Equation (17):  
\[f_{t}(\mathbf{x})=\rho\cdot f_{t-1}(\mathbf{x})+\delta_{t}(\mathbf{x}). \tag{17}\]  
In Equation (17), \(\rho\) serves as a scaling factor to denote the magnitude of correlation among different fidelity outputs, \(\delta_{t}(\mathbf{x})\) is a GP with mean \(\mu_{\delta_{t}}\) and covariance function \(k_{t}\), \(f_{t}(x)\), and \(f_{t-1}(x)\) are the GPs predicting data at fidelity level \(t\) and \(t-1\), respectively. This linear autoregressive scheme relies on the Markov property, asserting that when the closest point \(f_{t-1}(x)\) is known, no further information about \(f_{t}(x)\) can be gained from any other model output \(f_{t-1}(x^{\prime})\)[33]:  
\[\text{cov}\{f_{t}(\mathbf{x}),f_{t-1}(\mathbf{x}^{\prime})|f_{t-1}(\mathbf{x})\}=0\ \ \ \ \ \forall\mathbf{x}\
eq\mathbf{x}^{\prime}. \tag{18}\]
## 4 Discussion
### Simplifying Mutual Influences of Visual and Aural Attributes through Clustering  
This study introduced a typological perspective on sorting out the cross effects of environmental visual and aural attributes. Two considerations led to this step being taken before exploring the influence of these attributes on human emotions.  
First, mutual influences existing between environmental visual and aural aspects may constrain efforts to pinpoint appropriate design interventions [80]. Evidence has shown that visual information can supplement and enhance the meaning of sound as perceived by people [81]. Similarly, environmental soundscape can also affect the information received through visual sensations to a certain extent [82], but an excessive level of sound may have a negative effect on perceived environmental visual quality [59]. It is usually easier to improve environmental perception evaluation results when the audio-visual qualities appear to be consistent [83]. To simplify these mutual influences in design decision-makingprocesses while keeping them in consideration, this study obtained subjects' ratings of environmental visual and aural perceptive attributes. This was to make sure that the responses were grouped by environmental visual and aural attributes and their interactions. Then, classification was conducted on an individual basis to categorise settings according to their rated visual and aural perceptions so as to bypass their cross effects.  
Second, a typological perspective can also be helpful in broadening the application of research outcomes. Though many efforts have been made to identify environmental characteristics that are beneficial to human emotions [84], the disclosed design implications are largely dependent on contextual factors since they were developed under specific research contexts. The introduction of classifications at an early stage in human-environment studies allows design rules to be developed based on perceived soundscape typologies rather than the actual contextual features. This means that the classification-based conclusions in this study can also be applied to other settings with similar perceptual attributes and can predict human emotional responses accordingly. Moreover, this approach can include far more research sites than previous studies without complicating the analysis or compromising the accuracy of results with the help of classification.