esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Error Correction Approach to Improve Standard Point Positioning of Integrated BDS/GPS
## 4 Experimental Analysis
### Error Corrected Positioning Results and Evaluations  
#### 4.4.1 Static Corrected Results  
The prediction results were used to achieve the error correction. The LSTM error correction performed on the weighted least square method is called WLS-LSTM error correction, while the LSTM error correction performed on Kalman filter is called Kalman-LSTM error correction. The result of WLS-LSTM error correction and Kalman-LSTM error correction are presented in Figure 13. The positioning errors of the two error correction methods are shown in Table 5. The root mean square error of the Kalman-LSTM error correction method in the X-axis, Y-axis and Z-axis are 0.523, 0.705 and 0.554 m, respectively. The root mean square error of the WLS-LSTM error correction method in the X-axis, Y-axis and Z-axis are 0.880, 1.345 and 0.770 m, respectively. The experimental result illustrates that the positioning result of Kalman-LSTM error correction method is smoother and more accurate than the WLS-LSTM error correction method.  
Figure 11: Result of LSTM error prediction performed on Kalman filter: (**a**) result on X-axis; (**b**) result on Y-axis; and (**c**) result on Z-axis.  
Figure 12: Result of dynamic LSTM error prediction: (**a**) result on X-axis; (**b**) result on Y-axis; and (**c**) result on Z-axis.  
Figure 13: Result of LSTM-based error correction method on the three axes: (**a**) positioning error of WLS–LSTM; and (**b**) positioning error of Kalman–LSTM.  
The positioning results of the four different methods are presented in Figure 14. From the positioning results, compared to the positioning methods without LSTM correction, the positioning error of LSTM-based error correction methods is significantly reduced on three axes. The Kalman-LSTM error correction is the most accurate method for its positioning error is significantly smaller than the other methods. It is worth mentioning that the point positioning error of Kalman-LSTM error correction method can almost reach the sub-meter level, which is 1.038 m from the experimental result, while the point positioning error of WLS-LSTM error correction method, Kalman filter and WLS method are 1.782, 3.406 and 3.498 m, respectively. Moreover, the positioning error from 11:31 to 11:59, which is obviously larger, is effectively reduced by the proposed error correction method.
# Temperature Effect on Stability of Tunnel Face Under Unsaturated Seepage Condition
## 4 Parametric Studies
### The Impact of Temperature T  
Figure 4 illustrates the variation in the safety factor FS for sand, silt, and clay under different seepage conditions as the temperature increases from 10 \({}^{\circ}\)C to 40 \({}^{\circ}\)C. From the figure, it can be observed that as the temperature rises, the safety factor _F_S for both clay and silt soils decreases under all three seepage conditions. In contrast, the FS for sand remains essentially unchanged with temperature variations. Additionally, clay exhibits the highest sensitivity to seepage conditions, achieving the lowest safety factor under high infiltration conditions and the highest safety factor under high evaporation conditions. Under the three seepage conditions, the impact of temperature on silt is essentially consistent, while the influence of temperature on clay is significantly reduced under high infiltration conditions.
## 3 System Model  
In this section, we describe the mathematical system model for the scenario of the multi-domain network slicing service provision market in the satellite-terrestrial integrated network scenario. Compared with previous studies, our system model incorporates both competitive and cooperative relationships among different network service providers, with the simplest example as an illustration of a better and easier understanding. Some link properties are also simplified (e.g., delay and jitter) and the bandwidth and price are used to demonstrate the key ideas of our newly defined problem and proposed mechanisms. Our system model and proposed mechanisms can be easily extended to much more complex and realistic integrated satellite and terrestrial network scenarios without a heavy modification.  
In our problem formulation, the optimization objectives of both the network service providers and users are to maximize their own utilities, which are mathematically defined in this section. Since the value of providing/using the network service is the private information, the participants may want to behave strategically through their bids for their own interests, which would damage the overall efficiency. Thus, our design goals are the economic properties, which include the budget balance, individually rationality, and obvious truthfulness, to inhibit such selfish behaviors. We also demonstrate that our proposed multi-sided ascending-price auction mechanism guarantees these economic properties with theoretical proofs in Section 5 and achieves a desirable asymptotically optimal gain from trade with numeral experiments in Section 6.  
Table 3 provides a list of the major mathematical symbols used in this paper for a convenient reference.
# Analysis of Sentinel-1 Radiometric Stability and Quality for Land Surface Applications
## 1 Introduction  
Synthetic Aperture Radar (SAR) space-borne sensors have been widely used for soil and vegetation parameters estimation. SAR sensors operate in all temporal (day or night) and meteorological conditions, rendering SAR data operational for surface monitoring. Over recent decades, SAR sensors have been designed to fulfill a growing need for scientific land monitoring. The recent Sentinel-1 constellation has been scheduled to ensure continuous monitoring. The first Sentinel-1 sensor, S-1A, was launched on 3 April 2014, and the launch of the second Sentinel-1 sensor, S-1B, is scheduled for 2016. The two satellite constellations offer a 6-day repeat cycle (a 12-day repeat cycle for each Sentinel-1 satellite). Aboard Sentinel-1 is a C-band (\(\sim\)5.4 GHz) SAR sensor that facilitates high-resolution image acquisition according to four standard operational modes: the Strip Map (SM), Interferometric Wide swath (IW), Extra Wide swath (EW), and WaVe (WV) modes. In SM, IW, and EW, imaging can be carried out for single and dual-polarizations (HH, VV, HH-VV, and VH-VV), whereas in the WV mode imaging can only be carried out under single polarization conditions (HH and VV).  
SAR data of radiometric stability and quality are essential to accurate surface monitoring [1, 2]. Indeed, radar signal variations should only occur due to variations in soil and vegetation conditions and not due to the imprecise calibration of radar images. The S-1 IPF (Instrument Performance Facility) performs a number of radiometric corrections to achieve required S-1A image radiometric accuracy levels. During the commissioning phase (completed on 23 September 2014), the S-1 IPF only correctsfor the gain of EAP (Elevation Antenna Patterns). As an outcome of the S-1A commissioning phase, the S-1 IPF was upgraded to also correct for the EAP phase. This correction was introduced to the S-1A images acquired on 19 March 2015 with the new IPF version (V243) [3]. Moreover, an expected gain imbalance on acquired S-1A data was reported during the commission phase. This gain imbalance generated an overall gain offset of roughly 0.666 dB for all polarization and imagining modes [4]. The gain offset correction was applied to S-1A images acquired on 25 November 2015 at 10:40 UTC.  
The goal of the paper is to investigate the radiometric stability and quality of S-1A signals using a times series of S-1A images acquired over continental areas. To assess radiometric stability levels, S-1A images were first calibrated. Second, temporal variations in S-1A backscattering coefficients (\(\sigma^{\circ}\)) averaged over invariant targets were analyzed. To assess radiometric quality levels, a comparison between S-1A and RS-2 \(\sigma^{\circ}\) returned from the same invariant target was performed. This paper includes four sections. The second section of the paper describes the SAR images and studied areas. The third section presents the methodology and results. Finally, the fourth section presents our main conclusions.
# A Unified Denoising Framework for Restoring the LiDAR Point Cloud Geometry of Reflective Targets
## 2 Materials and Methods
### Methods
#### 2.2.3 Blooming Effect Denoising
As illustrated in Figure 6, when the alpha radius is small, the algorithm can capture fine and complex local details, but the extracted boundary contours tend to be complex and fragmented. As the alpha radius increases, the extracted boundary contours gradually become smoother. As the target distance increases, the point cloud becomes sparser. To address this, we adopt the average point spacing of the point cloud projected onto the YOZ plane as the alpha radius, ensuring adaptability for boundary extraction across targets at varying distances. ```
Input: Projected 2D points \(Q\) and alpha radius \(\alpha_{r}\) Output: Target points \(T\) (1) Extract the boundary of point cloud \(Q\) using alpha shape with alpha radius \(\alpha_{r}\) (2) Obtain the facets \(F\) (each facet represents an edge segment in a 2D plane) and the corresponding boundary points \(B\) (each facet contains two boundary points) (3) Initialize the normal vector for each boundary point with zero values (4) for each facet \(f_{i}\in F\)do (5) Compute the normal vector \(\overrightarrow{n_{i}}\) for the facet \(fi\) using its two endpoints \(b_{i}\) and \(b_{j}\) (6) Normalize \(\overrightarrow{n_{i}}\) to make it a unit vector (7) Accumulate \(\overrightarrow{n_{i}}\) to the normal of the two endpoints \(b_{i}\) and \(b_{j}\) (8) endfor (9) Initialize the corrected boundary points with \(B\) (10) for each boundary point \(b_{i}\in B\)do (11) Compute the unit normal vector \(\overrightarrow{v_{i}}\) for the point \(b_{i}\) (12) Compute the distance L from \(b_{i}\) to the origin of the LiDAR coordinate system (13) Define the adaptive error ellipse centered at \(b_{i}\); calculate the semi-major axis a and semi-minor axis b of the ellipse (14) Calculate the absolute value of the slope k between the original boundary point and the corrected boundary point \(b_{i}\prime\) (15) Calculate the corrected boundary point coordinate \(b_{i}\prime\) based on the adaptive ellipse parameters (16) endfor (17) Connect all points \(b_{i}\prime\) to form the new boundary (18) for each point \(q\in Q\)do (19) if\(q\) is located between the original and the corrected boundaries, then (20) Mark \(q\) as an inflated point and remove it (21) elseif\(q\) is located inside the corrected boundary, then (22) Mark \(q\) as a target point and retain it (23) endif (24) return\(T\)
```  
Figure 6: Illustration of boundary extraction based on alpha shape: (**a**) the original point cloud of the target; (**b–d**) the alpha shapes of the point cloud; the alpha radius is set to 0.01, 0.05, and 0.5, respectively.
## 4 Machine-Learning-Aided Positioning in 5G Networks
### Advances in Positioning Aided by Machine Learning
[66] proposed the following pipeline for indoor positioning. Firstly, they used a Kalman filter to preprocess the raw RSS values. Then, a universal Kiring algorithm is utilized in order to improve the resolution of the fingerprint database. Ultimately, a kNN model is used to estimate the position of the UE. Their experiments were performed in two indoor office rooms. In each room, one 5G NR BS was deployed 3.62 m high above the floor. The data for testing were collected in multiple unconnected test points. According to their results, in the first room, the Kalman filter improved the performance by 31% and the universal Kiring enhanced the accuracy by an additional 26%. In case of the other room, the Kalman filter increased the performance by 6% and the universal Kiring with 36%. Due to the improvements listed above, the achieved error was less than 1.6 m in more than 80% of the test samples. [PERSON] et al. [67] proposed a dataset generating method, whose output is suitable for 5G high-precision localization. This includes the multilevel feature synthesis method, which flattens the features of a multiple-input multiple-output channel into a single image in order to improve the information density and the robustness to noise. They also introduced the multipath res-inception, which is a deep learning solution for positioning. In this method, in order to extract position related features in the frequency domain and capture the fine channel differences between adjacent antennas, multiple specially sized filters are created. Their simulations were executed in an indoor scenario, and the positioning results show a 0.28 m mean error and 90% of the samples have an error less than 0.51 m. Moreover, in an urban canyon environment they achieved a 0.204 m mean error and less than 0.36 m for 90% of the samples. According to their comparison, the proposed method outperformed the results of four other positioning solutions (TDoA, AoA, kNN, and TDoA-AoA) in both environments.
###### Abstract  
Nd-doped CaWO\({}_{4}\) single crystals with dopant concentrations of 0.1, 0.5, and 1% were synthesized by the floating zone method. The transmission, photoluminescence, and scintillation properties were evaluated from ultraviolet to near-infrared (NIR) ranges. An emission peak due to transitions of the host was observed at 400 nm, and several sharp peaks originating from Nd\({}^{3+}\) 4f-4f transitions were confirmed at 900, 1060, and 1300 nm. The quantum yields of 0.1, 0.5, and 1% Nd-doped samples were 70.7, 79.5, and 61.2%, respectively, when monitored, and excited wavelengths were 750-1500 nm and 590 nm. Decay times consistent with typical Nd\({}^{3+}\) transitions were obtained when NIR ranges were monitored. Additionally, the energy transfer between the host and Nd\({}^{3+}\) occurred according to the decay measurement. The relationship between X-ray irradiated dose rate and intensity in the range of NIR was investigated by combining the crystals with an InGaAs-photodiode. The lowest detectable dose rate retaining the linearity of the present crystals was 0.3-0.06 Gy/h.  
scintillation; photoluminescence; 4f-4f transition; floating zone method +
Footnote †: journal: Applied Physics Letters  
Acticle Development of Nd-Doped CaWO\({}_{4}\) Single Crystalline Scintillators Emitting Near-Infrared Light
degradation in the visible range is largely even in radiation-resistive optical fibers [21]. High interaction probabilities for X- and \(\gamma\)-rays and high radiation hardness are desired for scintillators to use in the above applications where the dose rate is very high. A CaWO\({}_{4}\) single crystal is a candidate for the host material. This material has a large effective atomic number (\(Z_{\text{eff}}\), 62); thus, radiation detection efficiency can be improved as photoabsorption, Compton scattering, and pair creations are relatively proportional to \(\sim\)\(Z_{\text{eff}}\)4, -\(Z_{\text{eff}}\), and \(\sim\)\(Z_{\text{eff}}\)2 [22], respectively. Additionally, the transmittance of the material in NIR regions is reported to change little after high-dose irradiation [23], although the transmittance of UV-visible ranges degrades largely after a huge exposure. In terms of luminescence centers, some rare-earth ions have a function to show NIR luminescence [24]. Nd\({}^{3+}\) has especially attracted interest as NIR luminescent centers. Typical main emissions of Nd\({}^{3+}\) are confirmed at \(\sim\)1060 nm, and the emission is applied in laser fields such as Nd: Y\({}_{3}\)Al\({}_{5}\)O\({}_{12}\) lasers [25]. Further, NIR-scintillation has been observed in some Nd-doped scintillators [26; 27; 28]. In the study, Nd-doped CaWO\({}_{4}\) single crystals were grown as NIR-scintillators, and we evaluated the optical and scintillation properties.
## 3 Results and Discussion
### Aroma Production
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Volatile Compounds** & **SHM \({}^{\star}\)** & **YHM-G \({}^{\text{b}}\)** & **Aroma Descriptors** \\ \hline Isobutanol & - \({}^{\text{c}}\) & \(18\pm 3\) & Pine \\ Isopentanol & - & \(335\pm 27\) & Alcoholic, nail polish \\
2-Proyi-1-pentanol & - & \(1\pm 0\) & Mashroom, cream \\ Citronellol & - & \(6\pm 0\) & Lemon, lime \\ Phenylethyl alcohol & \(19\pm 3\) & \(212\pm 34\) & Rosy, honey \\ Trans-nerolidol & - & \(2\pm 0\) & Rosy, apple \\ Farnesol & - & \(5\pm 1\) & Sweet \\ \(\Sigma\)**Aleochs** & **19** & **579** & \\ Phenylethyl acetate & - & \(10\pm 1\) & Rosy, honey \\ Hexadecanoic acid ethyl ester & \(6\pm 0\) & - & Cream, herb \\ (E)-9-Ocadecenoic acid ethyl ester & \(3\pm 1\) & - & Lipid \\ Ethly Inlocate & \(2\pm 0\) & - & Lipid \\ Dihydro-5-pentyl-2(3H)-furanone & - & \(4\pm 1\) & Cecount \\ Dibbutyl phthalate & \(4\pm 1\) & \(4\pm 1\) & No \({}^{\text{d}}\) \\ \(\Sigma\)**Esters** & **15** & **18** & \\
2-Octanone & - & \(13\pm 2\) & Green herbaceous \\ Bernaldehyde & - & \(27^{\pm}\) & Almond \\
**Ketones** & - & \(40\) & \\ Guaiacol & - & \(2\pm 0\) & Fruify, flower, sweet, green grass, sauce \\
4-Ethyl-2-methoxyphenol & - & \(10\pm 0\) & Warm, sweet, spicy, medicinal, clove \\
3-Ethylphenol & - & \(2\pm 0\) & Sweet \\
2-Methyl-4-vinylphenol & - & \(315\pm 37\) & Sweet, flower, fruity, cassaba \\
2,4-Di-tert butylphenol & - & \(6\pm 0\) & Sweet \\
**L Phenols** & - & **335** & \\ Tetadecene & - & \(1\pm 0\) & Corn, popcorn, floral, \\ Pentadecane & - & \(2\pm 0\) & Crayons, sweet, green grass \\
**L Alkanes** & - & **3** & \\ Benzoiniazole & \(83\pm 6\) & \(11\pm 1\) & Smoke \\
4-Ehenyl-1-2-dimethoxy-benzene & - & \(5\pm 0\) & Green flower, grass, pericarp \\ N, N-dibutyl-formamide & - & \(3\pm 0\) & Roast \\ Aniline & \(5\pm 0\) & - & Sweet \\ Methyl-pyrazine & \(2\pm 0\) & - & Roast \\
2,4-Dmethyl-benzaldehyde & \(14\pm 2\) & - & Semen armeniaceae amarae \\
2,3-Dihydro-benzofuran & - & \(170\pm 29\) & Roast, sweet \\
**Chinese** & **104** & **126** & \\
**Sum** & **138** & **1011** & \\ \hline \hline \end{tabular} Note: “\({}^{\text{a}}\)”, the volatile compounds in SHM without YHM-G; \({}^{\text{b}}\)”, the volatile compounds in SHM with YHM-G; \({}^{\text{c}}\)”, not detected; \({}^{\text{d}}\)”, no flavor.  
\end{table} 
Table 8: The volatile compounds in SHM with or without YHM-G (\(\text{$\mu$g}/\text{L}\)).
# Research on Composite Control Strategy of Quasi-Z-Source DC-DC Converter for Fuel Cell Vehicles
###### Abstract  
The DC-DC converter for fuel cell vehicles requires high gain and wide voltage input range to boost the voltage of the fuel cell. However, with the traditional boost converter, it is difficult to meet the requirements of the fuel cell vehicle power system. Based on a quasi-Z-source network DC-DC converter, this paper proposes a composite controller, which includes a feedforward compensation network and feedback control to meet the control robustness requirement of the fuel cell vehicle power system. The dynamic model of the converter is obtained by using the state space averaging method and the small-signal dynamic modeling method. The input voltage and load disturbance experiments are performed on the DC-DC converter. Moreover, the converter is tested under the worldwide harmonised light vehicle test procedure (WLTP) to validate the effectiveness of the proposed composite controller. The simulation and experiment results show that the proposed composite controller effectively enhances the converter's ability to resist input and load disturbance, and improves the dynamic response performance of the DC-DC converter for fuel cell vehicles.  
Keywords:fuel cell vehicles; quasi-Z-source DC-DC converter; feedforward compensation; small-signal dynamic modeling; feedback control +
Footnote †: journal: Applied
## 5 Conclusions  
In this paper, an efficient method ACO-SPA for rate adaptive based resource allocation with proportional fairness constraints in OFDMA systems is proposed. As mentioned in Section 2, the rate adaptive based resource allocation mathematical model is given as a NP-hard combinatorial problem. With non-linear constraints of proportional fairness, optimization problem becomes non-convex. Therefore, the resource allocation is separated into two steps. In the first step, the swarm intelligence algorithm ACO is applied in order to solve the subcarrier allocation and to obtain the coarse proportional fairness allocation scheme. In the second step, a simplified suboptimal power allocation is performed by the strategy that the sums of power and the reciprocal of channel-to-noise ratio for each user in different subchannels are equal. In the simulation results, the performance of ACO-SPA method is put in contrast with the other two classical approaches, _i.e._, the root-finding method and the linear method. Simulation results show that with a relatively larger computational complexity, the proposed ACO-SPA method achieves a higher sum rate in the same situation than the other two methods and strictly meets the proportionality constraints.
# Experiment on Flexural Fatigue Performance of Precast Bridge Deck Joints with Loop Connections
## 4 Experiment Results and Analysis
### Variation of Load-Deflection  
The moment-deflection curves of specimens SF1 to SF3 under static loading after \(200\times 10^{4}\) fatigue cycles were drawn, respectively, as shown in Figure 7a. For the specimen B3-A, which are the same size as specimen SF1, the moment-deflection curves of the specimen B3-A under the static loading is also compared with that of the specimen SF1, which is also presented in Figure 7a. Each curve conforms to the characteristics of a typical pure flexural concrete beam.  
Figure 6: _Cont._  
By comparing the test results of the specimens SF1 and B3-A, it can be learnt that the load-deflection curves are basically consistent in the elastic stage. When the loading  
Figure 6: Crack propagation law and failure mode of specimens: (**a**) specimen B3-A; (**b**) specimen SF1; (**c**) specimen SF2; and (**d**) specimen SF3 (Unit: mm).  
Figure 7: Load–deflection curves under different fatigue cycles: (**a**) specimen B3-A; (**b**) specimen SF1; (**c**) specimen SF2; and (**d**) specimen SF3.  
value is close to the proportional limit, the specimen SF1 can still maintain a certain elastic performance. It indicates that the fatigue load has a certain beneficial effect on the performance of the specimen SF1 in the elastic stage. However, there are some differences in the behavior of specimens SF1 and B3-A in the yielding stage. After 200 \(\times\) 10\({}^{4}\) fatigue cycles, the ultimate strength of the specimen SF1 is improved to a certain extent, but the ultimate elongation is reduced. This phenomenon is similar to the age-hardening behavior of steel bars after cold stretching.  
According to the proposed loading process, each specimen was subjected to static loading after a certain number of fatigue cycles. The load-deflection curves of each specimen during static loading are shown in Figure 7b-d. The following results can be analyzed from the experimental data:  
* After the specimen SF1 is subjected to 50 \(\times\) 10\({}^{4}\) fatigue cycles, the slope of the load-deflection curve of this specimen under the static load increased slightly. However, with the increase of the number of fatigue cycles, the slope of the load-deflection curve under the static load tends to be stable, and there is no significant increase again.
* For the specimen SF2, the slope of the load-deflection curve of this specimen under the static load decreases slightly and tends to be stable after 100 \(\times\) 10\({}^{4}\) fatigue cycles. In general, the load-deflection curves of static loading after different durations of fatigue cycles are still basically linear. It indicates that the specimen SF2 is generally in an elastic state.
* While the slope of the load-deflection curve of the specimen SF3 under the static load does not change significantly, similarly, the specimen SF3 is still in an elastic state.