esa-sceva/satcom-chunk-collection huggingface dataset:
# Progressive Multiple Alignment of Graphs
## Appendix A Additional Information on Graph Similarities
### Insufficiency of the Standard Version of VF2  
The standard version of VF2 uses an optimized routine for proposing candidate matches that might extend \(M\) (see Algorithm A1). Here, \(\preceq\) denotes an arbitrary total order assigned by the VF2 to the vertices of \(G_{2}\), used to optimize the selection of these vertices.  
```
Data: Graphs \(G_{1},G_{2}\) and a matching \(M\subset V(G_{1})\times V(G_{2})\) Result: Set \(P\) of match candidates for extending \(M\) // initialize \(P_{0}\) \(P_{0}\leftarrow\emptyset\); // candidates are neighbors of match but not in the match for\((n1,n2)\in M\)do \(N_{1}\leftarrow\) neighbors of \(n_{1}\) in \(G_{1}\) not matched by \(M\); \(N_{2}\leftarrow\) neighbors of \(n_{2}\) in \(G_{2}\) not matched by \(M\); \(P_{0}\gets P_{0}\,\cup\,N_{1}\times N_{2}\);  end for // alternatively candidates are all unpaired vertices if\(P_{0}=\emptyset\)then \(N_{1}\leftarrow\) set of unmatched vertices in \(V(G_{1})\); \(N_{2}\leftarrow\) set of unmatched vertices in \(V(G_{2})\); \(P_{0}\gets N_{1}\times N_{2}\);  end if // get only the \(\preceq\)-minimum of such vertices \(m\leftarrow\min_{\preceq}\{y\,:\,(x,y)\in P_{0}\}\); \(P\leftarrow\{(x,y)\in P_{0}\,:\,y=m\}\); return\(P\)
```  
Figure A1: **Left**: correlation between kernel distance and MCIS-based distance. **Right**: distribution of \(|\text{MCIS}|\). The panels on the top compare the initial graph to its mutants. The panels below compare pairs of mutants over each of the 50 scenarios.  
The example in Figure A2 illustrates why the standard implementation cannot be used for an MCIS search. With the given total order \(\preceq\), every branch of the search space produced by the VF2 will start with a pair of the form \((\bullet,1)\). Then, one branch will be associated to the match \(\{(e,1),(c,2),(g,3),(f,4)\}\). Now, candidate_matches_original\(()\) has to chose from pairs \((\bullet,m)\) with \(m=5,6,7\) as candidates. But, from these, the valid candidate will be \((h,5)\) given the condition on the minimum second entry. Since this pair does not satisfy the syntactic compatibility, see Section 3.3, this pair would not extend the match and the algorithm would backtrack. Thus, the MCIS enclosed in red dotted lines cannot be found by such implementation.  
Similar examples show that other strategies such as \"taking the feasible pair from the minimum\" instead of the \"minimum pair from the feasible\" also fail for certain inputs. In general, the difference lies in that the VF2 assumes that there should be at least one match mapping _every_ vertex in these graphs if they are indeed isomorphic, a condition that is not compatible with the MCIS search.
# Secure Transmission in mmWave Wiretap Channels: On Sector Guard Zone and Blockages
## 4 Performance Analysis  
Hereinafter, we first analyze secrecy performance of the direct transmission in term of the COP, the SOP and the secrecy throughput. Then, according to the same performance metrics, AN assisted transmission is investigated. Actually, direct transmission and AN assisted transmission are two special cases of adaptive transmission. Finally, considering the probabilities aforementioned that eavesdroppers may be beyond the sector secrecy guard zone and within the sector secrecy guard zone, we study the secrecy performance of the adaptive transmission scheme.
# Stress Dispersion Design in Continuum Compliant Structure toward Multi-DOF Endoluminal Forceps
## 2 Stress Dispersion Design Method
### Basic Design  
The developed forceps structure consists of three segments: grasping, bending, and driving, as shown in Figure 1. Although the grasping and bending segments are monolithically structured, these segments provide 3-DOF, with 2-DOF of bending and grasping through a tendon-sheath mechanism. A single-piece construction prevents losing parts in the operation. In particular, the grasping segment has an outer diameter of 2.5 mm in the proposed design. Moreover, the bending segment has an outer diameter of 2.3 mm because the outer peripheral surface is assumed to be covered with a heat-shrinkable tube. The driving segment has a compact structure consisting of five wires with an outer diameter of 0.3 mm and five tubes with an outer diameter of 0.6 mm. The wires are driving using servo motors or a connected mechanical joystick. When tension is applied to the center wire, the driving force transmission part is deformed in the loading direction, the side surface part elastically deforms, and the tip opens and closes. The bending uses an antagonistic drive method that bends up, down, left, and right through four wires. When tension is applied to the wire, a bending moment is applied to the tip, deforming the continuous hinges to bend.  
Several studies [29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50] have introduced continuum robots using hinge designs. As shown in Table 1, the hinge design can be classified into several types: (i) discrete joint [29; 30; 31], (ii) uni-directional compliant joint [32; 33; 34; 35], (iii) bi-directional compliant joint [36; 37; 38; 39; 40; 41], (iv) cross-axis compliant joint [42; 43], (v) helical compliant joint [44; 45; 46], and (vi) symmetric compliant joint [47; 48; 49; 50]. We selected a symmetric compliant joint, which has the advantage of high flexibility and strength for grasping and bending. By using elastic deformation, the mechanism could be operated without mechanical backlash.  
Figure 1: Structure of the designed forceps.
## 2 Establishment of Motion, Force Diagram, and Coordinate System  
The coordinate system, as illustrated in Figures 2 and 3 [16], is employed: Two fixed coordinate systems, \(oxyz\) and \(O^{\prime}X^{\prime}Y^{\prime}Z^{\prime}\), are taken. The centerline of the output shaft rotary cone and the axis of the input shaft are designated as axes \(oz\) and \(O^{\prime}Z^{\prime}\), respectively. Axis \(oy\) coincides with axis \(O^{\prime}Y^{\prime}\), perpendicular to axes \(oz\) and \(O^{\prime}Z^{\prime}\), forming angle \(\beta\). Furthermore, at the intersection of the three-pronged linkage, an auxiliary coordinate system \(O^{\prime}X^{\prime\prime}Y^{\prime\prime}Z^{\prime\prime}\) is established with axis \(O^{\prime\prime}Z^{\prime\prime}\) aligned with the output shaft, and plane \(X^{\prime\prime}O^{\prime\prime}Y^{\prime\prime}\) representing the plane of the three-pronged linkage, with axis \(O^{\prime\prime}Y^{\prime\prime}\) always perpendicular to axis \(ox\). In the initial configuration, it is assumed that the axis \(m_{1}\) of the plunger groove lies in plane \(X^{\prime}O^{\prime}Z^{\prime}\), while the connecting rod \(n_{1}\) lies in the moving plane \(X^{\prime\prime}O^{\prime\prime}Z^{\prime\prime}\) that coincides with the fixed plane \(xz\). When the input shaft passes through any point \(\varphi_{1}\), the connecting rod \(n_{1}\) on the output shaft rotates by a corresponding angle \(\varphi_{0}\) around the output shaft.  
In the given scenario, \(m_{1},m_{2},m_{3}\) refer to the axis of the plunger groove, while \(n_{1},n_{2},n_{3}\) represent the axis of the connecting rod. It is known that there is no relative sliding between the output shaft and the inner ring of the self-aligning bearing. The distance from the intersection of the three-pronged linkage to the center of the self-aligning bearing, denoted as \(l\), remains constant. The motion of the output shaft is a conical motion with a cone angle of \(\theta\). The radius of the motion trajectory of the center of the three-pronged linkage is \(\rho\). The angle between the center lines of the input shaft and the output shaft is \(\beta\). The distance from the axis of the plunger groove to the axis of the input shaft is \(r\). The distance between the center of the ball joint and the center of the connecting rod is \(h\).  
Figure 3: Coordinate system for motion analysis when the output shaft is installed with aligning bearings.  
Figure 2: The position of the input axis in the coordinate system (\(O^{\prime}X^{\prime}Y^{\prime}Z^{\prime}\)) at the angle (\(\varphi_{1}\)) of rotation.
# Cutting Force Estimation Using Milling Spindle Vibration-Based Machine Learning
## 3 Cutting Force Estimation Using LSTM-Based Time-Series Machine Learning  
The relationship between cutting force and tool wear was identified through previous experiments, and the data are used to develop a machine learning model utilizing spindle vibration data to estimate cutting force without relying on a dynamometer. Among the various machine learning techniques, the LSTM model is particularly effective for cutting force estimation because it captures dependencies in time-series data. Since cutting force variations are influenced by tool wear over time, utilizing historical data is essential for accurate estimation [24]. The LSTM model is well-suited for learning both short-term and long-term dependencies in time-series data, making it an excellent choice for cutting force estimation, as described below.  
First, the LSTM model effectively captures both long-term and short-term dependencies, allowing it to retain essential patterns in time-series data while integrating new information. This capability is particularly useful for modeling the complex and dynamic relationships between cutting force and vibration data [25].  
Second, the LSTM model demonstrates strong robustness in handling noisy signals, which is crucial in machining environments where vibration data often contain significant noise. By selectively extracting relevant time-series features and filtering out irrelevant noise, the model enhances estimation accuracy [26].  
Third, the non-linear relationship between cutting force and tool wear poses a challenge for traditional modeling approaches. The LSTM model excels in capturing such complex dependencies, improving the accuracy of cutting force estimation as the tool condition deteriorates [25].  
Finally, the LSTM model enables real-time cutting force monitoring, making it well-suited for integration into industrial tool condition monitoring systems. By utilizing past data, the model estimates cutting force at each time step, facilitating real-time tracking of force variations due to tool wear and supporting optimized tool replacement strategies [25].  
In summary, this study presents an LSTM-based approach for estimating cutting force using spindle vibration data. The proposed method provides a cost-effective and practical alternative to direct dynamometer-based force measurements while enhancing the efficiency of tool condition monitoring.
# Elastic Modulus Prediction Model of Foamed Concrete Based on the Walsh Formula
## 4 Model Validation
### Comparison with Test Results  
In this test, an instron1346 universal testing machine produced by the Instron company of Britain is used to carry out the indoor compression test of foamed concrete test block. The raw materials of foamed concrete are 42.5-grade ordinary Portland cement, class F fly ash, and QW-100-type foaming agent. The size of the test block is set to 100 mm \(\times\) 100 mm \(\times\) 100 mm. The curing conditions are implemented in a sealed plastic bag for constant-temperature curing, and the curing temperature is 20 \(\pm\) 2 \({}^{\circ}\)C. The test process is shown in Figure 8.  
In the test, a controlled strain rate of 50 mm/min is used to load the foamed concrete test block, and the stress-strain curve is immediately generated using the universal testing machine. Loading is conducted until the foamed concrete specimen fails, and the maximum failure load is recorded. The elastic modulus of the foamed concrete test block can be obtained by measuring the slope of the straight-line segment on the stress-strain curve.  
In accordance with the compressive strength test results of foamed concrete cubes, the test data of the test blocks are listed in Table 1.  
Figure 7: Flowchart of the calculation for the elastic modulus of foamed concrete.  
Figure 8: Schematic of test.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test Group** & **Water-Solid** & **Bubble Rate** & **Wet** & **28-Day Unconfined** & **Elastic** \\
**Number** & **Ratio** & & **Severity/kN\(\cdot\)m\({}^{-3}\)** & **Compressive Strength/MPa** & **Modulus/MPa** \\ \hline
1 & 1:1.8 & 0.71 & 5.39 & 1.52 & 718 \\
2 & 1:1.8 & 0.68 & 5.70 & 1.79 & 826 \\
3 & 1:1.8 & 0.68 & 6.03 & 1.67 & 778 \\
4 & 1:1.8 & 0.65 & 6.19 & 1.74 & 806 \\
5 & 1:1.8 & 0.65 & 6.25 & 1.77 & 818 \\
6 & 1:1.8 & 0.65 & 6.32 & 1.90 & 870 \\
7 & 1:1.8 & 0.62 & 6.67 & 1.96 & 894 \\
8 & 1:1.8 & 0.62 & 7.05 & 2.12 & 958 \\
9 & 1:1.8 & 0.59 & 7.07 & 2.54 & 1126 \\
10 & 1:1.8 & 0.59 & 7.53 & 2.80 & 1230 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test value of the compressive strength of foamed concrete test blocks.  
The porosity of foamed concrete and the mechanical constants of the matrix material are mainly involved in the analysis and calculation of this model. The former is mainly obtained by measuring the bubble rate in indoor tests, and the relevant parameters of the latter are listed in Table 2.  
From the experimental-theoretical comparison results given in Figure 9, the elastic modulus of foamed concrete calculated using the theoretical model reported in this paper is in good agreement with the laboratory test data. The overall error is within a reasonable range. In addition, the distribution law of the elastic modulus of foamed concrete with bulk density indicates that a certain linear quantitative relationship exists between its elastic modulus and its density, which can be described by an empirical formula.
## 2 Testing Apparatus and Methods
### Experiment Procedures  
In order to study the initiation and evolution characteristics of water-sand inrush in practical situation, as well as the effects of hydraulic pressure, confining stress and initial compactness of filling medium on water-sand inrush progress, a series of seepage tests were conducted, and the operation procedure is summarized as follows:  
* Soil specimen loading: before start, Vaseline is smeared uniformly on the inside of specimen chamber to prevent water leakage along the inner wall; the soil sample is loaded into the specimen chamber by layers, then compacted to the specific compactness. The perforated plate I is placed on the top of the soil sample after loaded.
* Specim saturation: In order to simulate the hydraulic state of filling medium in karst cavity filled with water, the soil specimen will be fully saturated before seepage tests start. After the soil sample is loaded, the low-pressured water is applied on the top of soil sample to pre-saturate the soil sample, until the flow rate of discharged water maintains stable.
* Water and soil particles collection: after the saturation step, load the hydraulic pressure and confining pressure to launch the seepage test. Eroded sand particles are separated and assembled  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Number** & **Relative Density \(D_{\rm{r}}\)** & **Hydraulic Pressure \(P_{w}\) (MPa)** & **Confining Pressure \(P_{c}\) (MPa)** \\ \hline
1 & 0.25 & 0.4 & 0.2 \\
2 & 0.50 & & \\
3 & 0.75 & & \\
4 & 0.50 & 0.2 & 0.2 \\
2 & & 0.4 & \\
5 & & 0.6 & \\
6 & 0.50 & 0.4 & 0.0 \\
2 & & & 0.2 \\
7 & & & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experiment schemes.  
by a sieve, with the mesh size of 0.05 mm. Eroded sands and discharged water are collected and weighed every 1 minute until the test ends.
## 3 Results and Discussion
### Comparison of \(\alpha\)-Amylase Activity during AAO and ABA Production  
To gain a deeper understanding of saccharification during fermentation, the \(\alpha\)-amylase activity was measured for multiple time points. The \(\alpha\)-amylases are extracellular enzymes that randomly hydrolyze starch into disaccharides and oligosaccharides, ultimately leading to the production of monosaccharides and sugar alcohols through carbohydrate metabolism [22].  
The trends observed for the RS contents mirrored the \(\alpha\)-amylase activity rates for all samples (Figure 2B), with all measurements being significantly different for both time points across and between microorganisms. The AAO samples consistently exhibited higher enzymatic activity rates at every time point (\(p\) < 0.001), reaching a peak at 24 h (306.8 units/mL), while ABA also peaked at 24 h, albeit with a lower value (120.3 units/mL). Notably, at the start of the fermentation (0 h), AAO displayed significantly higher \(\alpha\)-amylase activity (148.5 units/mL) compared to ABA (71.2 units/mL), which is in line with previous findings for koji fermentation with the same organisms [22]. During the initial 8 h, the \(\alpha\)-amylase activity remained higher in AAO (276.4 units/mL) compared to ABA (110.4 units/mL). This is possibly attributable to different optimal temperature preferences for the enzymes in the two organisms [4]. It could also be due to greater production of extracellular enzymes by _A. oryzae_ during koji fermentation compared to _B. amyloliquefaciens_.  
As was also done for the RS content, the \(\alpha\)-amylase activity for the ABA was measured at both 70 \({}^{\circ}\)C and 55 \({}^{\circ}\)C (Figure 2B). The \(\alpha\)-amylase activity was higher for amazakes prepared at 70 \({}^{\circ}\)C than at 55 \({}^{\circ}\)C for all data points (\(p\) < 0.05). This was consistent with the fact that _B. amyloliquefaciens_ produces thermoresistant \(\alpha\)-amylases, which thrive at 65 \({}^{\circ}\)C [14].
# A Multiscale CNN-Based Intrinsic Permeability Prediction in Deformable Porous Media
## 2 Macroscopic Saturated Porous Media Model  
### Homogenization, Volume Fractions, and Densities  
For the continuum mechanical description, the following formulations consider a saturated, two-phase porous material consisting of a materially incompressible solid but compressible solid matrix and a materially incompressible pore fluid. The microscopically heterogeneous porous materials can be well described within the macroscopic TPM framework. Hereby, a homogenization process is applied to a representative elementary volume (REV), resulting in a smeared-out continuum \(\varphi\) with overlapping, interacting, and statistically distributed solid and liquid aggregates \(\varphi^{\alpha}\) (\(\alpha=S\) for the solid phase and \(\alpha=F\) for the pore-liquid phase). See [31] for details. Having immiscible solid and fluid aggregates, the volume fraction \(n^{\alpha}:=\mathrm{d}\
u^{\alpha}/\mathrm{d}\upsilon\) of \(\varphi^{\alpha}\) is defined as the ratio of the partial volume element \(\mathrm{d}\
u^{\alpha}\) to the total volume element \(\mathrm{d}\upsilon\) of \(\varphi\). Furthermore, the saturation constraint of the fully saturated material is expressed as  
\[\sum_{\alpha}n^{\alpha}=n^{S}+n^{F}=1\quad\text{with}\quad\left\{\begin{array} []{ll}n^{S}&:\!\!\text{solidity}\,,\\ n^{F}&:\!\!\text{porosity}\,.\end{array}\right. \tag{1}\]  
Two density functions are also defined, namely a material (effective or intrinsic) density \(\rho^{\alpha R}:=\mathrm{d}m^{\alpha}/\mathrm{d}\upsilon^{\alpha}\), and a partial density \(\rho^{\alpha}:=\mathrm{d}m^{\alpha}/\mathrm{d}\upsilon\), with \(\mathrm{d}m^{\alpha}\) being the local mass element of \(\varphi^{\alpha}\). While \(\rho^{\alpha R}=\mathrm{const.}\) in the current treatment, i.e., materially incompressible solid and fluid constituents, the solid (bulk) matrix is still compressible through the change in \(n^{\alpha}\). Hence, the partial and the effective densities are related by \(\rho^{\alpha}:=n^{\alpha}\,\rho^{\alpha R}\).
## 2 Materials and Methods
### EEG Recording and Data Analysis  
The SSVEP signals were recorded using the Brain Vision system (Brain Products Ltd., Munich, Germany) with 64 channels at a sampling rate of 5000 Hz. The electrodes arrangement was according to the international EEG system with 64 channels. The reference and ground electrodes were FCz and AFz, respectively. Six channels (O1, O2, Oz, PO3, PO4, and POz) were used to acquire the SSVEP signals. An electrooculographic (EOG) electrode was placed over the outer cannus of the right eye for blink-artifacts correction. The impedances of the electrodes were maintained below 10 k\(\Omega\). The layout of the electrodes is shown in Figure 1b.  
The EEGLAB toolbox and MATLAB 2019a platform (MathWorks Inc., Natick, MA, USA) was used to analyze the EEG data. First, the data were down-sampled to 1000 Hz and band-filtered between 0.5 and 49 Hz. Then the electrooculogram artifacts were rejected using independent component analysis (ICA). After that, the data was segmented into 6 s time length epochs (from the start to the end of the stimulation was selected). In addition, considering the DC offset after segmentation, baseline correction was required. Among the 15 participants, two participants' data were discarded due to big motion artifacts. Another one was unable to concentrate due to drowsiness and did not complete the entire experiment. The subsequent data processing was conducted using the data of the remaining 12 participants.
## 1 Introduction
It deals with the integration of satellites into a European large-scale 5G testbed with access to over-the-air demonstration test capabilities using GEO and MEO satellites. Figure 1: 5G use case scenarios. ESA's SATis5 testbed aims to practically demonstrate backhaul connectivity using 5G prototypes of satellite and terrestrial network elements working together to operate the satellite and terrestrial networks seamlessly. The testbed covers over-the-air satellite field demonstrations in addition to laboratory emulations and simulations in a federation of testbeds. The development of the testbed is a multistage process that has started with the deployment of a virtualized edge node. The edge node includes functions of the packet core and is able to connect to the central core network through either satellite or terrestrial backhaul. First, the satellite path was implemented locally via a satellite emulator. After the first tests with the satellite emulator, a real edge network was deployed in Berlin, Germany, in November 2018. This article describes two over-the-air demonstrations using geostationary satellite links as brought in by the project partners. This approach allows thorough testing and validation of all features required for the final phase. This is basically the deployment and simultaneous management of further static and nomadic edge nodes to prove the large-scale capabilities of the architecture. This article describes the first over-the-air demonstration tests using the SATis5 testbed based on a 3 GPP Release 15 core network. The rest of the paper is organized as follows. The SATis5 use cases are outlined in Section 2. In Section 3, the SATis5 testbed architecture is briefly presented. Section 4 describes the satellite emulator which allows a testbed utilization without satellite capacity. Section 5 presents the setup of two SATis5 over-the-air experiments utilizing a GEO satellite backhaul. Section 6 provides the conclusion.