esa-sceva/satcom-chunk-collection huggingface dataset:
# Collaborative Planning in Non-Hierarchical Networks--An Intelligent Negotiation-Based Framework
## 4 Application Case--Analysis and Results
### Capacity Models  
In order to allow the effective testing of the intelligent collaborative planning framework in the application case scenario, the implementation of capacity models that could reflect each partner behavior during the negotiation was required. Since this capacity model information was considered confidential by the industrial partners in the project, an approximated static capacity model was developed for each one of the involved partners.  
Using the experimental approach presented by [PERSON] [33], each one of the capacity models followed the behavior presented in the load chart represented in the Figure 7. The author sustained that static capacity modeling can simplify the data collection and the validation effort necessary to respond to the market demand.  
In reality, the concept is very straightforward. In the time horizon considered, it is possible to establish a relationship between the demand load and the cost reply for quotations. By analyzing the planning time horizon, it is possible to identify three areas. The first area corresponds to the short-term period where the production capacity of the partner is totally committed and the cost of producing in this period is higher. In the second area, a medium-term period, the load usually decreases over time, and the corresponding cost reply for quotations from the partner follows the same trend. On the other hand, the third zone corresponds to a long-term period where the capacity load is minimal, and the cost reaches its structural minimum.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Normalized Decision Matrix** & & & \\ \hline  & **Cost** & **Collaboration** & **Quality** & **Partners** & **Plan Score** \\ \hline Alternative Plan 1 & 0.98333333 & 0.862068966 & 0.666666667 & 1;5;10 & 92.741\% \\ \hline Alternative Plan 2 & 1.00000000 & 0.879310345 & 0.3333333333 & 3;5;11 & 90.920\% \\ \hline Alternative Plan 3  
Based on the described approach, and using some individual replies provided by the project industrial partners, it was possible to define the necessary capacity models used in the application case.
# Rheological Approaches of Wheat Flour Dough Enriched with Germinated Soybean and Lentil
## 4 Discussion
### Effects of SGF and LGF on Falling Number and Dough Rheology
The loss tangent increased at SGF levels up to 10%, then it was reduced and raised as the amount of LGF was higher, confirming the positive effects of germinated legume flours for wheat flour since dough from stronger flour has G\({}^{\prime}\) values higher compared to weaker ones [62]. The proteins found in legumes can influence water distribution within the dough matrix with significant implications in components interactions [63]. The incorporation of higher amounts of germinated flours could alter the starch-gluten matrix, influencing the viscoelastic behavior of dough and cumbering its handling, similar observations being made by [PERSON] et al. [57] for wheat dough enriched with germinated lentil. Germination causes the decrease of wheat starch crystallinity, the enzymes activated during germination preferentially hydrolyze the amorphous starch areas which led to the raised double-helical ordered structure, contributing to the increase in the formation of the gel structure [64]. The loss tangent could be a measure of the structural order (molecular interactions) of dough, with low tan \(\delta\) values suggesting a rigid and stiff mass, while higher values led to a moist and slack dough [65; 66]. Loss tangent increase indicated the depletion of the elastic character of dough, probably as a result of the incorporation of non-gluten flours, such as SGF and LGF. These changes could be possibly due to the presence of low molecular mass molecules caused by de-polymerization during germination of soybean and lentil which will contribute to the increase of the viscous character of dough samples [63]. Legume flours led to the increase of dough fiber proportion, the effect on the rheological behavior of dough being possibly also attributed to interactions between the fiber structure and wheat proteins [1]. The decrease of the initial gelatinization temperature T\({}_{\text{i}}\) with SGF addition level increase and the rise of T\({}_{\text{i}}\) in the case of LGF and T\({}_{\text{max}}\) in the case of SGF could be related to the starch structure which was proven to influence dough behavior during heating [67]. Furthermore, the amylose and amylopectin ratio, the degree of heterogeneity, and the amounts of amylase-lipid complexes could have been impacted the gelatinization temperatures [68]. Probably, these results could be also explained by the activation of enzymes during germination, increasing the \(\alpha\)-amylase, proteolytic and lipolytic activities [55]. The decrease of the maximum gelatinization temperature could suggest that germination altered soybean and lentil starch granule surface, determining higher resistance to temperature changes, complying with findings reported by [PERSON] et al. [69].
## 3 Blockchain-Based IIoT Data Security Sharing Mechanism
### Blockchain-Based IIoT Data-Privacy Security Framework  
This paper proposes a data-privacy security framework for the industrial Internet of Things based on blockchain, as shown in Figure 2. The Internet of Things is divided into a perception layer, transmission layer, and application layer [19]. In this framework, source data collected by nodes are uploaded to the industrial blockchain network, and  
Figure 1: Industrial data security protection scheme.  
technologies such as data blocks, Merkle trees, and timestamps are used to achieve the data security of the industrial control system.  
In order to improve the security of the industrial control network, all levels of servers, switches, PLC, and even the underlying equipment in the industrial control network can be connected to the blockchain as participating nodes. Each device node contains the entire database transaction information [20], called a block and a ledger. All device nodes of industrial control system (ICS) form the blockchain and distributed ledger. In order to help the industrial control network and the blockchain reach consensus, the respective blockchains need to establish each device node transaction and industrial control network connection, and they should follow certain rules. These rules are programmed into each blockchain's client nodes, which are then used to decide whether an incoming transaction is valid and whether it should be relayed into the network.  
When blockchain is applied to an ICS network consisting of field-bus connected device nodes, each block contains a Hash sequence and a timestamp combined key [21], forming \"blocks\" linked to the next block, which also contains ICS transaction data between devices in a network. Multiple \"blocks\" form a linear sequence. Since \"blocks\" are identified by hashed sequences of ciphers, and each block also references information from the previous block, it is possible to check whether the sender and receiver's data has been validated and tampered with through these blocks in the blockchain. When users trade data in the ICS network, they only need to trade between device nodes because each device node executes a smart contract and records transaction data, and the blockchain replicates itself between the device nodes of the ICS, further demonstrating that any device node in the ICS network can record all transaction data. As a result, device nodes on the ICS network are attached to the blockchain, and only authenticated, mutually agreed transactions are written to the underlying storage of the blockchain, ultimately ensuring the security of the data exchange.
## 1 Introduction
Most of the studies that characterize MCPM take into account the chemical, physical, and thermal properties, but few characterize their mechanical properties by Atomic Force Microscopy (AFM) with indentation mode [35]. The relevance of mechanical property evaluation lies in the importance of analyzing the MPCM in service conditions when the PCM is in solid and liquid states [36]. The goal of this paper was to test the mechanical properties (Effective Young's modulus, \(E\) and deformation) of two laboratory-made tetracosane MPCMs with nominal diameters of around 1-2 \(\upmu\)m, in order to evaluate their stiffness and decide the best shell between PMMA and PS based on their mechanical properties. Additionally, the mechanical response based on differences in the encapsulation ratio between shell and core--1:3 and 1:1--was also evaluated.
# On the Study of Influences of Different Factors on the Rapid Topospheric Tomography
## 1 Introduction
Test results indicate that the number of the voxels from GPS/GLONASS PPP that are passed by the satellite rays increased by 18%, and the water vapor accuracy from GPS/GLONASS PPP is 10% higher than with the GPS PPP method. Results in [PERSON] et al. [21] show that the integrated multi-GNSS can pronouncedly increase the number of effective signals, and three-dimensional water vapor results are better than those from the GPS-only system, improving by 5% with GPS + GLONASS or GPS + GLONASS + BeiDou Navigation Satellite System (BDS). [PERSON] et al. [22] found that the tomographic horizontal resolution can be improved by densifying the GNSS network, and the vertical resolution can be improved if receivers are deployed at different altitudes. However, in most current studies, the radiosonde or the reanalysis products are used as references to evaluate the tomographic results [16,18]. This kind of evaluation may be affected by the discrepancy among different techniques, for example, the differences of water vapor information between GNSS and radiosonde or reanalysis products rather than the tomography itself [23,24]. In this work, we will utilize the reanalysis products to simulate GNSS slant wet delays which will then be used as inputs in the tomography. The wet refractivity filed derived from the tomography is evaluated by comparing with reanalysis products, ERA5 (as a true value), to exclude the influence of differences between inputs and references. Influences of different factors including the ground meteorological observation data, the multi-GNSS observations, the ground-based station distribution and the horizontal resolution on the tomographic results, are discussed and analyzed. The NWP forecast products are used to avoid the rank-deficiency and an algebraic reconstruction technique is applied to tackle the massive parameter estimation issue in large-scale region tomography. The data used in this study is introduced in Section 2. The tomographic method is described in Section 3. The slant wet delay (SWD) simulation based on reanalysis products is depicted in Section 4, and the tomography experiments and result discussion are presented in Section 5, followed by the conclusions in Section 6.
# Numerical Investigations on the Blade Tip Clearance Excitation Forces in an Unshrouded Turbine
## 3 Results and Discussion
### Static Pressure Distributions  
To better understand the flow characteristics in this turbine and the cause of the tip clearance excitation forces, the cross-sections are illustrated in Figure 13a, while Figure 13b-d shows the normalized static pressure contours on the selected cross-sections. At this moment, the rotor displacement in the x-direction caused by the excitation was at the maximum (\(x\) = 65 \(\upmu\)m, \(ER\) = 0.13, shown in Figure 6a) and that in the y-direction was about 350 \(\upmu\)m (\(ER\) = 0.7) due to the rotor eccentricity. Meanwhile, the rotor was moving in the positive y-direction.  
According to Figure 13b, the eccentricity had an impact on the pressure distributions of upstream flow, which validates the hypothesis in [PERSON] and [PERSON] [8]. The static pressure at the smaller tip gap was relatively higher than that at the larger tip gap when comparing the result on the upper and right surfaces with that on the lower and left surfaces. According to Figure 13c, a larger tip gap led to a larger tip leakage flow, and therefore caused a decrease of static pressure at the tip gap. Due to the circumferentially uneven upstream flow and tip leakage flow, the torque on the blade was also circumferentially non-uniform, and hence caused the tip clearance excitation forces. According to Figure 13d, at the larger tip gap region, a low-pressure area caused by a larger tip leakage flow can be seen.  
Figure 14 shows the distributions of selected variables of numerical results (left) and experimental results in Song [9] (right). In the right figure, \(\rho\) is the density, \(m\) is the mass flow rate, \(U\) is the  
Figure 13: Illustrations of the selected cross-sections and normalized static pressure contour (\(P_{\text{s}}/P_{\text{t,0}}\)) on the cross-sections (\(ER\) = 0.7, \(t\) = 0.05s, \(n\) = 10000 r/min, \(\alpha\) = 48.36\({}^{\circ}\), \(t_{m}/\hbar\) = 2%, x-direction excitation).  
linear velocity at the average radius, and the subscript av indicates circumferential averaged value. Comparing these two figures showing the phase differences between the local torque and the local tip clearance, the local pressure and the local tip clearance were quite similar. According to the numerical results, the static pressure on the tip basically decreased with the local tip clearance. However, it should be noted that the phase difference between the local tip clearance and the local torque on the blade was not 180\({}^{\circ}\), but instead approximately 120\({}^{\circ}\), which indicates that the lowest local tip clearance could not lead to the highest local torque on the blade. Based on this, a positive direct stiffness, which indicates the direct force contribution in the tip excitation forces, was generated.
## 3 Result
### Correlation Analysis  
The relationship between biogas production (\(y\)-variable) and environmental parameters (\(x\)-variables) was calculated by the Pearson correlation coefficient r. The relationship between environmental variables and biogas yields was constructed using the Seaborn heatmap Python data visualization library, which revealed the correlation between temperature, moisture, pH, and biogas yield.  
Figure 7 presents how the inter-variables correlation matrix was constructed. The value of r fell within the range of \(-1\) to \(+1\). When r \(>0\), it indicated a positive correlation between the two variables, meaning that as one variable increased, the other variable tended to increase as well. Conversely, when r \(<0\), it indicated a negative correlation, whereas when one variable increased, the other variable tended to decrease, and the larger the absolute value of r, the stronger the correlation. It was important to note that this correlation did not imply causation. When r = 0, it indicated no linear correlation between the two variables.  
The Pearson correlation result showed that the indoor temperature and pH had a strong correlation of 0.77 and \(-0.6\), while the outdoor temperature and moisture presented a moderate correlation of 0.46 and 0.3, respectively.  
As presented in Figure 8, throughout the experiment, the matplotlib python library was imported to create graphs representing the correlation between variables. According to its constraints, the premise of the correlation analysis was that the distribution of environmental parameters conformed to a normal distribution. Figure 8a-d provides information regarding our findings.  
Figure 7: Inter-correlation matrix.  
In Figure 7(a), a correlation between the biogas yield and indoor temperature indicated an indoor temperature range between 37 and 39 \({}^{\circ}\)C, which was induced to a maximum production of about 0.10 dm\({}^{3}\). In Figure 7(b), the correlation between the biogas yield and outdoor temperature showed the maximum outdoor temperature of (23-24) \({}^{\circ}\)C, which resulted in a maximum biogas generation of about 0.10 dm\({}^{3}\). In Figure 7(c), the correlation between the biogas yield and pH showed a maximum biogas production of 0.10 dm\({}^{3}\) for a pH range (6-9). In Figure 7(d), a correlation between the biogas yield and moisture indicated a moisture range of 85-86, which presented the maximum biogas production. These results indicate that the environmental parameters had a high impact on biogas production. Moreover, the temperature increased both outdoors and inside during the daytime while it decreased at night. Thus, it could be concluded that the daytime period also had a significant impact on biogas prediction.
## 4 Results and Discussion
### Statistical Analysis Using \(t\)-Test
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cost** & **Iteration** & **HWACO** & **QANA** & **Improvement** & \(t\)**-Test Value** & \(p\)**-Value** \\ \hline \multirow{6}{*}{Number of Task 200} & 10 & 70 & 78 & 10.25641026 & 4.3534 & \(5.342\times 10^{-2}\) \\ \cline{2-6}  & 20 & 63 & 72 & 12.5 & 5.3503 & \(6.405\times 10^{-2}\) \\ \cline{2-6}  & 30 & 58 & 65 & 10.76923077 & 3.4533 & \(3.035\times 10^{-2}\) \\ \cline{2-6}  & 40 & 52 & 64 & 18.75 & 4.3422 & \(8.308\times 10^{-3}\) \\ \cline{2-6}  & 50 & 49 & 58 & 15.51724138 & 3.4508 & \(7.083\times 10^{-3}\) \\ \hline \multirow{6}{*}{Number of Task 400} & 10 & 110 & 118 & 6.779661017 & 5.3058 & \(9.304\times 10^{-2}\) \\ \cline{2-6}  & 20 & 103 & 110 & 6.363636364 & 3.3058 & \(5.204\times 10^{-2}\) \\ \cline{2-6}  & 30 & 89 & 95 & 6.315789474 & 4.2398 & \(3.038\times 10^{-2}\) \\ \cline{2-6}  & 40 & 80 & 95 & 15.78947368 & 3.0583 & \(5.035\times 10^{-3}\) \\ \cline{2-6}  & 50 & 75 & 92 & 18.47826087 & 4.3583 & \(9.083\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: \(t\)-Test for cost between HWACO and QANA.
## 4 Results and Discussion
### Evaluation of the Trap Static Sealing Capacity  
#### 4.3.1 Evaluation of the Static Sealing Capacity of Cap Rock  
According to the stratigraphic sequence and the main reservoir area of the underground storage in the depleted gas reserve, the cap rock of the gas storage reservoir is the second section of the Denglouku formation, mainly composed of dark purple mudstone and siltstone, with a thickness of \(300-400\) m. The porosity of the cap rock core of the two pilot test wells ranges from \(1.25\%\) to \(3.54\%\), with an average of \(2.08\%\); the permeability ranges from \(0.001\) mD to \(0.018\) mD, with an average of \(0.004\) mD. This indicates that the distribution of the cap rock in the region is stable, thick, and has good sealing properties.  
From the perspective of the planar distribution characteristics of the cap rock, the thickness of the cap rock is inverted. The characteristics of the cap rock thickness show the characteristics being thick around the perimeter and thin in the middle. As shown in Figure 18, the cap rock is thick, with the thickness mainly distributed between \(40\) m and \(120\) m above the reservoir range of the depleted gas reservoir, with an average of \(83\) m.  
The results of the mud content inversion of the cap rock indicate a high content, with the majority of the area having a mud content ranging from \(50\%\) to \(90\%\).  
Meanwhile, the relationship between the measured displacement pressure and the acoustic travel time of the cap rock mudstone. It can be seen that the displacement pressure of the cap rock mudstone mainly ranges from \(4\) MPa to \(7\) MPa, indicating a medium-to-good level of quality. Within the gas storage range, the displacement pressure of the D2 formation mainly exceeds \(6\) MPa, indicating a good sealing property.  
In addition, the analysis of the breakthrough pressure test in the pilot test wells also indicates that the breakthrough pressure distribution of the cap rock in well SPKP1 ranges from \(6.0\) MPa to \(20.5\) MPa for five samples, while in well SPKP2, the breakthrough pressure distribution ranges from \(4.7\) MPa to \(17.5\) MPa for five samples. The cap rock has relatively high breakthrough pressure, indicating good sealing properties.
## 2 The Numerical Tool Development  
### The Development of the Simulation Strategy  
#### 2.1.1 Thermal Anisotropy  
The coefficient of thermal expansion (CTE) is more significant in resins than fibers. This mismatch results in a phenomenon called thermal anisotropy, which can be defined as a higher contraction or expansion in the resin-dominant direction than in the fiber-dominant direction.  
Distortions of this nature do not occur in flat plates; rather, they manifest in curved geometries. This is due to the difference in the coefficient of thermal expansion (CTE) between the thickness direction and the circumferential direction, which decreases the enclosed angle in curved areas--a phenomenon referred to as spring-in. Such effects do not apply to isotropic materials, where contraction and expansion are consistent. In fiber-reinforced composites, the CTE and strains in the through-thickness direction are significantly greater than those in the fiber direction, leading to a reduction in the enclosed angle (\(\theta\)), as generically illustrated in Figure 2.  
The first attempt to calculate the magnitude of the enclosed angle was proposed by [PERSON] and [PERSON] [25] with the following:  
\[\Delta\theta=\theta\frac{(\alpha_{\theta}-\alpha_{R})\Delta T}{1+\alpha_{R} \Delta T} \tag{1}\]  
where  
* \(\Delta\theta\) is the spring-in angle;
* \(\theta\) is the subtended angle of the part;
* \(\alpha_{\theta}\) is the circumferential coefficient of thermal expansion;
* \(\alpha_{R}\) is the radial coefficient of thermal expansion;
* \(\Delta T\) is the temperature change.