esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Decentralization of VANETs
### Benefits and Limitations  
Threshold key management and homomorphic properties in VANETs have many obvious advantages, but their drawbacks need to be carefully considered. Enhanced network security is one of the main benefits. There is no such complete security breach because the system distributes cryptographic key sharing among several nodes. Even if one node is compromised, the entire key is secure. This strengthens the network's resistance to deliberate cyberattacks. Key management's decentralized structure has better fault tolerance. The system's overall integrity and functioning are preserved even if some nodes malfunction or are compromised. This is because the entire key may be rebuilt from the remaining shares. Furthermore, threshold cryptography works very well in VANET systems in terms of scalability and flexibility. The threshold values are adjusted to maintain a balance between security and performance as the network grows larger with the addition of more nodes. Additionally, the system is naturally resistant to some attacks, especially those that target a single crucial component, preventing the network from becoming inoperable due to a single point of failure.  
Homomorphic encryption in VANETs enhances security and ensures data integrity and confidentiality during transmission. However, this requires complex computation that can increase latency in communication [60; 69]. There are also certain difficulties in putting VANET threshold key management into practice. Because of the continuous movement of cars, network architecture is dynamic, making it challenging to maintain consistent levels of privacy and trust. This makes managing trust and privacy more challenging, especially in light of the need for processing and decision-making in real time. Additionally, the limited processing power in cars makes it difficult to implement sophisticated trust and privacy-preserving systems.
# Wide-TSNet: A Novel Hybrid Approach for Bitcoin Price Movement Classification
## 3 Wide-TSNet
### Image Generation Layer
#### 3.2.1 Data Classification  
In our strategy, a function is defined to calculate the percentage change in price between two points in time for each day. This function categorizes the price change into INCREASE', DECREASE', or 'STABLE'. After extensive data exploration and analysis concerning these categories and the thresholds defining them, we determined the following:  
* If the price change exceeds 5%, it is categorized as an 'INCREASE';
* If the price change is less than \(-\)5%, it is categorized as a 'DECREASE';
* Otherwise, it is categorized as 'STABLE'.
# A Novel Cloud Approach for Connected Vehicles
## 4 Analytic Model
### Approaches
#### 4.3.1 Network Overhead and Data Freshness Expressions for NDN  
A vehicle sends a request for each datum it needs and will repeat it at regular intervals to be sure that it is up to date. Each request and each reply is exchanged as a message which has to be signed and accompanied by its certificate. With this approach, the bandwidth is busy with heavy requests and we retrieve data for every request, even if the data have not been updated. Thus, the number of useless packets is high. Figure 15 shows the different exchanges that take place with the NDN approach in our model.  
Firstly, a vehicle sends an interest for each sensor that it needs (1). Then, the traffic light equipped with RSU receives the interests and answers with its data as a sensor and routes the other interests toward the relevant traffic light as a resolution handler (RH) (2). Each requested sensor answers the RH with its own data (3). Then, the RH can transmit the data from the other traffic lights to the vehicle (4). Additionally, these operations are regularly repeated.  
**Definition of \(Cr\)**  
We defined CR as being the sum of \(CR_{i}\) and \(CR_{d}\) (18), so for NDN:  
\[CR=PO_{i}*I_{s}+I_{RSU}*PO_{d} \tag{20}\]  
Thus, we get:  
\[\begin{split} CR&=(S+P_{if})*\omega(\alpha/100* \delta_{RSU})*F_{i}+\\ \omega(\alpha/100*\delta_{V})*F_{i}*(S+P_{df}+P_{if}),\end{split} \tag{21}\]  
**Definition of \(\alpha_{D}\)**  
Using NDN, the number of vehicles \(\alpha_{D}\) represents the number of vehicles that are in the transmission range of an RSU, to that we added the vehicles entering the range of an RSU between two data generations.  
\[\alpha_{D}=\frac{\alpha}{100}*\delta_{V}+\frac{F_{\lambda}}{\sigma} \tag{22}\]  
**Definition of \(Fr\)**  
Which we apply to the broad definition of the freshness (19) and give us:  
\[FR=\frac{\frac{\alpha}{100}*\delta_{V}+\frac{F_{\lambda}}{\sigma}*100}{\alpha } \tag{23}\]
# Dynamic UAV Inspection Boosted by Vehicle Collaboration Under Harsh Conditions in the IoT Realm
## 4 Algorithm
### Static Planning and Scheduling Algorithm  
To solve the initial static solution, we selected a genetic algorithm due to its simplicity and small number of parameters. However, traditional genetic algorithms have drawbacks such as a tendency to fall into local optima and relatively low convergence accuracy, making them unsuitable for direct use in the cooperative optimization of multi-UAV and vehicle systems. Therefore, by exploring the characteristics of the multi-UAV and vehicle cooperative point patrol problem, we specifically improved the genetic algorithm to obtain route solutions during the initial static planning stage. First, an initial feasible solution construction algorithm based on K-means was proposed, followed by the design of a Hybrid Genetic Algorithm (HGA) [43] to optimize the initial solution and achieve a static optimized solution. The HGA combines local search techniques with the Genetic Algorithm (GA) to overcome GA's tendency to converge prematurely. A set of feasible solutions is constructed using a greedy algorithm to generate the initial parent population. In each iteration, the HGA selects two parent solutions from the population, generates multiple offspring groups through merging and splitting, then evaluates and further optimizes the offspring solutions using local search. This determines whether to incorporate the optimized offspring into the current new population, replacing the parent solutions. Finally, an updated population is obtained.
## 3 Constitutive Model and Simulation Results with MATLAB
### Determination of Constitutive Model Parameters  
In addition to the parameters in the Ohno-Wang model determined above, the model parameters related to the martensitic transformation can be determined as follows.  
Cryogenic temperature torsion experiments were performed on specimens with a torque amplitude of 2.5 Nm and a cycle of 3 s [37]. It can be seen from Figure 6 that S30408 austenitic stainless steel showed rapid cyclic hardening in the first 10 cycles, and then the hardening rate decreased until 200 cycles. The maximum angular amplitude was approximately twice that of the stable angle. Therefore, S30408 is a cyclic hardening material at 110 K, and tends to be stable when the number of cycles exceeds 200.  
In this study, the cyclic hardening properties of S30408 austenitic stainless steel are all attributed to isotropic hardening. According to the isotropic hardening rule, the peak stress and accumulated plastic strain of the symmetric strain cycle meet the requirement  
\[\sigma_{peak}=\sigma-Q\xi[1-\exp(-bp)]. \tag{21}\]  
Figure 6: Angular amplitude vs. cycles.  
where \(\sigma_{peak}\) is the peak stress. As shown in Figure 7a, the isotropic parameters C and Q can be obtained by fitting the peak stress and cumulative plastic strain. The parameters related to the martensitic transformation are obtained by fitting the martensite content and cumulative plastic strain curve, as shown in Figure 7b, and the saturation value of the martensite content \(\xi_{\infty}\) = 0.9, which is influenced by the temperature. Parameter \(\omega_{m}\) is determined by trial at present, as shown in Figure 7c. The material parameters of the model are presented in Table 4.
## Nomenclature  
### Roman symbols (upper case)  
\(\mathrm{PM_{2,5}}\): Particulate matter with particle size below 2.5 micrometers  
\(\mathrm{PM_{10}}\): Particulate matter with particle size below 10 micrometers  
\(W_{i}\): Moisture content rates of dust samples in hour \(i\) (%)  
\(m_{i}\): Mass of Petri dish and dust sample in hour \(i\) (g)  
\(m_{a}\): Initial mass of dry Petri dish and dust sample (g)  
\(m_{0}\): The mass of dry dust sample (g)  
\(W_{g}\): The water loss rate of dust sample (%)  
\(G_{a}\): Clean and dry Petri dish and the mass of dust sample (g)  
\(G_{i}\): Mass of dust sample and Petri dish at hour \(i\)/day (g)  
\(C_{0}\): Mass of 11 nm of solution (g)  
\(I\): Leaf number of the plant at (t)  
\(N_{0}\): Leaf number in the initial plant  
t: Experiment duration (day)  
\(V\): Plant growth rate  
\(V_{0}\): Growth rate of control group  
\(V_{n}\): Growth rate of experimental group  
\(A_{665}\): The absorbance at the wavelength of 665 nm  
\(A_{649}\): The absorbance at the wavelength of 649 nm  
\(C_{a}\): Concentration of chlorophyll a (mg/L)  
\(C_{b}\): Concentration of chlorophyll b (mg/L)  
w: Chlorophyll content (mg/g)  
\(V\): Volume of chlorophyll-extracting solution (mL)  
n: Dilution ratio of chlorophyll-extracting solution, which is 1 in this experiment  
m: The mass of crushed leaves (g)  
\(E\): Wind erosion rates of dust samples (%)  
\(W_{1}\): Mass of dust sample before wind erosion (g)  
\(W_{2}\): Mass of dust sample after wind erosion (g)  
BS: Humectant  
BS1: Glycol  
BS2: Sucrose  
BS3: Triethanolamine  
XS: Hygroscopic agent  
XS1: Organic solvent glycerin  
XS2: Triethanolamine  
XS3: Trimethylolpropane  
XS4: The mixture of glycerin and magnesium chloride  
NB: Coagulant  
NB1: Polyacrylamide  
NB2: Methylcellulose  
NB3: Cassava starch  
NB4: Sodium polyacrylate  
NB5: Sodium carboxymethyl cellulose  
BZ, Surfactant  
BZ1: Sodium dodecyl benzene sulfonate  
BZ2: Sodium dodecyl sulfate  
BZ3: Phospholipid  
BZ4: Sodium lignocellulosicate  
BS5: The i indicates the concentration serial number of humectant in the orthogonal experiment  
XS1: The i indicates the concentration serial number of hygroscopic agent in the orthogonal experiment  
NBi: The i indicates the concentration serial number of coagulant in the orthogonal experiment  
BZi: The i indicates the concentration serial number of surfactant in the orthogonal experiment
# Considering Inter-Frequency Clock Bias for BDS Triple-Frequency Precise Point Positioning
## 5 Conclusions  
The BDS is providing signals on three or more frequencies. In multi-frequency PPP, a key issue is to estimate the high-accuracy IFCB. Datasets collected at 35 globally distributed stations are employed to analyze the characteristics of IFCB. Considering the effects of IFCB, a triple-frequency PPP model is developed. In the developed model, the ionospheric-free combined observations of \(L_{1}/L_{2}\), \(L_{1}/L_{3}\) and \(P_{1}/P_{2}\) are employed. The GPS corrections of receiver PCO and PCV are simply used for BDS signals. As to other data processing models and strategies, they are the same as those usually adopted in PPP. The triple-frequency PPP developed here can achieve a positioning accuracy better than 2 cm for all three coordinate components. The limitation of the developed model is that the \(P_{1}/P_{3}\) ionospheric-free combined code observations are not applied due to the lack of carefully considering the code hardware bias in IFCB estimation. The effects of stability of code hardware bias as well as the influence of SICB on its estimation need further investigation.  
The time series of B1/B2a/B3, B1/B2b/B3, B1C/B2a/B3 and B1C/B2b/B3 triple-carrier combinations are used to investigate the IFCB of BDS-3 satellites. The results indicate that no significant IFCB variations can be observed for BDS-3 satellites. In addition, the carrier phase noise and multipath errors with a varying range of 1-4 mm for BDS-3 satellites are comparable to those of BDS-2 satellites. As to BDS-2 satellites, the IFCB between B1/B2 and B1/B3 satellite clocks is investigated and the IFCB variations are generally confined to peak amplitudes of about 5 cm. Moreover, the IFCB time series of BDS-2 satellites show periodic variations, and the prediction accuracy of IFCB is 1.2 cm. The triple-frequency PPP improves the positioning accuracy by 12%, 25% and 10% over the B1/B2 PPP in the east, north and vertical coordinate components, respectively. Future work includes the development of the efficient undifferenced estimation approach to obtain the more precise phase-specific IFCB, which is a linear combination of phase biases at the satellite. In addition, the estimation approach for the code-specific IFCB, which is a linear combination of code hardware biases at the satellite, needs further investigation. We will also devote ourselves to the triple-frequency PPP ambiguity resolution. The PPP of the future will feature the joint use of multi-frequency signals as well as multi-constellation signals.  
The financial supports from National Natural Science Foundation of China (Grant No. 41474025) and Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University (Grant No. 15-02-06) are greatly appreciated.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments, analyzed the data and wrote the paper; [PERSON], [PERSON], [PERSON] and [PERSON] reviewed the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Fast Implementation of NIST P-256 Elliptic Curve Cryptography on 8-Bit AVR Processor
## 3 Range Shifted Representation for Modular Multiplication
### Range Shifted Representation  
We can apply RSR to represent 256-bit integers \(X,Y\) range from \(2^{-128}\) to \(2^{128-1}\), and their multiplication \(Z=X\cdot Y\) with 8-bit word size \((W=2^{8})\), as the following:  
\[X=\sum_{i=0}^{i=31}x_{i}W^{i-16}=x_{0}W^{-16}+\cdots+x_{31}W^{15}, \tag{1}\]  
\[Y=\sum_{i=0}^{i=31}y_{i}W^{i-16}=y_{0}W^{-16}+\cdots+y_{31}W^{15}, \tag{2}\]\[Z= X\cdot Y=\sum_{i=0}^{i=63}z_{i}W^{i-32}=z_{0}W^{-32}+\cdots+z_{63}W^{31}, \tag{3}\]  
where \(x_{i},y_{i},z_{i}\in[0,2^{8}-1]\). The result \(Z\) of multiplication \(X\cdot Y\) is expanded to both sides, where the shape of it is symmetric with respect to \(W^{0}\). In order to reduce the result \(Z\) into the range from \(2^{-128}\) to \(2^{128}-1\), we use shifted prime \(P_{256}\cdot 2^{-128}=2^{128}-2^{96}+2^{64}+2^{-32}-2^{-128}\). Subsequently, we can reduce it at both sides, where \(z_{0}W^{-32}+z_{1}W^{-31}+\cdots+z_{15}W^{-15}\) and \(z_{48}W^{12}+z_{37}W^{13}+\cdots+z_{47}W^{23}\) in Equation (3) is reduced while using the congruence relations  
\[\begin{split}& W^{16}\equiv W^{12}-W^{8}-W^{-4}+W^{-16}\pmod{P_{256} \cdot W^{-16}},\\ & W^{-32}\equiv 1-W^{-4}+W^{-8}+W^{-20}\pmod{P_{256}\cdot W^{-16}}. \end{split} \tag{4}\]  
For example, let  
\[\begin{split}& Z_{A}=z_{0}+z_{1}W+\cdots+z_{15}W^{15},\\ & Z_{B}=z_{16}+z_{17}W+\cdots+z_{47}W^{31},\\ & Z_{C}=z_{48}+z_{49}W+\cdots+z_{63}W^{15},\\ & Z= Z_{A}\cdot W^{-32}+Z_{B}\cdot W^{-16}+Z_{C}\cdot W^{16},\end{split}\]  
where \(z_{i}\in[0,2^{8}-1]\). Subsequently, we can reduce \(Z\), as follows:  
\[\begin{split} Z\equiv& Z_{A}-Z_{A}\cdot W^{-4}+Z_{A} \cdot W^{-8}+Z_{A}\cdot W^{-20}\\ &+Z_{B}\cdot W^{-16}\\ &+Z_{C}\cdot W^{12}-Z_{C}\cdot W^{8}-Z_{C}\cdot W^{-4}+Z_{C} \cdot W^{-16}\\ &\pmod{P_{256}\cdot W^{-16}}.\end{split} \tag{5}\]  
Note that this is not a complete reduction. The part of result in (5) is not included in the range from \(2^{-128}\) to \(2^{128-1}\). Here, we omit the complete step for simplicity.
## 3 Calculation Results and Discussion
### Influence Mechanism Analysis
#### 3.4.2 Graphene Content Effect
(**a**) global model at 0.0125 nm; (**b**) graphene stress at 0.0125 nm; (**c**) global model at 0.625 nm; (**d**) graphene stress at 0.625 nm. Based on the above analysis, it can be seen that the graphene content will affect the normal strength because the graphene sheet will hinder the dislocation expansion of the copper matrix. The pull-out force was only affected by the number of carbon atoms at the pull-out critical state. Therefore, the maximum pull-out force remained unchanged in this simulation, but the increase in graphene content resulted in an increase in the contact area and a decrease in interfacial shear strength. In the cohesive strength simulation, the cohesion and contact area increased simultaneously, and the cohesive strength was almost unaffected by the graphene content.
## 3 Production of Plant Secondary Metabolites Using Natural Hosts
### Comparison of Different In Vitro Plant Culture Systems
** \\ \hline Hydroxycinnamic acids & _Anchusa officinalis_ & High-density culture with optimized culture parameters & Suspension cells & 3.7 g/L & Stirred tank bioreactor [75] \\  & Rosmarnic acid & _Anchusa_ & High-density culture with optimized culture parameters & Suspension cells & 3.7 g/L & Stirred tank bioreactor [75] \\  & Rosmarnic acid & & & & & \\ \hline  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & 22 mg/g DW & \\  & & _Echinacan pur pururea_ & Addition of 2 mg/L indole butyric acid & Advertitious roots & Air-lift bioreactor [76] \\  & Chicoric acid & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline Alkaloids & & & & & \\  & & & & & \\  & & & & & \\ Tetrahydroisoquine alkaloids & _Thalictrum minus_ & Cells immobilized in calcium alginate beads & Suspension cells & 875 mg/L & Modified bioreactor [77] \\  & Berberine & & & & \\  & & & & &  
The synthesis of plant secondary metabolites is often subject to complex spatial (subcellular structures, tissues, organs, and entire plants) and temporal (different developmental stages) regulation. From an evolutionary perspective, this synthesis and regulation in plants are significant because they help avoid self-intoxication, enable better control of metabolic flux (by adding additional regulation and reducing feedback inhibition), and provide enzymes with more suitable catalytic environments. This complexity makes the synthesis of metabolites with complex biosynthetic pathways within a single cell a significant challenge. Selecting a high-yielding and fast-growing suspension cell line is a crucial step. Taking Taxol production as an example, among the eight species of _Taxus_ and two hybrids, _T. baccata_ and _T. chinensis_ are the main high-yield producers [45; 90]. Similarly, for morphine production, the suspension cell line from _Papaver bracteatum_ can achieve an approximately 10 times higher synthesis of morphine compared to _P. somiferum_, 243.2 mg/g FW and 2.5 mg/g DW, respectively [83; 85]. Aside from the influence of plant species, as suspension cell lines are sub-cultured, they often undergo varying degrees of differentiation, which can also affect yield stability. Additionally, the reduction in yield after long-term cultivation is also related to epigenetics [91]. In a study on the production of indole alkaloids by _Catharanthus roseus_ suspension cells, it was found that, after 8 years of continuous cultivation, the total yield gradually decreased. However, researchers were still able to identify some cells with highly productive phenotypes, demonstrating that the initial callus tissue is the source of subsequent heterogeneity [92]. [PERSON] et al. [93] partially addressed this issue by isolating and culturing undifferentiated cambial meristematic cells from _T. cuspidata_, achieving a high content of 263 mg/kg FW. Nevertheless, plant stem cell isolation technology, particularly for herbaceous plants, still requires improvement, and therefore, this technology has not seen more widespread application.
###### Abstract  
Current CubeSats usually exhibit a low structural mass efficiency and a low internal volume for their payloads. The present work aims to propose an advanced structural architecture for CubeSats that addresses the issues of low structural mass efficiency and payload volume. The starting concept is the smart tiles architecture for satellites developed for the ARAMIS (an Italian acronym for a highly modular architecture for satellite infrastructures) CubeSat project. By introducing multifunctional structures and lightweight, composite materials in the design of smart tiles, the volumetric and structural mass efficiency of the entire CubeSat are enhanced. The advantages of the chosen approach are preliminarily analyzed in terms of the volumetric efficiency and amplitude of the payload design space. A 1U battery tile design is then selected to investigate the multifunctional structures design aspects in the project of space structures. A battery tile prototype is designed, produced, and tested. The CubeSat volumetric increment and the payload volume gain with respect to the traditional architecture is shown to reach a maximum of 37%. The CubeSat structural mass ratio can be reduced to 16.7%.  
CubeSat; multifunctional structures; structural dynamics +
Footnote †: journal: _Aerospace_
## 3 Results and Discussion
### Overall Variability and Relationships Involving the Apparent Optical Properties
#### 3.2.1 Average Cosines
This is not consistent with a common assumption that the angular distribution of upwelling light field is more isotropic. Nonetheless, such low values of \(\overline{\mu}_{u}\) were also observed by [PERSON] et al. [29] and reported on the basis of RT simulations within highly-scattering bubble clouds at near-surface depths [91] and for certain combinations of absorption coefficient, scattering coefficient, and scattering phase function in the water column ([PERSON], personal communication). A closer inspection of our data indicates that the group with \(\overline{\mu}_{u}\) < -0.3 was generally characterized by relatively larger values of \(c_{p}\)(z, 660), especially within the top 30-40 m, compared to the group with \(\overline{\mu}_{u}\) > -0.3. This is seen in the example data shown in Figures 3, 5b,f and 7b, supporting the notion that certain suites of IOPs can play an important role for producing low values of \(\overline{\mu}_{u}\). Although the behavior of average cosines in natural waters can be complex, these quantities are usually assumed to be constant or vertically uniform when used in the context of ocean color remote sensing applications, bio-optical modeling, and/or estimation of water constituents, e.g., [39; 95; 96; 97; 98; 99]. The assumption that \(\overline{\mu}\) obtained from simulations for a homogeneous water column is representative of a stratified water column was also used [100; 101]. The present study demonstrates that these simplifying assumptions are not valid. Further studies to determine how such assumptions lead to errors in data products of interest are needed. For example, [PERSON] and [PERSON] [102] reported that oceanic primary productivity could be systematically underestimated by 5 to 13% when ignoring the angular distribution of the underwater light field, for which the average cosines serve as simple yet useful proxies.