esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
As it is well known, shrink fits exhibit a stress concentration at the hub edges that can cause the failure of such mechanical components. A method for reducing such a stress concentration is placing a contact ring between the hub and the shaft. To achieve the desired effect, the Young Modulus of the material used for the contact ring must be lower than the one used in the hub and the shaft. Unfortunately, there are no design methods for estimating the optimal dimensions or materials of the contact ring. To fill this gap, in this study, diverse numerical simulations by the finite elements method (FEM) were carried out considering different geometries and materials in order to obtain recommendations that allow mechanical designers to significantly reduce the stress concentrations in these components. According to the obtained results, a contact ring of 25% of the hub thickness allows to significantly reduce up to 40% of the stress concentration. In addition, a linear influence of the stress reduction with the Young modulus was found thereby, and the most recommendable material for the contact ring is the one with the lowest Young modulus. On the other hand, according to the obtained results, the influence of the Poisson coefficient can be considered negligible.  
shrink fits; stress concentration factor; finite element analysis; stress reduction Article
# Feasibility Study on Grouting Compactness Detection in Sleeves Using Piezoelectric Transducers
## 3 Experimental Setup
### Grouting Compactness Detection of Sleeves  
After 28 d, the sleeve grouting compactness experiment was conducted. As shown in Figure 5, the experimental instruments included a real-time data acquisition system (NI-USB 6363), a single specimen, a voltage amplifier and a laptop with the LabVIEW software installed. Excitation signals were applied to the PZT-based actuator using the LabVIEW program to generate stress waves. Additionally, the signal response of each PZT sensor was recorded. The excitation signals were sine sweep signal. It was found that the signal energy was concentrated at 50 kHz-100 kHz by pre-scanning before the test began, while the signal energy was lower when it was out of range of 50 kHz-100 kHz, making it difficult to correlate with the grouting compactness of sleeve. Therefore, the frequency range of the test was set as 50 kHz-100 kHz. In order to increase the accuracy of the original signal obtained from the PZT sensor, each specimen was scanned for 5 consecutive times. Therein, each interval time and collection time were set as 10 s and 1 s, respectively. In addition, the amplitude of the signal was 10 V. The sampling frequency of each channel was 1 MHz. The voltage amplifier amplifies by 10 times.  
Figure 4: Schematic of the test specimen.  
Figure 5: Experimental setup.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tested Property** & **Performance Index** & \multicolumn{2}{c}{**Measured Value**} \\ \hline  & Initial & \(\geq\)300 & 355 \\ Fluidity/mm & 30 min & \(\geq\)260 & 306 \\  & 1 d & \(\geq\)35 & 58.5 \\ \hline Compressive strength/MPa & 3 d & \(\geq\)60 & 90.7 \\  & 28 d & \(\geq\)85 & 122.2 \\ \hline Rate of upright direction expansion/\% & Difference between 24 and 3 h & \(\geq\)0.02 & 0.683 \\  & 0.02–0.5 & 0.225 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications for the grouting material characteristic experiment.
# R&D of an Innovative OFDM Communication Payload for Small-Size AUV
## 5 Conclusions  
With the interference of the Doppler effect in the UWA channels, the performance of an UWA OFDM communication system will be seriously degraded. In particular, for an UWA OFDM communication payload onboard small-size AUV, the serious limitations of load capacity, energy supply and cost pose significant challenges.  
In this paper, a novel UWA OFDM payload is designed and investigated for a small-size AUV by employing hardware implementation to enable agile Doppler compensation with low computational overhead. After Doppler estimation is completed by calculating the time compression or extension of preamble signal, the DDS chip AD9954 is adopted to enable hardware tuning of the ADC sampling rate via the external clock source of the I2S bus. As the Doppler compensation is fulfilled at the hardware level, any processing delay generated during the software resampling is avoided.  
Finally, HIP simulation on the UWA OFDM modem was conducted to demonstrate the effectiveness and superiority of the proposed scheme, with the potential of achieving an effective communication rate of 3.19 kbps with the admissible implementation overhead. The designed and implemented UWA OFDM communication payload has the potential of being applied to small-size AUV platform in multitudinous underwater missions associated with telemetry and remote control between underwater vehicles. Future work will contain the integration and performance evaluation of the proposed UWA OFDM communication payload on the practical small-size AUV platform.  
Conceptualization, B.L. and F.T.; methodology, B.L. and Y.Z.; software, B.L.; validation, Y.Z., J.Y. and B.L.; formal analysis, X.G. and H.H.; investigation, J.Y.; resources, X.G., H.H. and F.T.; data curation, F.T.; writing--original draft preparation, B.L.; writing--review and editing, F.T.; visualization, J.Y.; supervision, F.T.; project administration, F.T.; funding acquisition, F.T., B.L., X.G. and H.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (2018 YFE0110000), the National Natural Science Foundation of China (11574258 and 11274259), the Scientific Research Starting Foundation Project of Fujian University of Technology (GY-Z220198), the Key Scientific and Technological Innovation Projects of Fujian Province (2022G02008), and the Education and Scientific Research Project of Fujian Provincial Department of Finance (GY-Z220232).  
Not applicable.  
The data presented in this paper are available after contacting the corresponding author.  
The authors declare no conflict of interest.
## 3 Experimental Results and Discussions  
In this section, we first employed the three data augmentation methods mentioned in Section 2.2 to construct the distortion magnitude space. We evaluated the benefits of the distortion-based data augmentation method combined with the dual-path classification strategy for few-shot learning models. The datasets used for our experiments include the UCMerced Landuse dataset (UCM) [41], the Aerial Image dataset (AID) [42], and the NWPU-RESISC45 Remote Sensing Image Scene Classification dataset (NWPU) [43]. Finally, we conducted ablation experiments and analyzed the impact of feature distortion magnitude space dimensions on model improvements.
###### Abstract  
An estimated 15 million babies are born prematurely every year worldwide, and suffer from disabilities. Appropriate care of these pre-term babies immediately after birth through telemedicine monitoring is vital. However, problems associated with a limited bandwidth and network overload due to the excessive size of the electromyography (EMG) signal impede the practical application of such medical information systems. Therefore, this research proposes an EMG uterine monitoring transmission solution (EUMTS), a lossless efficient real-time EMG transmission solution that solves such problems through efficient EMG data lossless compression. EMG data samples obtained from the Physionet PhysioBank database were used. Solution performance comparisons were conducted using Lempel-Ziv Welch (LZW) and Huffman methods, in addition to related researches. The LZW and Huffman methods showed _CRs_ of 1.87 and 1.90, respectively, compared to 3.61 for the proposed algorithm. This was relatively high compared to related researches, even when considering that those researches were lossy whereas the proposed research was lossless. The results also showed that the proposed algorithm contributes to a reduction in battery consumption by reducing the wake-up time by 1470.6 ms. Therefore, EUMTS will contribute to providing an efficient wireless transmission environment for the prediction of pre-term delivery, enabling immediate interventions by medical professionals. Another novel point of EUMTS is that it is a lossless algorithm, which will prevent any misjudement by clinicians because the data will not be distorted. Pre-term babies may receive point-of-care immediately after birth, preventing exposure to the development of disabilities.  
compression; EMG; lossless; medical information system; pre-term birth; telemedicine; wireless +
Footnote †: journal: Applied Sciences
## 1 Introduction
Not requiring any signal transformation or postprocessing, a Recurrent Slice Network (RSNet) based on CNN was presented in the fault diagnosis of a high-speed train bogie [18]. However, most of the current studies discussed diagnosis methods with clear and definite fault modes, whereas concurrent compound faults are difficult to accurately classify because of the feature combination [19]. A concurrent fault diagnosis method for a centrifugal multi-level impeller blower with six states was proposed based on Bayesian discriminating [20]. Using the symplectic geometry mode decomposition method, a compound fault detection was performed [21]. For gearboxes, wavelet transform combined with a multi-label convolutional neural network was used in compound fault diagnosis [22]. These studies were still focused on the equipment with simple fault mechanisms, based on the accurate fault label. Due to the heavy workload of manual data labeling, some automatic methods were tried in practice, effectively with audio signal [23], still relying on data preprocessing and correction by expert intervention. Utilizing the BIT record to label the flight data might be a feasible approach for UAV compound fault diagnosis in the flight test stage, and would be beneficial. Establishing a compound fault diagnosis model of UAV, facing possible real-time update and online diagnosis requirements in the future, is also a development direction. To assist the sound and fast ground maintenance, this paper discusses a compound fault labeling and diagnosis method based on machine learning classification. The contributions of this paper are as follows: (1) According to the actual flight data and BIT records of a UAV in test flight, we recombined multiple single-fault alarms at the same time stamp in BIT records into a complicated state. By referring to Failure Mode, Effects and Criticality Analysis (FMECA) and ground maintenance experience, it is possible to correspond these complicated states to compound fault modes after analysis, improving the efficiency and accuracy of flight data labeling. Then, we labeled the flight data with compound fault mode names. (2) Using the labeled flight data, which inherited the original data-driven diagnosis framework (GBDT and FCNN), we proposed an improved compound fault diagnosis method package with an XGBoost-based model, LightGBM-based model and CNN-based model, that achieved a significant performance. The rest of this article is structured as follows. Section 2 provides a detailed description of flight data and BIT records of UAV with a practical data assembling and labeling approach, and two ensemble learning-based diagnosis methods (XGBoost, LightGBM) and a deep learning-based diagnosis method (CNN). The application and experiments of the proposed compound fault diagnosis methods are described in Section 3. Section 4 presents the comparison of methods applied to the actual UAV test flight data and the discussion. Finally, the conclusions are given in Section 5.
# Delamination Detection in Bimetallic Composite Using Laser Ultrasonic Bulk Waves
## 3 Experimental Setup and Specimen
### Cu/Al bimetallic Composites Sample  
The dimension of the Cu/Al bimetallic composites was 80 mm \(\times\) 40 mm \(\times\) 3 mm, which was produced by rolling cladding technology. The thickness of the aluminum layer was 2.5 mm and the thickness of the copper layer was 0.5 mm, as shown in Figure 3. The T-shaped groove was machined by wire-electrode cutting, which was cut vertically from the copper side, and after cutting to the composite interface, cut equal lengths symmetrically along the horizontal direction. The gap width of wire-electrode cutting was about 0.2 mm. The simulated delamination widths were 2 mm, 4 mm, 6 mm, 8 mm, and 10 mm, respectively.
## 4 Results
### Application of the FFISS in the Study Area
During the 2009 flood, the built environment sector experienced high impact severity, with classes ranging from VI to VIII observed at the estuary of the Lilas River. Class VI impacts were also recorded in Vassiliko, Filla, and along the Afrati-Pissonas road. Additionally, lower impact severity, such as Class III, was noted near the river's estuary. In the 2020 flood, the severity of the impacts for the same sector remained high, with classes ranging from VII to VIII observed in Vassiliko, Agios Nikolaos, Lefkadi, Filla, and Afrati. However, lower severity classes, ranging from I to III, were recorded near the Lilas estuary (Figure 14). The natural environment sector exhibited high impact severity values during the 2009 flood, with classifications ranging from VI to VII observed at the estuary of the Lilas River, both downstream and upstream of the Vassiliko bridge, as well as in the Afrati area. In the 2020 flood, similarly high impact severity values, ranging from VI to VII, were documented at the Lilas estuary, Agios Nikolaos beach, Vassiliko, upstream of Vassiliko, Filla, and Afrati, as well as along the Afrati-Pissonas road. Conversely, lower severity classes, ranging from II to III, were recorded at the river's estuary and near the settlement of Pissonas (Figure 15). No impact data were recorded for the sector of mobile objects and the human population during the 2009 flood. However, for the 2020 flood, in the sector of mobile objects, high impact severity classes, ranging from VI to VIII, were observed at Agios Nikolaos beach and in Afrati. In the human population sector, low impact severity classes, ranging from I to III, were recorded across the flood-affected areas, including Agios Nikolaos, Lefkadi, and Filla. An exception was observed at the Lilas River estuary, where a severity class of VIII was documented, corresponding to a human fatality (Figure 16). Figure 17: Maps of the maximum flash flood impact severity in the affected area in 2009 (**a**) and 2020 (**b**). In conclusion, across all the impact sectors following the 2009 flood, the highest impact severity values, ranging from Class VI to VIII, were recorded at the estuary of the Lilas River, downstream of Vassiliko, in Afrati, and along the Afrati-Pissonas road. Conversely, the lowest severity classes, ranging from II to III, were observed at Agios Nikolaos beach and along the road east of Pissonas. During the 2020 flood, high impact severity classes, ranging from VI to VIII, were observed in Agios Nikolaos, both upstream and downstream of Vassiliko, in Afrati, and along the Afrati-Pissonas road. Lower severity classes, ranging from II to III, were recorded at the Lilas River estuary and in Lefkadi (Figure 17).
# Sparse Representing Denoising of Hyperspectral Data for Water Color Remote Sensing
## 2 Materials and Methods  
We collected both field-measured and remotely sensed hyperspectral data in this study. For the field-measured data, the sampling stations were collected from four routes distributed in two coastal provinces of China (Figure 1). In the four routes, we collected 150 hyperspectral curves and water samples in total (Table 1). For the samples collected in Taihu Lake, the \(\text{C}_{\text{chla}}\) were measured in the library. The \(\text{C}_{\text{chla}}\) data were used to evaluated the effectiveness of the denoising method through some popular spectra indices. In addition, we collected a PRISMA hyperspectral image that covered part of Taihu Lake.  
\begin{table}
\begin{tabular}{c c c} \hline
**Sampling Station** & **Hyperspectral Sample Number** & \(\text{C}_{\text{chla}}\) \\ \hline Taihu Lake (1 August 2019) & 60 (OWTS: 3, OWT11: 37, OWT12: 20) & \(\surd\) \\ Hongze Lake (12 November 2020) & 29 (OWT4: 1, OWT5: 14, OWT11: 13, OWT12: 2) & - \\ Qiandao Lake (1 December 2021) & 10 (OWT2: 2, OWT3: 5, OWT9:2, OWT12: 1) & - \\ Hangzhou Bay (26 July 2017) & 51 (OWTS: 17, OWT11: 30, OWT12: 4) & - \\ \hline \end{tabular}
\end{table}
Table 1: Basic information of the four cruises. OWT represents optical water type. The detailed description of OWTs can be found in Section 2.2.1.
# Design and SAR Analysis of a Dual Band Wearable Antenna for WLAN Applications
## 3 Results
### On-Body Analysis
#### 3.2.1 Return Loss  
Figure 20 presents the return loss comparison of the proposed conventional antenna without a body, with the flat body phantom, and bent around rounded body parts (leg and arms). Both resonance frequencies increase as the antenna is placed over the body parts. This is due to the permittivity and conductivity of human tissues. Moreover, the shift in the resonance frequencies towards the right side increases as the bending radius decreases. This is due to the fact that the effective resonant length of the antenna depends on the bending radii. The smaller the bending radii, the greater the reduction in the effective length of the antenna, and therefore, shifting of the resonant frequency towards higher frequencies is observed [40]. However, in general, it can be observed that the proposed antenna is tuned all the time, irrespective of the different environmental conditions.
## 3 Results and Analysis
### Stress Level of Secondary Lining
Similarly, the static initial tensile stress and the maximum dynamic tensile stress of the tunnel lining under different work conditions were used as input values for the minimum and maximum stress levels of the fatigue loads. Table 7 presents the maximum dynamic tensile stress increment \(\Delta\sigma\) under different work conditions. It also shows that the stress increment of the tunnel lining is mostly affected by the way the train passes. Poor surrounding rock level, high train running speed, and thin surrounding rock thickness can also considerably increase the stress increment of the tunnel lining. Moreover, the tunnel lining would suffer a higher increment of dynamic stress, even under the same train load, when the static stress is relatively large due to the stratum. Assuming that the influencing factors are independent of each other, the fitting formula of the maximum increment of the dynamic tensile stress in the lining structure, with regard to the surrounding rock level (\(\lambda\)), train speed (\(v\)), rock pillar height (\(H\)), and cross-angle (\(\theta\)), can be obtained by considering the condition that the train passes through both the upper and lower tunnels simultaneously. As shown in Equation (3), where \(i=1\), \(2\), and \(3\) represents grade III, IV, and V surrounding rock, respectively, \(\lambda_{1}\), \(\lambda_{2}\), and \(\lambda_{3}\) are \(0.7\), \(0.79\), and \(1.0\), respectively. \[\Delta\sigma_{(\lambda,\
u,\theta,H)} =7.9\times 10^{-6}\lambda_{i}\Big{(}1.15\mathrm{e}^{\frac{\
u}{15 0.7}}-1.75\Big{)}\Big{(}10.53\mathrm{e}^{-\frac{H}{23.31}}-0.152\Big{)}\Big{(}2.62\mathrm{e}^{\frac{\theta}{21.33}}+820.8\Big{)} \tag{3}\] \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Condition**} & \multicolumn{2}{c}{**Dynamic Tensile Stress**} & \multirow{2}{*}{**Location**} \\  & & **Increment (MPa)** & \\ \hline \multirow{3}{*}{Rock level} & III & 0.357 & Side wall of upper tunnel \\  & IV & 0.404 & Invert of upper tunnel \\  & V & 0.513 & Invert of upper tunnel \\ \hline \multirow{3}{*}{The way the train passes} & Upper tunnel & 0.513 & Invert of lower tunnel \\  & Lower tunnel & 0.386 & Side wall of upper tunnel \\ \cline{1-1}  & Both tunnels & 0.789 & Invert of lower tunnel \\ \cline{1-1}  & simultaneously & 0.789 & Invert of lower tunnel \\ \hline \multirow{3}{*}{Train speed} & 250 km/h & 0.221 & Invert of upper tunnel \\ \cline{1-1}  & 300 km/h & 0.343 & Invert of upper tunnel \\ \cline{1-1}  & 350 km/h & 0.513 & Invert of upper tunnel \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Maximum dynamic tensile stress increment for different design parameters of the cross tunnel. Figure 11: Stress increment comparison due to the train loading: (**a**) and (**b**) show the major principal stress of the upper and lower tunnels, respectively; (**c**) and (**d**) illustrate the third principal stress of the upper and lower tunnels, respectively.
## 1 Introduction  
GNSS generally refer to various global satellite navigation systems and their augmentation systems, including the U.S. Global Positioning System (GPS), China's BeiDou, Russia's Global Navigation Satellite System (GLONASS), and Europe's Galileo, among others [1, 2]. GNSS is the preferred technology for most location- and time-dependent applications [3], supporting critical infrastructure closely tied to national operations and people's livelihoods [4]. The spread-spectrum modulation techniques employed in satellite navigation provide GNSS signals with a certain level of anti-jamming capability [5]. However,the low received power of GNSS signals makes the system vulnerable to electromagnetic environments or interfering signals [6; 7].  
Currently, GNSS systems face increasingly severe interference threats, with interference incidents occurring more frequently. Therefore, rapid and effective interference source localization has become a crucial means of ensuring GNSS application security [8; 9]. To enhance the effectiveness of interference while improving concealment, multiple interference sources are often employed cooperatively to target GNSS signals. Moreover, low-cost jammers are now readily available on the commercial market [10], making multi-source GNSS interference an even greater threat than single-source interference [11]. With the widespread adoption of null-steering antennas, an array antenna composed of M elements can suppress M-1 interference sources [12]. This has led to the evolution of interference methods from single-source to multi-source cooperative jamming [13]. Such cooperative interference typically employs distributed low-power directional jamming, leading to increasingly common yet challenging scenarios, including multiple directional interference sources, growing types and quantities of GNSS interference, and multiple weak interference signals. These developments pose significant challenges to existing GNSS interference localization methods. This paper focuses on addressing the multi-interference source localization problem in these challenging scenarios.