esa-sceva/satcom-chunk-collection huggingface dataset:
# A CNN-Based Approach for Driver Drowsiness Detection by Real-Time Eye State Identification
## 4 Experimental Results
### Comparison and Discussion  
A comparison of the accuracy of the facial methods used in the region of interest (ROI) for drowsiness detection is presented in Table 6. This research uses the NITYMED dataset, which differs from others, so the comparison is performed only considering its detection performance.  
The accuracy of the methods ranged from 73% to 98.15%, while the proposed method reached an accuracy higher than 99% in the experiments. The methods used by [17; 19; 20] focused on the whole face using classical face recognition methods. This approach may present limitations when making use of the whole face, where drowsiness prediction may focus on facial features other than the eyes, such as the nose, forehead, cheek, etc. Meanwhile, the methods used by [18; 21; 22; 26] focused on the eyes using Haar cascade and Dlib methods; these two classifiers are widely used but have limitations. Being so, Haar cascade tends to be prone to detecting false positives and lose information on head movements. In addition, the images used are mostly independent of each eye.  
The methods used by [23; 24] present another approach combining CNN and LSTM; this combination involves the use of convolutional layers of the CNN for feature extraction from the input data to then pass to the LSTM and make sequence prediction. The authors use this method by observing that drowsiness symptoms occur in small time sequences in the state of the eyes; it is a very robust technique. On the other hand, the method employed by [25], makes use of Dlib for the extraction of characteristic eye points and determining their open or closed state using the eye aspect ratio (EAR), which is a technique commonly used in various systems to detect drowsiness. The EAR determines the eye status by blink threshold values, which makes it dependent on those values when characterizing the size of the drivers' eyes; moreover, the thresholds are predefined for most drivers, while those thresholds vary for each driver.  
Figure 14: Driver drowsiness detection in a real environment: (**a**) Driver is in a normal state. (**b**) Driver is in a wakeful state. (**c**) Driver is with closed eyes, with a time of approximately 230 ms. (**d–f**) Driver goes to the drowsy state seen, with time longer than 300 ms, activating the visual alarm. (**g–i**) Driver with normal eye blink.  
This research proposes an efficient method for the correction and extraction of the region of interest of the eyes to be evaluated for drowsiness detection by means of transfer learning using deep learning with three CNNs (InceptionV3, VGG16 and ResNet50V2). In addition, a visual analysis is presented for each CNN, which other authors do not take into account or do not provide. The operation of the system can be downloaded from Supplementary Materials.
## 3 Example Analysis
### Performance Verification of Large Samples
#### 3.2.3 Analysis of Network Attack Detection Performance  
Under the condition of large samples, the comparison of the CIKS-MFS-FLGB method and the classic LightGBM algorithm for network attack detection performance is shown in Table 3. Among them, compared with the traditional LightGBM algorithm, the CIKS-MFS-FLGB method has a detection precision of 93.85% for network attacks, the detection precision of 94.66% for a remote tripping command injection attack, and a detection precision of attacks on relay delay setting changes of 82.86%. The precision of these three  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Algorithm/Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline LightGBM & 0.7822 & 0.7778 & 0.7579 & 0.7662 \\ CIS-LightGBM & 0.8879 & 0.8837 & 0.8842 & 0.8839 \\ CIS-MFS-LightGBM & 0.9146 & 0.9097 & 0.9121 & 0.9109 \\ CIS-MFS-FLGB & 0.9289 & 0.9345 & 0.9325 & 0.9335 \\ \hline \end{tabular}
\end{table}
Table 2: Overall performance comparison.  
types of attack detection is improved by 18.67%, 19.93%, and 11.90%, respectively. The F1 score is also improved by 22.92%, 20.62%, and 11.10%, respectively. In summary, the CIKS-MFS-FLGB method not only greatly improves the detection precision of network attacks, but also reduces the influence of false alarm rate on the stability of the model. It makes the model's detection performance of network attacks more reliable. That is, when the data samples are enough, the CIKS-MFS-FLGB method can effectively detect various network attacks and improve the stability of the power system.
## 4 System Model
### Communication Model  
We firstly introduce the communication model in edge-cloud system, in which our environment has a single base station connected with a \(\mathbb{N}\) set of VR game devices through a wireless channel as well as edge computing resources are associated with a single base station and connected with cloud computing via the core network. In addition, each device runs a VR mobile game application which has \(\mathbb{M}\) independent computation tasks that needed to be completed.  
Let us denote \(\alpha_{i,j,k}\) as a binary computation offloading decision for the computation task \(j\) of VR device \(i\) which defines the execution place for the computation task. More specifically, \((\alpha_{i,j,0}=1)\) indicates that the computation task \(j\) of VR device \(i\) will be executed locally by VR device resource, while \((\alpha_{i,j,1}=1\) and \(\alpha_{i,j,2}=1)\) indicates that the computation task \(j\) of VR device \(i\) will be offloaded and processed remotely at the base station and cloud server, respectively. Overall, each computation task \(j\) must be executed only one time whether locally (\(k=0\)) or remotely ( \(k\in\{1,2\}\) ), i.e., \(\sum_{k=0}^{2}\alpha_{i,j,k}=1\).  
Regarding Shannon law, the maximum uplink and downlink data rate for each VR device where its computation task data are transmitted over the communications channel can be calculated as [4]:  
\[r_{i}^{U}=B^{U}log_{2}(1+\frac{p_{i}^{T}G_{0}^{2}}{\omega_{0}B^{U}}) \tag{1}\]  
\[r_{i}^{D}=B^{D}log_{2}(1+\frac{p_{bs}^{T}G_{0}^{2}}{\omega_{0}B^{D}}) \tag{2}\]  
where \(B^{U}\) and \(B^{D}\) denote to the uplink and downlink channel bandwidth, \(p_{i}\), and \(p_{bs}\) denote the transmission power of VR device \(i\) and base station, \(\omega_{0}\) and \(G_{0}\) denote to the density of noise power and the corresponding channel gain between the VR device and the base station due to the path loss and shadowing attenuation.
## 4 Results
### Results of EMG Signal Processing and t-test
#### 4.3.2 \(t\)-test Results  
Table 7 and Figure 12 below shows the result of the evaluation and independent \(t\)-test result.  
Figure 11: Loss and Accuracy of the Proposed RNN Model. (a) Accuracy of the proposed RNN Model (b) Loss of the proposed RNN Model.  
Figure 10: EMG Sensor Attachment and Extracted EMG Signal Graph.  
Serial No. 1 is acceptance evaluation, No. 2 is usability evaluation, and No. 3 is reactivity and aesthetic evaluation. In the evaluation index, most users had difficulty 2-5 getting used to using the HoloLens2.  
In the comparison of the normal group (4.625 \(\pm\) 1.408) and amputation group (6.571 \(\pm\) 1.272), the normal group was high, and the evaluation comparison in various rehabilitation environments was significant (z = \(-\)2.245, * \(p\) = 0.025). In the comparison between the normal group (7.000 \(\pm\) 2.070) and the amputation group (4.857 \(\pm\) 1.215) of the 2-6 questions, the evaluation of the convenience of function use was significant (z = \(-\)2.102, * \(p\) = 0.036). The comparison between the normal group (4.625 \(\pm\) 1.408) and the amputation group (6.571 \(\pm\) 1.272) in the 3-5 questions showed high evaluation of the amputation group, and the comparison of the adaptive part evaluation using HoloLens 2 showed significance (z = \(-\)2.294, * \(p\) = 0.022). The comparison of the normal group (7.375 \(\pm\) 2.387) and the amputation group (5.428 \(\pm\) 1.397) in the 3-7 questions showed high evaluation of the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Serial**} & \multirow{2}{*}{**Items**} & **Public Group (N = 8)** & **Amputation Group (N = 7)** & \multirow{2}{*}{**z**} & \multirow{2}{*}{_p_-Value**} \\  & & **M \(\pm\) SD** & **M \(\pm\) SD** & & \\ \hline \multirow{2}{*}{\(1\)} & 1.1 & 6.375 \(\pm\) 1.847 & 7.000 \(\pm\) 1.291 & \(-\)0.652 & 0.514 \\  & 1.2 & 6  
normal group, and significance in the evaluation comparison of existing rehabilitation methods (z = \(-\)2.051, * \(p\) = 0.040).  
Older users had trouble adjusting to HoloLens2 and gave it a low score for needing a tutorial to control the rehabilitation system. Lower-aged users gave high ratings for reactivity and gaming ability. In both age groups, it was the motivational aspect that scored high in the detailed survey and evaluation. Rehabilitation training in a virtual environment was rated as motivating and fun in the evaluation process.  
In the usability evaluation of this program, the ease of use was judged from an expert's point of view, and the reactivity of the system was highly rated. However, when evaluated by amputee patients and the general population, the HoloLens2 adaptation method received a very low rating. Users provided feedback on the voice guidance system, the improvement of the attachment effect, and the addition of a number of 3D models. Among amputee patients, it was felt that a lot of practice on the HoloLens2 was needed before using the program. The overall usability rating has a strong motivating factor for simulating a virtual environment, but there were difficulties in using and customizing Hololesn2.
## 5 Conclusions  
In order to defend against the jamming attack, one of the most threatening attacks on a ubiquitous networking system, we have designed an immediate detouring service with multiple paths at the only boundary of the jamming area. The simulation results showed that our service enhanced considerably the PDS and average end-to-end delay of normal traffic in comparison with AODV routing and JAM routing. This service will minimize the influence of jamming attacks on real ubiquitous networking systems, such as home automation or industry control systems.
## 5 Results and Discussion
### Application and Limitation  
The method proposed in this paper is not applicable to plate structures or to other types of structures. In practical applications, how to obtain accurate modal pairs (frequencies and mode shapes) information of the structure is the primary problem to be solved. Regarding frequency measurement, it should be noted that the installation position of the sensor should not be arranged at the mode-shape node line, and the installation direction should be consistent with the excitation direction. In addition, if the mass of the structure to be measured is small, non-contact measurement is recommended. Regarding mode-shape measurement, the measurement method used in this paper is suitable for plate structures, and for non-plate structures, it can be obtained by rationally arranging multiple measurement sensors or using advanced scanning laser vibrometers. Building an optimization model for model-updating is another key step in the proposed method. In this step, it is necessary to select reasonable correction parameters according to the actual situation, and determine the value range of each parameter. Furthermore, this paper simplifies the boundary constraints into a series of springs, which can provide a solution for the quantitative characterization of complex boundary constraints in the FE modeling process.  
Nevertheless, this method still has certain limitations. Although LHS was used to count the mode-switching range, thereby reducing the identification orders, the calculation time remained as high as 33.34 h. This was due to the need to call the FE software to re-perform modal analysis of the FE model for each calculation. In future work, in order to further reduce the calculation time, it is necessary to introduce an approximate model instead of the FE model for modal analysis. Moreover, for complex engineering structures, it is often difficult to measure the mode shape. When the measured mode shapes are distorted or unavailable, this method will fail.
# Geographical Information System Enhancement Using Active Learning-Enhanced Semantic Segmentation
## 2 Materials and Methods  
### Methods for Geographical Information System Refinement  
The goal of this research was to investigate the building registration ledger on South Korean maps by following the building registration ledger inspection procedure illustrated in Figure 1. The process consisted of three steps: The process of finding the optimal algorithm, the process of refining data using active learning, and investigating the building registration ledger.  
As seen in the figure, the process progressed in the order of algorithm selection, data refinement, and checking the building registration ledger. In the process of finding the highest-performing algorithm for the semantic segmentation of drone images, the aim was to validate the performance of UNet, SegNet, DeepLab18, and DeepLab50. For accurate training and validation, manually labeled information was utilized. Once the algorithm was selected, a method was proposed to refine the dataset for training using this algorithm. Labeling every building for processing a vast range of images is challenging and difficult. However, the GIS of the building registration ledger can be used for automatic labeling, and there is a need to train and validate using these data. By measuring the training data similarity of the network trained with the algorithm and removing images with low similarity with human verification, the performance of the trained network can be improved. While the performance might be lower than when humans perform the labeling, it allows for the processing of a large amount of data without labeling. This process is referred to as active learning. After this process, if the IoU exceeded the threshold, the investigation results were recorded in the building database of the GIS system based on the building registration ledger.
## 6 Conclusions  
In this paper, we propose and evaluate the tap estimation schemes for the traditional TDE in optical fiber communications using the optimization algorithms commonly used in the NNs. The experimental results show that the optimization algorithms of NNs are also useful in tap estimation of optical communication system. BER performance of 129-Gbit/s PAM8 optical communication system adopting BGD method, AdaGrad, RMSProp, and Adam algorithms are all below \(3.8\times 10^{-3}\). It is also shown that Adam algorithm and BGD method perform better in the tap estimation of equalizer. Although Adam algorithm has the highest computational complexity compared to the other three algorithms, its performance is best and it is most robust with the effective step size range of \(\sim\)7.50. BGD method performs better than AdaGrad and RMSProp algorithms and it is more straightforward to implement, but it is less robust with the effective step size range of \(\sim\)0.01. In conclusion, the optimization algorithms of NNs are useful in the tap estimation of equalizer with traditional structures in optical communications.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **BGD** & **AdaGrad** & **RMSProp** & **Adam** \\ \hline
**Add.** & \((2N-1)(M-N+1)\) & \((2N-1)(M-N+1)+N\) & \((2N-1)(M-N+1)+N\) & \((2N-1)(M-N+1)\) +\(2N\) \\ \hline
**Mul.** & \(N[2(M-N+1)+1]\) & \(N[2(M-N+1)+3]\) & \(N[2(M-N+1)+5]\) & \(N[2(M-N+1)+9]\) \\ \hline
**Sqrt.** & 0 & \(N\) & \(N\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computational complexity per iteration of optimization algorithms of NNs introduced to tap estimation of traditional equalizer.  
Figure 5: BER versus step size for 129-Gbit/s optical PAM8 system with different optimization algorithms at ROP of \(-1\) dBm after 2-km SSMF transmission. (**a**) BGD method; (**b**) AdaGrad algorithm; (**c**) RMSprop algorithm; and (**d**) Adam algorithm.  
**Author Contributions**: Conceptualization, [PERSON], J.Z., J.W., W.L., C.Y. and Z.L.; methodology, [PERSON], J.Z., J.W., W.L., C.Y. and Z.L.; software, [PERSON], J.Z. and Y.W.; formal analysis, [PERSON] and J.Z.; investigation, [PERSON], [PERSON] and Y.W.; writing--original draft preparation, [PERSON], [PERSON] and Y.W.; writing--review and editing, [PERSON], [PERSON], [PERSON], C.Y. and Z.L.; funding acquisition, [PERSON], W.L. and Z.L.  
**Funding**: This research was funded by National Key R&D Program of China (2018 YFB1801704); The Science and Technology Planning Project of Guangdong Province (2017B010123005, 2018B010114002); Local Innovation and Research Teams Project of Guangdong Pearl River Talents Program (2017 Bt01X121); National Science Foundation of China (NSFC) (61525502, 61975242); The Fundamental Research Funds for the Central Universities (21619309).  
**Conflicts of Interest**: The authors declare no conflict of interest.
# SALMA: A Novel Middlebox Infrastructure System Based on Integrated Subnets
## 7 Evaluation
### Overhead Analysis  
In this experiment, we focused on three key benchmark metrics: the time to setup the network with proactive flow entries, the southbound communication overhead between the controller and the switches, and the flow setup delay of every new flow. We used Mininet to build large topologies, including Internet2/Abielene, Geant, and an abstracted enterprise network for one of the large enterprises that participated in our survey, as shown in Figure 8. We built the topology using Mininet where end nodes were connected via 1 Gbps links. In all the topologies, we gathered all the middleboxes into two subnets for redundancy and load balancing. The network setup details are shown in Table 3.  
Network setup time: In reactive mode, we needed to install proactive flow entries to build a tunnel between every switch and the two middleboxes' subnets. The reactive flow entries were also used to dynamically balance the traffic load among the two subnets of middleboxes. Furthermore, we proactively used the installed flow entries to maintain the policy sequence of the flows that exit from the proxy. Since SALMA needs only a few flow entries, the setup time was in the range of 30 ms, as shown in Table 3.  
The southbound communication overhead: Since the sizes of the OpenFlow messages are small and since only a few of them are needed to setup the network, the communication overhead between the switches and the middlebox manager is small. Table 3 shows that the communication overhead is in the range of few kilobytes.  
Flow setup delay: In order to dynamically assign the new flows to one of the middleboxes' subnets, the middlebox manager is involved in the flow setup process. We built a  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **SALMA** & **Distributed Middleboxes** \\ \hline Throughput & 100.5 Mbps & 4.1 Mbps \\ Congestion Window & 22.85 bytes & 22.47 bytes \\ Round Trip Time (RTT) & 8.9 ms & 15.7 ms \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average throughput, congestion window, and round trip time for SALMA compared to the conventional method.  
Figure 13: Percentage of CPU load for both SALMA and the distributed middleboxes approach.  
simple topology in Mininet to test the flow-setup process's delay and compare it with the proactive mode. The network was composed of only a single switch, a controller, five hosts (h1-h5), and one server (h6). We tested the effect of the flow-setup process on round-trip time; where the flow entry expiration time was 55 s. The flow-setup delay was zero in the proactive mode, since the flow entries were already installed, and there was no need to communicate with the controller to set up a flow. The comparison is shown in Figure 14. The average flow-setup delay for the reactive scenario was 3.3 ms.
# Real-Time Conveyor Belt Deviation Detection Algorithm Based on Multi-Scale Feature Fusion Network
###### Abstract  
The conveyor belt is an indispensable piece of conveying equipment for a mine whose deviation caused by roller sticky material and uneven load distribution is the most common failure during operation. In this paper, a real-time conveyor belt detection algorithm based on a multi-scale feature fusion network is proposed, which mainly includes two parts: the feature extraction module and the deviation detection module. The feature extraction module uses a multi-scale feature fusion network structure to fuse low-level features with rich position and detail information and high-level features with stronger semantic information to improve network detection performance. Depthwise separable convolutions are used to achieve real-time detection. The deviation detection module identifies and monitors the deviation fault by calculating the offset of conveyor belt. In particular, a new weighted loss function is designed to optimize the network and to improve the detection effect of the conveyor belt edge. In order to evaluate the effectiveness of the proposed method, the Canny algorithm, FCNs, UNet and Deeplab v3 networks are selected for comparison. The experimental results show that the proposed algorithm achieves 78.92% in terms of pixel accuracy (PA), and reaches 13.4 FPS (Frames per Second) with the error of less than 3.2 mm, which outperforms the other four algorithms.  
c 2019; 12, 205; doi:10.3390/a12100205
## 4 Status of PLC in Smart Metering Systems
One of the reasons for this is that most of the existing NB PLC systems [16] are LDR NB PLC systems, just capable of accessing meter readings once in several days and they cannot be considered as real-time systems. AMR systems with such LDR NB PLC technologies are not able to offer real-time capabilities [121]. New generation NB PLC systems (_i.e.,_ HDR) combine the real-time characteristics needed, with the low cost of the PLC platforms and deployments. This real-time data transfer enables optimized grid operation [122] and provides instantaneous information of the Points of Supply (PoS) in the LV grid [123]. This information may be used for the fast detection of faults in cables, voltage control, meter tampering detection, and LV monitoring capabilities, as those existing in MV and HV segments [124].
# Improved RSSI Indoor Localization in IoT Systems with Machine Learning Algorithms
## 3 System Model
### Machine Learning for Wireless Indoor Localization
#### 3.1.2 Support Vector Machine  
The SVM is a machine learning algorithm that uses labeled training samples to learn the classification of data points. For example, one way to detect malicious behavior on a node is to use SVMs to investigate the temporal and spatial correlation of data. To explain this point, the WSN observation is used as a point in the element space, and the SVM divides it into multiple parts. These parts are separated by the widest possible margin (the separation gap), and the new readings are sorted according to which side of the gap they are on. An SVM algorithm uses linear constraints to optimize a quadratic function (the problem of building a set of hyperplanes). It provides an alternative to Multi-layer Neural Networks with non-convex, unconstrained optimization problems.