esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Change Detection and Time Series Analysis with Applications
## 4 Change or Breakpoint Detection within Non-Stationary Time Series
### JUST Monitor  
The JUST Monitor is like BFAST Monitor and CCDC, proposed for the purpose of near-real-time monitoring [77]. It first searches to find a stable history period via JUST, and then it forecasts the stable historical time series into the monitoring period. Thus, the simultaneously estimated seasonal and trend components will be subtracted from the time series to obtain the residual series from which LSSA will determine whether the spectrum of the residual series remains statistically insignificant as the new observations become available. If a peak in LSS becomes statistically significant, then a change will be identified as abrupt or seasonal. If ALLSSA is used in JUST instead of OLS, then the over/under-fitting issue may not occur, resulting in more reliable change detection in near-real-time. Unlike BFAST Monitor and CCDC, JUST Monitor can explicitly consider the observational uncertainties, e.g., the error bars shown in Figure 5f, and is expected to perform well in estimating generally unknown seasonal components of any time series. JUST and JUST Monitor are new methods that promise nice features for change detection and classification; however, similarly to any other method, their usability in real-world applications needs to be further investigated. The MATLAB and python software package for JUST and JUST Monitor is freely available [63; 78].
## 2 Mathematical Framework
### Broadband Power Spectral Density
For instance, \(\overline{\sigma}_{\alpha}=2\cdot 10^{-3}\) s yields a cut-off frequency \(\omega_{c}=500\) rad s\({}^{-1}\) and \(f_{c}=79.5\) Hz, which, for an _alpha_ wave of 8 Hz, would allow about 10 harmonics to stand in the power spectrum up to 80 Hz with only a slight reduction, while for a wave of 20 Hz a reduction in the amplitude would occur after the 4 th harmonic. At frequencies lower than \(\omega_{c}\), the number and amplitude of the harmonics merely depend on the states characterizing the neurons forming the loops. In summary, the main effect of the fluctuations in \(N_{i}\) is the onset of a mixed power spectrum, which includes spectral lines at the angular frequencies \(n\cdot\omega_{0}\) and continuous broadband noise. Fluctuations in the parameters intrisic to a single loop give rise to changes in the synchronization with the other loops by changing the number \(N_{i}\). As observed in empirical EEG records, amplitude and waveform vary almost at every period of the detected signal. According to the model proposed in this work, this change may be due to fluctuations in the number of synchronized loops \(N_{i}\).
## 5 Results
### Dynamic Evaluation Analysis of Sci-tech Journals Based on LSTM  
This subsection analyzes the performance of LSTM in the experiment in detail.  
Figure 11 shows the comparison between the real results in the test set and the predicted results of the LSTM model. Blue dashes represent the actual values and green dashes represent the predicted values, revealing that the predicted results are well fitted. Considering that the difference between the metrics of other experiments and LSTM is not disparate, it can be demonstrated that the mainstream methods in the field of time series analysis are generalizable to the task of dynamic journal evaluation.  
Figure 11: Prediction results of the LSTM model for the test set compared with the actual results. Blue dashes represent the actual values and green dashes represent the predicted values.  
Table 4 shows the results of the evaluation metrics of the trained LSTM model in the test set for dynamic evaluation of journals in different classes of subjects. It can be seen that the dynamic evaluation of journals in the field of medicine is optimal, followed by agriculture; however, the overall dynamic evaluation of the subjects of economics and transportation is poor.  
Through detailed analysis of the journal metric time series dataset, we found that journals in the medical field are the most numerous and most completely and comprehensively recorded samples in the dataset, while journals in the field of agronomy are smaller in number, but their metric records are more comprehensive, with fewer missing values and a high degree of metric diversity; thus, both have better evaluation metric results under the trained LSTM model. In contrast, journals in the field of economics and transportation are smaller in number in the dataset, and it is verified that the original data of these two types of journals contain more outliers and missing values, and although they are processed through data cleaning, poorer evaluation metric results are still obtained under the LSTM model.  
The above analysis proves that the authenticity, completeness, and comprehensiveness of metric records are very important for the dynamic journal evaluation task, and the number of training samples is essentially positively correlated with the performance effect. Although the problem of abnormal data can be solved by data pre-processing, it still affects the final training effect.
## 3 Results and Discussion
### Influence of Moisture Level on Proton Transport in the Composite Membranes
It is considered that the differences in the water uptakes amid the membranes (at 20 \({}^{\circ}\)C in Table 1) played the main role. The hydrophilic channels, constructed by the -SO\({}_{3}\)H groups of Nafion and either s-HPS or c-HPS, can sustain higher proton flux than that of its pristine counterpart. With raising the single cell operation temperature to 70 \({}^{\circ}\)C (Figure 9b), the N/s-HPS membrane sustained the maximum power density of 525 mW/cm\({}^{2}\), representing an increment of 106% against the utmost value of 255 mW/cm\({}^{2}\) obtained at 20 \({}^{\circ}\)C. In contrast to this, the Nafion membrane achieved only an increment of 73% through this temperature rise. Figure 9: Comparison of the polarization curves of H\({}_{2}\)-PEMFC (proton exchange membrane fuel cell) loaded with the various membranes at **(a)** 20 \({}^{\circ}\)C and **(b)** 70 \({}^{\circ}\)C.  
As shown in the water-uptake/temperature measurement (Figure 5a), these two membranes had about 11 wt % difference in water uptake at 70 \({}^{\circ}\)C, responsible for the different energy output. The other important factor is the different proton transport efficiency as observed in examining conductivity under 10% RH. The N-112 membrane also possessed a lower power density than that of the composite membranes, validating the impact of the above two factors that originates from the structure of hydrophilic channel.
## 4 Experimental Results and Discussions  
### Mechanical Tests  
#### 4.1.1 Results of Unconfined Compressive Strength Test  
Tests were conducted to evaluate the effects of different ratios of eco-friendly stabilizer and wood chips, as well as varying curing times, on the unconfined compressive strength of soil bricks that had been stabilized with this mixture. The data presented in Figure 4 indicate a direct relationship between stabilizer content and unconfined compressive strength, as an increase in stabilizer content results in a higher strength. Based on multiple preliminary experiments, it was observed that a 14 day curing period is sufficient to achieve 90% of the reference strength when the 28 day strength is used as the reference. The moisture content for developing 90% of the reference strength was found, as a result of multiple preliminary experiments, to exhibit the maximum strength in the wetting phase, and was higher than the optimum water content of weathered granite soil.  
A soil brick reference strength of 2.0 MPa was achieved with a 7 day curing period when the stabilizer content was set at 15% or higher as shown in Figure 5. Therefore, if time is a constraint, a stabilizer content of 15% or higher can be utilized to attain the required brick strength within 7 days.  
Figure 4: Effect of stabilizer contents on unconfined compressive strength (UCS) containing 0.5% wood chip with various curing times.  
Figure 5: Unconfined compressive strength with various stabilizer contents containing 0.5% wood chip.  
The unconfined compressive strength of the soil bricks varied with changes in the wood chip content. It was observed that a lower wood chip content is positively correlated with a higher unconfined compressive strength, as shown in Figure 6. Specifically, the strength of soil bricks with 0.5% wood chip content was up to 1.3 times greater than that of soil bricks with 1.5% wood chip content, and this trend diminished as the stabilizer content increased.  
In conclusion, the integration of wood chips into soil brick pavement systems has been demonstrated to enhance pedestrian comfort. However, it is important to note that an increase in wood chip content coupled with a low stabilizer ratio can lead to a reduction in the pavement's overall strength. To overcome this limitation, it is recommended to maintain a stabilizer ratio of 10% or greater, regardless of the wood chip content. This approach ensures that the soil brick pavement achieves its target strength within a curing period of 28 days, thus maintaining structural integrity while providing a comfortable walking surface.
# Physically-Data Driven Approach for Predicting Formation Leakage Pressure: A Dual-Drive Method
## 3 Selection, Processing, and Correlation Analysis of Training Samples
### Data Preprocessing  
Preprocessing is essential prior to formally establishing the model due to the presence of substantial noise in the raw logging data, especially noticeable in outliers at the beginning and end, as well as some missing values. This process includes replacing or eliminating the outliers to enhance the quality of the training samples. For this study, the output layer data employed in model training (i.e., the formation pressure loss equivalent density of Well M-1) is computed by domain experts using a formula (refer to Equation (2)) and fine-tuned through calibration considering field conditions and years of experience. Consequently, it  
Figure 5: M-1 well input layer learning sample data violin diagram.  
demonstrates a high level of accuracy and can be regarded as the actual formation pressure loss. For more details, please consult Figure 6.  
\[P_{l}=\text{a}*P_{smin} \tag{2}\]  
In the equation, \(P_{l}\) denotes the formation pressure loss, measured in MPa. The variable \"a\" is a dimensionless empirical coefficient that pertains to the region, while \"\(P_{smin}\)\" corresponds to the minimum horizontal principal stress, expressed in MPa.  
The primary focus of this research is on the B Formation, C Formation, and D Formation in Block M. Following data preprocessing, Well M-1 covers a depth interval of 2127 to 4652 m, whereas Well M-2 spans from 2510 to 4770 m. In both instances, data were gathered at 5 m intervals to assemble the dataset, amounting to 506 rows for Well M-1 and 453 rows for Well M-2.
## 2 Literature Review
### Classification Algorithms Capable of Continual Learning  
In general, continual learning is categorized into three scenarios [3; 4]: domain incremental learning [32; 33], task incremental learning [34], and class incremental learning [5; 34; 35; 36]. In the deep learning domain, several approaches have been proposed to handle the above scenarios. A regularization-based approach selectively regularizes the variation in network parameters to preserve the important parameters of previously learned tasks [37; 38]. A replay-based approach maintains the performance of trained tasks by storing selected training samples in a limited memory buffer and using them when training new tasks [39; 40; 41; 42]. Although there are several approaches for continual learning, the ability of neural networks to perform continual learning without catastrophic forgetting is still not sufficient [43]. The major problem with the above approaches is that the structure of networks is basically fixed, and therefore, there is an upper limit on the memory capacity.  
One promising approach to overcome this difficulty caused by the fixed network structure is to apply a growing self-organizing clustering algorithm as a classifier. The growing self-organizing clustering algorithms adaptively and continually generate a node to represent new information. Typical classifiers of this type are the Episodic-GWR [44] and ASOINN classifiers (ASC) [8], which utilize GWR and ASOINN, respectively. One state-of-the-art algorithm is SOINN+ with ghost nodes (GSOINN+) [10]. GSOINN+ has successfully improved the classification performance by generating some ghost nodes near a decision boundary of each class.  
Another successful approach is ART-based supervised learning algorithms, i.e., ARTMAP [22; 23; 24; 26; 29; 45]. As mentioned in Section 2.1, ART-based algorithms theoretically realize sequential and class-incremental learning without catastrophic forgetting. However, especially for an algorithm with an ARTMAP architecture, label information is not fully utilized during a supervised learning process. In general, a class label of each node is determined based on the frequency of the label appearance. Therefore, there is a possibility that a decision boundary of each class cannot be learned clearly.
# DFT Characteristics of Charge Transport in DBTP-Based Hole Transport Materials
## 3 Results and Discussions
### Ionization Potential (IP), Electron Affinity (EA), and Reorganization Energy  
_IP_, _EA_, hole reorganization energy (\(\lambda_{h}\)), and electron reorganization energy (\(\lambda_{e}\)) were used to evaluate charge injection and transfer abilities of HTMs, which are listed in Table 1. A low _IP_ is advantageous to the generation of a hole, and a high _EA_ denotes a stronger charge injection ability [29]. Table 1 shows that the trends in _IP_s and _EA_s are both mDPA-DBTP \(<\) pTPA-DBTP. Therefore, mDPA-DBTP is better than pTPA-DBTP for the generation of holes. The stability is an important index for the use of cells. The absolute hardness (\(\eta\)) can be calculated to estimate the stability of cells using the following equation [30]:  
\[\eta=\frac{IP-EA}{2} \tag{1}\]  
As shown in Table 1, mDPA-DBTP is more stable than pTPA-DBTP.  
Figure 2: The partial density of states (PDOS) of the fragment A and D, where (**a**) mDPA-DBTP and (**b**) pTPA-DBTP.  
The reorganization energy (\(\lambda_{h|e}\)) refers to the energy change of the system, which is caused by the structural relaxation after the gain or loss of electrons. It can be calculated by [31; 32; 33]:  
\[\lambda_{h/e}=(E_{0}^{\pm}-E_{\pm})+(E_{\pm}^{0}-E_{0}) \tag{2}\]  
where \(\mathrm{E_{0}^{+}}\)\(\left(\mathrm{E_{0}^{-}}\right)\) and \(\mathrm{E_{+}^{0}}\)\(\left(\mathrm{E_{-}^{0}}\right)\) are the charged energy on neutral states and neutral energy used cation (anion) states, respectively; \(\mathrm{E_{+}}\)\(\left(\mathrm{E_{-}}\right)\) and \(\mathrm{E_{0}}\) are energies for cation (anion) states and neutral state, respectively. Low reorganization energy is beneficial to charge transport. In Table 1, the value of \(\lambda_{h}\) is in the following order: mDPA-DBTP \(>\) pTPA-DBTP, and pTPA-DBTP is favorable for transporting holes in the design. However, a parameter cannot perfectly estimate the performance of HTMs; we also need to calculate the transfer integral and mobility, which are discussed below.
# Deep Feature Fusion Based Dual Branch Network for X-ray Security Inspection Image Classification
## 4 Experiments
### Ablation Studies
#### 4.4.2 Level Selection of Features to Be Fused  
As for the deep feature fusion model proposed in this paper, we compare three level selections of features to be fused through adding low-level features to the fusion stepwise, i.e., level4/level3, level4/level3/level2, and level4/level3/level2/level1. We give the experimental results as shown in Table 4. The experiment here is based on the SIXray10 subset with the adoption of cross entropy loss function. To validate the effectiveness of the proposed model, the first row of Table 4 illustrates the experimental result of the baseline method.  
Through comparing the experimental results, we find that, as more levels attend to fusion, the classification performance of the model increases. On the other hand, this margin of growth is gradually reduced, from 4.39 points brought by merely adding level3 to the fusion, to 1.85 and 0.2 points brought by adding level2 and level1, successively. For the features of these four levels, higher-level ones need a larger parameter amount of occupancy. It implies that, for later added high-level features, the performance improvement gained by consuming so much computational cost is extremely limited. In other words, the improvement is inefficient, especially for the addition of level1. It also shows that the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Levels of Features to Be Fused** & **mAP/\%** \\ \hline Baseline (without fusion) & 74.83 \\ level4/level3 & 79.22 \\ level4/level3/level2 & 81.07 \\ level4/level3/level2/level1 & **81.27** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effect of level selection of features to be fused on model performance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{3}{c}{**SIXray10**} & \multicolumn{3}{c}{**SIXray100**} \\ \hline \(\gamma\) & \(\alpha\) & **mAP/\%** & \(\gamma\) & \(\alpha\) & **mAP/\%** \\ \hline – & – & 74.83 & – & – & 52.74 \\
0 & 0.5 & **82.021**  & 0 & 0.5 & 73.05 \\
0 & 0.75 & 81.35 & 0 & 0.75 & 72.94 \\
0.2 & 0.75 & 80.72 & 0.2 & 0.75 & **76.09** \\
0.5 & 0.5 & 76.21 & 0.5 & 0.5 & 73.28 \\
1 & 0.25 & 74.29 & 1 & 0.25 & 72.90 \\
2 & 0.25 & 67.18 & 2 & 0.25 & 69.80 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effect of varying parameters setting to model performance on SIXray10 and SIXray10 subsets. First row with symbol “–” shows the performance result on baseline.  
output feature of level1 is inappropriate to add into the feature fusion module. Getting to the bottom of this, the feature of this layer experiences few convolution calculations, which brings about poor expressive ability. In consequence, no more information will be obtained by fusing the feature of this layer. Considering performance and computational cost comprehensively, we finally choose features of level4/level3/level2 for the fusion procedure.
## 2 Models and Methods
### Dynamic Substructuring  
In the field of structural dynamics, dynamic substructuring methods have played a fundamental role because they allow studying the dynamic behavior of a complex system starting from the dynamic behavior of the component subsystems. The main advantages are the following [39]:  
* The possibility of studying the dynamic behavior of systems that would be too complex to analyze with a single model. In particular, at the design stage, it allows analyzing the effects of local modifications in the complete system, by changing only the model of the subsystems involved in the modification.
* The analysis of the single subsystems allows more immediate recognition of the local dynamic behaviors and therefore very efficient global optimizations. At the same time, in the analysis, it is possible to neglect the local dynamic behaviors that do not have any significant effect on the dynamics of the whole system.
* The possibility of assembling subsystems whose dynamic behavior is described using numerical or analytical models, and subsystems described using models derived from experimental measurements.
* The possibility of combining subsystems modeled by different project groups even at different times and places.  
Using the substructuring approach, once the numerical, analytical, or experimental models and the coupling conditions at the interface degrees of freedom (DoFs) between the subsystems are established, the dynamic behavior of the coupled system can be obtained. The coupling conditions impose the compatibility and the equilibrium at the interface between the given subsystems.  
Therefore, the substructuring method, used in this paper, provides the following advantages in the dynamic analysis of agricultural machinery:  
* It allows one to analyze the dynamics of the complete system (the agricultural machine) through the characterization of the single components (tractor, linkage system, and operating machine);
* It simplifies the analysis of the complete system when only one of the subsystems is modified (as in the case of several operating machines connected to the same tractor or of the same operating machine connected to different tractors);
* It allows one to focus the attention only on the elements that affect particular aspects of the dynamic behavior of the agricultural machine (e.g., the DoFs of the connector between the tractor and the operating machine to evaluate the vibration transmission from the machine to the tractor).
# Lightweight End-to-End Speech Enhancement Generative Adversarial Network Using Sinc Convolutions
## 7 Results
### Interpretation of Sinc Convolution  
Inspecting learned filters is a valuable practice that provides insights into what the network is actually learning. To this end, we visualize the learned low and high cutoff frequencies of Sinc-convolution filters in Figure 3. The green area demonstrates the low and high cutoff frequencies for each filter. In addition, we also give the corresponding results of mel filters (purple area) for comparison. We plot four examples of the learned Sinc filters of the proposed system in Figure 4. As shown in Figure 4, the learned Sinc filters are rectangular band-pass filters in the frequency domain, echoing its definition in Section 4. Furthermore, we exhibit examples of spectrograms of the speech signal enhanced by SEGAN [19], DSEGAN [30], SASEGAN-all [31], and the proposed Sinc-SEGAN-add, respectively, in Figure 5. As observed in Figure 3, the Sinc convolution learns a filter bank containing more filters with high cut-frequencies. In addition, a tendency towards a higher amplitude is noticeable, indicating an inclination of the Sinc convolution to directly process raw speech waveforms. As we can see from Figures 4 and 5, Sinc convolution is specifically designed to implement rectangular band-pass filters. Considering the speech waveform distribution in the time domain, the specific design makes Sinc convolution suitable for extracting narrow-band speech clues, e.g.  
Figure 4: Examples of the learned filters of the Sinc convolution. The upper row reports the filters in the frequency domain, while the lower row reports their corresponding magnitude frequency response. The orange dashed line depicts the corresponding mel-scale filter.  
Figure 5: Spectrograms of an example utterance enhanced by (**c**) SEGAN [19], (**d**) DSEGAN [30], (**e**) SASEGAN-all [31], and the proposed (**f**) Sinc-SEGAN-add. We also exhibit the (**a**) clean and (**b**) noisy spectrograms for reference.