esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Enumeration of the Viable Cells and Optical Density Measurements
#### 2.4.2 Biofilm Biomass Quantification through Optical Density Using Crystal Violet Assay  
The total biomass, which includes live cells, dead cells and EPS within the biofilm, was quantified through the measurement of the optical density following the CV assay [16,44]. After rinsing three times with 3 mL of PBS to remove the unattached cells, 1.5 mL of a 0.046% (_v/v_) CV solution (CV stain HT90132, Sigma Aldrich, St. Louis, Missouri) in water was added into the petri dish containing the biofilm. The dye was left to stain the biofilm for 15 min. Afterwards, it was rinsed twice with 2 mL of PBS to remove the excess of dye and it was left to dry for 30 min. Then, 1.2 mL of acetic acid solution 33% (_v/v_) in water was added to dissolve the dye. 200 uL of the dyed acetic acid solution was transferred in each of three wells of a 96-well microtiter plate. The blank was performed following the same protocol with petri dishes not containing biofilms. The measured blanks were subtracted from the measurements even though the OD related to these blanks was negligible. The quantification of the biofilm biomass was performed by reading the optical density at 590 nm (OD\({}_{590}\)), using a VersaMax tunable microplate reader (Molecular devices, Berkshire, UK). A single replicate corresponded to the average value corresponded to three wells. The wavelength of 590 nm represents the absorption peak of the CV spectrum. Three, three to five, and ten independent replicates were performed for experiments in Section 3.1 (where the influence of inoculum level is studied), Section 3.4.2 (where the development of the biofilm in time is investigated) and Section 3.5 (where the reproducibility is assessed), respectively.
## 3 Results and Discussion
### Technological Properties of Modified BCP
BCP modified with Celluclast(r) 1.5 L contained the lowest IDF content, which may significantly influence the creaming index. During hydrolysis, compounds with higher molecular weights are broken down into compounds with lower molecular weights, increasing the solubility of the compounds. [PERSON] et al. [50] investigated the influence of carboxymethyl cellulose molecular mass on emulsions stabilized with whey protein isolate. Their study demonstrated that carboxymethyl celluloses with higher molecular weights were more effective in stabilizing emulsions compared to those with lower molecular weights. The stability of emulsions prepared with berry pomace may be attributed to two main mechanisms proposed in the literature. The first suggests that the finest particles within the insoluble fraction of the pomace adsorb at the oil-water interface, forming a protective layer around the oil droplets that inhibits coalescence. The second mechanism proposes that the insoluble fraction, in conjunction with the soluble components of the pomace--particularly endogenous proteins--increases the viscosity of the continuous phase, thereby enhancing the physical stability of the emulsion through reduced droplet mobility [51]. [PERSON] et al. [52] investigated the influence of pH on the emulsifying properties of polysaccharides, noting that the effect is dependent on the conformation of the polysaccharide. Anionic polysaccharides, such as pectin, are more susceptible to changes in pH. At acidic pH (e.g., pH 4), anionic polysaccharides form a dense film on the surface of oil droplets. A weaker and more easily ruptured film is formed at alkaline pH (e.g., pH 8). In contrast, neutral polysaccharides, such as cellulose, are less affected by pH. The reduced emulsion stability in acidic conditions indicates that the SDF fraction of BCP (mainly pectin) contributes less to emulsion stabilization compared to the IDF fraction (mainly cellulose). Figure 3: Stability of emulsions prepared with hydrolyzed BCP under static conditions (**a**) and after heat treatment (30 min at 80 \({}^{\circ}\)C) (**b**), depending on pH value. Mean \(\pm\) standard deviation values (\(n=3\)).
## 3 Experimental Results  
### XRD Analysis  
Figure 4 shows the diffractogram of the material obtained from the chemical reaction between the nickel nitrate and the antimony chloride, plus ethylenediamine, and calcined at 800 \({}^{\circ}\)C. In the XRD pattern, the characteristic peaks of NiSb\({}_{2}\)O\({}_{6}\) located at points 20 = 19.23\({}^{\circ}\), 21.41\({}^{\circ}\), 27.15\({}^{\circ}\), 33.49\({}^{\circ}\), 34.99\({}^{\circ}\), 38.77\({}^{\circ}\), 40.16\({}^{\circ}\), 43.63\({}^{\circ}\), 44.71\({}^{\circ}\), 53.21\({}^{\circ}\), and 56.20\({}^{\circ}\) can be clearly observed.  
The main crystalline phase of the oxide was identified by means of the PDF file No. 86-0110. According to this, the NiSb\({}_{2}\)O\({}_{6}\) crystallized into a tetragonal structure with cell parameters a = b = 4.641 A and c = 9.219 A, with spatial group P4\({}_{2}\)/mmm (136). It has been previously reported that the peaks of the oxide's main phase (see Figure 4) belong to the trirutile-type family of structures or the trirutile-type antimonates [21; 28]. This characteristic is obtained by modifying the axis' c-direction of  
Figure 4: Diffractogram of the oxide NiSb\({}_{2}\)O\({}_{6}\) obtained by calcination at 800 \({}^{\circ}\)C in air.  
the rutile cell [31]. On the other hand, a small portion of inorganic material outside the main phase can be seen in the diffractogram. This reflection was located at point 2\(\theta\) = 37.1\({}^{\circ}\) and corresponds to a secondary phase of NiO according to the PDF file No. 65-2901.  
The results shown in Figure 4 are consistent with literature reports [32; 33] where different synthesis methods of the NiSb\({}_{2}\)O\({}_{6}\)'s crystalline phase were employed, like the solid-state reaction [31] and the colloidal method [34]. In this work, an economical and simple to implement alternative, a wet-chemistry process, was used successfully. It was possible to obtain with its particle sizes less than 100 nm.
## 3 Results
### Spectra Analysis and Preprocessing Results
The MSC can effectively reduce the spectral changes caused by object particle size and  
Figure 5: The spectral curves of (**a**) raw and preprocessed reflectance of all coal samples using (**b**) SG, (**c**) FD, and (**d**) MSC methods. The red curves indicate the mean spectra of all coal samples, and the gray shadows represent the spectra reflectance range. uneven distribution, resulting in more concentrated spectral reflectance with sharper features at characteristic reflection peaks and absorption valleys. The PLSR model was selected to compare the effects of different preprocessing methods using \(RMSE_{p}\) as the evaluation metric. The parameters are optimized using 10-fold cross-validation on the training set. Table 3 shows the PLSR modeling results based on the raw spectra and different preprocessed spectra. As shown in Table 3, the prediction accuracy of the SG is generally similar to or better than that of the Raw spectra. However, the spectral heterogeneity due to the coal complex conditions may hinder the effective differentiation of feature wavelengths, potentially leading to model abnormalities, such as the overfitting of OR and SG in MC. FD can emphasize the spectral change information. However, it is significantly affected by noise, which often makes the model unstable in accuracy. In conclusion, MSC achieves the optimal performance across all indices; therefore, this study will be analyzed based on MSC spectra.
## 2 Materials and Methods
### Drought Effect Analysis  
Pearson correlation analysis was performed between SPEI drought indices at different time scales and remote sensing indices to evaluate their impact on drought effects in rubber plantations [47]. To analyze the impact of drought on rubber forest GPP production, we first generated GPP data every 8 days from 2001 to 2020 using a model. These data were aligned with the SPEI drought index to plot a time series curve and observe trends.  
Second, we introduced GPP deviation to quantitatively analyze the impact of drought on GPP [52]. Rubber forest GPP was decomposed into the sum of the GPP trend component (T\({}_{\text{GPP}}\)) and GPP deviation (D\({}_{\text{GPP}}\)). GPP deviation represents the fluctuations caused by drought and other environmental factors in a given year. The GPP every 8 days from 2001 to 2020 is denoted as \(G_{\text{{{{{{{{{{{{\rm{\rm{\  
Figure 3 shows the annual GPP values for four different locations. The XR station in Southwest China has the highest annual GPP, reaching 3075.5 g C m\({}^{-2}\) year\({}^{-1}\). The DR station on Hainan Island, China, and the two stations in Thailand (NR and SR) have similar annual GPP values. The DR station has an average annual GPP of 2280.5 g C m\({}^{-2}\) year\({}^{-1}\). The value for the NR station is slightly higher at 2558.4 g C m\({}^{-2}\) year\({}^{-1}\), and the SR station has the lowest at 2189.5 g C m\({}^{-2}\) year\({}^{-1}\). In terms of variation, DR and XR show greater fluctuations, while NR and SR have smaller variations.  
The violin plot in Figure 4 shows the monthly variations in daily GPP for the four sites DR, XR, NR, and SR. At all sites, daily GPP values typically exhibit a pronounced seasonal trend, with lower values in winter, usually reaching their minimum in January or February, gradually increasing, peaking in summer or autumn, and then declining. The daily GPP at the XR site is relatively stable throughout the year, with monthly GPP values being relatively concentrated, and the average daily GPP in February is also above 5 g C m\({}^{-2}\) year\({}^{-1}\). The highest average daily GPP in July exceeds 10 g C m\({}^{-2}\) year\({}^{-1}\). The daily GPP variation in the same month for the other three sites is greater, with the highest average daily GPP generally below 10 g C m\({}^{-2}\) year\({}^{-1}\), and the lowest months below 5 g C m\({}^{-2}\) year\({}^{-1}\). Among all the sites, the monthly average daily GPP fluctuates the most at DR, while it is the most stable at XR.  
Figure 2: Monthly temperature and precipitation conditions in the areas of the four flux sites. (DR stands for China Danzhou rubber, XR represents China Xishuangbanna rubber, SR stands for Thailand south rubber, and NR represents Thailand north rubber).
## 5 Unbalanced PSI-CA Protocol Based on Cuckoo Filter
### Protocol Process
#### 5.2.1 Preprocessing  
In the preprocessing phase, the small health app developer and the large medical institution need to perform a series of preparatory works to ensure the security and efficiency of subsequent interactions. The specific steps are as follows:  
1. Security parameter negotiation: The small health app developer and the large medical institution agree on the large prime number \(q\) used in the DH encryption algorithm and the hash function \(H\) used.
2. Large Medical Institution Generates Private Key: The large medical institution generates its own private key \(\alpha\), used for the Diffie-Hellman (DH) encryption algorithm.
3. Data Scrambling: The small health app developer and the large medical institution scramble their own datasets \(Y\) and \(X\) for randomization, enhancing data privacy and security.
4. Small Health App Developer Data Preprocessing: The small health app developer calculates \(h_{j}=H(y_{j})\) and generates \(n_{2}\) random numbers \(\beta_{j}\), used for the Diffie-Hellman (DH) encryption algorithm.
5. Creation of Cuckoo Filter: The large medical institution generates a Cuckoo filter \(CF\) by using the operation \(CF.insert((H(x_{i}))^{\alpha})\) and sends the filter \(CF\) to the small health app developer for private-set intersection queries with privacy protection.
# Identification of High-Order Nonlinear Coupled Systems Using a Data-Driven Approach
## 1 Introduction
### Motivation
The limitation arises from the reliance on the least squares approach, favoring the selection of large values over small. Consequently, the SINDY algorithm may exclude functions with small coefficients in nonlinear models where coefficients vary significantly in magnitude. This can result in inaccurate or incomplete models that fail to capture the true dynamics of the system. For instance, in a recent work [13], the authors proposed a modification of the SINDY algorithm, aiming to outperform the classical SINDY approach in constructing a system of ordinary differential equations from stochastic simulations. Thus, despite the simplicity of SINDY, it requires constant modifications and improvements tailored to each new application. As a result, customized variants of the SINDY algorithm have begun to emerge in the recent literature [14; 15; 16; 17; 18].
## 5 Discussion
### Limitations and Future Work  
Although the proposed method effectively reduced the feature set while maintaining high detection accuracy, its applicability across other datasets and IoT protocols remains to be fully explored. The study leveraged the publicly available MQTSet dataset, which, although comprehensive and suitable for evaluating MQTT traffic, was generated in asimulated environment. Real-world IoT networks often feature additional complexities, such as dynamic traffic patterns, device heterogeneity, and environmental noise, which are not fully represented in the dataset. The selected features were optimal for the MQTTset dataset, but additional testing on datasets from other IoT ecosystems would help assess the scalability and adaptability of the approach to varying network conditions.  
The employed dataset also focuses on a subset of common MQTT-related attacks, including doS, flood, brute force, slowITe, and malformed data attacks. While these represent critical vulnerabilities, they do not encompass more sophisticated threats such as advanced persistent threats (APTs). Expanding the scope to include a wider variety of attack scenarios in future research could improve the generalizability of the method to diverse IoT environments and emerging cybersecurity threats.  
Furthermore, the computational efficiency observed in this study, particularly with lightweight models like DT and RF, was evaluated in a high-performance computing environment. While this demonstrates the suitability of the proposed method for resource-constrained devices, real-world testing on actual IoT hardware is necessary to validate its scalability and performance under live, large-scale deployment scenarios. Future work will focus on implementing the approach in real-time systems to evaluate its latency, throughput, and overall operational viability.
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 3 Methodology  
The optimization of grounding systems for different voltage levels (400 V, 10 kV, and 35 kV). It involves both computational modeling and experimental field measurements. The methodology integrates numerical simulations, measurement techniques, and analysis of various parameters that influence grounding performance.
# Experimental Study on the Effect of Wind Speed on Hail Impacts
## 3 Experimental Analysis and Results
### Factors Influencing the Damage Potential of Hailstorms  
In the assessment of hail-induced damage potential, the maximum impact force is a primary indicator, which is correlated with the maximum impact kinetic energy of hailstones. The kinetic energy of a hailstone depends on its velocity and mass [38]. The formula for calculating hailstone kinetic energy can be expressed as:  
\[E=\frac{1}{2}mv^{2} \tag{3}\]  
In the equation, \(E\): kinetic energy of hail (unit: J); \(m\): mass of the hailstone (unit: kg); and \(v\): velocity of the hailstone (unit: m/s).  
Figure 8: Time-course curves of hail impacts with different velocities at a 13 m/s wind speed.  
Figure 7: Time-course curves of hail impacts with different velocities at a 0 m/s wind speed.  
Figure 9 presents the relationship between the maximum impact force and the kinetic energy of hailstones under wind speeds of 0 m/s and 13 m/s. Linear regression analysis of the experimental data revealed a strong linear correlation between the maximum impact force and kinetic energy across different wind speeds, with good agreement. The results demonstrate that higher wind speeds lead to greater maximum impact forces.  
According to the momentum theorem, the maximum impact force is related to the momentum transfer capacity of the hailstone prior to complete fragmentation. During impact, a portion of the hailstone's momentum is converted into the impact force, while the remainder is transferred to the momentum of the fragmented cluster particles. Under strong winds, the momentum of these cluster fragments is further converted into additional impact force, thereby enhancing momentum transfer in the impact direction and generating higher impact forces. Consequently, the inclusion of wind significantly amplifies the impact forces during hail collisions.  
These findings highlight that wind speed is a critical factor in assessing hail-induced damage potential. Higher wind speeds correlate with stronger destructive capacity, underscoring the necessity of incorporating wind effects into hail risk evaluation frameworks for structural and agricultural applications.
## 2 Numerical Methodology
### Turbulence Simulation Methods
#### 2.1.3 Definition of Sub-Grid Scale  
The sub-grid scale \(\Delta\) plays a role in DES-type methods that is similar to its role in classical LES methods, controlling the computed wavelength and the level of vortex viscosity. [PERSON] [20] proposed a new sub-grid scale for LES, considering the slow development of turbulence in the mixing layer:  
\[\Delta_{\omega}\ =\ \sqrt{\frac{\left\|N_{i}\right\|^{2}}{s_{j+1/2}s_{k+1/2}}+ \frac{\left\|N_{j}\right\|^{2}}{s_{k+1/2}s_{i+1/2}}+\frac{\left\|N_{k}\right\|^ {2}}{s_{i+1/2}s_{j+1/2}}} \tag{12}\]where \(N_{i}\), \(N_{j}\), and \(N_{k}\) represent the components of the unit vector of the vorticity \(\omega\) along the three coordinate axes.  
This new definition of the sub-grid scale helps to prevent the delay of shear layer instability and the delay of the transition to full turbulence after separation. This new definition has been successfully validated in the literature [20].  
For the length scale associated with Type II separated flows, to ensure the rapid transition of the DES mode from the LES mode, the delay function \(f_{d}\) is introduced as a conversion channel from \(\Delta_{\text{max}}\) to \(\Delta_{\omega}\). Here, a value \(f_{d0}\) is introduced to mark the outer boundary of the boundary layer. \(\Delta_{\text{DES}}^{\text{II}}\) is expressed as follows:  
\[\Delta_{\text{DES}}^{\text{II}}\ =\ \left\{\begin{array}{l}\Delta_{\text{max}},\text{ if }f_{d}<f_{d0}\\ \Delta_{\omega},\text{ if }f_{d}>f_{d0}\end{array}\right. \tag{13}\]  
The range of values for \(f_{d0}\) is (0.75, 0.99). The minimum value of \(f_{d0}\) is intended to maintain the original DDES behavior within the boundary layer, while the maximum value corresponds to the sub-grid length scale \(\Delta_{\text{max}}\). Therefore, as long as \(f_{d}<f_{d0}\), Equation (13) remains in DDES mode.  
In summary, the definition of the sub-grid length scale for ZDES is as follows:  
For the first type of flow separation problem (fixed separation position), the sub-grid length scale in Equation (11) is given by  
\[\Delta_{\text{DES}}^{\text{I}}\ =\ \Delta_{\omega} \tag{14}\]  
For the second type of flow separation problem (uncertain separation position), the sub-grid length scale in Equation (11) is given by  
\[\Delta_{\text{DES}}^{\text{II}}\ =\ (0.5+sign(0.5,f_{d}-f_{d0}))\times\Delta_{ \text{max}}+(0.5-sign(0.5,f_{d}-f_{d0}))\times\Delta_{\omega} \tag{15}\]  
The sign function is defined as follows:  
\[\text{sign}(a,b)\ =\ \left\{\begin{array}{l}+a\,,\text{if }b\geq 0\\ -a\,,\text{if }b<0\end{array}\right. \tag{16}\]
# Popularity-Aware Service Provisioning Framework in Cloud Environment
## 4 Constrained MDP
### Optimization Formulation  
The average OPEX of the service provider, \(\zeta_{O}\), is defined as  
\[\zeta_{O}=\lim\limits_{t\rightarrow\infty}\sup\frac{1}{t}\sum\limits_{t^{\prime }}^{t}E\big{[}r(S_{t^{\prime}},A_{t^{\prime}})\big{]} \tag{14}\]  
where \(S_{t^{\prime}}\) and \(A_{t^{\prime}}\) are the state and the determined action at \(t^{\prime}\in\mathbb{T}\), respectively.  
The average response time of the service \(i\), \(\psi_{R,i}\), can be represented as  
\[\psi_{R,i}=\lim\limits_{t\rightarrow\infty}\sup\frac{1}{t}\sum\limits_{t^{ \prime}}^{t}E\big{[}c_{i}(S_{t^{\prime}},A_{t^{\prime}})\big{]}. \tag{15}\]  
We can express the CMDP model as follows:  
\[\min\limits_{\pi}\zeta_{O} \tag{16}\]  
**s.t.**\(\psi_{R,i}\leq\theta_{R,i}\), for \(\forall i\) (17)  
where \(\pi\) is a policy that represents the probabilities of deciding a specific action at each state. Moreover, \(\theta_{R,i}\) indicates the response time requirement of the service \(i\).  
To transform the CMDP model to an equivalent LP model, the stationary probabilities of state \(S\) and action \(A\), \(\varphi(S,A)\), can be defined as the decision variables for the LP model. Consequently, the LP model can be obtained by  
\[\min\limits_{\varphi(S,A)}\sum\limits_{S}\sum\limits_{A}\varphi(S,A)r(S,A) \tag{18}\]  
being subject to  
\[\sum\limits_{S}\sum\limits_{A}\varphi(S,A)c_{i}(S,A)\leq\theta_{R,i},\text{ for }\forall i \tag{19}\]  
\[\sum\limits_{A}\varphi(S^{\prime},A)=\sum\limits_{S}\sum\limits_{A}\varphi(S,A )P[S^{\prime}|S,A] \tag{20}\]\[\sum_{S}\sum_{A}\varphi(S,A)=1 \tag{21}\]  
and  
\[\varphi(S,A)\geq 0. \tag{22}\]  
The objective function, Equation (18), was used to minimize the average OPEX of the service provider. The constraint in Equation (19) was matched to the constraint of the CMDP model in Equation (17). Equation (20) presents the constraint for the Chapman-Kolmogorov equation. The constraints in Equations (21) and (22) ensure that the probability properties are satisfied.  
The above LP model can be solved by a conventional algorithm such as [PERSON]'s algorithm [21]. Then, we can obtain the optimal policy with a low complexity, which can be considered as the advantage of the transformation. Specifically, when using [PERSON]'s algorithm, the computational complexity is \(O\big{(}n^{2.5}\big{)}\), where \(n\) denotes the number of decision variables in the model. From the solution \(\varphi^{*}(S,A)\) of the LP model, we can obtain the optimal stochastic policy \(\pi^{*}(S,A)\) for the CMDP model as  
\[\pi^{*}(S,A)=\frac{\varphi^{*}(S,A)}{\sum\limits_{A^{\prime}}\varphi^{*}(S,A^ {\prime})}\text{ for }S\in\mathbf{S},\sum\limits_{A^{\prime}}\varphi^{*}\big{(}S,A^{\prime} \big{)}>0. \tag{23}\]  
By means of the optimal stochastic policy (i.e., optimal probability distribution), Instead of utilizing the conventional LP solving algorithms, various deep reinforcement learning approaches [22; 23] can be used to solve the CMDP model. The service provider requests appropriate resources to the cloud operator at a given state \(S\).