esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Data Cleaning and Labeling Process  
Data cleaning and preprocessing are the most important steps in preparing the dataset for the proposed two-stream CNN-LSTM approach. This process involves identifying and removing noisy data, such as duplicate entries, corrupted files, and samples of incorrect sizes or formats. It enhanced the quality of the dataset and improved the accuracy of model training. Although data cleaning can be time-consuming, often taking 50% to 80% of the total classification process, it remains a crucial component of deep learning algorithms. The data cleaning and labeling process consisted of three stages: annotating tasks, segmenting videos, extracting frames, and reviewing the refined data. This process was performed on approximately 495,000 labeled data samples, which included synchronized video frames and point cloud images. Once cleaned and synchronized, the data were systematically organized for analysis using the two-stream CNN-LSTM architecture. During this process, a standard labeling format was used to ensure consistency in data structure, based on data type, file name, and sensor position, as shown in Figure 4. All data labels were carefully reviewed to guarantee reliability and consistency throughout the dataset.  
Figure 3: Different work types and activities selected for the activity recognition of excavators.  
To synchronize RGB and point cloud data temporally, timestamp metadata were used to match corresponding frames. For each point cloud instance, a synchronized RGB image was selected based on the same acquisition time. The 3D point cloud data were visualized and processed with open-source software, allowing for region segmentation, zooming, viewpoint adjustment, and background customization. The point cloud was exported as an image with a white background and blue points at a resolution of 1920 \(\times\) 1080 pixels. Each point cloud image was paired with exactly RGB frame corresponding to the same timestamp. RGD and point cloud data were labeled simultaneously using the proposed format, which includes metadata such as equipment type, acquisition date and time, sensor type, frame index, site conditions, and activity class. The final labeling scheme, shown in Figure 4, consists of seven components, starting with an alphabet denoting equipment type (e.g., \"E\" for excavator), followed by the acquisition date and time, sensor source (e.g., CE for camera external or PC for point cloud), frame number, and a four-letter code describing the environment and activity. A detailed description of this format is provided in Table 2. This standardized format enables the construction of a structured, extensible database suitable for multi-stream activity recognition across various types of construction equipment and operational scenarios. The proposed format enables the researcher to develop a database of various construction equipment and their associated activities, following a standard data labeling format.  
Figure 4: Cleaning of input data and defining standard data labeling format for the video frames.
# Potential of Homomorphic Encryption for Cloud Computing Use Cases in Manufacturing
## 4 Use Cases for Homomorphic Encryption for Cloud Computing in Manufacturing
### Identification of Use Cases  
To identify use cases, the described findings of homomorphic encryption in manufacturing were combined; thus, potential benefits of cloud computing with the benefits and limitations of homomorphic encryption are considered.  
On the one hand, to identify, cluster, and analyze use cases, the creativity technique used was the morphological box, as it combines creativity, logic, and systematics in one approach [41]. The morphological box consists of four characteristics:  
* _Cloud benefits_: the reasons for using cloud computing as described in Section 2.1.
* _Cloud type:_ the type of cloud as described in Section 2.1.
* _Homomorphic encryption (HE) category:_ the applied homomorphic encryption category as described in Section 2.2.2.
* _Company size:_ the company size and associated characteristics.  
On the other hand, the restrictions that were concluded in Section 3.4 were used as boundary conditions and restrictions for the use case:  
* _Security:_ the use case must have high data security requirements, especially in the usage phase. Otherwise, classic encryption methods could be used.
* _Time:_ the time restrictions of the use case must not be too low, which means that latencies in the minute range must be acceptable.
* _Cost:_ the cost for homomorphic encryption compared to classic encryption are yet higher, despite savings due to lower data transfer. Thus, the implementation of homomorphic encryption should increase the revenue to cope with the increased cost.  
Figure 6 summarizes the above. Thus, to identify the use case, first, the categories of the morphological box were systematically combined (e.g., availability, public cloud, SWHE, and SME). Second, it was checked whether these use cases fulfilled the boundary conditions. For example, a use case with low latency requirements was excluded from the further analysis. In the end, a total of 13 promising use cases was identified, two of which are presented below.
## 2 Background and Related Work
### Related Work
#### 2.2.2 Deep Clustering  
Deep clustering refers to a clustering analysis technique that utilizes deep learning algorithms. Unlike traditional clustering algorithms, deep clustering is particularly adept at handling high-dimensional data. By leveraging the power of deep learning, it can automatically uncover hidden structures and patterns within the data, enabling the discovery of more complex data relationships and ultimately improving the accuracy of clustering results.  
The early deep clustering method [23; 24] used an auto-encoder to learn the representation of the data, and the data representation directly used k-means to obtain the clustering results. Later, the representation learning module of deep clustering added clustering prior, and IDFD [25] improved the performance of clustering by learning the similarity between sample instances and reducing the correlation of features. DeepCluster [26] introduced the features obtained by the feature extraction network into the k-means algorithm to generate pseudo-labels, and then reduced the gap between the classification model prediction results and pseudo-labels to update the network weights. SCAN [27] first uses comparative learning to obtain a feature extractor that can obtain image semantic information, and then uses this semantic feature to cluster, to prevent clustering from relying on low-level features. Experiments show that semantic features have a significant improvement in clustering results. The previous methods separate the representation learning and clustering modules, and perform optimization learning independently. There are also some algorithms that optimize the representation learning module and the clustering module in an end-to-end manner. The representative method is DEC [28]. DEC combines the autoencoder and the self-trainer, initializes the representation learning module through the encoder in the autoencoder, and uses the self-training strategy to optimize the representation learning module and the clustering module simultaneously.  
In summary, deep clustering leverages different network architectures to effectively process diverse types of data. For example, convolutional neural networks (CNNs) excel at handling image data, while recurrent neural networks (RNNs) or transformer models are well-suited for text sequence data. This versatility allows deep clustering to outperform traditional clustering methods when dealing with complex data. By leveraging specialized network structures, deep clustering can capture intricate patterns and dependencies within the data, leading to more accurate and robust clustering results.
# Novel Faulted-Section Location Method for Active Distribution Networks of New-Type Power Systems
## 3 Improved Matrix Algorithm
### Traditional Matrix Algorithm  
Firstly, the principle of the traditional matrix algorithm for the open-loop state is introduced. The number of switch nodes \(S_{j}\) and feeder sections \(L_{i}\) is equal and assumed as \(m_{l}\). The correlation matrix \(D^{\prime}\) is defined as follows:  
\[D^{\prime}=\left[\begin{array}{c}\begin{array}{c}S_{1}\ S_{2}\ \cdots\ S_{m_{l}}\\ \\ \end{array}\\ \end{array}\right]L_{1}\\ L_{2}\\ \vdots\\ L_{m_{l}}\\ \end{array} \tag{2}\]  
\[D^{\prime}_{ij}=\left\{\begin{array}{c}1,\ \text{When section $i$ fail, switch node $j$ sends an alarm}\\ 0,\ \text{When section $i$ fail, switch node $j$ does not send an alarm}\\ \end{array}\right. \tag{3}\]  
The elements of the \(1\times m_{l}\) feeder section status matrix \(L^{\prime}\) are defined as follows:  
\[l_{i}=\left\{\begin{array}{c}1,\ \text{Feeder section $L_{i}$ is faulty}\\ 0,\ \text{Feeder section $L_{i}$ is not faulty}\\ \end{array}\right. \tag{4}\]  
The elements of the \(1\times m_{l}\) fault information matrix \(I^{\prime}\) are defined as follows:  
\[I_{j}=\left\{\begin{array}{c}1,\ \text{Switch node $S_{j}$ sends an alarm}\\ 0,\ \text{Switch node $S_{j}$ doesn't send an alarm}\\ \end{array}\right. \tag{5}\]  
Define the following logical operation. Matrix \(K\) and matrix \(M\) are two matrices of the same dimension:  
\[K=\left|M\right| \tag{6}\]  
\[K_{ij}=\left\{\begin{array}{c}1,\ M_{ij}>0\\ 0,\ M_{ij}\leq 0\\ \end{array}\right. \tag{7}\]  
From the definition of the correlation matrix \(D^{\prime}\), the following relationship can be calculated for radial distribution networks:  
\[I^{\prime}=\left|L^{\prime}D^{\prime}\right| \tag{8}\]  
After simplification, we can obtain the equation for calculating the matrix \(L^{\prime}\):  
\[L^{\prime}=\left|I^{\prime}D^{\prime-1}\right| \tag{9}\]  
The fault section is the section in which the calculation result of \"1\" in matrix \(L^{\prime}\).  
Secondly, the calculation principle for the closed-loop state is introduced. The algorithm adopts the method of separate analysis and superposition judgment. When analyzingone of the power supplies, set the positive direction as the power supply pointing to each node. Open the interface circuit breakers of other power supplies. The results of individual power supply are calculated separately, and the sets of fault sections are \(F_{1}\), \(F_{2}\),..., and \(F_{i}\). The set of fault sections \(F\) of the entire active distribution networks is:  
\[F=F_{1}\cup F_{2}\cup\cdots\cup F_{i} \tag{10}\]
## 1 Introduction  
The dramatic needs in wireless communications service have resulted in higher requirements for more spectrums. Cognitive radio networks (CRNs) aim to effectively address spectrum scarcity and have attracted much attention from the research communities [1; 2; 3; 4]. In spectrum sharing CRNs, the unlicensed secondary users (SUs) can have access to the licensed primary users' (PUs) spectrum in the condition that the interference power at the PU does not surpass a certain value [5]. Not only are CRNs expected to increase spectrum reuse, but they are also taken the low complexity of implementation into consideration. Thus, the underlay scheme has attracted growing attention, where the interference of SUs to the PUs should be guaranteed not to exceed a given threshold.
## 3 Methodology
### Overall Transmission Quality  
All the individual transmission qualities have been computed in (2), (4), and (6) for \(TQ_{path}\), \(TQ_{asym}\) and \(TQ_{HP}\), respectively. To compute the overall \(TQ\) or combined \(TQ\) (as mentioned in open-mesh documentation), the product of all individual transmission qualities is taken, and since each \(TQ\) has a range of 0.255, to normalize the combined \(TQ\) to retain it in the same boundary, the product is then divided by the cube of \(TQ_{\text{max}}\), which has a default value of 255 and is presented below:  
\[TQ=\frac{TQ_{local}\times TQ_{path}\times TQ_{asym}\times TQ_{HP}}{\left(TQ_{ \text{max}}\right)^{3}} \tag{7}\]
### UAVs: Roots and Advancements  
UAVs have undergone significant advancements and have become increasingly prevalent in various fields. This section explores the history and development of UAV technology, highlighting its evolution and the factors contributing to its widespread adoption. The development of UAV technology was driven by the need for improved capabilities in military operations and the emergence of new applications in civilian sectors, such as agriculture, environmental monitoring, and infrastructure management. Additionally, the integration of advanced technologies, such as artificial intelligence (AI), Internet of Things (IoT), and machine learning (ML), further enhanced the capabilities and potential of UAVs.
## 5 MFAM Benchmark Results for the Anisotropic Diffusion Problem  
One of the main arguments against the use of MFAMs is that the numerical errors due to lack of mesh regularity are, potentially, of the same order than those caused by numerical diffusion in a regular Cartesian mesh (see [23]). This section presents a benchmark simulation to compare these errors. The numerical problem presented in Section 2.2 may be generalized to take into account a curved magnetic field (variable alignment, \(\alpha\), of the magnetic field directions with respect to the cartesian coordinates), so that a comparison between meshes is possible.  
Figure 13 shows a comparison of the solutions of a diffusion problem in two different meshes: A regular and a magnetically aligned meshes with the same number of elements. Once again, the main parameter in the simulation is \(\Theta=D_{\parallel}/D_{\perp}=10\) and the MFAM simulation is solved with a WLSQ method with second order reconstruction polynomial and inverse-distance weighting (which was found to produce the lowest GR errors for this particular case). The errors obtained in the MFAM mesh are found to be smaller than those from the Cartesian one.  
Figure 14 shows the average density evolution in the initial low density region (at \(t^{\prime}=0\) in Figure 13, which has been kept as similar as possible between both meshes, accounting for their intrinsic differences) in both meshes and accounts for the difference between the two solutions due to numerical errors. As evidenced by the results, the errors appearing in MFAM are small in comparison to global errors caused by numerical diffusion in the Cartesian mesh and imply that both solutions will diverge for sufficiently large times. Assuming that the simulation for the MFAM is solving the exact diffusion problem, because no numerical diffusion should be present in theory, even if numerical discretization errors are considered, it is demonstrated that, in general, MFAMs offer a solution which is more consistent with the actual evolution problem than the structured mesh for similar mesh refinement. It should be noticed that other discretization errors are still present in MFAMs  
Figure 14: Evolution of average density due to diffusion for the low density region of the domain (marked in blue in Figure 13) at \(t^{\prime}=0\) for simulations performed in Cartesian and MFAM meshes for two different \(\Theta\) values.  
Figure 13: Temporal evolution of the anisotropic diffusion problem solution in Cartesian (**a**) and MFAM (**b**) meshes. Variables are non-dimensionalized.  
as can be appreciated in Figure 13. These errors appear due to the truncation of the order on the reconstruction of the Taylor series, the loss of curvature on magnetic mesh lines and the machine rounding, amongst others. In any case these errors will appear in both Cartesian and Curvilinear meshes and so avoiding a key element of numerical diffusion represents an important asset in the selection of MFAM type meshes.
# State of the Art and Trends Review of _Smart Metering_ in Electricity Grids
## 4 Development of _Smart Metering_ Worldwide
### Asia-Pacific
#### 4.3.4 India  
Although India is one of the fastest-growing economies in the world, its industrial growth has been limited by inadequate energy availability, especially electricity transmission, distribution losses, and a mismatch of supply and demand in electricity [50]. This issue, along with theft, are the major concerns regarding power in India. Consequently, the Indian Ministry of Power is advocating for _SG_ investment to solve those issues and in 2012 unveiled eight _SG_ pilot projects that use a combination of _Smart Metering_ and various technologies to improve the efficiency and reliability of the power system for sustainable growth. Initial steps towards _Smart Metering_ in India have been encouraging, with the market for electricity meters both for static and electromagnetic witnessing a rapid expansion of 32% between 2008-2009 and 2010-2011. Under the India Smart Grid Task Force (ISGTF), an inter-ministerial group initiated by the Ministry of Power, a _Smart Meter_ Task Group was formed to discuss the development of cost-effective metering solutions that can be applied within the Indian context [76]. Some remarkable _Smart Metering_ pilots are the \"Puducherry Smart Grid Project,\" with more than 1400 _SM_s equipped with different technologies; the \"Bangalore Pilot Project,\" which will reach 2000 residential and commercial customers; and the deployment started in 2008 in New Delhi (with 500,000 _SM_s installed in 2011), where _SM_s include automated meter reading and a prepaid system utilizing _PLC_ technology [80]. Industry reports estimate that India will install 130 million _SM_s equipped with both _PLC_ and wireless technologies by 2021 [82].  
However, despite the progress in _Smart Metering_ across utilities at pilot level, there are still infrastructural development and capacity building issues that need to be addressed before a large-scale implementation. A considerable percentage of existing _SM_s are still being read manually; there is an absence of associated infrastructure for meter data analysis and also an insufficient regulatory focus and policy on _Smart Metering_[76].
# A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
## 5 Different VANET Technologies Based on Transmission Range
### Medium-Range Communication Technologies  
#### 5.2.1 Dedicated Short-Range Communication (DSRC)  
DSRC or wireless access in vehicular environment (WAVE) is a standard specifically proposed for reliable communication between vehicles and the network infrastructure. The technology can be deployed in OBUs and RSUs to help the vehicles to communicate effectively and create a wide area network [42]. The WAVE/DSRC is a modified version of the Wi-Fi technology (IEEE 802.11), and its set of standards such as IEEE 802.11p and IEEE 1609.x provides some useful results while applying for vehicular access. The interference between DSRC and Wi-Fi can be avoided by setting the frequency spectrum in DSRC much higher than the Wi-Fi spectrum. The difference between Wi-Fi and DSRC is given in Table 6.  
DSRC is utilized in V2V communication where OBUs of vehicles communicate with each other. It can also be deployed for V2I communication, where vehicles get assistance from the network infrastructure regarding traffic signals, accident alerts, etc. [25; 26]. In  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Standards** & **Bluetooth** & **BLE** & **ZigBee** & **UWB** \\ \hline Specifications & IEEE 802.15.1 & IEEE 802.15.1 & IEEE 802.15.4 & IEEE 802.15.3 \\ \hline Frequency & 2.402–2.481 & 2.402–2.481 & 868/902–968 MHz, 2.4 GHz & 3.1–10.6 GHz \\ \hline Bandwidth & 1 & 2 & 0.3/0.6 MHz, 2 MHz & 500 MHz–7.5 GHz \\ \hline Rate & 1–3 Mbps & 1 Mbps & 20–250 kbps & 480 Mbps \\ \hline Range & 10 m & 50 m & 75–100 m & 75 m \\ \hline Latency (msec) & 100 & 6 & 30 & 0.1 \\ \hline Modulation & GFSK & GFSK & BPSK,O-QPSK & BPSK-QPSK \\ \hline Data Protection & 16-bit CRC & 24-bit CRC & 16-bit CRC & 32-bit CRC \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of short-range technologies.  
addition, the Quality of Service (QoS) of DSRC communication can be improved by using the enhanced distribution channel access (EDCA) protocol [38]. DSRC security threats were classified into three levels: critical, major or minor [158]. Denial of service attack on DSRC was studied by [PERSON] et al. [159] and its potential countermeasures were analysed by [PERSON] et al. [160]. Finally, it was argued that DSRC cannot be the standalone system for ITS and a Het-Net approach would be required by [PERSON] et al. [161].
## 5 Conclusions
Data sharing is not applicable to this article. The authors would like to express their appreciation of the efforts of the journal's editor and the anonymous reviewers whose comments and suggestions have helped to improve the quality of the presented subject matter. The authors declare no conflict of interest. **Nomenclature**  
3D Three-Dimensional  
4G Fourth Generation New Radio Network  
5G Fifth Generation New Radio Network  
5G-R Fifth Generation New Radio Network for Railways  
AC Alternating Current  
AI Artificial Intelligence  
ANN Artificial Neural Network  
ARRT Autonomous Rail Rapid Transit  
CNN Convolution Neural Network  
R-CNN Region-Based Convolution Neural Network  
CO\({}_{2}\) Carbon Dioxide  
DC Direct Current  
DC/DC Direct Current to Direct Current (power converter)  
DER Distributed Energy Resource  
DGs Distributed Generators  
DME Dimethyl Ether  
DOEM Dynamic On-Board Energy Management  
DP Dynamic Programming  
EC European Commission  
ECo External Consumer  
EEE Energy, Economic, and Ecological (indicator)  
EMO Electricity Market Operator  
EMS Energy Management System  
ERA European Railway Agency  
ERTMS European Rail Traffic Management System  
ESO Electrical System Operator  
ESS Energy Storage System  
EU European Union  
FACTS Flexible Alternating Current Transmission Systems  
FBMC Filter Bank Multi-Carrier (modulation technique)  
FFT Fast Fourier Transform  
FLIR Forward-Looking Infra-Red (thermal imaging camera)  
H\({}_{2}\) Hydrogen  
HD High Definition (video camera)  
HESS Hybrid Energy Storage System  
HIL Hardware-in-the-Loop  
HPT Hypercomplex Fourier Transform (model)  
HVDC High-Voltage Direct Current  
IFFT Inverse Fast Fourier Transform  
GHGs Greenhouse Gases  
GS Geographic Information System  
GNSS Global Navigation Satellite System  
GSM Global System for Mobile Communication  
GSM-R Global System for Mobile Communication--Railways  
I2V Infrastructure-to-Vehicle (communication)  
ICT Information and Communication Technologies  
IoT Internet of Things  
IIoT Industrial Internet of Things  
IoV Internet of Vehicles  
IMU Inertial Measurement Unit  
IP Ingress Protection  
ISST Intelligent Sub-Station  
IT Information Technology  
ITC Information and Telecommunications  
ITS Intelligent Transportation System  
LCA Life Cycle Assessment  
LIDAR Light Detection and Ranging
## 1 Introduction
Alternatively, many studies have focused on remote sensing technology [16, 19, 22, 27]. Continuous data from remote sensing technologies have many advantages when compared to general point measurement data. In this study, we used the satellite remote sensing method to improve the understanding of spatio-temporal variations of the WUE and GPP [15, 22, 24]. The National Aeronautics and Space Agency (NASA) and United States Geological Survey (USGS) have provided the Moderate Resolution Imaging Spectroradiometer (MODIS) global GPP and ET products [10, 27, 28, 29, 30]. The main purposes of this study are to (1) quantify the carbon and water circulation in South Korea from 2007 to 2008 using MODIS satellite remote sensing technology, (2) validate the flux tower measurements with the MODIS products including the GPP and ET, and (3) understand the spatio-temporal variability using a distributed map of the GPP, ET, and WUE in South Korea through validation with ground measurements and MODIS products. This will improve our understanding of the primary mechanism for the coupling of the carbon and water cycles, which is of great importance in predicting the effects of climate change on the ecosystem carbon budget and water resources.