esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 State of the Art
### Graph-Based Design Languages Implemented with UML and DC43
#### 3.2.1 General Objectives of an Engineering Framework  
In the development of a complex technical system, often hundreds or even thousands of engineers have to contribute with their processes, ranging from creative thinking about computer-aided design (CAD) geometry description to simulation and verification tasks. The objects in these processes are components of the technical system and its abstract representation, but are also representations of performance, quality, cost and manufacturing  
Figure 5: Domain diagram of the entity relations in the IFM Framework. Reprinted with permission from [17].  
processes as well as operation process information. These objects dispose of numerous mutual relationships; an engineering framework seeks to represent a large share of this information in a sensible, interconnected way that facilitates effective and efficient processes for the involved engineers. Engineering frameworks seek to reduce unnecessary or inefficient activities, which may be caused, amongst other factors, by information deficits, insufficient communication, interface problems, data conversion necessities, inconsistencies and redundancies.  
Over the last decades, numerous approaches were proposed in engineering design science and adjacent research fields that may contribute to engineering frameworks. Notable are the scientific outcomes in the research area computational design synthesis (CDS), which also cover the early, abstract stages of the development process (e.g., [43]). The methods and tools of design automation (e.g., [44]) focus on the more concrete stages of this process, but can nevertheless contribute to an engineering framework. In recent years, research activities concern the development of cyber-physical systems (CPSs) and cloud-based systems (e.g., [45]). On a system development level, several strategies, methods and tools were proposed in the area of model-based systems engineering (MBSE) (e.g., [46]). This paper focuses on a specific engineering framework based on graph-based design language, which has the distinct objective of allowing the creation and evaluation of a topological diverse solution field while still enabling effective and efficient development processes.
# A Comparative Study for Control of Quadrotor UAVs
## 4 Conclusions  
In this paper, a control system architecture for 3D trajectory tracking is implemented and simulated in MATLAB/Simulink(r) for a micro-quadrotor UAV. The highly nonlinear, coupled, unstable and underactuated plant system, i.e., the quadrotor, is modelled with the Newton-Euler formalism. The control architecture is intended to stabilize the quadrotor attitude dynamics, which is fully actuated with a sufficiently fast inner loop controller, and to track velocity references in the outer loop. A comparative study of well-known linear and nonlinear model-free and model-based control laws applied to the attitude stabilization problem is proposed: PID, LQR, FL, SMC and MPC attitude stabilization laws are designed and implemented. The choice of the inner loop controller derives from either the fast dynamics requirement of the inner loop or an L\({}_{2}\) norm-based approximative evaluation of both tracking error and controller command signals since the relatively poor battery autonomy is still one of the main limitations for the deployment of multirotor UAVs in medium-to-long range missions. The reference attitude signals for the inner loop controller are derived from a simplified translational dynamics inversion performed in an outer loop controller, which is also responsible for altitude control. In the outer loop, the dynamics  
Figure 16: Euler angles signals [\(\phi(t)\), \(\vartheta(t),\psi(t)\)] of the controlled quadrotor system (blue) and reference attitude signals for trajectory 2 (red).  
Figure 15: Control input signals [\(u_{1}(t)\), \(u_{2}(t)\), \(u_{3}(t)\), \(u_{4}(t)\)] of the controlled quadrotor system executing trajectory 2.  
inversion is combined with three independent PID controllers, while the Sliding Mode attitude stabilizer is implemented in the inner loop. A velocity trajectory generator and a control input saturation block complete the proposed control architecture. The controlled closed-loop quadrotor system is proven to be able to execute complex 3D trajectories, such as ascent helixes. Consistent with the aim of this work, the main summarizing considerations are reported in Table 4 in order to highlight the main observations drawn from the study and provide guidelines for further research in this field.  
Future research directions will include the realization of other comparative studies for control of quadrotor UAVs, considering external disturbances, e.g., the wind, and parametric uncertainties, e.g., variable payloads.  
All authors contributed extensively to the current study. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This research received no external funding. Not applicable. Not applicable. Not applicable. The data used in the current study are available from the corresponding author upon reasonable request. The authors wish to thank the Piedmont Aerospace District for supporting the PhD activity of [PERSON]. The authors declare no conflict of interest.
# Image Segmentation Framework for Detecting Adversarial Attacks for Autonomous Driving Cars
###### Abstract  
The widespread deployment of deep neural networks (DNNs) in critical real-time applications has spurred significant research into their security and robustness. A key vulnerability identified is that DNN decisions can be maliciously altered by introducing carefully crafted noise into the input data, leading to erroneous predictions. This is known as an adversarial attack. In this paper, we propose a novel detection framework leveraging segmentation masks and image segmentation techniques to identify adversarial attacks on DNNs, particularly in the context of autonomous driving systems. Our defense technique considers two levels of adversarial detection. The first level mainly detects adversarial inputs with large perturbations using the U-net model and one-class support vector machine (SVM). The second level of defense proposes a dynamic segmentation algorithm based on the k-means algorithm and a verifier model that controls the final prediction of the input image. To evaluate our approach, we comprehensively compare our method to the state-of-the-art feature squeeze method under a white-box attack, using eleven distinct adversarial attacks across three benchmark and heterogeneous data sets. The experimental results demonstrate the efficacy of our framework, achieving overall detection rates exceeding 96% across all adversarial techniques and data sets studied. It is worth mentioning that our method enhances the detection rates of FGSM and BIM attacks, reaching average detection rates of 95.65% as opposed to 62.63% in feature squeezing across the three data sets.  
Keywords:adversarial attacks detection; autonomous driving cars; deep learning; image and mask segmentation; k-means +
Footnote †: journal: Applied sciences
# Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs
## 4 Performance Evaluation
### Federated Reinforcement Learning  
In this subsection, we give detailed explanations about the learning for the proposed system. We describe the implementation, the learning process, and the simple evaluation for validating the result of learning.
# Demand Flexibility: A Review of Quantification Methods, Models, and Required Data
## 3 Results
### Methods Deployed Across Studies
#### 3.3.1 Clustering  
Clustering techniques were used to group buildings together based on various attributes affecting their flexibility potential. Most studies employing clustering techniques did so based on consumption patterns [26; 27; 53; 63], while [25] used clustering to determine day-types. By grouping buildings into clusters, their potential flexibility levels can be estimated as an aggregate value. These clusters could also be targeted by DR providers and utilities to achieve desirable load reductions or shifts.
# A Project Scheduling Game Equilibrium Problem Based on Dynamic Resource Supply
## 2 Research Overview  
### Problem Description  
Project scheduling has always been considered the core of engineering supply chains, as the construction and operation of the supply chain are driven by the development of the project schedule. The engineering construction department first defines the resource demand of each demand point in each time period by forming a project schedule plan; next, the resource supplier attempts to meet the resource demand. However, as the resource supplier is also a decision-making subject with its own constraints, it optimizes its cost and time goals by formulating a resource transportation strategy and sends this information back to the engineering construction department, thus affecting the formulation of the project's schedule. Conflict and cooperation coexist in the engineering supply chain. The engineering construction department has higher decision-making power (i.e., the leader), whereas the resource supplier is subordinate (i.e., the follower). This \"leader-follower\" behavior is, in its essence, a Stackelberg game, with the characteristics of multi-periodicity in practice. Therefore, project scheduling and resource supply comprise an inseparable integrated system, which is the game analysis and dynamic coordination problem of the integrated system of \"project scheduling-resource supply\" from the perspective of the engineering supply chain. The successful operation of this system helps reduce project costs, shorten construction periods, and improve project quality and resource utilization.  
The research object of this project is a large hydropower construction project located in southeast China. A concrete double-curvature arch dam is the main project, with many construction activities with priority relationships and shared resources; each activity has several alternative modes, and each mode has a certain duration and resource demand. To meet the requirements of shared resources, it is necessary to specify the ordering time and quantity in each time period when making the project scheduling scheme, and the resource supplier further formulates the resource transportation strategy. These constitute the dynamic game decision-making system of the MRCPSP-MPSCP integrated system, and the structural model is shown in Figure 1.
# Effect of Performance by Excessive Advanced Fuel Injection Timing on Marine Diesel Engine
## 4 Discussion
In Figure 10b, it can be observed that the variation of the crankshaft angular velocity before the adjustment of injection timing was close to 20% at maximum. After the adjustment, the angular velocity variation occurred within 10%. The cause of this was attributed to the ignition timing and the different interval and magnitude of P\({}_{\text{max}}\). After the adjustment of fuel injection timing, the ignition timing was uniformly adjusted, and the position of P\({}_{\text{max}}\) shifted from BTDC to ATDC, resulting in a reduced difference in P\({}_{\text{max}}\). This adjustment is believed to have influenced the angular velocity variation. Since the significant fluctuation in crankshaft angular velocity corresponds to engine vibration, the reduction in engine vibration through the adjustment of injection timing can be indirectly confirmed by the data on angular velocity variation of the crankshaft. As shown in the graph in Figure 10c, the FFT result before the adjustment of injection timing exhibited significant amplitude at around 34 Hz. This frequency corresponded to the occurrence of four power strokes per revolution at 511 rpm. Continuous high amplitude at specific frequencies leads to engine vibration. Therefore, it can be inferred that the adjustment of injection timing reduced the amplitude and, consequently, decreased engine vibration.
# An Energy-Efficient Integration of a Digital Modulator and a Class-D Amplifier
## 6 Conclusions  
In this paper, the integration of the modulator of a transmitter and PW-modulator of a class-D amplifier is presented to improve the energy efficiency of the system. The concept stems from the idea of the class-D amplifier where a power switching block is driven by a digital carrier modulated by the signal of interest. A band-pass filter is then used to obtain an amplified PSK output. The concept can be of vital importance in the ultrasound band used in applications related to underwater acoustic  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & [3] & [4] & [5] & [6] & [7] & **This Work** \\ \hline Input signal type & Digital & Analog & Analog & Analog & Analog & Digital \\ Input signal voltage (V) & - & - & - & 1 & 1 & 1 \\ Input signal frequency (kHz) & - & 1 & - & \(1,5\) & 1 & 2 \\ Switching frequency (kHz) & 390 & 384 & 200 & 300–800 & 500–2000 & 19.8 \\ Class-D amplifier modulation & PWM & UPWM & PWM & PWM & PDM & PWM \\ Filter type & Digital & - & LPF & LPF & LPF & BPF \\ Output type & Amplified & Amplified & Amplified & Amplified & Amplified & PSK \\ Output power (W) & 1.2 & - & - & - & - & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with state-of-the-art.  
Figure 14: Waveform of the proposed circuit.  
communications. We use several steps to verify the concept. Initially, the hypothesis is verified using Matlab, and then a model is designed and simulated in Simulink. Finally an electronic circuit is built and experimentally tested to validate the feasibility of the concept. The results obtained by simulations and circuit implementation show that the proposed integrated system is an energy-efficient and cost-effective solution compared to existing solutions.  
Future work includes optimizing the performance of the electronic circuit by adjusting various parameters and component values. We intend to use this prototype in an energy-efficient underwater acoustic modem.  
[PERSON] and [PERSON] conceived the idea, [PERSON] proposed the model and wrote the manuscript, [PERSON]-A.L.-[PERSON] performed the model fitting in Matlab. [PERSON] designed and simulated the model in Simulink, built an electronic circuit in Multisim and validated the experimental results. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Universidad de Malaga, Campus de Excelencia Internacional Andalucia Tech.  
**Acknowledgments:** Authors express their gratitude to the Escuela Tecnica Superior de Ingenieria de Telecomunicacion, and the Instituto de Ingenieria Oceanica, University of Malaga, Malaga, Spain.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Adaptive Cruise Control for Cut-In Scenarios Based on Model Predictive Control Algorithm
## 4 Host Vehicle Trajectory Tracking Control
### Reference Trajectory Generation
#### 4.2.3 Cut In with Safe Distance  
In driving scenario 3, the relative distance between the host vehicle and the preceding vehicle does not meet the safe distance model of the cut-in vehicle. Therefore, the host vehicle needs to gradually slow down as the cut-in possibility of the nearby vehicle increases, which expands the longitudinal relative distance to the preceding vehicle and provides enough space for the cut-in vehicle to cut in. In this process, the host vehicle needs to assign the cut-in vehicle as the following target. The longitudinal trajectory and the cut-in possibility of the nearby vehicle are considered as the reference for the MPC-ACC. Similarly, the state quantities are rewritten as:  
\[\Delta x(k+1)=\Delta x(k)+v_{rel}(k)T_{s}+\frac{1}{2}(a_{L}(k)-a_{H}(k))T_{s}^ {2} \tag{51}\]  
\[v_{rel}(k+1)=v_{rel}(k)+[a_{L}(k)-a_{H}(k)]T_{s} \tag{52}\]  
Introducing cut-in possibility \(P_{cutin}\), obtained in Section 2, into the reference, the relative distance between the host vehicle and the cut-in vehicle needs to be changed with cut-in possibility \(P_{cutin}\) so as to realize coordinated control of the host vehicle with the cut-in vehicle, so the reference trajectory of the relative distance \(\Delta x(k)\) is:  
\[\Delta x_{ref}(k){=}\left[\Delta x_{des}(k)-\Delta x_{0}\right]P_{cutin}+ \Delta x_{0}\;0\leq P_{cutin}\leq 1 \tag{53}\]  
where \(\Delta x_{0}\) is the initial relative distance between the host vehicle and the cut-in vehicle, and \(P_{cutin}\) is the cut-in possibility. Reference trajectory \(\Delta x_{ref}(k)\) changes with the cut-in possibility, as shown in Figure 20.  
Reference trajectory vector of the MPC-ACC system in driving scenario 3 is:  
\[y_{ref}(k)=\left[(\Delta x_{des}(k)-\Delta x_{0})P_{cutin}+\Delta x_{0},0 \right]^{T} \tag{54}\]  
\[y_{ref}(k)=\mathbf{C}_{ref}x(k)+\mathbf{Z}_{ref} \tag{55}\]  
where,  
\[\mathbf{C}_{ref}=\left[\begin{array}{cc}0&0\\ 0&0\\ t_{h}P_{cutin}&0\\ 0&0\\ 0&0\end{array}\right]^{T},\mathbf{Z}_{ref}=\left[\begin{array}{cc}x_{0}P_{cutin}- \Delta x_{0}P_{cutin}+\Delta x_{0}\\ 0\\ 0\end{array}\right]\]
# Development and Pilot-Test of Blockchain-Based MyHealthData Platform
## 4 Discussion
### Strengths and Limitations  
The strong scientific contribution of this study is that a MyData system that can prove the authenticity of data using blockchain technology was created and verified by implementing the concept of dynamic consent required by the GDPR. This study developed the MyHealthData platform and was used by 1228 large-scale participants, and conducted user surveys at the same time. Users could optionally view or download medical records as agreed, check clinical trial matching information, or claim private insurance. In the user surveys, the satisfaction with claims through app was the lowest, which might be because only one of the private insurance companies was linked in the early stages of development in 2019. As of May 2021, insurance claim services are widely used in conjunction with more than 20 insurance companies. In addition, limited information, such as prescription drugs, simple test results such as blood and urine, diagnosis names, and diagnosis codes, was provided. In the future, the data types should be expanded so that even complex and large amounts of information, such as medical images and functional tests, can be part of MyData. Issues regarding data capacity and transmission will have to be considered.
# Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset
## 3 Methodology  
In this study, we propose ensemble deep-learning-based COVID-19 predictions from CXRs. Figure 3 presents a block diagram of the proposed study. Initially, image preprocessing was performed to reduce image variability and noise and enhance the contrast of the images. Lung segmentation was carried out using DeepLabV3+ semantic algorithm. The lung regions were cropped using the lung masks and inputted to deep learning models to classify COVID-19 CXRs from normal and pneumonia images. At first, we applied transfer learning of CNN models to classify CXRs. However, the dataset was highly unbalanced; therefore, we applied five different approaches: (i) class loss weighting; (ii) image augmentation; (iii) undersampling; (iv) oversampling; and (v) hybrid sampling, to prevent biased classifications. The best-performing models from each approach were combined as the ensemble classifier to improve the prediction accuracy. Finally, the indicative regions of COVID-19 lesions were detected by deep learning for better interpretability.
###### Abstract  
The rapid integration of the Internet of Medical Things (IoMT) into healthcare systems raises urgent demands for secure communication mechanisms capable of protecting sensitive patient data. Quantum key agreement (QKA), a collaborative approach to key generation based on quantum principles, provides an attractive alternative to traditional quantum key distribution (QKD), as it eliminates dependence on a trusted authority and ensures equal participation from all users. QKA demonstrates particular suitability for IoMT's decentralized medical networks by eliminating trusted authority dependence while ensuring equitable participation among all participants. This addresses fundamental challenges where centralized trust models introduce vulnerabilities and asymmetric access patterns that compromise egalitarian principles essential for medical data sharing. However, practical QKA applications in IoMT remain limited, particularly for schemes that avoid complex entanglement operations and authenticated classical channels. Among the few QKA protocols employing [PERSON]'s search algorithm (GSA), existing proposals potentially suffer from limitations in fairness and security. In this paper, the author proposes an improved GSA-based QKA protocol that ensures fairness, security, and correctness without requiring an authenticated classical communication channel. The proposed scheme guarantees that each participant's input equally contributes to the final key, preventing manipulation by any user subgroup. The scheme combines [PERSON]'s algorithm with the decoy photon technique to ensure secure quantum transmission. Security analysis confirms resistance to external attacks, including intercept-resend, entanglement probes, and device-level exploits, as well as insider threats such as parameter manipulation. Fairness is achieved through a symmetric protocol design rooted in quantum mechanical principles. Efficiency evaluation shows a theoretical efficiency of approximately 25%, while eliminating the need for quantum memory. These results position the proposed protocol as a practical and scalable solution for future secure quantum communication systems, particularly within distributed IoMT environments.  
Internet of Medical Things; quantum key agreement; [PERSON]'s search algorithm; quantum security; fairness +
Footnote †: journal:  
_Article_