esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Validation
### Cross-Domain Fault Diagnosis Case 1: CWRUi  
#### 4.2.1 Experimental Platform and Dataset  
The effectiveness of the proposed method is tested in this section using CWRU drive-end bearing failure data. Figure 5 depicts the layout of the test bench, which consists of an induction motor, a torque encoder, a dynamometer, control electronics and accelerometers.  
The bearing vibration signals were collected under different load conditions, including (0 HP, 1797 rpm), (1 HP, 1772 rpm), (2 HP, 1750 rpm), and (3 HP, 1730 rpm), respectively. According to the needs of the motor load on the mechanical conditions, four sets of bearing data were collected for the investigation. Dataset A, Dataset B, Dataset C, and Dataset D are the names of the four datasets. There are ten states for each bearing data set, including normal and three fault sizes (0.007, 0.014, and 0.021 inches), that correlate to three fault locations (inner ring, outer ring and rolling body). There are four modes, namely normal condition (NC), inner race fault (IF), ball fault (BF), and outer race fault (OF), for each situation, as depicted in Table 2.  
We initially trained the MiniImageNet based feature extractor that was utilized in the target dataset, and we divided the training and validation sets into 4:1 categories to test the performance of the proposed method. The driver-end data with a sample frequency of 48 kHz was then chosen from the CWRU database to create the target dataset. To simulate the case of small datasets, three few-shot training settings were used for each dataset, namely 10-way 3-shot, 10-way 5-shot, and 10-way 10-shot, respectively. In each round of the K-way N-shot experimental setup, N samples are taken from 200 test samples for each of the K categories, K \(\times\) N samples are used as the training set, and K \(\times\) (200-N) samples are utilized as the test samples. The specific description of the data set is shown in Table 3.  
Using the MEMTF multiscale morphological method, the original 1D vibration signal is transformed into a 2D image with more feature information. Figure 6 displays the results of the signal conversion to image under normal operating conditions. The resulting image has the dimensions 84 \(\times\) 84. The results of the conversion under the other 9 state conditions are shown in Figure 7. As can be seen from the converted image results, the images in different fault states look completely dissimilar, providing an intuitive way to classify the method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fault Size (Inches)** & \multicolumn{3}{c}{**Fault Mode**} \\ \hline
0 & & NC & \\ \hline
0.007 & BF-1 & IF-1 & OF-1 \\
0.014 & BF-2 & IF-2 & OF-2 \\
0.021 & BF-3 & IF-3 & OF-3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: CWRU bearing dataset faults classification.  
Figure 5: Bearing experimental platform of CWRU.
## 2 Experimental Set-Up
### Ultrasonic Data Acquisition System
#### 2.3.1 Ultrasonic Wave Probe  
In order to search for transmission laws of ultrasonic wave during the destruction of the rock sample, the load-bearing plates on the testing machine needed to be replaced by multi-frequency ultrasonic wave probes developed by ourselves, and the specific steps are as follows.  
The ultrasonic wave probe, an important sensor in the tests of rock physical and acoustic properties, can send ultrasonic wave and receives acoustic signals modulated by rock physical and mechanical properties. The acoustic wave test system is used to detect physical and acoustic properties of rock samples under different stresses; thus, the ultrasonic wave probe must be able to withstand high pressure.  
Figure 3 shows the structure of the ultrasonic wave probe. The ultrasonic wave probe is made up of piezoelectric crystal chips, resonance coils, a probe chamber, a probe cap, sound-absorbing material, signal leads and a joint. The ultrasonic wave probe uses the converse piezoelectric effect to send ultrasonic waves and uses direct piezoelectric effects to receive ultrasonic waves. In acoustic tests, the ultrasonic wave probe must withstand high pressures of up to 300 MPa; thus, the probe needed to be thickened, especially the contact between the probe and tested sample. The thickness of the face plate of the probe (the metal plate between the crystal chips and tested sample) was designed as an integer times the wave length of the transmitted ultrasonic wave to augment the energy of the transmitted or received ultrasonic waves.
## 7 Results and Discussion
### Results
#### 7.2.2 Packet Delivery Ratio  
Our proposed schemes CRP-GR and CRP-G have the best delivery packet delivery ratio compared to FBCFP, TEEN and LEACH. Figure 9a,b show packet delivery ratio versus the number of rounds with different mobility speeds of the nodes. The packet delivery ratio decreases due to the available fixed bandwidth of the network by increasing the number of nodes. The FBCFP, TEEN and LEACH delivery ratio is less due to selecting repeated routes for data transmission from source to destination. Our proposed schemes perform better than other schemes due to selecting the optimal cluster head (CH) for every cluster and better node distribution, making fluent data transmission between cluster head (CH) and base station. The average performances of the proposed schemes, CRP-GR and CRP-G, with respect to the packet delivery ratio were 67% and 49%. The existing schemes, such as FBCFP, TEEN and LEACH, had average performances of 42%, 25% and 19% accordingly.  
Figure 8: Throughput vs number of rounds.
## 3 Analytical Expressions
### Low-Frequency Amplifiers
#### 3.5.1 Transimpedance Amplifier (TIA)  
The envelope detector circuit can be modeled as a Thevenin equivalent with an internal current source that can be easily useful for the modeling with other WuRx stages. The TIA circuit acts similar to a current-to-voltage amplifier with an adjustable conversion factor. TIA circuits are typically implemented as OA-based amplifiers. Figure 10 shows the schematic used in this implementation. The TIA is supplied by \(V_{\text{V}+}=3.0\,\mathrm{V}\).  
The TIA output voltage is defined as shown in Equation (11), which depends on \(R_{\text{D}}\) the diode's Thevenin resistance, \(I_{\text{D}}\) the diode's source current, and \(R_{\text{f}}\) the TIA's feedback resistor.  
\[V_{\text{out,TIA}}=-R_{\text{f}}\cdot I_{\text{D}}=-\frac{R_{\text{f}}}{R_{ \text{D}}}\cdot U_{\text{d}} \tag{11}\]  
With \(R_{\text{f}}=200\,\mathrm{k}\Omega\) and \(R_{\text{D}}\approx 10\,\mathrm{k}\Omega\), the required voltage gain of 20 was reached. A reference voltage of \(1.2\,\mathrm{V}\) was used to ensure that the proper output voltage swings. \(C_{1}\) is the corresponding bias-blocking capacitor interrupting the DC current flow towards the envelope detector.  
The performance of the TIA is mainly related to the used OA where the supply current and gain bandwidth product (GBWP) are the main challenging elements during their selection within an application. In fact, the current requirement of the OA should be as small as possible, resulting in a low GBWP value.
# Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing
## 5 MASU Empirical Evaluation
### Evaluation Results
#### 5.4.4 Resource Consumption of Storages and Relays  
In order to assess the CPU consumption of the Storage and Relay roles, we performed a test where two Consumers subscribe to the GPS sensing services of a Producer and a Storage, respectively. This test is useful to evaluate the computational cost of both Storages and Relays since such a cost is only related with the rate at which Storages or Relays send data to Consumers. Therefore, it is not necessary to perform two separate tests to evaluate both types of roles.  
In this test the Storage role was assigned to the last node entering the network. This Storage receives the GPS data sensed by the Producer every 60 s and sends such data to the Consumer subscribed to it at the same rate. Results in Figure 20 show how the CPU utilization in the Storage is slightly higher than the in the Consumer but significantly lower than in the Producer. In addition, the CPU utilization of both Producers and Consumers in the case of having one Producer and two Consumers only increases slightly when we add one Storage. In this case, we have three units receiving the information generated by the Producer (i.e., two Consumers and one Relay) but at a lower cost for the Producer, reducing its CPU utilization a 23. These results indicate the usefulness of including Storages or Relays when we have two Consumers and one of them cannot receive the information from the Producer properly.
## 4 Experiments
### Results and Discussion
#### 4.4.3 Comparisons with Varying Class Imbalance Rates  
To verify the robustness of our method, we conducted experiments under five different class imbalance rates. According to the results of the previous two experiments, CART and ensemble learning are the next-best methods to our approach. Therefore, this section compares the performance of the proposed method with these two methods under different class imbalance rates. In this work, we define the class imbalance rate as follows:  
\[IR=\left|\begin{array}{c}M\textit{a}jority\\ \hline\textit{Minority}\end{array}\right| \tag{15}\]  
where \(\left|\begin{array}{c}M\textit{a}jority\\ \end{array}\right|\) and \(\left|\begin{array}{c}M\textit{a}ority\\ \end{array}\right|\) are the number of samples belonging to the majority and minority classes, respectively. Figure 6 compares the average F1-score results of three different methods at different class imbalance rates (i.e., IR equals 2, 6, 10, 14, and 18).  
Figure 5: The performance of our proposed method is compared with class imbalance methods.  
As shown in Figure 6, as the class imbalance rate increases, the F1-score of these three methods gradually decreases. For the ensemble learning method, when IR = 2, its F1 result is 77%, but its F1 value drops to approximately 55% as the IR increases to 18, causing an overall decline of 22%. The second method, CART, fell by 20%, i.e., from 71% to 51%. Our method also shows a downward trend as the imbalance rate increases from 78% when IR = 2 to 67% with IR = 18, but compared with the other methods, this downward trend is relatively gentle. For example, our method achieves the best F1 value (78% when IR = 2). As IR increases to 10, our result is 70%, the result of ensemble learning is 66%, and CART falls to 55%. When the class imbalance rate increases to 18, the F1-score of our method is 67%, which is only 3% lower than when IR = 10, but CART and ensemble learning methods are 4% and 11% lower, respectively. Finally, the comparison shows that our method maintains relatively stable overall performances with different class imbalance rates, and therefore it shows better robustness.
## 3 Experiments and Analysis
### Experimental Scheme  
In this article, three experiments were designed, all based on the LACB_ECBAM Net surface damage image dataset, but with different purposes. The first experiment was a training and testing of LACB_ECBAM Net, aiming to validate its effectiveness for  
\begin{table}
\begin{tabular}{c c} \hline
**CNN** & **Sample Size/(w,h)** \\ \hline AlexNet [37] & 224 \(\times\) 224 \\ VGG-16 [38] & 224 \(\times\) 224 \\ VGG-19 [38] & 224 \(\times\) 224 \\ GoogLeNet [39] & 229 \(\times\) 229 \\ MobileNet [33] & 224 \(\times\) 224 \\ MobileNet\_V2 [40] & 224 \(\times\) 224 \\ ShuffleNet & 224 \(\times\) 224 \\ ShuffleNet\_V2 [41] & 224 \(\times\) 224 \\ Xception [42] & 299 \(\times\) 299 \\ \hline \end{tabular}
\end{table}
Table 1: Common sample size.  
Figure 9: Three kinds of blade images. (**a**) Normal. (**b**) Cracks. (**c**) Surface shedding.  
LACB_ECBAM Net surface damage identification. The second experiment performed a comparison test by applying LAC Block and E_CBAM to AlexNet to verify the generalizability of the improved method. The third experiment compared various common lightweight networks based on transfer learning to measure the performance difference between LACB_ECBAM Net and other lightweight networks.  
For the experimental environment, the operating system was Windows 10 Professional Edition, Python 3.6.5, and the deep learning framework was Pytorch 1.7.1, developed by Facebook Ai Research (Silicon Valley, CA, USA, [[https://pytorch.org/](https://pytorch.org/)]([https://pytorch.org/](https://pytorch.org/)), accessed on 10 September 2022). The CPU was an 11 th Gen Intel Core i7-11800 H 2.30 GHz, 16 GB of RAM, and the GPU was an NVIDIA GeForce RTX 3060 Laptop GPU-6 GB. We used CUDA 11.2 and CUDNN 8.2.4, developed by NVIDIA (Santa Clara, CA, USA, [[https://developer.nvidia.com/cuda-zone](https://developer.nvidia.com/cuda-zone)]([https://developer.nvidia.com/cuda-zone](https://developer.nvidia.com/cuda-zone)), accessed on 10 April 2013), as training acceleration kits for the network model. In terms of experimental assessment, the effectiveness of the WTB damage identification approach was assessed using four evaluation metrics often employed in the field of classification, namely accuracy, recall, precision, and F1 score.
## 3 The Proposed Method
### Dimension Reduction  
From preliminary observations, we found that feature maps in images and texts usually have very high dimensions, which could be as high as 8192 after feature fusion. In addition to computational time complexity, it may also include a lot of noise inside. To find out the most important features, we further apply autoencoders for dimension reduction.  
Given a fixed dimensional representation of the user preferences as inputs, we design the autoencoder as a single-hidden-layer \(d\)-dimensional neural network. Autoencoders consist of an encoder that transforms input to a code, and a decoder which reconstructs the input from the code. When the number of neurons in the hidden layer of autoencoders is less than that in the input layer, it is forced to learn the compressed representation of the input data. After dimensionality reduction, our model performs similarity calculations among users to generate a list of recommendations. The architecture is shown in Figure 5.  
Since the goal of using autoencoders is to reduce dimensionality by simple neural networks, it would be beneficial if the dimensionality can be reduced while maintaining comparable performance in recommendation. In order to find out the most compact representation which is still effective in recommendation, we conducted experiments to verify the effects of different dimensionality \(d\) on the performance of user recommendation.
# A Multi-Frame PCA-Based Stereo Audio Coding Method
## 7 Conclusions  
The framework of proposed multi-frame PCA-based audio coding method has several differences compared to other codecs; therefore, there are lots of barriers to the design of an optimal algorithm. This paper proposed several ways to remove those barriers. For mono signal, the design of PCA-based coding method in this paper, including multi-frame signal processing, matrix design, and quantization design can hold it efficiently. As to stereo signal, PCA has high associativity with parametric stereo, which makes PCA-based parametric stereo certainly feasible and significant. Experimental results show satisfactory performance of the multi-frame PCA-based stereo audio coding method compared with the traditional audio codec.  
In summary, research on the multi-frame PCA-based codec, both for mono and stereo, has certain significance and needs further improvement. This kind of stereo audio coding method has good performance in processing different kinds of audio signals, but further studies are still needed before it can be widely applied.  
[PERSON] conceived the method and modified the paper, [PERSON] performed the experiments and wrote the paper, [PERSON] and [PERSON] contributed suggestions, and [PERSON] supervised all aspects of the research.  
National Natural Science Foundation of China (No. 61571044).  
The authors would like to thank the reviewers for their helpful suggestions. The work in this paper is supported by the cooperation between BIT and Ericsson.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 2 Materials and Methods
### Feature Selection Methods  
The radiomic features were subjected to a feature selection process in order to prevent overfitting, improve learning accuracy, and reduce computation time [29]. To this end, we considered three feature selection techniques widely used in the radiogenomics field: pairwise correlation filter, PCA, and QR decomposition. Additionally, a baseline was also implemented where no feature selection method was used to compare the results obtained with and without feature selection.  
The pairwise correlation filter removes variables whose pairwise correlation is greater than a specific cutoff. First, a correlation matrix is created with values representing the pairwise correlations for all feature combinations. Then, features that have an absolute pairwise correlation equal to or greater than the cutoff are excluded [30]. After investigating multiple cutoffs, the cutoff value was set to 0.95.  
PCA is a method that projects high-dimensional data into a new lower dimensional representation while keeping all relevant linear structure intact. This method generates new, uncorrelated variables that explain a large proportion of the variance in the original feature space [31]. PCA was implemented at seven different cutoffs, where the number of components accounted for percentages of variance in the feature set ranging from 65% to 95%.  
The QR decomposition along with an iterative procedure is used to remove features that are linear combinations of others. The feature matrix is decomposed into two matrices: the orthogonal matrix Q and the upper-triangular matrix R. The latter is used to determine which features are linearly dependent so that they can be sequentially removed [32].
# A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching
## 4 Results and Discussion  
In this study, we analyzed a total 85 feature detector-descriptor combinations pairs. It is not possible to show outputs of all combinations here; therefore, we show stitched image outputs of the top six combinations in each dataset category. Figure 4 shows the output stitched images of the top six combinations which produce better quality of stitched images in comparison to all other combinations for dataset 1. Upon close examination of the stitched images, it can be observed that AKAZE + AKAZE combination gives the best-quality image, which is further verified by the objective IQA metrics. Figure 4: Image stitching outputs obtained for different feature detector–descriptor combinations for dataset 1. For dataset 1, ORB + SIFT has a very good inlier ratio, but the quality of the stitched image is poor in comparison to other pairs. This pair has poor quality of stitched images despite its very good inlier ratio because in order to obtain a better-quality image, the matched points should be spread into the image, but for this pair, matched points are clustered. It also takes more execution time. ORB + BRISK also has a very good inlier ratio, but the quality of the stitched image is poor because of the low number of matched points. It has better execution time than ORB + SIFT, which may be because the BRISK descriptor takes less time to extract descriptors than SIFT. AKAZE + SIFT and AKAZE + DAISY both have very good inlier ratio, a little lower than ORB + BRISK. The quality of stitched image \begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline
**Det/Desc** & **AKAZE** & **BRIEF** & **BRISK** & **DAISY** & **FREAK** & **KAZE** & **ORB** & **SIFT** & **SURF** \\ \hline
**AGAST** & NC & 0.91869 & 0.92354 & 0.90394 & 0.90831 & NC & 0.92501 & 0.91243 & 0.00000 \\
**AKAZE** & 0.92945 & 0.87339 & 0.92881 & 0.91095 & 0.91303 & 0.91170 & 0.92436 & 0.91196 & 0.89611 \\
**BRISK** & NC & 0.92252 & 0.88374 & 0.91531 & 0.92080 & NC & 0.85495 & 0.91904 & 0.91795 \\
**FAST** & NC & 0.91688 & 0.92586 & 0.91589 & 0.90936 & NC & 0.91269 & 0.90447 & 0.91535 \\
**GFTT** & NC & 0.90719 & 0.92617 & 0.92024 & 0.91344 & NC & 0.89364 & 0.90513 & 0.00000 \\
**KAZE** & NC & 0.91274 & 0.89847 & 0.92245 & 0.90594 & 0.91285 & 0.91383 & 0.89385 & 0.91251 \\
**MSD** & NC & 0.85767 & 0.88436 & 0.90943 & 0.90857 & NC & 0.88385 & NC & 0.91923 \\
**MSER** & NC & 0.00000 & 0.91407 & 0.00000 & 0.00000 & NC & 0.00000 & 0.00000 & 0.91407 \\
**ORB** & NC & 0.88139 & 0.86445 & 0.88829 & 0.00000 & NC & 0.90260 & 0.83293 & 0.91399 \\
**SIFT** & NC & 0.90636 & 0.88154 & 0.91642 & 0.86461 & NC & NC & 0.91924 & 0.91398 \\
**Star** & NC & 0.90999 & 0.00000 & 0.89682 & 0.88858 & NC & 0.87969 & 0.88514 & 0.91265 \\
**SURF** & NC & 0.91988 & 0.91946 & 0.90890 & 0.91036 & NC & 0.92380 & 0.91110 & 0.90780 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: VSI for different feature detector–descriptor combinations for dataset 1.
## 2 Deep Learning and Dataset Preparation
### Simulation Dataset Preparation
#### 2.2.2 Phase Map  
When the deformation distribution \(w(x,y)\) is estimated from the above, a phase map \(\Delta(x,y)\) can be calculated according to the principle of digital shearography. Figure 2 is a schematic diagram of an out-of-plane Michelson-type digital shearography system. The laser is expanded with Lens 1 and then projected onto the object. The diffuse light from the object surface is divided into two beams by the Michelson device with a beamsplitter and two mirrors. Mirror 1 is tilted at an angle to produce image shearing, and Mirror 2 is usually fixed on a piezo-electric transducer (PZT) to generate phase shifting. Suppose the shearing direction is along \(\mathbf{k}=(\cos\,\alpha,\sin\,\alpha)^{T}\); the phase difference (or phase map) \(\Delta(x,y)\) between before and after loading is denoted as  
\[\begin{array}{l}\Delta=\frac{4\pi\delta_{k}}{\Lambda}\frac{\partial w}{ \partial k}\\ =\frac{4\pi\delta_{k}}{\Lambda}\left(\frac{\partial w}{\partial x}\cos\alpha+ \frac{\partial w}{\partial y}\sin\alpha\right)\end{array} \tag{1}\]  
where \(\lambda\) is the laser wavelength and \(\delta_{k}\) is the shearing value in the \(\mathbf{k}\) direction.  
Figure 1: YOLOv4 architecture.  
With different settings in shearing value and shearing direction of just one simulated deformation distribution, amounts of simulated phase maps will be calculated. Therefore, shearing settings can also meet the diversity consideration.