esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Multiphysics Modeling to Construct Dual-Porosity Sandstones
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Properties** & **Symbol** & **Values** & **Units** \\ \hline Reservoir thickness & h & 24.00 & meter \\ Reservoir temperature & T & 60.00 & \({}^{\circ}\)C \\ Irreducible water saturation & \(S_{wirr}\) & 0.10 & Fractional \\ Residual oil saturation & \(S_{or}\) & 0.03 & Fractional \\ Salinity & Sal & 120.00 & Kppm \\ Quartz density & \(\rho_{Q}\) & 2.65 & g/cm\({}^{3}\) \\ Feldspar (orthoclase) density & \(\rho_{fd}\) & 2.52 & g/cm\({}^{3}\) \\ Clay (illite) density & \(\rho_{sh}\) & 2.52 & g/cm\({}^{3}\) \\ Brine density & \(\rho_{w}\) & 1.05 & g/cm\({}^{3}\) \\ Oil density & \(\rho_{o}\) & 0.75 & g/cm\({}^{3}\) \\ Quartz bulk modulus & \(K_{Q}\) & 37.00 & GPa \\ Feldspar bulk modulus & \(K_{fd}\) & 29.00 & GPa \\ Quartz shear modulus & \(\mu_{Q}\) & 44.00 & GPa \\ Feldspar shear modulus & \(\mu_{fd}\) & 15.00 & GPa \\ Clay bulk modulus & \(K_{sh}\) & 25.35 & GPa \\ Clay shear modulus & \(h_{sh}\) & 16.26 & GPa \\ Clay gamma ray & \(GR_{sh}\) & 275.00 & API \\ Quartz gamma ray & \(GR_{Q}\) & 0.00 & API \\ Feldspar gamma ray & \(GR_{fd}\) & 220.00 & API \\ Brine bulk modulus & \(K_{w}\) & 2.50 & GPa \\ Oil bulk modulus & \(K_{o}\) & 0.75 & GPa \\ Critical porosity & \(\varnothing_{c}\) & 0.45 & Fractional \\ Micropore lithology exponent & \(\lambda_{m}\) & 0.75 & Fractional \\ Intergranular lithology exponent & \(\lambda_{i}\) & 1.75 & Fractional \\ Micropore length scale for bulk modulus & \(L_{m}^{k}\) & 1.00 & Fractional \\ Intergranular length scale for bulk modulus & \(L_{i}^{k}\) & 0.20 & Fractional \\ Micropore length scale for shear modulus & \(L_{m}^{h}\) & 1.00 & Fractional \\ Intergranular length scale for shear modulus & \(L_{i}^{h}\) & 0.20 & Fractional \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Reservoir, matrix, fluid, and model parameters for the dual-porosity sandstone formation. The second type of template is mainly derived from petrophysical properties including resistivity and compressional slowness (DTCO). These types of templates help petrophysicists to interpret well logs. For example, from the bottom left panel in this figure, one can conclude that oil-bearing and water-bearing zones can be separated using resistivity and DTCO well logs where all the points are color-coded by water saturation. From the bottom right panel in this figure, one can clearly see the gradual effect of intergranular porosity on resistivity and DTCO well logs where all the points are color-coded by intergranular porosity. Figure 6: Histograms of the true well-log responses for a dual-porosity sandstone formation containing intergranular pores and micropores as illustrated in Figure 5.  
Figure 7: Multiphysics rock templates created using different attributes highlighting the separation of water-bearing from oil-bearing zones (left panels) and the effect of intergranular porosity on resistivity, velocity ratio, acoustic impedance, and compressional or P-wave slowness (right panels).
## 4 Proposed Protocol
### Initial Setup  
The initial setup process is depicted in Figure 3; it begins with the global owner of the model performing signcryption on the model and then submitting it to IPFS. This setup process ends with the owner submitting the IPFS hash of the encrypted model and signcryption parameters to the smart contract. By utilizing the blockchain's immutable property, the model data can be prevented from being compromised by malicious actors. The details of the process are explained as follows.  
1. The owner of the model prepares the initial model \(\alpha\), consisting of \(M_{0}\mid t\mid x\mid r\). They then perform ZSS signcryption (as in Algorithm 1) to generate \((V_{\alpha},X_{\alpha},Y_{\alpha})\). In this case, \(V_{\alpha}\) is the signature of \(\alpha\), \(X_{\alpha}\) is the randomness of the key, and \(Y_{\alpha}\) is the encrypted version of \(\alpha\). Starting from the second iteration, the initial model will use the new global model, which is the result of the previous iteration's final model.
2. The owner sends the \(Y_{\alpha}\) to IPFS.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline \(M_{0}\) & Initial global model created by the owner \\ \(M_{u}\) & Trainer’s generated model update \\ \(MU\) & Array of \(M_{u}\) \\ \(t\) & Time limit for trainers to submit the model updates \\ \(x\) & Minimum token deposit \\ \(r\) & Minimum reputation threshold \\ \(\alpha\) & Consists of \(M_{0}\), \(t\), \(x\), and \(r\) \\ \(\delta\) & The accuracy claimed by the trainers \\ \(\beta\) & Consists of \(M_{u}\) and \(\delta\) \\ \(\gamma\) & Voters’ decisions, can be _accept/reject_ \\ \(\Omega\) & Resulting model from federated averaging \\ \(V\) & ZSS signature of the message \(m\) \\ \(X\) & Randomness of the encryption key generation \\ \(Y\) & Signcryption message (the result of signcryption) \\ \(H\) & Hash function \\ \(SC\) & Smart contract \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of notations used in this section.  
Figure 2: The overall proposed FL process.  
1. IPFS stores \(Y_{\alpha}\).
2. IPFS generates a unique cryptographic hash for the uploaded model (\(\alpha_{ipfs}\) and returns it to the owner.
3. The owner submits \(X,V,\alpha_{ipfs}\) in the \(SC\).
4. The \(SC\) stores \(X,V,\alpha_{ipfs}\).
5. The \(SC\) relays the successful transaction event to the owner of the model.
## 2 Analytical Methods
### Dimensionless Analysis  
Prior to conducting mathematical treatments, it is essential to bring the dimensional problem to a non-dimensional form for the purpose of generalization and simplification. We introduce the following dimensionless variables and parameters:  
\[\xi=\frac{r}{a},\qquad\qquad\beta=\frac{b}{a},\qquad\qquad\theta=\frac{T-T_{0} }{q_{0}a/k},\qquad\qquad\tau=\frac{\alpha t}{a^{2}}. \tag{3}\]  
The dimensionless problem is therefore expressed as:  
\[\begin{split}\frac{\partial^{2}\theta}{\partial\xi^{2}}+\frac{ \partial\theta}{\partial\xi}&=\frac{\partial\theta}{\partial \tau},\\ \frac{\partial\theta}{\partial\xi}\Big{|}_{\xi=1}& =-f(\tau)=F(\tau),\\ \theta(\beta,\tau)&=0,\\ \theta(\xi,0)&=0.\end{split} \tag{4}\]  
Note that \(f(\tau)\) is the dimensionless form of \(f(t)\) and the negative sign carries from the left-hand side; together, they are denoted as \(F(\tau)\) for simplicity. Since one of the boundary conditions depends on time, the method of separation of variables cannot be directly  
Figure 3: Flow chart of: (**a**) the presented analytical framework and (**b**) its computation.  
applied. Alternative approaches are required to convert the time-dependent boundary to a constant one.
## 5 Transmission Slot Selection Policy with Reinforcement Learning
\(CH^{k}\) represents the cluster head of \(k\)th cluster with a time slot allocation table and contains record of each packet reception from its cluster members. Here, cluster members, denoted as \(CM\), decide their transmission slot for a packet by selecting one of \(Q\) available time slots in current frame. Nodes observe energy harvesting rates and schedule their transmission to a time slot with less available energy compared to other slots. For instance, \(CM_{1}\) decides allocating time slot 2 according to its available energy. Depending on its value of Q table for time slot 2, the transmission decision is calculated based on its harvested energy in previous frame, with the lowest in time slot 2.
# Numerical Analysis of Seismic Pounding between Adjacent Buildings Accounting for SSI
## 3 Numerical Study
### Response Analysis
For Cases I and II, excellent agreement between the peak and phase responses for both methods has been found for the selected earthquakes. Notably, both methods produced slightly different response histories for the fourth story due to the larger forces under the small SSI effects, as shown in Figure 7. As a result of the high bounding forces in the first through third modes, the responses at the foundation obtained by the rigorous method in Figure 8 are not fully in agreement with those achieved by the approximate method in Case IV. Based on the comparison between the conventional rigorous method and the approximate method, it could be concluded that the approximate method markedly improves the accuracy of the analytical results without increasing the computational effort and by reducing the dimensions of the matrix. Regarding the large SSI effect (Case I), both methods produced the same results for the 1995 Kobe earthquake in terms of translation, twisting, and rocking. Figure 9 compares both methods based on the story shears and torque with and without bounding. Figure 9 shows the maximum story shears in the \(x\)- and \(y\)-directions, together with the maximum  
Figure 8: Time histories of Cases I and IV for the foundation during the 1995 Kobe earthquake. story torque in relation to the upright direction for shear waves traveling at 65 and 300 m/s. This is based on simulations by [PERSON] et al. [42; 43]. For validation purposes, these two methods used in this study are compared with the findings of [PERSON] et al. [42; 43] without considering the bounding effect. Based on the results for the large SSI case, the approximate and rigorous methods provided in Figure 9 for adjacent buildings are completely in agreement. Figure 9 also shows that, in Case IV, the maximum responses to the story shear and torque are reduced due to reduced bounding. [PERSON] [61] has studied the bi-directional excitation and biaxial interaction of the base isolation system, although there is still debate on the base isolation system's behavior under the SSI and hammering impacts. Two approaches utilized in this study will be applied to base-isolated buildings on various soil types, taking impact effects into account.
## 5 Conclusions  
We present a privacy-preserving object-detection (vePOD) framework that can protect both a vehicle's sensor data and object-detection results from being exposed to and used by edge servers. We leverage the additive secret-sharing theory to develop secure functions in detection CNNs. The secure layers in a vePOD CNN process secret shares received from a vehicle and produce detections with the same functional correctness as the original network. We implement proof-of-concept networks for vePOD Faster R-CNN and vePOD YOLO. Experimental results on a public dataset show that vePOD CNNs guarantee the same detection accuracy as the original CNNs. Most importantly, vePOD CNNs protect data privacy for a vehicle by assuring that each edge server only accesses an encrypted secret share and produces partial detection, which has no semantic importance for the edge server.  
In our experiments, we note the performance degradation in running vePOD CNNs, especially from the secure ROI pooling. The main goal of this paper is to develop proof-of-concept networks for privacy-preserving object detection in a vehicle-edge collaborative environment. We did not conduct any optimization on it. In our future research, we will speed up the execution of vePOD CNNs by exploring acceleration and parallel-processing techniques. Furthermore, we plan to develop novel approaches to enable the execution of the entire object-detection workflow on edge servers for all types of CNNs. This will free the limited resources on a vehicle to perform more mission-critical tasks.  
**Author Contributions:** Supervision, S.F. and Q.Y.; Writing--original draft, T.B.; Writing--review & editing, S.F. and Q.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work has been supported in part by the U.S. National Science Foundation grants CNS-2037982, ECCS-2010332, OAC-2017564, CNS-2113805, CNS-1852134, DUE-2225229, and CNS-1828105. We thank the reviewers for their constructive comments and suggestions, which helped us improve this paper.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Carbon Nanotubes-Based Hydrogels for Bacterial Eradiation and Wound-Healing Applications
## 4 CNT-Based Antibacterial Applications
### Antibacterial Properties of CNT Composites  
CNTs combined with nanoparticles exhibited an improved antibacterial effect. Several studies have been reported to check the antibacterial effect of CNTs composites with chitosan and silver NP [112]. [PERSON] et al., examined that CNTs conjugated with AgNPs exhibited superior antibacterial properties against _E. coli_[113]. This was attributed to the more significant disruption of the cell membrane in the presence of CNTs conjugated with AgNPs. [PERSON] et al., observed that the deposition of AgNPs on MWCNTs with poly(amidoamine) induced bacteriostatic effects against _S. aureus_, _E. coli_, and _Pseudomonas aeruginosa_[114].  
[PERSON] et al., prepared the MWCNTs/ AgNPsN composites and evaluated their biocompatibility and antibacterial potential [115]. Enhanced biocompatibility and antibacterial efficacy were observed in the developed composites. The antibacterial property of CNTs is explored to establish nanocomposite films. CNTs/poly(L-lysine) or poly(L-glutamic acid) composite films were synthesized and their antibacterial potential against _E. coli_ and _Staphylococcus epidermidis_ was evaluated. The composite films showed 90% of bacterial killing efficacy [116]. Gelatin/MWCNTs composite films demonstrated improved bacterial killing properties against Gram-negative and Gram-positive bacteria [117]. An improvement in physicochemical, mechanical, and electrical properties was observed in CNTs incorporated in gelatin. [PERSON] et al., synthesized the poly(lactic acid) (PLA)/AgNPs/CNTs composites and examined their antibacterial potential against _Staphylococcus haemolyticus_[118]. The developed composite showed superior antibacterial properties against _Staphylococcus haemolyticus_. They observed that the combination of two nano-fillers demonstrated synergistic features to the polymeric matrix. The nanocomposite also exhibited remarkable thermo-mechanical and antibacterial properties. [PERSON] et al., developed the low-density poly(ethylene) (LDPE)/MWCNTs nanocomposites and monitored antimicrobial activity against DH5\(\alpha\)_E. coli_[119]. A significant enhancement in the antibacterial potential was observed in 1% MWCNTs-added LDPE film. They also evaluated the effects of the developed nanocomposites on biofilms. They reported that the biofilms boosted the ability of _E. coli_ to adhere to the surface of the materials, thereby attenuating the antimicrobial properties of MWCNTs. [PERSON] et al., prepared the composites with a combination of SWCNTs and poly(lactic-\(\alpha\)-glycolic acid) (PLGA), which effectively declined the viability of both _E. coli_ and _S. epidermidis_[111]. Table 3 summarizes the antimicrobial effect of CNT and its composite materials.
# A Review of Autonomous Tugboat Operations for Efficient and Safe Ship Berthing
## 3 Previous Research
The perception problem can be divided into two categories: one dealing with localization that estimates accurate vehicle position and attitude, and the other dealing with the problem of situational awareness of the surrounding environment using sensor fusion technology that can integrate information with the remaining sensor information even if one sensor fails or a shadow zone occurs. In [36], an algorithm for determining the exact position through the convergence of relative and absolute position information in a sensor network was presented, while in [37], a complex navigation system for an autonomous vehicle was designed to obtain accurate location information. For the maritime domain, several works have been introduced to address the perception problem. In ship berthing, safe berthing is possible only when the distance between the quay wall and the ship is accurately estimated. In [38], the distance between the quay wall and the ship is estimated using the homodyne phase system, which is composed of an antenna, a reader, and transponders. Several works estimate the distance between the quay wall and the ship using vision sensors. In [39; 40], the ship's motion state is estimated in real time by using the SLAM algorithm using 3D LiDAR and monocular camera, respectively, when the ship is berthing. In the work by [41], a vision-based monitoring system for berthing, called AVMS, is proposed. In the works by [42; 43], a sensor fusion technique using vision sensors and other perception sensors is proposed to enhance the situational awareness performance in the marine environment. The decision-making problem can be divided into three categories: task assignment, formation maintenance, and path planning for the towing operation. While there have been no works specifically addressing the task assignment problem in tugboat operations, the Hungarian algorithm [31] has been widely used to assign tasks to a number of unmanned ships and robots. Additionally, to move a swarm of motherships and tugboats to the port for berthing, a certain formation must be maintained to avoid collisions within the swarm,  
Figure 9: Timeline of notable research on ship berthing with autonomous tugboats. as well as optimize path planning for avoiding obstacles. [PERSON] [32] used the FMS (Fast Marching Squares) method to control the formation of a number of autonomous swarm boats without any collisions. In the context of autonomous ship berthing using tugboats, the problem is often treated as a tracking and controlling problem for the desired trajectory in simulation environments, such as MATLAB or Pygame. [PERSON] [35] considered the berthing path planning problem in a complex port environment, while [PERSON] [34] addressed the route planning problem after reactive collision avoidance in the presence of sudden obstacles or disturbances. Finally, [PERSON] [33] solved the path planning problem for a swarm of tugboats, as opposed to the studies of [PERSON] and [PERSON], which focused on a single ship.
## 6 Evaluation Results
### Evaluation of Oil Leakage Mass and Duration (Number of Leakage Day Intervals)  
Six (Samples A, B, C, D, E, and G) out of the eight specimens were found with filler content settlement and leaked oil. Although the testing period was set to 28 days, none of the specimens leaked oil past the 25 th day, so results from past the 25 th day are not displayed. At the end of the evaluation period, the respective daily masses of the oil leak were summed up, and an average of the leakage mass between 3 specimens of each product was derived. The details are shown below in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{13}{c}{**Days**} & \multicolumn{13}{c}{**Avg. (0-1)**} & \multicolumn{13}{c}{**Avg. (0-1)**} & \multicolumn{13}{c}{**Avg. (1-2)**} & \multicolumn{13}{c}{**Avg. (1-3)**} & \multicolumn{13}{c}{**Avg. (1-4)**} & \multicolumn{13}{c}{**Avg. (1-5)**} & \multicolumn{13}{c}{**Avg. (1-6)**} & \multicolumn{13}{c}{**Avg. (1-7)**} \\ \multicolumn{13}{c}{**Sample**} & \multicolumn{1}{c}{**1**} & \multicolumn{1}{c}{**2**} & \multicolumn{1}{c}{**3**} & \multicolumn{1}{c}{**4**} & \multicolumn{1}{c}{**5**} & \multicolumn{1}{c}{**6**} & \multicolumn{1}{c}{**7**} & \multicolumn{1}{c}{**8**} & \multicolumn{1}{c}{**9**} & \multicolumn{1}{c}{**10**} & \multicolumn{1}{c}{**11**} & \multicolumn{1}{c}{**12**} & \multicolumn{1}{c}{**13**} & \multicolumn{1}{c}{**14**} & \multicolumn{1}{c}{**15**} & \multicolumn{1}{c}{**16**} & \multicolumn{1}{c}{**17**} & \multicolumn{1}{c}{**18**} & \multicolumn{1}{c}{**19**} & \multicolumn{1}{c}{**20**} & \multicolumn{1}{c}{**21**} & \multicolumn{1}{c}{**22**} & \multicolumn{1}{c}{**23**} & \multicolumn{1}{c}{**24**} & \multicolumn{1}{c}{**25**} & \multicolumn{1}{c}{**Mass (g)**} & \multicolumn{1}{c}{**Avg. (0-1)**} & \multicolumn{1}{c}{**No. Days**} \\ \hline
**1** & **X** & **X** & **0.35** & **0.21** & **1.25** & **2.16** & **1.32** & **1.23** & **1.05** & **8.07** &  
The above data suggests the following: (1) Samples F and H did not display any signs of leakage throughout the evaluation period, indicating the most stable performance out of the evaluated samples; (2) Sample A had the highest mass of leakage over the evaluation period; and (3) Sample E had the lowest mass of leakage. In this regard, the rest of the samples (D, C, G, and B in order of highest to lowest mass of cumulative oil leakage) displayed similar ranges of leakage mass.
## 2 Microgrid Optimization Scheduling Model
### Mathematical Model of Microgrid
#### 2.1.2 Restriction Condition  
The main constraint on electricity is the limit of the power generated by the generator set, which can be divided into power equilibrium conditions, climbing speed limit, and inequality constraint.  
**Power Equilibrium Conditions:** The constraint condition for power balance is to ensure dynamic balance between power production and consumption in microgrids  
\[\sum_{k=1}^{N_{K}}P_{LK}(t)=\sum_{i=1}^{N_{S}}[P_{Gi}(t)]+\sum_{j=1}^{N_{S}}[ P_{sj}(t)]+(P_{Grid}(t)) \tag{3}\]  
where \(P_{LK}\) represents the \(K\) quantity at the load level, and \(N_{k}\) represents the total number of available load levels in the power grid.  
**Climbing Speed Limit:** Climbing speed limit refers to the maximum rate at which a generator set or other component in the power system can change its output power per unit time  
\[R_{down}^{i}\cdot\Delta t\leq P(h)^{i}-P(h-1)^{i}\leq R_{up}^{i}\cdot\Delta t \tag{4}\]  
where \(R_{down}^{i}\) represents the decrease in output power of ithDG, and \(R_{up}^{i}\) indicates an increase in jthDG output power. In addition, \(\Delta t\) represents the time step, measured in hours.  
**Inequality Constraint:** All units discussed in this paper have upper and lower bounds on their power generation capability. The units include distributed generators, storage devices, and markets  
\[\begin{array}{l}P_{Gi,min}(t)\leq P_{Gi}(t)\leq P_{Gi,\max}(t)\\ P_{sj,min}(t)\leq P_{sj}(t)\leq P_{sj,\max}(t)\\ P_{Grid,\min}(t)\leq P_{Grid}(t)\leq P_{Grid,\max}(t)\end{array} \tag{5}\]  
where \(P_{Gi,min}(t)\) indicates the minimum output power in the open state; \(P_{Gi,max}(t)\) indicates the maximum output power in the open state; \(P_{Sj,min}(t)\) and \(P_{Sj,max}(t)\) represent the minimum and maximum output power in the closed state, respectively; and \(P_{Grid,min}(t)\) and \(P_{Grid,max}(t)\) represent the minimum and maximum values of the power exchanged between customs and the market, respectively.  
The constraint on the charging and discharging rate of storage devices is shown as  
\[\begin{array}{l}SOC_{ij}(t)=SOC_{ij}(t-1)+P_{chg}/D_{chg}(t)\\ 0\leq\left|P_{chg}/D_{chg}(t)\right|\leq P_{CDSj,\max}\end{array} \tag{6}\]  
where \(SOC_{ij}(t)\) represents the current charging amount of the storage unit at that time, \(SOC_{ij}(t-1)\) represents the amount of charge before the storage unit was charged, \(P_{chg}/Dechg(t)\) corresponds to the charging (discharging) amount at the th hour, and the maximum charging (discharging) rate is represented by \(PCD_{ij,max}\).