esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Results
### Experiments Setup  
#### 3.1.1 Data Augmentation and Preprocess  
We utilize the ZY-3 cloud dataset provided by [31]. In terms of data conditions, the quality of cloud datasets plays a crucial role in the training and processing of cloud detection. The variety and comprehensiveness of cloud datasets are of great importance. To enrich the dataset, we augment 1160 images with dimensions of 1024 \(\times\) 1020 \(\times\) 3. The newly added images predominantly feature cloud and snow instances within the same scene, as depicted in Figure 5. Notably, these cloud and snow patterns exhibit strikingly similar characteristics in the images, increasing the complexity of the training scenarios. This augmentation aligns more closely with real-world situations encountered in satellite imagery quality assessments. The dataset distribution after augmentation is outlined in Table 1. To ensure the accuracy of data, a meticulous labeling process is employed for the newly added images. In this process, an existing model is used for preliminary feature extraction, followed by manual refinement for each image using Photoshop. This iterative approach ensures the precision of the labels, as human expertise is applied to correct and validate the automatically generated labels. Regardless of cloud thickness, we label pixels as \"1\" and non-clouds as \"0\".  
Our augmented training data come from the ZY-3 satellite, which comes from the Land satellite Remote Sensing Application Center, Ministry of Natural Resources. The website is [[http://www.lasac.cn/](http://www.lasac.cn/)]([http://www.lasac.cn/](http://www.lasac.cn/)) (accessed on 5 April 2024). We use a square color pan-sharpened version of the image with a resolution of 5.8 m.  
In the discussion section, we use two datasets for further exploration, the 38-cloud dataset [32; 33] and the GF1_WHU dataset [34]. All data in the 38-cloud dataset were compiled by the Robot Vision Laboratory (LRV). It contains 38 Lansat OLI 8 scene images and their manually labeled ground truth for remote sensing cloud extraction. For the consistency of experimental comparison, the unified size of all pictures in the dataset is adjusted to 256 \(\times\) 256. The SENDIMAGE laboratory released this validation dataset, which includes 108 GF-1 Wide Field of View Level 2A scenes and their reference clouds and cloud shadow masks. Ground truth images are obtained by manually labeling cloud boundaries after a visual inspection conducted by an experienced user. The GF1_WHU dataset contains two labels: cloud and shadow. To make the dataset labels consistent, the cloud shadow labels summarized in the dataset are preprocessed and marked as the background. The scene image is converted from tf format to png format by ArcMap 10.3 software.
## Series Arc Fault Detection Algorithm Based on Autoregressive Bispectrum Analysis  
**[PERSON] 1, [PERSON] 1,*, [PERSON] 1, [PERSON] 1,2, [PERSON] 1 and [PERSON] 1**  
College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China;  
E-Mails: [EMAIL_ADDRESS] (K.Y.); [EMAIL_ADDRESS] (S.C.); [EMAIL_ADDRESS] (F.Z.);  
[EMAIL_ADDRESS] (J.Y.); [EMAIL_ADDRESS] (X.Z.)  
School of Mechanical and Automotive Engineering, Fujian University of Technology,  
Fuzhou 350118, China  
Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-592-6162-566.  
Academic Editor: [PERSON]  
_Received: 3 July 2015 / Accepted: 28 September 2015 / Published: 16 October 2015_  
**Abstract:** Arc fault is one of the most critical reasons for electrical fires. Due to the diversity, randomness and concealment of arc faults in low-voltage circuits, it is difficult for general methods to protect all loads from series arc faults. From the analysis of many series arc faults, a large number of high frequency signals generated in circuits are found. These signals are easily affected by Gaussian noise which is difficult to be eliminated as a result of frequency aliasing. Thus, a novel detection algorithm is developed to accurately detect series arc faults in this paper. Initially, an autoregressive model of the mixed high frequency signals is modelled. Then, autoregressive bispectrum analysis is introduced to analyze common series arc fault features. The phase information of arc fault signal is preserved using this method. The influence of Gaussian noise is restrained effectively. Afterwards, several features including characteristic frequency, fluctuation of phase angles, diffused distribution and incremental numbers of bispectrum peaks are extracted for recognizing arc faults. Finally, least squares support vector machine is used to accurately identify series arc faults from the load states based on these frequency features of bispectrum. The validity of the algorithm is experimentally verified obtaining arc fault detection rate above 97%.  
**Keywords:** arc fault detection; autoregressive model; bispectrum analysis; Gaussian noise; high frequency signal; electrical fire +
Footnote †: journal: Journal of Scientific Computing
# Computer Simulations of Dual-Antenna Microwave Ablation and Comparison to Experimental Measurements
## 3 Results  
### FEM Model Verification by Ex Vivo Experiments  
The simulation results of dual-antenna array temperature distributions and coagulation zones were validated using ex vivo experimental data. With the use of two parallel antennas under 50 W to be spaced 20 mm, 30 mm, and 40 mm apart, fifteen experiments were carried out for 8 min of treatment time. At the end of the experiments, the porcine liver was cut along the antenna longitudinal direction to obtain the section presented in Figure 4. The coagulation zone was rotationally symmetrical. From the inside to the outside are the carbonized zone, coagulation zone, and bleeding zone [40]. The values of the characteristic lengths \(R_{\rm max}\), \(R_{\rm L}\), and \(R_{\rm f}\) were measured. Furthermore, the temperatures at measurement point P1 were recorded during the MWA experiments. The average values of these parameters were used as the results of the ex vivo experiments and compared with the FEM simulation data.
## 1 Introduction
Building on recent advances in hybrid optimization algorithms, this study addresses container intermodal scheduling within inland port systems under low-carbon constraints. A multi-objective optimization model is proposed, considering transportation cost, carbon emissions, and transport time. To solve this model, an improved grey wolf-Harris hawks hybrid algorithm (IGWOHHO) is developed. This algorithm integrates Tent chaotic initialization, adaptive weight adjustment, and elite perturbation strategies to enhance search diversity and improve convergence performance. The main contributions of this study are threefold. First, it introduces a unified scheduling model that integrates economic, environmental, and temporal objectives under realistic constraints. Second, it proposes the IGWOHHO algorithm, which improves solution quality through coordinated hybrid search strategies. Third, it presents a case study that validates the model and algorithm, demonstrating notable reductions in cost and emissions and supporting flexible decision-making across different policy and preference settings. The structure of this paper is organized as follows. Section 1 introduces the research motivation, emphasizing the challenges of low-carbon container intermodal scheduling and the need for advanced optimization approaches. Section 2 reviews the background and related literature, discussing existing optimization methods for multimodal transport scheduling and low-carbon logistics, identifying current limitations, and highlighting the research gap. Section 3 presents the proposed multi-objective scheduling model and provides details on the design of the IGWOHHO algorithm. Section 4 outlines the algorithm parameters, the benchmark test functions used to validate algorithm performance, and the case study data. Section 5 evaluates the performance of the proposed algorithm and analyzes its application to intermodal transport scheduling under various scenarios. Section 6 concludes this study and offers suggestions for future research directions.
# Application of Virtual Reality in Learning Quantum Mechanics
## 3 Teaching Experiment  
This study adopted the quasi-experimental pretest-posttest design of nonequivalent groups. A total of 60 students (ages 16-18) from an upper secondary school in Taoyuan, Taiwan, were recruited as the research subjects (Figure 7), 30 students in the control group (using the traditional teaching method), and 30 students in the experimental group (using the virtual teaching modules). The teaching method is the independent variable, and the learning effectiveness is the dependent variable, the pre-learning ability of quantum mechanics is the co-variable, and the control variables include the teacher, learning content, and teaching time. The purpose of this teaching experiment is to analyze the impacts of using different teaching methods on the learning effectiveness of quantum mechanics. Questionnaire were used to measure the learning motivation, cognitive load, and system satisfaction of the experimental group students. In addition, the researchers have collected the teacher's opinions after applying virtual reality to teaching, which can be used to improve the virtual teaching modules.  
Figure 6: Virtual electron diffraction experiment.
# Effectiveness of Augmented Reality in Stroke Rehabilitation: A Meta-Analysis
## 3 Results and Discussion
### Potential Implications for Clinicians  
Although AR is still in the developing stage in stroke rehabilitation, AR systems showed they are promising for the improvement of lower limb function and upper limb function in this meta-analysis. In addition, physiotherapists' and patients' experiences were surveyed in some studies. Users preferred a system that provided automatic instructions and feedback. They discovered that the AR technique was useful, motivating, attractive, and valuable for their function recovery [16; 29; 30; 32]. This similar positive feedback was  
Figure 11: The funnel plot for the publication bias of the included investigations on (**a**) the upper limb rehabilitation, (**b**) the lower limb rehabilitation.  
Figure 10: The meta-analysis results of the included investigations in the Time Up and Go Test improvement of the lower limb rehabilitation.  
also found by therapists [30]. To obtain this positive feedback, they can be derived from multiple important factors of AR systems. The first reason is that AR systems employ important factors for promoting neuroplasticity such as repetition, intensity, and task-oriented training in the paretic extremity, which induces improvement in stroke rehabilitation. Moreover, the AR rehabilitation systems provide a sense of realism to the patients during the exercising process [29; 37; 42; 43] based on the range of sensory feedback provided by the AR systems. Various exercises in AR systems are another reason for the positive responses. Here, depending on the patients' needs and actual motor skills, the therapist or patients could select the appropriate therapy modes. Additionally, the training intensity could be automatically modified to suit the patients' motor condition by creating a personal training program in AR systems.  
However, some drawbacks of AR were described, such as the wearing of HMD inducing side effects during and after the intervention such as fatigue, and nausea. This simulator sickness is a critical issue in medical applications. Other challenges consisted of: (1) the patients might find it difficult to perceive depth of view, so they felt it was difficult to reach the desired position in the 3D environment; (2) the AR applications have relied on tracking methods in the prepared environment, meaning that it is almost impossible to apply in unprepared environments. Future research needs to overcome these shortcomings.
# The SSP-Tree: A Method for Distributed Processing of Range Monitoring Queries in Road Networks
## 2 Problem Statement and System Overview
### System Overview
Specifically, EI is traversed down to the leaf node that contains \(q.p\), and \(e\) is identified among the edges stored in this leaf node. * _Edge Table_ (_ET_): ET is a table hashed on the identifier of each edge \(e\). ET stores for \(e\): (i) its endpoints (i.e., \(v_{i}\) and \(v_{j}\)); (ii) its length (i.e., \(dist_{N}(v_{i},v_{j})=dist_{E}(v_{i},v_{j})\)) and (iii) the sets of edges adjacent to each of its endpoints. ET is used to maintain the connectivity information of the road network \(G\). * _Query Table_ (_QT_): QT is a table hashed on the identifier \(qid\) of each query \(q\). QT stores for \(q\): (i) its query point \(q.p\); (ii) its query distance \(q.d\); (iii) a set of its query segments; and (iv) its current result. QT is used to maintain the information of the registered queries. As the intermediary between moving objects and clients in the system, the server performs the following three main tasks. * **Query registration**: When a new query \(q\) is issued (or \(q\) is terminated) by a client, the server inserts \(q\) into (or deletes \(q\) from) QT, updates the SSP-tree (and the additional data structures that will be described in Section 3), and broadcasts messages (e.g., InsertQS, SplitRegion, DeleteQS, and MergeRegion) to all the moving objects to notify them of these changes. Please note that notifying such common information through broadcasting is desirable because the communication overhead is irrelevant to the number of moving objects in a sense that a single message transmission from the server can be received by all the moving objects. * **Region assignment**: When a Request/VR message is arrived from the moving object \(o\) that leaves its current vicinity region, the server searches a new vicinity region by traversing the SSP-tree, after which it sends an Assign/VR message to \(o\) for the purpose of assigning this new vicinity region (together with new query segments) to \(o\). Figure 3: System overview. **Query result update**: When an UpdateResult message is arrived from a moving object \(o\) that may affect the result of a query \(q\), the server checks whether the current result of \(q\) is affected by \(o\). If so, the server updates the result of \(q\). For example, in response to the UpdateResult message sent by the moving object \(o_{2}\) in Figure 3, the server updates the result of \(q_{2}\) (i.e., the server removes \(o_{2}\) from the result of \(q_{2}\)).
# Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review
## 3 Reviewing Relevant Technologies and DT Components
### Technologies Involved in DTs
#### 3.1.2 Artificial Intelligence  
Artificial Intelligence (AI) integration into physical assets that lack it inherently is a key advantage of DTs. This introduces a specialized intelligence capable of efficiently comprehending vast amounts of numerical data and deriving domain-specific insights faster than human experts. Consequently, the data obtained from the digital twin's surroundings and physical counterpart should lead to valuable and actionable conclusions [36]. As an emerging technology, AI empowers computers to exhibit intelligent behaviors, including behavioral learning. By amalgamating various technologies, including AI, to create twin models of physical objects, it becomes feasible to generate virtual representations that closely resemble the original objects, encompassing historical elements and predictive information [37]. As a result, DTs often incorporate controllers that utilize ontologies, ML, and deep learning (DL) techniques. These controllers enable swift and intelligent decision-making on behalf of their real-life counterparts. Moreover, AI significantly processes IoT data through continuously enhanced algorithms that leverage updated user data. Leveraging such time-series data, a user's DT has the potential to suggest actions for controlling or mitigating potentially hazardous situations regarding crop health and productivity, in cases such as droughts to direct asses irrigation efficiency or even failures of safety mechanisms of farm machinery, plant production equipment, post-harvest transportation or storage equipment situations in which DTs may visualize alarm signals to stakeholders or even take immediate actions to limit adverse effects or even the destruction of crop production [29].
## 5 Challenges and Open Issues
Therefore, systems are more vulnerable to attack if any part of a single system from a network is infected in intelligent factories. **Environment**: security is also a critical challenge in WSN [97; 100; 106]. WSN nodes are not secure when deployed in a prone environment due to the wireless transmission of data. An attacker can access them from anywhere in the world and manipulate them easily. Internet attacks can also affect the vulnerability of sensor nodes. **Supply chain management systems**: IoT devices are spreading every day, posing new challenges to the integrity and scalability of supply chain management systems [117; 118]. Simultaneously connecting IoT devices to the cloud or the Internet requires a lot of access control, fault tolerance, data management, privacy, and security. **Limited resources**: are other challenges in WSN domain that affect the energy of sensor nodes used in the network. Sensor nodes usually change their mode from sleep to active and vice versa. Therefore, sleep mode is considered as \"outside the network\" while active mode brings some other issues such as energy consumption [119]. Due to the high energy consumption, they also became dead. Sensor nodes usually have limited power, processing, and memory. In addition, sensor mobility is another problem that hinders the integration of mobile sensor nodes with the Internet.
## 2 Methodology
### Models for Extracting Code Biases  
In this section, we design three time-varying code biases extraction models to compare their effects on satellite clocks, which are (1) extracting an SCBs model (Undifferenced-SCB), (2) extracting an RCBs model (Undifferenced-RCB), (3) simultaneously extracting an SCBs and an RCBs model (Undifferenced-SRCB). For brevity, we assume that except for the code biases to be extracted, the other nuisance terms (such as time-varying phase biases) are driven into code residuals and ignored. Considering the time-varying characteristics of code biases, \(i\) is used to represent the current epoch number. Then, Equation (1) can be written as follows:  
\[\left\{\begin{array}{l}P_{3}(i)=\rho(i)+c\widehat{d}_{r}(i)-c\widehat{d}^{ s}(i)+T(i)+d_{r,3}(i)-d_{3}^{s}\left(i\right)\\ L_{3}(i)=\rho(i)+c\widehat{d}_{r}(i)-c\widehat{d}^{s}(i)+T(i)+\widehat{A}_{3} \end{array}\right. \tag{5}\]  
with:  
\[\begin{split} d\widetilde{t}_{r}(i)&=dt_{r}(i)+\frac{ \delta b_{r,3}(i)}{c}\\ d\overline{t}^{s}(i)&=dt^{s}(i)+\frac{\delta b_{3}^{s}(i)}{c}\\ \widetilde{A}_{3}&=A_{3}+\Delta b_{r,3}-\Delta b_{3}^{s} \end{split} \tag{6}\]
## 4 Comprehensive Performance Evaluation
### Computational Resource Analysis  
To quantitatively demonstrate the computational resource requirements of the two SLAM methods, we simultaneously record the CPU resource allocation during the map creation process. In this study, we run 10 datasets for the nondeterministic nature of the system and show average results for the CPU utilization under various types of tasks and memory usage increasing ratio in Table 3. \"User\" is responsible for time spent by normal processes executing in user mod [28]. \"System\" shows time spent by executing processes in kernel mode. And \"Idle\" is the portion of CPU time during which the processor is inactive. In addition to these three main states, there are other types of CPU usage; therefore, these three types will not add up to 100%. Moreover, \"Memory\" is the temporary storage for active processes. Finally, the increasing ratio is computed by subtracting the normal memory usage occupancy rate from the active occupancy rate and dividing the result by the normal memory usage occupancy rate.  
According to Table 3, we conducted comparisons from three different perspectives and the results are presented in Table 4. Firstly, we compare the CPU utilization statistics of the two SLAM methods indoors and outdoors (Scenario 1). In indoor environments, Visual SLAM occupies 51.25% more CPU compared to LiDAR SLAM, while outdoors, it occupies 35.02% more than LiDAR SLAM. This result confirms that Visual SLAM has higher computational requirements than LiDAR SLAM. The reason behind this lies in the fact that the map constructed by Visual SLAM stores more information.  
Next, we compare each CPU utilization of the two SLAM methods indoors and outdoors with the original CPU utilization without running any programs (Scenario 2). The CPU utilization of Visual SLAM during the execution of indoor and outdoor datasets is 77.80% and 70.67% higher compared to the original usage, respectively. On the other hand, for LiDAR SLAM, it is 17.55% and 26.41% higher than the original usage, respectively. The relatively minor increase in indoor CPU utilization may be due to the simpler structure of indoor walls, resulting in a lower point cloud volume and lower density for mapping.  
Finally, due to the indoor dataset recording time being approximately 350 to 450 s and the outdoor dataset recording time being around 200 s, we compare the memory usage increasing ratios of the two SLAM methods indoors relative to outdoors (Scenario 3). It can be observed that the memory usage increasing ratio of Visual SLAM indoors is approximately twice that of outdoors, consistent with the doubled dataset recording time. However, in LiDAR SLAM, the memory usage increasing ratio outdoors is slightly higher than indoors, possibly due to the larger scanning range and point cloud volume in outdoor environments.