esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Test Method  
In this study, CST tests and vacuum preloading tests combined with the PIV technique were conducted on untreated slurry, APAM-flocculated slurry, dual-use PAC and APAM-flocculated slurry, and lime-flocculated slurry. The detailed test procedure is as follows:  
1. Prepare the flocculated slurries according to the test schemes outlined in Table 2 and perform CST tests to determine the optimal flocculant proportions for the vacuum preloading tests.  
Figure 4: Vacuum preloading combined with PIV technique test apparatus. (**a**) Physical pictures. (**b**) Schematic diagram.  
2. Prepare the flocculant solutions based on the determined formulations. Gradually add the flocculant solutions into the slurry barrel in batches, using a hand-held electric mixer to ensure uniform mixing of the flocculants with the slurry.
3. Charge the viewing window with static electricity through friction, and then spray tracer particles (average particle diameter is 50 \(\upmu\)m) onto the inner side of the viewing window using a powder cluster. Install and secure the viewing window onto the model box. Next, install the PVD and pore water pressure sensors inside of the model box (Figure 4b). Carefully pour the prepared flocculated slurry into the model box, ensuring that no air cavities form during the process. The slurry fill height is 45 cm.
4. Turn on the LED light source and adjust the camera position and focal length to achieve a clear and fully captured imaging plane. Set the high-definition camera to capture images at 30 s intervals during the first 24 h of the test and at 10 min intervals thereafter.
5. Activate the vacuum pump and gradually increase the vacuum pressure inside of the model box to \(-\)85 kPa within 3 min. Throughout the test, continuously monitor and record imaging data, pore water pressure, and water discharge volume in real time. The test continues until no further water discharge is observed.
6. After the test, conduct vane shear tests and water content measurements at the detection points indicated in Figure 4b. Additionally, collect slurry samples within a 3 cm radial distance along the PVD using a ring cutter to perform permeability tests, following the Specification of Soil Test (GB/T 50123-2019).
## 3 Experiments for Jason-3 Attitude Modes and Receiver PCVs Calibration
### Effect of Jason-3 Attitude Modes on Receiver PCO and POD  
To verify the validity of the combined attitude mode, the experiments analyzed the effect of different attitude modes on receiver PCO, including the PCO estimation and its correction, and the effect on POD by internal and external orbit validation. The normal (fixed-yaw) attitude mode was selected in tests because it is adopted in more LEO satellites and beneficial to the combined attitude modeling.
## 2 Related Works
### Object Detection Based on Deep Learning  
While many object detection algorithms were already introduced, R-CNN was the first deep learning-based object detection algorithm [16]. After R-CNN, its successors, such as Fast R-CNN [17] and Faster R-CNN [18] have been devised. While these successors could not achieve real-time processing due to expensive training costs and region proposals, YOLO was introduced and has been evolving ever since. YOLO is fundamentally based on a Convolutional Neural Network (CNN). By processing the entire image in a single evaluation, YOLO accomplishes bounding box prediction (object detection) and inferring class probability (object classification) in a single pass through CNN. This makes YOLO different from other detection systems where multiple networks or passes might be necessary. YOLOv7 is a more recent model of the YOLO family. Its architectural design improved performance by focusing on optimization. These modules and optimization methods are called trainable bag-of-freebies; they improve detection accuracy without increasing the inference cost. Additionally, it is already proven that the YOLOv7 can detect small flying objects such as UAVs [19]. Due to the nature of the UAV surveillance setting--capturing images of flying UAVs from a distance--and the fast maneuverability of UAVs capable of flying 60 km/h, quickly determining the bounding boxes accurately makes YOLOv7 a suitable choice for the proposed system.
# Breast Cancer Image Classification Using Phase Features and Deep Ensemble Models
## 3 Proposed Methodology
### EfficientNetV2 Models  
Training a convolutional neural network (CNN) model typically requires a large number of images to reduce overfitting and improve accuracy. Transfer learning enables the reuse of pre-trained CNN models on extensive datasets for smaller datasets, either through fine-tuning or feature extraction [46]. In this work, EfficientNetV2 [47] is utilized for feature extraction due to its advanced architecture and efficiency.  
EfficientNetV2 is an improved version of EfficientNet [48] that incorporates a combination of a mobile inverted bottleneck convolution (MBConv) and fused MBConv layers in its structure, as illustrated in Figure 4. MBConv is a building block that enhances model efficiency by combining depthwise separable convolutions and inverted residuals, which help reducing computational cost while maintaining high accuracy. These enhancements allow EfficientNetV2 models to train faster and be more lightweight compared with other state-of-the-art models [47]. EfficientNetV2 models are pre-trained on the extensive ImageNet-21K dataset [49], which provides a robust reference for various computer vision tasks. A key difference between conventional EfficientNet and EfficientNetV2 is the incorporation of Fused-MBConv layers in the initial stages of the model. Specifically, EfficientNetV2 uses these layers in the first three stages, achieving a trade-off between training speed and network effectiveness [50]. Fused-MBConv layers combine the efficiency of standard convolutions with the benefits of depthwise separable convolutions, improving both computational efficiency and model performance.  
Figure 4: Structure of MBConv and Fused-MBConv. **(a)** MBConv layer; **(b)** Fused-MBConv layer.  
Figure 3: Proposed mammogram representation using phase-image features. (**a**) ROI of breast cancer image. (**b**) Local phase of ROI. (**c**) Local orientation of ROI. (**d**) Phase congruency of ROI.  
There are several variants of EfficientNetV2. These variants are designed to adapt to various applications, ranging from resource-constrained environments to high-performance computing scenarios. Table 5 presents the variants of the EfficientNetV2 model, detailing the input image sizes and the dimensions of the feature vectors in the final fully connected layer. This information is essential for integrating EfficientNetV2 into machine learning algorithms, as it ensures compatibility and optimized performance.  
In summary, EfficientNetV2 offers significant improvements over its predecessor, making it a highly effective choice for feature extraction in image classification tasks. Its ability to train rapidly while maintaining a lightweight structure and high accuracy makes it particularly suitable for applications with limited computational resources.
## 2 Materials and Methods  
### Sample Collection and Preparation  
The coal samples were obtained from the 2-2 Upper Coal Seam of the Shilawusu Coal Mine in Ordos, China, at a depth of 698 m. The samples consisted of freshly dislodged coal directly collected from the underground working face. To minimize environmental contamination and mechanical damage during transportation, the samples were immediately wrapped in plastic film and transported to the laboratory. The fresh coal blocks were ground and sieved to pass through a 200-mesh screen to produce coal powder for X-ray diffraction (XRD) analysis. Additionally, the coal blocks were cut into four rectangular specimens with dimensions of 10 mm \(\times\) 10 mm \(\times\) 5 mm, labeled as N1, N2, N3, and N4.  
The upper and lower surfaces of the specimens were ground to achieve parallelism. Considering the high surface smoothness required for nanoindentation, the specimens underwent a rigorous grinding and polishing protocol to ensure measurement accuracy. Sandpapers with grit sizes of 400, 800, 1500, 2000, 2500, and 3000 were sequentially applied during the grinding process. Subsequently, polishing was performed using diamond suspensions with particle sizes of 0.5, 0.25, and 0.1 \(\upmu\)m. After polishing, the specimen surfaces were cleaned with anhydrous ethanol to meet experimental cleanliness standards. Finally, each specimen was sealed in plastic wrap to prevent contamination and surface degradation. The specimen preparation and testing procedure is schematically illustrated in Figure 1.  
Figure 1: Sample preparation and test process.
## 1 Introduction
### Smart Logistics System and Its Utilization by Different Actors  
The SLS has four major components, as indicated in Figure 3 and presented in detail by [PERSON] et al. [2]. The smart box is a sensor unit for measuring parameters such as relative humidity, temperature, geographic position, and route tracking, and information associated with quick read (QR) codes. The onboard control unit is used for planning transport routes and monitoring recordings by Cargolog. The information platform is used for documentation and data sharing, and to facilitate biomass trading and management of the pruning supply chain and traceability. The central control unit is a point of administration of the biomass trading and logistics system and links the information platform and onboard control unit. The two components of the SLS (the smart box and information platform) have been integrated so that the smart box is functionally connected with the web address of the information platform [5]. The smart box records and transmits data to the central information platform. The information platform is designed to facilitate interactions among biomass supply chain actors and data collection, as well as the management of the entire logistics of the pruning biomass supply chain (see Figure 2 and Table 1). Table 1 presents how different actors use the platform to interact regarding the flow of materials and related information along the pruning biomass supply chain. Figure 2: Different stages of logistics chain in the process of biomass-to-energy conversion. The SLS will be used to improve the performance of the biomass supply chain focusing on pruning biomass. In evaluating the performance of supply chain management, it is important to identify the performance measurement metrics within the context of the supply chain under evaluation [6]. The overall performance of a supply chain depends on the role of each stakeholder in the chain [6; 7]. [PERSON] et al. [6] discussed that stakeholders' interest, value addition, and partners' performance play important roles in the overall performance of a supply chain. The rest of this paper is structured as follows. Section 2 describes the testing of the SLS tool and evaluation methodology. Section 3 presents the performance evaluation results and discussion, while Section 4 presents the conclusion.
## 1 Introduction
Researchers can simulate ToF raw data and ground truths for various tasks by themselves through the provided approach, as shown in Figure 2. Detailed generating methods are discussed in Section 4.1. Quantitative tests show that the proposed PcBD performs better as a processing flow for raw point cloud data and is expected to be applied to practical tasks. Our main contributions can be summarized as follows:  
* We propose a novel point cloud processing flow PcBD for outlier removal, boundary detection, and smoothing of raw point cloud data. Compared with traditional engineering application methods, PcBD can fulfill multiple tasks in one network using only 3D coordinates, significantly improving the real-time performance of 3D measurement. * We have improved various traditional 3D point cloud processing modules and combined them with local reference frame calculation and novel convolutional layers to achieve point cloud 3D feature extraction with strong expressiveness. In the multi-task processing flow of PcBD, the extracted 3D features are continuously strengthened to guide different tasks. * We propose a method to combine 3D and 2D features for projection boundary prediction and smoothing with a novel cross transformer structure to simultaneously search for the locations of target features in 2D and 3D point clouds, allowing the network to extract 2D projection boundaries in combination with 3D features, and de-noise the 3D coordinates of the projection boundaries in combination with 2D features. * We propose a new benbenchmark, Bound57, for multiple point cloud processing tasks, including outlier-removal, de-noising, boundary-detection and up-sampling. Our proposed method can be used to generate new 3D point cloud data and to test point cloud processing flows for arbitrary functions. The source code for the proposed method and instructions for obtaining the dataset are available at the following link: [[https://github.com/desperadossy/PcBD](https://github.com/desperadossy/PcBD)]([https://github.com/desperadossy/PcBD](https://github.com/desperadossy/PcBD)), accessed on 1 June 2025. Figure 1: The PcBD Network. The structure contains three parts: Feature-Extraction, Boundary-Detection and Smoothing. The network uses the predicted point-by-point labels and filters to remove outliers and predict projection boundaries, and finally outputs a set of 3D vectors to smooth the projection boundaries. Figure 2: The generation route of the proposed Bound57 dataset. By inputting an.obj model, a simulated output point cloud from a ToF camera and ground truths (GT) with labels for various point cloud processing tasks can be obtained.
# A Multitask Learning Framework for Abuse Detection and Emotion Classification
## 2 Related Work
### Abuse Detection  
The development of abuse detection can be divided into three stages: manual feature engineering, deep learning methods, and pretrained models.  
The earliest abuse detection works used rules to train a classifier [22]. This work creates rules manually on the text to generate feature vectors for learning. Next, many methods based on manual features have been proposed. There are two main sources of features: text and user attributes. The former attempts to use lexicon-based features [23], bag-of-words (BOW) or N-gram features [24] to extract features from users' posts on the network. Ref. [25] also shows that the dense comment representations generated by paragraph2 vec are superior to the bag-of-words feature. The latter tries to infer the likelihood of abuse based on the user's age [26], time of publication [27], and so on.  
With the development of deep learning, some works have used deep neural architecture to achieve impressive results on various abuse detection datasets. Ref. [28] reports different deep learning methods on a Twitter dataset, including CNN and LSTM with random embeddings and GloVe. Ref. [8] proposes a transformed word embedding model by pretrained word embeddings and max/mean pooling from simple, fully connected transformations of these embeddings. This approach can easily be extended to some unseen or rare tokens in the training dataset by projecting the pretrained embedding into a space that the encoder can understand. Some studies also try to extract better features by combining the structure of CNN and RNN [29]. Because abuse detection often contains some cryptic expressions, character-level methods also show effectiveness [30]. More recently, some studies have also expanded the application scenarios of abuse detection by studying cross-language and cross-domain aspects [31; 32; 33].  
Some works have also taken advantage of pretrained models to greatly improve abuse detection by pretraining large datasets of abuse. Ref. [10] collects a large dataset banned for being offensive, offensive or hateful comments on Reddit, and generates a pretrained model called HateBERT. HateBERT outperforms the corresponding general BERT model. In addition, [34] also enhanced the results through the use of German BERT, pretrained from German Wikipedia and German Twitter corpora.  
More recently, some works have begun to explore the role of emotion in abuse language detection [1; 3]. However, they rely on additional annotation of data and cannot be extended effectively.
# Optimal Placement of Multiple Feeder Terminal Units Using Intelligent Algorithms
## 3 Mathematical Model
### Constraint Condition  
Different reliability indices quantify the outage of distribution network from different perspectives. If we only consider the EENS which reflects the quantity of unsupplied energy merely, the impact of outage on users cannot be fully described. For example, when the annual EENS of a load point is certain, if the average time of each fault outage is shorter, the annual fault rate will rise, and the load point's user experience on electricity service will get worse. [PERSON] et al. fitted the outage loss curve by the outage duration and outage loss data of industrial users [16]. It can be seen that the loss caused by multiple short-term outages is greater than that caused by one long-term outage under the condition of unchanged average annual power shortage caused by the grid outage. In order to fully consider the influence of distribution network reliability on load, SAIDI and SAIFI are taken as constraints in this optimization model:  
\[SAIDI\leq SAIDI_{\max} \tag{13}\]  
\[SAIFI\leq SAIFI_{\max} \tag{14}\]where \(SAIDI_{\max}\) and \(SAIFI_{\max}\) are the maximum acceptable values of the corresponding reliability indices. The reliability algorithm proposed in Section 2 is used to calculate the reliability index of each load point firstly, and then the \(SAIDI\) and \(SAIFI\) can be calculated according to the formula defined in [17].
# An Inverter Topology for Wireless Power Transfer System with Multiple Transmitter Coils
## 1 Introduction
The detection method of the receiver coil position is introduced using detection switches on the secondary side. This method requires only a current sensor on the primary side. The proper transmitter coil pattern can be determined and energized by the proposed method. This improves the transfer efficiency of the WPT multi-coil system. This paper is organized as follows. The circuit configuration, timing diagram, and operating mode of the proposed system are described in Section 2. The detection method of the receiver coil position and selection of the transmitter coil pattern are proposed in Section 3. The proposed controller is outlined in Section 4. Experimental validation of the proposed system is presented in Section 5. Section 6 concludes this work.
## 3 Research and Development Methodology
### Android Interfaces  
Developing an operator interface for Digital Twin monitoring in manufacturing using Android Studio and Java [41] enables the creation of a highly customizable, portable, and efficient mobile application. With Android, operators can monitor real-time data from the manufacturing floor on tablets or smartphones, improving accessibility and response times.  
Figure 7: Dapper ORM architecture.  
Developing a Digital Twin operator interface for Android offers several key benefits to manufacturing environments, especially when real-time monitoring and quick decision-making are critical.
* Real-Time Data Access: Operators can monitor live data from the factory floor via an intuitive mobile app. The interface can display metrics such as machine status, energy consumption, temperature, pressure, or any other IoT data generated by the Digital Twin system.
* Remote Access and Mobility: Operators can access the manufacturing system's Digital Twin from anywhere on the plant floor or remotely, increasing responsiveness and decision-making speed. This is especially valuable for large-scale facilities where operators are not always near desktop workstations.
* Interactive Dashboards: By utilizing libraries like MPAndroidChart or Android Plot, developers can integrate interactive graphs, data trends, and KPIs (Key Performance Indicators) into the operator interface. This allows for more dynamic monitoring, enabling operators to zoom into specific data ranges or investigate anomalies.
* Alert and Notification System: with Android's notification APIs, the app can push critical alerts to the operators' devices when real-time data signal equipment failure or process inefficiency, enhancing proactive maintenance and reducing downtime.
* Seamless Integration with Existing Systems: the app can integrate with the backend infrastructure of the Digital Twin, pulling data from SCADA systems, PLCs, and ERP systems, providing operators with a unified interface to monitor the entire production environment in real-time.
* Offline Mode: for environments with unreliable connectivity, the app can be designed with an offline mode, allowing operators to view and update data locally, syncing with the backend when connectivity is restored.  
By developing an Android operator interface using Android Studio and Java, manufacturing operations can benefit from enhanced mobility, real-time insights, and faster response times, optimizing the performance of Digital Twin systems and overall production efficiency.
# The Evolution and Optimization Strategies of a PBFT Consensus Algorithm for Consortium Blockchains
## 3 An Introduction to Mainstream Consensus Algorithms
### The Mainstream Consensus Algorithms of Public Blockchains
#### 3.1.1 Pow
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author** & **Year** & **Improvement Method** & **Core Mechanism** & **Limitations** & **Reward/Penalty Mechanism** \\ \hline \multirow{2}{*}{[PERSON] [21]} & \multirow{2}{*}{2020} & \multirow{2}{*}{Green-PoW} & Allocates subsequent block priorities to mining runners-up, reducing redundant computation. & May lead to long-term benefits for runner-up nodes, affecting fairness. & Provides higher priority to mining runners-up as a reward. \\ \hline \multirow{4}{*}{\begin{tabular}{c} [PERSON] [22] \\ \end{tabular} } & \multirow{2}{*}{2020} & \multirow{2}{*}{Learning Proof (PoLe)} & Uses mining power to optimize neural network parameters, synchronizing blockchain and machine learning tasks. & \multirow{2}{*}{\begin{tabular}{c} Increases computational \\ complexity, potentially \\ affecting the decentralization \\ of POW. \\ \end{tabular} } & No explicitly defined incentive mechanism. \\ \hline \multirow{4}{*}{\begin{tabular}{c} [PERSON] [23] \\ \end{tabular} } & \multirow{2}{*}{2020} & \multirow{2}{*}{Post-Quantum PoW Variant} & Integrate quantum-resistant cryptographic algorithms (e.g., have a high computational overhead, potentially \\  & & & hash/lattice-based), ensuring blockchain security in smart cities. & \multirow{2}{*}{\begin{tabular}{c} Unmeasured, potentially \\ affecting mining efficiency. \\ \end{tabular} } & No specially designed incentive mechanism. \\ \hline \multirow{4}{*}{\begin{tabular}{c} [PERSON] [24] \\ \end{tabular} } & \multirow{2}{*}{2021} & \multirow{2}{*}{\
\begin{tabular}{c} Compute-and-Wait PoW \\ (CW-PoW) \\ \end{tabular} } & A multi-round proof verification mechanism that reduces redundant computation through a “compute-wait” protocol. & Increase block confirmation delay, reducing transaction speed. & \multirow{2}{*}{
\begin{tabular}{c} Reduced rewards for \\ high-energy consumption \\ needs to encourage \\ efficient computing. \\ \end{tabular} } \\ \cline{1-1}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A comparison of PoW improvement algorithms.