esa-sceva/satcom-chunk-collection huggingface dataset:
# Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network
## 5 A Case Study of UAV NTN Airborne Network in Mountainous Area
### Field Trial Results
#### 5.2.2 Results and Analysis
The operator-to-robot control instruction delay is less than 20 ms, which is sufficient to support inter-robot collaboration tasks such as joint environmental perception, formation control, and collision avoidance. The above-mentioned capabilities are crucial in successful emergency management scenarios ranging from earthquake victims searching to dangerous item detection. During the field trial, a group of four quadrupedal locomotion robots formed a team to perform joint environmental perception tasks while maintaining fixed formation and collision avoidance via the UAV network. (Figure 9)  
On the other hand, a long-range teleoperation experiment was also conducted during the test. The robot and the control terminal are all connected to the UAV base station, and the operator controls the robot via 5G UAV communication using the terminal equipment. It has been proven in the experiment that the robot can be teleoperated through a UAV network from a distance of up to 50 km. The result is show in Table 8. \begin{table}
\begin{tabular}{c c c} \hline
**Application** & **Item** & **Results** \\ \hline \multirow{3}{*}{Multi frequency integration} & Network edge rate & 14 Mbps \\  & Peak rate & 744 Mbps \\  & Average delay & 21 ms \\  & Total system bandwidth & 240 MHz \\ \hline \multirow{3}{*}{UE coverage} & Coverage & 36 km\({}^{2}\) (on demand) \\  & Number of UEs & 41 (\(>\)10 Mbps) \\ \cline{1-1}  & System outage probability & Less than 0.04\% \\ \hline \end{tabular}
\end{table} 
Table 7: UAV airborne network coverage field trial results. Figure 9: Field trial of quadrupedal locomotion robot formation: (**a**) quadrupedal locomotion robot formation control; (**b**) perspective of the robot in the group.
###### Abstract  
In recent times, security and safety are, at least, conducted in safety-sensitive or critical sectors. Nevertheless, both processes do not commonly analyze the impact of security risks on safety. Several scholars are focused on integrating safety and security risk assessments, using different methodologies and tools in critical infrastructures (CIs). Bayesian networks (BN) and graph theory (GT) have received much attention from academia and industries to incorporate security and safety features for different CI applications. Hence, this study aims to conduct a systematic literature review (SLR) for co-engineering safety and security using BN or GT. In this SLR, the preferred reporting items for systematic reviews and meta-analyses recommendations (PRISMA) are followed. Initially, 2295 records (acquired between 2011 and 2020) were identified for screening purposes. Later on, 240 articles were processed to check eligibility criteria. Overall, this study includes 64 papers, after examining the pre-defined criteria and guidelines. Further, the included studies were compared, regarding the number of required nodes for system development, applied data sources, research outcomes, threat actors, performance verification mechanisms, implementation scenarios, applicability and functionality, application sectors, advantages, and disadvantages for combining safety, and security measures, based on GT and BN. The findings of this SLR suggest that BN and GT are used widely for risk and failure management in several domains. The highly focused sectors include studies of the maritime industry (14%), vehicle transportation (13%), railway (13%), nuclear (6%), chemical industry (6%), gas and pipelines (5%), smart grid (5%), network security (5%), air transportation (3%), public sector (3%), and cyber-physical systems (3%). It is also observed that 80% of the included studies use BN models to incorporate safety and security concerns, whereas 15% and 5% for GT approaches and joint GT and BN methodologies, respectively. Additionally, 31% of identified studies verified that the developed approaches used real-time implementation, whereas simulation or preliminary analysis were presented for the remaining methods. Finally, the main research limitations, concluding remarks and future research directions, are presented  
graph theory; Bayesian networks; safety; security; critical infrastructures; literature review +
Footnote †: journal: 2 November 2021
# K/Ka-Band Very High Data-Rate Receivers: A Viable Solution for Future Moon Exploration Missions
## 1 Introduction
The main features of the receiver are: (i) to enable communication from Earth to lunar orbit (E-LO), from Earth to lunar surface (E-LS) and from a lunar orbit to lunar surface (LO-LS), even in low signal-to-noise ratio (SNR)conditions; (ii) to acquire, demodulate and decode, with a low-density parity-check (LDPC) approach, uplink signals in the 22.55-23.15 GHz or in the 27.5-30 GHz bands, covering different data rates up to 100 Mbps; (iii) to implement the modulation schemes required by the Consultative Committee for Space Data Systems (CCSDS) and to the European Cooperation for Space Standardization (ECSS), both in the classical way (based on ground-aided acquisition approach) and in autonomous mode (non-ground-aided). Another important requirement is to keep the power consumption below 12 W in such a way as to be compatible with the solar panels available on the market [5; 6]. The original contribution of the present paper is twofold. First of all we propose a Ka-band receiver suitable for deep space mission that is based on off-the-shelf components developed for commercial telecommunication systems. This is a quite novel approach that, once demonstrated, could dramatically reduce mission costs and open the way to the massive adoption of cubesats even in the Moon exploration context [7]. The Mars Cube One (MarCO) satellites, for example, are two notable precursors of such a vision [8]. Another original contribution of this work is related to the methodology used to model the phase-noise impairments. In low-cost radios the local oscillator signal is generated by an integrated frequency synthesizer, the phase-noise of which is typically worse than that obtained with expensive dielectric resonator oscillators (DRO). As a consequence the phase-noise impairments need to be carefully considered in the link budget. The state of the art for simultaneous orbit and link budget computations is Systems Tool Kit (STK), a multi-physics software from Analytical Graphics, Inc. [9]. This software, however, is unable to simulate the oscillator phase-noise. To solve such problem it is shown that, in the small phase errors approximation, the phase noise can be modeled as an equivalent thermal noise. This approach leads to the definition of an effective noise figure (accounting for both the thermal and the phase-noise): A simple formula that can be easily implemented in the STK to allow a preliminary study of the phase-noise impairments.
# Frequency Selective Surfaces: A Review
## 3 Frequency Selective Surfaces (FSSs)  
At microwave and optical frequency ranges, spatial filtering is the most desirable operation in all signal processing systems. Frequency selective surfaces (FSS), also called spatial filters, are used to modify the EM wave incident on such surfaces and provide dispersive transmitted and/or reflected characteristics. FSSs are usually designed by periodic metallic arrays of elements on a dielectric substrate. The change brought to the transmitted wave can be both in amplitude or phase when compared to the incident wave. In any case, selectivity may be introduced against the incident polarization to improve the irregularities in the emission pattern, which is exhibited through a change of the phase or amplitude of the transmitted wave. A variety of applications according to different requirements can be facilitated, depending on the nature of modification added to the transmitted wave.  
Low profile, reduced periodicity, dual polarization, angular stability, multi-pole frequency response with higher out-of-band rejections, and easy manufacturability are some of the desired properties of FSSs. Nevertheless, achieving all the aforementioned characteristics for an optimized design has been a challenge for the FSSs designers [2]. Different techniques to dig out best EM properties utilize various element shapes and varying geometric design parameters, such as adjusting the structure element size (patch or aperture), dielectric substrate, and tuning the inter-element spacing [2; 46; 47]. In this paper, we present different varieties of FSS structures that are used for specific EM applications. Additionally, approaches for implementation, fabrication, and testing of these structures are also studied.
# Latency Analysis of Blockchain-Based SSI Applications +
## 2 Related Works
Usually, the performance evaluations [23] can be classified into two categories: analytical modelling and empirical analysis. The empirical evaluation includes monitoring, benchmarking, experimental analysis, and simulation. The diversity of consensus algorithms and APIs makes it impossible to use traditional benchmarking tools for blockchains. The three most popular benchmarking tools for blockchains are Hyperledger Caliper [24], DAGbench [25], and Blockbench [26]. Blockbench, by default, can work with Ethereum, Parity, HLF, and Quorum, but it is possible to implement adapters to use other platforms. It measures throughput, latency, scalability, and fault-tolerance in private blockchains. The main focus of Hyperledger Caliper is Hyperledger blockchains, such as Hyperledger Fabric, Sawtooth, Iroha, Burrow, and Besu. DAGbench is dedicated to benchmarking DAG distributed ledgers such as IOTA, Nano,and Byteball. Scalability, throughput, latency, resource consumption, transaction fee, and transaction data size can all be measured. Though these works investigate SSI issues with Indy and Aries, none of them has performed such detailed latency analysis with different system settings as we have, concerning network sizes and TX arrival rates.
## 4 Payload and Subsystem Architectures and Design  
In this section, the working function of the proposed payload and subsystems of the nanosatellites' platform are presented and explained. The main payload for the proposed missions (AIS and fire detection) consists of a communication transceiver. This proposed payload consists of a VHF receiver (161.975 and 162.025 MHz, respectively) to collect data on AIS and fire detection. The remaining components of the platform are made up of different subsystems. Another independent UHF transceiver-based communication subsystem is in charge of sending data to the ground station and receiving commands. Furthermore, an On-Board Computer (OBC) that manages data and executes commands for the mission plan is used. To guarantee a constant supply of energy to the nanosatellite, according to the electrical specification of the nanosatellite platform, an appropriate Electrical Power Subsystem (EPS) is used. Finally, an Attitude Determination and Control Subsystem (ADCS) is also proposed to stabilize the nanosatellite so that it can conduct the mission effectively. The electrical architecture of the proposed nanosatellite platform is shown in Figure 13.  
Details of the payload and subsystems for the proposed nanosatellite platform are presented in the following subsections.  
Figure 12: BUAA Beihang Ground Station Implementation: (1) radio station, (2) TNC, (3) polarization switch, (4) mission computer, (5) power supply, (6) VHF/UHF antenna, and (7) antenna pedestal.  
Figure 13: Electrical architecture of the proposed nanosatellite platform.
# Fast and Accurate Multi-Task Learning for Encrypted Network Traffic Classification
## 2 Related Works
### Encrypted Traffic Classification
In [22], the authors propose the BFCN model, which combines BERT and CNN models to derive global traffic features with a pre-trained BERT model and byte-level local traffic features with a CNN model. The experimental results show F1 scores of 99.11% and 99.41% in the traffic service and application identification tasks operating on the ISCX 2016 VPN/Non-VPN dataset, respectively. In [23], similar to [22], a pre-trained BERT model and a bidirectional LSTM are applied together, with an accuracy of about 99%. In [33], the authors utilize DistilBERT to perform encrypted traffic classification research. They introduce comparative learning to enhance classification speed without degrading performance. Although our study is similar to [33], which focuses on STL, our study specifically targets MTL. We apply MTL to simultaneously learn three tasks on a single model, resulting in superior performance. In [24; 25], both studies utilize MAE for traffic classification research. The authors propose a pre-training model for MAE that introduces a mask patch model, a self-supervised learning pre-training task, to capture unbiased representations from bursts of varying lengths and patterns. Experiment results show that the proposed system achieves new high levels of accuracy of 98%, classification speed, memory efficiency, and robustness across a wide range of network traffic types.
# Magnetic Fields Trump Oxygen in Controlling the Death of Erythro-Leukemia Cells
## 2 Materials and Methods
### Cell Culture Measurements  
Cell counts, apoptosis, and necrosis readings were obtained by micro-capillary aspiration and miniaturized laser fluorescence detection, performed automatically by a Muse Cell Analyzer cytometer (Millipore Sigma, Oakville, ON, Canada). Total cell count and cell viability were obtained using Millipore assay kit MCH100102, based on the differential permeability of two DNA-binding dyes. Culture media containing K562 cells and the assay kit reagent were added directly to microcentrifuge tubes (VWR 16466-030, VWR International, Ville Mont-Royal, QC, Canada). Five minutes later, the Muse Cell Analyzer delivered counts and viability readings. These readings were used to adjust seeding density at each culture passage.  
The Muse Cell Analyzer with Millipore Annexin V & Dead Cell assay kit MCH100105 provided the early apoptosis, late apoptosis, and necrosis readings. K562 cells and the assay kit reagent were added directly to microcentrifuge tubes (VWR 16466-030). Twenty minutes later, the Muse Cell Analyzer delivered readings from evaluation of 1000 cells. The assay relied on the binding of fluorescently labeled Annexin V to phosphatidylserine, which translocates to the outer surface of the cell membrane, exposed in the case of dead and apoptotic cells. Annexin V-Phycoerythrin tagged phosphatidylserine on the external membrane of apoptotic cells. 7-Aminoactinomycin D (7-AAD), because it is excluded from live and healthy cells, and has a strong affinity for DNA, permeated the late-stage apoptotic and dead cells. These determinations allowed the distinction of three cell populations within a culture, as shown in Table 1.  
Although measurements on effects of EMR have been made previously using similar end-points [11; 12; 13; 14; 15], none used environmental level exposures (\"Incubator Field\") [16], nor, most importantly, pre-industrial fields (\"Zero Field\") as a reference.
## 2 Materials and Methods
### Wi-Fi Module  
The Wi-Fi module performs sensing-data transmission from the sensor node to the IoT system. Microchip's ATWINC1510 was chosen for this purpose due to its low-power characteristic. It is an IoT network controller based on the IEE 802.11 b/g/n IoT protocol. It can couple with a microcontroller chip to perform low-power IoT networking capability using SPI or UART for the Wi-Fi interface. The ATWINC1510 needs only an external clock source to work in operating frequency ranges from 2.412 to 2.472 MHz using a high-speed crystal or oscillator. The system software can use its 8 MB flash memory. It is equipped with a printed antenna or a micro co-ax (u. FL) connector to achieve a small form-factor design with a dimension of 21.7 \(\times\) 14.7 \(\times\) 2.1 mm, as shown in Figure 5.  
Microchip's ATWINC1510 is equipped with a switch, power management, Low noise amplifier (LNA), and power amplifier. Considering the low-power requirement, it works at an operating voltage range of 2.7 to 3.6 V, Tx current of 287 mA, Rx current of 83 mA, and power-down mode of 4 uA. In addition, it has an on-chip low-power sleep oscillator and fast host wake-up [47].  
Figure 4: CO\({}_{2}\) sensor module.  
Figure 5: Wi-Fi module WINC1510.  
A supercapacitor was selected for the main power storage of the sensor node. The high-power density with fast charge and discharge time was considered to support a low-power sensor node capable of harvesting micro energy from indoor ambient energy and store in energy storage (supercapacitor). Compared with a battery, a supercapacitor has better typical characteristics such as a recharger cycle life of more than 10\({}^{6}\) cycles, self-discharge rate of more than 30%, a wide-range power density of up to 500 W/m\({}^{3}\), faster charging time in the range of sec-min, faster discharging time less than a few min, and usage of the simple charging circuit [48].  
The developed sensor node works with an operating voltage of 5 V. A parallel of 2 units 5.5 V 5.0 F was used as power storage for powering up the sensor node with a total capacity of 5.5 V 10 F. The supercapacitor capacity can be increased by enlarging the capacitance of the supercapacitor. The built-in supercapacitor on the developed sensor node was considered to verify the potential of the supercapacitor as power storage for the sensor node that harvests energy from indoor ambient energy. A parallel of 2 units 5.5 V 5.0 F integration is shown in Figure 6.
# Enhancing Skin Lesion Classification Performance with the ABC Ensemble Model
## 3 Materials and Methods
### Data Preprocessing  
The raw data are subjected to basic preprocessing steps. The raw images in the HAM10000 dataset capture not only the lesion but also the surrounding skin, which often includes noise such as wounds or age spots. To avoid the effect of these noises, we apply a U-net model [44] to extract the Region of Interest (ROI) from the original image, isolating the lesion from the surrounding area.  
The U-net model applies two convolution layers with a \(3\times 3\) size kernel, and applies max pooling with stride of 2 to halve the image size and double the number of channels to extract the feature map by repeating downsampling. After that, the convolution layer is applied to increase the robustness of the model, and the feature map is upsampled to the resolution of the original image through a deconvolution layer with a stride of 2. This process allows U-net to reconstruct the image. After training the U-net, we can obtain the segmentation mask of the augmented dataset using the trained U-net model and generate a segmentation mask of 0 s and 1 s as shown in Equation (1) [45]. Using the obtained segmentation mask, we extract the ROI of the lesion, including its location and size, as defined in Equations (2) and (3). Then, the images are upsized by focusing only on the ROI to match the input dimensions required by each classifier, as shown in  
Figure 4: The overall architecture of the ABC ensemble model, consisting of five blocks: Generality Training, Upsize Training, Asymmetry Enhancement Training, Border Enhancement Training, and Color Enhancement Training. The two orange blocks learn the general features of the image, and the three blue blocks learn the features reflecting the ABCD rule.  
Figure 3: Four images of skin lesions captured from different angles and distances. Due to the variations in measurement angles and distances, the actual diameter of the lesions cannot be determined.  
Figure 5. Among the five blocks of our model, only the Upsize Training block produces preprocessed images of \(384\times 384\), while the remaining four blocks produce preprocessed images of \(256\times 256\).  
\[B(x,y)=\begin{cases}1&\text{if }I(x,y)>0.05\\ 0&\text{if }I(x,y)\leq 0.05\end{cases}, \tag{1}\]  
where \(B(x,y)\) is the pixel value of the binarized image, representing the result of converting the original image's pixel values into black and white based on a threshold, and \(I(x,y)\) is the pixel value at position \((x,y)\) in the original image.  
\[x_{min}=min(x\mid B(x,y)\in 1),\;y_{min}=min(y\mid B(x,y)\in 1), \tag{2}\]  
where \(x_{min},y_{min}\) are the coordinates of the pixels in \(B\) with a value of 1. \(x_{min},y_{min}\) represent the top-left corner of the ROI.  
\[x_{max}=max(x\mid B(x,y)\in 1),\;y_{max}=max(y\mid B(x,y)\in 1), \tag{3}\]  
where \(x_{max},y_{max}\) are the coordinates of the pixels in \(B\) with a value of 1. \(x_{max},y_{max}\) represent the bottom-right corner of the ROI.
## 3 Optimization Methods and Results
### Hardware System Optimization
#### 3.2.3 Memory Access Optimization Method 3: Use BRAM  
The external memory DDR provides a lot of extra cache space for user logic, which allows us to accelerate more complex algorithms. Nevertheless, because it has only a single port and cannot read and write simultaneously, it needs FIFOs as the data buffers, and complex control logic is added. Also, the throughput of DDR is limited by clock frequency, causing a lot of time consumption. Plus, many ports between FPGA and DDR can also lead to high power consumption.  
Using BRAM integrated into the FPGA chip will address the problems. We can employ two BRAM blocks (marked as RAMA and RAMB), to form the ping-pong structure to improve the throughput of memory access. The optimized system architecture is shown in Figure 11. When the request type is CONV, which means SYS_CTRL will perform CONV computation, the system will read RAMA and write RAMB. When the request type is ARMP, which means SYS_CTRL will perform ARMP computation, it will read RAMB and write RAMA. The ping-pong structure needs no more FIFOs to serve as data buffers because the two BRAM blocks can handle a read or write operation in a clock period.  
Figure 10: Task pipelining reduces the interval between tasks.
# Solving the Urban Transit Routing Problem Using a Cat Swarm Optimization-Based Algorithm
## 1 Introduction
### Comparison Criteria  
The formalism used to model the UTRP in this paper is the same with the one used in all contributions presented in the previous section. Moreover, we use an equivalent fitness function (see Section 3.1.3) and assess the resulting solutions' quality using analogous performance criteria. We decided to do so to be able to compare with other approaches' results on a fair basis. The criteria on which the assessment of the resulting bus transportation networks are based are five items. The 1 st criterion is each passenger's average in-vehicle travel time or alternatively the total time for all passengers needed to cover their transport demands. The 2 nd criterion is the percentage of passengers been able to travel directly from their origin to their destination. The 3 rd criterion is the percentage of passengers who must make only one transfer to go from their origin to their destination. The 4 th criterion is the percentage of passengers who must make exact two transfers to go from their origin to their destination. Finally, the 5 th criterion is the percentage of passengers who must make three or more transfers to travel from their origin to their destination or are not able to reach their destination using public network at all.  
The objective function implemented (the value of which must be maximized) is the sum of two conflicting parameters. The first is the passenger cost which is represented by the total journey time overall passengers. The second is the operator cost which is represented by the total length of the route set. There are two cases investigated in the current contribution. According to the 1 st case, designing each route set is completely based on passengers' profit (operator's cost is absent). According to the 2 nd case, the route set design also takes into account the operator's profit (see Sections 3.1.3 and 3.1.4).