esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results and Analysis
### Uniaxial Loading Conditions
#### 3.1.2 Anisotropic Characteristics of Failure Modes
Layered shale exhibits more diverse failure modes than isotropic rock masses due to the presence of these weak bedding planes. These modes can be categorized as follows: (I) \(Z\)-shaped vertical splitting failure, (II) tensile fracture along the bedding plane, and (III) serrated progressive failure through the bedding. Shale exhibits two failure modes: types I and III for bedding angles of 0\({}^{\circ}\), 30\({}^{\circ}\), and 90\({}^{\circ}\). The failure section morphology is relatively complex, with a serrated structure observed,  
Figure 10: Tensile failure results and AE distribution under different bedding angles and distributions. (**a**) Bedding 0\({}^{\circ}\); (**b**) Bedding 30\({}^{\circ}\); (**c**) Bedding 60\({}^{\circ}\); (**d**) Bedding 90\({}^{\circ}\). and the tensile strength is relatively high. In contrast, at a bedding angle of 60\({}^{\circ}\), the bedding plane significantly influences the tensile failure process, predominantly resulting in type II failure. In this case, the fracture position depends on the location of the weaker bedding plane, making the exact position of the failure section more random. Figure 10c shows distinct cross-sections and AE signals from the sample's upper, middle, and lower parts. Moreover, the different bedding angles lead to two distinct fracture forms. At low bedding angles, the crack cross-section exhibits a serrated failure form. As the bedding angle increases, the failure mode shifts toward the bedding plane, resulting in a smoother crack cross-section parallel to the bedding. This phenomenon reflects a change in the failure mechanism, driven by alterations in the bedding plane angle under tensile stress. This change is responsible for the pronounced strength and failure anisotropy observed in shale under tension loading conditions. The spatial distribution characteristics of AE signals under different bedding angles reveal that damage predominantly occurs along the bedding planes. This is because the bond between bedding planes is weak, and under tensile stress, the cracks typically initiate and propagate along the bedding planes. At a 0\({}^{\circ}\) bedding angle, tensile cracks develop along both the bedding and the matrix. As a result, the difference in the crack and AE signal morphology shown in Figure 10a is more pronounced compared to the other three angles. In conclusion, weak bedding planes in shale lead to significant anisotropy during tensile failure.
## 5 Simulation Experiments  
### DRL Model Training  
The SUMO simulation software (V 1.8.0) was utilized as the experimental platform and the neural network was constructed using Pytorch. The network comprises five layers. The first is an input layer with 128 nodes that employs the Leaky Relu activation function. The subsequent three hidden layers are structured as follows: the first hidden layer contains 64 nodes, the second contains 32 nodes, and the third contains 16 nodes. Finally, the output layer, which has 13 nodes (equal to the size of the action space of DRL), employs the Softmax activation function. The Adam optimizer was employed with a learning rate of 0.005 and a reward decay coefficient \(\gamma\) equal to 0.97. The training involved the adoption of a greedy strategy. In addition, the feedforward neural network is used in this study. Except that the number of neurons in the output layer should be equal to the size of the action space, the rest are superparameters including the network structure, which is determined by experience. Since the learning methods are not interpretable, this study evaluates the parameter settings from the perspective of results. Future research can adjust the parameters according to its needs to obtain better performance. Nine distinct decision networks were constructed for the purpose of gaming with MDVs at various positions and different driving styles. Figures 6 and 7, respectively, show the rewards and game rounds of CAVs and conservative MDVs in region 2; curves of different colors show the improvement brought by distinguishing driving styles. If different driving styles are not considered as the same driver, the model must deal with the several styles. However, training multiple models for different styles can effectively solve this problem. Because different drivers react differently, the DRL model is difficult to converge. The game results between CAVs and both aggressive and moderate MDVs in other regions are comparable to those presented cases. Through the results, it can be clearly seen that the rewards obtained by the CAVs through the training of reinforcement learning when merging different driving styles have been significantly enhanced and stabilized. Concurrently, the number of gaming sessions has been significantly reduced, thereby shortening the duration of the stalemate.  
Figure 6: The rewards of gaming CAVs with conservative MDVs and unclassified MDVs.
# Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion
## 2 Theoretical Background
### Recurrent Neural Network  
Recurrent Neural Networks (RNN) [31] consists of an essential memory element due to which the present output depends not only on current input but also on previous input. However, as the input sequence size increases, the problem of vanishing gradient is observed during backpropagation. This problem makes RNN unsuitable for applications requiring long-term dependencies. To overcome this, advanced versions of RNNsknown as Long Short-Term Memory (LSTM), consisting of gates and memory elements, were introduced. These gates help regulate and extract information from the input and pass on gradients to the next node enabling the new sequence to be trained as equivalent as the earlier sequence and prioritize learning [32]. Also, LSTMs are more effective than conventional RNN [33].  
Sensor measurements are a continuous stream of data, and hence LSTM framework is applicable for extracting the features from the sensor measurements. The thermal camera provides images, and CNN is an appropriate choice for feature extraction. The two considered modalities are having different characteristics and do not have any time-level correlation. Hence, in our proposed framework, we have employed early fusion of features extracted by the LSTM model from gas sensors and by the CNN model from the thermal images data. The further section provides the details of the pipeline for data collection using the specified sensors, preprocessing the collected data, and developing the fusion frameworks for the proposed work.
# A Geostationary Instrument Simulator for Aerosol Observing System Simulation Experiments
## 3 Results
### Application of Synthetic Observations to Study the Synergy between TEMPO and GOES-R  
The GOES-R Series is a four-satellite program including GOES-R, GOES-S, GOES-T and GOES-U that will extend the operational GOES satellite system through 2036, and provide advanced imagery and atmospheric measurements of the Earth. The GOES-R Series will maintain the two-satellite operational system implemented by the current GOES satellites. The satellites will be located at 75.2\({}^{\circ}\) W and 137\({}^{\circ}\) W, providing excellent overlap with the TEMPO field of view. In addition, TEMPO and the GOES-R Series will complement each other in terms of spectral coverage. GOES-R measures 6 bands in the visible to near infrared (NIR) spectral range from 0.47 to 2.25 \(\upmu\)m, while TEMPO will provide spectral observations in the UV-Vis from 290 to 740 nm. Thus, there is an interest in studying how observations from the two platforms can be used synergistically. One application for the synergy of TEMPO and GOES-R observations is aerosol type detection. The combination of UV-Vis observations from TEMPO and NIR observations from GOES-R has the potential to provide aerosol type discrimination that may not be possible from observations from each sensor alone. In this section, we will describe how simulated observation data for a biomass burning and a dust event were used in a case study designed to demonstrate how future observations from TEMPO could be used in conjunction with simultaneous observations from GOES-R for aerosol type detection. Specifically, the focus for the two case studies was on (1) detecting smoke from the Pacific Northwest fires on 27 July 2006 in the G5 NR simulation, and (2) detecting wind-blown dust coming from the Sonoran Desert on 13 May 2007 in the G5 NR simulation.
## 5 A Framework for SDN-SDR-Based Maritime Communications
### The SDR Approach
#### 5.1.2 SDR Adoption  
SDR solutions exist and may be implemented in maritime communication systems and enhanced to keep up with the increasing demand for novel functionality. The US Navy has been developing SDRs for implementation on surface ships and submarines, saving space, reducing maintenance requirements, and adding functionality [50]. There are over 900 radios that have been initially delivered, and these systems are being improved with new features that also include National Security Agency-certified encryption for both voice and data [51]. The maritime industry can make ready use of the lessons learned from the US Navy's investment and enjoy the benefits of capable SDRs. While still meeting SOLAS Convention and ITU requirements, SDRs offer an opportunity to improve current data rate capabilities and remain flexible for future changes in communication protocols. In [52], the authors present a prototype for AIS and VDES that is implemented on a low-cost SDR platform. Their prototype platform specifically executes a selectable code for the AIS and VHF data exchange system (VDES) services in transmission and reception modes. The prototype has implemented an additional platform for filtering and amplification in the AIS and VDES bands to improve the performance of the SDR platform.  
In [53], the authors propose the creation of a low-cost AIS transmitter using SDR technology and open-source software. Through tests, the authors confirm that SDR-based AIS transmitters are a feasible solution since the AIS messages they sent out were received by commercial AIS equipment. Table 1 provides a summary of various studies exploring the utilization of SDN and SDR in maritime communications.  
\begin{table}
\begin{tabular}{c p{227.6 pt}} \hline \hline
**Reference** & **Summary** \\ \hline
[14] & SDN-based framework for stable and efficient communication strategies for UAVs, addressing intermittent links, changing network topology, power constraints, link switching, routing, and load balance. \\ \hline
[54] & SDN-based UAV network controller collects network statistics, uses data for decision-making, emphasizing controller responsiveness, load, and device proximity for enhanced cost reduction, reduced latency, and reliability. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of Research Papers.
# Interference Alignment in Multi-Hop Cognitive Radio Networks under Interference Leakage
## 3 Outage Probability Analysis
### Outage Probability in the Presence of Interference Leakages  
To compute the outage probability of the first \(K-2\) hops in the presence of interference leakages, we express (8) as \(\Gamma_{R_{i}\to R_{i+1}}=\frac{\gamma_{R_{i}\to R_{i+1}}}{1+\gamma_{j\to R_{i+1} }^{J}}\), where \(\gamma_{R_{i}\to R_{i+1}}=\frac{P_{R_{i}}||\mathbf{H}_{R_{i}\to R_{i+1}}||^{2} }{\sigma_{N}^{2}}\) and \(\gamma_{j\to R_{i+1}}^{I}=\frac{\sum_{j=1}^{L}P_{j}||\mathbf{H}_{R_{j}\to R_{ i+1}}||^{2}}{\sigma_{N}^{2}}\). Then, the probability density function (pdf) of \(\gamma_{R_{i}\to R_{i+1}}\) can be expressed as [36]:  
\[f_{\gamma_{R_{i}\to R_{i+1}}}(\gamma)=\frac{\gamma^{M^{2}-1}\exp\big{(}- \gamma/\tilde{\gamma}_{R_{i}\to R_{i+1}}\big{)}}{\big{(}\tilde{\gamma}_{R_{i} \to R_{i+1}}\big{)}^{M^{2}}\left(M^{2}-1\right)!}, \tag{14}\]  
and the pdf of \(\gamma_{j\to R_{i+1}}^{I}\) can be defined as:  
\[f_{\gamma_{j\to R_{i+1}}^{I}}(\gamma)=\frac{\gamma^{LM^{2}-1}\exp\big{(}- \gamma/(\alpha\tilde{\gamma}_{j\to R_{i+1}})\big{)}}{\big{(}\alpha\tilde{ \gamma}_{j\to R_{i+1}}\big{)}^{LM^{2}}\left(LM^{2}-1\right)!}. \tag{15}\]  
Then, the cdf of \(\Gamma_{R_{i}\to R_{i+1}}\) can be written as [27]:  
\[F_{\Gamma_{R_{i}\to R_{i+1}}}(\gamma)=\int_{0}^{\infty}F_{\gamma_{R_{i}\to R _{i+1}}}((x+1)\gamma)f_{\gamma_{j\to R_{i+1}}^{I}}(x)dx. \tag{16}\]  
We can find \(F_{\gamma_{R_{i}\to R_{i+1}}}(\gamma)\) by taking the integral of \(f_{\gamma_{R_{i}\to R_{i+1}}}(\gamma)\) with respect to \(\gamma\). Then, by substituting \(F_{\gamma_{R_{i}\to R_{i+1}}}(\gamma)\) and \(f_{\gamma_{j\to R_{i+1}}^{I}}(\gamma)\) into (16), \(F_{\gamma_{R_{i}\to R_{i+1}}}(\gamma)\) can be obtained as:  
\[\begin{split}& F_{\Gamma_{R_{i}\to R_{i+1}}}(\gamma)=1-\exp \bigg{(}\frac{-\gamma}{\tilde{\gamma}_{R_{i}\to R_{i+1}}}\bigg{)}\sum_{n=0}^{M ^{2}-1}\bigg{(}\frac{\gamma}{\tilde{\gamma}_{R_{i}\to R_{i+1}}}\bigg{)}^{n} \frac{1}{n!}\\ &\times\frac{1}{(\alpha\tilde{\gamma}_{j\to R_{i+1}})^{LM^{2}}} \mathcal{U}\bigg{(}LM^{2},LM^{2}+1,\frac{\gamma}{\tilde{\gamma}_{R_{i}\to R_{ i+1}}}+\frac{1}{\alpha\tilde{\gamma}_{j\to R_{i+1}}}\bigg{)},\end{split} \tag{17}\]where \(\mathcal{U}(\cdot,\cdot,\cdot)\) is [PERSON]'s confluent hypergeometric function [37]. As \(P^{(i)}_{\text{out},\mathcal{I}}=F_{\Gamma_{R_{i}\to R_{i+1}}}(\gamma_{th})\), the outage probability for the first \(K-2\) hops can be derived. With the aid of (17), for the last two hops, \(P^{(K-1)}_{\text{out},\mathcal{I}}\) can be expressed as:  
\[\begin{split} P^{(K-1)}_{\text{out},\mathcal{I}}=F_{\Gamma_{R_{K-2 }\to R_{K-1}}}(\gamma_{th})+F_{\Gamma_{R_{K-1}\to D}}(\gamma_{th})\\ -F_{\Gamma_{R_{K-2}\to R_{K-1}}}(\gamma_{th})F_{\Gamma_{R_{K-1}\to D}}( \gamma_{th}).\end{split} \tag{18}\]  
By substituting (17) into (18) and after replacing superscripts \(R_{i}\) with \(R_{K-2}\) and \(R_{K-1}\) and \(R_{i+1}\) with \(R_{K-1}\) and \(D\), \(P^{(K-1)}_{\text{out},\mathcal{I}}\) can be obtained. With the aid of (9), outage probability can be derived.
# Design of Multi-Mode Antenna Array for Use in Next-Generation Mobile Handsets
## 8 Integration of a Compact MM-Wave Phased Array
Figure 20: (**a**) Radiation beams, (**b**) active reflection coefficient, and (**c**) efficiencies at different scanning angles.
# Transitional SAX Representation for Knowledge Discovery for Time Series
## 1 Introduction
Using financial time-series data, the research showed that representations of ESAX are more precise than those of SAX without losing the symbolic nature of the original SAX. On the one hand, the storage cost of ESAX is triple that of the original SAX, since it necessarily locates the maximum and minimum along with the sample mean for each segment. Since SAX representations have low accuracy when distinguishing time series with similar average values but different trends, several attempts were made to qualitatively define a few trends, such as slight up/down and substantial up/down. [PERSON] et al. defined a SAX-based trend distance (SAX-TD) quantitatively by using the starting and ending points of a segment and improved the original SAX distance [14]. [PERSON] et al. proposed trend feature symbolic approximation (TFSA) using a two-step segmentation technique for rapid segmentation in long time series data [15]. TFSA, satisfying a lower bound criterion, showed better segmentation and classification accuracy. [PERSON] et al. also represented a time series as a sequence of symbols consisting of the average and trend for each segment [16]. Basically, it is an application of linear regression to time series sub-segments, and symbols take into account information on the sample averages and slope values. This method, called 1d-SAX, improved retrieval performance, while the compression ratio remained similar to the original SAX. In this paper, we propose a new symbolic representation method that incorporates transitional information of values according to time, enabling the method to easily track the direction in which a current symbolic representation moves toward the symbolic aggregate approximation. We aimed to capture important patterns in a systemic and meaningful fashion and append them to a piecewise representation method, such as SAX or PAA, for time series. We chose the SAX method to associate with the proposed method with because of its popularity and performance. Since neither SAX nor PAA suffer from low classification accuracy due to a high level of information compression or information loss, the proposed representation improves classification tasks and preserves interpretability. The remaining part of this article is organized as follows: Section 2 contains the background of SAX. Section 3 describes the proposed approach for improving SAX with trend information. Section 4 shows experimental designs and results to verify the performance and interpretability of the proposed representation. Section 5 concludes the research with future research directions.
# Research on Environmental Vibration Induced by High-Speed Maglev Transportation
## 5 Vibration Propagation and Distribution Laws  
### Vibration Propagation Law in Guideway Structures  
From the measurement results presented above, it is clear that the viaduct structure works well in reducing the vibration propagated to the ground. However, the height of the columns varies at different parts of the magley line: some are as tall as 10 m high, as in the test, but some columns are no more than 1 m in height, such as those at Pudong International Airport station. In order to predict the attenuation effectiveness of the column height, an empirical formula needs to be proposed.  
In order to evaluate the vibration magnitude of each test point more directly, as well as reduce the effects of vibration randomness, the running one-second root-mean-square (Is RMS) value was chosen. Using root-mean-square (RMS) values for acceleration evaluation can reduce the influence of the occasional extreme value, as well as indicate the effects of vibration duration; thus, the approach is widely utilized in structural vibration serviceability evaluation [19]. To calculate the running one-second RMS value, a running time window covers the whole length of the acceleration time history, each single RMS value is calculated within the one-second time window, and the largest RMS value is chosen as the representation of the acceleration record. In each time window, the RMS value is calculated using Equation (1):  
\[a_{rms}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}a_{i}^{2}} \tag{1}\]  
where \(a_{rms}\) is the RMS value to be calculated, \(N\) is the number of discrete acceleration records in the time window, and \(a_{i}\) is the \(i\)th discrete acceleration record.  
In this research, the altitude of the test point was chosen as the major affecting factor in vertical vibration propagation. Vibration records for the functional part, the upper and lower boards of the bearing, and the bottom of the column were chosen. The calculated 1s RMS values for four repetitions are shown in Table 2 (altitude indicates the distance to the test point in the functional part).  
By calculating the average vibration magnitude at each altitude shown in the table above, an empirical formula can be given, as in Equation (2):  
\[a_{rms}(h)=a\cdot e^{-bh}+c \tag{2}\]  
where h is the distance to the functional part; \(a_{rms}(h)\) is the 1s RMS value to be calculated at a distance of \(h\); and factor \(a=3.696\), \(b=1.038\), and \(c=4.299\times 10^{-2}\). A comparison of the measured data and the fitted curve is shown in Figure 13, and the R-square value is 0.9978.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Altitude** & **0 m** & **2.2 m** & **2.48 m** & **12.48 m** \\ \hline
1 & 3.786 & 0.435 & 0.282 & 0.053 \\
2 & 3.912 & 0.469 & 0.304 & 0.056 \\
2 & 3.626 & 0.455 & 0.251 & 0.048 \\
4 & 3.630 & 0.493 & 0.255 & 0.049 \\ \hline \hline \end{tabular}
\end{table}
Table 2: One-second RMS values of vertical test points (m/s\({}^{2}\)).
## 2 Materials and Methods
### Time-Series MODIS Data Processing  
MODIS 250 m 8 day composite reflectance product (MOD09Q1, version 5) from 2003 to 2016, and the 500 m land cover type products (MCD12Q1) from 2000 to 2014, were obtained from the Land Processes Distributed Active Archive Center (LP DAAC). The study area is completely covered by two MODIS tiles, h11v04 and h12v04. MODIS tiles were mosaicked using the MODIS re-projection tool (MRT). There were 46 reflectance composites each year. The 250 m reflectance product allows for calculation of vegetation indices with a higher temporal resolution than the standard vegetation index product MOD13Q1.
# Pseudo-3D Receiver Deghosting of Seismic Streamer Data Based on \(l_{1}\) Norm Sparse Inversion
###### Abstract  
The ghost effect in marine seismic data causes low-frequency suppression and frequency notch, resulting in incomplete frequency information for seismic records, which poses challenges for high-resolution imaging. The deghosting effect depends on the approximation of the ghost delay operator. Due to the strict requirements of dense sampling, the 2D deghosting method for a densely sampled inline dataset is still the mainstream method for marine data processing. As the trade-off, inversion-based methods are widely used in the industry to reduce the influence of the inaccurate ghost delay operator. In order to overcome the sampling limits and improve the 2D deghosting effect, we propose a pseudo-3D deghosting framework based on an \(l_{1}\) norm sparse inversion. In the framework, the inversion problem is divided into two subproblems, i.e., pseudo-3D operator building and optimization inversion. Considering the data coherence along the shot direction, we derive a pseudo-3D ghost delay operator to deghost simultaneously for multi-shot gathers. We then introduce a sparse inversion method in the pseudo-3D radon domain (multi-shot gathers) to further improve the inversion accuracy. The proposed framework is easy to implement, is not sensitive to noise, and shows superior performance in synthetic and field examples.  
reciever deghosting; sparse inversion; pseudo-3D deghosting 2022
# Predicting Electromagnetic Performance Under Wrinkling in Thin-Film Phased Arrays
## 4 Discussion
### Phased Array Antenna Analysis
Combining the above analyses, we conclude that out-of-plane deformation of the copper/film patch antenna has a marked effect on electrical performance, making wrinkle suppression essential. Variations in \(c_{fz1}\) and \(c_{fz2}\) affect the phased array's matching characteristics, resonant frequency, and pattern stability: \(c_{fz1}\) influences the main-beam most strongly, whereas changes in \(c_{fz2}\) have a larger effect on sidelobe structure and far-field radiation, potentially causing beam distortion and scan errors. Hence, to ensure stable phased array operation, structural deformation--particularly out-of-plane wrinkling--must be rigorously controlled to prevent radiation pattern distortion and gain degradation.