esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Embedding-Based Methods  
Embedding-based methods [6; 16; 17; 18; 19; 20; 21] aim to map entities and relations in a knowledge graph into a low-dimensional vector space, enabling semantic similarity computations via vector operations. (Classical models like TransE [5]) learn such embeddings by minimizing the distance between head and tail entities via relation translation. Extensions such as TransR [22] and ComplEx [23] support more complex relation types via matrix or tensor decomposition.  
In the context of recommendation, methods like CKE [6] combine collaborative filtering with KG embeddings, integrating TransR with matrix factorization to jointly learn user-item relevance.Other works (e.g., KTUP [17]) employ joint optimization frameworks to enhance both recommendation quality and KG completion.  
Despite their efficiency, these methods face two core limitations:  
* They primarily capture first-order relations, lacking mechanisms for higher-order semantic propagation.
* They operate under static embedding assumptions, failing to reflect dynamic user interests or contextual shifts over time.
# Machine Learning-Based Satellite Routing for SAGIN IoT Networks
## 2 Architecture Design
### Application Layer  
Satellites play an irreplaceable role in IoT. Smart medicine, smart home, industrial Internet, and other IoT businesses have grown significantly in recent years. Some services need great efficiency over a long length of time and with high dependability. To meet the needs of these services and ensure quality of service, the network has been extended from the ground broadband network to the ground and satellite broadband network. SIoT also plays an important role in some mission-critical applications [13]. The failure of these tasks may lead to significant loss of life and property. For example, in mission-critical military applications, border protection and territorial defense are linked. These applications must successfully protect valuable resources. However, these mission-critical applications present many risks to human life. Therefore, the collection and driving of information should be completed by IoT sensors. The integration of satellites and the Internet of Things improves the coverage and reliability of these mission-critical application networks. At the same time, the wide coverage of SIoT enables application prospects in farms, oil/gas facilities, power grids, etc. [14]. The wide possibilities of SIoT make it an area of particular research interest.  
The above mainly introduces the content of satellite routing. It can be seen that, in SAGIN, how to ensure the reliability and effectiveness of satellite communication transmission is very important. Therefore, in the following sections, the method of improving the effective transmission of communication in the SAGIN architecture is mainly introduced, that is, the method proposed to use the extended ELM algorithm to predict the congestion of the communication path.
## 4 Discussion
To better analyze how the addition of a compressor improves the performance of the capsule in terms of transonic effects, the equivalent blockage ratio among the two cases is represented in Figure 15. This value has been obtained from the CFD curves shown in Figure 14, making both passage Mach numbers equal. From the plotted data, one can conclude again that the installation of a compressor allows the use of smaller tunnels if the same blockage is to be achieved, which implies a higher BR when using turbomachinery. In addition, note that the trend is linear for 500 km/h and also for 700 km/h until the Kantrowitz limit is achieved for the _compressor_ case (BR between 0.5 and 0.6). Due to this linearity, until the choke of the flow occurs, the following regression, independent of the speed, can be assumed:  
\[\beta_{Comp}=2.82\beta_{magelov}. \tag{9}\]  
This relation indicates that the BR of the tunnel can be 2.8 times higher when installing a compressor on the vehicle. In terms of physical parameters, this means that, for the same vehicle cross-section, the area of the tube can be 2.8 lower with aerodynamic propulsion. The final study that was conducted was to obtain the pressure for the _base_ case that matches the same power consumption as the _compressor_ one, using data from [15]. To do this, a linear relation of the drag with the pressure must be assumed. This trend was reported not only by the present authors in [15], but also by [PERSON] et al. in [17]. Having said that, the results of this study are shown in Figure 13.0 For medium or high blockage ratios, 0.02 atm at 500 km/h and 0.03 atm at 700 km/h is required to get the same performance. In terms of the real system, this fact implies an increase in the complexity and hazard of the case without a compressor. Figure 13.1 Blockage ratio comparison for the case with and without compressor.
###### Abstract  
In constructing high-geo-stress soft rock tunnels, the major deformation disaster of the surrounding rock has always been the main problem faced during construction. The research on the deformation and loose zone characteristics of large deformation tunnels has positive significance for the safe and rapid construction of tunnels. Therefore, based on the Yuntunpu large deformation tunnel, this article first analyzes the geological and deformation characteristics of the tunnel site area in response to the problem of high-geo-stress soft rock large deformation. Subsequently, on-site testing and analysis were conducted on the loose zone characteristics of four tunnel sections. Finally, based on the comprehensive analysis of tunnel deformation and loose zone characteristics, the causes of large deformation in the tunnel are analyzed. The results indicate that the large deformation characteristics of the Yuntunpu Tunnel are mainly manifested as a large initial deformation rate of the surrounding rock, a short self-stabilization time of the surrounding rock, a large cumulative deformation amount, and a long deformation duration. The Yuntunpu Tunnel is influenced by the grade and structure of the surrounding rock, with a loosening zone ranging from 12 to 14 m, and the wave velocity variation characteristics exhibited by different grades of surrounding rock vary greatly. Adopting collaborative active control of long and short anchor rods is recommended to limit the continued development of loose zones and the deformation of surrounding rocks. The large deformation of tunnels is mainly affected by high geo-stress, formation lithology, geological structure, engineering disturbance, and groundwater. Among them, high geo-stress and formation lithology are the decisive and important factors for the occurrence of major deformation disasters in the tunnel.  
high geo-stress; double-line railway; large deformation tunnel; characteristics of loose zone; deformation characteristics; field test +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### CNCs Characterization
#### 2.3.2 X-ray Diffraction (XRD) Analysis  
XRD (Bruker D8 Advance, Billerica, Massachusetts, MA, USA,) was later used to analyze the MCC and CNCs samples' crystalline, size, and structural characteristics; specifically, a technique employing Cu K\(\alpha\) (\(\lambda\) = 1.5406 A) was used to obtain the samples' structural information in diffraction angle 2\(\theta\) of 5\({}^{\circ}\) to 80\({}^{\circ}\) at a step angle of 0.1\({}^{\circ}\) with scanning rate of 0.025\({}^{\circ}\) s\({}^{-1}\). To perform the analysis, a small amount of sample powder was placed into a sample holder and inserted into the XRD instrument, which was equipped with the Cu K\(\alpha\) radiation source.
## 2 Methods
### Transform Subproblems from the MILP to QUBO  
In this section, we consider converting our MILP problem into the QUBO. The QUBO representation provides an opportunity to run the problem at quantum annealers (e.g., at the D-Wave computer) or at other machines capable of finding the ground state of the Ising model. Let us take the following linear system:  
\[A\vec{x}=\vec{b}\Leftrightarrow\sum_{j}a_{ij}x_{j}=b_{i}\quad(\forall i\in\{ 0\ldots M-1\}), \tag{33}\]  
where \(A\in\mathbb{R}^{M\times N},\vec{b}\in\mathbb{R}^{M},\vec{x}\in\{0,1\}^{N}\). Move all terms from the right-hand side to the left:  
\[\sum_{j}a_{ij}x_{j}-b_{i}=0 \tag{34}\]  
Square all terms in the lhs and sum up all of them:  
\[\left(\sum_{j}a_{0j}x_{j}-b_{0}\right)^{2}+\left(\sum_{j}a_{1j}x_{j}-b_{1} \right)^{2}+\cdots+\left(\sum_{j}a_{(M-1)j}x_{j}-b_{M-1}\right)^{2} \tag{35}\]  
One can see that minimizing the expression (35) is equivalent to finding the solution of Equation (33). Summing up, we write the equivalent form for MILP and QUBO as follows:  
\[A\vec{x}=\vec{b}\Leftrightarrow\min_{\vec{x}}\left(\left(\sum_{j}a_{0j}x_{j}-b _{0}\right)^{2}+\left(\sum_{j}a_{1j}x_{j}-b_{1}\right)^{2}+\cdots+\left(\sum_ {j}a_{(M-1)j}x_{j}-b_{M-1}\right)^{2}\right) \tag{36}\]
# An Insight into the Chemistry of Cement--A Review
## 3 Hydration Step Involving OPC
### Mechanism and Heat of Hydration of OPC
#### 3.3.4 Post-Acceleration Period  
During this step, the hydration rate decelerates, and the \"inner\" C-S-H starts forming from hydration reactions involving both C\({}_{3}\)S and C\({}_{2}\)S. According to [PERSON] et al. [79], the acceleration period involves the nucleation (and growth) of C-S-H clusters onto the surfaces of the cement grains. The deceleration period, instead, involves a substantial decrease in the growth rate of C-S-H that, at this point of the process, covers much of the grains surface. In fact, this final period of hydration involves a low hydration rate, probably caused by the less available space. In particular, the formation of hydrates occurs only in space filled with water. Such a lack of available space is attributable to two phenomena: (i) the depletion of the water volume (necessary for hydrates' precipitation), and (ii) the sequestration of the remaining water into pores whose critical pore sizes are smaller for precipitation [80].  
Moreover, together with the formation of \"inner\" C-S-H, there is a continuous consumption of gypsum, until it is fully consumed. In this way, the concentration of the sulphate ions in the liquid decreases, and the AFt phase (produced during the earlier steps of hydration) begins to chemically react with further C\({}_{3}\)A and C\({}_{2}\)(A,F), thus forming the AFm phase [81].
## 5 Experimental Results
### Experiment 2: Results Based on the ISIC-2016 Dataset
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Classifier** & **Accuracy** & **Recall** & **Precision** & **F1-Score** \\ \hline \multirow{4}{*}{ISIC-2016} & DT & 0.8259 & 0.8258 & 0.8192 & 0.8221 \\  & LDA & 0.8602 & 0.8601 & 0.8518 & 0.8541 \\  & SVM & 0.8602 & 0.8601 & 0.8546 & 0.8567 \\ \hline \multirow{4}{*}{PH2} & DT & 0.9179 & 0.9178 & 0.9206 & 0.9177 \\  & LDA & 0.9536 & 0.9535 & 0.955 & 0.9535 \\  & SVM & 0.9571 & 0.9571 & 0.9574 & 0.9572 \\ \hline \multirow{4}{*}{Chest-XRay} & DT & 0.8253 & 0.8253 & 0.8384 & 0.8156 \\  & LDA & 0.8478 & 0.8477 & 0.8739 & 0.8373 \\ \cline{1-1}  & SVM & 0.8718 & 0.8717 & 0.8906 & 0.8651 \\ \hline \multirow{4}{*}{Blood-Cell} & DT & 0.8786 & 0.8785 & 0.9001 & 0.8809 \\ \cline{1-1}  & LDA & 0.8834 & 0.8833 & 0.9041 & 0.8853 \\ \cline{1-1}  & SVM & 0.8846 & 0.8845 & 0.905 & 0.8865 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification results without using feature selection optimization. better with the features selected by the AHA-AO, PSO, and MFO; the LDA achieved the best results with the WOA, AO, and AHA. To conclude, the proposed AHA-AO achieved the best results with the three classifiers. This means that the AHA-AO selected the best features that captured the majority of the variance in the ISIC-2016 dataset.
## 3 Results and Analysis
### Shear Test of Filled Soil-Concrete Interface  
In order to study the influence of the filling materials on the shear characteristics of the soil-concrete interface, the soil-concrete interface was filled with 2 mm of medium-coarse sand or silty sand, and the silty sand-concrete interface was filled with 2 mm of silt. Figure 11 shows the variation of the shear stress with the shear displacement, and Figure 12 shows the variation of shear strength. Compared with the condition without filling materials, the shear stress of the concrete interface with a thin layer of sandy soil decreased slightly, indicating that the thin layer of sandy soil had the effect of reducing the shear strength of the interface. In terms of the strength parameters, the interfacial friction angle was \(35.28^{\circ}\) without filling material, which decreased to \(33.39^{\circ}\) and \(33.54^{\circ}\) after filling with thin layers of medium-coarse sand and silty sand, respectively. The cohesion without filling was \(21.32\) kPa, which changed to \(16.14\) and \(16.803\) kPa after filling with thin layers of medium-coarse sand and silty sand, respectively. The reduction in the friction angle and cohesion was mainly because the sliding friction on the soil-concrete interface was partially converted into rolling friction between the sand particles and the concrete, which reduced the friction coefficient. In addition, the sandy soil also played a certain role in reducing the adhesion of the clay to the concrete.  
Figure 9: Fitted shear strength curves for different interfaces: (**a**) silty sand, (**b**) medium-coarse sand, (**c**) clay, and (**d**) sandstone.  
Figure 11: Effect of filling material on shear characteristics of soil–concrete interface: (**a**) clay filled with sandy soil and (**b**) silty sand filled with silt.  
Figure 10: Variation of shear strength parameters of different interfaces with soil moisture content: (**a**) silty sand, (**b**) medium-coarse sand, (**c**) clay, and (**d**) sandstone.  
Silt with a 50% moisture content was filled in the silty sand-concrete interface, and the variation of the shear stress with the shear deformation was basically the same as that of the unfilled interface. The interfacial friction angles were 32.41\({}^{\circ}\) and 31.62\({}^{\circ}\) before and after silt filling, respectively, and the corresponding interfacial cohesion values were 6.37 and 6.577 kPa. Thus, the friction angle decreased, whereas the cohesion increased. However, the effect on the overall shear strength of the interface was minimal. This was mainly because the silt increased the interfacial adhesion between the soil and the concrete, resulting in an increase in the interfacial cohesion. Moreover, the silt had a certain lubricating effect, and therefore, reduced the interfacial friction angle.
# Mass Flow Scaling of Gas-Assisted Coaxial Atomizers
## 5 Conclusions  
This study aims to lay the groundwork for liquid mass flow scaling of gas-assisted coaxial atomizers. An approach that keeps the most relevant dimensionless parameters constant (\(We_{aero}\), \(GLR\)) was chosen. Four liquid mass flow steps (\(20/50/100/500\) kg\(\cdot\)h\({}^{-1}\)), each operated at \(We_{aero}\ =\ 250/500/750/1000\), were investigated in terms of spray quality (\(D_{32}\), \(ID_{32,m}\)) and primary breakup. For each liquid mass flow, a specific nozzle was designed. A high-speed camera as well as a phase Doppler anemometer were utilized for spray investigation. The following conclusions can be drawn:  
1. Comparable measurement planes for varying liquid mass flows based on the free jet theory were determined;
2. An increase in liquid mass flow led to an increase in droplet size while keeping \(We_{aero}\) and \(GLR\) constant;
3. An increase in \(We_{aero}\) led to a reduction in droplet size at constant liquid mass flow;
4. An empirical model for liquid mass flow scale-up of gas-assisted coaxial nozzles was derived, based on gas momentum flow \(J_{gas}\).  
Conceptualization, S.W. and T.J.; investigation, S.W.; data curation, S.W.; writing--original draft preparation, S.W.; writing--review and editing, T.J. and T.K.; visualization, S.W.; supervision, T.K. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the financial support of the Helmholtz Association of German Research Centers (HGF) in the context of the research program Materials and Technologies for the Energy Transition (MTET). We acknowledge support by the KIT-Publication Fund of the Karlsruhe Institute of Technology.  
The authors declare no conflict of interest.
# Dual-Boost Inverter for PV Microinverter Application--An Assessment of Control Strategies
## 2 Operation and Modeling of the DBI  
The DBI is composed by two DC-DC bidirectional boost converters, sharing the input source (PV panel), as illustrated in Figure 3. Note that both boost converters operate in a non-conventional manner; indeed, each boost generates a low-frequency (60 Hz)  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Type of Load** & **Control Strategy** & **Control Objectives—Surface** & **Ref.** \\ \hline \multirow{8}{*}{**1 st family**} & R and non-linear load & Linear control (PIs) & Capacitor voltage and inductor current & [7, 8] \\  & R and RC & Linear control (PIs) & Capacitor voltage and inductor current & [9] \\  & Grid-connection & Linear control (PRs) & Capacitor voltage and inductor current & [10] \\  & Grid-connection & Linear control (PR+PI) & Capacitor voltage and inductor current & [11] \\  & R, L, and non-linear load & Sliding mode control & \(\sigma=k_{\rm{(}}t_{\rm{L1}}-\dot{t}_{\rm{L1}}^{*})+k_{\rm{2}}(v_{\rm{c1}}-v _{\rm{c1}}^{*})-k\) & [12] \\  & Resistance & Dynamic linearizing modulator & Capacitor voltage & [13] \\  & Grid-connection & Flatness-based control & Energy stored in capacitors and inductors & [14] \\  & Grid-connection & Finite control set—MPC & Capacitor voltage and inductor current & [15] \\ \hline \multirow{2}{*}{**2 nd family**} & R and non-linear & Sliding mode control & \(\sigma=i_{\rm{L1}}-i_{\rm{L2}}-k\) & [16] \\  & Resistance & Sliding mode control & \(\sigma=i_{\rm{L1}}-i_{\rm{L2}}-k\) & [17] \\  & Grid-connection & Sliding mode control & \(\sigma=i_{\rm{L1}}-i_{\rm{L2}}-k\) & [18, 19] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Review of the control strategies for the dual-boost inverter.  
AC output voltage with a DC-bias. In order to obtain only an AC output, a differential connection between both boost converters (upper terminal of each boost's output capacitor) is performed. Moreover, a \(\pi/2\) phase shift between the AC components (and having the same DC-bias) allows eliminating the DC-bias while duplicating the AC component. As can be observed, the inverter presents four semiconductors, two inductors on the input and two capacitors in the output. The grid is connected between the positive nodes of the output of each boost converter, i.e., differential connection.  
The operation of the converter and related control depends on the adopted control strategy. In this work, two different switching strategies are considered. Indeed, the operation of the DBI can be defined by considering individual operation of each boost converter [7]. On the other hand, it is possible to use a second switching strategy, by considering a global scheme for the whole structure [17]. The two switching strategies are detailed in the following section, and a model of each operation is deduced.
# Finite-Time Tracking Control of a Flexible Link Manipulator Based on an Extended State Observer
## 4 Simulation and Experimental Verification
### Estimation of Unknown Parameters of the System
\(\hat{n}\) is the estimated coefficient related to the deformation angle of the flexible link, which is significantly different from its nominal value. This is because the flexible link is equivalent to a linear spring in the lumped parameter model, and the viscous damping coefficient of the link is ignored. Due to this simplified method, the lumped parameter model cannot accurately reflect the true deflection angle of the flexible link. In [25], the shortcomings of the lumped parameter model were verified by experiments in both the frequency domain and the time domain. Although there is a certain error between the parameter estimates and the nominal values, the results provide an important design basis for the controller design, especially the system input gain.
## 2 Methodology
### Description of Segment Twist Control Configuration  
The active twist control schedules and segment twist control configurations are investigated in this study to determine the optimal twist control configuration in order to reduce the rotor power. The segment twist control configuration is determined, as shown in Figure 7. The segment twist control configuration includes the segment layout and control schedule of each segment. The active twist actuator array embedded on the blade is divided into several segments spanwise, and different twist control schedules are applied. It is assumed that, within a control segment, the actuators generate a torsional couple, which results in a linear variation of the twist angle spanwise.  
\begin{table}
\begin{tabular}{c c c} \hline
**Condition** & **Weight Coefficient (\(C_{W}\))** & **Advanced Ratio (\(\mu\))** \\ \hline A & 0.0065 & 0.25 \\ B & 0.0065 & 0.35 \\ C & 0.0083 & 0.25 \\ D & 0.0083 & 0.35 \\ \hline \end{tabular}
\end{table}
Table 2: Summary of flight conditions.  
Figure 6: Comparison of the prediction result and flight test data.  
Figure 7: Schematic diagram of the segment twist control configuration.  
The active twist control model defines the local pitch angle increment that is generated by continuous torsional deformation of the blade, which is derived from the transient active twist rate.  
\[\theta(r_{B},\psi)=\theta_{0}+\theta_{1c}\cos\psi+\theta_{1s}\sin\psi+\int_{0}^{ r_{B}}\theta_{A}(r,\psi)dr \tag{3}\]  
where \(\theta\) is the local pitch angle, \(r_{B}\) is the location of the concerned blade section, \(\theta_{0}\) is the collective pitch angle, \(\theta_{1c}\) is the lateral cyclic pitch angle, \(\theta_{1S}\) is the longitudinal cyclic pitch angle, \(\theta_{A}\) is the local twist rate derived from active twist control, and \(\psi\) is the azimuth.  
The multi-harmonic input of active twist control is described in terms of Fourier series, as follows.  
\[\theta_{A}(r,\psi)=\left\{\begin{array}{c}\theta_{Amin}\\ A_{0}(r)+\sum\limits_{i=1}^{5}[A_{ic}(r)\cos(i\psi)+A_{is}(r)\sin(i\psi)]\\ \theta_{Amax}\end{array}\right. \tag{4}\]  
where \(A_{0}\) is the amplitude of the twist rate for the 0/rev twist control, \(A_{ic}\) and \(A_{is}\) are the amplitudes of sinusoidal and cosinoidal components of the twist rate for the \(i\)/rev twist control, and \(\theta_{Amax}\) and \(\theta_{Amin}\) are the upper and lower bounds of the saturation limit of the local active twist rate, respectively. Up to 5/rev of Fourier series is considered.  
The segment twist control with a zoned active twist actuator array is structurally feasible. However, too many segments may lead to a proliferation of control system complexity. Selecting fewer segments and applying the optimized segment twist control schedules are the key to obtaining the best benefit. The segment layout is another key factor of segment twist control. Therefore, the present study concentrates on the optimization of the amplitudes of the multi-harmonic input, segment number, and locations of segment points.