esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Deep Learning Applications in Tunneling and Underground Engineering in China
## 3 Intelligent Application of Deep Learning in Tunnel Engineering  
### Mountain Tunnel  
In 1979, the total length of mountain road tunnels in China was merely 52 km. Entering the 21 st century, this figure surpassed the 1000 km mark. By the end of 2022, China had constructed 24,850 mountain tunnels, totaling 26,784 km in length. The emergence of a large number of tunnel projects has led to \"multiplicity\" and \"length\" becoming the defining characteristics of mountain tunnel construction in this century [44]. Drawing on structural reliability theory, the main areas of research have focused on the stability and durability of tunnel engineering, alongside a trend towards more mechanized construction methods for mountain tunnels. Many scholars have investigated intelligent analysis methods for addressing various problems in tunnel engineering, primarily divided into (1) intelligent analysis methods that combine numerical simulation with deep learning (DL); (2) methods that integrate numerical simulation with machine learning (ML) optimization algorithms; and (3) approaches based on DL combined with advanced monitoring tools, such as geological forecasting. During the excavation of tunnels, the disturbance to the original stratum alters the actual rock stress, with the stress state of the surrounding rock mass in mountain tunnels experiencing significant variations.  
Therefore, when studying mountain tunnel engineering problems, the change of stress with time plays a vital role in its stability. By mastering the changing trend in the stress state of rock and soil mass, it can more accurately judge the stability of structures in engineering, which is also the reason why many scholars study the stress sequence in the construction process. The construction environment of mountain tunnels is complex; is often undertaken in high ground stress and deep buried environments, resulting in many geological disasters such as rockbursts, collapse, and water and mud inrush. In the past, the prediction methods of geological disasters included numerical simulation analysis,  
Figure 5: The development process of DL [24; 31; 33; 34; 35; 37; 40].  
fractal geometry, fuzzy mathematics comprehensive evaluation, extensive evaluation, etc. However, the complexity of deep rock mass and the high non-linearity between disaster mechanisms and environmental factors make it difficult for traditional methods to meet the needs of construction design. Accordingly, it is good to use intelligent analysis methods based on the deep network structure for related identification, prediction, and prevention. This subsection mainly discusses the innovation of the DL method in mountain tunnels to account for the above geological disasters.
# Autonomous Robotics for Identification and Management of Invasive Aquatic Plant Species
## 2 Methods
### Autonomous Boat Development
#### 2.1.1 Hull Design and Fabrication  
A multi-hull, V-shaped design was selected for each vehicle to provide optimal stability and drag characteristics in the low-speed operating range (0.5-1.5 m/s) best suited to hydroacoustic mapping. With the use of hydrostatic analysis, pontoon dimensions were computed to support the geometry and weight of fully loaded payload components (batteries, electronics, herbicide tank filled to 15 gallon capacity, electronics, propulsion system, etc.) with 50% or less pontoon submersion [23]. The resulting geometry of each pontoon was \(244\times 26\times 30\) cm (\(96.0\times 10.2\times 11.9\) in), not including heel dimensions.  
Production of fiberglass pontoons was a multi-step process involving the fabrication of polystyrene plugs (Figure 6a,b) and fiberglass models (Figure 6c) for the left and right halves of each pontoon, bolting the mold halves together (Figure 6d), and forming each pontoon within the mold (Figure 6e). Pontoon caps were fabricated using a separate mold (Figure 6f). A heel was integrated into the base of the pontoons for improved tracking performance. Multiple coats of polyester resin and liquid rubber were applied to the surface of the pontoons and caps for enhanced waterproofing and cosmetics.  
Figure 5: Autonomous boat prototype for identification and chemical treatment of invasive aquatic plant species.  
Internal and external struts were waterjet-cut from aluminum sheets, bent to final geometry, and bolted between and within the pontoons (Figure 7). The struts provided enhanced lateral support and load-bearing capabilities. Low-density (32 kg/m\({}^{3}\) or 2 lbf/ft\({}^{3}\)), high-buoyancy urethane foam was added to the interior of the pontoons both to cradle batteries and electrical components and prevent sinking following capsizing or hull penetration. Hatch doors with water-resistant draw latches were installed above this region (Figure 5) to provide access to the batteries and electronics for battery recharging and electronics troubleshooting.  
Figure 6: Fabrication of fiberglass pontoons: (**a**) cutting, sanding, and bonding polystyrene plug sections; (**b**) epoxy coating, sanding, and gel-coating of plug; (**c**) fiberglass lay-up of mold over polystyrene plug; (**d**) assembly of mold halves (shown with keel upward); (**e**) fiberglass pontoon after lay-up inside mold and application of multiple finish coats; and (**f**) separately cast pontoon cap installed.
## 2 Materials and Methods
### Isolation and Characterization of Mesenchymal Stem Cells Derived from Human Wharton's Jelly  
Fragments of human umbilical cords (hUCs) were obtained by full-term births (gestational age of 39-40) from the \"Azienda Policlinico Umberto 1\" of Rome, upon written parental informed consent and approval by the local ethical committee. Umbilical cords were deprived of vessels, and WJ-MSC were isolated as previously described [28]. WJ-MSC were cultured in Dulbecco's Modified Eagle Medium low glucose (D-MEM, EuroClone, Pero, MI, Italy) supplemented with 10% Fetal Bovine Serum (Hyclone, Thermo Scientific, South Logan, UT, USA), 1% Non-essential AminoAcids (Sigma, Saint Louis, MO, USA), 1% Antibiotics-Antimycotics (Gibco, Life Technologies, Grand Island, NY, USA), and 2 mM L-Glutamine (EuroClone, Pero, MI, Italy) and incubated at 37 \({}^{\circ}\)C in 5% CO\({}_{2}\). Medium was replenished every 3 days, and adherent cells were serially passaged at 80-90% confluence at 1:3 ratio. WJ-MSC were cryopreserved at a concentration of about 5 \(\times\) 10\({}^{5}\) cells/mL in liquid nitrogen until use. WJ-MSC met the criteria for MSC as defined by International Society for Cell & Gene Therapy (ISCT). Characterization of WJ-MSC was carried out as previously described [29].
## 4 Simulation Validation
### Inductance Parameter Mismatch Investigation  
In practical PMSM drive applications, inductance values may vary considerably due to magnetic saturation during high current injections. To fully emulate the actual scenario, a 50% reduction in inductance and the realistic error factors including dead time and sampling delay presented in Table 2 are considered in the simulation modeling. The results in Figure 6 clearly illustrate the performance differences across three scenarios: (a) FCS-MPC with mismatched model, (b) FCS-MPC with accurate model, and the (c) proposed ESN-MPC, which is not trained under this set of parameters.  
The waveform results show that when the inductance is mismatched, the traditional FCS-MPC is significantly affected and the ripple increases (up to 30 N\(\cdot\)m). In contrast, the FCS-MPC strategy with an accurate model reduces this ripple notably to 21.5 N\(\cdot\)m. In the test, the proposed ESN-MPC activates the online learning block to adapt to parameter variations and stops it at 0.1 s after the load step. After online learning, the proposed  
Figure 5: Control performance using (**a**) conventional FCS-MPC and (**b**) proposed ESN-MPC.  
Figure 6: Test results with \(-50\%\) to the nominal inductance under (**a**) a conventional FCS-MPC with mismatched model, (**b**) a conventional FCS-MPC with accurate model, and (**c**) the proposed ESN-MPC (not trained with this test set).  
ESN-MPC achieves a reduced ripple level (approximately 22 N\(\cdot\)m), even without prior training samples from the mismatch scenario.  
A more extensive investigation, depicted in Figure 7, was conducted by varying inductance values over a wide range. The test involved performing approximately 1000 simulations to comprehensively assess the system behavior. As shown in Figure 7a, the output current THD with conventional FCS-MPC exhibits large fluctuations, ranging notably from 4.11% to 9.48%, which indicates considerable sensitivity to parameter mismatch. These prominent THD variations mainly result from nonlinearities in the system, sampling errors, and dead-time influences. With a perfectly modeled FCS-MPC (Figure 7b), a relatively low current THD value can be obtained (4.11-7.93%). Furthermore, the proposed ESN-MPC (Figure 7c) effectively compensates for model uncertainties and improves robustness against inductance variations via online learning. It consistently achieves lower current THD values (4.15-7.79%).
# Off-Axis Diffractive Optics for Compact Terahertz Detection Setup
###### Abstract  
Medical and many other applications require small-volume setups enabling terahertz imaging. Therefore, we aim to develop a device for the in-reflection examination of the samples. Thus, in this article, we focus on the diffractive elements for efficient redirection and focusing of the THz radiation. A terahertz diffractive optical structure has been designed, optimized, manufactured (using extrusion-based 3D printing) and tested. Two manufacturing methods have been used--direct printing of the structures from PA12, and casting of the paraffin structures out of 3D-printed molds. Also, the limitations of the off-axis focusing have been discussed. To increase the efficiency, an iterative algorithm has been proposed that optimizes off-axis structures to focus the radiation into small focal spots located far from the optical axis, at an angle of more than 30 degrees. Moreover, the application of higher-order kinoform structure design allowed the maintaining of the smallest details of the manufactured optical element, using 3D printing techniques.  
Keywords:THz; diffractive optics; 3D printing; skin cancer diagnosis; iterative design; off-axis structures; beam redirecting +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Analysis of Volatile Compounds  
For the analysis of the volatile profile, a subset of 8 dry-cured hams (2 for each of the 4 groups, with 4 per effect of processing and diet) was examined. Volatile organic compounds (VOCs) were determined, based on the method used by [PERSON] et al. (2018). The extraction of headspace VOCs was performed using a solid phase microextraction (SPME) device containing a fused silica fibre (20 mm length) coated with a 50/30 \(\upmu\)m DVB/Carboxen/PDMS fibre (Supelco, Bellefonte, PA, USA). Muscle samples (BF only) were homogenized in distilled water that was saturated with NaCl, using a commercial blender. Ten mL of this mixture was placed into each 20 mL vial, 100 \(\upmu\)L of 4-methyl-2-pentanol (1.2 mg/kg) (internal standard) was added and the vials were tightly capped with a PTFE septum. Duplicate 20 mL vials were placed in a thermoblock at 40 \({}^{\circ}\)C. The SPME fibre was then exposed to the headspace for 180 min while maintaining the sample at 40 \({}^{\circ}\)C. After extraction, the SPME fibre was immediately injected into a 6890 N gas chromatograph coupled to a 5975 series mass selective detector (Agilent Technologies, Santa Clara, CA, USA). A capillary column DB-5 MS 30 m \(\times\) 0.25 mm, film thickness 0.25 \(\upmu\)m (Agilent Technologies, Santa Clara, CA, USA), was used with helium as a carrier gas at a 1.0 mL/min flow rate. The temperature of the injector, used in the splitless mode, was 230 \({}^{\circ}\)C and the desorption time was 5 min. The temperature programme was set at 40 \({}^{\circ}\)C, then isothermal for 10 min, then rising to 200 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min and finally rising to 250 \({}^{\circ}\)C at a rate of 20 \({}^{\circ}\)C/min. The final temperature was held for 5 min. The transfer line temperature was maintained at 280 \({}^{\circ}\)C. The mass spectra were obtained at 70 eV with a rate of 1 scan/s over an \(m/z\) range of 50-450. An in-house mixture of C8-C20 n-alkanes was run under the same chromatographic conditions to calculate the retention indices (RI) of the detected compounds. AMDIS 3.2, program version 2.62, was used for the identification of components, using the NIST 2005 version 2.0 spectral library (NIST, Gaithersburg, MD, USA) as well as for a comparison of the obtained RI with values given in the literature (([PERSON], 2018) and the in-house library).
## 3 Nonlinear Estimation of the Perturbed Keplerian Parameters
The minimization problem in (3) is linearized by taking \(\mathbf{f}(\mathbf{p}(t_{oe}))\) at each epoch \(t_{k}\) about an orbital set obtained with a first guess, denoted with \(\hat{\mathbf{p}}^{(0)}(t_{oe})\) such that  
\[\mathbf{f}(t_{k},\mathbf{p}(t_{oe}))\approx\mathbf{f}(t_{k},\hat{\mathbf{p}}^ {(0)}(t_{oe}))+\frac{\partial\mathbf{f}(t_{k},\mathbf{p})}{\partial\mathbf{p} }\big{|}_{\mathbf{p}=\hat{\mathbf{p}}^{(0)}(t_{oe})}\cdot\Big{(}\mathbf{p}(t_ {oe})-\hat{\mathbf{p}}^{(0)}(t_{oe})\Big{)}, \tag{4}\]  
where \(\frac{\partial\mathbf{f}(t_{k},\mathbf{p})}{\partial\mathbf{p}}\bigg{|}_{ \mathbf{p}=\hat{\mathbf{p}}^{(0)}(t_{oe})}\) is a \(((m+n)\times 15m)\) matrix containing the partial derivatives of the vector function \(\mathbf{f}()\) at time \(t_{k}\) with respect to the \(15\) perturbed Keplerian orbital elements of all \(m\) satellites, computed in \(\hat{\mathbf{p}}^{(0)}(t_{oe})\). Expression (3) can then be linearized and rewritten in matrix form as  
\[(\Delta\mathbf{\rho}-\mathbf{J}_{\mathbf{f}}\Delta\mathbf{p}(t_{oe}))^{ \mathsf{T}}\mathbf{Q}_{\mathbf{y}}^{-1}(\Delta\mathbf{\rho}-\mathbf{J}_{ \mathbf{f}}\Delta\mathbf{p}(t_{oe})), \tag{5}\]  
where \(\mathbf{J}_{\mathbf{f}}=\Big{[}\mathbf{J}_{\mathbf{f}t_{1}}\big{(}\hat{\mathbf{ p}}^{(0)}(t_{oe})\big{)},\ldots\big{)}_{\mathbf{f}t_{K}}\big{(}\hat{\mathbf{p}}^{(0) }(t_{oe})\big{)}\Big{]}^{\mathsf{T}}\) is the Jacobian matrix introduced in (4), containing all \(((m+n)K\times 15m)\) partial derivatives of the observations over the \(K\) epochs; \(\Delta\mathbf{\rho}\) is the residual range error, i.e., the difference between the observed ranges and the ranges computed from initial values of the orbital parameters at the linearization point; \(\Delta\mathbf{p}(t_{oe})\) is the correction to the initial state vector minimizing observation residuals; and \(\mathbf{Q}_{\mathbf{y}}\) is the block-diagonal covariance matrix of all observations, with diagonal components obtained from matrices \(\mathbf{Q}_{\mathbf{y}_{t_{k}}}\). The vector of orbital parameters is computed iteratively, starting from the initial guess:  
\[\hat{\mathbf{p}}^{(i+1)}(t_{oe})=\hat{\mathbf{p}}^{(i)}(t_{oe})+\Delta\hat{ \mathbf{p}}^{(i)}(t_{oe}),\quad\Delta\hat{\mathbf{p}}^{(i)}(t_{oe})=\Big{(} \mathbf{J}^{(i)}\mathbf{Q}_{\mathbf{y}}^{-1}\mathbf{J}^{(i)}\mathbf{f}\Big{)} ^{-1}\mathbf{J}^{(i)}\mathbf{f}\mathbf{Q}_{\mathbf{y}}^{-1}\Delta\mathbf{\rho}. \tag{6}\]  
The iterations are stopped when the norm of the increment, \(\Delta\hat{\mathbf{p}}^{(i)}(t_{oe})\), falls below a given threshold. Convergence of the iterative method to a global minimum depends, among other factors, on the choice of the initial conditions. It has been observed that convergence is achieved even with an initial orbital error of a few hundred metres.
# Gradient-Based Aerodynamic Optimization of an Airfoil with Morphing Leading and Trailing Edges
## 2 Methodologies
### Mesh Deformation
The mesh deformation algorithm is summarized below:  
* The construction of the RBF interpolation matrix \(\mathbf{H}\) by using the points \(\mathbf{x}_{t}^{u}\) on baseline CST airfoil and the mesh points on the corresponding undeformed surface \(\mathbf{x}_{k}^{u}\);
* The generation of the displacement field by evaluating \(\mathbf{v}_{t}=\mathbf{x}_{t}^{m}-\mathbf{x}_{t}^{u}\), in which \(\mathbf{x}_{t}^{m}\) on the morphed CST airfoil, sharing the same normalized arc length with the \(\mathbf{x}_{t}^{u}\);
* The projection of displacement field \(\mathbf{v}\) from CST airfoil surface \(\Omega\) to surface mesh \(\mathcal{S}\) through matrix \(\mathbf{H}\), yielding \(\mathbf{w}=\mathbf{H}\mathbf{v}\), then mesh points on the morphed surface \(\mathbf{x}_{k}^{m}=\mathbf{w}+\mathbf{x}_{k}^{u}\) are finally obtained;
* The propagation of the displacement on the surface to the entire volume mesh through solving the linear elastic deformation problem. The application of the mesh deformation algorithm to an airfoil with morphing leading and trailing edges is demonstrated in Figure 5. The presented method proved to be efficient and robust in the preliminary test, and can be naturally extended to aero-structural coupling when the internal morphing mechanisms are available. However, the only flaw in this method is the negative effects on the orthogonality of the boundary layer mesh. The authors have compared the result calculated on the deformed mesh with the one obtained from the rebuilt mesh, and the deviation on the drag is acceptable. Figure 5: Mesh deformations of airfoil with morphing leading and trailing edges. (**a**) Undeformed RAE 2822 mesh. (**b**) Morphing RAE 2822 mesh.
# PLI-VINS: Visual-Inertial SLAM Based on Point-Line Feature Fusion in Indoor Environment
## 4 Nonlinear Optimization Based on Sliding Window  
In this paper, the nonlinear optimization method based on the sliding window model is adopted, that is, to ensure that the number of optimization variables is maintained in a certain range, the optimization variables are dynamically added or removed through the sliding window, and only the key frame data in the current period of time participate in the position pose solution process.  
Figure 5: Reprojection error of line features.  
The complete state vector at moment \(i\) in the sliding window is defined as follows:  
\[\begin{split}&\chi=[x_{n},x_{n+1}\cdots x_{n+N},\lambda_{m},\lambda _{m}\cdots\lambda_{m+M},O_{l},O_{l+1}\cdots O_{l+L}]\\ & x_{i}=\left[p_{\omega b_{i}},q_{\omega b_{i}},r_{\omega b_{i}},b _{b_{i}}^{a},b_{b_{i}}^{b}\right]^{T},i\in[n,n+N]\end{split} \tag{12}\]  
where \(x_{i}\) is IMU state vector at window \(i\), \(p_{\omega b_{i}}\) is position information, \(q_{\omega b_{i}}\) is pose information, \(v_{\omega b_{i}}\) is velocity, \(b_{b_{i}}^{a}\) and \(b_{b_{i}}^{b}\) are accelerometer bias and gyroscope bias, respectively; \(\lambda_{m}\) represents the inverse depth of 3D points; \(O_{l}\) is the orthogonal representation of line features in the world coordinate system; \(N\) is the number of key frames in the sliding window, \(m\) is the number of point features observed by key frames in the sliding window, and \(l\) is the number of line features observed by key frames in the sliding window.  
On the basis of VINS-Mono, the residual term of line feature is added into the objective optimization function. That is, the objective optimization function includes marginal prior residual, IMU measurement residual, point and line residual. The specific form is as follows:  
\[\min_{\chi}\left\{\begin{array}{l}\rho(||r_{p}-H_{p}\chi||_{\Sigma_{p}}^{2})+ \sum\limits_{k\in B}\rho\left(\left||r_{B}(z_{b_{k+1}}^{b_{k}},\chi)\right||_{ \Sigma_{b_{k}+1}}^{2}\right)\\ +\sum\limits_{(i,j)\in D}\rho\left(\left||r_{D}(z_{j}^{c_{j}},\chi)\right||_{ z_{j}^{c_{j}}}^{2}\right)+\sum\limits_{(i,j)\in I}\rho\left(\left||r_{I}(z_{L}^{c_{j} },\chi)\right||_{p_{L}^{c_{j}}}^{2}\right)\end{array}\right\} \tag{13}\]  
where \(B\) is the IMU measurement data set, \(D\) and \(l\) are the collection of point features and line features observed at least twice in the image frame, respectively. \(\left||r_{p}-H_{p}\chi\right||_{\Sigma_{p}}^{2}\) is the marginal prior information, \(H_{p}\) is the marginal prior residual Jacobian matrix; \(r_{B}(z_{b_{k+1}}^{b_{k}},\chi)\), \(r_{D}(z_{j}^{c_{j}},\chi)\) and \(r_{I}(z_{L}^{c_{j}},\chi)\) are the residual terms of IMU, point feature and line feature, respectively. \(\rho(\cdot)\) is a Cauchy robust function for suppressing outliers.
# Effect of Nanodisks at Different Positions on the Fano Resonance of Graphene Heptamers
Therefore, it is predicted that graphene-based PMs possess fantastic properties compared to their noble metal counterparts. In this paper, a plasmonic heptamer based on graphene nanodisks is designed to study the effects of chemical potential and radius changes of nanodisks at different positions on the overall behaviors of the structure. At first, a graphene nanodisk with variable chemical potential is placed in the center of a hexamer to form a heptamer with a \(\mathbf{D_{6h}}\) symmetry. In this process, surface plasmon of central nanodisk hybridizes with other six satellite nanodisks, which provides a basis for the formation of Fano resonance in the extinction spectrum. Furthermore, the quality of Fano resonance is effectively adjusted by the chemical potential of central nanodisk. After that, the significance of the radius and chemical potential of the ring nanodisks are discussed, respectively. At the same time, the influence of the number of nanodisks on Fano resonance is also discussed.