esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Recent years have seen an increase in events of drone incursion into airport terminal areas, leading to safety concerns and disruptions to airline operations. It is of great importance to identify the potential conflict, especially for those non-cooperative drones, as their intentions are always unknown. For the safe operation of air traffic, this paper proposes a conflict risk assessment method between non-cooperative drones and manned aircraft in the terminal area. First, the trajectory data of manned aircraft and drones are obtained. Real-time cylindrical protection zones are established around manned aircraft according to the separation interval for safe operation between the drone and the manned aircraft at different altitudes. Secondly, trajectory predictions for the manned aircraft and the drone are conducted, respectively. A quartile regression bidirectional gate recurrent unit neural network is proposed in this research for the trajectory prediction of the drones. The model integrates the bidirectional gated recurrent unit structure and the quartile regression structure. The performance indicators confirm the superiority of the proposed model. Based on the trajectory prediction results, it is then determined whether there is a conflict risk between the drone and manned aircraft by comparing the position distribution of the drone as well as the real-time cylindrical protection zone of the manned aircraft. The conflict probability between the drone and the manned aircraft is then calculated. The prediction accuracy of conflict probability is estimated by Monte Carlo simulation methods. The collision probability prediction accuracy of manned aircraft and drones at different flight stages and altitudes ranges from 73% to 97%, which shows the reliability of the proposed method. Finally, the collision probability between the drone and the manned aircraft at the closest encountering point and the estimated time to reach the closest encountering point are calculated. This paper predicts the conflict risk between the drone and manned aircraft, thus providing theoretical support for the safe operation of air transport in low-altitude environments.  
non-cooperative drones; manned aircraft; trajectory prediction; machine learning; artificial neural networks; Monte Carlo simulation +
Footnote †: journal: Applied  
12 October 2022  
14 October 2022
## 2 Literature Review  
Construction machines are essential components of earthwork construction sites, performing various operations [31; 32]. It is necessary to detect and track the earthmoving activities of these machines to facilitate cycle time estimation, productivity assessment, and resource allocation [10; 33]. Traditional observational approaches to construction sites are time-consuming, costly, error-prone, and sensitive to the complexity of construction sites and the availability of manpower for monitoring [34]. Recently, extensive research has been conducted on the continuous monitoring of construction machines using various information technologies and artificial intelligence (AI) techniques to enhance construction efficiency, mitigate associated risks, and accelerate task completion at construction sites [35; 36]. Various data types, including surveillance camera footage, images, and Internet of Things (IoT) sensor data, are processed through these techniques to facilitate construction tasks and safety monitoring [37; 38; 39]. Considering the input data type, these techniques can be categorized into three primary methods: audio-based, motion-based sensors, and vision-based. The audio-based methods usually follow four steps: gathering the audio data, filtering and segmenting, selecting and extracting features, and modeling training for activity classification through SVM, Short-Time Fourier Transform (STFT), continuous wavelet transforms (CWT), and Bayesian statistical models [40; 41; 42]. However, audio-based methods can be adversely affected by crowded and noisy conditions, significantly reducing the accuracy of activity detection at construction sites [40]. In sensor-based methods, various motion sensors (accelerometers and gyroscope) have been used to monitor construction equipment's earthmoving activities [43]. Motion sensors often lack data acquisition capabilities for construction equipment, particularly upon reaching a certain level of maturity, and they encourage the use of additional sensors for mechanical parameters, such as pressure sensors and fuel consumption meters [44]. Bluetooth, ultrawideband, and pump pressure sensors have been utilized for working stage identification of excavators, while IMU sensors have been employed for automated cycle time estimation of excavators. Additionally, IMU sensors with GPS have been used to detect earthmoving activities of construction equipment [40; 45; 46; 47; 48; 49]. However, single-sensor solutions are sensitive to inherent sensor constraints, which limit their information acquisition capabilities [10]. Additionally, sensor-based approaches require a complex installation and setup, focusing solely on internal inertial or mechanical parameters without providing any external information regarding the terrain or earthen material.
## 4 Results and Discussion
### Test Results of Volume Fraction and Carbon Isotope Value  
According to the test scheme based on isotope geochemistry, CBM extraction samples were collected on-site on the 90 th day, and the volume fractions and carbon isotope values of 31 groups were tested. The test results are shown in Table 1.  
Figure 8: Extraction ratio of CBM in different zones. (**a**) 6u Coalbed; (**b**) 6m Coalbed; (**c**) 6l Coalbed. Figure 7: CBM extraction volumes of each group. (**a**) A–1 Group; (**b**) B–1 Group; (**c**) A–2 Group; (**d**) B–2 Group; (**e**) A–3 Group; (**f**) B–3 Group; (**g**) A–4 Group; (**h**) B–4 Group. Compared with normal values, several values with obvious deviations in Table 1 were eliminated. For the CH\({}_{4}\) carbon isotope, \(-32.97\%\)% of the No. 6u-5 sample in the 6u coaled and \(-35.01\%\) of the No. 4-10 sample in the 6l coalbed were deleted. For the CO\({}_{2}\) carbon isotope, \(-2.15\%\) of the No. 3-b8 sample in the 6m coalbed was deleted. For CH\({}_{4}\) gas components, \(0.72\%\) of the No. 6u-5 sample and \(59.88\%\) of the No. 6u-8 sample in the 6u coalbed, \(13.26\%\) of the No. 6m-6 sample and \(15.05\%\) of the No. 6m-7 sample in the 6m coalbed, and \(56.46\%\) of the No. 4-5 sample and \(8.09\%\) of the No. 4-b5 sample in the 6l coalbed were deleted. For CO\({}_{2}\) gas components, \(0.18\%\) of the No. 4-5 sample and \(0.07\%\) of No. 4-b5 sample in the 6l coalbed were removed. To show and compare the distribution of volume fraction and carbon isotope values of CBM extraction, the corresponding box plots were drawn as shown in Figure 9. The upper and lower bounds of box represent the 75% and 25% quantiles of data, respectively; the hollow point in the middle of box represents the average value of data, and the upper and lower bounds of the vertical lines represent the maximum and minimum values of data, respectively. It can be observed that CH\({}_{4}\) occupies the main component in CBM extraction, and the difference is significant. In which, the CH\({}_{4}\) gas components of the 6u, 6m and 6l coalbeds are 20%-40%, 30%-80% and 80%-90%, respectively. CO\({}_{2}\) is a secondary component in CBM extraction, and the overall distribution of gas components ranges from 0.1% to 0.4%. The CH\({}_{4}\) and CO\({}_{2}\) gas components in each coalbed show a linearly increasing trend with burial depth increase, and the order from highest to lowest is 6l, 6m and 6u. The CH\({}_{4}\) carbon isotope values of CBM extraction in different coalbeds range from \(-36\%\) to \(-32\%\), but there are great differences between each other. The CO\({}_{2}\) carbon isotope values of CBM extraction in the different coalbeds range from \(-9\%\) to \(-4\%\). For the 6u and 6l coalbeds, the CO\({}_{2}\) carbon isotope may be inaccurate, due to the extremely low content of the CO\({}_{2}\) gas component in some samples. The CH\({}_{4}\) and CO\({}_{2}\) carbon isotopes in each coalbed show a linearly increasing trend with burial depth increase, and the order from heavy to light is 6l, 6m and 6u.
## 2 CC&OV-PT Controlled Two-Stage WPT System  
### Equivalent Model of the WPT System  
Regarding WPT systems, the Serial-serial (SS) topology is widely used since it can achieve constant current output. The SS topology of a magnetically coupled resonance WPT system is shown in Figure 1.  
DC voltage \(V_{1}\) is converted into a high-frequency AC voltage \(v_{\rm P}\) with frequency \(f_{1}\) through a full-bridge inverter, and electric energy is wirelessly transferred to the secondary side through a resonance device composed of a capacitor and an inductor. AC voltage \(v_{\rm s}\) is induced with frequency \(f_{1}\) on the secondary side, which is then converted into a DC voltage by the full-bridge rectifier to energize load \(R_{\rm L}\).  
Generally, high-order harmonic components of square wave voltage \(v_{\rm P}\) can be attenuated to zero after passing through the resonant network. The fundamental voltage of \(v_{\rm P}\) here is defined as \(v_{\rm px}\), which can be expressed by:  
\[v_{\rm px}=\frac{4}{\pi}V_{1}\sin\omega_{1}t \tag{1}\]  
By defining \(R_{\rm Lx}\) as the equivalent load resistance and referring to [28], \(R_{\rm Lx}\) can be equivalently written by:  
\[R_{\rm Lx}=\frac{8}{\pi^{2}}R_{\rm L} \tag{2}\]  
For calculation convenience, the equivalent circuit model can be constructed by using controlled sources for the WPT system, as shown in Figure 2.  
Define \(R_{1}\) as the resistance of the primary coil and \(R_{2}\) as the resistance of the secondary coil. With the equivalent circuit shown in Figure 2, the currents of primary and secondary sides can be obtained by:  
\[\left\{\begin{array}{l}\dot{\rm I}_{\rm P}=\frac{4\dot{V}_{1}(R_{2}+R_{\rm Lx })}{\pi[\omega^{2}M^{2}+R_{1}(R_{2}+R_{\rm Lx})]}\\ \dot{\rm I}_{\rm S}=\frac{4\dot{V}\alpha V_{1}}{\pi[\omega^{2}M^{2}+R_{1}(R_{2 }+R_{\rm Lx})]}\end{array}\right. \tag{3}\]  
Figure 1: Schemes follow the same formatting.  
Figure 2: The equivalent circuit for SS wireless transmission.  
According to energy conservation, the input power of the rectifier circuit is equal to the output power, namely,  
\[\left(\frac{I_{\mathrm{S}}}{\sqrt{2}}\right)^{2}R_{\mathrm{Lx}}=\frac{V_{\mathrm{ o}}^{2}}{R_{\mathrm{L}}} \tag{4}\]  
Thus, the output voltage \(V_{\mathrm{o}}\) can be calculated as  
\[V_{\mathrm{o}}=\frac{8\omega_{1}MR_{\mathrm{L}}V_{1}}{\pi^{2}\left[\omega_{1}^{ 2}M^{2}+R_{1}(R_{2}+R_{\mathrm{Lx}})\right]} \tag{5}\]  
Equation (5) reveals that the output voltage is in proportion to the input voltage when the WPT system operates in a steady state. Hence, the output voltage \(V_{\mathrm{o}}\) can be timely regulated by adjusting the input voltage \(V_{1}\). A DC/DC converter is usually employed as the first power stage to regulate the output voltage of the WPT system.
## 1 Introduction  
Electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) have developed rapidly in recent years. The problems which people are most concerned with are driving distance, battery life, and safety, namely reliability of battery. Cathodes and anodes, as the most important parts of a battery, are two of the research focuses. Compared to anode materials, cathode materials have relatively lower specific capacity and twice as high a price [1]. Therefore, the key to increasing energy storage efficiency is to improve cathode materials with high specific capacity [2]. Layered cathode materials Li[Ni\({}_{x}\)Mn\({}_{y}\)Co\({}_{2}\)]O\({}_{2}\) (NMCs; x + y + z = 1) are successfully commercialized cathode materials [3]. Ni-rich NMC materials can provide higher energy density [3], but the application of Ni-rich NMCs is limited by their large capacity decay after long-term cycling [4]. One of the most important reasons is surface degradation from electrolyte attack [5]. At a highly delithiated state, the active Ni\({}^{4+}\) ions are unstable and have a tendency to form NiO-like rock-salt phase on the material surface. The formed NiO contributes to the formation of solid electrolyte interface (SEI), which increases the ion transfer resistance due to its more compact structure [6]. Moreover, dissolution of transition metal (TM) ions by HF attack can lead to more complex parasitic reactions, e.g., TM deposition on the anode surface or resistive metal fluorides on the cathode side [5].  
To overcome these drawbacks, much effort has been focused on surface modification of Ni-rich cathode materials. Various kinds of metal oxides like Al\({}_{2}\)O\({}_{3}\)[7; 8], phosphate compounds like Mn\({}_{3}\)(PO\({}_{4}\))\({}_{2}\)[9], polymer [10], etc., are applied as coating materials on cathode material surfaces. In this study, SiO\({}_{2}\) was chosen as a coating material because of its physical-chemical stability and scavenging effect on HF [11]. Additionally, SiO\({}_{2}\) is also a low-cost material that can be easily synthesized. [PERSON] et al. reported SiO\({}_{2}\) coating on LiFePO\({}_{4}\) (LFP); the coated LFP had better rate capability and higher capacity retention [12]. [PERSON] et al. reported application of SiO\({}_{2}\) coating on Li\({}_{3}\)V\({}_{2}\)(PO\({}_{4}\))\({}_{3}\)/C; the material had improved electrochemical performance [13]. In the frame of this work, SiO\({}_{2}\)-modified commercially available NMC622 materials were prepared through the same wet coating process, and their physicochemical properties and electrochemical performance were characterized and investigated.
# A Review of Artificial Intelligence in Enhancing Architectural Design Efficiency
## 3 Overview of AI Technology
### Application of AI in Architectural Design
#### 3.2.1 Design Automation Generation and Optimization
AI-assisted parametric design allows designers to explore multiple design possibilities and evaluate the performance of different solutions through simulation, supporting better initial stage decisions. With the popularity of building information modeling (BIM) technology, AI enhances building performance simulations by analyzing building performance such as energy consumption, sunshine, and lighting and assists designers in continuous optimization of the scheme. In addition, AI also shows great potential in structural performance optimization [30], such as optimizing the structural form of long-span buildings through GAs or predicting stress outcomes with machine learning models.
## 5 Experiments
### Performance Comparison of CRGPPO-TKL
#### 5.3.2 Comparison with Other DRL Algorithms  
This subsection evaluates the performance of CRGPPO-TKL against other DRL-based scheduling methods. As shown in Table 8, CRGPPO-TKL significantly outperforms both the DQN-based and PPO-based methods across 10 MK benchmark instances. It achieves the best performance in 9 out of 10 instances, with MK06 being the only case where PPO slightly outperforms CRGPPO-TKL (ODR = 1.8%). As shown in Figure 9, on average, CRGPPO-TKL achieves an ODR of 0.18%, compared to 8.19% and 10.54% for DQN and PPO, respectively. Furthermore, CRGPPO-TKL exhibits a lower standard deviation of 11.9, in contrast to 10.1 for DQN and a much higher 140.7 for PPO, indicating improved stability alongside superior performance. Notably, PPO exhibits large performance fluctuations on the larger and more complex instances MK08 and MK09, which are characterized by longer completion times. In contrast, CRGPPO-TKL stabilizes performance by dynamically adjusting its clipping range based on the candidate action set, thereby enhancing robustness in complex scenarios. Layout-based statistics (Table 9) further reveal that CRGPPO-TKL achieves the lowest average completion time under all three workshop layouts. In contrast, DQN and PPO record average ODRs of 8.5% and 13.3%, and standard deviations of 10.59 and 127.2, respectively. These results underscore the overall performance and robustness of CRGPPO-TKL.  
Additionally, independent two-sample \(t\)-tests were performed between CRGPPO-TKL and the baseline DRL methods (DQN, PPO) across the 10 MK benchmark instances. The results indicate that the observed improvements of CRGPPO-TKL over DQN and PPO are statistically significant (\(p\) < 0.05) in all cases except for MK06, where the difference is not significant. These findings further support the robustness and superior performance of CRGPPO-TKL in complex flexible job shop scheduling scenarios.  
Figure 8: Comparison of ODR mean-variance of selected composite scheduling rules.
## 3 Results
### Boreal Vegetation GPP Changes  
The spatiotemporal variations in annual GPP across the two distinct periods were also analyzed using the GLASS GPP product. As shown in Figure 7, although GPP in the boreal ecosystem steadily increased during both periods, the rate of increase significantly decelerated in P2 for all three vegetation types. The linear trend of forestlands GPP experienced a 63% reduction, declining from 3.27 \(\pm\) 0.86 g C m\({}^{-2}\) yr\({}^{-1}\) in P1 to 1.2 \(\pm\) 0.79 g C m\({}^{-2}\) yr\({}^{-1}\) in P2. Grasslands GPP witnessed a 70% reduction, weakening from 2.24 \(\pm\) 0.64 g C m\({}^{-2}\) yr\({}^{-1}\) in P1 to 0.68 \(\pm\) 0.38 g C m\({}^{-2}\) yr\({}^{-1}\) in P2. Shrublands GPP also exhibited a slight decline from 1.52 \(\pm\) 0.67 g C m\({}^{-2}\) yr\({}^{-1}\) in P1 to 1.35 \(\pm\) 0.58 g C m\({}^{-2}\) yr\({}^{-1}\) in P2. The area of forestlands exhibiting positive GPP trends decreased by 18%, with a similar decline of 17% observed in grasslands. Spatial patterns revealed an increased complexity in linear trends during P2, as  
Figure 6: Long-term trends in pixel-level and regional mean LOS of boreal vegetation during the two study periods. Panels (**a,b**) present the spatial distribution of GPP trends for the two study periods. Panel (**c**) shows the time series and linear trends of regional mean LOS for different vegetation types. The accompanying bar charts (**a,b**) depict the fraction of positive and negative pixels for each vegetation type. Panels (**d–f**) display the frequency histograms of trends in forestlands, shrublands, and grasslands for both periods. The right panel provides the linear trend values for each study period, with R\({}^{2}\) values in brackets.  
indicated by the higher standard deviation of pixel-level trends and the altered histogram (Figure 7d-f).
# Static Wind Reliability Analysis of Long-Span Highway Cable-Stayed Bridge in Service
## 4 Case Analysis of Static Wind Instability and Parameter Influence of Xiangshan Harbor Bridge
### Influence of Stay Cable Broken Position on Static Wind Stability  
During the use of the bridge, the stay cable will lose its function entirely (or called stay cable broken) due to the occurrence of some special circumstances or replacement of stay cables. To discuss the influence of the stay cable broken position on static wind stability, there are three cases about the broken position of the stay cable, that is, the stay cable breaks in the middle position of the main span, in the quarter position of the main span and near to the bridge tower, and they are called Condition 1, Condition 2 and Condition 3, as shown in Figure 15. The comparison of the maximum displacement of the main beam under different conditions is shown in Figure 16. The critical instability wind speed of Xiangshan Harbor Bridge under different conditions is listed in Table 3 at the initial wind attack angle of 0\({}^{\circ}\).  
From the above results, three broken positions of the stay cable are analyzed to get the aerostatic instability wind speeds of Xiangshan Harbor Bridge are 260 m/s, 295 m/s, and 290 m/s, respectively. It is proved that the broken stay cables near the main tower have almost no effect on the critical aerostatic instability wind speed, but the influence of stay cable breakage in mid-span is great, which makes the aerostatic instability wind speed decrease by 12.8%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Item** & **Normal** & **1.2 Times Moment Coefficient** & **1.2 Times Lift Coefficient** & **1.2 Times Drag Coefficient** \\ \hline Static wind instability speed (m/s) & 298 & 275 & 295 & 285 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Static instability wind speeds under different three-component force coefficients.  
Figure 15: Stay cable broken position (m).
# Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks
## 4 Supervised Learning for Estimating the Bit Error Rate (BER)
### Data Collection  
The data collection phase is crucial for establishing a comprehensive knowledge base for machine learning. In our study, data are collected through simulations, enabling us to obtain essential information. This includes the geometric structure of the building (\(X_{\text{min}}\), \(X_{\text{max}}\), \(Y_{\text{min}}\), \(Y_{\text{max}}\)), the signal-to-noise ratio, the number of transmitted and received bits, the percentage of received bits, and the Bit Error Rate (BER). These collected data points serve as the foundation for our subsequent machine-learning analyses.
# Exploiting Concurrent Wake-Up Transmissions Using Beat Frequencies
## 5 Experimental Results
### Free-Space Transmission  
To verify the concurrent wake-up, we performed two test setups in free space. The first setup consisted of a single sender and receiver, both at a height of about 1.2 m. The second test consisted of three sensor nodes. Two nodes, placed at a distance \(x=1\) m from each other, sent a concurrent wake-up message. The third node received the concurrent wake-up calls. Both tests consisted of 500 wake-up messages that were sent with a transmit power of 0 dBm. The receiver was placed at different distances \(d\) in the range from 1 to 50 m and counted each successfully-received wake-up message. Figure 20 shows schematically the experimental setups.  
Figure 21 depicts the number of successfully-received wake-up packets over distance. The black dashed curve illustrates the single sender case, and the red curve visualizes the data for the concurrent wake-ups. Both curves correspond well to the simulated free-space transmissions reported in Figure 14, although the local minimum was located at around a 15-m distance for the single wake-up calls and according to the simulation was located at a distance around 8 m. This discrepancy could be caused by a differing ground reflection coefficient from simulation to experiment. The receiver successfully  
Figure 19: Normalized signal amplitudes received in the case of (**a**) two, (**b**) four and (**c**) six concurrent senders.  
Figure 20: Schematics of the experimental setups for (**a**) single wake-up and (**b**) concurrent wake-up.  
detected each single wake-up message (black curve) until a distance of around 20 m, then the wake-up rate decreased to zero at 30 m. Between 30 and 35 m, the receiver started again to successfully detect wake-up messages that probably originated from additional multipaths generated by surrounding trees or buildings.  
The red solid curve in Figure 21 visualizes the amount of successfully-received wake-up messages for the concurrent wake-up calls. The wake-up range increased as expected. Additionally, the minimum at around a 15-m distance to the senders did not exist. The receiver successfully detected each concurrent wake-up call up to a distance of around 25 m. Then, the number of received packets decreased, but not to zero, and increased again at around a 30-m distance. Located at distances of 40 and 45 m were two additional maxima that probably also originated from additional multipath propagations due to scattering effects of surrounding trees, buildings, etc.