esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Analysis
### GA Experimental Verification
#### 4.2.2 GA Optimized for Low-Power Experimental Analysis  
In Figure 18, error plots of battery SOC \(\geq\) 20% and \(<\) 20% after GA optimization are shown. The red part represents battery SOC \(<\) 20% and the black part represents battery SOC \(\geq\) 20%.  
As demonstrated in Figure 19 and Table 5, after GA optimization, both the max error and the MSE were further reduced when the battery SOC was above or below 20%. In the DST dataset, the maximum error of the BP neural network for a battery SOC of below 20% was the same as that of above 20%. The max errors for CNN, RNN, and TSCNN were lower by 0.14%, 1.57%, and 0.17%, respectively. In the UDDS dataset, the maximum errors for a battery SOC of below 20% were lower by 1%, 0.02%, 0.41%, and 0.19%, respectively, compared to those of above 20%. Compared with before GA optimization, the deep learning neural networks optimized by GA not only reduced the overall error but also reduced the error gap between those of battery SOCs above and below 20%.  
Figure 19: Max error comparison for battery SOC \(\geq\) 20% against SOC \(<\) 20% on (**a**) DST and (**b**) UDDS datasets.  
Figure 18: (**a**) Error of GABP on DST; (**b**) error of GACNN on DST; (**c**) error of GARNN on DST; (**d**) error of GATSCNN on DST; (**e**) error of GABP on UDDS; (**f**) error of GACNN on UDDS; (**g**) error of GARNN on UDDS; (**h**) error of GATSCNN on UDDS.  
Compared with previous studies, the TSCNN model optimized by GA reduced the error when the battery SOC was less than 20% and narrowed the gap of errors for battery SOCs of greater than 20% and less than 20%. Therefore, using GA-optimized TSCNN not only solves the problem of poor estimation accuracy for a battery SOC of below 20% but also addresses the empirical errors caused by manually set parameters.
# Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning
## 3 Method and Algorithm
### Federated Network Algorithm
#### 3.2.1 Learning Client  
For learning clients, they have their own private data and, supposing all the data have been aligned, their quantitative dimensions with other learning participants. For the learning client, the main functions include initializing the same initial model with other clients, training data locally, extracting gradients during the training, computing the gradients with computing server, collecting server responses, passing the results, updating the model, and iterating repeatedly until the model converges.
## 1 Introduction
### Features of the Proposed Review  
The proposed survey mainly focuses on providing a thorough and comprehensive review of existing work carried out in deep learning-based object detectors, particularly showing a pathway for new researchers who wish to choose this field.  
* Moreover, differently from recently published review papers on object detection topics [18; 19; 20; 21; 22; 23], this paper comprehensively reviews modern deep learning-based object detectors starting from regions with convolutional neural networks (RCNN) and ending at CornerNet with its pros and cons.
* It also covers some specific problems in computer vision (CV) application areas, such as pedestrian detection, the military, crowd detection, intelligent transportation systems, medical imaging analysis, face detection, object detection in sports videos, and other domains.
* It provides an outlook on the available deep learning frameworks, application program Interface (API) services, and specific datasets used for object detection applications.
* It also puts forth the idea of deploying deep learning models into various embedded platforms for real-time object detection. In the case of a pre-trained model being adopted, replacing the feature extractor with an efficient backbone network would improve the real-time performance of the CNN.
* It describes how a GPU-based CNN object detection framework would improve real-time detection performance on edge devices.  
Finally, we intend to give an overview of various deep learning methods deployed on various embedded platforms in real-time objection and possible research directions.  
The rest of this paper is organized as follows. Section 2 covers various deep learning architectures used in object detection in detail. Frameworks and API Services, and available datasets and performance metrics for object detection, have been discussed in Sections 3 and 4. Application domains and deep learning approaches for object detection are explained briefly in Sections 5 and 6 respectively. Section 7 discusses various GPU-based embedded systems for real-time object detection implemented using deep learning techniques. Research directions, a conclusion and future research possibilities are presented in Sections 8 and 9.
## 5 Conclusions  
In this study, an automatic sleep state detection framework is presented for both male and female human subjects. The proposed framework uses the PSD of EEG signals collected from three important regions of the brain, i.e., the pre-frontal, central, and occipital, as input to a ResNet. The ResNet architecture automatically extracts the intrinsic information for each sleep stage to classify it correctly. The proposed framework was tested on the publicly available Dreams dataset. It achieved accuracies of 87.8% and 83.7% for female and male subjects, respectively. The proposed framework was compared with several state-of-the-art methods to justify the choice of a ResNet based classifier and PSD based input data, and it yielded significant performance improvements, i.e., 3.8-40.6% and 1.3-37.9% for female and male subjects, respectively. The dataset used for validation of the proposed framework is imbalanced in terms of gender, which inevitably affected the experimental results. The difference in performance is understandable as in machine learning as well as in deep learning, more data often translates into a better model. Nevertheless, in future, a more extensive and balanced dataset would be considered to validate the findings of this study. Furthermore, in addition to PSD, other time-frequency based analysis techniques will be explored to capture all the variations in the non-stationary EEG signals.  
**Author Contributions:** All the authors contributed equally to the conception of the idea, implementing, and analyzing the experimental results, and writing the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by an Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government. [20 ZS1200, Research on original technology of human-centered autonomous  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Classification Methods**} & \multicolumn{2}{c}{**Accuracy (\%) with the Amount of Improvement \({}^{*}\) (\%) by the Proposed Method**} \\ \cline{2-5}  & **Female** & **Improvement \({}^{*}\)** & **Male** & **Improvement \({}^{*}\)** \\ \hline
**Proposed** & 87.8 & - & 83.7 & - \\ \hline
**Raw EEG + ResNet** & 78.2 & 9.6 & 76.9 & 6.8 \\ \hline PSD + CNN (5 Layers) & 71.3 & 16.5 & 70.4 & 13.3 \\ \hline PSD + CNN (10 Layers) & 78.6 & 9.2 & 74.1 & 9.6 \\ \hline PSD + CNN (14 Layers) & 81.9 & 5.9 & 80.3 & 3.4 \\ \hline PSD + CNN (18 Layers) & 84.0 & 3.8 & 82.4 & 1.3 \\ \hline
**FE+ RF**[41] & 47.2 & 40.6 & 45.8 & 37.9 \\ \hline
**FFT+ MPC**[42] & 61.8 & 26.0 & 61.7 & 22.0 \\ \hline
**PSD + MPC** & 51.8 & 36.0 & 52.4 & 31.3 \\ \hline \hline \end{tabular}  
* Improvement = Performance of the proposed method – Performance of the considered method.  
\end{table}
Table 4: Accuracy comparison of different methods.  
intelligence system/20 AS1100, the development of smart HSE and digital cockpit systems based on ICT convergence for enhanced major industry].  
The authors declare no conflict of interest.
## 5 Packet Length Control for Sparse Signal Transmission
### Performance Improvement with Data Interleaving
#### 5.3.3 Interleaving Length  
Note that performance improvement is correlated with interleaving length, which is limited by memory size and the application's timeliness. Figure 13 further shows the relationship between mutual coherence and interleaving length when \(\frac{M}{N}=0.2\).  
The dashed line represents the mutual coherence guaranteeing accurate signal recovery (the error being zero) when using RCS as the projection matrix with packet lengths equal to 1, 8, 32, 64, 128 bytes. A larger interleaving length makes the data loss more random, therefore making the mutual coherence smaller. Longer packets lead to longer bursty loss, therefore needing larger interleaving length. However, larger interleaving length induces larger transmission delay and needs more memory space. A trade-off must be made between the application's requirements (signal recovery accuracy, latency, _etc._) and interleaving length, which will be our further work. In the following part of this paper, we leave this problem by considering the interleaving length equal to \(N\).  
Figure 12: Packet length effect on relationship between \(BER\) and mutual coherence using RCS projection with interleaving.  
Figure 13: Relationship between mutual coherence and interleaving length.
## 4 Discussion
In line with previous research, this study shows that the placement of the implant had beneficial effects on the bone; the stress on the implant increases as long as the force is applied more distally. In their 2016 study, [PERSON] et al. recommended that special care be taken in the selection of attachments that are used in implant supported RPDs, aiming to reduce stress levels, and to avoid mechanical problems [19]. The effect of the type of attachment on peri-implant and peri-abutment supporting structures was studied by [PERSON] in 2020; he did not find statistically significant differences between the attachment types [20]. Moreover, a recent study showed that the implant survival rate and patient satisfaction were not affected by the type of the attachments used in removable partial dentures on implants, on [PERSON] class II dentulous cases [21]; in our study, a double ball attachment was chosen, especially dedicated for those types of dentures. A study that aimed to evaluate the prognosis of combining natural teeth and implants in the same removable denture found that tooth-and-implant-supported and solely implant-supported double-crown-retained removable partial dentures, with both abutment types, showed high cumulative survival rate, most of which were approximately 100% [22]. Other authors also found that that the association of teeth and implants to support double crown-retained RDPs has a prognostic advantage [23]. None of the unilateral RDPs compared in our study benefit from cross-arch stabilization, since they do not have a major connector; the presence of the distal implant adds height and contributes to the limitation of such horizontal movements, during the action of the tangential forces, showing maximum values on the shoulder of the housing and on the apex of the implant. The limits of this study are that it is based on a numerical simulation, since it was not the authors' intention to perform a clinical trial; however, the finite element analysis method is well used in dental medicine and offers good results for analyzing the design and performance of dentures. Conserving the height of the bone, the vertical dimension of occlusion and the chewing ability of the patient for a long term has a huge importance for a good quality of life, therefore further clinical studies are necessary to confirm that positioning a distal implant to support a class II Kennedy unilateral RPD proves to be a valuable treatment solution.
## 4 Experiment and Discussion
### Undocumented Instruction Function Analysis  
According to the purpose of each undocumented instruction, we divided them into four categories: test, change function, denial of service, and temporarily unclear. Taking TMS320C6478 and TMS320C6678 processors as an example, we list their function of undocumented instructions in Table 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**DSP Processor Series**} & \multirow{2}{*}{**Processor Model**} & \multicolumn{2}{c}{**Number of Undocumented**} \\  & & \multicolumn{2}{c}{**Instructions**} \\ \cline{3-4}  & & **OP-16** & **OP-32** \\ \hline C28x & TMS320F28335 & 52 & 49 \\ \hline C54x & TMS320C5416 & 5 & 0 \\ \hline C64x & TMS320C6416 & - & 15 \\ \hline  & TMS320 DM6437 & 0 & 19 \\  & TMS320 DM6443 & 0 & 19 \\  & TMS320 DM6446 & 0 & 19 \\  & TMS320C6421 & 0 & 19 \\ C64x+ & TMS320C6424 & 0 & 19 \\  & TMS320C6454 & 0 & 19 \\  & TMS320C6455 & 0 & 19 \\  & TMS320C6472 & 0 & 20 \\  & TMS320C6474 & 0 & 20 \\ \hline C66x & TMS320C6678 & 2 & 18 \\ \hline C674x & TMS320C6748 & 2 & 19 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Undocumented instruction found. Figure 5: Experimental Setup of DSPUIM. First, the test function instructions such as DDBG and EDBG were related to debugging. Since they can set the DBGM bit of the TSR register to be one or zero, we judged that the two instructions were built-in debugging instructions of the manufacturer that are not disclosed to users. Second, the function-changing instructions included jump instructions and the instructions changing register. The jump instructions force the pointer to change to the address in the ARP register. If such instructions are maliciously used, the execution sequence can be disordered. On the other hand, PLD, STP, and STWP instructions will change the value of the target register. If they are used to maliciously modify register conditions, some important instructions will be invalidated. Therefore, these function-changing instructions pose a security threat to the DSP processor. Third, the denial-of-service instructions involve EFRW and EFRDW instructions. The execution of the two instructions on the C6678 processor will cause the processor to crash, and a \"Device core is hung\" error will be reported. Once a processor crashes, it cannot work until it is rebooted. In addition, we find that the undocumented instructions with the same instruction format have different execution effects on different series of processors. For example, the execution of EFRW and EFRDW on the C6748 processor will rewrite the target register, which is completely different from the execution effect of the C6678. Finally, there are some instructions whose functions are temporarily unclear, and we did not find any obvious phenomenon or register changes after the execution of these instructions. In addition, we found that although the format and function information
# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 6 Network Solutions at Data-Center Back-Ends to Meet Performance Requirements of 5G
### Kernel Bypassing Network Solutions  
The performance of the traditional kernel networking stack cannot meet the increasing requirements of 5G. Several papers had shown that kernel bypass networking, proposed by [63] among others (see Figure 6), significantly outperforms the traditional kernel based solutions. In [64], the authors showed that Remote Direct Memory Access (RDMA) and Intel's Data Plane Development Kit (DPDK) shows a huge performance increase to the kernel network solutions. However, to reach  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Delay** & **Bandwidth** & **User Mobility** &
\begin{tabular}{c} **Energy** \\ **Consumption** \\ \end{tabular} & **Research Efforts** \\ \hline Tactile internet & \textless{}=5 ms & \textgreater{}1 Gbps & ✓ & ✓ & [32; 33] \\ \hline Data/video analytics & \textless{}10 ms & 100 kbps-10 Mbps & ✓ & - & [49; 50; 51] \\ \hline XR and Industry 4.0 & \textgreater{}20 ms & \textgreater{}50 Mbps & - & ✓ & [52; 54; 55; 56] \\ \hline Caching in the edge & - & 20 Mbps & - & ✓ & [57; 58] \\ \hline Smart vehicles & \textless{}=100 ms & \textgreater{}20 Mbps & ✓ & ✓ & [59; 60; 61; 62] \\ \hline \hline \end{tabular}
\end{table}
Table 3: 5G Use-Case Requirements in Edge Computing.  
the best performance, RDMA and DPDK should be used in poll mode, which of course leads to high CPU utilization. However, DPDK shows unstable operations after a few millions of packets when using it in event mode. On the other hand, RDMA requires special hardware; therefore, it might be inconvenient to implement a heterogeneous infrastructure.
# Quad-Port Multiservice Diversity Antenna for Automotive Applications
## 3 Radiation and Diversity Characteristics  
The radiation patterns of the antenna in the E-plane and H-plane are displayed in Figure 12. Figure 13 depicts the gain and efficiency of the proposed antenna. The maximum gain is found to be 2.14 dBi, and the maximum efficiency is 87%.  
Figure 11: Proposed MIMO antenna prototype.  
Figure 12: Measured radiation patterns of the antenna: (**a**) when port 1 is excited, (**b**) when port 2 is excited.  
The diversity characteristics of the MIMO antenna are examined for its use in automobile applications. Envelope correlation coefficient (ECC) can be calculated with the S-parameter (Equation (1)) and the far-field (Equation (2)) [33], and the calculated ECC is less than 0.4, as shown in Figure 14.  
\[ECC(\rho_{e})=\frac{|S_{ii}^{*}S_{ij}+S_{ji}^{*}S_{ji}|^{2}}{(1-|S_{ii}|^{2}-|S_{ ij}|^{2})(1-|S_{ji}|^{2}-|S_{ii}|^{2})} \tag{4}\]  
\[ECC(\rho_{e})=\frac{|\iint\stackrel{{\rightarrow}}{{[F_{1}}( \theta,\varphi).F_{2}(\theta,\varphi)]d\Omega|^{2}}}{\iint|F_{1}^{*}(\theta, \varphi)|^{2}d\Omega\iint|\stackrel{{\rightarrow}}{{[F_{2}}( \theta,\varphi)]d\Omega}} \tag{5}\]  
Diversity gain (DG) is defined as an increase in signal-to-interference ratio without compromising quality [34]. It also demonstrates how much transmission power can be saved by employing a diversity scheme. It is measured in decibel or power ratio. DG can be calculated with the S-parameter and the far-field, and the DG of the antenna is shown in Figure 15.  
\[\text{DG}=10\sqrt{1-{|ECC|}^{2}} \tag{6}\]  
Figure 14: ECC of the MIMO antenna.  
Figure 13: Measured gain and efficiency of the designed antenna.  
The total active reflection coefficient (TARC) is the square root of the summation of outgoing powers divided by the summation of incident powers at any port of an \(N\)-port antenna [35]. TARC is estimated using the Equation (7), where \(a_{i}\) is the incident signal and \(b_{i}\) is the received signal. TARC of the proposed antenna is less than \(-10\) dB, shown in Figure 16.  
\[TARC=\frac{\sqrt{\sum_{i=1}^{N}{\left|{b_{i}}\right|^{2}}}}{\sqrt{\sum_{i=1}^{N }{\left|{a_{i}}\right|^{2}}}} \tag{7}\]  
Figure 16: _Cont._  
Figure 15: DG of the proposed antenna (**a**) using far-field, (**b**) using S-parameters.  
In high data rate transmission, the transmission loss is calculated using channel capacity loss (CCL) [36]. The CCL of the proposed antenna is shown in Figure 17, and it is less than the practical limit of 0.4 bits/s/Hz.  
Figure 16: TARC of the proposed antenna (**a**) with respect to port 1 and port 2, (**b**) with respect to port 3 and port 4.  
Figure 17: CCL of the proposed antenna (**a**) with respect to port 1 and port 2, (**b**) with respect to port 3 and port 4.
# Ship's Digital Twin--A Review of Modelling Challenges and Applications
## 4 Applications  
Generally, applications can vary; however, they could be classified into some basic groups such as condition-based maintenance and predictive maintenance, as in [8,23], decision-making support, as in [12,22,23], condition monitoring, as in [9,16,26], cost reduction, as in [17], testing and simulation, as in [24], testing the integration of various systems onboard, as in [1], training personnel, as in [50], and overall optimisation, as in [10,13,18].  
Figure 2: Communication layers and protocols.
## 5 Limitations and Future Research Directions
### Control and Stability of VSM in Faulty Conditions  
This research focuses on the control of ESS-based VSM in normal mode. However, in practical applications, actions must be taken to deal with contingencies, such as faulty events in the system. For the faults located behind the breaker, the protection relay will normally trip the device off by opening the circuit breaker. If the fault is before the breaker, there may be fault ride-through requirement, such as low voltage ride through (LVRT), depending on the grid code, especially for large-capacity facilities [42].  
LVRT strategies for grid-following converters, such as conventional renewable generators synchronized with PLL, have been extensively studied and validated in field, but it is still a challenge for grid-forming converter such as VSM, which needs further investigation into two key aspects [2; 3; 4; 5; 6]. (1) Current of VSM must be limited within the safe range in the faulty cases. As a voltage source intrinsically, VSM will experience overcurrent in the low-voltage or short-circuit fault, and may thus damage the converter or ESS associated with VSM, especially considering the overcurrent capability is smaller than conventional rotating generators [26]. There are no standard solutions at present, based on either software (for example, virtual impedance or saturation control) or hardware (overcurrent limiter or protection). (2) Research on transient stability should also be conducted to evaluate the stability performance during faults and provide guidelines for critical clearing time design [2]. This is more complex than conventional synchronous generator given the additional current limiting control introduced. Modified equal area criteria or Lyapunov energy function are two mainstream approaches for such studies.
## 3 Results  
### Drought Occurrence Detected by SPEI Index Monitoring  
SPEI index values over a three-month time scale were used here, since it is assumed that such scale allows effective assessment of meteorological drought intensity. The reference period ranges from 1950 to 2020, and the values of this index since 1981 are shown in Figure 5. Lower negative results indicate more severe drought in a given month. Extreme droughts are characterized by SPEI values of less than \(-2\). Such conditions occurred at ME study area in August 2019, as well as in July and August 2018. It should be noted that even though there were several dry months during both study years, fewer severe drought events occurred in 2020 than in 2019 (Figure 5).  
Figure 4: Schematic of data analysis and calculation combining results from dendrometric, biometric and EC measurements for obtaining net primary productivity (NPP), gross primary productivity (GPP) and carbon use efficiency (CUE).