esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Experiments and Discussions
### Fault Detection  
#### 3.4.1 GPS Fault  
To evaluate the performance of our approach, the GPS RAIM and the VA-RAIM were compared in terms of fault detection. We randomly selected one visual satellite and added the fault bias on the pseudorange. The fault detection results with different methods are shown in Figure 9, which shows that the proposed VA-RAIM performs better than the GPS RAIM method with a higher fault detection rate under the conditions of the same fault. For example, when the fault bias is 50 m, the fault detection rate of the GPS RAIM is 84.3%, and the fault detection rate of VA-RAIM with 300 dpi and 1 pixel DE is 97.5%.The VA-RAIM has an increase 13.2% when compared with the fault detection result of the GPS RAIM. Taking into account that the detection power is 99%, the minimal detectable bias (MDB) [16] of the VA-RAIM with 300 dpi and 2 pixels DE is 75 m, which is 21.9% less than the GPS RAIM's 96 m. Conclusively, the proposed VA-RAIM outperforms the GPS RAIM with a higher level of fault detection rate and lower MDB in the approach and landing phase.
# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 3 Analysis of Resource Management in the MLSCS
### Primary Challenges in Resource Management
#### 3.2.2 Scarce Global Spectrum Resources  
The development of satellite communication systems is currently constrained by the scarcity of spectrum resources, which cannot meet the increasing demand for new services and applications. This has led to escalating spectrum congestion and interference issues. Furthermore, competition for spectrum resources between satellite and terrestrial communication systems is intensifying, posing additional challenges for the growth and expansion of both systems.  
As communication systems increasingly use higher frequency bands, limited spectrum resources have always been a major constraint on satellite communication systems. As more services and applications adopt these higher frequencies, spectrum congestion is likely to become a significant barrier to the deployment and development of satellite systems. The scarcity of Ka-band and even higher frequency bands has affected the performance of broadband satellite communication systems. Effective spectrum management and planning are crucial for the design of these systems. To address this issue, some researchers have proposed cognitive radio-based satellite spectrum allocation. This approach allows satellite communication to share the spectrum with terrestrial systems under acceptable interference conditions, thereby improving the utilization efficiency of satellite spectrum resources.  
On the other hand, limited spectrum resources are divided into several independent frequency bands. To avoid interference, these bands are restricted to specific authorized users. Unauthorized users cannot access these bands even when they are idle. This model ensures that authorized users are not disturbed. However, the exclusivity of these bands in both the time and space domains means that a large number of authorized users have exclusive access. This leads to significant waste and reduced spectrum utilization efficiency.  
To address the problem mentioned earlier and reduce spectrum usage limitations, spectrum sensing is used to identify real-time spectrum holes, which can improve spectrum utilization efficiency. Additionally, optimizing spectrum allocation ensures that limited resources are used where they are most needed. This approach helps to make better use of available spectrum and reduces waste.  
Figure 1: The global coverage of a Walker LEO constellation.
# Optical Boundaries for LED-Based Indoor Positioning System
## 2 System Model  
The system model for investigating the optical boundaries is developed based on the transmitter front end as shown in Figure 2.  
Considering a typical room size of dimensions 5 m \(\times\) 5 m \(\times\) 3.5 m, where the receiver is on a horizontal plane at a distance of \(h\) m from the transmitter. The power received at a location in the room is given by \(P_{r}=H(0)P_{t}\), where \(P_{t}\) is the optical power transmitted from the LED, and \(H(0)\) is the DC channel gain for directed line of sight (LOS) given in Reference [34; 79; 80] as  
\[H(0)=\begin{cases}\frac{m+1}{2\pi d^{2}}A\cos^{m}(\phi)T_{s}(\varphi)g(\varphi )\cos(\varphi),&\text{for }0\leq\varphi\leq\varphi_{c}\\ 0,&\varphi>\varphi_{c}\end{cases} \tag{1}\]  
where \(A\) is the physical area of the PD, \(d\) is the LOS distance between the transmitter and the receiver, \(\phi\) is the angle of irradiance with respect to the transmitter's perpendicular axis, and \(\varphi\) is the angle of incidence with respect to the receiver axis. \(T_{s}(\varphi)\) is the transmission of the optical filter and is assumed to be in unity for this work as this assumption does not affect generality [81], \(\varphi_{c}\) is the field of view of the receiver, \(g(\varphi)\) is the gain of the optical concentrator given as a function of the refractive index \(n\) as  
\[g(\varphi)=\begin{cases}\frac{n^{2}}{\sin^{2}\varphi_{c}},&0\leq\varphi\leq \varphi_{c}\\ 0,&\varphi>\varphi_{c}.\end{cases} \tag{2}\]  
\(m\) is the order of the Lambertian source and is  
\[m=\frac{\ln(1/2)}{\ln(\cos(\Phi_{1/2}))} \tag{3}\]  
where \(\Phi_{1/2}\) is the half angle of the LED transmitter.  
In this work, the received optical power as the mobile receiver moves along the horizontal plane is expressed in terms of the angle of irradiance at the receiver with respect to the transmitter's perpendicular axis. On the basis of Figure 2, the horizontal displacement \(x\) can be evaluated from this figure as \(x=h\tan\varphi\).
###### Abstract  
This paper presents a multi-state adaptive early warning method for mechanical equipment and proposes an adaptive dynamic update model of the equipment alarm threshold based on a similar proportion and state probability model. Based on the similarity of historical equipment, the initial thresholds of different health states of equipment can be determined. The equipment status is divided into four categories and analyzed, which can better represent its status and provide more detailed and reasonable guidance. The obtained dynamic alarm lines at all levels can regulate the operation range of equipment in the different health states. Compared to the traditional method of a fixed threshold, this method can effectively reduce the number of false alarms and attains a higher prediction accuracy, which demonstrates its effectiveness and superiority. Finally, the method was verified by means of lifetime data of a rolling bearings. The results show that the model improves the timely detection of the abnormal state of the equipment, greatly reduces the false alarm rate, and even overcomes the limitation of independence between the fixed threshold method and equipment state. Moreover, multi-state division can accurately diagnose the current equipment state, which should be considered in maintenance decision-making.  
Keywords:fault early warning; similar proportion; adaptive threshold; kernel density estimation +
Footnote †: journal: Applied Sciences
# Z-OFDM: A New High-Performance Solution for Underwater Acoustic Communication
## 4 Computer Simulation Experiments  
This section primarily introduces the computer simulation experiments and experimental results of the Z-OFDM system. The parameter of the Z-OFDM system is given  
Figure 5: BER of Z-OFDM system over AWGN channel.  
in Table 1. This simulation aims to get the BER of the Z-OFDM system under a natural underwater channel.  
In this section, the channel data are obtained from the natural underwater channel in an experiment held by the Key Laboratory of Underwater Acoustic Communication and Marine Information Technology at Xiamen university. The experiment aimed to measure the time evolution of the magnitude impulse response at two different locations. The experiments are held in Fuxian Lake in Yunnan [7; 55; 56] (Figure 6a) and Wuyuan Bay in Xiamen [57; 58; 59] (Figure 6b).
# Hybrid Positioning for Smart Spaces: Proposal and Evaluation
###### Abstract  
Positioning capabilities have become essential in context-aware user services, which make easier daily activities and let the emergence of new business models in the trendy area of smart cities. Thanks to wireless connection capabilities of smart mobile devices and the proliferation of wireless attachment points in buildings, several positioning systems have appeared in the last years to provide indoor positioning and complement GPS for outdoors. Wi-Fi fingerprinting is one of the most remarkable approaches, although ongoing smart deployments in the area of smart cities can offer extra possibilities to exploit hybrid schemes, in which the final location takes into account different positioning sources. In this paper we propose a positioning system that leverages common infrastructure and services already present in smart spaces to enhance indoor positioning. Thus, GPS and WiFi are complemented with access control services (i.e., ID card) or Bluetooth Low Energy beaconing, to determine the user location within a smart space. Better position estimations can be calculated by hybridizing the positioning information coming from different technologies, and a handover mechanism between technologies or algorithms is used exploiting semantic information saved in fingerprints. The solution implemented is highly optimized by reducing tedious computation, by means of opportunistic selection of fingerprints and floor change detection, and a battery saving subsystem reduces power consumption by disabling non-needed technologies. The proposal has been showcased over a smart campus deployment to check its real operation and assess the positioning accuracy, experiencing the noticeable advantage of integrating technologies usually available in smart spaces and reaching an average real error of 4.62 m.  
hybrid positioning; fingerprinting; indoor positioning; smart buildings; mobile devices +
Footnote †: journal: Applied Sciences  
1
## 2 Data Processing  
The train's advanced on-board axle temperature monitoring system ensures the accurate collection of bearing temperature data. Using the axle temperature host, the system effectively captures temperature data transmitted by sensors on the bearings and axle boxes. These critical data can then be efficiently transmitted through the network to ground storage or downloaded by personnel using the on-board temperature detection system [17]. The axle temperature data collected by the on-board axle temperature monitoring system on the train often exhibit some deficiencies. In particular, the following two issues are more common:  
(1) Communication anomalies often result in partial periods of missing bearing temperature data when the train passes through tunnels or areas with weak network signals.  
(2) Temperature sensor failure due to poor connections, electromagnetic interference, and other factors can lead to transient abnormalities in certain axle temperature variables that subsequently recover. These anomalies usually do not occur simultaneously in multiple sensors and therefore lead to outliers in the bearing temperature data.  
The proposed methodology here is essentially based on a data-driven latent structure approach that requires normalized data during modeling. Introducing a small amount of missing axle temperature data or outliers during the modeling process could lead to noticeable modeling errors and thus affect the accuracy of fault diagnosis. As a result, appropriate data preprocessing is also performed on the acquired axle temperature data.
# Fault Diagnosis Algorithm of Beam Pumping Unit Based on Transfer Learning and DenseNet Model
## 3 Dynamometer Classification Model Based on the Transfer Learning Method
### Training Results and Discussion
#### 3.3.2 Comparative Experiments of Different Depth Convolutional Network Models
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fault Type** & \multicolumn{4}{c}{**Recognition Rate**} \\ \hline  & **ResNet** & **MobileNet** & **Xception** & **DenseNet** \\ \hline plunger up bump & 8.6\% & 4.3\% & 21.7\% & 91.3\% \\ \hline plunger down bump & 22.7\% & 54.5\% & 45.5\% & 90.9\% \\ \hline insufficient fluid supply & 86.6\% & 75.9\% & 67.1\% & 97.5\% \\ \hline double vale loss & 89.1\% & 48.6\% & 48.6\% & 100.0\% \\ \hline fixed vale loss & 0\% & 18.2\% & 45.5\% & 90.9\% \\ \hline broken rod & 95.4\% & 77.3\% & 100.0\% & 97.7\% \\ \hline normal operating conditions & 100\% & 46.2\% & 84.6\% & 100.0\% \\ \hline gas effect & 97.8\% & 41.3\% & 91.3\% & 97.8\% \\ \hline pump leakage & 86.9\% & 94.3\% & 47.2\% & 99.1\% \\ \hline plunger come out & 0\% & 28.6\% & 23.8\% & 100.0\% \\ \hline swimming vale loss & 80.8\% & 47.9\% & 54.8\% & 98.6\% \\ \hline sand effect & 19\% & 95.2\% & 71.4\% & 90.5\% \\ \hline heavy oil effect & 0\% & 12.5\% & 62.5\% & 87.5\% \\ \hline wax effect & 91.1\% & 97.1\% & 58.8\% & 95.6\% \\ \hline pump and eject & 0\% & 16.7\% & 66.7\% & 100.0\% \\ \hline Training set & 100.0\% & 99.3\% & 98.6\% & 99.8\% \\ \hline Testing set & 95.3\% & 97.4\% & 95.3\% & 96.9\% \\ \hline Average recognition rate & 51.8\% & 50.5\% & 59.3\% & 95.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of recognition rate of various models. recognition rate far exceeds that of the control group classification model, proving that it can effectively classify 15 types of working conditions
## 4 Case Studies Based on Real Data
### Influence of Extremely Large Passenger Flow on Exit Width Range  
Risks are always hidden in extreme scenarios. Consider the extreme situation of an oversized passenger flow (disembarking passengers) that may occur in the operation of a high-speed railway. The risk probability of passengers falling off the platform resulting from congestion is equivalent to the cumulative distribution probability of the number of passengers that can leave the platform within the maximum allowable travel time by optimizing the exit width. Section 4.1 discusses the width range of the platform exit corresponding to a 90% percentile cumulative probability of passenger flow. While this still means that there is still a 10% probability that cannot guarantee that passengers exit within the maximum allowable travel time, it results in accidents of falling off the platform when the exit width is between 6.43 m and 7.01 m.  
In order to study the interplay between passenger flow and exit width range, we set risk probabilities of 5% and 2%, respectively, to explore the impact of an extremely oversized passenger flow on exit width. Keeping the other parameters constant, the results of the platform exit width range under risk probabilities of 5% and 2% are shown in Table 9. It can be concluded that as the disembarking passengers increase, the exit width range shrinks. Because the exit width is limited by the total platform width of 10 m, there is no solution for optimization of the exit width under a 2% risk probability. At this time, the platform should be widened instead of expanding the exit. Finally, we determine that the current optimal exit width can only reduce the risk probability by about 3.1% at most.  
We further expand the scenarios of passenger flow to compute the range of exit widths. As shown in Figure 6, under the current maximum allowable travel time of 300 s, the exit width range is roughly inversely proportional to the number of passengers getting off the trains. When the number of disembarking passengers exceeds 2720, changing the exit width is no longer effective and the platform width needs to be redesigned.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Risk Probability** &
\begin{tabular}{c} **Passenger Flow** \\ **(Disembarking Passengers)** \\ \end{tabular} & **Exit Width** \\ \hline
10\% & 2614 & (6.43, 7.01) \\
5\% & 2678 & (6.61, 6.88) \\
3.1\% & 2720 & (6.71, 6.72) \\
2\% & 2751 & \(\varnothing\) (empty set) \\ \hline \hline \end{tabular}
\end{table}
Table 9: The width of a high-speed railway platform exit under different risk probabilities (detailed results under different risk coefficients).
# Seismic Responses of GRS Walls with Secondary Reinforcements Subjected to Earthquake Loading
## 4 Results
### Effects of Wall Height  
Figure 17a-d show the effect of wall height on the relative lateral facing displacement, connection load, maximum reinforcement load, and the acceleration amplification of the wall with/without secondary reinforcement, respectively. The results for the 3, 6, and 9 m high walls with and without secondary reinforcement are presented. As shown in Figure 17a, higher relative lateral facing displacements occur in the higher wall. Similar results can be seen in the higher walls, where the effect on reducing relative lateral facing displacement is insignificant.  
As shown in Figure 17b,c, secondary reinforcement can be an effective method for reducing the connection load and the maximum reinforcement load for various wall heights. To further evaluate the effect of the wall height, the maximum reinforcement loads of the 3, 6, and 9 m high walls in each primary reinforcement after shaking were added together, as shown in Table 7. From the results, it is evident that installing secondary reinforcement is more effective in the higher walls. Under the same Lsecondary/H ratio (0.15), the 9 m high wall experienced a 15.3% reduction in the overall reinforcement load, while the 3 and 6 m high walls experienced 1.06% and 12.9% reductions, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Height of GRs** **Walls (m)**} & \multicolumn{2}{c}{**Secondary**} & \multirow{2}{*}{**VS (m)**} & \multirow{2}{*}{**PGA (g)**} & \multirow{2}{*}{**Value (kN/m)**} & \multirow{2}{*}{**Difference**} \\  & **Reinforcement** & & & & \\  & **Length (m)** & & & & \\ \hline
3 & 0 & 0.6 & 0.8 & 25.714 & 1.06\% \\
3 & 0.45 & 0.6 & 0.8 & 25.442 & 1.06\% \\
6 & 0 & 0.6 & 0.8 & 165.95 & 12.9\% \\
6 & 0.9 & 0.6 & 0.8 & 144.58 & 12.9\% \\
9 & 0 & 0.6 & 0.8 & 363.89 & 15.3\% \\
9 & 1.35 & 0.6 & 0.8 & 308.31 & 15.3\% \\
6 & 0 & 0.4 & 0.8 & 181.87 & 9.4\% \\
6 & 0.9 & 0.4 & 0.8 & 164.72 & 9.4\% \\
6 & 0 & 0.6 & 0.2 & 102 & \\
6 & 0.9 & 0.6 & 0.2 & 78.77 & 22.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Sum of the maximum reinforcement loads of walls in each primary reinforcement layer.  
Figure 16: The variations of acceleration amplification out of the reinforced zone with different values of secondary reinforcement stiffness over the height of the backfill.  
As shown in Figure 17d, under the same seismic loading, secondary reinforcement does not notably affect the acceleration amplification for the three wall heights.  
Figure 17: The effect of wall height on **(a)** the relative lateral facing displacement; **(b)** connection load; **(c)** maximum reinforcement load; **(d)** the acceleration amplification of the wall with and without secondary reinforcement.
###### Abstract  
Flood hazards affect the local economy and the livelihood of residents along the South-Central Coast of Vietnam. Understanding the factors influencing floods' occurrence potentially contributes to establish mitigation responses to the hazards. This paper deals with an empirical study on applying a combination of the fuzzy analytic hierarchy process (AHP), the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS), and a geographic information system (GIS) to assess flood hazards along the South-Central Coast of Vietnam. Data are collected from focus group discussions (FGDs) with five communal authorities; a questionnaire completed by eight hamlet heads in the Phuoc Thang commune (Binh Dinh province); and documents, reports, and thematic maps provided from official sources. A total of 12 maps of flood factors are prepared. The results show that terrain elevation, creek-bottom terrains, high tide-induced flooding area, and distance to water body are the main factors affecting flood hazards. The An Loi hamlet faces the highest risk for floods, followed by Lac Dien, Luong Binh, and Pho Dong. The map of flood hazards indicates the western part is assessed as low hazard, whereas the eastern part is a very high hazard area. The study findings show that the hybrid approach using GIS-based fuzzy AHP-TOPSIS allows connecting decision makers with the influencing factors of flooding. To mitigate floods, both the Vietnam national government and the Binh Dinh provincial government should integrate natural hazard mitigation into socio-economic development policies.  
fuzzy AHP; fuzzy TOPSIS; GIS; flood hazards; South-Central Coast of Vietnam +
Footnote †: journal: Applied Sciences
# Feature-Model-Based In-Process Measurement of Machining Precision Using Computer Vision
## 2 Measurement Algorithm
### Establishing a Machining Feature Model for Design  
To effectively measure the machining of parts, it is necessary to construct a machining feature model for subsequent image matching. First, the part blueprint created by Computer-Aided Design (version: AutoCAD 2014) software or third-party tools is obtained. Drawings of parts are usually created directly in CAD software or imported through third-party tools. Subsequently, CAD standard data will be converted to DXF format, and each component of the input drawing will be analyzed, such as circles, lines, arcs, etc. As shown in Figure 3, this can capture relevant dimension information, such as radius size, starting point position, center position, and arc degree of the arc, as well as starting and ending point positions of lines. Then, the parsed parameter information will be integrated into a standardized data structure to keep data consistency. For each graphic element, the process generates a series of point sets based on its parameter information. The generated sets of points represent the position and shape of these transformed sets of points and feature points in a unified reference coordinate system. Taking a circle as an example, a series of points are generated based on the circle's center and radius that accurately represent the position of the circumference. Finally, according to the pixel standards of industrial cameras, the size of the machining feature model is adjusted to ensure dimensional consistency from the part drawing to the actual machining process. The specific conversion between arcs and line segments is as follows:
## 5 Experimental Tests
### DC-DC and Diode Efficiency  
The converter and diode assembly is characterized by a certain efficiency, which is a function of the output current and the output voltage. To measure and map the efficiency, a dedicated test was performed. Figure 8 reports the efficiency measured during the test of the converter and diode assembly as a function of the load current. The test was conducted by connecting the PS for the emulation of the FC to the input side of the power converter and the electronic load downstream from the diode; thus, the batteries were not included in this test setup. The output voltage of the converter was set equal to the nominal voltage of the test bench DC bus. The efficiency was measured at different load currents. During the test, the power supply followed the emulated FC V-I current so that the efficiency was evaluated with the proper input current and voltage to the converter. For each efficiency point, the system was brought to the regime. At low currents, the efficiency increases with  
Figure 7: Emulated FC V-I curve vs. real FC V-I curve.  
the load and reaches its maximum of about 93% at approximately 4.5 A; then, it starts decreasing. However, the efficiency is more than 88% for almost the entire load spectrum.