esa-sceva/satcom-chunk-collection huggingface dataset:
## Abbreviations  
* 3G Third Generation
* 4G Fourth Generation
* 5G Fifth Generation
* 5G Automotive Association
* AV Autonomous Vehicle
* C2C Car to Car
* C2I Car to Infrastructure
* C2X Car to All/Everything
* CSMA Carrier Sense Multiple Access
* C-V2X Cellular V2X
* D2D Device to Device
* EATA European Automotive and Telecom Alliance
* eV2X Enhanced V2X
* EWM Emergency Warning Messages
* FCC Federal Communications Commission
* FSU Field of View
* HetVNET Heterogeneous Vehicular Network
* I2I Infrastructure-to-infrastructure
* I2P Infrastructure-to-pedestrian
* I2V Infrastructure-to-vehicle
* I2X Infrastructure-to-everything
* ICT Information and Communication Technologies
* ICT Intelligent Connected Transport
* ICT Intelligent Connected Vehicle
* IEEE Institute of Electrical and Electronics Engineers
* IN-V In-vehicle
* ITS Intelligent Transport Systems
* IoT Internet of Things
* IOTA Internet of Things Application
* ITU International Telecommunication Union
* ITU Radiocommunication Sector
* LoRaWAN Long Range WAN
* LOS Line-of-sight
* LOW Power Wide Area Network
* LTE Long Term Evolution
* LTE Machine
* LTE for vehicles
* M2M Machine-to-machine
* MANET Mobile Ad Hoc Network
* MBDMS Multimedia Broadcast Multicast Service
* NBI-IoT Narrow Band IoT
* NDN Name Data Networking
* P2I Pedestrian-to-infrastructure
* P2P Pedestrian-to-pedestrian
* P2V Pedestrian-to-vehicle
* P2X Pedestrian-to-everything
* QoS Quality of Service
* R&D Research and Development
* RFID Radio Frequency Identification
* RSU Roadside UnitSON  
Software-defined Networking  
TaaS  
Transportation as a Service  
TDMA  
Time Division Multiple Access  
V2B  
Vehicle-to-broadband Cloud  
V2C  
Vehicle-to-cloud  
V2D  
Vehicle-to-device  
V2G  
Vehicle-to-grid  
V2H  
Vehicle-to-home  
V2I  
Vehicle-to-infrastructure  
V2N  
Vehicle-to-network  
V2M  
Vehicle-to-motorcycle  
V2P  
Vehicle-to-pedestrian  
V2R  
Vehicle-to-road  
V2S  
Vehicle-to-sensor  
V2V  
Vehicle-to-vehicle  
V2X  
Vehicle-to-everything  
VANET  
Vehicular Ad Hoc Network  
VFC  
Vehicular Fog Computing  
VLC  
Visible Light Communication  
VSN  
Vehicular Sensor Network  
WAVE  
Wireless Access in Vehicular Environments  
Wi-Fi  
Wireless Fidelity  
WSN  
Wireless Sensor Network  
WiMAX  
Worldwide Interoperability for Microwave Access
# Task-Oriented Motion Planning of a Space Manipulator under Multiple Constraints
## 3 General Idea of the Research  
The structure of the space manipulator is shown in Figure 1. The joints are numbered as \(1-n\) successively and indicated as \(J_{i}\) (\(i=1,\,2,\dots,\,n\)). \(\sum_{i}\) denotes the coordinate system of \(J_{i}\), and \(\sum_{i}\) denotes the inertial coordinate system.  
The control system of the space manipulator is divided into three levels: task planning, path planning and motion control. As the highest level of the manipulator control system, task planning is responsible for the reception, analysis and disassembly of task objectives and divides complex task objectives into a series of action sequences that the manipulator can directly plan and execute. Due to the diversity of ways for the manipulator to complete a task, task planning also involves the scheduling of various resources of the manipulator system, optimizes resource consumption in the whole task process and finally provides the planning results in the form of task intermediate point and resource allocation in each stage.  
The task planning of the manipulator is divided into two stages that complete the planning process: first, accept the task objectives and decompose the complex tasks into simple original task combinations based on the working ability of the manipulator, to ensure the logical feasibility of the tasks; then, considering various constraints and optimization indicators, decompose each original task into multiple simple paths to ensure the feasibility of task execution. As shown in Figure 2, task planning is divided into two levels.  
Figure 1: The space manipulator structure.  
The first layer is responsible for task profile analysis. Logically, complex tasks are disassembled layer by layer to obtain the original task sequence that cannot be further subdivided.  
The second layer is responsible for the selection of task intermediate points. Under the condition of optimizing and considering various available resources, iteratively search the task intermediate points for each original task in the original task sequence to obtain the number and coordinates of task intermediate points under constraints and with optimal resources.
## 3 DAS Field Site Deployment  
In 2019, our team conducted an experiment at Carbon Management Canada (CMC), Containment and Monitoring Institute Field Research Station (CaMI FRS), near Brooks, Alberta, Canada (Figure 2). The CaMI site had three primary wells, including one CO\({}_{2}\) injection well and two adjacent monitor wells (Wells 1 and 2), all completed at a depth of 350 m [35; 36; 37]. One of these observation wells had steel casing and had an integrated DAS and DTS fiber pack, pressure gauges, and a fluid-sampling U-tube mounted on the outside of the casing. The second observation well had fiberglass casing with 3C geophones, as well as DAS and DTS fiber and electrodes cemented behind the casing. The surface area and wells at CaMI were heavily instrumented with many different types of modern geophysical instrumentation, including Fiber Optics, Geophones (surface and borehole) and Gauges. In the two instrumented wells, #1 and #2, pre-existing fiber optic cables were installed behind fiberglass casing and cemented in place. This experiment was conducted to evaluate and study the effectiveness of DAS in monitoring CO\({}_{2}\) injection and subsurface changes in these wells.
# GenConViT: Deepfake Video Detection Using Generative Convolutional Vision Transformer
## 1 Introduction
Our proposed architecture leverages both visual artifacts and the latent data distribution of the data in its detection process. GenConViT has two main components: a generative part and a feature extraction part. The generative part utilizes two different models (an Autoencoder (AE) [33] and VAE), to learn the latent data distribution of the training data. The feature extraction component employs ConvNeXt [34] and the Swin Transformer [35] to extract relevant visual features. Our proposed model addresses the limitations of current detection methods by identifying both the visual artifacts and the latent data distribution, making it capable of detecting a wider range of fake videos. Extensive experimental results demonstrate that GenConViT produces competitive results compared to other state-of-the-art deepfake detection models. In summary, our paper makes the following contributions to this area of research:  
* GenConViT: We propose the Generative Convolutional Vision Transformer for deepfake video detection. Our method utilizes an AE and VAE to generate images, extracts the visual features of the original and generated image using ConvNeXt and the Swin Transformer, and then uses the extracted features to detect whether a video is a deepfake video. * By training our model on more than 1 million images extracted from five large deepfake datasets and detecting both their visual and latent features, our proposed method aims to enhance its deepfake detection performance compared to that of previously proposed methods. * We thoroughly evaluated GenConViT on more than 3972 videos collected from five deepfake video datasets, demonstrating its robust performance under diverse experimental settings. * We have open-sourced our code and weights at [[https://github.com/erprogs/GenConViT](https://github.com/erprogs/GenConViT)]([https://github.com/erprogs/GenConViT](https://github.com/erprogs/GenConViT)) (accessed on 9 June 2025). * While our approach achieves a strong performance, our comprehensive ablation study (a leave-one-dataset-out study) indicates a potential limitation in the generalizability of deepfake detection methods, particularly when faced with out-of-distribution data. This is an area for future work. The remainder of this article is structured as follows: An overview of existing studies is presented in Section 2. The proposed Generative Convolutional Vision Transformer (GenConViT) deepfake detection framework and the datasets used are detailed in Section 3. Experiments are discussed in Section 4, and an ablation study is performed in Section 4.4.2. Finally, the conclusions are drawn in Section 6.
## 1 Introduction
Most of them are described in the workbook [19]. Various full-frequency modeling methods may be chosen. Figure 1: Simulation and validation processes. The blue rectangles represent steps where various models can be chosen. The “Diffuse Acoustic Field” rectangle is colored black because it corresponds to a unique analytical definition. The red rectangles represent comparison with the experiment steps. Certain wave-based methods, such as the variational theory of complex rays [20] proposed a unique full-frequency framework. Industrial studies often prefer a combination of a FE model with a SEA model and may use a hybrid FE-SEA model [21] for mid-frequencies. An example of such full-frequency modeling of an aircraft fuselage is described in [22]. In this article, a FEM-BEM model and a SEA model of a ballistic re-entry aeroshell are developed. The final aim of such modeling is the computation of in-flight vibrations, which is the first process shown in Figure 1. The simulation results depend both on the modeling of the structure and the modeling of the in-flight pressure fluctuations. In this article, a process for validating the structure model alone is deployed--the third process shown in Figure 1.  
The ground experiment chosen is diffuse acoustic testing. As well as turbulent boundary layer noise, diffuse acoustic noise is a dynamic pressure load correlated in space and frequency. Using a dedicated test facility, this load can be precisely controlled. Such experiments are widely used for the modeling of plates and panels [23; 24]. Fewer test-simulation comparisons have been published on industrial structures (see [25] for example). The vibroacoustic response of a hypersonic aircraft external shape to a diffuse acoustic field has recently been studied by Pr. [PERSON]'s team. A FEM-BEM model [26], a SEA model [27] and a hybrid FE-SEA model [28] of the structure have been developed. Simulations have been compared with diffuse acoustic testing experiments. The approach deployed here uses diffuse field experimentation as a validation tool. The study focuses on the understanding, modeling and simulation of vibroacoustic phenomena. The aim is to simulate and interpret resonances in the low-frequency range on the one hand, and to simulate average responses at higher frequencies on the other. The experimental setup (test structure, diffuse acoustic testing facility and measurement tools) is described in the first section of this article. The second and third sections describe the model and the results obtained by FEM-BEM and SEA, respectively. Finally, the results obtained with the two models are combined to perform a full-frequency test-simulation comparison.
## 3 Study Data  
Daily meteorological data from 1999 to 2008 were collected from the national network of weather stations managed by UCEA ([[http://www.ucea.it](http://www.ucea.it)]([http://www.ucea.it](http://www.ucea.it))). In particular, monthly average minimum and maximum temperatures and total precipitation were collected from about 90 stations spread all over the national territory. These data were interpolated following the method described by [PERSON] _et al._[15], which provided digital maps of mean monthly temperatures and rainfall having a pixel size of 1 km\({}^{2}\).  
A digital forest map at the same resolution was derived from the original CORINE Land Cover 2,000 map of Italy [16]. This map was produced by manual photointerpretation of Landsat imagery supported by ancillary information [17]. The map classifies forests and other wooded land in 13 types on the basis of the prevalent species, maintaining the geometric and thematic congruency with the original dataset [18]. In the present work the five forest types (FT) which are most widespread over the Italian territory were selected: Norway spruce/white fir (FT 1), chestnut (FT 2), beech (FT 4), Holm oak (FT 7) and deciduous oak (FT 8). The main features of these five forest types are summarised in Table 1, whilst their spatial distribution is shown in Figure 1.  
NDVI images taken by the SPOT Vegetation (VGT) sensor were downloaded from the archive of VITO ([[http://free.vgt.vito.be](http://free.vgt.vito.be)]([http://free.vgt.vito.be](http://free.vgt.vito.be))), which freely distributes preprocessed ten-day Maximum Value Composite (MVC) images for the entire globe since April, 1998. The applied preprocessing steps comprise the radiometric calibration of the original channels and their geometric and atmospheric corrections [19]. The final product of these steps were 10-day NDVI MVC images having a pixel size of 1 km\({}^{2}\). These images were acquired from 1999 to 2008, a ten-year period which should be sufficient to depict the mean environmental situ
# The Power System and Microgrid Protection--A Review
## 3 Protection Scheme of Microgrid
### Modifications of Scheme  
Embedded various distributed resources in traditional distribution networks cause problems in the protection system. There are several simple solutions to improve the conventional structure of distribution networks such as changing relay settings and using DOC relays.
# A Novel Hierarchical Adaptive Feature Fusion Method for Meta-Learning
## 4 Experimental Results
### Ablation Study  
#### 4.4.1 Comparison of Different Feature Fusion Methods  
Generally speaking, a better feature fusion method can achieve better results. In this work, in order to further verify that the proposed method is state-of-the-art, comparative experiments are carried out on multiple feature fusion methods. The feature fusion methods chosen by the study include: directly fusing the output features by each stage of the embedding model according to the same size of channels, without flattening the output features; performing addition operations on the features output by stage 1 and stage 2 and inputting them into stage 3, in a similar way to [24]. The experimental results in Tables 4 and 5 show that the strategy of feature fusion according to the size of channels and the strategy of summing the output features of different stages and feeding them to other stages are inferior to the adaptive feature fusion method.  
Figure 8: The relationship between the accuracy and the generation in meta-testing on CIFAR-FS and FC100. (**a**) represents the relationship between meta-learning accuracy and the generation on CIFAR-FS. (**b**) represents the relationship between meta-learning accuracy and the generation on FC100. The 0-th generation represents the \(n\) groups of feature fusion weights randomly generated during population initialization.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Backbone**} & \multicolumn{2}{c}{**MUB 5-Way**} \\ \cline{3-4}  & & **1-Shot** & **5-Shot** \\ \hline Ours-simple & MobileNet-V2 & 39.57 \(\pm\) 2.42 & 52.92 \(\pm\) 2.19 \\ Ours-fuse & MobileNet-V2 & **49.48 \(\pm\) 2.05** & **68.25 \(\pm\) 2.03** \\ \hline \hline \end{tabular}
\end{table}
Table 3: We conduct experiments on the MUB dataset. Average meta-learning classification accuracies (%) with 95% confidence intervals on MUB meta-test splits.
## 4 Experiments and Results
### Evaluation  
To evaluate performance of the networks, we use recall, precision, and F-score (F1 score). These values are computed as:  
\[Recall=\frac{TP_{p}}{TP_{p}+FN_{p}} \tag{5}\]  
\[Precision=\frac{TP_{p}}{TP_{p}+FP_{p}} \tag{6}\]  
\[F1\ Score=\frac{2\cdot Precision\cdot Recall}{Precision+Recall} \tag{7}\]  
Recall means the portion of the cases that the defect detector indicates as the defects among actual defects, while precision means the portion of the cases that are the actual defects among the cases that the detector indicated as defect. Table 1 shows the comparison between the performance of the U-Net and the performance of our proposed system.  
Figure 3: Samples of the generated synthetic defects in our experiment: (**a**) non-defect images, (**b**) generated synthetic defect images, and (**c**) defect applied images.
## 3 Results
### Terpenoid Biosynthesis Pathway Analysis  
In the development of Caoyuanheimo-1, with some key periods including the primorudin, young fruiting body, and ripe fruiting body periods, we detected some genes and pathways of the terpenoids or terpenoid compounds. During the analysis of the metabolomic data, a total of 17 terpenoids were identified, including 5 terpenoid glycosides(tsangane L 3-glucose, foeniculoside VII, kenposide B, perilloside C, and (IS,2R,4R)-p-menth-8-ene-2,10-diol 2-glucose); 3 monotereponoids (linalyl benzoate, (R)-2-acetoxy-p-mentha-1,8-diene, and crispane); 2 diterpenoids (6,10,14-trimethyl-5,9,13-pentadecatrien-2-one and sclareol); 1 sesquiterpenoid (8, 12-epoxy-4(15), 7, 11-eudesmatrien-1-one); 1 seasterterpenoid (25-cinnamoyl-vulgaroside); 2 terpenoid lactones (eremopetasitenin C2 and lactupicrin); and 3 triterpenoids (ganoderiol H, lucidenic acid A, and lucidenic acid N). The content of these substances gradually accumulates during growth and development (Figure 5a).  
Figure 5: An analysis of the key genes in the synthesis pathway of terpenoids and the content of terpenoids during growth and development. (**a**) The bar chart represents the content of terpenoid metabolites and substrates at three different developmental stages; HY represents the primordium period of Caoyuanheimo-1; HZ represents the young fruiting period of Caoyuanheimo-1; and HC represents the rip fruiting period of Caoyuanheimo-1. (**b**) The heatmap represents the expression levels of key genes in the terpenoid biosynthesis pathway in the HY, HZ, and HC groups. The scale of the heatmap represents the FPKM of genes.  
In addition, we found that two differential metabolites were associated with the biosynthesis of terpenoids: pyruvate acid and methylglutaric acid. Moreover, the content of both was at a relatively low level during the HY period and began to increase significantly during the HZ period, reaching its highest value during the HC period (Figure 5a). The results indicate that the precursor content of some terpenoid compounds increases sharply in the HZ and HC periods of development. We also analyzed the transcription levels of various genes in the terpenoid synthesis pathway at different periods and found that the transcription levels of some key genes also underwent significant changes. For example, the transcription level of the DXS (1-deoxy-D-xylulose-5-phase synthesis) gene significantly increased during the HZ and HC periods. The transcription levels of the GGPPS (ger-arylgeranyl diphosphate synthesis) gene and GPPS (geranyl diphosphate synthesis) gene significantly increased during the HZ phase and decreased to the original level during the HC period (Figure 5b).
# Optimization of Coverage and Capacity Using Smart Antennae
## 3 Methodology
### Algorithms  
The calculation mechanism for adjusting the on and off functions of the intelligent directional antenna of the base station and for determining the transmission power value can now be presented as Algorithms 1 and 2, as follows:
# WFO: Cloud-Edge Cooperative Data Offloading Strategy Akin to Water Flow
###### Abstract  
The exponential growth of video data in networks has led to video flow occupying a significant proportion of network traffic, causing congestion and poor service quality. To address this issue, it is crucial to quickly offload data and ensure high-quality service for users, especially in the context of cloud-edge collaboration. We propose a strategy for collaborative data offloading between cloud and edge computing, analogous to water flow (WFO). When users simultaneously access the same data from the same data source, WFO can serve more users within the limited bandwidth of the cloud while maintaining the quality of service. WFO creates a water flow-like data link between nodes to enable data offloading, using multiple nodes in collaboration to offload data for a single node. Experimental results show that compared with typical methods, such as fair-queue and first-come-first-served, WFO can significantly reduce the data offloading delay, guarantee service quality, and effectively reduce network congestion. Moreover, the number of service nodes can be as numerous as possible.  
cloud computing; cloud-edge cooperative; data offloading; FQ; FCFS
## 4 Experimental Results and Analysis
### Discussion  
The foreign object identification and detection technique for conveyor belts presented in this paper, utilizing the improved YOLOv8, has yielded notable detection outcomes in both the evaluated dataset and laboratory settings. Although its recognition speed and accuracy are superior to most current classic algorithms, there are still some areas that can be improved.  
From the detection results in Figures 11g-i and 13g-i, it can be observed that although the network model underwent motion blur preprocessing on the non-coal foreign object data before training, the resistance to motion blur in the improved network model is not ideal. In other words, the detection results are still affected by image clarity, and as the degree of image blur increases, the detection accuracy decreases. Therefore, it is imperative to fine-tune certain parameters of the acquisition apparatus; examples include decreasing exposure duration and adjusting the installation angle, focal length, and height of the acquisition apparatus to expand the perspective, particularly along the conveyor belt's length. This can maximize the completeness and clarity of the acquired image data.  
In addition, noise interference is another important factor affecting image detection results. Excessive noise data can interfere with the network model's accurate judgments during recognition. As shown in Figure 16, we tested the model's resistance to noise interference on three different non-coal foreign object datasets. As the level of noise increases, there is a certain degree of loss in the model's recognition accuracy. In the industrial field, erroneous or delayed judgments can pose certain safety risks to the work site. Therefore, it is necessary to apply appropriate image-denoising techniques to the raw data during on-site debugging.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Parameters (M)** & **FPS** & **Inference (ms)** & **Hardware** \\ \hline Improved YOLOv3 [(16)] & 61.6 & - & - & GTX 2080 Ti \\ Improved YOLOv4 [(18)] & 6.50 & 70.1 & - & GTX 1650 \\ Improved YOLOv5 [(19)] & 54.6 & 59.9 & - & RTX A4000 \\ Improved YOLOv7 [(21)] & 37.1 & 25.6 & 25.0 & RTX 3080 \\ YOLOv8n & 3.10 & 72.1 & 12.1 & RTX 3080 \\ \hline FasterNet-YOLOv8 & 2.91 & 65.1 & 5.1 & \\ EfficientNet-YOLOv8 & 1.90 & 60.4 & 4.9 & \\ GhostNetv2-YOLOv8 & 3.78 & 40.4 & 20.7 & \\ PP-ICNet-YOLOv8 & 1.72 & 73.2 & 4.9 & \\ MobileNext-YOLOv8 & 2.05 & 20.8 & 81.5 & \\ Mobilenet-YOLOv8 & 2.35 & 75.1 & 10.2 & \\ \hline Our improved methods & 1.60 & 81.5 & 4.5 & RTX 3080 \\  & 1.60 & 75.3 & 5.6 & GTX 2080 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Inference speed and hardware condition results for different models.