esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Multi-Objective Optimization Results
Each solution corresponds to a real-world configuration of five input variables, as shown in the parallel coordinate plot (Figure 11), which allows practitioners to back-calculate the specific input mix associated with each point on the front. This capability aligns with best practices in decision support system design for infrastructure material selection, as outlined by [PERSON] et al. [81], and it represents a significant advantage over single-objective models or heuristic decision making. In summary, the NSGA-II optimization results provide a comprehensive, data-driven foundation for selecting optimal slag supply configurations that reflect context-specific priorities. While all Pareto-optimal configurations were generated under engineering feasibility constraints (e.g., energy-mass balance, processing bounds), the physical realism and constructability of extreme points--especially those in the high-cost/low-emission corner--require critical interpretation. For instance, configurations with minimal CO\({}_{2}\) emissions often involve very short transport distances (<10 km), advanced drying processes, and operation within near-zero carbon grids. While these setups are technically plausible, their implementation would require premium infrastructure, renewable-dominated energy access, and strict material controls--conditions that may not be widely available or economically feasible in many regions. Therefore, these solutions should be viewed as boundary references for best-case sustainability scenarios rather than directly actionable designs. By combining a predictive GBR model with an efficient evolutionary search algorithm, the framework generates technically feasible and environmentally responsive solutions that go beyond conventional cost-minimization strategies. The resulting Pareto front not only confirms trade-off structures consistent with theory and practice but also empowers stakeholders to implement more informed, transparent, and future-ready decisions. Figure 11: Parallel coordinates plot of input variables across selected pareto solution.
## 2 Method  
### Hardware Configuration  
Figure 1a shows the layout and configuration of the BFP ellipsometry measurement system, which includes four parts: an illumination light source, a measurement probe, a detection unit, and a motorized positioning unit. The white light generated by a halogen lamp is collimated, then passes through a band-pass filter and becomes monochromatic (@632.8 nm). A diaphragm is used to control the beam radius. The monochromatic light becomes linearly polarized after passing through a polarizer and is reflected by a beam splitter before becoming incident on the sample through a high-_NA_ objective lens at different incident angles. Then, the reflected light is recollected by the objective and focused on the BFP. After passing through the beam splitter and an analyzer, the BFP image is captured by a CMOS, as shown in Figure 1d. The motorized positioning unit uses a motorized translation stage to hold the sample and simulates the defocus of the sample generated by vertical vibration at different positions.
## 3 Preliminaries and Motivations
### RPL Fundamentals  
RPL is designed to enable joined nodes to perform three types of communications. Initially, LBR broadcasts the initial DODAG information object (DIO) message [30] to start establishing RPL topology. There is a lot of information involved in DIO to complete an instance; specifically, an instance number, DODAG ID, version number, and objective function [31] are decided in that initial DIO. Objective function is a method used by nodes to determine their ranks, which logically represent relative positions to LBR. In the general case, the calculation of rank is related to quality of service [32; 33].
## 6 Conclusions  
The existence of high-frequency noise brings great challenges when using the MRAS to identify the unknown parameters of the PMSM. Therefore, a variable-bandwidth linear ADRC adaptive law is proposed for the MRAS to enhance the anti-disturbance performance and improve the identification accuracy. By designing a variable bandwidth for the linear ADRC, its anti-disturbance ability is significantly improved while ensuring the response speed. Finally, some simulation experiments are designed to verify the performance of the proposed method. The results exhibit that the MRAS based on the variable-bandwidth linear ADRC adaptive law has good identification accuracy and anti-disturbance ability. In addition, facing the mutation of the identified parameters, it also exhibits a fast response speed under the premise of identification accuracy. Moreover, the experimental results of the double closed-loop control system designed in this study illustrate that the proposed MRAS can be used for high-performance controllers.  
Theoretically, the proposed ADRC adaptive-law-based MRAS can be implemented on a motor. However, the actual performance of MRAS will be limited by the operational performance of DSP. Since the interruption frequency of DSP is lower than the sampling step of the simulation, the response speed and identification effect of the parameters on the physical platform might not perform as well as those in the simulation. In the future, physical experiments will be carried out to verify the performance of the proposed method. A better identification effect is expected to serve for the high-performance controller design.  
Figure 15: SPMSM speed comparison diagram.  
**Author Contributions:** Writing--review and editing, X.Q. and C.S.; supervision, C.S., Y.G., T.S. and H.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant no. 62273213, 62073199, 62103245, 62203280; the Natural Science Foundation of Shandong Province for Innovation and Development Joint Funds, grant no. ZR2022 LZH001; the Natural Science Foundation of Shandong Province, grant no. ZR2020 MF095, ZR2022 MF341; the Taishan Scholarship of Construction Engineering.  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** This article does not use a dataset, and all the required parameter data are provided in the article.  
The authors declare no conflict of interest.
## 3 Proposed Methodology
### Overview of the Routing Framework
Note that, even if wireless link BD is selected, a packet can still be delivered to destination node G from source node A. Therefore, we propose a hybrid algorithm to select either the highest stable least delay path or the highest stable shortest path by estimating link lifetimes and link delay parameters, in order to increase the reliability of routing in VANETs. Thus, the proposed routing algorithm has a hybrid mode that switches the metric (delay or distance) based on the predictability of the delay (degree of contention in the network). However, in both modes, link stability is considered such that routing will occur only on stable links.
## 3 Results  
### Parameters' SI variation with Parameter Range  
In this experiment, the model output was GPP and three-parameter ranges were set as listed in Table 1: \(\pm\) 10% and \(\pm\) 30% perturbation of the values and between the minimum and maximum values. It was found that parameter sensitivities varied with the ranges (Figure 1). For the range of \(\pm\) 10% perturbation of value, only \(f_{ci}\) and \(V_{m}^{25}\) showed the highest SIs on the GPP (Figure 1a). The two parameters' MSIs all exceeded 0.1, and their TSIs were 0.67 and 0.22 respectively. For this experiment, however, the other 14 parameters had a relatively weak influence on the GPP. These observations indicated that in the given range, \(V_{m}^{25}\) and \(f_{ci}\) dominated model behavior. For the range of \(\pm\) 30% perturbation of value, Parameters \(f_{ci}\) and \(\alpha_{q}\), for which the TSIs were 0.54 and 0.43, were the most sensitive on GPP (Figure 1b). When the ranges were determined by the minimum and maximum values, the sensitivities parameters were as shown in Figure 1c. Parameters \(f_{ci}\) and \(\alpha_{q}\), for which the TSIs were 0.51 and 0.49, were the most sensitive on GPP, and the TSIs of \(V_{m}^{25}\) and \(E_{Vm}\) were 0.19 and 0.16, as well those of the other parameters.  
By comparing the two cases, it can be seen that the ranges of the parameter significantly influenced their sensitivities and importance order [18]. The range selected in the first experiment was narrower and limiting some important but small parameters, such as \(\alpha_{q}\), so the range was given in the second experiment was suggested for FBEM. Overall, in the two cases, the common findings were that the most sensitive parameters were \(f_{ci}\), \(\alpha_{q},V_{m}^{25}\), and \(E_{Vm}\), which was similar to the results of the earlier studies [57; 58]. \(V_{m}^{25}\) was the key parameter for estimating photosynthesis and respiration, its inaccurate estimation may lead to non-negligible uncertainty in GPP estimation [59]. In addition, \(f_{ci}\), \(\alpha_{q}\) were a highly sensitive parameter, and relative experiments were also focused on them [60].  
Figure 1: The main sensitivity indexes (MSIs) and total sensitivity indexes (TSIs) of 16 parameters in different ranges of variation: (**a**) 10% perturbation of model parameters value, (**b**) 30% perturbation of model parameters value and (**c**) between the minimum and maximum from Table 1.
# Practical 3-D Beam Pattern Based Channel Modeling for Multi-Polarized Massive MIMO Systems +
## 2 3-D Antenna Configuration
### 3-D AES and Antenna Element's Positioning  
Let us now consider an \(LK\) transmit antenna which is composed of \(l=0,...L-1\) and \(k=0,...,K-1\) identical antenna elements, which is indexed by \(l_{th}\) row and \(k_{th}\) column, respectively. Similarly, \(L^{\prime}K^{\prime}\) receive antenna which is composed of \(l^{\prime}=0,...,L^{\prime}-1\) and \(k^{\prime}=0,...,K^{\prime}-1\) identical antenna elements, where index by \(l^{\prime}_{th}\) row and \(k^{\prime}_{th}\) column. The outgoing/incoming wave directions are fixed along \(y\) axis and the antenna arrays are fixed at \(x\) and \(z\) axes both for TX and RX. Prior to this, \((\theta_{EAoD},\phi_{AAoD})\) and \((\theta_{EAoA},\varphi_{AAoA})\) are the elevation and azimuth angles for TX and RX, respectively. In the SRAA at the base station (BS) and SULA, the minimal distance between antenna elements denote as \(d^{BS}\) and \(d^{UE}\), respectively. In Figure 1a, \(lk\) is a point of \((x,y,z)\) of the distance above the origin \(O\) from an array at the \((x,z)\) axes and the distance vector between the TX and RX is \(\mathbf{D}=(D,0,0)\). The calculation of the antenna element's positioning, considering different antenna polarizations, can be expressed as follow sub-sections.
## 3 Proposed Frequency-Dependent Amplitude Panning for Stereophonic Image Enhancement
### Azimuth-Frequency Analysis Using Time Delay
For example, \(B(k)~{}=~{}6\) (150 Hz) when \(k=43\) (1 kHz). Thus, the direction at each frequency bin is estimated so that \(AF_{s}(k,\theta)\) is minimized, such that:  
\[\hat{\theta}(k)=\underset{\theta}{\text{argmin}}AF_{s}(k,\theta)\,. \tag{10}\]  
Figure 4 illustrates an A-F plane, \(AF(k,\theta)\), and a smoothed A-F plane, \(AF_{s}(k,\theta)\), computed for a stereo signal that is recorded from a stereo microphone array in an anechoic room, where a white noise source is angled at 15\({}^{\circ}\) and placed 1.5 m from the center of the microphone array. In the figure, a 2048-point STFT is applied to each frame of white noise, and \(\theta\) is changed from \(-90^{\circ}\) to \(90^{\circ}\) at 1\({}^{\circ}\) steps. In addition, the distance between the two microphones is \(l=3\) cm and \(f_{s}=48\) kHz. As shown in the figure, the direction of the white noise is easily estimated at low frequencies, but there are multiple minima at mid-to-high frequencies. As shown in Figure 4c, the estimated direction of the white noise is 15\({}^{\circ}\), which is identical to the direction at which the white noise is located for recording. Next, we repeat the experiment above by recording white noise in a reverberant room whose reverberation time (\(RT_{60}\)) is measured as 230 ms, and the A-F planes and estimated direction are shown in Figure 4. Comparing Figure 5a with Figure 4a, the A-F plane in the reverberant room is more blurred than that in the anechoic room. This is because the reverberation muddles the direction of the sound source, making it seem as though multiple sound sources are being recorded by the stereo microphones. Owing to the smoothing window, the smoothed A-F plane shown in Figure 5b becomes similar to that in Figure 4b. Therefore, as shown in Figure 5c, the direction of white noise can be estimated correctly, especially at mid-to-high frequencies, while there are some errors at low frequencies. Since it is known that stereophonic images are mostly affected by mid-to-high frequencies, the quality of stereophonic images is not significantly affected by such errors at low frequencies [24].
# Rain Monitoring with Polarimetric GNSS Signals: Ground-Based Experimental Research
## 1 Introduction
Recently, the Spanish satellite PAZ has been launched successfully in 2018 [26]. However, this concept is still under research. Based on previous theoretical research [12; 25], a ground-based experiment was conducted to study the feasibility of this concept. This experiment was carried out on a mountain to receive GNSS signals in grazing angle geometry [25]. A long term observation was implemented in order to collect a large number of data on no-rain days and catch more cases in rainy days to study the phase difference between horizontal and vertical GNSS signals. At the same time, nearby weather radar and weather station data was also collected. The major objectives of this study are to verify the reliability of our experimental system, which was specially designed for receiving dual-polarized GNSS signals and solve a series of problems related to data processing. For this purpose, the methodology of [PERSON] et al. [24], for radio-occultation data, is applied to this ground-based data records. Section 2 gives an overview of the experiment and details the data processing algorithm about how to solve quality control, unlocking, hardware effect, phase ambiguity, multipath effect and identify phase shift. The statistical results of no-rain and rainy days and comparison results of rainy cases with weather radar and weather station data are analysed and discussed systematically in Section 3. Finally, some conclusions are summarized in Section 4.
## 3 Operating Principle  
Two operating modes, A and B, of the stator are excited to generate the elliptical trajectory on the driving foot, as shown in Figure 2. Two-phase alternating current (AC) voltages are applied on the PZT ceramics of the horizontal transducer and vertical transducer with specific phase differences. Modes A and B, excited by the longitudinal vibration of the vertical and horizontal transducer, produce the vertical and horizontal displacements on the driving foot. The corresponding passive bending vibration is also generated by the coupling effect between transducers. When the resonance frequencies of the two operating modes are near enough, the elliptical trajectory will be generated on the driving foot. The driving ability comes from the frictional force between the interfaces when the slider is in contact with the stator where normal preload is applied. Figure 2a indicates the coupled bending vibration excited in the horizontal transducer, and Figure 2b indicates the coupled bending vibration shape excited in the vertical transducer.  
Figure 3 is the schematic diagram that shows the working principle of the motor. The vibrational shapes of the stator in one vibration cycle are listed in Table 1. According to Table 1, in one vibration cycle, the motor's working process can be divided into four phases, which are demonstrated as follows.  
During the first 1/4T phase, the driving foot moves from state 1 to state 2, which is equivalent to the driving foot moving from the top to the left in the anticlockwise direction. Meanwhile, the driving foot levitates over the slider and the motor does not produce the driving force.  
1. From the 1/4T to 1/2T phase, the driving foot moves from state 2 to state 3 and starts to make contact with the slider. During this period, the motor generates a rightward driving force to actuate the slider.
2. In the duration of the third phase, the driving foot moves from the bottom to the right and the motor departs from the slider gradually. The driving force decreases to zero slowly.
3. During the last 1/4T phase, the driving foot will be back in its original state. The driving force disappears completely.  
Figure 2: Two operating modes, A and B, of the stator: (**a**) Mode A; (**b**) Mode B.
## 3 Results and Discussion
### Absorption Rate  
Figure 5 plots the absorption rate of concrete according to the C/S molar ratio adjusted by silica fume and quartz powder. The results generally tended to be inversely proportional to compressive strength, showing low water-absorption rates at the C/S molar ratio range of 0.7 to 0.9, with specimens QP0.95 and SF0.95 showing the lowest and highest water-absorption rates of 0.28% and 1.34%, respectively, demonstrating that a silica fume-based mixing design had a higher absorption rate. This indicates that while C/S molar ratio is the most important determinant of concrete performance in high-temperature/high-pressure curing, the use of fine powders such as silica fume is also an important factor, and that concrete performance is adversely affected by insufficient or excessive content of fine powers compared to an appropriate level [23]. In particular, specimen SF0.95, a mixing design not containing silica fume, has suboptimal particle size distribution due to a low fine powder content in the mixing design, resulting in an increase in pores and, consequently, a high absorption rate.
## 4 Measurement Results and Discussion  
The wide-passband filters (both with and without the proposed tri-sub-band configuration) in this work were fabricated using standard PCB technology. The design was implemented on a 0.508 mm thick Rogers 5880 substrate (\(\varepsilon_{r}\) = 2.2) with 0.035 mm thick copper, as shown in the fabricated prototype in Figure 8a,b. The overall dimensions are 35 mm \(\times\) 6 mm, corresponding to 3.1 \(\lambda_{g}\)\(\times\) 0.53 \(\lambda_{g}\), where \(\lambda_{g}\) denotes the wavelength at the center frequency. Measurements were performed using a Keysight PNA N5247A vector network analyzer (Keysight Technologies, Santa Clara, CA, USA), with the test setup illustrated in Figure 8b. As demonstrated in Figure 8c,d, the simulated and measured S-parameters show good agreement, both with or without CSRR loading. For the tri-sub-passband filter, the measurement results indicate that within the 24 GHz-25.3 GHz frequency range, \(S_{11}\) is below \(-12.5\) dB with an insertion loss of 1 dB; in the 26.1 GHz-27.9 GHz range, \(S_{11}\) is below \(-12\) dB with an insertion loss of 1.5 dB; and in the 28.8 GHz-30.1 GHz range, \(S_{11}\) is below \(-11.5\) dB with an insertion loss of 2 dB. The single wide-passband filters yield better results. Minor discrepancies observed at certain frequency points can be attributed to fabrication tolerances and soldering variations, yet these deviations remain within acceptable ranges. This experimental validation confirms the validity of the design methodology.  
To further objectively demonstrate the advantages and utility of the design, Table 3 compares its performance with the results of previous studies. As can be seen in the table, Filter 1, Filter 2, and Filter 3 correspond to the single wide-bandpass filter, dual-sub-bandpass filter and tri-sub-bandpass filter in Section 3. The table compares key performance metrics for various types, including insertion loss, return loss, physical dimensions, and return loss characteristics beyond the high-frequency cutoff. The data in the table show that the structure presented in this paper achieves a multi-passband filter with low insertion loss,  
Figure 8: Fabricated prototype and measurement results. (**a**) photograph of the schematic structure; (**b**) measurement setup; (**c**) comparison of simulated and measured results of the single wide-passband filter; (**d**) comparison of simulated and measured results of the tri-sub-passband filter.  
while ensuring compact size, multi-band capability, and low insertion loss. Furthermore, the S\({}_{11}\) parameter beyond the high-frequency cutoff can achieve \(-70\) dB, which presents a notable advantage over other studies.
## 2 Materials and Methods
### Parameter Selection
#### 2.2.5 Range  
Range can be an important performance criterion in UAV design and in aircraft in general [19]. However, although the authors initially considered the range amongst the various parameters of this study, it was eventually dropped for a number of reasons. As a start, most fixed-wing UAVs conduct ISTAR missions, where loiter endurance is more important than cruise range, while when it comes to target drones or LM UAVs, cruise range may be calculated for a one-way sortie only. Moreover, in some sources, it is not specified if the maximum range of a given UAV is calculated with full payload or  
Figure 2: Percentage of UAV database entries per region (out of 202 total UAVs).  
not. Furthermore, in contrast to manned aircraft, UAV range does not refer to the usual performance criterion that maximizes either fuel capacity or aerodynamic and performance efficiency. That is, the operational range of an unmanned vehicle is also constrained by the maximum communications radius and, in turn, by its avionics. Therefore, the data available in the literature concerning the range parameter are not of practical use, simply because each source may refer to a different definition. For example, some of the manufacturers give an indicative range value as a result of performance calculation and others as telecommunication systems limitations.  
To support these arguments, the example of Guardian Eye is given, which is an electrically powered mini UAV with a marketed range of 46.3 km. At the same time, its endurance is given to up to 4 h (with solar panels) with a cruising velocity of 85 km/h [35]. It is apparent that the operational radius is constrained by non-performance factors and the exact configuration (with or without supplementary solar panels) is not specified in its datasheet.