esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Flyback Transformer Core Design  
In this work, the flyback transformer core considered a U-type-geometry ferrite-based core (N27 and N87 Siferrit(r) materials). Joining two U-type magnetic cores, an O-type core is formed, obtaining two symmetrical air gaps, as can be seen in Figure 12. According to this geometry, and considering the approach in [31], the expression that relates the product of the areas of the window \(A_{w}\) and transversal \(A_{e}\) to the parameters of the circuit is defined by Equation (15), where \(d_{max}\) is the maximum duty cycle, \(i_{prim_{rms}}\) is the rms primary winding current, \(\Delta B\) is the differential magnetic density, \(K_{P}\) is the primary utilization factor, \(K_{W}\) is the utilization factor of the winding area, and \(J\) is the current density. Since the converter operated in DCM, the expression (15) can be rewritten in terms of the input power, obtaining Equation (16), where \(P_{in}\) is the input power of one PV panel. The expression that defines the air gap \(\delta\) in terms of input power is defined by Equation (17).  
\[A_{e}A_{w}=V_{in}\ d_{max}\frac{i_{prim_{rms}}}{\Delta B\ f_{s}\ K_{P}\ K_{W}\ J} \tag{15}\]  
\[A_{e}A_{w}=P_{in}\ \frac{\sqrt{4/3}}{\Delta B\ f_{s}\ K_{P}\ K_{W}\ J\sqrt{d_{ max}}} \tag{16}\]  
\[\delta=P_{in}\ \frac{2\ \mu_{0}}{B^{2}\ f_{s}\ A_{e}} \tag{17}\]  
Figure 11: MPPT control scheme for each PV module.  
Figure 10: Schematic of the MPPT for the topology proposed.  
As can be seen in Equations (16) and (17), the input power is proportional to the product \(A_{e}\)\(A_{w}\) and the air gap \(\delta\). In the proposed topology, since each converter input module transferred energy to the secondary side during a period \(T_{s}/N\), the average power processed by the transformer in a switching period \(T_{s}\) was only the one produced by a single PV panel, whereas the total power managed by the converter corresponded to that generated by the \(N\) PV panels (three in this work). Therefore, the core was designed to process only one-third of the total power. This is an advantage in terms of cost-volume compared to other flyback topologies where the transformer must be designed for the total converter power [11; 12; 13; 16].
# Machine Learning Algorithms: An Experimental Evaluation for Decision Support Systems
## 4 Materials and Methods
### Training and Testing
#### 4.4.2 Support Vector Machine  
There are two variants of the supervised Support Vector Machine algorithm, linear and non-linear, where the difference between them is the fact that the first can easily separate data with a linear line, and the second cannot.  
For the _linear variant_, default values from the Scikit-learn library were used except for parameter C, which is a regularization parameter [37]. For large values of C, the optimization will choose a smaller-margin hyperplane to obtain all of the correctly classified training points. Conversely, a very small value of C will cause the optimizer to look for a larger-margin separating hyperplane, even if that hyperplane misclassifies more points. C parameter values of 0.1, 1 and 10 were defined.  
For the _non-linear variant_, default values were used, except for parameters C, gamma and kernel. The gamma parameter is used for non-linear hyperplanes, where higher values mean that the algorithm will try to exactly fit the training data [37]. The kernel parameter is a method used to take data as input and transform into the required form of processing data. C and gamma parameter values of 0.1, 1 and 10 were defined, and the kernel parameter values of 'linear' and radial basis function ('rbf').
# A New Windage Loss Model for S-CO\({}_{2}\) Turbomachinery Design
## 2 Description of S-CO\({}_{2}\) Windage Loss Experiment
### Uncertainty of Measured Windage Loss  
Since the temperature sensor, pressure sensor, and power analyzer all have measurement uncertainties and the geometric tolerance of the test section also contributes to measurement uncertainties, the measured windage loss and the skin friction coefficient will have uncertainties too. The measurement uncertainties and the geometric tolerance are summarized in Table 1. The skin friction, \(C_{f,exp}\), is obtained with Equation (8). Since the uncertainty sources in Equation (8) are \(P_{input}\), \(\rho\), \(r\), and \(l\), the relative uncertainty of \(C_{f,exp}\) can be described with Equation (9). \(W_{input}\) is measured from the power analyzer, and \(\rho\) is obtained from a property database with the measured temperature and pressure. Therefore, each term in Equation (9) can be expanded to Equations (10)-(14).  
\[C_{f,exp}=P_{input}/\left(\pi\rho\omega^{3}r^{4}l\right) \tag{8}\]  
\[\frac{\partial C_{f,exp}}{C_{f,exp}}=\frac{1}{C_{f,exp}}\left\{\left(\frac{ \partial C_{f,exp}}{\partial\rho}\tilde{\omega}_{\rho}\right)^{2}+\left(\frac {\partial C_{f,exp}}{\partial P_{input}}\tilde{\omega}_{p_{input}}\right)^{2 }+\left(\frac{\partial C_{f,exp}}{\partial r}\tilde{\omega}_{r}\right)^{2}+ \left(\frac{\partial C_{f,exp}}{\partial l}\tilde{\omega}_{l}\right)^{2} \right\}^{0.5} \tag{9}\]  
\[\tilde{\omega}_{\rho}=\left\{\left(\frac{\partial\rho}{\partial T}\tilde{ \omega}_{T}\right)^{2}+\left(\frac{\partial\rho}{\partial P}\tilde{\omega}_{p }\right)^{2}\right\}^{0.5} \tag{10}\]  
\[\frac{\partial C_{f,exp}}{\partial\rho}=-\frac{P_{input}}{\pi\rho^{2}\omega^ {3}r^{4}l} \tag{11}\]  
\[\frac{\partial C_{f,exp}}{\partial P_{input}}\tilde{\omega}_{P_{input}}=\frac {\tilde{\omega}_{T}}{\pi\rho\omega^{3}r^{4}l}=\frac{0.02\ \%\ P_{input}}{\pi\rho\omega^{3}r^{4}l} \tag{12}\]  
\[\frac{\partial C_{f,exp}}{\partial r}=-4\frac{P_{input}}{\pi\rho\omega^{3}r^ {5}l} \tag{13}\]  
\[\frac{\partial C_{f,exp}}{\partial l}=-\frac{P_{input}}{\pi\rho\omega^{3}r^{4 }l^{2}} \tag{14}\]  
Based on Equations (9)-(14), the uncertainty of \(C_{f,exp}\) is calculated and the maximum relative uncertainty of \(C_{f,exp}\) is 1.43%. Thus, the measurement uncertainty of the skin friction due to windage loss is very low.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Measurement Devices** & **Uncertainty, \(\mathbf{\phi}\)** \\ \hline Pressure transmitter (Rosemount 3051 s), \(P\) & \(\tilde{\omega}_{P}\): 0.025\% \\ Temperature transmitter (RTD, A class), \(T\) & \(\tilde{\omega}_{T}\): 0.2\% \(\pm\)0.015 K \\ Power analyzer (Yokogawa WT 3000), \(P_{input}\) & \(\tilde{\omega}_{P_{input}}\): 0.02\% \\ Shaft length tolerance, \(l\) & \(\tilde{\omega}_{l}\): 0.03 mm \\ Shaft radius tolerance, \(r\) & \(\tilde{\omega}_{r}\): 0.02 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Uncertainty of measurement from S-CO\({}_{2}\) windage loss test.
## 4 Ship Detection
It is proved that the rain removal network designed in this paper has a strong applicability in ship detection, which improves the stability of the training process of the ship detection network and also improves the detection accuracy.
# Review of Soft Actuators Controlled with Electrical Stimuli: IPMC, DEAP, and MRE
## 2 Recent Progress in Development and Applications of Soft Actuators
### Magnetorheological Elastomers (MRE)  
Magnetorheological elastomers (MREs) are composed of non-magnetic elastomeric polymer matrix filled with magnetizable particles and other additives (not always present in MREs). The elastomer selection plays a very important role as it greatly affects the mechanical properties of the MRE, such as Young modulus, elasticity, hardness, etc. Therefore, polymeric elastomers such as silicons [90, 91, 92, 93, 94], polyurethane [95, 96], natural rubber [97, 98, 99] and other rubbers [100, 101] are used. However, silicons, thanks to the possibility of selecting their mechanical properties in a wide range, ease of handling and processing, wide range of operating temperature, non-toxicity, and aging resistance, are the most commonly used polymer matrices for MRE fabrication. The second key component of MREs is magnetic particles. They are responsible for the interaction of the MRE with the magnetic field. Nowadays, carbonyl-iron powder (CIP), due to its high saturation magnetization and a wide range of particle size availability, is the most commonly used magnetic filler in MRE preparation. Apart from CIP, other magnetic filters are used, such as cobalt, cobalt ferrites, nickel, magnetite (Fe\({}_{3}\)O\({}_{4}\)), and others [90, 94, 98, 101, 102, 103]. Various non-magnetic modifiers can be added to improve the mechanical properties of MREs, including reinforcements, plasticizers, cross-linking agents, and more. The fabrication of MREs is similar to the fabrication of conventional elastomers containing fillers. The MRE manufacturing process involves mixing magnetic particles with an elastomeric resin and then curing it to produce an isotropic MRE. However, due to the interaction of the magnetic particles in the magnetic field, it is possible to apply an external magnetic field to force the magnetic particles to orient in space and then cure the resin. In this case, the resulting MRE will be anisotropic (Figure 8) [104].
# Proposed Fuzzy-NN Algorithm with LoRa Communication Protocol for Clustered Irrigation Systems
## 3 Irrigation Systems Algorithms
### FITRA Protocol Logic
The existence of an intermediate node (in our case the gateway for the LoRa OWS system set 1 km away) for the operation of the LoRa OWS system, increased the RTT frame time of the LoRa node up to 100-160 ms. For a watering system, such RTT variations are not significant. Moreover, closer gateway placements of less than 1 km can further increase frame count and decrease by more than 50% the Lora average RTT time.
## Abbreviations  
LLRB: lower Lancang River basin; TWS: terrestrial water storage; SWS: surface water storage; SMS: soil moisture storage; GWS: groundwater storage; EO: Earth observation; GRACE: Gravity Recovery and Climate Experiment; GLDAS: Global Land Data Assimilation System; GFZ: Geor-ForschungsZentrum Potsdam; CSR: Center for Space Re-search at the University of Texas, Austin; JPL: Jet Propulsion Laboratory; SWE: surface water extent; WSE: water surface elevation; TWV: total water volume; TAV: total adjustment volume; IMERG: Integrated Multi-Satellite Re-treivals for GPM; JRC: Joint Research Centre; GRAS: Denmark DHI-GRAS Company; MERRA-2: Modern-Era Ret-rospective Analysis for Research and Applications version 2; CMA: China Meteorological Administration; CMA-Grid: 0.5\({}^{\circ}\) gridded daily precipitation dataset from the China Meteorological Administration; STL: local regression; LOESS: local regression smoothing; TPS: Thin Plate Spline; OLTC: Open-Loop Tracking Command; NDVI: optical Normalized Difference Vegetation Index; NDWI: Normalized Difference Water Index; GLAD: Global Land Analysis and Discovery; SAR: synthetic aperture radar; DEM: digital elevation model.
# TSA-GRU: A Novel Hybrid Deep Learning Module for Learner Behavior Analytics in MOOCs
## 4 Experimental Results and Comparaison
### TSA-GRU Performance Across Epochs and with Other Models
#### 4.4.1 TSA-GRU Performance Across Epochs
Figure 6: Accuracy and loss curves with 20 epochs. Figure 7: Accuracy and loss curves with 30 epochs. Model Performance Across 100 Epochs  
Over 100 epochs (Figure 9), the TSA-GRU model showed a stable and robust performance in detecting student learning activities. The training loss of the model monotonically converged toward 0.0277, while the test loss stably converged to 0.0209. This was mirrored by an estimated training accuracy of 95.44% and a test accuracy of 95.25%. These results indicate that the TSA-GRU learns well from the sequential student interaction data, is capable of strong generalization, and performance consistency along knowledge tracing tasks, all in the context of some minimal fluctuations throughout training. These findings indicate that the TSA-GRU can learn effectively from the sequential data of the objects being interacted with by students, along with impressive levels of generalization and consistency in performance in knowledge tracing tasks, all within the context of some relatively small changes throughout the training. Besides that, we also monitored the model performance across training and validation sets to evaluate generalization. The training and test curves (shown in Figures 4-9) continuously and in parallel declined in loss, with no indication of diverging. TSA-GRU learned to converge very early on in training, and results remained constant in consecutive epochs. The patterns suggest that the model generalized well over unseen data and did not overfit. Generally, from the performance of the TSA-GRU model in run training for multi-epochs, it can be deduced that the model learns quickly and generalizes properly. All experiments conducted for 5, 10, 20, 30, 40, and 100 epochs accepted the persistent statement that training loss and test loss were constantly reduced. Notably, the highest test accur  
Figure 8: Accuracy and loss curves with 40 epochs. Figure 9: Accuracy and loss curves with 100 epochs. 95.96% is achieved after five epochs. Although a few epochs of training produce slight improvements in loss (from 0.0302 after 5 epochs to 0.0277 after 100 epochs) as well as variability in accuracy, the advantage gained after that is marginal. It can thus be deduced that, perforated by the TSA-GRU taken at the beginning of each of the five epochs into the training iterations, meaningful patterns regarding student interaction data were recognized; therefore, further epochs became a fading utility, promoting the entire model's capacity for performing tasks concerning knowledge tracing.
# Layered-Cost-Map-Based Traffic Management for Multiple AMRs via a DDS
## 3 Methods
### Layered Cost Map  
The cost map encodes the cost of individual grid cells as a gray scale value in a discrete fashion (Figure 2). Our AMRs utilize the cost of each cell so that the AMRs move toward the path with the lowest cost. AMR planning is categorized into global planning, which does not consider dynamic obstacles, and local planning, which considers dynamic obstacles. The algorithm using the cost map, known as the dynamic window approach (DWA) for local planning, is adopted in this paper. This algorithm is positioned as the primary planning algorithm of ROS 1 and ROS 2, considering its strength that the computational cost is extremely low. The cost map expresses objects or spaces that AMRs should avoid with regard to the cost. The layered cost map is expressed using the OR operation by stacking multiple cost maps for obstacles and spaces [33]. Like ROS 1, ROS 2 provides the layered cost map as a plugin library. Once a plugin library is created, a plugin is referenced for global and local planners. The most basic layer is the static map, which receives a map drawn from simultaneous localization and mapping (SLAM). It helps AMRs avoid walls and static obstacles. Although static and dynamic obstacles are reflected as a form of  
Figure 1: Data sharing in the global data space in the DDS domain.  
cost values, AMRs may approach very closely those obstacles. To increase the separation distance for navigation planning, it is necessary to express an area around the obstacle with a lower cost value than the obstacle cost to provide buffer areas before the collision. The inflation layer represents this area. One of our goals is to help multiple AMRs proceed to their destinations without collision; thus, the fleet layer is designed to share the locations among the AMRs. In addition, the master cost map grid stacks separate layers, including the static map layer, prohibition filter, lane filter, fleet layer, region filter, and inflation layer. The prohibition filter, lane filter, fleet layer, and region filter are stacked for no-trespassing, one-way, and collision avoidance, and exclusive areas, respectively.
# A High Full Quality CMOS Image Sensor for Space Applications
###### Abstract
Electrical crosstalk is the phenomenon that photo-generated electrons move to the neighboring pixel through the substrate. This can be mitigated by shallow trench isolation (STI) and deep trench isolation (DTI) processes. Optical crosstalk is caused by undesirable photo generated electrons as a result of light incident in the lateral direction of the pixel. There are two approaches for degradation of optical crosstalk. The first approach prevents light from entering the PPD of adjacent pixels using a light guide, which refracts undesirable directions of light [14]. This significantly reduces the optical crosstalk but increases cost due to the additional process. Another way, which is adopted in this work, is to physically block the lateral light using a metal layer placed around edges of the PPD [15]. While most electrical parts equipped in a spacecraft have radiation shielding using aluminum or other metallic materials, CISs are directly exposed to outer space for image capturing. This requires CISs to have an extremely high level of radiation tolerance. The effects of radiation on CMOS technology, including MOSFET, capacitors, and various pixel structures, have been individually researched previously [16; 17], but the pixel with an in-pixel capacitor has not been studied before. In this paper, a modified 4T pixel with a multiple charge transfer technique is proposed to extend the FWC without a non-linear response or SNR degradation. The measurement results show that the pixel can hold more than 100,000 electrons by means of the technique in a 6.5 \(\upmu\)m pixel pitch. This value is approximately two times higher than in previously reported works [18; 19; 20; 21; 22; 23]. Even though the MTF of a pixel can be improved either by using a specified fabrication process or design, these approaches could potentially increase the radiation damage effects. Therefore, metal shielding [24] was adopted to maintain the radiation tolerance of pixel devices. Two sensor chips were fabricated, one with metal shielded pixels and the other without metal shielding, and the measurement results show that an MTF improvement of approximately 307% was achieved by using metal shielding. In addition, the radiation damage effects of an in-pixel MOS capacitor are also investigated by comparing the performance of the sensor before and after irradiation. The remainder of this paper is organized as follows. We describe the pixel configuration of the proposed CIS with an in-pixel MOS capacitor and its operation in Section 2. The prototype CIS implementation results, characteristics, and evaluation results are presented in Section 3. Finally, conclusions are given in Section 4.
## 3 Results and Discussion
### Color  
The values of a* and b* parameters in the model products after production and after storage were similar and did not differ significantly (\(p>0.05\)). Statistically significant differences (\(p<0.05\)) were observed in the color lightness parameter (L*) in both treatments after storage. The lowest L* parameter values were observed 1 week after storage reaching 57.74 in the C treatment and 58.15 in the L treatment. A similar result was reported by [PERSON] et al. [48], who observed color lightness decrease after 10 days in the storage of cured (50 mg/kg) raw beef sausages without significant differences between the control sample (without bacteria) and the sample with _L. plantarum_. The highest share of the red color (a*) was reported in the C and L treatments directly after production reaching 17.33 and 17.22, respectively (Table 4).  
The b* parameter value was similar in both experimental treatments in the whole storage period and ranged between 5.26 and 5.49. A significant decrease of the a*/b* factor was observed in the C treatment after storage which indicated a lower share of red color in the products [31] (Table 4). A lower share of red color in the C treatment might have been caused by the significant increase of redox potential and by NOMb concentration decrease in this sample after storage [42; 49] (Table 3).
# Full-Body Motion Capture-Based Virtual Reality Multi-Remote Collaboration System
## 2 Literature Review  
### 2.1 Immersive Collaboration Platform  
An immersive collaboration system enhances the effectiveness of collaboration through rich information sharing and immersive interactions using VR and AR technologies [19]. Realistic collaboration technology can be used to share the work to be solved by reducing the ambiguity of communication. [20]. Problems can be solved accurately and quickly, even in non-face-to-face conditions. An immersive collaboration system supports various collaboration environments, such as idea visualizations or meetings, model design and evaluation environments, and presentation services [21; 22]. Table 1 analyzes 28 immersive collaboration systems as classified into four main items. All 28 immersive collaboration platforms can be accessed through VR. Among them, five platforms can be accessed through AR, and 22 platforms can be accessed through a PC. In addition, 13 platforms can be accessed through mobile devices such as smartphones and tablets. In the item analyzing the avatar type in the VR environment, four platforms have avatar types of head and hands. There is only one platform with the avatar type of only showing the head and upper body. There are 13 platforms where avatars have a head, hand, and upper-body shapes. Four platforms implement the avatar's head, arms, hands, and upper body. Five platforms use only the HMD and controller and use IK to implement the movements of the entire avatar body. One platform naturally implements the movement of the entire avatar body according to motion-capture equipment. Of the 28 platforms, 22 can freely convert the face shape of the avatar. Among them, spatial can generate an avatar by photographing a user's face with a camera. At least six people can collaborate in one room of the network on all platforms. Platforms such as FrameVR can accommodate up to 100 people. Figure 1 summarizes the avatar types found in the virtual world as six categories. Each realistic collaboration platform uses an avatar type based on its characteristics. Unlike hand-only avatars, full-body avatars can improve a user's presence in a virtual environment [48]. Rendering the user's body in VR increases immersion and the illusion of being in the virtual world [49]. Full-body tracking technology using full-body motion capture equipment can match body motion data with virtual character movements, thereby enabling specific body information transfer [50]. In addition, in previous studies, users felt that it was easier to visually track full-body characters and understand their behaviors than characters with only hands or upper bodies [51]. As an immersive VR environment is experienced by the user from a first-person perspective, it is essential to have a virtual character body. Therefore, it can be confirmed that a full-body full-tracking system provides higher user