esa-sceva/satcom-chunk-collection huggingface dataset:
# Automotive Cybersecurity Application Based on CARDIAN
## 4 Case Study
### System Architecture  
The system has been developed in such a way as to have direct access to the vehicle's CAN bus like a normal ECU (see Figure 3), which will be able to signal the presence of an attack by sending a file in json format via MQTT protocol to the node to which the device is connected.  
From a hardware point of view, the device is made up of the Minized board produced by Avnet. The board's SoC consists of an ARM-Cortex-A9 single core processor and an Artix 7 FPGA. A CAN controller is also directly connected to this SoC, making it necessary to install only the CAN transceiver. In order to connect it, it is necessary to configure, using the Vivado software, version 2020.1, the enabling of the CAN controller peripheral integrated into the System on Chip, as well as its connection to the pins of the Pmod2 port by modifying the xdc file associated with the project. The CAN controller used in this phase is the Transceiver SN65 HVD230. This device interfaces directly to the CAN bus via the CAN-H and CAN-L lines, and takes care of the conversion between the differential signal coming from the bus and the serial one used by the CAN controller.  
The AXI bus is also connected to the CPU of the device, which is used for the connection and transfer of data between the CPU and the FPGA. The following figure highlights the AXI peripheral, which is connected directly to the CPU unit of the Zynq SoC (Advanced Micro Devices, Inc., Santa Clara, CA, USA).  
Figure 2: Three-step algorithm.  
The Bayesian network will be connected downstream of the AXI peripheral, using the AXI GPIO component (see Figure 4). For each of these components, the manufacturer Xilinx makes IP available already integrated into Vivado.  
The system created in hardware, including a Bayesian network connected to the AXI bus and including the enabling of the CAN controller, is used as the basis for compiling the Petalinux operating system. Petalinux is a particular Linux distribution optimized for embedded systems and systems based on System on Chip containing FPGAs.  
Figure 4: AXI bus connection.  
Figure 3: System architecture.  
Using the Vivado software, the hardware file containing the part relating to the device peripherals and the bitstream relating to the Bayesian network are exported.  
Subsequently, the operating system is compiled and configured, which is finally installed on the board. The programs used for the acquisition of CAN messages, for communication with the AXI interface and for sending messages via MQTT, are written in C, with the integration of the Mosquito and cantools libraries.  
There are two scripts in use, and they are executed in parallel; the management of active threads is delegated to the operating system.
## 6 Conclusions and Future Works  
In this study, HM-SMC was used to control the output voltage and inductor current of the DC-DC boost converter. Additionally, the IT2 FS approach was used for the first time in this research to manage the switching frequency, which is intrinsically variable in HM-SMC. Through the integration of IT2 FS into SMC in MATLAB/Simulink, the effectiveness of both controllers was assessed under various scenarios such as load resistance, reference of output voltage, and input voltage. The outcomes showed that both SMC and IT2 FS are fairly successful in achieving good output voltage and switching frequency responses under the system disturbances.  
The IT2 FS faces a threat that will shrink its useable space despite offering extremely strong performance. Since IT2 FS requires a digital signal processing memory for its operation, it is not applicable in analog circuits. The difficulty of the design process and the absence of a systematic design process of the IT2 FS can be considered limitations of the proposed method. The experience of the designer has great importance in the design step.  
In the future, T1 FS and T2 FS will be extended to a grid-connected LCL-filtered voltage source inverter with a double-band hysteresis scheme.  
Conceptualization, A.N. and N.A.; Methodology, N.A.; Software, G.B.; Validation, N.A.; Formal analysis, G.B.; Investigation, G.B.; Writing--original draft, N.A. and G.B.; Writing--review & editing, A.N.; Project administration, A.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# A Comprehensive Review of Micro UAV Charging Techniques
## 5 Distributed Laser Charging (DLC) for UAVs
#### DLC Characteristics  
DLC is intrinsically safe, visibility agnostic, self-aligning and can support electromagnetic interference- (EMI) free operations and concurrent multiple-receiver hot-spot charging. DLC offers several benefits over other LPT techniques, such as large transmission distance, compact size and access control. However, DLC also has some critical concerns, including inherent LOS dependence, QoS, laser hazard, power conversion efficiency, propagation attenuation, DLC receiver tracking and DLC transmission distance under path-loss aspects. Some characteristics of DLC are as follows:  
1. _EMI-Free:_ There is no leakage of power outside the resonating beam in DLC. In contrast to other WPT techniques, DLC does not impose RF radiations. Thus, it does not inflict EMI on nearby electronic devices.
2. _Compact Size:_ In DLC, the beam diameter is almost one millimeter and the receiver can be compact in size e.g., a smartphone camera.
3. _Visibility Agnostic:_ DLC can depend on visible, as well as ultraviolet (UV) and infrared (IR), lasers. Both IR and UV laser beams are invisible, which is suitable for some applications because it does not impose visible light interference. This visibility agnostic feature of DLC makes it reliable and flexible in different applications.
4. _Concurrent Wireless Charging:_ A DLC system generates a resonating beam when a receiver is exposed to the LOS path of the transmitter. Hence, a single transmitter has the capability to create several resonating beams pointed towards multiple receivers, which enables concurrent WPT from one transmitter to multiple receivers.
5. _Intrinsically Safe:_ The DLC power level can reach up to tens of watts which raises safety concerns. However, the spatially distributed resonator structure of DLC makes it different from integrated resonating lasers. In DLC, when an obstacle comes into the resonating beam path, the laser is curtailed immediately without any additional decision-making circuit.
6. _Self-Aligning:_ In DLC, the charging process continues as the LOS path remains between the transmitter and receiver components. In such a scenario, the distributed resonator is capable of generating a resonating beam without any tracking or aiming feature. No user assistance in starting the charging process in DLC leads to a Wi-Fi-type experience. Several features of DLC. including concurrent charging, self-aligning and intrinsic safety, are shown in Figure 13.  
Figure 13: DLC features.
# A Rapid Verification System for Automatic Emergency Braking Control Algorithm of Passenger Car
## 6 Conclusions  
In order to quickly verify the AEB control algorithm for passenger cars, the working mechanism of the AEB system and the verification requirements of the AEB control method were analyzed, and a scheme of the AEB verification system for passenger cars was proposed. The dynamic pressure regulation performance of the pneumatic braking system was verified. The passenger car simulation parameters are configured, and the passenger car AEB verification system including the IPG simulation platform, dSPACE controller, and pneumatic braking bench is built. The verification system can provide SIL testing and HIL testing methods for AEB control algorithm verification, and provide a platform for the development of passenger car AEB controllers.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test** & \(t_{1}\)**/s** & \(t_{2}\)**/s** & \(t_{3}\)**/s** & \(t_{4}\)**/s** \\ \hline SIL & 89 & 40 & 54 & 45 \\ HIL & 146 & 74 & 54 & 45 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Typical values of test time.  
The HIL and SIL comparison tests of the AEB hierarchical control algorithm under typical test conditions verified the effectiveness of the proposed algorithm. In the test results, the SIL curve and the HIL curve have high consistency, and the average relative deviation is only 1.7 m. Without considering the time of building and debugging the verification system, the single test time of SIL testing is 228 s, and the single test time of HIL testing is 319 s. It is recommended to adopt SIL testing when verifying the function of the AEB control algorithm. If there is a higher requirement for accuracy, HIL testing is preferred.  
Conceptualization, J.X.; methodology, L.L. and R.Z.; software, L.L. and F.D.; data curation, L.L., R.Z. and F.D.; writing--original draft preparation, L.L. and R.Z.; writing--review and editing, J.X. and G.L.; visualization, J.X. and L.L.; project administration, G.L.; funding acquisition, J.X. and G.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Research and Development Program of Hubei Province of China, grant number YFXM2022000405 and 2022 BEC014.  
Not applicable.  
The study did not report any data.  
The authors declare no conflict of interest.
## 3 Application Example  
### Description of Target Bridge Transportation Network  
To demonstrate the ANN-based surrogate model with an MCS framework proposed in Section 2.3, an actual bridge transportation network in Pohang, South Korea, was adopted in this study. The target network lies on a dense fault in southeast South Korea, and a magnitude 5.4 earthquake occurred in 2017 [45]. South Korea is known to have a relatively low earthquake probability, but recent frequent earthquakes and aftershocks have increased the level of interest in seismic risk assessment [46].  
Figure 3 shows the topology of the target bridge transportation network, which comprises 1440 nodes and 3490 links. In addition, the bridge connecting each road network comprises 48 long-span bridges, including highways and national highways. The aim of this numerical analysis is to find TSTT of all nodes according to the damage states of 48 bridge structures. Figure 4 shows detailed structural information such as the superstructure type and traffic volume distribution for 48 bridges that compose the transportation network. The superstructure of the bridge is classified into six types, and the maximum volume of vehicles that can pass is up to 43,000 per day. In addition, travel demand of traffic analysis zones is determined based on the population and the number of households from the 2015 Census conducted by the Korean government [47]. The mean total span length of 48 bridges is 101.15 m with a minimum of 10 m and a maximum of 455 m, the mean length of each span is 23.68 m with a minimum of 9 m and a maximum of 70 m, and the mean pier height is 5.73 m with a minimum of 2.5 m and a maximum of 25 m.
## 1 Introduction
The design flexibility and modifiability of FPGAs also make it easier to map complex equations, such as Duffing equations, to hardware in a more efficient manner [19; 20]. So, we decided to verify the hardware circuit on the FPGA platform. Nowadays, the Duffing oscillator system has been widely used in the field of detection. [PERSON] et al. proposed a method of detecting intermittent chaos based on a variable step size dual Duffing oscillator differential system to detect the line spectrum of ship-radiated noise under the ocean background noise. Compared with the conventional Duffing oscillator detection method, this method can obtain a better signal-to-noise ratio [21]. [PERSON] et al. implemented an improved Duffing chaotic system for periodic weak signal detection and investigated dynamical characteristics. The advantage of the improved Duffing system for wide area detection is demonstrated by analyzing the corresponding Lyapunov exponent spectrum and bifurcation diagram [22]. [PERSON] et al. address a new modified hyperchaotic van der Pol-Duffing snap oscillator. Various dynamical properties of the proposed system are investigated with the help of Lyapunov exponents, stability analysis of the equilibrium, points and bifurcation plots, and it is implemented in field-programmable gate arrays [23]. In this study, semi-analytical solutions for periodic motions of a typical Duffing oscillator are predicted using the implicit mapping method. Through eigenvalue analysis, stability and bifurcations are identified. By optimizing and adjusting the model with dual parameters, namely excitation frequency and amplitude, we establish model parameters suitable for FPGA circuit implementation. Subsequently, a corresponding FPGA hardware circuit is constructed to validate complex bifurcation phenomena. The comparison between the numerical model and the FPGA hardware circuit exhibits strong consistency in the results.
## 4 Example Simulation and Analysis of SPH in Tunnel and Underground Engineering
### Establishment of Numerical Simulation Model
(**c**) Displacement of tunnel and surrounding rock. (**d**) Combined velocity of tunnel and surrounding rock. then shows a slight increase from the blasting surface along the circumference to the rear blasting surface. Figure 14c illustrates that the displacement and vibration velocity at the top end of the tunnel are lower compared to those at the bottom. This discrepancy is attributed to the greater blasting load borne by the bottom section. The vibration velocity recorded at the transverse measuring point reveals two distinct vibration periods within the initial 50 ms, with the amplitude of the first period significantly exceeding that of the second. During this period, the first four columns of steel bars experience tensile forces, while the bars from the fifth column onwards are subjected to compression, with tensile forces surpassing compressive forces. Figure 14d demonstrates that with a secondary lining thickness of 25 cm, the maximum vibration velocity reaches 49 cm/s, exceeding the allowable limit for safety. Increasing the thickness to 30 cm results in a reduction in the maximum vibration velocity to 32.6 cm/s, which conforms to the safety standard of 35 cm/s. Therefore, a secondary lining thickness of 30 cm is recommended for practical application. Although the stress on each component of the tunnel structure is maximized with a 25 cm thickness, all components remain within safe limits. Based on comprehensive analysis, the recommended thickness for the middle partition wall is 30 cm. These findings closely align with field measurement data and offer valuable theoretical guidance.
# A Framework for Proactive Resource Provisioning in IaaS Clouds
## 3 System Framework  
As shown in Figure 1, the proposed system framework consists of the data processing engine, the virtual cluster computing system, the cloud platform, and the physical hardware resources. In the virtual cluster computing system, there are the monitoring module, the resource management system, and the profiling module. In the resource management system, there are the over-commit mechanism and the resource estimating module. The virtual cluster computing system provides the computing environment of virtual clusters. The computing node in the virtual cluster is the virtual machine provisioned by the cloud platform. Several Spark data processing systems are executed on the virtual cluster computing system, which are integrated with the proposed proactive task scheduling algorithm aiming for shorter processing time. The historical log of resource usage is generated by the profiling system, and the current resource usage is captured by the monitoring system. All virtual resources are efficiently managed by the resource management system, in conjunction with the resource usage estimating module and the over-commit mechanism.  
When Apache Spark starts to execute an application, the profiling system first checks whether the profiled log has existed. If the profiled log has not existed, the profiling system would be activated to generate the corresponding profiled log for this newly-arrived application. In the profiled log, Spark could find the execution pattern of this application, such as the number of stages, the number of tasks within each stage, and data dependencies between stages. In the meantime, the resource management system would inform Spark about available resource information, and efficiently manage virtual resources based on the over-commit mechanism to improve the resource utilization. The goal of the resource estimating module is to estimate the resource demand of the launched application according to its historical executing log. Considering the current resource usage captured by the monitoring system, the over-commit mechanism would trigger the resource provisioning beyond the actual requirements in order to improve the resource utilization. The proactive task scheduling algorithm would decide an appropriate sequence to execute stages according to a profiled execution pattern and resource information, and aims to shorten the overall processing time.
# Online Streaming Feature Selection via Conditional Independence
## 5 Experiments and Analysis
### Comparison of \(ConInd\) with Two Markov Blanket Algorithms  
To observe the performance of the proposed \(ConInd\) algorithm in the aspect of mining _Markov blankets_, we compared \(ConInd\) with other methods of discovering _Markov blankets_, i.e., \(HITON\_MB\) and \(OSFS\). The \(HITON\_MB\) is the first concrete algorithm that would find sets of direct causes or direct effects and _Markov blankets_ in a scalable and efficient manner [20]. However, some error nodes may be introduced. The \(OSFS\) algorithm contains online relevance analysis and online redundancy analysis based on a _Markov blanket criterion_. The redundant features removed earlier remain redundant during the rest of the process when some features within its _Markov blanket_ are later removed [16]. There is a distinct difference between these two families of algorithms: \(HITON\_MB\) can only handle fixed feature sets. Therefore, we fixed feature sets instead of streaming features. The Probabilistic Network Learning Toolkit for Biomedical Discovery (_Causal Explorer_) [31] is the first comprehensive library for use in  
Figure 9: The variation of running time with the increase of ratio feature number with high redundancy and low relevance.  
MATLAB that implements the _HITON_MB_ algorithms for discovering _Markov blankets_. Meanwhile, _ContInd_ and _OSFS_ run datasets with streaming features. To compare _HITON_MB_ with _ContInd_ and _OSFS_, it is interesting and useful to compare them directly. According to the results of the algorithm, we mainly analyzed the following aspects: (1) degree of closeness among _HITON_MB_, _OSFS_, and _ConInd_; and (2) the reasons for the differences in the results of the _Markov blankets_.  
Table 9 presents the results of the number of selected features for _ContInd_, _HITON_MB_, and _OSFS_. We observed that _HITON_MB_ has more selected features than those in _ContInd_ and _OSFS_. This is because _HITON_MB_ discovers the _Markov blanket_ under fixed features, and mined features may not belong to nodes in its _Markov blanket_. Meanwhile, many error nodes will be produced in _HITON_MB_.  
Tables 5 and 10 present the results of comparison of prediction accuracy. Although the number of selected features for _ConInd_ is far less than that of _HITON_MB_, as indicated in Table A1, the prediction accuracies of _ConInd_ are not significantly lower than those of _HITON_MB_. Meanwhile, these selected features for _ContInd_ are approximately included in _HITON_MB_. Meanwhile, these features are indispensable to classification and prediction, and are strongly relevant or non-redundant to class attribute.
## 2 Study Area
### Selection of Typical Years and Water Demand Analysis  
There are no measured hydrological data at the Huangjinxia dam site of Hanjiang River. Yangvian hydrological station is located at 72 km upstream of the dam, with a controlled watershed area of 14,192 km\({}^{2}\). In this paper, the section flow of Yangvian hydrological station was taken as the inflow of Huangjinxia Reservoir. Through the hydrologic frequency analysis of the monthly average flow data of Yangvian hydrological station from 1967 to 2014, the designed runoff under 95%, 75%, 50%, 25% and 5% inflow frequencies were 1.299 billion m\({}^{3}\), 3.327 billion m\({}^{3}\), 4.799 billion m\({}^{3}\), 7.449 billion m\({}^{3}\) and 11.007 billion m\({}^{3}\), respectively.  
According to the principle of minimum root mean square error between designed runoff and measured runoff, July 1967 to June 1968 was selected as an extraordinarily dry year under 95% inflow frequency, July 1972 to June 1973 as a dry year under 75% inflow frequency, July 2001 to June 2002 as a normal year with 50% inflow frequency, July 1984 to June 1985 as a wet year under 25% inflow frequency and July 1983 to June 1984 as  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Category** & **Numerical Value** \\ \hline Dead water level/m & 440 \\ Normal pool level/m & 450 \\ Flood control level/m & 448 \\ Total reservoir storage/\(10^{8}\) m\({}^{3}\) & 2.29 \\ Effective storage capacity/\(10^{8}\) m\({}^{3}\) & 0.92 \\ Installed capacity/MW & 1.35 \\ Guaranteed output/MW & 8.6 \\ Installed capacity of pump station/MW & 129.5 \\ Pumping flow of pump station/(m\({}^{3}\)/s) & 70 \\ Ecological flow/(m\({}^{3}\)/s) & 25 \\ Design flow of fishway/(m\({}^{3}\)/s) & 1.51  \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristic parameters of Huangjinxia Reservoir and pump station.  
Figure 1: Location of Huangjinxia Reservoir.  
an extraordinarily wet year under 5% inflow frequency. The natural runoff processes in different typical years are shown in Figure 2. According to the water diversion plan, the water diversion volume of the Hanjiang-to-Weihe water diversion project will be 1.5 billion m\({}^{3}\) by 2030. The water demand processes in the water receiving area are relatively stable, and are mainly urban domestic water use, industrial and commercial enterprises water use. Therefore, the monthly average water demand flow is 47.53 m\({}^{3}\)/s.
# Roles of Modeling and Artificial Intelligence in LPBF Metal Print Defect Detection: Critical Review
## 3 Advances in Numerical Modeling Techniques for LPBF Defect Detection
### Melt Pool Defect Modeling  
Modeling the transient melt pool's hydrodynamic and heat transfer behaviors during the LPBF process can highlight in situ phenomena and quantitatively predict the resultant microstructure characteristics and residual stresses post-build. Defects such as residual-stress-induced cracks, solidification cracks, porosity increases, severe balling, undesired surface roughness, and alloy vaporization can be thoroughly analyzed using numerical and analytical techniques to model and simulate the melt pool's behavior. Various modeling efforts have been made to quantify certain melt pool behaviors occurring during the LPBF process. The modeling techniques and investigated defect behavior(s) occurring in the melt pool are categorized into three sections: (1) the finite element method (FEM), (2) finite volume method (FVM), and (3) analytical approaches. All presented modeling efforts have been validated in the report or against experimental dataset(s) from other reports. Due to the wide variety of reports available regarding this topic, inside each category of numerical method, modeling efforts are divided by powder material since the associated defects and behaviors can widely differ between powder materials.  
Figure 3: Mechanical defects of (**a**) liquidation cracking [20], (**b**) solidification cracking [22], and (**c**) delamination [25].