esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI
## 3 Results
### Enhancement of Scan Efficiency:  
CGAN enhances the scan efficiency of under-sampled and multi-contrast procured images. The shared high-frequency-prior present in the source contrast is used to maintain high-spatial-frequency features. The low-frequency-prior in the under-sampled target contrast is used to avert feature leakage or quality loss. The perceptual prior is used to upgrade the retrieval of high-level attributes. Reconstructing-synthesizing GAN (RS-GAN) generator estimates the target-contrast image from either fully sampled or partially under-sampled source-contrast image [128]. The tissue susceptibility in various brain diseases is measured via the quantitative susceptibility mapping (QSM) technique. The inherent issue of dipole inversion can affect the reliability of the susceptibility map. QSM-GAN is a 3D U-Net that solves the dipole inversion problem in QSM reconstruction [129]. A directed graph represents a brain-effective connectivity network where nodes denote brain regions. EC-RGAN is a recurrent GAN that applies effective connectivity generators to acquire the temporal information from the fMRI time series and refine the quality [130]. Double inversion recovery (DIR) improves FLAIR images acquired with a higher sensitivity for lesion diagnosis than conventional or fluid-attenuated T2-weighted scans. They are beneficial for detecting cortical plaques in MS. DiamondGAN can increase image details via multi-to-one mapping where various input modalities (in this case, T1, T2, and FLAIR) are utilized to produce one output modality (in this case, DIR) [131]. Three-dimensional multi-information GAN uses structural MRI to find cortical atrophy to predict disease progression. First, a 3D GAN model generates 3D MRI images at future-time points; then, a 3D-densenet-based multiclass classification identifies the stages of produced MRI [132]. Visual scenes can be reconstructed from human brain activity measured with fMRI. Dual-Variational Autoencoder/Generative Adversarial Network (DVAE/GAN) learns the mapping from fMRI signals to their corresponding visual stimuli (images). Cognitive Encoder, Visual Encoder, and GAN transform the high-dimensional and noisy brain signals (fMRI) into low-dimensional latent representation [133].
# Method to Predict Performances of PCB Silicone Conformal Coating under Thermal Aging
## 3 Results  
### Bi-Material Strip Bending (BMSB) Characterization and Modeling  
One well-known method to test the mechanical behavior of a substrate-coating assembly is to impose a deflection to a bi-layer strip set on two free rotation supporting points and to apply a force at an equal distance from each support to force bending of the strip. The well-known beam bending theory applied to a bi-layer material is used to calculate the residual stress in the coating [16; 17]. Figure 4 shows the typical BMBS test.  
When both coating thickness and the elastic modulus of the coating are small compared to corresponding values associated with the substrate, the relationship between the residual stress and beam deflection highly simplifies. In applications of the conformal coating  
Figure 4: Schematic view of BMSB modeling.  
Figure 3: (**a**) Thermal shock and (**b**) thermal cycling aging conditions.  
protection of PCBs, those conditions are always satisfied because the coating thickness is much less than PCB thickness. In addition, compared to alternative conformal coating technologies, silicone materials are soft, with [PERSON]'s modulus of the coating being much less than [PERSON]'s modulus substrate. The assumption of the ratio of Young's modulus of coating to the substrate's modulus to be small is satisfied in most cases. With these assumptions, the equation between the residual stress and the deformation takes the following form:  
\[\sigma_{a}=\frac{4}{3}\ \frac{E_{s}h_{s}^{2}}{h_{a}l^{2}}\ \delta \tag{1}\]  
Equation (1) predicts that both high substrate Young's modulus \(E_{s}\) and high substrate thickness \(h_{s}\) contribute to higher stress in the coating for any imposed displacement \(\delta\). On the contrary, higher coating layer thickness \(h_{a}\) and span length l both lead to lower coating stress. It must be recalled that Equation (1) calculates the stress associated with a forced plate bending, i.e., the stress created in the coating layer results from an imposed curvature. In the present application, the bending comes from the difference in thermal expansion between the two components of the bi-layer material. However, because of the large difference in Young's modulus between the substrate and the coating, as well as their different thicknesses, the CTE mismatch will not lead to any significant bending of the bi-material panel; the bi-layer panel stays undeformed, while the major fraction of differential displacement is absorbed by the deformation of the softer material, in our case, the silicone coating. In this case, the local stress built-up inside the coating must be dependent on Young's modulus of the coating, a variable not appearing in Equation (1). For this reason, this method is not appropriate for silicone conformal coating evaluation.
## 2 Materials and Methods  
### Patients and Inclusion Criteria  
This retrospective study included patients who underwent mandibular segmental resection at the Department of Oral and Maxillofacial Surgery of Kyung Hee University Dental Hospital between December 2016 and November 2017. The indications for mandibular resection included osteoradionecrosis, and nonmalignant and malignant tumors. The study included only patients who had undergone both preoperative CT and MRI and postoperative CT of the craniofacial region. Before starting this study design, ethical approval was received from the institutional review board (approval number KHD IRB 1806-2). Written informed consent was obtained from all patients in the study. Preoperative surgical simulations, designs of surgical guides, and operations were all performed by a single experienced surgeon under confirmation of head and neck radiologists.  
Data Acquisition and Three-Dimensional Virtual Models Preparation for Preoperative Surgical Simulation  
Before surgical simulation, the patient's craniofacial region was initially scanned using both CT and MRI. The CT scans of the craniofacial region were taken with the patient in the supine position at a slice thickness of 1 mm (Brilliance CT 64-channel scanner, Philips Healthcare, Best, Netherlands). The protocol of MRI consisted of T1-weighted and T2-weighted sequences, T1 fat-suppressed sequences, T2 fat-suppressed sequences, and diffusion-weighted sequences after intravenous administration of a contrast agent (Philips Achieva 3.0T TX, Philips Healthcare). Additionally, a positron emission tomography (PET)-CT was performed for the staging of malignant tumors.  
Image datasets were imported as Digital Imaging and Communications in Medicine (DICOM) files into Mimics 20.0 software (Materialise, Leuven, Belgium). The DICOM datasets of CT and MRI were segmented as the cranio-maxilla region and mandible. In the case of osteoradionecrosis, the lesion areas were segmented using CT images, and the tumors were segmented on MRI. Segmented cranio-maxilla regions, mandibles, and lesions were reconstructed into three-dimensional (3D) virtual models. With an additional medical software program (3-Matic 12.0, Materialise), the 3D reconstructed CT and MRI mandible models were superimposed images. As a result, tumors segmented in MRI were fused on the mandibular 3D virtual model converted from CT. Finally, the segmented tumors were uniformly increased to 10 mm for a safety margin, and the virtual model for preoperative surgical simulation was completed (Figure 1A-C).
## 4 Experiments
### Performance Metrics  
In order to gauge the efficacy of an algorithm, performance assessment words play a crucial role in machine learning approaches. The performance of algorithms may be measured in a variety of ways. There are problem-specific uses for each concept. It is assumed that FP denotes the misinterpretation of data values and TP represents correctly interpreted data points. FN denotes the number of data points identified incorrectly as normal, and TN represents data points correctly identified as normal ones. We selected the ideal time frame for testing the efficacy of our suggested approach to anomaly identification. In this study, the confusion matrix is defined before any specific measure is discussed. For example, a confusion matrix of 2 \(\times\) 2 is shown below.  
\[\begin{bmatrix}\text{TP}&\text{FP}\\ \text{FN}&\text{TN}\end{bmatrix}\]  
where TP is true positive, FP is false positive, FN is false negative, and TN is true negative.  
Algorithm performance is measured in a variety of ways. The choice of techniques, data collection, and attribute type all influence the vocabulary used. The term \"accuracy\" is first defined.  
\[\text{Accuracy}\ \frac{\text{TP}\ +\ \text{TN}}{\text{TP}\ +\ \text{TN}\ +\ \text{FP}\ +\ \text{FN}}\]  
ZDue to its imbalance, this dataset cannot be used for anomaly detection. It is expected that the number of anomalous occurrences is substantially fewer than the number of normal instances, such that \(\text{TN}\ +\ \text{FN}\approx\text{TN}\ \rightarrow\text{ACC}_{\text{null}} \approx 1\). Even a typical anomaly detector that tags every data point as normal (TP = 0) would have extremely high accuracy.  
However, the metrics of recall (also known as sensitivity or real positive rate) and false positive rate (FPR) are very significant since they indicate the features of a reliable detection system.  
\[\text{Recall}\ =\frac{\text{TP}}{\text{TP}\ +\ \text{FN}}\]  
\[\text{False}\ -\ \text{Positive rate}\ =\frac{\text{FP}}{\text{FP}\ +\ \text{TN}}\]  
The F1-score compares the false rate of two different metrics recall and precision. The F1-score is defined as the harmonic mean of recall and accuracy.  
\[\text{F1}-\ \text{score}\ =2\text{.}\ \frac{\text{precision}\ *\ \text{recall}}{\text{ precision}\ +\ \text{recall}}\]  
F1-score values vary from zero to one, where the latter indicates perfect precision and recall, and the former represents zero precision and recall.
# Context-Aware Gossip-Based Protocol for Internet of Things Applications
## 5 Simulations and Parameter Effects
### Simulation Results
#### 5.2.3 Saved Rebroadcasts  
The saved rebroadcasts performance measure is used to determine the bandwidth utilisation as fewer nodes are involved in rebroadcasting a message. This percentage is calculated as the number of nodes that received the message but did not rebroadcast it divided by the the total number of nodes that received the message. The percentages of saved rebroadcasts as the number of nodes in the  
Figure 3: The impact of parameter activation on the average percentage of rebroadcast nodes as the number of nodes increases from 50 to 250 nodes.  
network increases from 50 to 250 nodes are illustrated in Figure 4. This figure also shows the influence of parameter control on the percentage of saved rebroadcasts.  
The percentage of saved rebroadcasts appears to increase linearly as the number of nodes increases from 50 to 250 nodes for four of the controlled parameters: residual energy (\(f1\)), sink distance (\(f3\)), node density (\(f4\)), and message priority (\(f5\)). When analysed in tandem with Figure 3, the percentage of saved rebroadcasts is seen to exhibit the inverse trend compared with that shown for the percentage of rebroadcast nodes and thus shows similar parameter effects. Controlling the sink distance (\(f3\)) parameter results in a slight increase in the percentage of saved rebroadcasts, with values ranging from 94% to 99% as the number of nodes increases from 50 to 250. With the activation of the node density (\(f4\)) and message priority (\(f5\)) parameters, although the percentage of saved rebroadcasts remains relatively stable for networks of 50-150 nodes, it shows significant increases of approximately 7% and 10% as the number of nodes increases to 200 and 250, respectively. The percentage of saved rebroadcasts is shown to be stably increasing with an increasing number of nodes in the network when the residual energy parameter (\(f5\)) is controlled. The next-node distance parameter (\(f2\)) shows the least stability, as seen from the decrease in the percentage of saved rebroadcasts as the number of nodes increases to 150, followed by a slight increase as the number of nodes in the network grows to 250.
## 4 UAV Data Acquisition and Preprocessing
### UAV Imagery Data Preprocessing  
#### 4.2.1 Frame Extraction and Fusion from UAV Imagery Video  
Video frame data play a crucial role in acquiring UAV pavement crack images. In order to obtain and supplement the original pavement crack datasets, it is necessary to extract and crop the frames. During the frame extraction process, it is important to consider the overlap, spacing, and seamlessness of neighboring video frames to ensure the integrity and independence of each frame. Additionally, the setting of the frame extraction interval is of the utmost importance. If the interval is too large, it may result in a lack of seamless fusion and docking. On the other hand, if the interval is too small, there will be significant overlap between frames, leading to an excessive number of frames and an increased computing cost. The formula for calculating the extraction interval number (\(N\)) is as follows:  
\[N\leq\left[\frac{GSD\times F_{I}\times(1-r)}{v}\times fps\right] \tag{5}\]  
where \(F_{I}\) is the frame image size (px), i.e., the flight direction; _fps_ represents the frames per second in each video; and the other variables are described in the previous section.  
For this experiment, UAV imagery in the **DJI** Mini 2 was set to 4K HD, which corresponds to the **DJI** official frame image size of 3840 px \(\times\) 2160 px. Namely, \(\textbf{F}_{I}\) was taken as 3840 px. The _fps_ was officially 24 f\(\cdot\)s\({}^{-1}\), and **GSD** and \(v\) were calculated to be 0.4125 cm/pixel and 5.0 m/s as above, respectively. The overlap (_r_) was taken as 75%. Using Equation (5), the extraction interval number (_N_) was found to be [19.01], which was rounded to 19. Finally, to ensure sufficient overlap, this study extracted an image every 19 frames from the video. The extracted frame images were then used to stitch together overlapping parts of neighboring frames using the picture fusion technique.
## 3 Result and Discussion
### Functionality Validation Testing  
This testing was performed to verify the sensor-node functionality to measure indoor CO2 concentration data and transmit the acquired data to the cloud over the IoT system. Furthermore, the current and trend of CO2 data can be visualized and monitored in real time on the Adafruit dashboard. The testing configuration is presented in Figure 9a. The CO2 sensor node was connected to the monitoring terminal over a Wi-Fi/internet connection that was integrated with the IoT system.  
The testing process is shown in Figure 9b. The sensor node that was already integrated with the IoT system was placed on the table inside the indoor environment. During 2 h testing periods, the sensor node continuously measured and wirelessly transmitted the CO2 concentration data to cloud storage with a sampling rate of 1 min. At the same time, the CO2 concentration data was visualized on the Adafruit IO dashboard on the monitoring terminal.  
The testing results show that the developed sensor node has worked properly to measure indoor CO2 concentration, send the data to the cloud over an IoT system using a Wi-Fi/internet connection, and visualize the CO2 data on the Adafruit dashboard at the monitoring terminal. The range value of CO2 concentration was 500-850 ppm. The CO2 data was visualized with data history and a real-time graph using a graphical user interface, as shown in Figure 9b.  
Figure 8: The dashboard monitoring.
# Analysis and Design of Protection Device for Anchor Cable Pull-Out in High-Stress Roadways
## 3 Tensile Test of Spring Protection Devices
### Dynamic Impact Assessment of Spring Protection Device through Energy Conversion Analysis  
In case of anchor wire breaking and ejection, the ejecting steel wires hit the spring base of the protective device, subjecting the spring to deformation and allowing it to absorb energy and exert a protective effect. The protective device for anchor wire breaking is as shown in Figure 5. During the construction of mine roadway support, a steel chain equipped with hooks or D-shaped buckles at both ends serves as a connecting element. One end of this steel chain is suspended from the roof anchor net, while the other end is linked to the handle of the anchor cable protection device. Detailed implementation measures are depicted in Figure 6.  
Figure 5: Schematic of impact protection device for anchor wire breaking.  
Figure 6: Installation schematic of the anchor protective devices.  
The energy that can be absorbed by the protective device is expressed as follows:  
\[W=\int_{0}^{H}k\mathrm{d}y, \tag{11}\]  
The safety criterion for energy conversion between anchor wire breaking and ejection and the protective device is described as follows:  
\[W_{x}=\frac{1}{2}mv_{1}^{2}\leq W=\int_{0}^{H}ky\mathrm{d}y, \tag{12}\]  
where \(H\) is the stretching amount of the spring, \(k\) stands for the elastic coefficient of the protective device for anchor wire breaking, and \(W\) represents the effectively absorbed energy by the protective device for anchor wire breaking and ejection.
# Blockchain Applications in Forestry: A Systematic Literature Review
## 6 Managerial Implications  
This study provides several important managerial implications related to the blockchain applications for decision-makers, managers, consultants, and practitioners in the forestry field. The primary applications are implementing blockchain-based applications for forest products, forest fire detection, and preserving forests' biodiversity and resources. The main managerial implications include (1) blockchain-based IoT systems can increase the efficiency of traceability to establish the purchaser's trust in wood products and minimize illegal logging; and (2) the features of blockchain, such as decentralization, transparency, confidentiality, and data integrity, could provide competitive advantages for enterprises in the forestry sector. If managers would like to use cryptocurrency and blockchain, they may need to hire new skilled employees to specialize in cryptography and smart contracts.  
Although blockchain has multiple benefits in global timber trading and forest management, there are a few risks and disadvantages that managers and practitioners should consider before implementing blockchain in their enterprises: (1) due to the nature of blockchain, data breach and hacking is relatively difficult, but not impossible. In fact, in the last ten years, there have been multiple cybercrimes, such as cryptocurrency hackings, data breaches, and Sybil attacking reported worldwide [54]. It is important to understand that the blockchain is not un-hackable and immutable [55]. The security of blockchain should be listed as a potential risk for forestry enterprises. (2) Even though blockchain was reported for cost reduction on forest farmers' abatement and processing of wood products, the enormous energy consumption and electric waste of cryptocurrency transactions is also a disadvantage. The infrastructure of blockchain would be costly for startups. It should be considered by consultants and decision-makers in the forest sector. (3) Every startup could create its own cryptocurrency. However, there are many unsuccessful cryptocurrencies. The enterprise should realize that not every cryptocurrency is successful as bitcoin.
# Resource Allocation in Downlink VLC-NOMA Systems for Factory Automation Scenario
## 4 Evaluation and Analysis
### Evaluation of the Proposed User-Pairing  
In this subsection, the simulation results of scheduling by OMA with deadline monotonic scheduling (DMS), NOMA with the next-largest-difference-based user pairing algorithm (NLUPA) [30], and NOMA with PBUP are dealt with. Simulation parameters are shown in Table 5. In the case of NLUPA, the transmission order is as the following example. For example, if there are four nodes released, the first user pair, which consists of the node with the largest channel gain and the node with the lowest channel gain, will be transmitted first, and the next transmission is on the next user pair that consists of the node with the second largest channel gain and the node with the second lowest channel gain. Except for OMA, DBPA is adopted for NOMA systems as the power allocation scheme. In this simulation, 10 nodes are deployed at locations shown in Table 5. Released time means when the messages for nodes are released. Computing time means how many timeslots are required for transmission. Deadline means when the transmission should be performed by after being released. Period means the time when messages appear periodically. Simulation time is 10,000 ms, which means 1000 timeslots.  
As shown in Table 6, missed deadlines are evaluated by simulation. Missed deadline means that a transmission is not finished by the deadline. By comparisons among OMA with DMS and NOMA with NLUPA, the simulation results show that PBUP has fewer missed deadlines than the others. The OMA system handles only one transmission during a timeslot; the NOMA system handles a maximum of two transmissions during a timeslot  
Figure 9: BER comparisons (**a**) BER comparison (\(h_{f}=0.0251\), \(h_{n}=0.4010\)). (**b**) BER comparison (\(h_{f}=0.0402\), \(h_{n}=0.3675\)). (**c**) BER comparison (\(h_{f}=0.0445\), \(h_{n}=0.1002\)). (**d**) BER comparison (\(h_{f}=0.0395\), \(h_{n}=0.3290\)).  
using superposition coding. Comparing PBUP with NLUPA, PBUP considers the deadline-based priority; instead, NLUPA only considers user-pairing. Therefore, PBUP has fewer missed deadlines than the others.  
The packet delivery ratio (PDR) means transmissions succeed without packet errors and missed deadlines. The evaluation results are shown in Table 7. The evaluation proceeded with OMA with DMS, NOMA with PBUP, and NOMA with NLUPA. The NOMA systems adopted the DBPA as the power allocation scheme between NU and FU. At transmitted SNR 40 dB, OMA with DMS showed higher PDR than the others, because NOMA systems should divide transmitted power between NU and FU. It causes higher packet errors than OMA. However, from 41 dB, the NOMA systems start improving PDR until 49 dB. From 49 dB, the saturation starts because of missed deadlines and no packet errors. No more reduction of packet errors and missed deadlines are generated from 49 dB. Table 7 shows that the NOMA system with PBUP and DBPA outperforms the others.