esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion and Conclusions
Technologies of the laser in-frame robot developed for VHE electron beams will pave the way for laser-driven proton beam therapy as well, which uses protons with the kinetic energy range in the medical window 70-300 MeV, though much higher intensity lasers will be needed for the production of such high-energy protons due to laser-plasma interactions. Although the achievement of current laser plasma electron acceleration experiments basically carried out in a single-shot mode may not ensure the performance of laser-driven electron beams in practical applications, the requirements for VHE electron beams with energies of 50-250 MeV, an rms energy spread of the order of 5%, and an electron charge of the order of 10 pC have been demonstrated by previous experiments on a two-staged laser wakefield accelerator comprising an injector and accelerator cells in conjunction with ionization-induced injection [24; 25]. The most crucial requirement for VHE electron/photon beam radiation therapy may be a shot-to-shot energy stability of the order of 1% and pointing stability of the order of 1 mrad for IMRT with PBS. Since these stabilities are dominantly attributed to the stability of drive lasers, the current limits of a few percent-level stability at the repetition rate of the order of 1 Hz operation would be improved to the level of less than 1% by increasing the repetition rate of drive laser pulses to 100 Hz. Furthermore, the use of coherent combining fiber lasers capable of producing VHE electron beams could replace the rotary vacuum channels with flexible fibers for transporting laser pulses to the gas cell, removing the need for a large part of the guiding optics and aligning system as well as increasing the repetition rate and efficiency of the drive laser. In conclusion, the very high energy electron/photon beam radiation therapy robotic system proposed is more compact and efficient, less expensive, and offers simpler and safer operation in addition to higher performance compared to previous techniques. This will lead to cost-effective, ubiquitous high-technology radiation therapy including photon, electron, and proton beams, in conjunction with advanced robot technologies. This work was supported by the National Natural Science Foundation of China (Project No. 51175324). [PERSON] was also supported by the Project Code (IBS-R012-D1).
###### Abstract
For the design presented in this paper, we have concentrated on dual-band frequency behavior with the improvement in bandwidth, gain, efficiency, and radiation pattern in the boresight at the same time, respectively. Therefore, this paper initially presents a single-element dual-band operation Minkowski-Sierpinski fractal antenna design using two different substrates, i.e., a high-end Rogers TMM4 substrate and a low-end FR4 substrate. The performance of both antenna designs is compared. Then, a hybrid four-element (2 \(\times\) 2 configuration) dual-band Minkowski-Sierpinski fractal antenna array (MSFAA) design is proposed for 802.11 ax (Wi-Fi 6E), sub-6 GHz 5G, and next-generation wireless applications. The proposed single-element antenna and the 2 \(\times\) 2 configuration antenna array are operating at 4.17 and 5.97 GHz frequency bands, respectively. The proposed fractal antenna array has many attractive features, such as a high gain and directional radiation pattern at the boresight. Both features make the proposed antenna array a promising candidate for the 802.11 ax (Wi-Fi 6E), sub-6 GHz 5G, Internet of Things (IoT), and next-generation wireless applications. All four elements of the 2 \(\times\) 2 configuration fractal antenna array are individually mounted on an aluminum sheet, which acts as a holding mount and extended ground plane for the individual array elements. Then, a delta-stub-based 1 \(\times\) 2 Wilkinson power divider (WPD) feed network is also designed, which is used with the help of coaxial cables to feed the individual array elements. The rest of the paper is organized as follows: Section 2 briefly discusses the WPD design configuration as well as its simulation and measured results. The single-element fractal antenna design and its results, as well as the four-element fractal antenna array design and characterization results, including return loss (\(S_{11}\)), 2D and 3D radiation patterns, gain, and surface current distribution, are discussed and summarized in Section 3. Section 4 provides a comparison of the results and overall discussion of the paper and its findings, and finally, Section 5 draws conclusions.
# Reducing Detrimental Communication Failure Impacts in Microgrids by Using Deep Learning Techniques
## 3 Link Loss Effects  
Based on the descriptions of the multilayer communication system provided in Section 2 it can be understood that link failures can happen at each of the levels with different consequences. For example, at the device level, where the acceptable level for  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & **Level** & **Node Types** & **Characteristics** & **Protocols** & **References** \\ \hline
**DAN** & _Device_ & Converters, V/I Sensors, Data Loggers, Data Acquisition & Low-Range, High-Speed, Ultra-reliability & Can-Bus, Modbus, Interbus, Profibus, Ethernet [19; 20; 21] \\
**SHAN** & _Smart Home_ & Smart Applications, Control, Monitoring, Processing & Heterogeneity, Scalability, Mobility & ZigBee, WiFi, Zwave, Bluetooth, 6 LoWPAN [22; 23; 24] \\
**MAN** & _Microgrid_ & DGs, ESSs, EV Charging & Long-Range, Independent, Security & LoRaWAN, SigFox, Dash7, Ingenu, NB-IoT \\
**MMAN** & _Multi- Microgrid_ & Energy Internet & Ultra Long-Range, Terrain affected, Standardization & 4G, 5G, Direct to satellite LPWAN & [28] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different network levels, devices, communication requirements, and protocols.  
Figure 1: Multilevel communication network for a system of residential MGs for remote applications.  
latencies are very small (in the order of 10 milliseconds or less) even small delays can cause stability problems and be very harmful. In contrast, at multimicrogrid level, a total link failure may cause serious economic problems or voltage and frequency deviations.  
In this paper, we are primarily interested in the MG-level and the details of its link failure effects will be discussed in this section. Based on the configuration of a specific MG the role of communication in the control architecture varies. For example, in an MG architecture based on voltage source inverters (VSIs) the secondary control is the most recognizable point of communication interaction with MG control. This level deals with voltage and frequency deviations caused by the primary control by restoring the reference values. Furthermore, it contributes to proper reactive power sharing since there is a trade-off between voltage regulation and reactive power sharing in the primary level [29; 30]. In another grid-connected MG topology, that is formed of grid-feeding inverters, the secondary control functionality is completely different. Figure 2 illustrates the difference between the secondary-level of control for the two aforementioned types of inverter. As it can be observed, when voltage-source and VSIs are used the secondary control inputs are voltage/frequency references and measurements while in the grid-feeding and current source inverters (CSIs) externally generated power references and power measurements are transmitted to the secondary controller. As a result of this argument it can be easily understood that the effects of communication link losses can be quite different depending on which types of inverters are utilized.
# Development of Fast Protection System with Xilinx ZYNQ SoC for RAON Heavy-Ion Accelerator
## 4 Development of FPS with Xilinx ZYNQ for Minimizing a Latency  
In order to reduce the latency for quick shut off, the device drive software and the minimization of the distance between the two gates in _FPGA_ were optimized. As shown in Figure 5, two mitigation nodes and seven acquisition daisy chains can be found in each of the _FPS_ primary products. One of the two mitigation nodes acts as a backup in case of failure. Each acquisition chain is composed of seven acquisition nodes that are connected linearly. One mitigation node and one acquisition chain are randomly selected to be tested. With the test result, the remaining one mitigation node and six acquisitions chains are modified for improvement. In the case of the mitigation node, the _SoC_ chip was replaced with XC7Z100-2 FFG900I for bidirectional communication with the acquisition node.  
Minimizing latency is a crucial aspect of developing a highly efficient Fast Protection System (_FPS_). This article presents a descriptive analysis of a study conducted to optimize latency reduction in _FPS_ using Xilinx ZYNQ, a powerful embedded OS porting system. This research aims to enhance device drive software and _FPGA_ gate distance for quick shut off. Xilinx ZYNQ emerges as a powerful solution for minimizing latency in the _FPS_. Its versatile functionality, adaptability to changing requirements, real-time processing capability, reduced power consumption, and independent power rails enable the development of a highly efficient and flexible _FPS_ control system. By leveraging the advantages and features of Xilinx ZYNQ, developers can enhance the device drive software and _FPGA_ gate distance, resulting in significant latency reduction and improved performance in the _FPS_. ZYNQ XC7Z100-2 FFG900I [14] and ZYNQ XCZU9 EG-2 FFVB1156I [15] are implemented for RAON fast protection system.  
Figure 4: Configuration of the acquisition node firmware.  
Figure 5: _FPS_ layout.  
A spare of the front SFP+ port is created and modified, giving a total of nine SFP+ ports. A masking function is added to prevent a BPS mode setting or input/output of a false trigger signal. In the case of the acquisition node, a spare SFP+ port was also created, giving a total of five SFP+ ports. To receive the BPM output signals directly, the converter terminals were modified to 16 SMA-type input ports. A software change was made to check the BIT of communication status, temperature, time, and timing through LED, LCD, and \(OPI\) on the node front. The IP and configuration files for each node were automatically updated at each node boot. Additionally, a node self-test mode was added via a software developed.
# Analysis and Experimental Test of Electrical Characteristics on Bonding Wire
## 4 Bonding Wire Design and Measurement Experiment
### Circuit Board Design  
In order to reduce the measurement error, it is necessary to shorten the length of the microstrip line and make the impedance matching design. Since the measurement frequency band had reached the GHz level, a Rogers RO4350B high-speed plate was chosen as substrate in accordance with simulation model. The test board length and width were 3.5 cm \(\times\) 5.0 cm.  
By using the empirical formula of the characteristic impedance for microstrip line, the approximate calculation of characteristic impedance is [20]:  
\[Z_{0}=\frac{87}{\sqrt{\varepsilon_{r}+1.41}}\cdot\ln\left(\frac{5.98H}{0.8W+T}\right) \tag{8}\]  
In Equation (1), \(Z_{0}\) is the characteristic impedance of the microstrip line. As shown in Equation (1), \(\varepsilon_{\mathrm{r}}\) is the dielectric constant of the substrate, H is the thickness of the substrate, W is the width of the microstrip line, and the T is the thickness of the microstrip line. The test board was connected with the VNA using the SMA connector. In order to weld with the SMA joint and shorten the microstrip line, the microstrip line length L was designed as 8 mm. The plate thickness H was 0.508 mm. A Rogers RO4350B was selected as double-layer plate with a dielectric constant of 3.48. Keeping the \(Z_{0}\) as constant 50 \(\Omega\) based on Equation (1) and Cadence two-dimensional field solver impedance design tool, the rest of design parameters are obtained after adjustment: microstrip width W is 0.85 mm, thickness T is 0.05 mm. The specific microstrip line design parameters are as shown in Figure 17.  
A TPT HB05 type semi-automatic bonding machine works on the basis of thermal compress bonding method. High pressure and high temperature caused metal plastic deformation in the bonding region, which realized the connection between the pad and the lead. Therefore, the hot pressing bonding method needs the hot pressing area to adopt the same metallurgy material, so that the hot pressing bonding process can be completed. The conventional PCB technique is not suitable for the bonding. This means that depositing Sn on copper pads as tin does not make the wire pressing successful. Considering about the bonding machine hot press temperature, hot press reliability and cost, bare gold process is the final choice for the microstrip line. There is no solder layer on the line, while the back of circuit board is deposited with gold as well as ground plane. Finally, the front and back of the test board are shown in Figure 18a,b.  
Figure 17: Microstrip line design parameters.
###### Abstract  
The combination of mobile edge computing (MEC) and wireless power transfer (WPT) is recognized as a promising technology to solve the problem of limited battery capacities and insufficient computation capabilities of mobile devices. This technology can transfer energy to users by radio frequency (RF) in wireless powered mobile edge computing. The user converts the harvested energy, stores it in the battery, and utilizes the harvested energy to execute corresponding local computing and offloading tasks. This paper adopts the Frequency Division Multiple Access (FDMA) technique to achieve task offloading from multiple mobile devices to the MEC server simultaneously. Our objective is to study multiuser dynamic joint optimization of computation and wireless resource allocation under multiple time blocks to solve the problem of maximizing residual energy. To this end, we formalize it as a nonconvex problem that jointly optimizes the number of offloaded bits, energy harvesting time, and transmission bandwidth. We adopt convex optimization technology, combine with Karush-Kuhn-Tucker (KKT) conditions, and finally transform the problem into a univariate constrained convex optimization problem. Furthermore, to solve the problem, we propose a combined method of Bisection method and sequential unconstrained minimization based on Reformulation-Linearization Technique (RLT). Numerical results demonstrate that the performance of our joint optimization method outperforms other benchmark schemes for the residual energy maximization problem. Besides, the algorithm can maximize the residual energy, reduce the computation complexity, and improve computation efficiency.  
mobile edge computing; Wireless Power Transmission (WPT); task offloading; joint optimization; resource allocation +
Footnote †: journal: 12 March 2021
## 4 Results  
### Perceived Quality Scores  
Figure 5 displays the mean and standard error of perceived quality scores. The results of the one-way RMANOVA revealed a significant impact of sound on the perceived quality of the hair dryer [F(2,34) = 5.057, \(p\) = 0.012, np2 = 0.229]. Post hoc tests indicated that the hair dryer with high sound had the highest perceived quality score (M = 5.61, SD = 0.99), whereas the hair dryer with no sound had the lowest perceived quality score (M = 4.85, SD = 1.13). The difference between these two conditions was statistically significant (\(p\) = 0.01). Notably, no significant differences were observed between the low sound condition (M = 5.28, SD = 0.89) and the high sound condition (\(p\) = 0.518) or between the low sound condition and the no sound condition (\(p\) = 0.252).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Area of Interest (AOI)**} & \multirow{2}{*}{**Channel**} & \multicolumn{3}{c}{**MNI Coordinates**} & \multirow{2}{*}{**Brodmann Area**} & \multirow{2}{*}{**Overlap**} \\ \cline{3-3} \cline{5-7}  & & **X** & **Y** & **Z** & & **(BA)** \\ \hline \multirow{3}{*}{Right DLPFC} & CH03 & 25.13 & 53.20 & 37.93 & BA9—DLPFC & 0.75 \\  & CH10 & 37.69 & 55.19 & 24.78 & BA46—DLPFC & 0.95 \\  & CH17 & 47.37 & 50.74 & 12.48 & BA46—DLPFC & 0.82 \\ \hline \multirow{3}{*}{Left DLPFC} & CH05 & \(-\)20.13 & 54.09 & 38.59 & BA9—DLPFC & 0.80 \\  & CH13 & \(-\)32.04 & 57.43 & 24.35 & BA46—DLPFC & 0.90 \\ \cline{1-1}  & CH21 & \(-\)41.63 & 55.24 & 10.02 & BA46—DLPFC & 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The MIN coordinates of fNIRS channels and corresponding brain regions.  
Figure 5: Perceived quality scores for each condition. Error bars represent the standard error of the mean. A rating of 7 indicates the highest perceived quality, and 0 indicates the lowest. * denotes statistical significance at \(p<\alpha=0.05\).
## 4 Experimental Setup and Results Analysis
### Experimental Method
#### 4.3.2 Model Training  
A K-means++ model is established with \(K=2\). A spectrogram with clear and abundant vertical lines, such as shown in Figure 10a, is selected. After the aforementioned preprocessing steps, each column of pixels is treated as a sample vector and undergoes dimensionality reduction. These vectors are then fed into the model for training. By accessing the label  
Figure 8: Recognition results marked on the grayscale.  
Figure 9: Step-by-step results of data preprocessing. (**a**) Original spectrogram; (**b**) grayscale; (**c**) vertical line feature enhancement; (**d**) binarization.  
attribute of the K-means++ model, the clustering labels of each column vector sample can be obtained. The labels representing the vertical lines are visualized in Figure 10b.
# Pointer Meter Reading Recognition Based on YOLOv11-OBB Rotated Object Detection
## 3 Materials and Methods
### Pointer Rotation Object Detection Based on YOLOv11-OBB  
#### 3.1.1 YOLOv11-OBB Object Detection Model  
The YOLOv11 algorithm, developed by Ultratics in 2024, is an object detection model designed to accurately locate and classify objects within complex environments [28]. This is achieved through the integration of efficient feature extraction networks, adaptable parameter settings, and robust detection mechanisms. Notably, YOLOv11 has demonstrated substantial enhancements in precision, speed, and computational efficiency, particularly in the realm of oriented bounding boxes (OBB). To cater to diverse application requirements, YOLOv11 offers model size options including n, s, m, l, and x. The framework of YOLOv11 comprises three primary components: the head, backbone, and neck, as illustrated in Figure 2.  
YOLOv11 introduces novel features in various aspects.  
Backbone network: The backbone network of YOLOv11 comprises a distinctive architecture composed of convolution layers (Conv) and specialized modules (C3K2, SPPF, C2 PSA). The convolutional layer extracts local features and performs downsampling. The C3K2 module efficiently integrates multi-level features, reducing computational load. The SPPF module captures context information at various scales, while the C2 PSA module refines the integrated features. These modules extract multi-scale feature information, providing a robust foundation for subsequent detection tasks.  
Figure 1: Proposed method overall framework.  
Neck network: The YOLOv11 architecture introduces a novel neck network design that integrates upsampling and feature stitching to merge feature maps of varying scales. The incorporation of the C3K2 module further enhances the expressive capacity and discriminative power of the processed features. This design addresses the detection needs of targets across diverse scales while improving the precision of target localization.  
Detection head: The YOLOv11 object detection model employs an Oriented Bounding Boxes (OBB) module to extract multi-scale feature maps. This module utilizes convolutional, classification, and regression layers to accurately predict the position, category, and orientation of rotated target objects. The model's multi-scale detection strategy leverages the semantic information from small-scale feature maps to identify large targets, while utilizing the detailed features preserved in large-scale feature maps to detect small targets. This approach significantly enhances the model's ability to detect objects of diverse sizes.  
In summary, YOLOv11 demonstrates exceptional performance in object detection, especially for rotating object detection. This is achieved through innovative designs in the backbone network, neck network, and detection head, providing novel technical strategies for pointer meter reading recognition.
# A Study of the Thermodynamic Properties of Nd-C and Ce-C TRISO Fission Product Binary Systems
## 2 Materials and Methods
### Experimental Procedures
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Test ID** & **Sample** & **Mol\%C** & **Mol\%Nd** & **Mass [mg]** & **Crucible Material** & **Powder** & **Note** \\ \hline \multicolumn{8}{c}{Campaign \#1–Alumina Crucibles} \\ Nd1-1-1 & Nd1 & 10 & 90 & 21.1 & Al\({}_{2}\)O\({}_{3}\) & Al\({}_{2}\)O\({}_{3}\) & No phase changes observed. \\ Nd1-1-2 & Nd1 & 10 & 90 & 20.0 & Al\({}_{2}\)O\({}_{3}\) & Al\({}_{2}\)O\({}_{3}\) & Re-tested Nd1-1-1, \\ Nd1-1-3 & Nd1 & 10 & 90 & 125.3 & Al\({}_{2}\)O\({}_{3}\) & Al\({}_{2}\)O\({}_{3}\) & no changes. \\ \multicolumn{8}{c}{Campaign \#2–BN Crucibles and Powder Test} \\ Nd1-2-1 & Nd1 & 10 & 90 & 31.2 & BN & None & 0.25\% H\({}_{2}\) gas isothermal; \\ Nd1-2-2 & Nd1 & 10 & 90 & 147.4 & BN & None & 0.25\% H\({}_{2}\) gas isothermal; \\  & & & & & & sample stuck to crucible. \\ Nd2-2-1 & Nd2 & 20 & 80 & 125.9 & BN & BN & 0.25\% H\({}_{2}\) gas isothermal; \\  & & & & & & 0.25\% H\({}_{2}\) gas isothermal; \\  & & & & & & powder, no adhesion; \\  & & & & & & bad DSC. \\ \multicolumn{8}{c}{Campaign \#3–BN Crucibles and BN Powder Additional Tests} \\ Nd1-3-1 & Nd1 & 10 & 90 & 40.1 & BN & BN & 0.8 mg BN powder, excellent \\  & & & & & & DSC, sample stuck. \\ Nd1-3-2 & Nd1 & 10 & 90 & 94.2 & BN & BN & 0.25\% H\({}_{2}\) gas isothermal; \\  & & & & & & sample stuck to crucible. \\  & & & & & & 0.25\% H\({}_{2}\) gas isothermal; \\  & & & & & & powder, no adhesion; \\  & & & & & & bad DSC. \\ \multicolumn{8}{c}{Campaign \#4–BN Crucibles Only} \\ Nd2-4-1 & Nd2 & 20 & 80 & 22.8 & BN & None & Clean DSC. \\ Nd3-4-1 & Nd3 & 30 & 70 & 33.7 & BN & None & Clean DSC. \\ Nd4-4-1 & Nd4 & 40 & 60 & 16.0 & BN & None & DSC response noisy. \\ Nd1.5-4-1 & Nd1.5 & 16 & 84 & 36.7 & BN & None & Three possible phases \\ Nd1.5-4-2 & Nd1.5 & 16 & 84 & 36.7 & BN & None & Clean DSC. \\ Nd2.5-4-1 & Nd2.5 & 25 & 75 & 61.6 & BN & None & Clean DSC. \\ Nd3.5-4-1 & Nd3.5 & 35 & 65 & 55.7 & BN & None & Clean DSC. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Rare earth carbide DSC campaigns and test sequences.