esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Constitutive Model of Rock Mass Under THMC Coupling
### THM Coupling Models
#### 3.2.2 Evolution of Permeability and Chemical Reactions  
Recent theoretical models aim to capture the evolution of permeability in fractured rocks under flow-chemical and flow-stress-chemical coupling. [PERSON] and [PERSON] [171] propose a thin-film theoretical model that examines calcite dissolution and precipitation under various chemical and flow conditions. Similarly, [PERSON] et al. [100] extended this approach by creating a partial differential equation system to simulate water flow-chemical interactions in single rock fractures, showing how inhomogeneities in rock surface chemistry and flow paths can affect the dissolution-precipitation processes. These models emphasize the complexity of coupling processes in which water chemistry, driven by ion concentrations, influences rock integrity and porosity evolution.  
Pressure Dissolution and Permeability Evolution  
[PERSON] [172] introduced a pressure-dissolution model for rock particles, examining how fracture permeability evolves under stress through analogies with spherical-particle dissolution. His work reveals that as rock particles dissolve under pressure, the permeability within fractures changes dynamically. Meanwhile, [PERSON] and [PERSON] [173] present a rock chemical reaction transport model, treating fractured rock as a heterogeneous, anisotropic porous medium. This model accounts for solute migration, precipitation, redox reactions, adsorption, ion exchange, and acid-base interactions, although it lacks the ability to represent tubular fracture evolution and pore structure changes during dissolution and precipitation.
## 4 Proposed Hybrid Model of NLMPC and Data-Driven Approach
### Objective Function  
The NMPC aims to optimize the operation of the power system by minimizing a multi-objective cost function that considers generation cost, voltage deviation, and frequency deviation, incorporating both the physical model and the data-driven correction.  
\[J=\sum_{i=1}^{N_{\textit{open}}}\sum_{k=0}^{N-1}\left[\alpha_{\text{c}}\ C(\mathbf{u}_{i}(k))+\alpha_{V}\Big{(}V_{i}(k)-V_{ \textit{ref}_{i}}\Big{)}^{2}+\alpha_{f}\Big{(}f_{i}(k)-f_{\textit{ref}}\Big{)} ^{2}\right] \tag{20}\]where the system dynamics include the data-driven correction:  
\[x_{i}(k+1)=f_{i}(x_{i}(k),u_{i}(k))+\delta_{i}(x_{i}(k),u_{i}(k)) \tag{21}\]  
In this context, the following equation can be utilized to achieve the economic objective within the overall objective function, which focuses on minimizing the total generation cost of the system.  
\[C(u_{i}(k))=\sum_{i=1}^{N}C_{Gen,i,t}^{OP}+\sum_{i=1}^{N}C_{Gen,i,t}^{EMI} \tag{22}\]  
In Equation (22),  
\(C_{Gen,i,t}^{OP}\) represents the generation cost of each power plant unit.  
\(C_{Gen,i,t}^{EMI}\) denotes the cost related to the pollution of the units in each time interval \"t\", which can be defined as follows:  
\[C_{Gen,i,t}^{OP}=\left\{\begin{aligned} & aP_{Gen}^{2}+bP_{Gen}+c\ if\ 0<P_{Gen}<P_{Gen}^{rated}\\ & 0\ if\ \ P_{Gen}=0\end{aligned}\right. \tag{23}\]  
\[C_{Gen,i,t}^{EMI}=\left(C_{CO_{2}}\times CO_{2}+C_{SO_{2}}\times SO_{2}+C_{ NO_{x}}\times NO_{x}\right)\times P_{Gen} \tag{24}\]  
where the parameters in Equation (24) are defined as follows:  
\(C_{CO_{2}}\): Penalty factor for \(CO_{2}\) production.  
\(C_{SO_{2}}\): Penalty factor for \(SO_{2}\) production.  
\(C_{NO_{x}}\): Penalty factor for \(NOx\) production
## 3 Calculation of Propagation Time and Attenuation Rate Using the Amiet Method
### Attenuation Rate
#### 3.3.4 Derivation of \(P_{b}\) and \(P_{e}\)  
Equation (24) can be obtained by substituting Equations (11), (21)-(23) into Equation (9):  
\[P_{b}^{2}=P_{e}^{2}\cdot(1-M\cos\theta^{\prime})^{2}\cdot\frac{\sin\theta^{ \prime}}{\sin\theta_{0}}\cdot\frac{R^{\prime 2}}{R_{0}R_{a}}\cdot\frac{ \mathrm{d}\theta^{\prime}}{\mathrm{d}\theta_{0}}. \tag{24}\]  
The relationship of \(P_{b}\) and \(P_{e}\) is given by substituting Equation (17) into Equation (24).  
\[P_{b}^{2}=P_{e}^{2}\cdot(1-M\cos\theta^{\prime})^{2}\cdot\frac{R^{\prime 2}}{R_{0 }R_{a}}\cdot\left(\frac{\cos\theta^{\prime}}{\cos\theta_{0}}\right)^{2} \tag{25}\]  
Here, \(P_{e}\) is given, as follows.  
\[P_{e}=\frac{1}{4\pi\sqrt{(1-M^{2})\{(x_{s}-x_{0})^{2}+(y_{s}-y_{0})^{2}+(z_{s} -z_{0})^{2}\}-M^{2}(x_{s}-x_{0})^{2}}} \tag{26}\]  
\(P_{b}\) can also be derived by substituting Equation (26) into Equation (25).
# Policy-Compliant Maximum Network Flows
## 4 Policy-Compliant Paths and Algorithms
### First Lower Bound
Taking everything together, we have \[c_{f}(p)=min\{c_{u_{j}},c_{v_{k}u_{k}},\ldots,c_{v_{j}u_{j}},c_{v_{k}}\}\] (19) (cf. (8)). This denotes the additional flow \(c_{f}(p)\), under the assumption that \(c_{f}(p)>0\), that can now be sent over the newly found augmenting path \(p\), such that the resulting flow \(f\) can be realized via only policy-compliant paths. 9. We now want to update the value of the flow \(f\). This can be done via (9). However, we also need to update the specific values of the function \(T\), for all edges involved in the augmenting path. First, we update \(T\) for all edges \(e=(u_{x},u_{y})\) in the forward section \([e_{1},\ldots,e_{i}]\) via \(T^{\prime}(u_{x},u_{y},q_{u_{x}},q_{u_{y}})=T(u_{x},u_{y},q_{u_{x}},q_{u_{y}}) +c_{f}(p)\). Thus, for such an edge \(e=(u_{x},u  
the transitions-function \(T\) and the work that needs to be done when an augmenting path has been found. 10. Iterate steps (3)-(9), that is, keep searching for augmenting paths that fulfill conditions (16)-(18) and update the flow \(f\) according to (9) and the transition-function \(T\) via the equations above. Once no more augmenting paths are found, stop iterating. 11. The resulting flow \(f\) can be realized via a set \(\{p_{1},\ldots,p_{n}\}\) of \(n\) policy-compliant paths \(p_{i}\). Thus, the value \(|f|\) is a lower bound for the value of the policy-compliant maximum flow problem.
## 3 Proposed GIS-Based Approach
### GIS-Based Input Data Preparation
#### 3.1.3 Population Data and PSs Dimension  
The last information taken into account is related to population density. As shown in the left picture of Figure 12, the population is deduced by the census tracts downloaded from the Milano dataset website [32]. We consider the following classification: low-density area: population less than 6000 people; medium-density area: population greater than 6000 and less than 15,000; high-density area: population greater than 15,000. It is worth noticing that the thresholds are chosen by calculating the difference between the census tracts' maximum and minimum population and dividing it by three. Based on the classification in the righthand picture of Figure 12, the squares are colored in green, low-density; yellow, medium-density; and red, high-density.  
Figure 11: Left picture: Google satellite image of several squares; right picture: the layer of green areas reported in green.  
Figure 10: Location of green areas and disused or abandoned buildings (yellow points).  
In the proposed approach, the population defines the type of PS, either indoor or outdoor. Considering the dimension of the current Unareti PSs, we set up the following criteria:  
* Outdoor PS: allowed only in low-density areas; needs an available area greater than 6200 m\({}^{2}\) (corresponding to 2.5% of the size of each square).
* Indoor PS: mandatory in medium and high-density areas; requires at least 2500 m\({}^{2}\) of available area (corresponding to 1% of the size of each square).  
Finally, mixing all the information, we ended up with the map shown in Figure 13. The green squares are a candidate to accommodate a PS, while the red squares are not. Moreover, the map highlights in blue the squares that contain an MV substation without enough space for a conversion in a PS. The orange squares contain either an MV substation with enough space to be converted into a PS or the location of expected coming PSs, as indicated in the Unareti developing plan.  
As a final statement, it is worth noticing that the approach is highly flexible regarding a possible change in the input data: additional scenarios can be easily considered by adjusting the grid maps shown in Figures 9 and 13 with the potential new information available.
# A Tale of Many Networks: Splitting and Merging of Chord-like Overlays in Partitioned Networks
## 2 Related Work
### Further Surveys on Overlay Network Management  
Since 2015, only limited attention has been paid to the explicit merging of disjoint overlay networks--a challenge still faced by long-lived P2P systems subject to network partitions, e.g., through Internet shotdowns or extreme churn; while recent efforts have focused on ring optimization and fault tolerance (e.g., DGRO [15], PSPChord [16]), none address the reintegration of separate ring-based overlays--a gap we address with Ring Reunion. Further surveys on \"Churn Handling Strategies to Support Dependable and Survivable Structured Overlay Networks\" [17] and \"Churn Handling Strategies for Structured Overlay Networks: A Survey\" [18] elaborate on various stabilization strategies in (structured overlay networks from 2002 to 2020) but do not address the topic of completely partitioned overlay networks.  
A third thorough overview on strategies against high churn is given in [19]; while strategies for increasing the overlay stability under churn are discussed, cases of network separation are only mentioned as a \"current research challenge\". A single paper is mentioned, namely [20], which looks into multi-dimensional routing inside partitioned social networks. The authors propose a localized focus of the network in order to support local communication in case of a partitioning. We proposed a similar concept (friend-to-friend routing) with FRoDO [21], Friendly Routing over Dunbar-based Overlays. Such focus on social networks limits the applicability of the overlay network, as it assumes a social/friendship relationship underlying the overlay network, which is not the case in regular structured overlays. In summary, even surveys on similar research question rarely address the issues of splitting and merging peer-to-peer overlays, or do not mention them at all.
## 2 Materials and Methods
### Method Validation  
The proposed method was validated in terms of accuracy, precision, linearity and sensitivity. The linearity of the ICP-AES method was evaluated using least square linear regression. The slope, intercept and coefficients of determination for the calibration curves of each element were calculated. Aqueous calibration curves, calibration curves in 2% \(w/v\) sucrose and standard addition curves using sample JS-4 were constructed. The sensitivity of the method was assessed by calculating the limits of detection (LODs) and the limits of quantification (LOQs). For this purpose, ten blank solutions were prepared and analyzed. The LOD and LOQ values were the concentration that correspond to three and ten times the standard deviation of the measurements for the blank solutions divided by the slope of calibration curves, respectively. Finally, the accuracy and precision of the method was assessed by analyzing spiked sample solutions in three replicates. The accuracy of the  
\begin{table}
\begin{tabular}{c c} \hline \hline RF Generator & 40 MHz \\ Radiofrequency incident power & 1300 W \\ Auxiliary argon gas flow rate & 0.6 L min\({}^{-1}\) \\ Plasma argon gas flow rate & 15.0 L min\({}^{-1}\) \\ Nebulizer argon gas flow rate & 0.85 L min\({}^{-1}\) \\ Sample aspiration flow rate & 2.5 mL min\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Working parameters of the ICP-AES system.  
method was expressed as relative recovery (bias between the theoretical and the found concentrations of the spiked samples), while the precision of the method was expressed as the relative standard deviation (RSD%) of the replicate measurements [8; 23].
## 6 Conclusions  
A simplified analysis model for the deformation of buried pipelines based on the theory of elastic foundation beams caused by excavation is proposed in this study, and the influences of various parameters on the settlement, namely, ground displacement, calculated length, subgrade modulus, diameter, and elastic modulus, are also investigated here. The feasibility of this simplified analytical method is verified by comparing it with the results of earlier cases, and the practicality of this method is also verified by an engineering example. The main conclusions are su mnarized as follows:  
(1) The settlement displacement of the buried pipeline obtained using this present method is slightly larger than the observed data of earlier cases and the engineering example, but the shapes of the settlement curves of calculated and recorded results are consistent, which is beneficial for a reasonable evaluation of buried pipeline deformation.  
(2) The effects of different parameters on the maximum settlement displacement are discussed, and the order of correlation is as follows: subgrade modulus \(k>\) ground displacement \(\delta>\) calculated length \(l>\) diameter \(d>\) elastic modulus \(E\). The parameters \(k\), \(\delta\), and \(l\) are more sensitive to the settlement displacement, and the effects of \(d\) and \(E\) can be ignored. The settlement displacement only increases linearly with an increase in the ground displacement \(\delta\); when subgrade modulus \(k\) and calculated length \(l\) increase to a certain value, the settlement displacement will remain stable.  
(3) In order to overcome the drawback of the pipeline not being parallel to the edge of the foundation pit, this study proposes using the actual length of the pipeline within the length range of the outmost boundary of the subsidence area as the calculated length \(l\), which shows satisfactory results.  
(4) According to the principle of bending stiffness equivalence, the simplified analysis model is used not only for the pipeline of a circular cross section, but also for the pipeline of a non-circular cross section.  
Conceptualization, investigation, methodology, Y.L. and X.C.; data processing, J.H.; original draft, Y.L.; writing and editing the draft, Y.L. and Y.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Hebei Natural Science Foundation (No. E2021512002); Langfang Science and Technology Research and Development Plan Project (No. 2021011065); and the Key Laboratory Building Collapse Mechanism and Disaster Prevention, China Earthquake Administration (No. FZ211104).  
Not applicable.  
Data Availability Statement: The data used to support the finding of this study are available from the corresponding authors upon request.  
The authors express appreciation to [PERSON] for editing and English language assistance.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Ultrasonic Extraction Procedure  
Optimal extraction of OM peels and pulp was achieved using a multifactorial extraction procedure using a US probe chamber cell, the details of which are shown in Table 1 below. These parameters include the investigated liquid-to-solid ratio (_R_, mL/g), duration (_t_, min), and the concentration of ethanol in water (\(C_{\text{EOH}}\), % \(\
u/\
u\)). The pulsation process (_P_, pulses/min) was also investigated, as it is considered to have a vast impact on the extraction process. All extractions were conducted utilizing the highest amplitude (i.e., 80%, which was equal to 120 W) with a temperature <40 \({}^{\circ}\)C using an ice bath to prevent depigmentation of extracts. The temperature ranges were recorded before, during, and after the procedure. The samples underwent centrifugation at 3600\(\times\)_g_ for 10 min to isolate the supernatant, after which the solid portion was removed and preserved at -40 \({}^{\circ}\)C. To compare it with UPAE, a 2-h conventional extraction with a stirring hotplate was conducted at 40 \({}^{\circ}\)C with a 20 mL/g liquid-to-solid ratio with water as the extraction solvent, using the optimized extraction technique by [PERSON] and [PERSON] (20). Preliminary experiments revealed that extraction at temperatures as high as 80 \({}^{\circ}\)C could lead to the decomposition of betalains and the depigmentation of the extracts. The stirring hotplate was set at 500 rounds per minute (rpm) of stirring.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Independent Variables**} & \multirow{2}{*}{**Code Units**} & \multicolumn{3}{c}{**Coded Variable Level**} \\ \cline{3-5}  & & **1** & **2** & **3** \\ \hline Liquid-to-solid ratio (_R_, mL/g) & \(X_{1}\) & 20 & 40 & 60 \\ Ethanol in water concentration (_C_, \% \(v/v\)) & \(X_{2}\) & 0 & 50 & 100 \\ Pulsation (ON/OFF) (_P_, pulses/min) & \(X_{3}\) & 12 & 30 & 48 \\ Extraction time (_t_, min) & \(X_{4}\) & 4 & 12 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The actual and coded levels of the independent variables used to optimize the process.
## 3 Stiffness Matrix of Spectral Element Method  
The forward analysis algorithm forms the basis for pavement modulus inversion, and it employs the spectral element method for dynamic elastic mechanical response analysis of asphalt pavement. In their seminal work, [PERSON] [22] proposed the fundamental structural form of spectral elements. During the calculation and analysis process, two types of spectral elements are used to partition the pavement structure. Dual-node spectral elements simulate the finite thickness of the pavement structure layer, while single-node spectral elements simulate the infinite depth of the subgrade layer. The schematic diagrams for these two types of spectral elements are shown in Figure 1. In this method, a single spectral element can accurately describe the distribution of inertial forces within a pavement structure layer [35]. Therefore, the number of elements required is equal to the number of layers in the pavement structure. Compared to the finite element method, this not only significantly reduces the number of element divisions, improving computational efficiency, but also yields precise results.  
Figure 1: Schematic diagram of the spectral element structure. (**a**) Two-node axisymmetric layer spectral element. (**b**) Single-node axisymmetric layer spectral element.  
The single-node spectral element is a specific instance of the two-node spectral element and exhibits semi-infinite characteristics. When an incident wave propagates within the single-node spectral element, the wave's energy gradually dissipates along its path until it diminishes completely. In the case of a single-node spectral element, the incident wave encounters no boundaries during its propagation, resulting in the absence of reflected waves. By utilizing the solution process that relies on the stiffness matrix of a two-node spectral element, it can derive the stiffness matrix of a one-node spectral element. It can be shown as Equation (22).  
\[\begin{bmatrix}\widetilde{T}_{mm1}\\ \widetilde{T}_{mm1}\end{bmatrix}=\frac{\mu}{p_{mn}}[P]\begin{bmatrix}\widetilde {u}_{mm1}\\ \widetilde{w}_{mm1}\end{bmatrix} \tag{22}\]
## 3 Results  
### Olfactometer Assays  
_D. citri_ females were significantly repelled by fir oil at both the 5 and 15 mg doses; however, their behavior was not affected by litesea oil or citronella oil vs. ethylene glycol (Table 1). In a subsequent experiment, 15 mg of fir oil was tested in the presence of crushed citrus against crushed citrus alone. In this case, the fir oil with crushed citrus did not repel _D. citri_ females as compared with crushed citrus alone (Table 2). After modifying the assay to use intact, undamaged citrus seedlings in place of crushed citrus, we found that fir oil repelled _D. citri_ at the 15 mg dose, but not at the 1 or 5 mg doses, as compared with undamaged citrus odor (Table 2).  
74% relative humidity. Therefore, the conditions were fairly consistent during the two field trials and the measure of the release rate of the fir oil devices.  
In 2013, there was a slight difference in the mean number of \(D\). _citri_ adults found between plots designated for treatment and control before treatments were applied (\(t\) = 2.339, df = 8, \(p\) = 0.048). However, there were no significant differences in the numbers of \(D\). _citri_ in plots treated with fir oil vs. the control at 7 (\(t\) = 0.472, df = 8, \(p\) = 0.650), 14 (\(t\) = 1.019, df = 8, \(p\) = 0.338), 21 (\(t\) = 1.833, df = 8, \(p\) = 0.104), and 28 days (\(t\) = 0.557, df = 8, \(p\) = 0.593) after dispensers with fir oil were deployed (Figure 3A).  
Figure 2: Average, minimal, and maximal temperatures recorded in Lake Alfred, FL during examination of dispenser release rates in 2013 (**A**); in Winter Garden, FL during 2013 (**B**) and 2014 (**C**) field trials of fir oil as a repellent for \(D\). _citri_.  
In 2014, the pre-treatment numbers of \(D\). _citri_ did not differ between plots designated for fir oil treatment vs. the control plots (\(t\) = 1.347, df = 8, \(p\) = 0.215). With the number of fir oil dispensers doubled (8 per tree), compared to 2013, the mean number of \(D\). _citri_ in treated plots was reduced significantly at 7 (\(t\) = 4.487, df = 8, \(p\) = 0.002) and 14 (\(t\) = 3.968, df = 8, \(p\) = 0.004) days after dispensers were deployed as compared with controls, but not at 21 days following deployment (\(t\) = 0.904, df = 8, \(p\) = 0.392) (Figure 3B).