esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Parallel Computing on MPI Version PHITS Code
## 3 Results
### Computational Simulation Evaluation
The same magnification was utilized for 15 mm, 50 mm, 100 mm, and 200 mm. There are two criterion values for the PDD value, \(\delta_{1}\) and \(\delta_{2}\). Both evaluated data met the reference value. Specifically, evaluation of the fall-off region after the build-up area, \(\delta_{1}\), was 2.0% and 1.1% for the 4 cm \(\times\) 4 cm and 10 cm \(\times\) 10 cm field sizes, respectively. The  
Figure 4: Superimposed beam profiles of measured and simulated values for Varian Clinac 6 MV: the in-plane was evaluated by dividing by field size of 5 mm, 50 mm, 100 mm, and 200 mm depths (4 cm \(\times\) 4 cm, 10 cm \(\times\) 10 cm). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**In-Plane**} & \multicolumn{3}{c}{**Cross-Plane**} & \multicolumn{3}{c}{**Reference Value**} \\ \cline{2-10}  & **15 mm** & **50 mm** & **100 mm** & **200 mm** & **15 mm** & **50 mm** & **100 mm** & **200 mm** \\ \hline \(\delta_{2}\) & 22.3\% & 19.4\% & 5.7\% & 13.9\% & 6.0\% & 12.5\% & 11.2\% & 5.3\% & 3\% \\ \(\delta_{3}\) & 0.5\% & 0.9\% & 0.5\% & 0.5\% & 0.6\% & 0.9\% & 0.8\% & 0.5\% & 2\% \\ \(\delta_{50-90}\) & 2 mm & 1 mm & 2 mm & 1 mm & 2 mm & 1 mm & 2 mm & 1 mm & 2 mm \\ \(\delta_{4}\) & 15.1\% & 18.8\% & 5.5\% & 3.4\% & 30.1\% & 22.2\% & 4.6\% & 15.7\% & 30\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Beam profile analysis of measured and simulated values for Varian Clinac 6 MV: Data of 10 cm \(\times\) 10 cm field size were analyzed at 15 mm, 50 mm, 100 mm, and 200 mm depths. Each part of beam profile was analyzed in order of \(\delta_{2}\), \(\delta_{3}\), \(\delta_{50-90}\), \(\delta_{4}\). In this case, result of \(\delta_{4}\) slightly exceeded reference value of 30%. \(\delta_{2}\) also showed the result of exceeding the criterion. discrepancy of the build-up region was 2 mm and 0 mm for 4 cm \(\times\) 4 cm and 10 cm \(\times\) 10 cm field sizes, respectively (Table 4). In the case of PDD data, no magnification constant has been applied. Discrepancy of the build-up region for measured data was figured out as 2 mm because each of the build-up regions was defined as 14 mm and 16 mm for measured data and simulated data (field size: 4 cm \(\times\) 4 cm), respectively. An exact match was found when it comes to comparison between the cases of 10 cm \(\times\) 10 cm field size. Those build-up regions were 14 mm. These discrepancies are acceptable for our purpose, giving consideration for the abovementioned criterion (Figure 6).
# An ERNIE-Based Joint Model for Chinese Named Entity Recognition
## 4 Experiments and Results
### Results
#### 4.3.1 MSRA-NER Dataset  
Table 5 shows the experimental results of diverse models on the MSRA-NER dataset, which is provided by [PERSON] et al. [23]. The training set, validation set, and test set used to test the models in Table 5 are all identical. In the first block, we give the performance of previous methods. [PERSON] et al. [5], [PERSON] et al. [26], and [PERSON] et al. [27] exploited multi-prototype embeddings and leveraged rich hand-craft features for the Chinese NER task, and [PERSON] et al. [26] obtained the F1-score of 91.18%. [PERSON] et al. [28] applied a BiLSTM-CRF model which utilizes radical features and achieved the F1-score of 90.95%. [PERSON] et al. [29] obtained the F1-score of 91.67% by proposing a CNN-BiRNN-CRF model, which incorporates stroke features. [PERSON] et al. [30] utilized adversarial transfer learning to conduct the NER task. [PERSON] et al. [25] investigated a Convolutional Attention Network (CAN) for Chinese NER task. The model [PERSON] et al. proposed achieved the highest F1-score of 93.18% in the first block, but the result heavily depends on the quality of external lexicon data. The results of the baseline, BERT, ERNIE, and our model are listed in the second block. The pre-training models (BERT and ERNIE) outperform all the previous methods and baseline model without additional features. However, the ERNIE-Joint we proposed achieves the SOTA result with the F1-score of 94.20%.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyper-Parameters** & **Values** \\ \hline Batch size & 32 \\ Epoch & 6 \\ Learning rate & \(5\times 10^{-5}\) \\ Weight decay & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyper-parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Precision/\%** & **Recall/\%** & **F1-Score/\%** \\ \hline [PERSON] et al. [5] & 91.22 & 81.71 & 86.20 \\ [PERSON] et al. [26] & 92.20 & 90.18 & 91.18 \\ [PERSON] et al. [27] & 91.86 & 88.75 & 90.28 \\ [PERSON] et al. [28] & 91.28 & 90.62 & 90.95 \\ [PERSON] et al. [29] & 92.04 & 91.31 & 91.67 \\ [PERSON] et al. [30] & 91.30 & 89.58 & 90.64 \\ [PERSON] et al. [11] & 93.57 & 92.79 & 93.18 \\ [PERSON] et al. [25] & 93.53 & 92.42 & 92.97 \\ \hline Baseline [25] & 92.54 & 88.20 & 90.32 \\ [PERSON] [7] & - & - & 92.60 \\ [PERSON] [8] & 93.35 & 94.82 & 94.08 \\ [PERSON] & **93.58** & **94.82** & **94.20** \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results on the MSRA-NER.
# Application of Augmented Reality in Waterway Traffic Management Using Sparse Spatiotemporal Data
## 4 Methodology
### Ship Target Detection Under Low Visibility Conditions
#### 4.2.2 Ship Target Detection in Nighttime Environments  
In environments with insufficient lighting, the performance of object detection algorithms often deteriorates. To address this, we process low-light or high-contrast images to restore the true visual perception of the image. First, given an image \(I(x,y)\), its brightness value can be decomposed into the product of the reflection component \(R(x,y)\) and the illumination component \(L(x,y)\):  
\[I(x,y)=R(x,y)\cdot L(x,y) \tag{19}\]  
Here, \(R(x,y)\) represents the reflection component, and \(L(x,y)\) represents the illumination component. The goal is to recover the reflection component \(R(x,y)\) from the observed image \(I(x,y)\) while suppressing the influence of the illumination component \(L(x,y)\).  
To extract the reflection component from \(I(x,y)\), the Retinex algorithm employs a logarithmic transformation. First, the original image \(I(x,y)\) is log-transformed to obtain the following:  
\[\log\left(I(x,y)\right)=\log\left(R(x,y)\right)+\log L(x,y)) \tag{20}\]  
Then, the image is processed using filters at different scales to estimate the illumination component \(L(x,y)\).  
\[L(x,y)=G(x,y)\;*I(x,y) \tag{21}\]  
Here, \(G(x,y)\) is the Gaussian kernel, and \(*\) denotes the convolution operation. In this way, the illumination component is estimated through filtering, and the reflection component \(R(x,y)\) is obtained.  
\[R(x,y)=\exp(\log(I(x,y))-\log(L(x,y))) \tag{22}\]  
To further enhance low-light images, we need to process the extracted reflection component. At this point, the reflection component \(R(x,y)\) represents the object features of the image but may still lack brightness or detail. To address this issue, we can apply local contrast enhancement techniques to adjust the brightness or contrast of the reflection component, further enhancing the image. Finally, by combining the illumination component \(L(x,y)\) with the enhanced reflection component \(R(x,y)\), the enhanced image is restored. The final enhanced image can be expressed as follows:  
\[I_{enhanced}(x,y)=R_{enhanced}(x,y)\cdot L(x,y) \tag{23}\]
# NOMA-Based VLC Systems: A Comprehensive Review
## 4 Potential Challenges and Open Research Issues
### Decoding Complexity  
Due to the fact that the receiver must decode the information of other users before it can decode its own information, implementing SIC-based signal decoding is more difficult than orthogonal systems. More users in the targeted cell mean a higher complexity level. Users, though, can be sorted into groups, with a few poor channels in each category. Then, inside each group, SC/SIC procedures can be carried out. There is a tradeoff between implementation complexity and performance improvements offered by this group-wise SC and SIC operation. In addition, SIC introduces extra delay and complexity, which can increase with the number of users. Several works have addressed these issues by replacing SIC with low-complexity treating interference as noise decoding, i.e., single-user decoding [162].
# GPS/BDS RTK Positioning Based on Equivalence Principle Using Multiple Reference Stations
## 2 Methodology
### Equivalent DD Observation Equations  
In Equation (2), the coordinate corrections and ambiguities are of interest, and the SD receiver clock errors can be eliminated directly from the observation equations based on equivalence principle [31; 32]. Thus, the equivalent DD observation equations for single satellite system can be expressed as  
\[\left\{\begin{array}{c}Rv=RAx+RBN-RL,\quad P\\ R=E-C(C^{T}PC)^{-1}C^{T}P\end{array}\right. \tag{6}\]  
where \(E\) is an identity matrix. \(R\) is the transformation matrix of eliminating the clock errors with a rank defect of three. It means that the three SD ambiguities of reference satellite can be set to zero to keep the parameters independent, and the other SD ambiguities can be converted to the equivalent DD ambiguities.  
Considering the GPS/BDS positioning, the equivalent DD observation equations of MBRTK can be obtained as  
\[\underbrace{\left[\begin{array}{c}v^{\prime}_{G}\\ v^{\prime}_{C}\end{array}\right]}_{V}=\underbrace{\left[\begin{array}{cc}A^{ \prime}_{G}&B^{\prime}_{G}&0\\ A^{\prime}_{C}&0&B^{\prime}_{C}\\ \end{array}\right]}_{H}\underbrace{\left[\begin{array}{c}x\\ N_{G}\\ N_{C}\\ X\end{array}\right]}_{X}-\underbrace{\left[\begin{array}{c}L^{\prime}_{G}\\ L^{\prime}_{C}\\ \end{array}\right]}_{Z}\text{where the subscripts \(G\) and \(C\) denote the satellite system. \(A^{\prime}_{G}=R_{G}A_{G}\), \(A^{\prime}_{C}=R_{C}A_{C}\), \(B^{\prime}_{G}=R_{G}B_{G}\), \(B^{\prime}_{C}=R_{C}B_{C}\), \(L^{\prime}_{G}=R_{G}L_{G}\), \(L^{\prime}_{C}=R_{C}L_{C}\), \(v^{\prime}_{G}=R_{G}v_{G}\), \(v^{\prime}_{C}=R_{C}v_{C}\). \(x\) is a column vector of coordinate corrections, \(N_{G}\) and \(N_{C}\) are the column vector of the equivalent DD ambiguities for GPS and BDS systems.  
As shown in Equation (7), the observations of four receivers can be modelled and processed strictly to obtain the positioning results of the MBRTK, which is not only equivalent to the conventional observation equations of the SBRTK, but also the correlative characteristics of differenced observations are solved clearly. If there are multiple reference and rover receivers tracking multiple satellites, the similar equations can be obtained for RTK positioning. As a special case, when there is only one reference receiver and one rover receiver, the equivalent DD equations can be reduced to the conventional DD equations of the SBRTK.
# Exploring the Cutting Process of Coaxial Phase Change Fibers under Optical Characterization Tests
## 2 Materials and Methods
### Fourier-Transform Infrared Spectroscopy (FTIR) and Index of Functional Groups
#### 2.7.2 Index Calculation  
To calculate the area index, it was necessary to perform (i) identification of FTIR peaks, (ii) establishment of a relationship between chemical groups of PCFs (pre and post-controlled washing) and (iii) comparison of indices calculated before and after controlled washing. Structural and functional indices are calculated from the areas of the valley-to-valley strip. The analysis focused on areas rather than band heights because vibrations of the same type can exist simultaneously (e.g., CA has a C-O-C stretching bond that extends up to 1030 cm\({}^{-1}\), while PEG also exhibits strong absorption bands for C-O-C stretching  
Figure 8: Fourier transform infrared spectroscopy (ATR-FTIR). (**a**) Placement of the PCF sample in the ATR-FTIR. (**b**) PCF analysis in the ATR-FTIR. (**c**) Visualization of the PCF sample spectrum.  
Figure 7: Bright-field microscope. (**a**) Placing the PCF sample in the microscope. (**b**) PCF analysis at \(5\times\) magnification. (**c**) Visualizing the morphology of the PCF sample. (**d**) Visualizing the cut of the PCF sample.  
vibrations in the range 1000-1150 cm\({}^{-1}\)) [12]. The use of the index calculation type with the band area was defined [22]. The chemical structure of the materials (CA, DMF and PEG) was analyzed using the FTIR index (I). Equations (1)-(3) present I, which is calculated by the ratio between the peak area of the identified band and the total spectrum area (\(\sum A\)). Each peak is assigned to a functional group that remains unchanged during useful life, but also to groups responsible for the presence of polymeric materials.  
\[I_{Aliphatic\ structures\ (PEG):}=\frac{A_{(1465,1368)}}{\sum A} \tag{1}\]  
\[I_{Carbonyl\ (CA):}=\frac{A_{(1745)}}{\sum A} \tag{2}\]  
\[I_{Carbonyl\ (DMF):}=\frac{A_{(1657)}}{\sum A} \tag{3}\]  
where,  
\[\begin{array}{c}\sum A=A_{2885}+A_{1745}+A_{1657}+A_{1465}+A_{1376}+A_{1368 }+A_{1328}+A_{1278}+\\ A_{1148}+A_{1105}+A_{1047}+A_{939}+A_{901}+A_{835}+A_{600}\end{array}\]
## 3 Experimental Setup and Numerical Models
### CFD Models  
A crucial aspect is the ability to correctly replicate the slot outlet conditions (velocity profile). For this reason, an upstream extension of the computational domain has been adopted (Figure 4); it essentially consists of a plenum. Downstream of it there is a duct, at the top of which a restricted section is mounted to accelerate the flow and to assume the geometry configuration of the slot.  
Figure 3: Experimental test apparatus [12].  
A 2D plane grid equivalent to the real 3D case was adopted for the single slot configuration. The velocity profile is developed only in the vertical direction, while the orthogonal variation is entirely negligible. Steady simulations with the following boundary conditions were set. Inlet mass flow rate condition \(\dot{\mathrm{m}}\) = 0.197 [kg/s] (calculated from the Reynolds number at the outlet jet Re = 1.1 \(\times\) 10\({}^{4}\), at temperature 30 [\({}^{\circ}\)C] with B = 3.175 [mm]) on the three plenum rectangular sides (excluding the lower side) with turbulence intensity equal to 1%. On the side opening B a pressure ambient outlet condition was fixed with temperature of 30 [\({}^{\circ}\)C]. All the remaining walls were set as adiabatic no-slip boundaries. This simulation provides the velocity profile to fix as boundary condition in the simulations of the slot jet impingement problem.  
In Figure 5 the computational domain with the relative grid for the jet impingement study is shown. A multi-block structured mesh was generated using Ansys v. 17 platform. A further thickening in the jet area and near the target surface (where the jet impacts) was introduced. The proper wall treatment is guaranteed by y+ values close to one. The total mesh size amounts to about 900 kCells.  
In Figure 5 the patches of the following boundary conditions were also highlighted. In the outlet jet (green) the velocity and turbulence profile (\(k\) and \(\varepsilon\)) generated by the previous upstream model with the plenum was set. On the lateral surfaces, placed sufficiently far from the jet axis (20 times), the outlet ambient pressure was imposed. The upper surface  
Figure 4: Calculation domain for the velocity profile slot jet generation.  
Figure 5: Computational grid of a single slot configuration.  
was set as adiabatic wall free slip, while on the target surface (to be cooled) a wall no-slip condition with constant temperature of 50 [\({}^{\circ}\)C] was imposed, to match with the test case [12]. All the governing and transport equations were solved with SIMPLE second order pressure based computational schemes using Ansys Fluent v 17 code. The modeled fluid is air treated as an ideal gas, the viscosity is computed with Sutherland's law and the specific heat with a polynomial law to consider temperature variations.
# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
## 2 Methodology
### Adaptation Methodology
#### 2.3.1 Domain-Focused Adaptation  
We propose a targeted adaptation approach that addresses a key domain mismatch problem in end-to-end speaker diarization models. Specifically, our initial model was trained on simulated conversations with up to 4 speakers, while the target CallHome corpus contains conversations with up to 7 speakers but with limited adaptation data available. This mismatch presents a fundamental adaptation challenge: should we expose the model to all available target domain data regardless of speaker count distribution, or should we restrict adaptation to maintain consistency with training conditions? Our methodology explores two strategic approaches:  
1. Traditional adaptation using the complete CallHome adaptation set (CH1 all), which includes conversations with 2-7 speakers. This approach potentially extends the model's capabilities to handle higher speaker counts but risks degrading performance on the more common 2-4 speaker scenarios due to the limited examples of higher speaker counts in the adaptation set.
2. Restricted adaptation using only conversations with 2-4 speakers (CH1 2-4 spk), which maintains alignment with the original training distribution. This approach prioritizes robust performance within the speaker range the model was originally trained on, potentially sacrificing adaptability to higher speaker counts.
## 5 RTIoT System Design Methodology
### Mapping Specifications to RTIoT System Architecture  
The assignment of tasks to the RTIoT architecture layer is performed by the designer at the system specification stage. The next step is to map the specifications to the different layers of the RTIoT architecture. Figure 7 shows the results of the specification mapping introduced in Section 5.1.  
For simplicity, only one instance of each graph was assumed. This corresponds to a system with one user and single sensors (one parking lot and one parking space). In a real system, one instance is created for each user and one instance is created for each sensor that can activate the TG. For this purpose, it is necessary to assume the maximum load on the system, i.e., the maximum number of simultaneous users and the maximum number of parking lots and stands served by the system. For clarity, the figure also does not indicate transmission volumes and time constraints. Based on the mapping, it is possible to obtain information about the transmissions between the layers and the sequence dependencies between these transmissions. For example, there may be 10 transmissions between the UL and CL layers: T0_4\(\rightarrow\)T0_5, T0_10\(\rightarrow\)T0_5, T0_12\(\rightarrow\)T0_5, T0_17\(\rightarrow\)T0_5, T0_5\(\rightarrow\)T0_6, T0_14\(\rightarrow\)T0_15, T0_15\(\rightarrow\)T0_16, T2_3\(\rightarrow\)T2_4, T2_9\(\rightarrow\)T2_4, and T2_4\(\rightarrow\)T2_5. It can also be noted that there are sequence dependencies between some transmissions, which means that these transmissions will not compete with each other for the communication link. This is important information from the point of view of transmission scheduling.  
Assuming the maximum frequency of activation of individual tasks and the size of individual transmissions, it is possible to estimate the throughput requirements of communication links. These requirements can be reduced by appropriately scheduling transmissions so that time-constrained transmissions are executed in an order that ensures the maximum QoS.
# Design and Analysis of the Composite Stability Control of the Reflective Optoelectronic Platform
## 4 Experiments and Analysis
### The Anti-Disturbance Comparison Experiment  
#### 4.2.1 The Comparative Experiment of Fixed-Frequency Angular Velocity Disturbance  
The azimuth axis and pitch axis channels were subjected to a fixed frequency disturbance of 1 degree and 1 Hz, respectively. The motion response of the line of sight (LOS) for the traditional reflector stabilization and composite stabilization control systems is presented in Figures 14 and 15. As evident from the figures, the traditional reflector has a two-axis stabilization error range of approximately 3.6 \(\upmu\)rad, whereas the composite stabilization mode has a range of only 0.048 \(\upmu\)rad. This represents a significant improvement of 75 times in stabilization precision.  
Figure 13: The experiment results of the closed-loop bandwidth of the two subsystems. (**a**) The azimuth motion of the LOS. (**b**) The pitch motion of the LOS.  
2.2 The Comparative Experiment of Translational Disturbance with Traditional Reflector Stabilization  
The LOS motion results of the traditional reflector stabilization and composite stabilization control systems under the influence of a flat vibration disturbance applied to the base can be observed in Figures 16 and 17.  
The traditional reflector stabilization exhibits an error range of approximately 3.7 \(\upmu\)rad in azimuth of the LOS and 3.0 \(\upmu\)rad in pitch, while the error range under composite stabilization control is about 2.2 \(\upmu\)rad in azimuth of the LOS and 1.9 \(\upmu\)rad in pitch, resulting in a 37% reduction in error.  
In comparison to angular disturbance, composite stabilization mode demonstrates superior suppression effects. However, it is observed that the suppression effect is not as pronounced for flat vibration acceleration disturbance. This is largely due to the error induced by dynamic unbalance torque acting on the fast reflector assembly under flat vibration disturbance, which falls beyond the compensation mechanism. Hence, to effectively suppress the error, it is necessary to improve the precision of dynamic balance during the design and machining process while also enhancing the control stiffness of fine-stable components.  
Figure 14: The anti-disturbance results of the traditional reflective stabilized LOS under fixed-frequency angular velocity disturbance. (**a**) The azimuth motion of the LOS. (**b**) The pitch motion of the LOS.  
Figure 15: The anti-disturbance results of the reflective composite stabilized LOS under fixed-frequency angular velocity disturbance. (**a**) The azimuth motion of the LOS. (**b**) The pitch motion of the LOS.
## 2 Materials and Methods
### Dataset and Statistical Analysis
To verify the correlation between the corresponding rasters of the same day (from satellite and from the sensor network) and between these and the 1 relating to the geoelectric reading, the analysis of correlation was applied based on the moving window of Dutilleul [35]. The analysis of variance (ANOVA) was applied to the values of the correlations obtained for each available date, considering as independent variables the conditions of the field based on the coverage (trticale, bare soil, and maize) and based on the water conditions. This took into consideration if the soil had received in the previous 5 days a quantity of water greater than 40 mm, in the form of both rain and irrigation.