esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Scale Effect Analysis of Influence Factors  
The spatial distribution of organic matter is obviously different at different research scales, which is due to the comprehensive effect of different types of influencing factors. Provincial scale can effectively reflect the spatial distribution of organic matter in a large spatial range. In this study, it was found that soil type and parent material were the main controlling factors of spatial variation of soil organic matter in the Shandong Province, followed by soil texture and land use type. The influence of terrain and climate factors was relatively small, and human activities overshadowed the influence of climate, to a certain extent. In other provinces of China, such as Guangxi Province [13], soil type was the most important influencing factor, independently explaining 36.0% of the variation, followed by parent material and elevation. Jiangsu Province is located in the humid area of eastern China and the terrain is flat; there, soil texture was the main factor affecting the variation of organic matter content [14]. In North China's Hebei Province, soil type and land use mode have great influence on the spatial distribution of soil organic matter. The lower the soil classification level, the greater the ability to reflect the spatial variation of soil organic carbon density [15]. It can be seen that, at similar provincial scales, the influence factors of different regions will also differ due to the different physical and geographical environments.  
For the smaller spatial scale, the influence of topography and soil type was relatively large, but the influence of temperature and precipitation was relatively small. In some areas at the county scale, such as Hengshan County on the Loess Plateau and eastern China, topographic factors such as elevation and slope direction had significant effects on organic matter [8; 9; 10]. The main factors of soil organic matter distribution in Pinggu County were terrain, soil type, texture, and land use type. At the township scale, texture and tillage methods were dominant factors [11]. In Huzhu County, north-east Qinghai Province, the three main factors affecting soil organic matter variation were soil type, average annual precipitation, and altitude [12].  
For some larger scales, it is mainly the topographic climate that plays a prominent role. For example, some studies found that 41.50% to 56.20% of SOM changes in different geographical areas in China were predominated affected by temperature, precipitation, and altitude [17]. On a national scale, many studies have shown that climate variables and land use are the most significant influences on soil organic matter change [18; 19].  
These studies further indicate that the relationship between environmental factors and organic matter content is different at different scales and in different regional environmental conditions. Therefore, it is necessary to constantly summarize the spatial variation information among various scales in future research to strive for the accuracy of the research.
# Model Predictive Control of Non-Linear Systems Using Tensor Flow-Based Models
## 3 Benchmark Models
### Coupled Tanks System  
In several industry processes, it is often required to process liquids within storage devices. Many times, they are solely pumped across reservoirs but can also be part of chemical reactions where sudden volumetric changes can happen. Anyhow, in any scenario, the level of a fluid within a storage tank must be controlled according to its capacity limits.  
In the present scenario, a system consisting of two tanks is used, having each one an independent pump to control the inflow of liquid (\(q_{1}\) and \(q_{2}\)) and an outlet at the bottom responsible for the liquid leakage. The tanks are interconnected by a channel which allows the liquid to flow between them and the variables under control are the liquid heights in each tank (\(h_{1}\) and \(h_{2}\)) [27], as depicted in Figure 2,The dynamic of this system can be described by the set of non-linear differential Equations (2) and (3) [27].  
\[a_{1}\frac{dh_{1}}{dt} =q_{1}-\alpha_{1}\sqrt{h1}-\text{sgn}(h_{1}-h_{2})\alpha_{3}\sqrt{h1 -h2} \tag{2}\] \[a_{2}\frac{dh_{2}}{dt} =q_{2}-\alpha_{2}\sqrt{h2}+\text{sgn}(h_{1}-h_{2})\alpha_{3}\sqrt{h1 -h2} \tag{3}\]  
where \(a_{1}\) and \(a_{2}\) denote the cross-sectional area of the tank 1 and 2, \(h_{1}\) and \(h_{2}\) are the liquid level in tank 1 and 2, \(q_{1}\) and \(q_{2}\) are the volumetric flow rate (cm\({}^{3}\) s\({}^{-1}\)) of Pump 1 and 2, \(\alpha_{1}\), \(\alpha_{2}\) and \(\alpha_{3}\) are proportionality coefficient corresponding to the \(\sqrt{h_{1}}\), \(\sqrt{h_{2}}\) and \(\sqrt{h_{1}-h_{2}}\) terms which depend on the discharge coefficients of each outlet and the gravitational constant. In the present evaluation, \(q_{2}\) will be used as an unmeasured external disturbance for the control system. The reservoir model parameters were obtained from the setup described in [28], and are presented in Table 1:  
As evidenced by Figure 2(a),b the system's steady-state gain is of non-linear nature and its incremental gain highly dependent on the current operation point.
# A Simple and Efficient Method for RSS-AOA-Based Localization with Heterogeneous Anchor Nodes
## 1 Introduction
Through simulation experiments, we found that the proposed method has good performance in different positioning scenarios and is very close to the CRLB. Although the computational complexity is slightly higher than that of the traditional LWLS (higher computational complexity when calculating weights), the computational complexity is still within an acceptable range, and the calculation speed is fast, which can achieve real-time positioning. This indicates that the proposed method can achieve high accuracy, reliability, and real-time performance. The main contributions of this paper are as follows:  
1. Diverse Anchor Node Scenarios: This paper considers a more practical scenario, specifically localization scenarios involving different types of anchor nodes, which closely reflects real-world situations. 2. Innovative Algorithm: A novel localization method based on hybrid RSS-AOA measurements is proposed, which accounts for the differences in measurement noise distributions across different nodes to enhance localization accuracy and reliability. 3. Two-stage LWLS Method: This paper introduces a two-stage LWLS method for estimating the residual variance of each LWLS equation, thereby achieving more accurate target node localization. 4. RSS-AOA Time Series Model: This paper incorporates an RSS-AOA time series model that utilizes the time series information of RSS-AOA measurements to further improve localization accuracy. 5. CRLB Derivation: A detailed derivation of the CRLB of the RSS-AOA time series model is provided, establishing a theoretical foundation for evaluating the performance of localization methods. In the remaining sections of this paper, the sequential hybrid RSS-AOA system model is presented and the CRLB of the measurement model is derived in Section 2. The details of the proposed method are shown in Section 3. The extensive simulation results demonstrate the good performance of the proposed method in Section 4. We conclude the paper with a summary and discussion of the future work in Section 5.
# Bluetooth Communication Leveraging Ultra-Low Power Radio Design
## 3 BLE Radio Design Trends and Considerations
### Transmitter Design Trends
#### 3.2.4 Transmitter-Focused Design Requirement for System Efficiency  
The brief survey below summarizes recent design trends in ultra-low power BLE transmitter designs. In order to reduce power consumption as well as the overall cost and improve the system efficiency, a lot of techniques are used for different applications. In order to design the BLE transmitter at its physical limit in terms of power and cost, designers have to adjust their design specifications according to application needs. For example, the frequency synthesizer specifications in traditional full function BLE transceivers are set by the receiver adjacent channel rejection requirements which requires exceptional performance of the VCO. However, from the transmitter's point of view, such performance far exceeds its need according to either spectrum emission or frequency modulation requirement [50; 54; 60]. Thus, designing the transmitter frequency synthesizer for a BLE network according to the receiver requirement is wasting power and cost. For power amplifier design in BLE transmitters, it is better to optimize the output power and system efficiency according to application needs as well. For example, if the target communication range is only within 3-4 m, optimizing the PA for its highest efficiency at 10 dBm would not be as efficient as optimizing its highest efficiency at 0 dBm.  
Table 2 presents a comparison table for state-of-the art BLE transmitters with different architectures. It can be seen that BLE transmitters have an average overall efficiency around 25%. Therefore, in order to achieve longer transmission range, the power consumption is expected to be in mW levels. Aggressive duty-cycling techniques can be implemented for discontinuous transmission events/advertisements to bring the average power consumption to \(\upmu\)W levels for ULP applications.
## 4 Design and Analysis of TRA Metasurface
### Transmit Array Functionality  
The functionality of the transmit array metasurface is presented in this section depending on the incident wave's polarization. Specifically, in the TM mode, the array functions as a transmitter at 6.3 GHz, whereas for TE polarized waves, it acts as a transmitter at 9.3 GHz.  
Figure 9 depicts a 2D transmission plot, providing insight into the array's transmission characteristics. Evidently, the array operates as a transmitter for the TM mode at 6.3 GHz, and correspondingly, for the TE mode, it functions as a transmit array at 9.3 GHz. In the TE mode, the array achieves a gain exceeding 20 dB, whereas for the TM mode, the gain reaches 12 dB. This variation in gain can be attributed to losses and significant coupling between distinct components of the multilayer unit cell.  
Figure 10 illustrates the 3D pattern for the transmit array scenario, offering a visual representation of its radiation characteristics. As depicted in Figure 10a, the proposed array functions as a transmit array for the TE mode at 9.3 GHz. Conversely, for the TM mode, it operates as a transmit array at 6.3 GHz.  
Figure 7: A 2D reflection.  
Figure 8: Reflection: (**a**) TE mode 6.3 GHz and (**b**) TM mode 9.3 GHz.  
Figure 10: Transmission: (**a**) TE mode 9.3 GHz and (**b**) TM mode 6.3 GHz.  
Figure 9: A 2D transmission.
## 5 Experimental Results
### Verifications of the Proposed DC Bus Voltage Compensator  
The DC bus voltage, the load power increase, the battery currents, and the supercapacitor current of HESS when the PI double closed-loop control strategy is used without voltage compensation are shown in Figure 21. The signal on the left at the bottom of the oscilloscope result graph represents the voltage and current per grid scale value, and the signal on the right represents the value of the voltage and current. When the load power increases from 80 W to 150 W, the average DC bus voltage is 62.4 V.  
in Figure 24. When the load power decreases from 150 W to 80 W, the average DC bus voltage is 64.6 V.  
Figure 21: The DC bus voltage, the load power increase, the battery currents, and the supercapacitor current of HESS when the PI double closed-loop control strategy is used without voltage compensation. The non-English word “\(\overline{\textbf{\mu}}\)” in the figure means “average”.  
Figure 22: The DC bus voltage, the load power decrease, the battery currents, and the supercapacitor current of HESS when the PI double closed-loop control strategy is used without voltage compensation. The non-English word “\(\overline{\textbf{\mu}}\)” in the figure means “average”.  
Comparing Figures 21 and 23, on condition of the same load power increase (from 80 W to 150 W), the DC bus voltage varies from the original 62.4 V to 63.4 V when DC bus voltage compensation is added. Similarly, comparing Figures 22 and 24, on condition of the same load power decrease (from 150 W to 80 W), the DC bus voltage also increases from primary 63.2 V to 64.6 V using voltage compensation.  
The experimental results agree with the simulation results in Figure 16. The verification of proposed DC bus voltage compensator is verified.  
Figure 23: The DC bus voltage, the load power increase, the battery currents, and the supercapacitor current of HESS when PI double closed-loop control strategy is used with voltage compensation. The non-English word “\(\mathcal{H}\)” in the figure means “average”.  
Figure 24: The DC bus voltage, the load power decrease, the battery currents, and the supercapacitor current of HESS when PI double closed-loop control strategy is used with voltage compensation. The non-English word “\(\mathcal{H}\)” in the figure means “average”.
# An Integrated Safety Monitoring and Pre-Warning System for Fishing Vessels
## 4 Experiment Results and Discussion
### Results and Discussion  
#### 4.2.1 Vessel Attitude Motion Prediction  
Figure 10 shows the vessel's roll angle of the research vessel between 09:00 and 15:00. The length of each time interval is 1 s. It can be clearly seen that the highest roll motion is \(6.25^{\circ}\), the minimum roll motion is \(-10.31^{\circ}\), and the roll's average value is less than zero, indicating that the starboard side seems higher than the port side of the vessel.  
Figure 11 gives the vessel's pitch angle of the research vessel between 09:00 to 15:00. It can be found that the maximum pitch motion is \(3.19^{\circ}\) and the minimum pitch motion is \(-3.68^{\circ}\). The pitch's average value is higher than zero, which means that the bow is more likely to sink than to rise.  
Figure 10: The vessel’s roll angle.  
Figure 9: Vessel’s navigation routes.  
Figure 12 shows the vessel's heave acceleration of the research vessel between 09:00 and 15:00. It can be concluded that the maximum heave motion is 0.156 g and the minimum heave motion is \(-0.10\) g, and the heave's average value is close to zero, which suggests that the heave motion is negligibly small.  
Figure 13 shows the prediction \"danger score\" based on data from Figures 10-12 using the fuzzy logic control method. It can be found that, normally, large rolling motions occur together with large pitch motions. Furthermore, it can be concluded that during the experiment, the \"danger score\" is less than \(-1\) at all times, indicating that the vessel is under a safe navigation condition. However, the decision support subsystem will send text messages to the vessel crew's smartphones and pop up the security alert on the web platform if the \"danger score\" is above 1.  
Figure 11: The vessel’s pitch angle.  
Figure 12: The vessel’s heave acceleration.
# Localization of Radio Sources Using High Altitude Platform Station (HAPS)
###### Abstract  
In Japan, the DEURAS system has been deployed to detect and locate illegal radio sources that either exceed permissible transmission power limits or operate on unauthorized frequencies. This system utilizes receiving antennas installed on high-rise buildings and radio towers to capture radio signals and estimate the location of the transmission source. However, in densely built urban environments, the accuracy of location estimation is often compromised due to signal reflections and diffractions. Additionally, in large-scale disasters such as earthquakes, terrestrial infrastructure may be severely damaged, making it essential to develop a localization system that operates independently of ground-based stations. To overcome these limitations, this study proposes a localization system based on a high-altitude-platform station (HAPS), which operates at an altitude of approximately 20 km. The feasibility and effectiveness of the proposed system are evaluated through numerical simulations, considering various environmental conditions. The results demonstrate that HAPS-based localization significantly improves positioning accuracy, offering a robust and high-precision alternative for radio source detection, particularly in scenarios where traditional ground-based systems are unreliable or unavailable.  
H  
HAPS; MUSIC; DOA
## 3 Results and Discussion
### Single-Array Antenna
The peak gain reflects the antenna's efficiency in transmitting and receiving signals with enhanced precision and effectiveness across the designed frequency range. Regarding radiation efficiency, the simulation results confirm that this gain corresponds to a radiation efficiency of 91.85% and a total efficiency of 82.9%. Figure 3: A parametric analysis of the inset length for the proposed antenna design. Figure 2: Simulated reflection coefficient of the single-port antenna as a function of frequency. The highlighted area shows bandwidth range. To ascertain the primary direction(s) in which the proposed antenna predominantly concentrates EM radiation, an analysis of the radiation pattern is conducted. The investigation aims to determine the main lobes and nulls in the radiation pattern, which provide crucial information about the antenna's directional characteristics. This analysis helps in identifying the specific azimuth and elevation angles where the antenna exhibits maximum radiation concentration, thereby aiding in optimizing the antenna's orientation and performance for targeted communication or sensing applications. The radiation patterns displayed in Figure 5 offer a detailed insight into the performance of a single 1 \(\times\) 8 series-fed array antenna across various frequencies in both the electric field plane (E-plane) and magnetic field plane (H-plane). At 28.8 GHz in the E-plane, the antenna realizes a gain of 11.6 dBi, with the main lobe pointed at \(\theta\) = 3\({}^{\circ}\) and a beamwidth of 64.2\({}^{\circ}\). In the H-plane, the major lobe displays a gain of 12.7 dBi, directed at \(\theta\) = 5\({}^{\circ}\), with a beamwidth of 19.3\({}^{\circ}\). Figure 4: Frequency dependence of peak gain of single-array antenna. Figure 5: Radiation patterns of 1 \(\times\) 8 series-fed array; (**a**) E-plane, (**b**) H-plane computed at 28.8 GHz.
## 3 Results
### Accuracy Optimization Assessment  
The primary trade-off to consider when setting a minimum change probability threshold is whether the increased change mapping is commensurate with the increased time to review additional candidatechange polygons. We plotted the observed change proportion in 5% probability bins over the bin median predicted probability of change (Figure 7). The proportion of polygons observed as change had a sigmoidal relationship with increasing probability. For the lowest bin, 0%-5%, only 0.2% of observed sample polygons were actual change while, in the highest bin, 95%-100%, 100% were actually change.  
Figure 7: Plots of observed change proportion and polygon counts by 5% probability bins. The upper plot shows the number of observed polygons in each bin plotted on the y-axis as the percentage of observed change polygons for that bin. For example, the uppermost value of 1734 represents the 95%–100% probability bin for reviewed polygons which contained 1734 polygons of which all were actually change. Values to the right of 25% are complete censuses. Values to the left of 25% represent proportionate samples with change probabilities <25%. The darker areas represent errors of commission and omission. The light green area between 25 and 50% provides a graphical representation of additional change mapped from accuracy optimization technique 2, threshold lowering. The bottom histogram shows the total population of polygons in each bin.  
The lowest 5 bins, which were assessed during the omission analysis, had rates from 0.2% to 19% (Table 3). We used these percentages to estimate the cost of reviewing subsequent bins. To estimate utility we would need to assign a value on mapping additional change and compare it to our estimated cost [84]. For example, the 10%-15% bin had 23,090 polygons in it from which we observed a sample of 290 polygons. Those polygons covered 69 hectares of which we observed 1.3 were omitted change. If we extend the proportion of change in this bin, 1.89%, by the 5754 acres covered by all polygons in this bin, we might expect to find 108 total change hectares if we performed the census. The cost to map these additional change locations would be about 92 hours of analyst time.  
The review of the 5734 training polygons for input to the Random Forests model took about 24 hours. The workload for the Commission analysis is proportional to the number of polygons in each prediction bin. The perceived value for reviewing a certain prediction bin is proportional to the change rate observed in that bin. The RF50 accuracy assessment required the review of 7832 polygons for commission errors and 5250 polygons for omission errors which totaled 13,108 polygons and required 50-60 analyst hours. The additional polygons for the RF25 analysis raised the accuracy assessment work load to 21,774 polygons, added 40 hours of processing and raised the number of mapped change locations from 2348 to 5607.
# HFDF-EffNetV2: A Lightweight, Noise-Robust Model for Fault Diagnosis in Rolling Bearings
## 4 Discussion
### Case 1: CWRU Bearing Dataset  
In the experiment, data from the drive-end part of the CWRU bearing dataset with a motor load of 0 horsepower (HP), a speed of 1797 r/min, and a sampling frequency of 12 kilohertz (kHz) were used. The data were divided into four states: normal, inner-ring faults, outer-ring faults, and rolling-ball faults, with single-point fault diameters corresponding to 0.1778 mm (mm), 0.3556 mm, and 0.5334 mm. To ensure the capture of critical fault features, the sample length was set to 1024 data points, with an overlap window of 512, covering approximately 2.5 turns of bearing rotation. The data points were calculated as shown in Equation (18), below:  
\[\mathrm{N_{P}}=\frac{60f}{R} \tag{18}\]  
where N\({}_{\mathrm{P}}\) is the number of data points collected per revolution; \(f\) is the sampling frequency; and R is the bearing speed under the corresponding operating condition.  
The raw vibration signals were standardized, and then the CWT was applied to generate a total of 2000 samples. Each class contained 130 training samples and 70 test samples, with the detailed composition of the dataset shown in Table 2.