esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Analysis  
### Experimental Data  
The open dataset of the China Jiangnan University (JNU) bearing fault dataset [32] was selected to verify the validity of the model. The experimental platform is shown in Figure 5. The platform consists of an inductor motor (Mitsubishi SB-JR), accelerometer (PCB MA352A60), and signal conditioner (PCB ICP Model 480C02).  
There are four states in the collected bearing data: normal, inner ring fault, outer ring fault, and rolling element fault. The experiment was conducted at 600 r/min, 800 r/min, and 1000 r/min, and different speeds were taken to represent different tasks: dataset A represents the data collected at 600 r/min, B represents the data collected at 800 r/min, and C represents the data collected at 1000 r/min. Datasets A, B, and C are composed of four health states [33]. The health status and corresponding labels are shown in Table 3.  
Figure 5: JNU bearing test bench.  
The number of points in each state is 410,112, and 1024 points are intercepted as a group of samples by overlapping sampling, and 800 groups of samples for each state are obtained. The final A, B, and C data sets have 3200 samples. Before the experiment, CMOR wavelet was used to perform wavelet transform on all samples to obtain time-frequency image samples.  
The code uses Pytorch as the deep learning framework [34] and a Windows 10 operating system. The experiments were set to a number of 32 samples per batch, the initial learning rate was set to 0.0001, and the total number of training rounds was set to 1500. All compared methods, except the method proposed in this paper, use SGD [35] to optimize the network model.
## Appendix A  
def envelope(y, rate, minThreshold, maxThreshold):  
mask = []  
y = pd.Series(y).apply(np.abs)  
y_mean = y.rolling(window=int(rate/10), min_periods=1, center=True).mean()  
for mean in y_mean:  
if mean > minThreshold and mean < maxThreshold:  
mask.append(True)  
else:  
mask.append(False)  
return mask  
Figure 11: Dependencies and Libraries used in this experiment.  
Figure 12: Envelope Function Code.  
def reshape(X,y):  X, y = np.array(X), np.array(y)  X = X.reshape(X.shape[0], X.shape[1], X.shape[2], 1)  return X, y  
def sampling(data, rate):  step = int(rate/2)  index = np.random.randint(0, data.shape[0]-step)  sample = data[index:index + step]  return sample  
def pickRandomFile():  random_class = np.random.choice(class_dist.index, p=[0.1]*10)  filename = np.random.choice(df[df.label == random_class].index)  return filename, random_class  
def extract_feature():  X = []  y = []  n_samples = int(df['length'].sum()) * 50  for  in tqdm(range(n_samples)):  filename, random_class = pickRandomFile()  rate, data = sio.wavfile.read(DIR+'MF10 PeopleClean/'+filename)  sample = sampling(data, rate)  S = librosa.feature.melspectrogram(y=sample, sr=rate, hop_length=700)  x = librosa.feature.mfcc(n_mfcc=32, n_fft=512, S=librosa.power_to_db(S))  X.append(x)  y.append(classes.index(random_class))  X, y = reshape(X, y)  return X, y  
def BasicCNNModel(X):  i = Input(shape=X[0].shape)  x = Conv2D(32, (3, 3), activation='relu', padding='same')(i)  x = MaxPooling2D((2, 2))(x)  x = Flatten()(x)  x = Dense(1024, activation='relu')(x)  x = Dense(10, activation='softmax')(x)  model = Model(i, x)  return model def AdvancedCCNModel(X):  i = Input(shape=X[0].shape)  x = Conv2D(32, (3, 3), activation='relu', padding='same')(i)  x = BatchNormalization(()(x)  x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)  x = BatchNormalization()(x)  x = MaxPooling2D((2, 2))(x)  x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)  x = BatchNormalization()(x)  x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)  x = BatchNormalization()(x)  x = MaxPooling2D((2, 2))(x)  x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)  x = BatchNormalization()(x)  x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)  x = BatchNormalization()(x)  x = MaxPooling2D((2, 2))(x)  x = Flatten()(x)  x = Dense(1024, activation='relu')(x)  x = Dense(10, activation='softmax')(x)  model = Model(i, x)  return model ```  
Figure 6: Advanced Model CNN Structure Parameters Table.
## 1 Introduction
#### Our Contribution  
This work provides the following key contributions.  
1. We propose a novel software-defined MIoT-based model that provides security communication in the underlying data plane. The outstanding computing power of the control plane greatly relieves the overhead of the upper layer, which offers users higher-quality services.
2. We design an authentication system to ensure the authenticity and reliability of messages, so that OLDs are encouraged to spread real information. Otherwise, they will be punished. In addition, an emergency-dealing scheme is offered to provide in-time services based on multicast, which not only takes current network situations into consideration, but also predictions of instantaneous changing.
3. A security analysis demonstrates that the proposed scheme is capable of achieving the security objectives of software-defined MIoT. In addition, adopting elliptic curve cryptography avoids heavy overhead brought by bilinear pairing operations, which is demonstrated by the comparison results.  
The structure of the remaining sections in this paper is as follows. Section 2 provides an overview of related works. Section 3 presents the system models and outlines our design objectives. In Section 4, we provide a comprehensive and elaborate explanation of the proposed scheme. The security analysis is presented in Section 5, followed by a comparison of computation overhead in Section 6. Finally, Section 7 concludes the paper.
## 3 Analysis and Results
### Effects of Structural Parameters on Slip Anchoring Performance
#### 3.2.1 The Effect Law Analysis of Slip Tooth Apex Angle on Anchoring Performance  
Finite element models were developed for slip tooth apex angles of 60\({}^{\circ}\), 65\({}^{\circ}\), 70\({}^{\circ}\), 75\({}^{\circ}\), 80\({}^{\circ}\), and 90\({}^{\circ}\). Numerical simulations were conducted to investigate the influence of varying slip tooth apex angles on the contact pressure distribution between the slip and casing wall. The resulting contact pressure cloud diagrams for each apex angle configuration are presented in Figure 11a, while the corresponding contact pressure indicators are summarized in Table 2.  
Figure 11: Stress curves of slips and casing under varying slip tooth apex angles. (**a**) Contact pressure between slips and casing. (**b**) Equivalent stress of slips. (**c**) Equivalent stress of casing.  
As the apex angle of the slip tooth was increased, the contact pressure between the slip and the casing wall rose. However, when the apex angle reached 90\({}^{\circ}\), the peak contact pressure between the slip and the casing was found to decrease.  
The standard deviation of the contact pressure between the casing and the slip was determined to be the smallest at slip tooth apex angles of 60\({}^{\circ}\) and 65\({}^{\circ}\), while the largest deviation was recorded at 70\({}^{\circ}\). Nevertheless, the deviation remained at a relatively low level, and the contact pressure distribution between the slip and the casing was noted to be fairly uniform, with no significant stress concentration observed.  
When the slip tooth apex angle fell within the range of 70\({}^{\circ}\) to 80\({}^{\circ}\), the contact pressure between the slip and the casing was measured to be higher, which was considered beneficial for achieving a secure anchoring effect.  
The stress distribution of the slip block at different slip tooth apex angles was analyzed and presented in Figure 11b. As the slip tooth apex angle was increased, a decreasing trend in slip block stress was observed. At an apex angle of \(\alpha=80^{\circ}\), the slip block stress was found to increase slightly compared to \(\alpha=75^{\circ}\), though it remained at a relatively low level.  
With lower stress exerted on the slip block, the slip was deemed safer during seating and fracturing operations. Within the slip tooth apex angle range of 75\({}^{\circ}\) to 90\({}^{\circ}\), the slip block was subjected to minimal stress, enhancing structural safety.  
The casing stress distribution, illustrated in Figure 11c, exhibited minimal variation across different slip tooth apex angles. During slip anchoring, the casing was found to experience only minor damage, with no significant casing failure observed. Based on the comprehensive stress analysis of the casing, the optimal slip tooth apex angle \(\alpha\) was preliminarily determined to range between 75\({}^{\circ}\) and 90\({}^{\circ}\).
## 2 Study Area and Data
### Remote Sensing Data  
Landsat TM and MODIS data were used to generate the primary inputs for the STARFM fusion model. We obtained 12 scenes of Landsat 5 TM images (path 124/row 29, path 124/row 30, cloud  
Figure 1: The location of Xilinhot. The elevation in the left-bottom corner was generated by using Shuttle Radar Topography Mission (SRTM) data ([[http://srtm.csi.cgi.org/](http://srtm.csi.cgi.org/)]([http://srtm.csi.cgi.org/](http://srtm.csi.cgi.org/))). The image on the right is the RGB (red, green, blue) composite of Landsat bands 5, 4, and 3 overlaid with field locations (orange dots) where samples were collected in 2011.  
cover <10%) covering the years of 2005-2011 (Table 1), and the data were processed with Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) tools. The LEDAPS software was developed specifically for processing Landsat directional surface reflectance data through radiometric calibration, atmospheric correction, image registration, and image orthorectification [49]. The digital numbers (DNs) of the Landsat TM images were converted to reflectance values based on the atmospheric calibration procedure that was implemented by using LEDAPS [50].  
The MODIS product (MOD9Q1, tile h26v04) at 250 m and 8-d intervals for the study area during the growing season (May to September) in 2005-2013 was obtained from the National Aeronautics and Space Administration's (NASA's) Earth Observing System Data and Information System ([[http://earthdata.nasa.gov](http://earthdata.nasa.gov)]([http://earthdata.nasa.gov](http://earthdata.nasa.gov))). The MOD9Q1 product contains optimal observations during the 8-d periods given the effects of clouds, cloud shadows, and aerosol loading [51]; pixels with poor quality were excluded by using the quality assurance layer. The MODIS reflectance imagery were projected to Universal Transverse Mercator (UTM) projections and resampled to a 30-m resolution by nearest neighbor resampling to meet the projection and resolution of Landsat TM data.
## 6 Case Study Validation  
To validate the effectiveness of the proposed IRDM and the rationality of its parameter configuration, the underground structure model described in Section 3.1 is adopted. Six SSI models are established with dimensionless boundary distance ratios \(R\) = \(R_{S}\) = \(R_{B}\) set to 0.5, 1.0, 2.0, 3.0, 4.0, and 5.0. Seismic responses under the Kobe, Loma Prieta, and Northridge earthquake records are computed via three approaches: the dynamic time history method, the conventional IRDM, and the proposed improved IRDM (with artificial boundary parameters of \(\alpha_{T}\) = 0.5 and \(\alpha_{N}\) = 1.0). The ground surface acceleration time histories of the three seismic motions are shown in Figures 4 and 12, with the peak ground acceleration (PGA) normalized to 0.1 g and a time step of 0.01 s.  
Figure 11: Computational accuracy of the proposed method under different \(\alpha_{T}\) values, where \(\alpha_{T}\) is the parameter of the static artificial boundary element.  
Figure 12: Acceleration time histories of seismic ground motions.
## 4 Discussion
### Effects of Exercise on Aerobic Capacity in HF Patients  
This meta-analysis suggested exercise significantly improved aerobic capacity in HF patients. Extensive evidence indicates that in cardiovascular disease patients, improved aerobic capacity negatively correlates with all-cause mortality [83]. Previous studies have shown that high-intensity interval training, circuit resistance exercise, aquatic exercise, yoga, and Tai Chi significantly improved aerobic capacity in HF patients [84; 85; 86; 87]. Similarly, [PERSON] et al. [88] also indicated that exercise had a significant positive effect on improving aerobic capacity in HF patients. However, [PERSON] et al.'s [89] meta-analysis found that exercise did not significantly improve VO2 peak in HF patients. Two factors may contribute to this difference. First, this meta-analysis included only three studies, and the sample size of three trials was relatively small. Second, among the included studies, two were performed before beta-adrenergic blockade became the main focus of therapy in HF. Our results are consistent with [PERSON] et al. [88], indicating that exercise plays a significant role in the rehabilitation of HF patients.  
The mechanisms by which exercise improves aerobic capacity are multifaceted. First, exercise alleviates endothelial dysfunction in HF patients by increasing endothelial nitricoxide (NO) levels [90] and promoting angiogenesis and vascular repair [91]. Additionally, exercise improves left ventricular function and reverses cardiac remodeling [92; 93] while reducing pro-inflammatory cytokines in the serum [94], which helps alleviate systemic metabolic imbalances and improve prognosis. Second, exercise positively impacts the autonomic nervous system. A 24-week exercise program significantly improved heart rate variability in stable chronic heart failure (CHF) patients [95]. Reducing sympathetic and renin-angiotensin system activity while enhancing vagal activity is crucial for improving exercise performance in HF patients.  
Furthermore, exercise benefits the peripheral system. HF patients often suffer from severe peripheral system dysfunction, characterized by reduced skeletal muscle capillary density, decreased activity of mitochondrial oxidative enzymes such as cytochrome c oxidase and citrate synthase, and impaired oxygen uptake and transport in muscles. Moderate exercise increases blood flow and capillary density in skeletal muscles and promotes NO synthesis and release, thereby enhancing angiogenesis and reducing systemic peripheral resistance [96; 97]. This process helps improve peripheral oxygen uptake and utilization, increases adenosine triphosphate (ATP) synthesis in skeletal muscle mitochondria, and ultimately enhances skeletal muscle function and exercise performance in patients [98; 99].
# Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference
## 3 Materials  
### Dataset Creation  
This paper employs quick-look images of Sentinel-1 Level-1 SLC and GRD products due to their various merits. Firstly, these data are available orderly and free of charge for online downloading. Moreover, they feature a wide swath and high resolution as the Sentinel-1 SAR mainly functions in the interferometric wide (IW) mode over land [27; 28]. Last but not least, the quick-look images are colored, lower-resolution versions of the original images. There are three channels in quick-look images, with the red channel (R) representing the co-polarization VV, the green channel (G) is the cross-polarization VH, and the blue channel (B) is the ratio of the cross and co-polarizations. These three channels are mapped to RGB colors to form a pseudo-color image as shown in Figure 3, which provides convenience for direct screening of interference through visual observation in the process of data labeling. The original SLC and GRD products are on the order of several gigabytes, while their quick-look images have a size of tens to hundreds of kilobytes. Considering storage and processing issues, the selection of quick-look images facilitates the creation of datasets and the application of deep learning techniques. Thus, it detects RFI artifacts effectively.
# A Universal Low-Complexity Demapping Algorithm for Non-Uniform Constellations
## 5 Performance Evaluation
### Complexity Comparison
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**CR**} & **Demapping Algorithm** & **Number of One-Dimensional EDs to be** & **Number of EDs to be Compared** \\  & **Algorithm** & **Calculated and Reduction Percentage** & **and Reduction Percentage** \\ \hline All & Max-Log-MAP & 64 & 320 \\
2/15 & SCSR & 6 (90.63\%) & 10 (96.88\%) \\
6/15 & SCSR & 20 (68.75\%) & 104 (67.50\%) \\
10/15 & SCSR & 28 (56.25\%) & 138 (56.87\%) \\
13/15 & SCSR & 34 (46.88\%) & 162 (49.38\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Complexity comparison when demapping 1D-1024 NUC.
## 3 Results and Discussions
### Microhardness and Wear Properties
The rapid solidification conditions during laser cladding increased the limit of solid solubility of the atoms in the solid solution. Thus, more B atoms  
Figure 7: Micro-hardness profile along the cross section of the FeCoCrNiB\({}_{x}\) HEA coatings with various boron (B) additions. were solidified in the solid solution. According to the XRD results, the addition of the element B can increase the lattice constant of the FCC solid solution. Thus, the increase of micro-hardness was caused by the increasing crystal packing density and lattice distortion of the FCC crystalline. Figure 8 shows the wear morphology of FeCoCrNiB\({}_{x}\) HEA coating. It can be seen from Figure 8 that the wear volume of the coating decreased with the increase of the B ratio. Figure 9 shows the relationship between the wear volume and the average hardness of FeCoCrNiB\({}_{x}\) HEA coatings. The wear volume of the coating increased gradually with decreasing average hardness. The wear resistance of FeCoCrNiB\({}_{x}\) HEA coatings was positively correlated with the hardness. The wear resistance of FeCoCrNiB\({}_{1.5}\) HEA coating was the highest and the maximum hardness value of three HEA coatings was the FeCoCrNiB\({}_{x}\) HEA coating, and the hardness was around 1025 HV\({}_{0.2}\), whereas that of the FeCoCrNiB\({}_{0.5}\) HEA coating was the lowest. In this work, the wear resistance and hardness of the coating conformed to the classic Archard law [24], which states that the wear resistance of materials is proportional to the hardness.
## 5 Conclusions and Future Directions
### Future Directions  
Despite the satisfactory traffic flow forecasting results using the GAHD-VAE methodology, future works will be aimed at improving the robustness of the GAHD-VAE model to noisy traffic flow measurements by developing a wavelet-based GAHD-VAE approach. Another direction of improvement is to incorporate explanatory variables, such as meteorological measurements and spatial information, in constructing the deep learning models to further improve forecasting quality. Moreover, this deep learning approach ignores the spatiotemporal correlation in the traffic network. Thus, we plan to develop a more flexible forecasting approach that considers spatiotemporal correlations of the traffic network and captures spatiotemporal features. We will also investigate the capacity of applying the GAHD-VAE approach in other applications that need forecasting like environment, health, energy, big data, and many others. We also plan to improve the robustness of the GAHD-VAE model to noisy measurements by developing a wavelet-based GAHD-VAE forecasting model. Moreover, it will be interesting to investigate the forecasting capability of this deep learning method in other applications, such as predicting bike-sharing demand.  
F.H.: Conceptualization, Formal analysis, Investigation, Methodology, Software, Writing-original draft, Writing-review & editing. A.D.: Conceptualization, Formal analysis, Investigation, Methodology, Software, Supervision, Writing-original draft, Writing-review & editing. A.Z.: Investigation, Conceptualization, Formal analysis, Methodology, Writing-original draft. Y.S.: Investigation, Conceptualization, Formal analysis, Methodology, Writing-review & editing, Funding acquisition, Supervision. All authors have read and agreed to the published version of the manuscript.  
This work was supported by funding from King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No: OSR-2019-CRG7-3800.  
The authors declare no conflict of interest.
# Tertiary Waves Measured during 2017 Pohang Earthquake Using an Underwater Glider
## 3 Results
### Pohang 2017 Earthquake: Analysis of in Situ Observations
#### 3.2.4 Correlation with Situ Seismic Waves and Earthquake Sound  
Earthquake generated acoustic waves in the ocean are referred to as T-wave because in certain conditions, when they reach the shore, they may convert back to seismic waves and arrive third after the P- and S-seismic waves on near-shore seismological stations.  
IRIS (Incorporated Research Institutions for Seismology) archives waveform (time-series) data from stations around the world. Seismic wave data of the Pohang Mw 5.5 earthquake was collected by the Busan (35.25\({}^{\circ}\)N, 129.11\({}^{\circ}\)E), Chungju (36.87\({}^{\circ}\)N, 127.97\({}^{\circ}\)E), Namwon (35.42\({}^{\circ}\)N, 127.40\({}^{\circ}\)E), Seawha (38.27\({}^{\circ}\)N, 128.25\({}^{\circ}\)E), Seoul (37.49\({}^{\circ}\)N, 126.92\({}^{\circ}\)E) station in Figure 7. The Busan (BUS) station was about 92 km away from the Pohang epicenter; the Chungju (CHJ) station was 147 km, Namwon (NAWB) station was 183 km, Seawha (SEHB) station and Seoul (SEO) station were 255 km same distance. The underwater glider was about 140 km away from the Pohang epicenter.  
The time series of the seismic and earthquake acoustic signal detected at the five stations and the underwater glider are presented in Figure 10. The time of arrival of the seismic waves observed at the station is proportional to the distance from the epicenter. Since the underwater glider was closer than Chungju station to the epicenter, the arrival time of the T-wave (i.e., earthquake acoustic signal) observed by the underwater glider was earlier than that of the seismic waves observed at the Chungju station. The seismic P- and S-waves travel at velocities from 2000 to 7000 m/s in the crust just up to the seafloor bottom (about 430 m) of detection point, then in the water column and finally detected by the underwater glider (about 200 m). It is clearly shown that the acoustic signal detected by the glider from the Pohang earthquake is a Tertiary wave (T-wave) in Figure 11.  
Figure 9: The earthquake acoustic signal: (**a**) Time reconstructed signal, (**b**) frequency spectrum and (**c**) Spectrogram.  
Figure 11: The creation of T-waves from seismic waves and detection by the underwater glider.  
Figure 10: Time signal of the seismic and T-waves observed by the seismic observatory and underwater glider position from the Pohang earthquake epicenter.