esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Non-Isothermal Crystallization Behavior of P3 HBHrk--Effect of Nucleating Agents
#### 3.1.4 Nucleation Efficiency
PHB, which is structurally similar to P3 HBHrk, allows epitaxial crystallization due to molecular compatibility, leading to efficient chain alignment and crystal growth. Talc also demonstrated strong nucleation effects, particularly at a concentration of 2 wt%, where it reached an % _NE_ value of 40.2%, which is comparable to those of BN and PHB. This enhanced crystallization behavior is particularly beneficial in industrial processes, such as injection molding, where reduced cycle times are essential. In contrast, PHH_05 UFC and PHH_05 LAK exhibited considerably lower NE values of 3.4% and 18.9%, respectively, indicating limited nucleating activity. These low values suggest that both ultrafine cellulose and the organic potassium salt provide insufficient nucleation sites to significantly influence the crystallization of P3 HBHrk. The poor performance of UFC may be related to its abundant hydroxyl group, which can form hydrogen bonds with the polymer matrix and interfere with local chain mobility, as well as its tendency to agglomerate, reducing the effective number of nucleating surfaces. Similarly, LAK may suffer from poor dispersion in the hydrophobic P3 HBHrk matrix and lack chemical affinity with the polymer. These characteristics limit their ability to trigger early or uniform nucleation, resulting in a minimal impact compared to more efficient nucleating agents such as BN, PHB, and talc.
# Development of a Radio-Frequency Quadrupole Accelerator for the HL-2A/2M Tokamak Diagnostic System
## 4 Beam Commissioning
### Comparison with Other Deuteron RFQs  
The main parameters of the deuteron RFQs in the world are listed in Table 2. The SPIRAL2 project located at GANIL in Caen aims at delivering stable and rare isotope beams with intensities. The SPIRAL2 88 MHz RFQ is designed to accelerate light and heavy ions with A/Q from 1 to 3. The power consumption of SPIRAL2 RFQ is 35 kW/m. It has demonstrated a 1.34 mA He\({}^{2+}\) cw beam.  
Figure 17: The measured distance of the two BPMs.  
Figure 18: The beam signals detected by the two BPMs.  
Soreq Applied Research Accelerator Facility (SARAF) is a multi-user facility for basic research, medical and biological research, neutron-based non-destructive testing, and radio-pharmaceuticals research [28]. The SARAF 4-rod RFQ was built in 2006, and then RF conditioning and beam commissioning were conducted. Due to the power consumption of up to 65 kW/m when accelerating the D\({}^{+}\) beam, the operation of the SARAF 4-rod RFQ was extremely difficult [29]. The SARAF 4-rod RFQ was redesigned in 2015 and the power consumption of the upgraded SARAF 4-rod RFQ was reduced to 50 kW/m. The upgraded SARAF 4-rod RFQ has achieved the acceleration of a 1.15 mA deuteron cw beam.  
The International Fusion Materials Irradiation Facility (IFMIF) is a fusion neutron source intended for use as a test facility to find and qualify new advanced materials for the plasma-facing components in future fusion reactors. IFMIF RFQ is characterized by very challenging specifications, accelerating a 125-mA cw deuteron beam to 5 MeV within 9.8 m length. The power consumption per unit length is 56 kW/m. A nominal beam current of 125 mA deuteron beam was accelerated up to 5 MeV through the IFMIF RFQ at a duty cycle of 0.1%.  
China Material Irradiation Facility (CMIF), which is a compact neutron source with less cost and low-level risk, is designed to provide a material test for fusion reactors. The power consumption per unit length is 23 kW/m. CMIP RFQ has completed the acceleration of a 7.8 mA \({}^{2}\)H\({}^{+}\) beam at the pulse mode.  
973-RFQ is collaborating with PKU and the Institute of Modern Physics (IMP). It aims to accumulate experience in the design and fabrication of high-current cw RFQs. 973-RFQ adopts a 4-vane structure with magnetic coupling windows, which is more compact and has sufficient mode separation. The power consumption of 973-RFQ is 27 kW/m. It has successfully accelerated a 1.78 mA \({}^{2}\)H\({}^{+}\) cw beam.  
Our RFQ will be used to study the migration and deposition of impurities on the first wall of HL-2A/2M tokamak. The power consumption of our RFQ is 20 kW/m. The low power consumption per unit length is beneficial to the long-term stable operation of the RFQ. A 10 mA \({}^{2}\)H\({}^{+}\) beam was successfully accelerated through our RFQ.
# A Comprehensive Survey for Deep-Learning-Based
###### Abstract  
In this paper, we provide a comprehensive survey of the recent advances in abnormality detection in smart grids using multimodal image data, which include visible light, infrared, and optical satellite images. The applications in visible light and infrared images, enabling abnormality detection at short range, further include several typical applications in intelligent sensors deployed in smart grids, while optical satellite image data focus on abnormality detection from a large distance. Moreover, the literature in each aspect is organized according to the considered techniques. In addition, several key methodologies and conditions for applying these techniques to abnormality detection are identified to help determine whether to use deep learning and which kind of learning techniques to use. Traditional approaches are also summarized together with their performance comparison with deep-learning-based approaches, based on which the necessity, seen in the surveyed literature, of adopting image-data-based abnormality detection is clarified. Overall, this comprehensive survey categorizes and carefully summarizes insights from representative papers in this field, which will widely benefit practitioners and academic researchers.  
abnormality detection; smart grids; multimodality; image data; intelligent monitoring; statistical inference; deep learning +
Footnote †: journal: _sciences_  
scenarios, avoiding manual work at heights. Moreover, one can utilize satellite images to perform large-scale abnormality detection. As a result, these multimodal image data play a vital role in abnormality detection in power systems. With the rapid development of advanced data-processing methods, especially deep learning, this role for image data will be further enhanced.  
Here, we provide a comprehensive survey of abnormality detection in smart grids using multimodal image data, aimed at providing recent advances and new perspectives to practitioners, academic researchers, and industrial professionals.
## 4 Analysis and Discussion
### Influence of Elastic Modulus of Bol \(E_{b}\) on Radial Displacement \(u_{r}\) of Joint  
To study the influence of the bolt elastic modulus \(E_{b}\) on the radial displacement \(u_{r}\) of bolted joints, six groups of different numerical tests were conducted. The elastic modulus of each group of bolts is shown in Table 2, and \(Er\) and \(R_{b}\) remain unchanged. The variation law of \(u_{r}\) with the change of \(E_{b}\) could be obtained from Figure 12. \(u_{r}\) (\(r=6R\) in this study) decreased first and then tended to be stable with the increase of \(E_{b}\). According to Figure 12, when \(E_{b}\) was less than or approximately equal to the \(E_{r}\), which was 2 \(\times\) 10\({}^{4}\) MPa in this example, the curve showed an obvious downward trend. However, when \(E_{b}\) was greater than \(E_{r}\), the decline speed of the curve decreased. As \(E_{b}\) continued to increase, no significant change occurred in the value of \(u_{r}\). This test also suggested that appropriate bolts should be selected for reinforcement according to the specific mechanical properties of the reinforced rock when strengthening rock mass in engineering practice. Otherwise, the bolt will not be fully utilized, causing unnecessary economic losses.  
Figure 11: Relationship between bolt radius and radial displacement \(u_{r}\) of rock joints.
# Soil Moisture Retrieval by Integrating SAR and Optical Data over Winter Wheat Fields
## 2 Study Area and Data  
### Study Area and Study Sites  
An agricultural region in Hebei province of China is selected as the study area (37.70\({}^{\circ}\)-37.90\({}^{\circ}\) N, 116.29\({}^{\circ}\)-116.54\({}^{\circ}\) E, Figure 1). This area is located at the junction of Hengshui and Cangzhou, where it consists mainly of winter wheat agricultural fields on a flat landscape. The winter wheat crop is usually sown in October of the previous year, and it is harvested in June of the following year. The predominant soil texture of agricultural fields is loam with a clay percentage of about 16% and sand percentage of about 44 %.
# Concise Robust Control of the Nonlinear Ship in Course-Keeping Control
## 1 Introduction
For instance, the ship course-keeping and track-keeping control based on sliding mode control [17; 18; 19] has certain robustness to system uncertainty and external disturbance, but the high-frequency oscillation phenomenon of sliding model control is difficult resolve. Furthermore, the nonlinear robust controller for ship course control has been proposed by introducing an integral item to eliminate the static error [20]. The stability of the course controller is guaranteed by the construction of the Lyapunov candidate function [21; 22]. Nevertheless, the control target of this method only has one DOF in ship yawing and lacks the coupling of longitudinal and lateral velocity, so there is a limitation in its practical application. For nonlinear ship control, the \(\upmu\)-synthesis method has an advantage in terms of robustness, but it is difficult to determine the parameters in the controller [23], which limits its application in practical use. While another method based on the Lyapunov candidate function has attracted much attention in the ship course control field [16; 24; 25], this method deals with the nonlinear term recursively and it is also capable of tracking the course for under-actuated ships with stochastics disturbances [26]. The contribution and novelty of our method:  
1. A concise robust controller design method for a nonlinear 3 DOF model based on the Lyapunov stability principle is proposed, and the method takes the longitudinal and the lateral velocity coupling into consideration. Our method is easy to implement with few parameters to determine. 2. The performance of our new method is verified on a 3 DOF ship mode; the new method can achieve better steering performance with less rudder wear.
## 1 Introduction
In addition, a 4 \(\times\) 4 antenna array based on a differential SIW feeding network is designed in [25]. The H-shaped four-way power divider with equal amplitude and 180 \({}^{\circ}\) out-of-phase output signals formed with five LTCC layers is fed by rectangular waveguide transition. However, the measured cross-polarization levels are higher than the simulated results because of the slight shift of the coupling slot. In this paper, a dual-polarized differential-fed phased array patch antenna with high gain, low sidelobes, and light weight is proposed. The 1D phased array is grouped into non-uniform subarrays along the direction of beam scanning with the suppression of sidelobe levels. Horizontal polarization radiation at the transmitting band (Tx, 14-14.5 GHz)and vertical polarization radiation at the receiving band (Rx, 12.25-12.75 GHz) share slot-coupled octagonal patches loaded by Via-rings. In addition, minimized three-port ring couplers are designed for differential feeding and further suppress the cross-polarization level. Two prototypes based on the SICL and MS feeding structure are demonstrated and fabricated. To the best of the authors' knowledge, this is one of the few dual-polarization phased array antenna designed with T/R RF phase-shifting modules in recent years. The rest of this paper is structured as follows. The antenna element and 4 \(\times\) 4 subarray are presented in Section 2. Section 3 studies the 16 \(\times\) 24 array design and measurement. After that, the 32 \(\times\) 24 phased arrays and related discussions are elaborated in Section 4. Finally, conclusions are drawn in Section 5.
# Kernel Point Convolution LSTM Networks for Radar Point Cloud Segmentation
## 4 Evaluation
### Moving Vehicle Data Set  
Additionally, we evaluate the models for segmenting moving vehicles against the background in the _Moving Vehicle Data Set_. The moving vehicle class compromises all bounding boxes with the nuScenes attribute _vehicle.moving_. This includes cars, trucks and buses. Table 1 shows the results for this data in the right column.  
The results show that moving vehicles can be distinguished from the background more successfully than static vehicles, due to the overall higher F1-score achieved. At the same time, the performance levels out at higher overall scores for this type of input data.
# Deep Learning in Financial Modeling: Predicting European Put Option Prices with Neural Networks
## 3 Materials and Methods
### Model Architecture and Training Strategy  
The DNN architecture was developed after extensive experimentation with various configurations to identify an optimal balance between accuracy, computational efficiency, and scalability. Initial attempts included deeper architectures with more layers and neurons, as well as alternative activation functions and regularization techniques. However, these configurations often led to overfitting or diminishing returns in predictive performance. After iterative testing, the final architecture was selected as follows:  
* Hidden layers: Three fully connected hidden layers with 128 neurons each were found sufficient to model the relationships between input parameters and option prices. This configuration strikes a balance between capturing complex non-linearities and maintaining computational efficiency.  
Figure 1: European put option valued using the Black–Scholes pricing equation.  
* Activation function (ReLU) [12]: ReLU was chosen for its ability to avoid vanishing gradients, ensuring faster and more stable convergence during training.
* Regularization techniques: A dropout rate [13] of 0.2 and an L2 penalty [14] term of \(10^{-4}\) were incorporated to mitigate overfitting, particularly on the training dataset.
* Optimizer and learning rate: The Adam optimizer [15] was used with an initial learning rate [16]: of \(10^{-3}\), as it demonstrated stable convergence across diverse parameter settings during preliminary tests.
* Batch size: A batch size of 32 was selected to balance computational efficiency and gradient stability [17].  
This final architecture represents an optimized trade-off based on systematic experimentation, achieving strong predictive performance with a Mean Squared Error (MSE) of 0.0021 and a coefficient of determination (R\({}^{2}\)) of 0.9533. The iterative design process highlights the adaptability of the DNN framework to the specific requirements of option pricing under the Black-Scholes model.
# Automated Detection of Multi-Rotor UAVs Using a Machine-Learning Approach
## 2 Existing Methods for Detection and Identification of Objects in an Image  
### Background Subtraction  
For ease of operation in particular, the background subtraction method is one of the most basic methods for detecting objects in an image. As described in [10], this method needs to accurately identify the background model. After completing this step, the background model is compared to the current image, and the known background parts are subtracted. Objects that are not subtracted are with certain probability new objects in the foreground. Normally the background is defined as any static or periodically moving parts of the scene. The entire scene may have time-varying components, such as tree leaves that move at some time but are static at another time. A common element of systems whose purpose is to monitor the objects by a static camera is a module whose task is to subtract a background to distinguish static objects from dynamic objects. A substantial and complex part of the background subtraction process is maintaining the background model. According to [11], there are some situations when it is difficult to read or detect the background: inhomogeneous and variable illumination of the scene, changing spectral characteristics of the illumination and consequently different color of the object, overlapping objects, different camera angles, and object variations within one category. In [12] a detailed comparison of different background subtraction techniques is elaborated. The purpose of this comparison was to find out which of the techniques could cope best with the problems mentioned above.
# A System Architecture of a Fusion System for Multiple LiDARs Image Processing
## 3 Proposed System Architecture for Multiple LiDARs Image Processing
### Client Structure  
The processes of a fusion system involve multiple clients and a server. A fusion system receives data from multiple LiDAR sensors through UDP communication. The client is an I/O-bound process and is deployed with the same number of LiDAR sensors. The client repeatedly loads and executes a process on the CPU through context switching. Each client includes each socket, so the processed data is transmitted to the server. As the client process frequently switches context, scheduling for processes is needed to optimize the usage of the core.  
The scheduler is composed of a task scheduler and a CPU scheduler. The task scheduler is responsible for deciding the process to insert into the ready queue. The CPU scheduler considers the priority among the processes loaded in memory and decides which process should be loaded on the CPU in the order of priority. The CPU scheduler chooses a process to allocate to the CPU as per a defined scheduling algorithm. Performance metrics for scheduling are generally based on processor utilization, throughput, turnaround time, waiting time, and response time. In this study, the scheduling algorithm is implemented considering the turnaround time, waiting time, and response time. Turnaround time refers to the time wait from arrival to the first start. Waiting time refers to waiting time in the Ready Queue. Response time refers to the total time taken from arrival to completion. To optimize the scheduler, the turnaround time, waiting time, and response time need to be minimized.  
Scheduling for the client processes operates by non-preemptive scheduling. In this study, a round-robin method is introduced to process the point cloud from multiple LiDAR sensors in real time. When the point cloud for multiple LiDAR sensors is input in real time, it is difficult to determine the priority based on a numerical value such as the amount of work. To have a multiple LiDAR system, a round-robin method that can be performed only within a limited processing time (called a quantum) without priority was considered. The quantum is set within the time when the next data is input from multiple LiDAR sensors. If the quantum is set to be long, it operates in a similar manner to the non-preemption method, whereas if it is set to be short, context switching occurs frequently, increasing overhead. The performance of the round-robin method depends on the quantum setting.  
Figure 4: IPC in the fusion system.
# An Efficient Routing Protocol with Overload Control for Group Mobility in Delay-Tolerant Networking
## 4 Simulation Results
Figure 10 shows the delivery latency for varying message size with a buffer size of 50 Mbytes. The delivery latencies of all the considered protocols decreased as message size increased. This is because when the message size increases, the average number of messages stored in buffer decreases, and thus each message has a higher chance for forwarding and results in a lower delivery latency. For a given message size, delivery latency of overload control protocols was higher since they had smaller copies of messages and there was a smaller chance of message delivery. The proposed protocol had the largest delivery latency due to proactive overload control. Figure 10: Delivery latency for varying message size. Figure 9: Overhead ratio for varying message size. Figure 11 shows the delivery ratio for varying time-to-live (TTL) with a buffer size of 50 Mbytes. The delivery ratio of protocols without overload control increased as TTL value increased from 60 m to 180 m, but it decreased as TTL value increased from 180 m to 600 m. Since the number of message copies increased as TTL increased, thus the delivery ratio also increased because of more message dissemination by increasing the TTL value for small values of TTL. However, if TTL increases for large values of TTL, it is highly likely that more messages are dropped due to buffer overflow at fixed buffer size, since fewer messages are removed because of TTL expiration, and thus delivery ratio decreases. The delivery ratio of protocols with overload control increased as TTL values increased, since the number of message copies could be controlled efficiently using overload control. The proposed protocol had the largest delivery ratio in most of the considered TTL values, except for small TTL values, where the protocol with reactive overload control had a higher delivery ratio than the proposed protocol because of the effect of more message dissemination in small TTL values. Figure 12 shows the overhead ratio for varying TTL with a buffer size of 50 Mbytes. The overhead of all the protocols increased as TTL values increased due to more message forwarding. The overhead of the protocols with overload control was significantly smaller than those without overload control. Especially, the overhead ratio of the proposed protocol was the smallest due to proactive overload control. Figure 11: Delivery ratio for varying TTL. Figure 12: Overhead ratio for varying TTL. Figure 13 shows delivery latency for varying TTL with a buffer size of 50 Mbytes. The delivery latencies of all considered protocols increased as TTL values increased, since more time was needed to deliver a message to the destination node for large values of TTL using limited transmission speed because of more message copies in the buffer. The proposed protocol had the largest delivery latency, since the proposed protocol proactively removed message copies if they were delivered to any node in the same group of the destination node, and thus more time was needed to deliver a message to a destination node.