esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Evaluation of DPPH Radical Scavenging Activity (Antioxidant Capacity)  
The antioxidant capacity was evaluated by measuring the DPPH (2,2-diphenyl-1-picrylhydrazyl) free radical scavenging activity of the horseradish extracts, according to the method described by [PERSON] et al. [23]. A fresh 0.1 mmol/L DPPH solution (diluted in methanol) was prepared, and 5 mL of this solution was added to 1 mL of each extract previously prepared. The samples were kept for 30 min at room temperature, in the absence of light. Since the samples underwent a flocculation process upon contact with the DPPH reagent, before measuring the absorbance (Shimadzu 300 UV-VIS-NIR spectrophotometer, Tokyo, Japan) at a wavelength of 571 nm [18; 20; 21; 24; 28], they were filtered through a Millipore syringe filter (Merck, Darmstadt, Germany) with a pore size of 0.45 \(\upmu\)m to avoid affecting the accuracy and precision of the measurement. The antioxidant activity of the extracts was evaluated by the percentage of DPPH radical inhibition, which reflects their ability to neutralize free radicals, according to the following equation:  
\[\text{Scavenging activity (\%)}=(\frac{\text{A control}-\text{A sample}}{\text{A control}}) \times 100 \tag{1}\]  
\(\text{A}_{\text{control}}\)--control absorbance;  
\(\text{A}_{\text{sample}}\)--sample absorbance.
## 5 Analysis of the Scheme
### Comparative Analysis of Performance  
In this section, a comparative study of the proposed approach with other classical CP-ABE schemes is presented in Table 1. It is observed that all the works employ LSSS-based access structures, with [44] utilizing a hierarchical access structure. FAN [7] uses q-PBDHE as its hardness problem without adopting multi-authority mechanisms, which means that the secret key cannot be cracked and the one-way function cannot be inverted within polynomial time. However, there is a negligible probability of cracking it. [PERSON] [9] utilizes d-DDH hardness problems as security assumptions, providing an ECC scheme instead of a bilinear pairing in terms of computational complexity overhead. Additionally, it employs a multi-authority CP-ABE scheme with a hierarchical LSSS access structure. [PERSON] [35] and [PERSON] [45], on the other hand, separately employ LWE and R-LWE CP-ABE to enhance their security through a multi-authority scheme, achieving resistance against quantum attacks, although they are not based on blockchain and introduce a significant increase in computational complexity.  
Our scheme extends the previous works by utilizing a lattice-based CP-ABE scheme in conjunction with blockchain-based PRE threshold networks to manage the master secret key. It provides automated verification and consensus of transactions through smart contracts,  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Scheme** & [7] & [9] & [35,44] & **Ours** \\ \hline Blockchain & \(\bigvee\) & \(\bigvee\) & \(\bigvee\) & \(\bigvee\) \\ Privacy Preservation & \(\bigvee\) & \(\bigvee\) & \(\bigvee\) & \(\bigvee\) \\ Anti-Quantum Attack & \(\times\) & \(\bigtimes\) & \(\bigvee\) & \(\bigvee\) \\ Provable Security & Not always provable & Provably secure & Provably secure & Provably secure \\ Hardness Problem & q-PBDHE & d-DDH problem & LWE, R-LWE & LWE \\ Multi-Authority & \(\times\) & \(\bigvee\) & \(\bigvee\) & \(\bigvee\) \\ Access Structure & LSSS & LSSS & LSSS, Hierarchical LSSS & LSSS \\ \hline \end{tabular} Note: \(\bigvee\) means the solution satisfies this characteristic, \(\times\) means it does not.  
\end{table}
Table 1: Comparison with related schemes.  
ensuring confidentiality, accountability, and traceability of transactions. Moreover, it is a security type based on the worst-case problem, where the cryptographic algorithm needs to be solved in the worst-case scenario if it is cracked. Lastly, other CP-ABE schemes described in the references require more expensive operations such as multiplication, modulus exponentiation, and bilinear mapping, while our scheme only requires addition operations with lower expense, thereby significantly improving operational efficiency.
# Cryobiotechnology of Plants: A Hot Topic Not Only for Gene Banks
## 3 Pathogen-Free Plant Material
### Conventional Methods for Pathogen Eradication  
Conventional methods for pathogen eradication are based on in vitro meristem culture, as well as heat treatment (thermotherapy) and chemical treatment (chemotherapy), both of which are followed by meristem culture [37; 38; 39; 40; 41]. Although these traditional methods have been widely used for acquiring virus-free plants, all of them have some drawbacks.  
The meristem culture method is based on the assumption that the youngest meristematic cells are free of viruses and other plant pathogens. Therefore, the extension and regeneration of small shoot  
Figure 2: Schematic diagram of the encapsulation-dehydration cryopreservation method.  
tips containing the meristem (0.2-0.4 mm) and two to four leaf primordia should lead to pathogen-free plants [36; 39]. The major limitations of meristem culture techniques are difficulties in excising tiny merristens and a low regeneration rate of shoot tips.  
The use of heat treatment is another well-known conventional method for pathogen eradication in plants. Generally, thermotherapy is based on keeping the target in vitro cultivated plants at a temperature of 35 \({}^{\circ}\)C-42 \({}^{\circ}\)C for four to six weeks. Both temperature range and duration of thermotherapy are virus-type- and plant-species-dependent [42; 43]. The need for specific and expensive laboratory equipment such as a growth chamber with precise temperature control is the major limitation of thermotherapy [44]. Moreover, this method cannot be used for infected plants that are not resistant to higher temperatures and it does not work for all viruses [45].  
Another method used for plant pathogen elimination is chemotherapy. In this technique, in vitro-grown plants are treated with antiviral chemicals like ribavirin, whose positive effect on virus eradication has been reported [46; 47]. In this method, optimization of the dose and duration of the treatment is crucial because a high concentration of antiviral compounds in the culture medium can negatively affect the growth of in vitro plants through their phytotoxic activity. The sensitivity of in vitro plants to antivortics is species- and genotype-specific [45; 47]. Because of the abovementioned limitations linked to traditional methods for pathogen eradication, it would be beneficial to develop some more efficient and simpler methods for obtaining pathogen-free plants. In this respect, cryotherapy, i.e., treatment with ultra-low temperatures, could be a useful biotechnological tool with great potential.
# Optimal Trajectories of Diffractive Sail to Highly Inclined Heliocentric Orbits
## 3 Transfer Strategy
Note that the eccentricity at the end of the first phase is zero, in accordance with the fact that the osculating orbit is circular. The first phase lasts 662 days, and the second 358 days, for a total of 1020 days of transfer time. As expected, the spacecraft trajectory tends to reach a minimum distance below \(r_{f}\), but does not fall below \(r_{c}\) (dashed red line). In the second phase, the distance from the Sun varies slightly with small fluctuations, while the inclination increases until it reaches the final desired value. The overall trajectory is shown in Figure 5, where the blue star represents the perihelion of the Earth's heliocentric orbit. Figure 3: Illustration of the two-phase strategy with the approach phase and the cranking phase. The green and red solid line represent the orbit at the end of the first and final phase, respectively. Figure 4: Time variation of orbital parameters and control variable in a near-optimal transfer to a circular heliocentric orbit of radius \(r_{f}=0.32\,\mathrm{au}\) and inclination \(i_{f}=60\,\mathrm{deg}\) using the two-phase strategy (\(a_{c}=0.26\,\mathrm{mm}/\mathrm{s}^{2}\)). Black circle \(\rightarrow\) start; black square \(\rightarrow\) arrival; blue triangle \(\rightarrow\) first phase end; dashed red line \(\rightarrow\) minimum solar distance. Figure 5: Near-optimal trajectory to a circular heliocentric orbit of radius \(r_{f}=0.32\,\mathrm{au}\) and inclination \(i_{f}=60\,\mathrm{deg}\) using the two-phase strategy (\(a_{c}=0.26\,\mathrm{mm}/\mathrm{s}^{2}\)). Black circle \(\rightarrow\) start; black square \(\rightarrow\) arrival; blue triangle \(\rightarrow\) first phase end; blue star \(\rightarrow\) perihelion; orange circle \(\rightarrow\) the Sun; blue line \(\rightarrow\) parking orbit; red line \(\rightarrow\) target orbit; black line \(\rightarrow\) transfer trajectory.
## 2 Materials and Methods  
### Dielectric Properties of Soils  
Water and sediment have different dielectric properties. Freshwater has a relative dielectric constant (\(\varepsilon_{\mathrm{r}}\)) of 81, and sediment is in the range of 2-10, depending on the soil type [2]. In an earthwork, such as a dike, sand and gravel, which have an \(\varepsilon_{\mathrm{r}}\) of 4, are usually used as core. Completely saturated gravel-water and sand-water mixtures, therefore, have different dielectric properties. Due to the larger grain sizes of gravel, there is more water in the same volume than in a sand-water mixture. Therefore, \(\varepsilon_{\mathrm{r}}\) is higher in a gravel-water mixture than in a sand-water mixture. In case of an erosion process, it can be assumed that the soil is completely saturated with water. During the erosion process, the sand migrates into the pores of the gravel and thus displaces the water. This effect leads to a change in \(\varepsilon_{\mathrm{r}}\). \(\varepsilon_{\mathrm{r}}\) of the mixture can be described with the mixture model of [PERSON] [15]. This was developed for mixing two material phases. Assuming that gravel and sand have the same \(\varepsilon_{\mathrm{r}}\), it can also be used in this case.  
\[\varepsilon_{M}=\bigg{(}\sqrt[3]{\varepsilon_{S}}+\theta_{W}\bigg{(}\sqrt[3]{ \varepsilon_{W}}-\sqrt[3]{\varepsilon_{S}}\bigg{)}\bigg{)}^{3}. \tag{1}\]  
In the equation above, \(\varepsilon_{M}\) is the resulting relative dielectric constant, \(\varepsilon_{s}\) is the dielectric constant of the soil, \(\varepsilon_{W}\) is the complex dielectric constant of water and \(\theta_{W}\) is the volume fraction of water.  
To determine an order of magnitude of the expected change in \(\varepsilon_{\mathrm{r}}\), the volume fraction of the water can be varied. Planning engineers assume that, with a 4-8 mm grain size of gravel, the proportion of pores is approximately 35% of the volume [16]. This corresponds to the water content at complete saturation. If the water content drops by 1% due to entering sand, \(\varepsilon_{\mathrm{r}}\) of the mixture is reduced by about 0.5. \(\varepsilon_{\mathrm{r}}\) as a function of the volume fraction of water by Looyenga (Equation (1)) is shown in Figure 2.
## 9 Results and Discussion
### Multi-Introker Scheduling  
To validate the performance of the hybrid model using the synthetic dataset in complex multi-invoker environments, we assess its ability to optimize resource efficiency by minimizing the average number of invokers used while maximizing their resource utilization. For this evaluation, we compare the hybrid model to commonly used baseline load-balancing methods in both research and industry for distributing function requests across available invokers: _Round Robin (RR)_: Distributes requests sequentially across  
Figure 12: (**a**) Overall container utilization percentage for each method. (**b**) Utilization distribution across the tested applications.  
Figure 13: Cold start penalty scaling evaluation results.  
invokers in a fixed rotation. This method is simple to implement and ensures an even distribution under steady workloads. _Least Loaded (LL)_: Routes requests to the invoker with the shortest queue or fewest active requests. LL adapts to real-time load and reduces queuing delays. _Random (Ran)_: Assigns requests to invokers randomly without tracking state. This method is stateless and lightweight.  
The results of the four methods across the four workload types are presented in Figure 14. Based on these results, several observations can be made from Figure 14a: both RR and LL significantly reduce the number of active invokers compared to the Random (Ran) load-balancing strategy. Notably, LL initializes approximately 40% fewer invokers than Ran across all workload scenarios. The hybrid model achieves even further reductions in the number of active invokers, ranging from 27% in short-lived workloads to 8% in resource-intensive workloads. Additionally, we observe that in resource-intensive workloads, the hybrid model's performance converges with that of LL; however, it consistently requires the fewest invokers for the same workload across all scenarios. These results demonstrate that the combination of hybrid prediction and skewness-aware scheduling effectively reduces the required resources--specifically, the number of invokers--by preventing resource starvation in serverless environments. In Figure 14b, we use the median skewness score to validate the efficiency of resource provisioning. The lower average skewness score achieved by the Hybrid Model indicates more effective and balanced resource allocation. Finally, in Figure 14c, the Random (Ran) algorithm shows clear underutilization of available resources, while both the Hybrid Model and the Least Loaded (LL) method demonstrate more balanced allocation. This balanced distribution contributes to a reduced number of active invokers.
## 4 Discussion  
### Minimum Friction Coefficient of Interface  
At present, there are two main ways to determine the interface friction coefficient, namely fitting the linear relationship equation between the shear strength and the normal Mohr-Coulomb on the Mohr-Coulomb criterion and taking its slope as the friction coefficient. This method is used to determine the friction coefficient in a document [17]. The other way is to approximate the average value of the interface friction coefficient obtained from each group of tests. In fact, according to the definition of interface friction coefficient:  
\[\mu=\frac{F}{N}=\frac{\tau_{\text{u}}A}{\sigma_{\text{n}}A}=\frac{\tau_{\text {u}}}{\sigma_{\text{n}}} \tag{1}\]  
where: \(\mu\) is the interface friction coefficient; \(F\) and \(N\) are the interface ultimate shear force and the interface normal pressure, respectively; \(\tau_{\text{u}}\) and \(\sigma_{\text{n}}\) is the ultimate shear stress and normal stress of the interface, respectively; and \(A\) is the contact surface area.  
When the interfacial shear strength \(\tau_{\text{u}}\) satisfies the Mohr-Coulomb criterion and the normal stress \(\sigma\). When \(\sigma_{\text{n}}\
eq 0\), Formula (1) can be changed to:  
\[\mu=\frac{\text{c}+\sigma_{\text{n}}\tan\phi}{\sigma_{\text{n}}}=\tan\phi+ \frac{\text{c}}{\sigma_{\text{n}}} \tag{2}\]  
where c is the interfacial cohesion and \(\phi\) is the interface friction angle.  
According to Formula (2), the friction coefficient \(\mu\) and normal stress \(\sigma_{\text{n}}\) are inversely proportional, and this test well verified this rule. In practical engineering, the normal stress \(\sigma_{\text{n}}\) shall be the maximum vertical stress at the bottom of the foundation or the ultimate bearing capacity of the underlying soil mass of the foundation, and the interface friction coefficient shall be calculated according to Formula (2). However, from the perspective of safety and convenient application, the limit value of Formula (2) can be taken as the minimum friction coefficient of the interface \(\mu_{\text{min}}\):  
\[\mu_{\text{min}}=\lim_{\sigma_{\text{n}}\to\infty}\left(\tan\phi+\frac{c}{ \sigma_{\text{n}}}\right)=\tan\phi \tag{3}\]Formula (3) is the theoretical basis for directly taking the slope of the shear failure fitting curve of the contact surface as the friction coefficient. As [PERSON] [27] demonstrated, the dependence of friction on the normal load must be clear on the area of contact or on the sliding speed. In this work, the influence of sliding speed with 0.05 mm/s was too small to ignore. Similarly, the slight change of shear area could have also been ignored because of the large size of the test equipment. Thus, this paper focused on the change of normal pressure.
###### Abstract  
Carbon quantum dots (CQDs), a distinctive class of fluorescent carbon nanomaterials, exhibit considerable potential for widespread application across several industries due to their safety, environmental sustainability, excellent water solubility, and tunable yet stable fluorescence properties. Nevertheless, the mass field is limited, and the cost of production is higher for the majority of methods. This study examines a cost-effective approach for the hydrothermal synthesis of nitrogen-doped carbon quantum dots (N-CQDs) from wood using NH\({}_{3}\)-H\({}_{2}\)O as the nitrogen precursor, facilitated by H\({}_{2}\)O\({}_{2}\) and ultraviolet light. The produced N-CQDs demonstrate superior crystallinity and solubility in water, with the average particle size of 5.02 nm. After 10 experiments under the same conditions, a significant and stable yield of 5.04 g (42 wt%) was finally obtained by hydrothermal synthesis. The N-CQDs solution exhibits green fluorescence when exposed to ultraviolet light, and its fluorescence performance is influenced by concentration and excitation wavelength. Furthermore, it explores their application in identifying Fe (III) in water. The surface of N-CQDs is abundant in hydrophilic hydroxyl groups, distinctive nitrogen-containing groups, and various oxygen-containing functional groups. Fe (III) can extinguish fluorescence in water. The ratio of fluorescence intensity before and after to the addition of Fe (III) solution to the N-CQDs solution (\(F_{0}/F\)) exhibits the effective linear correlation within the concentration range of 0.1 to 100 umol/L. Within the concentration range of 100 to 1000 umol/L, the increase in Fe (III) concentration results in substantial aggregation of Fe (III) and N-CQDs, along with a blue shift in the fluorescence wavelength. This discovery possesses significant potential for the synthesis and application of environmentally friendly, high-yield N-CQDs.  
N-CQDs; gram-scale; hydrothermal method; Fe (III) detection; wood +
Footnote †: journal: Applied Sciences  
1
## 1 Introduction
A new technology, which would provide result in a few hours, is needed to enable the public to be informed effectively and to improve measures of health protection through \"One Health\" approach. In this work, we tested the feasibility to detect _E. coli_ in seawater by a direct PCR method after original pre-concentration step. Because pathogens are typically low concentrated in seawaters, direct monitoring of waterborne _E. coli_ is costly, technically challenging, and in some cases not feasible. High concentration of NaCl in seawater may interfere with commonly used molecular detection technics based on PCR or ELISA. That is because proteins (antibodies or enzymes, like DNA polymerase) may be inhibited by NaCl at high concentration. To overcome problems of low pathogen and high salt concentration in seawater, we combine the aptamer coated magnetic beads pre-concentration modality with the direct PCR detection of _E. coli_. A specific DNA aptamer selected by bacterial cell- systematic evolution of ligands by exponential enrichment (SELEX) that binds whole _E. coli_ cells with high affinity and specificity [6] was chosen for the development of the inexpensive and rapid pre-concentration step. The analytical performance of the direct PCR on _E. coli_ whole cells carried by magnetic beads is evaluated using a procedure without any DNA extraction or purification.
# Advancements in the Intelligent Detection of Driver Fatigue and Distraction: A Comprehensive Review
## 3 Intelligent Detection Methods for Driver Fatigue and Distraction
### Intelligent Detection Methods Based on Vehicle Travel Data  
When drivers are fatigued or distracted, their driving skills and reaction times are compromised, leading to deviations in heading angle and difficulties in maintaining the correct direction of travel on the road. Furthermore, the emergence of fatigue and distraction can reduce a driver's steering capability, potentially resulting in insufficient steering actions that affect their driving behavior. In severe cases, this may even lead to significant lane departures. Therefore, analyzing vehicle driving data can indirectly assess a driver's fatigue and distraction levels.  
[PERSON] et al. [60] assessed the state and behavior of drivers based on vehicle GPS data and the Gaussian Mixture Model (GMM). [PERSON] et al. [61] utilized a Bidirectional Long Short-Term Memory network (Bi-LSTM) with an attention mechanism to propose a method for indirectly detecting drivers using mobile phones based on vehicle performance parameters, achieving an accuracy rate of up to 91.2%. [PERSON] et al. [62] also employed a Bi-LSTM network to process data on vehicle steering wheel angles, steering wheel angular velocity, vehicle yaw rate, lateral acceleration, and longitudinal acceleration. They used a wavelet packet analysis to extract characteristic frequency bands, thereby indirectly identifying driver fatigue and distraction.  
Intelligent detection methods based on vehicle driving data generally involve data collection through a vehicle's original onboard sensors, offering advantages in convenience and cost. However, this approach has difficulty in directly reflecting changes in drivers' physiological and psychological states, especially under simple driving conditions, where vehicle driving data show minimal variations, making it challenging to reflect a driver's state accurately.
# A Novel PCA-Based Method for PRNU Distillation to the Benefit of Source Camera Identification
## 4 Experiment and Evaluations  
### Experimental Setup  
In this section, we evaluated the performance of the proposed SCI scheme and compared it with some state-of-the-art works [6; 7; 9]. All cameras used in the experiments were obtained from the \"Dresden\" dataset [22], a large dataset widely used for research on source camera identification problems. The cameras in Dresden consist of common camera brands in the market, such as Nikon, Canon, Sony, etc. Table 1 presents the basic information of the used cameras, including 15 camera models and 49 camera devices. For each chosen camera, the reference PRNU noise is estimated using 50 flat field images with simple textures. Then, 150 test images of these camera are selected as the intraclass sample and 1440 test images of the other 48 cameras are selected as the interclass samples. In total we acquire 150 x 49 intraclass and 1440 x 49 interclass samples of correlation values for the overall 49 cameras. To obtain convincing results, all the 150 x 49 intraclass and 1440 x 49 interclass samples from the 49 cameras are used together to draw the overall ROC curve.