esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Outcome Measures  
Time intervals between the initial injury, preoperative testing, surgery, postoperative testing, and second injury were well documented and calculated. Isokinetic, concentric knee extension, and flexion strength (peak torque) were measured bilaterally between 0\({}^{\circ}\) and 90\({}^{\circ}\) using a Humac Norm isokinetic dynamometer (Humac Norm Testing & Rehabilitation System; CSM Inc., Stoughton, MA, USA) at speed of 60\({}^{\circ}\)/s, as used in other similar published studies [15; 25; 37]. Knee extension and flexion strength testing using the Humac Norm dynamometer is reliable when measuring the isokinetic muscle strength (Intraclass Correlation Coefficient = 0.89) [38]. All strength assessments were performed identically before and after ACLR. The second testing was performed six months after ACLR; this being the usual time to begin light non-contact sport activities, it seemed appropriate for evaluating the rehabilitation progress [20].  
LSIs were calculated for the muscle strength of knee extension and flexion before and after ACLR using the Equation (1) [36]:  
\[\text{LSI}=((\text{Involved limb})/(\text{Uninvolved limb}))\times 100. \tag{1}\]  
In addition to computing LSIs after ACLR, EPIC indexes were calculated for the muscle strength of knee extension and flexion using the Equation (2) [36]:  
\[\text{EPIC}=((\text{Involved Limb (after ACLR)})/(\text{Uninvolved Limb (initial evaluation)})\times 100 \tag{2}\]  
To determine limb-to-limb differences of the uninvolved knee extension and flexion between the initial testing before ACLR and the second one after ACLR, we calculated the EPIC indexes for the uninvolved, healthy limb (EPIC-H) using the Equation (3) [37]:  
\[\text{EPIC-H}=((\text{Uninvolved Limb (after ACLR)})/(\text{Uninvolved Limb (initial evaluation)})\times 100. \tag{3}\]  
A cut-off of 90% was operationally defined as achieving symmetry for all indexes, which is a generally accepted RTS criterion that allows returning to sport activities [15; 23; 31].
# A Study of Eye-Tracking Gaze Point Classification and Application Based on Conditional Random Field
## 3 Experiment
### Experimental Data  
The existing public saliency image datasets _CSSD_, _DUTS_, _ECSSD_, _HKU-IS_, _PASCAL-S_ and _SOD_[37], contain different complex scenes and targets. In this study, the existing saliency datasets are used and 100 images that contain \"person\" are selected as shown in Figure 8, where the \"person\" in the images is the gaze target. In addition, other 6 scene images in our real scene are selected for gazing target \"person\". In order to ensure the uniformity of the gaze result, each image is set as 1920 \(\times\) 1080 pixels. The head-mounted eye-tracking device (Figure 6) collects human eye images and calculates the gaze point data. Each image is gazed at 5 s. 200 gaze points and 5 groups of gazes are completed by 5 experimenters, leading to a total of 100,000 gaze points data. The collected data are divided into 3 groups for training and 2 groups for testing, and the gaze point sequence classification experiments are carried out. Figure 9 presents an example of the distribution of gaze points obtained by gazing at different images collected from the same group of gaze point sequences representing the same graph shape color size. The study in this experiment uses python language based on pytorch library for training, prediction, and completing the comparison tests, with the learning rate of 0.0001.  
Figure 8: Example of experimental gaze images.  
Figure 7: Relationship between gaze point and visual aiming of servo platform.
# Establishment of a Pressure Variation Model for the State Estimation of an Underwater Vehicle
## 2 Methodology and Results  
### Test Model  
The autonomous underwater vehicle (AUV) used in this study is a slender body by REMUS. The primary dimensions are 1.345 m in length and 0.191 m in diameter (Table 1).  
Figure 1 shows the shape of the AUV and the arrangement of 0.01 m diameter pressure sensors (red circles with numbers) located on the front and sides. Each of the pressure sensors is named and positioned as described in the bottom left of Figure 1, with \(R\), \(L\), \(T\) and \(B\) representing the pressure sensors on the right (starboard), left (port), top, and bottom sides, respectively. Through prior research, it was confirmed that placing sensors in locations with significant changes in pressure with speed leads to a higher accuracy in predicting the speed and drift angle of AUVs. Therefore, pressure sensors were arranged at the front of the vehicle, considering the size of the pressure sensors, and 11 sensors were arranged at appropriate intervals.  
\begin{table}
\begin{tabular}{l c} Length overall [m] & 1.345 \\ Max. diameter [m] & 0.191 \\ Design speed [knots] & 3.0 \\ \end{tabular}
\end{table}
Table 1: Geometric details of the test model.
# Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework
## 4 Results
### Case Study
When users post messages and activate location-sharing features on their mobile devices, the associated social media data can include geographic coordinates. However, a study shows that only about 3.1% of users choose to share their location when posting content [28]. In this study, the original dataset [4], designed specifically for classification tasks, excluded all location-related information by applying a geographic filter, which resulted in the removal of precise latitude and longitude coordinates. To address this limitation and demonstrate the effectiveness of spatial visualization, we selected a subset of the Hurricane Harvey test data from the original dataset [4] and assigned randomly generated geographic coordinates to each social media post. For model deployment and simulation, our system retrieved 10 social media data instances from the simulated stream at a time, effectively mimicking a continuous data feed for reasoning on the server. Each batch was processed in real time, allowing us to demonstrate the system's ability to handle ongoing data streams, perform text classification, and update the visual map dynamically. Figure 6 shows the social media information visualization of Hurricane Harvey on interactive map. This methodology highlights the system's data processing abilities, including rapid data ingestion, the accurate classification of social media posts, and the visualization of geolocated information on a dynamic map interface. The assigned random locations allow for a practical demonstration of how geographic data can enhance situational awareness and support decision-making in a real-world disaster response scenario. This visualization tool enables emergency responders to monitor trends geographically, identify critical areas needing immediate attention, and coordinate resource allocation more effectively. Figure 5: Words cloud for Hurricane Harvey.
# A New Method for Fatigue Evaluation of Titanium Alloy Welded Structures
## 1 Introduction
According to the analysis results, the stress field at the tip of the singular crack is further deduced. The proposed stress field model is validated by calculating the Von Mises stress of the model. [PERSON] et al. [18] studied the notch stress existing in the direction of crack propagation at the weld position of steel welded structures. Based on the crack propagation principle, the singular stress function is determined. The calculation model of the equivalent value point \(x_{as}\) is obtained through calculation and analysis, and the concept of singular strength \(as\) is proposed. A method for solving the fatigue life of welded structures with singular stress is found: the equivalent method of notch stress. In summary, it is found that there are relatively few studies on fatigue assessment of titanium alloy welded structures with singular stress, so it is of great significance to conduct in-depth research on them. In order to study the fatigue behavior of titanium alloy welded structures with stress singularities in the field of rail transit, this paper proposes a new fatigue life prediction method that takes into account the influence factors of stress singularities, namely the augmented-reverse notch equivalent stress method. The method is based on the new three-parameter power function model and the notch stress equivalent value method with the introduction of a correction factor and is derived by combining the two. Next, combined with the fatigue test data of a limited number of titanium alloy welded structures, a new three-parameter power function model of the titanium alloy welded structure proposed in this paper is established. According to the calculation method of the notch equivalent stress correction factor proposed in this paper, the correction factor suitable for the titanium alloy welded structure is obtained. The fatigue life of the titanium alloy welded structure was predicted by combining the above two and compared with the test life. Finally, the proposed augmented-reverse notch equivalent stress method is verified using fatigue test data different from the above-mentioned titanium alloy welded structures.
## 4 Modes Identification and DoA Determination Classifiers
The idea of the LDA technique is to project the original dataset of size (\(N\times D\)) onto a new space of size (\(N\times d\)), where \(D>>d\), as shown in Figure 5b. In our development, \(N\) represents the number of mode images in the training dataset and \(D\) is the number of features (image pixels). Therefore, the LDA can be performed using the following steps [24]:  
1. Compute the between-class covariance matrix \(\mathbf{S}_{B}\) of the training dataset. 2. Compute the within-class covariance matrix \(\mathbf{S}_{W}\) of the training dataset. 3. Find the transformation matrix \(\Phi=\mathbf{S}_{W}^{-1}\mathbf{S}_{B}\) that maximizes \(\mathbf{S}_{B}\) and minimize \(\mathbf{S}_{W}\). The eigenvectors and the corresponding eigenvalues of the transformation matrix \(\Phi\) provide the information about LDA space. The directions of LDA space is represented by the eigenvectors, while the associated eigenvalues denote the magnitude of the eigenvectors. Therefore, every eigenvector indicates one axis of the LDA space; however, the eigenvalue represents the robustness of this eigenvector. The robustness of the eigenvector reflects its potential to distinguish between the various classes, i.e., increase \(\mathbf{S}_{B}\) and decreases \(\mathbf{S}_{W}\). Thus, it achieves the goal of LDA. The LDA space is constructed using the eigenvectors that have the largest eigenvalues. It is worth noting that for \(K\) number of classes, the number of non-zero eigenvalues will be \(\leq K-1\)[22]. Finally, the training dataset is projected on LDA space. The classifier then designed to have a decision threshold (\(i_{\text{th}}\)) that separates the two adjacent classes. The decision threshold can be calculated as [22]  
\[i_{\text{th}}=\frac{\mu_{1}-\mu_{2}}{2} \tag{4}\]  
where \(\mu_{1}\) and \(\mu_{2}\) represent the training dataset means of the two adjacent classes in LDA space. Once the threshold value is calculated, it can easily be used to classify a new data by first projecting it on LDA space, and then comparing its value with the value of \(i\)th. Figure 5b illustrates an example of classification using LDA. It can be observed that there is a significant overlap between the classes in case of the eigenvector \(\mathbf{v}_{2}\) (i.e., with lowest eigenvalue) which leads to error in classification. However, the eigenvector \(\mathbf{v}_{1}\) (i.e., with largest eigenvalue) shows a considerable improve in separation between classes.
## Methods
### Area-Based Approach  
The authors of [10] proposed a generalised approach to estimate \(\mathrm{ACD}\) using top of canopy height estimated by LiDAR. As recommended by [10], least squares regression was used to relate field measured TCH and \(\mathrm{ACD}\) as follows:  
\[\mathrm{ACD}=\mathrm{a}\ \mathrm{TCH^{b}} \tag{2}\]  
This model was fitted to data from the 27 1-ha SAFE plots and the estimates were obtained by leave-one-out cross validation. TCH was the mean height of CHM pixels within each 1-ha plot extracted using the raster package of R, while a and b are the parameters of the nonlinear model.  
We also tested whether canopy cover (CC)--the proportion of area occupied by crowns at a given height aboveground--could be used as an alternative LiDAR metric to predict \(\mathrm{ACD}\). CC is calculated by creating a horizontal plane in the canopy height model (CHM) at height h above ground, and calculating the number of pixels for which the CHM lies beneath the plane divided by the total number of pixels in the plot. CC was calculated for h from 1 m to the maximum canopy height encountered in the plots (23 m). There were a few individuals of other tree species encountered in the 1-ha plots.  
\[\mathrm{ACD}=\mathrm{a}\ \mathrm{CC^{b}} \tag{3}\]  
We also tested whether TCH and canopy cover could both be included as a predictor of \(\mathrm{ACD}\)[12; 31]. The model requires accounting for multicollinearity, considering that TCH is closely correlated to CC. This can be done by regressing one variable against the other, and replacing the variable with the residuals from the regression [37]. We therefore first modelled the relationship between CC and TCH using logistic regression, validated the model using a leave-one-out procedure, and then used the residuals of this model (CCres in Equation (4)) to identify plots that have higher or lower than expected canopy cover for a given TCH. Equation (5) was used following [31], who carefully compared alternative functional forms.  
\[\mathrm{CCres}=\ \mathrm{CC}-\frac{1}{1+\mathrm{e}^{-\mathrm{d}TCH-f}} \tag{4}\]  
\[\mathrm{ACD}=\mathrm{a}\ \mathrm{TCH^{b}}\ (1+\mathrm{c}\ \mathrm{CCres}), \tag{5}\]  
where lowercase letters indicate regression coefficients. To aid interpretation, Spearman rank correlation coefficients (\(r\)) between structural variables and area-based approach metrics were calculated to investigate the relationship of both average tree height and tree density with both TCH and CC LiDAR metrics.
# Key Strata Identification of Overburden Based on Magnetotelluric Detection: A Case Study
## 3 Identification Mechanism of MT Detection  
### The Characteristics of Overburden AR  
The characteristics of overburden AR can be obtained by using the inversion technology [27]. The instrument used this time was a CAN-I MT lithology detector produced by Zhengzhou City, Henan Province, China. Through the comparison and analysis of the detected characteristic points and the borehole histogram, the relationship between the AR and the stratum lithology was obtained. For the mining area without accurate mechanical parameters, the AR characteristics of different strata can be used to determine the lithology of strata. Figure 5 shows the AR characteristic of 10 kinds of rock strata, which come from the Buertai coalmine near the study area.  
Figure 4: The main influencing factors of AR: (**a**) non-physicochemical factors; (**b**) physicochemical factors.  
As shown in Figure 5, it can be seen that the AR of different strata have different trends. The AR curve of a dense and single component stratum is smooth, and the curve fluctuation is small, while the unconsolidated layer has a significant fluctuation because of its porosity and many components. In addition, under the dry condition, the denser the stratum is, the larger the AR is, and vice versa.
## 1 Introduction  
Recent advancements in digital technology have enabled the use of virtual reality (VR) to enhance the shopping experience online. In the fashion industry, VR applications, which are part of the Metaverse, are categorized into experiential, entertainment, and informational types [1]. The informational category specifically aims to provide detailed product information, such as catalogs. It has been argued that VR shopping, through three-dimensional graphics and natural expressions, can provide superior product information compared to existing 2D image-based online shopping (e-commerce) [2]. Moreover, the product information provision method in VR shopping allows products to be rotated 360 deg and viewed from any angle, combining the benefits of offline shopping with the advantages of online shopping, such as unrestricted access to a variety of product information, irrespective of store hours [3]. Thus, VR shopping represents an extension and fusion of offline and online shopping experiences, offering a superior shopping experience, particularly during the 'information search' and 'alternative evaluation (product comparison)' stages of the consumer decision-making process, which are the most time-consuming and critical stages for making purchase decisions [4].  
Due to these advantages, global corporations, including eBay's 'Myer', Alibaba's 'Buy+', inVRison's 'ShelfZone', and Hyundai's 'VR store' have launched VR shopping malls. Fashion-related companies like Lurckychouette and Esquire are also introducingVR shopping through methods including VR runways and the use of VR codes in magazines [2; 5; 6]. Some of these initiatives are still not commercialized, and VR shopping is generally in its early stages of growth, with a focus on experiential uses. Thus, to progress toward sustainable VR shopping, product information must be better conveyed to consumers [4]. In particular, for fashion products such as shoes and clothing--where consumer emotions vary with the appearance of the product and significantly influence purchase decisions [7; 8; 9; 10; 11; 12]--it is more important that product information be provided in a manner that is easily understandable by consumers. To address these requirements, the company Fibbl offers a solution in online shopping by reconstructing fashion products, including shoes and glasses in 3D graphics, making it easier for consumers to understand product information. However, research on how product information provision methods affect consumer perceptions of 'product affect' and the ease of recognizing product information remains inadequate. Therefore, we have moved beyond existing studies that focus on enhancing consumer immersion and the overall shopping experience using VR shopping platforms. We conducted a comparative study to examine the differences in 'product affect' and the ease of recognizing product information between the information provision methods used in VR shopping and existing online shopping platforms (e-commerce).
## 2 Materials and Methods  
### Fabrication of Phantom and Acquisition of Phantom's CT Data  
A phantom was fabricated with ABS-like material using a 3D printer (RSPro600, Shanghai 3D Union Technology, Shanghai, China) according to ASTM F2554-10 [26] (Figure 1). The phantom contained forty-seven cone-shaped divots, each 8 mm in diameter, 4 mm in depth, and having a semi-vertical angle of 22.5\({}^{\circ}\). Eight of them were oriented at an angle of 45\({}^{\circ}\), and the remaining 39 divots were oriented at an angle of 90\({}^{\circ}\). The positions of the divots were the same as those specified in ASTM F2554-10 [26]. A three-dimensional coordinate measuring machine (global advantage silver model, hexagon metrology, Stockholm, Sweden) was used to measure the positions of all holes on the phantom. Then, we acquired a CT image of the phantom with MDCT (SOMATOM Sensation 10, Siemens, Munich, Germany) under 120 kVp and 80 mAs with a slice thickness of 0.75 mm.  
Figure 1: A phantom for evaluating registration accuracy with forty-seven cone-shaped divots.
# Numerical Simulation-Based Study on the Arching Effect in Subsequent Backfill
## 3 Numerical Simulation
### Effect of Backfill Cohesion on Stress Distribution  
As shown in Figure 7, through the numerical simulation study of the stress distribution at the vertical centerline of the stop filling body, the influence mechanism of the cohesive force on the stress evolution law and arching effect of the filling body was revealed (research conditions: cohesive force at the contact surface = 0, friction angle within the filling body = 35.7\({}^{\circ}\)). The results show that the cohesive force, as it is a key parameter controlling the mechanical properties of the filling, then its variation will significantly affect the stress distribution characteristics of the filling body and the development degree of the arching effect.  
As the cohesive force gradually increases from 0 kPa to 1000 kpa (0, 10, 20, 30, 100, 500, 1000 kpa), both the vertical stress and the horizontal stress inside the filling body show a significant decreasing trend. In the low cohesion range (0-30 kPa), the stress distribution shows a relatively simple linear characteristic. When the filling depth is within the range of 0-20 m, the stress value is close to the theoretical calculation result of the overburden, which indicates that the arching effect has not been fully developed at this time, and the filling body mainly bears the self-weight stress. When the cohesive force enters the medium-high range (100-1000 kpa), the arching effect is significantly enhanced. The filling body shows good load transfer and stress redistribution capabilities, and the stress value is significantly lower than the calculation result of the overburden.  
It is worth noting that when the cohesive force exceeds 500 kPa, the enhancing effect of further increasing the cohesive force on the arching effect tends to level off, and the stress distribution no longer changes significantly. This phenomenon indicates that there is a critical value for the influence of the cohesion force of the filling body on the arch-forming effect. Beyond this value, the contribution of the increase in cohesion force to the arch-forming effect will significantly decrease. This discovery provides an important reference for mine filling engineering: when designing the strength and analyzing the stability of the filling body, it is necessary to fully consider the favorable influence of the arching effect on the stress redistribution, and at the same time avoid overestimating the effect of the cohesive force. In engineering practice, it is recommended to control the cohesion within a reasonable range to achieve the best balance between economy and stability.