esa-sceva/satcom-chunk-collection huggingface dataset:
# A Mobile LiDAR-Based Deep Learning Approach for Real-Time 3D Body Measurement
## 2 Mobile Device 3D Measurement Technology
### 3D Measurement Technologies and Applications of LiDAR  
3D measurement technologies overcome the limitations of 2D images and enable more accurate body measurements. Representative 3D measurement technologies include Structured Light [16], Stereo Vision [17], and LiDAR [8]. Among these, LiDAR is a technology that can generate high-resolution 3D point clouds by measuring distances to objects using lasers, and is widely utilized in various fields such as autonomous vehicles, robotics, and Geographic Information Systems (GIS) [18]. LiDAR technology has several advantages. It allows for highly accurate distance measurements through laser emission and reflection, can generate a large number of point clouds at high speeds enabling real-time data processing, and can be utilized in various fields such as construction, surveying, archaeology, and environmental monitoring [19]. However, in the field of body measurement, the application of LiDAR is relatively insufficient. This is mainly due to the need for specialized equipment, high costs, and complex data processing requirements, making it perceived as a technology that is difficult for general users to access [20; 21]. Although some recent studies have proposed body recognition and measurement methods using LiDAR data, these studies remain mostly at the research stage, and implementation as practical solutions is limited [22].
# Rapid In Situ Biomonitoring of Subsoil Contamination by Applying an Algae-Soaked Disc Seeding Assay
## 2 Materials and Methods
### Test Soil  
A natural loamy sand, LUFA 2.2 soil (LUFA-Speyer, Sp 2121, Speyer, Germany), was used as the test soil. The physicochemical properties of LUFA 2.2 soil were as follows: a water-holding capacity of 0.54 mL/g, an organic matter of 3.39%, a pH of 5.6, a total nitrogen of 375 mg/kg, an available phosphate of 24 mg/kg, a calcium of 9.43 Cmol\({}^{+}\)/kg, a potassium of 0.18 Cmol\({}^{+}\)/kg, and a magnesium of 0.4 Cmol\({}^{+}\)/kg. AgNPs were spiked in LUFA soil that was autoclaved for 15 min at 121 \({}^{\circ}\)C, at the nominal concentration of 100 mg AgNPs/kg dry weight, and then thoroughly mixed with a roller at 40 r/min for 24 h.
algae, except the vials were not shaken. The process was repeated after 3, 6, 9, 12, 15, 18, and 21 weeks. The entire experiment lasted approximately six months and was performed with six vials per exposure scenario to obtain three replicates. The first three vials were used to analyze biomass and cell morphology; the others were used to analyze esterase activity, oxidative stress, and membrane permeability.
## 6 Traditional and DL Approaches for Object Detection in AVs
### DL Approaches for Object Detection in AVs
#### 6.2.1 DL Approaches for Vehicle Detection
In contrast, the study in [139] focused on harsh weather conditions, introducing YOLOv4 with an anchor-free and decoupled head, albeit achieving a 60.3% mAP and focusing exclusively on a single class. Moreover, the goal of [140] was to enhance self-driving vehicle detection in adverse weather using YOLOv5 with Transformer and CBAM modules, achieving an impressive mAP of 94.7% and FPS of 199.86. The DL approach proposed in [141] for nighttime vehicle detection in autonomous cars, combining a Generative Adversarial Network for image translation and YOLOv5 for detection, achieved a high accuracy of 96.75%, significantly enhancing the reliability of AV recognition models for night conditions. This study in [12] presents a DL-based intelligent AV weather-detecting system. Using a combined dataset from MCWDS2018 and DAWN2020, the performance of three deep convolutional neural networks was evaluated by categorizing six weather categories: overcast, rainy, snowy, sandy, sunny, and sunrise. The CNNs are SqueezeNet, ResNet-50, and EfficientNet-b0. The ResNet-50, EfficientNet-b0, and SqueezeNet models achieved 98.48%, 97.78%, and 96.05% accuracy, respectively, in the experiments, demonstrating remarkable accuracy rates while preserving quick inference times using GPU acceleration. By combining previously disparate datasets, the study's novel methodology makes a significant addition to DL applications for autonomous systems' weather detection. In simple traffic situations, the aforementioned extraction and classification algorithms have shown themselves to be successful at detecting vehicles. However, issues including scale sensitivity, occlusion, and a high number of false positive results limit their efficacy in more complex traffic conditions [142]. These algorithms perform well in simple traffic scenarios, but they are limited in complex traffic conditions due to a significant number of false positive detections. As a result, an explanation connected with vehicle detection is intended for this part, and the difficulties associated are also highlighted.
## 4 Discussion  
### Detectability of Demersal Fish with P900-130 BlueView Acoustic Camera  
Until recently, many studies using an acoustic camera have focused on pelagic fish [18; 21; 48]. The use of acoustic cameras with the specific goal of studying demersal species was mentioned once [29], but mullet, eels, and barbel were identified during scientific surveys [3; 49]. Benthic species on the sediment could be confused with inanimate objects, but remaining in a stationary position for 15 minutes increased the probability of detecting motionless individuals if they move in that time frame.  
In this study, the goliath grouper was chosen as a target demersal fish to evaluate the capabilities of the acoustic camera. This species was identified by combining morphological and behavioral characteristics (1) the shape of the acoustic signal, (2) the size of the signal, (3) swimming behavior and (4) depth of occurrence. Such criteria were supported by the comparisons between the acoustic signal and visual identification of 7 captured individuals when both methodologies (fishing and acoustic camera) were used on the same individuals at the same time. Acoustic shadow and swimming behavior were considered major criteria for identifying species because cubera snapper (_Lutjanus cyanopterus_), morphologically similar to goliath groupers and occupying the same habitat [50], would have been difficult to distinguish with the P900-130 BlueView camera. However, _L cyanopterus_ is behaviorally more active and swims faster than goliath groupers. Thus, it is considered unlikely that this species would bias data on goliath groupers. In all cases, acoustic imagery was analyzed by a specialist of marine species of French Guiana in order to minimize identification errors.  
Identification errors were also minimized by removing individuals < 40 cm. Since the goliath grouper population around rocky sites was described as composed of few individuals < 60 cm, the abundance estimated with the acoustic camera should not be biased significantly [51]. Indeed, juveniles of goliath groupers (<120 cm) occupy another habitat (magnroves) during their first years of life [32; 52].
# Unified Genetic Algorithm Approach for Solving Flexible Job-Shop Scheduling Problem
## 4 Numerical Experiments
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{5}{c}{**Existing GA**} & \multicolumn{5}{c}{**u-COGA**} \\ \cline{2-7}  & PS & PT & PU & SS & TT & UU \\ \hline \(\overline{C_{max}}\) & 48 & 47.8 & 48.1 & 48.45 & 47.45 & 46.9 \\ \hline SD(\(\overline{C_{max}}\)) & 2.10 & 1.40 & 1.29 & 1.82 & 1.67 & 1.62 \\ \hline Rank & 4 & 3 & 5 & 6 & 2 & 1 \\ \hline \multicolumn{7}{c}{**vData 10 \(\times\) 10 (mt10)**} \\ \hline \multirow{2}{*}{Algorithm} & \multicolumn{5}{c}{**vData 10 \(\times\) 10 (mt10)**} \\ \cline{2-7}  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \cline{2-7}  & PS & PT & PU & SS & TT & UU \\ \hline \(\overline{C_{max}}\) & 945.35 & 938.45 & 933 & 934.15 & 940.6 & 920.25 \\ \hline SD(\(\overline{C_{max}}\)) & 21.61 & 19.22 & 27.08 & 26.31 & 23.86 & 22.73 \\ \hline Rank & 6 & 4 & 2 & 3 & 5 & 1 \\ \hline \multicolumn{7}{c}{**vData 20 \(\times\) 5 (mt20)**} \\ \hline \multirow{2}{*}{Algorithm} & \multicolumn{5}{c}{Existing GA} & \multicolumn{5}{c}{u-COGA} \\ \cline{2-7}  & PS & PT & PU & SS & TT & UU \\ \hline \(\overline{C_{max}}\) & 1074.8 & 1068.85 & 1072.5 & 1055.05 & 1053.2 & 1054.55 \\ \hline SD(\(\overline{C_{max}}\)) & 28.81 & 31.23 & 31.61 & 30.82 & 33.71 & 27.59 \\ \hline Rank & 6 & 4 & 5 & 3 & 1 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Experiment results for vData. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**Existing GA**} & \multicolumn{3}{c}{**u-COGA**} \\ \cline{2-7}  & PS & PT & PU & SS & TT & UU \\ \hline \(\overline{C_{max}}\) & 489 & 4.67 & 4.33 & 3.33 & 2.56 & 1.11 \\ \cline{2-7}  & \multicolumn{3}{c}{4.63} & \multicolumn{3}{c}{2.33} \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Average ranks of the algorithms. Table 10 compares the best makespan of the existing GAs and u-COGAs. For instance, the best makespan for the existing GAs for mt20 of vData is 1068.85, while the best makespan for a u-COGA for the same instance is 1053.20, as shown in Table 8. Moreover, the reduction ratio in Table 10 was calculated as follows: (the best makespan of the existing Gas--the best makespan of u-COGAs)/(the best makespan of the existing GAs). This index was used to measure how much improvement was achieved by the u-COGA. In Table 10, we can see that the u-COGA always produces better solutions for the FJSP than the existing GA. The reduction ratio values range from 1.36% to 5.29% (average reduction ratio = 2.79%). That is, the existing GA sometimes demonstrates a similar performance to the u-COGA; however, the u-COGA generally produces superior results for the FJSP.
# Two-Stage Classification Method for MSI Status Prediction Based on Deep Learning Approach
## 2 Methods
### Two-Stage Classification  
A two-stage classification pipeline is proposed for MSI status classification, inspired by the findings that the network performs better at binary classification than at multiple classification. As a preliminary experiment, we trained the network with Inception-Resnet-V2 that classified three classes simultaneously with \(20\times\) and \(10\times\) magnified WSIs. The proposed pipeline included two main steps: tumor segmentation and patch-wise MSI status classification. In the tumor segmentation step, a pre-trained network on the ImageNet dataset [31] is used to divide the patches into two types: normal tissues and tumors (MSI-L and MSI-H). Then, the patch-wise MSI status classification is performed on only the patches labeled as tumors. A schematic of the overall pipeline is presented in Figure 3.  
Figure 1: Overview of the patch preparation procedure: foreground mask extraction, new tissue and mask creation, and patch extraction.  
Figure 2: The original whole slide image (WSI) and the associated tumor mask: (**a**) the original WSI, (**b**) the tumor mask created from the given XML annotation, (**c**) the segmented tumor area from WSI, and (**d**) the created tumor mask without background.  
The patches were prepared similar to the training patches described in Section 2.2. Particularly, the patches were split with a size of \(224\times 224\) pixels, similar to the training patches, but without overlapping areas. Patches with more than 50% foreground pixels were fed into the pre-trained segmentation network based on FPN. The segmentation network produced a tumor mask for each patch, annotating one for tumor area and zero for normal tissue area. The masks were then stitched into the overall tumor mask, which had the same size as the WSI. This whole-slide-sized mask and the original WSI were used to extract the area containing tumors using the pixel-wise AND operation. The resultant images were again split into patches. If more than 50% of a patch corresponded to background or normal tissue area, that patch was discarded and not used for MSI status classification. The second network, which was a patch-wise MSI status classification network, was then used to classify the patch into MSI-L or MSI-H based on Inception-Resnet-V2. Finally, the patch-wise classification results were collected to determine the MSI status of the given WSI. Among the various ensemble methods, the majority vote method was chosen in this study.
# Immersive Virtual Reality Experience of Historical Events Using Haptics and Locomotion Simulation
## 2 Materials and Methods
### Hand Tracking and Haptic Feedback  
For our application, we utilized the Vive Trackers to support hand tracking. Vive Trackers are enabled with SteamVR and can be programmed through UE4 to update the position and rotation of our virtual hands so they correspond to the user's hand movements. Specifically, we used them to track the position and rotation of the wrist.  
For the haptic feedback, we used a commercially available pair of haptic gloves. These gloves act like wireless controllers for Extended Reality (XR) providing precise hand and finger tracking along with a haptic feedback effect for each finger. It allows interaction with five fingers, with one vibration motor under the last phalange of each finger. The measurements of the fingers and hand movements are provided by inertial sensors. These features create a cheap device, easy to calibrate with a guaranteed high refresh rate, but at the cost of precision.  
The integration of these gloves was done with the UE4 plugin provided by the company. The position of the fingers is automatically provided by the sensors on the gloves, but for the haptic feedback, data must be sent dynamically through a User Datagram Protocol (UDP). After a connection is established, the proper JSON string is being sent, which contains information about the desired vibrations. The string includes arguments for the left or right hand, finger index, vibration type, and vibration duration. The whole process is accomplished with two plugins for socket communication. For the visualization and animation of the hand, the default hand mesh of the UE4 is used, but in future implementations, a more realistic hand and/or body mesh with elements of the proper historical time can be used.  
The gloves contain several vibration types which can be sent as information in the JSON string with the proper id. Consequently, we altered the vibration type to accompany each interaction with the appropriate tactile feedback. Specifically, in the case of the steering wheel (Section 2.1), we used a pulsating vibration both to the fingers and the wrist of the glove. This recurrent pulse resembles the buoyancy of the ship on the water, which is transferred to the user's hand, through the wheel. The feedback remains, as long as the user holds the wheel. On the other hand, in the case of the Antikythera Mechanism, we used a continuous vibration only to the fingers of the glove. This feedback is sent only when the user holds and rotates the mechanism's handle. The motion for reaching, holding and turning the wheel or the handle is performed by the user with their natural hand movement. This is achieved with the combined integration of the Vive Trackers for tracking the wrist and the gloves' detailed tracking of the fingers' joints and bones. Our whole system can be seen in Figure 4.
## 3 Results  
### Stress-Strain and Pore Water Pressure Behavior  
Figure 6a presents the curves of global deviatoric stress (\(\sigma_{z}-\sigma_{x}\)) versus axial strain (\(\epsilon_{z}\)) from plane strain compression tests. As shown in Figure 6a, for specimens consolidated to confining pressures lower than \(\sigma_{yield}\) = 350 kPa, the \((\sigma_{z}-\sigma_{x})-\epsilon_{z}\) curves show post-peak reductions in deviatoric stress and strain-softening behaviors. As the axial strain increases, the deviatoric stress first increases to a peak and then decreases, and the peak deviatoric stress appears at a small axial strain (\(\epsilon_{z}\) = 2-3%), exhibiting the typical stress-strain behavior of naturally structured clay. Significant differences in stress-strain behavior are observed between the other natural clays and Zhanjiang clay; the stress-strain curves of the former either do not indicate a remarkable peak deviatoric stress, or the peak deviatoric stress appears at \(\epsilon_{z}\geq\) 5% [18; 19; 26]. For specimens consolidated to confining pressures higher than \(\sigma_{yield}\) (i.e., \(\sigma_{x}\) = 400 and 500 kPa), no significant strain-softening behavior is observed. The deviatoric stress first increases to a peak (at approximately \(\epsilon_{z}\) = 6%) and then decreases slightly (\(\sigma_{x}\) = 400 kPa) or remains flat (\(\sigma_{x}\) = 500 kPa) as \(\epsilon_{z}\) increases. Similar results were obtained in conventional triaxial tests performed on naturally structured clays [7; 33].  
Figure 6b presents the curves of global pore water pressure (\(u\)) versus axial strain (\(\epsilon_{z}\)). First, the pore water pressure increases with increasing \(\epsilon_{z}\), and, once the pore water pressure reaches its peak, it decreases as \(\epsilon_{z}\) increases. The value of \(\epsilon_{z}\) at the peak pore water pressure is slightly higher than that at the peak deviatoric stress, which is a typical phenomenon of naturally structured clay.  
Figure 6: Global stress–strain and pore water pressure behavior for plane strain compression tests: (**a**) deviatoric stress (\(\sigma_{z}-\sigma_{x}\))—axial strain (\(\epsilon_{z}\)) curves; (**b**) pore water pressure (\(u\))–axial strain (\(\epsilon_{z}\)) curves.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Number of Tests**} & \begin{tabular}{c} **Confining Pressure** \\ **(kPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Shearing Rate** \\ **(mm/min)** \\ \end{tabular} &
\begin{tabular}{c} **Testing Conditions** \\ \end{tabular} \\ \hline T1 & 100 & & \\ T2 & 200 & & \\ T3 & 300 & 0.075 & undrained \\ T4 & 400 & & \\ T5 & 500 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test program.
## 3 Unconventional Fibers
### Application to Elliptic Core Fibers
In the following Figure 6, the \(\beta\) diagram of the fundamental even mode of an elliptic fiber with semi axis ratio \(aa/bb=2\), \(n_{1}=1.54\) and \(n_{2}=1.47\) and variable factor defined by: \(V=bb\cdot k_{0}\cdot\sqrt{n_{1}^{2}-n_{2}^{2}}\) is shown for completeness in agreement with [32]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**a/b = 2** & **Mathieu** & **RTL** & **Differences** & **Relative Diff. (0/00)** \\ \hline
**V** & **b11, No** & **b11, No** & & \\ \hline
1.5 & 1.49939050000000 & 1.497675992184210 & 0.001714507815790 & 1.143469840 \\
1.7 & 1.50472725000000 & 1.502885066432660 & 0.001842183567340 & 1.224264110 \\
1.9 & 1.50911088000000 & 1.507251064452310 & 0.001859815547690 & 1.232931584 \\
2.1 & 1.512712712900000 & 1.501915134944720 & 0.00179705055280 & 1.1879698913 \\
2.3 & 1.515667864000000 & 1.51400653909580 & 0.001661324904120 & 1.096100896 \\
2.5 & 1.518085750000000 & 1.516632675098700 & 0.001453074901300 & 0.957175773 \\
2.7 & 1.520047200000000 & 1.518879733445780 & 0.001167466554220 & 0.768046252 \\
2.9 & 1.521631209000000 & 1.520816115993170 & 0.000815093068630 & 0.535670578 \\
3.1 & 1.522869900000000 & 1.522496066092140 & 0.00037389397860 & 0.245483418 \\
3.3 & 1.523799100000000 & 1.523962746538420 & -0.000163646538420 & 0.107393775 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of ellipse for the fundamental modes. Figure 6: \(\beta\)-V diagram of the even fundamental mode an elliptic fiber of semi axis ratio \(aa/bb=2\) and core refractive index 1.54 and cladding index 1.47.
## 3 DFIG Model
### Proposed Rotor-Side Controller  
The proposed controller for the RSC is depicted in Figure 6. This controller consists of three PI controllers and one FLC. The main motivation behind using one FLC in the inner loop of the cascaded controller is maximization of the reactive power injection. The FLC offers variable gain depending on the system parameters. Due to the variable gain, the FLC can inject reactive power (Q_dfg) more effectively in the fault condition. Thus, the grid voltage can quickly be retraced back to the nominal value. Moreover, the FLC can stabilize a higher rating of the SCIG as compared to the conventional PI-based controller of the RSC in the inner loop.  
The active power (\(P_{dfg}\)) and reactive power (\(Q_{dfg}\)) outputs of the DFIG are controlled by regulating the rotor winding current. The reference active power (\(P_{rpf}\)) is calculated by subtracting the losses (\(P_{loss}\)) from the MPPT output (\(P_{mppt}\)). In the upper loop portion, the grid voltage (\(V_{g}\)) is taken as feedback to regulate the terminal voltage constant at 1.0 pu. The q-axis current (\(I_{nq}\)) controls the active power delivered to the grid, and the d-axis current (\(I_{nq}\)) controls the reactive power delivered to the grid.  
Figure 6: Proposed rotor-side controller.  
In the normal operating condition (\(V_{g}>0.9\) pu), the RSC regulates the active power delivered to the grid. During a fault condition (\(V_{g}<0.9\) pu), a comparator sends a signal so that active power transfer to the grid becomes zero. By controlling the power in this manner, the reactive power injected to the grid can be maximized.  
The detailed design procedure of the FLC will be discussed in Section 4.
## 3 Experiment
### Simulation Experiment
#### 3.2.1. _S_epp-Logan_ Phantom
Here, 60+ indicates that Poisson noise is added to 60 projections. Rows from top to bottom are reconstructed by using FBP, SIRT, SIRT-TV, SIRT-WTDM, and SIRT-MWTDM, respectively. number of projections compared with the other four algorithms. The RMSE values of each algorithm decrease with the increase in projection number. The RMSE value of the image reconstructed by the SIRT-MWTDM algorithm is 0.0145 for 30 projections, which indicates that the difference between the reconstructed image and the reference image is smaller and the image quality is higher. Then, we analyze the evaluation index of the image reconstructed by using the noise projection data. As can be seen from the SSIM and PSNR histogram, when the same number of noisy projections is used for reconstruction, both SSIM and PSNR values of the image reconstructed by using SIRT-MWTDM are the largest. As can be seen from the RMSE histogram, the RMSE value of the image reconstructed by using SIRT-MWTDM is the smallest when the same number of noisy projections is used for reconstruction. Therefore, from the reconstructed results obtained by using projection data with and without noise, it can be concluded that the objective evaluation indexes of images reconstructed by using our algorithm are the best compared with the other four algorithms under the same conditions. To evaluate the performance of the SIRT-MWTDM algorithm, we plot the profile of the blue line position in Figure 2a as shown in Figure 5. From left to right, the number of projections is 30, 60, and 60+. The profiles demonstrate that the amplitude of the FBP and SIRT algorithms considerably fluctuates and differs from the true value of the pixel. SIRT-TV and SIRT-WTDM perform much better than the FBP and SIRT algorithms, but there is still a certain gap between their amplitude and the true value. However, the result of the SIRT-MWTDM algorithm matches that of the original model and is much closer to the true value. It also shows the effectiveness of SIRT-MWTDM algorithm in sparse-view CT reconstruction. Figure 4: The histograms of evaluation index for reconstructed images of the Shepp–Logan phantom with different numbers of projections. (**a**) SSIM; (**b**) PSNR; (**c**) RMSE. Here, 60+ indicates that Poisson noise is added to 60 projections. Figure 5: The profile of the blue line position in Figure 2a. The number of projections is (**a**) 30; (**b**) 60; (**c**) 60+. Here, 60+ indicates that Poisson noise is added to 60 projections.