esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Review of the Application Characteristics of Biodiesel Blends in Diesel Engines
## 3 Engine Performance
### Coefficient of Variation in Indicated Mean Effective Pressure  
During the operation of the engine, each cycle slightly varies due to changes in the initial temperature, pressure, fuel composition and thermodynamic conditions in the cylinder [56]. The indicated mean effective pressure (_IMEP_) represents the work done per unit cylinder volume per cycle [57]. It can reflect the extensive pressure information of the entire operating cycle, and is often used to evaluate the combustion changes of the engine each cycle [58]. The coefficient of variation in indicated mean effective pressure (\(COV_{IMEP}\)) is an important indicator reflecting this periodic change, which indicates the cyclic variability of operation. A smaller coefficient of variation indicates a more uniform the combustion process and more stable engine operating conditions. Generally, it is believed that \(COV_{IMEP}<10\%\) is the upper limit of stable combustion [59]. \(COV_{IMEP}\) is calculated by the expression [57]:  
\[COV_{IEMP}=\frac{\sigma_{IEMP}}{IEMP}\times 100 \tag{4}\]  
where, _oimep_ is the standard deviation in _IMEP_, and _imep_ is the mean _IMEP_.  
Most studies show that the \(COV_{IMEP}\) of biodiesel-diesel blends is similar to or smaller than diesel [35; 36; 44]. [PERSON] et al. tested linseed oil biodiesel and found that the \(COV_{IMEP}\) of biodiesel blends decreases as the concentration of biodiesel increases, and the advantages compared with diesel become more obvious [35]. [PERSON] et al. compared the \(COV_{IMEP}\) of diesel, ethyl ester biodiesel, methyl ester biodiesel, chicken fat biodiesel, and fleshing oil biodiesel. It was found that under low and high load, diesel has a higher \(COV_{IMEP}\), but that of the medium load is just the opposite [36]. As shown in Figure 4, the other blends are much smaller except that the \(COV_{IMEP}\) of B10 is similar to diesel [44]. These phenomena may be attributed to the higher cetane number and oxygen content of biodiesel, which can be burned quickly and smoothly [59]. Although biodiesel has a lower calorific value, higher oxygen content will make combustion more complete, so the \(COV_{IMEP}\) of biodiesel blends tends to be lower [56]. Of course, the lower calorific value is considered to be the cause of fluctuations at higher loads [50].  
Figure 3: Variation of brake thermal efficiency of fish oil biodiesel blends with load at 21\({}^{\circ}\)bTDC [33].
# Emergency Repair Scheduling Model for Road Network Integrating Rescheduling Feature
## 3 Methodology
### Model Formulation  
To summarize the basic assumptions of the model in the previous subsection, this subsection introduces the mathematical equations of the proposed model, and the definition of model parameters and variables are shown in Table 1. IBM ILOG CPLEX Optimizer software and the constraint programming (CP) approach are adopted for model development [53]. * Objective function \[\textit{Minimize T}\] (1)
* Constraints \[\textit{T}\ =\ \textit{Max}\ \{\ \textit{F}_{i}\}\,\forall\ i\ \in\ \textit{SI}\] (2)
2. Finish time of repair activity. Equation (3) indicates that the finish time of repair activity at the disaster point is the sum of activity start time and selected activity duration. \[\textit{F}_{u}=\ \textit{S}_{u}+\textit{d}_{u}\,\ d_{u}\ \in\ \textit{D}_{u}\, \forall\ u\ \in\ \textit{SU}\] (3)
3. Finish time of transit activity. Finish time of transit activity is the sum of activity start time and transit activity duration. Transit activities can be divided into two categories: the first group--the ones traveling from the resource point to the disaster point; the second group--the ones traveling from the preceding disaster point to the succeeding disaster point. Therefore, the restrictions for each category are made separately as Equations (4) and (5). \[\textit{F}_{nj}=\ \textit{S}_{nj}+\textit{LT}_{nj}\,\forall\ n\ \in\ \textit{SN}\, \forall\ j\ \in\ \textit{S}\textit{J}\,\ \textit{LT}_{nj}\ \in\ \textit{D}_{nj}\] (4) \[\textit{F}_{ij}=\ \textit{S}_{ij}+\textit{LT}_{ij}\,\forall\ i\ \in\ \textit{S}\textit{I}\, \forall\ j\ \in\ \textit{S}\textit{J}\,\ \textit{LT}_{ij}\ \in\ \textit{D}_{ij}\] (5)
4. Transit activity execution. If a specific transit activity is selected for resource dispatching, the number of equipment resources is greater than zero (i.e., this transit activity is really in execution status), then the duration of this transit activity is equal to the corresponding duration \(lt_{ij}/lt_{ij}\); otherwise, it is not executed and its corresponding duration should be equal to zero, where such logical expression is shown as Equations (6) and (7) separately for two categories of transit activities. \[If\,LR_{nj}^{k}\,>\,0\,,\,\,then\,LT_{nj}\,=\,lt_{nj}\,If\,LR_{nj}^{k}\,=\,0\,,\, \,then\,LT_{nj}\,=\,0\,\forall\,n\,\in\,SN\,,\,\forall\,j\in\,SJ,\,\forall\,k\in SK \tag{6}\]  
\[If\,LR_{ij}^{k}\,>\,0\,,\,\,then\,LT_{ij}\,=\,lt_{ij}\,If\,LR_{ij}^{k}\,=\,0\,, \,\,then\,LT_{ij}\,=\,0\,\forall\,i\,\in\,SI\,,\,\forall\,j\in\,SJ,\,\forall\,k \in SK \tag{7}\]
## 3 Silicon Germanium Waveguides and Nonlinear Applications
### Linear and Nonlinear Optical Characterization  
Below we summarize the optical properties of SiGe waveguides for different Ge concentrations and waveguide widths at telecommunication wavelengths (\(\lambda\) = 1530-1580 nm). The impact of these two parameters on the linear and nonlinear loss and Kerr nonlinearity were studied and presented in detail in [55]. The waveguides studied were strip-waveguides encapsulated in a 12 um top silicon cladding layer and fabricated by means of the Reduce Pressure Chemical Vapour Deposition (RP-CVD) technique.
## 4 Numerical Experiments
### Example 2  
In the practical application of portfolio optimization, often faced with a large number of assets for investors to choose, which requires the algorithm maintain a good performance to large-scale operations. In order to test the computational power of \(\varepsilon\)BBO algorithm on large-scale SSD constraints, we collect 249 daily historical returns of 101 FTSE100 assets prior to December 2016 to construct the portfolio strategy. Therefore, we get a historical of percentage returns for \(m=249\) time periods and a group of \(n=101\) assets. Besides, we use \(y=(\frac{1}{101},\ldots,\frac{1}{101})\) as the benchmark and we discuss the following SSD constraints model:  
\[min 2-g(x,\xi)\] \[s.t. \sum_{i=1}^{249}\frac{1}{249}(g(y,\xi^{i})-g(x,\xi^{i}))_{+}\leq \sum_{i=1}^{249}\frac{1}{249}(g(y,\xi^{i})-g(y,\xi^{i}))_{+},j=1,\ldots,249\] \[x\in X\]  
Due to the fact that the stock certificate can not always guarantee positive returns, we discuss the suitability function as \(2-g(x,\xi)\) to ensure its value is positive. In order to ensure the diversity of the portfolio, we discuss the X constraint as \([0,0.1]\) and \([-0.05,0.15]\) which stand for two conditions: short-selling is allowed and short-selling is prohibited. Comparing with the FTSE100 Index we get the test results shown in Table 4. The specific index weight data of FTSE100 Index is shown in Appendix A.  
The Figures 8 and 9 show the specific asset structure of optimal portfolio in two kinds of X constraints of model (30). The Figures 10-13 show the performance of the \(\varepsilon\)BBO and the optimal portfolio in the two kinds of \(X\) constraints of model (30).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**X constraint** & **Portfolio** & **Iteration** & **Time** & **NO.Assets** & **E[g(x,\xi)]** \\ \hline \([0,0.1]\) & \(\varepsilon\)BBO & 92 & 1.6 & 101 & 0.1325 \\ \([-0.05,0.15]\) & \(\varepsilon\)BBO & 150 & 2.64 & 101 & 0.3197 \\ / & FTSE100 & / & / & 101 & 0.0937 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The model (30) using date of 249 daily historical returns of 101 FTSE100 assets prior to December 2016. Time is in minute and the expected return of the benchmark portfolio \(E[g(y,\xi)]=0.0595\).  
Figure 7: The performance of the \(\varepsilon\)BBO and the optimal portfolio in \(X\in[-1,2]\) of model (29).  
Figure 8: The specific asset structure of optimal portfolio in \(X\in[0,0.1]\) of model (30) for 101 assets.  
Figure 10: The return of optimal portfolio in different iterations in \(X\in[0,0.1]\) of model (30).  
Figure 9: The specific asset structure of optimal portfolio in \(X\in[-0.05,0.15]\) of model (30) for 101 assets.  
Figure 11: The return of optimal portfolio by \(\varepsilon\)BBO in each period with benchmark portfolio and the FTSE100 Index portfolio in \(X\in[0,0.1]\) of model (30).  
Figure 12: The return of optimal portfolio in different iterations in \(X\in[-0.05,0.15]\) of model (30).  
Figure 13: The return of optimal portfolio by \(\varepsilon\)BBO in each period with benchmark portfolio and the FTSE100 Index portfolio in \(X\in[-0.05,0.15]\) of model (30).
# Experimental Test and Prediction Model of Soil Thermal Conductivity in Pernafrost Regions
## 5 Thermal Conductivity Prediction for Three Soil Clusters  
### Thermal Conductivity Prediction Models  
#### 5.1.1 Binary Fitting  
The previous research indicates that dry density and water content have obvious positive correlation with thermal conductivity. Therefore, the curve estimation of the fitting relationship between thermal conductivity, dry density and water content has been taken and exponential fitting formulas for three soil clusters are obtained:  
\[\lambda=a_{1}\mathrm{e}^{b_{1}\rho_{d}+c_{1}w} \tag{2}\]  
where \(a_{1}\), \(b_{1}\) and \(c_{1}\) are the fitting coefficients of the equation.  
Figure 10: Thermal conductivities of three soil clusters with different water content.  
Figure 9: Thermal conductivities of three soil clusters with different dry density.  
It is also found that the influence of water content on thermal conductivity is inconsistent with different dry density. When the dry density is small, increasing of water cannot play a significant role in increasing soil connectivity as well as increasing dry density. On the contrary, the increasing of water is the most effective approach to promote soil thermal conductivity under the high dry density condition. For this reason, the fitting formulas are stepwise fitted according to their dry density. The detailed fitting parameters of three soil clusters are listed in the Table 4.
## 3 Material and Methods
### Optical Satellite Images  
We used the time series of optical satellite images Landsat 7-8 [105] acquired in the time span 2001-2019 and multispectral WorldView 2 acquired in 2011 to evaluate the horizontal migration of the sandy shorelines that underwent to significant retreat. The effective shoreline migration was obtained following the method described in [PERSON] et al. [80]. From all the selected images, the shorelines were digitalized in GIS environment for each year reported in the satellite images. In the coastal areas affected by several meters of coastal retreat, an estimation of the rate of total shoreline retreat was performed.  
To this aim, we applied a linear regression of the shoreline position digitalized with respect to years of the satellite images. This approach involves the coastal retreat as a result of all contribution that cause the sediment movements and shoreline migration. Firstly, to assess the coastal retreat only due to SLR contribution, we assumed the investigated coasts to be in steady-state approach, without significant sediment movements and the shoreline migration caused only by land subsidence estimated by the geodetic measurements. Secondly, the eustatic sea-level component was added to the local land subsidence analysis to obtain horizontal displacements in function of the coastal slope. The latter has been extracted from DEM of LiDAR data. The most relevant shoreline changes have been observed on the coastal plain of Catania, the sandy coast of Siracusa and the coast of Vendicari, near Marzamemi (Figure 6).  
To project the shoreline migration at 2050 and 2100 epochs, we incorporated the sea-level projections in the model reported in [PERSON] et al. [80]. Starting from the present-day coastline extracted from high-resolution satellite image of ESRI ArcGIS service (year 2019), the coastline in 2050 and 2100 was mapped.
# Data Hiding of Multicompressed Images Based on Shamir Threshold Sharing
## 1 Introduction
### Multi-image secret sharing data hiding  
In 2018, [PERSON] et al. proposed an image encryption algorithm based on Shamir secret sharing [26], which uses secret sharing technology to encrypt the original image into multiple meaningless ciphertext shadow images and hides the original reversible data in shadow images; the proposed method is extended using two algorithms, Difference Expansion and Histogram Shift. The experimental results show that the method has low computational complexity, large embedding capacity, and can restore the original image losslessly.  
In 2020, [PERSON] et al. proposed a homomorphic encryption reversible data hiding algorithm based on secret sharing [27]. The algorithm first uses the Shamir secret sharing scheme to encrypt the image. The secret message is embedded by using the homomorphic properties of the Shamir secret sharing scheme. The encryption and embedding stages use the same operation. Compared with similar algorithms, this algorithm has lower time complexity.  
In 2020, [PERSON] et al. proposed a novel reversible data hiding over Distributed Encrypted-Image Servers (RDH-DES) based on secret sharing [28]. The Chinese Remainder Theorem and block-level scrambling were developed as a lightweight cryptography to generate the encrypted image shares. However, the image shares are meaningless and easily attract attackers' attention.  
The data hiding technology based on secret sharing is mainly divided into double-image data hiding and multi-image data hiding. Double-image data hiding is a secret sharing data hiding technique in extreme cases, which has considerable hiding performance but no fault tolerance. Although multi-image data hiding has certain fault tolerance, the shadow images embedded in the secret message are transmitted and stored as a ciphertext image, which cannot avoid the defects of traditional encryption technology and reduces the security of the secret message. Considering the advantages and disadvantages of the above two hiding techniques, the algorithm proposed in this paper uses the secret sharing technique to divide the secret message into multiple shares and embed them into multiple images, to ensure that it is fault-tolerant. The embedded carrier selects meaningful digital images to ensure the concealment and security of secret messages. At the same time, in practical application, the proposed method embeds the secret message into multiple AMBTC compressed images to reduce the cost for transmission and storage.  
The organization of this paper is as follows: Section 2 revisits related works, and briefly describes the Shamir threshold secret sharing scheme and AMBTC. The proposed method is elaborated in Section 3. The experimental results are demonstrated in Section 4. Finally, the conclusions and future works are given in Section 5.
## 5 Results
### Performance Comparison with State-of-the-Art Studies  
A performance comparison is also made with other recent studies to show the significance of the proposed approach. For this purpose, two recent studies have been selected including [12; 17]. The study uses several models for citation sentiment analysis, such as support vector classifier (SVM), multinomial naive Bayes, k-nearest neighbor, and LR. On the other hand, Ref. [12] has been selected, as it uses an ensemble of CNN and LSTM deep learning models. Comparison results provided in Table 8 suggest that the proposed framework with the CNN model using blended features shows better results than the state-of-the-art models for the sentiment analysis of in-text citations.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Reference** & **Model** & **Accuracy** \\ \hline
[17] & SVC & 0.85 \\ \hline
[17] & Multinomial NB & 0.87 \\ \hline
[17] & KNN & 0.79 \\ \hline
[17] & Logistic regression & 0.86 \\ \hline
[12] & CNN+LSTM & 0.85 \\ \hline Proposed & CNN & 0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance comparison with other approaches.  
Figure 3: Sentiment distribution of the collected dataset.
## 5 Simulation
### LOS Signal Received by the LHCP Antenna  
As we mentioned before, the LOS signal received by the LHCP antenna leads to bias of the estimation of multipath and finally affects the estimated TOA of the LOS signal. Here we increase the power of LOS signal received by the LHCP antenna gradually from \(-15\) dB to \(-6\) dB to evaluate this impact. If the power of LOS signal received by the LHCP antenna reaches \(-9\) dB, it means the AR of the LHCP antenna is 4.7 dB. This level of AR is often measured when the LOS signal comes from a low elevation angle [34]. Table 5 shows the resultant RMSEs of \(f_{0}\). As we increase the power of the LOS signal in the LHCP channel, the RMSE increases gradually. When the power is \(-6\) dB, which is the same as that of the multipath, the performance of the DP-SIMLE algorithm degrades to that of RHCP-MEDLL. In this critical case, the impact of ignoring the power of the LOS signal in the LHCP channel becomes perceptible. Fortunately, such a high power of the LOS signal in the LHCP channel barely happens if well-designed dual-polarization antennas are used (\(-6\) dB of the LOS power corresponds to the AR of the LHCP antenna to be about \(10\) dB which is barely seen in a typical circular polarization antenna). To sum up, as long as the multipath dominates the received signal power in the LHCP channel, the DP-SIMLE algorithm using a dual-polarization antenna can outperform the RHCP-MEDLL algorithm using a single-polarization antenna. Therefore, it is verified that ignoring the LOS signal in the LHCP channel is reasonable.
## 3 State of the Art
### Digital Product Passport and Regulatory Framework in Europe  
The new construction products Regulation (new CPR) [13] and the ecodesign for sustainable products Regulation (ESPR) [40], both published in 2024, require manufacturers to provide a digital product passport (DPP) when placing their products in the EU Single Market. The DPP is a digital record of product information related to a model, a batch or an individual item. A data carrier, linked to a unique product identifier physically attached to the product or the accompanying documentation, enables data sharing along the value chain.  
The legal provisions of the CPR require interoperability of the DPP data with BIM, with the objective of delivering regulatory-compliant information to design or construction companies, facility managers, public authorities, and asset managers. Data must be provided in accordance with strict content requirements derived from the CPR, facilitating the comparison of performance metrics. This regulatory framework imposes constraints on the available approaches to digitalizing data.  
The DPP will include information related to the environmental performance, to be defined for each product family. For construction products, a detailed set of life cycle environmental indicators aligned with EN 15804 (and the Product Environmental Footprint (PEF)) is listed in annex II of the new CPR.  
All products covered by the CPR, and many of the products covered by the ESPR, are installed in built assets. Examples of products covered by the ESPR include water pumps, light sources and space heaters. To perform calculations and assessments for buildings or infrastructures, the environmental data of these products must be stored in IFC models.  
The DPP concept can be aligned with other models, such as the Internet of Materials described in [41], to support the validation of claims or its application beyond the European regulatory context. At the international level, ISO and UNECE launched an initiative to align the DPP across industries and regions [42].  
DPPs should be integrated into digital building logbooks (DBLs), envisioned as a centralized digital repository for all relevant information throughout a building's entire lifecycle. The European Commission has identified the potential for harmonizing this tool and has proposed a definition of DBL to facilitate its implementation and ensure the interoperability [43]. The integration of IFC into DBLs is feasible, provided that their structures and methodologies are aligned. [PERSON] et al. [44] proposed a data structure for the DBLs, which can be considered in the description of the requirements for new versions of IFC.  
The integration of LCA data contained in the CPR-DPP and the ESPR-DPP into the IFC schema will allow for the direct use of reliable environmental product data in current BIM software tools, thereby reducing the resources required for the sustainability assessment of buildings and infrastructure. Integration into DBLs has the potential to enhance the efficiency of these assessments, as it ensures that all asset-related information is accessible within a single digital model.
# Modeling and Experimental Study of the Dual Cylinder Fluid Inerter
## 2 Structure and Principle of the Dual-Cylinder Fluid Inerter
### Condition of Free Motion  
Suppose that the effective area of main chamber 2 is \(A_{2}\), that of main chamber 6 is \(A_{6}\), that of annular chamber 15 is \(A_{15}\), and that of annular chamber 9 is \(A_{9}\). Suppose that the relative movement velocity between terminal 1 and terminal 16 is \(\dot{x}\), and the volume flow discharged from main chamber 2 is \(A_{2}\dot{x}\). According to the conservation rule of oil  
Figure 1: Schematic drawing of dual-cylinder fluid inerter. 1—terminal; 2—main chamber; 3—oil port; 4—pipeline; 5—oil port; 6—main chamber; 7—cylinder; 8—piston; 9—annular chamber; 10—oil port; 11—pipeline; 12—piston; 13—cylinder; 14—oil port; 15—annular chamber; 16—terminal; 17—piston rod; 18—piston rod.  
volume, the volume flow \(Q_{9}\) out of annular chamber 9 is equal to the flow \(Q_{15}\) into annular chamber 15:  
\[Q_{15}=Q_{9}=\frac{A_{2}\dot{x}}{A_{6}}\cdot A_{9} \tag{1}\]  
To make sure there is no motion interference in the reciprocating process of the piston, the discharge (or inflow) \(Q_{2}\) of main chamber 2 and the inflow (or outflow) \(Q_{15}\) of annular chamber 15 should obey the following relationship:  
\[\frac{Q_{2}}{Q_{15}}=\frac{A_{2}}{A_{15}} \tag{2}\]  
Substitute Equation (1) into Equation (2), and obtain:  
\[\frac{A_{6}}{A_{9}}=\frac{A_{2}}{A_{15}} \tag{3}\]  
\(A_{2}/A_{15}\) is the ratio of the effective area of the main chamber of the inertia force output cylinder to the annular chamber. \(A_{6}/A_{9}\) is the ratio of the effective area of the main chamber and the annular chamber of the flow-regulating cylinder. As long as they are equal, they can move back-and-forth without motion interference.