esa-sceva/satcom-chunk-collection huggingface dataset:
# Presenting a Human Pupil Orbit Model (HPOM) for Eye-Gaze Tracking
## 2 Materials and Methods  
### Theoretical Background  
The pupil is a circular hole on the surface of an eyeball that is surrounded by the iris. Information about the shape or color of an object is transmitted through the pupil to the visual nerve. The pupil is a black circle regardless of race, age, or gender. It is well known that when the eye gazes at an object, it rotates about a central point inside the eye so that the pupil can be directed at the object in question.  
[PERSON] and [PERSON] observed the rotational movement of the eye using magnetic resonance imaging. This led to the ORCP reporting an average position change of 0.77 mm when the eye moved in the abduction direction, and a change of 0.14 mm when the eye moved in the adduction direction [30]. Their study showed the pupil moves along an ellipsoid orbit and the distance between the Pupil Centre Point (PCP) and the real ORCP changes according to the gaze direction. However, when one uses a camera to observe eye movement, the pupil is seen to move along a spherical orbit. This is because when an object is photographed, the light from the object passes through the camera's lens where distortion occurs according to the law of refraction. In addition, the image of the distorted object is then projected onto the camera's image sensor and converted into a digital image through an analog-to-digital converter [31]. Due to the characteristics of the lens and those of the analog-to-digital conversion process, the camera turns an area that is, in reality, only several millimetres into an area of several centimetres. This is because the size of 1 pixel depends on the distance from the object combined with the distortion of the object's shape caused by the camera and lens. The HPOM is designed to overcome these problems by converging the pupil to the correct point on a sphere. This can be achieved by estimating the pupil's position based on interpreting its elliptical shape. In order to create the three-dimensional HPOM, the shape of the pupil, which deforms into elliptical shapes according to gaze direction, is analysed and its position converted into three-dimensional coordinates. The positional relationship between the ORCP and the three-dimensional PCP can then be analysed [19].
# Hypergraph Position Attention Convolution Networks for 3D Point Cloud Segmentation
## 4 Experiment
### Implementation Details  
Our proposed framework uses the PyTorch 1.8.1 deep learning framework with hardware configuration CPU:16-core Intel(R) Xeon(R) Platinum 8350C CPU @ 2.60 GHz and GPU: RTX 3090. In the S3 DIS dataset, the training dataset was generated following the same preprocessing scheme as well-known networks such as PointNet++ [5] and PAConv [42]. Specifically, the room is divided into blocks using the sliding window method, and then, 4096 points are randomly sampled as samples in each block. We utilize the farthest point sampling and ball querying schemes for hypergraph construction of XYZ and RGB feature information of the sample point clouds for the point cloud semantic segmentation task. We train the model using the SGD optimizer with the cross-entropy loss function minimized, the number of weight matrices set to 16, the PAConv convolution kernel set to [16; 16; 16], and the empirical learning rate set to 0.05. We maintain the same training and testing batches as PAConv so that we can better evaluate the performance of the network.  
In the ShapeNet Part dataset, we use the same hardware and software configuration. We set the training batch size to 32 and use 200 epochs for training, the number of sample points is set to 2048, the model [42] convolution kernel is set to [16; 16; 16], and we also train the model using the Adam optimizer, set the momentum to 0.9 for batch normalization, set the minimum learning rate to 0.003, and use a fixed-step decay scheme to test our network. Our comparison with other models can demonstrate the effectiveness of our proposed method. To enhance the data, we also perform random scaling, rotation and dithering on the points of the tested model.
# Seismic Exploration Methods for Structural Studies and for Active Fault Characterization: A Review
## 3 Developments in Seismic Imaging
### Pre-Stack Depth Migration
The FWI-RTM workflow involves the following steps: (1) an initial model of the subsurface is created based on available geological data; (2) RTM is performed using the initial model to generate a preliminary image of the subsurface; (3) FWI is used to update the model parameters used in RTM to minimize the difference between the observed and synthetic data. The updated model parameters are used in RTM to generate a more accurate and high-resolution image of the subsurface. FWI is particularly useful in RTM, as it can help to correct inaccuracies in the initial model and improve the imaging of complex geological structures, such as faults and fractures. An interesting example of the integration between RTM and FWI is provided by [PERSON] et al. [29] using seismic data acquired above salt domes in the Atlantis field in the Gulf of Mexico. Their study shows that an improved velocity model provided by the FWI can greatly enhance the quality of the RTM images (see Figure 4a-d). The FWI modified the salt geometry imaged by seismic tomography (compare Figure 4a,b), which results in substantial enhancements in the RTM image, such as a considerable uplift of the salt (as shown in Figure 4d).
# Modified Kapok Fibers (_Ceiba pentandra_ ([PERSON]) for Oil Spill Remediation
## 3 Results and Discussion
### Sorption Assay
Finally, at 2.0 mol/L of HCl, a reduction of 11.6% compared to 1.0 mol/L is seen, suggesting the presence of a large number of surface fissure points and links with the lumen, making the low-viscosity diesel unsupported in these internal structures, and flowing out of the sorbent. Through the analysis of the acid treatment by FTIR, we can see the spectral patterns shown in Figure 4b. It can be noticed, according to Figure 3c, that treatments with NaOH at 0.01 and 0.1 mol/L yielded sorptive capacities of 56.21 and 58.23 g/g, respectively, i.e., within the expected value range of the in natura fibers. Therefore, it is safe to assume that, at these concentrations and within the experimental time of 50 min, no modifications that justify an improvement nor decline in the sorption process took place. Notwithstanding, from the NaOH concentration of 1.0 mol/L on, there is an appreciable demotion in the sorptive character of the fibers, with 50.99 g/g of sorption, which represents 12.4 less than those treated with one-tenth of the concentration. This result indicates the beginning of the surface degradation process, known as mercization, which is a common procedure that applies an alkaline attack to lignocellulosic biomasses, aiming to dismantle the lignin and hemicellulose structure and expose the cellulose nucleus of the material or to grant a higher surface roughness. This effect is further developed when the base concentration is increased to 2.0 mol/L, which leads to the lowest diesel oil sorption by kapok fibers seen in this work, at 38.56 g/g, a value 35% lower than the natural material. The fiber most likely suffered the advanced degradation of functional groups, with a reduction in lipophilicity and intensification of hydrophobicity, since the process tends to allow access to the cellulose content.
## 3 Properties and Characteristics of LoRaWAN Technologies
### Data Rate and Payload  
The maximum payload length and related data rate in LoRaWAN are significantly affected by the interaction of spreading factors, bandwidth, and coding rate. In general, choosing lower bandwidths and spreading factors increases transmission ranges but decreases data rates; on the other hand, choosing lower bandwidths and spreading factors increases data rates but reduces transmission ranges [19].  
Various factors influence the maximum payload length and data throughput. These factors include error correction, frame format, and other variables that may change throughout LoRaWAN networks and devices. The LoRaWAN specification describes data rates for 125 kHz bandwidth that range from DR0, which is the least resilient and considered the highest data rate, to DR5, which is the most robust and considered the lowest data rate, and for 500 kHz bandwidth, which vary from DR6 to DR7. Payload lengths range from 222 bytes (DR0 to DR2) to 51 bytes (DR3 to DR5) and 1152 bytes (DR6 and DR7) [20]. LoRa data rates are not limited to any specific range, but they can range from 0.162 kb/s to 50 kb/s. Depending on the deployment location, LoRaWAN-based systems using LoRa in the physical layer (PHY) can achieve data speeds of 5.47 kb/s as well as 11 kb/s, or 21.9 kb/s [18]. The frequency shift keying (FSK) modulation technique enables the LoRaWAN to achieve a maximum data rate of 50 kb/s [21].  
To achieve an extended range and less inference, it is important to balance considerations such as lower data rates with higher spreading factors or higher data rates with lower spreading factors to improve throughput and overall data transmission. The choice can be according to the requirements of each application or device, which offers an optimal trade-off between data transmission efficiency and network performance [21].
# ASDNet: An Efficient Self-Supervised Convolutional Network for Anomalous Sound Detection
## 2 Proposed Method
### Different Methods for Anomaly Score Calculation
#### 2.4.4 Local Outlier Factor  
The LOF [36] algorithm is a density-based anomaly detection method that identifies outliers by comparing the local density of a data point to that of its neighbors. Specifically, LOF evaluates whether a data point is an outlier by calculating the ratio of its local density to the local densities of its neighboring points. If a data point's local density is significantly lower than the densities of its neighbors and the ratio falls below a certain threshold, the point is considered an outlier. The main advantage of LOF is its ability to detect anomalies in datasets with varying local density distributions, making it particularly effective for cases where the data exhibits local density variations.  
The core idea of LOF is to assess the anomaly degree by computing the local reachability density (LRD) for each data point and its neighbors. For each data point \(p\), its LOF value is the average ratio of the LRD of its neighbors to the LRD of the point \(p\). The formula for calculating LOF is as follows:  
\[LOF(p)=\frac{1}{|N_{k}(p)|}\sum_{o\in N_{k}(p)}\frac{LRD(o)}{LRD(p)} \tag{15}\]  
where \(N_{k}(p)\) is the set of \(k\)-nearest neighbors of point \(p\), and \(LRD(p)\) is the local reachability density of point \(p\).  
In this study, for each newly identified data point, we compute its LOF value and use the negative of this value as the corresponding anomaly score. The anomaly score \(A_{LOF}\) is given by the following:  
\[A_{LOF}=-LOF(p) \tag{16}\]  
A lower anomaly score indicates that the data point is more typical, whereas a higher score suggests a greater likelihood of it being an anomaly.
## 3 Results of Data Training and Discussion
### Logistic Regression Modeling  
An exhaustive evaluation of various models was performed, and the accuracy achieved by each model is presented in Table 4, with features including age, gender, hours of training, days of training, previous injuries, corrective treatment, sport with injury, preventive treatment, stress, kinesiophobia, fatigue, previous warmup, average hydration on event day and outcome. In particular, the logistic regression model demonstrated exceptional accuracy, reaching an impressive accuracy rate of 90.0%. This means an outstanding ability to accurately classify athletes based on the presence or absence of injuries. Interestingly, the SVM Kernel, Linear Discriminant, and Binary GLM Logistic Regression models also showed high levels of accuracy, reaching 89.2%, 89.0%, and 89.0%, respectively. In contrast, the models with the lowest accuracy were the Fine Tree and the Gaussian Naive Bayes, with 83.2% and 84.8%, respectively. The application of logistic regression, known for its reliability, is crucial to achieving the desired results, and these findings have significant implications for identifying and preventing injuries in athletes.  
The confusion matrix for the logistic regression model is illustrated in Table 5. It reveals that the model correctly predicted 6 cases without injuries and 354 cases with injuries. However, there were 37 cases where the model incorrectly predicted that there were no injuries when there were, and 3 cases where it incorrectly predicted injuries when there were none.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Accuracy** \\ \hline Fine Tree & 83.2\% \\ Linear Discriminant & 89.0\% \\ Binary GLM Logistic Regression & 89.0\% \\ Gaussian Naive Bayes & 84.8\% \\ Linear SVM & 89.2\% \\ Fine KNN & 86.0\% \\ SVM Kernel & 89.2\% \\ Boosted Trees & 87.8\% \\ Logistic Regression & 90.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Response variable.
## 4 Conclusions  
In this study, the overlying water and sediment samples of six rivers in Changchun City were analyzed, and the content distribution of N and P was clarified. Based on the surface water environmental quality standards, the N and P contamination degree of each river was evaluated. From the time perspective, the pollution level of N and P in the key rivers of Changchun was observed and analyzed. Additionally, the relationship between N, P, and water quality was comprehensively considered. In the main rivers of Changchun City, there were differences in the spatial distribution of N and P contents in the overlying water and sediment. Specifically, the contents of N and P in the overlying water were high in the north and west of Changchun City, but low in the south. In the south and west, the contents of N and P in the sediment were high, while those in the north were low. The pollution of NH\({}_{3}\)-N, TN, and TP in Xinkai River and Chuan Lake was extremely serious, with a quality rating of V or above. The N pollution degree in Yitong River, Jingshui River, and West Lake was acuter than that of P pollution. In Yongchun River, the N and P environmental quality was the best. Pollution sources, mainly industrial wastewater, should be the primary treatment object of N and P pollution prevention and control of the water environment. Figure 7: True and predicted values of chemical oxygen demand (COD) predicted by partial least squares regression. Through the time analysis of a crucial river in Changchun City, it was found that the N and P pollution in Yitong River gradually improved since 2016, due to the government's vigorous comprehensive water quality control work, and the exceeding multiple of NH\({}_{3}\)-N and TP has decreased to less than 1 in 2021. Under the condition of balanced water nutrient load, the change in external environment might still lead to eutrophication. Therefore, in the comprehensive management of surface water environmental quality, climate and social trends should not be ignored. The correlation analysis showed that there was indeed an internal correlation between the water quality and the distribution of N and P. P content in the overlying water and sediment had the most powerful correlation with the water quality parameters. However, because of the complex interaction between N, P, and water quality parameters, there was no single parameter acting alone. Based on the high correlation between COD and N and P in the overlying water, COD could be predicted to some extent using the partial least squares method. In conclusion, with the development of social industry and urbanization, more and more surface water pollution has occurred. The pollution of nutrition elements was closely connected with water quality. Taking Changchun, a typical representative of a Chinese old industrial city, as an example, investigating the temporal and spatial distribution of N and P pollution and exposing the correlation between N, P, and water quality parameters could be used for reference in the surface water pollution control in other industrial cities.
###### Abstract  
This paper deals with an enhanced robust design optimization (RDO) method and its application to the strength design problem of seat belt anchorage, related to the front crash safety of multi-purpose vehicles. In order to determine the rational design safety of the newly developed automotive part, such as the seat, in which the reliability of the evaluation data is not sufficient at the design stage, it is necessary to implement a probabilistic design considering uncertainties. Thickness size variables of the seat frame structure's members were considered random design variables, including uncertainties such as manufacturing tolerance, which are an inevitable hazard in the design of automotive parts. Probabilistic constraints were selected from the strength performances of the seat belt anchorage test, which are regulated in Economic Commission for Europe (ECE) and Federal Motor Vehicle Safety Standard (FMVSS), and the strength performances were evaluated by finite element analyses. The RDO problem was formulated such that the random design variables were determined by minimizing the seat frame weight subject to the probabilistic strength performance constraints evaluated from the reliability analyses. Three sigma level quality was considered for robustness in side constraints. The mean value reliability method (MVRM) and adaptive importance sampling method (AISM) were used for the reliability analyses in the RDO, and reliability probabilities from the MVRM and the AISM on the probabilistic optimum design were assessed by Monte Carlo simulation (MCS). The RDO results according to the reliability analysis methods were compared to determine the optimum design results. In the case of the RDO with the AISM, the structure reliability was fully satisfied for all the constraint functions, so the most reliable structural safety was guaranteed for the seat frame design.  
robust design optimization; reliability analysis; mean value reliability method; adaptive importance sampling method; seat belt anchorage
## 3 Methodology
### Modeling
#### 3.4.3 XGBoost  
XGBoost (extreme gradient boost), developed by [PERSON] et al. in 2014 [35], is a highly effective ensemble learning method that builds on the principles of gradient-boosting decision trees. It introduces regularization techniques to smooth the loss function and prevent overfitting, resulting in improved control over model complexity. XGBoost utilizes an efficient approximation algorithm to optimize splits, enhancing the efficiency and scalability of the gradient boosting process. Additionally, it exhibits robustness in handling missing or sparse values by allocating specific branches to address these scenarios.  
The pseudocode in Algorithm 3 presents the comprehensive step-by-step procedure and hyperparameter choices used for constructing the XGBoost model in this research. Essential hyperparameters, such as learning rate, maximum tree depth, column subsampling by the tree, the number of estimators, and subsampling, were utilized to construct the model. The algorithm began by iterating a specified number of times to compute the gradients and Hessians for each sample in the training dataset. These gradients and Hessians played crucial roles in guiding the construction of the decision trees. In each iteration, a decision tree was built with a specified maximum depth, and the training dataset predictions were updated using the tree and the set learning rate. This iterative process allowed the XGBoost classifier to progressively enhance its predictions by incorporating knowledge from each decision tree. Once the ensemble of decision trees was constructed, the algorithm employed a prediction procedure to generate predictions for newly input data. The ensemble prediction started with an initial value of zero and then for each decision tree, a prediction was made based on the input data. The ensemble prediction was then updated by aggregating the predictions from each tree [36; 37].
```
1:training dataset: \(D\)
2:\(learning\_rate\): [0.1, 0.01, 0.001]
3:\(colsample\_bytree\): [0.8, 1.0]
4:\(max\_depth\): [3; 5; 7]
5:\(n\_estimators\): [100, 200, 300]
6:\(random\_state\): [42; 123]
7:Subsample: [0.8, 1.0]
8:
9: Ensemble prediction: \(E(X)\)
1:procedure XGBoostClassifier(\(D\), \(learning\_rate\), \(colsample\_bytree\), \(max\_depth\), \(n\_estimators\), \(subsample\), \(Random\_state\))
2:for\(i=1\) to \(N\)do
3: Compute gradients and Hessians for each sample in \(D\)
4: Build a decision tree with maximum depth \(t\)
5: Update training set predictions using the tree and learning rate
6:endfor
7: Ensemble prediction \(E(X)\leftarrow\) Make prediction using the ensemble of decision trees on input \(X\)
8:return\(E(X)\)
9:endprocedure
10:procedureXGBoostPrediction(\(X\))
11: Ensemble prediction \(E(X)\gets 0\)
12:for each decision tree do
13: Make prediction using the tree on input \(X\)
14: Update \(E(X)\) by adding the prediction
15:endfor
16:return\(E(X)\)
17:endprocedure
```
**Algorithm 3** The extreme gradient boost (XGBoost) pseudocode
## 3 Results
### Effects of PGPB on Canola Exposed to Saline Conditions
#### 3.3.2 Membrane Injury Index (MII)  
The membrane injury index (MII) is a useful biomarker for providing information about the level of damage of cell membranes; higher MII values are related to greater membrane damage. In saline soil, non-inoculated plants showed a significantly higher MII index in comparison with plants grown in control soil. However, PGPB strains significantly lowered the MII of plants exposed to salt, compared with the non-inoculated ones grown under the same conditions (Figure 3).  
\begin{table}
\begin{tabular}{c c c} \hline  & **Proline (nmoles/mg f.w.)** & **Phenolic Compounds (ug Chlorogenic Acid eq./g f.w.)** \\ \hline N.I.C. & 2.17 \(\pm\) 0.21 a & 8.57 \(\pm\) 0.58 a \\ N.I.S. & 6.07 \(\pm\) 0.28 b & 10.46 \(\pm\) 0.06 a \\ A.C. & 1.57 \(\pm\) 0.12 a & 10.57 \(\pm\) 0.13 a \\ A.S. & 2.95 \(\pm\) 0.17 a \({}^{*}\) & 16.67 \(\pm\) 0.13 b \({}^{***}\) \\ B.C. & 0.98 \(\pm\) 0.09 a \({}^{*}\) & 8.84 \(\pm\) 0.18 a \\ B.S. & 0.88 \(\pm\) 0.18 a \({}^{***}\) & 9.67 \(\pm\) 0.06 a \\ G.C. & 14.62 \(\pm\) 0.87 a \({}^{***}\) & 18.72 \(\pm\) 0.26 a \({}^{***}\) \\ G.S. & 39.68 \(\pm\) 2.09 b \({}^{***}\) & 20.23 \(\pm\) 1.22 b \({}^{***}\) \\ H.C. & 0.69 \(\pm\) 0.31 a \({}^{*}\) & 8.50 \(\pm\) 0.31 a \\ H.S. & 0.75 \(\pm\) 0.17 a \({}^{***}\) & 9.25 \(\pm\) 0.07 a \\ U. C & 1.54 \(\pm\) 0.23 a & 8.37 \(\pm\) 0.17 a \\ U. S & 5.85 \(\pm\) 0.22 b & 12.77 \(\pm\) 0.31 b \({}^{*}\) \\ \hline \end{tabular}
\end{table}
Table 2: Proline and phenolic compounds of _B. napus_ cv. SY Saveo grown for 30 days on non-saline soil (C) or saline soil (S). Plants were inoculated with plant growth-promoting bacteria (A = _A brasilense_, B = _B ambifaria_, G = _A globiformis_, H = _H seropedicae_, U = _Pseudomonas_ sp. UW4) or not (N.I. = non-inoculated). Different letter means significant difference within the same bacterial treatment. Significant differences between inoculated or not inoculated plants grown at the same soil conditions are marked as \({}^{*}\) (\(p<\) 0.05), \({}^{***}\) (\(p<\) 0.001) (\(n=9\)).  
Figure 2: Water content of the plants in non-saline (C) and in saline soils (S). Data are expressed as means \(\pm\) SE (\(n=8\)). N.I. = non-inoculated. Mean values in the column marked by different letters are significantly different within the same group (\(p\leq\) 0.05; ANOVA and Tukey–Kramer test). Significant differences between groups are reported as \({}^{**}\)\(p<\) 0.01; \({}^{***}\)\(p<\) 0.001.