esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result and Discussion
### NMR Pore Size Distribution  
Porous media exhibit a wide range of pore throat sizes. In MICP, external pressure is gradually increased on a rock sample to force mercury into its pores, thereby obtaining a distribution of pore sizes. In contrast, NMR detects the magnetic resonance signals from hydrogen nuclei within the rock, providing insight into its pore size distribution. Importantly, NMR is capable of effectively representing the entire volume of pores in the rock, including disconnected pores. However, the PSD from NMR requires assistance from MICP for calibration.  
To ensure precision in the calculations, it is necessary to remove the influence of the pockmark effects from the MICP results. Hence, only portions of the T\({}_{2}\) relaxation time distribution spectrum that match the MICP pore throat radius distribution are chosen for comparison. At points in the NMR T\({}_{2}\) cumulative distribution curve that coincide with the cumulative distribution frequency of the MICP pore throat curve, multi-point sampling is executed [59]. Using sandstone S1-1 and shale H3-5 as examples, the cumulative distribution curves for NMR T\({}_{2}\) relaxation time and MICP pore throat radius are illustrated in Figure 6. Subsequently, based on the cumulative distribution frequencies from both testing methodologies, the relationship between the rock's T\({}_{2}\) relaxation time and pore throat radius is determined, as presented in Figure 7.  
Based on the fitting results from Figure 7, the conversion coefficients C and n values correlating the NMR T\({}_{2}\) relaxation time and pore throat radius for the sandstone S1-1 and shale H3-5 samples were derived. This pore size matching method has received consensus among multiple researchers [34; 60; 61]. By method from Section 2.3.3, the C and n values for the 8 sandstone samples from Tuha and the 8 shale samples from Hechuan were determined. The specific conversion coefficients are listed in Table 3.  
Figure 6: The cumulative frequency distribution of NMR and MICP (S1-1 and H3-5).
## 3 Proposed Job Recommendation Method
### Ranking Stage
#### 3.2.2 Salary Matching  
In this study, salary matching is conducted using a two-dimensional Euclidean metric that considers the job's average and maximum salary against the graduate's desired entry and incentive salary. The average salary is the monthly pay received by an employee, while the maximum salary is the highest amount offered by the company. Entry-level salary refers to the minimum salary required for college graduates, and incentive salary refers to the maximum salary desired by college graduates. The job's average and maximum salaries are viewed as a two-dimensional point \(\alpha=({\rm x_{w}},{\rm y_{w}})\), where \({\rm x_{w}}\) represents the average salary for the job, and \({\rm y_{w}}\) represents the maximum salary for the job. Similarly, the graduate's entry and incentive salaries are viewed as two-dimensional points \(\beta=({\rm x_{s}},{\rm y_{s}})\), where \({\rm x_{s}}\) denotes the entry salary and \({\rm y_{s}}\) denotes the incentive salary.  
\[dis(\alpha,\beta)=\sqrt[2]{(x_{w}-x_{s})^{2}+(y_{w}-y_{s})^{2}} \tag{9}\]  
\[if:(x_{w}-x_{s})<0,default(x_{w}-x_{s})^{2}=0 \tag{10}\]  
\[if:(y_{w}-y_{s})<0,default(y_{w}-y_{s})^{2}=0 \tag{11}\]  
The value of \({\rm x_{w}}-{\rm x_{s}}\) defaults to zero when the average salary of the job is lower than the entry salary expected by college graduates. Similarly, the value of \({\rm y_{w}}-{\rm y_{s}}\) defaults to zero when the maximum salary of the job is lower than the incentive salary desired by college graduates. When choosing a job, college graduates look at the average and highest salary of the job with a bias, so when calculating the weight, \(\delta\) is added to improve the accuracy of the calculation, and the value of \(\delta\) ranges from 0 to 1. In this study, \(\delta\) is experimentally set to 0.6, and is calculated as follows:  
\[dis(\alpha,\beta)=\sqrt[2]{(x_{w}-x_{s})^{2}\cdot\delta+(y_{w}-y_{s})^{2}\cdot (1-\delta)} \tag{12}\]
# Development of a Magnetorheological Damper with Self-Powered Ability for Washing Machines
###### Abstract  
Magnetorheological (MR) dampers have been widely investigated and proposed for vibration mitigation systems because they possess continuous variability of damping coefficient in response to different operating conditions. In the conventional design of MR dampers, a separate controller and power supply are required, causing an increment of complexity and cost, which are not suitable for home appliances like washing machines. To solve these issues and to reuse wasted energy from vibration of washing machines, in this study, a self-powered shear-mode MR damper, which integrates MR damping and energy-harvesting technologies into a single device, is proposed. The MR damper is composed of an inner housing, on which magnetic coils are wound directly, and an outer housing for covering and creating a closed magnetic circuit of the damper. The gap between the inner housing and the moving shaft is filled with MR fluid to produce the damping force. The energy-harvesting part consists of permanent magnets fastened together on the shaft and induction coils wound directly on slots of the housing. The induced power from the induction coils is directly applied to the excitation coils of the damping part to generate a corresponding damping force against the vibration. In order to achieve optimal geometry of the self-powered MR damper, an optimization for both the damping part and the energy harvesting part of the proposed dampers are conducted based on ANSYS finite element analysis. From optimal solutions, a prototype of the proposed damper is designed in detail, manufactured, and experimentally validated.  
magnetorheological fluid; self-powered; MR damper; shear-mode MR damper; suspension system; washing machine vibration +
Footnote †: journal: Applied Physics Letters  
1
## 2 Air Quality Monitoring in Terms of Particulate Matter
### Localization of Monitoring Sites and the Spatial Distribution of PM Concentrations  
Monitoring of air pollution by PM is not an easy task, due to the complex nature of atmospheric particulate matter [3]. Difficulties arise mainly when comparing and analysing the results of PM measurement gathered from different countries. They are also related to the differences in locationcriteria for measurement stations, limited data sets and the use of different sampling equipment at various stations [34; 58]. There are many methods for measuring the concentration of particulate matter that have been developed and improved over the last decades [3; 34; 63]. National and international guidelines recommend the gravimetric method as a standard method for the determination of the PM\({}_{2.5}\) and PM\({}_{10}\) concentrations [21; 64]. However, as the provisions of Directive 2008/50/EC indicate, Member States may use any other method of collecting PM samples if they can prove its compliance with the reference method. The principles and methodology for demonstrating the equivalence of alternative methods are described in detail in the [65].  
The concentration and size distribution of particulate matter as well as its chemical composition varies considerably in time and space [3] and therefore it is necessary to carry out measurements of PM on different types of stations and at different seasons of the year [31]. The detailed requirements for the location of individual types of monitoring stations for particulate matter (and other criterion pollutants) have been specified in Directive 2008/50/EC (Annex III). In addition to meteorological conditions, seasonal changes or long-range transport of air pollutants [66; 67; 68; 69], local conditions may also affect the measurement point [1; 70; 71]. For a specific urban area, a uniform distribution of PM\({}_{2.5}\), PM\({}_{10}\) or PM\({}_{2.5-10}\) is often only a guess, usually due to the limited availability of measuring equipment [4; 34; 35; 72; 57]. The impact is particularly strong for sources within a radius of around 10 km (neighbourhood scale) and very weak for the entire region [70]. Typically, the spatial distribution of PM\({}_{2.5}\) is relatively homogeneous; only in the vicinity of the PM emission source the concentrations might be elevated in relation to the background concentrations [46; 71]. For larger particles (PM\({}_{2.5-10}\)), spatial changes are significant [71; 73]. This also applies to bioaerosol, due to the strong influence of local sources of pollen, bacteria and spores on the total load of particles in ambient air [74].
# 6D Pose Estimation of Objects: Recent Technologies and Challenges
## 3 Non-Learning-Based Approaches
### 3D-Information-Based Approaches
#### 3.2.2 Local Descriptor Approaches  
Approaches based on the local descriptor define and calculate a global descriptor on the model offline. The global descriptor should be invariant with respect to rotation and translation. Then, the local descriptor is calculated and matched with the global descriptor online. The iterative closest point (ICP) [74; 75] algorithm is a classical one that is able to calculate the pose relation between two coordinates according to two sets of point clouds. 6D pose can be measured by the correspondence between them or as the result of voting. [PERSON] et al. [76] proposed a global method named Super Key 4-Points Congruent Sets (SK-4 PCS),combined with invariant local features of 3D shapes, thus reducing the amount of processed data. [PERSON] et al. [77] proposed a method aimed at everyday tools that does not rely on the 3D model of the measured object. It assumed that the same kinds of tools possess the same part-affordance. Therefore, using the 3D models for similar kinds of object, the method was able to measure the pose of objects belonging to this kind on the basis of the spatial relationships of part-affordance. [PERSON] et al. [78] used the improved Oriented Fast and Rotated Brief (ORB) [79] feature and rBRIEF descriptor (a descriptor developed based on binary robust independent elementary features (BRIEF) [80]) to obtain a coarse match, and then culled mismatches to retain more correct matches. They also proposed a hybrid reprojection errors optimization model (HREOM) to improve the accuracy of the result by minimizing 3D-3D and 3D-2D reprojection errors. To measure the 6D pose of large-scale objects that are partially visible, [PERSON] et al. [81] proposed a method based on semi-global descriptors. They used semi-global descriptors for scene segments and model views in combination with up-sampling and segment label merging techniques and obtained more reliable results than with other descriptors.
## 7 Results and Discussion
### Confusion Matrix  
Figure 10 is a confusion matrix of the Mask R-CNN detection model [23]. The confusion matrix is basically the relationship of the actual answer and the prediction of the model. When the answer can be either true or false, the prediction of the model can be also true or false. In the confusion matrix, there are four possibilities: true positive (TP), where the model predicts true while the answer is true; false positive (FP), where the model predicts true while the answer is false; false negative (FN), where the model predicts false while the answer is true, and true negative (TN), where the model predicts false while the answer is false. The confusion matrix gives precision and recall as the outputs. Precision is the ratio of what the model classifies as true to what is true. Recall is the fraction of what the model predicts to be true among what is true. To evaluate the performance of an algorithm, both precision and recall must be considered. The precision and recall can be expressed in the following way.  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}, \tag{1}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}, \tag{2}\]  
Class A, located in the first row and first column of the confusion matrix, recorded 0.00%, because Class A is the background. In the case of acceptable, Class B, 29 out of 29 flaw images were correctly detected and the recall recorded 100%, but the actual precision was 82.86%, as the acceptance flaws were incorrectly detected six times in the background. In the case of Class C, 27 flaws were correctly detected in 31 defect images, but the recall recorded 87.10% as it failed to detect four flaw images in the background. On the other hand, in the case of precision, it was detected incorrectly once in the background and recorded as 96.43%. Table 6 below shows the results of the confusion matrix in the table.  
Figure 9: Not acceptable (NA) flaw result, flaw length: 4 mm.
# Review on Silver Nanoparticles as a Novel Class of Antibacterial Solutions
## 4 Silver Nanoparticles and Their Antibacterial Role
### Factors Impacting the Antibacterial Activity of Silver Nanoparticles
#### 4.1.3 Nanoparticle Surface Chemistry  
Surface charge also plays an important role into the antibacterial activity. Different coated silver nano particles were used, with values ranging from \(-\)38 mV to +40 mV, to test the antibacterial activity against _Bacillus_ species. An important step is purification to accurately measure toxicity levels, which decreased the antibacterial activity [45].  
Silver ion release affects antibacterial activity of silver nano particles. This release can be triggered by oxidative dissolutions combining silver nano particles with oxygen. Silver ions also have high affinity to electron donating groups which can be easily found in membranes and proteins. Silver ions are capable of combining with DNA, RNA and peptides to form a barrier that stops cell division and reproduction [45]. Attachment of functional groups has been recently reported to differently alter the transcriptome. While negative bovine serum albumin-functionalized nanoparticles and neutral polyvinylpyrrolidone-binded silver nanoparticles significantly impair and down-regulate ribosomal translation and protein synthesis, while the positive, chitosan-based nanoparticles induce limited effects over the respective mechanisms [49].
# LoRaWAN Path Loss Measurements in an Urban Scenario Including Environmental Effects
## 4 Experimental Setup  
We have deployed a LoRaWAN setup to collect the variables included in this dataset. The system architecture follows the model previously explained in Figure 1. Mainly, we implemented the network shown in Figure 4. First, we deployed four ENs and one GW in different locations in the urban area of Medellin, Colombia, keeping LoS between each EN and the GW. Medellin is a medium-sized city with an area of 328 km\({}^{2}\) and about 4 million people. The city is located in the central part of the Andes Mountain Range, and its topography is a valley surrounded by mountains. The GW is connected to the Internet using an Ethernet connection. Once the GW receives a frame from an EN, it resends it to the selected NS. We used the things network (TTN), a widespread open-source NS. Because there are several LoRaWAN GWs in Medellin, we filtered the duplicated information in the NS to preserve only data from our GW. Subsequently, we enabled the MQTT broker provided by TTN and used a database cloud-based MySQL server to subscribe to the broker and get all the messages the NS receives. The following subsections describe each component deeply.  
Figure 3: ESP and noise power of ENs.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Variable** & **Unit** & **Min** & **1 st Quartile** & **Median** & **Mean** & **3 rd Quartile** & **Max** \\ \hline distance & m & 2140 & 3450 & 6100 & 5107 & 6100 & 8260 \\ ht & m & 8 & 8 & 12 & 16.67 & 12.5 & 40 \\ hr & m & 5 & 5 & 5 & 5 & 5 & 5 \\ ptx & dBm & 20 & 20 & 20 & 20 & 20 & 20 \\ ltx & dB & 1 & 1 & 1 & 4.824 & 11.75 & 11.75 \\ gtx & dBi & 2.903 & 2.923 & 2.923 & 4.094 & 2.995 & 8.536 \\ lrx & dB & 4.25 & 4.25 & 4.25 & 4.25 & 4.25 & 4.25 \\ grx & dBi & 4.161 & 4.161 & 4.161 & 4.161 & 4.161 & 4.161 \\ frequency & MHz & 903.9 & 904.1 & 904.7 & 904.59 & 904.9 & 905.3 \\ sf & bit/sym & 7 & 8 & 10 & 9 & 10 & 10 \\ frame\_length & byte & 9 & 9 & 10 & 39.91 & 53 & 241 \\ temperature & \%C & 13.9 & 18.8 & 21.1 & 21.91 & 24.7 & 35.1 \\ rh & \% & 1 & 64.1 & 84.5 & 77.87 & 93.2 & 99.8 \\ bp & hPa & 822.8 & 828.4 & 845.1 & 840.5 & 849.5 & 854.8 \\ pm2\_5 & \(\upmu\)g/m\({}^{3}\) & 0 & 3 & 6 & 10.01 & 15 & 93 \\ rssi & dBm & \(-\)105 & \(-\)93 & \(-\)82 & \(-\)83.12 & \(-\)75 & \(-\)63 \\ snr & dB & \(-\)18.5 & \(-\)11.5 & \(-\)1.5 & \(-\)3.064 & 4.8 & 12.8 \\ toa & second & 0.04122 & 0.2409 & 0.24781 & 0.2399 & 0.28877 & 0.379 \\ exp\_pl & dB & 84.91 & 96.81 & 103.81 & 102.2 & 107.08 & 117.4 \\ energy & Joule & 0.0093 & 0.0955 & 0.1038 & 0.09993 & 0.116 & 0.165 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical description of the dataset fields.
# Fault Diagnosis of a Solenoid Valve Based on Multi-Feature Fusion
## 2 Analysis of Solenoid Valve Current Characteristics
### Equation of Motion  
(1) Spring force \(F_{1}\)  
Figure 1: Force analysis diagram of solenoid valve spool.  
Figure 2: Equivalent circuit diagram of solenoid valve.  
According to [PERSON]'s law, the formula for calculating spring force is:  
\[F_{1}=k(x_{0}+x) \tag{5}\]  
where \(k\) is the elastic coefficient of the spring, \(x_{0}\) is the initial deformation of the spring (preload) and \(x\) is the deformation of the spring, which is the displacement of the spool movement in the solenoid valve.  
(2) Friction \(F_{2}\)  
During the valve core motion, friction is generated from two primary sources: the friction between the valve core and the valve body, \(fv\), and the friction between the valve core and oil, \(f\). Therefore, total friction \(F_{2}\) is:  
\[F_{2}=f_{v}+f=\left(C_{v}+C_{f}\right)\frac{\mathrm{d}x}{\mathrm{d}t} \tag{6}\]  
where \(C_{v}\) is the coefficient of the dynamic friction between the solenoid valve spool and the valve body, \(x\) is the displacement of the spool movement, \(t\) is the time of motion and \(C_{f}\) is the viscous damping coefficient of oil.  
When the solenoid valve is energized, the electromagnetic suction force generated by the coil overcomes the spring's elastic force and the friction force of the valve core, resulting in pushing the push rod through the armature to make the valve core move. Hence, Equations (3), (5) and (6) become:  
\[\frac{1}{2}i^{2}\frac{\mathrm{d}L}{\mathrm{d}x}-kx-\left(C_{v}+C_{f}\right) \frac{\mathrm{d}x}{\mathrm{d}t}=m\frac{\mathrm{d}^{2}x}{\mathrm{d}t^{2}} \tag{7}\]
# Secure Beamforming Design for UAV-Empowered Integrated Sensing and Communication
## 3 Joint Optimization Algorithm
### Transmit Beamforming Optimization
}&\text{C5}:\text{tr}\big{(}g_{j}(\mathbf{q}, \mathbf{e}_{j})g_{j}^{\text{H}}(\mathbf{q},\mathbf{e}_{j})\mathbf{W}_{k}^{\ast}\big{)}\\ &\quad-c_{j,k}\left(\sum\limits_{i=1,j+k}^{K}\text{tr}\Big{(}g_{j} (\mathbf{q},\mathbf{e}_{j})g_{j}^{\text{H}}(\mathbf{q},\mathbf{e}_{j})\mathbf{W}_{i}^{r}\Big{)}+ \sum\limits_{n=1}^{N}\text{tr}\Big{(}g_{j}(\mathbf{q},\mathbf{e}_{j})g_{j}^{\text{H}}( \mathbf{q},\mathbf{e}_{j})\mathbf{W}_{n}^{\ast}\Big{)}+\sigma_{e}^{2}\right)\leq A,\forall j,k\\ &\text{C6}:\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!Ignoring the rank-one constraint, problem P4 becomes a SDR problem, which we solve using the CVX toolbox. Since the rank-one constraint is ignored, we perform eigenvalue decomposition to obtain \(\omega_{k}^{c}\), \(\forall k\) and \(\omega_{n}^{r}\), \(\forall n\), and check if their ranks are one. If not, Gaussian randomization is applied to convert the high-rank solution of problem P4 into a feasible rank-one solution. The details are shown in Algorithm 1. ```
Input:\(\mathbf{u}_{k},\mathbf{e}_{j},\mathbf{q},\mathbf{P}_{\max},\Gamma_{k}^{\text{con}}\), \(\Gamma_{e}^{\text{sen}}\) Output:\(\mathbf{\omega}_{k}^{c},\forall k\) and \(\mathbf{\omega}_{n}^{r},\forall n\)
1. Given the initial value \(c_{j,k},\forall j,k\), set the minimum increment \(\epsilon_{\min}\). 2. Repeat:
3. Solve the optimization problem P4 to obtain \(\mathbf{W}_{k}^{c}\), \(\forall k\), \(\mathbf{W}_{n}^{r},\forall n\). 4. Perform EVD decomposition on \(\mathbf{W}_{k}^{c},\forall k\) and \(\mathbf{W}_{n}^{r},\forall n\) to obtain \(\mathbf{\omega}_{k}^{c},\forall k\) and \(\mathbf{\omega}_{n}^{r},\forall n\). 5. Update \(c_{j,k},\forall j,k\) according to Equation (15). 6. Until: The objective function increment is less than \(\epsilon_{\min}\). ```  
**Algorithm 1** UAV transmit beamforming optimization
## 1 Introduction
Laser light is sent into the fiber, and Rayleigh backscattered light will be analyzed to calculate strain in the fiber. Backscattering happens at irregularities of the fiber material. This means strains can be measured along the entire length of the fiber, thus allowing one to monitor large structures. It is basically a long array of many individual strain gauges. OBR is a relatively new technique and has been used so far in structures to, e.g., determine strain fields under load, assess crack development or in general structural health monitoring [31; 32; 33; 34; 35; 36; 37; 38]. To the authors knowledge, OBR has not been used so far to determine the internal shrinkage of, e.g., large cementitious or polymer structures. This may be due to the forces acting on the fiber during the curing process, which in return deteriorate the backscattered signal greatly, and thus, only noise is extracted in traditional OBR analysis. This issue will be discussed below, in addition to how OBR works in detail. The main part of this article discusses how a new method to analyze the data can tremendously improve the obtained strain information. The need to improve the OBR analysis came out of a project to analyze the curing behavior of epoxy in large models. Epoxy is a two-component polymer. The two components are mixed, and the epoxy is cast into a form. The two components react with each other, forming chemical bonds. This creates a network of bonds, and the epoxy turns into a solid block of material. The curing reaction is exothermic, i.e., heat is released. The forming of the bonds reduces the volume of the material, the so-called cure shrinkage. This shrinkage causes also strains in the material. The curing process is complex, being different at different locations inside a cast component, while the whole block exhibits an average shrinkage. This research is described in detail in [17]. Hence, the paper at hand does not focus on the curing characteristics, but on the challenges of measuring the strain distribution along the fiber inside the hardening material. Solutions to the encountered measurement challenges are described here. The solutions will be useful for many applications of the OBR technique where local forces act on the fiber and high strain gradients occur.
## 4 Results
### Monit4 Healthy Platform Development
#### 4.2.4 Real-Time Data Transmission and Alerts  
A fundamental aspect of the Monit4 Healthy platform is its real-time data acquisition and visualization, with continuous patient health metric monitoring. As soon as a connected medical device receives new information, the data are transmitted to the platform, processed, and presented on the user's dashboard with minimal latency. To ensure patient safety, the system includes an alert mechanism to notify users of deviations from predefined physiological thresholds, with the system instantly generating visible alerts for both the patient and the physician.