esa-sceva/satcom-chunk-collection huggingface dataset:
# Compositional Modeling of Dimethyl Ether-CO2 Mixed Solvent for Enhanced Oil Recovery
## 2 Materials and Methods
### Fluid Modeling
Additionally, values of the constants are given as follows:  
\[\alpha=\Big{[}1+m\Big{(}1-\sqrt{T_{r}}\Big{)}\Big{]}^{2}\ \alpha=\Big{[}1+m\Big{(}1-\sqrt{T_{r}} \Big{)}\Big{]}^{2} \tag{8}\]  
The parameter \(m\) is expressed as a function of the acentric factor \(\omega\):  
\[m=0.37464+1.54226\omega-0.26992\omega^{2} \tag{9}\]  
[PERSON] and [PERSON] [30] proposed a modified expression for \(m\) for heavier components (\(\omega>0.49\)):  
\[p=\frac{RT}{v-b}-\frac{a}{v(v+b)+b(v-b)} \tag{1}\]0 \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Fluid Model** & **Experimental Data** \\ \hline Saturation Pressure (kPa) & 4784.95 & 4920 \\ Oil density at \(p_{\mathrm{sat}}\) (kg/m\({}^{3}\)) & 805.73 & 806.4 \\ Oil viscosity at \(p_{\mathrm{sat}}\) (mPa\(\cdot\)s) & 1.75 & 1.76 \\ Formation volume factor & 1.11 & 1.12 \\ API (\({}^{\circ}\)) & 34.8 & 31 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the fluid model and experimental data for Weyburn W3 fluid. In PR EOS, the fugacity of component \(i\) of the hydrocarbon mixture, \(\Phi_{i}\), is expressed through the following equation:  
\[\ln\Phi_{i}=\frac{B_{i}}{B}(Z-1)-\ln(Z-B)+\frac{A}{2\sqrt{2}B}\Bigg{(}\frac{B_{ i}}{B}-\frac{2}{A}\sum_{j=1}^{N}y_{j}A_{ij}\Bigg{)}\ln\Bigg{[}\frac{Z+\left(1+ \sqrt{2}\right)B}{Z-\left(1-\sqrt{2}\right)B}\Bigg{]} \tag{11}\]  
where \(y_{j}\) is the gas mole fraction of component \(i\). The mixing rules for expressing multi-component fugacity are as follows:  
\[A=\sum_{i=1}^{N}\sum_{j=1}^{N}y_{i}y_{j}A_{ij} \tag{12}\]  
\[B=\sum_{i=1}^{N}y_{i}B_{i} \tag{13}\]  
\[A_{ij}=\left(1-K_{ij}\right)\sqrt{A_{i}A_{j}} \tag{14}\]  
where \(K_{ij}\) is the binary interaction parameter. The parameters of DME used in PR EOS were obtained from literature (Table 3) [13; 32]. DME is also soluble in the aqueous phase, and [PERSON]'s law was used to determine the fugacity in the aqueous phase. The phase of the gas that dissolves in the water is obtained through [PERSON]'s law as [33]:  
\[f_{iw}=y_{iw}H_{i},\ i=1,\ldots,n_{c} \tag{15}\]  
where \(H_{i}\) is [PERSON]'s constant of component \(i\) and \(y_{iw}\) is the mole fraction in the aqueous phase of component \(i\). [PERSON]'s constant for the pressure change is calculated by:  
\[\ln H_{i}=\ln H_{i}^{*}+\frac{\overline{v_{i}}(p-p^{*})}{RT} \tag{16}\]  
where \(H_{i}^{*}\) is [PERSON]'s constant of component \(i\) at a reference pressure (Pa) and temperature (K) \(p^{*},\ T\) and \(\overline{v_{i}}\) is the molar volume of component \(i\). The values of the solubility properties of DME in the aqueous phase were obtained from literature [34]. The phase behavior and solubility in the aqueous solution were determined through fugacity obtained by PR EOS and [PERSON]'s law. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline  & **N\({}_{2}\)** & **CO\({}_{2}\)** & **H\({}_{2}\)S** & **C\({}_{1}\)** & **C\({}_{2}\)** & **C\({}_{3}\)** & **DME** & **C\({}_{4}\)** & **C\({
## 6 MSE of Direct Position Determination Method in Presence of Array Model Errors
### Second-Order Perturbation Analysis on the Cost Function
Let \(\lambda_{1}\leq\lambda_{2}\leq\cdots\leq\lambda_{N}\) and \(\mathbf{u}_{1},\mathbf{u}_{2},\cdots,\mathbf{u}_{N}\) be the eigenvalues and relevant unit eigenvectors of matrix \(\mathbf{C}_{0}\), respectively. Additionally, it is not unreasonable to assume that the source location parameters are identifiable, which means that \(\mathbf{C}_{0}\) has unique maximal eigenvalue \(\lambda_{N}\). Meanwhile, it is noteworthy that the eigenvalue perturbation theory is extensively applied to the performance analysis in array signal processing for DOA estimation. To our best knowledge, there is no relevant mathematical tool that can be used to prove that the eigenvalues of \(\mathbf{C}_{0}\) are distinct. However, a large number of numerical investigations demonstrate that the possibility of the case of equal eigenvalues is small enough that we can ignore it. As a result, we define the matrix  
\[\mathbf{U}_{N}=\sum\limits_{n=1}^{N-1}\frac{\mathbf{u}_{n}\mathbf{u}_{n}^{\rm H}}{\lambda_{ N}-\lambda_{n}}\,. \tag{33}\]  
By combining Proposition 1 and (31), the cost-function value at point \(\hat{\mathbf{p}}\) is given by  
\[ \begin{array}{rl}\hat{f}(\hat{\mathbf{p}})&=\lambda_{\max}\{\mathbf{B}^{\rm H}(\hat{ \mathbf{p}})\hat{\mathbf{B}}(\hat{\mathbf{p}})\}=\lambda_{\max}\{\mathbf{C}_{0}\}+\mathbf{u}_{N}^{ \rm H}(\mathbf{C}^{(1)}+\mathbf{C}^{(2)})\mathbf{u}_{N}+\mathbf{u}_{N}^{\rm H}\mathbf{C}^{(1)}\mathbf{U} _{N}\mathbf{C}^{(1)}\mathbf{u}_{N}+o(||\widetilde{\mathbf{\xi}}||_{2}^{2})\\ &=\lambda_{N}+\widetilde{\lambda}_{N}^{(1)}+\widetilde{\lambda}_{N}^{(2)}+o(|| \widetilde{\mathbf{\xi}}||_{2}^{2})\,,\end{array}  \tag{34}\]  
where  
\[\left\{ \begin{array}{l}\widetilde{\lambda}_{N}^{(1)}=\mathbf{u}_{N}^{\rm H}\mathbf{B} _{0}^{\rm H}\widetilde{\mathbf{B}}^{(1)}\mathbf{u}_{N}+(\mathbf{u}_{N}^{\rm H}\mathbf{B}_{0}^{ \rm H}\widetilde{\mathbf{B}}^{(1)}\mathbf{u}_{N})^{\rm H}=O(||\widetilde{\mathbf{\xi}}||_{2 })\,,\\ \widetilde{\lambda}_{N}^{(2)}=\mathbf{u}_{N}^{\rm H}\mathbf{B}_{0}^{\rm H}\widetilde{\mathbf{B }}^{(1)}\mathbf{U}_{N}\mathbf{B}_{0}^{\rm H}\widetilde{\mathbf{B}}^{(1)}\mathbf{u}_{N}+(\mathbf{u }_{N}^{\rm H}\mathbf{B}_{0}^{\rm H}\widetilde{\mathbf{B}}^{(1)}\mathbf{U}_{N}\mathbf{B}_{0}^{ \rm H}\widetilde{\mathbf{B}}^{(1)}\mathbf{u}_{N})^{\rm H}+\mathbf{u}_{N}^{\rm H} \widetilde{\mathbf{B}}^{(1)\rm H}(\mathbf{I}_{K}+\mathbf{B}_{0}\mathbf{U}_{N}\mathbf{B}_{0}^{\rm H} \widetilde{\mathbf{B}}^{(1)}\mathbf{u}_{N}\\ \mathbf{u}_{N}^{\rm H}\mathbf{B}_{0}^{\rm H}\widetilde{\mathbf{B}}^{(1)}\mathbf{U}_{N}\mathbf{B}_{0}^ {(1)}\mathbf{B}_{0}\mathbf{u}_{N}+\mathbf{u}_{N}^{\rm H}\mathbf{B}_{0}^{\rm H}\mathbf{B}^{(2)} \mathbf{u}_{N}+(\mathbf{u}_{N}^{\rm H}\mathbf{B}_{0}^{\rm H}\widetilde{\mathbf{B}}^{(2)}\mathbf{u}_ {N})^{\rm H}=O(||\widetilde{\mathbf{\xi}}||_{2}^{2})\,.\end{array} \right. \tag{35}\]  
Additionally, using (45) and the second equality in (46), the localization error \(\widetilde{\mathbf{p}}=\mathbf{p}-\mathbf{p}\) is obtained by  
\[
## 3 Example Test and Result Analysis
### Test Environment Analysis
It has abundant static reserves and regulated reserves, from high to low runoff and discharge. Figure 1: Schematic diagram of the tunnel lining structure. (3) Water conducting structure  
The main recharge sources of groundwater in the tunnel area are atmospheric precipitation, surface springs and gully flows. The terrain in the section is high in the middle and low at both ends. The tunnel is located at the highest peak, which is the first level watershed. The tunnel area is affected by multi-stage structures, secondary fault structures have developed and the irregular frequent intrusion of granite and the contact of multiple strata and lithology constitute different types of water-conducting structures in the tunnel area. The connectivity between surface water, groundwater and groundwater units is good, and groundwater mainly exists near the fault structure zone, the rock erosion zone and the lithologic contact zone. The fracture water in the K13 + 060-K13 + 100 section is relatively developed, with a weak water yield property, and the fracture water in the mud inrush section (K13 + 612) is relatively developed, with a strong water yield property.
# Fault Tolerant DHT-Based Routing in MANET
## 4 Fault Tolerant DHT Network (FTDN)  
FTDN employs localized distributed algorithms and solves the problem of fault tolerance in a DHT-based MANET. FTDN, using a cross-layer design approach, investigates network dynamics in the physical network and adaptively makes arrangements to tolerate faults in the logical, structured DHT network. For this purpose, FTDN exploits k-hop topological information along with the philosophies of detection, recovery, avoidance, and redundancy. Specifically, FTDN considers pre-failure measures (i.e., measuring network dynamics, replication, and partition detection) and post-failure measures (i.e., finding alternate paths to avoid failure, merging detection, and reconfiguring). These measures ensure DHT network availability, successful lookup resolution and make the system reliable, fault-tolerant in the adversarial environment of MANET.  
Overview: FTDN builds a logical tree-based structured network(Section 4.1), analyzes network dynamics (Section 4.2), replicates index information in the address publication process (Section 4.3), differentiates between occasional node failure (finds alternate paths avoiding the failure) and detects partition event (Section 4.4), and performs merging detection and merging (Section 4.5).
# LoRa Technology Propagation Models for IoT Network Planning in the Amazon Regions
## 4 Propagation Model
### Close-In Model  
The close-in model estimates path loss both indoors and outdoors, with the path loss exponent as its adjustment parameter. Utilizing minimum mean square error (MMSE), the CI model employs linear regression to minimize error and standard deviation [37]. It is grounded in the free space model, using a reference distance as a physical basis that characterizes the path loss dependence between the transmitter and the receiver. In this study, we adopted a reference distance value of 1 m [38]. The CI model equation is given by:  
\[PL^{CI}(f,d)=FSPL(f,d_{0})+10n\log_{10}\biggl{(}\frac{d}{d_{0}}\biggr{)}+X_{ \sigma}^{CI} \tag{2}\]  
\[FSPL=10\log_{10}\biggl{(}\frac{4\pi d_{0}}{\lambda}\biggr{)}^{2} \tag{3}\]  
where \(FSPL\) is the initial loss in dB and represents the anchor point of the model, \(n\) is the loss exponent, \(d\) is the distance to estimate the loss amount, \(d_{0}\) is the reference distance, which is 1 m, \(X_{\sigma}^{CI}\) is a random variable with zero mean Gaussian distribution, and standard deviation \(\sigma\) also in dB, and \(\lambda\) is the wavelength as a function of frequency [38].
## 4 Fuzzy Decision Model for Battery Recharging  
We propose a system in [33], designed from the literature, in particular [26], which aims to manage decisions regarding energy levels and tasks for a robot or autonomous agent. The primary goal is to decide whether the agent should recharge its energy or continue with its current task, based on its energy level and distances to the target and energy source.  
The description of the model uses fuzzy logic as described in [10]. This approach relies on a formal knowledge representation using fuzzy sets, built on fuzzy elementary propositions of the form \"\(V\) is \(A\)\". These propositions are defined from a set, \(L\) (\(V\), \(X\), \(D\_V\)), consisting of the linguistic variable, \(V\), the universe of possible values, \(X\), and a set of descriptions, \(D\_V\) for \(V\), which are represented by fuzzy subsets of \(X\).  
Then, our proposed system uses three input variables and one output variable:  
* **Energy Level**--input linguistic variable: _EnergyLevel_.
* **Distance to Target**--input linguistic variable: _DistanceToTarget_.
* **Distance to energy source**--input linguistic variable: _DistanceToEnergySource_.
* **Decision** (recharging or finishing the task)--output linguistic variable: _Decision_.  
Each variable is defined over a specific range and described using linguistic terms, represented as fuzzy sets. We will describe each linguistic variable in the subsections below.
# Task-Aware Feature Composition for Few-Shot Relation Classification
## 5 Experiments
### Experiments on FewRel2.0 (Cross Domain)
#### 5.2.3 Results and Discussions  
Table 2 presents comparisons between the proposed model and baseline models. For FewRel2.0, our model achieves a huge gain of 12.98% on average in four settings compared with BERT-PAIR, while our model outperforms BERT-PAIR by 3.35% on FewRel1.0. The reason why a greater performance boost is observed for FewRel2.0 is that there exists a huge domain gap between the training and testing dataset, and this exacerbates the severe data sparsity problem. Since our proposed methods encode task-aware information as additional guidance for the model training, they are more immune to the data sparsity issue, leading to more improvements in FewRel2.0.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **5-Way 1-Shot** & **5-Way 5-Shot** & **10-Way 1-Shot** & **10-Way 5-Shot** \\ \hline Finetune (CNN) & 44.21 & 68.66 & 27.30 & 55.04 \\ Finetune (PCNN) & 45.64 & 57.86 & 29.65 & 37.43 \\ kNN (CNN) & 54.67 & 68.77 & 41.24 & 55.87 \\ kNN (PCNN) & 60.28 & 72.41 & 46.15 & 59.11 \\ Meta Network & 64.46 & 80.57 & 53.96 & 69.23 \\ GNN & 66.23 & 81.28 & 46.27 & 64.02 \\ SNAIL & 67.29 & 79.40 & 53.28 & 68.33 \\ Proto & 69.20 & 84.79 & 56.44 & 75.55 \\ Proto-HATT & - & 90.12 & - & 83.05 \\ HAPN & - & 91.02 & - & 84.16 \\ MLMAN & 82.98 & 92.66 & 75.59 & 87.29 \\ BERT-PAIR & 88.32 & 93.22 & 80.63 & 87.02 \\ CTEG & 88.11 & 95.25 & 81.29 & 91.33 \\ TFC (BERT) & 86.31 & 92.97 & 78.13 & 87.01 \\ \hline TFC+Mode 1 (BERT) & 89.71 & & & \\
**95.42** & 82.31 & 89.33 & & \\ TFC+Mode 2 (BERT) & **89.76** & 95.13 & **82.81** & **92.76** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Accuracy (%) of all models on FewRel1.0 under four different settings.
# Location-Awareness for Failure Management in Cellular Networks: An Integrated Approach
## 3 Proposed System  
The Context-aware Automated FAILure Management (CAFAIM) framework is proposed to overcome the described challenges caused by the huge increase in the number of indicators in context-aware scenarios. CAFAIM provides an automatic integrated approach aiming to, firstly, integrate network and positioning data; secondly, to copy with the increased number of indicators generated by the contextualized approach by applying dimensionality reduction; and, thirdly, to implement the necessary diagnosis mechanisms.  
As shown in Figure 2, CAFAIM is in this way structured into five main blocks: _data acquisition, indicators generation, feature engineering, inference, and Machine/Learning Monitoring and Maintenance (MLMM)_; all of which are described in the next subsections.
## 4 Simulation Results and Analysis
Table 7 shows the distance to the nearest obstacle for both routes and the number of waypoints determined by both algorithms. It can be seen that the proposed algorithm ensured that each waypoint was at least 4 m away from obstacles (excluding case 3). In comparison, for the basic A* approach, this parameter was 1 m in almost all cases. It is worth noting that due to the map resolution, a distance of 1 m means that the pixel to which a specific waypoint is assigned is a direct neighbour of the obstacle. Analysing the number of waypoints determined by both algorithms, it can be seen that the proposed method provides a smaller number of waypoints, which is beneficial from the point of view of processing a set of waypoints in order to smooth the route and using the determined path to generate trajectories for real ASVs or AUVs. The above images and results prove that the proposed method, including map image processing, reduces computation time and increases the path's safety. This is particularly important from the point of view of the vehicle's operation in an underwater environment, where finding a lost vehicle, e.g., due to a collision, requires a time-consuming search with the appropriate equipment, and in a port environment, also the special coordination of ship traffic. The benefit from shortening the calculation time significantly outweighs the losses associated with the difference in path length, which does not exceed 3%. However, from a practical point of view, to accelerate the entire trajectory planning process, more efficient computing hardware and parallel processing should be used, which would significantly shorten the trajectory calculation time.
## 1 Introduction
Because zirconium oxide does not discolor or change the material's hydration, it has been asserted that Biodentine produces less coronal discoloration than MTA [27; 28]. Biodentine, as opposed to MTA, has the advantage of looking more like natural tooth color and reducing discoloration brought on by material translucency through hard tissues of the teeth [3]. Previous studies comparing color stability in Biodentine have reported a higher color stability compared to ProRoot MTA [29; 30]. The one-year follow-up finding of another study reported that Biodentine placed on extracted bovine teeth had a lower staining potential than bioaggregate and Angelus MTA [15]. BIOfactor MTA (Imicryl Dental, Konya, Turkey) is a new type of MTA that has recently been introduced to clinical use in pulp capping, pulpotomy, apexification, retrograde filling, apical plug procedures, and root perforation repair, as with other calcium silicate-based materials. The material powder contains tricalcium and dicalcium silicate, tricalcium aluminate, and ytterbium oxide, which acts as a radiopacifier. Demineralized water and a 0.5-3% water-soluble carboxylated polymer are the components of BIOfactor MTA fluid. The consistency of the material can be varied to suit the treatment protocol [31]. BIOfactor MTA has previously been studied for bond strength, vital pulpotomies of primary molars [32], and shear bond strength compared to compomer [33]. The manufacturer claims that BIOfactor MTA does not cause discoloration. Color change that follows as a result of irrigation solutions and calcium silicate-based materials interacting or the tooth discoloration efficacy of the materials will have a substantial impact on clinical outcomes and treatment planning. The objective of this study is to evaluate the color stability of Angelus MTA, Biodentine, and BIOfactor MTA interacting with various irrigation solutions (chlorexidine gluconate, sodium hypochlorite, and distilled water) and to compare the coronal discoloration effect at varying time points following the application of these materials on teeth.
# Efficient Data Gathering in 3D Linear Underwater Wireless Sensor Networks Using Sink Mobility
## 2 Related Work
### Counterpart Schemes in Brief
#### 2.1.2 Data-Driven Routing Protocol  
The data-driven routing protocol (DDRP) has static nodes, and the MS can move in the network. The deployed nodes and MS have the same communication range. Two nodes can communicate if they are within the communication range. The MS has unlimited energy, while the nodes are equipped with limited energy. The MS broadcasts a control packet, and the nodes that receive that packet set themselves as one-hop neighbors. This control packet contains the MS identification, time stamp (when the control packet is issued) and the control interval, which can be affected by the speed of the MS or the communication range. The nodes that receive this control packet do not further broadcast it. The data packets contain information about the distance from the MS, and it records the shortest distance for data sending. If the value of \(Dist2 mSink=2\), this means that the node is two hops away from the MS. The maximum allowed value of \(Dist2 mSink=K\), where, \(K\geqslant 1\). If the value of \(Dist2 mSink\) is greater than \(K+1\), this means that the node has no route to the MS. Further, the deployed nodes are divided into three types on the basis of their distance from the sink:  
1. The nodes that are present in the communication range of the MS send their data directly, and they are one-hop neighbors (O-nodes).
2. The nodes sharing the communication range and that have a valid path to the MS are called M-nodes.
3. The nodes that do not have a valid route to the MS and that have infinite hops are called I-nodes.  
This protocol has the following working principle: Initially, at the start of the network, each node is an I-node; because at that time, the MS has not started gathering data and none of the nodes have received any control packet. As the network evolves, the MS starts it tour, and nodes receive control packets and set themselves as O-nodes in its path. When a potential two-hop neighbor overhears a data transmission made by an O-node, it updates its routing table. When this two-hop neighbor sends its data packet, it sets its \(Dist2 mSink\) as two and also includes it in the data packet sent. In this way, the M-nodes set their routing table for data delivery. The authors give a brief analysis of the proposed scheme by varying the number of MSs in the same network area. To perform a fair analysis, we selected the case of four MSs.
# Predicted Future Changes in the Mean Seasonal Carbon Cycle Due to Climate Change
## 4 Discussion
### Changes in NEE Dynamics under Different Climate Scenarios  
Climate change impacts plant phenology by altering the start and end dates of the growing season, which influences when photosynthesis can begin and consequently affects C-fluxes [20; 29; 30; 32; 86].  
A primary finding is that, regardless of the scenario analyzed, the number of days identified as a C-sink in evergreen forests increases significantly over time, indicating a consistent upward trend. Similarly, the number of days classified as a C-source decreases over the decades, showing a general reduction. The second finding is that for evergreen forests, the DoY to C-sink tends to increase (indicating a forward shift in the year when the system becomes a sink), and the DoY to C-source decreases (indicating a backward shift in the year when the system becomes a source), aligning with the overall trend of fewer C-sink days and more C-source days over time. In contrast, deciduous forests maintain a relatively stable number of C-sink (and C-source) days throughout the century, reflecting a steady DoY when the system becomes a C-sink (or a C-source), despite an anticipated beginning of the growing season but compensated by higher respiration rates. Indeed, for the deciduous, the 3D-CMCC-FEM simulates the bud-brake through a thermic sum function and leaf and fine root development (and the relative growth respiration peak) in a well-defined and short time during spring [36]. Conversely, for the evergreens, leaf and fine root growth development is spread all over the spring. At the same time, leaf fall in the deciduous starts under certain hours of solar radiation, and thus, this is not under the control of climate, while in the evergreen, it happens all over the year, and under the control of climate, balanced by incoming photosynthesis for new leaves and fine roots. Ultimately, deciduous spring C-sink capacity is counterbalanced by high C-emissions mainly because of growth respiration in spring. Such behavior is different for evergreens, which lengthen their C-sink capacity during spring. However, the lengthening of the growing season does not automatically mirror an increase in the net sink capacity because \(\text{R}_{\text{eco}}\) shows an increase much more than GPP. It is generally acknowledged that the changing temperature response of respiration transforms forests from C-sinks to C-sources [26; 76; 87; 88; 89], while the stability of the carbon sink/source dynamics over the decades for deciduous ecosystems is a relatively recent finding. The DoY for deciduous forests remains unchanged because the earlier start of the growing season, triggered by rising temperatures, is balanced by an earlier increase in respiration. This compensates for the earlier rise in GPP at the level of NEE. Overall, the lack of change in the number of C-sink (and C-source) days across decades and the reduction in the NEE suggest that, over the long term, deciduous forests are more efficient in using photosynthates compared to evergreen forests [90; 91].