esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Material and Methods
### Bi-Directional Lateral Scanning Protocol and Image Correction for Doppler Analysis
(**c**) Schematic of the imaged glass capillary in respect to the defined coordinate system. (**e**) Resulting M-scan and B-scans (forward and backward lateral scan: FL- and BL-scan) due to the modified scanning protocol for LR-DOCT. Figure 5: (**a**) Conventional processing of the detected interferometric signals for structural and Doppler flow imaging on the example of the FL-scan of the 1% Intralipid flow within the glass capillary. (**b**) Correction of the obliquely running direction of the glass capillary by insertion of an additional phase term \(\varphi_{\text{shifted}}(k,j)\) to the real-valued interference signal and subsequent conventional processing. Note the tilted signal from zero delay at the top.
## 1 Introduction
The results presented below are the continuation of years-long research focused on the application of independent data used to construct seismic wave velocity fields [1; 2; 21; 22; 23]. In addition to a number of publications dealing with this problem, attention must be paid to the solution proposed by the working group from the Polish Oil and Gas Co. (POGC) for seismic, gravimetric and magnetotelluric datasets and applied to the comprehensive interpretation of regional seismic data acquired within the project of Tran-scarpathian Seismic Profile (POLCRUST) [7; 24; 25; 26]. For that solution, several analyses and tests were performed with the application of various seismic wave velocity fields [7; 25; 26]. Particularly, the modeling of full wavefield was carried out in order to evaluate the possible advantages provided by the application of such inversion to seismic data processing [26]. The analyses presented below aimed to test the influence of standard generation procedures of velocity fields not only on the PreSDM results but more importantly on the effects of geological interpretation of the Raczyna gas deposit area [27]. The latter aspect of the presented research seems to be crucial to the effectiveness of petroleum exploration in the transitional zone between the Carpathian orogen and the Miocene Carpathian Foredeep. The research included both the geophysical and geological datasets originating from the study areas of the following project: \"The new 3D seismic-geological model testing the perspectives of discovery of hydrocarbon unconventional deposits within the Miocene formations of the Carpathian Foredeep\" performed within the INGA 2 research project. Both were co-financed by POGC and the National Centre for Research and Development. The article has been divided into six chapters. In Section 1, \"Introduction\", a general description of the purpose of this material was provided. Section 2, \"Location of ResearchArea\", includes information about the location and geological characteristics in relation to archival seismic surveys conducted in the studied area. Section 3, \"Methods and Materials\", is a crucial part of this work as it describes the applied methodology for validating independent velocity models used in the seismic data processing. Additionally, the seismic and gravimetric data used for calculations are described in this chapter. To facilitate a better understanding of the subject matter, Section 3 has been divided into five subsections. Section 4, \"Research Results and Discussion\", presents the obtained results along with a discussion on the possibilities offered by the application of independent velocity models in the seismic data processing. The last section, \"Summary and Conclusions\", includes a summary of the described procedure presented by the authors. Both the benefits and limitations of this methodology were indicated.
# Iterative Decoding of LDPC-Based Product Codes and FPGA-Based Performance Evaluation
## 4 Hardware Evaluation for Iterative Decoding of Product Codes
### Experimental Results Based on Field Programmable Gate Array
\begin{table}
\begin{tabular}{c l c c} \hline \hline  & **Module** & **Block RAMs** & **Slices** \\ \hline \multirow{4}{*}{Encoder} & Hamming encoder & 9 & 356 \\  & LDPC encoder & 42 & 2687 \\  & Memory for outer codewords & 54 & \(\backslash\) \\ \hline \multirow{4}{*}{Decoder} & Hamming decoder & 18 & 385 \\  & LDPC decoder & 200 & 18,932 \\  & Memory for prior information & 216 & \(\backslash\) \\  & Memory for intermediate dec results & 54 & \(\backslash\) \\ \hline \multicolumn{3}{c}{XC6 VLX240T usage (percent)} & 832 (71.27\%) & 22,360 (59.34\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The resource usage of hardware evaluation platform. Figure 12: FPGA evaluation results of product codes with LDPC (576, 288) and Hamming (127, 120). Figure 14: FPGA evaluation results of product codes with LDPC (576, 288) and Hamming (511, 502). Figure 13: FPGA evaluation results of product codes with LDPC (576, 288) and Hamming (255, 247).
## 6 Limitations and Future Directions
### Threats to Validity  
The TREQS dataset, while derived from the widely documented and widely used MIMIC-III database, presents potential limitations that must be addressed to ensure generalizability and robustness. Although the dataset includes a curated collection of question-query pairs, its reliance on manually rephrased questions introduces the risk of linguistic bias. These rephrasings may inadvertently overfit the models to specific phrasing patterns, limiting their ability to generalize to diverse, real-world queries. In addition, the dataset focuses primarily on English queries, raising concerns about its applicability to multilingual healthcare systems commonly found in global contexts. Expanding the dataset to include queries in multiple languages and integrating colloquial and domain-specific medical terms would significantly enhance its relevance and usability in diverse healthcare settings.  
In addition, reliance on proprietary models, such as GPT-4o and Claude 3.5, poses reproducibility and external validation challenges. These models operate as black boxes with limited access to training data, fine-tuning processes, and architectural specifics. Although we have included open-source models such as LLaMA 3.3-70B and Qwen-2.5-72B in our evaluation, the comparative emphasis on proprietary solutions could hinder broader validation efforts.  
Another limitation arises from the reliance on structured tables derived from MIMIC-III, which may not fully encapsulate the complexities of real-world electronic health record (EHR) systems. Healthcare databases often exhibit variability in schema design, naming conventions, and data formats, necessitating further validation of models on additional datasets representing broader medical domains. Addressing these issues would involve developing benchmarking datasets that capture the heterogeneity and nuances of healthcare queries, thus ensuring that the proposed models perform effectively in diverse operational settings.
## 4 Sensing-Oriented Issues and Solutions
### Defect Types and Antenna Topologies
#### 4.1.1 Strain Detection and Characterization
\begin{table}
\begin{tabular}{p{108.4 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt}} \hline \hline
**Sensing Principle (Antenna Type)** & **Sensing Variable** & **Feature** & **Pros.** & **Cons.** & **Refs.** \\ \hline Conductor loss resistance (stretchable dipole on fabric substrate) & Backscattered power at turn on threshold & Power variation & Strain level up to 50\%-Sensitivity can be modified by conductive material; Read range: 1.5 m & Power is susceptible to wireless channel & [39] \\ \hline Deformation of shape factor (meander line dipole) & Backscattered power & Power variation & Sensitivity: 16\%, Read range: 0.6 m & Strain level up to 6\% (poor-elastic conductor leads to small yield point) & [40] \\ \hline Coupling (slotted circular patch) & Reflection coefficient (S\({}_{11}\)) & RPS & Omni-directional strain sensing & VNA is required; Not compatible with Gen2 regulation & [41] \\ \hline Electrical length (fabric-based embroidered dipole) & Dual-interrogation-mode (read range/RCS) & RPS & Strain level up to 16\%-Sensitivity: 0.66 parts per million (ppm)/ \(\mu\epsilon\) & Read range: 20 cm; Need calibration; Dedicated receiver & [107] \\ \hline Elastic deformation (patch) & Turn on power & RPS & Read range: 2.1 m; Can be mounted on metallic surface & Sensitivity: 0.7907 ppm/ \(\mu\epsilon\) & [36] \\ \hline LC resonator (PDMS substrate stamped with sliver nano ink) & Reflection coefficient (S\({}_{11}\)) & RPS & Strain level up to 7\%, Chipless, Good mechanical & Sensitivity: 0.51 ppm/ \(\mu\epsilon\); Read range: 20 cm; VNA is required; Dedicated receiver & [42] \\ \hline Deformation of slot width (dipole on PDMS substrate with stretchable conducting Lycra fabric containing silver threads) & Turn on power & Power variation & Sensitivity: strains of up to 10\% causing transmit power differences of about 4 dB; Read range: 1.6 m; Good repeatability & Power is susceptible to wireless channel & [43] \\ \hline Elastic deformation (folded patch) & Turn on power & RPS & Can be mounted on metallic surface & Sensitivity: \(-\)0.7404 ppm/ \(\mu\epsilon\); Read range: 30 cm & [46] \\ \hline Elastic deformation (dual patches) & RCS & RPS & Sensitivity: \(-\)5.232 kHz/ \(\mu\epsilon\) & Not compatible with Gen2 regulation Dedicated receiver & [38] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of RFID tag antenna sensors for strain detection and characterization.
# Development of a Magnetostrictive FeNi Coated Surface Acoustic Wave Current Sensor
###### Abstract  
A magnetostrictive FeNi-coated surface acoustic wave (SAW)-based current sensor was proposed in this work. The weak remanence and hysteresis effect of the FeNi itself contributes to suppress the asymmetry in sensor response at increasing and decreasing current. The sensor response was simulated by solving the coupled electromechanical field equation in layered structure considering the magnetostrictive effect and an approach of effective dielectric constant. The effects from the aspect ratio and thickness of the FeNi film on sensor response were analyzed to determine the optimal design parameters. Differential oscillation structure was used to form the sensor, in which, the FeNi thin film was deposited along the SAW propagation of the sensor chip by using RF magnetron sputtering. The magnetostrictive effect of the FeNi coating induced by the magnetic loading generates the perturbation in SAW velocity, and corresponding oscillation frequency. High sensitivity of 10.7 KHz/A, good linearity and repeatability, lower hysteresis error of 0.97% were obtained from the developed prototype 150 MHz SAW FeNi coated current sensor.  
current sensor; differential oscillator; FeNi; magnetomechanics effect; surface acoustic wave +
Footnote †: journal: Applied Physics Letters  
_Applied_
###### Abstract  
Maintaining the data freshness and completeness of road intersection information is the key task of urban road map production and updating. Compared to professional surveying methods, crowdsourced trajectory data provide a low-cost, wide-coverage and real-time data resource for road map construction. However, there may exist the problems of spatio-temporal heterogeneity and uneven density distribution in crowdsourced trajectory data. Hence, in light of road hierarchies, the paper proposes a hierarchical segmentation method to generate road intersections from crowdsourced trajectories. The proposed method firstly implements an adaptive density homogenization processing on raw trajectory data in order to decrease the uneven density discrepancy. Then, a hierarchical segmentation strategy is developed to extract multi-level road intersection elements from coarse scale to fine scale. Finally, the structural models of road intersections are delineated by an iterative piecewise fitting method. Experimental results show that the proposed method can accurately and completely extract road intersections of different shapes and scales, with an accuracy of about 87-90%. Particularly, the precision and recall of road intersection detection are obviously increased by about 7% and 20% by adaptive density homogenization, indicating the advantages of dealing with uneven trajectory data.  
crowdsourced trajectory data; road intersection; density homogenization; hierarchical segmentation; routable road map +
Footnote †: journal: Medical Imaging  
12 October 2022
## 5 Conclusions  
SNNs represent a new generation of artificial neural network models inspired by the activity of biological neurons in the nervous system, which have lighter parameters and computational overheads compared to CNNs, but the higher performances with proper optimization methods still needs to be investigated for object detection tasks. Our work has demonstrated the higher performances for the object detection in the proposed SNNs with proper optimization methods, integrating neuronal variants and self-attention mechanisms. Our work shows that the optimization in the SNN structure with the attention mechanism outperforms that of neuronal variants for object detection. Combining with the PLIF model and CBAM mechanism, AMS_YOLO achieved an 11.4% performance boost in static COCO datasets, and AMSpiking-VGG achieved a 6.7% improvement in the dynamic GEN1 dataset.  
Since the optimization in the SNN structure with neuronal variants outperforms that in the attention mechanism, future research could investigate other types of neuron models that improve the efficiency and reduce the number of parameters for more efficient object detection tasks. The different SNN structures should be designed and modified by adapting them to the different inputs; therefore, general SNNs with self-adaptive inputs could be studied to overcome their limitations. Moreover, we could expand the proposed optimization methods to complex tasks such as behavior recognition and target tracking with SNNs, applying to practical applications in more scenarios that require low energy consumption, such as UAV navigation and robot vision.  
Conceptualization, L.S. and W.L.; methodology, W.L.; software, W.L. and J.Z.; validation, W.L., J.Z. and L.S.; formal analysis, N.J.; investigation, W.L., L.S. and Q.H.; resources, L.S. and N.J.; data curation, J.Z.; writing--original draft preparation, W.L. and J.Z.; writing--review and editing, W.L. and L.S.; visualization, W.L.; supervision, L.S., N.J. and Q.H.; project administration, L.S.; funding acquisition, L.S. and N.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China: 12272039; R&D Program of Beijing Municipal Education Commission: KM202110028010 and Beijing Natural Science Foundation: QY24309.  
Not applicable.  
The data presented in this study are openly available in COCO2017 at [[https://cocodataset.org/#home](https://cocodataset.org/#home)]([https://cocodataset.org/#home](https://cocodataset.org/#home)) accessed on 9 September 2024 and Propahsee GEN1 at [[https://www.prophesee-cn.com/development-center](https://www.prophesee-cn.com/development-center)]([https://www.prophesee-cn.com/development-center](https://www.prophesee-cn.com/development-center)) accessed on 9 September 2024.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Photocatalytic Tests of Nanocrystalline Metal Oxide Nanofibers  
The photocatalytic activities of metal oxide nanofibers were studied by the degradation of MB. In this process, 20 mg nanofibers were suspended in 200 mL MB aqueous solution (10 mg/L). The suspension needed to be stirred continuously for 30 min to ensure adsorption/desorption equilibrium in the dark and then exposed to a 40 W UV lamp at room temperature. The supernatant was collected and separated by centrifugation at irradiation time intervals, and then determined by UV spectrophotometer. The degradation efficiency of dye is calculated by the following equation [32]:  
\[\text{Degradation}(\%)= \frac{\text{C}_{0}-C}{C}\times 100 \tag{1}\]  
\(\text{C}_{0}\) is the initial concentration of dye before irradiation and \(C\) is the concentration of dye after a certain irradiation time. The photocatalytic degradation kinetics were analyzed using pseudo-first-order kinetics:  
\[\text{ln}(\text{C}_{0}/C)=\text{k}t \tag{2}\]  
\(C\) is the concentration and \(\text{C}_{0}\) is the initial concentration of the reactant. k refers to the reaction rate kinetic constant and \(t\) is the irradiation time.
## 4 Results  
This research is designed to screen the audience by obtaining 5G base station positioning data. Compared with the advertising system that uses traditional GPS positioning data, its advertising effectiveness will be significantly different. Before starting the comparison, the usage rate and usage context of the GPS service to be compared will be defined and explained.  
The traditional advertising system is mainly limited by whether the positioning function of the audience is enabled for the information collection of the geographic location. When the positioning function is not enabled, the traditional advertising system has no way of knowing where the audience actually is. Therefore, when the placement needs are generated, it can only be inferred based on past data that the placed audience once appeared in a specific location.  
In addition, observe the actual data collection situation. Except for several apps with a large number of users, such as Google and Facebook, there are generally not many apps that use mobile phone GPS, and these apps do not have many users. If the advertising system cooperates with these APP services to obtain the geographic location of the audience, its audience coverage is obviously less than 10% of the total audience. Generally speaking, when the APP service uses GPS, most of it is turned on when there is a temporary positioning need. It is basically impossible for the user to keep the GPS continuously turned on. Therefore, the inference of the movement path is actually very difficult to do. If you further observe the GPS data of the user's location occasionally, you can find that most of the location data is generated by special scenic spots or when traffic positioning is required, so the reference of the obtained location data is open to question.  
To sum up, this study assumes that the users who use mobile GPS account for 10% of the total number of users, and that all the information obtained is correct, and based on the following types of advertising scenarios and audiences to compare the performance between using traditional GPS data and using 5G base stations data. For some experimental background condition parameters, please refer to Table 15.
## 3 The Index System of Risk Assessment for Tunnel Failure
### Weight of Evaluation Indexes  
The weight of the nine evaluation indexes was calculated by using the entropy weight method, according to Equations (1)-(4). Any single index does not play a dominant role in risk assessment compared to other indexes (Figure 8). Thus, multi-indexes models should be applied during the risk assessment of tunnel damage. Among the nine indexes, the accumulated energy and accumulated events have the largest weights (0.211 and 0.21, respectively) and have the greatest impact on the results of tunnel failure risk assessment. The weight of the buried depth index is smallest, which is consistent with the scattered distribution and indistinct boundary of this index at different damage levels (Figure 7). It is worth noting that the evaluation indexes with the five largest weights are MS parameters and account for a cumulative 83.8% of the total weight. This indicates that the MS parameters play a leading role in the risk assessment of tunnel failure.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Damage Level** & **Statistical Parameters** & **Accumulated Events** & **Accumulated Energy** & **Distance** & \(\sigma_{a}\) & \(\overline{u}\) & **Density** & **Depth** \\ \hline \multirow{4}{*}{No damage (\(n=24\))} & Minimum value & 0 & 0 & 24.95 & 0 & 0 & 0 & 559 \\  & Maximum value & 6 & 22.7 & 175.2 & 2.13 & 0.004 & 7.58 & 909 \\  & Truncation mean & 1.43 & 4.5 & 88.87 & 0.42 & 0.0013 & 0.82 & 650.9 \\  & Standard deviation & 1.72 & 5.82 & 49.07 & 0.6 & 0.0014 & 1.97 & 103.95 \\ \hline \multirow{4}{*}{Slight damage (\(n=44\))} & Minimum value & 3 & 9.08 & 21.24 & 0.01 & 0.001 & 1.73 & 558 \\  & Maximum value & 23 & 40.8 & 162.74 & 1.12 & 0.014 & 19.78 & 910 \\  & Truncation mean & 9.8 & 25.67 & 58.85 & 0.52 & 0.0047 & 8.75 & 730.49 \\  & Standard deviation & 2.96 & 5.67 & 34.34 & 0.29 & 0.0027 & 3.54 & 104.44 \\ \hline \multirow{4}{*}{Moderate damage (\(n=22\))} & Minimum value & 8 & 28.8 & 20.11 & 0.03 & 0.003 & 3.58 & 609 \\  & Maximum value & 65 & 192.7 & 139.83 & 1.61 & 0.019 & 34.63 & 910 \\ \cline{1-1}  & Truncation mean & 26.52 & 79 & 53.91 & 0.69 & 0.0067 & 12.21 & 797.32 \\ \cline{1-1}  & Standard deviation & 19.35 & 51.93 & 29.5 & 0.39 & 0.0038 & 8.05 & 73.46 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistical distribution parameter table of evaluation indicators for different damage levels.  
Figure 6: Box plot of the distribution of the seven evaluation indexes.
## 2 Materials and Methods
### Physical Characterization of CMSC
#### 2.5.3 Determination of Solubility and pH  
Quantitative solubility tests were carried out in 3% (\(w/v\)) CMSC in water. The pH was measured using a pH meter (Mettler-Toledo S20; Mettler-Toledo, Greifensee, Switzerland). The mixture was stirred at room temperature for 16 h to obtain a uniform suspension. The sample was then centrifuged for 10 min at 1500 rpm. After centrifugation, 25 mL of clear supernatant was transferred by pipette into a dry pre-weighed sample bottle. The weighing bottle was dried at 105 \({}^{\circ}\)C in an oven until constant weight (approx. 24 h). It was then cooled in a desiccator and weighed. The solubility was calculated using Equation (5) as follows:  
\[\mathit{The\ solubility\ of\ sample\ in\ 3\%}\ \left(w/v\right)\ \mathit{aqueous\ solution}\ =\frac{W_{d}\times 100}{25\times 0.03} \tag{5}\]where:  
\(W_{d}\) = weight of dried soluble sample in 25 mL
# Design and Performance Analysis of BDS-3 Integrity Concept
## 2 BDS Integrity Concept
### \(P_{sat}\) and \(P_{const}\) Calculations
#### 2.3.3 Pconst Calculation  
\(\mathrm{P_{const}}\) is obtained through statistics, which can be expressed as following:  
\[\mathrm{P_{const}}=\frac{\mathrm{N_{const}}\cdot\mathrm{MTTN}}{\mathrm{N_{hours}}} \tag{8}\]  
where \(\mathrm{N_{const}}\) represents the total time in a year that two or more satellites fail due to a common cause at the same time.