esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Impact on Clinical Decision Making  
Table 6 reports the results for the \(MC\)\(\overline{AD}\), the activity that is as high as safely administrable (AHASA), and the maximum activity in the lungs (MLA) not to be exceeded, ensuring that the average absorbed dose to the lungs does not exceed the 30 Gy threshold [2,3] for the specific simulated case.  
It is worth noting that the safe activity in the lungs is almost the same for all \(LS\) values due to the negligible cross-irradiation contribution from the liver to the lungs. It is noteworthy that, for this specific patient and the simulated liver biodistribution of treatment microspheres, the maximum lung activity (MLA) is significantly lower than the typically expected MLA of approximately 600 MBq according to international guidelines [2,3]. This difference primarily arises from the physical assumptions underlying the computation of MLA using the MIRD approach. Additionally, computations using any of the other tested methods would similarly result in an overestimation of the MLA compared to the \(MC\) simulation.  
It should be noted that all the presented results are specific to the particular morphological information of the case in this study and are not intended for general application.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Lung Shunt** & \(MC\)\(\overline{AD}\) (Gy/GBq) & **AHASA (GBq)** & **MLA (GBq)** \\ \hline
10\% & 11.9 & 2.52 & 0.25 \\
20\% & 23.0 & 1.30 & 0.26 \\
30\% & 34.3 & 0.87 & 0.26 \\
40\% & 45.6 & 0.66 & 0.26 \\ \hline \hline \end{tabular}
\end{table}
Table 6: \(MC\)\(\overline{AD}\) results for all the \(LS\) cases and the corresponding activity that is as high as safely administistable (AHASA) and the maximum lung activity (MLA).  
Figure 6: From left to right, this figure shows an example of the activity biodistribution for the \(LS\) = 20% case and the corresponding \(AD\) distribution maps for the \(MC\) simulation. Also included are the \(AD\) distribution maps obtained from several convolution approaches for visual comparison. The colors used to represent the activity biodistribution are purely illustrative, indicating that the activity was uniformly distributed within each region (for further details, see Section 2.1). The \(AD\) distributions are reported as Gy per GBq of administered activity. For Lung_296 and Lung_L, the \(AD\) distribution does not display any dose in the liver region, reflecting the computational choice to crop the activity map to the lung region only. Note that the \(AD\) distribution for Lung_296 is represented with a different color scale, from 0 to 40 Gy/GBq, compared to the other methods, which are shown with a scale from 0 to 120 Gy/GBq, because using the latter scale, the dose values would not have been visible.  
None of the results should be interpreted as general criteria or as a basis for corrections to computations performed with any of the tested approaches for patients other than the one studied here. However, the overall performance of each method is indicative of its limitations, with the primary differences arising from the underlying physical assumptions of each tested approach.
# Multi-Constrained Optimization Method of Line Segment Extraction Based on Multi-Scale Image Space
###### Abstract  
Image-based line segment extraction plays an important role in a wide range of applications. Traditional line segment extraction algorithms focus on the accuracy and efficiency, without considering the integrity. Serious line segmentation fracture problems caused by image quality will result in poor subsequent applications. To solve this problem, a multi-constrained line segment extraction method, based on multi-scale image space, is presented. Firstly, using Gaussian down-sampling with a classical line segment detection method, a multi-scale image space is constructed to extract line segments in each image scale and all line segments are projected onto the original image. Then, a new line segment optimization and purification strategy is proposed with the horizontal and vertical distances and angle geometric constraint relationships between line segments to merge fracture line segments and delete redundant line segments. Finally, line segments with adjacent positions are optimized using the grayscale constraint relationship, based on normalized cross-correlation similarity criterion for realizing the second optimization of fracture line segments. Compared with mainstream line segment detector and edge drawing lines methods, experimental results (i.e., indoor, outdoor, and aerial images) indicate the validity and superiority of our proposed methods which can extract longer and more complete line segments.  
line segment extraction; multi-scale image space; optimization; purification; geometric constraint; grayscale constraint +
Footnote †: journal: Geo-Informat
# Bearing Fault Diagnosis Approach under Data Quality Issues
## 2 Experimental Apparatus and Data Sets  
### Case Western Reserve University (CWRU) Testing Rig  
The vibration data sets were provided by the Case Western Reserve University (CWRU) bearing data center [31]. In the experiment, a testing rig was used to extract the vibration signals from the bearings, as shown in Figure 1. The apparatus consisted of an electric motor, torque transducer, a dynamometer, and tested bearings. A 1.491 kW reliance electric motor drives a shaft that is connected by a coupling. On the driving shaft, a torque transducer and encoder were mounted to measure speed and horsepower data. Moreover, the vibration signals were gathered using accelerometers attached to the motor housing using a magnetic base. Accelerometers were located at the 12 o'clock position at both the drive end and fan end of the motor housing.  
In this experiment, a dynamometer with an embedded electronic control unit was used to apply different motor loads (0-2.237 kW). It is known as absorbing dynamometer that acts like a load driven by the motor. The amount of applied torque was proportional to the motor load that was managed via variable frequency drive through the control unit. Therefore, the increase in motor load resulted in an increase in torque and a reduction in shaft speed. The 0 kW motor load represented the no-loading condition and the shaft speed was slightly slower than synchronous speed. However, under the three loading conditions (0.746-2.237 kW), the shaft speed was less than the synchronous speed.  
The tested bearings were steel, sealed, deep-groove ball bearings (SKF 6205-2 RS JEM) installed at the drive end of the motor. The ball bearing and its dimensions are shown in Table 1. Each bearing had a limiting speed, 18,000 rpm, and a maximum basic dynamic load, 14,800 N. Artificial fatigue cracks were seeded on the bearings using electro-discharge machining (EDM), as shown in Figure 2. Faults with diameters, 0.18 mm and 0.53 mm, were introduced on different bearings at the rolling element (ball), the inner race, and the outer race. After seeding the bearings with faults, they were installed into the motor to test them and record the vibration data sets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bearing Model** & **Bore (mm)** & **Outside Diameter (mm)** & **Width (mm)** & **Ball Diameter (mm)** \\ \hline SKF 2605 JEM & 24.89 & 51.82 & 14.99 & 7.87 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ball bearing dimensions.  
Figure 1: The experimental testing rig.
## 3 Methodology
### Analysis Software Developments  
Once the data have been logged, it needs to be processed in order to analyze and use the sensor's measurements for specific target applications; in our case, the target applications are based on accurate and/or robust positioning. The aforementioned GNSSLogger app is widely used by the research community because of its companion processing software called \"GNSS Analysis App\" [35], allowing plotting of the logged data. It is developed in Matlab and the source code can be found on GitHub. During the processing of the data, multiple unknown errors arose that prevented its usage for our analysis goals. Thus, similarly to how we developed the Mimir app [36], we have decided to build our own software to process the measurements.  
For processing the data, we developed a Python library called \"MimirAnalyzer\", with its code also available in open source on Github [37]. The library allows parsing of the CSV data into a Pandas DataFrame format, to be later analyzed. For a quick review of the measurement quality, we also created a local web dashboard. It allows visualization of the trajectory, comparison with a reference and review of the measurements logged into the file. For a deeper analysis, we have created also some Jupyter Notebook templates that allow for visuals and statistical analysis. More details about the performance metrics are provided in Section 5.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
\begin{tabular}{l} Android 12 \\ (API 31) \\ \end{tabular} & Unknown & L1, L5 & ✓ & ✗ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selected Android devices included in the dataset.
## 3 Components of VPN Architecture
### Linux Daemons
#### 3.3.1 Libreswan/Strongswan  
LibreSwan [32] and StrongSwan [33] are open source implementations of the IPsec protocol, powered by OpenSwan and based on the FreeSwan project, available as a ready-to-use package on Linux distributions on RedHat. However, detailed instructions are provided in the project's code for compiling on non-packaged Linux platforms. After the installation process, after the proper configuration, one will have an IPsec VPN gateway capable of protecting data in transit between the members of the network. Below, Table 1 compares LibreSwan and Strongswan features.  
Libreswan configuration files are not compatible with strongSwan ones, although the format of ipsec.conf is identical. Different options have different meanings, others are mutually absent due to the supported architectures. For example, in Libreswan it is not possible to enable the L2 TP IPSec [25] support of Android, because it is fixed in the client with the DH2 version (MODP1024), considered too weak to be supported, and it is disabled in the compilation phase. Strongswan and Libreswan both support both Policy-Based and Route-Based VPN, but in the former case they can be used in a mutually exclusive way unless you switch from the \"classic\" configuration (ipsec.conf) to the new one (swantcl.conf), while in the second, there are special flags, both policies are supported by default.
###### Abstract  
In today's advanced network and digital age, the Internet of Things network is experiencing a significant growing trend and, due to its wide range of services and network coverage, has been able to take a special place in today's technology era. Among the applications that can be mentioned for this network are the field of electronic health, smart residential complexes, and a wide level of connections that have connected the inner-city infrastructure in a complex way to make it smart. The notable and critical issue that exists in this network is the extent of the elements that make up the network and, due to this, the strong and massive data exchanges at the network level. With the increasing deployment of the Internet of Things, a wide range of challenges arise, especially in the discussion of establishing network security. Regarding security concerns, ensuring the confidentiality of the data being exchanged in the network, maintaining the privacy of the network nodes, protecting the identity of the network nodes, and finally implementing the security policies required to deal with a wide range of network cyber threats are of great importance. A fundamental element in the security of IoT networks is the authentication process, wherein nodes are required to validate each other's identities to ensure the establishment of secure communication channels. Through the enforcement of security prerequisites, in this study, we suggested a security protocol focused on reinforcing security characteristics and safeguarding IoT nodes. By utilizing the security features provided by Elliptic Curve Cryptography (ECC) and employing the Elliptic Curve Diffie-Hellman (ECDH) key-exchange mechanism, we designed a protocol for authenticating nodes and establishing encryption keys for every communication session within the Internet of Things. To substantiate the effectiveness and resilience of our proposed protocol in withstanding attacks and network vulnerabilities, we conducted evaluations utilizing both formal and informal means. Furthermore, our results demonstrate that the protocol is characterized by low computational and communication demands, which makes it especially well-suited for IoT nodes operating under resource constraints.  
authentication protocol; key-agreement protocol; IoT security; mutual authentication; network security +
Footnote †: journal:  
00 2024 by the authors. Licensee 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/))).
# Secure On-Off Transmission in UAV Relay-Assisted mmWave Networks
## 1 Introduction  
Millimeter wave (mmWave) has attracted considerable research attention due to its plentiful available spectrum resources [1; 2; 3]. Nevertheless, with the high frequencies, mmWave signals are extremely sensitive to the blockage effect, and their path losses are severe. As such, the mmWave communication links are prone to outage, especially in an environment with dense blockage, e.g., a mountainous area. To this end, deploying the unmanned aerial vehicle (UAV) relays in the air has been investigated for performance improvement and disconnection recovery in the mmWave networks [4; 5].  
Although the performance improvement can be obtained by employing the UAV relays, it also makes the transmission vulnerable to eavesdroppers due to the inherent broadcast of the wireless networks. As such, it is one of the key concerns to provide a secure communication in UAV relay-assisted mmWave networks. In contrast to the conventional upper-layer security methods mainly based on cryptographic protocols [6], physical-layer security has been emerged as a powerful approach to protect information transmission against wiretapping by exploiting the randomness ofwireless medium, e.g., noise and interference [7]. Some potential application scenarios and challenges in terms of physical-layer security are presented in [8; 9; 10; 11]. In this paper, we focus on secure on-off transmission in UAV relay-assisted mmWave networks.
## 4 The Performance Parameters and Application Scenarios of 6G Networks
### Application Scenarios
#### 4.2.7 Cyber Robots and Autonomous Systems  
Additionally, 6G technology can potentially improve the use of network robots and autonomous systems, such as UAV mail delivery systems. Self-driving automobiles that use 6G wireless connectivity have the potential to significantly alter daily life. The 6G system's capabilities will drive the wider deployment and implementation of autonomous vehicles. Self-driving vehicles are equipped with a variety of sensors, including inertial measuring tools, light detection, LiDAR, radar, GPS, sonar, and odometer. These sensors allow the automobiles to observe and understand their surroundings. Vehicle-to-server services and Vehicle-to-Everything (V2X) connectivity will have strong support in the 6G system.  
Notably, 6G will help in the communication between UAVs and ground controllers. Numerous industries, including the military, commerce, science, agriculture, entertainment, government, logistics, surveillance, aerial photography, emergency rescue, and disaster relief, are among the many that employ UAVs. Furthermore, UAVs can act as high-altitude platform stations (HAPSs) in places lacking or unable to operate cellular base stations, providing broadcast and high-speed Internet services to local consumers.
## 3 Results and Discussion  
### Decontamination of the Native Load via PPA Treatment  
Figure 5a summarizes the time-dependent growth of the culturable part of the native microbiome on apples after PPA treatment. These findings have been compared with two kinds of reference. First, PPA-untreated apples were carried throughout the experiment (negative reference). Additionally, apples treated with compressed air instead of PPA have also been observed (positive reference). For the references, a huge variation of almost half a log\({}_{10}\)-step is observed. In accordance with our expectations, the number of microorganisms found on the surface increases over the given observation period of 26 days. These findings appeared to be true for both controls. Although not statistically significant, the microbial growth on positive references exceeds that on the negative controls for longer observations times. Nevertheless, the reduction of the microbial load on PPA-treated apples exceeds that on the references. Repeatedly, these offsets are not statistically significant. Based on the presented data, the successes of PPA treatment, i.e., a reduction of the natural microbiome or a hampered growth, cannot be proven based on statistics.  
However, Figure 5b summarizes all reduction factors (RF) of all experiments for treated apples. The outcome has been compared with the positive reference as described above (see Section 2.5). Since Figure 5b is only a different presentation when compared with Figure 5a, it just offers the reader a better access to the reduction factors after a PPA treatment. It shows that the bacterial growth is mainly hindered and that a large-scale killing of the organisms is observed. The figure supports the observation that the highest RF are observed for longer treatment times (>14 days, day 3: 1.51 \(\pm\) 0.42 log\({}_{10}\)-levels vs. day 24: 2.16 \(\pm\) 0.00 log\({}_{10}\)-levels).
## 8 Technical Challenges for 6G Development
### Terahertz Frequency
#### 8.1.5 Processing Power Consumption  
Implementing very large antennas in terahertz systems introduces a complication regarding the power needed for analog-to-digital (A/D) conversion. The power required is directly proportional to the sampling rate and increases exponentially with the number of bits per sample. Due to the extensive bandwidth and the use of large antennas in the terahertz band, there is a demand for high-resolution quantization, which poses a substantial hurdle in developing devices that are both low in power consumption and cost-effective [42].
# An Analytic Transform Kernel Derivation Method for Video Codecs
###### Abstract  
In the standardization of versatile video coding (VVC), discrete cosine transform (DCT)-2, discrete sine transform (DST)-7, and DCT-8 are regarded as the primary transform kernels. However, DST-4 and DCT-4 can also be considered as the transform kernels instead of using DST-7 and DCT-8 owing to their effectiveness in smaller resolution test sequences. To implement these different block size transform kernels, a considerable amount of memory has to be allocated. Moreover, memory consumption to store different block size transform kernels is regarded as a major issue in video coding standardization. To address this problem, a common sparse unified matrix concept is introduced in this study, where any block size transform kernel matrix can be obtained after some mathematical operations. The proposed common sparse unified matrix saves approximately 80% of the static memory by storing only a few transform kernel elements for DCT-2, DST-7, and DCT-8. Full-required transform kernels are derived using the stored transform kernels and generated unit-element matrices and a permutation matrix. The static memory required is only for 1648 elements instead of 8180 elements, each with 8-bit precision. The defined common sparse unified matrix is composed of two parts: a unified DST-3 matrix and a grouped DST-7 matrix. The unified DST-3 matrix is used to derive different points of DCT-2 transform kernels, and the grouped DST-7 matrix is used to derive different points of DST-7 and DCT-8 transform kernels. The new technique of grouping concept is introduced, which shows the relationship between different rows of DST-7 transform kernels with various block sizes. The proposed grouping concept supports the fast algorithm of DST-7 by implementing the proposed method of the \"one group one feature\" principle. The simulation was conducted using the VTM-3.0 reference software under common test conditions. The simulation result of the all intra (AI) configuration is Y = 0.00%, U = -0.02%, V = 0.00% with an encoding time of 100%, and a decoding time of 100%. Similarly, the simulation results of random access (RA) configuration are Y = -0.01%, U = 0.09%, V = 0.06%, and the encoding and decoding times are 101% and 100%, respectively. The simulation result of the low delay B (LDB) configuration is Y = 0.01%, U = 0.08%, and V = -0.27%, for encoding and decoding times of 101% and 100%, respectively.  
VVC; DCT-2; DST-7; DCT-8; DST-3; DST-4; DCT-4; common sparse unified matrix; unified DST-3 matrix; unit element matrices; grouped DST-7 matrix; permutation matrix +
Footnote †: journal: Applied Sciences
## 3 Results
### Model Estimations and Comparisons
#### 3.3.2 BPNN Model Prediction Results  
The selected characteristic bands were used to predict the heavy metal content using a three-layer BP neural network. The numbers of input and output nodes of the neural network were 8 and 1, respectively. Here we used trial and error method to determine the number of hidden layer nodes, which was 8. The scatter plot of the estimated and measured values of heavy metal content is shown in Figure 8. It can be seen that among all of the optimal BPNN models, Cr and Cd exhibit the best modeling effect, as reflected by: R\({}^{2}\) = 0.728, 0.737 and RPD = 1.950, 1.944. Then is Pb, with prediction accuracies (R\({}^{2}\)) of 0.675.
# Stream-DBSCAN: A Streaming Distributed Clustering Model for Water Quality Monitoring
## 4 Experiment and Analysis
### Distributed Time Consumption Analysis  
After conducting multiple experiments, we recorded the time required for the Stream-DBSCAN model to process 200,000 datasets while setting different numbers of nodes. The time consumption curve is presented in Figure 9. It is evident from the graph that the processing time gradually decreases with an increase in the number of nodes. However, the rate of change of the curve gradually decreases as the number of nodes increases. This is because the increased number of distributed nodes leads to increased computational complexity during the merger process. Consequently, when the number of nodes approaches infinity, the time consumption curve reaches a minimum value while ensuring the same dataset.
## 3 Current Sensor Fault Detection
### Current Sensor Fault Detection  
Theorem 1 provides a scheme to design a robust observer for residual generation sensitive to current sensor faults. For the purpose of fault detection, a residual evaluation function is defined by a moving window \(\left[1,N\right]\)  
\[J_{d}=\frac{1}{N}\sum_{i=l+1}^{l+N}r^{T}\left(i\right)r\left(i\right), \tag{31}\]  
where \(N\) is the sampling length related to the current frequency. This paper follows \(\mathcal{L}_{2}\) re-constructible condition [27] to set the evaluation threshold. Recalling Theorem 1, the error system (22) satisfies the \(\mathcal{L}_{2}\) re-constructible condition with Equation (24). The detection threshold of evaluation function (31) is  
\[J_{th}=\frac{1}{N}\left(\gamma^{2}d_{\max}^{2}+\sup_{x\left(0\right),\xi\left( 0\right)}\gamma V\left(x\left(0\right),\hat{x}\left(0\right)\right)\right). \tag{32}\]  
The detection logic is defined as follows:  
* \(J>J_{th}\), sensor fault alarm,
* \(J\leq J_{th}\), no fault alarm.  
Although the proposed residual generator is designed for fault detection of three-phase current sensor faults, it can be utilized to detect the component or actuator faults in the system. Nevertheless, this is not in the scope of this work.