esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Computational Fluid Dynamics Simulations
#### 3.2.4. CFD Model Validation with Experiments
This difference can be attributed to the inherent assumptions and limitations of the NTC model used in the CFD simulations for modeling droplet dynamics. While the NTC model effectively captures the average droplet size, it may not fully represent certain physical processes involved in droplet formation and behavior. This partial representation could lead to a realistic estimation of the mean diameter, yet produce a variation in the droplet size distribution that differs from what is observed experimentally. This discrepancy underscores the complexity of accurately modeling droplet interactions and dynamics and highlights the need for careful consideration of model selection and parameterization in CFD simulations. It is interesting to note that when droplets are at a height (H0) of 0 mm, their diameter distribution can be adequately characterized by a log-normal curve. Nevertheless, the log-normal distribution eventually changes into a normal distribution as these droplets start to fall. This shift suggests that the processes of collision and coalescence are predominantly influenced by a normal probability function. The transition from an initial log-normal distribution of water droplets to a normal distribution as these droplets descend within a square tube over an increased height is the result of a complex interplay of various contributing factors. First and foremost, the process of coalescence comes into play as the droplets interact and collide within the confined space of the tube. This collision-induced merging and growth of droplets lead to a gradual shift in the distribution pattern, eventually aligning it with a normal distribution. One key factor that plays a crucial role in this transformation is the Weber number (We), a dimensionless number used in fluid dynamics to predict when the inertial forces of a fluid become dominant over its surface tension [42]. A Weber number significantly less than 1 (We \(\approx\) 1) signifies that surface tension forces are dominant, ensuring that the droplets remain intact throughout the CFD domain. When examining droplets through CFD simulations, it has been consistently observed that the Weber number remains below 0.14. This observation suggests that droplet breakup is not occurring due to the dominance of inertial forces. When the Weber number is less than 0.14, the inertial forces are typically not strong enough to overcome the cohesive forces holding the droplet together, thus preventing breakup. In summary, the complex interaction between gravity, air resistance, droplet collisions, and coalescence leads to the change of droplet sizes from an initial log-normal distribution to a normal distribution inside a square tube.
## 3 Materials and Methods
### Data Parsing and Final Dataset Preparation  
Once each LLM agent (VectorRAG, VectorGraphRAG, Fine-Tuned LLM) completed its assigned tasks, providing alignment scores, improved questions, and newly generated items, these outputs were consolidated into a single dataset. During this process, multiple versions of both the agent outputs and the meta-evaluation were created to refine quality and ensure final decisions were based on robust data:  
* **Agents1 (Version 1)--**VectorRAG with a top-20 retrieval setting, VectorGraphRAG with top-50, and Fine-Tuned LLM v1 (the initial fine-tuned model).
* **Agents2 (Version 2)--**VectorRAG with top-5 retrieval, VectorGraphRAG with top-20, and Fine-Tuned LLM v2 (the improved fine-tuned model).
* **Meta_auto--**The \"raw\" outputs of the meta-evaluator (LLM-based) when scoring Agents2 responses.
* **Meta_human--**The final ground-truth dataset after human experts supervised and corrected the meta-evaluator's judgments from Meta_auto.  
In the first version (Agents1) dataset, a preliminary human review of these outputs identified several issues with alignment, clarity, and difficulty, leading the team to conclude that they were insufficient for final analysis. To address these shortcomings, both the retrieval parameters and the fine-tuning process were refined. In the second version (Agents2) dataset, human experts conducted another review of the outputs and deemed them acceptable for statistical modeling and further study.  
Following the collection of Agents2 responses, a Meta Evaluator was employed to produce meta_auto dataset: initial \"ground-truth\" scores generated automatically by an LLM-based system. However, because the study implies rigorous human supervision to mitigate biases or factual inaccuracies, domain experts manually examined and corrected the meta_auto outputs, creating a final reference dataset known as meta_human.  
Consequently, meta_human represents the combined product of automated scoring and expert oversight.  
With Agents2 outputs serving as the primary agent-based dataset and meta_human as the authoritative set of ground-truth evaluations, several comparative analyses were performed. This multi-stage workflow ensured that each iteration of data collection and evaluation was systematically validated, ultimately enabling robust conclusions about agent performance, fairness, and real-world applicability of the proposed LLM-based exam-question framework.  
The final datasets (agents1, agents2, meta_auto, and meta_human) contain 3605 valid agent and meta responses, encompassing both original question evaluations and newly generated items.
## 2 Materials and Methods
### Chemical Analysis Methods  
Influent and effluent samples were regularly collected and filtered using a 0.22 \(\upmu\)m membrane filter (RephlQuik syringe filter, RephiLe Bioscience, Ltd., Boston, USA ). The nitrification trend was evaluated by measuring the concentrations of NH4+-N, NO2-N, and NO3-N. NH4+-N was analyzed using a kit (HS-NH3(N)-H, Humas Co., Daejeon, Republic of Korea) and a UV spectrometer (HS 1000, Humas Co., Daejeon, Republic of Korea), according to standard methods [28]. The concentrations of NO2-N and NO3-N were analyzed by ion chromatography (ICS-1000, Dionex, Sunnyvale, CA, USA) with a Dionex IonPac(tm) AS14 analytical column (4 \(\times\) 250 mm), and the eluent used was a 3.5 mM Na2 CO3/1.0 mM NaHCO3 solution with a flow rate of 1.2 L/min. Mixed liquor volatile suspended solids (MLVSSs) were measured according to standard methods [28]. The pH was measured using a pH meter (pH 33, Horiba Scientific, Kyoto, Japan), and DO concentration was measured using a DO meter (Pro 20i, YSI Inc., Yellow Spring, OH, USA).  
To evaluate and compare the nitrification efficiency of each reactor, the ammonium removal efficiency (ARE) and nitrite accumulation efficiency (NAE) were calculated using Equations (3) and (4). Additionally, the FA concentration was determined based on the equation proposed by [PERSON] et al., 1976 (Equation (5)) [22].  
\[\text{ARE }(\%)=\frac{\left(\text{NH}_{4}^{+}-\text{N}\right)_{\text{inf}}- \left(\text{NH}_{4}^{+}-\text{N}\right)_{\text{eff}}}{\left(\text{NH}_{4}^{+}- \text{N}\right)_{\text{inf.}}}\times 100 \tag{3}\]  
\[\text{NAE }(\%)=\frac{\left(\text{NO}_{2}^{-}-\text{N}\right)_{\text{eff}}}{ \left(\text{NO}_{2}^{-}-\text{N}\right)_{\text{eff}}+\left(\text{NO}_{3}^{-}- \text{N}\right)_{\text{eff}}}\times 100 \tag{4}\]  
\[\text{FA}_{\text{inf}}\Big{(}\frac{\text{mg}}{\text{L}}\Big{)}=\frac{17}{14} \times\frac{\left(\text{NH}_{4}^{+}-\text{N}\right)_{\text{inf}}\times 10^{ \text{pH}}}{\frac{6344}{\text{e}^{273+\text{i.e.}}\left(\text{CN}\right)}} \tag{5}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Operational Condition** & **PN1** & **PN2** \\ \hline Influuent NH4+-N (mg/L) & 50 & 300, 200, 100, 50 \\ Influuent FA (mg/L) & 10 & 50, 35, 18, 10 \\ HRT (h) & 5-7.5 & 5-10 \\ DO (mg/L) & & \textless{0.5} \\ pH & & 8.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Operational conditions of nitritation in PN1 and PN2.  
where (NH4\({}^{+}\)-N)\({}_{\rm inf}\) is the influent NH4\({}^{+}\)-N concentration, while (NH4\({}^{+}\)-N)\({}_{\rm eff}\), (NO2\({}^{-}\)-N)\({}_{\rm eff}\), and (NO3\({}^{-}\)-N)\({}_{\rm eff}\) are the effluent concentrations of NH4\({}^{+}\)-N, NO2\({}^{-}\)-N, and NO3\({}^{-}\)-N, respectively.
# Event Detection and Spatio-temporal Analysis of Low-Altitude Unstable Approach
## 2 Methodology  
### Data  
A database station was constructed by the China Academy of Civil Aviation Science and Technology to collect all the QAR data of domestic flights for flight operations quality assurance (FOQA). The conduction of a wide range of flight risk studies with this QAR data is important for promoting civil aviation safety. In total, QAR data of over 3500 aircraft or airlines are aggregated daily into the base station, and up to 2000 parameters are recorded in the data records, including the time, latitude and longitude, flight altitude, flight speed, wind speed, temperature, ground proximity warnings, and flight attitude. These parameters comprehensively reflect the characteristics of a flight by recording its time, space, pilot control, and engine performance. Most of the parameters are recorded every second, while some sensitive parameters are recorded as often as every 1/8 second. QAR data can be widely applied in condition monitoring, fault diagnosis, flight quality assessment, flight process visualization, mechanics maintenance, and fuel consumption control.  
In this work, QAR data collected from more than 10,000 flights from 1 January to 30 June 2018 were utilized. The UA events were explored with the proposed method, and it was found that approximately 5000 flights seemed to be affected by UA. Moreover, the spatio-temporal patterns of UA events during January 2018 were explored via ESDA techniques, as well.
# Attention-Gate-Based Model with Inception-like Block for Single-Image Dehazing
## 4 Experiments and Analysis
### Parameters of Neural Network  
The input image specification of the dehazing CNN is \(12\times 256\times 256\) after resizing the original image. An image pre-processing block is used to obtain a pre-processed image with 12 channels. The internal parameters of the proposed image-dehazing CNN are listed in Table 1, including five inception-like blocks, two attention gates, two downsamplings, and two upsamplings. The dehazing output image is an RGB image; therefore, there are three convolutions at the end of the neural network.  
The size of the trainable parameters of the proposed image-dehazing CNN affects the time required for training and inference speed. Although a larger number of trainable parameters can improve the image-dehazing quality, the number of trainable parameters should not be too large for real-time dehazing. Table 2 shows a comparison of the number of trainable parameters with the state-of-the-art model. Although FFA-Net [12] has outstanding dehazing quality, it has too many trainable parameters to meet a lightweight model and will not be applied in the proposed approach.  
\begin{table}
\begin{tabular}{c c c} \hline
**Blocks** & **Layers** & **Num** \\ \hline \multirow{4}{*}{Inception-like block 1 and 5} & Conv2D(3,3) 1  & 8 \\  & Conv2D(3,3) & 8 \\  & Conv2D(3,3) & 8 \\  & Conv2D(3,3) & 8 \\  & Conv2D(1,1) & 32 \\ \hline \multirow{4}{*}{Inception-like block 2 and 4} & Conv2D(3,3) & 16 \\  & Conv2D(3,3) & 16 \\  & Conv2D(3,3) & 16 \\  & Conv2D(1,1) & 64 \\ \hline \multirow{4}{*}{Inception-like block 3} & Conv2D(3,3) & 32 \\  & Conv2D(3,3) & 32 \\  & Conv2D(3,3) & 32 \\  & Conv2D(3,3) & 32 \\  & Conv2D(1,1) & 128 \\ \hline \multirow{4}{*}{Pyramid pooling block} & Conv2D(1,1) & 32 \\  & Conv2D(1,1) & 32 \\  & Conv2D(1,1) & 32 \\  & Conv2D(1,1) & 32 \\ \hline \multirow{4}{*}{Attention gate 1} & Conv2D(1,1) & 32 \\  & Conv2D(1,1) & 32 \\  & Conv2D(1,1) & 32 \\  & Conv2D(1,1) & 32 \\ \hline \multirow{4}{*}{Attention gate 2} & Conv2D(1,1) & 64 \\  & Conv2D(1,1) & 64 \\  & Conv2D(1,1) & 64 \\ \hline Downsampling 1 & Conv2D(2,2) & 32 \\ \hline Downsampling 2 & Conv2D(2,2) & 64 \\ \hline Upsampling 1 & ConvT2D(2,2) 2 & 32 \\ \hline Upsampling 2 & ConvT2D(2,2) & 64 \\ \hline Final convolution & Conv2D(1,1) & 3 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of proposed network.
# A Single Wearable Sensor for Gait Analysis in Parkinson's Disease: A Preliminary Study
## 3 Experimental Protocol
### Statistical Analysis  
In order to evaluate the performance of the proposed algorithm, a statistical analysis of the tasks carried out in laboratory and home scenarios was performed. In the tasks of the laboratory scenario the performance of the proposed algorithm was evaluated in terms of absolute _Percentage error_, _Sensitivity_ and _Specificity_. These metrics were computed as follows:  
\[Percentage\ error=\frac{|N_{A}-N_{V}|}{N_{V}}*100 \tag{5}\]  
\[Sensitivity=\frac{TP}{TP+FN} \tag{6}\]  
\[Specificity=\frac{TN}{FP+TN}, \tag{7}\]  
where \(N_{A}\) is the value count estimated by the algorithm, \(N_{V}\) is the total count number of Step (S) or Bout (B) obtained by the video recording, \(TP\) is the True Positives, \(TN\) is the True Negatives, \(FP\) is the False Positives, and \(FN\) is the False Negatives.  
Subsequently, we tested the algorithm in the home scenario, during the daily living activity in an unsupervised environment. In this scenario, Bland-Altman plots were adopted to check for nonlinear distributions of error between the video recording and the proposed algorithm. In particular, Bland-Altman plots were used to assess the agreement between the measures obtained from algorithm and the videos, where the difference (\(D\)) and the average (\(M\)) were computed as [34]:  
\[D=N_{A}-N_{V} \tag{8}\]  
Figure 6: Laboratory trial: (**a**) Timed Up and Go (TUG) task; (**b**) Stair task; (**c**) Free walking task.  
\[M=\frac{N_{A}+N_{V}}{2}. \tag{9}\]
###### Abstract
This paper reviews the utilization of a non-linear system identification technique called parallel cascade identification (PCI) to improve the overall navigation solution by modeling errors at the sensor and measurement level.
# A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data
## 2 Approaches to Modeling, Determining, and Mitigating Errors in MLS Point Clouds
### Correction  
As mentioned in the Introduction, random and systematic errors are unavoidable in MLS point clouds under certain circumstances. Therefore, many researchers have tried to eliminate them as much as possible during post-processing. Ref. [28] presented an approach to compensating for height errors in airborne laser scanning data. The idea is to correct the heights of a scan strip using an estimated algebraic surface. They calculated such a correction surface using tie points in overlapping scan strips and control points. [PERSON]. [29] demonstrated a method to correct trajectory errors of land-based MLS in the absence of GNSS satellite visibility using GCPs. They extracted the time of sampling of the control point and introduced the difference vector as an observation update in the trajectory filtering at that GNSS time. According to their results, the position error within a tunnel does not exceed 5 cm, provided that GCPs are available every 400 m. [PERSON]. [30] introduced the so-called multi-pass approach. The motivation is also to minimize the number of control points. They manually digitized line features in point clouds that occurred in multiple passages of the same scene. These control lines were used on the one hand to evaluate positioning errors and, on the other hand, to average the height of multiple point clouds. The estimated trajectory error of each pass determines the weight for averaging. The authors argued that employing the multi-pass strategy reduces time and costs compared to the multi-target approach.  
Other freely available sources such as 3D city and terrain models are also suitable for removing systematic errors. The combination of these two provides a three-dimensionalreference for MLS point clouds. For example, [PERSON]. [31] registered their recorded MLS point clouds to city models using ICP. To account for the changing positioning accuracy along the path, they subdivided the point cloud into sections and performed the calculations section-wise. They avoid discontinuities by transforming each point with multiple sets of parameters and computing a weighted average.  
These models are an abstraction of reality, in which building facades are vertical and streets are perfectly flat. [PERSON]. [32] made similar assumptions without actually using city models. Namely, they introduced parallelism and orthogonality constraints for the detected planes in their point cloud adjustment procedure. They transformed individual profiles and estimated the plane parameters simultaneously, following the example of [15].
## 2 Theory and Formulation
### Vibration Study  
The vibration studies can be divided into two parts. First, we studied the influence of different parameters (such as the geometric ratios of the plate, distribution patterns of CNT, volume fraction of carbon fibers, weight fraction of CNT, temperature gradient, etc.) on the natural frequencies of the MHCAP. Their effect on the vibration performance of the system was important for the design of such structures. Then, the sensitivity of the vibration response to nonlinear temperature gradients are presented and discussed.
## 3 Parameter Optimization of PRPS and CGPR
### Numerical Model  
The longitudinal length, the width, and the height of this model were 100, 250, and 200 m, respectively (Figure 9). The tunnel lining was simulated as shell elements. The pipe roof and the surrounding rock were modeled using solid elements. The very fine mesh was used to accurately simulate the pipe roof and the neighboring grouting rock mass [25]. This model was initially built in the MIDAS (a finite element analysis software) program, then the mesh information data were input into the FLAC 3D program. The bottom of the model was constrained vertically, whereas four sides were fixed horizontally. The middle section of the model along the longitudinal direction (i.e., y = 50 m) was selected for analysis.  
The ratio of horizontal to vertical in situ stress was set to 1.8 by referring to the geotechnical report. According to real circumstance, the excavation sequence in the numerical simulation was Left-Right-Middle, each tunnel was divided into three parts, each excavation circle was 2 m, and the tunnel lining was installed immediately after excavation. The top heading face was 10 m ahead of the bench face, and the invert face was 10 m behind the bench face. The lagging distance between the left tunnel and right tunnel was set to 30 m, which was the same as that between the right tunnel and the middle tunnel. The advanced lengths of the PRPS and the CGPR were set to 40 m and 15 m, respectively.  
Figure 9: Three-dimensional finite element model of triple tunnels.  
The mechanical behavior of the surrounding rock was simulated with the Mohr-Coulomb failure criterion. The reinforcement effect of CGPR was simulated by increasing the mechanical parameters of the rock mass in the reinforced zone. The physical and mechanical properties of each material are listed in Table 2.
# Route Risk Index for Autonomous Trucks
## 4 Results
### Risk Factor Relationships  
The histograms along the diagonal plots of Figure 6 show the frequency distributions of each variable, normalized to fit the same scale for comparison. The AADT distribution is right-skewed, indicating a higher frequency of lower traffic volumes. The IRI distribution is also right-skewed, indicating a higher frequency of lower smoother interstate segments. The distribution for fatalities is heavily right-skewed, with most data points toward the lower end, indicating fewer fatalities are more common. The precipitation data show a peak near the center of the distribution, indicating that the precipitation of most interstate segments is near the average, but a minority have either low or high precipitation levels. One caution in interpreting these results is that they do not account for possible confounding variables. Furthermore, correlation does not imply causation, and the relationships observed here would need further analysis to establish any causal links.  
Figure 7 is a histogram overlaid with a Gaussian (normal) probability density function. The histogram represents the observed distribution of the composite risk index (Risk Index), while the red curve indicates the estimated normal distribution based on the data. The inset provides key statistics including the mean (1.023) and the standard deviation (STD), which is 0.322. The histogram shows the frequency of the risk index in terms of density, with the x-axis representing the risk index value and the y-axis representing the density of these values. The distribution is symmetric around the mean, which aligns with the assumption of normality. The formal test for normality evaluated the Kolmogorov-Smirnov (KS) statistic (0.023), which is a measure of the maximum distance between the observed cumulative distribution and the expected cumulative distribution. The KS test is preferable when assessing the similarity between distributions without making assumptions about their underlying shapes or parameters [30]. The null hypothesis is that the data are normally distributed. The \(p\)-value (0.941) of the test indicates the probability of observing the KS statistic (or one more extreme) under the null hypothesis. The high \(p\)-value suggests that the test cannot reject the null hypothesis, implying that the distribution of the composite risk index does not significantly differ from a normal distribution. Hence, the interpretation is that risks associated with interstate routes are normally distributed. This facilitates the categorization of risks at the standard deviation boundaries. For example, a risk index of less than or greater than one standard deviation, respectively, can represent low and high risk, respectively, whereas risk indices within one standard deviation can represent medium risk.
# Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest
## 2 Materials and Methods  
### Materials  
#### 2.1.1 FY-4B/AGRI Levell Data  
FY-4B is the first operational satellite of FY-4. The Advanced Geostationary Radiation Imager (AGRI) is one of the main payloads of the FY-4B. FY-4B/AGRI has four full-disk observations per hour and ninety-five full-disk observations per day (at 14:15 UTC, the satellite undergoes maintenance, so no observations are made). FY-4B/AGRI has a total of fifteen channels covering a wavelength range of 0.45\(\sim\)13.6 \(\upmu\)m, including six visible/near-infrared channels (1-6 channels) and nine infrared channels (7-15 channels), as shown in Table 1. The range of FY-4B/AGRI Levell data used in this study is 73\({}^{\circ}\)E-135\({}^{\circ}\)E, 3\({}^{\circ}\)N-60\({}^{\circ}\)N (including Chinese mainland and adjacent to the Bohai Sea, the Yellow Sea, the East China Sea and the South China Sea on the edge of Chinese mainland).