esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result
### Evaluation of Data Consistency
#### 3.1.2 Spatiotemporal Consistency on the Regional Scale  
The FPAR\({}_{\rm ANN}\) was compared with FPAR\({}_{\rm MCD15A2}\) for interannual trends in overlapping periods from 2000 to 2015 (Table S2). The analysis of variance showed that the differences between the two datasets were insignificant in terms of their regional means (\(p=0.95\)) and their trends (\(p=0.18\)), but they were significantly different in terms of their coefficients of variance (\(p=0.046\)), with the FPAR\({}_{\rm ANN}\) capturing the magnitude and trend well but not capturing the inter-annual variability well.  
The spatial similarity was quantified through \(R^{2}\), SSIM, KGE, and MSSIM index between the FPAR\({}_{\rm ANN}\) and FPAR\({}_{\rm MCD15A2}\) (Table S3). The results show that the FPAR\({}_{\rm ANN}\) can explain spatial variance of 72% to 80% and has more spatial similarity with the KGE of 0.86 to 0.90, and the MSSIM of 0.74 to 0.76, compared to the FPAR\({}_{\rm MCD15A2}\) in the three years of 2005, 2010 and 2015. However, the NDVI3g and FPAR\({}_{\rm BNU}\) showed \(R^{2}\) lower than 0.60 and 0.66 with the FPAR\({}_{\rm MCD15A2}\). The FPAR\({}_{\rm BNU}\) was less spatially similar to the FPAR\({}_{\rm MCD15A2}\) as indicated by an MSSIM lower than 0.54 and a KGE lower than 0.78. The FPAR\({}_{\rm ANN}\) indicated a larger extent of similarity with an SSIM higher than 0.8 over 53.51% of the area compared to the FPAR\({}_{\rm MCD15A2}\) (Figures S3 and S4). The results illustrated that the FPAR\({}_{\rm ANN}\) is much more consistent with FPAR\({}_{\rm MCD15A2}\) than the other two products.
## 6 Case Study: Application of the Method on LoA 4 Valet Parking with V2X
### Decision Making--AVP  
Decision making is one of the key elements of LoA 4-5 automated driving, which is particularly challenging in mixed--AD and non-AD--environments. In the case of fully automated AVP scenarios (no human driver are allowed), V2X serves as the interface for the swarm intelligence of active self-driving vehicles in the parking garage, transferring information back and forth between the actuators and a cloud-based computing unit to find the optimal planning and execution of the parking maneuver. This requires a low-latency, high bandwidth communication protocol to avoid delays and potential damage. In mixed environments, the decision making process is more complex, as the cloud-based system is not aware of the intention of human drivers, therefore more attention is given to the sensors mounted on the vehicles. In this case, the central computing unit relies on information transmitted from V2X-capable vehicles and static sensors mounted on the garage to estimate the intention and plan accordingly for human-operated vehicles.
# DQN-Based Shaped Reward Function Mold for UAV Emergency Communication
## 1 Introduction
Furthermore, with the progression of artificial intelligence, there is an escalating demand for heightened speed and accuracy in data processing, necessitating improvements in the timeliness of current technological approaches. Currently, the majority of research on UAV emergency communication networks centers on routing planning for UAVs with limited exploration of deep learning and other methodologies. A systematic investigation into these areas is lacking. Additionally, the existing theoretical frameworks and models may exhibit constraints when applied in practical scenarios. Consequently, developing an algorithm that not only facilitates rational action selection but also accelerates the learning pace of the agent has emerged as a crucial research gap. In this paper, the application of reward shaping to the DQN algorithm is explored, resulting in a certain enhancement of the algorithm's training efficiency. The primary contribution of this work lies in the continuous adjustment of parameters, optimization of network architecture, and improvement of agent performance. Furthermore, a reward function grounded in potential functions is incorporated to further elevate the algorithm's overall performance. The work of this paper includes three aspects:  
* Abstract modeling is carried out for typical application scenarios of communication equipment such as UAV, and environmental design is carried out based on Atari platform. * A more effective action selection strategy is applied to replace the greedy strategy with the traditional DQN algorithm, and its performance is better than before. * The reward function is redesigned to improve the efficiency and stability of the algorithm, and experimental verification is carried out. In Section 1, we summarize the current research status. In Section 2, we establish the system model and introduce the DQN algorithms. Section 3 contains the experiments and analysis of the experimental results. Then, Section 4 provides a summary and directions for further improvement.
# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 4 Carbon Flux Data Assimilation and Inversion Studies
### Framework and Techniques for Assimilation of Atmospheric CO\({}_{2}\)
#### 4.3.1 Prior Flux  
Prior flux is divided into four parts: ocean carbon flux, fossil fuel carbon-emission inventory, ecosystem carbon flux, and fire carbon-emission data. Due to the large differences in the absolute values of prior flux in different regions, it is generally not directly assimilated as a system state variable. Instead, assimilation is performed by adding a difference or multiplying by a scaling factor to the prior flux. This adjusted flux is then used as the posterior flux. The difference or scaling factor, being dimensionless, can be better used as a system state variable for statistical calculations in the carbon-assimilation system.
# Quadrotor UAV Dynamic Visual Servoing Based on Differential Flatness Theory
## 2 The Dynamic Model of the Quadrotor  
The commonly used quadrotor dynamic model [35; 36; 37] is given by Equation (1). This model has been proven by numerous experimental tests.  
\[\left\{\begin{array}{l}\ddot{x}=u_{1}(cos\psi sin\theta cos\phi+sin\psi sin \phi)-\frac{K_{3}\dot{x}}{m}\\ \ddot{y}=u_{1}(sin\psi sin\theta cos\phi-cos\psi sin\phi)-\frac{K_{2}\dot{y}}{m} \\ \ddot{z}=u_{1}(cos\theta cos\phi)-g-\frac{K_{3}\dot{x}}{m}\\ \ddot{\theta}=u_{2}-\frac{lK_{3}\dot{\theta}}{l_{1}}\\ \ddot{\phi}=u_{3}-\frac{lK_{3}\dot{\phi}}{l_{2}}\\ \ddot{\psi}=u_{4}-\frac{K_{4}\dot{\psi}}{l_{3}}\end{array}\right., \tag{1}\]  
where \((x,y,z)\) are the three positions; \((\theta,\phi,\psi)\) are the three Euler angles, representing pitch, roll, and yaw, respectively; \(g\) is the acceleration of gravity; \(l\) is the distance from the center of gravity to each rotor; \(m\) is the total mass of the quadrotor; \((I_{1},I_{2},I_{3})\) are the moments of inertia along \(x\), \(y\), and \(z\); \((K_{1},\ K_{2},\ K_{3},\ K_{4},\ K_{5},\ K_{6})\) are the drag coefficients (in the rest of our work, we assume that the drag is zero since the drag is negligible at low speed); \((u_{1},u_{2},u_{3},u_{4})\), are the control inputs defined Equation (2) [36]:  
\[\left\{\begin{array}{l}u_{1}=\frac{(T_{1}+T_{2}+T_{3}+T_{4})}{l(-T_{1}-T_{2 }+T_{3}+T_{4})}\\ \frac{l}{l_{1}}\\ u_{3}=\frac{l(-T_{1}+T_{2}+T_{3}-T_{4})}{l_{3}}\end{array}\right., \tag{2}\]  
where \((T_{1},T_{2},T_{3},T_{4})\) are the thrusts generated by the four rotors and can be considered as the actual system control inputs; \(C\) is the force-moment scaling factor; \(u_{1}\) represents the total thrust on the quadrotor UAV body according \(Z\); \(u_{2}\) and \(u_{3}\) are the pitch and roll inputs; and \(u_{4}\) is the yaw input.
## 7 Conclusions and Future Work  
In this paper, we analyzed the relationship between the final expected proportion of remaining normal operational nodes and distribution of generators, unintentional random initial failure, interdependence, network topology, and load redistribution. We gave a CCF simulation algorithm and a robustness evaluation algorithm. The robustness of the ADN against the CCF was simulated based on the ER network topological structure and the BA network topological structure. The model analysis and the simulation analysis showed that given the specific network topology parameters and the unintentional random initial failure rate, the more the average control fan in of a node in the PN and the more the average power fan in of a node in the CN, the better the robustness of the ADN, the distribution of generators improved the robustness of the ADN against the CCF. In addition, the robustness of the ADN was improved when the values of the nodes tolerance parameters were greater. Compared with the traditional power distribution network, because of the physical distribution of generators, the CCF caused by the interdependence between the PN and CN were more difficult to occur.  
This work is helpful in understanding the CCF in the ADN. Based on our above analysis on numerical simulation results, if we want to improve the robustness of the ADN against the CCF, we should take the following measures: using the more uniform degree distribution of the ADN network topology, improving the values of \(<\)\(O_{1}\)\(>\) and \(<\)\(K_{2}\)\(>\), improving the number of distributed generators, improving the tolerance parameters of the nodes in the ADN and protecting the nodes from the random failures. In practice, the PN and CN of the ADN are non-linear and the topological structure of the network is asymmetric due to natural and artificial factors. Considering the effect of factors of non-linear and asymmetry on the robustness of the ADN is one of our future directions. Accordingly, making preventive control measures for the CCF in the ADN is also one of our future directions.  
Conceptualization, P.S. and Y.D.; methodology, P.S. and Y.D.; software, C.W. and C.L.; validation, P.S., Y.D., C.W., C.L., and K.Y.W.; formal analysis, P.S.; investigation, P.S. and Y.D.; resources, P.S., C.L.,and Y.D.; data curation, P.S.; writing--original draft preparation, P.S.; writing--review and editing, P.S., Y.D., and K.Y.W.; visualization, P.S. and K.Y.W.; supervision, Y.D. and P.S.; project administration, P.S.; funding acquisition, Y.D.; All of the authors approved the final version of the manuscript.  
This work was supported by the National Key Research and Development Program of China (Basic Research Class) under Grant No. 2017 YFB0903000--Basic Theories and Methods of Analysis and Control of the Cyber-Physical Systems for Power Grid.  
The authors declare no conflict of interest.
## 1 Introduction
### Model Categories  
In STLF, both univariate and multivariate models have been discussed in the literature [7; 11; 12]. Univariate models use only historical demand data to make future predictions, while multivariate models consider other variables such as atmospheric variations and calendars along with historical demand data. [PERSON] et al. [11; 12] developed both univariate and multivariate models, and stated that the univariate models had good prediction capability. In univariate time series models, the historical electricity demand data are arranged with correlated past lags to capture the demand patterns even when the data are limited [13]. [PERSON] et al. [6] obtained improved accuracy by including the temperature as a variable, recognizing that weather conditions play a crucial role in forecasting performance.  
While factors such as humidity, wind, rainfall, and cloud cover have been identified as influential in meteorological analysis [8], temperature is widely recognized as the most crucial weather variable [6]. In fact, the temperature variable alone is capable of explaining more than 70% of the load variance in the GEF2012 Com dataset [14; 15]. Therefore, other weather variables can be excluded when the temperature variable is included in the model [6]. There are a several reasons for this decision: (i) other weather variables have shown lower impact on electricity demand when the temperature is already included; (ii) the cost of collecting weather data is considerable due to the need to install weather stations; and (iii) collinearity problems can arise multiple weather-related variables are employed simultaneously [8].
## 3 Results and Discussion  
### Fresnel Reflection Losses  
As we explained in Section 2, we have designed two sensors with two different structures MgF\({}_{2}\)-Ag and MgF\({}_{2}\)-Al.  
Losses by Fresnel Reflection occur at any boundary of a medium where the refractive index changes, causing a part of the incident radiation to be reflected to the first medium and another part to be refracted to the second medium.  
The losses caused in the changes of media of the two optical fibers are the same since the light is refracted or reflected in the same media.  
The equation that gives us the Fresnel reflection losses is  
\[L_{F}=-10\log\left[\frac{n_{1}-n_{2}}{(n_{1}-n_{2})^{2}}\right] \tag{1}\]where \(n_{1}\) is the refractive index of the medium in which the light is incident and \(n_{2}\) is the refractive index in which the light is refracted. Applying Equation (1) to the different interfaces we have:  
Losses in the optical fiber-epoxy resin interface are 4.61598 \(\times\) 10\({}^{-5}\) dB.  
Losses in the epoxy resin-prism interface are 0.00360481 dB.  
Losses in the hemispherical prism-MgF\({}_{2}\) interface are 0.005565023 dB.  
Losses in the hemispherical prism-epoxy resin interface are 0.00360481 dB.  
Losses in the epoxy resin-optical fiber interface are 4.61598 \(\times\) 10\({}^{-5}\) dB.  
Therefore, to sum up, the total losses are 0.013 dB, a very low value.
## 4 Results and Discussion
### Code Verification  
In the present simulations, uniform Cartesian grids with increasing nodal resolution are used, ranging from \(51\times 51\) up to \(201\times 201\). A grid-independent numerical solution was obtained by a grid of \(101\times 101\) resolution, as seen in Figure 3. The average Nusselt number (\(Nu_{avg}\)) on the left vertical wall (hot wall) was computed for validation purposes. The average Nusselt number for the natural convection in the square porous cavity is presented for \(Ha\ =\ 100,\ \varepsilon=0.7,\ \varphi=0.01\), and two different values of \(Ra\ =\ 5\times 10^{7}\) and \(Ra\ =\ 5\times 10^{8}\). For higher accuracy, a denser grid consisting of \(151\times 151\) nodes was used. The accuracy of the proposed scheme was tested against verified numerical results obtained for MHD natural convection in a square cavity [54] (Table 2).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(Gr\) & \(Nu_{avg}\) (\(Ha\) = 0) & \(Nu_{avg}\) (\(Ha\) = 100) \\ \hline \multirow{2}{*}{\(10^{3}\)} & 3.745834 & 3.678740 \\  & 3.735471 & 3.716622 \\ \hline \multirow{2}{*}{\(10^{4}\)} & 4.771623 & 3.680210 \\  & 4.781083 & 3.757865 \\ \hline \multirow{2}{*}{\(10^{5}\)} & 6.677280 & 3.885215 \\  & 6.728638 & 3.825346 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average Nusselt numbers at the hot wall for characteristic Grashof and Hartmann numbers obtained by the present method (underlined) and [54].  
Table 2 shows the average Nusselt number on the left vertical wall, as presented in the work of [PERSON] et al. (underline values) [54] and the results of the proposed method. In these cases, the medium consisted only of the fluid (\(\varepsilon=1\), \(Da=\infty\), \(\varphi=0\)), the Hartmann number ranged from \(Ha=0\) to \(Ha=100\), and the Grashof number varied in the range \(10^{3}\leq Gr\leq 10^{5}\). The Grashof number is inherently related to the Rayleigh and Prandtl numbers through the relationship \(Gr=Ra/Pr\). The comparative results are in good agreement.
###### Abstract  
OBJECT: To evaluate the effect of the orthokeratology (OK) lens design, used in the Montreal Experience cohort, on corneal treatment zone characteristics and their relationship to the pupil. METHODS: This retrospective study follows previously published work and refers to the analysis of 4 different OK lenses. Tangential topography maps were obtained at baseline and after 1 month of OK lens wear. The extracted parameters are: distance treatment zone diameter (DTZD (mm)); relative peripheral power (RPP (D)); mid-peripheral width (MPW (mm)); a new concept, the plus power ratio (PPR (%)), corresponding to the coverage of the pupil area by the positive power zones. RESULTS: DTZD and MPW were significantly different between the lens designs ([PERSON]'s ANOVA). (DTZD (OK 1: 3.68 \(\pm\) 0.46 mm; OK 2: 3.06 \(\pm\) 0.67; OK 3: 2.83 \(\pm\) 0.54; OK 4: 3.20 \(\pm\) 0.53) MPW (OK 1: 1.65 \(\pm\) 0.21 mm; OK 2: 1.31 \(\pm\) 0.40 mm; OK 3: 1.46 \(\pm\) 0.17 mm; OK 4: 1.57 \(\pm\) 0.17 mm). PPR was significantly lower in OK 1 (40.1 \(\pm\) 22.1%) than the other designs (OK 2: 53.8 \(\pm\) 18.4%; OK 3: 60.3 \(\pm\) 13.6; OK 4: 54.7 \(\pm\) 15.3). CONCLUSIONSION: This study shows that the corneal response to OK lens wear varies with lens design. When analyzed, topographic analysis shows that OK 1 is associated with a larger DTZD, which produces a lower PPR. This may explain why previously published results showed significantly faster axial length (AL) progression with this lens.  
orthokeratology; myopia management; axial length; pupil diameter +
Footnote †: journal: Applied Sciences  
**Clation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]-M. The Montreal Experience: Impact of Different Orthokeratology Lens Designs on Comneal Treatment Zone Characteristics. _Appl. Sci._**2024**, _14_, 4067. [[https://doi.org/10.3390/app14104067](https://doi.org/10.3390/app14104067)]([https://doi.org/10.3390/app14104067](https://doi.org/10.3390/app14104067))
# An Image Hashing-Based Authentication and Secure Group Communication Scheme for IoT-Enabled MANETs
###### Abstract
To provide consistent, protected, and well-timed network packages, this work is based on a cluster in MANET-IoT [5]. The MANET-IoTnodes were grouped into single clusters [6], and a predictive model based on trust management was established to calculate and distribute trust. Moreover, an ideal model for identifying malicious nodes and progressing network functioning was also established. The main problem addressed in the paper is the mobility of the nodes. Therefore, a node is out of the group and wants to join another group so it can be easily attacked from the middle. Attackers can now easily find the device and link the cluster with official approval. The group head (GH) should not permit the pairing of soldiers and devices that are attacker nodes. This paper presents an efficient authentication scheme based on image hashing (IHA) to provide the secure joining and mobility of nodes in the network. In the system model, asymmetric keys are utilized for secure communication in the network. GHs and the network administrator (NA) collaborate to authenticate the node based on security credentials and image hashing. The main contributions of this paper are as follows:  
1. We evaluated the literature and identified the research from recent and related schemes. 2. An image hashing-based authentication scheme is proposed to migrate the nodes to other groups securely. Asymmetric keys were employed for secure communication. 3. The proposed image hashing-based node joining and migration mechanisms are presented. These mechanisms provide secure mobility of the node within the network. 4. Finally, extensive simulations were conducted to extract the results from trace files. The rest of the paper is organized as follows: Section 2 provides a review of the existing and related literature; Section 3 demonstrates our system model; Section 4 presents the proposed work; Section 5 provides the results and analysis; finally, Section 6 concludes the paper.
## 4 Experiments
### Dataset  
To evaluate the model's performance on phishing URL detection, this study utilized the PhishTank dataset. PhishTank is a publicly available community-driven database of phishing URLs comprising a substantial number of verified phishing websites. The data used for this research were obtained from the PhishTank website ([[https://www.phishtank.com/](https://www.phishtank.com/)]([https://www.phishtank.com/](https://www.phishtank.com/))). A total of 2,198,157 URLs were collected, predating November 2022, with 3637 online webpages identified as verified phishing webpages.  
Given the continuous expansion of the internet, which includes a vast number of URLs, ensuring diversity in the training samples of benign URLs is crucial for training a model so as to accurately reflect the real-world online environment. This diversity ensures that the model is exposed to a wide range of legitimate URLs, helping to mitigate the bias between experimental results and the real world. In order to construct a diverse collection of benign webpages, we sampled a significant number of legitimate URLs from the top 10 million pages based on Open PageRank. Specifically, we initially selected the top 100,000 pages, as they are popular and highly ranked, making them less likely to be disguised as phishing websites. Subsequently, we randomly sampled from the remaining 9.9 million pages to ensure a diverse distribution of the collected benign webpages, approximating the overall distribution of the internet. Finally, we verified the sampled webpages using the VirusTotal tool to ensure their legitimacy.  
Consequently, a curated set of 40,066 benign webpages was obtained. An unlabeled URL dataset was created using the 2,198,157 URLs combined with 40,066 benign URLs, which was utilized for the adversarial pretraining of \(BERT_{url}\). For the training and evaluation of the final phishing detection model, 3637 verified phishing webpages along with 40,066 benign webpages were employed.  
This meticulous data collection process, encompassing a comprehensive range of both phishing and benign URLs, ensures the robustness and reliability of the model's performance assessment in the phishing URL detection domain.