esa-sceva/satcom-chunk-collection huggingface dataset:
# A Privacy-Preserving Scheme for a Traffic Accident Risk Level Prediction System
## 4 Materials and Methods
### Our Security Approach  
Considering that, first, approximately one of every two proposals use any cryptography to improve their security [34], and second, the messages circulating in the telecommunications infrastructure include sensitive information, and also taking into account that adversaries with specific capabilities can compromise security requirements, we consider the cryptographic mechanisms as a solution to guarantee the confidentiality and integrity of the information.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Scope** & **Target** & **Assumptions** & **Capabilities** \\ \hline Type I & Internal & VANET & Member of the vehicular network & They can only listen to channel communication and intercept messages \\ \hline Type II & External & VANET & Not a member of the vehicular network & They can perform Tampering, Impersonation, MitM, DoS, Replay, and other attacks. \\ \hline Type III & External & DSRC/CV2X & Member of the any other network & They can perform DoS, Hijacking, Malware Injection, Side-Channel, Man-in-the-Cloud, and other attacks. \\ \hline Type IV & Internal & Cloud Service Provider & Member of the cloud service provider network & They can only listen to channel communication and intercept messages. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Adversary model.  
Figure 2: Location of adversaries into the system architecture.  
We initially considered using public key cryptography. While it could work, there is a risk of authorized adversaries within the cloud service provider network intercepting sensitive information once it is unencrypted to be processed. Then, we analyzed the implementation of a cryptography computing technique, specifically HE, as a solution. Despite the high computational cost of using HE, we chose this technique because we need to perform operations over encrypted data. Also, using this type of cryptography would prevent internal or external adversaries from seeing the content of the messages, which is possible because the processing would be done over encrypted data. Thus, our privacy-preserving scheme will be based on Homomorphic Encryption, specifically Fully Homomorphic Encryption. We chose FHE rather than Partially or Somewhat Homomorphic Encryption because it can perform unlimited evaluation operations from many available operations.  
Referring to the Risk Level Prediction System for Traffic Accident Prevention, our scheme will permit encrypting the outgoing data provided by the acquisition agent, processing that encrypted data through the processing agent with its encrypted learning model, and sending back the encrypted result to the response agent. This circuit is performed without decrypting the data at any time. Figure 3 presents our privacy-preserving scheme for the learning model.
## 2 Materials and Methods
### Methods Used
#### 2.2.3 Quality Assessment Criteria  
In order to train and validate the ML model, several quality assessment criteria can be used. In this study, three criteria--namely Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Coefficient of Determination (R\({}^{2}\))--were used. The expressions of these criteria are [92; 93; 94; 95; 96; 97]:  
\[\text{RMSE}=\sqrt{\sum_{i=1}^{N}{\left(y_{0}-y_{p}\right)^{2}/N}} \tag{5}\]  
\[\text{MAE}=\frac{1}{N}{\sum_{i=1}^{N}{\left|y_{0}-y_{p}\right|}} \tag{6}\]
# A Bayesian Network-Based Information Fusion Combined with DNNs for Robust Video Fire Detection
## 1 Introduction
SRoF areas, including both flame and smoke, are then calculated, and their temporal changes are reported to incorporate the dynamic fire behavior into the final fire decision. However, the DTA assigned no role to environmental information (such as humidity, weather conditions, fire location) in improving the performance of fire detection. Some information, such as fireplaces location and the existence of smoke, can be obtained from the same visual sources as used by Faster R-CNN, but other environmental information, such as humidity or weather conditions, must be obtained from other sensors. This paper proposes to use Bayesian network to combine all of this information to improve the accuracy and the latency of perfect fire detection. This extended version of DTA is called Detection and Temporal Accumulation with Information Fusion (DTAIF). Experiments show that a simple Bayesian network can improve the fire detection accuracy when compared with the prior exclusively video-based method, and results in the state of art performance when applied to the public dataset. The proposed method with the simple Bayesian network also reduces the time to reach (latency for) a perfect fire decision as compared to the prior video-based method. Although a simple Bayesian network is designed here only to show the potential, we believe such systems that combine all sensor information by Bayesian net are useful for improving fire detection performance. In addition to DTA-based fire decision approach, our contributions can be summarized as follows. The Bayesian network-based information fusion combined with deep neural net (DNNs) goes one step further than previous DTA, whereby SRoFs were detected in a scene and the temporal behaviors were continuously monitored and accumulated to ultimately decide whether a fire existed. Humans, however, do not determine the existence of fire, based solely on the localized visual behavior within a fire-like region. In addition to location of the scene, people account for environmental information, such as humidity and weather conditions, to make robust decision. In order to further emulate this human approach, this paper proposes to use a Bayesian network that fuses various kinds of information (DTAIF). To verify the proposition, a simple Bayesian network is designed as an example and its effectiveness is shown in terms of accuracy and latency for fire decision in the experiments. The remainder of this paper is organized as follows. Related work is introduced in Section 2, the details of our proposed method are given in Section 3, and our experimental results and discussions are presented in Section 4. In Section 5, we offer our concluding remarks.
# Motion Simulation and Human-Computer Interaction System for Lunar Exploration
## 3 Simulation Results
### Multi-Channel Interactive System Experiment  
#### 3.4.1 Visual and Force Interaction  
We invited seven volunteers who had no experience in driving this device to participate in the experiment.  
In the preparation of the experiment, we designed six routes of similar length on the simulated terrain and optimized the routes according to the terrain slope and the frequency of undulations. To ensure the validity of the experimental results, the experimenters learned and practiced driving with the simulation platform. Three routes were randomly selected from the preset routes for the training. During the training, each experimenter needed to drive the rover along three routes.  
In the experiment, we randomly selected one of the remaining routes as the experimental route. First, the selected route was blurred to obtain a recommended driving area, which is visually displayed on the terrain as a driving reference. Each volunteer operated the platform with a single-channel interaction method that only included vision and an interaction method that combined the two channels of vision and force feedback. As shown in Figure 21, the experimenter drove twice along the same route, and we recorded the driving path and time (Figure 21).  
Figure 22 shows the path traveled by the experimenter driving the rover. Among the paths shown, the red path is the optimal path calculated by the system. The blue path  
Figure 20: Comparison of rover attitude.  
Figure 21: Experiment procedure.  
indicates the one taken by the experimenter with only visual judgments during driving. The green path indicates when the experimenter was able to judge the terrain through a combination of vision and the force feedback of the handle.  
We found that when the experimenters considered the force feedback information, the driving route was closer to the optimal route. This shows that in this study, the force feedback handle helped astronauts choose a safer driving route. Table 2 shows the data under two driving modes.  
From the above data, it can be seen that when the experimenter judged the terrain through a combination of vision and the force feedback, the posture of the rover was more stable and the route was closer to the optimal path. Although the experiment time was generally increased, after interviews, we reached the following conclusion: When the number of channels increased, the factors that affect the experimenter's judgment increased. Therefore, the judgment time increased, which eventually led to a longer operation time. However, the increase in time was not significant, and the impact on work efficiency was small.
## 2 Materials and Methods
### Performance Evaluation  
To provide a more intuitive and easier-to-understand method to measure the prediction quality, the following equation set is often used in literature for examining performance quality as follows [32]:  
\[\left\{\begin{array}{c}Sn=\frac{\mathit{TP}}{\mathit{TP}+\mathit{FN}}\\ Sp=\frac{\mathit{TN}+\mathit{FP}}{\mathit{TP}+\mathit{FN}}\\ Acc=\frac{\mathit{TP}+\mathit{TN}+\mathit{FP}+\mathit{FN}}{\mathit{(TP+FN)}- \mathit{(TP\times FN)}}\\ \sqrt{\left(\mathit{TP}+\mathit{FP}\right)\left(\mathit{TP}+\mathit{FN} \right)\left(\mathit{TN}+\mathit{FP}\right)\left(\mathit{TN}+\mathit{FN} \right)}\end{array}\right. \tag{22}\]  
where _TP_ (true positive) represents the number of fatigue EEG signals identified as fatigue EEG signals; _TN_ (true negative), the number of normal EEG signals classified as normal EEG signals; _FP_ (false positive), the number of normal EEG signals recognized as fatigue EEG signals; _FN_ (false negative), the number of fatigue EEG signals distinguished as normal EEG signals; _Sn_ represents sensitivity; _Sp_ represents specificity; _Acc_ represents accuracy; and _MCC_ represents Mathew's correlation coefficient.
###### Abstract  
Plant growth-promoting rhizobacteria (PGPR) can potentially be used as an alternative strategy to control plant diseases. In this study, strain ST-TJ4 isolated from the rhizosphere soil of a healthy poplar was found to have a strong antifungal activity against 11 phytopathogenic fungi in agriculture and forestry. Strain ST-TJ4 was identified as _Pseudomonas_ sp. based on 165 rRNA-encoding gene sequences. The bacterium can produce siderophores, cellulase, and protease, and has genes involved in the synthesis of phenazine, 1-phenazine-carboxylic acid, pyrrolirn, and hydrogen cyanide. Additionally, the volatile compounds released by strain ST-TJ4 can inhibit the mycelial growth of plant pathogenic fungi more than diffusible substances can. Based on volatile compound profiles of strain ST-TJ4 obtained from headspace collection and GC-MS/MS analysis, 1-undecene was identified. In summary, the results suggested that _Pseudomonas_ sp. ST-TJ4 can be used as a biocontrol agent for various plant diseases caused by phytopathogenic fungi.  
Keywords:diffusible substances; volatile compounds; phytopathogenic fungi; _Pseudomonas_ sp. +
Footnote †: journal: Microorganisms
# Enhanced High-Definition Video Transmission for Unmanned Driving in Mining Environments
## 3 Design and Implementation of Video Transmission
### Architecture of Mine Unmanned Driving System  
The mine unmanned driving system consists of a vehicle control subsystem, an information transmission subsystem, and an unmanned vehicle subsystem, as depicted in Figure 1. This proposed architecture requires the deployment of a private mine communication network connecting to tunnel 5G base stations for mine unmanned driving system. The vehicle control subsystem is situated in the indoor machine room on the ground, and typically includes the remote-control platform, video server, network switch, and display. Its primary functions are to achieve vehicle control, provide vehicle attitude display, offer vehicle environment video display, and enable human-computer interaction. The process of the vehicle control subsystem for remote monitoring and emergency takeover is illustrated in Figure 2. This subsystem relies on high-definition video environmental information transmitted through the 5G network and is monitored by the operator. In the event that the vehicle issues an emergency takeover request, or the operator detects risk factors in the operation of the autonomous vehicle, remote operation, safety monitoring, and emergency takeover of the mining vehicle can be implemented.  
Figure 1: Architecture of mine unmanned driving system.  
Figure 2: Remote monitoring and emergency takeover work control.  
The information transmission subsystem facilitates wireless network communication between the unmanned vehicle subsystem and the vehicle control subsystem. It supports remote control of unmanned vehicles through the transmission of vehicle monitoring data, video, and other services using 5G super uplink technology. The system is designed to achieve intelligent unmanned assisted transportation and enhance safety with minimal human intervention.  
The unmanned vehicle subsystem serves as the primary component of the entire system and is responsible for vehicle perception, decision-making, control, and execution. This paper focuses primarily on optimizing video transmission within the unmanned vehicle subsystem.
# Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information
## 7 Experimental Evaluation
### Angle of Arrival Estimation Accuracy of Different Clients  
In this part of the experiment, we tested AoA's accuracy on 5G band clients, including APs and mobile phones. Let the transmitter be at 11 degrees, which ranges from 15\({}^{\circ}\)-165\({}^{\circ}\) by steps of 15\({}^{\circ}\). The distance between the transmitter and the receiver is 3 m. After collecting the CSI data, we changed the distance to 2 m and carried out the same experiment. Through our improved MUSIC algorithm, we can determine the inherent phase offsets \(\langle 8^{\circ},20^{\circ}\rangle\) of Antennas 2 and 3. Figure 9a shows the error bar of the traditional MUSIC algorithm and our algorithm in different angles' measured data, which randomly selected 300 packets. We can see that the average of our algorithm is almost the same as the true angle with the average error being 6.3\({}^{\circ}\), but the average error of the traditional operator is 11.9\({}^{\circ}\). For mobile phone testing, we changed the experimental environment to the office, filled with desks and computers. The size is about 64 m\({}^{2}\). Then, we put the phone and the antennas at the same level of height and adjusted the distance between the receiving antennas to 6 cm to prevent interference between the receiving antennas. Due to the many obstacles in the environment, we are making sure that the external environment has not changed. On the semicircle with a distance of 1 m, we changed the position of the mobile phone [30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), 90\({}^{\circ}\), 120\({}^{\circ}\), 135\({}^{\circ}\), 150\({}^{\circ}\)] as the measurement angles, respectively. Figure 9b shows that we can also effectively detect the angle of the phone in the wireless network with an accuracy of 7.2\({}^{\circ}\). The average RSSI difference in different locations is 1.6 dB.  
Figure 10: AoA Estimation error. (**a**) CDF of access points; (**b**) CDF of access points of different packets.  
Figure 9: AoA Error bar of different clients. (**a**) Error bar of access points; (**b**) error bar of mobile devices.  
Figure 10a shows the AOA's CDF estimate error with all APs, and we can see that 80% of the AP's detection error in 300 packets does not exceed 10\({}^{\circ}\), whereas the traditional algorithm is only 50%. We simulate the number of different packages as shown in Figure 10b, and the experiment shows that our algorithm can effectively improve the positioning accuracy with the CSI packets' increase. The average RSSI of 8000 packets, shows that the difference between 2 m and 3 m is 1.3 dB. The CSI amplitude values decrease by more than 1.4 per meter.
###### Abstract
\begin{table}
\begin{tabular}{l l c c c c c c c c c c c} \hline \hline
**References** &
\begin{tabular}{c} **Mode of** \\ **Operation** \\ \end{tabular} & **(G)** & **(ii)** & **(iii)** & **(iv)** & **(v)** & **(vi)** & **(vii)** & **(viii)** & **(viii)** & **(viii)** & **(io)** & **(io)** & **(io)** \\ \hline \([9]/2003\) & MISO & 6-OTA & 2C & Yes & Yes & No & No & No & Yes & Yes & Yes & - \\ \([7]/2004\) & MISO & 7-CCII & 2C+8R & No & Yes & No & Yes & No & Yes & Yes & No & - \\ \([12]/2006\) & MISO & 3-CCII & 3C+4R\({}_{+}\)2-switch & No & No & No & Yes & No & Yes & Yes & No & - \\ \([13]/2008\) & MISO & 4-OTA & 2C & Yes & Yes & No & No & Yes & Yes & Yes & 2.25 MHz \\ \([19]/2010\) & MISO & 5-OTA & 2C & Yes & Yes & No & Yes & No & Yes & No & Yes & 1.59 MHz \\ \([20]/2010\) & MISO & 2-MOCCII & 2C+2R & No & Yes & Yes & Yes & No & Yes & Yes & Yes & 1.27 MHz \\ \([27]/2010\) & MISO & 1-CRO & 2C+3R & No & No & Yes & No & Yes & No & No & 12.7 MHz \\ \([24]/2013\) & MISO & 4-MOCCII & 2C & Yes & Yes & No & Yes & Yes & Yes & No & Yes \\ \([25]/2013\) & MISO & 1-FDCCI & 2C+2R & No & Yes & No & No & Yes & Yes & No & 10 MHz \\ \([26]/2013\) & MISO & 2-VOTA & 2C & Yes & Yes & Yes & No & Yes & Yes & Yes & 1 MHz \\ \([27]/2016\) & MISO & 1-FDCCI+, 1-DDCC & 2C+6R & No & Yes & Yes & Yes & No & Yes & No & 1.59 MHz \\ \([40]/2016\) & MISO & 1-MCCTA & 2C+2R & No & Yes & Yes & Yes & No & Yes & Yes & Yes & 12.16 MHz \\ \([37]/2018\) & MISO & 5-DVC & 2C+5R & Yes & Yes & Yes & Yes & No & Yes & Yes & No & 1 MHz \\ \([41]/2018\) & MISO & 4-CCII & 2C+4R & Yes & Yes & Yes & No & Yes & Yes & No & 31.8 MHz \\ \([38]/2019\) & MISO & 5-OTA & 2C & Yes & Yes & Yes & No & Yes & Yes & Yes & 3.390 MHz \\ \([39]/2020\) & MISO & 3-DDCCII & 2C+4R & No & Yes & No & Yes & No & Yes & Yes & No & 3.978 MHz \\ This work & MISO & 1-VD-EXCCII & 2C+3R & No & Yes & Yes & Yes & Yes & Yes & Yes & 8.0844 MHz \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative study of the state-of-the-art MISO mixed mode filter designs with the proposed filter.
## 2 Related Work
### Multiple Edge Servers and Remote Cloud
To optimize the data transfer, lazy object transfer algorithms and differential object update algorithms are used. In the first algorithm, when traffic is unloaded, a proxy is created to transfer related objects. In the second method, only the parts of objects that differ when loading several methods on a mobile device are updated. The proposed Echo code was found to have lower average task execution time and power consumption for mobile devices compared to existing traffic offloading systems. Recently, a multi-user with multi-UAV-enabled and cloud servers' model is presented in [33; 34]. Specifically, in [33], a new weighted cost model is proposed with the goal of minimizing the energy consumption and execution time. Additionally, an efficient placement algorithm is developed to assign the tasks to the most suitable server in an efficient manner. Moreover, a lightweight failure recovery algorithm is designed to optimize the parallel execution for tasks. [PERSON] et al. [34], however, proposed an intelligent task offloading model for a multi-tier MEC network, in which minimizing the execution delay of tasks and meeting the UAVs' computing capacity are the main goals. In addition, an equivalent and convex form of the formulated problem is given using two-layer optimization technique. In the first layer, differential evolution learning approach is utilized, while a distributed deep neural network is used in the other layer. However, measuring the execution time is not considered in [34], which is one of the major downsides. Due to the high dynamics in urban networks, which lead to an overload of the connection between the UAV and the boundary server (which also leads to an increase in the time spent on tasks), and due to the large queues that occur on the boundary servers when processing several tasks, the total time of n-th task transfer to the boundary server and the task processing time may exceed the time spent analyzing the task locally on the UAV. Here, we present an optimization process that allows the user to choose where the task will be processed on a UAV. We consider the communication, computational, and energy aspects of the proposed process. Table 1 introduces a summary of the related works reviewed in terms of number of users, edge, and remote cloud servers, and highlights the main weaknesses of the studies. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Objective**} & \multirow{2}{*}{**Proposed Solution**} & \multicolumn{3}{c}{**User**} & \multicolumn{3}{c}{**UAV Sensor**} & \multirow{2}{*}{**Remote Cloud**} & \multirow{2}{*}{**Evaluation Methods**} & \multirow{2}{*}{**Weathers**} \\ \cline{3-4} \cline{6-9}  & & & & **Single** & & & **Multiple** & & & **Range Cloud** & **Evaluation Methods** & **Weathers** \\ \hline
[24] & Minimize energy & An energy-efficient framework for & & & & & & & & & & \\ \multicolumn{9}{c}{} \\  &
# Sub-Band Backdoor Attack in Remote Sensing Imagery
## 2 Related Work  
### Deep Learning  
Deep learning models utilize multiple layers of processing units to extract features from inputs for subsequent tasks such as regression, classification, or object identification. Recently, deep learning methods have achieved state-of-the-art performance in many applications, including computer vision [20; 21; 22; 23; 24; 25], hyperspectral imaging [26], object detection [27], remote sensing [28; 29], medical imaging [30; 31], cybersecurity [32], speech recognition [33], and audio classification [34]. Deep learning models can have different architectures, and the deep convolutional neural network (DCNN) is a popular one that revolutionized the field of computer vision since the 2012 ImageNet competition [20]. Due to its effectiveness, researchers have developed numerous DCNN architectures, including AlexNet [20], VGGNet [35], ResNet [21], DenseNet [36], InceptionV3 [37], and Efficient-Net [38]. The ResNet [21] uses a residual connection to train large-scale neural network layers and achieved state-of-the-art performance in the 2015 ImageNet competition. Efficient-Net is a recent approach that uses efficient compound coefficients to scale up the internal layers in width, depth, and resolution with balanced ratios [38]. DCNN architectures have also been successfully applied to remote sensing applications [1].
# A Novel Approach for Improving XML Querying over Wireless Broadcast Channels
## 4 Analysis
### Sample Data  
1. XML Data Size and Complexity: * Size of XML data (\(size_{xml}\)): 10 MB * Complexity of XML data (\(complexity_{xml}\)): Medium (2) * Data Partitioning: * Number of partitions: 5 * Time to partition each MB of XML data: 0.1 s per MB * Partitioning time for each partition (\(T_{partition}\)): \(size_{xml}\times\)0.1 \(\times\ complexity_{xml}/\ number_{puttitions}\)
2. Transmission: * Size of each partition (\(size_{partition}\)): 2 MB (since 10 MB/5 partitions) * Bandwidth of each channel (\(bandwidth_{channel}\)): 1 MBps * Number of channels (\(number_{channels}\)): 5 * Transmission time for each partition
3. Routing: * Complexity of routing (\(complexity_{routing}\)): Low (1) * Routing time (\(T_{routing}\)): 0.5 s
4. Query Processing: * Complexity of query (\(complexity_{query}\)): High (3) * Efficiency of processing (\(efficiency_{processing}\)): Medium (processing time is 1 s per query complexity unit) * Processing time (\(T_{processing}\)): \(complexity_{query}\times\ efficiency_{processing}\)