esa-sceva/satcom-chunk-collection huggingface dataset:
# Applications of Nanomaterial Coatings in Solid-Phase Microextraction (SPME)
## 6 SPME Based on Nanoporous Structures
When combined with gas chromatography-microelectron capture detection (GC-\(\mu\)ECD), the approach demonstrated large enhancement factors (984-2869), low detection limits (0.1-0.5 ng g\({}^{-1}\)), and good linearity (0.3-50 ng g\({}^{-1}\)) for detecting pyrethroid pesticides in fruits and vegetables. The RSDs for six replicate extractions ranged from 4.3% to 8.0%. The recoveries of spiked pesticides (5 ng g\({}^{-1}\) and 20 ng g\({}^{-1}\)) were between 86.0 and 104.0% and between 86.0 and 103.5%, with RSDs of 4.8-12.9% and 5.0-10.8%, respectively. The fiber performed consistently during 100 extraction cycles, with no notable loss of efficiency. The approach was successfully used to identify pyrethroids in fruit and vegetable samples [131]. According to [PERSON] et al., CMK-3 nanoporous carbon was prepared and characterized as a highly porous fiber coating for solid-phase microextraction (SPME) that possesses an ordered carbon structure. According to their explanation, the material was immobilized onto copper, stainless steel, and platinum wires to produce new SPME fibers, with the copper-CMK-3 fiber exhibiting the best performance. The fiber was tuned for the gas chromatography-mass spectrometry (GC-MS) extraction of phenolic chemicals, the researchers observed. According to them, the following ideal conditions were achieved by applying a simplex optimization method: 13 mL sample volume, 56 \({}^{\circ}\)C extraction temperature, 7 min extraction time, 5.5 min ultrasonic duration, pH 5, and 8.9% salt concentration. In comparison to a commercial product, [PERSON] et al. demonstrated the fiber's selectivity for polar phenolic chemicals and observed higher extraction efficiency. They added that linear calibration curves with correlation coefficients above 0.99 and detection limits between 0.002 and 0.068 \(\upmu\)g/mL were provided by the fiber. For at least 40 applications, the fiber's extraction effectiveness remained constant, and it was effectively used to examine phenolic chemicals in natural water samples [132].
## 2 Materials and Methods
### Electronic Tongue Analysis  
Electronic tongue analysis was conducted according to the method of [PERSON] et al., with modifications [PERSON] et al. (2017). Each 10 g sample was homogenized and heated in a water bath until the core temperature reached 40 \({}^{\circ}\)C. Subsequently, 80 mL of water at the same temperature was added and further mixed for 1 min. After centrifugation at 3000\(\times\)\(g\) for 10 min at 4 \({}^{\circ}\)C, the supernatant was collected for electronic tongue testing. A SA402B taste sensing system (Insent, Kanagawa, Japan) was used to characterize the taste profile. The system was equipped with five lipid membrane sensors--SB2 CA0 (sourness), SB2C00 (bitterness), SB2 AE1 (astringency), SB2 AAE (umami), and SB2 CT0 (saltiness)--along with an Ag/AgCl reference electrode.  
Before measurement, the taste sensors and reference electrode were activated for 24 h in a reference solution (30 mmol/L KCl + 0.3 mmol/L tartaric acid) and saturated KCl solution, respectively, to stabilize signal responses. Measurements were conducted at room temperature (25 \(\pm\) 2 \({}^{\circ}\)C) following a standardized five-step procedure: cleaning, equilibration, sample measurement, re-cleaning, and aftertaste recording. Sensors were first cleaned with cation and anion cleaning solutions for 90 s, rinsed in two reference solutions for 120 s each, and the baseline potential (Vr) was recorded for 30 s. The sensors were then immersed in the sample solution to measure the sample potential (Vs, 30 s), followed by two brief rinses in reference solution (3 s), after which the aftertaste potential (Vr\({}^{\prime}\), 30 s) was recorded. Each sample was tested in quadruplicate, with the first reading discarded and the mean of the remaining three used to ensure signal stability and reproducibility. Data were processed using the instrument's dedicated software and expressed as relative taste intensity values. A principal component analysis (PCA) was applied to identify taste distribution patterns and visualize differences among sample groups.
# Privacy-Preserving Data Analytics in Internet of Medical Things
## 4 Differential Privacy  
Differential privacy is a mathematical mechanism that offers a robust privacy guarantee throughout data analysis, enabling the public exchange of data. This idea was first presented by [PERSON] and associates in the early 2000s [30]. It protects an individual's privacy by making sure the the presence or absence of an individual in the dataset cannot impact the results of any search. It helps to make guarantee that private information about an individual is kept hidden upon aggregated data analysis. The fundamental concept of differential privacy is the introduction of controlled randomness into the data analysis process. Differential privacy guarantees that no single data point's privacy is compromised in the output by carefully adding the noise to query results [31].  
**Definition 1**.: _A randomized algorithm (\(R\)) is \((\epsilon,\delta)\)-differentially private for any two adjacent datasets (\(O_{1}\) and \(O_{2}\)) for all subsets (\(Q\)) of the output space of \(R\)[32]._  
\[\Pr[R(O_{1})\in Q]\leq e^{\epsilon}\Pr[R(O_{2})\in Q]+\delta \tag{1}\]  
Neighbouring datasets \(O_{1}\) and \(O_{2}\) are adjacent datasets that differ by no more than one element. Here, a positive privacy parameter called epsilon (\(\epsilon\)) is used to evaluate the loss of privacy. A smaller value of epsilon indicates stronger privacy. While \(\delta\) is a positive parameter, which is usually close to zero, that permits a minor relaxation of the strict privacy guarantee. It is pure differential privacy if the value of \(\delta=0\); then, we obtain a stricter definition of differential privacy.  
\[\frac{\Pr[R(O_{1})\in Q]}{\Pr[R(O_{2})\in Q]}\leq e^{\epsilon} \tag{2}\]
# Applying Deep Learning to Medical Imaging: A Review
## 3 Applications in Medical Imaging
### Image Classification  
3.2.1. Techniques and Approaches  
Image classification in medical imaging involves assigning a label to an input image, typically indicating the presence or absence of a specific condition or abnormality [114]. DL techniques, particularly CNNs, have demonstrated exceptional performance in image classification tasks [63]. Transfer learning, where pretrained models on large-scale natural image datasets (e.g., ImageNet) are fine-tuned on smaller medical imaging datasets, has been widely adopted to overcome the limitations of scarce labeled data in medical imaging [76]. Additionally, DL techniques such as DenseNets [68], ResNets [53], and multitask learning approaches [115] have been used to improve classification performance in various medical imaging applications, including the detection of cancerous lesions, identification of diseases, and assessment of treatment response. Figure 6 indicates the application of DL approach to mammography images.
# Feature Mining: A Novel Training Strategy for Convolutional Neural Network
## 5 Conclusions  
In this paper, we proposed a novel training strategy named Feature Mining that aims to strengthen a CNN's learning of the local features. Feature Mining divides the complete feature into two complementary parts and reuses the divided feature to make the network capture different local information for efficient training. Our method has a plug-and-play nature and can be applied to any CNN model. Extensive experiments proved that Feature Mining brought stable improvement to different classification datasets on various models and could be applied to various tasks, including fine-grained image classification, object detection, label noise, and the small data regime. On CIFAR-100 classification, applying Feature Mining to ResNet-164 brought +3.33% top-1 accuracy improvement. On CUB-200-2011, fine-grained image classification, applying Feature Mining to ResNet-50, significantly improved the performance of the baseline by +9.53%. Besides, Feature Mining  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & **GPU Memory** & **Training Time** \\  & **(MB)** & **(s/iter)** \\ \hline ResNet-56 & 1740 & 0.094 \\ ResNet-56+FM (1 layer) & 1773 & 0.098 \\ ResNet-56+FM (2 layers) & 1959 & 0.113 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Computation cost introduced by Feature Mining. The statistics were obtained on a Tesla M40 GPU with a batch size of 128. The training time is averaged over the first 300 iterations.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy (\%)** \\ \hline ResNet-56 & 73.71 \(\pm\) 0.28 \\ ResNet-56+FM (1 layer) & 74.55 \(\pm\) 0.11 \\ ResNet-56+FM (2 layers) & 75.35 \(\pm\) 0.07 \\ ResNet-56+FM (3 layers) & 75.29 \(\pm\) 0.12 \\ \hline ResNet-56+Dropout-FM & 74.12 \(\pm\) 0.23 \\ ResNet-56+SpatialDropout-FM & 74.01 \(\pm\) 0.17 \\ ResNet-56+non-complementary FM & 74.23 \(\pm\) 0.09 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Performance of Feature Mining variants on ResNet-56.  
is complementary to data augmentation and self-distillation methods. For future work, we plan to find better segmentation strategies for Feature Mining by using reinforcement learning.  
Conceptualization, X.W., M.L. ([PERSON]), and T.X.; methodology, T.X.; validation, X.W., T.X. and J.D.; formal analysis, T.X. and J.D.; writing--original draft preparation, J.D.; writing--review and editing, X.C. and M.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Science and Technology Program of Quzhou under Grant 2021D007, Grant 2021D008, Grant 2021D015, and Grant 2021D018, as well as the project LGF22G010009.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CNN & Convolutional neural network \\ FM & Feature Mining \\ BYOT & Be your own teacher \\ CS-KD & Classwise self-knowledge distillation \\ CAM & Class activation mapping \\ \end{tabular}
## 3 Numerical Results and Discussion
### Effect of Random Cumulative Pitch Deviations on Unmodified HGRS  
#### 3.1.1 Quasi-Static Analysis  
For a gear pair with manufacturing errors, the contact patterns will differ under different load conditions. The change of contact patterns will cause the changes of dynamic mesh excitations. In order to further analyze the vibration responses of the HGRS, the quasi-static mesh excitations of the helical gear pair with random cumulative pitch deviations are investigated in this section. LPMS, STE, and CMS of the helical gear pair with random cumulative pitch deviations under different load conditions are given in Figure 9. The load torques are 200 N-m, 350 N-m, 600 N-m, and 1000 N-m, respectively. It can be seen that the cumulative pitch deviations have significant influence on LPMS, STE, and CMS. From Figure 9a, it can be found that the values of LPMS under light load conditions in some mesh cycles are obviously smaller than that under heavy load conditions, and the corresponding curve shapes are notably different from each other. This is because partial contact loss occurs in these mesh cycles under relatively light load conditions. In Figure 9b, the shapes of STE curves under different load conditions are always consistent with each other, but the values become larger in each mesh cycle when the load torque increases. This is because the increased load torque results in larger tooth deflection which contributes to the STE values. From Figure 9c, it can be found that the CMS is related to the load condition. When the load torque is larger than 600 N-m, the curve of CMS remains constant. The changes of LPMS, STE, and CMS can be further explained from the remarkable change of corresponding load distribution. Figure 10 shows the load distributions in the twelfth mesh cycle at different load torques. It can be seen that partial contact loss occurs when the load torque is less than 600 N-m. Moreover, the contact loss region has a tendency to become smaller with the increase of load torque, and the mating gear teeth reaches full contact condition in each mesh cycle when load torque is larger than 600 N-m.  
Figure 9: Long-period mesh stiffness (LPMS), static transmission error (STE), and composite mesh (CMS) at different load torques considering random cumulative pitch deviations.
# Low-Cost Automation for Gravity Compensation of Robotic Arm
## 2 Related Works
Gravity compensation is a well-known technique in robot design to achieve equilibrium throughout the range of motion and as a result to reduce the loads on the actuator. The paper in [16] proposes an overview of gravity compensation methods applied in robotics. The characteristics of gravity compensation are disclosed and illustrated via kinematic schemes. In order to classify the considered balancing schemes three principal groups are distinguished due to the nature of the compensation force: counterweight, spring or active force developed by an auxiliary actuator. The author of this paper believes that such an arrangement of gravity compensation methods allows one to carry out a systematized analysis and provides a comprehensive view of the problem. While benefits of the compensation of the gravity force effects seem to be more difficult to implement, it brings additional enhancement of the control quality. Several types of research works [17; 18] describe approaches to the robot manipulator control based on a compensation of non-linear gravity terms and inertial terms in the robot motion equations and applying the PD or PID-type control law. Unlike the inverse dynamics methodology, the precise mathematical model of the robot is not needed, which is an important advantage, since models of robots get more than three links are usually very complex and difficult to obtain. The research work [19] shows a gravity force methodology compensation in order to improve the control precision and efficiency. This methodology is used in a spatial manipulator for trajectory tracking control. This paper proposed a simple and practical gravity compensation algorithm that does not need complex computation of coordinate transformation and the accurate dynamic models and parameters, only needs the largest gravity torque estimation of the manipulator and the calculation of simple trigonometric function, while the algorithm is also suitable for non-parallel axis spatial manipulators with N joints. As can be seen in this section, technological progress has allowed the development of gravity compensation algorithms solutions that have contributed to the automation of industrial processes using robotics arms, this paper proposes a low-cost platform that has covered the needs of having controllers based on gravity compensation with a high processing capacity into the IoT paradigm.
# A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots
## 2 Noise K-Means Clustering Algorithm  
### The Ide of the Noise K-Means Clustering Algorithm  
A K-means clustering algorithm is an iterative clustering analysis algorithm that has been regarded as an effective means to solve urban road planning problems by scholars for the past several decades. The algorithm has been widely used in the fields of document classification, customer classification, ride data analysis, criminal network analysis, the detailed analysis of call records, and so on. It is very difficult to determine the number of clusters and sensitively initialize the cluster center. Noise can be used to simulate noise phenomena in nature. Because of its continuity, if an axis in two-dimensional noise is taken as the time axis, the result is a continuously changing one-dimensional function. As such, in order to solve the existing problems of the K-means clustering algorithm and make use of the merits of the noise algorithm, a novel noise-based K-means clustering algorithm is proposed to obtain a better clustering center and capture urban hotspots in this paper. The proposed noise-based K-means clustering algorithm consists of three parts. Firstly, the noise algorithm is employed to randomly enhance the attribution of data points and the output result of clustering by adding noise judgment in order to automatically obtain the number of clusters of the given data and initialize the cluster center. Secondly, the K-means clustering algorithm is employed to optimize the clustering center generated. It is fused with noise algorithm to form a novel noise-based K-means clustering algorithm. Finally, the proposed noise-based K-means clustering algorithm is used to obtain the clustering results for the given data and capture an excellent clustering center, i.e., and urban hotspot. Four unsupervised evaluation indexes of DB, PBM, SC, and SSE are directly used to evaluate and analyze the clustering results, and nonparametric Wilcoxon statistical analysis is employed to verify the distribution states and differences between clustering results.
## 3 Effect of Incident Angle of the Light on the Measurement Sensitivity
### Simulation Assessment of Sensitivity
This may be because the experimental parameters such as the diameter of the stylus shaft and the lens performance do not exactly match the theoretical parameters in the simulation. Consequently, it is possible to increase the measurement sensitivity. Therefore, because the sensitivity of the optical fiber probe increases with the increase in the incident angle of the laser beam on the stylus shaft, it is desirable that the stylus shaft is irradiated at as large an incident angle as possible by using the prism with a mirror. \begin{table}
\begin{tabular}{l} \hline \hline X, Y, Z: Coordinates Used for Ray-Tracing \\ \hline \hline \(D\): Diameter of the stylus shaft \\ \hline \(L_{0}\), \(L_{1}\): Length from between plane 0 to plane 1, plane 2 to plane 3 \\ \hline \(n_{0}\), \(n_{1}\): Refractive indices of the air and optical fiber (stylus) \\ \hline \(W_{1}\), \(W_{2}\), \(W_{3}\): Heights of laser beam at respective planes \\ \hline \(\theta_{0X}\), \(\theta_{1X}\)\(\theta_{2X}\)\(\theta_{3X}\): Inclination angles of rays in the X-direction \\ \hline \(\theta_{02}\), \(\theta_{1Z}\)\(\theta_{2Z}\): Inclination angles for rays in the Z-direction \\ \hline \(\beta\), \(\gamma\): Angles between the normal line at the laser irradiation position of the stylus shaft and X-axis \\ \hline \(\theta_{a}\), \(\theta_{c}\): Incident angle \\ \hline \(\theta_{b}\), \(\theta_{d}\): Refracting angle \\ \hline \hline \(\Delta\)1: Length from a laser irradiation position on the surface of the stylus shaft to plane-1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters used for ray-tracing.
## 3 Results and Discussion
### Optimization for the Removal of the FD&C Red 40 Dye in an Aqueous Solution  
Table 3 shows the matrix of the CCD experimental variables and FD&C Red 40 dye removal efficiency responses using the CS-TiO\({}_{2}\)-GLA beads. The range of parameters was determined based on preliminary experiments. The concentrations of the solutions were calculated from a previously prepared calibration curve (Figure S1).  
The interactions and effects between the independent variables in the removal efficiency were determined by ANOVA, as shown in Table 4. The effectiveness of the model and its predictive ability were evaluated based on the R\({}^{2}\) and adjusted R\({}^{2}\) coefficients (R\({}^{2}\)\({}_{\text{adj}}\)) for removal efficiency. The model presented significant variability with an R\({}^{2}\) of 0.7555 and an R\({}^{2}\)\({}_{\text{adj}}\) of 0.6391, where the R\({}^{2}\)\({}_{\text{adj}}\) is the most appropriate parameter for comparing models with different independent variables [27].  
For a confidence level of 95% (\(p<0.050\)), it was observed that the main effects (B: initial dye concentration and C: pH) and the interaction effect BC are significantly different from zero. The mean square of the total error is much smaller or very different from the mean square of the effects, so they introduce variability to the response, and the alternative hypothesis is approved. On the other hand, factor A, the amount of TiO\({}_{2}\), has a \(p>0.050\), indicating that TiO\({}_{2}\) in the beads does not significantly influence dye removal efficiency. Consequently, the mathematical model that best fits the removal percentage with a significance level of 95% is verified in Equation (3).  
\[\begin{array}{ll}Removal\ efficiency\ (\%)&=91.063+0.161854A-0.726989B+3.55169C-0.00036359A^{2} \\ &+0.00175667 AB-0.0208 AC+0.0327055B^{2}-0.423125 BC\\ &+0.456848C^{2}\end{array} \tag{3}\]  
Figure 1: SEM morphology images for the nanocomposite beads CS-GLA: (**A**) 100\(\times\), (**B**) 500\(\times\), (**C**) 50,000\(\times\); CS-TiO\({}_{2}\)-GLA: (**D**) a 100\(\times\), (**E**) 500\(\times\), (**F**) 50,000\(\times\) and CS-GO-TiO\({}_{2}\)-GLA: (**G**) a 100\(\times\), (**H**) 500\(\times\), (**I**) 50,000\(\times\).
## 5 Conclusions  
To improve the measurement accuracy of the vehicle state in the tracking control, based on the extended Kalman filter theory, we integrated the vehicle GPS and IMU information and designed a vehicle state estimator with three inputs, six states and four outputs. To meet the requirements of multi-target tracking control in the longitudinal and lateral directions of autonomous vehicles, based on the 3 DOF vehicle dynamics model and the curvilinear road coordinate system, we carried out dimensionality reduction on the longitudinal and lateral errors in the path tracking. To realise the online application of predictive control algorithm, based on the convex optimisation and the online active set theory with an open source QP solver, we designed and implemented a fast-rolling optimisation algorithm for longitudinal and lateral tracking control of autonomous vehicles and completed the verification of the algorithm and tracking performance in the reconstructed curve road scene based on real GPS data. The hardware-in-the-loop simulation results show that the proposed control can meet the tracking control need of autonomous driving. Compared with the PID-based controller, the proposed MPC controller has apparent advantages in lateral errors, lateral acceleration and front tire slip angle.  
In the next step, we will further apply the proposed algorithm on other autonomous vehicles with specific driving environment. Moreover, the proposed strategy will be tested  
Figure 8: Acceleration comparison of two controllers, PID and MPC: (**a**) Longitudinal acceleration of two controllers; (**b**)Lateral acceleration of two controllers.  
Figure 9: Front tire slip angle comparison of PID and MPC controller.
## 3 Methodology
### Data Pre-Process  
To ensure the accuracy of the data, pre-processing of the scanned data is essential. The first step involved meshing the point cloud data. In this study, a 1600 \(\times\) 1000 grid was constructed with 0.1 mm intervals in both x- and y-directions. To accurately assign values to the z-direction, an interpolation method was used. Subsequently, normalization was carried out by subtracting the mean value.  
However, the presence of bumps and concave data can cause noise in the scanned data. Additionally, diffused or refracted laser light sources through bitumens or aggregates can cause noise. A procedure for pavement texture noise removal was utilized in this study, as depicted in Figure 7. A two-dimensional median filter was employed to remove outliers from the data. For this, a 3 \(\times\) 3 sliding window was used to detect outliers in both the x- and y-directions, with a step size of 1. Points with z-value variance greater than a defined threshold (0.1 mm) were treated as abnormal points and replaced with the median value of the z-value in the surrounding points of the sliding window.