esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Biological Imaging for Intracellular Micromanipulation: Methods and Applications
## 3 Intracellular Micromanipulation with Computational Image Information  
Intracellular micromanipulation is difficult to perform and has a low operation success rate because of the small sizes, irregular shapes, and irregular distribution of targeted specimens and the complexity of intracellular environments. However, specimen morphology, position, orientation, manipulation speed, force, and other operation details are hidden in sampled images. Advanced computational image processing methods can extract such details from high-quality sampled images and facilitate complicated intracellular manipulation tasks.  
The following section summarizes different types of computational processing methods, such as planar localization, depth acquisition, and 3D analysis. It also discusses their suitability and limitations.
## 2 Materials and Methods
### Motion Sensor Device (MSD)
#### 2.1.1 Wearable IMU-Based Sensors  
The IMU-based sensors comprised six-axis microelectromechanical systems, composed of accelerometers and gyroscopes, and the sensors collected information on the angular motion of the knee of interest. These 2-sensor systems were used in the MSD to accurately record the knee's angular motion. To perform the recording, 2 sensors were strapped on the anterior midline of the distal third of the femur (i.e., 10 cm proximal to the upper border of the patella) and the proximal third of the tibia (i.e., 10 cm distal to the tibia tuberosity) (Figure 1a). An algorithm was used to convert raw data from the accelerometer and gyroscope into a quaternion. The sensors were calibrated in the position of the knee flexion at 90\({}^{\circ}\) when the measurement began (Figure 1b). The initial calibration process involved placing the sensor on a horizontal fixture to measure the offset for each axis and to eliminate nonzero deviations. The sensors on the thigh served as a reference point, enabling raw data from the sensor on the proximal tibia to be used to construct a quaternion using the algorithm; thus, the relative angle changes on the knee joint could be converted into a structure based on the motion of the knee. The high accuracy of repeated angular measurements taken using the 2-sensor system on simulated knee motion was also validated ex vivo (see Supplementary Materials Files).  
InvenSense's built-in library was utilized to setup the algorithm [21]. The fusing algorithm combined gyroscope and accelerometer measurements into a single measurement of orientation as a quaternion by drifting in the yaw component of orientation only. The application could reset the drift in yaw by setting the yaw to a specified angle at any time. The algorithm also contained functions for converting this quaternion into a rotation matrix and Euler angles. In addition, the algorithm provided a measurement of linear  
Figure 1: The position of motion sensors worn on the lower limb: (**a**) sensor position; (**b**) calibration position.  
acceleration (equal to the accelerometer measurement with the 1 g of gravity removed) and Earth acceleration (a measurement of linear acceleration in the Earth coordinate frame). The example of pseudo-code was provided in Supplementary Materials Files.
# Research Status and Progress of All-Position Narrow-Gap GMAW for Pipelines
## 3 Methods to Improve Weld Bead Formation in Narrow-Gap GMAW  
To address issues such as lack of side wall fusion and interpass lack of fusion that occur during the construction of all-position narrow-gap GMAW, researchers have explored several approaches, including optimizing welding process parameters, controlling arc shape using external magnetic fields, and combining with other heat sources. These investigations have yielded many important research findings.
# Effect of Soil Reinforcement on Tunnel Deformation as a Result of Stress Relief
## 3 Results and Analysis
### Analyses of Soil Responses Caused by Excavation  
#### 3.5.1 Stress Distributions of Soil Elements in the Tunnel's Longitudinal Direction  
To better understand the tunnel behavior in the transverse and longitudinal directions, the stress distribution of the soil elements around the tunnel are presented in Figures 10 and 11. Figure 10a shows the computed changes in the vertical stress at the tunnel crown in the longitudinal direction for different reinforcement Young's moduli. The positive and negative values denote the increase and decrease in the stress acting on the tunnel lining, respectively. Along the tunnel crown, the vertical stress beneath the basement reduced significantly as a result of the excavation. An almost uniform stress relief was observed beneath the basement. The stress was concentrated beneath the diaphragm wall. This is because the tunnel moved upward due to stress relief, whereas the trend was blocked by the diaphragm wall. The concentrated stress may increase largely beneath the diaphragm wall and then dissipate behind the wall. Thus, it is possible that there will be an increase in stress, which has also been observed in the literature [14]. The vertical stress in the soil increased by more than 100 kPa. After the basement excavation, the maximum change in the vertical stress at the tunnel crown exceeded the allowable limit (i.e., \(\pm\)20 kPa) set by the Building Department of Hong Kong [27]. In the case where the soil element was not reinforced, the maximum reduction in the vertical stress was 159 kPa. When the reinforced soil's Young's modulus increased from \(E_{8}\) to \(5E_{8}\), the maximum reduction in the vertical stress was 120 kPa. This was because the reinforced soil blocked the stress transfer and thus reduced the stress release around the tunnel. However, obvious stress changes were not observed when  
Figure 9: Measured and computed change in tunnel diameter with different soil reinforcement areas.  
the reinforced soil's Young's modulus increased from 5\(E_{\text{s}}\) to 10\(E_{\text{s}}\), which can explain the phenomenon shown in Figure 6. Figure 10b shows the computed changes in the vertical stress at the tunnel crown in the longitudinal direction for different reinforcement depths. As can be seen, the maximum stress reduction was restrained from 159 kPa to 120 kPa when the reinforcement depth changed from 0 to 0.6 m. Further obvious changes were not observed for a reinforcement depth larger than 0.6 m, which is consistent with the tunnel heave change shown in Figure 8.  
Figure 10: Computed vertical stress change in the soil element at the crown in the circumferential direction for (**a**) different reinforcement Young’s moduli and (**b**) different reinforcement depth.  
Figure 11: Computed earth pressure change in the soil element along the tunnel transverse direction for (**a**) different reinforcement Young’s moduli and (**b**) different reinforcement depth.
## 2 Methods
### The Quasi Steady Forcing  
The drag and lift components provided by the CFD model, as described in Section 2.1, are determined considering the VI at rest, therefore the total force \(\mathbf{F}_{stat}\) depends on the undisturbed upstream flow velocity \(U_{\infty}\) and on the VI AoA \(\alpha\), namely:  
\[\mathbf{F}_{stat}(U_{\infty},\alpha)=-F_{D}(U_{\infty},\alpha)\mathbf{i}+F_{L} (U_{\infty},\alpha)\mathbf{k}, \tag{6}\]  
where \(\mathbf{i}\) and \(\mathbf{k}\) are the unit vectors, respectively, of the \(x\) and \(z\) axes as depicted in Figure 3. The evaluation of the force acting on the moving VI needs to account for its instantaneous AoA and velocity. Being the VI mounted on the tip of the oscillating beam, its instantaneous configuration can be described in terms of the motion and orientation of the tip. With reference to Figure 3, the quasi steady assumption is here enforced, which states that the force \(\mathbf{F}_{dyn}\) acting on the VI during its motion can be determined from the components of the static force in Equation (6) as:  
\[\mathbf{F}_{dyn}(U_{r},\alpha_{r})=-F_{D}(U_{r},\alpha_{r})\mathbf{i}_{r}+F_{ L}(U_{r},\alpha_{r})\mathbf{k}_{r}, \tag{7}\]  
where the relative velocity \(\mathbf{U}_{r}\), the relative AoA \(\alpha_{r}\) depend on the tip motion and orientation as follows:  
\[\begin{cases}\mathbf{U}_{r}=-U_{\infty}\mathbf{i}-\dot{w}(L,t)\mathbf{k},\\ \alpha_{r}=\underbrace{\arctan\left[w^{\prime}(L,t)\right]}_{\alpha}-\arcsin \left(\frac{\dot{w}(L,t)}{U_{r}}\right).\end{cases} \tag{8}\]  
and the unit vectors \(\mathbf{i}_{r},\mathbf{k}_{r}\) are respectively aligned and perpendicular to \(\mathbf{U}_{r}\), and depend on the tip motion as:  
\[\begin{cases}\mathbf{i}_{r}\equiv\frac{U_{\infty}\mathbf{i}+\dot{w}(L,t) \mathbf{k}}{U_{r}},\\ \mathbf{k}_{r}\equiv-\text{sgn}(\alpha_{r})\frac{-\dot{w}(L,t)\mathbf{i}+U_{ \infty}\mathbf{k}}{U_{r}}.\end{cases} \tag{9}\]  
\(F_{l}\) and \(F_{t}\) appearing in Equation (4) are the longitudinal and transverse components of \(\mathbf{F}_{dyn}\), that is:  
\[\begin{cases}F_{l}=\mathbf{F}_{dyn}\cdot\mathbf{i},\\ F_{t}=\mathbf{F}_{dyn}\cdot\mathbf{k}.\end{cases} \tag{10}\]  
To compute them, according to Equations (7) and (8), the domain \((U_{\infty},\alpha)\) needs to be continuously sampled. Therefore, the discrete values of \(F_{D}\) and \(F_{L}\) as yielded by the CFD simulations have been suitably interpolated. For a comprehensive discussion on the effect of such interpolation, the reader is referred to [36] and to the works therein cited.  
Quasi steady modelling of the galloping motion of such devices has been extensively employed 10/1 [15, 18, 20, 36, 37, 38, 39], being supported by the difference in peak frequencies between vortex shedding in the wake of the VI (\(f_{\text{VI}}\)) and oscillation of the FEH (\(f_{\text{FEH}}\)). In our case, the two stand in the ratio \(f_{\text{VI}}/f_{\text{FEH}}\simeq 4:1\).  
The moment \(M_{dyn}\), as computed from the above described quasi steady assumption, resulted to be negligible for the case considered (i.e., \(\left|{}^{L_{F_{dyn}}/M_{dyn}}\right|\gg 1\)), and it has not been included in the modelling.
## 6 Discussion
### Privacy Issues  
Finally, the recent DL approaches for PD classification can also lead to privacy, ethical, legal, and safety issues. This implies a scarcity of publicly available speech-basedPD datasets due to the sensitivity of medical data, challenges in standardizing and collecting high-quality speech recordings, and variability in symptoms among patients [27]. [PERSON] et al. [65] explain that such approaches can improve diagnostic accuracy, but strict patient data privacy regulations often prevent institutions from sharing their data. To address this, the authors developed an FL method. FL has the advantage of not requiring institutions to share any of their data, which is crucial for protecting patient privacy. In their work, [PERSON] et al. [65] focused on German, Spanish, and Czech speech data, all from separate institutions, none of which had access to the other's training data or network parameters. The institutions only had access to the aggregated network, without knowing the contributions of each institution. FL clearly performed better than using local models alone. However, the performance of FL was similar to that of centrally combining data from all the institutions. [PERSON] et al. [43] also employ FL to promote more privacy-preserving methods, showing promise for scalable, privacy-preserving healthcare applications. However, the results of this study are not completely satisfactory (the highest accuracy obtained is 61.49%) and they point out the necessity of exploring more FL strategies and incorporating additional data modalities.  
[PERSON] et al. [44] use an auto-encoder and two auxiliary modules to retrieve speaker identity-invariant representations, thereby preserving privacy. The first module is an adversarial speaker identification module, and the second is a PD classifier. To obtain speaker-invariant representations, the speaker identification module is trained together with the auto-encoder in an adversarial manner. The auto-encoder is trained with both the speaker identification module and the PD classifier to classify PD based on speaker-invariant representations. The highest accuracy that was achieved with this method was 75.4%.  
While the privacy-aware method developed by [PERSON] et al. [70] shows promising results, with AUC values of 0.84, 0.93, and 0.83 for the English, Greek, and German sub-cohorts, respectively, there remains room for improvement in classification accuracy. These results indicate that local feature extraction on smartphones can be a reliable approach for distinguishing PWP from HC, balancing both privacy and performance. However, most current studies on privacy-preserving methodologies for PD diagnosis lag behind other methods that do not prioritize privacy to the same extent. Future research should explore how DL techniques can be enhanced to be more privacy-preserving while achieving state-of-the-art performance, bridging the gap between privacy concerns and diagnostic accuracy.
## 4 Experimental Results and Discussion
### Results and Discussion
#### 4.2.2 Contrastive Denoising with Continuous Increase in Noise Density
Additionally, with increasing pixel quantities, the level of similarity between the denoised image and the original image exhibited a minor decline; however, stability was basically maintained. Thus, the processing of more kinds of grayscale images should be conducted to explore the applicability of the proposed TSETMF algorithm and further improve the denoising performance and higher densities. Figure 12: Change map of PSNR with continuously increasing noise density for different algorithms processing COVID. Figure 13: Change map of SSIM with continuously increasing noise density for different algorithms processing COVID. Furthermore, the minor descending PSNR values achieved by the proposed improved TSETMF algorithm demonstrate its better robustness and filtered image quality at higher-density noise levels. Also, the minor descending SSIM values achieved by TSETMF indicate that the algorithm has excellent denoising ability in the processing of higher-noise grayscale images. Moreover, this method can provide a unique approach to addressing denoising issues. Further, it can be advanced to promote high-noise filtering and stability when processing high-density salt and pepper noise in grayscale images. Prospectively speaking, salt and pepper noise can be produced by transmission, analog-to-digital conversion, and data acquisition errors. In particular, the processing of high-density salt and pepper noise is often required for medical images, for instance, magnetic resonance images [66] and X-ray images [67]. By preliminarily analyzing the processing results for medical brain magnetic resonance images, the ability of the proposed TSETMF algorithm to produce relatively stable and excellent noise reduction effects may be demonstrated. This would also demonstrate the algorithm's strong robustness in terms of denoising high-density-noise grayscale images.
# Fuzzy PID Control of the Three-Degree-of-Freedom Parallel Mechanism Based on Genetic Algorithm
## 4 PM Control System  
The servo control system of the PM is a complex, nonlinear, and strong coupling system. Since the parameters in the system are time varying, only relevant approximate models can be established, which hinders control. Therefore, the combination of a genetic algorithm and fuzzy PID controller has the reliability of PID control, the robustness of fuzzy control, rapid adjustments, and the global optimization of the GA.
## 3 Results
From Sweden North to Hedmark South, the rate was M = 47.2 (CI: 29.3-64), while from Hedmark South to Sweden North, the rate was M = 52.8 (CI: 34-72). From Sweden South, the rate was M = 37.1 (CI: 19.3-55) into Hedmark North, and M = 49.0 (CI: 30.7-66) into Hedmark South. Rates to Sweden South from Hedmark North and Hedmark South was M = 32.6 (CI: 1.3-47.3) and M = 62.7 (CI: 45.3-81.3), respectively (Table 3). The inferred contemporary migration rates from BAYESASS (fraction of population i that originates from population j) indicated a significantly higher migration from Sweden North to Hedmark North with a rate of m = 0.30 (CI: 0.28-0.32), while the current migration from Hedmark North to Sweden North was estimated to be m = 0.007 (CI: 0.0003-0.0014, Figure 3, Table 4). There was also a significant degree of migration from Sweden South to Hedmark North with a value of m = 0.30 (CI: 0.28-0.32), and from Hedmark South to Hedmark North with m = 0.31 (CI: 0.30-0.32). All other migration rates were small and not significantly different, with a range from 0.006 (Hedmark South to Sweden South) to 0.01 (Sweden North to Hedmark South). \begin{table}
\begin{tabular}{c c c c c} \hline  & **Hedmark S.** & **Hedmark N.** & **Sweden N.** & **Sweden S.** \\ \hline Hedmark S. & & **0.3139 (0.0108)** & 0.0060 (0.0058) & 0.0060 (0.0059) \\ Hedmark N. & 0.0113 (0.0092) & & 0.0063 (0.0061) & 0.0070 (0.0067) \\ Sweden N. & 0.0118 (0.0113) & **0.2979 (0.0189)** & & 0.0119 (0.0114) \\ Sweden S. & 0.0116 (0.0113) & **0.2986 (0.0189)** & 0.0116 (0.0111) & \\ \hline \end{tabular}
\end{table} 
Table 4: Inferred recent migration rates from BAYESASS (B) (m\({}_{\rm ij}\) = proportion of individuals from population i which are migrants from population j) with standard error (average results of two independent runs). Significant migration rates are marked in bold. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Hedmark S.** & **Hedmark N.** & **Sweden N.** & **Sweden S.** \\ \hline Hedmark S. & & 77.24 (20.09) & 77.24 (13.24) & 49.02 (16.98) \\ Hedmark N. & 49.02 (54.98) & & 38.35 (16.98) & 37.05 (17.62) \\ Sweden N. & 52.84 (19.16) & **115.75 (21.58)** & & 49.82 (3.51) \\ Sweden S. & 62.73 (18.6) & 32.58 (14.75) & 73.91 (19.42) & \\ \hline \end{tabular}
\end{table} 
Table 3: Coalescent estimates of historic mutation-scaled population sizes (\(\Theta\) = 4\(N_{\rm E}\)\(\mu\)) and migration rates (M\({}_{\rm ij}\) = m\({}_{\rm ij}\)/\(\mu\)) with standard error between the four study areas from Norway and Sweden, estimated with MIGRATE (M). Significant migration rates are marked in bold.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis of the data gathered during the research was conducted using appropriate software (IBM SPSS Statistics, v26; IBM Corp., Armonk, NY, USA). The Shapiro-Wilk test confirmed a normal data distribution, allowing for the selection of parametric tests due to this normal distribution. Because of the data normality, the three-way analysis of variance (ANOVA) test was employed for the statistical analysis (final irrigation protocol, sealer type, and root third). Whenever a statistically significant interaction was found, the post hoc Tukey test was utilized for multiple comparisons in the presence of significance. For all analyses, a 5% level of statistical significance was used.
# Seismic Performance of Modal Transfer Stations on Soft Clays
## 3 Seismic Environment Characterization
#### 4.2.2 Site Response Analyses  
Initially, the computer code SHAKE-91 [42] was used to conduct one-dimensional equivalent linear site response analyses and deconvolve the motion to the base of the soil column. Afterwards, the site response analysis in the time domain was carried out with the software FLAC3D v.6.0 [43]. The deconvolved ground motion was applied at the base of the model. The compliant-base approach was employed. Thus, seismic motion was applied as a shear stress history. To validate the site response analysis model, the records of the seismic  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Seismogenic Zone**} & \multirow{2}{*}{**Earthquake Name**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Mw, Moment Magnitude**} & \multicolumn{2}{c}{**PGA, Peak Ground Acceleration (g)**} & \multirow{2}{*}{**T\({}_{\text{D}}\)**} & \multirow{2}{*}{**Frequency Content (Hz)**} & \multirow{2}{*}{**Arias Intensity (cm/s)**} \\  & & & & & & & \\ \hline \multirow{2}{*}{Intraplate} & Puebla, Mexico, CU17 & \multirow{2}{*}{2017} & \multirow{2}{*}{7.1} & \multirow{2}{*}{0.059} & \multirow{2}{*}{29.6} & \multirow{2}{*}{0.23 to 3.5} & \multirow{2}{*}{12.7} \\  & Michoacan, Mexico, CU85 & \multirow{2}{*}{1985} & \multirow{2}{*}{8.1} & \multirow{2}{*}{0.033} & \multirow{2}{*}{49.7} & \multirow{2}{*}{0.38 to 1.36} & \multirow{2}{*}{15.5} \\ \hline \hline \end{tabular} Note: T\({}_{\text{D}}\) is the significant duration of the ground motion defined as the difference between T-95 and T-5, which are the times where 95% and 5% of Arias intensity is reached, respectively.  
\end{table}
Table 4: Earthquakes considered in the analysis.  
Figure 8: Input motions considered in the seismic analyses. (**a**) Synthetic acceleration history for the CU17 earthquake (intraplate). (**b**) Response spectra and UHS for an intraplate event. (**c**) Synthetic acceleration history for the CU85 earthquake (interface). (**d**) Response spectra and UHS for an interface event.  
station SCT of the 2017 earthquake were used. [PERSON] and [PERSON] [22] compared the response spectrum calculated with the numerical model with the response spectrum of the SCT ground motion record. Good agreement was observed between the computed and measured responses. Following this validation, the same dynamic properties were considered for the 3D model.
## 4 Experimental Validation  
### System Implementation  
The AUV incorporates multiple mechatronic systems to perform the path-following task and the motion control of the vehicle (surge, sway, heave, and yaw). Figure 10 presents the system overview of the AUV, which can be defined as high-level and low-level control. The high-level control involves the ROS computers with the GNC algorithms, and the  
Figure 8: Bathymetry map with the coverage area (blue line) and predefined path (dotted red line) for the AUV in the harbor of Sant Feliu de Guixols (Girona, Spain): (**a**) Map view, (**b**) Zoom view.  
Figure 9: Modular GNC architecture schematic. The two GNC modules (guidance and control and coverage path modules) run independent ROS nodes.  
low-level control involves the sensors and actuators of the AUV, which comprise an AHRS, depth sensor, DVL, thrusters, and the manual joystick controller.  
The AUV runs most of the architecture inside the vehicle. The proposed GNC algorithms are included in an external Linux computer (ROS node) located onshore, connecting to the AUV by a gateway buoy via WiFi. Then, this buoy is connected to the AUV via ethernet. The implemented algorithms are obtained using the same procedure as the Standalone ROS-node [41], which provides a solution to the time-consuming process of C++ programming. The ROS master runs the previously described COLA2 navigation system and the developed GNC algorithms and sends the necessary thruster setpoints commands obtained from the motion control system.  
The priority control level of this study is the same as presented in [28]. The highest priority in the control level is the manual joystick controller operated onshore, providing the necessary safety features by stopping the AUV movement at the user's convenience.