esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Fault Tolerant Control  
The nonlinear function is selected as the ideal control input, and the system state error is defined as \(x_{c}(t)=\alpha In(1+ex)\).  
\[e_{t}=x(t)-x_{c}(t) \tag{31}\]  
Let \(\Delta f\) is the estimation error, then \(f=\hat{f}+\Delta f\).  
**Assumption 4**.: \(\Delta f\) _is bounded and satisfies \(\Delta f=sup_{t>0}\|\tilde{f}_{e}\|=\xi\).__The derivative of Equation (31) is obtained_  
\[\dot{e}_{t}=Ax(t)+A_{m}x(t-d(t))+B(I+F(t))u_{c}(t)+\hat{f}+\Delta f+Dv(t)-\dot{x}_ {c}(t) \tag{32}\]  
_Note \(g(x(t),x(t-d(t)))=Ax(t)+A_{m}x(t-d(t))\), then Equation (32) is rewritten as:_  
\[\dot{e}_{t}=g(x(t),x(t-d(t)))+B(I+F(t))u_{c}(t)+\hat{f}+\Delta f+Dv(t)-\dot{x}_ {c}(t) \tag{33}\]  
_The integral sliding surface is defined as follows:_  
\[S=e_{t}(t)-e_{t}(t_{0})+\int_{t_{0}}^{t}e_{t}(\tau)d\tau \tag{34}\]  
_To ensure the continuity of sliding control, \(\dot{S}=0\)._  
**Remark 4**.: _The introduction of the integral term ensures that the sensitivity of the sliding controller when the state error of the driving system reaches the sliding surface is reduced._  
_Let \(O=B(I+F(t))\), pseudo inverse matrix of defined \(O\) is \(O^{+}=O^{\rm T}(OO^{\rm T})^{-1}\), which satisfies \(OO^{+}=I\). The fault tolerant control rate of the system is designed as follows:_  
\[u_{c}(t)=[B(I+F(t))]^{-1}\Big{[}x(t)-g(x(t),x(t-d(t)))-\hat{f}-\overline{d}+ \chi sat(S)\Big{]} \tag{35}\]  
_where \(\chi\) is the positive diagonal gain matrix, and the saturation function is defined as:_  
\[sat(S)=\begin{cases}sign(S),&if\|S\|>\Phi\\ \frac{S}{\Phi},&if\|S\|\leq\Phi\end{cases} \tag{36}\]  
_where \(\Phi\) is the thickness of boundary layer._  
**Theorem 3**.: _In the error differential Equation (33) with the premise of Assumptions 3 and 4, combining with Equations (19), (34) and (35), when \(\chi\geq\widetilde{\chi}+\xi\), the system state is supposed to remain stable and converge to the expected value in fault conditions._  
**Proof of Theorem 3.** By Considering the Lyapunov candidate as:  
\[V_{2}=\frac{1}{2}s^{T}s \tag{37}\]  
The derivation of Equation (37) shows that:  
\[\dot{V}_{2}=s^{T}\bigg{[}g(x(t),x(t-d(t)))+B(I+F(t))u_{c}(t)+\hat{f}+\Delta f +Dv(t)-\frac{\alpha\varepsilon}{1+\varepsilon x}+x_{c}(t)-x(t)\bigg{]} \tag{38}\]  
Consider the following inequality:  
\[\frac{\alpha x}{1+x}\leq\alpha In(1+x)\leq\alpha x\quad(x>-1) \tag{39}\]  
Combining with Equation (35) and Assumptions 3 and 4, the following is obtained from inequality (39) amplification and reduction.  
\[\dot{V}_{2}\leq s^{T}\Big{(}-\chi sat(s)+f-\hat{f}\Big{)}\leq s^{T}(-(\xi+ \widetilde{\chi})sat(s)+\xi)\leq-\widetilde{\chi}\|s\| \tag{40}\]  
\(\square\)
## 3 Numerical Simulation
### Influence of Burial Depth on Mechanical Response of Buried Pipeline
In addition, the vibration effect had a significant impact on the pipeline, making the deformation of the pipeline and the displacement of the pipe top significantly greater compared with the static effect. Additionally, it can be seen from Figures 14 and 15 that the strong vibration state had a greater impact on the pipeline than the weak vibration state. In the case of 0.8 m buried depth, the vertical deformation of the pipeline under strong vibration was 1.25 times that under weak vibration, and the displacement of the pipe top under strong vibration was 1.20 times that under weak vibration. When the buried depth was 1.7 m, the vertical deformation of the pipeline under weak vibration was 91% of that under strong vibration, and the displacement of the pipe top under weak vibration was 93% of that under strong vibration. This also demonstrates that the difference in the influence of weak and strong vibration states on the pipeline is inversely proportional to the increase in the buried depth. According to the current regulation in China, vibratory rollers can only be used to compact the backfill soil after the burial depth of the pipe has reached 1.5 m, and the vertical deformation of the pipeline must be kept below 5% of the pipeline diameter (which is 40 mm in this study). The maximum vertical deformation of the pipeline at 0.8 m burial depth was 14.93 mm, which was much smaller than the regulation limit of 40 mm. From the graph, it can be seen that the displacement at the top of the pipeline was greater than the vertical deformation, indicating that the pipeline had experienced significant overall displacement. When the pipeline experienced settlement, a three-dimensional soil arching effect was formed above it, which could effectively resist some of the upper loads and reduce the impact of the upper loads on the buried pipeline [24]. Therefore, the deformation of the pipeline was much smaller than the regulation limit. Figure 18 shows the Mises equivalent stress distribution of the pipe ring section under strong and weak vibration states, both of which are \"butterfly-shaped\" and symmetrical about the vertical direction. With the increase of burial depth, the equivalent stress of the ring section increases gradually, and the equivalent stress of the sidewall of the pipeline increases most obviously, followed by the top of the pipe and the slowest increase at the bottom of the pipe. Under the two vibration states, the maximum equivalent stress of the pipe ring section appears on both sides of the pipe, namely at 75\({}^{\circ}\) and 285\({}^{\circ}\), which is similar to distribution under static force. Under the weak vibration state, the equivalent stress of the ring section at the top of the pipe is approximately the same as at the bottom of the pipe. While under the strong vibration state, the equivalent stress of the pipe top is significantly larger than the bottom. Figure 17: Displacement of the pipe top under earth pressure.
# Hybrid Engineered Water-Polymer Flooding in Carbonates: A Review of Mechanisms and Case Studies
## 4 Hybrid Engineered Water-Polymer Flooding (EWFF)
### Recovery Mechanisms  
The hybrid method under study involves combined mechanisms of both EW and polymer methods. EW helps to detach oil from the rock surface, creating moveable oil saturation areas in situ, which are later displaced by polymer flooding. Figure 7 shows the main mechanisms known to be responsible for EOR using the hybrid method. However, in order to obtain the maximum benefit from the hybrid method, there should be an optimum design in terms of the EW composition, selection of PDIs to be used in a certain carbonate rock type, polymer concentration, slug sizes for EW and PF, optimum injection rate for PF, and injection scheme used for the hybrid process (continuous or slug-wise injection). The mechanisms reported in the literature are briefly discussed in the following section.
## 2 Study Area Description  
The Marvdasht Plain in Fars Province, Iran, home to the UNESCO World Heritage Site of Perseopolis and the ancient rock reliefs of Naqsh-e Rustam [29, 30, 31] (Figure 1a), faces critical challenges from groundwater overexploitation and resulting land subsidence.  
Scientific explorations and excavations conducted in various hills of the Marvdasht plain indicate that thousands of years before [PERSON] chose the rocky hill at the foot  
Figure 1: Study area of the Marvdasht Plain in Fars Province, Iran: **(a)** Satellite image and aquifer delimitation, highlighting geographical features, land subsidence evidence, and UNESCO World Heritage Sites; **(b)** Digital Elevation Model (DEM) with spatial distribution of groundwater monitoring piezometers (train and test points) and production wells; **(c)** monthly mean hydrograph of the Marvdasht aquifer, illustrating water table decline from October 2004 to October 2017 along with precipitation data.  
of Mount Rahmat to build his grand palaces, civilized peoples had lived and cultivated in its vast plain [32]. Owing to favorable geographical conditions, including abundant water and fertile land, this region enjoyed a strong economic status. However, the Marvdasht Plain now confronts significant threats due to unsustainable water management practices, with groundwater depletion reaching approximately 6 billion cubic meters annually [33]. This overexploitation has led to land subsidence, which affects about 56,000 km\({}^{2}\) of Iran, with some areas experiencing rates exceeding 10 cm/year [34]. The region's historical significance and agricultural potential are at risk as aquifers continue to deplete.  
Agriculture in this area is carried out both industrially and traditionally, using both dry farming and irrigation methods, using more than 8000 exploitation wells (Figure 1b). The extensive agricultural lands of this region are divided into seven homogeneous areas with similar characteristics, covered by seven rural agricultural service centers and 13 rural production cooperatives. In 1995, Marvdasht County produced 215,000 tons of wheat, and by 2001-2002, wheat production had reached 304,000 tons, earning the county the top position in wheat production. Marvdasht has 170,000 hectares of agricultural land, including 148,000 hectares of irrigated land and 22,000 hectares of dry farmland. However, excessive irrigation practices have made the plain particularly vulnerable to groundwater depletion and land subsidence [34; 35]. The region's main agricultural products include irrigated wheat and barley, rice, sugar beets, grain corn, and fodder corn, with melon fields covering the largest cultivated area. Given sufficient water resources, the region is also well-positioned for horticulture. It is worth mentioning that the water required for agriculture and horticulture in the region is supplied from wells, qanats, and rivers.
# The Potential of Cannabidiol in the Management of Oral Infections
## 6 Limitations of Current Studies  
Despite promising outcomes, current research on CBD in oral infections is constrained by several methodological limitations, as follows: (i) Sample Size: most clinical studies are small-scale trials or case reports, limiting statistical power and generalizability. (ii) Heterogeneity of Formulations: differences in CBD delivery methods (e.g., oils, gels, rinses, or systemic administration), dosage, and purity (full-spectrum vs. isolate) make it difficult to compare results or establish standardized treatment protocols. (iii) Short Study Durations: many studies assess only acute outcomes; there is a lack of long-term data on chronic conditions such as periodontitis or peri-implantitis. (iv) Lack of Blinding and Controls: several trials are either open-label or lack appropriate control groups, introducing potential bias in outcome assessment. (v) Regulatory and Legal Constraints: variability in cannabis legislation across jurisdictions limits the scalability and reproducibility of CBD-related research.  
Preclinical and early clinical evidence supports the antimicrobial, anti-inflammatory, and analgesic potential of CBD in the management of oral infections. Outcomes from animal models, ex vivo tissue studies, and small human trials collectively suggest that CBD may serve as an effective adjunct in treating conditions such as periodontitis, mucositis, and oral ulcers. However, larger, well-controlled clinical trials with standardized formulations and dosing protocols are essential to validate these findings and facilitate integration into mainstream dental practice [19; 77].
# Greenhouse Residues' Potential for Biogas Production
## 2 Materials and Methods
### BMP Test  
The inoculum was collected from a biogas plant in \"Avato\" of Xanthi in Greece, mainly treating cattle manure, chicken manure, and energy crops. After collection, the inoculum was sieved through a 2 mm mesh sieve to remove the large particles. The BMP test was conducted in 250 mL and 200 mL working volume vessels. Each vessel was inoculated with approximately 152 mL of the biogas plant's inoculum containing 4.12 g VS, and 2.06 g VS of the substrate was added to keep the ratio of substrate/inoculum (S/I) at 1:2 on a VS basis [52]. Each substrate was studied in triplicate, along with the blank tests in which no substrate was added. The reactors were flushed with a gaseous mixture of N\({}_{2}\)/CO\({}_{2}\) (80/20)  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Sieve Diameter (mm)** & **T.L (\%)** & **T.S. (\%)** & **T.St. (\%)** & **C.L. (\%)** & **C.S. (\%)** & **C.St. (\%)** \\ \hline \(>\)1.7 & 12.32 & 16.29 & 22.52 & 16.99 & 7.35 & 23.21 \\
1.7–1.4 & 5.63 & 6.72 & 9.35 & 2.68 & 6 & 5.46 \\
1.4–1 & 10.98 & 11.81 & 10.81 & 5.1 & 14.53 & 10.52 \\
1–0.85 & 4.61 & 4.7 & 2.22 & 2.42 & 5.94 & 5.97 \\
0.85–0.71 & 5.46 & 5.33 & 6.5 & 2.9 & 7.08 & 3.23 \\
0.71–0.6 & 3.71 & 3.81 & 4.73 & 2.5 & 5.13 & 4.79 \\
0.6–0.5 & 5.21 & 5.05 & 3.68 & 4.05 & 6.76 & 6.49 \\
0.5–0.25 & 13.98 & 12.97 & 13.88 & 14.6 & 15.3 & 12.93 \\
0.25–0.15 & 9.62 & 9.34 & 9.97 & 15.12 & 6.91 & 8.25 \\ \(<\)0.15 & 28.47 & 23.98 & 16.33 & 33.64 & 25.02 & 19.16 \\ \hline \end{tabular}
\end{table}
Table 1: Particle size distribution of residual biomass.  
for two minutes, sealed with a rubber stopper, and placed on a magnetic stirrer (rpm) in an incubator for about 40 days at a constant temperature of 37 \(\pm\) 1 \({}^{\circ}\)C. The headspace of each vessel was connected with a NaOH trap to absorb the CO\({}_{2}\) of the produced biogas (Figure 1). The remaining gas after absorption was the CH\({}_{4}\), which displaced an equivalent volume of the NaOH aqueous solution of the trap. The displaced volume was recorded daily. The volume of the CH\({}_{4}\) was expressed at standard conditions of temperature and pressure (STP) after subtracting the CH\({}_{4}\) produced from the blank tests and was divided by the substrate VS added.
# Real-Time Optical Flow Estimation Method Based on Cross-Stage Network
## 2 Related Research  
### FlowNet  
FlowNet is an algorithm to obtain optical flow through supervised learning. The FlowNet method has the problem of a lack of datasets, which results in the limitation of optical flow estimation for supervisory learning. FlowNet is CNNs end-to-end learning method, and for end-to-end learning using two images, a correlation layer is used to perform pixel-by-pixel localization. FlowNet proposes two structures, FlowNetSimple and FlowNetCorr, as shown in Figure 1.  
FlowNetSimple uses two images required for optical flow calculation as input through the number of input channels of 6. Subsequent network structures follow traditional CNN structures. Based on supervisory learning, it allows the network to decide on its own how to extract the optical flow. FlowNetCorr has a more complex structure and no concat process. Instead, the input stage is divided to receive input for the two images. In the previous process of the correlation layer, which is characteristic of FlowNetCorr, both inputs had the same structure. This structure extracts each feature map from the two images and combines them into one through the correlation layer.  
To improve the performance of FlowNet, FlowNet2 is designed using a learning method that can detect both large and small movements well by combining the FlowNetC and FlowNet5 networks. There are three main improvements in the scheduling of datasets, building networks, and additional areas dealing with small displacements. Several experiments were conducted in each area to determine the structure and learning method of the model. Figure 2 shows the structure of FlowNet2. FlowNet2 has a variant structure of FlowNet in which a cascaded network finesses the previous flow field to overcome the limitations of accuracy. Therefore, the FlowNet2 model consists of more than 160 M parameters, which has a problem that cannot be applied to applications that require real-time estimation.  
are concatenated, and optical flow is extracted immediately through an inference. However, SpyNet performs downscaling and obtains optical flows at the smallest scale. Based on this procedure, the optical flow at the next scale is calculated, and this process is repeated. Therefore, in terms of parameters, SpyNet has the advantage of having much smaller parameters compared to previous networks that obtain optical flows. However, the SpyNet method did not show much advantage when comparing accuracy with previous methods. Figure 3 shows the structure of SpyNet.  
PWC-Net [20] is small but uses an effective CNN model for optical flow, applying well-known design principles of pyramid, warping, and cost volume. This method warps the CNN features of the second image using current optical flow estimates cast onto a learnable feature pyramid. The cost volume is then constructed using the warped features of the first image, which is processed by the CNN network to decode the optical flow. Figure 4 shows the structure of PWC-Net.
# Research on the Correlation Filter Tracking Model Based on the Deep-Prunned Feature Network
## 3 Methods  
In view of the problems faced by visual object tracking based on the correlation filter proposed in Section 1 and potential solutions to these problems, this paper proposes a \"correlation filter tracking model based on the deep-pruned feature network\", which mainly includes the following: (1) PCIP-based pre-pruning of deep feature network; (2) optimal determination of global tracking pruning rate based on tracking response contribution; (3) definition and initialization of alternative convolutional kernels based on global tracking pruning rate, and a secondary pruning of the deep feature extraction network; (4) correlation filter tracking model based on the deep-pruned feature network; (5) online updating of deep-pruned feature network (including alternative kernels). A detailed description of each part will be given in the following sections.
# Research on Optimal Voltage Control of Distribution Network with the Participation of EVs and PVs
## 3 Case Studies
### Simulation Results of the Intraday Control Stage  
To further improve the voltage profiles, intraday control has to be applied with the voltage support capability of solar systems and EV charging stations. During the intraday control stage, the control objects are the six solar systems and the 700 EVs, while the OLTC transformers and the CBs are operating at their optimal operating curves achieved from the day-ahead control (i.e., the operation settings in Figure 16). The grid voltage support is based on the voltage droop control of solar systems and the ROCS-based EV  
Figure 16: The optimal operation settings for OLTC transformers and CBs.  
Figure 15: The boxplot of bus voltages before the day-ahead control.  
charging/discharging schedule. When the intraday control is not applied, all solar systems operate at a unity power factor, and EVs are charged without a schedule. The boxplot represents the corresponding simulation results of bus voltages in Figure 18, where the voltage limit violations are observed.  
After the intraday control is enabled, voltage droop control is activated for the six solar systems while ROCS is applied to schedule the charging/discharging behaviors of all the 700 EVs. The voltage distribution pattern under the intraday control is shown in Figure 19, and the achieved bus voltage improvement is illustrated in Figure 20. It is observed that all bus voltages are within the safe operation margin, and the bus voltage fluctuation gets alleviated after the intraday control is applied. The total reactive power provided by the six solar systems is 378.07 kVArh, while the optimum EV charging/discharging scheme is illustrated in Figure 21.  
Figure 19: The boxplot of bus voltages achieved from the intraday control.  
Figure 17: The boxplot of bus voltages achieved from the day-ahead control.
## 5 Transmission Rate Control Scheme Based on Network Node Bandwidth Allocation
### Scheme Process
```
1 The router receives a negotiation message;
2 Read the \(Type,ID,Max,Min,Rtt\) field in the header of the message;
3 if\(Type==Negotiation\) requestthen
4\(B_{c},L_{i},L_{r}=\texttt{Allocation}(Rtt,V_{Q},B_{v})\);
5 if\(B_{c}<Min\)then
6 send negotiation failed message;
7 else
8 if\(B_{c}<Max\)then
9 set Max field in message to \(B_{c}\);
10
11 end if
12 add \(B_{c}\) in the node speed record list;
13 forward message;
14 for\(Tr_{i}\in L_{i}\)do
15 send increase transmission rate message to \(Tr_{i}\) server;
16
17 end if
18
19 end for
20 elseif\(Type==Negotiation\) successfulthen
21 if\(speed_{ID}>Max\)then
22 set \(ctl\_flag\) to false in the node control record list;
23 elseif\(speed_{ID}==Max\)then
24 set \(ctl\_flag\) to true in the node control record list;
25
26 end if
27
28 elseif\(Type==Negotiation\) successful Or Record timeoutthen
29
30 end if
```  
**Algorithm 4**Router negotiation process. The _User_ processing process is shown in Algorithm 5. The _User_ receives a negotiation message, reads the header information (line 2) in the message, and determines whether it is a negotiation request message (line 3). Firstly, it obtains the maximum transmission rate it can provide, known as the _provide_speed_ (line 4). If it is less than the \(Min\) field (line 5) in the message, then it sends a negotiation failed message (line 6); otherwise, it takes the smaller value of \(Max\) and the allocated bandwidth as the negotiation result (lines 8-12) and sends a negotiation successful message (line 13). ```
1 The user receives a negotiation message;
2 Read the \(Type,ID,Max,Min,Rtt\) field in the header of the message;
3 if\(Type==Negotiation\)requestthen
4 get the maximum transmission rate that a node can provide, called \(provide\_speed\);
5 if\(provide\_speed<Min\)then
6 send negotiation failed message;
7
8 else
9 if\(provide\_speed<Max\)then
10 set negotiation result to \(provide\_speed\);
11
12 elseif\(provide\_speed>=Max\)then
13 set negotiation result to \(Max\);
14
15 end if
16
17 send negotiation successful message with negotiation result;
18
19 end if
20
21 end for
```  
**Algorithm 5**User negotiation process.
## 2 Methodology
### Multi-Fidelity SPCE-Kriging  
The descriptions given in Sections 2.1 and 2.3 show that kriging is interpolatory in nature and PCE is a regression. Thus, kriging usually recreates the local variations well, whereas PCE captures the global trends better [32; 33]. Therefore, it is natural to try to combine the advantages of both kriging and SPCE for a more efficient meta-modeling technique. The resulting SPCE-Kriging model is a kriging surrogate, whose trend functions are determined by an SPCE model using the same underlying training data. The construction of a single-fidelity SPCE-Kriging consists of the following three main steps:  
1. Choose PCE orthogonal bases associated with the probability distributions of the (random) inputs;2. Solve for the PCE coefficients using the LASSO algorithm as described in Section 2.3 to determine which bases are most important;
3. Use only those important bases as trend functions \(h_{k}\) in the universal kriging mean value given by Equation (2) as well as in the maximum likelihood estimation using Equation (5).  
For the construction of a multi-fidelity SPCE-Kriging, which to the authors' knowledge is a novel contribution, the algorithm presented in Section 2.2 is employed. However, there is an important caveat: SPCE-Kriging is only used in the first step (the training of the lowest fidelity surrogate model, \(\hat{f}_{LF_{1}}\)) and for the additive bridge function, \(\hat{\gamma}_{2}\), an ordinary kriging model is constructed. The reason for this is that in step two, the genetic algorithm requires a lot of log-likelihood evaluations to compute the optimal \(\hat{\rho}_{2}\) (and \(\hat{\theta}_{2}\)), which is required to compute \(\hat{\phi}_{2}(x)=g^{T}(x)\hat{\rho}_{2}\) and thus the \(N_{LF_{2}}\) training points for the additive bridge function, \(\gamma_{2}(x_{i})=f_{LF_{2}}(x_{i})-\hat{\phi}_{2}(x_{i})\hat{f}_{LF_{1}}(x_{i})\). Hence, using SPCE-Kriging for the construction of the additive bridge function, \(\hat{\gamma_{2}}\), would require a lot of SPCE evaluations, which are much more expensive than simple ordinary kriging constant trend computations.