esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 MPC Scheme Design
### Lateral Safety Corridor  
To ensure driving safety, the AGV needs to be fully contained within the feasible region, determined by the environmental constraints such as road boundaries or lane marks. Here, we define a lateral safety corridor, according to the pre-defined longitudinal position and correlated with the road properties, to represent the feasible region. The lateral safety corridor is expressed as time-varying upper and lower bounds on the AGV's cross track error (\(e_{y}\)).  
Figure 7 illustrates the methodology to generate the lateral safety corridor. Based on the constant longitudinal acceleration assumption, the reference road can be sampled at discrete points along the road centerline, which correspond to the AGV's future position \(k\) steps into the prediction horizon. The feasible lateral gap at each time step \(k\) is identified mainly considering the lane boundaries' and vehicles' effective width. Starting at the AGV's current position, the lateral safety corridor is generated by linking the adjacent feasible gaps throughout the prediction horizon. Therefore, the lateral safety corridor is defined as a set of lateral deviation bounds \(e_{ymax}\) and \(e_{ymin}\) and can be compactly written as:  
\[\textbf{{H}}_{\text{env}}\hat{\varphi}^{(k)}_{v}\leq G^{(k)}_{\text{env}} \tag{17}\]  
with:  
\[\textbf{{H}}_{\text{env}}=\left[\begin{array}{cccccc}0&0&0&0&1&0\\ 0&0&0&0&-1&0\end{array}\right],\textbf{{G}}^{(k)}_{\text{env}}=\left[ \begin{array}{c}\varepsilon^{k}_{y,\text{max}}-d_{s}\\ -\varepsilon^{k}_{y,\text{min}}+d_{s}\end{array}\right]\]  
This provides a convex method of enforcing environmental constraints on the position of AGVs. \(d_{s}\) is a user-defined comfort distance.  
Moreover, the steering input should also satisfy the limits of the actuator system:  
\[\left|\delta_{f}\right| \leq\delta_{f,\text{max}} \tag{18}\] \[\left|\Delta\delta_{f}\right| \leq\Delta\delta_{f,\text{max}} \tag{19}\]  
where \(\delta_{f,\text{max}}\) and \(\Delta\delta_{f,\text{max}}\) reflect the maximum allowable steering angle and the physical slew rate capabilities of the vehicle steering system.
## 3 Topological Path Planning (TPP)
### Particular Case of TPP  
A particular case of TPP is obtained when the influence zone rule is not relaxed. In this case, the homotopic regions are shrunk until they are only one point wide. The result is a skeleton, a structure that has similarities with the GVD-based path planning method, as shown in Figure 5c. Note that the skeleton surrounds all obstacles numbered 1, 2, and 3 in Figure 5a. As a particular case, we call it TPP*, a version of TPP that delivers a more conservative and safe roadmap, although much faster because it contains fewer nodes. The main disadvantage of the TPP* path is that it often contains unnecessary detours, as highlighted in Figure 5d.
## 1 Introduction  
Common optical components of optical lenses can be divided into two types: optical glass and optical lenses. Process optimization is the key factor to accurately control the quality of the injected polymer, such as the accuracy of the size and the material's mechanical properties. This research mainly discusses injection molding plastic processing to form optical lenses. Injection molding is a one-time forming technology that is low cost, high speed, has a stable yield, and high production efficiency. Since the optical plastic lens forming does not require the secondary processes of polishing and grinding, the production time is shortened, and the cost is reduced.  
There is currently a growing demand for light guide plate backlight modules and plastic optical lenses, which has indirectly driven many optical plastic parts manufacturers who originally produced automobile and motorcycle lamp housings, lighting equipment, or cosmetics to upgrade and meet these additional manufacturing requirements. In the field of high-value precision optical products, due to the extremely high precision requirementsof the molds of optical products as well as the reproducibility of dimensions during mass production, the stability of accuracy and yield is particularly important. The injection of polymer material optical products has the same shrinkage and warpage deformation problems as general injection molded components, with the additional issues of optical refraction, including the relationships between the radius of curvature, the residual flow stress, birefringence, mold temperature, injection temperature, and injection speed, all of which must be improved to reach a better accuracy and yield. In addition to the accuracy requirements of the process equipment itself, the key to the process method is accurately controlling the parameters.  
In the early days of injection molding technology, the mold was simply filled directly with the injection material, but research has proved that the key to flow wavefront processing injection quality accuracy is understanding the polymers' material mechanical properties. Therefore, the injection molding process needs to use high speed, high pressure, and high temperature for molding, which causes stress to remain in the injection part, which is called residual stress. When the stress is generated in the transparent optical component, the phenomenon of birefringence occurs, which has the potential to cause large errors between the final product and the optical design values. Therefore, a method to effectively determine the residual birefringence in the injection molded part is the core goal of this article.
# Advanced Statistical Analysis of 3D Kinect Data: A Comparison of the Classification Methods
## 1 Introduction
### Biomedical Background
#### 1.1.3 Evaluation of Facial Nerve Function  
For clinicians, it is essential to evaluate the function of the facial nerve objectively. In everyday practice, the most commonly used tool is the standard grading system, such as the **House-Brackmann scale**. This system involves a six-point scale (with I being normal and VI total) of flaccid paralysis (Table 1). Group I represents normal facial movement with no weakness or synthesis. A patient placed in group II has only slight asymmetry of facial movements with a possible slight synkinesis. Patients in group III have an obvious asymmetry with obvious secondary defects but some forehead movement. The presence of forehead movement indicates that there has not been total degeneration of the nerve. Patients in group IV have an obvious asymmetry, no forehead movement, and weakness with possible disfiguring synkines or mass action. When there is only slight movement of the face, no forehead movement, and not enough facial function to return to have secondary defects, the patient is placed into group V. The absence of any movement or tone places the patient in group VI [23].  
The House-Brackmann scale produces comparable results between different observers in patients with normal or only mildly impaired facial nerve function. Interobserver variability can increase depending on the severity of facial nerve paresis. However, House-Brackmann does not promote uniformity of reporting and comparison of outcomes in patients with moderate or severe facial nerve paresis [24].
# Needle Segmentation in Volumetric Optical Coherence Tomography Images for Ophthalmic Microsurgery
## 2 Method  
The schematic diagram of needle segmentation in OCT cube is shown in Figure 2. In order to preserve as much information as possible, the highest resolution of OCT scan on RESCAN 700 is selected which is 128 B-scans each with 512 A-scans in \(3\times 3\) mm. Each A-scan has 1024 pixels for the 2 mm depth information (see Figure 1(a)). Figure 1(b) shows the needle in an ex-vivo pig eye experiment, which is an en-face plane view obtained by the ophthalmic microscope. The scan area is decided by the rectangle and can be adjusted by a foot panel connected to the RESCAN 700. Afterwards, the volumetric OCT images are generated (see Figure 1(c)). Figure 1(d) is the needle segmentation based on the process of OCT image cube. By taking consideration of different needle rotations and positions in OCT cube, the needle in B-scan can be seen as the following patterns in Figure 3. It shows that most of the needle fragments have the clear shadow except for the needle tip part. The refraction fragment usually has a larger amount of pixels in comparing to normal imaging parts. The qualified method should be able to segment as many needle pixels as possible in various conditions.  
Figure 2: The schematic diagram of needle segmentation in an Optical Coherence Tomography (OCT) cube. (**a**) The needle in OCT cube; (**b**) The microscopic image of the needle with ex-vivo pig eyes (the white box indicates the OCT scan area); (**c**) The output of B-scan sequence; (**d**) The needle (yellow) and background (gray) segmentation point cloud.
## 2 Materials and Methods
### Photoacoustic Imaging Experimentation and Machine Learning-Based Crack Detection
#### 2.1.4 Regression Models  
Three different regression models are used for crack size estimation. The following subsections give a brief introduction of each of these models:  
In machine learning, linear regression is a statistical method to model the relationship between a dependent variable and one or more independent variables [35]. The model's objective is to evaluate the best-fitting linear relationship predicting the value of the dependent variable based on the independent variables [36]. Mathematically, linear regression may be represented by Equation (1),  
\[Y=XM+\beta \tag{1}\]  
where \(Y\) is the output vector, \(X\) is a matrix of the observed value of independent variables, \(M\) is the vector of coefficients to be optimized during the training to minimize the error between actual and the predicted values of \(Y\), and \(\beta\) represents the error term showing the difference between actual and predicted values.  
Support Vector Regression (SVR)  
Support vector regression is a powerful technique for regression tasks adapted from the powerful machine learning classifier, the support vector machine (SVM). SVR is a good choice in situations of nonlinear relationships, and where there is an acceptable error margin between the actual and predicted values [37; 38]. Mathematically, a simple SVR model may be represented by Equation (2),  
\[y_{i}=f(X_{i})+\varepsilon \tag{2}\]  
where \(f(X_{i})\) represents the predicted value for the \(i\)th input data point \(X_{i}\), and \(\varepsilon\) is the error between the predicted value and the actual value \(y_{i}\). The objective function of SVR is to minimize feature coefficients and the error margin defined by \(\varepsilon\), as defined by Equation (3).  
\[min_{w,b,\xi,\xi^{*}}\frac{1}{2}\|w\|^{2}+C\sum_{i=1}^{N}\left(\xi_{i}+\xi_{i} ^{*}\right) \tag{3}\]  
The objective function defined above is subjected to the constraints given by Equations (4)-(6),  
\[y_{i}-(w.X_{i}+b)\leq\varepsilon+\xi_{i} \tag{4}\]  
\[(w.X_{i}+b)-y_{i}\leq\varepsilon+\xi_{i} \tag{5}\]  
\[\xi_{i},\xi_{i}^{*}\geq 0 \tag{6}\]  
where \(w\) is the weight vector, \(b\) is the bias term, \(\xi,\xi^{*}\) are slack variables that allow for some points to fall outside the margin, \(C\) is a regularization parameter that controls the trade-off between minimizing the error and minimizing the magnitude of the weights, and \(\epsilon\) is a parameter that defines the width of the margin.  
Random forest regression (RFR) is a decision tree-based ensemble learning technique that is based on random forest algorithm [39; 40]. The mechanism of random forest regression is to build and train multiple trees on different subsets of data predicting the output values (length and width of crack) individually. These individual estimates from all the trees are then averaged to get the final value of the crack size. This scheme makes the model robust and avoids overfitting.
# Dual-Modality Cross-Interaction-Based Hybrid Full-Frame Video Stabilization
## 3 Proposed Method: Cross-Interaction-Based Video Stabilization
### Implementation  
#### 3.2.1 Stage 1: Motion Compensation  
We began by synchronizing sensor data with video footage through communication between the IMU sensor and a Raspberry Pi. We utilized the ROBOR RB-SDA-V1 IMU sensor for data collection, setting the initial state by resetting angle and position, and collecting timestamp, gyroscope, accelerometer, and magnetometer data. Given the original video data's specifications of a 1920 \(\times\) 1080 resolution at 25 fps, we fixed the transmission period for sensor data collection at 40 ms to ensure consistency in data points. The sensor data utilized for motion compensation underwent noise reduction through a VQF filter. Gyroflow [21], utilizing gyroscope data for stabilizing a video along with optional accelerometer and magnetometer data, was chosen for motion compensation. Motion compensation at the image level was performed using the AKAZE optical flow [7] with sensor data from which noise had been removed. Subsequently, to maintain execution speed while applying neural rendering [5], we used \(ffmpeg\)[22] to resize the video data from 1920 \(\times\) 1080 (1080p) to 854 \(\times\) 480 (480p) as part of the preprocessing steps.
###### Abstract  
This study proposes a mechanical model for evaluating the stability of thickened structural walls (TSWs) under complex local loading conditions. The model allows for the calculation of stress distribution and yielding status of TSWs based on the Drucker-Prager (D-P) yield criterion. Compared with two existing theoretical models, the proposed model improves calculation accuracy by approximately 5% and 53%, respectively. The analysis results indicate that the maximum principal stress of TSWs primarily occurs at the midpoint of the left boundary (0, h/2), the center of lateral loading on the bottom boundary (LP, 0), or the center of lateral loading (LP, h/2). As the lateral load position (LP) and width (LW) increase, both the maximum principal stress and the yielding area increase. Increasing the sidewall thickness (ST) and length (SL), while reducing the sidewall height (SH), significantly enhances the overall stability of TSWs. To meet residual ore recovery requirements, it is recommended to increase SL and reduce LP, LW, and SH. In the residual ore recovery project of the Jubankeng tungsten mine, the critical thicknesses of four TSWs were calculated using the proposed mechanical model, yielding values of 4.6 m, 4.2 m, 2.6 m, and 11.9 m. Based on field validation conducted in stopes V3412 and V3301, the discrepancy in maximum principal stress (MPS) between the mechanical model and numerical simulations was within 4% for both cases, further confirming the accuracy and applicability of the proposed model in engineering practice.  
Keywords:thin sidewall stability; mechanical model; critical thickness; localized complex load; residual ore mining _Article_
# Integration of Organic Waste for Soil Stabilization through MICP
## 3 Results
### Selection of a Growth Medium for Ureolytic Bacteria  
The production of biomass of an individual ureolytic strain in the used bacterial consortium was necessary due to mutual inhibition of the strain's growth during preliminary co-cultivation in the LB medium. Thus, to obtain an inoculum for each ureolytic bacterial strain, the reduction of the growth medium cost was necessary. This challenge was solved by replacing expensive components of the LB medium (yeast extract and peptone) with an extract prepared from spent brewer's yeast. The composition of the spent brewer's yeast extract is presented in Table S1 (Supplementary Materials). The final component ratio was selected in such a way that the developed medium did not reduce the biomass yield and biomineralizing properties of bacteria (specific urease activity as a marker) compared to those observed for the control LB medium.  
Figure 3 shows the biomass growth characteristics of bacterial strains during their cultivation in media with different component ratios (see details in Table 1). After a 24-h growth period of all bacterial strains in Medium 4 (BSYE, 5 g; glucose, 10 g; NaCl, 1 g, per liter of distilled water), the highest values of optical density were observed compared to those obtained for the control LB medium. The growth biomass characteristics for _B. subtilis_ K51, _B. cereus_ 4b and _M. luteus_ 6 increased by 1.4, 1.5 and 1.8 times, respectively, while for _B. subtilis_ 168, values were comparable to those of the control medium. Therefore, Medium 4 (hereinafter referred to as BY medium) was chosen for further experiments.  
An analysis of the strains' specific urease activity during cultivations in BY and control LB media revealed a sharp increase in the activity of _B. subtilis_ 168 and _B. subtilis_ K51 during the entire observation period in BY medium compared to that in the LB medium (Figure 4B,C). The urease activity of _B. cereus_ 4b (Figure 4A) in the BY medium was slightly lower than in the LB medium. The specific urease activity of _M. luteus_ 6 (Figure 4D) reached its maximum after 27 h of cultivation in the LB medium but then decreased within two days. This contrasts with the continuous slow increase observed after 27 h and subsequent three days of cultivation in the BY medium.  
Thus, when ureolytic bacteria were cultivated on an inexpensive medium based on spent brewer's yeast extract, biomass yields and urease activity values were generally comparable to those obtained on the standard commercial LB medium. For further experiments on bioementation, each strain of the resulting bacterial mixture was grown in the BY medium.  
Figure 3: Biomass growth characteristics of ureolytic bacteria selected for this study after 24-h cultivation in different media. Media composition, g/L: BSYE, 5; NaCl, 10 (Medium 1); BSYE, 10; NaCl, 10 (Medium 2); BSYE, 5; Glucose, 5; NaCl, 10 (Medium 3); BSYE, 5; Glucose, 10; NaCl, 10 (Medium 4); LB medium was used as a control. * Significantly different from control LB medium (\(p<0.05\)).
## 4 Discussion  
This study assessed the reliability and agreement between anthropometric measurements obtained automatically from a photogrammetric analysis, which generated a 3D digital avatar, and those measured manually using a tape measure. Overall, the new technology demonstrated strong reliability for most of the measurements, particularly in the limbs, effectively distinguishing between subjects with different morphologies. However, measurements from the trunk area, such as chest, breast, waist, and belly, showed notably lower reliability and wider confidence intervals, with higher random error and absolute overestimation compared to the manual method. Bland-Altman analysis confirmed systematic error and proportional bias in these regions, especially the chest, whereas limb measurements showed high agreement and low variability. Compared to the state of the art, these findings align with prior research reporting similar limitations in trunk measurements [6; 8; 14; 15], but also demonstrate that limb measurements using this technology can achieve precision comparable to trained human raters [1; 16]. The main advantage of this technology is improved efficiency and automation, while the main disadvantage is lower accuracy in specific anatomical areas, particularly the trunk. These results are consistent with prior research on digital anthropometry [6; 8; 14; 15; 16; 17; 18], which has repeatedly documented the challenges of accurately assessing body measurements using photogrammetric methods. Specifically, the highest random errors of trunk circumferences have already been observed in several reliability and agreement studies [8], confirming chest and waist circumferences, but not that of the hip [6], as having the lowest accuracy and consistency [14; 15]. It has also been highlighted that difficulties may arise when measuring circumferences of hidden regions or others that strongly depend on body posture [16; 18]. Several factors likely contributed to the inaccuracies observed. First, the scanning cabin required participants to stand with arms slightly raised, making it difficult to capture hidden anatomical areas like the armpits, which is a known challenge even when scanning inanimate objects [19]. Second, trunk measurements are influenced by respiration. While manual measurements were standardised to the end of expiration, photogrammetry lacked control over the respiratory phase, introducing variability. This may have caused (for example, for chest, breast, waist, belly, and hip measurements) a collection of photos at different phases of inspiration and expiration between subjects compared to a manual assessment, which was instead performed at the same respiration phase. Additionally, manual measurements compress soft tissue, while photogrammetry does not, likely explaining why automatic measurements overestimated trunk dimensions on average. Importantly, our findings contribute to the field by identifying specific anatomical
# Data-Driven Optimization of CNC Manufacturing Using Simulation and DOE Techniques
## 7 Input Analyzer  
As shown in Figure 3a-c, the Arena Input Analyzer was used to determine the best-fit probability distribution for a given dataset for parts by comparing the square error values of various distributions. A lower square error indicated a better fit to the data. The analysis results are as follows: Based on the square error values, a uniform distribution provided the best fit for the data, having the least square error (0.113) among all the values considered in the distribution (Figure 3a). Therefore, the uniform distribution was recommended for use in the simulation model for an accurate representation of the input data in the same way the input analyzer was used for all the dataset examples shown in (b) and (c) distributions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Process Step** & **Component 1** & **Component 2** & **Component 3** & **Component 4** & **Component 5** \\ \hline Lathe & 15 & 18 & 16 & 18 & 20 \\ \hline CNC Turning & 30 & 27 & 29 & 27 & 25 \\ \hline Drilling & 4 & 5 & 5 & 4 & 5 \\ \hline Inspection & 30 & 32 & 35 & 34 & 30 \\ \hline \end{tabular}
\end{table}
Table 1: Part 1 processing times (mins).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Process Step** & **Component 1** & **Component 2** & **Component 3** & **Component 4** & **Component 5** \\ \hline Lathe & 15 & 10 & 12 & 13 & 15 \\ \hline CNC Turning & 20 & 25 & 22 & 23 & 20 \\ \hline Milling & 20 & 15 & 18 & 20 & 19 \\ \hline Drilling & 3 & 3 & 4 & 4 & 4 \\ \hline Inspection & 22 & 22 & 25 & 22 & 25 \\ \hline \end{tabular}
\end{table}
Table 2: Part 2 processing times (mins).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Process Step** & **Component 1** & **Component 2** & **Component 3** & **Component 4** & **Component 5** \\ \hline Lathe & 18 & 17 & 18 & 18 & 18 \\ \hline CNC Turning & 20 & 18 & 20 & 20 & 18 \\ \hline Milling & 12 & 12 & 15 & 15 & 15 \\ \hline Drilling & 3.1 & 2.5 & 3.3 & 3.0 & 3.5 \\ \hline Inspection & 20 & 22 & 20 & 22 & 20 \\ \hline \end{tabular}
\end{table}
Table 3: Part 3 processing times (mins).  
As shown in Table 4, the processing time data for different parts and operations were analyzed using the Arena Input Analyzer, selecting the best-fit distributions for simula  
Figure 3: (**a**) Uniform distribution using the Arena Input Analyzer; (**b**) normal distribution using the Arena Input Analyzer; (**c**) beta distribution using the Arena Input Analyzer.  
tion modeling. Lathe and CNC turning mainly followed uniform and beta distributions, indicating bounded variability. Milling, applicable to Part 2 and Part 3, showed higher variation when modeled by beta distributions. Drilling exhibited triangular and normal distributions, reflecting defined ranges and symmetric variation. Inspection processes varied, with Poisson and beta distributions capturing both discrete and continuous behavior. Rework steps exhibited triangular and normal distributions, representing short, consistent tasks. These distributions ensure a realistic simulation of each process and part type in the production system.