esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Task Learning for Small Brain Tumor Segmentation from MRI
## 4 Experimental Results
### Results and Analysis  
#### 4.5.1 Small Tumor Segmentation Performance  
We evaluated the effectiveness of the proposed model with other models on small-sized tumors. We randomly split training into 80% for training and 20% for evaluation. We trained all models on the training set and get the best results of each model on the evaluation set. As shown in Figure 4, the enhancing tumor is a tumor type with the smallest size in brain. Therefore, we proved our effectiveness on small-sized tumors by comparing the segmentation results of our models with other models on small enhancing tumors. The criteria for selecting small-sized tumors are the volume size of tumors in voxels, and thresholds of the volume are selected as 2000 voxels and 1000 voxels respectively.  
Figure 4: Box plot of tumor volume with different tumor subregions. Outliers in the circle, the box with 25–75% with the orange line of median, and dash bars indicating 0–100% except outliers.  
As shown in Tables 1 and 2, our model performed better with enhancing tumors smaller than 2000 voxels. Other models such as DMFNet [43], ReversibleUnet [48], and No New-Net [49] often overlook tumors of a very small size. However, our method can detect and segment them.  
Figure 5 shows the input images, ground truth, and segmentation results of the enhancing tumor predicted by four models including our model, ReversibleUnet [48], No New-Net [49], and DMFNet [43]. As shown in the first row of the figure, ReversibleUnet and No New-Net predicted high false negatives on the enhancing tumor (yellow color), DMFNet failed to segment the enhancing tumor area while our model can accurately segment them. In the second and third rows of Figure 5, we can see that our model could segment the area of small enhancing tumor while other models failed to segment them. Figure 6 shows the dice similarity coefficient (DSC) stratified by tumor size.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Dice Score** & **TPR** & **FPR** \\ \hline DMFNet [43] & 0.2720 & 0.2316 & 0 \\ ReversibleUnet [48] & 0.2828 & 0.2479 & 0 \\ No New-Net [49] & 0.3154 & 0.3322 & 0.0001 \\
**Ours** & 0.4499 & 0.4269 & 0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The performance comparison of small tumors (tumor volume \(<\)2000 voxels).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Dice Score** & **TPR** & **FPR** \\ \hline DMFNet [43] & 0.1638 & 0.1456 & 0 \\ ReversibleUnet [48] & 0.1645 & 0.1462 & 0 \\ No New-Net [49] & 0.2147 & 0.2751 & 0.0001 \\
**Ours** & 0.3854 & 0.3963 & 0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The performance comparison of small tumors (tumor volume \(<\)1000 voxels).  
Figure 5: Qualitative comparison of different models for small tumor segmentation. (**a**) Input fluid attenuated inversion recovery (FLAIR) image. (**b**) Input T1 image and (**c**) ground truth images. (**d**) Results of our method. (**e**) Results of ReversibleUnet [48]. (**f**) Results of No New-Net [49]. (**g**) Results of DMFNet [43].
## 3 Proposed Cost-Effective Auto-Scaling (CEAS) Framework
### Monitoring and Provisioning Amazon EMR Clusters for Auto-Scaling
#### 3.3.1 Elastic Amazon EMR Cluster  
As discussed in the previous subsection, an Amazon EMR cluster which is made of Amazon EC2 instances has sufficient instances to process the streaming data. Each EC2 instance in the cluster is classified as a node and the cluster can monitor the requirements as the data size increases. The cluster also coordinates the resizing of the cluster to suit everyday data request needs [15; 25]. There are basically three types of nodes available within the Amazon EMR cluster, including:  
1. _Master node_: executes NameNode and JobTracker, coordinates data distribution and tasks among other nodes, while monitoring the cluster health.
2. _Core/Slave nodes_: executes DataNodes and TaskTracker, runs the tasks and stores data in the HDFS.
3. _Task nodes_: are optional nodes added to handle the TaskTracker when a high load is required for data processing.  
On AWS cloud, CloudWatch offers a periodic monitoring service for applications and resource use at every 5 min. The EMR CloudWatch metrics can be used to automate the task by adding the required data nodes to the cluster. The Amazon EMR uses a variety of CloudWatch metrics like the number of mappers and/or reducers (active/inactive), the status of the cluster (idle/active), and HDFS use to resize the cluster.  
Using Amazon Simple Notification Service (SNS) along with CloudWatch metrics, a notification service can be enabled for resource use within an EMR cluster. For instance, the EMR cluster with a large number of jobs during peak hours needs to be resized rather than increasing the queuing time of the jobs. Such process can be monitored using CloudWatch metrics and auto-scaling action could be performed. By using the Amazon EMR, the processing time can be controlled and further improved by adding the required capacity. Moreover, the CloudWatch can trigger an alarm when CPU use increases. This alarm can be associated with an auto-scaling policy via SNS notifications, i.e., a scaling event is logged when the scaling is triggered.
###### Abstract  
The hollow metal waveguides are attractive components for millimeter-wave circuits owing to low loss. To integrate with the front-end circuit, a transition from microstrip line to waveguide is required. In this article, a microstrip-to-waveguide transition is presented in the W-band by using an E-plane probe with a parasitic patch. The probe is embedded into the waveguide along the center of the broad wall to excite the TE\({}_{10}\) mode. A backshort-circuited waveguide with a quarter wavelength is used to obtain sufficient energy coupling and achieve good impedance matching. The additional parasitic patch can introduce a new resonance at a low frequency to enhance the working bandwidth. Hence, the proposed transition achieves wide working bandwidth and low insertion loss. For verification, a back-to-back transition is constructed and measured. The measured results indicate that the proposed transition has a wide working bandwidth covering the entire W-band. The measured reflection coefficient is below \(-13\) dB from 70 to 110 GHz and the average insert loss is 1.1 dB. Attributed to wide working bandwidth and simple structure, the proposed transition is attractive for W-band circuit systems.  
E-plane probe; microstrip-to-waveguide transition; parasitic patch +
Footnote †: journal: Applied Sciences  
**Citation:** [PERSON], [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A Wideband Mistoristor-to-Waveguide Transition Using E-Plane Probe with Parasitic Patch for W-Band Application. _Appl. Sci._**2022**, 12, 12162. [[https://doi.org/10.3390/app12231262](https://doi.org/10.3390/app12231262)]([https://doi.org/10.3390/app12231262](https://doi.org/10.3390/app12231262))  
Academic Editor: [PERSON]  
Received: 3 November 2022  
Accepted: 25 November 2022  
Published: 28 November 2022  
**Published:** **Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  
**Copyright**\(\copyright\)\(\copyright\)\(\copyright\) 2022 by the authors. Liconsee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# A Critical Review of Flood Risk Management and the Selection of Suitable Measures
## 4 Conclusions and Recommendations  
The concept of risk-based flood management is not new and has evolved over time. Risk-based flood management is replacing the probability-based (return period) approach, but the design standards, implementation procedures, and detailed protocols are yet to be determined. Risk-based flood management is popular and has received broader appraisal in many parts of the world, yet the approach needs a few finer refinements before being accepted as a standard approach at national levels.  
Comprehending the role of risk parameters is important for the scrutiny of flood management schemes. A careful valuation of the economic, social, and environmental assets is indispensable in order to develop a balanced flood management approach. Understanding risk phenomenon is very important in order to devise the most effective solutions under the socioeconomic and environmental constraints. The basis of effective and efficient risk reduction measures lies in risk assessment, which considers the different aspects of flood risk, e.g., hydrological, hydraulic, economic, social, and ecological aspects. The presented risk definition and mechanism provide a standardized approach and establish common grounds to compare different flood management options.  
Many options and measures exist for flood management. Their effectiveness, suitability, and role within a plan are highly associated with the characteristics of a floodplain and the combination of measures selected. Every floodplain has its own circumstances and needs a tailor-made solution based on thorough analysis. The initial shortlisting of measures should be done considered alongside other models suitability matched to the floodplain, as well as the combined efficiency of the measures themselves. However, the final scheme of measures must undergo a detailed risk assessment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Software** & **GIS-Based** & **Country** \\ \hline ANUFLOOD & No & Australia \\ ESTDAM & No & UK \\ FAT & Yes & Czech Republic \\ FDAM & Yes & Japan \\ FLODSIM & No & South Africa \\ FloodAUS & Yes & Australia \\ HAZUS-MH & Yes & USA \\ HEC-FDA & No & USA \\ HIS-SSM & Yes & Netherlands \\ HWSCalc & No & Germany \\ TEWA & Yes & South Africa \\ MDSF & Yes & UK \\ \hline \hline \end{tabular}
\end{table}
Table 3: Popular software tools, their compatibility to geographic information systems (GIS), and their country of origin.  
**Author Contributions:** Conceptualization, M.A.U.R.T. and N.v.d.G.; Data curation, M.A.U.R.T.; Formal analysis, M.A.U.R.T. and R.F.; Investigation, M.A.U.R.T. and R.F.; Methodology, M.A.U.R.T. and R.F.; Software, M.A.U.R.T. and R.F.; Supervision, M.A.U.R.T. and N.v.d.G.; Validation, M.A.U.R.T.; Visualization, M.A.U.R.T. and R.F.; Writing--original draft, M.A.U.R.T. and R.F.; Writing--review & editing, M.A.U.R.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 The Algorithm: Space of Convergence Vectors (SCV)
### The Proposed Algorithm
#### 4.2.3 Termination Condition  
The termination condition refers to the reasonable number of iterations of the SCV algorithm. In this respect, the number of \(H\) possible combinations (see Formula (6)) and the number of particles \(s\) in the system are considered. The highest integer \(N\), which is \(\leq H/s\), reveals the maximum number of iterations to be covered by the relevant combinations; or in other words, the position where the convergence condition should be obvious. However, from a practical perspective, if solutions generated at each iteration are defined by the creative intervention of people and not by an automatic artificial intelligence process, it would be desirable to define the mature solution as soon as possible (after a reduced number of iterations, e.g., \(Q=7\div 10\) iterations).
## 3 Antenna Sensor Simulation and Parameter Optimization
### Influence of the Feed Position on the Antenna Parameters  
Considering the symmetry of the antenna, feed points are selected on its symmetry axis. Figure 6 shows two symmetry axes of the antenna, denoted as the \(X\)-axis and \(Y\)-axis. Five points (Points 1-5) are selected to be the feed points, which are used to analyze the main resonant frequency and impedance matching degree at different feed positions. Points 3 and 5 along with Points 2 and 4 are respectively located on the \(X\)-axis and \(Y\)-axis. Point 1 is located at the geometric center of the antenna.  
In addition, although the electromagnetic waves caused by the PD are distributed in the range of 300 MHz to 3 GHz, the main electromagnetic energy of the PD UHF signals concentrates under 1 GHz due to the significant attenuation of high-frequency signals. Therefore, the resonant points between 300 MHz and 1 GHz are considered in this study. The low frequency band mentioned below refers to this frequency band.  
The simulation results, shown in Table 1, indicate that the antenna exhibits different resonant frequencies at different feed points, and these differences are embodied not only in the value of the resonant frequency but also in the number of resonant points. When the feed position is located at the geometric center of the antenna (Point 1), the antenna can only stimulate a single resonant point with a large resonant frequency. Because this point is situated at the core of the antenna, current paths in all directions are similar and singular. The simulation results of feed Points 2 and 4 on the \(Y\)-axis show that the antenna can stimulate three resonant points in the low-frequency band. By contrast, four resonant points can be stimulated when the feed points are Points 3 or 5 on the \(X\)-axis. By analyzing the current distribution of the antenna shown in Figure 7, it is found that the current path is more complicated when the feed point is located on the \(X\)-axis, which stimulates more resonant points.  
Figure 6: The antenna sensor.  
Additionally, it also could be concluded from Table 1 that the resonant frequency at the minimum VSWR differs significantly as the feed point changes on the same axis, whereas the resonant frequency at diverse resonant points exhibits a slight change. According to the aforementioned simulation analysis, the feed position has a considerable influence on the resonant frequency and impedance matching degree at the resonant points. Thus, it is necessary to optimize the feed position of the antenna sensor. In this work, the optimal feed position will be selected from the \(X\)-axis.
## 5 Implementation and Evaluation
### Evaluation  
For 53,000 devices, we examine the identification capability via 10-fold cross-validation. Figure 6 shows how the success rate changes with the maximum number of probe methods. The X-axis represents the maximum number of probe methods, and the Y-axis represents the success rate. The success rate equals \(N_{get}/N_{all}\), where \(N_{all}\) is the number of target devices, and \(N_{get}\) is the number of devices that return responses containing device fingerprint information.  
Figure 5: The probability of obtaining device fingerprints.  
Figure 6: The success rate of device fingerprint identification.  
Our approach selects the optimal probe method in the first step of identification and achieves a high success rate. Furthermore, the success rate stabilizes when the maximum number of probe methods reaches five. At this stage, the identification success rates for device brand, model, and firmware version reach 96.89%, 93.43%, and 83.71%, respectively. Therefore, when performing large-scale device identification, we can ignore the probe methods in the tail of the optimal probe sequence to improve efficiency.  
Different types of IoT devices can use our approach to obtain high efficiency and success rates, i.e., our approach has wide applicability.
## 4 Simulation Results
### Simulation Setup  
During the first step of the simulations, the parameters assume the following values: \(\alpha\in\{0.01,0.02,0.04,0.06,0.08,0.1,0.2,0,4\}\), number of nodes \(N=100\) and network average degree \(k\in\{4,6,8,10\}\). During the second step of the simulations, the parameters assume the values: \(\alpha=0.1\), \(N\in\{50,100,150,200,250,300\}\) and \(k=8\). For each number of nodes in the network, we considered a set of 10 randomly-generated topologies. The following metrics are used: number of messages required for the tree construction, runtime of the algorithm, average distance to the sink and average number of hops to the sink. The first two metrics (number of messages and runtime) are intended to evaluate the efficiency of the algorithms. Messages retransmitted due to collisions are not accounted for. The number of retransmissions is unlimited in order to create an ideal scenario to simplify the evaluation of the algorithms. The last two metrics (distance and number of hops) aim to evaluate the quality of the tree. The shorter the distance and the lower the number of hops from sensors to the sink, the higher the quality of the tree, because distance and number of hops impact the energy consumption and end-to-end delay during the operation of the network. Table 2 summarizes the simulation parameters. The transmission and interference range are based on typical IEEE 802.15.4 parameters along with the log-distance path loss model. We choose the distance between nodes to compute the cost of the links, because it is one of the methods most commonly used to relay messages in a wide variety of networks [9]. However, EBF and DBF algorithms can use any metric described in the literature. To estimate the distance to the sending node, the receiving node can use the RSSI (received signal strength indicator) [28].
# Omnidirectional Haptic Guidance for the Hearing Impaired to Track Sound Sources
## 2 Methods
### Relationship between Interaural Time Difference and Source Direction  
The relationship between the interaural time difference and the direction of the sound source was determined by experiment. In order to measure the sound source direction accurately, the experiment was conducted using a binaural head-torso simulator instead of a human subject. The loudspeaker was fixed at a distance of 2 m from the head-torso simulator, and the direction of the simulator was changed to measure the interaural time difference. The relationship between the interaural time difference and the sound source direction is shown in Figure 3.  
When the interaural time difference was 0 us, the source direction was considered 0. It can be seen that the interaural time difference increases in proportion to the source direction. Using this relationship, we can estimate the source direction from the interaural time difference. When a robot's head is mechanically rotated, it is preferable to calculate the angle at which it should be rotated, but when a person's head is pointed in the direction of the sound source, as in this study, even if the person is told the angle, they cannot rotate their head accurately. Therefore, a relationship such as the one shown in Figure 3 is not necessary; it is sufficient to convert the time difference between the two ears, which has a one-to-one relationship with the direction of the sound source, into the intensity of the vibrations of the vibrator.  
Figure 3: Relationship between interaural time difference and direction of sound source obtained experimentally.  
Figure 2: (**a**) Example of frequency response of interaural time difference when sound source is located at 140 behind and to the left, and (**b**) corresponding histogram. In this example, time difference between ears is 360 μs.
# Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information
###### Abstract  
With the development of the Internet-of-Things (IoT), wireless network security has more and more attention paid to it. The Sybil attack is one of the famous wireless attacks that can forge wireless devices to steal information from clients. These forged devices may constantly attack target access points to crush the wireless network. In this paper, we propose a novel Sybil attack detection based on Channel State Information (CSI). This detection algorithm can tell whether the static devices are Sybil attackers by combining a self-adaptive multiple signal classification algorithm with the Received Signal Strength Indicator (RSSI). Moreover, we develop a novel tracing scheme to cluster the channel characteristics of mobile devices and detect dynamic attackers that change their channel characteristics in an error area. Finally, we experiment on mobile and commercial WiFi devices. Our algorithm can effectively distinguish the Sybil devices. The experimental results show that our Sybil attack detection system achieves high accuracy for both static and dynamic scenarios. Therefore, combining the phase and similarity of channel features, the multi-dimensional analysis of CSI can effectively detect Sybil nodes and improve the security of wireless networks.  
channel state information; Sybil attack; indoor AoA technology; DBSCAN algorithm Article
## 2 Materials and Methods
### Human Spine FE Model  
#### 2.2.1 Intact Model  
The intact model was created based on CT images of males in their thirties without spinal pathologies. To create a geometric model, a healthy spine is preferred over a spine with a specific disease [8]. Accordingly, this present study established the intact model based on one previous study, which created an intact model using the spine of a 32-year-old male with no pathological problems [32]. The spinal range of the intact model was from T9 to L5, and the model was constructed with nine vertebra and eight IVDs (Figure 1). The CT image was converted into a three-dimensional geometric model using Mimics software (Version 23.0, Materialise NV, Leuven, Belgium), and the vertebral body and IVD were simplified to facilitate the generation of a hexahedral element. The IVD was composed of the ground substance of the annulus fibrosus and nucleus pulposus as shown in the Figure 1, and the vertebral body and IVD were connected in a bonded state. Six major ligaments of the vertebral body are connected to the top and bottom of the vertebral arch via a two-node element [33].  
Ansys Workbench software (Version 2019 R1, ANSYS Inc., Pittsburgh, PA, USA) was used for material property settings and mesh generation in the FE model. Element generation information of the FE model was as follows: the element type of the vertebral body corresponded to a hexahedral element, which consisted of 5278-7648 elements per segment. The IVD was created as a hexahedral element in a similar manner as that used in the vertebral body. The vertebral arch was a tetrahedral element, with 26,372 to 35,838 elements per segment. Furthermore, the screw was penetrated with a complex shape. Thus, more elements were created here than in other parts. The percentage of elements with a quality index over 0.85 (quality range: 0-1) was 92.09%. Also, 72.87% of the total number of elements were trilinear hexahedral while the rest corresponded to linear tetrahedra. We created an FE model that seemed like a vertebral arch of a complex shape with high quality elements.