esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Encoding Scheme for AMBTC Compressed Images Using Ternary Representation Technique
## 4 Experimental Results
### The Performance of the Proposed Method
#### 4.1.1 Coding Efficiency Comparisons  
In the coding of smooth blocks, the original bitmaps of smooth blocks are used to obtain the cluster centers, and the quantized values are adjusted using the QA technique to lower the distortions. Tables 2 and 3 show how the QA technique improves the image quality when the block size is set to \(4\times 4\) and \(8\times 8\), respectively. In this experiment, \(\tau_{1}=16\) and \(\gamma=32\) are set. As seen from the tables, the QA technique effectively enhances the quality of reconstructed images for every \(k\). For example, in Table 2 and \(k=128\), the averaged quality of the reconstructed images with and without the QA technique is \(34.63\) and \(34.52\) dB, respectively. The averaged quality has improved by \(34.63-34.52~{}=~{}0.11\) dB. Similarly, in Table 3 when \(k=128\), the PSNR improvement is \(31.86-31.76~{}=~{}0.10\) dB. Therefore, the QA technique indeed reduces the distortion caused by replacing the original bitmap with a cluster center. Tables 2 and 3 also reveal that the increase in cluster size \(k\) also enhances the image quality. For example, in Table 3 when \(k=128\), the averaged PSNR of eight test images is \(31.86\) dB. When \(k=256\) and \(512\), the PSNR increases \(31.91-31.86=0.05\) dB and \(32.00-31.86=0.14\) dB, respectively. The reason is that a larger cluster size provides a greater chance to reduce the difference between the cluster centers and original bitmaps. To evaluate how threshold \(\tau_{1}\) affects the performance of the QA, we plot the gain of PSNR when using the QA for various \(\tau_{1}\) with \(k=128\) and \(512\). The results are shown in Figure 5. Note that, in this experiment, a block size of \(4\times 4\) is set. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{\(k=128\)} & \multicolumn{2}{c}{\(k=256\)} & \multicolumn{2}{c}{\(k=512\)} \\ \cline{2-10}
**Images** & **Bitrate** & \begin{tabular}{c} **w/o** \\ **QA** \\ \end{tabular} & **w/QA** & **Bitrate** & \
\begin{tabular}{c} **w/o** \\ **QA** \\ \end{tabular} & **w/QA** & **Bitrate** &
\begin{tabular}{c} **w/o** \\ **QA** \\ \end{tabular} & **w/QA** \\ \hline Lena & 1.60 & 35.83 & 35.96 & 1.63 & 35.95 & 36.05 & 1.68 & 36.07 & 36.15 \\ Jet & 1.52 & 35.85 & 35.91 & 1.55 & 35.92 & 35.97 & 1.58 & 35.99 & 36.03 \\ Baboon & 2.73 & 30.80 & 30.85 & 2.76 & 30.85 & 30.89 & 2.79 & 30.89 & 30.93 \\ Tiffany & 1.47 & 37.18 & 37.38 & 1.50 & 37.39 & 37.55 & 1.55 & 37.57 & 37.69 \\ Boat & 2.00 & 33.97 & 34.13 & 2.05 & 34.15 & 34.27 & 2.10 & 34.27 & 34.36 \\ Stream & 2.68 & 32.43 & 32.49 & 2.70 & 32.49 & 32.53 & 2.73 & 32.55 & 32.57 \\ Peppers & 1.68 & 35.34 & 35.57 & 1.73 & 35.52 & 35.71 & 1.78 & 35.71 & 35.85 \\ House & 1.95 & 34.73 & 34.78 & 1.97 & 34.79 & 34.82 & 2.01 & 34.85 & 34.88 \\ Average & 1.95 & 34.52 & 34.63 & 1.99 & 34.63 & 34.72 & 2.03 & 34.74 & 34.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Peak signal-to-noise ratio (PSNR) and bitrate of compressed images with \(4\times 4\) block size.
# Design and Dynamic Simulation Verification of an On-Orbit Operation-Based Modular Space Robot
## 2 Configuration Design of Reconfigurable Modular Space Robot
### Unit Module Mechanism Design
#### 2.3.2 Power System  
Inside the module, the module is monitored and controlled by connecting the communication module to the motor servo control system. The joint modular design requires that each module be an independent unit, so that the module is equipped with an independent power supply. Inside the module, the motor and its drive circuit board require a 24 V DC power supply, and the wireless module requires a 5 V DC power supply. Thus, a lithium battery and a voltage stabilizing module are provided to supply power to each device of the system. The wiring diagram of the entire system is shown in Figure 10.  
In order to make full use of the internal space of the module, each subsystem is installed in two sub-cavities. The main motor and transmission system are installed in the active sub-module of the system, that is, the wireless module and power supply are installed in the passive sub-module. In order to solve the problem of wire winding during rotation, the transmission shaft is designed as a hollow shaft, and the wiring is arranged inside the shaft. The corresponding wires on the stator of the conductive slip ring are connected to the active module motor and motor control board to achieve alignment. Conductive slip ring and DC step-down module as shown in Figure 11 below.  
Figure 11: Conductive slip ring and DC step-down module.  
Figure 9: Transmission slide rail.
###### Abstract  
The plant-parasitic nematode _Meloidogyne graminicola_ causes considerable damages to rice (_Oryza sativa_) culture. Resistance to _M. graminicola_ in the related species _Oryza glaberrima_ reduces root penetration by juveniles and stops further nematode development. _M. graminicola_ genes expressed during _O. sativa_ infection were previously characterized but no information is available about the molecular dialogue established with a resistant plant. We compared the _M. graminicola_ transcriptomes of stage-two juveniles (J2s) before and during infection of susceptible or resistant rice. Among 36,121 _M. graminicola_ genes surveyed, 367 were differentially expressed during infection of resistant or susceptible plants. Genes encoding cell wall-degrading enzymes, peptidases and neuropeptides were expressed for a longer time in resistant plants compared to susceptible plants. Conversely, genes related to nematode development were not activated in the resistant host. The majority of _M. graminicola_ effector genes had similar expression patterns, whatever the host genotype. However, two venom allergen-like protein (VAP)-encoding genes were specifically induced in resistant plants and _Mg-VAP1_ silencing in 12s reduced their ability to colonize roots. This study highlighted that _M. graminicola_ adapts its gene expression to the host susceptibility. Further investigation is required to assess the role of _Mg-VAPs_ in the rice-nematode interaction.  
plant-parasitic nematode; dual RNA-Seq; resistance; virulence effector; cell wall-degrading enzymes; cuticle; neuropeptide; venom allergen-like protein +
Footnote †: journal:  
_pathogens_
## 1 Introduction
Moreover, the extension to vector-valued processes or multi-output GPs (MOGPs) adjusts the probabilistic model to map inputs into a multidimensional output space as an EEG channel array [20; 21]. Former GP applications to EEG analysis in stress detection and cognitive stimulus recognition demonstrated the potential of GPs for BEA data modeling [22; 23]. Furthermore, a recently proposed covariance function, known as the multi-output spectral mixture (MOSM) analyzes dependencies with spectral information for multidimensional output processes [24]. The proposal novelty relies on the PSD design, ensuring the frequency constraints for real-valued signals. Additionally, the inverse Fourier transform of the MOSM covariance results in a temporal kernel with positive definiteness conditions that are harder to accomplish during kernel design. In this work, we propose the extension of MOGPs with an MOSM kernel for BEA discrimination, termed MOSM-GP. To this end, we learn an MOSM-GP for each EEG trial in a training set to model and quantify channel relationship patterns associated with particular EEG conditions. Then, we implement a likelihood measure to label new trial data into a specific class. The proposed framework of discriminative MOSM-GP, termed DMOSM-GP, is tested in two publicly available EEG datasets acquired under emotional [25] and motor imagery [26] conditions. The attained results show that the likelihood measure of testing data on the trained MOSM-GPs corresponds to a reliable discriminative index. The manuscript organization is as follows: Section 2.2 describes the theoretical background of MOSM-GPs and introduces the proposed framework. Section 3 presents the attained results on both EEG modeling and classification performance. Finally, Section 4 concludes the works with the most relevant findings.
## 3 Results  
### Optimization of Operating Variables  
Three-dimensional response surfaces and two-dimensional contour plots were presented to describe the optimization: the diagrams illustrate the effects of two factors on NP yield at a time. Figure 1 shows the effect of the fungal biomass (extract) and the concentration of total metals on NP yield after 15 min with 350 W microwave power. Increasing the metal concentration from 0.5 mM to 1.5 mM doubled the NP yield. Increasing the fungal extract from 1 mg to 1.5 mg reduced the yield. When the concentration was 1 mM, the maximal yield was achieved between 300 and 500 W and between 0.8 and 1.25 mg extract (Figure 2).  
A concentration of 1.5 mM caused a decline in the NP yield (Figure 3). Considering the reaction time, the optimal time was 15 min (Figure 4). When the MW power and the concentration were kept constant, first an increase and then a decrease in the NP yield were observed along with both time and extract (Figure 4a). Increasing the concentration from 0.5 mM to 1.5 mM increased the NP yield gradually when the extract and MW power were constant (Figure 4b). The optimal MW power was 350 W and time 15 min (Figure 4c). In summary, the yield was, at lowest, \(<\)15 mg and, at highest, 32 mg. The final conditions were 1.5 mM total metal concentration, 1.25 mg fungal biomass, 350 W, and 15 min.
## 3 Methods
### Feature Analysis  
The supervised algorithms provided within our framework are naturally equipped with methods to assess the importance of the input features by computing weighted rankings:  
* Support vector machines: we used the popular recursive feature elimination (SVM-RFE) algorithm [38]. The method is based on criteria derived from the SVM model to assess feature importance and thus to remove features having small criteria. The process is computed iteratively until all features are removed from the feature set: the final output is a ranked feature list.
* Random forests: for each tree, the feature importance was calculated as the decrease in node impurity weighted by the expected fraction of the samples reaching that node. For the overall forest, the normalized feature importance were simply summed.
* Artificial neural networks: we used the well-known Gedeon method [39]: it computes a feature ranking by considering the weights connecting the input features to both the two hidden layers.
# Evaluation of Multi-Stream Fusion for Multi-View Image Set Comparison
###### Abstract  
We consider the problem of image set comparison, i.e., to determine whether two image sets show the same unique object (approximately) from the same viewpoints. Our proposition is to solve it by a multi-stream fusion of several image recognition paths. Immediate applications of this method can be found in fraud detection, deduplication procedure, or visual searching. The contribution of this paper is a novel distance measure for similarity of image sets and the experimental evaluation of several streams for the considered problem of same-car image set recognition. To determine a similarity score of image sets (this score expresses the certainty level that both sets represent the same object visible from the same set of views), we adapted a measure commonly applied in blind signal separation (BSS) evaluation. This measure is independent of the number of images in a set and the order of views in it. Separate streams for object classification (where a class represents either a car type or a car model-and-view) and object-to-object similarity evaluation (based on object features obtained alternatively by the convolutional neural network (CNN) or image keypoint descriptors) were designed. A late fusion by a fully-connected neural network (NN) completes the solution. The implementation is of modular structure--for semantic segmentation we use a Mask-RCNN (Mask regions with CNN features) with ResNet 101 as a backbone network; image feature extraction is either based on the DeepRanking neural network or classic keypoint descriptors (e.g., scale-invariant feature transform (SIFT)) and object classification is performed by two Inception V3 deep networks trained for car type-and-view and car model-and-view classification (4 views, 9 car types, and 197 car models are considered). Experiments conducted on the Stanford Cars dataset led to selection of the best system configuration that overperforms a base approach, allowing for a 67.7% GAR (genuine acceptance rate) at 3% FAR (false acceptance rate).  
image sets; set similarity metric; same object verification; same view verification; DeepRanking; BSS error index +
Footnote †: journal: Applied  
000-0001-9980-3300][PERSON] and [PERSON]  
17 May 2021
## 4 Design Scheme of SIMP Method in the Abaqus-Matlab Topology Optimization Integration Framework
### Advantages of the Abaqus-Matlab Integration Framework  
This study combines the finite element analysis capability of Abaqus and the efficient computing characteristics of Matlab to achieve the integration of the SIMP method in the ABAQUS-MATLAB environment for the topological optimization of subframes. It is difficult to precisely define complex optimization models using Matlab alone, while using Abaqus alone is limited by the flexibility of its built-in optimization algorithms. Therefore, combining the two can fully leverage the powerful analytical capabilities of Abaqus and the rapid computing capabilities of Matlab to enhance optimization efficiency.  
The specific advantages of the Abaqus-Matlab integrated framework are as follows:  
1. Adapt to complex design requirements: Abaqus can conduct various working condition analyses, while Matlab controls the optimization process to ensure that the design meets all kinds of conditions.
2. Flexible optimization algorithm design: Matlab offers a flexible programming environment that allows for the customization of optimization algorithm parameters, such as penalty factors and filter radii. Combined with the simulation support of Abaqus, multi-objective optimization and complex constraint condition setting can be achieved.
3. Enhance optimization efficiency: Although the finite element solution of Abaqus is time-consuming, Matlab can quickly process gradient information and sensitivity analysis. Matlab is seamlessly integrated with Abaqus, simplifying the optimization process, accelerating the adjustment of material distribution, and enhancing work efficiency.
# Research on the Remaining Life Prediction Method of Rolling Bearings Based on Multi-Feature Fusion
## 1 Introduction
Firstly, the multi-sensor data is normalized, and the sensor data is combined in each channel to produce an optimal fusion of data from several sensors; Then, a parallel multi-branch feature learning network was constructed using TCN, LSTM, and Transformer, where TCN analyzes the data to identify and extract the long-time series features, LSTM captures the time-correlated features in the series data, and Transformer extracts the global feature representation of the bearing data. Meanwhile, a parallel multi-scale attention mechanism that captures both local and global dependencies is designed to go a step further in capturing the global and local contextual information of the sequence in order to accomplish adaptive weighted fusion from the output features of the three feature extractors. Secondly, the shallow features obtained by the parallel feature extractor are residually connected to the deeper features through the attention mechanism in order to enhance the efficiency of utilizing the information from the before and after features. Finally, the fused features output RUL predictions through the fully connected layer. The predictive model allows for a more comprehensive description of the operating condition of ball mill rolling bearings by fusing multiple features multiple times and capturing factors that may have an impact on their remaining life. This improves the accuracy of the prediction model and more accurately forecasts the RUL of rolling bearings. This paper's contribution can be summarized as follows:  
1. By fusing information from multiple sensors, more comprehensive, accurate, and reliable information is obtained, and feature extraction by parallel processing TCN, LSTM, and Transformer gives full play to their respective advantages to enhance the efficiency of the prediction model and the precision of the forecast outcomes. 2. A parallel, multi-scale attention mechanism is designed. By fusing features in both the time and frequency domains, we are able to capture comprehensive and specific contextual information from sequential data while capturing local and global dependencies. A more comprehensive representation of the data can be achieved. 3. A multi-feature fusion model for predicting the RUL of rolling bearings is proposed, which can enhance valuable information while reducing redundant information, ultimately achieving the effective fusion of multiple features. Better prediction results than the current prediction methods are achieved in the experimental validation. The subsequent sections of the paper are structured in the following manner: Section 2 provides a concise overview of the relevant background information; Section 3 provides a comprehensive explanation of the proposed methodology; Section 4 demonstrates the efficacy of the proposed method by analyzing two bearing datasets; and Section 5 provides the conclusion.
# Resilience Analysis of Traffic Network under Emergencies: A Case Study of Bus Transit Network
###### Abstract
Some networks become paralyzed after experiencing emergencies, unable to quickly digest and eliminate the negative impacts of the emergencies, while others can quickly overcome the adverse impacts of emergencies, and even take this opportunity to improve their own mechanisms for dealing with emergencies. The essential reason for the different results is the difference in resilience of the public transportation networks: a network with strong resilience has a strong ability to adapt and recover from disturbances caused by emergencies, while a network with weak resilience has a relatively lagging reaction capability, insufficient adaptability, and a longer recovery period. Based on the above background, in order to further explore the performance changes in the bus network under emergency situations, modeling and analyzing the resilience of the bus network has become the key to bus operations. However, in the process of modeling and analyzing the actual resilience of the bus network, how should the indicators that best represent the operating performance of the bus network be selected? How should the spatiotemporal dynamics and structural statics of the bus network be combined to analyze its resilience? These are urgent issues that need to be addressed in the current research on bus network resilience. In the context of practical problems, establishing a framework for studying the resilience of the bus network undoubtedly has significant academic and social value.
## 5 Relationship between Connectivity and Capacity  
Network capacity or throughput is an important constraint when considering the connectivity of ad hoc networks. In a wireless ad hoc network, the increase of the transmission power could increase the transmission distance of each node, which leads to the increased probability of network connectivity. However, the large power results in severe interference within the network, which reduces the network capacity and degrades the performance of decoding at receivers. On the other hand, reducing the transmission range by reducing the transmission power can limit the interferences, but reduces the probability of connectivity and increases the number of hops required to reach the destination. Therefore, the trade-off between connectivity and network capacity has been widely studied.
# Hand Gesture Recognition on Edge Devices:
## 8 Efforts to Bring HGR to the Edge
The small gesture classification algorithm can be achieved, as the pre-processing includes relative ToF calculation. Paper [23] suggests a solution for HGR based on Infineon BGT60 TR13C 60 GHz FMCW radar with one transmitter and three receivers. To prove edge applicability of the proposed algorithms, they are implemented on a Raspberry Pi4 with and without an Intel Neural Compute Stick 2. The algorithm is an optimization-based meta-learning approach. That means the main goal is to train the network to find a low-dimensional representation of the input that provides a basis for classification on new data--in the case of HGR, on user defined gestures. In [23], this representation occurs on the basis of RTI, RAI, and RDI, with VAE and a dense network or with a CNN. Ref. [77] captures data with TI IWR1443 76-81 GHz FMCW radar with three transmitters and four receivers. A Temporal Graph K-NN algorithm is generated on three radar point cloud data sets. Inference is implemented on a Raspberry Pi4, proving the edge applicability of this approach. The authors of [78] use Actioneer A1 pulsed radar. For classification, CNN-based automated feature extraction from RFDM is followed by a TCN. The final solution presented in [78] is specifically tuned for low power usage and memory footprint. It is implemented to run on a RISC-V microcontroller, the GAP8 from Greenwaves Technologies. Ref. [79] presents a transformer structure to replace the TinyRadar network, trained on the same data set. It outperforms the [78] solution in accuracy, power consumption and latency. Ref. [80] improves the edge applicability of the transformer presented in [79] by presenting a framework for edge transformers leveraging the RISC-V instruction set, applicable in several MCU classes. One possibility for showing edge applicability is by doing an HIL test. This was done for FMCW radar HGR in [75]. One of the earliest HGR based on WiFi [74] already introduces a real-time system. The output of this system is the calculated doppler shifts. An explanation is given how the four coarse gestures detected differ in their doppler shifts. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification** & **Data type** & **Accuracy** & **Size** & **Inference Time** & **Effort for Edge** \\
**Algorithm** & & & & & **Applicability** \\ \hline \hline \multirow{3}{*}{TCN [43]} & \multirow{3}{*}{Ultrasound} & \multirow{3}{*}{95.8\%} & Area \(\leq\) 13,829 \(\upmu\)m\({}^{2}\) & \multirow{3}{*}{Latency \(\leq\) 302,945 cycles (analysis based on HLS)} & Data flow transformation \\  & & & & (analysis based on HLS) & with FIR filter, sharing of hardware units \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of algorithms used for HGR with focus on real-time.