esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Setup
### Scenario 1: DRL-Based Scenario at the Signalized Intersection
#### 3.2.5 Determine the Hyperparameters  
Hyperparameters are parameters used to control the learning process in DNN. Their values need to be determined before starting the learning process. These hyperparameters affect the output of the model. There are no specific rules for choosing the number of neurons or the number of hidden layers. The selection of too many layers or neurons causes the model over-fitting and increases the time to train the model. Conversely, choosing too few layers or neurons causes model under-fitting and high statistical bias. In this work, we performed trial and error tests to determine the hyperparameters by changing the number of hidden layers and neurons.  
We first created three models with 2, 4, and 8 hidden layers, respectively, to determine the number of hidden layers that fit the model. In the case of a platoon with three vehicles, the results of these models are shown in Table 2.  
We can see that the model performance decreased when increasing the number of hidden layers. The cumulative negative reward (total waiting time) of vehicles was reduced from 11,974 s to 9947 s when changing the number of hidden layers from 8 to 2. The model with two hidden layers gave the best results, and we chose this number of hidden layers for the next steps. Next, we changed the number of neurons on hidden layers between 128, 256, and 512. When we decreased the number of neurons on hidden layers from 512 to 128, the total waiting time was reduced by 15%. Having too many neurons is undesirable, but the model needs to have enough neurons to be able to acquire the complexities of the input-output relationship. We performed the same steps to determine other hyperparameters in the model to obtain the optimal model. The final structure of the DNN and agent parameters are shown in Tables 3 and 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Cumulative Negative Reward (s)** & **Cumulative Delay Time (s)** \\ \hline Hidden layers = 2, & \(-\)9947 & 25,677 \\ Neurons = 128 & \(-\)10,671 & 28,115 \\ Hidden layers = 8, & \(-\)11,974 & 29,652 \\ Neurons = 128 & \(-\)12,824 & 30,235 \\ Hidden layers = 2, & \(-\)11,508 & 30,654 \\ Neurons = 512 & \(-\)11,508 & 30,654 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters of DNN.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Gui & False \\ \hline Total Episode & 300 \\ \hline Max\_steps & 3600 s \\ \hline Green duration & 5 s \\ \hline Yellow duration & 3 s \\ \hline Learning rate & 0.001 \\ \hline Batch\_size & 100 \\ \hline Training\_epochs & 800 \\ \hline Num\_states & 120 \\ \hline Num\_actions & 4 \\ \hline gama & 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of agent.
###### Abstract  
Ionospheres over sea areas have an inevitable impact on maritime-satellite communications; however, due to geographic constraints, ionospheric observation and analysis over sea areas are far from adequate. In our paper, slant total electron content (STEC) along small-satellite constellation-automatic identification system (AIS) signal rays is used for computerized ionospheric tomography (CIT) over sea areas, and small-satellite constellations can provide more effective signal rays than a single satellite. An adjustment factor \(\delta\) is introduced to optimize the initial electron density for the multiplicative algebraic reconstruction technique (MART). The CIT results reconstructed by a traditional MART and our new method at 00:00 and 06:00, 15 March 2022, are compared, and our new method produces about a 15% and over 40% improvement in average deviation (AD) and root-mean-square error (RMSE). The results show that the bigger the difference between \(\delta\) and 1, the better improvement will be in the 3D CIT process. The initial electron density is well selected during CIT when \(\delta\) is approximate to 1, which is the case at 12:00, and the reconstructed 3D electron density, applying the initial n\({}_{\text{e}}\) and the adjusted initial n\({}_{\text{e}}\), are both close to the true electron density. The small-satellite constellation-AIS signals are valuable resources for electron density reconstruction in sea areas.  
small-satellite constellation; automatic identification system (AIS); sea areas; multiplicative algebraic reconstruction technique (MART); 3D computerized ionospheric tomography (CIT) +
Footnote †: journal: _atmosphere_
# Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network
## 2 Proposed Resilience Measurement Methodology
### Information Entropy Theory  
Currently, the Arctic NSR is not a well-developed commercial waterway; as a result, some information associated with the NSR may be absent, which makes it difficult to  
Figure 4: Typical Noisy-OR model.  
quantify the uncertainty of the unknown information. To improve the reliability of the underlying information, information entropy theory is applied in the present study. In the framework of information entropy, the uncertainty of a variable can be measured by a probability distribution. Suppose \(X\) is a discrete random variable with the probability distribution \(P\), where \(P_{i}\) represents the probability of \(x_{i}\in X\). Then, the information pertaining to a specific value, \(x_{i}\), for this random variable can be defined as:  
\[I(x_{i})\ =\ -\log(P_{i}) \tag{27}\]  
where the base in the logarithm is usually taken as 2 ore; in the present study, the base is 2.  
The information function theoretically controls the volume of information conveyed by the specific state, such as \(x_{i}\). The information entropy function can be defined as the expectation of the information, which is expressed as:  
\[H(X)\ =\ \mathrm{E}[I(X)] \tag{28}\]  
The information entropy function can be further processed mathematically as:  
\[H(X)\ =\ \sum_{i\ =\ 1}P_{i}\cdot I(x_{i})\ =\ -\sum_{i\ =\ 1}P_{i}\cdot\log_{2}(P_{i}) \tag{29}\]  
For the discrete random variable, \(X\), the Function (29) can be expressed as:  
\[H(X)\ =\ -\sum_{x\in X}P(x)\log_{2}P(x) \tag{30}\]  
where \(0\leq P(x)\leq 1\) and \(\sum_{x\in X}P(x)\ =\ 1\). The higher the value of the information entropy is, the more uncertainty the variable contains. The entropy can reach the maximum when the probability distribution is uniform, i.e., \(P(x)\) is equal for all \(x\).  
Suppose \(X\) is a target variable and \(Y\) is a contributing factor, similar to the relationship between the child node and parent node in the Bayesian network. If the target variable \(X\) is conditionally dependent on the dependent variable \(Y\), then we have:  
\[H(Y|X)\ =\ \sum_{i}P(Y_{i})\cdot H(Y_{i}|X_{i}) \tag{31}\]  
where \(i\) denotes the number of states, \(i\ =\ 2\) for a Boolean variable, and \(H(Y|X)\) represents the conditional entropy of target \(X\) given the contributing factor \(Y\). Then, the mutual information between the target variable \(X\) and the contributing factor \(Y\) can be defined as:  
\[I(X,Y)\ =\ H(X)-H(Y|X) \tag{32}\]
## 3 Results
### Relationships among Climate, Phenology and GPP
#### 3.5.2 Responses of Seasonal and Annual GPP to Phenology Changes  
Figure 8a shows the Pearson correlation coefficients of seasonal and annual GPP with phenology factors and GPPmax in the SRYR from 2001 to 2019. We found negative correlations between spring-summer phenology indicators and the corresponding seasonal GPP and a positive correlation between GPPmax and summer GPP, indicating that advanced SOS and POS and increasing GPPmax all enhanced the corresponding seasonal GPP. GPPspr was significantly and negatively correlated to SOS (\(p<0.01\)), and a one-day advance of SOS increased GPPspr by \(0.75\) g\(\cdot\)C\(\cdot\)m\({}^{-2}\). GPPsum was significantly and negatively correlated to POS (\(p<0.05\)), and a one-day advance of POS increased GPPsum by \(3.73\) g\(\cdot\)C\(\cdot\)m\({}^{-2}\). GPPsum had a larger rate increase (\(>2.0\) g\(\cdot\)C\(\cdot\)m\({}^{-2}\) yr\({}^{-1}\)) (Figure 6e) in the north and southeast where POS had a larger rate advance (\(<-1.0\) d yr\({}^{-1}\)) (Figure 4d). Autumn GPP had weaker relationships with each phenology factor.  
Due to the fact that average GPPsum accounted for more than 70.0% of average GPPann in the SRYR, the advanced POS had a stronger impact on GPPann than SOS and EOS, and hence POS was significantly and negatively correlated to GPPann (\(p<0.05\)) (Figure 8a). A one-day advance of POS increased GPPann by \(4.23\) g\(\cdot\)C\(\cdot\)m\({}^{-2}\). GPPmax had significant and positive correlations with GPPsum (\(p<0.01\)) and GPPann (\(p<0.01\)), indicating that a greater GPPmax leads to a larger GPPsum and GPPann. Moreover, GPPann had a stronger correlation (r = 0.765) with GPPmax than every phenology factor.  
We also found a significant correlation between SOS and GPPspr/GPPann (\(p<0.05\)) (Figure 8b), indicating that an earlier SOS resulted in a larger contribution of spring GPP to annual GPP. The correlations between POS and GPPsum/GPPann and between EOS  
Figure 7: Pearson correlation coefficients between vegetation phenology and GPPmax and their corresponding seasonal precipitation (P) and air temperature (T\({}_{\text{a}}\)). The asterisk on the bar chart indicates the level of significance. Single asterisk (“*”) indicates statistically significant correlation coefficients with \(p<0.05\), and double asterisks (“***”) indicate \(p<0.01\).  
and \(\mathrm{GPP_{aut}/GPP_{ann}}\) were not significant. Moreover, there was no significant correlation between \(\mathrm{GPP_{max}}\) and each phenology factor.
# Application of the Improved Entry and Exit Method in Slope Reliability Analysis
## 3 Limit Equilibrium Method
### Improved Entry and Exit Method  
The essence of the improved entry and exit method is to adjust the searching range according to the previous search results and then perform a second search to decide the critical slip surface of slope. The general steps are as follows:  
1. The entry and exit approach is employed to search for the potential key slip surface. The distances of adjacent two entry points, adjacent two exit points, and adjacent two tangent lines are \(D_{1}\) (\(D_{1}\) = \(L_{1}\)/20), \(D_{2}\) (\(D_{2}\) = \(L_{2}\)/20), and \(D_{3}\) (\(D_{3}\) = \(H\)/10), respectively. The simplified Bishop approach is used to calculate the security factors of all trial slip surfaces. The slip surface with the minimum factor of security is taken as \"the potential critical slip surface (Css\({}^{\prime}\))\", as shown in Figure 2.
2. The key points (key entry point, key exit point, and key tangent line) are determined according to the location of the potential critical slip surface (Css\({}^{\prime}\)), and the searching range is reduced. Based on the reduced searching range, the entry and exit approach is adopted again to determine the critical slip surface (Css) of the slope. The reduced searching range is set to unilateral \(4D_{1}\), \(4D_{2}\), and \(4D_{3}\), as shown in Figure 3.  
Figure 1: Search for critical slip surfaces.  
Figure 3: The reduced searching range according to the location of the potential critical slip surface.  
3. On basis of the reduced searching range, we increase the multiple of points within the reduced searching range according to the requirements of target search accuracy, as shown in Figure 4. In this paper, the factor of safety obtained by analyzing 81 \(\times\) 81 \(\times\) 41 = 269,001 trial slip surfaces is used as the target accuracy. The distances of adjacent two entry points, adjacent two exit points, and adjacent two tangent lines are \(L_{1}\)/80, \(L_{2}\)/80, and \(H\)/40, respectively.  
It can be seen that when using traditional methods to achieve target search accuracy, it is necessary to analyze 81 \(\times\) 81 \(\times\) 41 = 269,001 trial slip surfaces. When using the improved entry and exit method to achieve target search accuracy, [21 \(\times\) 21 \(\times\) 11 (first search) + 33 \(\times\) 33 \(\times\) 33 (second search)] = 40,788 trial slip surfaces need to be analyzed (reduced searching range equal to unilateral 4\(D\)). The number of trial slip surfaces calculated by the improved entry and exit method is 15% of the traditional entry and exit method, and its calculation efficiency is significantly better than the traditional entry and exit method.
# Inversion Analysis Method for Tunnel and Underground Space Engineering: A Short Review
## 2 The Development and Main Research Contents of Inversion Theory
### Development of Inversion Theory
#### 2.1.2 Evolutionary Phase  
The evolutionary phase of inverse theory began in the early 1980s and ended in the early 1990s. During this period, researchers expanded the scope of application and accuracy of inversion analysis with the help of simulation methods such as finite element and discrete element. They also performed regression analysis of the rock stress field and displacement field.  
Field monitoring technology developed based on large-scale tunnel and underground space engineering construction provided a basis for the inversion of ground stress. The combination and introduction of numerical computation and field monitoring technology made inversion analysis research more focused on practical engineering applications. As a result, inversion analysis is commonly used in linear elastic, viscoelastic, or elasto-plastic problems.
## 6 Results
### Real-World Constrained Design Problems
* For the PV case: \(f_{PV}=6059.7208\) with \(T_{h}=0.4375\), \(T_{s}=0.8125\), \(L=176.6372\), and \(R=42.0984\) (see [34]);
* For the SpRe case: \(f_{SpRe}=2994.4798\) with \(b=3.5000\), \(m=0.7000\), \(z=17\), \(l_{1}=7.300\), \(l_{2}=\)7.7153, \(d_{1}=3.3503\) and \(d_{2}=5.2867\) (see [37]). Given that the real form of the PV and SpRe landscapes are not fully described in the literature, the following analysis first assumed that they could comprise multiple optima. Thus, the parameter setting defined in Table 2 was kept. Only \(\gamma^{*}\) was changed to \(2\times 10^{4}\). The simulations used two pre-probing zones along each dimension. In addition, since both optimum correspond to large values, in order to avoid working with particle quality \(Q_{0}\) close to zero (see Figure 3), the \(Fv\) results introduced into Equation (9) were first divided by \(5\times 10^{3}\). As before, all test series included 50 runs. The first simulation groups led to a unique optimum for both cases. Therefore, to focus on unique optimal positions, a second series of 50 runs with \(\gamma^{*}\) set at 0.05 repeated the optimizations. Table 7 shows the obtained results. The second series confirm the \(\gamma^{*}=2\times 10^{4}\) predictions. Table 7 shows that the S-EPSO predictions are in perfect agreement with the reference evaluations. This result demonstrates that, although the algorithm was originally designed for low-dimensional landscapes with multiple optima, it is not restricted to low-dimensional spaces and can also effectively handle higher-dimensional domains. Furthermore, the last two problems confirm that S-EPSO is well suited to constrained optimization scenarios. This is an important outcome, as real-world engineering applications typically involve various types of constraints.
# A Deep Learning Framework for Signal Detection and Modulation Classification
## 3 Deep Learning Framework for Signal Detection and Modulation Recognition
### The Description for Deep Learning Framework  
According to above introduction of the signal detection network and the modulation recognition network, we describe the use of our DL framework. Figure 9 presents the system model. The steps of the model used are as follows:  
Figure 8: The basic structure of ResNet.  
Figure 7: The network model for signal modulation recognition.  
**Step 1:** We construct the signal detection network and modulation recognition network and train each network with their appropriate samples.  
**Step2:** In the online testing phase, we perform STFT for the wideband signals, and use the trained SSD networks to detect the signals in the time-frequency spectrum. In this step, we can obtain the center frequency and start-stop time of each signal. And for MFSK signal, we can get its modulation format.  
**Step 3:** For the amplitude-phase modulation signal, we can get the central frequency and start-stop time in step 2. With this knowledge, we filter the target signal and use the envelope spectrum to estimate the signal symbol rate. Then we down convert the signal and perform the matched filter by using the estimated symbol rate.  
**Step 4:** If the timing deviation exists in the target signal, it is necessary to extract the sample value at the optimum sampling position for signal eye diagram and vector diagram. We use the non-data-aided timing estimation algorithm in [52]. The specific expression is as follows, where \(L_{0}\) is the length of the signal symbols, \(T\) is the sampling period and \(N\) is the oversampling number:  
\[\overset{\wedge}{t}=\arg\Biggl{\{}\sum_{k=0}^{NL_{0}-1}\Biggl{|}\Biggl{(} \frac{kT}{N}\Biggr{)}\Biggr{|}^{2}\mathrm{e}^{-\mathrm{i}2\pi k/N}\Biggr{\}} \tag{28}\]  
**Step 5:** We alter the target signal sampling rate, and obtain the baseband signal with a maximum delay 32 sampling period. Moreover, we generate the eye-diagram and vector diagram with the processed signal.  
**Step 6:** We use the trained modulation recognition network to identify the signal by its eye diagram and vector diagram. Finally, we complete the signal detection and modulation recognition.
## 3 Results and Discussions
### Diffusion Modelling and Numerical Simulation for Polyphenol Extraction  
The predicted extraction yields were compared with the experimentally determined extraction yields and plotted in Figure S2. It can be seen that despite of some divergences in extraction kinetic curves, especially in the latter stage of ultrasound-assisted extraction, the diffusional model can satisfactorily describe the evolution of total phenolic yield during extraction with and without sonication. Similar results were also reported in the studies of [PERSON] et al. (2017) [24] and [PERSON] et al. (2019) [38]. The low values of \(RMSE\) and \(E\%\) (lower than 10%) and high \(R^{2}\) value (higher than 0.93) listed in Table S2 all demonstrate the considerably high accuracy of diffusional modeling results.  
The \(D_{e}\) value derived from diffusional modeling for ultrasound-assisted extraction was 7.17 \(\times\) 10\({}^{-10}\) m\({}^{2}\).s\({}^{-1}\), and the corresponding value for non-ultrasound incorporated extraction was 1.83 \(\times\) 10\({}^{-10}\) m\({}^{2}\).s\({}^{-1}\). Both \(D_{e}\) values were in the similar range to that of phenolics during ultrasound-assisted extraction from chokeberry [24]. The higher diffusivity of phenolics for ultrasound-assisted extraction was consistent with the kinetic results, verifying that ultrasound can intensify the diffusion of phenolics within almond skins.  
Based on the numerical results, the distributions of phenolics within almond skin at different stages of extraction are visualized in Figure 6. As can be seen, the concentration gradient of phenolics within almond skin during extraction can be ignored due to the low thickness value. At the same time point, a lower retention of phenolics within almond skins under sonication was observed, which denoted to the higher extraction yield.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Protein Content (\%)** & **PDI (\%)** \\ \hline NA & 26.127 \(\pm\) 0.081 & 49.239 \(\pm\) 0.517 \\ BA & 25.734 \(\pm\) 0.298 & 39.260 \(\pm\) 0.579 \\ UA & 25.396 \(\pm\) 0.155 & 45.804 \(\pm\) 1.055 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Protein content (%) and Protein Dispersibility Index in for NA, BA and UA.
## 3 Datasets and Methodology
### Methodology
#### 3.2.4 Time-Lagged Cross-Correlation Analysis  
Time-lagged cross-correlation analysis (TLCC) is a statistical method used to quantify the lag-dependent relationship between two time series [30]. The core concept involves shifting the time axis of one series and calculating correlation coefficients at different lag intervals to reveal potential causal links or response delays between the signals [30]. The Spearman Rank Order Correlation Coefficient (SROCC) is a non-parametric statistical measure used to assess the correlation between two variables [30]. It is particularly effective for analyzing time series that do not follow a normal distribution or contain outliers. The SROCC is calculated using the following formula:  
\[\rho_{SROCC}=1-\frac{6\sum_{i=1}^{n}d_{i}^{2}}{n(n^{2}-1)} \tag{16}\]  
In the equation, \(\rho_{SROCC}\) represents the Spearman rank-order correlation coefficient; \(d_{i}\) denotes the rank difference for each pair of observations--that is, the difference between the ranks of corresponding data points in the two time series; and \(n\) is the length of the time series (i.e., the sample size).  
For two time series \(x=\{x_{0},x_{1},\ldots,x_{N-1}\}\) and \(y=\{y_{0},y_{1},\ldots,y_{N-1}\}\) (where \(x_{0}\) and \(y_{0}\) are the initial observations, and \(x_{N-1}\) and \(y_{N-1}\) are the final observations), the time-lagged cross-correlation coefficient at lag \(k\), denoted as \(L_{k}\)(\(x\),\(y\)), is defined as  
\[L_{k}(x,y)=\frac{\sum_{i=0}^{N-1-k}\left(x_{i}-\overline{x}\right)\left(y_{i+ k}-\overline{y}\right)}{\sqrt{\left(x_{i}-\overline{x}\right)^{2}\left(y_{i+k}- \overline{y}\right)^{2}}} \tag{17}\]  
In the equation, \(k\) denotes the lag order, representing the number of time units by which one time series is shifted relative to the other; \(y_{i+k}\) is the observation of time series \(y\) at time \(i+k\); and \(\overline{x}\) and \(\overline{y}\) represent the mean values of time series \(x\) and \(y\), respectively.
## 3 Results and Discussion  
To test the effectiveness of the similarity measurement with the entropy-based weighting proposed in this paper, two different types of datasets, mixed and numerical datasets, were selected from the UCI Machine Learning Data Repository [26], and most datasets were collected from the field of biology and medicine. The iterative clustering algorithm based on the proposed similarity measurement was compared with existing clustering algorithms, including OCIL [12], K-Prototype [9] and k-means [4]. k-means was used for dataset made of numerical variables only. In the experiments, the clustering accuracy [27] was adopted to evaluate the three mentioned methods. The clustering accuracy is defined as \(AC=\frac{\sum_{i=1}^{m}\delta(\{\bigcup_{i=1}^{m}\{idx_{i}\})\}}{m}\), where \(m\) denotes the number of instances of the dataset, \(l_{i}\) denotes the provided label, \(idx_{i}\) denotes the obtained cluster label, \(map(idx_{i})\) is a mapping function that maps \(idx_{i}\) to the equivalent label from the data corpus, and the function \(\delta(l_{i},map(idx_{i}))=1\) only if \(l_{i}=map(idx_{i})\); otherwise, the value is 0. Correspondingly, the clustering error rate is defined as \(error=1-AC\).  
In the experiments, considering that the clustering results are affected by the selected initial centroids, we set the same initial centroids for all methods during each test, and the following experimental results were averaged from 100 random runs. In addition, k-means was chosen as the clustering method in the automatic categorization process that transforms numerical attributes into categorical attributes. Before using k-means method to cluster, the original data were normalized to be between 0 and 1.