esa-sceva/satcom-chunk-collection huggingface dataset:
# A Network-Based Model for Optimization of Container Location Assignment at Inland Terminals
## 7 Discussion  
Few studies have examined container assignment location at inland terminals. Here we develop a new network-based mathematical model that integrates the most important criteria for inbound container location assignment (container dimension, its occupancy, cargo type, container owner, and distance to final user) under conditions when the container's departure time is unknown. The model determines the locations of inbound containers based on the retention time estimated according to the container criterion and category. The ranking of container storage tiers according to retention time is necessary to reduce unproductive manipulations in container storage and shipping. A higher ranking implies better availability for faster shipping.  
To develop our model, inland terminal operators (decision makers) were asked to indicate their preference stream for criterion replacements. For each subjective preference stream, the consistent approximation preference stream with minimal deflection was determined. For each consistent approximation preference stream, the potentials of the criteria and categories were calculated separately. We implemented a novel method to determine the hierarchy of parameter potentials for optimizing container storage. Implementation of the final model showed that the most important criteria for container assignment location were the container's dimensions, the container's final destination, and cargo type. Other criteria were less important, including the container's owner, container's occupancy, and distance to the final user.  
The proposed model determines a feasible sequence of stacking operations that minimize the costs associated with the storage of containers while taking into account the spatial and operational constraints. Its implementation can optimize the storage of inbound containers, reducing unproductive manipulations as well as the costs associated with the use of the inland terminal. The model was validated against data from one of the observed inland terminals, and the model was able to significantly reduce unproductive manipulations.  
Figure 8: Numbers of total and productive manipulations as well as the percentage of unproductive manipulations in the simulation.  
Thus, the presented model may help optimize technological processes. It may also harmonize technological processes between seaports and inland terminals, which in turn may increase productivity and reduce operating costs at both nodes in cargo distribution.  
Future research should examine whether determining the statistical distributions of retention times can improve the estimation of container retention time and the ranking of criteria. It may be possible to reduce even further the number of unproductive manipulations and thereby further improve inland terminal productivity.
## 2 Materials and Methods
### Data
#### 2.2.2 Typhoon and Rainfall Data
Among their data sources, CWB has made station weather data available, which includes hourly precipitation for each typhoon event for all the automatic rain gauge stations in Taiwan. For this study, we identified the three closest stations to the Butangbunasi landslide, all located within the Laonong River basin, which are summarized in Table 3. The locations of the three selected CWB stations are shown in Figure 3. Since station C1V210 was upgraded to a new weather station coded C0V210, which continues the data registration, we will refer to it as a single station called Fuxing. Since the entire study period was not covered by the CWB rain gauge station data available on the Typhoon Database, we also collected data from the CHIRPS (Climate Hazards Group Infrared Precipitation with Station data) dataset. CHIRPS combines information from the Tropical Rainfall  
Figure 3: Locations of the three selected CWB rain gauge stations shown on the Landsat image from 8 November 2018. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Station** & **Station** & **Operation Period** & **Latitude** & **Longitude** & **Elevation** & **Distance to Butangbunasi** \\
**Code** & **Name** & & & **(m a.s.l)** & **Landslide (km)** 1 \\ \hline C1V200 & Meishan & 21 January 1992–Present & 23.2684 & 120.8236 & 870 & 8.3 \\ C1V210 & Fuxing & 21 January 1992–March 2013 & 23.2224 & 120.8059 & 700 & 3.3 \\ C0V210 & Fuxing & 18 April 2013–Present & 23.2224 & 120.8061 & 734 & 3.3 \\ C1V220 & Xiaoguanshan & 22 January 1992–Present & 23.1542 & 120.8136 & 1781 & 6.8 \\ \hline \end{tabular} Note: 1 Distance calculated to the centroid of the Butangbunasi landslide area.  
\end{table} 
Table 3: Selected Central Weather Bureau (CWB) rain gauge stations. Measuring Mission (TRMM) 3B42 product, the NOAA Climate Forecast System (CFS) and other precipitation data sources to provide daily precipitation in grid format at a resolution of 0.05\({}^{\circ}\), for latitudes between 50\({}^{\circ}\) S and 50\({}^{\circ}\) N [65; 66]. We accessed the data through the Google Earth Engine, where we obtained the mean rainfall in the area of interest for the 19 typhoon events selected for analysis. For each event, we obtained the daily precipitation on the day the typhoon passed closest to the Butangbunasi landslide area.
# Parametric Simulations on Leakage and Performance of a Miniature Free-Piston Generator (MFPG)
## 3 Modelling of the MFPG System
### Heat Transfer Model  
The heat convection across the cylinder wall is the dominant mode of heat dissipation in the MFPG system [17; 44; 45]. Therefore, in order to simplify the model and accelerate the calculation, the composition inhomogeneity and temperature gradient inside the combustion cylinder can be reasonably neglected [33; 34]. [PERSON] et al. [55] and [PERSON] et al. [56] adopted these assumptions of uniform charge in modelling the heat transfer. Their simulation showed good agreement against test results. Thus, the heat loss of the MFPG system is simply approximated by [PERSON]'s law of cooling [57]:  
\[\frac{dQ_{w}}{dt}=\alpha_{w}\cdot A_{w}\cdot(T-T_{w}) \tag{2}\]  
where \(Q_{w}\) is the heat loss across the cylinder wall, J; \(A_{w}\) is the effective area of heat transfer, m\({}^{2}\); \(T\) is the in-cylinder gas temperature, K; \(T_{w}\) is the wall temperature, K; \(a_{w}\) is the convective heat transfer coefficient, W/(m\({}^{2}\cdot\)K), which is usually estimated by experiments or simulations.  
Two commonly used estimation formulas of \(a_{w}\) are the Woschni model [58] and the Hohenberg model [59], as shown in Equations (3) and (4).  
\[\alpha_{w}=820p^{0.8}T^{-0.53}D^{-0.2}\left[C_{1}v_{m}+C_{2}\frac{T_{EPC} \cdot V_{h}}{p_{EPC}\cdot V_{EPC}}(p-p_{0})\right]^{0.8} \tag{3}\]  
Figure 3: Configuration of the piston coordinates.  
\[\alpha_{w}=130V^{-0.06}\left(\frac{P_{EPC}}{10^{5}}\right)^{0.8}T_{EPC}^{-0.4}[v_{m} +1.4]^{0.8} \tag{4}\]  
where \(p\), \(T\), \(V\) are the pressure, temperature and volume of the in-cylinder gas, with the unit of Pa, K, m\({}^{3}\), respectively; \(p_{EPC}\), \(T_{EPC}\) and \(V_{EPC}\) are the pressure, temperature and volume of the in-cylinder gas at the exhaust port closing, with the unit of Pa, K and m\({}^{3}\), respectively; \(V_{h}\) is the swept volume, m\({}^{3}\); \(v_{\max}\) is the max piston velocity, m/s.  
Figure 4a compares the effects of the two heat transfer models on the in-cylinder pressure. Compared to the Woschni model, the Hohenberg model predicts higher in-cylinder gas pressure and shows better agreement against the test data. Since the real in-cylinder gas temperature of the MFPG is unavailable due to the technical obstacles in the field of internal combustion design [60; 61], Figure 4b only displays the simulated in-cylinder gas temperatures of the two models, in conjunction with the adiabatic condition. It shows that the Hohenberg model also predicts higher in-cylinder gas temperature than that of the Woschni model. This explains the cause of the higher in-cylinder gas pressure calculated by the Hohenberg model. The better agreement for the Hohenberg model on the in-cylinder pressure indicates that this model is more suitable for the MFPG prototype studied in this paper.
## 3 PolyCISAMR Algorithm
### Meshing an Individual Grain Using CISAMR
In such cases, picking either of the intersection points to relocate the mesh nodes leads to misrepresentation of the grain geometry, as shown in the _without treatment_ scenario in Figure 17b. To handle such cases, a new hanging node is added on the edge with the intersection point that has a larger distance to \(\mathcal{N}_{1}\) (here, edge \(\mathcal{N}_{1}\)-\(\mathcal{N}_{2}\)) before performing \(r\)-adaptivity. Relocating this hanging node to the intersection point followed by performing the sub-triangulation phase of CISAMR allows the grain geometry to be accurately captured in the final mesh. Another necessary modification in the CISAMR algorithm for meshing individual grains is the treatment of background elements with a very small edge in the vicinity of sharp corners after performing \(r\)-adaptivity, as shown for edge \(\mathcal{N}_{1}\)-\(\mathcal{N}_{2}\) in Figure 18. Without proper treatment, the sub-triangulation of such excessively deformed background elements leads to the creation of sub-triangles with an unacceptably high aspect ratio. Such cases can be identified by checking the ratio of the minimum element edge length \(h_{\text{min}}\) connected to a sharp corner (after \(r\)-adaptivity) to the original background mesh size \(h_{0}\). If \(h_{\text{min}}/h_{0}<\zeta\), where \(\zeta=0.25/2^{k}\) and \(k\) is the number of SAMR levels, we apply an _edge collapse_ algorithm to that edge by snapping the node that is not located on the sharp corner to the corner node. In Figure 18, this treatment strategy leads to collapsing \(\mathcal{N}_{2}\) to \(\mathcal{N}_{1}\), which eliminates the small background element edge and results in elements with proper aspect ratios after applying the sub-triangulation phase. Figure 17: Treatment of special cases leading to an inaccurate representation of the grain geometry in CISAMR due to the excessively small angle of a grain corner: (**a**) Cases 1 and 2; (**b**) Case 3. Figure 18: Removing high-aspect-ratio triangular elements from the final mesh by collapsing the small edge (\(\mathcal{N}_{1}\)-\(\mathcal{N}_{2}\)) of an excessively deformed background element in the vicinity of the sharp corner before performing sub-triangulation.
## 5 Discussion
### Efficacy of NN in Lung Cancer Diagnosis  
The DL models surpassed the conventional ML classifiers, with a three-hidden-layer NN trained for 800 epochs attaining a maximum accuracy of 92.86%. This result emphasizes the capability of DL to enhance lung cancer detection since NN can identify intricate nonlinear patterns within the dataset, yielding improved classification accuracy relative to the conventional ML models.  
The research investigated several NN topologies, including single-, dual-, and triple-hidden-layer models. The three-hidden-layer NN with a learning rate of 0.1 consistently surpassed the other models, indicating that deeper structures and prolonged training durations improve the performance. Nevertheless, increasing the number of hidden layers beyond three did not provide any improvements, suggesting that model optimization and hyperparameter adjustment are essential for reconciling the accuracy and computational efficiency.  
Figure 7: ROC curve for the NN model with one hidden layer, trained for 800 epochs.
## Abbreviations  
* EVI2 2-band Enhanced Vegetation Index
* AVHRR Advanced Very High-Resolution Radiometer
* AOI Area-of-Interest
* EVI Enhanced Vegetation Index
* GDVI Generalized Differential Vegetation Index
* GRVI Green-Red-Vegetation-Index
* GPP Gross primary production
# Evaluation of the Impact of Fines on the Performance of Sub-Base Materials
## 3 Test Results and Discussion
### Effect of Fines on the Compaction Characteristics of Soil Aggregate Mixtures  
The compaction curves of the samples belonging to gradation 'B' are shown in Figure 2. The figure depicts that, with the increase in fines from 0 to 15%, the OMC increases from 5.5 to 8.8%, whereas MDD decreases from 21.75 to 20.68 kN/m3. Likewise, for the samples with the AASHTO gradation 'C' that showed similar trends, i.e., with an increase in fines up to 15%, OMC increases in the range of 5.8-9% with a reduction in MDD value from 20.90 to 20.20 kN/m3, as presented in Figure 3.  
Specifically for gradation 'B', with the addition of 5% of non-plastic and medium-plasticity fines, MDD first increased from 21.64 KN/m3 to 21.92 kN/m3 and 21.74 kN/m3, respectively; however, with the inclusion of 5% of high-plasticity fines, the value of MDD  
Figure 3: Compaction curves of the composite samples (gradation C).  
Figure 2: Compaction curves of the composite samples (gradation B).  
reduced to 21.53 kN/m\({}^{3}\). Moreover, the addition of 10 and 15% of non-plastic fines reduced the MDD value to 21.62 kN/m\({}^{3}\) and 21.35 kN/m\({}^{3}\), respectively. Similarly, MDD values for medium-plasticity fines were 21.33 kN/m\({}^{3}\) and 21.15 kN/m\({}^{3}\) and, for high-plasticity fines, they were 21.01 kN/m\({}^{3}\) and 20.84 kN/m\({}^{3}\). The variation in MDD with the change in fine content for gradations B and C is shown in Figures 4 and 5, respectively.  
It can be inferred from Figures 4 and 5 that the addition of 5% of non-plastic and medium-plasticity fines caused an almost 10% increase in the value of MDD for both gradations. However, beyond this, the maximum dry density decreased with the further addition of fines. The increase in the value of MDD may be attributed to the filling of void spaces, thus causing the denser packing of the samples, which resulted into a higher unit weight. However, after 5% fines, the aggregates are being replaced by fines that have a lesser specific gravity, resulting in a lower maximum dry density of the matrix.
## 3 Results and Discussions  
### FTIR Analysis of Untreated and Treated WSP  
Figure 6 shows the FTIR spectrums of untreated and treated WSP. The spectrum of untreated WSP conforms to that of the natural fibers proved in references [7; 40]. The band at 1744 cm\({}^{-1}\), contributing to the stretching vibration in carbonyl C = O groups, vanished after the introduction of NaOH into WSP, indicating that the hemicelluloses, along with ash, oil, and other impurities had been removed. The band of lignin at 1038 cm\({}^{-1}\) cleaved into several small peaks after the treatment with NaOH. This implied that the number of lignin increased due to the disappearance of hemicellulose.  
After KH550 modification, the peak at 829 cm\({}^{-1}\) due to the C-H out-of-plane bending vibration faded away when the silane concentration was increased. Meanwhile, the-Si  
Figure 6: The Fourier-Transform Infrared Spectra (FTIR) spectra of WSP treated with NaOH/KH550. The orange box highlights the removing of hemicelluloses. The gray box highlights the cleavage of a lignin peak and the dark green box highlights the condensation reaction and auto polymerization.  
band presented at 749 cm\({}^{-1}\), indicated that a condensation reaction had happened between the WSP (-C-H-) and silane coupling agent (-Si-O-H-). A downward peak that was relevant to the -Si-O-Si symmetric stretching showed up at 690 cm\({}^{-1}\), because of the auto polymerization among the silanol groups. The auto polymerizate deposited on the surface of WSP, went into the micro pores of WSP and then formed an intertangling effect. The condensation reaction and the auto polymerization can improve the surface compatibility between the polymer matrix and the WSP.
# Bistatic Sea Clutter Suppression Method Based on Compressed Sensing Optimization
## 2 Overview of Signal Modelling and Compressed Sensing
### Signal Modelling
If other interference signals are ignored, the received echo is mainly composed of target echo \(x\), sea clutter \(c\), and Gaussian noise \(n\). Therefore, \(x\) can be expressed as:  
\[x=s+c+n \tag{4}\]
## 2 Materials and Methods
### The Phase Equilibrium Calculation Theory Considers the Capillary Force Effect
\(Z_{\alpha}\) is the phase compressibility factors; \(A_{\alpha}\) and \(B_{\alpha}\) are the PR-EOS parameters for the liquid and gas phases, with the specific expressions given as:  
\[A_{\alpha}=\frac{ap_{\alpha}}{R^{2}T^{2}}\quad B_{\alpha}=\frac{bp_{\alpha}}{ RT} \tag{6}\]  
According to Gibbs free energy principles [12], the smallest real root of \(Z_{l}\) represents the liquid-phase compressibility factor, while the largest real root of \(Z_{v}\) corresponds to the gas-phase compressibility factor. Using these factors, fugacity and fugacity coefficients can be determined. At phase equilibrium, the fugacity of the gas phase equals that of the liquid phase:  
\[f_{l}^{\ i}(T,P_{l},x_{i})=f_{v}^{\ i}(T,P_{v},y_{i}) \tag{7}\]  
where \(f_{l/v}^{\ i}\) are the fugacities of component \(i\) in the gas and liquid phases, respectively. For a gas-liquid two-phase system, fugacity is defined as:  
\[f=\varphi p \tag{8}\]  
where \(\varphi\) is the fugacity coefficient, which is expressed as follows [12]:  
\[ln\varphi=Z-1-\ln Z+\frac{1}{RT}\int\limits_{\infty}^{V}\left(\frac{RT}{V}-p \right)dV \tag{9}\]  
Based on [PERSON]'s research [52], the modified fugacity coefficients for the gas and liquid phases, incorporating capillary forces, are expressed as:  
\[\ln\varphi_{v}^{i}=\frac{b_{i}}{b}(Z_{v}-1)-\ln(Z_{v}-B_{v})- \frac{Z_{v}}{2\sqrt{2}B_{v}}\left(\frac{2\sum\limits_{j=1}^{N}y_{j}a_{ij}}{a} -\frac{b_{i}}{b}\right)\ln\left[\frac{Z_{v}+(1+\sqrt{2})B_{v}}{Z_{v}+(1-\sqrt {2})B_{v}}\right] \tag{10}\] \[\ln\varphi_{l}^{i}=\frac{b_{i}}{b}(Z_{l}-1)-\ln(Z_{l}-B_{l})- \frac{Z_{l}}{2\sqrt{2}B_{l}}\left(\frac{2\sum\limits_{j=1}^{N}x_{j}a_{ij}}{a} -\frac{b_{i}}{b}\right)\ln\left[\frac{Z_{l}+(1+\sqrt{2})B_{l}}{Z_{l}+(1-\sqrt {2})B_{l}}\right]\]  
where \(a\) and \(b\) represent the PR-EOS parameters for the mixture, while \(a_{i}\) and \(b_{i}\) denote those for pure component \(i\). The calculation methods for these parameters are detailed in Reference [12].
# A Coarse-Grained Molecular Model for Simulating Self-Healing of Bitumen
## 2 Establishment of Molecular Model
### Coarse-Grained Force Field
#### 2.3.1 Non-Bond Potential  
Since van der Waals potential energy dominates the bitumen system's non-bonded potential, the van der Waals force is described as the Lennard-Jones potential [27] in this work. Combined with the \"bottom-up\" coarse-grained method developed by [PERSON] [28] and [PERSON] et al. [29] and starting from the microscopic molecular model, the radial distribution function \(g(r)\) is obtained by all-atom simulation of every two coarse beads with van der Waals force. The Boltzmann inversion formula [30] is used to calculate the Lennard-Jones potential function from the radial distribution function, as shown in Formula (2). The Lennard-Jones potential function is provided in Formula (3). It is used to define the non-bonded potential function.  
\[U_{non}(r)=-K_{B}T\ \ln[g(r)] \tag{2}\]  
where \(K_{B}\) is the Boltzmann constant, \(T\) the temperature, and \(g(r)\) is the radial distribution function.  
This potential is recast into a coarse-grained Lennard-Jones potential  
\[U_{LJ}(r)=4\epsilon\bigg{[}\Big{(}\frac{\sigma}{r}\Big{)}^{12}-\Big{(}\frac{ \sigma}{r}\Big{)}^{6}\bigg{]} \tag{3}\]  
where \(\epsilon\) and \(\sigma\) are the parameters of the force field. These parameters are estimated by comparing Equation (3) to the all-atom potential: \(\epsilon\) corresponds to the lowest energy point of the Lennard-Jones function, while \(\sigma\) to the value of \(r\) when the potential is 0. Table 4 shows \(\epsilon\), and Table 5 shows \(\sigma\).
# A Lightweight Chaotic Map-Based Key Agreement Scheme for the Advanced Metering Infrastructure
## 8 Conclusions  
The AMI is an important part of the smart grid. In this article, a lightweight key agreement scheme based on chaotic map is designed for the AMI. The proposed scheme realizes mutual authentication, key agreement and multicast key generation between entities, so that secure data transmission can be carried out between entities. Security analysis shows that the proposed scheme can meet the security requirements of the AMI and can resist commonly known attacks. The performance analysis result shows that compared with the existing schemes, our scheme requires the lowest computation and communication cost. Designing a secure and efficient key agreement protocol has always been the focus of the field of information security in the smart grid environment. The proposed scheme fully considers the message transmission mode of the AMI system and the limitations of resource constraints, and minimizes the overhead caused by security measures. Compared with the existing schemes, the proposed scheme is more suitable for the AMI scenario.  
Conceptualization, B.W. and Y.F.; methodology, Y.F.; validation, S.Z. and Y.F.; formal analysis, Y.F.; data curation, Y.F.; writing--original draft preparation, Y.F.; writing--review and editing, B.W. and S.Z.; visualization, S.Z.; supervision, B.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Fundamental Research Funds for the Central Universities (2018 ZD06).  
Not applicable.  
The original contributions presented in the study are included in the article material, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 5 Enhancement of Biological Treatment Systems
### Constructed Wetlands (CWs)  
SDB is gaining attention as a potential substrate or additive in treatment wetland applications. Compared to gravel, biochar provides greater surface area and adsorption capacity for contaminants in wetland beds. CWs with SDB remove 60-95% inorganic or organic contaminants, based on complexation, ion exchange, precipitation, and pore-filling [77]. [PERSON] et al. [78] explored the effectiveness of SDB as a substrate in CWs for nitrogen removal efficiency and discovered that it promoted the enrichment of genes involved in nitrate metabolism. While a study of [PERSON] et al. [79] indicated that denitrification in CWs benefits from SDB, it supplies additional electron donors, and its redox-active surface can facilitate a partial reduction in nitrates. Integrating wetlands with electrochemical components, suchas MFC-CW hybrids, is an innovative strategy in which SDB serves both as a conductive medium and a biofilm support [58]. Even in traditional CWs, SDB aids in regulating ammonium and phosphate levels by adsorbing these nutrients when biological uptake is low, effectively buffering concentration fluctuations. As SDB gradually reaches saturation, it can be collected and repurposed as a nutrient-rich fertilizer, contributing to a sustainable nutrient recycling system [80]. Recent publications have highlighted the potential of SDB-based CWs in advancing carbon neutralization [81,82]. CWs are vital in mitigating greenhouse gases (GHGs), such as N\({}_{2}\)O and CH\({}_{4}\) emissions from CWs, with hydraulic retention time and wetland type playing key roles in mitigating CH\({}_{4}\) emissions [83].  
In summary, SDB revolutionizes constructed wetlands by synergizing adsorption, catalysis, and microbial enhancement, yet critical gaps persist. While its multifunctional improves nitrogen removal and GHG mitigation, long-term stability under dynamic wetland conditions and potential ecological risks (e.g., metal/nanoparticle leaching) remain underexplored. The integration with hybrid systems (e.g., MFCs-CWs) showcases innovation but lacks cost-benefit analyses for scalability. Future research must prioritize standardized life-cycle assessments to balance environmental benefits with unintended consequences, ensuring SDB-driven CWs align with circular economy and carbon neutrality goals.