esa-sceva/satcom-chunk-collection huggingface dataset:
# A Mobile LiDAR-Based Deep Learning Approach for Real-Time 3D Body Measurement
## 2 Mobile Device 3D Measurement Technology
### Data Collection for 3D Measurement  
In this study, uniform 3D data, such as cylindrical object and mannequin, were used to create initial datasets and later expanded to actual human body measurements. The experiments began with relatively simple models that resemble specific human body parts to minimize deformation, enhance the accuracy of the measurement algorithm, and enable a systematic evaluation across various body parts. Notably, previous studies such as [22; 23] primarily conducted experiments on real human subjects. In contrast, this study verified the performance of the algorithm in the early stages using uniform 3D objects. This approach helped secure fundamental accuracy and minimize variables that could arise when measuring different body parts. Additionally, the use of cylindrical object and mannequin ensured data consistency, which played a critical role in the training and evaluation processes of the deep learning model.  
In this study, we first verified the basic performance of the measurement algorithm using uniform 3D data, such as cylindrical objects and mannequin, and then extended our experiments to actual human subjects. A total of 150 Korean male participants were involved, each wearing black tights during the experiment to minimize potential errors caused by garment wrinkles or reflections.  
Four Captures (Front/Side, Upper/Lower Body): We divided the upper and lower body and conducted a total of four captures--one each from the front and side views.  
Front View: Participants stood with their arms extended to the sides and feet slightly apart, ensuring the torso outline was clearly visible, while capturing RGB images and LiDAR depth maps simultaneously.  
Side View: Participants extended their arms forward to facilitate accurate chest circumference measurement. Again, RGB and LiDAR depth maps were collected in the same manner. Lighting, camera distance, and angles were kept consistent, and if a large discrepancy was found by comparing the results with ground-truth measurements over two or more captures, a retake was performed.  
We recruited participants with waist circumferences from below 72 cm (approximately 11% of the subjects, considered slim) to above 92 cm (about 16% of the subjects), thereby confirming that the proposed algorithm can function reliably over a broad range of body sizes. Each participant underwent four total captures (front/side, upper/lower body), yielding both RGB and depth map sets for a detailed evaluation of full-body circumference measurement.
## Appendix A Full Questionnaire (Updated Version--60 Questions)
### Data Debt--Questions 10 to 15  
10. Question 10: Have you checked for spurious data?  
Stakeholder: Participants  
Score: 4  
Justification: Spurious data can introduce noise and lead to overfitting, which increases technical debt due to the need for additional debugging and retraining.  
Example: Identifying and removing outliers from player score datasets that do not align with expected patterns.  
11. Question 11: Have you checked your data for accuracy?  
Stakeholder: Participants  
Score: 5  
Justification: Inaccurate data can mislead the training process, resulting in models that perform poorly in real-world scenarios, thus accumulating technical debt.  
Example: Verifying the correctness of reward signals in the game environment to ensure they reflect the intended game mechanics.  
12. Question 12: Have you checked your data for completeness?  
Stakeholder: Participants  
Score: 4  
Justification: Incomplete data can result in underfitting and poor generalization, leading to technical debt when the model fails to perform as expected.  
Example: Ensuring that the dataset includes a wide range of scenarios that a player might encounter in the game.  
13. Question 13: Have you checked your data for trustworthiness?  
Stakeholder: Participants  
Score: 4  
Justification: Untrustworthy data can stem from biased or manipulated sources, increasing technical debt by causing the model to learn incorrect behaviors.  
Example: Assessing the reliability of data sources, such as player feedback, to confirm they are not influenced by external incentives.  
14. Question 14: Have you performed testing on the input features?  
Stakeholder: Participants  
Score: 3  
Justification: Testing input features is essential to ensure they are predictive and relevant, reducing technical debt by preventing the inclusion of irrelevant or redundant features.  
Example: Conducting feature selection to determine the most significant inputs for predicting player engagement.  
15 Question 15: Have you checked your data for data relevance?  
Stakeholder: Participants  
Score: 3  
Justification: Irrelevant data can lead to a model that does not adapt well to the task, increasing technical debt through unnecessary complexity and maintenance.  
Example: Filtering out gameplay data that does not contribute to the learning objective, such as background art elements.
###### Abstract  
Surface defect segmentation supports real-time surface defect detection system of steel sheet by reducing redundant information and highlighting the critical defect regions for high-level image understanding. Existing defect segmentation methods usually lack adaptiveness to different shape, size and scale of the defect object. Based on the observation that the defective area can be regarded as the salient part of image, a saliency detection model using double low-rank and sparse decomposition (DLRSD) is proposed for surface defect segmentation. The proposed method adopts a low-rank assumption which characterizes the defective sub-regions and defect-free background sub-regions respectively. In addition, DLRSD model uses sparse constrains for background sub-regions so as to improve the robustness to noise and uneven illumination simultaneously. Then the Laplacian regularization among spatially adjacent sub-regions is incorporated into the DLRSD model in order to uniformly highlight the defect object. Our proposed DLRSD-based segmentation method consists of three steps: firstly, using DLRSD model to obtain the defect foreground image; then, enhancing the foreground image to establish the good foundation for segmentation; finally, the [PERSON]'s method is used to choose an optimal threshold automatically for segmentation. Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches in terms of both subjective and objective tests. Meanwhile, the proposed method is applicable to industrial detection with limited computational resources.  
surface defect of steel sheet; image segmentation; saliency detection; low-rank and sparse decomposition +
Footnote †: journal: Applied Mechanics and Physics
## 6 Numerical Examples
### Implemented Variants  
For the numerical examples presented in this section, the standard XFEM with shifted enrichment functions is employed. The enrichment radius assumes a value equal to \(r_{e}=3.5\) h, with h denoting the element size, while the radius used for the interaction integral is \(r_{d}=1.5\) h. Element partitioning and almost polar integration are employed for the integration of jump and tip enriched elements respectively. Finally, levels sets are updated using the \(\phi\psi r\theta\) method from the work of Duflot [139].  
The specific realization of SBFEM employed in the presented examples is based on balanced hybrid-polygon quadtrees, unless otherwise explicitly stated, and thus discretises the boundary with linear line elements. The Gauss-Lobotto integration scheme is employed, to offset computational effort for the numerical examples where hp-refinement is introduced (Section 6.2). Decoupling of the linear system of ordinary differential equations (Equation (36)) is performed by block diagonal Schur decomposition. The gSIFs are estimated by means of the spline fitting approach. For the case of the tension test, the domain is approximated via use of a single subdomain with hp-refinement on the boundary to produce gSIFs of highest possible accuracy. Results obtained by this variant are termed SBFEM hi-fi, acknowledging the high fidelity solutions they produce [276].  
For the PF-FEM case, 4-noded quadrilateral plane strain/stress elements with bilinear basis functions and based on a full integration technique have been adopted. A displacement-controlled nonlinear static analysis scheme is utilized with constant displacement increments. Displacement is monitored and controlled at any single node on the loading edge, to which all other nodes on the edge are kinematically coupled in the direction of loading. Unless explicitly stated, the solution is implemented within a stagger phase-field solution algorithm with a single prediction step (\(N_{staggs}=1\)) and a displacement norm convergence tolerance \(tol_{u}=10^{-5}\). In all the numerical experiments conducted in this work, the mesh size is consistently smaller than the length scale, i.e., \(h\leq l_{0}\) to accurately resolve the crack path.  
In case the phase field functional definition and associated length scale parameter initially adopted by [232] is employed (see, also Remark 1), then the corresponding mesh size inequality becomes \(h\leq l_{0}/2\).
# Modeling Soil Temperature with Fuzzy Logic and Supervised Learning Methods
## 2 Materials and Methods
### Machine Learning and Fuzzy Logic Models for Soil Temperature Estimation
#### 2.2.1 Mamdani Fuzzy Inference System (MFIS)  
Fuzzy logic was introduced by [PERSON] [43], and has since been used in many scientific studies. Fuzzy logic divides data into classes and assigns them degrees of membership between 0 and 1. There are different types of fuzzy membership functions (MFs) (triangular, trapezoidal, Gaussian, etc.). However, Gaussian MFs are generally preferred [44].The FIS is a rule-based computational framework designed to model complex systems using approximate reasoning. It comprises four fundamental components: fuzzification, knowledge base, inference engine, and defuzzifier [45]. During the fuzzification process, input data are transformed into linguistic variables--such as _low_, _high_, _small_, or _large_--and assigned corresponding membership degrees within predefined fuzzy sets. Subsequently, a set of _IF-THEN_ rules is applied to infer outputs based on the fuzzy inputs. An example of such a rule set is presented below.  
_Rule 1: IF \(x_{1}\) is low and \(x_{2}\) is low THEN \(y\) is low_  
_Rule 2: IF \(x_{1}\) is low and \(x_{2}\) is high THEN \(y\) is medium_  
_Rule 3: IF \(x_{1}\) is high and \(x_{2}\) is high THEN \(y\) is high_  
With these rules, \(x_{1}\) and \(x_{2}\) are the inputs, while \(y\) is the output. Defuzzification is applied to obtain crisp values from the fuzzified set and to determine the output values of the model.
## 3 Results  
The analytical model presented in Section 2 allows the normal incidence sound absorption coefficient to be calculated for any combination of thickness and apparent density of Posidonia fibers.  
To verify the reliability of the method, the absorption curves of several different experimentally tested combinations of thickness and density were calculated with the analytical model.  
Table 1 shows the physical parameters calculated as a function of density using the Formulas (9)-(13).  
Figure 7 presents the comparison between the experimental and calculated sound absorption curves; very good correspondence can be seen between the analytical model and the experimental measurements.  
From Figure 7, it is evident that the Posidonia fibers have promising sound-absorbing properties in a wide range of densities.  
Figure 8: Comparison between the values of \(\Lambda\) and \(\Lambda^{\prime}\) obtained by inversion from the experimental measurements and interpolation curves (12) and (13).  
Thanks to the analytical model, the sound absorption can be compared as the bulk density varies for a constant thickness (for example, 50 mm); in Figure 9, the variation of sound absorption can be seen as the density increases from 50 kg/m\({}^{3}\) to 200 kg/m\({}^{3}\).  
When comparing the acoustic performance of Posidonia fibers with traditional fibrous materials, analytical models present in the literature can be used, such as:  
* The model proposed in [33] for polyester fibers;
* The model proposed in [34] for mineral fibers.  
Figure 9 shows comparisons between polyester fibers and mineral fibers with commonly used densities (50 kg/m\({}^{3}\)) with the same thickness of 50 mm.  
As the figures show, due to their larger diameter, the Posidonia fibers have lower absorption at the same density of 50 kg/m\({}^{3}\). When the density of the fibers is increased to 100 kg/m\({}^{3}\), the performance of the polyester fibers (50 kg/m\({}^{3}\)) is reached, and when it is increased between 150 and 200 kg/m\({}^{3}\), the performance of mineral wool (50 kg/m\({}^{3}\)) is reached.
# Large Scale Implementations for Twitter Sentiment Classification
## 6 Results and Evaluation
### Our Datasets for Evaluating MapReduce versus Spark Framework
#### 6.1.2 Effect of \(k\)  
In this subsection, we attempt to alleviate the problem of low classification performance for binary classification without Bloom filters. To achieve this we measure the effect of \(k\) in the classification performance of the algorithm. We test four different configurations where \(k\in\{50,100,150,200\}\). The outcome of this experimental evaluation is demonstrated in Table 4. For both binary and multi-class classification, increasing \(k\) affects slightly (or not at all) the harmonic f-score when we embody Bloom filters. On the contrary (without Bloom filters), there is a great enhancement in the binary classification performance for hashtags and emoticons and a smaller improvement in case of multi-class classification. The inference of this experiment, is that larger values of \(k\) can provide a great impulse in the performance of the algorithm when not using Bloom filters. However, larger values of \(k\) mean more processing time. Thus, Bloom filters manage to improve the binary classification performance of the algorithm and at the same time they reduce the total processing cost.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline
**Framework** & \multicolumn{3}{c}{**MapReduce**} & \multicolumn{3}{c}{**Spark**} \\ \hline
**Setup** & **BF** & **NBF** & **Random Baseline** & **BF** & **NBF** & **Random Baseline** \\ \hline Binary Emoticons & 0.77 & 0.69 & 0.5 & 0.77 & 0.76 & 0.5 \\ Binary Hashtags & 0.74 & 0.53 & 0.5 & 0.73 & 0.71 & 0.5 \\ Multi-class Emoticons & 0.55 & 0.56 & 0.25 & 0.59 & 0.56 & 0.25 \\ Multi-class Hashtags & 0.32 & 0.33 & 0.08 & 0.37 & 0.35 & 0.08 \\ \hline \end{tabular}
\end{table}
Table 2: Classification results for emoticons and hashtags (BF stands for Bloom filter and NBF for no Bloom filter).  
\begin{table}
\begin{tabular}{l c c} \hline
**Setup** & **BF** & **NBF** \\ \hline Binary Emoticons & 0.08 & 0.06 \\ Binary Hashtags & 0.05 & 0.03 \\ Multi-class Emoticons & 0.05 & 0.02 \\ Multi-class Hashtags & 0.05 & 0.01 \\ \hline \end{tabular}
\end{table}
Table 3: Fraction of tweets with no matching vectors (BF for Bloom filter and NBF for no Bloom filter).
# The Necessity of LED to Ambient Light Ratio Optimization for Vehicular Optical Camera Communication
## 4 Simulation
### Effect of Communication Distance  
This section provides simulation results to illustrate the effect of communication distance. The LED panel images were replicated with the communication distance varying from 30 to 70 m. The L2A ratio of 1 and the LED interval of 1 are assumed in all replicated frames. The ambient light is set randomly in the range from 50 to 16,000 lux. The LED luminance is set based on the ambient light and the L2A ratio. The LED surface reflectance is set randomly in the range from 0.3 to 0.5. The ISO is set based on the ambient light. The remaining parameters are set to their fixed values given in Table 1.  
Figure 18 shows examples of LED panel frames replicated at communication distances of 30, 50, and 70 m, respectively. As both LED interval and communication distance affect the LED feature variance through LED spacing, the effect of these two parameters are similar. More specifically, increasing the distance has the same effect as decreasing LED interval. When the distance increases, the LED panel image becomes smaller and thus makes the LED spacing smaller. Consequently, the LED interference gets stronger as the distance increases.  
Figure 19 shows examples of the distribution of LED features in ten frames replicated with different communication distance but the same L2A ratio of 1 and the same LED interval of 1. Because of the increase of the LED interference as the distance increases, the cluster of each class of LED spread spreads more, which makes the Wvar increases. Moreover, because of these spreading, the clusters of On and Off LED features get closer to each other, making the Bvar decreases.  
Figure 17: Variances corresponding to LED intervals. (**a**) Bvar and Wvar corresponding to LED intervals. (**b**) B2W variance ratio corresponding to LED intervals.  
Figure 20a shown the Bvar and Wvar at different communication distances. As suggested from Figure 19, the Bvar decreases while the Wvar increases as the distance increases. This makes the B2W variance ratio decreases sharply as the distance increases as shown in Figure 20b.
## 4 Experiments and Discussion
### Ablation Experiments
#### 4.5.3 Parameter Sensitivity Study on Compound Loss  
In the ablation experiments, we confirmed the complementary nature of the region classification loss and the boundary modeling loss. To further examine the impact of their relative weighting within the combined loss function, we conducted a parameter sensitivity analysis on the Vaihingen dataset. This analysis aims to evaluate how different  
Figure 10: Comparison of segmentation results between models: (**1**) original image; (**2**) label; (**3**) baseline model; (**4**) model with PSFM module only; (**5**) model with GFM module only; (**6**) our proposed model.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Setting** & \(L_{region}\) & \(L_{boundary}\) & **mIoU (\%)** & **mF1 (\%)** \\ \hline Baseline & ✓ & ✓ & 72.85 & 83.80 \\ Multi-Class Region Focal Loss Only & ✓ & & 61.62 & 70.09 \\ Multi-Scale Boundary Loss Only & & ✓ & 63.43 & 74.06 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation results on the Vaihingen dataset with different loss settings. ✓ indicates the specific loss is added.  
configurations influence the model's segmentation performance and to identify the optimal balance between regional and boundary supervision. We examined four key parameters: (1) \(\alpha_{\text{region}}\), which is the weight for the region focal loss; (2) \(\beta\), the balance coefficient between region and boundary losses; (3) \(\gamma\), the focusing parameter in the focal loss; and (4) \(\alpha_{\text{boundary}}\), the weight for the boundary-specific loss term.  
In each experiment, one parameter was varied across five representative values while fixing the others to their default configuration (\(\alpha_{\text{region}}=1.0\), \(\beta=0.3\), \(\gamma=2.0\), \(\alpha_{\text{boundary}}=0.3\)). The evaluation metrics include mean intersection over union (mIoU) and mean F1 score (mF1) on the validation set. As shown in Table 6, the model is relatively insensitive to \(\alpha_{\text{region}}\) and \(\gamma\), maintaining stable performance across a wide range of values. In contrast, it is mildly sensitive to \(\beta\), with noticeable performance degradation when deviating from the balanced setting of \(\beta=0.3\). For \(\alpha_{\text{boundary}}\), the best performance is observed when it is set to 0.3. Increasing its value disrupts region classification, while reducing it weakens boundary clarity. This configuration achieves the best performance, consistent with the configuration used in the main experiments.
# FedRDS: Federated Learning on Non-IID Data via Regularization and Data Sharing
## 4 Experiments and Analysis
### Effect of the Regularization Coefficient  
How to choose the proper regularization coefficient \(\sigma\) is a crucial problem. If the \(\sigma\) is too large, it will make the client and global model too similar and the fitting of local data insufficient, increasing the degree of client drift. Choosing \(\sigma\) as a relatively small value may result in insufficient constraints on local updates. Therefore, we explored the regularization coefficient on the MNIST and SVHN datasets. The impact on the algorithm's accuracy is shown in Figure 7. Throughout all trials, we fine tune the optimal variable \(\sigma\) from a restricted pool of candidates set {0.001, 0.01, 0.1, 1.0}. When \(\sigma=0.001\), the regularization term has insufficient generalization ability to the full according model, so the algorithm's accuracy is relatively low. With the increase in \(\sigma\), the advantage of the regular term gradually becomes noticeable and the accuracy reaches the highest at \(\sigma=0.01\). But, as the \(\sigma\) value further increases, the accuracy begins to decrease. The reason for this is that if \(\sigma\) is too large, it can worsen the problem of client drift, which will negatively impact the model's overall performance. While increasing \(\sigma\) can improve the model's generalization ability, this benefit is not significant enough to offset the adverse effects of client drift. Therefore, the experimental results confirm our previous conjecture that the optimal \(\sigma\) values for both datasets in Figure 7 are 0.01.