esa-sceva/satcom-chunk-collection huggingface dataset:
# A Fault Section Location Method for Distribution Networks Based on Divide-and-Conquer
## 5 Simulation Analysis
### IEEE 69-Node Distribution Network  
The IEEE 69-node distribution system is shown in Figure 15, which has 1 substation power supply, 4 distributed power supplies, 1 circuit breaker, 4 DG grid-connected switches, 68 intelligent switches, and 69 sections. The population size of the intelligent optimization algorithm was set to 100, and the maximum number of iterations was set to 70.  
To further demonstrate the performance advantages of the proposed method, this section describes taking the IEEE69-node distribution system as an example to test the accuracy and computational efficiency of the algorithms. The test cases in this section are shown in Table 9, which covers various fault scenarios such as single-fault, multiple-fault, sound fault information, and distorted fault information. Among them, the application vectors \(K_{1}\), \(K_{2}\), \(K_{3}\), and \(K_{4}\) represent the dynamic throwing cases of the distributed power supply. When DGi is put into operation, \(K_{i}=1\); otherwise, \(K_{i}=0\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Equipment Number** & **Preset Fault Section** & **Preset Information** & \multicolumn{5}{c}{**Accuracy (\%)**} \\ \cline{3-7}  & **Dislution** & **Algorithm 1** & **Algorithm 2** & **Algorithm 3** & **Algorithm 4** \\ \hline
1 & \(x_{3}\) & \(I_{16}\): \(0\to 1\) & 100 & 84 & 100 & 100 \\
2 & \(x_{18}\) & \(I_{7}\): \(1\to 0\), \(I_{32}\): \(0\to 1\) & 100 & 80 & Misjudgment & 100 \\
3 & \(x_{23}\) & \(I_{4}\): \(1\to 0\) & 100 & 88 & 100 & 100 \\
4 & \(x_{33}\) & \(I_{11}\): \(0\to 1\), \(I_{20}\): \(0\to 1\) & 100 & 86 & 99 & 100 \\
5 & \(x_{7}\), \(x_{22}\) & \(I_{14}\): \(1\to 0\) & 100 & 90 & 100 & 100 \\
6 & \(x_{10}\), \(x_{33}\) & \(I_{6}\): \(1\to 0\), \(I_{30}\): \(1\to 0\) & 100 & 70 & 98 & 100 \\
7 & \(x_{16}\), \(x_{27}\) & \(I_{7}\): \(1\to 0\), \(I_{22}\): \(0\to 1\), & 100 & 76 & Misjudgment & 100 \\
8 & \(x_{14}\), \(x_{26}\) & \(I_{10}\): \(1\to 0\) & 100 & 72 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Fault tolerance test results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Equipment Number** & **Preset Fault Section** & **Preset Information** & \multicolumn{5}{c}{**Computational Efficiency (ms)**} \\ \cline{3-7}  & **Dislution** & **Algorithm 1** & **Algorithm 2** & **Algorithm 3** & **Algorithm 4** \\ \hline
1 & \(x_{18}\) & & 13.27 & 131.43 & 75.27 & 118.18 \\
2 & \(x_{24}\) & \(I_{9}\): \(0\to 1\), \(I_{20}\): \(1\to 0\) & 13.86 & 128.29 & 79.34 & 116.76 \\
3 & \(x_{6}\) & \(I_{12}\): \(0\to 1\) & 15
## 4 Numerical Simulation Applied to the FFF Process
### Cooling and Solidification
At higher build rates, core temperatures drop, highlighting the reduced thermal residence time. These findings support the recommendation to implement extended heating zones and underscore the critical role of balanced thermal management in ensuring efficient material flow and preventing mechanical instabilities during the printing process. Enhancing print reliability, [PERSON] et al. (2020) [11] developed an AI-based real-time monitoring system using computer vision and embedded strain sensors to promptly detect  
Figure 12: COMSOL® model showing the temperature evolution (°C) for ABS during the extrusion process. Adapted from [49]. interlayer defects such as warping and delamination. Their system successfully identified defects early, significantly improving reliability and reducing material waste. The relationship between cooling dynamics and residual stress generation further clarifies the role of thermal management in FFF. [PERSON] et al. (2018) [51] employed CFD simulations to reveal that uneven cooling leads to differential thermal contraction, causing localized stresses and dimensional inaccuracies. By incorporating temperature-dependent shrinkage models, their simulations improved predictions of residual stresses. Extending these findings, [PERSON] (2020) [52] developed a comprehensive numerical model, indicating that the rapid cooling near the nozzle exit increases residual stresses, resulting in geometric distortions. Experimental validation confirmed that higher extrusion temperatures enhance adhesion but simultaneously increase the residual stress of thermal origin. Together, these studies underscore the role of temperature evolution in determining bonding quality and dimensional outcomes during FFF. However, the development of internal stresses resulting from non-uniform cooling and constrained thermal contraction cannot be fully explained by thermal considerations alone. To address this, the following section focuses on how residual stress accumulation and thermally induced deformation evolve during the printing process, and how they influence the structural integrity and reliability of FFF-manufactured parts.
## 2 Development of a Prototype of Industrial Metaverse for Teaching/Learning Activities  
### Design Methodology  
The methodology proposed in this work for designing an industrial metaverse is graphically depicted in Figure 3, which shows the systematic progression through key phases. It initiates with client specification gathering, involving the establishment of initial contact to comprehensively understand the client's specific needs and vision for utilizing the virtual environment. Following this, the collaborative preliminary design phase unfolds, where, based on the client's specifications, a joint effort is made to conceptually outline each element of the metaverse without delving into programming. Mock-ups are generated during this phase, providing a tangible representation for client feedback before transitioning to the subsequent development stage. After the client's validation, the selection of tools, software development kits (SDKs), platforms, and hardware is carefully undertaken, ensuring technical viability and effectiveness for the industrial metaverse's unique requirements. The implementation phase starts, presenting an alpha version for validation by testers and subsequently generating a beta version for client validation. This iterative process incorporates feedback, addressing necessary adjustments and refinements. The evaluation and delivery phase involves subjecting the beta version to a thorough evaluation during an agreed-upon period with clients and testers, culminating in the delivery of the finalized industrial metaverse. The methodology concludes with a prolonged validation period and continuous improvements, where the final version undergoes extended validation by users within the industrial environment, with ongoing refinements based on user feedback to ensure continuous adaptation and optimal efficiency.  
Unity, renowned for its versatility and user-friendly interface, has been selected as the preferred software for this work [15]. The platform, excelling in the development of interactive applications in 2D and 3D, empowers developers to create immersive virtual environments and interactive applications effectively. With a robust game engine and a diverse set of tools, developers can seamlessly integrate innovative technologies like NeRFs to achieve a highly realistic 3D representation in the industrial metaverse under development. The specific version utilized for this work is Unity 2021.3.2f1. This choice over alternatives like Unreal Engine [16] is motivated by Unity's simplicity, efficiency in disk space usage, and compatibility with C# language. Unity's smaller file sizes reduce the demand for disk space and system resources, making it particularly suitable for the requirements of this work, even though Unreal Engine may be more attractive for larger projects with advanced graphics needs. In this specific context, Unity emerges as the most fitting and advantageous choice.
# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
## 3 Survey of State-Of-The-Art MAC Protocols
### Time-Slotted Protocols
#### 3.2.3 Concurrent Access MAC Protocol  
Concurrent Access MAC (CA-MAC) [75] tries to achieve low access delay by creating several communication pairs concurrently, which are able to start transmission simultaneously. Each node has two radio front-ends for control signals and data transmission. Therefore, control overhearing and data transmission are concurrently possible. In CA-MAC, each channel has a unique time slot, and each channel is accessed only in a time slot associated with channels.  
CA-MAC uses two different channel lists, which are sorted according to priority, one is the Sorted Channel List (SCL), and the second is the Common Channel List (CCL). SCL is an arranged list of all of the channels available to members, which is based on accessibility, while CCL is a local list of channels available between a communication pair. CA-MAC uses the SCL to choose the data channel in a way that it should be a channel that is most common to all of the members. In contrast, CA-MAC uses the least common channel from CCL for the data channel between a communication pair to lower the chance of collision. In each time slot, all of the nodes set their listening radio to the associated channel. The sender chooses the first free channel from the CCL and sends the channel reservation request. Once the request is received, during the signaling period, the receiver checks the channel. When the channel is being used, the receiver suggests the next free channel from CCL. Otherwise, it sends the acknowledgment packet to the sender. By overhearing the channel reservation between a pair, other nodes update their available channel information, so they do not attempt to use the reserved channel.
# Sentiment Interaction Distillation Network for Image Sentiment Analysis
## 2 Related Work  
### Image Sentiment Prediction  
Existing image sentiment analysis methods can be summarized as two groups: dimensional emotion spaces (DES) and categorical emotion states (CES). DES methods usually employ valence-arousal-dominance space [12] or activity-weight-heat space [13] to represent human emotion. On the contrary, CES methods model emotions with categories [14; 15], which is more intuitive, and our work falls into categorical states.  
Figure 1: Examples of image sentiment. The blue border represents positive sentiment, while the green border represents negative. We show the categories of objects obtained by panoramic segmentation under the images.  
Some researchers have devoted themselves to discovering sentimental features in the images and bridging the \"affective gap\", which can be defined as a lack of strong connection between visual features and sentiment [16]. On the basis of psychological research, [PERSON] and [PERSON] [15] proposed to utilize the low-level features of images, such as texture, color, and composition, to achieve the image sentiment classification.  
With the development of deep learning, [PERSON] et al. [17] employed AlexNet to achieve the classification of emotions. [PERSON] et al. [18] proposed sentimental regions based on object proposal method and employed the corresponding features to achieve emotion classification. With the help of attention mechanism, [PERSON] et al. [19] achieved a higher performance by enhancing the local features in the image, which proves that local features have a promoting effect on image sentiment classification.  
Further, [PERSON] et al. [7] proposed the \"Affective Region\" and designed three fusion strategies to utilize the features of \"Affective Region\". [PERSON] et al. [8] proposed to leverage the salient region of images and made efforts to fuse the features of local and global, which achieve a large performance improvement. Recently, through the use of multimodal information, some researchers have proposed more methods combined with the attention mechanism [20; 21; 22], which further promote the development of emotion analysis tasks. These methods make efforts in extracting visual features to improve performance while ignoring the interaction information among objects. In contrast, we propose utilizing the sentimental interaction information and realize the sentiment analysis task.
## 2 Literature Review
### Zigbee
#### 2.1.1 Overview of Zigbee Protocol  
Zigbee protocol uses IEEE 802.15.4, it has protocol layers above the IEEE 802.15.4 standard and provides a full protocol stack for cheaper, low-energy, small data rate wireless communication applications. The layered architecture is shown in Figure 1.  
ZigBee architecture is also called a ZigBee stack and contains the Foundation Layer, and the Application and Interface Section. The Foundation layer of Zigbee is both physical and MAC layers, on top of its Application and Interface section which consists of Network and application layers [33].  
The Physical layer defines the physical and electrical characteristics and is responsible for the modulation and demodulation of transmitted and received signals, respectively [34]. The functions of the physical layer are enabling and disabling the transmission and reception, choosing a channel, sending and receiving data in packets, and calculating energy within the channel.  
The MAC layer acts as an interface between the physical and network layer, its main function is to establish reliable data transfer communication by accessing various networks with the carrier sense multiple access collision avoidance (CSMA), and it is responsible for handling data and data management.  
All of the network-related operations are taken care of by the network layer, such as establishing a connection between the router and other end devices, routing, the configuration  
Figure 1: Zigbee Architecture (Zigbee Stack) Functions [31].  
of other devices, and disconnection to network. Its main functions are: network initiation; assigning address to nodes; configuring new devices; and providing secure transmission.  
The Application Layer in Zigbee architecture has two sub layers namely: Application Support Sub Layer and Application Framework.  
The Application support sublayer filters the packets for end devices, and checks for duplicate packets within a network that supports automatic retries. To improve the probability of successful transmission, this layer performs automatic retries, when an acknowledgment is requested by the sending device.  
The Application Framework is vendor-dependent, who has picked for specific applications to communicate with the Zigbee protocol. This represents implementation of endpoints, and how data requests and data confirmation are executed for that vendor.
# Shell Waste Management and Utilization: Mitigating Organic Pollution and Enhancing Sustainability
## 3 Environmental Applications
### Acid Mine Drainage  
Acid mine drainage (AMD) is a global problem associated with sulfates and heavy metals entering the environment, mainly from coal mining when pyrite is exposed to water and oxygen. Sulfide is than oxidized to sulfuric acid, which lowers water pH and catalyzes leaching of metals [8]. Such drainage has harmful effects particularly on aquatic environment [52].  
Biomaterials retrieved from dried and pulverized shrimp shell and mussel byssus had significant effects on both sorption process and pH of the AMD, and the enhanced precipitation of metal hydroxides. The kinetic studies in microcosms [53] demonstrated that only a 200 min contact time with this mixture is sufficient to transform AMD into water suitable for non-potable reuse. Clam shells also have a high capacity for removing heavy metals from simulated, synthetic AMD in lab-scale constructed wetlands [54]. Constructed wetlands have been recognized as a potential green technology for mine drainage treatment due to their effectiveness, low cost, simple maintenance, and high biodiversity value [55,56]. In a constructed wetlands study, both oyster shells and limestone had high AMD neutralization potential during a 7-month operation. Accumulation in the substrates was a principal pathway for removing heavy metals [57]. Therefore, the recycling of waste bivalve shells as substrates in constructed wetlands for AMD treatment might encourage the reduced usage of natural minerals such as limestone.
## Theoretical Modeling
### Mixed Time-Marching Integration Algorithm
#### 2.4.1 Predictor Step with End-Point Algorithm  
Assuming all of the required information is available at step \(n\), we can adopt the standard Newmark interpolation to get the vectors of velocity and acceleration at time \(n+1\), and then the corresponding internal and inertial forces. Here, we use the HHT-\(\alpha\) time integration scheme, thus the end-point dynamic equilibrium equation (Equation (1)) is now expressed as:  
\[(1+\alpha)(\mathbf{q}_{i,n+1}-\mathbf{q}_{e,n+1})-\alpha(\mathbf{q}_{i,n}- \mathbf{q}_{e,n})+\mathbf{q}_{mas,n+1}=\mathbf{0} \tag{21}\]  
The values at time \(n\) and \(n+1\) are then substituted into Equation (21), which finally leads to a set of equations of the form:  
\[\Delta\mathbf{\tilde{q}}=\tilde{\mathbf{K}}_{t,n}\Delta\mathbf{p} \tag{22}\]  
where  
\[\tilde{\mathbf{K}}_{t,n}=(1+\alpha)\mathbf{K}_{t,stat}+\mathbf{K}_{mas1}+ \frac{1}{\beta\Delta t^{2}}\mathbf{K}_{mas2}+\frac{\gamma}{\beta\Delta t} \mathbf{K}_{mas3} \tag{23}\]  
is the equivalent dynamic tangent stiffness matrix, which includes contributions from both the internal and inertial terms.  
\[\Delta\mathbf{\tilde{q}}=(1+\alpha)\mathbf{q}_{e,n+1}-\alpha\mathbf{q}_{e,n}- \mathbf{q}_{i,n}-\mathbf{q}_{mas,n}+\mathbf{K}_{mas2}\frac{1}{\beta\Delta t} (\tilde{\mathbf{p}}_{n}+\frac{\Delta t}{2}\tilde{\mathbf{p}}_{n})+\mathbf{K} _{mas3}[\frac{\gamma}{2}\tilde{\mathbf{p}}_{n}-\Delta t(1-\frac{\gamma}{2p}) \tilde{\mathbf{p}}_{n}] \tag{24}\]  
is the equivalent incremental loads, and \(\Delta\mathbf{p}\) is the predicted incremental displacement from time step \(n\) to \(n+1\).
## 1 Introduction
### Visual Preference Comparison between LDR and HDR Images  
To determine whether the visual preference for HDR images is greater than for LDR images, we subjectively evaluated the HDR and LDR images. A total of 190 LDR images and 27 tone-mapped HDR images from the EMPA-HDR database [39] were subjectively evaluated by 10 researchers who specialize in image processing for analysis, image adjustment, and artifact reduction in a dark room display and the same display. The random order images were individually evaluated on a scale of 1 (minimum score) to 5 (maximum score). The subjective scores evaluated by 10 observers of each images were averaged to yield mean opinion scores (MOSs) and the MOSs of all LDR and tone mapped HDR images were also averaged to yield total MOSs of LDR and tone mapped HDR, respectively. Figure 2 shows that the subjective scores for the HDR images are greater than those for the LDR images. While the mean score of the LDR images is 2.44, the mean score of the HDR images is 4.39. This indicates that the HDR images are visually preferred by HVS over the LDR images.  
Our proposed assessment method, therefore, uses HDR-derived features extracted from HDR images that are closer to the HVS properties in order to improve our method's correlation with subjective evaluations.  
Figure 1: Subjective evaluation scores (x-) and calculated feature values (o-) for different adjustment levels on (**a**) colorfulness adjustment, (**b**) contrast adjustment, and (**c**) sharpness adjustment. While the calculated feature values keep increasing as the level of adjustment increases, the subjective scores increase only until the level of adjustment of about four or five levels and then they decrease thereafter until the eighth adjustment level.
# Influence of Contacts and Applied Voltage on a Structure of a Single GaN Nanowire
## 3 Results
### Influence of Contacts on the NWs Structure  
The 3D intensity distribution around 10\(\bar{1}\)0 GaN Bragg peak for the contacted nanowires is shown in Figure 5. It is well seen in Figure 5a that the Bragg peak of the contacted 350 nm thick GaN NW has obtained an additional Bragg peak due to the induced strain from the deposited contacts.  
The free-lying 200 nm thin GaN NWs were already bent before contacts deposition, but the bending angle was increased by 2 degrees after the contacts were applied (see Figure 5b). The nanowire fixation and bending mechanism were different for 350 nm and the 200 nm GaN NWs as can be seen from comparison of corresponding diffraction patterns (see Figure 5a,b).  
Further, influence of the second type of Au contacts on the GaN NWs was studied (see Figure 2c). The corresponding distribution of intensity in reciprocal space around 10\(\bar{1}\)0 GaN Bragg peak of a contacted 200 nm GaN NW is presented in Figure 5c, and shows the bending effect. Finally, we can conclude that all investigated types of contacts had a similar bending effect on the GaN NWs.  
Figure 5: Comparison of the intensity distributions around 10\(\bar{1}\)0 GaN NWs Bragg peak for the different types of Au contacts. (**a,b**) The contacts deposited on the top of the nanowires with the diameters of 350 nm (**a**) and 200 nm (**b**). (**c**) The intensity distribution for a NW with the diameter of 200 nm contacted on the top of the Au electrodes by melting procedure. For all types of the contacts, a significant influence on the scattered X-ray intensity due to the induced strain during the contacting process could be seen. To enhance the diffraction pattern details, the intensity distributions are represented by two different iso-surfaces. The length of coordinate arrows corresponds to 0.1 nm\({}^{-1}\). The intensity is normalized to the maximum and the iso-surface values are \(10^{-35}\) and \(10^{-4.5}\) (**a**), \(10^{-1.5}\) and \(10^{-2.85}\) (**b**), \(10^{-2.5}\) and \(10^{-3.5}\) (**c**).
## 3 Method
### Waveform Retracking Method  
To improve the data accuracy and enhance the data application of HY-2B satellite altimeter in coastal areas, this study designs a coastal waveform retracking strategy to process the HY-2B altimetry coastal waveform data. The strategy picks the effective part of the leading edge and trailing edge of coastal waveforms out then retrieves the accurate parameters of the observed sea surface based on the classic waveform models. The details are shown in Figure 4. The main processing of this method includes thermal noise removal and power normalization, specular echo processing and non-specular echo processing. The specular echo waveform is fitted by the MB4. The crucial steps of non-specular echo processing are as follows: first, confirming the rough leading edge part of the waveform and then obtaining the accurate leading edge according to the reference gates; second, identifying the steady trailing edge part of the waveform by searching the effective trailing edge; finally, weighting and fitting the processed leading edge and trailing edge of the waveform by the WLS4 to derive the parameters of the sea surface and according to the mispointing angle to recognize and refit the sharpening waveform by the MB4.