esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### NSGA Analysis  
The total thickness and number of layers optimization of epoxy/GNP/PP composite structures in the X-band have been conducted using NSGA-II. Different multi-layered structures (2, 3, 4, 5, and 6) are considered and analyzed, and the results are presented in this section. Upon completion of optimization, the Pareto front for all multi-layered structures (2 to 6) are obtained, which depicts the variation of RL along with total material thicknesses presented in Figure 9a-e, respectively. The microwave absorption performances of all structures are measured using EM wave theory, using the optimization results as inputs. The RL measurements concerning the frequency of all optimized composite structures (2 layers to 6 layers) are presented in Figure 10. From the results, it is observed that a two-layered nanocomposite structure with 1.5% MWCNT/PP/epoxy (1.0520 mm) and 2.7% GNP/PP/epoxy (4.4569 mm) with a total thickness of 5.506 mm has shown feasible RL (\(-\)20 dB) in an entire X-band among all combinations of composite structures. This means this thin composite structure can absorb 99.7% incident radiation in the X-band frequency range.  
Figure 9: Pareto front for different layers composed of GNP/PP and MWCNT/PP using NSGA in X-band. (**a**) 2 layered; (**b**) 3 layered; (**c**) 4 layered; (**d**) 5 layered; (**e**) 6 layered.
# Robust Near-Optimal Aerocapture Guidance Method Based on Saturation Function
## 4 Simulations and Result Analysis  
In this section, Monte Carlo simulation is used to test the performance and robustness of the proposed aerocapture guidance algorithm. In the simulation, we select the Orion multipurpose crew vehicle (MPCV) as the spacecraft model, including the spacecraft mass \(m=8983.4\) kg, reference area \(S=19.86\) m\({}^{2}\). Considering that the spacecraft flies at a high Mach number in an aerocapture scenario, and the aerodynamic coefficients hardly change at very high velocity, we can set the coefficients as constants during aerocapture [33; 34]. Therefore, drag coefficient \(C_{D}=1.37\), lift-to-drag ratio \(L/D=0.27\), bank angle rate limit \(\left|\sigma\right|_{\text{max}}=15\) deg/s, and bank angle acceleration limit \(\left|\sigma\right|_{\text{max}}=5\) deg/s\({}^{2}\)[24; 35].  
With the accelerating process of human exploration of the moon and Mars, the energy-saving capture at the Moon-Earth return and Mars has become a research hotspot [36; 37; 38]. Therefore, to fully test and verify the proposed algorithm's performance, simulations will be carried out in two scenarios: Earth and Mars. Table 1 shows the nominal entry initial state under the two scenarios [15; 35]. In the Earth scenario, the target orbit is a circular orbit with an altitude of 200 km and an inclination of 89 deg; In the Mars scenario, the target orbit is an elliptical orbit with an apogee altitude of 3000 km, a perigee altitude of 300 km, and an inclination of 89 deg.  
\begin{table}
\begin{tabular}{c c c} \hline
**EI State** & **Value (Earth Aerocapture)** & **Value (Mars Aerocapture)** \\ \hline Altitude & \(121.92\) km & \(128\) km \\ Longitude & \(-116.5\) deg & \(-116.5\) deg \\ Latitude & \(-46.67\) deg & \(-46.67\) deg \\ Velocity & \(11.02\) km/s & \(5.9\) km/s \\ Flight-path angle & \(-5.91\) deg & \(-11\) deg \\ Heading angle & \(0.00\) deg & \(0.00\) deg \\ Orbital inclination & \(90\) deg & \(90\) deg \\ \hline \end{tabular}
\end{table}
Table 1: Nominal inertial entry conditions.  
Figure 3: Architecture of the predictor-corrector guidance procedure.
## 5 Experimental Results
### Experimental Verification of Luenberger-Type Back-EMF Observer in Sensorless FOC
The result is that the estimated rotational speed is tracking the real motor speed with very high accuracy, which is evident from the speed error. Slight deviations are present in the motor transient due to switching between two methods during motor start-up. The other reason for the small oscillations lies in the controllers bandwidth. Comparison of the Main Features for Stable Vff with CPF and Sensorless FOC with Luenberger-Type Back-EMF Observer  
The comparison of the main features regarding control performance of both sensorless control strategies is presented in Table 3.  
Some of the statements were verified experimentally within the article, e.g., accuracy, load disturbance response, start-up, and structure tuning. The dynamics of both control structures are good. However, sensorless FOC could provide a faster speed response when increasing the bandwidth of the controllers. In the case of stable \(V\)/\(f\) control, the parameters of the stabilizing loop should be modified to improve the control dynamics. Parameters dependence of the back-EMF observer was discussed in Section 3.2, while the stable \(V\)/\(f\) is completely independent of the machine parameters. This is considered as the biggest advantage, because the machine parameters change during motor operation, e.g., with saturation and temperature. Furthermore, these parameters are required for controller tuning in sensorless FOC, as derived in Sections 3.1.1 and 3.1.2. Figure 31: Speed profile up to nominal reference speed with no-load: speed, speed error, \(dq\) voltages, \(dq\) currents. Computational time can be determined analytically based on the complexity of both sensorless control structures. Comparing Tables 1 and 2, it is evident that sensorless FOC is more complex and requires more computational time and high-performance MCU. Furthermore, the start-up control strategy has to be used regarding sensorless FOC, which further increases the complexity of the whole structure. Structure tuning is more complex for sensorless FOC, where the gains of speed and current controllers have to be calculated. For the calculation, it is necessary to know the machine parameters, which have to be measured or obtained by the machine manufacturer. In stable \(V/f\) control, just a few parameters have to be set up properly, including stabilizing loop parameters and the controller gains in the CPF loop. Low-speed operation is not acceptable for both sensorless control strategies. In the stable \(V/f\) control with CPF, boost voltage has to be applied in such a region to properly control the machine at low speeds. Sensorless FOC with Luenberger-type back-EMF observer cannot even be used in such a region due to the unfavorable signal-to-noise ratio of the voltage measurements, which is the main disadvantage of the back-EMF-based sensorless methods.
# LSOS: An FG Position Method Based on Group Phase Ranging Ambiguity Estimation of BeiDou Pseudolite
###### Abstract  
Due to the influence of indoor space environments, the carrier phase information obtained by the BeiDou pseudo-satellite often has the issue of cycle slips, which makes the user unable to carry out high-precision positioning. Aiming at the problem of ambiguity resolution (AR) and location in large-scale occluded space (LSOS), a factor graph (FG) position method based on group phase ranging ambiguity estimation of BeiDou pseudolite is proposed. Firstly, by introducing the principle of group phase period quantization and utilizing the multi-frequency characteristic of the BeiDou pseudo-satellite, the carrier phase propagation ambiguity of the BeiDou pseudo-satellite can be estimated quickly. On this basis, by introducing the shuffled frog leading algorithm (SFLA) assisted factor graph optimization location estimation method, the BeiDou pseudo-satellite positioning process in LSOS is realized. The experimental results show that the proposed method can solve the problem of fast estimation of ranging ambiguity of BeiDou pseudolite in LSOS, and the ranging accuracy can be improved to two wavelength ranges. In the further location experiment, it is found that the algorithm can not only guarantee the real-time location output but also improve the location precision to sub-meter level under the multi-frequency combination; the optimal location test precision is 9 cm, the maximum positioning error is 50 cm. This method successfully solves the problem wherein the BeiDou pseudo-satellite cannot provide real-time, continuous, and high-precision positioning in LSOS.  
BeiDou pseudo-satellite; integer ambiguity; group phase ranging; Global Navigation Satellite System (GNSS) rejection environment; positioning; large-scale space +
Footnote †: journal:  
0023  
LSOS: An FG Position Method Based on Ambiguity Estimation of BeiDou Pseudolite
# Weigh-in-Motion Method Based on Modular Sensor System and Axle Recognition with Neural Networks
## 4 Results and Discussion in Weighing on Modular WIM System
### Toll Station Gross Vehicle Weight Method Test Based on Transformer-GRU  
To verify the accuracy of the modular WIM system based on the Transformer-GRU axle recognition model in a complex environment, we analyzed data of sunny days and rainy days recorded by the modular WIM system which was deployed at the highway toll station. The actual weight value of each vehicle was provided by a whole-vehicle-load system operated by the toll station, which was authenticated by a national metrology institution. The results of the five-platform WIM system are listed in Table 9.  
As shown in Table 9, 96.50% of the vehicles had an error of less than 1%, indicating a high level of accuracy at the test site. Due to the complexity of the vehicle's traveling conditions in real situations, there were various special cases, such as parking and reversing, which made it challenging to achieve a 100% accuracy rate. Nevertheless, the vehicles with errors of less than 2% and 5% reached 98.16% and 99.18%, respectively, demonstrating that the modular system using the five-platform system achieved a high weighing accuracy. The accuracy of trucks with more than three axles on sunny days is better, but the accuracy of two axles is lower, mainly due to the two-axle vehicles including a large number of passenger cars, which tend to travel faster on sunny days. The increased speed leads to more vibration, which will not affect the axle recognition, but affect the weighing error.  
At a highway toll station, the real-time processing capability of the system is essential. Our edge device, the Jetson Xavier NX, was equipped with 384 CUDA Cores, 6 Carmel ARM CPUs, two Deep Learning Accelerator Engines, and 21 TOPS computational performance. The length of the signal collected in real-time is longer than the signal of the test set, and the inference time will be increased. The inference time was approximately 500 ms, and the system could also downsize the sample to reduce the input sequence length, accelerating the inference process. After post-processing, it could quickly obtain the number of axles and gross vehicle weight as vehicles traveled through.
## 2 Related Work
### Query Optimization Techniques  
Leveraging blockchain and IPFS in decentralized storage systems has transformed data storage and retrieval, particularly in sectors demanding high integrity and efficiency. These studies illustrate how blockchain and IPFS can improve query optimization, security, and scalability in distributed storage systems. In [19], the authors focus on agricultural product traceability, using IPFS to store multiple data types and blockchain for securing IPFS hash addresses to improve query efficiency and data authenticity. However, the sensor data are collected and processed on a centralized private server before storing data on IPFS. Therefore, this approach may pose security risks related to a single point of failure, data loss, or compromised server. In [20], the authors integrate blockchain, IPFS, and Elastic-search to address big data storage challenges in distributed systems, resulting in reduced storage overhead, search latency, and access control. The proposed approach relies on blockchain's inherent security features. Moreover, the elastic search approach focuses primarily on search latency and precision, without considering the security implications of query handling mechanisms. The work in [21] proposes a secure and decentralized framework for managing cloud data provenance by integrating blockchain technology with the IPFS. The proposed solution ensures provenance data availability, security, and integrity by utilizing decentralized storage and blockchain for immutability. The method for verifying data integrity is secure; however, it might be computationally intensive, particularly in scenarios where the frequent validation of large data sets is necessary.  
Despite these advancements, existing solutions have faced challenges, particularly in their reliance on decryption, which has associated security risks. Namely, the exposure of private keys remains a major concern, as discussed in [22]. This justifies the importance of a solution that could ensure data security and privacy without relying on decryption. The solution proposed in this paper addresses these challenges by optimizing and securing data storage on IPFS without relying on decryption. This novel approach, based on storing only the Content Identifier (CID) in blockchain and the actual data on IPFS, along with sophisticated encryption and hashing techniques, is a significant step forward from traditional methods. This innovation enhances data privacy and improves query optimization, as detailed in our methodology.
## 7 Effect of Zeolite Addition on WMA Properties
### Resistance to Permanent Deformation  
Surface resistance to permanent deformations is one of basic road users' expectations. Ruts affect both driving comfort, as well as safety. Water pooling in the ruts after rain and snow increases thepotential for traffic accidents [50]. Warm climate and the high traffic share of heavy vehicles favour the formation of permanent deformations. The necessity to identify resistance to permanent deformation indicated as a necessary stage in WMA design [51]. Several methods based on different mix asphalt parameters were developed for the assessment of resistance to permanent deformations [52]. According to EN 12697-22 (the wheel-tracking test) and AASHTO TP 63 (APA) procedures, the rut depth induced on the material increases as the number of wheel passes raises or on vertical load.  
Studies on WMA with zeolites, conducted according to EN 12697-22 standard showed decrease in resistance to permanent deformations for samples with Linde A synthetic zeolites, compared to HMA [13].  
On the other hand, when using Na-P1 synthetic zeolite and clinoptilolite, the resistance increased [20]. The results of APA (asphalt pavement analyzer) wheel-tracking tests conducted in the United States indicate the negative effect of zeolite addition at a minimum production temperature of 129 \({}^{\circ}\)C. In the case of lower production temperatures, the addition of zeolite improved resistance to permanent deformations [28]. Resistance to permanent deformations of WMA samples with zeolites, cut from the surface of experimental section is not lower than for HMA samples [25; 49].  
The results of studies on resistance to permanent deformations, similar to those regarding water sensitivity are not unambiguous. Presumably, too little \"zeolite water\" was released during WMA production, thus, asphalt foaming did not occur in correct manner. When it comes to asphalt foaming technology, the optimal amount of water is 2%-4% in terms of asphalt mass [53; 54]. With too small an amount of water, the foaming is ineffective, whereas with too high an amount, there is a risk of washing the binder off the aggregate [55]. This confirms the need to conduct thermal studies on zeolite.
# News Recommendation Method Based on Candidate-Aware Long- and Short-Term Preference Modeling
## 3 Method
### Short-Term User Preference Encoder
#### 3.3.3 Candidate-Aware Attention Network That Incorporates News Subtopics  
To enable candidate news to adequately match user interests, we design a candidate-aware attention network that incorporates news subtopics (Sub-CAAN). The structure is shown in the upper right corner of Figure 5. We first compute an affinity matrix to measure the relevance of a user's preference matrix corresponding to a subtopic to the candidate news and its corresponding subtopic,  
\[\mathbf{C_{k}^{s}}=\phi(\mathbf{u_{k}^{s}}\mathbf{W_{5}}\mathbf{N_{c}}+ \mathbf{u_{k}^{s}}\mathbf{W_{6}}\mathbf{e}(\mathbf{s_{c}^{n}})) \tag{24}\]  
where \(\mathbf{W_{5}}\) and \(\mathbf{W_{6}}\) are the trainable parameter matrix. \(\mathbf{e}(\mathbf{s_{c}^{n}})\) is the embedding of the candidate news subtopic \(s_{c}^{n}\). We then compute the attention score of the user's \(k\)-th subtopic about the candidate news based on the previously computed affinity matrix,  
\[b_{k}=\mathbf{q_{u}}^{\mathbf{T}}\tanh\big{(}\mathbf{W_{7}}\mathbf{u_{k}^{s}} +[\mathbf{N_{c}}:\mathbf{e}(\mathbf{s_{c}^{n}})]\mathbf{W_{8}}\mathbf{C_{k}^ {s}}\big{)} \tag{25}\]  
where \(\mathbf{W_{7}}\) and \(\mathbf{W_{8}}\) are the trainable parameter matrix. \(\mathbf{q_{u}}^{\mathbf{T}}\) is an attention query vector. the candidate-aware user subtopic-level short-term interest representation \(\mathbf{u_{s}}\) based on candidate perception can be obtained,  
\[v_{k}=\frac{\exp(b_{k})}{\sum\limits_{p=1}^{f}\exp(b_{p})} \tag{26}\]  
\[\mathbf{u_{s}}=\sum\limits_{k=1}^{f}v_{k}\mathbf{u_{k}^{s}} \tag{27}\]  
Finally, short-term user interests \(\mathbf{h_{u}^{short}}\) can be obtained from Equation (28),  
\[\mathbf{h_{u}^{short}}=\mathbf{W_{9}}([\mathbf{u_{s}};\mathbf{u_{c}}]) \tag{28}\]  
where \(\mathbf{W_{9}}\) is the trainable parameter matrix. In this way, we consider both the contextual information of the candidate news on the user's recent interactions and the influence of fine-grained changes in preferences.
## 3 Results
### The Effect of Protrusion Height
#### 3.1.1 Enhancement of Heat Transfer
The effect of a distance smaller than \(D\) between an orifice and a CP top face on the local \(Nu\) number distribution can be clearly seen in Figure 10. For a jet \(Re\) number of 15,000, the \(\overline{Nu}\) number on the top faces of CPs increases slightly from 79.39 to 81.82 as \(H_{p}/D\) is increased from 3 to 4. However, the \(\overline{Nu}\) number on the top faces of CPs rapidly rises to 94.47 for \(H_{p}/D\) of 4.5. The highest overall \(\hat{Q}\) achieved for \(H_{p}/D\) of 4.5 is due to the very high \(Nu\) numbers in the stagnation zone of the impinging jet. On the other hand, for a smaller \(H_{p}/D\), as the stagnation region deforms and enlarges, the momentum in the core of the impinging jet decreases compared with its periphery. Therefore, the \(Nu\) numbers in the immediate vicinity of the jet axis are lower than those in the surrounding oval or kidney-shaped region. As presented in Figure 11, in the case of a flat target plate, the crossflow disrupts the heat transfer in the stagnation zone by significantly deflecting and deforming the impinging outer jet, reducing the coherence of its core velocity profile and steepness of its shear layer velocity gradients. As seen in Figure 10, in the case of the flat target plate, the deformation of the kidney-shaped \(Nu\) number contours appearing in the impingement zone of the outer jet due to the interaction of the jet with the crossflow increases with the jet \(Re\) number. The CP-CGV pairs installed on the flat target plate to enhance heat transfer influence the crossflow as well. As \(H_{p}/D\) increases, velocities in the core of impinging jets increase and shear layers become thinner due to less advection and diffusion of the jets in the lateral directions. Because the spent fluid is directed further away from the jets, the reduced interactions of the crossflow with the impinging jets decrease the deflection of the jets, mitigating the displacement, deformation, and enlargement of the jet stagnation zones, thereby improving impingement heat transfer. The overall \(\hat{Q}\) increases even further due to the increased lateral surface area of the CPs. Stated effects are more pronounced at higher jet \(Re\) numbers. As \(H_{p}/D\) is increased, the jet impingement region shape progressively becomes oval and then circular. Increasing \(H_{p}/D\) shortens the impinging jets while CGVs direct the wall jets away from the stagnation region down to the lateral faces and then toward the base plate, ensuring that their momentum decreases as they move away from the CPs, thus reducing the deflection of the impinging jets due to their interaction with the crossflow. Figure 10: For \(D_{p}/D\) of 3.5, \(Nu\) number contour plots in the top view and unrolled views of the lateral faces of the CPs, at various jet \(Re\) numbers, for \(H_{p}/D\) of (**a**) 4.5; (**b**) 4; (**c**) 3; (**d**) 2.5; (**e**) 2; (**f**) 1.5; (**g**) 1; (**h**) flat target plate.
# A Highly Integrated Millimeter-Wave Circularly Polarized Wide-Angle Scanning Antenna Unit
## 3 Two-Dimensional Wide-Angle Scanning Antenna Array
### Measurement Results and Analysis  
To verify the performance of the proposed antenna design, a SIW-based antenna array is manufactured using standard PCB fabrication methods. Figure 11 displays the fabricated antenna array, consisting of several antenna units. Testing is conducted in a planar near-field anechoic chamber equipped with a Keysight vector network analyzer. The gain characteristics at various scanning angles are depicted in Figure 12. By comparing the measured results with the simulation results, we can obtain the gain curves of simulation and measured comparison at different scanning angles, as shown in Figure 12. As can be  
Figure 10: The simulation of the active radiation pattern at frequency \(f_{0}\) GHz.  
Figure 9: The illustration of the array layout.  
seen from Figure 12, the circularly polarized wide-angle scanning antenna can achieve wide-angle scanning from \(-60^{\circ}\) to \(+60^{\circ}\) in azimuth and elevation at the operating frequency. The measured results are consistent with the simulation results. When the scanning angle is \(0^{\circ}\), the gain of the phased array antenna test is 15.7 dB, which is 0.2 dB lower than the gain in simulation. When the antenna array scanning angles are \(\pm 30^{\circ}\) and \(\pm 60^{\circ}\), respectively, the gain is basically consistent with the simulation results. In addition, when the theta is greater than \(60^{\circ}\), the simulation and test curves are quite different, which is mainly due to the error of the test system when scanning at a large angle of the theta.