esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Stage 1: TS Modeling
### Model Parameter Determination
#### 6.2.4 Model Residual Diagnosis  
After fitting, the residual diagnosis was performed. From the results, the three models achieved similar performances, Figure 11. Only a few outliers fell outside the two STD residuals, and most points were within one STD residuals, indicating a normal distribution. The correlation of the residuals using the ACF diminished and remained bounded by the white noise bounds (dash lines), thus, indicating a low correlation between time lags of the residuals. From the QQ-plot, most points aligned along one line, which implies i.i.d. Hence, all three models satisfied the residual assumptions where the ARIMA(6,1,6) and ARIMA(7,1,6) models were slightly superior, compared with the ARI(7,1) model, with fewer outliers in the ACF, STD residuals, and QQ-plots.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Set** & **Pattern** & **ARI(7,1)** & **ARIMA(6,1,6)** & **ARIMA(7,1,6)** \\ \hline \multirow{2}{*}{Training} & Non-OL & \(-417.11\) & \(-442.54\) & \(-448.97\) \\ \cline{2-5}  & OL & \(-444.50\) & \(-470.51\) & \(-477.09\) \\ \hline Test & Non-OL & \(-431.11\) & \(-459.06\) & \(-464.14\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average AIC of the three candidate models.  
Figure 11: Residual diagnosis for the three candidate models.
# Multimodal Summarization of User-Generated Videos
## 3 Multimodal Video Summarization
### Segment-Level Classification  
According to the process presented above, the content of each video has been described by an audio and a visual feature vector that represents each 1-s segment of the video. In addition, as described in Section 4, each 1-s segment of the video has been characterized either as \"informative\" or as \"uninteresting\". Informative segments are the ones that belong to the summary according to the aggregation process of the annotation data described in Section 4, while \"uninteresting\" are all other 1-s segments that the annotators have agreed that do not belong in the video summary. Based on this separation, a binary classification task can be formulated.  
In order to properly classify each segment of the video with regards to this binary classification task of important vs uninteresting video segments, a variety of classifiers have been trained on three different feature categories:  
* audio features: the 136-D audio feature vectors
* visual features: the 88-D visual feature vectors
* audio-visual features: the merged 224-D feature representation (as an _arly_ fusion approach)  
For the training procedure, a training set, which consisted of a proportion of 80% of the dataset's videos, has been created. The rest of the data has been used for validation purposes. The following classifier types have been evaluated for all three feature modality setups: Naive Bayes, k-Nearest Neighbors, Logistic Regression, Decision Tree, Random Forest, XGBoost and a Fully Connected Neural Network (FNN). For the first four, the implementation of [37] with the appropriate parameter tuning was used. For the k-Nearest Neighbors classifier the k parameter, which represents the number of neighbors, was optimized. As for the Logistic Regression, the inverse regularization parameter (i.e., \(C\)), was tuned. Decision Tree classifier was optimized with respect to the split quality measure criterion (i.e., Gini impurity, entropy etc.) and the the maximum tree depth. The Random Forest was based on the balanced classifier provided by [38], while for the XGBoost the classifier of [39] was used. Both of these classifiers were optimized in regard to the split quality measure criterion and the number of tree estimators. Finally, the Fully Connected Neural Network consists of eight layers (one input layer, six hidden layers and one output layer); the \"ReLU\" activation function has been used for all layers. Each layer is followed by a batch-normalization layer. Moreover the first five layers are also followed by a dropout layer. We should herein note that we have also experimented with Perceptron classifiers and Support Vector Machines (SVMs). The former showed poor performance, that is, not significantly better than random. Moreover, the latter, although they consist a very powerful and robust classifier, are not of practical use in terms of memory and training time, when the training dataset becomes significantly large [40; 41].
## 3 Proposed Model
### Outline  
The outline of the proposed model is illustrated in Figure 2, which is composed of three modules: (i) ROI extraction module (REM), (ii) feature extraction module (FEM), and (iii) matching module (MM). In the first module, palmprint ROIs are extracted using the bounding box concept. A pre-processing procedure is applied to the input images. Thereafter, the location of the boxes on the palmprint images are specified using a CNN transfer learning approach. The output of this module is palmprint ROIs, which are the inputs of the feature extraction module. The FEM employs a pre-trained CNN architecture to represent features. Finally, this feature vector feeds the MM as the input to perform the verification task using a machine learning classifier.
## 3 UWA Receiver Structure  
### Signals Suffering from UIWA Channel with Large Delays  
The UWA channel differs from the terrestrial electromagnetic channel [38]. The ray theory reveals that the delay of an underwater channel is decided by the path length and sound speed [7], and in addition, according to [39], the motion of the transmitter/receiver pair, the scattering of the moving sea surface and the refraction due to sound speed variations. For a received symbol, the time-varying UWA channel impulse response (CIR) can be written as  
\[h(t,\tau)=\sum_{i=0}^{N_{p}-1}c_{i}(t)\delta(\tau-\tau_{i}(t)), \tag{3}\]  
where \(N_{p}\) is the number of paths, and \(\tau_{i}(t)\approx\tau_{i}-a_{i}t\) is the time-varying delay of the \(i\)-th path, and \(a_{i}\) is the Doppler factor. \(c_{i}(t)\) is the channel coefficient of each path varying with time. In the receiver, after resampling, FFT, and low-pass filtering [4], the channel function in frequency domain is written as  
\[H(t,f)=\sum_{i=0}^{N_{p}-1}c_{i}(t)e^{j2\pi f\tau_{i}(t)}. \tag{4}\]  
As a result of the slow sound speed and low reflection loss, \(\tau_{i}(t)\) is large in long distances. These recognizable paths result in a small coherence bandwidth for the OFDM symbol. Figure 2 shows a CIR and corresponding transfer function caught in the water tank of Xiamen University. It can be seen from Figure 2b that the frequency selectivity is severe because of the long delay of the recognizable paths in Figure 2a.  
Figure 1: A classic UWA OFDM system. The performance of the channel estimator and equalizer are challenged by the UWA channels with large delays and impulsive noise.
## 2 Results and Discussion
### Ptr-Ms Analysis  
#### 2.3.1 Fragmentation Pattern  
Terpenes tend to fragment in the ionization region, namely, the drift tube of the PTR-MS instrument. Therefore, the determination of the optimal E/N value (where E is the electric field strength and N is the numerical density of the buffer gas in the drift chamber) is an important part of studying terpene compounds. There are reports on the fragmentation patterns of selected terpenes determined by the PTR-MS technique [20; 21].  
Figure 2: (**a**) Principal component analysis of kaffir lime juice stored at different temperatures based on the terpene profile. (**b**) Plot of factors (varimax rotation) indicating the importance of individual terpenes for kaffir lime juice discrimination. (**c**) Canonical discriminant analysis of kaffir lime juice according to the storage temperature based on the terpene profile.  
However, the transmission coefficients and thus the ion product distributions in PTR-MS depend on the type of mass spectrometer, so fragmentation patterns should be determined for each instrument [22]. Based on the library of spectra prepared, it was found that in the case of this group of compounds, their differentiation and determination by the PTR-MS technique may be impossible because of too similar fragmentation patterns. In any case, ions 81 and 137 are the dominant fragment ions, which is in agreement with previous literature reports [23]. For other terpen present in the sample, it can be assumed that their fragmentation patterns will be similar.  
The fragmentation patterns of many terpen have been determined previously [21; 23]. Based on the above results, we decided to determine the changes in the total content of compounds of \(m/z\) 137 and 81 (characteristic for terpen) and 150.22 Da, 152.23 Da, 154.25 Da, and 172.26 Da (characteristic for terpene degradation products) during the storage of fruit under different conditions and at different times. Based on the chromatographic measurements, the identification of the detected chemical compounds was performed and the percentage content of selected terpen was determined in order to predict their metabolic pathway. The generated spectral library was used to select the best E/N value to measure the terpene group. The highest relative contents of the main fragmentation ions (81 and 137) were recorded at E/N equal to 120 (Table S2 in Supplementary Materials); therefore, this value was used in further analyses. This is in agreement with current literature reports [21; 23]. Higher E/N values can lead to greater fragmentation, while lower values can lead to increased hydronium/water clustering, which in turn makes data difficult to interpret [24].
# Hazard Assessment for a Glacier Lake Outburst Flood in the Mo Chu River Basin, Bhutan
## 3 Materials and Methods  
### Digital Elevation Models  
In order to test the robustness and computational requirements during the model set-up and first model runs, we used NASA Shuttle Radar Topography Mission (SRTM) v. 3.0. This dataset has a ground resolution of 30 m and was created from Synthetic Aperture Radar observations on the Space Shuttle Endeavour in February 2000. In version 3.0, voids are filled by using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Global Digital Elevation Model 2 (GDEM2), USGS Global Multi-resolution Terrain Elevation Data (GMTED) 2010, and USGS National Elevation Dataset (NED). SRTM elevation products are very common, freely available used in many GLOF studies [22; 23].  
The resolution of SRTM faces its limits for hydrodynamic routing, especially in steep or narrow valley sections. We therefore used Euro-Maps 3D DEM (EM3D) Level-A Data with a spatial resolution of 5 m for our final GLOF simulations. EM3D is derived from 36 stereo images acquired by the Indian IRS-P5 Cartosat-1 satellite. One advantage of this DEM is that a rough hydrological correction has been applied to the data by default [24]. The dataset is free of charge for scientific use through the ESA Third Party Mission program, which provided the acceptance of a project proposal. In the study area, EM3D is the most detailed of available DEMs. Although we are aware that 5 m resolution is not yet the ne plus ultra when it comes to the representation of terrain details, we refer to EM3D as \"high-resolution\" data. Compared to 30-m products, which are often used in GLOF studies, this seems to be justified. The best possible DEM resolution at the moment is 30 cm, but this is beyond our financial and computational resources.  
In order to get a constant flow, the DEM was edited by erasing bridges and bumps in the river valley. EM3D is delivered as 16-Bit integer pixel type. It was smoothed by converting it to 32-bit floating point and applying a \(5\times 5\) box filter. In the last step, we inserted the elevation data of the lake floor into the DEM.  
Compared to SRTM, the higher spatial resolution of EM3D provides a better representation of the terrain (see Figure 2). It is expected to result in a more realistic simulation of local hydraulic phenomena in a complex topography.
# An Adaptive INS-Aided PLL Tracking Method for GNSS Receivers in Harsh Environments
## 6 Hardware Simulation
### The Test for the Trajectory with Lower Dynamic Condition  
The vehicle is stationary at the beginning, a transient jerk is added to keep the vehicle with a linear motion of 10 g acceleration heading east after 60 s, and a noise is added into the GPS signal with a rate of 0.2 dB/s at the same time. The sky plot is shown in Figure 17, and the Doppler and \(C/N_{0}\) variations of the visible satellites are shown in Figure 18.  
Three INS-aided PLL tracking methods are tested and compared here, which are the adaptive method proposed in this paper, and the two methods presented in [8; 15], respectively. Figure 19 shows the tracking performance of SV29. It can be shown that the adaptive INS-aided PLL tracking method has a much better tracking performance than the others with the tracking sensitivity increased by about 3.2 dB.  
Figure 16: Hardware simulation testing platform.  
Figure 17: Sky plot of the satellites in Trajectory 1.  
The root mean square (RMS) of three-dimensional position and velocity error is adopted for the navigation performance comparison in this paper. The position and velocity errors are shown in Figure 20. It can be seen that the adaptive tracking method has the best performance, and a same conclusion can be also drawn from Table 2, where the RMSs of the position and velocity errors are given. From the table, we can see that the velocity error is decreased by 37% and 40%, and the position error decreased by 24% and 17%.  
Figure 19: The output of PD (**left**) and the corresponding PLI (**right**) for SV29 in Trajectory 1.  
Figure 20: Position (**left**) and velocity (**right**) errors during the simulation in Trajectory 1.  
Figure 18: The Doppler (**left**) and \(C/N_{0}\) (**right**) for the satellites in Trajectory 1.
## 2 Materials and Methods
### Model Scenarios
#### 2.2.4 Machine Learning Implementation  
As outlined in Figure 1, we used three different approaches for the implementation. The Direct Model used machine learning inputs and outputs as follows:  
\[Neural\ Network\ (P,T,z_{i})=[x_{i},y_{i}] \tag{2}\]  
For Hybrid Model 1, we generated \(K_{i}\) from the neural network and then used one Rachford-Rice iteration. Such an approach ensures that mass balance is honored for \(z_{i}\), \(x_{i}\), \(y_{i}\), and the vapor-liquid fractions at all times.  
\[Neural\ Network\ (P,T,z_{i})=[K_{i}] \tag{3}\]  
\[Rachford-Rice\ (K_{i})=[x_{i},y_{i}] \tag{4}\]  
For Hybrid Model 2, we used the neural network to generate \(\ln(x_{i})\) and \(\ln(y_{i})\). We then calculated \(K_{i}\) and used one Rachford-Rice iteration. This way, we address the highly nonlinear nature of the \(K_{i}\) variables, large and small values of \(x_{i}\) and \(y_{i}\), and mass balance consistency between \(z_{i}\), \(x_{i}\), \(y_{i}\), and the vapor-liquid fractions.  
\[Neural\ Network\ (P,T,z_{i})=[\ln x_{i_{nn}},\ln y_{i_{nn}}] \tag{5}\]  
\[K_{i}=\frac{e^{\ln y_{i_{nn}}}}{e^{\ln x_{i_{nn}}}} \tag{6}\]  
\[Rachford-Rice\ (K_{i})=[x_{i},y_{i}] \tag{7}\]  
The next section demonstrates the results for the Direct, Hybrid 1, and Hybrid 2 Models. Each performance is compared to the ground reference from the physical model of the Peng-Robinson equation of state. We compare the physical model and machine learning approaches in both the near-critical region, as well as far away from critical conditions. The results compare the entire compositional space in ternary diagrams in contrast to the typically checked individual flash calculations that do not capture the holistic performance of machine learning models.
# Design of a Modified Compact Coupler with Unwanted Harmonics Suppression for L-Band Applications
## 2 Design Procedure
### Horizontal Resonator Design
These added stepped impedance resonators in the first and second steps are shown by Resonator1 and Resonator2 in Figure 8. Resonator1 is added in the first step to form the improved initial horizontal resonator, and Resonator2 is added in the second step to form the final horizontal resonator. The stepped impedance resonators can create poles and zeros in the frequency response. The added poles improve the pass band, flatten the S\({}_{21}\) parameter and decrease the return loss parameters. In addition, the added zeros widen the suppression band and improve the harmonic suppression ability of the resonator. Figure 7: Improved initial horizontal resonator: (**a**) layout and (**b**) frequency response. All dimensions are in the millimeter unit.
# Influence of Different Base Materials on the Microstructure and Properties of Clad Rebar
## 3 Results and Discussions
### Diffusion Analysis of Elements at the Composite Interface  
Figure 10 shows the results of the EPMA analysis at the composite interface. The figure shows that when the carbon content in the base layer is 0.22%, the diffusion distance of carbon is ~30 \(\upmu\)m. When the carbon content in the base layer is 0.53%, the diffusion distance of carbon increases to 80 \(\upmu\)m. The diffusive capacity of carbon increases due to the increase in the carbon content of the base layer, and the diffusion distance of carbon increases accordingly. Moreover, an \"uphill\" phenomenon occurs in all rebars as carbon diffuses. The mechanism of the \"uphill\" phenomenon relies on the fact that the austenitic structure is face-centered cubic with dense alignments of atoms and that carbon has a far slower  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **C** & **Fe** & **Ni** & **Cr** \\ \hline Spot 1 & 20.24 & 45.1 & 3.12 & 31.54 \\ Spot 2 & 14.78 & 46.67 & 3.23 & 35.32 \\ Spot 3 & 18.45 & 47.78 & 3.45 & 30.32 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Point scan analysis of the precipitates (wt. %).  
Figure 9: Diffraction analysis of the stainless-steel side of the 55#/316L rebar.  
diffusion speed in austenite than in ferrite. Therefore, when carbon crosses the composite layer, it accumulates at the stainless-steel side, resulting in the \"uphill\" phenomenon.  
Figure 10e shows that the diffusion distance of chromium in the 55#/316L rebar was -45 \(\upmu\)m, while the diffusion distance of chromium in the 20 MnSiV/316L, 35#/316L, and 45#/316L rebars was only \(\sim\)25 \(\upmu\)m. Combined with the information obtained from the metallographic images of the composite interfaces of various rebars, it is revealed that a large amount of chromium-rich carbides (C15.58 Fe7.42 Cr6) was formed at the composite interface of the 55#/316L rebar, which reduced the chromium content on the composite interface. Consequently, chromium far away from the composite interface continuously diffused to the composite interface, finally resulting in the increased diffusion distance of chromium. When studying the influence of the carbon content in the base on the microstructure of the stainless-steel composite interface, [PERSON] et al. used an electron probe to analyze the contents of various elements on the composite interface. As a result, the diffusion range of chromium increased noticeably when the carbon content increased from 0.06 to 0.2% [22].  
Due to the relatively weak affinity of iron, nickel, and carbon, stable iron-nickel carbides could not be formed. Therefore, no significant changes occurred in the diffusion distances of iron and nickel with increasing carbon content in the base layer. The diffusion distances of iron and nickel were approximately 30 and 10 \(\upmu\)m, respectively.
## 2 Thermal Error Modeling
### Introduction of CNN-LSTMNN  
#### 2.2.1 Convolutional Neural Network  
A convolutional neural network (CNN) enables the effective extraction of spatial features. CNN is utilized to extract features from input data by leveraging convolutional kernels. The extracted characteristics are subsequently fed into a fully connected network for recognition and forecasting tasks. The process of feature extraction includes four steps: convolutional computation, activation, batch normalization, and pooling.  
Convolution computation is an effective method for feature extraction. Convolution calculation usually uses a square convolution kernel to perform a sliding window operation on the input data at a specified step size to traverse the input features. At each step, the convolution kernel will appear to overlap regions with the input features. The corresponding elements of the overlapping regions are multiplied and summed, and the bias terms are added to obtain a fraction of the output features. The depth of the convolutional kernel should match the depth of the input features. After the convolution kernel is finished traversing the input features according to step size, the extracted output feature data can be obtained.  
Activation means choosing an activation function to introduce nonlinearity. Linear models have limited expressive capacity. Adding nonlinear factors will make the expression ability of neural networks more powerful.  
Batch normalization (BN) is a technique that standardizes the input data for every layer, centering their mean around 0 and scaling their variance to 1 and following a standard normal distribution. This technique helps mitigate the issue of gradient vanishing to some extent.  
Pooling involves downsampling feature data, reducing the number of features and decreasing dimensionality through sampling. Popularly utilized techniques comprise maximum pooling and average pooling. Maximum pooling can keep the most significant features in the input feature map and, at the same time, decrease the size of the characteristic graph to avoid the problem of overfitting. Average pooling involves computing the arithmetic mean of all pixels of the characteristic data map as the output in each pooling window. Both pooling methods are effective in enhancing the model s capacity for generalization, making it less sensitive to minor variations in the input data. Figure 2 illustrates the essential computation process of CNN.
## 2 Materials and Methods
### Sample Conditioning Prior and during Natural and Accelerated Carbonation  
For the test campaign on HVFA mortar, 160 \(\times\) 20 \(\times\) 40 mm\({}^{3}\) mortar prisms were cast. Sample preconditioning consisted of 1 day of in-mold optimal curing at 20 \({}^{\circ}\)C and 95% relative humidity (RH) while sealed with plastic film, 6 days of the same optimal curing conditions after demolding, 4 days of drying at 40 \({}^{\circ}\)C and 3 days of storage at 20 \({}^{\circ}\)C and 60% RH. The latter exposure conditions correspond with the temperature and RH prescribed during subsequent natural/accelerated carbonation tests. This way of sample preconditioning was similar to the one used in earlier carbonation related research by the authors [12]. After preconditioning the mortar prisms were equally divided in three groups for carbonation at 0.04%, 1% and 10% CO\({}_{2}\).  
For the CEM II/B-V 42.5 N mix and reference CEM I 42.5 N mix, regular \(160\times 40\times 40\) mm\({}^{3}\) mortar prisms were cast. Sample preconditioning also lasted for 14 days, yet differed somewhat given the test protocols prescribed by RILEM TC 281-CCC. After 24 h of in-mold optimal curing at 20 \({}^{\circ}\)C and 95% RH while sealed with plastic film, the demolded samples were kept completely wrapped in plastic film at 20 \({}^{\circ}\)C and 60% RH for the remainder of the preconditioning period (13 days). Only two carbonation exposure conditions were considered, i.e., natural carbonation at 0.04% CO\({}_{2}\), 20 \({}^{\circ}\)C and 60% RH, as well as slightly accelerated carbonation at 1% CO\({}_{2}\), 20 \({}^{\circ}\)C and 60% RH. The findings for HVFA mortar led to the decision not to consider highly accelerated carbonation at 10% CO\({}_{2}\) any further (see Section 3.1).