esa-sceva/satcom-chunk-collection huggingface dataset:
# Modular Design and Decentralized Control of the Recupera Exoskeleton for Stroke Rehabilitation
## 2. Mechatronic System Design
### Electronic Design and Processing Architecture
#### 2.2.1 Decentralized Actuator-Level Controllers  
Each actuator is controlled by a separate self-designed modular Actuator Control Unit (ACU) that is located in close proximity of the corresponding actuator that has been specifically developed to control BLDC motors. The ACU consists of three self-designed distinct PCBs (for power electronics, data acquisition and communication, and computing, respectively), which can be combined as required to satisfy the specific requirements of each actuator. An overview on the configurations used in the Recupera exoskeleton is shown in Table 2. All functions are realized using a hardware implementation in the Xilinx XC6 SLX45 Spartan(r) 6 FPGA located in the computing PCB of the ACU. The control is performed using a cascaded position-velocity-current PID controller, see Section 3.2.1.  
Each ACU is supplied with two different voltages: 48 V for the motor phases and with 12 V for the computing and communication components. The supply voltage and current are continuously monitored and controlled by the FPGA, which permits to implement a configurable firmware-based fuse for safety reasons. In addition, each ACU is protected by a hardware fuse with a rated current of 5 A for the linear leg actuators and 3 A for all other actuators.  
Figure 5: Hybrid centralized-decentralized control and network architecture. The exoskeleton is controlled by two central systems, called ZynqBrain (ZB), and a network of decentralized Actuator Control Units (ACU) for BLDC motor control. Each motor is controlled locally by an adjacent ACU. The distributed ACUs are interconnected via an NDLCom network. ACUs that belong to a specific limb form a subsystem. Bottom left: cascaded actuator-level control architecture implemented on the FPGA of each ACU; \(u\) is the angular position, \(u\) is the angular velocity and \(i\) is the motor current. The reference values are provided by the mid-level control on the ZBs. All ACUs continuously send telemetry status data including \(u\), \(i\) and \(i\) to ZB1 with a frequency of 100 Hz using the NDLCom network.
## 4 Discussion
### LiDAR Processing Chain and ALS Data Collection  
In addition to the type of data implemented, the classification methodology is also crucial in the analysis of LiDAR data. The implementation of a decision tree algorithm was consistently applied. Inspections of point clouds contributed to determining the parameters for classification thresholds. Thus, the presented methodology is objective and easily reproducible without the subjective influence of operators. For monitoring purposes, this also allows a more reliable comparison of results when datasets from different dates are compared. The uncertainty in the multi-temporal analysis is reduced since the resulting classification maps are obtained with the same thresholds and classification methodology.  
Our results also confirmed the efficiency of ALS in terms of data collected per unit of time. ALS gathers information within a couple of hours, under the same water level, phenological and climatic conditions. With the proposed transect aquatic reed bed mapping protocol, a team comprising three researchers collecting biometric parameters in cross-sections every 10-m interval for 100 m needed approximately 3 h of work. The length of the Chiemsee (including the two islands) shoreline is 82.811 km. Thus, mapping the entire lakeshore would take approximately 10 months if a team were to work 8 h per day and 7 days per week. Due to the fast monthly growth rate of _Phragmites australis_ and the fact that new stems are produced until late summer [2], a considerable number of inconsistencies in data collection would be acquired, based on such field monitoring. Recording data within a short time period not only reduces the uncertainty of the measurements but also of the classification procedures. For instance, all AOIs were classified in the LiDAR data collections at once and the thresholds were suitable for all kind of _Phragmites australis_ structures at the Chiemsee. However, during field work, every AOI was treated independently and classified according to the vegetation structures present on each AOI. Thus, a classification developed for all objects cannot lead to the same result when every sample is independently categorised.
## 5 Simulation and Performance Analysis
### Simulation Example  
#### 5.2.1 Effect of _rang_ on Network Reliability and Network Coverage Rate  
During the adjustment of the depth of in-cluster nodes, the cluster head node finds the optimum location for its in-cluster nodes within the _rang_ of the minimum _CRR_; thus, the value of _rang_ has an influence on the results of network deployment. This study explores the changes in network coverage rate and network reliability when the number of network nodes is 200 and the value of _rang_ varies from zero to 0.5. Figure 10 shows the network coverage rate of URSA for varying _rang_, in which the network coverage rate decreases and the rate of decrease increases with increasing _rang_. Figure 11 presents the network reliability of URSA for varying _rang_, in which the number of nodes within the \(R_{c}\) range of the sink node immediately shows an inconsiderable fluctuation. However, its overall tendency is growing, and the rate of increase increases gradually with increasing _rang_. The average number of neighbor nodes increases all of the time, and the rate of increase accelerates with increasing _rang_. When the cluster head node selects the best locations for its in-cluster nodes, the possibility dominated by the number of hops increases and the possibility dominated by the value of _CRR_ decreases with increasing _rang_. This phenomenon increases the number of nodes near the cluster head node, shortens the path in the cluster, increases the chance of the node close to the sink node and increases the possibility of nodes to gather. Therefore, an increasing _rang_ improves the network reliability, but reduces the network coverage rate. The preceding analysis implies that the value of _rang_ can be set according to the importance of each indicator in the actual application. In this study, considering the compromise between the network coverage rate and network reliability, _rang_ is set to 0.2 in every example below.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Length of data packet \(l\) & 1000 bits \\ Energy consumption of data reception \(P_{r}\) & 2 nJ/bit \\ Energy diffusion factor \(\lambda\) & 1.5 \\ Carrier frequency \(F_{r}\) & 24 kHz \\ Data transmission speed underwater & 300 bits/s \\ Initial competition radius \(R_{0}\) & 80 m \\ Probability threshold parameter \(T_{h1}\), \(T_{h2}\), \(T_{h3}\) & 0.8, 0.6, 0.5 \\ Parameter \(k\) & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 10: Network coverage rate of the radius-adjusting self-deployment algorithm (URSA) for varying _rang_.
# A Data-Driven Deep Learning Framework for Prediction of Traffic Crashes at Road Intersections
###### Abstract  
Traffic crash prediction (TCP) is a fundamental problem for intelligent transportation systems in smart cities. Improving the accuracy of traffic crash prediction is important for road safety and effective traffic management. Owing to recent advances in artificial neural networks, several new deep-learning models have been proposed for TCP. However, these works mainly focus on accidents in regions, which are typically pre-determined using a grid map. We argue that TCP for roads, especially for crashes at or near road intersections which account for more than 50% of the fatal or injury crashes based on the Federal Highway Administration, has a significant practical and research value and thus deserves more research. In this paper, we formulate TCP at Road Intersections as a classification problem and propose a three-phase data-driven deep learning model, called _Road Intersection Traffic Crash Prediction_ (_RoadInTCP_), to predict traffic crashes at intersections by exploiting publicly available heterogeneous big data. In Phase I we extract discriminative latent features called _topological-relational features (tr-features)_, of intersections using a neural network model by exploiting topological information of the road network and various relationships amongst nearby intersections. In Phase II, in addition to _tr-features_ which capture some inherent properties of the road network, we also explore additional thematic information in terms of environmental, traffic, weather, risk, and calendar features associated with intersections. In order to incorporate the potential correlation in nearby intersections, we utilize a Graph Convolution Network (GCN) to aggregate features from neighboring intersections based on a message-passing paradigm for TCP. While Phase II serves well as a TCP model, we further explore the signals embedded in the sequential feature changes over time for TCP in Phase III, by exploring RNN or 1 DCNN which have known success on sequential data. Additionally, to address the serious issues of imbalanced classes in TCP and large-scale heterogeneous big data, we propose an effective data sampling approach in data preparation to facilitate model training. We evaluate the proposed _RoadInTCP_ model via extensive experiments on a real-world New York City traffic dataset. The experimental results show that the proposed _RoadInTCP_ robustly outperforms existing methods.  
machine learning; artificial intelligence; neural network; data mining; road intersection; traffic crash prediction; intelligent transportation systems
## 4 Decoupling Inverse Compensation  
On the basis of the newly developed SDCHM model, an inverse and decoupling compensator is constructed to mitigate coupled hysteresis. In order to compensate for the SDCHM model with three submodules, the stiffness-dependent and decoupled inverse compensator (SDIHC) also includes three parts: the inverse hysteresis part, the decoupling part, and the inverse stiffness-dependent part, as shown in Figure 8.  
First, the inverse stiffness-dependent submodule is constructed. \(g^{-1}(\cdot)\) in Figure 8 defines the inversion of \(g(\cdot)\). Because \(g(\cdot)\) is calculated by the TSFNN, \(g^{-1}(\cdot)\) is also constructed by the same structure, as shown in Figure 7. The inverse modeling based on neural networks has been utilized for a valid method for identifying the inversion of a known model. Particularly, analytical solutions of the inverse model are challenging to determine.  
Second, in the decoupling submodule, the inverse operation of Equation (8) is derived as follows:  
\[\begin{cases}v_{1d}(t)=\frac{1}{C_{51}}(h_{1d}-G_{21}v_{2d}(t)-G_{31}v_{3d}(t) ),\\ v_{2d}(t)=\frac{1}{C_{52}}(h_{2d}-G_{12}v_{1d}(t)-G_{32}v_{3d}(t)),\\ v_{3d}(t)=\frac{1}{C_{53}}(h_{3d}-G_{13}v_{1d}(t)-G_{23}v_{2d}(t)).\end{cases} \tag{14}\]  
Thirdly, the inverse hysteresis component is constructed. The inverse compensation method for the GPI model is applied as the following Equation (15):  
\[\begin{cases}\hat{p}_{1}(t)=GPI^{-1}[v_{1d}](t),\\ \hat{p}_{2}(t)=GPI^{-1}[v_{2d}](t),\\ \hat{p}_{3}(t)=GPI^{-1}[v_{3d}](t).\end{cases} \tag{15}\]  
Figure 8: Decoupled inverse hysteresis compensator.  
Because the envelop functions \(\gamma_{R}\) and \(\gamma_{L}\) are invertible, the inverse model of the GPI model is expressed as  
\[\dot{p}_{k}(t)=GPI^{-1}[v_{kd}](t)=\left\{\begin{array}{ll}\gamma_{R}^{-1} \circ\Gamma^{-1}\circ v_{kd}(t),&\text{if }v_{kd}(t)>v_{kd}(t-1),\\ \gamma_{L}^{-1}\circ\Gamma^{-1}\circ v_{kd}(t),&\text{if }v_{kd}(t)<v_{kd}(t-1), \quad(k=1,2,3)\\ \dot{p}_{k}(t-1),&\text{if }v_{kd}(t)=v_{kd}(t-1).\end{array}\right. \tag{16}\]  
where:  
\[\begin{array}{ll}\Gamma^{-1}\circ v_{kd}(t)&=\Gamma^{-1}[v_{kd}](t)\\ &=\hat{x}^{k}(r_{0}^{k})v_{kd}(t)+\sum\limits_{i=1}^{N}\hat{x}^{k}(p_{i}^{k}) F_{p_{i}^{k}}[v_{kd}](t)\end{array} \tag{17}\]  
with  
\[\hat{x}^{k}(r_{0}^{k})=\frac{1}{x(r_{0}^{k})} \tag{18}\]  
\[\hat{r}_{i}^{k}=x(r_{0}^{k})r_{i}^{k}+\sum\limits_{j=1}^{i-1}x^{k}(r_{j}^{k}) (r_{i}^{k}-r_{j}^{k}) \tag{19}\]  
\[\hat{x}^{k}(p_{i}^{k})=-\frac{x^{k}(r_{i}^{k})}{(x^{k}(r_{0}^{k})+\sum\limits_ {j=1}^{i}x^{k}(r_{j}^{k}))(x^{k}(r_{0}^{k})+\sum\limits_{j=1}^{i-1}x^{k}(r_{j }^{k}))} \tag{20}\]
###### Abstract  
This study evaluates the manufacturing accuracy of Merlon fracture models produced using two vat-photopolymerization-based three-dimensional (3D) printers: digital light processing (DLP) and liquid-crystal display (LCD). The Merlon fracture model is used to assess dimensional precision and machining accuracy. The root mean square (RMS) values, wall and bottom thicknesses, and field-emission scanning electron microscopy images are analyzed. The DLP-based printers exhibit lower RMS values and superior accuracy compared with LCD-based printing and subtractive milling. Polymer-based slurries for permanent dental applications exhibit better dimensional stability than those for temporary restorations. This study also highlights the significant impact of postprocessing and cleaning procedures on the final model accuracy. These findings suggest that optimizing the postprocessing parameters is crucial for enhancing the precision of 3D-printed dental restorations. The Merlon fracture model is a viable method for evaluating additive manufacturing accuracy, contributing to the improved clinical application of vat photopolymerization in dental prosthetics.  
vat photopolymerization; merlon fracture model; root mean square (RMS); additive manufacturing accuracy
## 4 Experiments and Analysis
### Experimental Data  
To assess the efficacy of our proposed method, two sets of HRRS images are used: One is used for model training, the other is used to test the robustness and tamper sensitivity of the proposed algorithm.  
Launched in August 2014, the GaoFen-2 (GF-2) satellite is the first civilian optical remote sensing satellite in China with a resolution below 1 m [47; 48; 49]. The GF-2 is equipped with two panchromatic/multispectral (PAN/MSS) cameras that can be used simultaneously, resulting in a high resolution of 0.81 m. It can obtain a 45 km width strip, which is four times that of IKNONS. Thus, GF-2 has the potential use in disaster assessments, high-accuracy navigation, mapping surveys, and other scientific research. In this paper, both of the testing set and the training set are acquired by the GaoFen-2 (GF-2) satellite with a spatial resolution of 0.8 m.  
The training set consists of 50 pairs of 256 \(\times\) 256 pixel HRRS images, and each pair images includes an HRRS image and a corresponding robust edge image generated based on the method in Section 3.1.1. Examples of training HRRS images are shown in Figure 8.  
In this paper, since the perceptual hash algorithm requires enough data to prove the robustness of the algorithm, the test sample is much more than the training sample. To build the testing set, we selected six three-band HRRS images in Tiff format denoted as image A to image F, as shown in Figure 9. Each of them is acquired by GaoFen-2 and is 4200 \(\times\) 5000 pixels in size. Due to the limitation of the graphics memory, we divide each of them into 16 \(\times\) 19 images, and then use a bilinear interpolation algorithm to resize them to 256 \(\times\) 256 pixel. Eventually, we produced a testing set of 1824 HRRS images.  
Figure 8: Examples of the training images: (**a**) Examples of original HRRS images, (**b**) corresponding images of robust edge feature.
###### Abstract  
This work aims to improve the characterization of petrophysical properties by accurately estimating subsurface porosity using seismic and well data. The study includes Bayesian Linearized Inversion to obtain elastic parameters (e.g., compressional e shear wave velocities and densities). This reduces processing uncertainty and provides a reliable substitute for the standard Amplitude versus Offset inversion method. Furthermore, incorporating sparse spike wavelets with Bayesian Linearized Inversion refines the inversion output, facilitating the extraction of petrophysical properties. Combined with log data from seventeen wells, these inverted parameters serve as inputs for two porosity prediction models: the empirical Han's equation and a more adaptable Support Vector Regression model, the latter demonstrating superior precision in most cases due to its flexible fitting and calibration capabilities. Results from the Norne field in the North Sea confirm the approach's viability, with the Support Vector Regression model achieving a significant Pearson correlation coefficient of 90% in porosity prediction, underscoring the potential of machine learning techniques in improving subsurface exploration results.  
Bayesian linearized inversion; porosity; machine learning Article  
1
# Effect of Process Parameters on Mode Conversion in Submicron Tapered Silicon Ridge Waveguides
## 2 Structure and Analysis
### Taper Design for Efficient Mode Conversion  
Due to mode hybridization at W\({}_{\rm h}\), mode conversion between TM\({}_{0}\) and TE\({}_{1}\) will occur when light propagates along an adiabatic taper if its end widths W\({}_{1}\) and W\({}_{2}\) satisfy the condition: W\({}_{1}\) < W\({}_{\rm h}\) < W\({}_{2}\). In our design, the taper end width is chosen as W\({}_{1}\) = 1 \(\upmu\)m and W\({}_{2}\) = 2 \(\upmu\)m (Figure 3). This will satisfy the mode conversion condition for a ridge waveguide with all the selected etch depths (W\({}_{1}\) = 1 \(\upmu\)m < W\({}_{\rm h}\) = [1.47 \(\upmu\)m, 1.37 \(\upmu\)m, 1.23 \(\upmu\)m] < W\({}_{2}\) = 2 \(\upmu\)m). For TM-type PICs, where low loss TM\({}_{0}\) tapers are desired, mode conversion is harmful and causes unwanted losses. One of the simplest methods to avoid such unwanted mode conversion is to design tapers whose end widths are either (W\({}_{1}\), W\({}_{2}\)) < W\({}_{\rm h}\) or (W\({}_{1}\), W\({}_{2}\)) > W\({}_{\rm h}\). However, if some design requirements put constraints on the end widths (W\({}_{1}\) < W\({}_{\rm h}\) < W\({}_{2}\)), ultra-short non-adiabatic tapers should be implemented to preserve the TM fundamental mode.  
An Eigen-Mode-Expansion method (EME-Ansys Lumerical) is used to simulate the mode conversion efficiency as the taper length (L\({}_{\rm tp}\)) increases. For scanning length in a very large range, the EME algorithm is very efficient compared to Finite-Difference-Time-Domain solver (FDTD). The simulations are performed for three different ridge heights under three different process conditions. The beam propagation simulation is also done in 3D FDTD to visualize the light propagation along the taper. For beam propagation simulation in FDTD, the same taper lengths of 200 \(\upmu\)m (adiabatic) and 10 \(\upmu\)m (TM-preserving) are chosen for different process conditions, and conversion ratios obtained are shown as well. The difference between EME and FDTD algorithm in determining the conversion efficiency is only 0.35%. The results are as follows:
## 3 Proposed Methodology
### Initial Curvature Estimation  
After the image preprocessing is completed, the next step involves defining the horizontal boundaries of the black pixels. This is achieved by determining the minimum and maximum x-coordinates of the black pixel regions and extending these boundaries slightly. Specifically, the boundaries are extended by 5% of the total width of the black pixel region to the left and right, providing a margin that ensures sufficient coverage of the text curvature. Subsequently, the coordinates of all black pixels within these extended boundaries are extracted. These coordinates serve as the data points for fitting a generalized additive model (GAM). The GAM is chosen for its ability to model complex, non-linear relationships, making it well-suited for capturing the curvature of the text. The fitting process involves using the black pixel coordinates as input data for the GAM. The number  
Figure 2: Example text “Lorem ipsum dolor sit amet” is warped following the \(y=x^{2}\) polynomial function.  
of splines, which determines the flexibility of the fitted curve, is a user-defined parameter. This allows the user to adjust the model's complexity based on the specific requirements of the curvature estimation task. By fitting the GAM to the data points, we obtain a smooth, continuous representation of the text's curvature, which can be used for further analysis and processing. Increasing the number of splines enhances the accuracy of the text curvature estimation, as it allows the model to capture more detailed variations in the curve. A similar approach to curve estimation has been previously explored by [PERSON] et al. [15]. However, it is important to note that using an excessively large number of splines can result in overfitting. Overfitting occurs when the model becomes too sensitive to the specific data points, capturing noise and letter fluctuations rather than the underlying trend. This can significantly degrade the quality of the curvature estimation, leading to poor generalization and a less robust final alignment. Therefore, it is crucial to strike a balance in selecting the number of splines to achieve an accurate yet generalizable curvature estimation. Figure 3 shows the padded image, with a red outline indicating the estimated curve. The number of splines used in this estimation is 6.
## 3 Methodology and Models
### Model Optimization  
#### 3.5.1 Java Optimization Algorithm  
To enhance the model's predictions, the Java optimization algorithm was implemented. This algorithm is designed to find the optimal input feature set that maximizes the model's output. The Java algorithm iteratively refined the input feature set for 3000 iterations, each time adjusting the inputs to identify the feature combination that yielded the highest energy production predictions. This optimization process was essential for identifying the best possible conditions for maximizing energy output.