esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Bio-2 FA-IoD Protocol
### Authentication and Key Exchange Phase  
This phase (illustrated in Figure 3) describes how the operator \(U_{O}\) (via their OD) authenticates to the GCS, potentially using a drone \(D_{i}\) as an intermediary communication link.  
1. **Operator** biometric input and credential activation (\(U_{O}\to OD\)): * The operator \(U_{O}\) provides a fresh biometric sample \(\beta^{\prime}_{O}\) to the biometric sensor on their OD. * The OD uses the stored helper data \(HD_{O}\) to reconstruct the biometric key: \(K^{\prime}_{BO}=Rec(\beta^{\prime}_{O},HD_{O})\)[12]. If reconstruction fails, the process aborts. * Operator \(U_{O}\) enters \(P_{ID_{O}}\). The OD regenerates \(K_{O}=KDF(P_{ID_{O}},Salt_{2})\). * The OD decrypts \(RP_{ID_{O}}=D_{K_{O}}(C_{RP})\). If decryption fails, the process aborts.
2. **OD--**OTAC generation: * The OD generates current timestamp \(T_{ID}\) and a fresh random nonce PIN.  
Figure 2: Bio-2 FA-IoD—registration phase. This diagram illustrates the physical interpretation of interactions between the operator (\(U_{O}\)), operator’s device (OD), and trusted authority (TA) for initial setup and credential provisioning. It depicts the flow of biometric enrollment, key generation, and secure provisioning of secrets to the OD.  
* OD generates ephemeral session key \(KS=KDF(P_{ID_{O}},T_{ID})\).
* OD computes \(OTAC=E_{KS}(PIN\|T_{ID}\|RP_{ID_{O}})\).
* **Authentication** request (\(OD\to D_{i}\to GCS\)):
* OD sends \(M_{1}=(ID_{O},OTAC,T_{ID})\) to drone \(D_{i}\).
* Drone \(D_{i}\) forwards \(M_{1}\) to the GCS.
* **GCS**--verification process:
* Upon receiving \(M_{1}\), GCS retrieves \((RPMD_{ID_{O}},Salt_{1},LLT_{O})\) for \(ID_{O}\).
* GCS computes \(KS_{GCS}=KDF(P_{ID_{O}},T_{ID})\).
* Decrypts \((PIN^{\prime},T^{\prime}_{ID},RP^{\prime}_{ID_{O}})=D_{KS_{GCS}}(OTAC)\).
* Verifies \(H(RP^{\prime}_{ID_{O}}\|Salt_{1})==RPMD_{ID_{O}}\).
* Verifies \(T^{\prime}_{ID}==T_{ID}\).
* Verifies \(T_{ID}>LLT_{O}\) and \(T_{current\_ GCS}-T_{ID}\leq\Delta_{t}\).  
* authentication response (\(GCS\to D_{i}\to OD\)):
* If all checks pass, GCS updates \(LLT_{O}=T_{ID}\), sends \(M_{2}=(PIN^{\prime})\) to \(D_{i}\).
* Drone \(D_{i}\) forwards \(M_{2}\) to the OD.
* **OD**--final verification (mutual authentication):
* OD receives \(M_{2}\) containing \(PIN^{\prime}\).
* OD compares \(PIN^{\prime}\) with its original \(PIN\). If match, GCS is authenticated.  
Figure 3: Bio-2 FA-IoD—authentication and key exchange phase. This diagram illustrates the physical interpretation of interactions between the operator (\(U_{O}\)), OD, drone (\(D_{i}\)), and ground control station (GCS) for mutual authentication. It visually represents the sequence of biometric input, credential activation, OTAC generation, message exchange, and verification steps across the physical entities.
# A Fuzzy-Logic-Based Load Balancing Scheme for a Satellite-Terrestrial Integrated Network
## 3 Results  
In this section, the simulation scenario is presented and parameters are set to train the networks both by the proposed scheme and several typical algorithms, including DDPG, proximal policy optimization (PPO) [32] and the adaptive multi-RAT mobile offloading algorithm (AMMO) [20]. Simulation results show that compared with baseline schemes, the proposed scheme solves the overloading problem in STIN more effectively.
# High Throughput and Acceptance Ratio Multipath Routing Algorithm in Cognitive Wireless Mesh Network
## 6 Simulation and Results
### Comparison Average Throughput  
The average throughputs with different numbers of requests are shown in Figure 2. From Figure 2, we can see that the average throughput decreased with the increments of the number of requests for the three algorithms. In the case that network resources were unchanged, this was because the number of wireless requests increased, therefore increasing the probability of link failure. The average throughput of the MRIR was higher than the Route-Stability and Route-Closeness. This was because the MRIR can save network resources based on channel interference and reusability for accepting more wireless requests, which can improve the average throughput for the MRIR. When the wireless request was \(|\Delta|\) = 40, the average throughput of MRIR was 33% and 35% higher than that of the Route-Stability and Route-Closeness.  
From Figure 3, we can also see that the average throughput increased with the increment of the number of available channels for the three algorithms. This was because the probability of link failure was reduced when the available channel increased. When the number of available channels was \(|K|\) = 6, the average throughput of the MRIR was 46% and 51% higher than that of the Route-Stability and Route-Closeness.
## 2 Numerical Modeling
### Model Validation
#### 2.4.2 A Highway Embankment in Shanghai  
The piled highway embankment is located in a northern suburb of Shanghai, China. The embankment was 5.6 m high with a 1 V:1.5 H side slope. The fill material consisted mainly of pulverized fuel ash with an average unit weight of 18.5 kN/m\({}^{3}\). A profile of the embankment is shown in Figure 6. The embankment is supported by rigid floating piles with 1 m diameter and 16 m length. The Young's modulus of the pile is 20 GPa, and Poisson's ratio is 0.2. The pile ends in a medium-stiff sandy silt stratum. The material parameters for each soil stratum can be found in [PERSON] et al. (2018).  
Figure 4: Settlements on the top of the pile and subsoil surface.  
Figure 5: Vertical stress acting on the top of the pile and subsoil over time.  
Field experiments were conducted for 125 days after the completion of embankment construction (55 days). Figure 7 shows the vertical stress acting on top of the pile and the subsoil obtained from on-site soil pressure cells, along with the corresponding results from finite element calculations. The embankment load is also included in the figure for reference. It has been found that the actual stress acting on the subsoil is significantly reduced. When the embankment construction was completed (with an embankment load of 104 kPa), the stress acting on the subsoil was 31 kPa, which is only 30% of the embankment load, whereas the load above the pile top at this time was 553 kPa, 5.3 times the embankment load. The finite element results exhibit good agreement with the actual vertical stress at the soil surface, albeit slightly overestimating the vertical stress at the pile top, with a maximum error of 18.6%.  
Figure 8 shows the pore pressure at depths of 4 m and 8 m below the interface between the embankment and the subsoil at the center line of the embankment. It has been found that the finite element results capture the overall trend of the pore pressure, with a maximum error of 2 kPa. As expected, both the measured and finite element results illustrate that the pore pressure gradually increases due to an additional load of 104 kPa applied in the embankment construction and subsequently decrease during the consolidation phase after construction until reaching a stable state.  
Figure 6: Cross section of the embankment.  
Figure 7: Vertical stress acting on the top of the pile and subsoil at the center line of the embankment.  
In general, the maximum error between the finite element results and field measurements is less than 20%, indicating that the finite element modeling method is suitable for simulating embankments supported by rigid floating piles.
## 1 Introduction
Implementation of GPSTPS policy contributes to improving the supply efficiency of S&T public services and stimulating the vitality of regional innovation. However, as an important S&T policy tool, does GPSTPS policy effectively promote the regional S&T innovation ability? For this question, there is still little convincing empirical research, and the current paper tries to answer it scientifically. Considering the phenomenon of policy diffusion in GPSTPS, the current research takes spatial dependence and correlation into account. Based on the panel data of 30 provinces in China from 2008 to 2017, this paper constructs the spatial econometric model, so as to empirically analyze the driving effect and spatial spillover effect of GPSTPS on the regional S&T innovation ability, and further explore the regional differences of above effects. Different from the previous studies, the main contributions of this paper are as follows. 1. This paper explores the impact and the spatial spillover effect of GPSTPS policy on the regional S&T innovation ability in China. At present, most studies assume that there is no spatial dependence between different regions, which greatly reduces the credibility of the conclusions. The current research adopts the spatial econometric model and explores the influence of GPSTPS policy on regional S&T innovation ability, considering the spatial spillover effect and regional regression differences. 2. To investigate the relationship between GPSTPS policy and regional S&T innovation ability, this paper measures GPSTPS policy from the perspective of policy texts. The policy modeling consistency (PMC) index model, a prevailing policy evaluation method, is creatively applied to GPSTPS policy. By constructing a PMC evaluation index system and PMC evaluation model of GPSTPS policy, the quantitative score, which is the PMC score of GPSTPS policy, can be obtained. Taking the evaluation score of GPSTPS policy at each region as the core explanatory variable, this paper constructs a spatial econometric model to empirically analyze the effect of GPSTPS policy on regional S&T innovation ability. The rest of this paper is organized as follows. Section 2 is a literature review and mechanism analysis, which introduces the research objects by reviewing literature. Section 3 introduces the research methods and materials, and variables are determined in this part. A spatial econometric model is selected and constructed in Section 4. We discuss the results in Section 5, which includes the regression analysis of spatial econometric model, subregional heterogeneity analysis and analysis of spatial spillover effect. Section 6 is a conclusion.
## 5 Results
### Model Sensitivity  
As mentioned above, the sensitivity analysis of the model involved adding a noise vector with a given standard deviation to each test record and then assessing the prediction accuracy obtained on the test set. The results, presented graphically in Figure 9, indicate that the addition of noise with a standard deviation of 0.05 (5%) causes a minimal decrease in the prediction precision for both the Failure 2 and Failure 3 datasets. For the Failure 3 dataset, a significant drop in accuracy is observed for \(\sigma>0.05\), and for \(\sigma>0.1\), the decline is linear. In contrast, for the Failure 2 dataset, where the size of the extent of valve plate damage is similar to the Failure 1 data, a significant deterioration is observed for \(\sigma>0.1\), with a linear decline in accuracy occurring for \(\sigma>0.15\). It is also noteworthy that, despite adding noise at a level of 0.25 (25%), the results obtained for Failure 3 were higher than those obtained for the Failure 2 dataset without any noise. In conclusion, considering the measurement errors of the sensors used in the study, as shown in Table 10, the developed model should behave stably, and measurement errors should not affect the quality of the predictions.  
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**Pos.** & **Description** & **Symbol** & **Accuracy** \\ \hline
1 & Temperature sensor Pt1000 \(-\)50...150 C & TA2105 & 0.1\% FS \(\pm\) 3 K \\
2 & Pressure sensor \(-\)1...1 bar & PA3509 & 0.8\% FS \\
3 & Pressure sensor 400 bar & PT5400 & 1.05\% FS \\
4 & Turbine flow meter 1...15 L/min & PPC-04/12-SFM-015 & 1\% FS \\
5 & Pressure sensor 10 bar & PT5404 & 1.05\% FS \\
6 & Gear wheel flow meter 1...250 L/min & DZR-1015 S25 & 0.3\% FS \\
7 & Torque meter 1 kNm & T22/1 kNM & 0.5\% FS \\
8 & Pressure sensor 250 bar & PT5401 & 1.05\% FS \\ \hline \hline \end{tabular}
\end{table}
Table 10: Sensors accuracy. FS = full scale.  
Figure 8: Feature importance plot obtained for Failure 2 and Failure 3 datasets; (a) Failure 2; (b) Failure 3.
# Traffic Conflict Prediction for Sharp Turns on Mountain Roads Based on Driver Behavior Patterns
## 3 Results
### Driving Behavior Characteristics Analysis Results
#### 3.1.4 Steering Characteristics  
The ability of drivers to adjust to curve features and their accuracy in path tracking is indicated by the steering characteristics. We studied the 19,860 dissections of the simulation data while concentrating on the steering wheel angle and the turn-on angle or steering rate. The characteristics of the steering wheel angle and the steering rate of drivers on sharp turns were analyzed, and the steering control index is shown in Table 6, indicating that the average steering angle and steering rate of drivers in curve sections are significantly higher than those in straight sections, and the degree of dispersion of steering actions is  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Unit** & **Straight Section** & **Curved Section** \\ \hline Average headway & s & 2.15 & 1.38 \\ Standard deviation of headway & s & 0.92 & 0.75 \\ Minimum front time & s & 0.85 & 0.47 \\ Hazardous headway ratio * & \% & 12 & 28 \\ \hline \hline \end{tabular}  
* Note: Hazardous headway ratio refers to the percentage of time headways are less than 1.0 s.  
\end{table}
Table 5: Statistical indicators of time headway in different segments.  
also significantly increased, reflecting more frequent and intense steering behaviors. The steering risk index was constructed to characterize the stability of steering behavior:  
\[SRI=\frac{\sigma_{\delta}}{R} \tag{8}\]  
where SRI is the steering risk index (deg/m), \(\sigma_{\delta}\) is the standard deviation of the steering angle (deg), and R is the radius of the bend (m). The greater the steering risk index, the more unstable the steering action of the driver and the lower the path-tracking accuracy. Based on the steering risk index, a steering ability evaluation model was proposed:  
\[SC=e^{-0.24 SRI} \tag{9}\]  
where SC is the steering ability score (0-1); the higher the score, the stronger the steering ability of the driver. This model can be used to evaluate the driver's ability to adapt to different cornering alignments.
## 3 Results and Discussions
### Reactivity Insertion Accident
#### 3.1.2 Protected Reactivity Insertion  
After a serious accident occurs, the shutdown protection system is activated so that the reactor can quickly return to a safe state. For the CNES reactor, the safety control rod is quickly inserted to shut down the reactor. This process takes about 1\(\sim\)2 s. The ADS reactor could be shut down by cutting off the proton beam to interrupt the external neutron source in the subcritical core. The time required from detecting the accident information to cutting off the beam is only about 1 ms, which is faster than the activation of the mechanical device of shutdown rod.  
The transient responses of protected reactivity insertion for ADS and CNES are shown in Figure 9, in which the positive reactivity is inserted at 5 s and then the shutdown protection system is activated at 12 s. For the ADS reactor, it can be seen that the power and the temperatures of fuel, cladding, and coolant sharply decrease to the safety level after the proton accelerator is closed. After introducing 700 pcm reactivity for the CNES reactor, the prompt supercritical accident occurs, and then the power and the fuel temperature rise sharply to very high values. With the rapid insertion of the safety control rod, the power declines to the shutdown level, and the peak temperatures of the fuel center and the cladding surface are 2552 K and 1035 K, respectively. It can be seen from Figure 9c that  
Figure 8: Transient responses of unprotected reactivity insertion of 700 pcm between the ADS core and the CNES core.  
the cladding still has a short-term rupture failure risk in the CNES reactor during this transient state.
## 3 Results
### Spatiotemporal Changes in the VMFDI  
#### 3.2.1 The Spatiotemporal Heterogeneity of the VMFDI in the Yellow River Basin  
Figures 5 and 7 show the monthly spatiotemporal patterns and variations in the VMFDI of the Yellow River Basin from January 2003 to December 2020. The results in Figure 5 show that the mean VMFDI value was 0.861 \(\pm\) 0.07 (mean \(\pm\) standard deviation) and fluctuated annually, with the largest value occurring in August and the smallest value in March. Spatially, the multiyear average monthly VMFDI in the southern region of the middle and lower reaches of the Yellow River Basin is always lower than that in other regions, and the low-value areas shifted northward with seasonal changes (Figure 7a). This indicates that the middle and lower reaches of the Yellow River Basin were relatively dry and drought-prone. From 2003 to 2020, VMFDI values from November to March showed a decreasing trend over time, while those from April to October showed an increasing trend (Figure 7a). This suggests that the Yellow River Basin becomes drier in winter and wetter in spring, summer, and autumn. Specifically, the areas where the VMFDI decreased over time accounted for more than 80% of the total area in winter, with areas showing significant decreases (\(p<0.05\)), accounting for 23.72-40.65% of the total area (Figure 7b,c). The regions with significant decreases were mainly in Inner Mongolia and Shanxi, Shaanxi, and Henan provinces (Figure 7c). From March to June and September, the areas where the VMFDI increased significantly (\(p<0.05\)) over time covered more than 15.6% of the total area, primarily in parts of the regions of Qinghai, Gansu, and Shaanxi provinces. Figure 7 also shows that the VMFDI decreased over time in regions with initially lower VMFDI values, while it increased in regions with higher VMFDI values. This indicates that drier areas are becoming drier, while wetter areas are becoming wetter in the Yellow River Basin.  
Figure 7: _Cont._
# Research on Rapid and Accurate 3D Reconstruction Algorithms Based on Multi-View Images
## 4 Results
### Experimental Results and Analysis
#### 4.2.1 Improving the Effectiveness Analysis of the RANSAC Algorithm  
The RANSAC algorithm's performance in research and use is vulnerable to outliers and noise interference found in high-resolution datasets. Furthermore, the fixed threshold mechanism has inherent shortcomings, including a high rate of misclassification and a heavy reliance on empirical data. In order to overcome these problems, this study suggests an adaptive threshold adjustment technique based on statistical features. By using dynamic threshold settings, the possibility of incorrectly classifying inliers is decreased, improving the model's resilience to noise. The RANSAC optimization algorithm has a threshold range of 10 to 100, a threshold increment of 5, a threshold initialization of 30, and a total of 100 iterations.  
Using the Statue1 dataset as a reference, Figure 11 illustrates the effectiveness of the proposed algorithm in this study.  
Figure 11a displays initial matching results using SIFT features and KD-Tree descriptors (green lines), revealing mismatches caused by endpoint misalignment. Figure 11b demonstrates the optimized results with our improved RANSAC algorithm: green lines represent matches under original thresholds, while red lines indicate additional matches from adaptive threshold adjustment. This highlights the algorithm's enhanced robustness through increased valid matching pairs.  
The quantitative results of the detailed autonomous threshold adjustment RANSAC algorithm are presented in Table 3.  
Compared to fixed-threshold RANSAC (threshold = 40), the proposed adaptive threshold algorithm dynamically adjusts parameters (e.g., Statue1 = 50, Archi1 = 65), significantly enhancing feature-matching performance. Experimental results show: statue datasets (Statue1/Statue2) achieve 57.0%-80.7% growth in matched pairs, while architectural datasets (Archi1/Archi2) exhibit 45.2%-71.1% increases. Additionally, dynamic parameter optimization improves inlier selection accuracy by 23.6% (\(p<0.01\)), effectively overcoming empirical threshold limitations, particularly in complex scenarios with significant resolution and noise variability.  
Figure 11: Analysis of the Effectiveness of Improved RANSAC Matching Result Optimization Algorithms under Self-Collected Data for Statue1: (**a**) Feature matching results without RANSAC optimization; (**b**) The feature matching results optimized through the improved RANSAC algorithm The green line represents the optimization results of the original RANSAC algorithm, while the red line indicates the increase in the number of matched point pairs after optimization using the proposed algorithm in this paper.
## 2 Materials and Methods
### Data Variability  
Coefficients of variation (CVs) were calculated for the number of protein groups, peptide sequences, and peptide spectrum matches (PSM) to assess the repeatability of the replicate results. In analyses utilizing Mascot, the score variances exhibited CVs ranging from 3.6% to 21.4% for protein groups, 1.6% to 24.8% for peptides, and 1.2% to 21.5% for PSM. A similar range of score variance was observed for data processed by PEAKS, with CVs between 2.9% and 20.3% for protein groups, 3.8% to 21.3% for peptides, and 3.6% to 20.1% for PSM. The median CV values for PSM were less than 12% for Mascot data and less than 14% for PEAKS data (Figure 5).  
Figure 4: Peak capacity calculation results (**a**) and comparison of increments (in %) of protein identifications after a change in frequency, output file extension, gradient time, or the DLboost feature (**b**). Increments were calculated using the formula: (\(\rm{N_{k}-N_{l}}\))/\(\rm{N_{l}}\)\(\times\) 100. N is the mean number of identified protein groups; **k—**parameter after changing, e.g., higher frequency (B or D), d file extension, longer gradient time (C or D), DL boost feature enabled; **l—**parameter before changing, e.g., lower frequency (A or C), mgf file extension, shorter gradient time (A or B), DL boost feature disabled. Error bars are standard deviations of data from three biological replicates in (**a**) and pairwise comparison of 8 mean numbers of protein groups (**b**).  
Figure 5: Box plots showing the CVs of PSM for methods A, B, C, and D (Mascot is red-colored, PEAKS Studio 11 is blue-colored. Diagrams were created using OriginPro 2024b software (version 10.1.5.132).  
Variations in peptide content between biological replicates may contribute to the observed increase in variation. Additionally, unfavorable co-elution of peptides in specific column-gradient-sample combinations could occur, leading to interference effects, such as charge competition during ionization, which may affect MS/MS selection periods. Notably, the highest CVs were consistently associated with the shorter 48 min gradient, while these values decreased with longer gradient lengths and higher MS acquisition frequencies. The median CV for protein groups was 13% for method A, whereas it was less than 9% for methods B, C, and D. A similar trend was observed for the number of peptide sequences, with median CVs of 15% for method A and less than 8% for methods B, C, and D, which aligns with results previously published by [PERSON] et al. [20].
## 5 Data Preparation and Filtering
### Filtering of Raw Operational Data Techniques  
Several methods could be utilized to remove the errors as presented in the previous sections. Some of the commonly used filtering techniques are given in the following sections. The pros and cons for each technique are summarized at the end of this section in Table 2.