esa-sceva/satcom-chunk-collection huggingface dataset:
# A Tube Linear Model Predictive Control Approach for Autonomous Vehicles Subjected to Disturbances
## 2 Tube Linear MPC Methodology
### Computation of the Approximate Invariant Set  
To obtain the input of the autonomous system, the maximal robust positively invariant set should be calculated first. In [26], the authors employed MPT to calculate the robust positively invariant set of the non-linear MPC in the autonomous system. As a series of Minkowski sums need to be calculated when the MPT is employed, the computational burden of the robust positively invariant set is big. To reduce the computational burden, this paper adopted a linear programming (LP) method to solve the approximately robust positively invariant set [31].  
Based on Definition 2, a robust positively invariant set can be expressed [31] as follows:  
\[\varepsilon_{\infty}=\oplus_{i=0}^{\infty}A_{r}^{i}W, \tag{13}\]where \(\varepsilon_{s}\) is an \(\delta\) approximation of the minimal robust positively invariant set \(\varepsilon_{\infty}\).  
\[\varepsilon_{s}=\oplus_{i=0}^{s-1}A_{r}^{i}W, \tag{14}\]  
where \(s\in N_{+}\), and a scalar is \(\alpha\in[0,1)\). In this paper, \(s\) is equal to the predict horizon \(N-1\).  
If \(A^{s}W\subseteq\alpha W\) is satisfied, then \(\varepsilon(\alpha,s)=(1-\alpha)^{-1}\varepsilon_{s}\) is convex and \(\varepsilon_{\infty}\subseteq\varepsilon(\alpha,s)\).  
The support function of a set \(W\subset R^{m}\), which is evaluated at \(a\in R^{m}\), is defined as follows [31]:  
\[h_{W}(a)=\sup_{w\in W}a^{T}w. \tag{15}\]  
The scalar can be calculated as  
\[\alpha(s)=\max_{i\in I}\frac{h_{w}(A^{s}f_{i})}{g_{i}}. \tag{16}\]  
The approximation of the robust positively invariant set can be expressed as  
\[m(s)=\max_{j\in N+}(\sum_{i=0}^{s-1}h_{w}\bigg{(}\Big{(}A^{i}\Big{)}^{T}e_{j} \bigg{)},\sum_{i=0}^{s-1}h_{w}\bigg{(}-\Big{(}A^{i}\Big{)}^{T}e_{j}\bigg{)}). \tag{17}\]  
In general, the computation of the minimal robust positively invariant set by LP can be described with Algorithm 1[31]:  
```
1:Input \(A,\ W\) and \(\delta\)>0
2:\(s\gets 0\)
3:Repeat
4:Compute \(\alpha=\alpha^{0}(s)\) with Equation (16)
5:Compute \(m(s)\) with Equation (17)
6:\(s=s+1\)
7:Until \(\alpha=\delta/(\delta+m(s))\)
8:Compute \(\varepsilon_{s}\) with Equation (14) and \(\varepsilon(\alpha,s)=(1-\alpha)^{-1}\varepsilon_{s}\)
```  
**Algorithm 1** Computation of the minimal robust positively invariant set
# Upgrade of the Universal Testing Machine for the Possibilities of Fatigue Tests in a Limited Mode
## 9 Cyclic Load Tests
### Experimental Measurements TD2b  
In this test, the loading parameters were set with a lower (minimum) force limit of \(F_{\text{min}}=0\) kN and an upper (maximum) force limit of \(F_{\text{max}}=16\) kN (in Figure 4b as \(F_{\text{TD2b}}\)), and \(\sigma_{\text{max}}=339\) MPa, respectively. This stress value for the positively vanishing load also corresponded to the normal stress range \(\Delta\sigma\). The Scale Factor SF was set to 500:1. The specified number of cycles was set to \(N=400\). The values obtained were recorded in two files, the first for force and the second for strain.  
The data were processed in the same way as in the previous case. The obtained signal attributed to the deformation was transformed to strain values using the transformation dependence calibration curve according to Figure 13b. In this way, the time dependence of the strain \(\varepsilon\) for the TD2b measurement was obtained according to Figure 22.  
To construct the normal stress vs. strain relationship, i.e., also possible hysteresis loops, cycles of 1-5, 10, 20, 50, 100, 200, and 360 were used according to Figure 23.  
Figure 23: Dependence of the normal stress \(\sigma\) on the strain \(\varepsilon\) for selected cycles for TD2b measurements with parameters: transducer TD2, Specimen No. 4, \(F=0\)–16 kN, \(N=1\)–5, 10, 20, 50, 100, 200 and 400, and Scale Factor SF = 500:1.  
Figure 22: Time dependence of the relative deformation \(\varepsilon\) for TD2b measurement with parameters: transducer TD2, Specimen No. 4, \(F=0\)–16 kN, \(N=400\), and Scale Factor SF = 500:1.  
As can be seen from the obtained dependences, during cyclic loading in the first stage (up to about 130 cycles or time 4000 s) there is an alternating strain hardening and softening of the material. At cycle numbers above 150 cycles, it is already possible to observe a strain hardening of the material.  
In Table 2, the main parameters of the fatigue tests performed are listed for recapitulation, with the following meaning of each parameter:  
TD (me.)--transducer (measurement), Steel No.--steel modification, \(\sigma_{\text{y}}\)--yield strength, \(l_{0}\)--measurement (gage) length of the specimen, \(d\)--diameter of the specimen, \(T\)--period, \(f\)--frequency, \(F_{\text{min}}\)--minimum loading force, \(\sigma_{\text{min}}\)--minimum loading stress, \(F_{\text{max}}\)--maximum loading force, \(\sigma_{\text{max}}\)--maximum loading stress, \(N\)--number of cycles, \(SF\)--scale factor, and \(t_{\text{total}}\)--total measurement time.
###### Abstract
Furthermore, the intricate micro- and nano-processing procedures involved in the preparation of the metallic mesh make excessive thickness a hindrance, leading to increased processing difficulties and costs. Highly efficient mesh-based EMI shielding materials have still been underachieving. It is expected that outstanding overall performance can be realized by engineering and optimizing the structural parameters, i.e., materials, density, mesh pattern, linewidth, spacing, and particularly the film thickness. In this study, we developed an IR transparent EMI shielding window that uses thin metallic meshes to provide efficient and stable EMI shielding properties over a broad frequency spectrum. The irregular polygonal metallic meshes with optimized dimensional parameters were fabricated on a sapphire substrate and exhibited efficient EMI shielding effectiveness of more than 20 dB in the wide frequency range of 1.7-18 GHz while maintaining high optical transparency in the infrared range. Additionally, the shielding effectiveness did not significantly decline in the high frequency range, indicating a balanced shielding property over a wide frequency spectrum. Our results also suggest that mesh thickness is a critical parameter that impacts EMI shielding properties over a wide range of frequencies. This work has important implications for the protection of infrared optoelectronic systems from electromagnetic interference in practical applications.
## 1 Introduction  
Cereal crops remain a cornerstone of global plant-based food production, contributing more than half of the world's agricultural output [1]. Wheat is especially important to human nutrition and caloric intake. In Poland, the winter-wheat cultivar 'Memory' is among the most widely grown because of its high yield potential and favourable processing characteristics [2].  
Understanding how wheat kernels respond mechanically and biologically to external loading is increasingly important in agricultural engineering, particularly for mechanical harvesting, pneumatic conveying and milling operations.  
During transport and handling, kernels often strike hard surfaces at high speed, causing structural damage, reduced germination and loss of product quality [3]. The coefficient of restitution (\(CoR,e\)) quantifies how much kinetic energy is retained after such impacts [4]. \(CoR\) is widely used in discrete element method (DEM) simulations of grain flows.  
Previous work shows that \(CoR\) depends on kernel geometry--including asymmetries created by the embryo or brush (Figure 1) [2,5,6]--as well as surface texture, moisture content and impact velocity. Most experimental studies, however, have focused on maize, rice or soy; data for wheat (and for cultivar-level differences) remain scarce.  
The aims of the present study were as follows:  
* To capture and analyse the force--time curves of single-kernel impacts with high-frequency piezoelectric sensing and high-speed imaging;
* To determine how kernel moisture content and impact velocity influence \(CoR\);
* To provide validated data for DEM calibration and for optimising grain-handling equipment so as to minimise mechanical damage during processing.  
These objectives were pursued under controlled, perpendicular impacts between single kernels of winter wheat 'Memory' and a flat metal surface.
## 3 System Architecture
### Strategy of Each Player
#### 3.3.6 MEC/Cloud Orchestrator  
The MEC/Cloud Orchestrator needs to consider two optional model use cases: centralized and decentralized. In the centralized type, the Orchestrator supports multiple Private/Local Telecom Operators and is held by the Cloud Owner. On the other hand, functions may exist on the Internet in the distributed type, while other parts may be retained by each Private/Local Telecom Operator. For the centralized type, it is possible to deploy an application to MEC that considers the end user usage information (e.g., usage access log) on the cloud. For the distributed type, it is possible to deploy an application to MEC that guarantees high security. The functions provided as Orchestrator manage the catalog image of the application and support the northbound and southbound interfaces (MEC platform, cloud platform, DNS entry, user request management), with the main function being application life cycle management.  
On the other hand, various studies have been conducted from the user point of view regarding MEC and locality. Because there are many unstudied factors regarding how to control MEC, this paper provides a detailed explanation of the MEC/Cloud Orchestrator. In addition, we explain the implementation of the MEC/Cloud Orchestrator in the next section.
## 3 Results and Discussion  
### Cement Paste Analysis  
#### 3.1.1 Cement Slurry Fluidity  
According to Table 4, summarizing the flow performance of the cement paste in the presence of various functional polycarboxylic acid superplasticizers, group B, as a blank (control) group, contains no water-reducing agent, while the other groups are mixed with different water-reducing agents. Under the same water consumption (water-to-cement ratio), the fluidity of groups B and H is poor in the initial state. Groups Z and H offer the best fluidity (280 mm), and group ZF has intermediate fluidity (160 mm). After 1 and 2 h, groups B and NF do not flow, but the fluidity of the cement paste in group H was 230 and 255 mm respectively after 1 and 2 h, indicating that the cement paste of group H has a sustained-release effect mainly due to the slow-release property of the water-reducing agent H. In fact, hydroxyethyl groups available in the water-reducing agent H undergo hydrolysis reaction in the presence of alkaline substances (mainly calcium hydroxide) in cement and gradually release carboxyl water-reducing groups, therefore the fluidity of the cement paste gradually increases. The fluidity of the cement paste of groups Z and J gradually decreases, implying that superplasticizers Z and J do not have a sustained-release effect on the cement.
## 2 CFD Model of Incompressible Flow in the Larynx
### Mathematical Model
#### 2.1.1 Smagorinsky SGS Model  
One of the first and simplest SGS models was the Smagorinsky algebraic model [36], based on the eddy-viscosity assumption, which states that the deviatoric part of the SGS stress tensor is proportional to the symmetric part of the velocity gradient tensor \(\overline{S}_{ij}=\frac{1}{2}(\partial\overline{U}_{i}/\partial x_{j}+ \partial\overline{U}_{j}/\partial x_{i})\):  
\[\tau_{ij}\quad\frac{1}{3}\tau_{ik}\delta_{ij}=\quad 2\
u_{t}\overline{S}_{ ij}=\quad\
u_{t}\left(\frac{\partial\overline{U}_{i}}{\partial x_{j}}+ \frac{\partial\overline{U}_{j}}{\partial x_{i}}\right). \tag{6}\]  
The constant of proportionality in this relation, \(\
u_{t}\) is called the kinematic sub-grid scale eddy-viscosity (turbulent viscosity), which eventually adds to the kinematic molecular viscosity \(\
u\). The Smagorinsky model assumes that the small scales dissipate instantaneously all energy transferred from the resolved scales. From this, [PERSON] derived that the SGS viscosity may be estimated as  
\[\
u_{t}^{S}=(C_{\mathrm{S}}\Delta)^{2}\sqrt{2\overline{S}_{ij}:\overline{S}_{ ij}}, [PERSON]tag{7}\]  
where \(C_{\mathrm{S}}\)\(0.18\) is the Smagorinsky constant describing the rate of break-up of isotropic vortices in the viscous subrange of the turbulence energy spectrum, and where the colon operator denotes the double inner product.  
The main limitation of the Smagorinsky model, which was used in the previous simulations [37], lies in the assumption of local [PERSON] between the production and dissipation of turbulent SGS energy. In many real cases, notably, free shear layer flows, separating and reattaching flows, and wall-dominated flows (which is also the case of glottal airflow), this assumption is not fulfilled. This is why more accurate SGS models have been tested in our study, as described in the following sections.
## 3 Materials and Methods
### Text Preprocessing and Sub-Attributes Selection
#### 3.2.2 Sub-Attributes Selection  
In this study, six key attributes including value, location, service, room, cleanliness and sleep quality [9; 10; 35; 36; 46] are selected to explore the role of their bidirectional performance on customer overall ratings. These six attributes are provided by TripAdvisor.com as significant factors for customers to review [35]. Hotel customers use a variety of elements to evaluate the performance of the same attribute [46; 93; 94]. For example, customers may use \"locate\", \"place\" and \"distance\" to describe the attribute \"location\". Therefore, extracting words that are semantically similar to each hotel attribute is essential to comprehensively understand customers' opinions.  
In this study, we use Word2 Vec algorithm to extract words semantically similar to each hotel attribute from textual reviews. Word2 Vec is a generative similarity analysis method used to compare the degrees of semantic similarity between two words or two texts. Given a text corpus, Word2 Vec learns a vector for each word in the vocabulary using the Continuous Bag-of-Words or the Skip-Gram neural network architectures [95]. Continuous Bag-of-Words is suitable for a small corpus, while Skip-Gram performs better in a large  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Hotel Star Ratings**} \\ \cline{2-5}  & **Two-Star and Below** & **Three-Star** & **Four-Star** & **Five-Star** \\ \hline Total number of hotels & 72 & 231 & 237 & 100 \\ negative reviews & 21,771 & 86,720 & 106,585 & 21,997 \\ positive reviews & 41,486 & 231,938 & 408,553 & 171,291 \\ Total & 63,257 & 318,658 & 515,138 & 193,288 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distribution of sub-datasets according to the hotel star and overall customer ratings.  
corpus. After training the word vector model, the similarity of the words can be obtained. For this study, gensim is used as library which provides ready-made implementation of Word2 Vec algorithm. We trained word vectors from each dataset of different hotel star using the Skip-Gram model. With the pre-trained Word2 Vec model for each dataset, the similarity value between attribute \(A_{i}\) and each word in dataset is calculated, where \(i=1,2,3,4,5,6\). The words with similarity value under attribute \(A_{i}\) greater than 0.5 are selected as the sub-attribute \(B_{ij}\) of attribute \(A_{i}\), where \(j=1,2,\ldots,P\) is the number of sub-attributes. Let \(SS_{ij}\) denoted the similarity value between sub-attribute \(B_{ij}\) and attribute \(A_{i}\).
# Adaptive Sparse Cyclic Coordinate Descent for Sparse Frequency Estimation
###### Abstract  
The frequency estimation of multiple complex sinusoids in the presence of noise is important for many signal processing applications. As already discussed in the literature, this problem can be reformulated as a sparse representation problem. In this letter, such a formulation is derived and an algorithm based on sparse cyclic coordinate descent (SCCD) for estimating the frequency parameters is proposed. The algorithm adaptively reduces the size of the used frequency grid, which eases the computational burden. Simulation results revealed that the proposed algorithm achieves similar performance to the original formulation and the Root-multiple signal classification (MUSIC) algorithm in terms of the mean square error (MSE), with significantly less complexity.  
frequency estimation; sparse estimation; coordinate descent algorithms Article
## 2 Materials and Methods
### Evaluation of Model Performances  
The following evaluation parameters were used to assess model accuracy: determination coefficients (\(R^{2}\)), mean absolute error (\(MAE\)), root-mean-squared errors (\(RMSE\)), corrected efficiency index (\(\eta\)), and corrected summation index (\(d_{1}\)). \(R^{2}\), \(\eta\), and \(d_{1}\) values close to 1 and \(MAE\) and \(RMSE\) values close to 0 represent better simulation performance.  
In addition, global performance indicator (\(GPI\)) was used to provide a comprehensive assessment of model performance. First, different indexes were normalized as below:  
\[x_{\text{scaled}}=\frac{x_{0}-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}} \tag{35}\]  
where \(x_{\text{scaled}}\), \(x_{0}\), \(x_{\text{min}}\), and \(x_{\text{max}}\) denote the scaled, actual, minimum, and maximum values of an evaluation parameter, respectively.  
Then, \(GPI\) is summed as follows:  
\[GPI_{i}=\sum_{j=1}^{5}a_{ij}(g_{ij}-y_{ij}) \tag{36}\]  
where \(a_{ij}\) is a constant equivalent to 1 for normalized \(RMSE\), \(\eta\), and \(MAE\) and to \(-1\) for normalized \(R^{2}\) and \(d_{1}\); \(g_{ij}\) is the median of the normalized evaluation parameter \(j\); and \(y_{ij}\) is the normalized evaluation parameter \(j\) (\(x_{\text{scaled-j}}\)) corresponding to the LUE-GPP model \(I\), calculated using Equation (35).  
The comprehensive performance and ranking of the different LUE-GPP models were estimated based on \(GPI\) values, and a higher \(GPI\) value indicated better performance.
## 3 Results
### GPRC5A Is Significantly Correlated with Mesenchymal Stem-like Phenotype
Next, to evaluate the association between GPRC5A and stemness, we focused on the enrichment of stem cell-related pathways comparing the high and low GPRC5A groups. Our GSEA showed strong enrichment of several stem cell signals in GBM, including integrin \(\alpha\)\(\beta\)3 (\(p<\) 0.0001) [51], VEGFA (\(p<\) 0.0001) [52], uPA-uPAR (\(p<\) 0.0001) [53], CXCR4 (\(p=\) 0.00012) [54], NRF2 (\(p=\) 0.0002) [55], and PDGFRB (\(p=\) 0.0001) [56] (Figure 3E). Interestingly, VEGFA, uPAR, and CXCR4 were particularly associated with mesenchymal stem cell signatures. We also observed enrichment in canonical pathways associated with stemness signaling, such as hematopoietic stem cell differentiation and SMAD2-3 nuclear pathways (\(p=\) 0.0002). Correlation analysis between GPRC5A and these stem cell markers showed overall significance (Figure 3F). These results suggest that GPRC5A is strongly associated with various markers related to mesenchymal stem-like cells, indicating its potential role in the mesenchymal phenotype of GBM. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Gene Name** & **Correlation Coefficient** & \(p\)**-Value** \\ \hline _OLIG1_ & \(-\)0.401 & 5.03 \(\times\) 10\({}^{-8}\) \\ _OLIG2_ & \(-\)0.3732 & 4.62 \(\times\) 10\({}^{-7}\) \\ _SOX9_ & \(-\)0.05221 & 0.4964 \\ _HOPX_ & \(-\)0.06765 & 0.3779 \\ _HEPACAM_ & \(-\)0.1669 & 0.02868 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Spearman correlation between GPRC5A expression and various genes related to oligodendrocytes and glial/astrocytes.
## 4 Plasma Interaction with Surfaces
On the other hand, atmospheric plasmas tend to produce filamentary or micro discharges, leading to non-uniform treatment and potential damage. Furthermore, vacuum chamber-based plasmas allows for fine-tuned chemistry, enabling the selective removal of organic residues, oxides, or specific layers without affecting underlying materials, as well as the possibility of conducting effective ion bombardment, where low-pressure plasmas allow for controlled ion energy, thus avoiding excessive surface damage [24]. Another advantage of vacuum plasma systems is the persistence of reactive radicals such as hydrogen, which would have longer lifetimes due to reduced collisions. This also enhances their effectiveness in cleaning processes because, in a vacuum, there would be less gas phase recombination. In a plasma operating at atmospheric pressure, reactive species quickly recombine, thus reducing cleaning efficiency. Vacuum plasmas also have reduced arc formation when compared with atmospheric plasmas, which often suffer from arcing. Such arcing can damage delicate semiconductor structures [68]. In this section, we will review how the species described earlier in this paper interact with the surfaces being cleaned. The interaction of two different states of matter makes the characterization process immeasurably more complex than when one is studying an unbound plasma. When plasma particles collide with the particles on the lead frame's surface, they can either be reflected or else adsorbed. The latter occurs due to the formation of bonds between the plasma and the surface particles. At the lead frames' surface, the particles can react and form bonds with atoms found on the surface of the lead frame or other adsorbed particles. The particles can bind two surfaces through one of two regimes, either physisorption or chemisorption. The bonding regime underlying physisorption is based on the interaction of electrostatic forces, such as dipole-dipole interactions. Physisorption is therefore a surface phenomenon where gas molecules or atoms adhere to a solid or liquid surface due to weak van der Waals forces (or electrostatic interactions) without forming chemical bonds. Chemisorption, in contrast, involves chemical bonds formed by valence electron interactions, such as sharing or transfer. Oxygen and hydrogen are examples of plasma particles that react with the surface in this way. In the case of the latter, the hydrogen molecules break apart upon impact with lead frame surface, with the products being chemisorbed separately [27]. Apart from the chemical aspects, plasmas also interact with surfaces physically through bombardment processes, as will be explained in the next subsection.