esa-sceva/satcom-chunk-collection huggingface dataset:
# MALDI-TOF/MS Analysis of Extracellular Vesicles Released by Cancer Cells
## 2 Applications
### Liver Cancer  
Hepatocellular carcinoma is the most common type of liver cancer and among the most lethal malignancy worldwide [57]. It shows a poor prognosis and frequent recurrence, regardless of the extensive advances in treatment approaches such as surgery, liver transplant, radiation, ablation, embolization, targeted therapy, and immunotherapy. It is then crucial to understand the mechanisms at the base of hepatocellular carcinoma occurrence, development, and progression, as well as to individuate new biomarkers for its early diagnosis. To reach this aim, exosomes represent an ideal analytical target [57], playingsignificant roles in tumor occurrence, development, metastasis, immune regulation, and drug resistance.  
An epithelial-to-mesenchymal transition is a crucial event that enables epithelial cancer cells to differentiate and acquire a mesenchymal phenotype [58]. Losing their cell-cell adhesions and becoming more motile, these differentiated cells can migrate to distant anatomical sites leading to the development of metastatic growth. It has also been observed that many cancers can metastasze even if the differentiation process is not complete. The mesenchymal transition is regulated by specific zinc fingers (Snail, Slug ZEB1, ZEB2) and helix-loop-helix (E47, Twist) transcription factors, through many events which have not yet been fully deciphered. For instance, Snail and Slug repress the expression of the epithelial marker E-cadherin silencing the CDH1 gene promoter through the methylation of its DNA sequences. [PERSON] et al. [59] compared complete and partial epithelial-to-mesenchymal transition in different Slug over-expressing hepatocellular carcinomas derived cell lines (HepG2 and Huh7). MALDI-TOF/TOF and ELISA were used to identify exosomal proteins, looking for the presence of chemo-resistance and markers for the transition. The results indicated that Slug over-expression induced both complete (with downregulation of E-cadherin and upregulation of ZEB2) and partial transitions (with upregulation of E-cadherin and downregulation of vimentin and ZEB2), together with chemo-resistance through the expression of CD133, the downregulation of ABCB1, and the upregulation ABCG2. Furthermore, fibronectin 1, collagen type II alpha 1, and fibrinogen gamma chain were suggested as biomarkers for chemo-resistance and partial epithelial-to-mesenchymal transition.
# Prediction of Cutting Parameters of HVOF-Sprayed Stellite 6
## 2 The Methodology of the Work
### Theoretical Background  
#### 2.1.1 Analysis of the Main Parameters of the Cut  
In the tool-material contact, we consider physico-mechanical parameters of the machined material, the physico-mechanical and geometric parameters of the cutting tool and the selected geometric and dynamic parameters of the cut to be the main parameters. Physical-mechanical parameters are the main elastic and strength properties. The selected parameters form a set given mainly by the values of speed \(v_{c}\) (m\(\cdot\)min\({}^{-1}\)), feed \(f\) (mm) and depth of cut \(a_{p}\) (mm). Due to their action, the contact induces an instantaneous stress-strain state, chip separation and the creation of a new surface with a specific roughness parameter \(Ra\). The roughness of the new surface is thus an integrated image of the chip forming mechanism as well as the topographic type of the substrate.  
In this section, we will focus on the analysis of functional relationships between the selected cut parameters. At the same time, we will try to analyse these relations in a new way. In practice, for the purpose of designing machining, these can be calculated in advance for the purpose of an exact selection, i.e., optimization. In this case, it will also be a verification of functionality and verification of our newly designed quantities \(R_{mp}\) [-], and \(Ra_{mp}\) [\(\upmu\)m], where \(R_{mp}\) is \"Ratio of machining parameters\" and \(Ra_{mp}\) is \"Ratio of machining parameters with direct roughness calculation \(Ra\)\". New parameters \(R_{mp}\) and \(Ra_{mp}\) were derived by the authors in order to be able to easily quantify the complex influence of a set of selected cutting parameters on the mechanical state in the cut and also on the topographic state of the final surface after cutting.
## 3 Results and Discussion
### Simulations
#### 3.1.2 Effect of Depth  
We tested how deep the perturbation can be detected by the system. In this case, the chosen inclusion is the one with \(\Delta\mu_{a}=0.017\) mm\({}^{-1}\), since it reasonably represents a localized brain activation. The depth was varied from 5 to 20 mm, increasing by 5 mm steps. In Figure 4, the tomographic reconstructions are reported. When we simulated the inclusion at increasing depths, we retrieved the perturbation quite well localized both in \(x\) and \(y\). Disregarding the superficial artifacts, the most  
Figure 3: Tomographic reconstructions showing the effect of the perturbation: \(x\)-\(y\) images at specific \(z\) values (**rows**) of decreasing \(\Delta\mu_{a}\) (**columns**) perturbations placed at \(z=15\) mm. White circles of 10 mm in radius are placed with the center on the actual position of the perturbation volume and are used to assess with more ease the accuracy in the localization of the retrieved perturbations.  
relevant \(\Delta\mu_{a}\) variations are localized correctly in \(z\) down to 10 mm, but reconstructed at a lower depth than desired for \(z=15\) mm.  
Nevertheless, this issue is quite common in tomographic reconstructions, since the sensitivity is lower at increasing depth [45; 46]. Unfortunately, for \(z=20\) mm, the perturbation seems invisible to the system.
# Dynamic Downsampling Algorithm for 3D Point Cloud Map Based on Voxel Filtering
## 6 Discussion  
The findings presented in this study underscore the efficacy of the proposed dynamic downsampling algorithm for 3D point cloud maps, particularly in terms of simplification rate, processing speed, and the reduced necessity for manual parameter adjustment. This discussion further explores these results in the context of existing research, practical applications, and future directions.
## 2 Materials and Methods
### Experimental Design and Procedure
#### 2.2.5 Statistical Analysis  
The normality of the data distribution was verified with the Shapiro-Wilk test. Homogeneity of variances were checked for all variables using by the Levene-Test. Descriptive statistics are reported as mean \(\pm\) standard deviation. Differences in anthropometric, academic performance and physical activity parameters between males and females were tested using Kruskal-Wallis H test and Mann-Whitney-U test for post-hoc testing, as the data were not normally distributed. Furthermore, we performed an analysis of variance (ANOVA), with gender (female vs. male) as between-subject factor, and academic performance parameters as the within-subject factor in order to evaluate longitudinal differences. The threshold for statistical significance and relevance was set at \(\alpha<0.05\) and \(\eta_{\mathrm{p}}\)\({}^{2}>0.10\).  
A power calculation (nQuery Advisor 4.0; Statistical Solutions, Saugus, MA, USA) was performed using previous data [6]. Based on the main parameter Science, a \(t\)-test for independent groups, a mean difference of 7.4 (pooled SD: 8.00; d = 0.89), an \(\alpha\) level of 5%, and a power of 80%, a sample size of 21 participants in each group was necessary [41].  
Effect size (Cohen's d) was calculated for each pairwise comparison [42], and interpreted as trivial (\(<\)0.20), small (\(\geq\)0.20-0.49), moderate (\(\geq\)0.50-0.79), and large (\(\geq\)0.80) [43]. Relationships between variables were analyzed with [PERSON]'s product-moment correlation (r) and interpreted as negligible (\(<\)0.1), weak (0.1-0.4), moderate (0.4-0.7), strong (0.7-0.9), and very strong (\(>\)0.9) [43]. A correlation of \(r^{2}>0.5\) (explained variance \(>50\%\)) was considered relevant and marked in bold. Regarding the sample size of n = 52, the critical value for the product moment correlation based on a two-sided \(t\)-test and a = 5% is r = 0.273 [44]. Statistical analyses were completed with SPSS (version 28.0, SPSS Inc., Chicago, IL, USA).
# Joint Interference and Phase Alignment among Data Streams in Multicell MIMO Broadcasting
## 6 Algorithm Performance Analysis
### Performance Analysis under Nonideal CSI  
At progressively high SNR, nonideal CSI with delay \(\tau\) and error variance \(\delta_{\text{e}}^{2}\), the system rate loss is expressed as:  
\[\begin{split}\Delta R=E\Big{(}\mathbf{R}_{\text{perfect CSI}}\Big{)}-E\Big{(}\mathbf{R}_{\text{ imperfect CSI}}\Big{)}\\ \leq\sum\limits_{\text{g}=1}^{G}\sum\limits_{k=1}^{K}\sum\limits_{w =1}^{d_{\text{{k}}_{\text{g}}}}\text{log}_{2}\left(\frac{E\Big{(}\mathbf{J}_{ \text{{k}}_{\text{g}}}\Big{)}+\delta_{n}^{2}}{\delta_{n}^{2}}\right)\end{split} \tag{47}\]In order to guarantee the freedom of the system, the loss of the system capacity is required to be a constant, defined as \(\varepsilon\) which is expressed as:  
\[\varepsilon=GKd\log_{2}\left(\frac{E\left(\mathbf{I}_{k_{g}}\right)+\delta_{n}^{2}}{ \delta_{n}^{2}}\right) \tag{48}\]  
We bring Equation (43) into (48) and obtain:  
\[\frac{\left|\rho\right|^{2}}{1+\delta_{\varepsilon}^{2}}=1-\frac{\left(2^{ \frac{\varepsilon}{GKd}}-1\right)\delta_{n}^{2}}{(GK-1)P} \tag{49}\]  
From Equation (49), it can be seen that in order to guarantee the ideal degree of freedom, it is required that as the power \(P\) increases, \(\left|\rho\right|^{2}/\left(1+\delta_{\varepsilon}^{2}\right)\) becomes closer and closer to 1, that is, the channel becomes more and more ideal.
## 2 Theoretical Background  
### Hydrodynamic Model  
In our study, the fluid domain was assumed to be a boundary element method, assuming a recently used, three-dimensional potential flow. A Rankine source and a higher-order spline function were used. For the boundary value problem in the fluid domain, a linearized boundary condition at the average position was used. The coordinate system with the ship's forward speed was defined, as shown in Figure 1.  
Figure 1: Linear pressure distribution.  
Here, \(\mathbf{\Omega}_{F}\) is the fluid domain, \(S_{F}\) denotes the free surface, and \(S_{\infty}\) represents the infinite surface. The velocity potential satisfies the Laplace equation, and the solution can be obtained by applying the boundary conditions, where \(\phi\) denotes the total velocity potential.  
\[\
abla^{2}\phi=0\text{ in }\mathbf{\Omega}_{F} \tag{1}\]  
The set of the boundary value problems is expressed in Equations (2)-(5). Equation (2) is an expression of the body surface boundary condition. \(\mathbf{U}\) represents the ship's forward speed vector, \(\mathbf{n}\) is the normal vector on the body surface, \(\overset{\rightarrow}{u}\) is the translation displacement vector, and \(S_{B}\) is the exact body surface.  
\[\frac{\partial\phi}{\partial n}=\mathbf{U}\cdot\mathbf{n}+\frac{\partial \overset{\rightarrow}{u}}{\partial t}\cdot\mathbf{n}\text{ on }S_{B} \tag{2}\]  
Equations (3) and (4) are expressions of the kinematic boundary condition and the dynamic free surface boundary condition in the boundary value problem, respectively. \(g\) is gravity and \(\mathbf{\zeta}\) is total wave elevation.  
\[\left[\frac{d}{dt}+\
abla\phi\cdot\
abla\right]\left[Z-\mathbf{\zeta}(x,y,t) \right]=0\text{ on }Z=\mathbf{\zeta}(x,y,t) \tag{3}\]  
\[\frac{d\phi}{dt}=g\mathbf{\zeta}-\frac{1}{2}\
abla\phi\cdot\
abla\phi\text{ on }Z=\mathbf{\zeta}(x,y,t) \tag{4}\]  
The open boundary in the free surface is expressed as follows:  
\[\
abla\Phi\to 0\text{ at }S_{\infty} \tag{5}\]  
To linearize the boundary problem, the velocity potential is decomposed into the basis potential \((\mathbf{\Phi})\), incident potential \((\mathbf{\Phi}_{I})\), and disturbed potential \((\mathbf{\Phi}_{d})\). Additionally, the free surface elevation is decomposed into the incident wave \((\mathbf{\zeta}_{I})\) and the disturbed wave elevations \((\mathbf{\zeta}_{d})\) ([PERSON], 2014).  
\[\mathbf{\Phi}=\mathbf{\Phi}(x,y,t)+\mathbf{\Phi}_{I}(x,y,t)+\mathbf{\Phi}_{d}(x,y,t) \tag{6}\]  
\[\mathbf{\zeta}=\mathbf{\zeta}_{I}(x,y,t)+\mathbf{\zeta}_{d}(x,y,t) \tag{7}\]  
Through double-body  
\(m_{j}\) is the nodal mass of the \(j\)th node and \(\mathbf{A}\) is the eigenvector of the \(j\)th mode.  
\[n_{j}=\mathbf{A}\cdot\mathbf{n} \tag{12}\]  
\[m_{j}=(\mathbf{n}\cdot\
abla)(\mathbf{A}\cdot(\mathbf{U}-\
abla\mathbf{\Phi})) \tag{13}\]  
Here, subscript \(j\) denotes that the rigid body motions are from modes one to six. The flexible motions are after mode seven. Detailed numerical solutions are given by WISH-FLEX [18].
# The Optimal Erection of the Inverted Pendulum
## 4 Numerical Solution
### Sensitivity to \(\epsilon\)  
The aim of this section is to examine the control structure for different values of the mass ratio for \(0\leq\epsilon<1\).  
The \(\epsilon=0\) case is of little physical relevance because either the pendulum bob is massless, or the mass of the cart is infinite. In this case, the problem can be computed, with the result being little more than the limit case for small \(\epsilon\). The \(\epsilon=1\) case corresponds to a massless cart or a bob of infinite mass. In this case, numerical issues arise from singularities in (7) when \(\theta=0\) or \(\pi\). Neither singularity is avoidable because they will occur, respectively, at the beginning and end of the pendulum-erection maneuver. For these reasons, these extreme cases are not considered here.  
Figure 5 shows the structure of the optimal solution as a function of the model parameter \(\epsilon\). A hundred simulations were performed with \(\epsilon\) ranging from 0 to 0.98 in steps of 0.01, with the corresponding switching times retained. The solution has a pure bang-bang structure with four bangs in the range \(0<\epsilon<0.23\)--no singular arc occurs when most of the system mass in concentrated in the cart. For \(\epsilon>0.23\), a singular arc arises at \(t\approx 1.076\,\mathrm{s}\), splitting the second bang in a singular-bang sub-arc. As \(\epsilon\) is increased, the solution structure becomes bang-singular-bang with a lengthening singular sub-arc and with the initial bang-bang sub-arc decreasing in duration. When \(\epsilon>0.56\), the second bang sub-arc vanishes, and the structure becomes bang-singular-bang up to \(\epsilon\approx 0.98\) with an ever increasing singular arc duration. When \(\epsilon>0.98\), there are numerical issues relating to the denominator in (7), which terminates the investigation. The results suggest that the greater the mass of the pendulum, the larger the duration of the singular arc.
# A UWB-Driven Self-Actuated Projector Platform for Interactive Augmented Reality Applications
## 3 System Design  
### UWB Wireless Sensor Network Infrastructure Configuration  
In this work, the UWB positioning system is composed of 6 anchors (A1-A6), 1 master (M), and 2 tags (one for the USAP platform and one for the user's hand-held device). The chosen UWB positioning system is developed by Ciholas [48]. The Ciholas UWB module comes in a single hardware unit and plays multiple roles (such as anchor, master, and tag) by means of varying the software, and each module mainly consists of a Decawave's DW1000 transceiver and an ARM's 32-bit Cortex-M4 processor. The master is responsible for controlling and collecting the time of flight (TOF) measurements based on the time difference of arrival (TDOA) technique among anchors and tags; after that, it sends the collected TOF values to the server for position processing via serial communication. According to [PERSON], their UWB positioning system can track up to 48 tags with 10 anchors and 1 master at a 20 Hz location update rate and achieve a position accuracy of about \(+/-0.1\) m laterally and about \(+/-0.2\) m vertically in a typical gymnasium-size environment.  
We deployed a total of 6 anchors in a room to exhibit 3D position tracking. Anchors were installed over two perpendicular walls; this installation reveals a cost-effective physical effort to track 3D spaces and decreases the non-line-of-sight and multi-path effects while performing UWB signaling among anchors and tags. Only the three anchors are installed on ground corners, while three other anchors are attached on the ceiling parallel to the ground anchors. All six surfaces (S1-S6) can be tracked under the L-shape infrastructure to exhibit reliable user interaction. The UWB position system's deployment is possible in a similar fashion from a single room to multiple rooms. Only the master of each room needs to be equipped with a Raspberry PI as a local server to communicate with the central server on the WLAN for position processing. Besides, as Ciholas UWB positioning system can track up to 48 tags, multi-agent tracking is also possible when it is required to add more than one USAP platform and multi-users in our presented indoor interactive system. Figure 1 shows the detailed UWB wireless sensor network installation for a single room (Room 1) while proposing a multiple room (Rooms 1-4) configuration concept.
## 2 Overview of Recent Radio Trends
### Cognitive Radio Networks  
The radio spectrum resources must be efficiently allocated and managed to assure the QoS for the end users while minimizing interference. Cognitive radio is an intelligent system which occasionally discovers an available spectrum part that can be allocated in a time/space and frequency manner. The spectrum allocation is time- and space-dependent, and therefore, the joint design of the routing and spectrum access is needed, with distributed implementation instead of the use of a common control channel.  
The global architecture of a cognitive radio system is illustrated in Figure 2. It includes a cognitive unit that takes decisions based on various inputs and an SDR bloc whose operating software provides a range of possible operating modes.  
The system also includes multiple sensors, a learning capability unit (external environment and an RF channel), and a virtual decision table (policy engine). In fact, the policy engine defines allowed/disallowed actions and sets the rules of communication (policies). The rules established must follow the telecommunication regularization defined by national or international organizations (namely the SDR forum): the spectrum, manufacturer, frequency band,... [17].  
It is extremely important to implement an efficient radio managing methodology which allows a continuous update of the policy rule definitions to meet the geographic location of the network and the radio standards. The policy engine is always controlling  
Figure 1: Overview of the discussed axes of the current review paper: in Section 3, three main axes will be analyzed: the MIMO systems’ recent hardware and architecture innovations, MIMO waveforms, and MIMO channel estimation and equalization.  
the behavior of the cognitive radio system, and the policy changes must be validated by trusted entities that distribute the policies.
# Comparative Study of Different Approaches for Islanding Detection of Distributed Generation Systems
## 3 Method  
There are varieties of the methods for the islanding detection of a DGS, each has its own advantages and disadvantages. In this study, two different approaches: islanding detection by the auto-grounding method and the data mining-decision tree algorithm has been presented. The detailed description of the approaches are described in Sections 3.1 and 3.2 below.
# Optimization of Sensor Placement for Modal Testing Using Machine Learning
## 2 A Review of Traditional Sensor Placement Techniques for Modal Testing
### Mass-Weighted Effective Independence (MEIM)  
A drawback to EIM is that it selects sensors by only considering the contribution to the linear independence of the mode shapes and neglects their orthogonality constraints through the mass matrix [24].  
In fact, the _MAC_ matrix is generally not an identity matrix; therefore, it will not directly show the mode shapes to be linearly independent. As a consequence, the sensor locations selected by the effective independence method may not always be appropriate.  
When a mass-weighted approach is used, such as the Mass-Weighted Effective Independence (MEIM), modes shapes that contribute the least to self-orthogonality are removed in each iteration as opposed to focusing purely on linear independence when selecting features. Cross-orthogonality checks are used to determine how analytical and empirical modal testing results correlate.  
Based on Equation (11), a new Mass-Weighted Effective independence parameter is defined, such that  
\[\mathbf{M}\mathbf{W}\mathbf{E}\mathbf{I}=\widehat{M}\mathbf{\Phi}_{s}\left(\mathbf{\Phi}_{s}^{T} \widehat{M}^{T}\widehat{M}\mathbf{\Phi}_{s}\right)^{-1}\mathbf{\Phi}_{s}^{T}\widehat{M }^{T} \tag{11}\]  
where matrix \(\widehat{M}\) is defined to allow \(\mathbf{M}=\widehat{M}^{T}\widehat{M}\); therefore, \(\widehat{M}=\mathbf{M}^{1/2}\).  
One of the drawbacks, however, is that the Mass-Weighted Effective Independence requires the decomposition of the mass matrix to obtain \(\widehat{M}\), which can be computationally prohibitive, as it requires the calculation of the eigenvalues and eigenvectors of the mass matrix \(\mathbf{M}\) or its Cholensky decomposition. The problem is reduced if the mass matrix \(\mathbf{M}\) is diagonal, and \(\widehat{\mathbf{M}}\) could then be found by taking the square root of all diagonal elements of \(\mathbf{M}\)[24].
## 1 Background and Motivation
**HSL_MA86 [3]**: Solves sparse symmetric indefinite systems using a task-based algorithm; designed for multicore architectures. **HSL_MA87 [4]**: Solves sparse symmetric positive-definite systems using a task-based algorithm; designed for multicore architectures. **HSL_MA97 [5]**: Solves sparse symmetric positive-definite and indefinite systems using a multifrontal algorithm, optionally using OpenMP for parallel computation; a key feature is bit-compatibility. The remainder of this paper is organised as follows. In Section 2, we provide a brief overview of sparse direct methods (for further details, the reader is referred to the books [6, 7] and the references therein). We explain the different phases that are common to all modern sparse direct solvers (not just those used in this study). There are two main approaches to performing the numerical factorization: supernodal and multifrontal. The codes HSL_MA86 and HSL_MA87 employ a supernodal approach, and this is explained in Section 3, while HSL_MA77 and HSL_MA97 implement a multifrontal algorithm and this is discussed in Section 4. For both approaches, we highlight how parallelism is exploited. Numerical results are presented in Section 5. Finally, concluding remarks are made in Section 6. The main contribution of this paper is to present a brief overview of sparse direct methods, aimed at those who have an interest and need to solve such systems efficiently and accurately. Furthermore, through numerical experiments on a range of problems from different applications, we are able to demonstrate and compare the performance of different algorithms in a multicore environment. Note that it is beyond the scope of this paper to present a comprehensive and detailed study of all available direct solvers (see [8] for a comparison of serial codes that was carried out a few years ago).