esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Temperature Direct Bonding of SiC to Si via Plasma Activation
## 3 Results and Discussion  
### Effects of Bonding Parameters  
Plasma activation time is a crucial parameter for PAB. The relationship between the bonding efficiency and the O\({}_{2}\) plasma activation time was investigated. An efficient bonding area ratio can lead to the full combination of two different materials. Therefore, we mainly adopted the bonding area ratio to present the bonding efficiency of our experiments. A higher bonding area ratio means a better connection between the bonded samples, which is crucial for direct wafer bonding. The influence of plasma activation time and the optical images of the bonding results are shown in Figure 2.  
As the bonding results reveal in Figure 2a, when the wafer surface was not activated, the bonding area ratio was insufficient. As the activation time increased to 30 s, the bonding efficiency exceeded 90%, which was fairly high for direct bonding. Then, with the increase in activation time, the bonding efficiency showed a fluctuating decrease. After 30 s of RIE O\({}_{2}\) plasma activation, the optical images of SiC/Si samples under the annealing temperatures of 150 \({}^{\circ}\)C and 300 \({}^{\circ}\)C are shown in Figure 2b,c, respectively. As SiC is a transparent material, the bonding area of the SiC/Si samples can be directly seen from the optical images in Figure 2b,c, in which the front layer was the SiC layer and the back was the Si layer. The unbonded region was marked, and the bonding area ratio can be calculated by its area ratio with the whole sample. Except for some inevitable voids caused by remaining contaminants, the optical images showed a fair bonding result. Therefore, 30 s of plasma activation time was adopted to further investigate the bonding mechanism.
## 2 Establishment of Induction Motor Defect Model and Fault Diagnosis Technology
### Establishment of Common Fault Types and Defect Model of Induction Motors  
Induction motors play vital roles in the industry; they also are essential equipment in various fields. Ref. [17], the sources of induction motor faults can be divided into internal and external factors, and the types of faults can result from mechanical and electrical causes. Figure 1 shows the statistics of the types of common induction motors faults including bearing faults, stator faults, and rotor faults [18].  
In addition, this research has made four induction motor defect models based on the statistics of fault types in Figure 1, including bearing outer ring damage, stator turn short circuit, rotor broken bars, and centering faults, as shown in Figure 2. We captured different operating status data by the laboratory measurement platform under both full loads as well as half load operation.
# DRL-Based Dynamic Destroy Approaches for Agile-Satellite Mission Planning
## 3 Modeling Process
### GAT-Based Network
#### 3.2.2 GAT-Based Actor-Network  
The architecture of GAT-based actor-networks is illustrated in Figure 3. The input and output of the actor-network are described in detail in Section 3.3. To extract node features, we employ a multi-layer GAT approach. Initially, three graph attention layers are utilized to form a GAT block. For each block, the structure of GAT\(\rightarrow\)GAT\(\rightarrow\)GAT facilitates spatial exploration and expands the acceptance field to the current solution. Subsequently, the GAT block is repeated \(N\) times to decrease the graph's density. The value of \(N\) is dependent on the node density of the actual graph, and a larger value can be selected for denser graphs.  
A Gated Recurrent Unit (GRU) is employed to integrate information from historical solutions [39]. At each iteration, the hidden state \(h_{t}\) helps calculate the output as a multiplicative adaptive weight in the final layer. The configuration of network layers and GRU is informed by the approach outlined in [40].  
Figure 2: The process of attention and aggregation operation in graph attention layer.  
The output of the last layer in the actor-network represents the probability of removing each node, and it is obtained using a softmax function. We sampled nodes based on the normalized removal probability. The total count of selected removal action nodes \(M\) is determined according to Equation (11):  
\[M=\left\lfloor n\times r^{d}\right\rfloor \tag{11}\]  
In the above formula, \(n\) denotes the initial total number of task nodes, and \(r^{d}\) is the preset destroy rate, which ranges from 0 to 1. We sampled the top \(M\) nodes with the highest removal probability to create a set of removal action nodes:  
\[\mathbf{\Omega}=\left\{\Omega_{1},\Omega_{2},\ldots,\Omega_{M}\right\} \tag{12}\]  
The hidden state \(h_{t}\) of GRU and the ELU function are utilized to generate two node coefficients, \(\beta_{i}\), and \(\gamma_{i}\), for each node \(i\), which are then used to construct a sampling probability distribution \(Beta(\beta_{i},\gamma_{i})\). The corresponding removing coefficients were sampled from \(Beta(\beta_{i},\gamma_{i})\) for each removing action node:  
\[\mathbf{\Lambda}=\left\{\Lambda_{1},\Lambda_{2},\ldots,\Lambda_{M}\right\} \tag{13}\]  
The concept of \(\mathbf{\Omega}\) and \(\mathbf{\Lambda}\) constitute two essential parameters in the \"destroy\" process, and their specific usage is elaborated on in Section 3.3.2.
## 2 Methodology
### Compression Tests of Lattice Structures  
As a result of the AM process, it was assumed that changes will occur in the mechanical properties of the 3D printed parts. Therefore, the mechanical properties of polymeric lattice structures were evaluated via compression tests. To characterize the compression behavior of the lattice structures, specimens were fabricated firstly with dimensions of 25 \(\times\) 25 \(\times\) 25 mm, in accordance with the standard for the Compression Testing of Rigid Cellular Plastics [54] (Figure 4). Mechanical tests were carried out with an Instron 5969 compression testing machine and the results were processed by a computer. To determine the strength properties of the lattice structures under load and to observe the effect of compression speed on mechanical behavior, the specimens were compressed 15 mm by applying a load of 50 kN at a 3 mm/min rate during the test.
## 5 Case Study
### Parametric Causality Inference and Anomaly Detection on a Real Satellite Telemetry Dataset
#### 5.2.2 Parametric Causal Network Inference  
In this section, we applied MESS and IRESS for parametric causal network inference. Similarly, we employ precision, recall, and F1-score as the evaluate metrics of (see Equation (18)). Figure 11 shows the parametric causal network inferred by MESS and IRESS. Table 4 shows all the target parameters and their corresponding source parameters of all causalities. Regarding the inferred causalities, we verify the reliability by analyzing the physical properties the parameters represented and consulting experts in satellite development, design, operation, and maintenance. After comprehensive analysis, combined with the experimental results of Section 5.1, we have reason to believe that the causality inferred by IRESS is reliable and more reasonable than that of MESS (although we do not know the true mechanism of the satellite system and causality of these parameters).
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 4 Applications
### Robotic Surgery  
Robotic surgery driven by AI utilizes various AI models, including classification and regression trees, SVMs in ML, ANNs, and CNNs in DL [74]. AI-driven robotics have revolutionized surgical procedures, enhancing precision and improving patient outcomes. In complex surgeries, AI-controlled robots improve surgeons' visualization, dexterity, and control, while their precise movements reduce invasiveness, complications, and recovery times [59]. A prime example is the Da Vinci Surgical System, which employs robotic arms controlled by surgeons using a console. The system utilizes AI to assist with intricate movements, providing a high-definition, 3D view of the surgical area. This capability is particularly valuable in urological surgeries, such as prostatectomies, where precision is crucial to avoid damage to surrounding tissues and nerves [75].  
AI-driven robotic surgery systems can be categorized along an assistive-to-autonomous spectrum. Assistive systems enhance user capabilities by analyzing data, executing tasks under human supervision, or learning from professional demonstrations [76]. For instance, in orthopedic surgery, the MAKO system uses AI to create 3D models of a patient's anatomy and assist surgeons in performing precise joint replacements. The AI system providesreal-time feedback, ensuring implants are placed accurately, which improves patient outcomes and the longevity of the implants [77]. Autonomous systems respond to real-world conditions, make decisions, and operate with minimal or no human interaction [76]. An example of this is the Smart Tissue Autonomous Robot (STAR), which has demonstrated the ability to perform soft tissue surgeries, such as suturing, autonomously. STAR utilizes computer vision and AI algorithms to adjust its actions in real time based on the tissue's response, achieving consistent and precise results that are comparable to those of experienced surgeons [78].  
While AI-driven robotic surgery systems with autonomous capabilities and independent robots represent the future, they are intended to supplement, rather than replace, surgeons in surgical care. For instance, during minimally invasive heart surgeries, AI-assisted robots can stabilize the surgeon's hand movements, filter out tremors, and enhance precision. However, the surgeon remains in control, making critical decisions and adjustments during the procedure [79].
# Overview and Perspectives for Vehicle-Integrated Photovoltaics
## 1 Introduction  
The transport sector is one of the main contributors to the emissions of greenhouse gases (close to 25% in Europe). One solution is the electrification of transport via electrical vehicles. However, electric vehicles have limitations despite their purchase price such as limited autonomy and long or frequent recharge times. Vehicle-integrated photovoltaics may help mitigate these downsides. Electrical vehicle-integrated photovoltaics has untapped potential [1] and could accelerate mutual development. On-board photovoltaic energy generation is driven by technological, environmental, and legislative motivations. Among the promises are added range, fewer charging sessions, and a reduced carbon impact. Hybrid electric vehicles (HEV) and plug-in hybrid electric vehicles (PHEV) may also gain reduced CO\({}_{2}\) emissions as eco-innovation [2] in the future with on-board photovoltaic energy generation.  
The integration of photovoltaics into vehicles requires aesthetic, low-weight, and curved modules unlike standard modules used for utility application. Of course, other key metrics such as performance (Wp/m\({}^{2}\)), reliability, and safety are still applicable. Materials and technologies should remain cost-competitive to enable large-scale production. In addition to the significant technology change, another drawback is the lack of standards in the field of energy yield calculation from on-board photovoltaics, as well as adapted automotive and photovoltaic standards [3, 4].  
This paper reviews the state of the art in terms of VIPV performance and available cell and module technologies, as well as module materials to meet the stringent weight and flexibility demands. Firstly, the status of commercial offers and research and development (R&D) projects involving vehicles with on-board PV is presented. Secondly, current flexible and lightweight PV technologies are reviewed, and their potential for integration into EVs is discussed. Nonintegrated PV solutions are outside the scope of this paper.
# Reference Broadcast-Based Secure Time Synchronization for Industrial Wireless Sensor Networks
## 3 System Models and Problem Analysis
### Clock Model  
In general, the node's time is measured based on counting the output pulse of an equipped crystal oscillator. Furthermore, the clock reading of each node \(i\) is modeled as a first-order dynamic function with reference to the absolute time \(t\), i.e.,  
\[\tau_{i}(t)=\alpha_{i}t+\beta_{i}, \tag{1}\]  
where \(\alpha_{i}\) is the clock skew that determines the timing rate and \(\beta_{i}\) is the initial clock offset. Since the frequency of the oscillator determines \(\alpha_{i}\), each \(\alpha_{i}\) is slightly different due to the imperfect oscillators, ambient temperature, battery voltage, and oscillator aging [15]. Hence, \(\alpha_{i}\) satisfies \(1-\rho\leq\alpha_{i}\leq 1+\rho\), where \(\rho\) is the clock drift and typically in the range of \([10^{-5},10^{-4}]\)[9]. Even if \(\beta_{i}\) is equal at the starting moment, the time between nodes is still out of synchronization with the existence of \(\alpha_{i}\). In addition, the absolute \(t\) is unavailable, and it is impossible to compute \(\alpha_{i}\) and \(\beta_{i}\) directly.  
In order to maintain synchronization, it is crucial to find the relationship between the two nodes' clocks and the clock compensation parameters. Observing the clock readings of two nodes, it is indicated that  
\[\tau_{j}(t)=\alpha_{j}\Big{(}\frac{\tau_{i}(t)-\beta_{i}}{\alpha_{i}}\Big{)}+ \beta_{j}=\alpha_{ij}\tau_{i}(t)+\beta_{ij}, \tag{2}\]  
where \(\alpha_{ij}=\alpha_{j}/\alpha_{i}\) and \(\beta_{ij}=\beta_{j}-\alpha_{ij}\beta_{i}\) denote the relative clock skew and offset, respectively. The goal of synchronizing with node \(j\) for node \(i\) is to find the compensation parameters \(\alpha_{ij}\) and \(\beta_{ij}\). Meanwhile, each node only synchronizes with its parent node. After multi-hop iteration, the local node achieves synchronization with the reference node.
###### Abstract  
With the continuous development of science and technology, UAV technology has been widely used in various fields. As an essential part, light and small UAV platforms have the advantages of being small, lightweight, and easy to operate and thus have been widely used in aerial photography, monitoring, rescue, and other fields. However, the traditional data transmission method can no longer meet the demand for the data transmission speed and stability of small and light UAV platforms. Wireless optical communication has the advantages of being high-speed and license-free, as well as having a large bandwidth strong anti-interference ability, and good confidentiality. Therefore, this paper aims to study high-speed data transmission technology for light and small UAV platforms to improve their transmission speed and stability. In this paper, the characteristics and advantages of UAV airborne laser communication are first elaborated to illustrate the advancements and importance of data transmission technology based on light and small UAV platforms. Then, the composition of a UAV airborne laser communication system is clarified to illustrate the feasibility of conducting airborne laser communication research. Then, the current domestic and international research status of people on light and small UAV airborne laser communication high-speed data transmission systems is reviewed, and its key technical features are analyzed. Finally, the prospects for its application and development trend are investigated.  
light and small UAV optical communication; high-speed data transmission technology; optical communication terminal; modulation and demodulation technology +
Footnote †: journal: selected
# Development of a Prototype Solution for Reducing Soup Waste in an Institutional Canteen
## 2 Prototype
### Architecture  
Figure 1 shows the architecture of the prototype solution developed, which makes it possible to quantify the food waste of soup in an institutional canten, in the process of delivering the tray after the meal has been consumed.  
Figure 1: Prototype architecture.  
The physical component consists of a camera and an infrared sensor connected to a Raspberry Pi [6], with the aim of capturing images of the trays. The captured images are not stored locally on this device, given its processing and storage limitations. Therefore, the image of the tray is transferred to a local Ubuntu server [7], which is running on a virtual machine.  
On this server, a Python script was created with the purpose of observing the destination directory of the images and then running the detection and classification model. First, it tries to detect the presence of a bowl in the captured image, using the ResNet-50 and Faster R-CNN computer vision models. Then, from the image of that bowl, it performs feature extraction and calculates the volume of soup remaining (i.e., wasted). This server has a MySQL database [8], implemented in a Docker container [9], which will store the data collected in the previous stage, i.e., the volume wasted and a corresponding date.  
To enable the integration and consultation of the information collected, as well as management and support for decision making, a web application based on microservices was developed, made up of individual containers that communicate with each other. All these services, from the database to the microservices, are implemented in Docker containers. The Frontend microservice is responsible for the user interface, allowing the stored data to be consulted. The Backend microservice interacts directly with the MySQL database. The Eureka microservice acts as a microservice discovery service and provides communication between them.  
Choosing an architecture based on microservices is a future-oriented decision, as it offers several important advantages. Microservices increase resilience, ensuring that component failures do not affect the entire system. They facilitate scalability, allowing each service to grow as needed. They improve testing efficiency by allowing each service to be checked in isolation. In addition, they enable the use of multiple technologies, increasing adaptability and continuous innovation [10].
# Low-Complexity GSM Detection Based on Maximum Ratio Combining
## 4 MRC-MP Detection Algorithm
For each entry contained in set \(\psi_{\beta}\), each signal can be obtained as  
\[\mu_{i}=\mathbf{h}_{\psi_{\beta}^{l}}^{\dagger}\mathbf{y}=\left(\mathbf{h}_{\psi_{\beta}^ {l}}^{H}\mathbf{h}_{\psi_{\beta}^{l}}\right)^{-1}\mathbf{h}_{\psi_{\beta}^{l}}^{H}\bm {y},\qquad 1\leq i\leq\left|\psi_{\beta}\right|, \tag{23}\]  
\[\widetilde{\mathbf{x}}_{\beta}=\left(\mu_{1},\cdots,\mu_{\left|\psi_{\beta} \right|}\right)^{T}, \tag{24}\]  
where \(\mathbf{h}_{\psi_{\beta}^{l}}\) represents the \(\psi_{\beta}^{l}\)-th column of channel gain matrix \(\mathbf{H}\), \(\psi_{\beta}^{l}\) denotes the \(i\)-th element in set \(\psi_{\beta}\). Step 5: The residual \(\mathbf{r}_{\beta}\) is updated in \(\beta\)-th iteration,  
\[\mathbf{r}_{\beta}=\mathbf{y}-\mathbf{H}_{\psi_{\beta}}\widetilde{\mathbf{x}}_{\beta} \tag{25}\]  
Step 6: Judge the iteration termination condition. If \(\beta>N_{p}\), stop the iteration and output \(N_{p}\) antenna index sets \(\left\{\psi_{\beta}\right\}_{\beta=1}^{N_{p}}\); otherwise, \(\beta=\beta+1\) and return to Step 2 to continue the iteration. Step 7: \(N_{p}\) antenna index sets \(\left\{\psi_{\beta}\right\}_{\beta=1}^{N_{p}}\) are obtained through \(N_{p}\) iterations. Combining the \(N_{p}\) antenna index sets, remove the duplicate antenna indices, and only leave the non-duplicate antenna indices. Thus, we obtain the new TACs by deleting the TACs that do not include any of the elements in set \(\left\{\psi_{\beta}\right\}_{\beta=1}^{N_{p}}\) from all the TACs. The new TACs are considered as quantified TACs with high possibility. Step 8: Quantitate the modulation symbol. Step 9: The TAC and the modulation symbol are estimated using ML algorithm. The proposed MRC-MP detection algorithm can be described in Table 2.
# Multi-Feature Guided Low-Light Image Enhancement
## 3 Materials and Methods
### Noise Estimation Network  
When performing noise processing in the image enhancement process, there are generally two ideas: one is to perform denoising operations on the basis of low-illumination images, and then image enhancement, and the other is to perform image denoising on the enhanced images. Both of these two methods have certain shortcomings. The former will blur the enhanced image, and the latter will inevitably amplify the noise. Therefore, we  
Figure 2: Schematic diagram of network structure.  
use the method of multi-feature map guidance to simultaneously perform image denoising and enhancement.  
Most of the existing denoising networks are based on additive white Gaussian noise (AWGN) modeling for denoising. This method tends to cause network overfitting, and the real-world noise is caused by complex reasons, so the denoising method based on AWGN modeling does not work well on real data. Inspired by [23], in order to establish a model closer to real noise, we carry out noise modeling based on the Poisson Gaussian distribution and the simulation of the real camera imaging process. For a given input noise image \(\mathrm{I}\), the output noise map is \(\hat{\mathrm{n}}\) after being processed by the noise subnet. Given a certain pixel point \(\mathrm{i}\) in the feature map, the estimated noise level is \(\hat{\mathrm{n}}_{\mathrm{i}}\), and the true noise level is \(\hat{\mathrm{n}}_{\mathrm{i}}\). According to the sensitivity of the denoising model to the estimation error, when \(\hat{\mathrm{n}}_{\mathrm{i}}<\mathrm{n}_{\mathrm{i}}\), that is, when the standard deviation of the estimated noise is lower than the standard deviation of the real noise, the mean square error (MSE) should be penalized. Set the loss here as:  
\[\mathcal{L}_{\mathrm{n}}=\sum_{\mathrm{i}}\lambda_{\mathrm{n}}(\hat{\mathrm{n }}_{\mathrm{i}}-\mathrm{n}_{\mathrm{i}})^{2} \tag{3}\]  
The value of \(\lambda_{\mathrm{n}}\) is expressed as: \(\lambda_{\mathrm{n}}=|\mathrm{b}-\alpha|\), the value range of \(\alpha\) is \((0,0.5)\), and \(\mathrm{b}\) is a constant. When \(\hat{\mathrm{n}}_{\mathrm{i}}<\mathrm{n}_{\mathrm{i}}\), the value of \(\mathrm{b}\) is \(0\), otherwise the value is \(1\).  
Considering the performance and efficiency of the denoising network, we compress the depth of the network and adopt a fully convolutional network structure with a number of layers of \(4\), set the number of convolution kernels in each layer to \(16\), and set ReLu activation after each convolution layer function to add nonlinearity. In order to better capture the contextual information of the image, increase the receptive field of the network, and reduce the computational cost, we use the method of dilated convolution with a sampling rate of \(2\) to extract the features.