esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Training-anomaly-based, machine-learning-based, intrusion detection systems (AMiDS) for use in critical Internet of Things (CIoT) systems and military Internet of Things (MIoT) environments may involve synthetic data or publicly simulated data due to data restrictions, data scarcity, or both. However, synthetic data can be unrealistic and potentially biased, and simulated data are invariably static, unrealistic, and prone to obsolescence. Building an AMIDS logical model to predict the deviation from normal behavior in MiT and CIoT devices operating at the sensing or perception layer due to adversarial attacks often requires the model to be trained using current and realistic data. Unfortunately, while real-time data are realistic and relevant, they are largely imbalanced. Imbalanced data have a skewed class distribution and low-similarity index, thus hindering the model's ability to recognize important features in the dataset and make accurate predictions. Data-driven learning using data sampling, resampling, and generative methods can lessen the adverse impact of a data imbalance on the AMIDS model's performance and prediction accuracy. Generative methods enable passive adversarial learning. This paper investigates several data sampling, resampling, and generative methods. It examines their impacts on the performance and prediction accuracy of AMIDS models trained using imbalanced data drawn from the UNSW_2018_IoT_Botnet dataset, a publicly available IoT dataset from the IEEEDataPort. Furthermore, it evaluates the performance and predictability of these models when trained using data transformation methods, such as normalization and one-hot encoding, to cover a skewed distribution, data sampling and resampling methods to address data imbalances, and generative methods to train the models to increase the model's robustness to recognize new but similar attacks. In this initial study, we focus on CIoT systems and train PCA-based and oSVM-based AMIDS models constructed using low-complexity PCA and one-class SVM (oSVM) ML algorithms to fit an imbalanced ground truth IoT dataset. Overall, we consider the rare event prediction case where the minority class distribution is disproportionately low compared to the majority class distribution. We plan to use transfer learning in future studies to generalize our initial findings to the MiT environment. We focus on CIoT systems and MioT environments instead of traditional or non-critical IoT environments due to the stringent low energy, the minimal response time constraints, and the variety of low-power, situational-aware (or both) things operating at the sensing or perception layer in a highly complex and open environment.  
imbalanced learning; data-driven learning; intrusion detection; machine learning; military Internet of Things; critical Internet of Things; oversampling; ensemble; generative; rare events +
Footnote †: journal: Cybersecur. Priv. 2023
## 4 Conclusions and Future Work  
This paper presents an experimental study focusing on the influence of the seawater on the property and damping ability of the viscoelastic material to ensure the vibration control effectiveness of the PTMD in a subsea environment. A batch of viscoelastic tapes were submerged in seawater for up to 5 years. Impact tests were conducted to compare the bounding behavior of the samples with the initial condition. The bounding stiffness, hysteresis loops and the energy dissipated per bounding cycle are used to interpret the impact property and damping ability of the viscoelastic material. The experimental results imply that seawater has little influence on the bounding behavior of the viscoelastic material. Therefore, the Pounding Tuned Mass Damper (PTMD) with viscoelastic material to dissipate the bounding energy can be used for vibration control of structures in a subsea environment. We notice that there is a slight decrease in the stiffness of the viscoelastic material measured during the 5 th year as compared to previous years. As a future task, we will investigate the cause of this decrease in stiffness of the viscoelastic material over a long period in the seawater.  
All authors discussed and agreed upon the idea and made scientific contributions. [PERSON], [PERSON] and [PERSON] designed the experiments. [PERSON] wrote the paper. [PERSON] and [PERSON] performed the experiments and analyzed the data. [PERSON] proofread and revised the paper.  
This research was financially supported by National Natural Science Foundation of China (No. 51808092) and the Fundamental Research Funds for the Central Universities (No. 3132014326). We also appreciate the support from the Scientific Research Program of State Nuclear Electric Power Planning, Design & Research Institute (No. 100-KY2018-DYZ-A14).  
The authors declare no conflict of interest.  
Figure 5: Dissipated energy per cycle during the testing period.
## 2 Material and Methods
### Software Design
#### 2.2.1 Device Firmware  
The firmware core, running on the microcontroller, is categorized into four layers to provide abstraction, as shown in Figure 8. The hardware abstraction layer is dependent on the microcontroller and is based on the Arduino libraries (arduino core) ported for STM-32 microcontrollers (STM32 duino). The device manager layer contains managers, implemented as state machines, specific to each electronic component connected (e.g., sensors and actuators). These managers are responsible for: data access from sensors, driving the actuators, monitoring for faults, scheduling the measurements at specified intervals, intercepting external communication, and logging data. The middleware layer contains managers, which serve as brokers that direct the flow of data between the modules of different layers. Data from the sensors are accessed by the modules of different layers using a data structure managed by data manager. The message broker directs the function calls and data between the external system (e.g., decision support system or user) and the corresponding manager modules. The system manager checks if all other managers are running, periodically servicing the watchdog timer, and re-initializing their state machines in the case of error. The task scheduler implemented is preemptive priority based, with highest priority assigned to the components of device manager layer and lower priority to the layers above it. The application interface translates control decisions made by the application layer into suitable actuator commands, thus abstracting the application logic from the hardware. The application layer contains the experiment regimes or apps (e.g., climate control, respiration test, etc.) that are custom developed. This abstraction provides the system its programmability aspect that enables users to develop these regimes and execute them without having to deal with the underlying hardware and firmware.  
Figure 8: Firmware architecture with its layers, components, and their interaction. The layers (**a**–**e**) correspond to application, middleware, device manager, hardware abstraction, and hardware layer, respectively.  
Figure 7: Software and information exchange framework: consisting of code generation framework for application development, hardware setup for experiment and data transmission, and cloud platform for data collection and user interaction.
## 2 Models and Methods
### Solar Irradiation Model and Mission Conditions  
During the flight, with the change of flight altitude and attitude, the intensity of solar radiation received by photovoltaic cells on the wing surface of the aircraft will change accordingly. As the main power source of SPA, the amount of solar energy obtained will directly affect its flight performance. Therefore, a relatively accurate solar flux model needs to be established to calculate the energy collection for SPA. In this study, based on the solar radiation intensity calculation model in the earth-fixed frame, the integration of coordinate conversion principle and the flight attitude angle is used to obtain the solar radiation intensity for SPA in the body coordinate system. The details are as follows.
## 4 Performance Evaluation and Obtained Results
### Real-Life Application Scenario under Simulation  
The realistic case scenario considered in our study is one of the smart water metering applications. As we know, the water meter, unlike the other utility meters, is very energy-critical because of the regulation in many countries like South Africa which forbids to bring electrical lines on a water utility. Most water supply companies request a guaranteed minimum of 10 years lifetime of smart water meter design. The smart water meters are fixed sensor nodes and the ones considered for this research are assumed to have a Tunnel Magneto Resistance (TMR) active magnetic sensor to detect and convert the turning of the mechanical register. Every turning cycle of the mechanical register corresponds to a well-defined water flow value and results in a digital pulse transition on the output of the TMR sensor. The digital processor of the smart water meter counts the number of digital pulses it receives. The water volume flow is computed based on the total number of counted TMR digital pulses. The smart water meter is also able to detect any tampering activities. There is an NB-IoT radio module in each smart water meter and the assumption that there is one smart water meter per household. The choice of 1000 to 150,000 NB-IoT nodes represents the number of smart water meters and other battery-powered environment monitoring smart devices such as geyser leak detectors, door sensors, room occupancy detection sensors, fire detection sensors, water tank level monitoring systems, smart fridges, smart TVs etc. The set of nodes considered in this study are normally static sensors and limited in terms of energy resources. With the increasing number of IoT devices applications, this study caters for up to 150,000 nodes within the geographical area of an NB-IoT cell. In terms of the data traffic, data packets are sent on an average of 1 packet per day by these devices. Hence our consideration of up to 3960 rounds of transmission packets for 11 years of operation. The daily data packet sent as an uplink packet by each smart water meter contains information about the device's status (water meter daily consumption, any sensed possible leaks as well as the remaining battery level percentage of the smart water meter for example). The other environment monitoring smart devices are also assumed to send their daily status once a day either in the case of a triggering event (alarm) or around midnight for a summary daily report. The traffic data model used in our simulation is therefore made up of a combination of deterministic daily packet transmissions coupled with a uniformly distributed stochastic event-triggered packet transmission. In essence, every single node has a minimum of 1 packet per day.
### Antimicrobial and Toxicity Screening of Virtual Peptides Reveals High AMP Diversity
Similarly, higher sequence charge (db3: \(p\) = 1.62 \(\times\) 10\({}^{-78}\); db4: \(p\) = 4.50 \(\times\) 10\({}^{-6}\); db5: \(p\) = 1.23 \(\times\) 10\({}^{-45}\); db6: \(p\) = 0.001; db7: \(p\) = 1.30 \(\times\) 10\({}^{-15}\)) and isoelectric point (db1: \(p\) = 0.003; db3: \(p\) = 2.27 \(\times\) 10\({}^{-161}\); db4: \(p\) = 1.34 \(\times\) 10\({}^{-69}\); db5: \(p\) = 4.53 \(\times\) 10\({}^{-163}\); db6: \(p\) = 6.23 \(\times\) 10\({}^{-21}\); db7: \(p\) = 6.19 \(\times\) 10\({}^{-24}\)) in non-haemolytic and non-toxic AMPs enhance their putative interaction with negatively charged microbial membranes. An increased hydrophobic ratio (db1: \(p\) = 0.008; db3: \(p\) = 0.0; db4: \(p\) = 6.38 \(\times\) 10\({}^{-147}\); db5: \(p\) = 1.20 \(\times\) 10\({}^{-203}\); db6: \(p\) = 1.43 \(\times\) 10\({}^{-40}\); db7: \(p\) = 2.47 \(\times\) 10\({}^{-21}\)) also favours targeted interactions with microbial membranes over mammalian ones. While the median of molecular weight (db3: \(p\) = 1.20 \(\times\) 10\({}^{-31}\); db4: \(p\) = 5.69 \(\times\) 10\({}^{-37}\); db5: \(p\) = 1.77 \(\times\) 10\({}^{-31}\); db6: \(p\) = 2.89 \(\times\) 10\({}^{-16}\)) and sequence charge (db3: \(p\) = 1.62 \(\times\) 10\({}^{-78}\); db4: \(p\) = 4.50 \(\times\) 10\({}^{-06}\); db5: \(p\) = 1.24 \(\times\) 10\({}^{-45}\); db6: \(p\) = 0.001; db7: \(p\) = 1.30 \(\times\) 10\({}^{-15}\)) of AMPs are slightly higher than those of the precursors, they slightly decrease in non-haemolytic and non-toxic AMPs. This reduction benefits diffusion to microbial membranes and facilitates rapid antimicrobial activity. Conversely, the instability index shows minimal variation, but lower median values in AMPs and non-haemolytic and non-toxic AMPs suggest greater stability, albeit with slightly reduced flexibility (db3: \(p\) = 4.64 \(\times\) 10\({}^{-136}\); db4: \(p\) = 7.59 \(\times\) 10\({}^{-24}\); db5: \(p\) = 1.46 \(\times\) 10\({}^{-15}\); db6: \(p\) = 1.35 \(\times\) 10\({}^{-08}\); db7: \(p\) = 3.49 \(\times\) 10\({}^{-07}\)). Additionally, the Boman Index median, which measures the potential for protein-protein interactions, is slightly lower in both AMPs and non-haemolytic and non-toxic AMPs (db1: \(p\) = 0.0004; db3: \(p\) = 0.0; db4: \(p\) = 2.28 \(\times\) 10\({}^{-152}\); db5: \(p\) = 9.66 \(\times\) 10\({}^{-250}\); db6: \(p\) = 2.55 \(\times\) 10\({}^{-50}\); db7: \(p\) = 3.94 \(\times\) 10\({}^{-22}\)). This implies fewer interactions with host proteins, making these peptides less toxic and more specific to microbial membranes. Overall, these characteristics align with typical AMP properties, contributing to their antimicrobial efficacy and selectivity (Figure 3).
###### Abstract
[26] developed the application of a neural network model to predict UCS based on \(n\), bulk density (\(\rho\)) and water saturation (S\({}_{\text{r}}\)), demonstrating the applicability and feasibility of the tool to model the response (validated by the goodness-of-fit indicators) and finally, [PERSON] et al. [27] develop a comparative analysis of different machine learning models for the prediction of UCS in traverting rocks, presenting good indicators of goodness of fit, however, in this work, the number of parameters of the neural network model is also greater than number of samples, making it possible that the models developed by [PERSON] et al. [27] are over-adjusted. The aim of this paper is to develop a comparative analysis between conventional statistical fitting methods and the machine learning (ANN) of the UCS of traverting, by means of an experimental correlation between UCS and \(v_{p}\), \(\rho_{dy}\) and \(n\) for a minimum of stress tests in a controlled environment. UCS of traverting rocks is a mechanical process that presents a great dispersion in the results of laboratory tests (62 MPa between the maximum and minimum values), which makes it difficult to fit conventional statistical models. On the other hand, machine learning techniques are effective at modeling nonlinear systems; however, they generally require a larger data set to represent the system to be studied.
# A Dual-Band Modified Franklin mm-Wave Antenna for 5G Wireless Applications
###### Abstract  
Franklin array antennas are considered as one of the most competitive candidates for millimeter-wave (mmW) 5G applications due to their compact size, simple geometry and high gain. This paper describes a microstrip Franklin antenna array for fifth generation (5G) wireless applications. The proposed modified Franklin array is based on a collinear array structure with the objective of achieving broad bandwidth, high directivity, and dual-band operation at 22.7 and 34.9 GHz. The designed antenna consists of a 3 \(\times\) 3 array patch element as the radiating part and a 3 \(\times\) 3 slotted ground plane operating at a multiband resonance in the mmW range. The dimensions of the patch antennas are designed based on \(\lambda/2\) of the second resonant frequency. The designed antenna shows dual band operation with a total impedance bandwidth ranging from 21.5 to 24.3 GHz (fractional bandwidth of 12.2%) at the first band and from 33.9 to 36 GHz (fractional bandwidth of 6%) at the second band in simulation. In measurement, the impedance bandwidth ranges from 21.5 to 24.5 GHz (fractional bandwidth of 13%) at the first band and from 34.3 to 36.2 GHz (fractional bandwidth of 5.3%) at the second band, respectively. The performance of the antenna is analyzed by parametric analysis by modifying various parameters of the antenna. All the necessary simulations are carried out using HFSS v.14.0.  
collinear array; dual band; Franklin antenna; millimeter-wave (mmW) 2021
## 4 Numerical Simulation Analysis
### Seismic Response Influence of Structure-Electrical Equipment Interaction  
Due to its heavy weight and huge size, electrical equipment fixed on the floors of the substation usually produced additional inertial force, which had an additional effect on the structure under seismic load and bending torsional load.  
In previous seismic designs of underground substations, the interaction between structure and equipment was usually ignored. When the size and weight of electrical equipment is low, such a method is acceptable. Nowadays, the size and weight of electrical equipment is larger than before, and such an equivalent load method would result in some error. For this reason, Model 1 (M1) of the underground substation with the interaction between electrical equipment and structure and Model 2 (M2) without the interaction between them were established. According to a comparison and analysis of the internal force and seismic response in these two models, the seismic performance effect of electrical equipment on the underground substation can be determined.  
Figure 21: The peak internal force of each section on surface-6 under El-Centro motion.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Section**} & \multicolumn{4}{c}{**Peak Vertical Pressure**} & \multicolumn{4}{c}{**Peak Shear Force**} & \multicolumn{4}{c}{**Peak Bending Moment**} \\  & & & \multicolumn{2}{c}{**(10\({}^{5}\) N/m)**} & \multicolumn{4}{c}{**(10\({}^{5}\) N/m)**} & \multicolumn{4}{c}{**(10\({}^{3}\) N/m)**} \\ \cline{3-11}  & & & **M1** & **M2** & **M3** & **M1** & **M2** & **M3** & **M1** & **M2** & **M3** \\ \hline \multirow{4}{*}{Floor 3} & El-Centro & Top & 4.43 & 4.28 & 4.40 & 5.42 & 5.67 & 5.81 & 8.28 & 8.28 & 8.36 \\  & motion & Bottom & 5.57 & 5.32 & 5.39 & 6.17 & 6.80 & 6.85 & 7.16 & 7.67 & 7.87 \\  & Lazhou & Top & 4.61 & 4.14 & 4.50 & 5.44 & 5.47 & 5.61 & 8.01 & 8.28 & 8.61 \\  & motion & Bottom & 5.30 & 4.83 & 5.27 & 6.13 & 6.26 & 6.59 & 7.17 & 7.18 & 7.36 \\ \hline \multirow{4}{*}{Floor 2} & El-Centro & Top & 7.14 & 6.69 & 6.81 & 7.50 & 8.72 & 8.94 & 7.45 & 7.77 & 7.96 \\  & motion & Bottom & 10.51 & 9.80 & 9.92 & 7.92 & 9.40 & 9.79 & 9.32 & 10.01 & 10.33 \\  & Lazhou & Top & 6.94 & 6.19 & 6.70 & 7.22 & 7.51 & 8.20 & 6.97 & 7.62 & 8.02 \\  & motion & Bottom & 9.84 & 9.20 & 9.71 & 7.35 & 7.57 & 8.44 & 9.93 & 10.10 & 11.06 \\ \hline \multirow{4}{*}{Floor 1} & El-Centro & Top & 12.77 & 11.91 & 12.11 & 6.64 & 8.00 & 8.51 & 5.39 & 6.47 & 6.75 \\  & motion & Bottom & 36.70 & 32.79 & 32.48 & 10.33 & 12.27 & 13.22 & 5.21 & 5.46 & 5.67 \\ \cline{1-1}  & Lazhou & Top & 12.25 & 11.32 & 12.00 & 5.26 & 5.55 & 6.62 & 6.91 & 6.98 & 7.22 \\ \cline{1-1}  & motion & Bottom & 35.11 & 31.27 & 34.19 & 8.22 & 8.47 & 9.91 & 5.26 & 5.52 & 5.69 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The peak internal forces of each section on surface-6 under 0.20 g.
## 3 Methods
### Geometric Calibration Model  
#### 3.1.1 Description of Geometric Calibration Model  
A rigorous geometric calibration model was constructed for transforming the image coordinates to the geographic coordinates for the BNU-1 images. The timing, position, and altitude of satellites and camera position parameters are used as inputs of the model. The model is shown as follows [27]:  
\[\left[\begin{array}{c}X\\ Y\\ Z\end{array}\right]_{\text{WGS84}}=\left[\begin{array}{c}X_{s}(t)\\ Y_{s}(t)\\ Z_{s}(t)\end{array}\right]_{\text{WGS84}}+m\left(R_{J2000}^{\text{WGS84}}R_{ body}^{J2000}\right)_{t}\left[\begin{array}{ccc}D_{x}&d_{x}\\ D_{y}&+&d_{y}\\ D_{z}&d_{z}\end{array}+R_{camera}^{body}\left[\begin{array}{c}\tan\psi_{x} \\ \tan\psi_{y}\\ 1\end{array}\right]\right] \tag{1}\]  
where \(t\) is the scanning time of an imaginary line. \(\left[\begin{array}{cc}X_{s}(t)&Y_{s}(t)&Z_{s}(t)\end{array}\right]_{\text {WGS84}}^{T}\) indicates the coordinates of the Global Positioning System (GPS) antenna phase center, which are measured by a GPS receiver on the satellite in the WGS84 coordinate system (derived from ECEF) at \(t\). \(m\) is the scale factor determined by the orbital altitude. \(\left(R_{J2000}^{\text{WGS84}}\right)_{t^{\prime}}\left(R_{body}^{J2000} \right)_{t}\) and \(R_{camera}^{body}\) are the rotation matrix of the coordinate system from J2000 to WGS84 at \(t\), the rotation matrix from the satellite's body-fixed coordinate system to J2000 coordinate system at \(t\), and the rotation matrix from the camera coordinate system to the satellite's body-fixed coordinate system, respectively. \(\left[\begin{array}{ccc}D_{x}&D_{y}&D_{z}\end{array}\right]^{T}\) is the coordinates of the GPS antenna phase center in the satellite's body-fixed coordinate system. \(\left[\begin{array}{ccc}d_{x}&d_{y}&d_{z}\end{array}\right]^{T}\) is the translations of the origin of the camera coordinate system relative to the satellite's body-fixed coordinate system. \(\left[\begin{array}{ccc}\tan\psi_{x}&\tan\psi_{y}&1\end{array}\right]^{T}\) represents the value of the coordinates of point \((x,y)\) corresponding to the detector direction angle model composed of the camera's principal point, focal length, charge coupled device (CCD) installation position, and lens distortion. \(\left[\begin{array}{ccc}X&Y&Z\end{array}\right]_{\text{WGS84}}^{T}\) represents the ground coordinates of the point \((x,y)\) in the World Geodetic System 1984 (WGS84) coordinate system.  
\(\tan\psi_{x}\) and \(\tan\psi_{y}\) describe the directional angle of point \((x,y)\) in the camera coordinate system [27; 28; 29], and this can be calculated by Equation (2), where f is the focal length of the camera.  
\[\tan\psi_{x} = \frac{x}{f} \tag{2}\] \[\tan\psi_{y} = \frac{y}{f}\]  
This step is conducted on the Windows Server 2016 Standard operating system on the Intel(R) Xeon(R) Gold 5220R CPU @2.20 GHz, 256 GB RAM. It is a whole-day unattended automatic data production system.