esa-sceva/satcom-chunk-collection huggingface dataset:
# Hardware Model Checking Algorithms and Techniques
## 2 Background and Notation
### Decision Problems
Eventually, either a conflict is found when no decisions have been made, meaning that the problem at hand is Unsat, or every variable has been assigned without incurring a conflict, meaning that the problem at hand is Sat. State-of-the-art SAT solvers are sophisticated artifacts of engineering that often include various techniques and heuristics to enhance their performance. Among such techniques, there are those to periodically _restart search_ in order to better explore the space [27], simplifying the problem at hand either before starting searching for a solution or during search [28] and periodically trimming the database of learned clauses. In many applications, SAT solvers are required to answer a sequence of related calls. In order to preserve useful information about the problem at hand, maintained in the state of the solver, many SAT solvers expose an _incremental interface_. Incremental interfaces enable the user to load a formula in the solver, solve the corresponding SAT problem, then modify the formula and solve the related SAT problem without losing inferences or other useful information about the problem stored in the state. Between incremental calls, the formula can usually be modified by either specifying _variable assumptions_ or _adding or removing clauses_. Many modern SAT solvers can be instructed to provide a _reutation proof_ in the case a problem is declared to be Unsat. Such a proof can be thought of as a NO-certificate that can be used by an independent checker to assess the correctness of the SAT solver's answer. Refutation proofs are usually produced as either _resolution proofs_[29] or _clausal proofs_[30]. A resolution proof is a series of applications of the _resolution rule_ that derives the empty clause from the clauses of an unsatisfiable propositional formula. **Definition 35** (Binary Resolution Rule). : _Given two clauses \(C_{1}\) and \(C_{2}\), the (binary) resolution rule is the inference rule:_  
\[\frac{(C_{1}\lor a)\wedge(\
eg a\lor C_{2})}{(C_{1}\lor C_{2})} \tag{8}\]  
Modern SAT solvers are capable, without incurring too large an additional cost, to generate a resolution proof from unsatisfiable runs [31]. Clausal proofs can be seen as logs of the sequence of clauses learned by the solver in the order in which they were learned. A resolution proof can be constructed starting from a clausal proof using BCP [32]. Another common feature of modern SAT solvers is the extraction of _unsatisfiability cores_. Given an Unsat problem, an unsatisfiability core is a subset of the clauses of the original problem that is still unsatisfiable. The set of clauses in a resolution proof can be seen as an unsatisfiability core.
# Performance Analysis of Time Synchronization Protocols in Wireless Sensor Networks
## 5 Evaluation Result
### Effect of Synchronization Period  
Choosing a proper synchronization period when implementing a time synchronization protocol is a trade-off problem. A short synchronization period allows a network to become synchronized quickly, whereas a long period allows the nodes to save energy. In this simulation scenario, the effect of the synchronization period of each protocol is analyzed from the perspective of synchronization speed (convergence time) and accuracy. We consider only the number of synchronization rounds instead of the amount of time for comparison. It is apparent from Figure 7 that the number of rounds required to synchronize the entire network is lower for a shorter period with GTSP, while that in FTSP tends to be constant regardless of the duration of the synchronization period. The reason for this phenomenon is that GTSP gradually adjusts the clock rate of each node to a common clock rate. A \"common clock rate\" implies that the difference in clock rate between nodes must be trivial such that the offset will be small after a synchronization period. However, with a short synchronization period, the logical clock values are synchronized regularly even though the clock rate of each node is not strictly common. In other words, GTSP does not require many rounds to achieve a common clock rate in a short synchronization period.  
Regarding the synchronization error, it is interesting that the short period does not improve the accuracy of both protocols but slightly increases the error in FTSP as shown in Figure 8. The simulation is repeated 10 times, and 300 points of data were collected after the network had achieved synchronization. The boxplots describe the distribution of the maximum network error and maximum neighbor error for each protocol. In general, changing the synchronization period does not increases the synchronization error of GTSP or E-FTSP. However, FTSP exhibits a higher synchronization error when it sends a message in over short period. As explained previously, FTSP incurs clock skew fluctuations due to message delay jitter. This problem is even more serious in a short synchronization period. Meanwhile, E-FTSP always demonstrates outstanding performance; its synchronization error is smaller than those of FTSP and GTSP for any synchronization period.  
Figure 7: Number of rounds required to synchronize entire network in FTSP and GTSP with different synchronization periods.
# Evaluation of Different Drying Methods on the Quality Parameters of _Acanthopanax semicosus_ Fruits
## 2 Materials and Methods
### Determination of Color Properties and Moisture Content  
The color parameters of AS samples were evaluated by employing a SY-1 colorimeter (Tianjin Tianda Tianfa Technology Co., Ltd., Tianjin, China). In the color measurement system, the L\({}^{\star}\) value stands for the degree of lightness or darkness, the a\({}^{\star}\) value represents the degree of redness versus greenness, and the b\({}^{\star}\) value corresponds to the degree of yellowness compared to blueness. The total color difference (\(\Delta\)E), which reflects the extent of overall color changes in the samples, was calculated in accordance with Equation (1).  
\[\Delta\mathrm{E}=\sqrt[2]{\left(L^{\star}-L_{0}^{\star}\right)^{2}+\left(a^{ \star}-a_{0}^{\star}\right)^{2}+\left(b^{\star}-b_{0}^{\star}\right)^{2}} \tag{1}\]  
\(\Delta\)E: The total color difference;  
L\({}^{\star}\): Lightness value;  
a\({}^{\star}\): Chromaticity coordinate on the red-green axis;  
b\({}^{\star}\): Chromaticity coordinate on the yellow-blue axis;L0*, a0*, and b0*: The corresponding L*, a*, and b* values of the reference sample (fresh fruit).  
Moisture content of dried AS fruits was determined by volumetric Karl Fischer titration using a fully automated titrator (Metrohm 915 KF Ti-Touch, Metrohm AG, Herisau, Switzerland). Approximately 1 g of powdered sample was dispersed uniformly in anhydrous methanol (Sigma-Aldrich, St. Louis, MI, USA) under continuous stirring to facilitate moisture extraction. Prior to analysis, instrument calibration was performed using a standard water solution (Merck KGaA, Darmstadt, Germany), and environmental moisture interference was eliminated by blank titration. The volumetric [PERSON] reagent (water equivalence: 5.00 mg H\({}_{2}\)O/mL, Merck KGaA, Darmstadt, Germany) was titrated until a stable endpoint was reached, and titrant consumption (mL) was recorded. The moisture content (MC, %) was calculated using the formula:  
\[\text{MC }(\%)=(\text{F}\times\text{V})/\text{W}\times 100 \tag{2}\]  
F: The water equivalence of the titrant (5.00 mg H\({}_{2}\)O/mL);  
W: Sample weight (mg);  
V: The consumed titrant volume (mL).
# First-Principles Study for Gas Sensing of Defective SnSe\({}_{2}\) Monolayers
## 4 Discussion
### Gas Molecules on the N-Doped SnSe\({}_{2}\) Monolayer  
When NO\({}_{2}\) adsorbed on the N-doped SnSe\({}_{2}\) monolayer, \(\rm E_{ad}=-2.98\) eV is the maximum among the NO\({}_{2}\) adsorption and \(\rm\Delta Q_{b}=-0.368\)e has relatively high value. It indicates that the strong interaction occurred between NO\({}_{2}\) molecule and the N-doped SnSe\({}_{2}\) monolayer. Moreover, the DOS of the N-doped SnSe\({}_{2}\) monolayer before/after NO\({}_{2}\) adsorption are totally different, as shown in Figures 2d and 4d.  
Compared to the DOS of the N-doped SnSe\({}_{2}\) monolayer before/after NO\({}_{2}\) adsorption, the DOS crossover E\({}_{\rm F}\) of the N-doped SnSe\({}_{2}\) moves to below the E\({}_{\rm F}\).  
When NH\({}_{3}\) is adsorbed on the N-doped SnSe\({}_{2}\) monolayer, E\({}_{\rm ad}=-0.36\) eV also has a greater value than that on the pristine SnSe\({}_{2}\) monolayer and \(\Delta\)Q\({}_{\rm b}=0.215\)e has a maximum value. In the DOS of NH\({}_{3}\) adsorbed on the N-doped SnSe\({}_{2}\) monolayer, the trap state is induced to decrease the carrier mobility. This implies that there would be an obvious change of electrical conductance. The N-doped atom is high sensitivity gas sensor for NO\({}_{2}\) and NH\({}_{3}\) shown as in our DFT calculation.  
When setting gas molecules on the N-doped SnSe\({}_{2}\), it reveals obvious enhancement for both NO\({}_{2}\) and NH\({}_{3}\) adsorption.
## 1 Introduction
In the rest of this paper, we establish the anti-jamming model in the proposed relay communication scenario, and give the relative problem formulations in Section 2. In Section 3, we formulate the anti-jamming joint optimization problem as a Stackelberg game and prove the existence of SE. Moreover, a hierarchical joint optimization algorithm is proposed. Simulation results show the user's strategy selection probability and the jammer's regret in Section 4, besides, utility comparison under different algorithms with feedback error and eavesdropping error is also given. Finally, we draw a conclusion in Section 5.
# Uncertainty Theory-Based Resilience Analysis for LEO Satellite Communication Systems
## 3 Belief Resilience Measure
### Belief Resilience  
**Definition 9**.: _The **belief resilience** of a system with epistemic and aleatory uncertainties is the chance that it can withstand disruption and recover quickly._  
Define \(\varepsilon\) as an uncertain random variable that describes a system's behavior after a disruption, with feasible domain \(\Xi\). The belief resilience can be measured as  
\[R_{E}=\mathrm{Ch}\{\varepsilon\in\Xi\}. \tag{10}\]  
If \(\varepsilon\) is a random variable, we have \(R_{E}=\Pr\{\varepsilon\in\Xi\}\). If \(\varepsilon\) is an uncertain variable, we have \(R_{E}=\mathcal{M}\{\varepsilon\in\Xi\}\).  
We focus on the belief instantaneous availability change after a disruption, and the belief resilience can be defined as  
\[R_{E}=\mathrm{Ch}\left\{\begin{array}{l}\int\limits_{t_{0}}^{t_{0}+T_{a}}A_ {B}(t|d)dt\\ \int\limits_{t_{0}}^{t_{0}+T_{a}}A_{B}(t|ns)dt\end{array}>r_{eth}\right\}, \tag{11}\]  
where \(t_{0}\) is the time when a disruption occurs; \(T_{a}\) is the maximum allowable recovery time determined by users; \(A_{B}(t|d)\) and \(A_{B}(t|ns)\) are, respectively, the belief instantaneous availability under a specific disruption \(d\) and in the normal state \(ns\) (without disruption); and \(r_{eth}\) is the requirement for the system's behavior after a disruption.  
Based on Definition 8, the belief instantaneous availability under a specific disruption and normal state (without disruption), respectively, can be calculated as  
\[A_{B}(t|d)=\mathrm{Ch}\{m(t|d)>0\}\text{ and }A_{B}(t|ns)=\mathrm{Ch}\{m(t|ns)>0\}, \tag{12}\]  
where \(m(t|d)\) and \(m(t|ns)\) are respective performance margins under specific disruption \(d\) and in the normal state \(ns\) (without disruption).
# Bi-LS-AttM: A Bidirectional LSTM and Attention Mechanism Model for Improving Image Captioning
## 3 Methodology
### Long Short-Term Memory  
The LSTM cells form the basis of this work. They are a unique form of RNN able to memorize long-term associations. Figure 2 shows that an LSTM cell is made up of four important components: a memory cell \(g\) and three gate circuits (\(i\) is the gate of the input, \(f\) is the gate of forget, and \(o\) is the gate of the output) [9].  
In the formula below, \(f(t)\), \(i(t)\), and \(o(t)\) are the values of forget, input, and output at time \(t\), respectively. \(a(t)\) is the intermediate feature extract result of \(h_{t-1}\) and \(x_{t}\) at time \(t\):  
\[a(t)=\tanh(W_{a}h_{t-1}+U_{a}x_{t}+b_{a}) \tag{2}\]  
\[i(t)=\sigma(W_{i}h_{t-1}+U_{i}x_{t}+b_{i}) \tag{3}\]  
\[f(t)=\sigma\Big{(}W_{f}h_{t-1}+U_{f}x_{t}+b_{f}\Big{)} \tag{4}\]  
\[o(t)=\sigma(W_{o}h_{t-1}+U_{o}x_{t}+b_{o}) \tag{5}\]  
where \(x_{t}\) is the entrance, and \(h_{t-1}\) is the hidden state value at time \(t-1\). The results calculated by forgetting and inputting operate on the cell state, expressed as the formula below:  
\[c(t)=c(t-1)\odot f(t)+i(t)\odot a(t) \tag{6}\]  
where \(\odot\) represents the Hadamard product. Finally, the hidden state is at \(t\). \(h(t)\) is obtained by multiplying the gate output \(o(t)\) and the current cell state \(c(t)\) using the Hadamard product:  
\[h(t)=o(t)\odot tanh(c(t)) \tag{7}\]  
The following equation uses the parameters \(W_{s}\) and \(b_{S}\) to predict the next word:  
\[F(p_{ii};W_{s},b_{S})=\frac{\exp(W_{s}h_{ti}+b_{S})}{\sum_{j=1}^{K}\exp\big{(} W_{s}h_{ij}+b_{S}\big{)}} \tag{8}\]  
where \(p_{ii}\) is the tipping probability of the forecast value.
# Progress in Remote Sensing of Photosynthetic Activity over the Amazon Basin
## 4 Case Study of Seasonal Variation in Light-Use Efficiency and SIF across the Amazon Basin
### Data Retrieval
#### 4.1.2 Sun-Induced Fluorescence  
The SIF signal was measured from Fraunhofer lines as described in Section 3. This signal was recorded by the Global Ozone Monitoring Experiment-2 (GOME-2) onboard the operational European Meteorological (MetOp) satellites launched in October 2006. Currently, GOME-2 provides daily fluorescence data with a nominal footprint of 40 \(\times\) 40 km. Monthly mean (Level 3) data are available, gridded to a spatial resolution of 0.5\({}^{\circ}\) latitude by 0.5\({}^{\circ}\) longitude, from 2007 to 2012 [44]. Details of the retrieval of SIF from GOME-2 measurements are provided in [26]. In this work, we used v. 26 data obtained between 2007 and 2012 [45]. Fluorescence values were normalized by the cosine of the solar zenith angle to minimize variations in SIF associated with fluctuations in irradiance.  
Because of the high noise associated with GOME-2 measurements [25], we aggregated monthly values of fluorescence into two periods: the months of June, July and August (JJA) and those of December, January and February (DJF). These two periods showed the largest differences in SIF. Following the approach of [PERSON] et al. [27], we subtracted the annual means from the respective seasonal means to enhance and to normalize the differences in fluorescence recorded across the Amazon.
# Application of Image Recognition Methods to Determine Land Use Classes
## 6 Conclusions and Future Works  
This study demonstrates the feasibility of using Sentinel-2 imagery and ML techniques for land use classification. The proposed methodology, incorporating cloud interpolation and vegetation indices, has been successfully implemented to enhance the accuracy of classification in Lithuania. Furthermore, these findings contribute to the development of automated, scalable land monitoring systems applicable to broader geographic regions. However, additional research is necessary to fully explore this potential.  
Future research should focus on enhancing classification robustness by integrating deep learning techniques such as Convolutional Neural Networks (CNNs) and Transformer-based models. Additionally, exploring multi-temporal analysis has the potential to enhance classification accuracy by incorporating seasonal variations into the model. Implementing semi-supervised learning can reduce the need for manually labeled datasets and improve adaptability to different regions. In order to reduce processing time and improve efficiency for large-scale applications, it is advisable to explore computational optimizations. Finally, testing the proposed methodology in diverse geographic regions would help assess its generalizability and refine the classification approach for broader applications.  
Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON]; software, [PERSON]; validation, [PERSON] and [PERSON]; investigation, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON] and [PERSON]; writing--review and editing, [PERSON] and [PERSON]; visualization, [PERSON] and J.J.; supervision, [PERSON]; funding acquisition, [PERSON] and J.J. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The datasets generated and analyzed during the current study are not publicly available to preserve individuals' privacy under the European General Data Protection Regulation, but are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.
# A More Efficient Conditional Private Preservation Scheme in Vehicular Ad Hoc Networks
## 3 System Model and System Security
### System Model  
In this subsection, the system model of a single geographic region for the scheme is illustrated in Figure 1.  
**System roles:** VANETs generally consist of vehicles equipped with wireless communication devices, which are called On Board Unit (**OBU**), infrastructure units such as Road Side Units (**RSUs**) which are located on the roadside or at a street intersection providing wireless interfaces to vehicles within their radio coverage, and a centralized Trusted Authority (**TA**) which is responsible for the RSU and OBU Registration, and what is more, recovering the vehicle's identity if it is necessary.  
**Channels:** To secure the vehicular communications which are mainly used for civilian applications, we have been following assumptions about the channels:  
* OBU communicates with RSU or other OBU through wireless links, which is unsecured.
* RSU is assumed to connect with the TA by wired links or any other creditable links with high bandwidth, low bit error rates and low delay.
## 5 Conclusions and Prospects
Four schemes of BDS-3 orbit determination were designed by one-month observations to fully illustrate the reliability and advantages of the improved strategies proposed in this study. The orbit accuracy and improvement of each scheme were calculated. Results showed that the NES based on the improved cycle-slip detection and repair was optimal for BDS-3 orbit determination compared with other schemes. The accuracy of the BDS-3 orbit improved by 34.07%, 41.05%, 72.29%, and 74.33%, for C31, C32, C33, and C34, correspondingly. However, the limited tracking observations and unknown satellite parameters resulted in low accuracy in BDS-3. Therefore, the ISL and low Earth orbit satellites will be considered in the orbit determination to further improve the accuracy of BDS-3 orbit in the follow-up research tasks. Moreover, the application of precise BDS-3 orbits based on the new signals will be discussed as given in [36; 37]. [PERSON] and [PERSON] proposed research ideas; [PERSON], [PERSON], [PERSON], and [PERSON] conducted the theory studies; [PERSON], [PERSON], and [PERSON] designed the experiments and analyzed the results; [PERSON] and [PERSON] wrote the paper; and [PERSON] and [PERSON] offered guidance and supervision. This research was funded by the National Science and Technology Basic Work of China grant number [2015 FY310200], the National Natural Science Foundation of China grant number [41404033] and [51374209], the State Key Program of National Natural Science Foundation of China grant number [41730109], the Fundamental Research Funds for the Central University grant number [2018 GF15] and Postgraduate Research & Practice Innovation Program of Jiangsu Province [KYLX16_0542]. The author appreciates \"The Jiangsu Dual Creative Teams Program Project Awarded in 2017\" and the International GNSS Monitoring and Assessment Service (iGMAS) and the International GNSS Service (IGS) for the data and products. Dr. [PERSON] and two anonymous reviewers are acknowledged for their valuable suggestions. The authors declare no conflicts of interest.
# A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning
## 2 Materials and Methods
### Target Carbon Fluxes and Predictors  
The predicted target variables for the models were three carbon fluxes, namely, GPP, RECO, and NEE from flux tower sites. There are mainly two categories of predictors used in this research: remote sensing and meteorological variables. Remote sensing variables include fraction of absorbed photosynthetically active radiation (FAPAR), leaf area index (LAI), minimum LAI of each year (LAI_MIN), maximum LAI of each year (LAI_MAX), and the number of LAI exceeding the average of LAI_MAX and LAI_MIN in each year (LAI_COUNT). The meteorological variables include 2 m temperature (TA), surface solar radiation downward (SW_IN), latent heat flux (LE), sensible heat flux (H), soil temperature (TS) and soil water content (SWC). These predictors were selected for the current modelling according to previous studies [25]. The remote sensing variables were used as indicators of vegetation conditions. [PERSON] et al. proved that there is a good statistical relationship between carbon fluxes and FAPAR (or LAI) [25]. In addition, LAI_MIN, LAI_MAX, and LAI_COUNT can also represent different plant functional types to some extent. The meteorological variables were used as environmental condition indicators that affect plants' activities.  
Figure 1: The framework of this study. The red dots are sites in the training set and the blue dots are site in the test set.
## 2 Mission Concept
### Mission Analysis
#### 2.4.1 Lifetime Analysis  
The lifetime of a spacecraft is the main aspect that is focused on in the mission planning step. Many factors affect the lifetime of a CubeSat, such as solar flux, atmospheric drag,Earth gravity, orbit configuration, satellite mass, and drag area. The solar radiation pressure caused by solar flux is insignificant in the low Earth orbit (LEO) compared to the other factors. To ensure Light-1 meets the mission lifetime requirement of 6 months, all the parameters were evaluated as if in the worst case. In the unpredictable space environment, the attitude is the only controllable variable that affects the drag area directly. Thus, different drag areas were considered based on the CubeSat orientation, assuming vertical orientation with deployed solar panels (0.09 m\({}^{2}\)), undeployed solar panels in vertical orientation (0.03 m\({}^{2}\)), and a horizontal orientation with undeployed solar panels (0.01 m\({}^{2}\)). For the worst-case drag area, 0.09 m\({}^{2}\) was used with the worst space environment condition parameters, such as the highest solar flux activity and high coefficient of drag. CubeSats usually have a drag coefficient between 2 and 2.2. In this simulation, 2.2 was used as the worst case. Implementing this simulation in STK with three different drag areas as mentioned yields a mission lifetime of 191 days, 1.3 years, and 2.2 years, separately. This is to emphasize that while an active drag area of 0.09 m\({}^{2}\) achieves the mission lifetime requirement, the required orientation of Light-1 is horizontal, hence an active drag area of 0.01 m\({}^{2}\) and a resultant lifetime of 2.2 years.