esa-sceva/satcom-chunk-collection huggingface dataset:
# VIS-HAPT: A Methodology Proposal to Develop Visuo-Haptic Environments in Education 4.0
## 5 VIS-HAPT Methodology, Results and Discussion
### Description of the Selected Visuo-Haptic Environments  
The four selected visuo-haptic environments common to both cases--with and without methodology--are described as follows:  
1. _Block on a rough incline._ The main purpose of this scenario is that students understand the nature of the friction force that a rough incline exerts on a block located on it (Figures 5 and 6). The user manipulates a virtual hand or a haptic sphere to push the block upwards or downwards the incline with an applied force, so as to feel the actual force that the block exerts on the virtual hand or the haptic sphere. The simulator reproduces the resulting block's motion. The user can select the values of the static and kinetic friction coefficient, the block's mass, and the angle of the incline.
2. _Point charge._ This simulator allows exploring the electric force exerted between two-point charges (Figures 7 and 8). A point charge is fixed at the origin of the reference frame and another point charge can be displaced around the former by the user,changing the distance between the charges. The magnitudes and signs of the charges can be changed by the user. The purpose of the simulator is for the user to feel the strength of the attractive or repulsive force that the charge at the origin exerts on the other one at different distances and for different charge magnitudes.
3. _Line charge._ This simulator is aimed at assessing the direction and strength of the electric force exerted by a long straight-line charge on a point charge (Figures 9 and 10). The line charge is fixed along the z axis and the point charge can be displaced at any desired position around the line charge. Users can select the values and signs for both the linear charge density and the point- charge, so as to feel the force that the line charge exerts on the point charge at different distances and for different charge magnitudes.
4. _Plane charge._ In this simulator, the force exerted by a very large plane charge on a test point charge is explored (Figures 11 and 12). The plane charge is fixed at the YZ plane, and the users can select the values and signs of the plane charge density and the point charge. Displacing the point charge at any desired position around the plane in the environment, the users can feel the strength and direction of the electric force exerted by the plane charge on the point charge at different distances and for different charge magnitudes.
## 4 Conclusions  
In conclusion, a novel solution based on a multi-twin-SSB technique for long-reach high-speed PON downstream was proposed and investigated in this paper. Multi-twin-SSB combined the advantages of multi-CAP and twin-SSB modulation. Different ONUs could be supported by flexible multi-band allocation based on the software-reconfigurable optical transceivers. Compared with the conventional multi-CAP system, the employment of the twin-SSB technique generated two SSB signals without increasing the DAC bandwidth requirements. The CD-induced power fading could also be eliminated and the transmission distance could be improved. A single DDMZM was applied to modulate the twin-SSB signal on the transmitter-side while a low-cost single-end PD was used on the receiver-side. To efficiently remove the SSBI from the square-law detection, the KK-receiver was adopted with a careful optimization on the guard band between the two sidebands and the OMI of the DDMZM. To validate the proposed scheme, numerical simulations with four ONUs using 50 Gbps 16-QAM modulation for each ONU was conducted for transmission over 50-km SSMF. Successful separation and receiving of each sub-band from the twin-SSB signal was achieved, which was able to almost double the transmission capacity. By the application of KK-receiver, the BER performance was improved by almost an order of magnitudes for the central sub-bands near the optical carrier. Digital pre-equalization was used on the transmitter-side DSP to balance the performance between the sub-bands. After pre-equalization, the required ROP was about \(-9.5\) dBm for all sub-bands to reach the BER threshold for the commonly-used HD-FEC code with a 7% redundancy ratio. We believe that the multi-twin-SSB technique with direct detection based on a KK-receiver will be a simple and low-cost solution for long-reach high-speed PON downstream in the future.  
Conceptualization, X.G. and X.Z.; methodology, X.G. and Y.C.; software, X.G.; validation, B.X. and K.Q.; formal analysis, B.X.; investigation, X.G.; resources, X.G. and B.X.; data curation, X.G. and Y.C.; writing--original draft preparation, X.G. writing--review and editing, Y.C., B.X., and X.Z.; visualization, X.G. and X.Z.; supervision, B.X. and K.Q.; project administration, B.X. and K.Q.; funding acquisition, B.X. and K.Q.  
This research received no external funding.  
Figure 4: Bit-error-rate (BER) versus received optical power (ROP) for 12.5 Gbaud per sub-band 16-QAM modulation after 50-km transmission. **(a)**: The BER performance without pre-equalization; **(b)**: The BER performance with pre-equalization.  
**Acknowledgments:** This work is supported in part by National Natural Science Foundation of China NSFC (No. 61420106011, No. 61871082 and No. 61471088) and the National High Technology Research and Development Program of China (863 Program) (2015 AA015501).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Development of Navigation Network Models for Indoor Path Planning Using 3D Semantic Point Clouds
###### Abstract  
Accurate and efficient path planning in indoor environments relies on high-quality navigation networks that faithfully represent the spatial and semantic structure of the environment. Three-dimensional semantic point clouds provide valuable spatial and semantic information for navigation tasks. However, extracting detailed navigation networks from 3D semantic point clouds remains a challenge, especially in complex indoor spaces like staircases and multi-floor environments. This study presents a comprehensive framework for developing and extracting robust navigation network models, specifically designed for indoor path planning applications. The main contributions include (1) a preprocessing pipeline that ensures high accuracy and consistency of the input semantic point cloud data; (2) a moving window algorithm for refined node extraction in staircases to enable seamless navigation across vertical spaces; and (3) a lightweight, JSON-based storage structure for efficient network representation and integration. Additionally, we presented a more comprehensive sub-node extraction method for hallways to enhance network continuity. We validated the method using two datasets--the public S3 DIS dataset and the self-collected HoloLens 2 dataset--and demonstrated its effectiveness through Dijkstra-based path planning. The generated navigation networks supported practical scenarios such as wheelchair-accessible path planning and seamless multi-floor navigation. These findings highlight the practical value of our approach for modern indoor navigation systems, with potential applications in smart building management, robotics, and emergency response.  
Keywords: indoor navigation; semantic point clouds; navigation network model; path planning
## 5 Experiments and Results
### Evaluation Metrics
#### 5.2.1 PSNR (Peak Signal-to-Noise Ratio)  
A measure of the quality of a reconstructed image compared to the original, expressed in decibels. Higher values indicate better quality.  
\[\text{PSNR}=20\cdot\log_{10}\left(\frac{\text{MAX}_{I}}{\sqrt{\text{MSE}}}\right) \tag{3}\]  
where  
* \(\log_{10}\) is the base-10 logarithm;
* \(\text{MAX}_{I}\) is the maximum possible pixel value of the image;
* MSE represents the mean squared error between the original and the corrupted image, calculated without applying the square root.
## 5 Stage-Wise Role of SiL and HiL Across Development Phases
### Test Coverage Across Development Stages
#### 5.4.2 Phase II--Validation and Integration (G70-G110)  
The second phase begins with DT calibration within G70. Simulation environments are refined using data from prior SiL tests and physical system constraints. From G80 to G90, HiL testing enables real-time performance validation under realistic electrical and sensor conditions. Fault injection, timing analysis, and ISO 26262 compliance are key activities during this phase. At G100, a continuous integration and testing pipeline emerges, synchronizing SiL and HiL outputs within a centralized DT platform. Testing becomes iterative, coordinated, and traceable. Ultimately, G110 pre-deployment activities encompass scenario-based simulation, diagnostics, and operational readiness verification through hybrid test environments.
# IOF-Tracker: A Two-Stage Multiple Targets Tracking Method Using Spatial-Temporal Fusion Algorithm
## 2 Methods
### Two-Stage Tracking Framework  
The primary function of the two-stage tracking framework is to process high and low-scoring detection boxes through different association methods in separate stages, followed by a comparative loop to filter out high-quality target detection boxes. First, for high-scoring detection boxes, they participate in the first-stage association, which results in three outcomes: matched tracklets (MT), unmatched detections (UD), and remaining tracklets (RT). Matched tracklets enter tracklets update (MTU), unmatched detections enter new tracklets (UDN), and remaining tracklets (RT) are those that are neither new nor matching with previous trajectories and require further correction. Then, UDN continues with the first association in subsequent frames, while RT associates with low-scoring detection boxes in the second round. This results in matched tracklets (MT) and re-remaining tracklets (RRT). Matched tracklets enter tracklets updation (MTU), and re-remaining tracklets (RRT) undergo similar operations as before. If the loop repeats for 30 frames without a match, the RRT is discarded. The principle is illustrated in Figure 4.
## 4 Results and Discussion
### Synthetic Signals  
As illustrated in Section 3, ten voltage disturbances were established in MATLAB. For each disturbance, there are 400 sample signals, where 320 are for training and 80 are for validation.  
A CNN model is established for the PQD 2D image training. In total, ten epochs are adopted in the training progress to obtain optimal parameters of the model. The hardware for the model training is based on an Intel (R) Core (TM) i7-6700 HQ CPU @ 2.6 GHz, 16 GB RAM and NVIDIA GeForce GTX 970M GPU with 192-bit 4 GB GDDR5 memory. The training process lasts 111 min and 52 s. The training progress and the confusion matrices of training and validation results are shown in Figures 5 and 6, respectively.  
In Figure 5, the high classification rate can be obtained only after five epochs and the loss of the cost function was almost zero. In Figure 6a, the classification rate in the training confusion matrix was 100% for both single and mixed synthetic disturbances. While 80 events were used for validation, the total classification rate was 99.8%, as shown in Figure 6b. The results demonstrate that the proposed method based on the PSR and CNN can achieve an excellent classification rate for PQDs. Additionally, the accuracy comparison of the proposed PQ assessment framework with other methods was illustrated in Table 2 with five methods, including EMD with balanced neural tree [41], ST with NN and DT [25], Hybrid ST with DT [42], ADALINE with FNN [43], and VMD with DSCN [22]. In Table 2, the proposed method is shown to be as good as the current best methods in terms of accuracy, as the 99.67-99.90% accuracy is well within the measurement uncertainty. The novelty of this work lies in the deep learning-based classification method and automatic feature extraction, while the existing methods are handcrafted. The features extracted by the convolution layers are shown in Figure 7. It can be seen that the feature maps are different even in one layer. For the different convolution layers, the extracted features are more specific when going to deep levels. Moreover, the weights of a convolution kernel are different too, leading to different pattern calculations for different variables. The evaluation results show that the proposed framework has comparatively better performance than the existing methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Feature Extraction** & **No.** & **Accuracy.** & **Ref.** \\  & **(Handcrafted/Automatically)** & **PQDs** & **(\%)** & **Ref.** \\ \hline EMD+Balanced Neural Tree & Handcrafted & 8 & 97.9 & [41] \\ ST+NN+DT & Handcrafted & 13 & 99.9 & [25] \\ Hybrid ST+DT & Handcrafted & 11 & 94.36 & [42] \\ ADALINE+FNN & Handcrafted & 12 & 90.58 & [43] \\ VMD+DeepSCN & Handcrafted & 7 & 99.4 & [22] \\ PSR+CNN & Automatically & 10 & 99.8 & Proposed method \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the proposed PQDs with other methods.  
Figure 6: The confusion matrices of training and validation results: (**a**) the confusion matrix of training; (**b**) the confusion matrix of validation.
## Proposed Proportionate NMCC Algorithms  
### Proportionate NMCC (PNMCC) Algorithm  
In order to get the PNMCC, we reinvestigate the MCC in the AF framework again. Here, the NMCC is also rewritten in Equation (3). The differences between the MCC and NMCC algorithms are that we use \(\xi=\chi\) in NMCC algorithm to replace the \(\xi=\chi_{\text{MCC}}\left\|\mathbf{x}(n)\right\|^{2}\) in the MCC. Similarly, the NMCC is to implement the following updating equation:  
\[\mathbf{\tilde{w}}(n+1)=\mathbf{\tilde{w}}(n)+\chi\frac{\exp\left(-\frac{e^{2} (n)}{2\sigma^{2}}\right)}{\left\|\mathbf{x}(n)\right\|^{2}}e(n)\mathbf{x}(n). \tag{10}\]  
Then, we use a reweighting controlling matrix \(\mathbf{G}(n)\) for reassigning different weights to the magnitudes of the filter coefficients, where the idea is obtained from the known Pt algorithms. Introducing \(\mathbf{G}(n)\) into (10), we get  
\[\mathbf{\tilde{w}}(n+1)=\mathbf{\tilde{w}}(n)+\chi\frac{\mathbf{G}(n)\exp \left(-\frac{e^{2}(n)}{2\sigma^{2}}\right)}{\mathbf{x}^{T}(n)\mathbf{G}(n) \mathbf{x}(n)+\vartheta}e(n)\mathbf{x}(n). \tag{11}\]  
Here, \(\vartheta>0\) represents a constant to get a stable solution. We know that a diagonal matrix \(\mathbf{G}(n)\) is used to give a modification of NMCC's step size under a rule given as follows. In general, \(\mathbf{G}(n)\) is [2; 3; 4]  
\[\mathbf{G}(n)=\text{diag}(g_{0}(n),g_{1}(n),\cdots,g_{N-1}(n)), \tag{12}\]  
where the individual gain \(g_{i}(n)\) is  
\[g_{i}(n)=\frac{\kappa_{i}(n)}{\sum\limits_{i=0}^{N-1}\kappa_{i}(n)},0\leq i\leq N-1 \tag{13}\]  
with  
\[\kappa_{i}(n)=\max[\gamma_{g}\max[\rho_{p},\left|\tilde{w}_{0}(n)\right|, \left|\tilde{w}_{1}(n)\right|,\cdots,\left|\tilde{w}_{N-1}(n)\right|],\left| \tilde{w}_{i}(n)\right|]. \tag{14}\]  
Here, \(\rho_{p}>0\) and \(\gamma_{g}>0\) are constants, and they are usually set to be \(\rho_{p}=0.01\) and \(\gamma_{g}=5/N\)[2]. \(\rho_{p}\) is implemented to avoid the stalling. The parameter \(\gamma_{g}\) is used for preventing \(\tilde{w}_{i}(n)\) from stalling.
# Attention-Based Color Difference Perception for Photographic Images
###### Abstract  
Traditional color difference (CD) measurement methods cannot adapt to large sizes and complex content of photographic images. Existing deep learning-based CD measurement algorithms only focus on local features and cannot accurately simulate the human perception of CD. The objective of this paper is to propose a high-precision image CD measurement model that simulates the perceptual process of the human visual system and apply it to the CD perception of smartphone photography images. Based on this, a CD measurement network called CD-Attention is proposed, which integrates CNN and Vision Transformer features. First, a CNN and the ViT are used separately to extract local features and global semantic features from the reference image and the distorted image. Secondly, deformable convolution is used for attention guidance, utilizing the global semantic features of the ViT to direct CNN to focus on salient regions of the image, enhancing the transformation modeling capability of CNN features. Thirdly, through the feature fusion module, the CNN features that have been guided by attention are fused with the global semantic features of the ViT. Finally, a dual-branch network for high-frequency and low-frequency predictions is used for score estimation, and the final score is obtained through a weighted sum. Validated on the large-scale SPCM dataset, the CD-Attention model has achieved state-of-the-art performance, outperforming 30 existing CD measurement methods and demonstrating useful generalization ability. It has been demonstrated that CD-Attention can achieve CD measurement for large-sized and content-complex smartphone photography images. At the same time, the effectiveness of CD-Attention's feature extraction and attention guidance are verified by ablation experiments.  
image quality assessment; color difference perception; attention mechanism; vision transformer; deformable convolution +
Footnote †: journal: Applied Sciences
## 3 Data Used and Methodology  
The data used were from the freely available climatological dataset, CRU, which provides land-based observations from 1901 to 2018 [20]. The dataset is prepared at a resolution of \(0.5^{\circ}\times 0.5^{\circ}\) and covers the entire world, except for Antarctica; the format is netCDF (Network Common Data  
Figure 1: Study area in (**a**) Australia, and (**b**) mean annual rainfall of New South Wales (NSW) (1901–2018), based on Climate Research Unit Time Series (CRU-TS) dataset.  
Form). The dataset is prepared by using an angular distance weighting interpolation technique, with no missing pixels [41]. The use of the CRU gridded dataset has been found to be a good representative of climatic conditions over dry regions [42]. The CRU dataset has three distinct variable types, which cumulate to ten variables and have been widely used for several applications for assessing climatic variability [43]. The present study uses three types of variables, (i) primary (mean temperature and precipitation); (ii) secondary (vapor pressure and cloud cover); and (iii) derived variables (potential evapotranspiration, maximum and minimum temperature). The potential evapotranspiration (PET) has been defined by using the Penman-Monteith technique [44]. Figure 2 depicts the annual variation of monthly rainfall and monthly mean temperature measures in NSW region for 1901-2018. The bottom and top of the rectangular boxes represent the 25 th and 75 th percentiles, respectively, with the horizontal thick lines in the boxes depicting median values (50 th percentiles); the whiskers indicate 1.5 times the interquartile range with the points reflecting outliers [45]. The annual variation of rainfall and mean temperature across all the pixels for the NSW region are depicted in Figure 3. The present study uses the CRU dataset first to determine the SPEI index and thereafter uses all the variables mentioned above to be used as predictive factors to forecast SPEI index, using Random Forests.  
Figure 3: Variation of annual rainfall and mean temperature across the entire time duration (1901–2018) for the NSW region. The orange line represents the moving average of the past 20 years.  
Figure 2: Box and plot chart of the annual variation of monthly rainfall and mean temperature values based on CRU TS dataset for the NSW, region from 1901 to 2018.
## 3 Advantages of the Proposed Configuration
### Segregating Noisy Equipment  
The cryogenic chillers for the 12 cryogenic test masses require a power of the order of 150 kW [34]. Chillers are notoriously noisy, especially at low frequency. To isolate the vibrations that they generate, it is necessary to house them in separate excavations, with anchoring to rock designed to filter out compressor vibrations and heat links running through boreholes. Several meters of rock efficiently shield the vibrations while the machinery can remain continuously accessible for maintenance via separate tunnels. It should be noted that the longer lengths involved may require replacing the external metallic heat links with liquid helium lines, which further reduce transmission of vibrations.  
It is worth mentioning that water cooling will be necessary to evacuate the power consumed by the chillers, as well as for any other instruments requiring significant power. Air cooling cannot be relied on because fast air flows through the tunnels, producing unacceptable levels of induced noise, including Newtonian noise.
## 2 Methods
### Calibration Procedures  
According to the measuring principle of the SL system using a grid pattern, there are three categories of parameters which need to be calibrated: intrinsic parameters (\(f_{x}\),\(f_{y}\), \(u_{0}\), \(v_{0}\)), distortion coefficients (\(k_{1}\), \(k_{2}\), \(p_{1}\), \(p_{2}\)) and multiple light planes (\(\Pi_{Hi}\), \(\Pi_{Vj}\)). By obtaining different pose views of a 2D coplanar point calibration board under several angles, all the parameters can be calibrated with a simple operation (Figure 7).  
The procedures of the system calibration are shown in Figure 8. The calibration board as a target are placed in different poses (Figure 7). The grid pattern is projected on the board at each position. A set of target images and corresponding grid pattern images are acquired first.  
Figure 6: Extraction of grid stripe feature in captured image.  
Figure 7: Schematic diagram of the system calibration.  
The elliptic centers on each target image are extracted to provide the data for the camera calibration. By adopting [PERSON]'s method [40], the intrinsic parameters and distortion coefficients can be obtained. In addition, the rotation matrix \(\mathbf{R}_{i}\) and translation vector \(\mathbf{T}_{i}\) of each target pose can be calculated. The equations of the target planes in \(O_{c}\)-\(X_{c}Y_{c}Z_{c}\) can be formed as:  
\[a_{ti}x_{c}+b_{ti}y_{c}+c_{ti}z_{c}+d_{ti}=0 \tag{5}\]  
where \(\overset{\rightarrow}{n}=[a_{ti},b_{ti},c_{ti}]\) is the third column of \(\mathbf{R}_{i}\), \(d_{tn}=\overset{\rightarrow}{n}\cdot\mathbf{T}_{i}\). \(n=1\), \(2\), \(\ldots\), \(N\) is the total number of the positions.  
Meanwhile, the stripe centers extraction is performed on the grid pattern images. The actual (distorted) pixel coordinates of the centers are labelled according to the topological relationship of the grids. The values \(m_{Vij}^{m}\) and \(m_{Hij}^{m}\) are the actual pixel points on the vertical and horizontal stripes, respectively. Subsequently, the ideal (distortion-free) pixel points \(m_{Vij}^{n}\) and \(m_{Hij}^{n}\) are calculated by substituting the actual pixel coordinates and calibrated distortion coefficients into Equation (4). By combining Equations (1) and (5), the 3D coordinates of the ideal pixel points in \(O_{c}\)-\(X_{c}Y_{c}Z_{c}\) can be obtained. The values \(M_{Vij}^{n}\) and \(M_{Hij}^{n}\) are the 3D grid feature points on the target planes and also the points on the grid light planes. Finally, the equations of the vertical and horizontal light planes can be fitted with the corresponding \(M_{Vij}^{n}\) and \(M_{Hij}^{n}\).