esa-sceva/satcom-chunk-collection huggingface dataset:
# Nb\({}_{3}\)Sn Cavities Coated by Tin Vapor Diffusion Method at Peking University
## 2 Experimental Descriptions  
### Coating Procedure at PKU  
The schematic diagram of the furnace for Nb\({}_{3}\)Sn coating at PKU is shown in Figure 1. The furnace consists of two vacuum systems. The inside chamber is made of pure niobium, and the initial vacuum can reach \(10^{-5}\) Pa after 48 h of 200 \({}^{\circ}\)C degassing. The furnace has a total of six C-type thermocouples to measure the temperature of the outer wall of the inner chamber and one thermocouple to measure the temperature of the outer wall of the evaporating boat area. The temperature control system was experimentally calibrated twice to eliminate the error between temperatures inside and outside the wall of the chamber. This furnace was located in a relatively clean room.  
In other typical coating devices for single-cell cavities, the tin source is put at the bottom of the cavity and tin vapor rises to the inside of the cavity [3,6,8,9,10,13,14,15]. We found that the location of the tin source influences the distribution of the tin vapor pressure, which is one of the key parameters during Nb\({}_{3}\)Sn growth. In our previous single-tin-source experiments, the grain sizes and morphology varied at the top and bottom of the cavity. Therefore, a new tin source arrangement was proposed: three tin sources were put inside the cavity, along the center cavity axis. The tin was stored in small tungsten evaporation boats supported by a 2.5 mm diameter niobium wire, as shown in Figure 2. The distances of the sources from the cavity top were 100 mm, 230 mm, and 420 mm. Detailed comparisons are described in Section 3.3.  
The upper beampipe of the niobium cavity was covered by niobium foil. The nucleation agent SnCl\({}_{2}\) was stored in a tungsten crucible with an independent heater at the bottom of the chamber. Several small niobium strips or sheets were suspended on the central axis of the cavity as witness samples in every coating experiment. The samples were as small as possible considering that big samples may disturb the tin vapor flow resulting in nonuniform Nb\({}_{3}\)Sn coatings. Each cavity was buffered chemical polished (BCP) and high-pressure water rinsed (HPR) before loading into the furnace.  
The typical temperature profile and the vacuum measurements are shown in Figure 3. In the nucleation stage, the SnCl\({}_{2}\) crucible was heated rapidly to 800 \({}^{\circ}\)C and kept for 30 min. The vacuum valve of the molecular pump was closed, and during the whole coating process, the vacuum was measured by a film gauge with a measurement range of 0.01-10 Pa. During this procedure, SnCl\({}_{2}\) was fully evaporated, and its pressure reached 1 Pa. Then, the SnCl\({}_{2}\) crucible cooled down to 500 \({}^{\circ}\)C or 650 \({}^{\circ}\)C for 4.5 h. The cavity was mounted on a niobium tube to maintain distance from the high-temperature region so that the two temperature regions did not interfere with each other. Then, the cavity temperature was ramped up to 1150-1200 \({}^{\circ}\)C and maintained for 2-5 h. Lastly, the system cooled down naturally.
## 3 Theoretical Development
### Hydraulic Properties  
The porosity of the REV can be calculated as the ratio of the total pore volume \(V_{p}\) and the total volume \(V_{REV}\) of the REV:  
\[\begin{split}\phi&=\ \frac{V_{p}}{V_{REV}}=\frac{\int_{w _{min}}^{w_{max}}(2 aw)(L_{\tau})(-dN)}{L_{o}A_{REV}}=\frac{2\beta\tau D_{f}w_{ max}^{D_{f}}}{A_{REV}}\int_{w_{min}}^{w_{max}}w^{1-D_{f}}dw\\ &\quad=\frac{2\beta\tau D_{f}w_{max}^{2}}{A_{REV}(2-D_{f})}(1-a ^{2-D_{f}}),\end{split} \tag{8}\]  
where \(L_{o}\) is the length of the REV, \(L_{\tau}\) is the real length of the fracture and \(\tau=L_{\tau}/L_{o}\) is the dimensionless hydraulic tortuosity of the microfracture, e.g., [77]. For the sake of simplicity, the length of the fractures is assumed to be independent of the fracture width, so an average tortuosity \(\tau\) is considered in the model. It is trivial to take into account the correlation between the tortuosity and the capillary size as performed by [68; 78; 79], for example. However, this extra complexity does not really make the model any more representative of real porous media or affect the key results of the model, e.g., [46].  
For a laminar flow, the velocity profile and average velocity (see Figure 2) inside the fracture are, respectively, given by, e.g., [57; 80]  
\[v(y)=\frac{a^{2}}{2\eta}\bigg{[}1-\frac{y^{2}}{a^{2}}\bigg{]}\frac{\Delta P}{ L_{\tau}}=\frac{a^{2}}{2\eta\tau}\bigg{[}1-\frac{y^{2}}{a^{2}}\bigg{]}\frac{ \Delta P}{L_{o}}, \tag{9}\]  
and  
\[v_{aw}=\frac{a^{2}}{3\eta\tau}\frac{\Delta P}{L_{o}}, \tag{10}\]  
where \(\Delta P\) is the fluid pressure difference, \(\eta\) is the dynamic viscosity of the fluid, \(a\) is the half aperture and \(y\) is the coordinate along the aperture as shown in Figure 2.  
Figure 1: Schematic view of a fractured medium composed of a bunch of parallel slits.  
The flow rate through a fracture is therefore given by  
\[q=v_{av.}(2 aw)=\frac{2a^{3}w}{3\eta\tau}\frac{\Delta P}{L_{o}}. \tag{11}\]  
It is noted that Equation (11) is the well-known cubic law, e.g., [81; 82].  
Consequently, the total volumetric flow through the REV is the sum of volumetric flow rates through all fractures and is given by:  
\[V_{REV}=\int_{w_{min}}^{w_{max}}q(-dN). \tag{12}\]  
Combining Equations (5), (7), (11) and (12) yields:  
\[\begin{split} V_{REV}=&\int_{w_{min}}^{w_{max}} \frac{a^{2}}{3\eta\tau}\frac{\Delta P}{L_{o}}(2 aw)[D_{f}w_{max}^{D_{f}}w^{-D_{ f}-1}dw]=\\ &\frac{2}{3\eta\tau}D_{f}\beta^{3}w_{max}^{4}\frac{1-\alpha^{4-D_ {f}}}{4-D_{f}}\frac{\Delta P}{L_{o}}.\end{split} \tag{13}\]  
According to [PERSON]\({}^{\prime}\)s law (macroscopic scale) for Newtonian fluid flow in porous media, \(V_{REV}\) is expressed as  
\[V_{REV}=\frac{kA_{REV}}{\eta}\frac{\Delta P}{L_{o}}. \tag{14}\]  
Combining Equations (8), (13) and (14), the following is obtained  
\[k=\frac{\beta^{2}w_{max}^{2}\phi}{3\tau^{2}}\frac{1-\alpha^{4-D_{f}}}{4-D_{f} }\frac{2-D_{f}}{1-\alpha^{2-D_{f}}}. \tag{15}\]  
In case of \(w_{max}>>w_{min}\) (\(\alpha\to 0\)), Equation (15) reduces to  
\[k=\frac{\beta^{2}w_{max}^{2}\phi}{3\tau^{2}}\frac{2-D_{f}}{4-D_{f}}. \tag{16}\]
## 9 Conclusions  
Energy consumption is one of the most important issues for above-knee prosthesis since an amputee desires to use her/his prosthesis for as long as possible. The long-term usage of an electrically controlled above-knee prosthesis mostly depends on the energy consumption of the batteries. Although increasing the number of joints offers comfort and aesthetic walking ability to the amputees, it also increases energy consumption at the battery. Electrical energy supplied by the battery should be carefully consumed for long-term usage. Considering this fact, in this paper, link mass optimization of the new above-knee prosthesis is performed for minimizing the electrical energy consumption while providing acceptable prosthesis stiffness. The PSO algorithm is used as the optimization  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Studies** & **Consumed Power in One Cycle (Watt)** & **Battery Life (Hour)** & **Gait Cycle Life (Steps)** \\ \hline Results of this paper & 32.07 & 3.69 & 10,543 \\ \hline [PERSON] and colleagues [35] & 66 & 1.8 & 9000 \\ \hline [PERSON] and [PERSON] [36] & — & — & 7000–13,000 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Power consumptions, gait cycle life and battery life durations.  
algorithm. A discrete-time PID controller is used for representing the energy saved by means of mass optimization.  
As a result, the battery life is determined as 3.69 h, corresponding to 10,543 steps in one battery charge. Considering the data in Table 7, the new prosthesis proposed in this study provides almost one full day of service in one charge. Another important remark is that the new prosthesis proposed in this study consumes about 51% less power than the prosthesis developed by [PERSON] and his colleagues [35]. Thus, the new prosthesis provides 1.89 h of additional battery lifetime compared to the prosthesis developed by [PERSON] and his colleagues [35].  
The optimized stiffness values provide less than 1 mm displacement errors (\(\delta\chi\)) along the \(x\), \(y\) and \(z\) axes. This displacement error, determined with the stepping pressure of a 120 kg weight of an amputee, can provide aesthetical movement and balance with acceptable accuracy. In conclusion, the proposed above-knee prosthesis can provide acceptable stiffness performance and almost one full day of walking service in one charge cycle for an amputee under 120 kg weight.  
The future studies of this research will include the results of different types of algorithms and controlling strategies by using experimental setup and testing on amputee for different environmental conditions. In addition, new designs can be worked on by considering hip amputation for lower-limb amputees.
## 5 Further Validation
### Second-Order Natural Frequency  
When the sinusoidal excitation frequency of the input structure changes to the second natural frequency of the structure, the vibration responses (with different noise contents) of the structure after damage are shown in Figure 18. Similarly, the spectra plots obtained by FFT analysis are shown in Figure 19a-d. Then, WPD technology is used to obtain the energy ratio of each frequency band, as shown in Figure 19e-h.  
Figure 16: Acceleration spectra and the proportion of energy in each frequency band. Spectra: (**a**) Condition 2 with SNR of 20 dB, (**c**) Condition 2 with SNR of 10 dB, (**e**) Condition 3 with SNR of 20 dB, and (**g**) Condition 3 with SNR of 10 dB. The proportion of energy in each frequency band: (**b**) Condition 2 with SNR of 20 dB, (**d**) Condition 2 with SNR of 10 dB, (**f**) Condition 3 with SNR of 20 dB, and (**h**) Condition 3 with SNR of 10 dB.  
Figure 17: PED value under different white noise degree (1 st natural frequency). (**a**) No noise. (**b**) SNR = 20 dB. (**c**) SNR = 10 dB.  
Figure 18: Response acceleration signals with varying degrees of white noise when excited at 2 nd-natural frequency. **(a)** Condition 2. **(b)** Condition 3.  
As can be seen from Figure 19, regardless of damage Condition 2 or Condition 3, although the SNR of background noise changes from 20 dB to 10 dB, the frequency band with the highest energy ratio can be clearly and accurately found after WPD and used for signal reconstruction.  
Figure 19: _Cont._  
The results in Figure 20 show that the PED value trend when ambient noise of 10-30 dB is applied is basically consistent with the results without noise interference. When the input frequency of the excitation load matches the second-order natural frequency of the structure, significant spikes in the PED values between the location of sensors 2-4 and 2-5 are observed for both Condition 2 and Condition 3. And the PED value of Condition 3 is slightly higher than that of Condition 2. This is consistent with when the input frequency of the excitation load is the first-order natural frequency of the structure. Therefore, when the input sinusoidal excitation load coincides with the structure's second-order natural frequency, the robustness of the WYPE technique is still evident despite interference from background noise.
## 2 Optical Inspection Technologies in Railways
### Sensor Systems
#### 2.3.5 Contact Wire  
Typical faults in contact wires are wear and tear (caused by constant contact with the pantograph), excessive sag, corrosion of metallic parts of the contact wire, incorrect alignment, damage caused by weather, and environmental influences and soiling.  
To counteract contact wire failure, various measuring systems for the regular inspection of height and stagger have proven themselves in practice. To determine positions, bvSys has installed digital line-scan cameras with a laser-based lighting unit on the roof of the measuring train. At speeds of up to 120 km/h, graphic data of the overhead line is continuously collected [29]. The evaluation is performed in the post-process. A triangulation method is used to determine the wear of the contact wire. A cross-sectional plane is generated and recorded using a camera and a laser. At a speed of 200 km/h of the carrier vehicle, the contact wire thickness can be determined [30]. At a speed of up to 320 km/h, the MERMEC system uses laser scanners and high-resolution cameras to record up to eight contact wires and determine their position and residual thickness [31]. SelectraVision has two systems for use on vehicles. CAT-V can measure height and stagger using a laser scanner mounted on the roof. Cameras can also be optionally installed in the system [32]. The CAT-VW can also determine wear, which achieves a resolution of 5 mm at 320 km/h [60]. With its Contact Wire Recording System (CRS), Fraunhofer IPM offers a sensor system that uses a laser scanner to determine the position of up to eight wires. Depending on the reflection and the distance of the system to the wire, an accuracy of 5 mm can be achieved [34]. The Wire Wear Monitoring System (WWS) uses cameras to determine the degree of wear. The uncertainty is described with \(\pm\) 0.3 mm to 0.5 mm (mainly determined by the degree of wear) [35]. Both systems are part of the Contact Wire Inspection System (CIS) (Figure 7) and can be used for measurements up to a speed of 250 km/h independent of ambient light [33]. The system is also available in a slower version [61].  
Figure 6: Damages failure of the contact strip of the pantograph. (**a**) Material melting as a result of arcing; (**b**) detachment of a piece of carbon strip; (**c**) crack of a strip; and (**d**) the top layer of the strip is peeling off [59].  
Due to the different heights of the contact wire along a route, the camera-based systems have to adjust their focus, which is why it is essential to determine the relevant distance. Different ambient lighting can also lead to overexposure of the images, which makes it difficult to evaluate the degree of wear. Contamination of the wire can also lead to incorrect determination of the residual thickness.
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
## 5 Conclusions  
In this work, we presented a review of the Narrow-Band Internet of Things (NB-IoT) and its application in industries (NB-IIoT). The reviews of Wireless Sensor Networks (WSN), as well as IoT, were made. These were presented in a manner that shows the relationships between these technologies. The review, among other things, covers the technological features, applications, and challenges. A novel presentation of the transmission tone mechanism of NB-IoT was made. Comparisons were made between NB-IoT and other Low Power Wide Area Networks (LPWAN). The paradigm presented by NB-IoT allows a brief and unique pictorial overview of its standardization process as provided by 3 GPP. While NB-IoT is an integration into the legacy LTE and increases its application in industries, this work presents the wireless communication challenges that the technology faces in the industrial environment. As a future research direction, we present the need for future research into wireless communication of NB-IoT in industrial environments. These include the need for NB-IoT networks to be self-organizing in a manner that meets the industrial requirements. Furthermore, we also discussed the application of edge computing to improve the scalability of the network management, securing the data to maintain confidentiality, integrity, and availability, and finally, providing a suitable propagation model that describes the industrial wireless system.  
Conceptualization, M.D.; methodology, M.D.; software, M.D. and S.A.; formal analysis, S.A. and S.H.; resources, S.A., S.H. and Q.H.A.; writing--original draft preparation, M.D.; writing--review and editing, M.D., S.A., S.H. and Q.H.A; visualization, M.D.; supervision, S.H. and M.A.I.; project administration, S.H. and M.A.I.; funding acquisition, Q.H.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by PTDF 1671/19 and the APC was funded by EPSRC EP/R511705/1.  
Not applicable.  
This work is supported by the Petroleum Technology Development Fund (PTDF) of the Federal Republic of Nigeria with Grant No.: 1671/19 and also in part by EPSRC EP/R511705/1.  
The authors declare no conflict of interest.
# Application of Machine Learning for Fault Classification and Location in a Radial Distribution Grid
## 3 Proposed Method  
In this paper, knowledge-based fault location and classification is proposed using advanced signal processing and machine learning tools. The three-phase measured current signal is fed into the tool as an input and the type of fault and its location are determined as output. Wavelet decomposition and statistical measures are used to extract the key features from the measured signal. MLP and ELM are then used to make a decision based on the features extracted. All of the distribution network model, signal processing, and ML tools are implemented in Matlab. The procedures are discussed in the following sections.
## 1 Introduction to the Tracking and Imaging Radar TIRA
### Current Configuration of the Imaging Radar  
The TIRA system also has a broadband, coherent imaging radar system which works in the Ku band with a center frequency of 16.7 GHz. It is always used when not only very precise orbit data are required, but also the imaging of a space object is needed.  
This is the case, for instance, during a contingency support when a satellite is not behaving nominally, and a visual analysis is needed. Indeed, two-dimensional radar images can be used to detect mechanical damage to the satellite that may have been caused by a collision with space debris. If one processes 2D radar images one after the other over the whole flyover, one can even generate a radar video which shows the rotational motion of the satellite and eventually different sides of the object. From the radar image series, one can calculate the attitude motion of a satellite and assess a potential tumbling motion. Due to the higher frequency compared to the L-band tracking radar, the antenna lobe is significantly narrower with 0.031\({}^{\circ}\), which corresponds to a lobe diameter of 540 m at a distance of 1000 km. Since only the L-band radar has a tracking capability, it is needed to steer the imaging radar. Due to the significantly narrower antenna lobe in the Ku band, the tracking radar must track space objects extremely precisely so that the Ku-band antenna lobe stays on the object.
## 3 Methodology
### Underwater Magnetic Induction MIMO Communication System Model
#### 3.1.2 The Model of Underwater MI-MIMO
\(M_{i}\) represents the mutual inductance between the \(T\)-th transmitting coil and the \(R\)-th receiving coil, \(Z_{t}\) is the impedance of the transmitting circuit, \(Z_{r}\) is the impedance of the receiving circuit, and \(Z_{L}\) is the load impedance of the receiving circuit. \[C_{MI-MIMO}=\log_{2}\Big{[}\det\Big{(}I_{N}+\frac{\rho}{N}HH^{*}\Big{)}\Big{]} \text{bps/Hz} \tag{7}\]  
In (7), \(I_{N}\) is the identity matrix, \(\rho\) is the signal-to-interference-plus-noise ratio, \(H\) is the channel transmission matrix, which represents the channel coefficient between antennas, and \(H^{*}\) is the complex conjugate transpose of the matrix \(H\) for \(N\) receive antennas.
## 5 Experimental Validation
### Experimental Results  
The sampling frequency is 51,200 Hz and the acquisition time is 15 s. Intercepting 2 s of data in the acquisition duration for calculation, each snapshot has a length of 0.1 s, has a Hanning window weighting, and corresponds to a frequency resolution of 10 Hz. Setting the overlap of two consecutive snapshots to 50% results in 39 snapshots.  
When sound passes through the shear layer of the flow field, the sound propagation route will be deflected due to the refraction effect of sound. The sound propagates from  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Frequency (Hz)** & **300** & **500** \\ \hline Sound pressure level of Source A (dB) & 106.7 & 107.0 \\ Sound pressure level of Source B (dB) & 106.7 & 107.0 \\ Sound pressure level of Source C (dB) & 86.7 & 87.0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Actual sound pressure levels of loudspeakers at different frequencies.  
Figure 10: The experimental setup. (**a**) Relative locations between the wind tunnel, the aircraft standard model, and the microphone array. (**b**) Localized view of aircraft standard model and the microphone array. (**c**) Arrangement of the three Bluetooth speakers on the aircraft standard model.  
the sound-generating point in the jet to the receiving point outside the jet, and a certain modified equivalent uniform flow field can be used instead of the non-uniform flow field [65]. Assuming that the speed of sound remains constant in the uniform flow field and no-flow field, the new flow velocity in the uniform flow field is replaced by an equivalent flow velocity that makes the sound source propagate directly along a straight line to the receiving point without sound deflection. In the preprocessing stage, the equivalent flow velocity method corrects the acoustic refraction phenomenon caused by flow velocity.
# Elderly Fall Detection in Complex Environment Based on Improved YOLOv5s and LSTM
## 6 Experimental Results and Analysis
### Performance of Fall Detection Algorithm Based on Improved YOLOv5s
#### 6.1.4 Experimental Results  
Figures 15-17 show that the detection precision, recall rate, and average precision of the improved model are 92.7%, 91.4%, and 95.1%, respectively, which are 7.2%, 9%, and 7.6% higher than that of the original model. As shown in Figures 18 and 19, regarding the actual detection effect of fall, the observation results show that the original model missed detection of distant targets due to environmental occlusion, too far distance, and other problems, and the overall detection accuracy was low, as shown in Figure 18. Compared with the original model, the proposed model can detect more targets in the same image or frame with severe occlusion and far distance, with the overall detection accuracy being high, as shown in Figure 19. These results demonstrate that after the improvements, the expressive ability of the model and its understanding of the input data's spatial structure are enhanced, helping the network better focus on the key parts of the target and, therefore, increasing the model's fall detection accuracy.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hardware Configuration** & **Version** \\ \hline CPU & 14-core Intel Xeon Gold \\ Memory & 12 Gb \\ GPU & NVIDIA 3080 RTX \\ Operating System & Windows10, 64 Bit \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental hardware environment.  
Figure 14: The process diagram of fall labeling.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Software Configuration** & **Version** \\ \hline Language & Python 3.8 \\ Deep learning framework & Pytorch 1.5.1 \\ GPU engine & Cuda 10.1 \\ Virtual environment & Miniconda3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental software environment.  
Figure 16: Comparison of recall between original YOLOv5s and improved YOLOv5s.  
Figure 17: Comparison of average precision between original YOLOv5s and improved YOLOv5s.  
Figure 15: Comparison of detection precision between original YOLOv5s and improved YOLOv5s.