esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### ML, FR and MS Variables
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**RSG**} & \multirow{2}{*}{**IRPSG**} & \multicolumn{3}{c}{**Tie**} & \multicolumn{3}{c}{**Win**} & \multicolumn{3}{c}{**Loss**} \\ \cline{3-8}  & & \(\mathbf{n}\) & \% & \(\mathbf{ASRs}\) & \(\mathbf{n}\) & \% & \(\mathbf{ASRs}\) & \(\mathbf{n}\) & \% & \(\mathbf{ASRs}\) \\ \hline \multirow{3}{*}{Immediate} & Positive impact & 12 & 50.00 & 0.00 & 25 & 75.80 & 3.70 * & 8 & 24.20 & \(-3.70\) * \\  & Negative impact & 12 & 50.00 & 0.00 & 8 & 24.20 & \(-3.70\) * & 25 & 75.80 & 3.70 * \\ \cline{2-10}  & Positive impact & 15 & 50.00 & 0.00 & 85 & 90.40 & 10.40 * & 9 & 9.70 & \(-10.30\) * \\ \cline{2-10}  & Negative impact & 15 & 50.00 & 0.00 & 9 & 9.60 & \(-10.40\) * & 84 & 90.30 & 10.30 * \\ \cline{2-10} Medium-term & Positive impact & 17 & 50.00 & 0.00 & 58 & 89.20 & 8.20 * & 7 & 10.60 & \(-8.20\) * \\ \cline{2-10} Medium–long-term & Negative impact & 17 & 50.00 & 0.00 & 7 & 10.80 & \(-8.20\) * & 59 & 89.40 & 8.20 * \\ \cline{2-10} Medium–long-term & Positive impact & 8 & 50.00 & 0.00 & 31 & 88.60 & 5.90 * & 4 & 11.40 & \(-5.90\) * \\ \cline{2-10} Long-term & Negative impact & 8 & 50.00 & 0.00 & 4 & 11.40 & \(-5.90\) * & 31 & 88.60 & 5.90 * \\ \cline{2-10} Long-term & Positive impact & 17 & 50.00 & 0.00 & 82 & 91.10 & 10.20 * & 8 & 8.90 & \(-10.20\) * \\ \cline{2-10}  & Negative impact & 17 & 50.00 & 0.00 & 8 & 8.90 & \(-10.20\) * & 82 & 91.10 & 10.20 * \\ \hline \hline \end{tabular} Note: \(n\) = Frequency; RPSG = Representation of the Player Substitutions on Goals; FR = Final Result; IRPSG = Impact of the Repercussion of the Player Substitutions on Goals. * \(ASRs>1.96\).  
\end{table} 
Table 4: Degree of relationship between the MS, FR and IRPSG variables. The \(ASRs\) of the crossover of the ML, FR and MS variables are shown in Table 7. In terms of the relationship between MS and FR, there are no major differences between playing as home teams or visiting teams when looking at \(ASRs\), although the percentage of the home teams winning the matches is somewhat higher.
# LoRA-Tuned Multimodal RAG System for Technical Manual QA: A Case Study on Hyundai Staria
## 3 Data Construction Methodology  
### Dataset Construction Pipeline  
In this study, we constructed a high-quality question-answering dataset suitable for multimodal RAG system learning by performing systematic data extraction and refinement from Hyundai Staria PDF manuals. The entire data construction process consists of a systematic approach including PDF extraction, text processing, image-text mapping, and QA pair generation, as illustrated in Figure 1.  
In the first stage, we simultaneously extracted text and images from the original PDF document using the PyMuPDF library. The analyzed Staria manual consists of 522 pages total, containing approximately 1.06 MB of text data and 878 images. During extraction, we preserved the original layout information, paragraph structure, and font styles to maintain the document's hierarchical structure as much as possible.  
In the second stage, we identified document structure based on extracted text and identified hierarchical patterns in chapter-section-subsection format. Analysis results detected approximately 748 items corresponding to upper-level numbering systems (e.g., \"1.\", \"2.\"), while lower-level structures (e.g., \"1.1\", \"1.1.1\") existed sporadically in limited sections. Accordingly, additional rule-based parsing and manual organization were performed for accurate hierarchization.  
Figure 1: Dataset construction process.  
In the third stage, we performed image-text mapping work based on manual annotation to ensure semantic connectivity between each paragraph and images. Approximately 200 high-quality visual-text pairs were constructed, serving as the foundation for subsequent QA pair generation.  
In the fourth stage, we generated various types of question-answer pairs through template-based automation. To reflect users' actual maintenance query situations, we included not only single-sentence queries but also Multi-Turn query types.  
In the final stage, we performed response quality evaluation using GPT-4 based large language models. Two researchers collaborated to evaluate each QA pair focusing on technical validity, contextual consistency, and image-text correspondence, with manual supplementation when necessary. This human-machine collaboration-based review contributed to ensuring high refinement consistency and efficiency.
# Review on Active and Passive Remote Sensing Techniques for Road Extraction
###### Abstract  
Digital maps of road networks are a vital part of digital cities and intelligent transportation. In this paper, we provide a comprehensive review on road extraction based on various remote sensing data sources, including high-resolution images, hyperspectral images, synthetic aperture radar images, and light detection and ranging. This review is divided into three parts. Part 1 provides an overview of the existing data acquisition techniques for road extraction, including data acquisition methods, typical sensors, application status, and prospects. Part 2 underlines the main road extraction methods based on four data sources. In this section, road extraction methods based on different data sources are described and analysed in detail. Part 3 presents the combined application of multisource data for road extraction. Evidently, different data acquisition techniques have unique advantages, and the combination of multiple sources can improve the accuracy of road extraction. The main aim of this review is to provide a comprehensive reference for research on existing road extraction technologies.  
road extraction; high-resolution image; hyperspectral image; synthetic aperture radar (SAR); light detection and ranging (LiDAR) +
Footnote †: journal: Remote Sensing  
**2021**, 13, 4235. [[https://doi.org/10.3390/rs13214235](https://doi.org/10.3390/rs13214235)]([https://doi.org/10.3390/rs13214235](https://doi.org/10.3390/rs13214235))
## 3 Construction of the Model
### Level 5 Evaluation: Communication Performance
#### 3.5.2 Communication Quality  
There are two types of communication systems: digital communication systems and analog communication systems.  
(1) Speech articulation  
The signal-to-noise ratio (SNR) is the key criterion by which to evaluate the performance of analog communication systems, and it can be calculated using Equation (28). When a radio's voice communication function is being used, the system performance can be evaluated via speech articulation, which refers to the quality characteristics of human speech sound and can be calculated using Equation (29).  
\[SNR=10\lg\frac{S}{N} \tag{28}\]  
\[D=1-\exp\left[-0.06128(SNR+12)^{1.6951}\right] \tag{29}\]  
Here, \(D\) is speech articulation, \(S\) is signal power, and \(N\) is noise power.  
(2) Bit Error Rate  
In digital communication systems, the bit error rate (BER) is the key criterion used to evaluate the communication performance of a system [36; 37]. However, there are many issues that can affect the signal BER, such as the channel-coding mode, the channel environment, or the modulation type. The equations used to calculate the BER for the different modulation types are given in Table 4. The impact of different modulation types and different channel-coding methods on the BER is analyzed in detail in the next section.
# Fabrication and Characterization of Modified Graphene Oxide/PAN Hybrid Nanofiber Membrane
## 4 Conclusions  
In this work, we focused on the improvement of GO's dispersivity in a PAN precursor solution for electrospinning and the fabrication of GP/PAN (GP: GO modified by PDDA) hybrid nanofiber membranes. To the best of our knowledge, according to the simple surface modification, the GO content in the polymer precursor solution can be added more than 0.4 wt.% (up to 1.0 wt.%) under the same electrospinning conditions for the first time. The prepared GO nanomaterials and hybrid nanofiber membranes were characterized by SEM, FT-IR, Raman spectra, contact angle, thermal and physical properties, etc. These results showed that the pore size and the thickness can be easily controlled by the content of GP. According to contact angle analysis, these fabricated PAN/GP hybrid nanofiber membranes exhibited better hydrophilic characteristics than the PAN nanofiber membrane. Lastly, the incorporation of the GP into the PAN nanofiber membranes generally improved their thermal  
Figure 8: Thermogravimetric curves of polyacrylamide(PAN) and graphene oxide modified by polydiallyldimethylammonium chloride/polyacrylamideonitrile (GP10/PAN) hybrid nanofiber membranes.  
Figure 9: Tensile strength of polyacrylamide(PAN) nanofiber and graphene oxide modified by polydiallyldimethylammonium chloride/polyacrylamideonitrile (GP/PAN) hybrid nanofibers.  
stability and mechanical properties, compared to the PAN nanofiber membrane. Based on all the above results and the original advantage of nanofibers, the PAN/GP hybrid nanofiber membranes could be a promising candidate for water treatment membranes. Further research about water purification and battery performance is being investigated.  
The experiments were conceptualized by [PERSON] and [PERSON] performed the experiments. [PERSON] analyzed the data. [PERSON] and [PERSON] wrote the manuscript.  
This research is supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2018R1A2B6008854).  
The authors declare no conflict of interest.
# Performance Prediction of Composite Marine Propeller in Non-Cavitating and Cavitating Flow
## 2 Methodology and Results
### Structural Modeling
#### 2.3.2 Material Properties  
In this study, the material properties of the composite propellers are described below. The composite material has isotropic like material properties without the distinction of core and skin.  
Young's modulus: \(E_{1}\) = 52.1 GPa, \(E_{2}\) = \(E_{3}\) = 18.3 GPa  
Shear modulus: \(G_{12}\) = \(G_{13}\) = \(G_{23}\) = 5.2 GPa  
Poisson's ratio: \(v_{12}\) = 0.29  
Density: \(\rho\) = 1498 kg/m\({}^{3}\)
## 3 Results and Discussion
### Numerical Examples
Details of the parameters in Figure 6a,b are taken as \(\varepsilon_{r}=1\), \(\varepsilon_{r}=2.25\), and \(\varepsilon_{r}=3\), \(\varepsilon_{r}=11.2\) with \(p=16\) mm, \(h_{1}=4\) mm, \(h_{2}=h_{3}=8\) mm, \(a=4\) mm, and \(b=6\) mm for all cases, respectively. The variations in the dielectric constant have evidently induced adjustments in the passband and stopband regions of the structure, and the auxiliary functions effectively demonstrate the band separation via zero crossings. We have modeled the single-corrugated and glide-symmetric double-corrugated PPW unit cell for multimode excitation (ten waveguide modes for each geometry) using the CST frequency domain solver. In the CST frequency domain solver, the S-parameters associated with the dominant mode of multimode excitation have been extracted and included in the eigenvalue equation shown in Equation (2). Furthermore, all S-parameters derived from CST have been included in Equation (4). Figures 7 and 8a illustrate the dispersion diagrams for all operations and the characteristics of the auxiliary functions. These results indicate that the simulator-assisted AFGSM functions are indistinguishably identical to the eigenvalue equations derived from CST and the equivalent circuit (EC) at the band edges. In Figure 8a, the band edge frequency derived from the eigenvalue equation of EC at about 12 GHz seems higher. This variation is due to a limited level of high-order mode interaction inside the circuit model. Parametric details of Figures 7 and 8 are given as \(\varepsilon_{r}=1\), \(p=8.28\) mm, \(h_{1}=1\) mm, \(h_{2}=1.8\) mm, \(a=\)1.08 mm, and \(b=3.6\) mm and \(\varepsilon_{r}=1\), \(p=26.5\) mm, \(h_{1}=0.5\) mm, \(h_{2}=h_{3}=2\) mm, \(a=1.5\) mm, and \(b=12.5\) mm, respectively. Figure 7b demonstrates that the stopband of the structure extends exclusively owing to the gradual increase in \(h_{2}\). We investigated manufacturing tolerances in Figure 8b by slowly changing the glide symmetry of the structure. The offset or gradual breaking of the glide symmetry of one corrugation results in the broadening of the structure's stopband. Figure 5: Dispersion diagram and \(X_{+}\), \(X_{-}\) frequency behavior of single-corrugated PPW for different dielectric loading for (**a**) \(\varepsilon_{r}=1\), \(\varepsilon_{r}=2.25\) (**b**) \(\varepsilon_{r}=3\), \(\varepsilon_{r}=11.2\).
## 5 Experimental Results and Discussions
### Experimental Setup  
The experiments simulate a scenario with \(N_{U}=3\) UAVs, \(N_{S}=3\) charging stations, and an initial set of \(N_{D_{\text{initial}}}=5\) demand points, with a maximum of \(N_{D_{\text{max}}}=7\) demands over a planning horizon of \(H=3\) stages. The UAV parameters include a maximum energy capacity \(E_{\text{max}}=100\), flight velocity \(v=10\), flight power consumption \(P_{\text{fly}}=1\), computation power consumption \(P_{\text{comp}}=2\), and service rate \(S_{\text{rate}}=1\). Charging stations have a charging rate \(r_{\text{charge}}=5\) and capacity limit \(C_{\text{max}}=2\). Demand workloads are randomly initialized between 5 and 15, with new demands added or workloads increased by up to 3 units per stage, as implemented in the \"runOnce\" function.  
The APSO-LS algorithm is configured with \(N_{\text{particles}}=20\) particles and a maximum of \(N_{\text{iterations}}=50\) iterations per stage, using an initial perturbation rate \(\rho=0.3\), inertia weight \(w=0.7\), cognitive coefficient \(c_{1}=1.5\), and social coefficient \(c_{2}=1.5\). The objective function balances makespan and energy consumption with a weighting factor \(\alpha=0.1\), as defined in (7). In this study, simulation experiments are independently conducted three times to obtain relatively reliable results.
# Visual Ranging Based on Object Detection Bounding Box Optimization
## 3 Methods
### Object Detection Bounding Box Optimization
#### 3.2.2 Evaluation Indicator  
To achieve a quantitative evaluation of the results of the object detection bounding box optimization, this paper sets two evaluation indicators, the width optimization value (WOV) and height optimization value (HOV), to evaluate the optimized results. The calculation formula is as follows:  
\[\text{WOV}=\frac{\text{w}_{1}-\text{w}_{2}}{\text{w}_{1}}\times 100\%, \tag{4}\]  
\[\text{HOV}=\frac{\text{h}_{1}-\text{h}_{2}}{\text{h}_{1}}\times 100\%. \tag{5}\]  
In Formula (4), w\({}_{1}\) and w\({}_{2}\) represent the pixel width of the object detection bounding box before and after optimization, respectively; in Formula (5), h\({}_{1}\) and h\({}_{2}\) represent the pixel height of the object detection bounding box before and after optimization, respectively.
## 1 Introduction
The imaging algorithm under this mode usually performs the dechirp operation on the range-compressed echo signals in the azimuth time and range frequency domain first [20; 21], that is, by multiplying the dechirp factor that changes with azimuth time, so as to straighten the curved range history of all signals caused by squint and long synthetic aperture time, reduce the total doppler bandwidth and the difficulty of the imaging algorithm design. Then the traditional imaging algorithm can be performed on the dechirped output echo signal to generate a focused image [22]. However, the image is generated on the signal whose real range history has been modified after the dechirp. In this virtual range history space, the focusing and positioning of the image point are uniform and self-consistent, but the real geometric position relationship between the image point and the satellite has been destroyed by the dechirp, so the range-Doppler (RD) model cannot be directly adopted in the geometric processing of this image. How to establish a geometric processing scheme suitable for the dechirp imaging algorithm is an urgent problem to be solved. This paper analyzes the influence of the dechirp operation on the high-resolution squint SAR sliding-spot imaging and presents a geometric constellation model based on the antenna pointing vector. The method starts from the new relative positional relationship between the SAR satellite and the image point after dechirp, and establishes a strict conversation model between image coordinates and geometric coordinates using the accurate satellite-target geometric conditions. Then the forward and reverse geometric processing flows for the calibration and positioning of the high-resolution sliding-spot mode are given. Finally, using the actual spaceborne SAR data on the calibration fields, experiments are carried out to verify the image geometric accuracy of this model. After the calibration and compensation of all the error factors, including SAR transceiver channel delay, atmospheric propagation delay, platform position error, and target elevation error, the geometric positioning accuracy can be improved to the decimeter level, which fully proves the effectiveness of our method.
# A Survey of Target Detection and Recognition Methods in Underwater Turbid Areas
###### Abstract  
Based on analysis of state-of-the-art research investigating target detection and recognition in turbid waters, and aiming to solve the problems encountered during target detection and the unique influences of turbidity areas, in this review, the main problem is divided into two areas: image degradation caused by the unique conditions of turbid water, and target recognition. Existing target recognition methods are divided into three modules: target detection based on deep learning methods, underwater image restoration and enhancement approaches, and underwater image processing methods based on polarization imaging technology and scattering. The relevant research results are analyzed in detail, and methods regarding image processing, target detection, and recognition in turbid water, and relevant datasets are summarized. The main scenarios in which underwater target detection and recognition technology are applied are listed, and the key problems that exist in the current technology are identified. Solutions and development directions are discussed. This work provides a reference for engineering tasks in underwater turbid areas and an outlook on the development of underwater intelligent sensing technology in the future.  
turbid water; underwater operation; image processing; target detection and recognition; intelligent sensing +
Footnote †: journal: Applied sciences  
_Applied_  
_sciences_
## 5 Results
### Knee Flexion Angle  
#### 5.1.1 Within-Participant Cross-Validation  
Table 1 shows the mean MAE and RMSE of knee flexion angles for all movements in within-participant cross-validation. Table 1 indicates that both the MAE and RMSE were smaller with Feature B, suggesting that when estimating knee flexion angles using the user's data, it is better to use instantaneous values and the past 20 samples of instantaneous values.  
To confirm the estimation accuracy of each machine learning algorithm, we compared the knee flexion angle estimation results using instantaneous values and the past 20 samples of instantaneous values. Figure 7 shows the within-participant cross-validation results for running for participant E using RFR and LSVR. In the analysis of Figure 7a,b, both algorithms did not significantly deviate in their estimations. However, based on the minimum values of the estimation results for each algorithm, the RFR had a higher accuracy for smaller knee flexion angles. Thus, for movements with significant knee flexion angle changes like running, the RFR is more suitable for estimating knee flexion angles.  
Figure 8 shows the within-participant cross-validation results for three consecutive jumps for participant E using the RFR and LSVR. In the analysis of Figure 8a,b, both algorithms did not significantly deviate in their estimations. In the analysis of the minimum values of the estimation results from different algorithms, the RFR had a higher accuracy for smaller knee flexion angles. Thus, for movements that impact the knee joint like jumps, the RFR is more suitable for estimating knee flexion angles.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**RFR a**} & \multicolumn{2}{c}{**LSVR b**} \\ \hline  & **Feature A** & **Feature B** & **Feature A** & **Feature B** \\ \hline MAE [\({}^{\circ}\)] & 4.38 & 3.33 & 4.93 & 4.75 \\ RMSE [\({}^{\circ}\)] & 5.76 & 4.33 & 5.96 & 5.71 \\ \hline \hline \end{tabular}  
* Random Forest Regressor. b Linear SVR.  
\end{table}
Table 1: Estimation errors of knee flexion angle in within-participant cross-validation.  
Figure 7: Within-participant cross-validation results for running for participant E.  
Figure 9 shows the within-participant cross-validation results for cutting for participant D using the RFR and LSVR. From the comparison of Figure 9,b, it is evident that the RFR accurately estimated both larger and smaller knee flexion angles. Thus, for movements with significant knee valgus angle changes like cutting, the RFR is more suitable for estimating knee flexion angles.  
The results of these three movements suggest that when estimating knee flexion angles using the user's data, it is better to use the RFR as the machine learning algorithm and instantaneous values and the past 20 samples of instantaneous values as features.
## 2 Materials and Methods
### Driving Circuit  
A fast galvanically decoupled driving circuit had to be introduced to enable MHz switching with sub-100 ns pulses. Therefore, we introduced a 3-stage, 8-channel driver including a microcontroller, the main driver (DRV0), and the respective drivers for MOSFETs (DRV1-4). The simplified block scheme is shown in Figure 2.  
The microcontroller was a 32 MHz XMEGA128A3U (Atmel, San Jose, CA, USA) operating at 3.3 V, which was driving the main 4-channel ADUM140D0 BRWZ driver (Analog Devices Inc., Norwood, MA, USA). This stage was introduced to make the driving signal 5 V and galvanically decoupled, ensuring better resistance to ground bouncing and electromagnetic noise. The DRV1-DRV4 were the ADUM4223 ARWZ (Analog Devices Inc., Norwood, MA, USA) operated at 15 V and limited by a gate resistance of 4.7 \(\Omega\). Connecting the microcontroller directly to DRV1-DRV4 was unsuccessful due to high electromagnetic noise.  
Figure 1: The pulse forming circuit of the electroporator, which is based on H-bridge circuit topology.