esa-sceva/satcom-chunk-collection huggingface dataset:
# Stitching-Based Resolution Enhancement in Wavefront Phase Measurement of Silicon Wafer Surfaces
## 2 Materials and Methods
### Quantitative Phase Imaging  
As mentioned above, the shape of the reflective surface measures is obtained from two slightly defocused images. Both defocused images can be defined as continuous functions \(f_{1}(x,y)\) and \(f_{2}(x,y)\). If a projection of these images is made in a certain direction in a Radon-like way, then this can be expressed for a given angle, as shown below.  
\[p_{1}(s,\theta)=\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}f_{1}(x,y)\delta (xcos\theta+ysin\theta-s)dydx \tag{3}\]  
\[p_{2}(s,\theta)=\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}f_{2}(x,y) \delta(xcos\theta+ysin\theta-s)dydx \tag{4}\]  
Now, if we integrate along the \(s\) coordinate, a cumulative distribution function (CDF) for both \(f_{1}(x,y)\) and \(f_{2}(x,y)\) along the given projection is obtained.  
\[c_{1}(s,\theta)=\int_{-\infty}^{s}p_{1}(s^{\prime},\theta)ds^{\prime} \tag{5}\]  
\[c_{2}(s,\theta)=\int_{-\infty}^{s}p_{2}(s^{\prime},\theta)ds^{\prime} \tag{6}\]  
The differences observed along the ordinate axis (also referred to as histogram specification) between the two CDF curves correspond to the mean slope in the average plane between the two defocused images, as described in [35; 36]. This relationship can be expressed mathematically using the directional derivative of the wavefront along \(s\):  
\[\
abla W(s)\cdot\vec{v}=\frac{c_{1}(s,\theta)-c_{2}(s,\theta)}{2z} \tag{7}\]  
where \(\vec{v}=\cos(\theta)\hat{x}+\sin(\theta)\hat{y}\) and \(z\) is the distance between the plane conjugate to the sample to one of the defocused planes. By computing this expression for all angles from 0 to \(\pi\) and integrating, the wavefront phase can be determined.
## 4 Performance Evaluation Results and Discussion
### Experiments and Results
#### 4.2.1 Result of Experiment 1
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **Dataset** & **Base-CNN** & **SDK-Injection** &
\begin{tabular}{c} **Increagement by** \\ **SDK-Injection** \\ \end{tabular} \\ \hline \multirow{4}{*}{Accuracy (\%)} & AF-D1 & 26.67 & 46.67 & 20.00 \\  & AF-D2 & 40.00 & 60.00 & 20.00 \\  & ECG200 & 92.00 & 95.00 & 3.00 \\  & ECG5000 & 93.76 & 94.38 & 0.62 \\  & TwoLead & 69.89 & 85.25 & 15.36 \\ \hline \multirow{4}{*}{Precision} & AF-D1 & 0.30 & 0.47 & 0.17 \\  & AF-D2 & 0.33 & 0.70 & 0.37 \\  & ECG200 & 0.92 & 0.95 & 0.03 \\  & ECG5000 & 0.70 & 0.75 & 0.05 \\  & TwoLead & 0.72 & 0.86 & 0.14 \\ \hline \multirow{4}{*}{Sensitivity} & AF-D1 & 0.27 & 0.47 & 0.20 \\  & AF-D2 & 0.40 & 0.60 & 0.20 \\  & ECG200 & 0.92 & 0.95 & 0.03 \\  & ECG5000 & 0.52 & 0.58 & 0.06 \\  & TwoLead & 0.70 & 0.86 & 0.16 \\ \hline \multirow{4}{*}{Specificity} & AF-D1 & 0.63 & 0.73 & 0.10 \\  & AF-D2 & 0.70 & 0.80 & 0.10 \\  & ECG200 & 0.92 & 0.95 & 0.03 \\  & ECG5000 & 0.98 & 0.98 & 0.00 \\  & TwoLead & 0.70 & 0.86 & 0.16 \\ \hline \multirow{4}{*}{F1-Score} & AF-D1 & 0.28 & 0.46 & 0.18 \\  & AF-D2 & 0.35 & 0.60 & 0.25 \\ \cline{1-1}  & ECG200 & 0.92 & 0.95 & 0.03 \\ \cline{1-1}  & ECG5000 & 0.56 & 0.63 & 0.07 \\ \cline{1-1}  & TwoLead & 0.70 & 0.86 & 0.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance evaluation of Base-CNN and SDK-Injection. Figure 6: Performance evaluation when the SDK-Injection method is applied to each dataset: (**a**) test accuracy when SDK-Injection is applied to each dataset (red) and test accuracy of Base-CNN for each dataset (sky blue); (**b**) standard deviations of ratio of class labels (gold) for each dataset and difference in accuracy between SDK-Injection method and Base-CNN (dark blue). Ratio of class labels is shown in Table 1. The datasets with standard deviations close to 0 are balanced datasets. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Class**} & \multirow{2}{*}{ \begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Precision** \\ **Per Class** \\ \end{tabular}  } & \multicolumn{2}{c}{**Sensitivity**} & \multicolumn{2}{c}{**Specificity**} & \multicolumn{2}{c}{**F1-Score**} \\ \cline{5-10}  & & & & **Per Class** & **Average** & **Per Class** & **Average** & **Per Class** & **Average** & **Per Class** & **Average** \\ \hline \multirow{8}{*}{\ \begin{tabular}{c} Base- \\ CNN \\ \end{tabular}
# A Modular Mobile Robotic Platform to Assist People with Different Degrees of Disability
## 1 Introduction
The modularity of the presented mobile robotic platform can be exploited by adapting the multimodal interface to the residual capabilities of the disabled person. In particular, the platform can be mainly adapted to three groups of end users with different residual capabilities:  
* Group 1: users with residual motor capabilities to control the arm and/or hand, but who need assistance to carry out activities of daily living in an efficient way. In this group of users, residual EMG signals could be used to control a wearable robot to assist in performing ADLs. In addition, the multimodal interface could be composed of a voice semantic recognition system (for users with non-speech disorders) or a wearable EOG system (for users with speech disorders) to tune some parameters of the high-level controller of the wearable robot and to interact with the user control software, a commercial wearable device for physiological signal monitoring, and RGB depth cameras to sense and understand the environment and context to automatically recognize the abilities necessary for different ADLs;
* Group 2: users without functional control of the arm and/or hand and who are unable to speak (due to a speech disorder or aphasia). In this group, the multimodal interface could be composed of a hybrid BMI system to send commands to the high-level control of the wearable robot, a wearable EOG system to interact with the user control software, a commercial wearable device for physiological signal monitoring, and RGB depth cameras to sense and understand the environment and context to automatically recognize the abilities necessary for different ADLs;
* Group 3: users without functional motor control of the arm and/or hand, with speech disorders, and with limited ability to control the movement of their eyes. In this case, the multimodal interface could be composed of a BMI system to send commands to the high-level control of the wearable robot and to interact with the user control software, a commercial wearable device for physiological signal monitoring, and RGB depth cameras to sense and understand the environment and context to automatically recognize the abilities necessary for different ADLs. For users belonging to Groups 1 and 2, a set of application scenarios was identified as possible targets for the AIDE system: drinking tasks, eating tasks, pressing a sensitive dual switch, performing personal hygiene, touching another person, and so on. For users, belonging to Group 3, the identified scenarios were related to communication, the control of home devices, and entertainment.
# Improvement of Network Flow Using Multi-Commodity Flow Problem
## 5 Experiment
### Routing Using Multi-Commodity Flow Problem
#### 5.2.2 Results of Experiment 2  
**Number of sources: 3, number of destinations: 3**  
Figure 11 shows the result when there are three commodities in total, with commodity 1 flowing from node 1 to node 11, commodity 2 flowing from node 2 to node 10, and commodity 3 flowing from node 8 to node 7. Because the bandwidth of all links is 1 Mbps, the metric for OSPF-ECMP is the number of hops.  
In OSPF-ECMP, the performance of each commodity differed depending on the number of equal-cost paths. In particular, the number of paths for commodity 3 was 2, so the packet arrival rate dropped considerably. However, the proposed method achieved a higher arrival rate than any of the OSPF-ECMP commodities (Figure 11).  
Table 6 compares the average end-to-end delay of OSPF-ECMP and the proposed method at 800 kbps.  
Figure 10: Packet arrival rate for 3 sources and 3 destinations.  
\begin{table}
\begin{tabular}{c c c c} \hline  & & **OSPF** & **Proposed Method** \\ \hline Commodity 1 & 300 kbps & 59 ms & 59 ms \\ \hline Commodity 2 & 300 kbps & 43 ms & 53 ms \\ \hline  & 300 kbps & 40 ms & 46 ms \\ Commodity 3 & 400 kbps & 53 ms & 46 ms \\  & 500 kbps & 421 ms & 49 ms \\ \hline \end{tabular}
\end{table}
Table 5: Average end-to-end delay for 3 sources and 3 destinations.  
As in Experiment 1, the average end-to-end delay was shorter for OSPF-ECMP based on the optimal path than for the proposed method (Table 6).
# Numerical Analysis of Cyclic Impact Damage Evolution of Rock Materials under Confining Pressure
## 2 Numerical Modeling and Verification
### Numerical Modeling  
Based on the actual size of a SHPB apparatus with a diameter of 50 mm, the finite element model of the incident bar, transmission bar, and rock specimen is established using the solid element in Hypermesh software. In the numerical model, both the incident bar and transmission bar have a diameter of 50 mm and a length of 2 m, and the rock specimen has a diameter of 50 mm and a length of 25 mm. To accurately represent the damaging effect and damage evolution of rock under cyclic impacts, the rock specimen is meshed into 20 grids along the axial direction and 30 grids along the radial direction, as shown in Figure 1.  
During the cyclic impact test, both the incident bar and transmission bar are in a linear elastic state. Therefore, the isotropic linear elastic model, namely MAT_ELASTIC, is applied for both the incident bar and transmission bar. Considering the damage evolution induced by cyclic impacts, a dynamic damage constitutive model accounting for strain rate effects, namely Holmquist-Johnson-Cook (HJC) model, is applied to rock material [31; 32]. The HJC model can effectively simulate the dynamic response of rock-like materials under high strain rates, large deformation, and high confining pressure [33]. Tables 1 and 2 give the basic physical and mechanical parameters of the steel pres  
Figure 1: Grid diagram of the incident bar, transmission bar, and rock specimen.  
In Tables 1 and 2, \(\rho\) is density, \(\mu\) is Poisson's ratio, \(E\) is elastic modulus, \(f_{c}\) is static uniaxial compressive strength, \(G\) is shear modulus, and \(K\) is bulk modulus.  
Keyword MAT_ADD_EROSION defines the failure threshold and controls the damage incurred by the material, and the maximum failure principal strain is set as 0.006. To make stress wave propagation closer to reality, the contact between the steel pressure bar and the rock specimen is set as an erosion contact, namely CONTACT_ERODING_SURFACE_TO_SURFACE, with the rock face being dominant. The remaining parameters in both contact types are set as the default values.
## 4 Discussion
The proposed model has the potential for cross-domain applications, and the application requirements vary significantly among different scenarios. Therefore, this study chooses to prioritize strengthening the underlying architecture of the model, laying a foundation for subsequent diversified applications. In summary, the fatigue assessment method based on optical motion capture technology has significant innovation and practical value. This model overcomes the limitations of traditional methods, particularly in accurately quantifying physical task loads and assessing occupational injury risks. It is especially useful in environments where precise measurement of workload and fatigue accumulation is crucial, such as in physically intensive industries and aircraft maintenance. The model supports real-time, dynamic fatigue monitoring, enabling managers to monitor workers' health, adjust work schedules as needed, and prevent excessive fatigue and potential injuries.
## 7 Experimental Results
### Analysis of the Recorded GNSS Raw Samples  
In order to study the dynamic range of the recorded signal, the GNSS raw samples were analyzed in the time/frequency domain prior to being fed to the playback system. The analysis in the time domain is shown in the top panel of Figure 8, where the power spectral density (PSD) of the signal is shown in the bottom panel. Since the AGC was not present within the front-end architecture, the gain provided by the front-end itself was manually adjusted in order to trigger 12 bits of the ADC, out of its maximum resolution of 14 bits. The histogram, highlighting the Gaussian shape of the bins, shows its distribution over 12 bits where the output values were placed between \(-\)2048 and +2048. This design choice was twofold: on the one hand, it allows the recorded signal to be represented with a very high resolution, which might be needed to catch all the features of constrained environments. On the other hand, in the case of signal impairments such as RFI which requires higher signal power levels, it gives the possibility of enlarging the dynamic of the signal, as 2 bits are still available within the ADC. Note that this design choice also has an important role in the playback operations--namely when the recorded signal is converted back to RF and fed to a GNSS receiver, provided that the GNSS receivers are designed to receive signals within a certain power level.
# Advancing TinyML in IoT: A Holistic System-Level Perspective for Resource-Constrained AI
## 5 Opportunities and Cross-Cutting Themes
### Agriculture and Environmental Monitoring  
Resource-constrained AI also finds significant application in smart agriculture, enabling low-cost sensors and drones to track soil moisture, temperature, crop vitality, and pest levels. By analyzing these signals in situ, farmers can make data-driven decisions about irrigation schedules, pesticide use, and harvesting times, improving yields and minimizing waste [30]. Importantly, many agricultural lands are remote, lacking stable internet connectivity, making local AI inference essential. Processing data locally can drastically reduce the volume of transmissions--limited to event triggers or summary statistics--thus preserving battery life for devices powered by solar panels or other off-grid sources [78].  
Similarly, environmental monitoring efforts depend on scattered sensors tracking air, water, or wildlife conditions across large geographic areas, including wilderness or marine habitats. Resource-constrained devices capable of on-site inference can detect pollution spikes, illegal deforestation, or changes in animal populations, alerting authorities only when anomalies occur [26]. This approach is more sustainable than continuously streaming high-resolution data, which may be difficult in areas with limited infrastructure [32]. Additionally, distributed sensor networks can offer robust resilience if certain nodes go offline, since intelligence is not solely centralized. By empowering local AI capabilities, environmental monitoring systems foster more immediate and effective conservation interventions while operating under constrained power and connectivity conditions [68].
###### Abstract  
Class II malocclusions, characterized by the mesial positioning of the maxillary molars relative to the mandibular molars, are among the most frequently encountered orthodontic issues. One of the widely adopted non-extraction approaches for addressing this malocclusion is maxillary molar distalization, which has been utilized for decades to create space within the dental arch. Historically, extraoral appliances such as headgear were commonly employed. However, with technological advancements, intraoral distalization devices, particularly those incorporating temporary anchorage devices (TADs), have gained prominence due to their compact size, not being visible externally, and improved patient acceptance. These appliances offer significant advantages, including being less invasive compared with extraction-based camouflage treatments, being more readily accepted by patients due to the absence of extraction spaces, and not requiring the complex biomechanical principles involved in extraction-based methods. TADs have revolutionized distalization techniques by providing superior anchorage control, reducing treatment duration, and offering a straightforward, patient-friendly application. The purpose of this comprehensive narrative review is to focus specifically on intraoral distalization techniques utilizing TADs, summarizing their efficacy and outcomes in the management of Class II malocclusions as reported in clinical studies over the past three decades.  
Keywords:Class II malocclusion; intraoral molar distalization; skeletal anchorage; TADs; camouflage treatment +
Footnote †: journal: applied sciences
# Spatial Autocorrelation Incorporated Machine Learning Model for Geotechnical Subsurface Modeling
## 2 Study Area and Methodology
### Machine Learning Modeling Process  
#### 2.4.1 Machine Learning Algorithms (MLA)  
The Support Vector Machine (SVM) is a supervised learning algorithm for classification and regression. The SVM algorithm for classification works by finding a hyperplane from the algorithm that maximizes the distance between classes by using samples (support vectors) closest to the hyperplane as the separation margin [22]. The greater the margin, the better the algorithm ability to predict value for previously unseen data (lower generalization error). The SVM algorithm for regression works similarly to the classification SVM, where the algorithm is trained to detect the hyperplane with the maximum number of points fitted to it. Figure 5a shows an illustration of SVM algorithm for regression and classification.  
Figure 5: _Cont._Artificial Neural Network (ANN)  
The artificial neural network (ANN) consists of three different types of layers, which are the input layer, the hidden layer(s), and the output layer [14], as presented in Figure 5b. The input layer is constructed of features that contain observed data that will be input into the training process. The hidden layer(s) is where the input data, together with weights and bias, undergo a mathematical process to generate the output value. The output layer is the last layer where the final output value is calculated and presented.  
The applied neural network is iteratively trained with backpropagation algorithm. Then, the output of the model is compared with the targets from the training-validation set to calculate the error and update the weights and bias in the hidden layer(s). In recent research, ANNs have been used to classify soils, identify soil parameters, and predict the SPT-N value [5; 7; 23].
# Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform
## 3 Proposed DWT_LSTM Forecasting Method
### The Proposed DWT_LSTM Method  
This section presents the DWT_LSTM prediction method, as shown in Figure 4. The method mainly has two stages: the preprocessing of original wind power data, and the training and forecasting of sub-signal data.  
Figure 3: The Mallet decomposition algorithm.  
Wind power data are divided into a training set, a validation set, and a test set according to the order of the time series. The training set, validation set, and test set account for 70%, 20%, and 10% of the original data, respectively. Approximate signal and detail signal values vary widely, which is not conducive to the training of LSTM. Therefore, the z score normalization method shown in Equation (16) was used to standardize the decomposed data in this paper. The normalized data \(z(t)\) obeys the standard normal distribution.  
\[z(t)=\frac{x^{\prime}(t)-x^{\prime}_{mean}}{x^{\prime}_{std}} \tag{16}\]  
\(x^{\prime}(t)\) represents one of the sub-signals (\(a_{n},d_{1}\cdots d_{n}\)), \(x^{\prime}_{mean}\) and \(x^{\prime}_{std}\) are the mean and standard deviation of \(x^{\prime}(t)\), respectively.  
Figure 4: The forecasting process of the discrete wavelet transform and long short-term memory network (DWT_LSTM).  
After the training of the LSTMs, each sub-signal of the test set was predicted, and the normalized predicted value \(p(t)\) of each sub-signal was obtained. Then \(p(t)\) was denormalized to \(y(t)\) of the corresponding sub-signal. The formula of \(y(t)\) is shown in Equation (17).  
\[y(t)=p(t)*x^{\prime}_{std}+x^{\prime}_{mean} \tag{17}\]  
Finally, the wind power prediction results of the original data were obtained by superimposing all denormalized predicted values \(y(t)\).
## 5 Case Study: SFRC Beam
### PCE Metamodeling  
The PCE metamodel of the probabilistic numerical model of the RB-SF specimen used in the previous section is obtained using the UQLab software [41]. Separate PCE metamodels are trained for each of the seven considered Quantities of Interest (QoIs): first-crack displacement and force; yield displacement and force, collapse displacement and force and ductility. An experimental design of 5000 samples is used to train PCEs for all QoIs. Adaptive PCE with a hyperbolic norm truncation scheme is used, and the accuracy of the PCE is measured using the leave-one-out error (LOO error). The details regarding the PCE metamodels for each of the considered QoIs are given in Table 2.  
Figure 13 compares the histogram of the ductility values obtained from the probabilistic numerical model and from the corresponding PCE. A single run of the numerical model takes around 110 milliseconds, while the corresponding PCE metamodel takes 0.96 milliseconds on the same hardware, showing an acceleration of more than 100 times.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Quantity of Interest** & **PCE Degree** & **q-norm** & **LOO Error** \\ \hline First-crack Displacement & 5 & 0.7 & \(1.45\times 10^{-2}\) \\ First-crack Force & 5 & 0.7 & \(2.55\times 10^{-3}\) \\ Yield Displacement & 13 & 0.7 & \(2.36\times 10^{-2}\) \\ Yield Force & 14 & 0.6 & \(1.52\times 10^{-4}\) \\ Collapse Displacement & 20 & 0.6 & \(2.97\times 10^{-2}\) \\ Collapse Force & 16 & 0.6 & \(2.78\times 10^{-5}\) \\ Ductility & 9 & 0.7 & \(3.71\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Degree, q-norm and LOO error of PCE metamodels.  
Figure 12: Load–deflection relations for 50 realizations of the proposed probabilistic numerical model obtained using MC sampling.
# Biogenic Nanoparticles: Synthesis, Characterisation and Applications
## 2 Biogenic Synthesis of Nanoparticles
### Types of Nanoparticles
#### 2.1.2 Gold  
As another metal that has been studied extensively, gold nanoparticles (AuNPs) have found numerous applications relating to cancer therapy, drug delivery and other biomedical applications such as antibacterial [32]. Studies have shown that nanobubbles containing gold nanoparticles can be targeted to a tumorous area and burst due to increased heat from a laser or infrared radiation source [33]. These nanoparticles enter the cancer cell, decreasing its growth rate. AuNPs are also being used for various applications such as Raman spectroscopy and imaging [34].