esa-sceva/satcom-chunk-collection huggingface dataset:
# Development and Prospect of UAV-Based Aerial Electrostatic Spray Technology in China
## 4 Analysis and Prospects
### Comparative Study of Various Charging Methods  
The induction charging mode was determined from the early stage of aerial electrostatic spray technology research in the United States, and it has been adopted in commercial electrostatic spray systems. Induction charging has the advantages of low charging voltage and low application threshold. It is a safe and effective method for charging droplets. Most Chinese scholars have also applied the induction charging method in their research. However, in addition to induction, there are also contact and corona ways to charge droplets. Corona charging voltage is very high, up to 30 kV. It can be used for conductive and non-conductive liquids with low insulation requirements. The contact charging voltage is required to be between corona type and induction type, but the insulation requirements are higher. Although the corona type with high charging voltage and the contact type with high insulation requirements still need a lot of basic research to clarify the charging mechanism and eliminate application risks, the disadvantages of the induction charging mode with poor charging effect cannot be ignored. Previous studies have shown that the contact charging method is able to generate a larger target current when compared with the induction charging method [47]. In recent years, there have been reports on contact charging methods in the research of UAV-based electrostatic spray technology with gratifying test results. In the future, it is necessary to carry out comparative studies of various charging methods in order to evaluate the operation effect of aerial electrostatic spray technology under different charging methods.
## 2 System Model Description
### Equivalent Degradation Model  
Fuel economy and system durability are generally treated as a multi-objective optimization problem. To simplify the analysis, this study converts the durability metric into an economic indicator for assessment. The equivalent hydrogen consumption due to fuel cell degradation is expressed as follows:  
\[C_{fc\_deg}=\frac{\Delta\mathcal{O}_{FC_{depal}}\text{M}_{fc}P_{fc,max}}{10 \%\text{M}_{\text{H}_{2}}} \tag{13}\]  
where M\({}_{fc}\) is the price of the fuel cell price per kilowatt and M\({}_{\text{H}_{2}}\) is the price of hydrogen. The 10% represents the maximum allowable performance degradation of the fuel cell.  
For battery degradation, a similar approach can be adopted. However, considering that changes in battery capacity affect the battery price, it is important to note that the battery price does not increase linearly with capacity. As the battery capacity increases, the cost growth rate tends to diminish. Therefore, a price decay factor is introduced, allowing the battery cost to be expressed as follows in Equation (14):  
\[C_{bat\_deg}=\frac{\Delta\mathcal{O}_{BAT_{depal}}\text{M}_{bat}Q_{bat}V_{bat}} {20\%\text{M}_{\text{H}_{2}}} \tag{14}\]  
where M\({}_{bat}\) is the price of the battery per kWh, with the price parameters detailed in Table 3. The specific pricing data are based on [18], which remain accurate for the costs of both batteries and hydrogen. However, as the price of an FCS has significantly decreased since the publication of the reference, according to [34], the cost of fuel cells decreases by approximately 5% annually. To align with current market trends, slightly lower values are adopted in this study to account for the ongoing reduction in fuel cell prices.
## 5 User Preference Modeling
### User Preference Modeling for Social Tags
#### 5.2.2 User Preference Modeling for Social Tags with EMA  
User preference modeling for social tags is built based on two intuitions: (1) The frequency of a social tag represents the strength of user preference on this category defined by this social tag. A social tag with a higher frequency implies that the user might prefer those videos labeled with this social tag. In addition, a social tag with a lower frequency represents that the user might not be interested in this social tag. (2) Social tag data close to the current temporal period are usually more important than data temporally far from the current period. For example, a certain user was interested in a personal digital assistant (PDA) six months ago. He is currently interested in the iPad, and the social tag of an iPad is used frequently. It is more appropriate to recommend an iPad over a PDA to this user.  
If only the social tag frequency is considered, the influences of time will be ignored in user preference modeling for social tags. Therefore, EMA is utilized to calculate the social tag frequency, which is a weighted method that can make full use of time information. Here, the weight of the social tag that is closer to the current time will be higher than the social tag that is farther from the present moment [37]. Suppose the social tag representation of a video is \(t_{\text{v}}\), the user preference model for the social tag is \(U_{0}\) before it is updated, and \(U_{\text{t}}\) is the user preference model for the social tag after it is updated. Their relationship is illustrated as  
\[U_{\text{t}}=\alpha t_{\text{v}}\ +\ (1\ -\ \alpha)U_{0} \tag{12}\]  
where \(a\) is an updating parameter of a user who has watched \(m\) videos and \(n\) is the number of videos with the social tag. The above equation can be rewritten as  
\[U_{t}=\frac{t_{\text{v}_{1}}\ +\ (1-\alpha)t_{\text{v}_{2}}\ +\ (1-\alpha)^{2}t_{\text{v}_{3}}\ +\ \ldots\ +\ (1-\alpha)^{n}t_{\text{v}_{n}}}{1+\ (1-\alpha)\ +\ (1-\alpha)^{2}\ +\ \ldots\ +(1-a)^{m}} \tag{13}\]  
With the increase of viewed videos, the denominator of Equation (12) is equal to \(1/a\). Therefore, for a user who has watched \(m\) videos, his or her user preference for a social tag can be modeled by  
\[U_{t}=\alpha\Big{(}t_{\text{v}}^{1}\ +\ (1-\alpha)t_{\text{v}}^{2}\ +\ (1-\alpha)^{2}t_{\text{v}}^{3}\ +\ \ldots\ +\ (1-\alpha)^{n}t_{\text{v}}^{n}\Big{)} \tag{14}\]
# Kolmogorov-Arnold Finance-Informed Neural Network in Option Pricing
## 5 KAFIN for Option Pricing  
### European Option Pricing  
In financial markets, options offer investors the right to buy or sell an underlying asset at a predetermined price within a specified period. European options, known for their simplicity and fixed expiration date, are favored for cost-effective hedging strategies and stability amid low market volatility. Institutional investors often utilize European options for precise risk management and predictable cash flows, although their fixed nature limits flexibility in responding to market changes before expiration.  
The Black-Scholes (BS) model, established by [PERSON] and [PERSON] in 1973, is pivotal in financial mathematics for pricing European options. This model provides a closed-form solution based on key variables such as the underlying asset's current price, the option's strike price, time to expiration, risk-free interest rate, and asset volatility. By employing partial differential equations, the BS model accurately predicts option prices, enabling traders to assess fair values, hedge risks, and execute effective derivative trading strategies in dynamic markets.
## 4 Practical Algorithms for Action Masking
### Off-Policy Algorithm  
In the Off-Policy Invalid Action Masking (Off-PIAM) algorithm, we treat \(\pi^{\varnothing}_{\theta}\) as the behavior policy, and we still optimize the original policy (\(\pi_{\theta}\)). We can adapt the Importance Sampling (IS) technique to derive an unbiased estimator of a policy gradient that relies on off-policy trajectories. This is known as an _off-policy_ policy gradient [28]. The gradient of \(J(\pi_{\theta})\) corresponding to its parameter \(\theta\) is [29]:  
\[\
abla_{\theta}J(\pi_{\theta})=\mathbb{E}_{\tau\sim\pi^{\varnothing}_{\text{ old}}}\left[\left(\prod_{t=0}^{H}\frac{\pi_{\theta}(a_{t}|s_{t})}{\pi^{ \varnothing}_{\theta\text{old}}(a_{t}|s_{t})}\right)\sum_{t=0}^{H}\
abla_{ \theta}\log\pi_{\theta}(a_{t}|s_{t})\hat{A}_{t}\right]. \tag{27}\]  
Even though the above policy gradient is unbiased, the product of the important sampling ratio results in high variance [30]. We can clip the gradient the same way as PPO [27; 31]:  
\[L^{CLIP}_{\text{Off-P}}(\theta) =\mathbb{E}_{\tau\sim\pi^{\varnothing}_{\theta\text{old}}}\left[ \min\left(r^{\text{IS}}_{t}(\theta)\hat{A}_{t},\text{clip}\left(r^{\text{IS}}_ {t}(\theta),\epsilon\right)\hat{A}_{t}\right)\right], \tag{28}\] \[\text{where }r^{\text{IS}}_{t}(\theta) =\frac{\pi_{\theta}(a_{t}|s_{t})}{\pi^{\varnothing}_{\theta\text {old}}(a_{t}|s_{t})}. \tag{29}\]  
By adding the entropy item, the optimization objective is:  
\[L^{PPO}_{\text{Off-P}}(\theta)=L^{CLIP}_{\text{Off-P}}(\theta)+c_{e}\mathcal{H }(\pi^{\varnothing}_{\theta}). \tag{30}\]  
Different from the On-PIAM algorithm, the Off-PIAM algorithm focuses on training the original policy \(\pi_{\theta}\). After the training, the agent can work well even if we remove the masking. That is, the agent trained by the Off-PIAM algorithm explicitly learns to avoid invalid actions.
## 3 Experimental System Setup  
A direct circuit system was used to study the DC forced-commutation interruption, as shown in Figure 5. The instantaneous short-circuit current was supplied from the capacitor \(C_{0}\) with the capacitance value of 2 F, by triggering the bidirectional thyristor \(VT_{0}\) controlled by the measure-and-control system. The main current ratings were obtained by adjusting \(R_{0}\). In the breaking cycle, the experimental results showed that the total attenuation of the circuit current was less than 10%, which could provide a stable short-circuit current. As the contact separation time was uncertain, the time of arc ignition was also uncertain; thus, the arc time was incorrect. An ignition circuit branch was designed to ignite the arc before the main current was injected. When contact separation occurred, an arc was formed between the anode and the cathode in the VI. At this time, the VI current was small, as long as the arc could be maintained, until the contacts were fully open. This current was provided by \(VT_{2}\), \(C_{2}\), and \(R_{2}\). The branch of \(R_{3}\) and \(C_{3}\) was composed as a frequency-modulation circuit. The frequency-modulation circuit was used to guarantee the same rate of rise of recovery voltage parameters after interruption. The forced-commutation branch included \(VT_{1}\), \(C_{1}\), and \(R_{1}\). The amplitude of the oscillation current was determined by the main circuit current \(I_{0}\) and the reverse peak-current coefficient \(k\). The arc voltage and arc current were recorded by a multichannel isolated oscilloscope. The arc voltage was obtained from the voltage drop across the VI, and was corrected for the induced voltage drop caused by the resistance of the VI. The arc current was measured using a Roche Coil. A Phantom v7.3 high-speed video camera and a PC were used to record the arc appearances and the arc moving processes with a camera sample rate of 72,000 frames/s and exposure time of 1 \(\upmu\)s. The parameters of the experimental system for DC forced interruption are listed in Table 2.  
The detailed experimental operation is shown in Figure 6, and is described below. At the start of the experiment, the charging voltage of the capacitor \(C_{0}\) is 270 V, and the precharged voltage of the capacitor \(C_{2}\) is approximately 200 V. The precharged voltage of \(C_{1}\) varies according to the current rating of the arc current. The VI is in the closed position at this time. The trigger thyristor \(VT_{2}\) is triggered first to ignite the direct current, followed by the opening of the contacts, and a vacuum arc is maintained with a small current of approximately 80 A until the contacts are fully open. The opening of the contacts is actuated by the operation mechanism signal. Then, the camera signal and the thyristor \(VT_{0}\) are triggered sequentially. The direct current is injected and the DC-arcing stage begins. \(VT_{2}\) is switched off by the negative voltage. After the arcing time, the thyristor \(VT_{1}\) is triggered, and the forced-commutation process occurs.
## 3 Results
### Repeated Freezing and Thawing Cycles  
The study evaluated the freeze-thaw resistance of pavement concrete for rural roads with the ACS aggregate under repeated cycling of freezing and thawing. In Korea, the four seasons are distinct. The temperatures in the morning, day, and night can vary significantly and tend to have a cycle of 3 cold days followed by 4 warm days. The externally exposed concrete road pavement is exposed to humidity via moisture, rain, ice, and snow, as well as variable loads due to vehicle travel, such as in snow, during winter. Thus, freezing and thawing effects were expected to be significant. The damage caused to the concrete pavement by repeated freezing and thawing is attributable to the filling of concrete pores with water. When the pore water turns to ice, the ice cracks and grows inside the concrete, thereby ultimately destroying the concrete pavement. Therefore, in this study, we assessed the effect of freeze and thawing repetition to more closely model the weather patterns. The test results are shown in Figure 9. The test samples with the ACS aggregate did not appear to be affected by freezing and thawing. Our results showed that applying the ACS aggregate to the plain mix provided better freeze-thaw resistance. The relative elastic modulus ratios were 1.00, 1.00, and 1.05 after 300 repetitions for plain, GG 100%, and GS 50%/GG 100% mixes, respectively. The dynamic elastic modulus increased with the ACS aggregate's substitution ratio in freeze-thaw repetitive testing over 90 days. Materials that undergo a similar pozzolanic reaction (blast furnace slag powder and ACS aggregate) exhibit long-term strength as opposed to higher initial strength. Here, the initial hydration reaction is delayed; thus, the initial strength tends to be lower. However, the long-term strength is excellent. The target relative dynamic elastic modular ratio of rural-road pavement concrete was set by referring to the relative dynamic elastic modulus presented in the Concrete Pavement Standards of the Korea Expressway Corporation. As a result of the test, the relative dynamic elastic modulus was shown to be 80% or greater. In this study, the air volume was designed to be 4.5% \(\pm\) 1.5%, having the greatest effect on freezing and thawing. In all the mixes, this standard was satisfied. The pores in the ACS aggregate provide resistance against freezing and thawing effects. As the water absorption rate increases, the damage may become more extensive due to repetitive freezing and thawing. The ACS aggregate applied in this study achieved a 3% absorption rate, which is the standard for the blast furnace slag aggregate. The results of the repeated freezing and thawing tests indicated that the ACS aggregate applied to rural-road pavement concrete plays a major role in securing freezing and thawing resistance.  
Figure 8: Test results of impact ratio.
# An Overview of Infrared Remote Sensing of Volcanic Activity
## 3 Specific Considerations for the Infrared Remote Sensing of Volcanic Activity
In comparing the detection limits of Landsat 8's OLI SWIR bands, with those of its predecessor, Landsat ETM+, the enhanced dynamic range of the former means that temperatures of up to 747.9 K and 570.4 K can be detected without saturation in Bands 6 (1.56-1.66 \(\upmu\)m) and 7 (2.10-2.30 \(\upmu\)m), as compared with those for the corresponding ETM+ bands (724.5 and 552.8 K, respectively) [33]. The problem of saturation was demonstrated in [19], in which it was found that 16% of the volcanic ASTER SWIR scenes analysed displayed saturation, thereby preventing their quantitative analysis. One solution for saturation is the use of varying gain settings to extend the maximum detectable temperatures at the higher end. The SWIR bands of the ASTER sensor, for example, were equipped with low-gain settings for the purpose of volcanic monitoring (a \"volcano mode\" [34; 35]), raising the saturation temperature; in doing so however, it had the effect of reducing sensitivity to cooler surface phenomena [19].
# Inverse Properties in Neutrosophic Triplet Loop and Their Application to Cryptography
## 1 Introduction
### Generalized Group
\((X,*)\) will be called a right weak inverse property neutrosophic triplet loop (RWIPNTL) if it obeys the right weak inverse property (RWIP)_  
\[x*anti(y*x)=anti(y) \tag{5}\]  
\((X,*)\) will be called a left weak inverse property neutrosophic triplet loop (LWIPNTL) if it obeys the left weak inverse property (LWIP)_  
\[anti(x*y)*x=anti(y) \tag{6}\]  
\((X,*)\) will be called a weak inverse property neutrosophic triplet loop (WIPNTL) if it obeys both (5) and (6). \((X,*)\) will be called an automorphic inverse property neutrosophic triplet loop (AIPNTL) if it obeys the automorphic inverse property (AIP)_  
\[anti(x*y)=anti(x)*anti(y) \tag{7}\]  
\((X,*)\) will be called an antiautomorphic inverse property neutrosophic triplet loop (AAIPNTL) if it obeys the antiautomorphic inverse property (AAIP)_  
\[anti(x*y)=anti(y)*anti(x) \tag{8}\]  
\((X,*)\) will be called a semi-automorphic inverse property neutrosophic triplet loop (SAIPNTL) if it obeys the semi-automorphic inverse property (SAIP)_  
\[(0,0,0),(0,0,5),(2,4,0),(2,4,5),(4,8,0),(4,8,5),(6,2,0),(6,2,5),(8,6,0),(8,6,5)\]0  
Definition 8: (Associators and Commutators of Neutrosophic Triplet Loop)  
Let \((X,*)\) be an NTL. For any \(x,y,z\in X\),  
1. \((x,y,z)\in X\) is called the right associator of \(x,y,z\) if \(xy*z=(x*yz)(x,y,z)\). 2. \([x,y,z]\in X\) is called the left associator of \(x,y,z\) if \(xy*z=[x,y,z](x*yz)\). 3. \((x,y)\in X\) is called the right commutator of \(x,y\) if \(x*y=(y*x)(x,y)\). 4. \([x,y]\in X\) is called the right commutator of \(x,y\) if \(x*y=[x,y](y*x)\). This paper is the first study of a class of neutrosophic triplet loop (NTL) containing varieties of inverse property NTLs and the application of some of them to cryptography. The second section contains the main results on the varieties of inverse property NTLs in Definition 7 and the interrelationships. The algebraic properties of their neutrality and opposite were investigated, and were found to share some properties with the neutrosophic triplet group. An example of these varieties of NTL is given. Summaries of the results in the second section are exhibited as two Hasse diagrams in \[(0,0,0),(0,0,5),(2,4,0),(2,4,5),(4,8,0),(4,8,5),(6,2,0),(6,2,5),(8,6,0),(8,6,5)\]1 The third section discusses the application of some of these varieties of inverse property NTLs to cryptography.
## 6 Conclusions  
This research aimed to use geospatial modelling to draw a map of soil erosion susceptibility in the study region. Three GIS and RS-based models (WOE, FR, and IV) were examined for soil erosion susceptibility mapping. The causal parameters of soil erosion were investigated based on their relevance in the literature. Elevation, slope, aspect, curvature, stream, precipitation, LULC, lithology, soil, NDVI, and distance to road were all investigated in ArcGIS and analyzed for their relationship with soil erosion utilizing WOE, FR, and IV approaches. Furthermore, the present study's results were considered using the AUC approach, which revealed that the SRC for WOE, FR, and IV were 88%, 91%, and 87%, respectively. Validation findings showed that the PRC for WOE, FR, and IV were 92%, 94%, and 90%, respectively. These results demonstrate that the FR approach with the specified parameters is the most appropriate for soil erosion. The outcomes show that the three models used in this study, namely WOE, FR, and IV, are extremely suitable techniques. For regional land, data for all current research approaches is easily accessible, cost-effective, and time-consuming. The approach used in this research may be suggested for regions influenced by similar natural (topographic, geologic, hydrologic, and climatic) and man-made activities, such as road networks, and for mapping soil erosion susceptibility. The final susceptibility map of soil erosion developed by three GIS and RS-based models can help policymakers reduce soil erosion in the study region.  
Conceptualization, F.I. and M.N.A.; methodology, F.I., M.N.A., H.T.J., M.U., I.U.I. and G.K.; software, F.I., M.N.A., H.T.J., M.U., I.U.I. and G.K.; validation, F.I., M.N.A., H.T.J., M.U., I.U.I. and G.K.; formal analysis, investigation, F.I. and M.N.A.; resources, F.I., M.N.A.; data curation, writing--original draft preparation, F.I. and M.N.A.; writing--review and editing, F.I., M.N.A., H.T.J., M.U., I.U.I. and G.K.; visualization, H.T.J. and G.K.; supervision, F.I.; project administration F.I.; funding acquisition, H.D.S. and G.D.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.
## 5 Conclusions and Future Work
The performance of FLARoute under different network conditions also showed that observing our fair routing guidelines to realize routing fairness does not only achieve user satisfaction, but benefits the network in terms of delivery guarantees as well. FLARoute's performance has provided evidence that our suggested guidelines can be followed to improve fairness and throughput in sensed data collection scenarios. While FLARoute has set the foundation for fair routing in these scenarios, more work is required to ready it for deployment. A comprehensive investigation of the effect of different FLARoute parameters on network performance could lead to further research contributions. As a future work, three main areas in which FLARoute leaves room for improvement could be addressed. The first improvement is concerning ReBurD and MaxBE's reset interval for transmission counters. Since different nodes may experience inactivity in different periods, a preferable solution is a mechanism to provide a dynamic reset interval that adapts to the inactive periods of different nodes. However, this may lead to desynchronization of time slots and the effect needs to be investigated. Also, ReBurD computes burden from only successful transmissions. However, failed transmissions also impact on resource consumption. This brings about the second area of improvement, which is to investigate how accounting for failed transmissions in determining node burden would impact network performance and routing fairness. The third aspect is concerning energy in acquiring location-based information for computing the fair forwarding utility (i.e., PoiUltility) via GPS. There is the possibility that users may have the GPS on their device turned off and may not be willing to turn it back on just to answer the needs of a specific routing solution. Since human movement exhibits a reasonable amount of regularity, the GPS could be used sparingly by relying on prediction techniques. However, further investigation is required to unfold the reliability of this approach in real-world scenarios. [PERSON] conceived the original idea, designed the figures, implemented the proposals in the simulator and wrote the manuscript with input from all authors. [PERSON] and [PERSON] contributed to the problem background the evaluation methodology. [PERSON] assisted with the simulation set up, helped carry out the experiments, and was also involved in processing the experimental data. [PERSON] and [PERSON] aided in interpreting the results and supervised the findings of this work. All authors provided critical feedback and helped shape the research, analysis and manuscript. The authors declare no conflict of interest.