esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theory and Analysis
### Nonlinear Coefficient  
Small defects cause the solid material to exhibit non-linear characteristics while causing nonlinear effects in the ultrasonic waves propagating in the material. However, the high-order harmonics in the received signal are extremely weak, and the use of large-value and high-energy ultrasonic signals can effectively enhance nonlinearity. The micro-defects in the material are thus evaluated by analyzing the nonlinear signals. When the excitation is a single-frequency sine wave, \(\mathrm{u}=\mathrm{A_{1}sin}\mathrm{\SIUnitSymbolMicro}\), the expression of the ultrasonic wave in the solid medium is obtained by an approximate iterative method [3]:  
\[u(x,t)=A_{1}sin(kx-\omega t)-A_{2}cos2(kx-\omega t)+ \tag{3}\]  
where \(k\) is the wave number, so the second-order ultrasonic nonlinear parameters can be expressed as [3]:  
\[\beta=\frac{8}{\mathrm{k^{2}x}\mathrm{A_{2}}} \tag{4}\]  
Figure 4: Comparison of actual distance and theoretical distance.  
Figure 3: Single mode signal received in non-damaged sheet.  
In engineering applications, the relative ultrasonic nonlinear parameters are used to study the nonlinear characteristics. The relative ultrasonic nonlinear parameters caused by microcracks are expressed as [17; 21; 34]:  
\[\beta^{\prime}=\frac{\mathrm{A}_{2}}{\mathrm{A}_{1}} \tag{5}\]  
When the crack appears on the lamb wave propagation path, the nonlinear effect is the strongest and the relative nonlinear coefficient is large. When there is no damage on the lamb wave propagation path, the higher harmonics are not generated, and the relative nonlinear coefficient is smaller. Therefore, the relative nonlinear coefficient is detected and calculated at different distances from the microcrack. Then the law is found through experiment.  
In the Figure 5a, the transmission to reception path is parallel to the X-axis, and the center of the microcrack is taken as the coordinate origin. The distance from the damage to the center line of the left scanning path is set to be negative. As can be seen from Figure 5b, the closer the distance to the microcrack, the larger the relative nonlinear coefficient; the farther the distance to the microcrack, the smaller the coefficient. In this paper, the relative ultrasonic nonlinear parameters are used as the damage index [25]. The relative ultrasonic nonlinear parameters are detected by the single S0 mode lamb wave.  
Figure 5: Research on the relative nonlinear coefficient: (**a**) The probe layout for looking for the law; (**b**) the distribution law of \(\beta\).
# Dynamic Path Planning for Vehicles Based on Causal State-Masking Deep Reinforcement Learning
## 5 Comparison of Simulation Results  
To validate the effectiveness of the proposed improved DRL algorithm (CSM-TD3) in dynamic path planning, we compared the performance of the CSM-TD3 algorithm with that of the TD3, proximal policy optimization (PPO) and DQN algorithms in terms of convergence and generalization through simulation experiments. All experiments were conducted on a hardware platform equipped with an Intel Core i9-13900K processor, sourced from Intel Corporation, Santa Clara, CA, USA, and an NVIDIA GeForce RTX 4080 graphics card, sourced from NVIDIA Corporation, Santa Clara, CA, USA.
# Deployment Optimization of Defense Stations in an Attack-Defense Aerial War Game
## 2 Game Design
### Defense Cost Function  
The deployment of defense stations is optimized by minimizing a defense cost function, which is tailored to the prioritization of defense assets. In this work, four different types of defense cost function are specified:  
\[\zeta_{1} =N_{dt}+0.5N_{dt^{\prime}}+0.2N_{ds}+0.02N_{db}\] \[\quad\quad(\text{benchmark}) \tag{11}\] \[\zeta_{2} =N_{dt}+0.05N_{dt^{\prime}}+0.02N_{ds}+0.002N_{db}\] \[\quad\quad(\text{major post dominant})\] (12) \[\zeta_{3} =N_{dt}+N_{dt^{\prime}}+0.2N_{ds}+0.02N_{db}\] \[\quad\quad(\text{minor post on par with major post})\] (13) \[\zeta_{4} =N_{dt}+0.5N_{dt^{\prime}}+0.5N_{ds}+0.02N_{db}\] \[\quad\quad(\text{defense station on par with minor post}) \tag{14}\]  
where \(N_{dt}\) is the number of hits on the major post, \(N_{dt^{\prime}}\) is the number of hits on the minor posts, \(N_{ds}\) is the number of defense stations lost, and \(N_{db}\) is the number of interceptors consumed. The cost function \(\zeta_{1}\) can be used as a benchmark. One hit on the major post is arbitrarily allocated a relative cost of 1. Each minor post can serve as backup to the major post, thus 1 hit is allocated a relative cost of 0.5. A defense station is lost by taking 1 hit and is allocated a relative cost of 0.2. Each interceptor is allocated a relative cost of 0.02.  
In cost function \(\zeta_{2}\), the importance of the major post is emphasized by reducing tenfold the cost of hit on minor posts and defense stations as well as the cost of interceptors, as compared to their counterparts in \(\zeta_{1}\). In cost function \(\zeta_{3}\), the importance of a minor post is promoted on par with the major post by imposing the same cost per hit. In cost function \(\zeta_{4}\), the importance of each defense station is promoted to match one hit on a minor post. The defense cost is counted at the end of each game for further statistical analysis.
## 4 Numerical Simulations  
### Numerical Modelling  
In order to understand the force situation of the support structure, ABAQUS 2020 numerical simulation software was used to simulate the mechanical performance of the third ring of the main structure based on the 'load-structure' 3D model. Based on the field test scheme and actual soil layer information, the linear elastic constitutive model is used for the stress-strain relationship of each material in the model. The physical and mechanical parameters of each material are listed in Table 1. Determine the magnitude of external loads on the structure according to the parameters listed in the table.  
3D reduced integration and eight-node linear solid elements, referred to as C3D8R by ABAQUS [31], are used to construct the meshes of the steel corrugated plate, the mold bag concrete, and the connecting bolt. Referring to Section 5.2, the dimensional parameters of each component in the model are modeled 1:1, however, some simplifications are made to the M22-type bolts. The nut and screw were merged to model as one piece, and the nut shape was changed to a round shape, as shown in the bolt in Figure 6. The mesh division of each component is shown in Figure 6, with a total mesh of 103,296 after division.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & \begin{tabular}{c} **Mass Density** \\ **(kg\(\cdot\)m\({}^{-3}\))** \\ \end{tabular} & \begin{tabular}{c} **Cohesion** \\ **(kPa)** \\ \end{tabular} & \begin{tabular}{c} **Internal Friction** \\ **Angle (\({}^{\circ}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Poisson’s** \\ **Ratio** \\ \end{tabular} &
\begin{tabular}{c} **Elastic** \\ **Modulus (MPa)** \\ \end{tabular} \\ \hline Soil & 1900 & 8 & 36 & 0.25 & 10 \\ Steel-Q235 & 7850 & - & - & 0.33 & 210,000 \\ Concrete-C30 & 2500 & - & - & 0.2 & 30,000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main physical and mechanical parameters of each material.
## 3 Outage Probability Performance Analysis  
When the targeted data rates, \(R_{1}\) and \(R_{2}\) are determined by the users' QoS requirements for user \(D_{1},D_{2}\). In fact, the outage probability is an important performance criterion which needs to be investigated. If the outage event occurs at the non-SIC user, the SIC user does not use the D2D signal, and the outage of the SIC user does not allow D2D transmission from the SIC user to the non-SIC user.
# Thermal Effects on Photovoltaic Array Performance: Experimentation, Modeling, and Simulation
## 5 Results
### CFD Results  
CFD simulations were conducted to study the influence of wind speed on thermal loss factor/heat transfer coefficient, temperature distribution, and modes of heat transfer. Figure 12 shows CFD simulation and experimental measurement of thermal loss factor/heat transfer coefficient as a function of wind speed for a heat flux of 800 W/m\({}^{2}\). Heat transfer coefficients/thermal loss factors were calculated using Equation (2), where temperature and solar irradiance data were obtained from experiments. The efficiency values were calculated based on power production and solar irradiance. Simulations were also conducted for 600, 800, and 1100 W/m\({}^{2}\); however, the results are very similar, so the data for 800 W/m\({}^{2}\) were chosen to be presented in this work. The CFD and experimental results match closely with a similar trend. Their suggestion was that the heat transfer coefficient increases nearly linearly with wind speed, as assumed by the PVsyst model in Equation (3). However, the free-standing thermal loss factor values suggested from PVsyst, \(U_{c}=25\) W/m\({}^{2}\cdot\)K and \(U_{V}=1.2\) W/m\({}^{2}\cdot\)K/(m/s), do not produce comparable results with that of experimental and CFD simulations. However, it is interesting to note that at low wind speeds (<4 m/s), the suggested values from PVsyst do fall within the range seen in our experimental data. Based on our empirical results, we recommend a new range of values for \(U_{c}\) and \(U_{v}\) for free-standing PV, listed in Table 5.  
Figure 11: Array efficiency at different wind speeds.
# Towards an Ergonomic Assessment Framework for Industrial Assembly Workstations--A Case Study
## 1 Introduction
Some of the most highly cited reviews on COBOT technology [19; 20; 21] defend that with human-robot interaction it is possible to combine the adaptability and fast decision making from humans with the precision and consistency of the robot system. Human-robot collaboration (HRC) is an appealing prospect to the industry in general: to Small and Medium-sized enterprises due to the high degree of adaptability and flexibility, as well as to mass production companies which are rapidly shifting into mass customization. With ever-changing customer demands at an increasingly faster pace, companies look to proactive answers to boost their competitiveness and take the factories to the next level of automation and manufacturing advancement [19; 21]. Smart factories are one of the pillars of the 4 th industrial revolution (Industry 4.0). Of those, flexible robotic solutions with intuitive and natural human-machine interfaces and capable of intelligent decision making--COBOTs--are key players. COBOTs or collaborative robots are a sub-type of robots specially tailored to work in close proximity to humans or other robots. Through a closer interaction between the machine and the operator, it enables collaborative scenarios where the continuous accuracy, speed, and repeatability typical of robots can be combined with the innate adaptability, dexterity, perception, and intelligence distinctive of humans. This mutualist relation between both parts leads to a powerful collaborative framework to positively impact productivity, flexibility, and most importantly with a positive net effect on the creation of new jobs rather than replacing workers [18; 20; 22]. Therefore, the current study is integrated into the first phase of a research project of the Collaborative Laboratory DTx--_\"Associacao Laboratorio Colaborativo em Transformacao Digital\"_. This laboratory is a non-profit private association, which carries out its activity doing applied research in different areas linked to the digital transformation of industry and society, promoting cooperation between the academic research and the industry. In this case, the project results from a cooperation between the Laboratory DTx and a large Portuguese site of furniture manufacturing, foreseeing the future implementation of collaborative robotics in the manual assembly section. In this industrial context, the company's reports pointed out the need for an ergonomic intervention across the manual assembly workstations, where most of the workers were continuously exposed to WMSD risk factors and presenting already several musculoskeletal complaints. The current study aims to identify the characteristics of the workstation problems and to define the ergonomics requirements as we look forward to integrating a COBOT system in the workflow.
# Complex Battery Storage Fire Propagation Translational Forensic Study Using Cellular Automata
## 2 Materials and Methods
### Experimental Test  
An experimental study was performed to calibrate the battery fire propagation with actual battery fire scenarios. Three cylindrical INR (Lithium Nickel Cobalt Manganese Oxide, NMC) 18650 battery cells (Samsung SDI, Yongin-si, Republic of Korea), each at 100% state of charge (SOC), were tested in series with positive-to-negative end alignment. The cells were placed in direct contact, with no spacing between them, to simulate a worst-case scenario that maximizes thermal interaction between the cells. This configuration mirrors real-world applications, particularly the fire propagation in compact battery arrangements found in consumer electronics such as laptops. The cells were secured together with tape wound around the circuit board strips acting as supports along the edges of the cells. An Oster electric hotplate was used to induce thermal abuse, facilitating and simulating a real-world LIB failure mode during the experimental tests. The bottom cell was in contact with the hot plate through its negative end cap, directing any potential positive end-fire event upward into the cell above. Type-K thermocouples were affixed at mid-height positions  
Figure 8: Simulation mechanism and rules.  
Figure 9: Principle governing fire propagation.  
along the length of each cell. The thermocouples were connected to an Omega signal conditioner, which converted the signal for the node device. The node transmitted data to a gateway located at a safe distance, connecting via USB to the monitoring computer. Data were logged and displayed in real time using SmartConnect and National Instruments Compact Data Acquisition (DAQ). The experimental setup is shown in Figure 10.  
Using the recorded time from the experiment as an estimation for each timestep in the simulation, the actual time of burn at the module level is formulated for different fire propagation probabilities:  
\[Actualestimationtimepertimestep=t_{fire2}-t_{fire1} \tag{2}\]  
\[Estimatedfirepropagationtime=Firespreadtimestep\times\left(t_{fire2}-t_{fire1}\right) \tag{3}\]  
where \(t_{fire}\) is the discrete time at a particular fire state.
###### Abstract  
Existing smart farming technology faces sustainability challenges due to high costs and environmental pollution. This study introduces a novel, sealed smart farming system utilizing misting technology to address these limitations. The system is designed to efficiently use water and nutrients, making it particularly suitable for high-value crop cultivation in urban environments with architectural constraints. Over a one-month experimental period, we monitored the system's performance in a controlled environment. The methodology included setting up the system and regularly measuring water usage, nutrient delivery, and plant growth metrics. The experimental results showed a significant reduction in water usage compared to traditional methods, with precise control of micronutrient delivery. Additionally, the system's ability to maintain a consistent sealed environment was demonstrated, which is crucial for optimal plant growth. The system's portability and space utilization efficiency were also highlighted as major advantages. Furthermore, the system demonstrated potential for cultivation in extreme environments, such as water-scarce regions, by maintaining optimal indoor conditions for crop growth. Challenges such as nozzle clogging and uneven mist distribution were identified, indicating the need for further research in cartridge design and misting methods. Overall, this smart farming technology shows significant promise for enhancing global food security and contributing to sustainable agricultural development by minimizing water usage and optimizing nutrient management.  
Keywords:inflatable smart farm; aeroponics for high-value crops; sustainable system +
Footnote †: journal: Applied sciences
## 2 Materials and Methods
### Total Phenolic and Flavonoid Contents  
The total phenolic content (TPC) of the horse chestnut extracts was evaluated according to the Folin-Ciocalteu method proposed by [PERSON] et al. [17], with some slight modifications. An aliquot of 0.5 mL horse chestnut solution was mixed with 3.0 mL distilled H\({}_{2}\)O, 0.25 mL of Folin-Ciocalteu reagent, and 0.75 mL of Na\({}_{2}\)CO\({}_{3}\) (20% \(w/v\)). After incubation, the absorbance of the prepared mixture was measured at 765 nm against a blank sample. Gallic acid was used for calibration of the standard curve (y = 0.0101x \(-\) 0.0178, R\({}^{2}\) = 0.9991). The results for total content of phenolics were expressed as mg of gallic acid equivalents (GAE/mL of sample).  
The total flavonoid content (TFC) was evaluated by the aluminium chloride method. Two procedures according to quercetin and catechin standards were used for estimation of TFC. For the first procedure, 0.5 mL measured extract was mixed with 1.5 mL 95% EtOH, 0.1 mL 10% AlCl\({}_{3}\) solution, 0.1 mL Na acetate, and 2.8 mL distilled H\({}_{2}\)O. After 40 min incubation, the samples were measured at 415 nm wavelength. The total flavonoid content was evaluated using the standard curve of quercetin (20-100 ug/mL). For another procedure, TFC was determined as follows: to the mixture of 0.25 mL horse chestnut extract, 1.25 mL distilled H\({}_{2}\)O, and 0.075 mL of a 5% NaNO\({}_{2}\) solution, there were added 0.15 mL 10% AlCl\({}_{3}\) solution, 0.5 mL 1 M NaOH, and 0.775 mL distilled H\({}_{2}\)O. The calibration curve using catechin as a standard (50-400 ug/mL) was then constructed [18], and then the absorbance was read at 510 nm wavelength. Analyses were carried out in triplicate. TFC was expressed as mg of quercetin equivalents (QE) and catechin equivalents (CE) per mL of sample.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dose** & **Range** & **Regression Equation** &
\begin{tabular}{c} **TEAC 1** \\ **(\(\upmu\)mol/mL)** \\ \end{tabular} \\ \hline
0 & PS & y = 151.1x \(-\) 0.06 & 30.8 \({}^{\text{a}}\) \(\pm\) 0.22 \\
1 & PS & y = 135.8x \(+\) 0.42 & 28.4 \({}^{\text{b}}\) \(\pm\) 0.28 \\
5 & PS & y = 131.5x \(+\) 3.2 & 28.15 \({}^{\text{b}}\) \(\pm\) 0.23 \\
10 & PS & y = 123.5x \(-\) 0.7 & 26.2 \({}^{\text{c}}\) \(\pm\) 0.36 \\
0 & SS & y = 951x \(-\) 14.58 & 1056.2 \({}^{\text{a}}\) \(\pm\) 2.68 \\
1 & SS & y = 959.5x \(-\) 7.99 & 1083.8 \({}^{\text{b}}\) \(\pm\) 2.85 \\
5 & SS & y = 998.5x \(-\) 7.67 & 1116.0 \({}^{\text{c}}\) \(\pm\) 1.69 \\
10 & SS & y = 986x \(-\) 9.32 & 1100.5 \({}^{\text{d}}\) \(\pm\) 2.9 \\ \hline \hline \end{tabular} \({}^{\text{i}}\) Trolox equivalent antioxidant capacity. Different superscript letters (a–d) indicate significant differences between values (\(p<0.05\)).  
\end{table}
Table 1: Regression equations and TEAC values for non-irradiated and irradiated with 1, 5, and 10 kGy peeled (PS) and shell seed (SS) horse chestnut samples determined by EPR measurements.
## 3 Security Aspects Regarding Wireless Body Area Framework
### Authentication and Key Agreement
#### 3.2.3 Symmetric Cryptographic-Based Authentication  
In addition to PUFs and ECQV, symmetric cryptographic-based authentication mechanisms are also suitable for WBANs [19]. Symmetric key-based authentication proposed in [19] is lightweight and efficient and is also suitable for post-quantum deployment scenarios.  
The emergence of quantum computers poses a significant threat to traditional asymmetric cryptographic algorithms, including those employed for WBAN communication like ECQV [20]. As quantum computers gain computational power, they will be able to break the encryption used in these algorithms, rendering WBANs vulnerable to eavesdropping and tampering. To safeguard WBAN security in the face of quantum computing, alternative authentication mechanisms that are resilient to quantum attacks are being developed [21; 22; 23].  
The development of these alternative authentication mechanisms has significantly enhanced the security of WBAN communication. By addressing the inherent vulnerabilities of IEEE 802.15.6 and leveraging novel authentication techniques, WBANs can provide a secure and reliable platform for healthcare applications.
# A Review of Research on Spectrum Sensing Based on Deep Learning
## 6 Challenges and Perspectives
### Investigating the Robustness of Sensing Systems under Various Malicious Attacks  
Although current deep-learning-based spectrum-sensing systems have achieved satisfactory detection performance, the sensing systems may be subject to various attacks, such as adversarial attacks, SSDF, etc. Therefore, it is crucial to ensure their security and improve their robustness. Currently, this is generally achieved through deep reinforcement learning, adversarial training, designing hybrid and dynamically tuned models, and incorporating autoencoders. For example, in [114], in order to defend against primary user adversarial attacks (PUAAs), it is proposed to apply self-encoders in LSTM and CNN to extract temporal and local features for effective defense. Another example is the above-mentioned work [109], that bypasses malicious data in FC by using agents in deep reinforcement learning, which can effectively improve the error caused by SSDF in sensing. However, due to the variety of malicious attacks and gaps in application practice, future research and the selection of appropriate strategies or new techniques based on specific application scenarios and threat models in the spectrum-sensing domain are needed by scholars.
## 1 Introduction
### Related Work
The communication and registration of drones are executed through a secure channel with the control room and verified by a registration authority as a trusted third party. Drones are resource-constrained devices; therefore, certificate-based cryptography for the secure channel can be inefficient. [PERSON] et al. suggest a distributed key management system ([17]) for a flying ad hoc network (FANET) using blockchain technology in their research. The system includes multiple clusters of UAVs, each consisting of a head UAV with strong transmission power, ample storage space, high computing capabilities, and multiple-member UAVs, which can only handle lightweight tasks. The head UAVs are responsible for distributing cluster keys, updating public/private key pairs, migrating between clusters, and securely revoking any malicious UAVs. Security analysis and performance evaluations demonstrate that this system can withstand various attacks. Although [PERSON]'s key management system takes advantage of blockchain, drones use symmetric and asymmetric encryption and Schnorr signatures, but compared to our scheme, we find that ours is more lightweight. Some studies are reviewed and compared that are relevant to our solution. In this survey, you can find more comprehensive reviews of these approaches [18].