esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment and Analysis
### Evaluation of Position Performance
#### 5.2.2 Evaluation on the Modified Euroc Dynamic Dataset
* Comparative Analysis of Positioning Performance Figure 11 shows the heat maps of the positioning trajectory of DSO (Figure 11a-c) and Dynamic-DSO (Figure 11d-f) in V101_syn, V201_syn, and V202_syn respectively. From three heat maps, it can be seen that the trajectory estimated by Dynamic-DSO is closer to the groundtruth than DSO in three image sequences, and the maximum, minimum and average values of trajectory error are less than DSO. Figure 12 compares the trajectory error with time of Dynamic-DSO and DSO in three synthetic sequences. The trajectory error of DSO exceeds 1m for quite a long time and the fluctuation is obvious. The maximum error in the V202_syn sequence exceeds 4 m, which cannot meet the accuracy requirements in indoor positioning. The trajectory error of Dynamic-DSO is much lower than DSO. The maximum value is 0.557 m in the V202_syn sequence, and the error fluctuation of the whole positioning process is small. Figure 13 shows the positioning results of DSO and Dynamic-DSO in three axes x, y, z. Compared with DSO, the positioning results of the proposed method are closer to the groundtruth in each axes in dynamic environments. Figure 10: The processing of the V202_syn sequence. (**a**): Images in original Euroc V2_02 sequence. (**b**): Images collected by ourselves, containing a pedestrian. (**c**): Sematic images obtained by Mask-RCNN. (**d**): Images in the modified V202_syn sequence. Figure 9: The acquisition equipment includes a laptop (**a**) and an Intel zr300 camera (**b**), which are fixed on the desktop. Figure 14 is the boxplot of the trajectory error between Dynamic-DSO and DSO, which is used to analyze the discrete distribution of the error. The box of Dynamic-DSO is flatter, meaning that the trajectory error of the proposed system is more concentrated. Compared to DSO, the upper and lower limiting values of Dynamic-DSO are lower, the median, the upper and lower quartiles are smaller, and the outliers are very few--verifying that the positioning accuracy and robustness of Dynamic-DSO are more superior than those of DSO in three dynamic sequences. Figure 11: Heat maps of the positioning trajectory of Dynamic-DSO and DSO in three sequences. (**a**), (**b**), (**c**): Positioning results of DSO in V101_syn, V201_syn and V202_syn respectively. (**d**), (**e**), (**f**): Positioning results of Dynamic-DSO in V101_syn, V201_syn and V202_syn respectively. Figure 12: Comparison of the trajectory error with time between Dynamic-DSO and DSO in three synthetic sequences. Comparison in V101_syn, V201_syn and V202_syn is shown from left to right. Table 2 shows statistics for the trajectory error of the DSO and Dynamic-DSO in V101_syn, V201_syn and V202_syn. Comparing the RMSE of the two methods, the positioning accuracy of the proposed method in the dynamic environment is significantly higher than that of DSO.
## 3 Results and Discussion
### Multivariate Data Analysis
#### 3.2.2 Comparisons Between Groups B and C
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Feature Selection Mode** & **Feature Subsets** & **AUC** & **Accuracy** & **Precision** & **Recall** & **Specificity** \\ \hline \multirow{10}{*}{**Dataset**} & Full dataset & All 25 cytokines & 0.445 & 0.450 & 0.450 & 0.450 & 0.450 \\ \cline{2-8}  & & HGF, IL-10, IL-2R, IL-13, IL-7, CXCL10, & 0.731 & 0.650 & 0.650 & 0.650 & 0.650 \\  & & PARK7, CXCL9, IFN-\(\gamma\), VEGF & & & & & \\ \cline{2-8}  & & HGF, IL-10, IL-2R, IL-13, IL-7, CXCL10, & 0.744 & 0.700 & 0.708 & 0.700 & 0.700 \\  & & PARK7, CXCL9 & & & & & \\ \cline{2-8}  & & HGF, IL-10, IL-2R, IL-13, IL-7, CXCL10, & 0.740 & 0.600 & 0.601 & 0.600 & 0.600 \\  & & PARK7, CXCL9 & & & & & \\ \cline{2-8}  & & HGF, IL-10, IL-2R, IL-13, IL-7, & 0.746 & 0.725 & 0.726 & 0.725 & 0.725 \\  & & HGF, IL-10, IL-2R, IL-13, IL-7, CXCL10 & 0.731 & 0.650 & 0.652 & 0.650 & 0.650 \\ \cline{2-8}  & & HGF, IL-10, IL-2R, IL-13, IL-7 & 0.719 & 0.650 & 0.652 & 0.650 & 0.650 \\ \cline{2-8}  & & HGF, IL-10, IL-2R, IL-13 & 0.728 & 0.700 & 0.708 & 0.700 & 0.700 \\ \cline{2-8}  & & HGF, IL-10, IL-2R & 0.724 & 0.725 & 0.726 & 0.725 & 0.725 \\ \cline{2-8}  & & HGF, IL-10 & **0.772** & **0.775** & **0.813** & **0.775** & **0.775** \\ \cline{2-8}  & & HGF & 0.713 & 0.750 & 0.750 & 0.750 & 0.750 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Univariate \\ Analysis \\ \end{tabular} } & VEGF & 0.541 & 0.575 & 0.575 & 0.575 & 0.575 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Naive Bayes models for mortality in the ICU, i.e., discriminating between discharged patients with IMV (Group B) and deceased patients with IMV (Group C). Models were based on full datasets or feature subsets of cytokines, routine blood analyses, or hybrid data selected by an information gain algorithm or univariate data analysis. Figure 5: t-SNE representations for Group B (blue) versus Group C (red), considering 25 serum cytokines (**a**), 9 routine blood analyses (**b**), and 34 cytokines and routine blood analyses (**c**); and feature subsets of serum cytokines (**d**), routine blood analyses (**e**), and cytokines plus routine blood analyses (**f**) that led to the best predictive models. Since the best results from the routine blood analyses’ dataset were achieved using a single variable, the t-SNE (**e**) was obtained using the features from the second-best model (lymphocyte and platelet counts). The t-SNE plots using the best model's features (Figure 5d-f) led to evident separations between patients from Groups B and C. The second best model for the subset including solely routine blood analyses was employed in the t-SNE plot analysis, leading to a better cluster formation (Figure 5e) than when using the entire dataset (Figure 5b). The best separation was achieved when using lymphocyte counts, IL-2R, and HGF (Figure 5f),
# Considering the Consequences of Cybersickness in
## 3 Results
### VR Hardware and Cybersickness  
When it comes to hardware factors, in this study, we mainly considered locomotion, control, and display type.  
In Table 4, the total SSQ score (Q-value = 7.864, \(p\) = 0.020, \(p\)\(\leq\) 0.05 *) and disorientation (Q-value = 8.396, \(p\) = 0.015, \(p\)\(\leq\) 0.05 *) are significantly different among the modes of locomotion. In our study, there were a total of four different modes of locomotion: sitting (n = 6, 23.1%, total mean score = 23.044, 95% CI: 10.847-35.240), standing (n = 7, 26.9%, total mean score = 24.122, 95% CI: 14.779-33.464), walking (n = 11, 42.3%, total mean score = 13.686, 95% CI: 10.173-17.199), and bicycling (n = 2, 7.7%, total mean score = 22.330, 95% CI: 14.310-30.349). Table 6 shows that the bicycling mode had the highest nausea score (mean = 21.491, 95% CI: 14.254-28.727). Meanwhile, standing led to higher oculomotor (mean = 20.162, 95% CI: 10.883-29.441) and disorientation (mean = 22.378, 95% CI: 12.316-32.440) sickness than other locomotion modes. The rankings are shown in Figure 4; the subscale scores of walking were lower than other locomotion.  
As for the control aspect, we studied controller-based, gesture-based, and gazed-directed modes. A general controller for virtual rehabilitation equipment in a virtual environment (n = 9, 34.6%, mean = 17.537) was less susceptible to cybersickness. However, there is still proof that the gaze-directed (n = 9, 34.6%, mean = 23.953) and gesture-based control modes (n = 6, 23.1%, mean = 21.451) were significantly more uncomfortable than the typical controller, as shown in the SSQ total scores (Q-value = 21.936, \(p\)-value = 0.000, \(p\)\(\leq\) 0.001 ***). Additionally, gesture-based training may have resulted in greater oculomotor (mean = 24.669, 95% CI: 10.800-38.538) and disorientation (mean = 20.369, 95% CI: 8.010-32.727) symptoms. These were brought on by the patients' high vulnerability and high motion degree. The biggest range value in Figure 4 is for gaze-directed. The patient has complete freedom to stare anywhere they choose while traveling with the gaze method. This function causes dizziness and strain on the eyes, which increased the disorientation score (mean = 30.953, 95% CI: 14.490-47.416).  
In Table 4, the values for the display types are significant in the total SSQ scores (Q-value = 7.660, \(p\) = 0.006, \(p\)\(\leq\) 0.01 **), nausea scores (Q-value = 21.058, \(p\) = 0.000, \(p\)\(\leq\) 0.001 ***), oculomotor scores (Q-value = 16.903, \(p\) = 0.000, \(p\)\(\leq\) 0.001 ***), and disorientation scores (Q-value = 31.741, \(p\) = 0.000, \(p\)\(\leq\) 0.001 ***). According to the boxplot, three degrees of freedom (n = 12, 60%) had higher subscale scores on the SSQ than six degrees of freedom (n = 17, 35%).
# Experimental Study on Cavity Formation and Ground Subsidence Behavior Based on Ground Conditions
## 4 Correlation Analysis Based on Ground Conditions  
Based on the model test results, a [PERSON]'s correlation analysis was conducted to examine the correlations between ground conditions (relative density, groundwater level, and fines content), the time until cavity formation and ground subsidence, and the cavity volume. A [PERSON]'s correlation analysis helps identify the magnitude of the correlations by examining linear relationships between variables. A correlation analysis is conducted using Equation (3). A higher absolute value of the correlation coefficient indicates a higher correlation between the two variables. The test statistic (\(p\)-value) should be examined for the correlation analysis results with statistical reliability. It is an indicator of statisti  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Fines (\%)**} & \multicolumn{2}{c}{**Groundwater Level**} & \multirow{2}{*}{**Soil Discharge**} & \multicolumn{2}{c}{**Cavity Volume**} \\  & **(cm)** & & **(cm3)** \\ \hline \multirow{4}{*}{5} & 10 & 11.88 & 7968.13 \\  & 20 & 7.82 & 5245.01 \\  & 30 & 4.73 & 3172.50 \\  & 10 & 9.34 & 6260.94 \\
7.5 & 20 & 3.65 & 2446.73 \\  & 30 & 1.77 & 1186.49 \\  & 10 & 0.00 & 0.00 \\
10 & 20 & 10.84 & 7262.31 \\  & 30 & 1.03 & 690.05 \\  & 10 & 4.98 & 3334.48 \\
12.5 & 20 & 4.42 & 2959.51 \\  & 30 & 3.64 & 2437.25 \\  & 10 & 0.00 & 0.00 \\
15 & 20 & 2.88 & 1927.28 \\  & 30 & 1.25 & 836.49 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Amount of soil discharged and cavity volume with respect to the fines content.  
cal significance. If the value is 0.05 or less, the two variables are said to be statistically significant [29; 30].  
\[\text{Corr}(\text{X},\text{Y})=\rho(\text{X},\text{Y})=\frac{\text{Cov}(\text{X},\text{Y})}{\sigma_{\text{x}}\sigma_{\text{y}}} \tag{3}\]  
where \(\sigma_{\text{x}}\), \(\sigma_{\text{y}}\) are the standard deviations of the variables.  
Table 8 presents the correlation analysis results. The correlation coefficient between the fines content and cavity volume and that between the relative density and cavity volume were \(-0.837\) and \(0.612\), respectively, indicating strong correlations. In addition, the \(p\)-values between the variables were less than \(5\%\), confirming statistical significance [31]. Therefore, attention must be paid to cavity formation in the ground by managing the relative density and fines content of the ground. Our correlation analysis was conducted using a limited amount of data. This appears to have resulted in no statistical significance for most factors. If the correlation analysis is conducted by considering sufficient parameters through experiments under various conditions in the future, the importance of factors influencing ground subsidence can be revealed.
## 4 Performance of Waste-Plastic-Modified Bitumen Binder and Mixture
### Characteristics of Plastic-Modified Bituminous Mixtures
#### 4.2.4 Challenges and Practical Considerations  
Despite the advantages of utilizing waste plastics as alternative materials in asphalt mixtures, several challenges impede extensive use. A significant issue is the diversity in efficacy contingent upon plastic type, dosage, and shape [18, 63]. Higher plastic contents often reduce mixture density and amplify air spaces, adversely affecting compaction and moisture resistance [60, 64].  
Field investigations by [65] emphasize that the adhesion between plastic and aggregates is significantly influenced by aggregate mineralogy. Basic rocks such as dolomite and limestone exhibit superior bonding with plastic in contrast to acidic rocks like granite. Another practical concern is the limited availability of comprehensive long-term field performance data. Despite promising outcomes in experiments conducted in Singapore, India, and the UK with 5-8% plastic content, widespread implementation necessitates thorough monitoring across various climates and traffic circumstances.  
Quality control is another critical issue. Variable properties of plastic feedstock, such as polymer type, contamination level, and particle size, can influence the consistency of outcomes. The processing infrastructure requires enhancement to provide secure handling, mixing, and emission management, especially during the heating of plastic waste.
## 3 Geodesic Paths on Surface Mesh
In practice, we start from the origin and project a ray upward along the z-axis. The intersection of this ray with the top of the mesh gives us our starting point. Figure 11: Determining geodesic path. (**a**) Point in triangle; (**b**) Point on edge; (**c**) Point on vertex; (**d**) Point on boundary (Green: geodesic path, Red: boundary). ```
Input: Point p, Triangle T Output: Boolean, true if p is inside or on edges of T Normal N of T is known T has vertices as point A, B, C \(\theta_{PN}\gets cos^{-1}((\mathbf{P}-\mathbf{A})\cdot\mathbf{N}/\|\mathbf{P}- \mathbf{A}\|)\) /* If p is in the plane of the triangle, then P - A is normal to N */ if\(|\theta_{PN}|>0\)then  return false end /* Calculating barycentric coordinates u and v */ \(u\leftarrow((\mathbf{P}-\mathbf{A})\times(\mathbf{C}-\mathbf{A})\cdot\mathbf{N })/\|\mathbf{N}\|^{2}\) \(v\leftarrow((\mathbf{B}-\mathbf{A})\times(\mathbf{P}-\mathbf{A})\cdot\mathbf{N })/\|\mathbf{N}\|^{2}\) /* For p to be inside the triangle \(0\leq u,v\leq 1\) and \(u+v\leq 1\) */ if\(0\leq u\leq 1\) and \(0\leq v\leq 1\) and \(u+v\leq 1\)then  return true else  return false end
```  
**Algorithm 1**Checking if point p is in triangle T  
```
Input: Triangle set S for point p where p is in T for all T \(\in\) S Output: unit normal vector n ifsize(S) = 1 then /* The point lies inside triangle T */ return n\({}_{T}\) else /* The point lies on an edge or vertex */ return avg(n\({}_{T}\)) for all T \(\in\) S end
```  
**Algorithm 2**Calculating tessellation normal of point p  
The reason for using a geodesic path algorithm to generate a path of the surface is that geodesics are very well-defined curves that are easy to understand and model for parametric curved surfaces. However other techniques such as using slicing planes to generate a path could also be used and this is a topic that could be explored in the future. Also, the algorithm given in [11] is essentially an approximate way of computing a geodesic and this can also be improved. In the next section, we will see how the geodesic path algorithm will be used to generate the child nodes from parent nodes during node insertion. An example output is shown in Figure 12b. A panel that is part of a launch vehicle propellant tank (it is an octant of a sphere with its top cut off) is shown in Figure 12a. We compute a set of geodesic paths on the 3D mesh model of this panel, starting from the origin point and length of 0.65 m, with directions of \(5^{\circ},45^{\circ},75^{\circ},155^{\circ},-75^{\circ}\).
# Single-Task Joint Learning Model for an Online Multi-Object Tracking Framework
###### Abstract
However, its performance declines with prolonged occlusions due to its reliance on local frame information. This study proposes a novel online tracking framework using deep feature models, called single-task joint learning (STJL). The main goal is to incorporate training data from diverse environments to enhance target recognition across various scenarios. Although it uses a tracking-by-detection approach, the model's addresses the existing limitations by introducing a strict initialization judgment to reduce incorrect detection boxes, minimize errors, and maintain the tracking accuracy. This framework will improve MOT identification accuracy in the application of developed surveillance systems [30; 31; 32]. The STJL model is first trained offline as shown in Figure 1. Multiple training datasets are standardized and relabeled to avoid duplicate labels and then combined into a single massive STJL dataset. This dataset is used for single-task training with cosine metric learning [33], resulting in the STJL model. After the offline training phase, the STJL model is used for the tracking. It extracts features from detection boxes, predicts object states, and uses deep features to determine whether boxes belong to the same object, linking them. New trackers are initialized for new objects. It is recommended to use both the detection box confidence and track confidence as criteria to differentiate accurate boxes from erroneous, thus excluding faulty detections. This study proposes two key contributions: the STJL model, which enhances feature extraction in diverse and challenging environments, and a refined tracker initialization strategy that combines detection and tracklet confidence, significantly reducing false positives and ID switches for improved tracking performance. This paper is structured as follows: Section 2 presents an initialization technique combining detection and tracklet confidence to enhance tracking precision. Section 3  
Figure 1: STJL model. validates the proposed deep model through experiments and compares its performance with state-of-the-art methods. Section 4 offers concluding remarks.
# Slope Stability Analysis of Open-Pit Mine Considering Weathering Effects
## 2 Laboratory Tests of Weathering Effects  
### Testing Specimens  
The engineering background of this study was one of the phosphate mines located in Yunnan Province, China. The predominant rock type surrounding the ore body in this mine is black shale. Given the low strength and rapid weathering characteristics of black shale in the mining area, the stability of the slopes, as well as the optimization of slope angles and boundaries, pose significant concerns. To address the concerns, black-shale samples collected from the mining areas were utilized as specimens to determine their fundamental mechanical properties under weathering effects. This particular phosphate mine was located at the northern edge of the Yunnan-Guizhou Plateau, where the terrain is complex with intense cutting and well-developed valleys, belonging to the deeply incised karst highland mountain landforms. The topography of the mining area is steep, with ore outcrops at altitudes ranging from 2600 to 3000 m. These topographical conditions have a significant impact on the preservation and enrichment depth of weathering products. The rock specimens were drilled perpendicular to the bedding direction at the depth of 600 m and processed into standard cylindrical specimens, which was in accordance with the rock mechanics test specification recommended by the International Society for Rock Mechanics (ISRM) [39]. Weathering may alter the mineral composition and physical properties of rocks, as well as their mechanical properties, but only the mechanical properties were focused on in this study. For the uniaxial compression test, the specimens had a diameter of 50 mm and a height-to-diameter ratio of 2. For the direct shear mechanical test, the height-to-diameter ratio was set to 1, while for the Brazilian splitting test it was 0.5. The specimens used for each test are shown in Figure 1.
# An Overview of the IberSpeech-RTVE 2022 Challenges on Speech Technologies
## 2 IberSpeech-RTVE 2022 Evaluation Databases
### The Basque Parliament Dataset
#### 2.5.3 The Ground Truth  
The ground truth is based on manually generated rich text transcriptions (including spontaneous speech events). These transcriptions follow the acoustics even though the syntactical correctness is lost. The timestamps of sentences were manually added, so they are fully reliable. Word-level timestamps inside sentences were obtained automatically by forced alignment of each sentence transcription with the corresponding audio. To verify the accuracy of word-level timestamps, an informal test was carried out using several randomly chosen sentences, by manually adding word-level timestamps and comparing them with the automatically generated timestamps. It was observed that differences between manual and automatic timestamps spanned from 0 to 20 ms. Thus, the automatic segmentation was considered good enough for the purposes of this evaluation, provided that a reasonable collar time was applied.  
For this evaluation, only the words appearing in the paired text are kept in the ground truth, the remaining elements of the rich text transcription being hidden. Note that the evaluation focuses on how well the paired text is aligned with the audio. Taking this into account, if a word \(w\) in the paired text is aligned with an audio segment that is not included in the ground truth, it is guaranteed that neither the word \(w\) nor any other word in the paired text appears in that segment, so the time span of such segment is counted as error no matter the exact transcription of it. Also, to be fair with the participants, if a word \(w\) of the paired text (e.g., a proper name) appears in a part of the audio spoken in Basque, the corresponding segment is included in the ground truth, just to cover the case that the word \(w\) was aligned with that segment. Finally, to account for the uncertainty when defining the borders between words, a collar time can be established so that a certain amount of time around the borders is not evaluated at all.