esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Volume Estimation
#### 2.3.3 Learning-Based Multi-View Stereo Volume Estimator  
In E-Volume [7] network architectures, a learning-based multi-view stereo (MVS) technique is used to overcome the reliance on depth cameras in traditional methods, reducing hardware requirements. This system captures multiple images from different angles and uses the backbone network of MVSNet to generate depth maps. These depth maps are then converted into a complete scene point cloud, providing a three-dimensional representation of the scene. Next, a segmentation mask extracts the target object from the point cloud, ensuring that only the relevant data are considered for volume estimation.  
For volume estimation, a neural network composed of four convolutional layers and batch normalization layers is used to extract features from the target object point cloud. These features are downsampled and passed through pooling layers to form a global feature vector, which is then fed into fully connected layers to predict the object's volume, as shown in Figure 2.  
This innovative approach eliminates the need for specialized depth cameras and enhances the flexibility and applicability of volume estimation in various environments. By leveraging learning-based MVS and advanced neural network architectures, the learning-based multi-view stereo volume estimator provides an efficient and accurate volume estimation solution suitable for multiple applications, including dietary management and health monitoring.
## 4 Discussion
### Uncertainty of Input Data in MODIS GPP Estimation in Diversity Ecosystems in the Arid Region
For example, improving the estimation of MODIS FPAR and land cover classification products in arid regions using data-driven approaches [59] and improving model structures [60] could be better choices for improving GPP simulation in arid regions. Meanwhile, since the biome-specific look up tables (BPLUT) are constant for a given biome at any time. Since the current BPLUT of the MOD17 cannot meet the needs of accurate definition of the parameters for all ecosystems [19; 61], especially for the diverse and complex ecosystems in arid regions, further research needs to be done to update these BLUPT of the model. In addition to update the parameter of \(\varepsilon_{\text{max}}\), the water and temperature-limited parameters are also of great importance in GPP estimation, especially for the ecosystems in arid regions. As the development of eddy covariance technique, there are more than 900 EC flux sites in the world currently [62]. With the availability of these large number of flux datasets, it provided us the opportunity to retrieve the biome specific parameters for each vegetation type more reasonable, which may improve the accuracy of the current GPP simulation in the arid region.
## 3 Results
### In Vitro Antifungal Activity of VOCs Produced by Strain CT32  
The VOCs emitted by strain CT32 significantly (\(p<0.05\)) inhibited the mycelial growth of all tested pathogens, although with different inhibition extents (Figure 1). _V. dahliae_ was the most susceptible fungus to VOCs emitted by strain CT32, and its mycelial growth was inhibited by 66.67%. In contrast, _T. cucumeris_ showed the greatest resistance to bacterial volatiles. Additionally, the VOCs produced by strain CT32 significantly reduced the mycelial growth of _G. cingulata_, _B. cinerea_, _B. dothidea_, _F. oxysporum_ f. sp. _niveum_, FOF and _F. oxysporum_ f. sp. _cucumerinum_, with percentages of inhibition ranging from 19.76% to 55.02%. The suppressive effect of the VOCs emitted by strain CT32 on the in vitro growth of eight pathogenic fungi is presented in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Bacterial Isolate**} & \multicolumn{2}{c}{**Colony Diameter of Control (mm)**} & \multicolumn{2}{c}{**Colony Diameter of Treatment (mm)**} & \multicolumn{2}{c}{**Inhibition Rate (\%)**} \\ \cline{2-7}  & _V. dahliae_ & **FOF** & _V. dahliae_ & **FOF** & _V. dahliae_ & **FOF** \\ \hline CT11 & 81.22 \(\pm\) 0.70 & 87.47 \(\pm\) 0.64 & 56.12 \(\pm\) 0.89 & 54.18 \(\pm\) 2.58 & 32.93 \(\pm\) 1.17 d 1 & 40.36 \(\pm\) 3.13 b \\ CT32 & 81.22 \(\pm\) 0.70 & 87.47 \(\pm\) 0.64 & 30.20 \(\pm\) 1.57 & 49.77 \(\pm\) 1.53 & 66.94 \(\pm\) 20.6 a & 45.72 \(\pm\) 1.86 a \\ CT56 & 81.22 \(\pm\) 0.70 & 87.47 \(\pm\) 0.64 & 47.85 \(\pm\) 0.60 & 68.55 \(\pm\) 1.70 & 43.78 \(\pm\) 0.79 b & 22.94 \(\pm\) 2.06 d \\ CT58 & 81.22 \(\pm\) 0.70 & 87.47 \(\pm\) 0.64 & 52.55 \(\pm\) 2.19 & 65.37 \(\pm\) 1.03 & 37.61 \(\pm\) 2.87 c & 26.80 \(\pm\) 1.25 c \\ CT64 & 81.22 \(\pm\) 0.70 & 87.47 \(\pm\) 0.64 & 67.82 \(\pm\) 1.26 & 79.38 \(\pm\) 0.83 & 17.58 \(\pm\) 1.65 e & 9.80 \(\pm\) 1.01 e \\ \hline \hline \end{tabular}  
* Data are the mean \(\pm\) standard deviation. In the same column, data with different lowercase letters are significantly different according to [PERSON]’s multiple range test (\(p<0.05\)).  
\end{table}
Table 1: Inhibitory effect of volatile compounds produced by antagonistic bacteria on the mycelial growth of _V. dahliae_ and _F. oxysporum_ f. sp. _fragariae_ (FOF).  
Figure 1: Inhibitory effect of volatile organic compounds (VOCs) produced by strain CT32 on the mycelial growth of 8 plant pathogenic fungi. Values with different lowercase letters are significantly different according to [PERSON]’s multiple range test at the 0.05 level.
# Ultrasonic Through-Metal Communication Based on Deep-Learning-Assisted Echo Cancellation
###### Abstract  
Ultrasound is extremely efficient for wireless signal transmission through metal barriers due to no limit of the Faraday shielding effect. Echoing in the ultrasonic channel is one of the most challenging obstacles to performing high-quality communication, which is generally coped with by using a channel equalizer or pre-distorting filter. In this study, a deep learning algorithm called a dual-path recurrent neural network (DPRNN) was investigated for echo cancellation in an ultrasonic through-metal communication system. The actual system was constructed based on the combination of software and hardware, consisting of a pair of ultrasonic transducers, an FPGA module, some lab-made circuits, etc. The approach of DPRNN echo cancellation was applied to signals with a different signal-to-noise ratio (SNR) at a 2 Mbps transmission rate, achieving higher than 20 dB SNR improvement for all situations. Furthermore, this approach was successfully used for image transmission through a 50 mm thick aluminum plate, exhibiting a 24.8 dB peak-signal-to-noise ratio (PSNR) and a about 95% structural similarity index measure (SSIM). Additionally, compared with three other echo cancellation methods--LMS, RLS and PNLMS--DPRNN has demonstrated higher efficiency. All those results firmly validate that the DPRNN algorithm is a powerful tool to conduct echo cancellation and enhance the performance of ultrasonic through-metal transmission.  
echo cancellation; deep-learning; ultrasonic communication +
Footnote †: journal: Accepted: 26 March 2024
# Fault Diagnosis of Rolling Bearings Based on Acoustic Signals in Strong Noise Environments
###### Abstract  
Compared to vibration sensors, microphones offer several advantages, including non-contact detection, high sensitivity, low cost, and ease of installation. To address the challenges posed by the complex components and significant interference in rolling bearing sound signals, we proposed a fault diagnosis method for rolling bearing acoustic signals based on Secretary Bird Optimization Algorithm (SBOA)-optimized Feature Mode Decomposition (FMD). Initially, a microphone is utilized to collect sound data while the bearing operates, followed by the application of S-FMD (Secretary Bird Optimization Algorithm-optimized Feature Mode Decomposition) to decompose the sound signal and extract components that may contain fault information related to the bearing. The SBOA is employed to adaptively optimize four influencing parameters of FMD: mode number \(n\), filter length \(L\), frequency band cutting number \(K\), and cycle period \(m\). By minimizing envelope entropy as the objective function, we achieve FMD of the bearing sound signal with the assistance of the SBOA. Additionally, this paper introduces an Integrated Signal Evaluation Index (ISEI) to extract potential bearing failure characteristics from the filtered components. Simulation experiments and test results indicate that, compared to Empirical Mode Decomposition, Complementary Ensemble Empirical Mode Decomposition, fixed-parameter FMD, and adaptive variational mode decomposition methods, the proposed approach more effectively extracts weak characteristic information related to early faults in bearing sound signals.  
r 2025 15, 1389 +
Footnote †: journal: Applied Sciences  
16 November 2024  
Revised: 26 January 2025  
Accepted: 27 January 2025  
Published: 29 January 2025  
**Citation:** [PERSON]; [PERSON] Fault Diagnosis of Rolling Bearings Based on Acoustic Signals in Strong Noise Environments. _Appl. Sci._**2025**, _15_, 1389. [[https://doi.org/10.3308/app15031389](https://doi.org/10.3308/app15031389)]([https://doi.org/10.3308/app15031389](https://doi.org/10.3308/app15031389))
## 3 Results and Discussion
### Raman Spectroscopy Characterization  
The average Raman spectra of cured pure resin and resin with 15%, 25%, and 35% alcohol content are shown in Figure 4. No additional bands were observed due to the addition of ethanol. The addition of alcohol causes the disappearance of the band located at 1409 cm\({}^{-1}\) attributed to C=CH\({}_{2}\) scissoring deformation vibrations in acrylate resin [34]. This is probably due to the easier diffusion of solvent molecules during the polymerization process than of macroradicals and the effective quenching of active centers by ethanol, which increases the conversion rate by making it more difficult to combine macroradicals into long chains, which correlates with previous considerations. A change in the integral intensity ratio of the bands 1609/1634 cm\({}^{-1}\), associated with the C=C phenyl vibrations and C=C meth-acrylate stretching vibrations [35] due to the use of ethanol, can be observed. This also confirms the reduction in the proportion of double bonds present in the structure of the resin after curing, which is caused by the cross-linking process.  
Figure 4: The averaged Raman spectra of resin (black), and resin with an alcohol content of 15% (red), 25% (blue), and 35% (green).
###### Abstract  
Device-to-device (D2D) communication is a key enabler to facilitate the realization of the Internet of Things in which direct communication between mobile users is allowed without the intervention of the base station. One of the most challenging issues in the D2D cellular network is a low-complexity and reliable initial synchronization. To solve this issue, an efficient joint detection of integer carrier frequency offset (IFO) and set information (SI) is proposed for D2D communication in a long term evolution (LTE) system. Unlike the conventional approach, the primary sidelink synchronization signal (SPSSS) and secondary sidelink synchronization signal (SSSS) transmitted in the LTE-D2D system are used together to remove the effect of channel fading and symbol timing offset (STO). In addition, the conjugate property between two PSSS sequences is utilized. This design facilitates robust sequential detection of the IFO and SI without priori information on two synchronization signals when residual STO is present. It has been demonstrated that the inherent properties of the PSSS and SSSS sequences are effectively used for efficient cell search in the presence of residual STO, compared to existing methods.  
device-to-device; internet of things; sidelink synchronization; set information; frequency synchronization +
Footnote †: journal: Applied Sciences
# EGCY-Net: An ELAN and GhostConv-Based YOLO Network for Stacked Packages in Logistic Systems
## 1 Introduction
* To tackle the problem of capturing complex patterns and information in the data, we propose the implementation of a Conv-GhostConv Stack (CGStack) block that consists of a 1 \(\times\) 1 convolution, ghost convolution, and another 1 \(\times\) 1 convolution. * The framework of the ELAN-based ghost convolution network (EGCNet) is proposed. It includes three convolution layers, two CGStack blocks, and concatenation with ELAN-based design strategies. This module helps the model to aggregate information across many layers. * We built a dataset to compare the suggested approaches (in terms of classification accuracy) with several state-of-the-art (SOTA) methods, to demonstrate the practicality and applicability of the proposed method. * By comparing our method with other SOTA methods, EGCY-Net improves the inference speed while balancing detection accuracy. It provides significant advantages to implementing this method in an embedded system. The rest of this paper is structured as follows. Section 2 details the EGCY-Net architecture. Section 3 explains the results from comparison and ablation experiments. The discussion, limitations, and future works are presented in Sections 4 and 5, respectively. Our conclusions are presented in Section 6. Figure 1: The subfigures (**a**,**b**) depict Box class 13 and 04, respectively. These classes exhibit similar shapes and dimensions in terms of length and width, while differing notably in height.
# Wireless Strain Gauge for Monitoring Bituminous Pavements
## 2 Electronic Design of the Sensor
To improve the precision of  
Figure 2: Gauge wiring diagram. the zero adjustment, the program includes a zero-strain voltage measure to set precisely at zero the measured strain. For gauge conditioning, we use a thermistor mounted on the test body to compensate for the slope change due to the temperature variation. The divider bridge formed by the gauge and the thermistor delivers half of VCC at zero strains. The signal is digitized by the embedded ADC of the MCU. Also, the MCU converts data from the ADC to the UART protocol to interface with the Bluetooth transceiver. We use a fast Fourier transform (FFT) of the expected signal to estimate the needed sampling frequency. As we can see in Figure 3, the frequencies contained in the signal do not exceed 10 Hz. To keep a high signal-noise ratio (SNR) and a nice curve shape, we choose a minimum sampling frequency of 200 Hz. To improve the quality of the analog-to-digital conversion, we place an anti-aliasing filter. The cut-off frequency is set by the maximum sampling frequency divided by two to avoid spectrum aliasing. In our case, if the sampling frequency is 1000 Hz, the cut-off frequency of the anti-aliasing filter should be set at 500 Hz. The use of an anti-aliasing filter prior to analog-to-digital conversion improves the digital signal quality by reducing quantization noise and preventing spectrum aliasing. As a result of this design, the sensor resolution is 1 \(\upmu\)m/m, its span is \(-250\) to 750 \(\upmu\)m/m with an accuracy of 5% and a sampling frequency up to 1 kHz.
# An Electroglottograph Auxiliary Neural Network for Target Speaker Extraction
## 3 Materials and Methods
### Methods
#### 3.2.3 Model For Electroglottograph Speech Extraction  
In our experiments, the extra speech was replaced by EGG signals. As a result, the auxiliary network for extracting the features of target speakers needed to be improved. As EGGs come from vibrations in the throat, only the fundamental frequencies of the EGG waveforms carry information about speech. However, the inputs for our auxiliary network were speech spectrograms, which were two-dimensional data. So, the EGGs had to be transformed into two-dimensional data. In our study, we generated spectrograms of the EGG signals, which encoded information about the glottal pulses.  
As Figure 5 indicates, the spectrograms of the recorded EGG signals contained extra information such as signals from the throat, which can provide more information about the identity of the speaker. Based on previous studies on EGGs and speech extraction, we eventually developed a speech extraction model that was fit for EGG signals.  
Based on the features of the EGG signals, we designed a speech extraction neural network, which is shown in Figure 6. The main network accepted the spectrograms of mixed speech as inputs while the auxiliary network accepted the spectrograms of EGG signals as inputs. In the main network, Tanh layers were used after each linear layer for nonlinearity. The spectrograms of target speakers were obtained by multiplying the binary masks and the spectrograms of mixed speech. The MSE loss between the estimated target speaker speech spectrogram and the original target speaker speech spectrogram was taken as the cost function. The LSTM layers in the bottom half of the network were removed because of the gradient disappearance that occurs in the training process of multi-layer convolutional networks. Our model used a logistic sigmoid to activate the output layer. The auxiliary network for extracting the characteristics of target speaker EGGs consisted of two convolution layers, one ReLU activation layer, and a Softmax layer. This new network reduced computational complexity and produced a better speech extraction performance by using EGGs.
# A Lightweight Visual Odometry Based on LK Optical Flow Tracking
## 2 System Description
### Strategy for Removing Mismatched Points
\tag{11}\]  
The discrimination between true matching and false matching is defined as \(p\):  
\[p=\frac{m_{t}-m_{f}}{s_{t}+s_{f}}=\frac{knp_{t}-knp_{f}}{\sqrt{knp_{t}(1-p_{t}) +knp_{f}\Big{(}1-p_{f}\Big{)}}}\propto\sqrt{kn} \tag{12}\]  
When the value \(p\) is larger, the difference between true and false matching is greater, and the ability of the model to distinguish true and false matching is stronger. The outlier rejection threshold is set to \(\tau_{i}=\alpha\sqrt{n_{i}}\), where \(\alpha\) is a weight factor and \(n_{i}\) is the average matching number of the key points in 9 neighborhood grids. It has been determined experimentally that better results can be obtained when \(\alpha=6\). If \(S_{i}>\tau_{i}\), \(x_{i}\) is considered a correct matching; otherwise, it is an incorrect matching and will be removed. Thus, the matching point set after preliminary screening is obtained. The RANSAC algorithm is used to purify the key point matching set after the preliminary screening. The steps are as follows:  
Figure 6: GMS algorithm diagram. 1. Randomly select eight pairs of matching points from the matching points after the preliminary screening, and use the eight-point algorithm to solve the fundamental matrix \(F\). 2. Calculate the Simpson distance \(d\) between the remaining matching point pairs according to Formula (13). If \(d<\tau\), it is considered that the matching point pair is an internal point. Otherwise, it is considered an external point. Record the number of internal points at this time. \[d(x_{n1},x_{n2})=\frac{\left(x_{n2}^{\top}Fx_{n1}\right)^{2}}{\left(Fx_{n1} \right)_{x}^{2}+\left(Fx_{n1}\right)_{y}^{2}+\left(x_{n2}^{\top}F\right)_{x}^ {2}+\left(x_{n2}^{\top}F\right)_{y}^{2}}\] (13)
3. Repeat the above steps. If the number of iterations reaches the set threshold or the ratio of the number of internal points to all matching points reaches the set threshold during an iteration, stop the iteration. Then, select the fundamental matrix with the highest confidence and eliminate all matching points that do not meet the condition of \(d<\tau\) in the matching point set after preliminary screening, to obtain the optimal matching point set. A robust initial rough matching relationship of key points between the adjacent images can be established via the pyramid optical flow tracking matching algorithm based on the motion model, and then use the combined mismatch elimination algorithm to eliminate the possible external points to ensure the accuracy of the camera pose solved by the system, providing a strong guarantee for the accurate positioning of the robot and the construction of an accurate environmental map.
## 4 Results
\begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{1}{c}{**SCAN-ECHO Pain Management Consult Satisfaction Survey (_n_ = 15_)**} \\ \hline \hline  & **Question** & **Mean (SD)* & **\%** \\ \hline My diagnostic question(s) were answered clearly. & 4.47 (1.06) & 89.33\% \\ My diagnostic question(s) were answered completely. & 4.43 (1.09) & 88.57\% \\ I feel confident in my ability to implement the recommended diagnostic plan. & 4.40 (1.12) & 88.00\% \\ My treatment question(s) were answered clearly. & 4.40 (1.06) & 88.00\% \\ My treatment question(s) were answered completely. & 4.40 (1.06) & 88.00\% \\ I feel confident in my ability to implement the recommended treatment plan. & 4.33 (1.11) & 86.67\% \\ \hline \hline \end{tabular}
* Likert scale: 5 = strongly agree, 4 = somewhat agree, 3 = neither agree/disagree, 2 = somewhat disagree, 1 = strongly disagree.  
\end{table} 
Table 3: Responses to SCAN-ECHO pain management post-meeting Consult Satisfaction Survey (sent only to providers who had a case processed). half of the cases, it was determined that the treatment would best be carried out at the Los Angeles VA Medical Center by a member of the pain management team; these were cases that involved specialized treatment or treatment involving medical equipment that is not available at the CBOCs (e.g., MRI, CT scan, surgery, image-guided injection). The majority of these cases involved patients who receive their primary care at one of four CBOCs affiliated with the Greater Los Angeles VA Medical Center (Santa Maria, Santa Barbara, San Luis Obispo, Bakersfield), an average round-trip distance of 287 miles, equivalent to 5.5 h of travel time (Figure 2). In total, these patients (the half whose pain management was handled by their local CBOC provider per SCAN-ECHO treatment recommendations) were spared a total of approximately 14,500 travel miles, translating to about 277 travel hours. Reducing the time, expense and rigors of lengthy travel are advantageous to patients, particularly for those experiencing high levels of pain. In terms of wait time, SCAN-ECHO pain management consultations met or outperformed conventional in-person visits: the typical wait time for an in-person appointment at the Los Angeles VA Pain Management Clinic is about 30 days; in months wherein one SCAN-ECHO session was convened, the wait was less than 30 days, depending on the date that the consult was submitted. In six of the months, two SCAN-ECHO sessions, scheduled two weeks apart, were convened due to the higher volume of results received. These more frequent sessions essentially halved the wait time.