esa-sceva/satcom-chunk-collection huggingface dataset:
In this paper, we propose an anomaly detection model called Bi-directional Long Short-Term Memory with Working Memory Connections-Autoencoder (Bi-LSTM-WMCs-AE). It applies Bi-LSTM with WMCs to an AE network. While the Bi-LSTM with the WMCs network effectively learns the time-related information inherent in the multivariate timeseries dataset, the AE part acquires the ability to reconstruct normal data by learning from normal data. The difference between the input data and the output data, known as the reconstruction error, is used for anomaly detection. The main contributions of our study are as follows:  
1. We propose a novel anomaly detection model for multivariate time series data. Building on the widely adopted Bi-LSTM-AE framework, we introduce a variant of LSTM, referred to as Working Memory Connections for LSTM, to replace the traditional LSTM units. This variant enables the memory cell to influence the gate values, thereby facilitating more effective processing of time series data. To the best of our knowledge, our study is the first to apply this novel LSTM variant to the field of anomaly detection. 2. Our model leverages a reconstruction-based framework, making it inherently suited for semi-supervised learning. Unlike approaches that rely on extensive labeled anomaly data, our method requires only normal data for training, significantly reducing the dependency on scarce and resource-intensive expert-labeled anomaly datasets. This advantage addresses a critical challenge in real-world applications, where labeled anomaly data are often limited or unavailable. 3. We evaluated the performance of our model on diverse datasets that include both continuous sensor data and mixed data containing sensor and actuator reports. Our model demonstrated consistently strong performance across these scenarios, highlighting its robustness and scalability in handling various types of multivariate time series data. The rest of this paper unfolds as follows. Section 2 discusses the related work. Section 3 describes the proposed model for multivariate time series anomaly detection. Section 4 presents a case study using the proposed model. Section 5 consists of our concluding remarks.
## 3 5G HetNet Services, Architecture, and Related Technologies
### 5G Technologies
#### Beamforming  
In WCS, beamforming is a technique that directs the transmission of a signal in a specific direction instead of spreading it equally in all directions. Beamforming is an essential component of 5G technology, enabling 5G networks to deliver high-speed, low-latency, and reliable communication services. It is crucial to realize the full potential of 5 Gby enhancing the coverage, capacity, and quality of the wireless signal. The beamforming technique allows 5G networks to focus the energy of the signal toward the user device, providing a more robust and stable connection. The beamforming technology improves the signal-to-noise ratio (SNR). It reduces the impact of interference, which is especially important in dense urban areas where multiple devices are trying to access the network. One of the benefits of beamforming in 5G is its ability to dynamically adjust the beam direction in real-time to adapt to changing environments, such as user device mobility. This feature helps to ensure a reliable and steady connection, even when the user device is on the move [63; 64; 65; 66; 67].  
There are two main types of beamforming utilized in 5G UD-HetNet: analog beamforming and digital beamforming. Due to the use of multi-RATs, the 5G system utilizes both analog and digital beamforming approaches to enhance the system's throughput and reliability.  
In addition, analog beamforming uses amplifiers and phase shifters to adjust the signal phase and amplitude at each antenna element to achieve the desired shape of the beam. This approach is well-suited for mm-Wave frequencies, where high-gain antennas are required to compensate for the high signal loss. Digital beamforming, on the other hand, uses mathematical algorithms to process the signal in the baseband processing unit. This approach provides flexibility in the frequency spectrum, as the beam direction can be changed dynamically based on the changing environment. Digital beamforming is well-suited for sub-6 GHz frequencies, where the frequency spectrum gain is lower but more processing power is available [68].
## 5 Case Study
### Simulation Parameters  
Assume that grid dispatching department will purchase DR resource from community operators during 18:00-21:00, and each scheduling cycle is 15 min. Accordingly, there are 12 cycles with \(\mathcal{T}=[1,2\cdots 12]\). Additionally, DR resource's grades are divided into 3 kinds: grade 1 for \(\gamma_{1}^{\text{min}}=0\) and \(\gamma_{1}^{\text{max}}=3\%\); grade 2 for \(\gamma_{2}^{\text{min}}=3\%\) and \(\gamma_{2}^{\text{max}}=8\%\); grade 3 for \(\gamma_{3}^{\text{min}}=8\%\) and \(\gamma_{3}^{\text{max}}=13\%\)[32]. The price parameters broadcasted by grid dispatching department to the market is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**DR Resource** & **18:00–19:00 (\(t=1\)-4)** & **19:00–20:00 (\(t=5\)-8)** & **20:00–21:00 (\(t=9\)-12)** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Grade 1 \\ \end{tabular} } & \(a=-0.01\) & \(a=-0.03\) & \(a=-0.01\) \\  & \(b=1.44\) & \(b=1.8\) & \(b=1.44\) \\  & \(q=1.8\) & \(q=2.2\) & \(q=1.8\) \\  & \(c_{\text{ele}}=0.3\) & \(c_{\text{ele}}=0.5\) & \(c_{\text{ele}}=0.3\) \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Grade 2 \\ \end{tabular} } & \(a=-0.02\) & \(a=-0.04\) & \(a=-0.02\) \\  & \(b=1.2\) & \(b=1.5\) & \(b=1.2\) \\  & \(q=1.8\) & \(q=2.2\) & \(q=1.8\) \\  & \(c_{\text{ele}}=0.3\) & \(c_{\text{ele}}=0.5\) & \(c_{\text{ele}}=0.3\) \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Grade 3 \\ \end{tabular} } & \(a=-0.03\) & \(a=-0.05\) & \(a=-0.03\) \\  & \(b=0.86\) & \(b=1.08\) & \(b=0.86\) \\  & \(q=1.8\) & \(q=2.2\) & \(q=1.8\) \\  & \(c_{\text{ele}}=0.3\) & \(c_{\text{ele}}=0.5\) & \(c_{\text{ele}}=0.3\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Price parameters for different grades of DR resource.  
We assume that, in \(\mathcal{T}=[1,2\cdots 12]\), there are 3 community operators participating in the bidding mechanism for DR market share. Considering the protection of players' private information, DR purchaser only broadcasts the overall situation of players, such as community operators' types and probability distribution. Here, suppose that the proposed DR project is implemented in initial stage, and there exists 3 possible types in 3 community operators: type 1 is that community operator equips gas boiler; type 2 is that community operator equips energy storage; type 3 is that community operator only has DR resource. And, the probability of community operator being each type is equal to 1/3 [33]. Actually, for each community operator, it knows its own type but doe not know its opponents' types. Assume that the actual type of community operator 1 is type 1, community operator 2 is type 2, and community operator 3 is type 3. As for the randomness of users' daily energy consumption, assume that \(u=0\), \(\sigma=0.15x_{n}^{t}\). Additionally, the maximal discharging energy in one time slot is 0.4 MWh, and the maximal percentage of heat energy demand in total energy demand varies from 20%-60% during 18:00-21:00. DR resource of 3 community operators in each time slot is shown in Figure 2.
# Research on Urban Traffic Incident Detection Based on Vehicle Cameras
## 3 Data and Methods  
### Dataset Production  
There is no applicable dataset for traffic event target detection, and the current open-source image samples are generally vehicle and pedestrian data sets such as KITTI data sets, which make it difficult to meet the needs of traffic incident detection. Therefore, the data set is established by acquiring pictures by supplementing the network picture crawler, road shooting, KITTI data set [32; 33], and KMU Fire & Smoke Database [34; 35; 36; 37; 38] data set. The KITTI dataset provides real-world picture data taken in urban, rural, and highway environments, with up to 15 cars and 30 pedestrians in each image with varying degrees of occlusion and truncation. The KMU Fire & Smoke Database dataset contains four categories of data such as indoor and outdoor (short-range) flames, indoor and outdoor (short-range smoke), wildfire smoke, and smoke or flaming moving objects. In addition, we use search engines in different countries to crawl traffic accident pictures and select some pictures from the above datasets to form our dataset. Sorting and screening invalid pictures and pictures that are difficult to label, a total of 8890 valid pictures were screened. After labeling with the labeling software, it counts the labeled image labels. The following Figure 1 shows an example of different categories of objects in the dataset.  
In order to achieve better practical detection of convolutional neural networks, a data augmentation method is used to make the network label categories more balanced. Based on the distribution of the different types of existing labels, we target categories with a low number of labels (e.g., trucks, traffic accidents) and add images containing such labels. In the process of adding samples, we selectively added images in rain and fog scenes, as shown in Figure 2. The statistics of each type of label after enhancement are shown in Figure 3.  
Figure 1: The figure shows examples of data sets of cars, pedestrians, traffic accidents and fires.  
Figure 2: Example of a rainy weather image added by sample enhancement.
# Virtual Reality for Spatial Planning and Emergency Situations: Challenges and Solution Directions
## 1 Introduction
Despite existing works demonstrating the effectiveness of VR, there is a lack of guidance on how to integrate VR and SCDL specifically for emergency situations. Thus, the objective of this research is to perform a review of the existing uses of VR for spatial planning and emergency situations and collate the findings to provide guidance for researchers on best practices for developing VR applications for emergency situations. This will be achieved by addressing the following research questions:  
1. What are the challenges and solution directions of VR applications for emergency situations in buildings? 2. What roadmap/recommendations can be derived from the scientific literature when designing and developing a VR application for the evaluation of emergency routes? To address these questions, this study involved a literature review of related survey articles on the use of VR for spatial planning and emergency situations followed by a domain analysis by means of a keyword search in the Scopus digital library, whereby challenges and solution directions were identified through the literature analysis. As such, the remainder of this paper is as follows. Section 2 provides a background investigation into related work, highlighting key differences to this study. Section 3 documents the methodology adopted for the review. The findings are presented in Section 4, a discussion is presented in Section 5 and the paper is concluded in Section 6.
# Holographic Mixed Reality System for Air Traffic Control and Management
## 5 Design and Development Process
### Conversion of WGS-84 Geodetic Locations (GPS Values) to Cartesian Coordinates  
The radar system gave the data about the location of the airplanes in the real world according to the World Geodetic System (GPS values). However, the Unity 3D engine used for the development of the mixed reality world followed a Cartesian coordinates system. Hence, for mapping the air traffic in the mixed reality world, the Cartesian coordinates were required in the form of x, y and z coordinates. Therefore, in order to find the Cartesian coordinates for the positions of the airplanes, it was required to convert the geodetic coordinates to the local tangent plane given by the radar system [23]. The basis for the implementation of the conversion between the values of coordinate systems was a book by [PERSON] and [PERSON] [24]. The Cartesian coordinates in terms of x, y and z were derived from the GPS information by using the system of equations described below.  
\[\mathrm{x=}(\mathrm{h+M})\mathrm{cos}\,\lambda\ \mathrm{cos}\,\varphi \tag{2}\] \[\mathrm{y=}(\mathrm{h+M})\mathrm{cos}\,\lambda\ \mathrm{sin}\,\varphi\] (3) \[\mathrm{z=}\Big{(}\mathrm{h+}\Big{(}1-\mathrm{e}^{2}\Big{)} \mathrm{M}\Big{)}\mathrm{sin}\,\lambda \tag{4}\]  
where in Equations (2)-(4), the height is denoted by h, latitude by \(\lambda\) and longitude by \(\varphi\), while M and e were calculated by using the equation below:  
\[\mathrm{e=f(2-f)} \tag{5}\]  
where ellipsoid flatness is denoted by f and calculated using Equation (6).  
\[\mathrm{f=}\frac{(\mathrm{a-b})}{\mathrm{a}} \tag{6}\]  
For calculating the ellipsoid flatness, the WGS-84 Earth semi-major axis was required, which is denoted as a, and the derived Earth's semi-minor axis, denoted as b in Equation (6). The value of M used in Equations (2)-(4), can be calculated by using the e in Equation (7).  
\[\mathrm{M=}\frac{\mathrm{a}}{\sqrt{1-\mathrm{e(s^{2})}}} \tag{7}\]  
The value of s can be calculated as follows:  
\[\mathrm{s=sin}\,\lambda \tag{8}\]  
Figure 9: The arrow points representing the distance between the control tower and the corner of the map.  
Therefore, the Cartesian coordinates for an airport's control tower and airplane locations were calculated by substituting their GPS locations in Equations (2)-(4).
## 3 Proposed System and Architecture
* _Service Agent_: This agent has the functionality of arranging and distributing the system's functionality through web services. Its role could be considered as a Gateway style; it allows for the communication of external services with the virtual organization of agents. This allows agents to be easily integrated and built in any programming language. * _Manager Agent_: This agent is important within the whole system, as it is in charge of checking the status of the system periodically. It detects a load in a part of the system, any overloaded functionality, or possible failures in agents from different organizations. * _Organization Agent_: This agent is responsible for verifying all operations of virtual organizations, checking security and load balancing, and offering encryption for communication between agents. Within the system, it is worth noting that two databases with well-defined functionalities are used. The PANGEA database is only used by the PANGEA virtual organization and is intended to store the information on the organizations and the services available to each of their agents. The APP Database Storage contains information specific to the use case. In this particular case, patient profiles, information on the experts responsible for the rehabilitation, exercises to be performed, exercise and monitoring histories, alerts, and incidents are stored. The proposed architecture indicates the existence of external agents. These represent the two available roles within the application that have direct interaction with it. The first user who makes use of the application is the patient who is the user of the application carrying out the rehabilitation task. The second user of the application is the expert or doctor who is in charge of setting up the user profile, proposing the exercises, checking the progress of the recovery that must be validated manually by an expert. The modules and agents of architecture are specialized in a specific objective or task. The advantage of this architecture is that it allows the replacement of either one agent or a set of agents that have similar characteristics without affecting the rest of the system. Anexample of this would be the use of a different motion capture suit. To do so, we would only have to replace the Node Position agents that allow them to communicate with the suit to be incorporated.
# Information Fusion in Autonomous Vehicle Using Artificial Neural Group Key Synchronization
## 3 Proposed Methodology
### Assessment of Security
#### 3.3.2 The Consistency of Information Content  
The data content integrity is achieved by generating a hash, that is always sent with the data to identify any change at the recipient's end. SHA-2 functions, which generally include SHA-256 and SHA-512 functions, are the most commonly used Hash functions. The TUAK method set [40], a more newly formed second method set for validation and key formation, will be used in V2X transmissions, according to the most current ESTI publication.
## 3 Results
### A. Results with the 3-DOF Simulated Model without Reduction Ratio  
Figure 4A illustrates a typical learning curve obtained with the simulated model from Figure 3A by using both methods. One can observe that the INN method requires 100 trajectories, while the GNN method needs 250 trajectories of 1 s each to maximize their accuracy.  
The learning curves for motors 1 and 2 highlight the differences in tracking accuracy between the two studied architectures when more than 100 trajectories are available. The INN method achieves an RMSE of 0.2 radians, whereas the GNN method achieves an RMSE of less than 0.04 radians--or 2.3 degrees. Conversely, the learning curve for the third motor shows an RMSE of 0.016 radians--less than 1 degree--for both methods, representing a 91% reduction in tracking errors compared to the PD controller without feedforward compensation.
## 3 Results and Discussion
### High-Resolution Transmission Electron Microscopy (HRTEM)  
The HRTEM images of AgNPs synthesized using latex extracts of _T. ventricosa_ are displayed in Figure 4A, whereas the particle size distribution is shown in Figure 4B. The morphological distribution of the AgNPs highlighted in Figure 4A reveals the absence of agglomeration, which is indicative that the synthesized AgNPs had good stability and were well dispersed (monodispersed) in the solution [35, 44, 45]. The thin matrix layer observed around the NPs is most likely due to the presence of biomolecules from _T. ventricosa_ latex during the synthesis of the AgNPs, which provided further stability and prevented the formation of aggregates in the solution [1, 5]. The particles appeared spherical and ovate, and some were triangular (Figure 4A). According to [PERSON] et al. [46], natural rubber AgNPs' characteristic features are spherical to ovate shapes. However, [PERSON] et al. [42] investigated the AgNPs using latex of _Syandenium grantii_ Hook F. and reported various morphologies of NPs such as spherical triangles, truncate triangles, and decahedral shapes. Many researchers have observed that NPs synthesized using latex extracts are often spherical [47, 48, 49, 50]. The HRTEM analysis in the present study (Figure 4A) is consistent with these reports.  
Figure 4: (**A**) High-Resolution Transmission Electron Microscopy micrographs of AgNPs synthesized using latex extracts from _T. ventricosa_. Arrows indicate film surrounding NPs. (**B**) Particle size (nm) of AgNPs synthesized using latex extracts from _T. ventricosa_. _n =_ 20.  
The particle size distribution obtained by Image J analysis showed an average diameter of 11.68 \(\pm\) 3.96 nm, which ranged from 5.00 nm to 17.50 nm (Figure 4B). In addition, we noticed two broad size distributions of the particles, one displaying smaller diameters ranging from 5 nm to 7.50 nm, and other particles with larger diameters, ranging from 15 nm to 17.50 nm. [PERSON] et al. [3] observed a similar occurrence, which was supported by UV-vis spectra analyses, which displayed two Ag peaks (425 nm and 464 nm). However, unlike the present study, we had only observed one Ag peak at 410 nm. The differences in the composition and concentration of extracts may be accountable for these variations [6; 12; 35; 45]. Overall, despite the variations observed in the present study, the diameters of the AgNPs are similar to those of [PERSON] et al. [3], [PERSON] et al. [14], and [PERSON] et al. [35].
# Three-Dimensional Division of Visible Light Communication Irradiation Area
## 4 VLC Experiment Setup and Discussion
### Establishment of 3D Model
Therefore, using LDI and LLD as the evaluation standard of the system, the lampshade height was 6 cm, LDI = 1060 cm, and LLD = 31 cm. This is the optimal parameter setting configuration. Analysis of Figure 11d and Table 2 indicates that when the volume ratio (VR) of the signal receiving area is used as the evaluation index of the VLC system, the maximum VR is expressed as  
\[VR=\underset{i,j}{max}\,\frac{V_{i}}{\sum V_{i}}\times 100\%,\,(i\text{=}I,II,III;\text{=}2,3,4,5,6), \tag{5}\]  
where \(V_{i}\) is the volume of the signal receivable regions in areas I-III. From the above analysis, the volumes of the signal receivable range of areas I-III were calculated and are shown in Figure 12. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Lampshade Height (cm)** & **2** & **3** & **4** & **5** & **6** \\ \hline d\({}_{1}\) (cm) & 24 & 25 & 25 & 24 & 24 \\ \(L_{I-1}\) (cm) & 10 & 10 & 20 & 20 & 20 \\ d\({}_{2}\) (cm) & 12 & 12 & 13 & 14 & 14 \\ \(L_{I}\) (cm) & 100 & 100 & 150 & 150 & 150 \\ d\({}_{3}\) (cm) & 22 & 24 & 24 & 26 & 31 \\ \(L_{I}\)+\(L_{II}\) (cm) & 400 & 500 & 600 & 600 & 600 \\ \(L_{I}\) + \(L_{II}\) + \(L_{III}\) (cm) & 725 & 923 & 930 & 938 & 1060 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental data. Figure 12: (**a**) Volume distribution of signal receivable area. (**b**) Volume ratio of area II. It can be seen from Figure 12a that the increase in the lampshade height had a certain enhancement effect on the entire communication range. Considered alongside the data analysis in Table 2, it can be deduced that area II was least affected by the external environment in the entire area that can receive signals. We extracted the VR of area II occupying the entire communication area, and Figure 12b was obtained. The volume proportion of area II gradually increased with increasing lampshade height and then decreased (see Figure 12b). This shows that the continuous increase in the lampshade height could not increase the proportion of area II continuously. For different lampshade heights, the volume proportion of area II in the entire signal receivable area was greater than 50%, which indicates that area II was the main communication area in the entire communication range. When the lampshade height was 5 cm, VR was 58.8%, which indicates that the signal stable area in the signal receiving range of VLC was the largest. To further increase the volume of area II, methods such as increasing the transmitting power and improving the light detection sensitivity at the receiver end could also be used to obtain a more stable and larger communication area.