esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of the New Thomson Scattering Diagnostic System on WEST [PERSON]
## 4 Discussion  
Global and local thermomechanical studies have demonstrated a foreseen correct functioning of the Thomson diagnostic during normal operation of WEST tokamak. The stress in the structure is lower than the allowable value. The deformation and the tilt of the mirror still ensure proper functioning for the optical system. The temperature of the mirror and its bracket is lower than 200 \({}^{\circ}\)C; thus, there will not be impact on the measurements due to interferences with infrared light.  
In addition to the normal operation checking, the test operations have already been verified, while the electromagnetic disruption [12] (vertical displacement Event) still has to be checked.  
Problems due to vibration have not been identified. The lower natural frequencies of the structure are lower than 20 Hz. Some natural frequencies of the structure are near the working frequency of external perturbation assemblies. Nevertheless, the excited mass at these frequencies is low; hence, its effects can be neglected.  
In order to increase the first mode of the structure, the frame should be reinforced to improve the mechanical strength. In the same way, the link between the frame and the endoscope could be reconsidered.  
After some optimizations, pressure drop in all components was low. Moreover, since specific water mass flow rates need to be provided for each component, diaphragms will be designed and located in appropriate positions.  
Conceptualization, B.C., F.F., J.-M.V. and A.B.; methodology, B.C., N.L., T.B. and S.G.; software, B.C.; validation, N.L., T.B. and S.G.; resources, B.C. and A.B.; writing--original draft preparation, B.C.; writing--review and editing, S.G., N.L., T.B., G.C. and L.D.; supervision, L.D. and N.L.; project administration, G.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 17: Pressure drop depending on velocity.
# Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods
## 3 Results
### PWCL Location Results  
In this method, many attempts were made to shift the location towards stronger signals. Figure 8 illustrates a complete improvement of the WCL algorithm.  
Figure 7: Bar chart of average error (in meters) at 47 points in the WCL method.  
In this method, the closer the user is to a transmitter, the less error is observed; more error is observed in the central points of the transmitter triangle. The average error in this method is 1.84 m. The error variance in this method is 0.52 and the standard deviation is 0.72.
## 4 Results and Discussion
### Experimental Determination of the Permeability
#### 4.2.1 Sintering  
The sample was placed inside the sample holder and then the corresponding working fluid was pumped through it. Measurements of the flow rate through the wick and its corresponding pressure drop were recorded, so that the permeability of the sample could be obtained using [PERSON]'s law. Thus, the results for the case of 95 \(\upmu\)m copper sintering in methanol are shown in Figure 12.  
The influence of pore size on permeability can be clearly observed from the tests of the different pore size samples shown in Figure 13a. Permeability presented a uniform linear increase in behavior by pore size; the larger the pore size the higher the permeability.  
The permeability of the sintering copper 95 sample was \(4.9\times 10^{-12}\) m\({}^{2}\), just within the admissible limit for the application (\(4\times 10^{-12}\) m\({}^{2}\)). It means that the drop in pressure in the wick was precisely the admissible maximum (100 Pa). This configuration of the wick hardly met the application specifications with regard to permeability.  
Figure 12: [PERSON]’s law for a 95 \(\upmu\)m copper sintering sample in methanol.  
Figure 13: _Cont._
# A Multinomial DGA Classifier for Incipient Fault Detection in Oil-Impregnated Power Transformers
## 3 Methods and Modeling
### The \(k\)-Near Neighbors (\(k\)-Nn)  
\(k\)-NN is a pre-trained ML process that works by first learning on a labelled dataset and then classifying new objects by recalling the examples on which it was trained [54; 55; 56; 25]. \(k\)-NN hinges on the assumption that data points that are similar most often lie close to each other. The \(k\) in \(k\)-NN signifies the number of adjacent neighbors that are retrieved by the classifier and used to predict a new data point. A value for \(k\) must be chosen iteratively such that it is just right for the data. It must be seen to reduce the number of errors encountered while maintaining the algorithms ability to make accurate predictions [25].  
The \(k\)-Nearest Neighbors classifier algorithm works in the following manner. If _xtrain_ is a training set with labels _ytrain_, and given that a new instance _xtest_ is to be classified:  
1. First look for the most related occurrences (say _XNN_) to _xtest_ that are in _Xtrain_.
2. Obtain the labels _yNN_ for all the occurrences in _XNN_.
3. Predict the label for _xtest_ by relating the labels _yNN_.  
A sample is categorized by determining the majority vote cast by its \(k\)-neighbors, with the sample being assigned to the most popular vote. The metric that is traditionally used is the Euclidean distance, which is calculated as shown in Equation (6).  
\[D_{m,n}=\sqrt{\sum_{i=1}^{k}\left(m_{i}-n_{i}\right)^{2}} \tag{6}\]  
where \(m\), \(n\) are two observations in the Euclidean space.  
The \(k\)-NN algorithm is easy to implement but performs poorly as the number of predictor variables increase [24].
# A Data Secured Communication System Design Procedure with a Chaotic Carrier and Synergetic Observer
## 3 Methodology and the System Design Procedure
### Two-Channel Synergetic Observer Analytical Design Procedure and Two-Channel Receiver Block Diagramm
\tag{15}\]  
Since the system of Equation (15) is of the fourth order, but has six unknown terms, starting from the stability conditions of the observer, and taking into account Equation (9), we choose  
\[L_{11}=\alpha y^{2},\ L_{12}=0,\ L_{21}=0,\ L_{22}=\alpha z^{2}. \tag{16}\]  
where \(\alpha\) is the observer tuning parameter introduced to determine the rate of estimation. Then  
\[ \begin{array}{l}\frac{\partial Q_{1}}{\partial y}=\frac{L_{11}}{y}=ay,\frac {\partial Q_{1}}{\partial z}=-\frac{L_{12}}{z}=0,\\ \frac{\partial Q_{2}}{\partial y}=\frac{L_{21}}{y}=ay,\frac{\partial Q_{2}}{ \partial z}=-\frac{L_{22}}{z}=-az,\end{array}  \tag{17}\]\[Q_{1}(x,\,y,\,z)=\frac{1}{2}\alpha y^{2}, \tag{18}\] \[Q_{2}(x,\,y,\,z)=-\frac{1}{2}\alpha z^{2}.\]  
Thus, taking into account Equations (17) and (18), the observer's Equation (14) take the following form:  
\[ \begin{array}{l}\dot{v}_{1}=-\alpha xy-\alpha y^{2}\dot{w}_{1},\\ \dot{v}_{2}=\alpha z(b+xz)-\alpha z^{2}\dot{w}_{2},\end{array}  \tag{19}\]  
where the estimates of variables \(\hat{w}_{1}\) and \(\hat{w}_{2}\) are calculated according to Equation (11). So, the receiver MATLAB/Simulink model structure according to model (5), (19) has the form shown in Figure 11.  
Figure 11 displays our MATLAB/Simulink model of the receiver with the two-channel synergetic observer and implements the initial model and observer equations from Section 3.5. In Figure 11 the blocks \(a1\) and \(c1\) consist function (18).
###### Abstract  
Aiming at the problem of poor spatial resolution of low-frequency noise sources in a small-aperture spherical microphone array (SMA), this paper proposes a method for localizing and identifying low-frequency noise sources based on virtual-vector open SMA ('p+\(\
u\)' joint processing method of pressure and velocity). Firstly, a virtual open SMA with a larger aperture is obtained using a virtual array extrapolation method. In this method, the virtual SMA and the actual SMA are regarded as a dual-radius SMA, and velocity information is obtained using finite difference elements of the same direction (azimuth and elevation) array of the virtual and actual SMA. At the same time, the sound pressure at the velocity position is obtained using the virtual SMA extrapolation method and the virtual vector array element SMA, whereby both velocity and sound pressure information is obtained. Finally, the vector signal processing technology is introduced into the generalized inverse beamforming algorithm (GIB). After determining the vector transfer function of the 'p+\(\
u\)' joint processing mode, a low-frequency-noise-source localization and identification method based on the vector signal processing GIB is proposed. The simulation and experiment results show that a virtual SMA with a large aperture can be obtained using a virtual array extrapolation method, and the GIB with sound pressure and velocity joint processing has a better spatial resolution.  
low frequency noise source; vector signal processing; spherical array; virtual spherical array extrapolation; generalized inverse beamforming
# A Data Sharing Scheme for GDPR-Compliance Based on Consortium Blockchain
## 3 Scheme Design
### Evaluation of Node Reputation  
The incentive mechanism aims to motivate the nodes abided by the rules, participate in the blockchain work, and punish the nodes that do not follow the rules. As a result, the entire network can develop in the direction of a virtuous circle since the security and availability of the blockchain require the participation of most honest nodes. In the consortium blockchain, to prevent the service node from doing nothing or doing evil, our scheme adopts the identity credibility as an incentive mechanism, which will reward the nodes abided by the rules and participate in the authentication, and punish the nodes that do not follow the rules. The specific factor thresholds in the reputation evaluation mechanism vary with the size of the company, so there is no clear definition. The method of calculating the initial reputation of a service node is presented as follows:  
* Server Factor \(S_{1}\): \(S_{1}\) includes the processor, memory, service speed, and operating performance, etc.
* Network Factor \(S_{2}\): \(S_{2}\) includes blocking the size of the internal network, server band-width, network latency, and server vulnerability, etc.  
Figure 3: CEM-PoA architecture.  
* Reputation Factor \(S_{3}\): \(S_{3}\) includes company size, revenue, user size, whether having been punished or not recently, and compliance history, etc.
* Assign a different weight \(W_{\text{ij}}\) to each \(S_{\text{ij}}\), and then calculate the weighted average as the node reputation, giving: \[R=\frac{(\sum\limits_{\text{i}=1}^{I}S_{\text{1i}}W_{\text{1i}}+\sum\limits_{ \text{j}=1}^{J}S_{\text{1j}}W_{\text{1j}}+\sum\limits_{\text{k}=1}^{K}S_{\text {1k}}W_{\text{1k}})}{I+J+K}\] (1)
## 3 Results
### Analysis of PAEs in Commercially Bottled Tea Samples  
The developed SPME-GC-FID method was employed for the determination of trace PAEs in bottled tea beverages (Table 4). The recoveries ranged from 85.5% to 115%, obtained by spiking the standard PAEs solution in the bottled tea beverages, which corresponded to spiking 30 \(\upmu\)g\(\cdot\)L\({}^{-1}\) DIBP, 10 \(\upmu\)g\(\cdot\)L\({}^{-1}\) DBP, 10 \(\upmu\)g\(\cdot\)L\({}^{-1}\) DPP, 50 \(\upmu\)g\(\cdot\)L\({}^{-1}\) BBP and 50 \(\upmu\)g\(\cdot\)L  
Figure 8: (**a**) Analyte selectivity analysis of the TPB-DMTP-GS-SSF; (**b**) the service time of the TPB-DMTP-GS-SSF; (**c**) comparison of the TPB-DMTP-GS-SSF with commercial SPME fibers. The extraction solution contained DIBP 30 \(\upmu\)g\(\cdot\)L\({}^{-1}\), DBP and DPP 10 \(\upmu\)g\(\cdot\)L\({}^{-1}\), and BBP and DEHP 50 \(\upmu\)g\(\cdot\)L\({}^{-1}\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Coating** & **Matrix** & **Analytes** & \begin{tabular}{c} **Linear Range** \\ **(\(\upmu\)g\(\cdot\)L\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **LODs** \\ **(\(\upmu\)g\(\cdot\)L\({}^{-1}\))** \\ \end{tabular} & **Ref.** \\ \hline HPLC-UV & ZIF-8-90@graphene oxide & Water, juice & DMP, DEP, DBP & 0.1–500 & 0.026–0.058 & [59] \\ \hline GC-MS & MWNTs@PS & water & \begin{tabular}{c} DBP, BBP, DEHP, \\ DOP \\ \end{tabular} & 0.001–5 & 0.0012–0.018 & [24] \\ \hline GC-MS & HCP-1 & Bottled water & \begin{tabular}{c} DMP, DEP, DAP, \\ DPR, DIBP, DBP, \\ BBP, DEHP \\ \end{tabular} & 0.01–10 & 0.003–0.033 & [60] \\ \hline GC-MS-MS & TpBD-TiO\({}_{2}\) & Vegetables & \begin{tabular}{c} DMP, DEP, DIBP, \\ DBP, DMP, DPP, \\ DHXP, BBP, \\ DCHP, DPHP, \\ DNOP \\ \end{tabular} & 0.005–50 & 0.001–0.430 & [29] \\ \hline GC-FID & MNP@P3 TAH & Bottled mineral & \begin{tabular}{c} DMP, DEP, DPP, \\ DBP, BBP, CDF, \\ DEHP, DNOP \\ \end{tabular} & 0.1–50 & 0.054–0.468 & [61] \\ \hline GC-FID & OH\({}_{\text{avg}}\)-TPB-COF & Water & \begin{tabular}{c} DEP, DPP, DAP, \\ DBP, BBP, DEHP \\ \end{tabular} & 1–100 & 0.032–0.451 & [62] \\ \hline GC-FID & COF-TPB-DMTP & \
\begin{tabular}{c} Bottled tea \\ beverages \\ \end{tabular} &
\begin{tabular}{c} DIBP, DBP, \\ DPT, DEHP \\ \end{tabular} & 0.04–500 & 0.04–0.44 & This work \\ \hline \hline \end{tabular}
\end{table}
Table 3: The comparison of the developed method with other reports on the extraction of PAEs.  
DEHP in a working solution. The recoveries ranged from 86.53% to 106.07%, which were obtained by spiking the standard PAEs solution, which corresponded to spiking 15 \(\upmu\)g\(\cdot\)L\({}^{-1}\) DIBP, 5 \(\upmu\)g\(\cdot\)L\({}^{-1}\) DBP, 5 \(\upmu\)g\(\cdot\)L\({}^{-1}\) DPP, 25 \(\upmu\)g\(\cdot\)L\({}^{-1}\) DBP and 25 \(\upmu\)g\(\cdot\)L\({}^{-1}\) DEHP in a working solution.
## 2 Materials and Methods
### Lidar Technology
This layer exhibits turbulent flow, significantly influenced by friction against the Earth's surface. Processes such as turbulence, convection, and radiative transfer within the boundary layer facilitate the exchange of heat, moisture, and momentum between the Earth's surface and the free troposphere. Therefore, the thickness of the boundary layer is a sought-after parameter for various applications, such as dispersion models. Lidars are particularly apt for gauging the thickness of the planetary boundary layer or the mixing layer's thickness during the day when convection is evident. Typically, lidar uses the lowermost aerosol layer as an indicator to estimate the thickness of the boundary layer, as the aerosol concentration shows a pronounced shift in the transition to the free atmosphere. Traditional approaches determine the thickness of the boundary layer by gauging the gradient at this transition point [26] or employing more recent image processing methodologies [27]. For all the previously described applications, denoising methods are essential for lidar data processing, as they help mitigate the effects of noise and improve the accuracy of the retrieved information. Lidar measurements are susceptible to various sources of noise, including solar background light, detector noise, and atmospheric variability. Denoising techniques aim to enhance the signal-to-noise ratio (SNR), allowing for a clearer detection of aerosol layers and clouds. These methods often involve sophisticated filtering algorithms, statistical approaches, and signal processing techniques, to effectively remove unwanted noise, while preserving the underlying atmospheric characteristics [28]. In this context, machine learning algorithms, together with denoising techniques, are of paramount importance to improve the retrieval accuracy of the less sophisticated and cheaper lidar instruments that are often deployed in networks [29]. The raw signals acquired from the different lidar techniques, as defined in Table 1, frequently exhibit a considerable amount of noise. This noise is predominantly a challenge during daytime measurements, when the solar background significantly degrades the signal-to-noise ratio (SNR). To mitigate this issue and extract meaningful information from these signals, various denoising techniques are employed. The overarching objective of these algorithms is to improve the SNR. However, it is imperative that while the SNR is amplified, the intrinsic geometrical and physical properties of the aerosols and clouds remain undistorted. This careful balance ensures that the refined data retain their precision and relevance in atmospheric studies. Traditional denoising algorithms are summarized in Table 2.
# The Applications of MALDI-TOF MS in the Diagnosis of Microbiological Food Contamination
## 4 Limitations and Challenges
### Limited Discrimination Between Closely Related Strains  
While MALDI-TOF MS is effective at genus- and species-level identification, its ability to discriminate at the subspecies or strain level is inherently limited. Closely related species, such as _Escherichia coli_ and _S Shigella_ spp., or _Bacillus cereus_ group members, often share highly similar ribosomal protein profiles, making them difficult to resolve without additional confirmatory tests. For applications requiring high-resolution typing--such as outbreak tracking, virulence profiling, or regulatory differentiation--more discriminatory techniques like WGS or multi-locus sequence typing (MLST) are preferred [66; 71; 138].  
Ongoing efforts to enhance strain-level resolution through improved algorithms, machine learning, and biomarker discovery are promising, but not yet widely implemented in commercial platforms.
## 4 Procedures
### Recptance Coupling Algorithms
#### 4.1.1 D.D. Klerk Algorithm (LM FBS)  
The LM-FBS method integrates Lagrange multipliers (\(\lambda\)) to ensure equilibrium and compatibility between coupled substructures. The equations of motion for rigid and flexible coupling scenarios are presented in the following [14]:  
* Rigid Coupling Considerations  
In the rigid coupling case, the interface points between the connected substructures have no relative motion, simplifying the compatibility condition. The displacements at these interface points are identical across substructures, allowing for a straightforward prediction of the system's dynamic behavior. The equilibrium equation for the rigid coupling scenario is as follows:\[\left\{\begin{matrix}[\text{Z}]\{\text{X}\}=\{\text{F}\}+\{\text{G}\}\\ \quad[\text{B}]\{\text{X}\}=\{0\}\\ \quad\{\text{G}\}=-[\text{B}]^{\text{T}}\{\text{\lambda}\}\end{matrix}\right. \tag{9}\]  
where [B] is a Boolean mapping matrix that enforces compatibility conditions on the interface DoFs. Here, \(\{\text{G}\}\) and \(\{\text{X}\}\) represent interface forces and generalized coordinates for points of interest, respectively. The matrices [L] and [B] are orthogonal, such that \([\text{L}]^{\text{T}}\{\text{G}\}=\{0\}\), ensuring interface equilibrium. The dynamic stiffness matrix [Z] is the system's impedance matrix, and \([\text{Z}]^{-1}=[\text{H}]\) is the receptance matrix. The Lagrange multipliers \(\lambda\) are expressed as follows:  
\[\{\text{\lambda}\}=\Big{(}[\text{B}][\text{H}][\text{B}]^{\text{T}}\Big{)}^{-1 }[\text{B}][\text{H}]\{\text{F}\} \tag{10}\]  
Substituting Equation (10) into Equation (9), the coupled receptance matrix of the system assembly is obtained [13]:  
\[[\text{H}]_{\text{coupled}}=[\text{H}]-[\text{H}][\text{B}]^{\text{T}}\Big{(} [\text{B}][\text{H}][\text{B}]^{\text{T}}\Big{)}^{-1}[\text{B}][\text{H}] \tag{11}\]  
Figure 2 illustrates FBS method for assembling substructures A and B, depicting the interface forces \(\lambda\) used in the LM-FBS method.  
\[[\mathrm{H}]_{\mathrm{coupled}}=[\mathrm{H}]\{\mathrm{I}-[\mathrm{B}]^{\mathrm{T}}([ \mathrm{B}][\mathrm{H}][\mathrm{B}]^{\mathrm{T}}+[\mathrm{H}]_{\mathrm{m}})^{-1} [\mathrm{B}][\mathrm{H}]\} \tag{15}\]  
The interface forces \(\lambda\) ensure compatibility between substructures, maintaining the balance of the system.  
The LM-FBS method, however, offers enhanced robustness under noisy or inaccessible interface conditions, making it particularly advantageous for experimental NVH modeling and determining internal forces at interfaces. Despite these strengths, its dependence on the mapping matrix [B], which defines interface compatibility, can pose challenges in systems with complex non-linear interface dynamics, underscoring the need for careful consideration in practical applications.
# Enhancing Railway Network Safety by Reproducing Wheel-Rail Electrical Contact on a Laboratory Scale
## 3 Results
### Dependence of the Resistance on the Applied Current  
The main objective of this experiment was to explore the impact of the applied current on contact resistance. To this end, contact resistance measurements were carried out on different areas of the rail under static conditions. Increasing and decreasing currents were applied, from 100 mA to 1 A and vice versa, with 100 mA steps every 60 s. The evolution of the applied current intensity is shown in Figure 9. The contact voltage was measured every second.  
Figure 8: With a nitrogen jet—Log-log \(R_{\mathrm{c}}-F\) characteristics at imposed DC current \(I=0.1\) A. Increasing the load (\(\bullet\)) then decreasing it (\(\bigtriangledown\)). (–): experimental fits of the various sections.  
Figure 9: Current \(I\) (\(A\)) versus time \(t\) (s). The current was increased from 0.1 A to 1 A, then decreased from 1 A to 0.1 A by 0.1 A every 60 s.  
These tests were conducted on both a clean and oxidized contact area (oxide thickness = 57 \(\upmu\)m).
# Overview of Mobile Communications in Colombia and Introduction to 5G
## 3 Analysis and Results
### Deployment of 5G in Colombia
\begin{table}
\begin{tabular}{c c} \hline \hline
**Applicant** & **Frequency Band** \\ \hline Colombia Telecomunicaciones S.A. E.S.P. & 3500 MHz–3600 MHz \\ Comunicacion Celular S.A. Comcel S.A. & 3500 MHz–3600 MHz \\ Empresa de Telecomunicaciones de Bogotiá S.A. & 3500 MHz–3600 MHz \\ E.S.P. & 3500 MHz–3600 MHz \\ ITICS S.A.S. & 3500 MHz–3600 MHz \\ Xiro Investment Group SAS & 3300 MHz–3400 MHz and 587 MHz–592 MHz \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Companies with approved permits to use radio spectrum for 5G pilots. Source: [35]. The results of the executed tests have not been published and are still being conducted, not least because of the COVID-19 pandemic that started in 2020, with social, commercial, and legal restrictions that prevented their normal development. Currently, the MinICT, in collaboration with the ANE, is working on the future issuance of a Decree (an administrative act enacted by the executive branch) that will enable the development of 5G mobile services. The rule aims to increase spectrum caps in bands below 3 GHz, as well as to enable a new category between 3 GHz and 6 GHz for future 5G services. This decree will benefit 5G applications and scenarios requiring higher speeds [68]. As for spectrum auctions for 5G, in July 2022 there were no known dates, caps, bands, or economic aspirations on the part of state entities. However, in June 2021, the Vice Minister of Connectivity stated that they are working together with the ANE and the CRC on structuring the future auction to grant licenses to commercially offer 5G. Figure 11 shows the different processes implemented in the framework of the action plan.