esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Magnetoelectric Effects on Coaxial Fibers of Ferrites and Ferroelectrics
## 8 Conclusions  
This review article on coaxial fibers of ferrites and ferroelectrics provided a comprehensive overview of the efforts during the past decade. There have been very few reports on this topic so far, even though one can anticipate a strong ME coupling in fibers due to (i) the high surface-area-to-volume ratio compared with bulk or layered composites and (ii) the absence of clamping effects related to the presence of a substrate in thin-film or nanopillar structures. Scanning probe microscopy techniques were utilized for investigations on ME coupling in individual fibers. Films of fibers assembled in a magnetic field or disc of the fibers were used for MEVC and H-induced variations in ferroelectric order parameters. PFM under H and FMR under E on individual fibers with spinel ferrite and PZT revealed strong direct and converse ME effects. Collections of fibers with NFO, NiZn-ferrite, or CFO for the ferromagnetic phase and PZT or BTO for the ferroelectric phase in general were found to show a strong direct ME coupling by measurements of static magnetic field-induced variations in the ferroelectric polarization, whereas low-frequency MEVC values were much smaller than the reported values for bulk or thick-film-layered composites. Three-dimensional films of hexagonal ferrites with M-, Y-, or W-type structures and PZT or BTO were reported to show an MEVC much higher than for spinel ferrite/ferroelectric films but an order of magnitude smaller than for thick-layered composites.  
All the authors contributed equally to the literature survey, collection of relevant contents for the review, and preparation of the manuscript. All authors have read and agreed to the published version of the manuscript.  
The research at Oakland University was supported by grants from the National Science Foundation (ECCS-2415328, ECCS-EAGER-2236879) and the Air Force Research Laboratory. The research at AFRL was supported by a grant from the AFOSR (Award No. FA9550-23 RXCOR001).  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Numerical Simulation  
Instead of modeling each BMHV model from the manufacturer's provided data and including other BMHV not included in this study, a sensitivity analysis was performed to quantify the BMHV design parameters (the leaflet opening angle, leaflet distance, and diameter) and patient hemodynamics (stroke volume and heart rate). In order to compare the results from the simulation with the tested BMHV, measurements of the orifice size, height, leaflet spacing, and thickness are provided (Table 2). For display only, St Jude BMHV and OnX were designed for each diameter, with size modifications made using FreeCAD (open source license). The inflow boundary condition was defined at the entry of a tube located 5 diameters upstream of the leaflet entry to ensure a parabolic velocity profile. The outflow was also located after a similar downstream length.  
Numerical simulations were performed using AcuSolve Software (Altair Engineering, Troy, MI, USA). The BMHV is considered stationary, being maintained in a fully opened state, and 10 cycles were simulated using the results of the 10 th cycle for post-processing. A Newtonian fluid (viscosity of 0.0038 Pa.s and density of 1050 kg/m\({}^{3}\) for a blood temperature of 37.0 \({}^{\circ}\)C) was used. The transient inflow boundary condition is provided in (Figure 2e) by applying a user function and a flat profile. The meshes comprised 408,000 to 4,210,000 elements (Figure 2c,d). Mesh size analysis was run on four mesh sizes (0.3, 0.5,  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Design of Experiment Parameters** & **Range** & **Increments** & **No. of Simulations** \\ \hline BMHV internal diameter (mm) & 20–24 & 1 & 5 \\ Distance between leaflet (mm) & 3–4 & 0.5 & 3 \\ Leaflet opening angles (\({}^{\circ}\)) & 80–90 & 5 & 3 \\ BMHV Dysfunction (opening angle of one leaflet \({}^{\circ}\)) & 0–15 & 15 & \(2\times 2\) \\ Stroke volume (mL) & 70–120 & 70; 90; 120 & 3 \\ Heart Rate (ppm) & 60–120 & 60; 70; 120 & 3 \\ Mesh size & & & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the numerical simulation: Tested prosthesis and conditions with parameter values and increments—BMHV Dysfunction: Dysfunction with one leaflet blocked.  
Figure 2: Parametric numerical simulation. BMHV designs: OnX (**a**) and St Jude Medical (**b**), Numerical simulation settings: BMHV in the tube (**c**) and details of computational mesh (**d**), and inflow velocity curve (**e**).  
0.7, and 0.9 mm), and three fluid layers of 0.5 mm were defined at the wall and around the leaflet. The mesh size analysis aims at finding the mesh size for which the change in results between different mesh size simulations is below 3%. The influence of the blocked leaflet was tested with similar boundary conditions and settings. Simulation parameters are listed in Table 3 as the design of the experiment of 17 simulations.
## 4 Experiments and Results: Performance Analysis
### Simulation Results
The improved detection rate, reduced latency, and enhanced scalability and fault tolerance highlight its potential as a robust and efficient framework for securing VANETs. Figure 11:0 Visual representation of the comparative analysis between the proposed dual-layer blockchain method and the basic method in detecting security threats in VANETs. Figure 11:1 Performance comparison between traditional and proposed blockchain-based solution.
## 6 Results
### Combinations of the Studied System Components
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline
**Hybrid Power System** & **Algorithm** & \multicolumn{2}{c}{**PV**} & \multicolumn{2}{c}{**Wind**} & \multicolumn{2}{c}{**Diesel**} & \multicolumn{2}{c}{**Battery**} & \multicolumn{2}{c}{**Inverter**} & \multicolumn{2}{c}{**Biomass**} \\ \hline Costs & Inv & OdM & Rep & Inv & OdM & Rep & Inv & OdM & Rep & Fuel & Inv & Rep & Inv & Rep & Inv & OkM & Fuel \\ \hline \multirow{4}{*}{Scenario I} & QQBO & 145,423 & 11,588 & 0 & 0 & 0 & 0 & 303 & 1792 & 465 & 17,680 & 1343 & 90 & 28,400 & 158 & // \\  & BO & 74,400 & 5913 & 0 & 124,813 & 9920 & 0 & 162 & 526 & 284 & 5244 & 1480 & 99 & 28,400 & 158 & // \\  & HHO & 153,931 & 12,234 & 0 & 38,161 & 3033 & 0 & 130 & 330 & 199 & 3729 & 1465 & 98 & 28,400 & 158 & // \\  & AEFA & 98,747 & 7848 & 0 & 22,034 & 1751 & 0 & 1367 & 537 & 2097 & 44,524 & 1865 & 125 & 28,400 & 158 & // \\  & IWO & 249,237 & 19,809 & 0 & 17,069 & 1356 & 0 & 2574 & 40 & 3949 & 8,025 & 582 & 39 & 28,400 & 158 & // \\ \hline \multirow{4}{*}{Scenario II} & QQBO & 88,191 & 7009 & 0 & // & // & // & // & // & // & // & // & // & 28,400 & 158 & 1040 & 103 & 4696 \\  & BO & 88,191 & 7009 & 0 & // & // & // & // & // & // & // & // & 28,400 & 158 & 1040 & 103 & 4696 \\  & HHHO & 89,658 & 7126 & 0 & // & // & // & // & // & // & // & 28,400 & 158 & 2080 & 129 & 5907 \\  & AFEA & 90,894 & 7224 & 0 & // & // & // & // & // & // & // & // & 28,400 & 158 & 1208 & 105 & 4820 \\  & IWO & 109,654 & 8715 & 0 & // & // & // & // & // & // & // & // & 28,400 & 158 & 2792 & 97 & 4416 \\ \hline \multirow{4}{*}{Scenario III} & QQBO & 122,803 & 8965 & 0 & // & // & // & 335 & 1869 & 514 & 19,339 & 580 & 395 & 28,400 & 158 & // \\  & BO & 100,875 & 8017 & 0 & // & // & // & 729 & 1994 & 1118 & 43,812 & 5219 & 350 & 28,400 & 158 & // \\  & HHHO & 144,826 & 11,510 & 0 & // & // & // & 446 & 1792 & 684 & 25,102 & 1375 & 92 & 28,400 & 158 & // \\  & AFEA & 116,007 & 9220 & 0 & // & // & // & 417 & 1855 & 641 & 24,008 & 557 & 374 & 28,400 & 158 & // \\  & IWO & 224,516 & 17,844 & 0 & // & // & 1002 & 1756 & 1383 & 55,545 & 5145 & 345 & 28,400 & 158 & // \\ \hline \multirow{4}{*}{Scenario IV} & QQBO & // & // & // & 340,787 & 27,085 & 0 & 18,093 & 921 & 27,759 & 720,800 & 9114 & 612 & 28,400 & 158 & // \\  & BO & // & // & // & 352,917 & 28,050 & 0 & 18,092 & 912 & 27,757 & 717,633 & 4263 & 286 & 28,400 & 158 & // \\  & HHHO & // & // & // & 351,094 & 27,805 & 0 & 18,641 & 915 & 25,899 & 740,465 & 7323 & 491 & 28,400 & 158 & // \\  & AFEA & // & // & // & 376,885 & 29,955 & 0 & 18,240 & 887 & 27,985 & 744,885 & 7265 & 488 & 28,400 & 158 & // \\  & IWO & // & // & // & 539,845 & 42,907 & 0 & 20,699 & 774 & 31,757 & 766,838 & 7821 & 525 & 28,400 & 158 & // \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Convergence of objective function of different scenarios.
# Effectiveness of Lower-Cost Strategies for Running Gait Retraining: A Systematic Review
###### Abstract
It was also observed that gait retraining to minimize heel striking increased the knee flexion at the initial contact. However, trials that reported on peak tibial acceleration (in the skin surrounding the tibia) and the peak patellofemoral joint reaction force were too different to pool their data. Results from individual trials demonstrated reductions in these outcomes across multiple retraining interventions. However, in a recent systematic review of randomized clinical trials on strategies to prevent and manage running-related knee injuries [50], low-quality evidence was found to indicate that retraining on running techniques may reduce the risk of running-related knee injuries by two-thirds. These findings highlighted the effectiveness of gait retraining in runners to alter movement-related risk factors that are potentially associated with the development of musculoskeletal injuries. Even though the efficiency of running retraining in reducing the risk of injuries is debatable, many studies and clinicians still utilize these interventions to improve outcomes for individual runners. However, most studies seem to rely on high-cost instruments such as force platforms (on the ground or on treadmills) and three-dimensional (3D) motion-analysis systems to provide real-time biofeedback. Unfortunately, these devices are unaffordable to many and are rarely available to coaches or clinicians. Clinical equipment generally includes a simple treadmill, a high-definition video camera, and computer applications or smartphones to identify variables such as the step cadence, foot strike angle, and foot strike pattern [40; 51]. There is still a need to examine the effectiveness of lower-cost equipment utilized in running retraining interventions. Therefore, the objective of this systematic review was to evaluate the effects of lower-cost equipment on running gait retraining. As a definition, in this study we assumed that lower-cost equipment would have a reduced cost compared to gold-standard devices.
## 3 Proposed Method
### Detection of Overlapping Lines  
1. Find the average value of the height of the characters (\(Avg_{c_{ht}}\)) in the preprocessed handwritten image, \(I_{b}\). This is obtained by finding the average height of the connected components in \(I_{b}\).
2. Identify the region containing the lines in each vertical stripe. Then, find the median value of the number of rows in each region, which indicates the line height, \(L_{ht}\). The obtained value is the median value of the line heights, \(M_{L_{ht}}\), in a vertical stripe.
3. The threshold value, \(T_{ov}\), for identifying the overlapping lines is calculated based on the values obtained from step 1 and step 2. Threshold \(T_{ov}\) is computed as follows. If (\(M_{L_{ht}}>4\times Avg_{c_{ht}}\)), \[T_{ov}=1.5\times M_{L_{ht}}.\] (1) Otherwise, \[T_{ov}=1.9\times M_{L_{ht}}.\] (2)
4. Compare the height of each line with the threshold value, \(T_{ov}\).
5. If the height of the line segment is above or equal to \(T_{ov}\), it is detected as an overlapped line.
6. If a line is detected as an overlapped line segment, then the number of lines \(L_{cnt}\) in the overlapped line is calculated as follows. If (\(M_{L_{ht}}>5\times Avg_{c_{ht}}\)), \[L_{cnt}=\frac{L_{ht}}{5\times Avg_{c_{ht}}}.\] (3) If (\(M_{L_{ht}}>4\times Avg_{c_{ht}}\) and \(M_{L_{ht}}\leq 5\times Avg_{c_{ht}}\)), \[L_{cnt}=\frac{L_{ht}}{1.4\times M_{L_{ht}}}.\] (5) If (\(M_{L_{ht}}>2\times Avg_{c_{ht}}\) and \(M_{L_{ht}}\leq 3\times Avg_{c_{ht}}\)), \[L_{cnt}=\frac{L_{ht}}{1.7\times M_{L_{ht}}}.\] (6)  
The size of the characters written may vary from person to person. This is considered while framing Equations (1)-(6) to compute the threshold value \(T_{ov}\) and the number of lines in the overlapped segment \(L_{cnt}\) in the proposed technique. Once a line segment is identified as having overlapping lines, it has to be segmented into individual lines. The steps involved in segmenting the overlapping lines are shown in Figure 3 and a detailed description is given in the next subsection.
# Adaptive Algorithm for Fast 3D Characterization of Magnetic Sensors
###### Abstract  
Magnetic sensors are highly relevant in clinical and industrial applications such as localization tasks and geological investigations. The spatial behavior of these sensors is of great interest for accurate forward modeling and the consequential possibilities for sophisticated applications, e.g., solutions to inverse problems. In this contribution, we present a novel characterization approach using adaptive system identification approaches. We utilize a gradient-based algorithm for estimating impulse and corresponding frequency responses for a directivity analysis in 1D, 2D, and 3D. For this, we built a triaxial Helmholtz coil setup to generate a 3D directive field. This is controlled by an algorithm that exploits similarities in sensor behavior with respect to small differences in excitation field angles. We found advantages for a controlled adaptation, with faster convergence and a smaller system distance between estimations and measurements with a proposed control based on the contraction-expansion approach (CEA). With runtimes averaging less than 1.5 s per direction for full impulse response estimation, this proof of concept shows the potential of the proposed algorithm for enabling a feasible frequency and directivity characterization method.  
m pacs: 25, 995 +
Footnote †: journal: Sensors  
apactical sensor characterization; estimation algorithms; adaptive filters; NLMS control; magnetic field steering; impulse response; frequency response; directivity
## 2 Materials and Methods
### Plant Growth Environmental Conditions  
Lettuce (_Lactuca sativa_ cv. 'Outredgeous') was grown in environmental growth chambers (Percival PGW-48, Perry, IA, USA) located in the Space Station Processing Facility at NASA Kennedy Space Center, Florida (located at sea level with an atmospheric pressure of ~101 kPa). The chamber air temperature, relative humidity (RH), and CO\({}_{2}\) levels were maintained at 23 \({}^{\circ}\)C, 50%, and 3000 ppm, respectively, for all tests. These set points were selected to simulate an average environment of the ISS. Lighting was provided by overhead light-emitting diode (LED) grow lights (Percival, Perry, IA, USA). The photosynthetic photon flux density (PPFD) target was 300 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\) averaged over the entire canopy, with a 16 h (16 h light/8 h dark) photoperiod. Spectral quality consisted of 23% blue (400-500), 27% green (500-600 nm), 50% red (600-700 nm), and 25 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\) of far red using a combination of 450 nm, 660 nm, 730 nm, and white (6000 K) LEDs.
## 4 Analysis and Findings of Research Questions
#### 4.1.1. Smartphone User Activities Based on Sensor Modality  
In several studies [89; 90], the survey aims to illuminate various activities, including storing personal passwords on the phone, ensuring that the mobile device software is updated, obtaining software from third-party websites, and searching for free Wi-Fi. Nevertheless, in research by [91], the user behavior was classified as local device management, network management, and remote service management. Only 36% of smartphone users use a four-digit PIN screen lock, with the remainder using anti-virus software, a PIN or an unlock pattern, data cleansing software, and encryption. Contrastingly, 34% of users do not utilize any such measures.  
The network management category specifies how stringent or flexible the user's options are for connecting to unknown communication nodes. Furthermore, both studies by [92; 93] focused on behavior targeting the GPS sensor of smartphones relating to the user activity (continuously enabling GPS, Bluetooth, or enabling remote tracking of the device) [93], making a wide landscape for vulnerability.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Modality** & **Sensors and Method** & **Activities** \\ \hline \multirow{3}{*}{Sensors} & \multirow{3}{*}{Positioning (GPS, Wi-Fi, and Bluetooth)} & How restrictive or permissive the user’s selection of unknown communication nodes is [90]. \\  & & Constantly enabling GPS, remote device tracking, and Bluetooth [92]. \\ \cline{1-1}  & & Connecting to insecure Wi-Fi hotspots [87; 89; 92]. \\ \cline{1-1}  & & Gaining the user’s location through GPS or the position of the closest cell tower [91]. \\ \hline \hline \end{tabular}
\end{table}
Table 9: Activities of the Smartphone User.  
Figure 9: Smartphone User Activities Classifications.
## 2 Materials and Methods
### Calculation of Electrical Energy per Order (EE/O)  
Economics is a crucial factor in selecting a wastewater treatment technology [46]. To compare the energetic efficiency of CP with other AOPs, its electrical energy per order (EE/O) was computed as described in Equation (6) [47; 48]. EE/O represents the energy, measured in kilowatt-hours (kWh), required to degrade a contaminant by an order of magnitude in a unit volume (1 m\({}^{3}\)).  
\[\text{EE/O}=\frac{\text{P}\cdot\text{t}\cdot\text{1000}}{\text{V}\cdot\text{ log}\Big{(}\frac{\text{C}_{0}}{\text{C}_{\text{t}}}\Big{)}} \tag{6}\]  
where P, t, V, C\({}_{0}\), and C\({}_{\text{t}}\) represent the power of the equipment (kW), the reaction time (h), the solution volume (L), the concentration of initial PFOS (mg/L), and the measured concentration of PFOS at time t (mg/L), respectively.
# Low-Cost Dual-Frequency GNSS Receivers and Antennas for Surveying in Urban Areas
## 3 Results and Discussion
### Results from Quality Control of GNSS Observations
#### 3.1.3 Ambiguity Resolution Results  
The ambiguity fix ratio is a key factor for a high-precision positioning solution when phase observations are considered. The multipath error can affect the ambiguity fix ratio and provide float instead of fix solutions. This can be more evident when using low-cost GNSS equipment in urban areas.  
The ambiguity resolution ratio results are shown in Table 3 for LC\(-\)1 and LC\(-\)2. In the open sky, with less multipath and higher values of C/N\({}_{0}\), the ambiguity fix ratio is more than 95%, LC\(-\)2 achieves an ambiguity fix ratio of 98.3%, while for LC\(-\)1 it is 96.8%. The difference is more obvious in urban areas, where the LC\(-\)2 has an ambiguity fix ratio of 93.4%, while it is only 75.0% for LC\(-\)1. Using a geodetic antenna improves the ambiguity fix ratio and provides fix solutions most of the time. In the case of the low-cost antenna,  
Figure 8: Multipath for code observations in F1 and F2 frequency in urban areas: (**a**) F1 multipath for LC\(-\)1, (**b**) F2 multipath for LC\(-\)1, (**c**) F1 multipath for LC\(-\)2, (**d**) F2 multipath for LC\(-\)2, (**e**) F1 multipath for GD\(-\)1, and (**f**) F2 multipath for GD\(-\)1.  
for intervals longer than 1 h, only float solutions are obtained. This may indicate the inappropriateness of using low-cost devices in similar conditions. The obtained positioning solutions for easting, northing, and ellipsoid height are shown for LC\(-\)1 and LC\(-\)2 in Figure 9 for both open-sky as well as urban conditions. In the open sky, fix solutions are obtained most of the time, while in urban areas more float solutions are present, which are shown with gaps in Figure 9.
## 1 Introduction
The experimental results show the feasibility of the controller and assess its performance in a real system. In Reference [24], a low-cost air levitation system to be used in both a virtual and a remote version is proposed. It provides a complete open-source and open-hardware remote lab solution that is low-cost and easy to replicate. This work applies several neural control schemes to a challenging system like the nonlinear and unstable magnetic levitation system to test the performance of neural controllers in a low-cost hardware. Nowadays this type of hardware is extensively used both in academia and scientific projects and its validation to incorporate more recent technologies such as neural networks is needed. The main differentiation of this contribution with respect to former control methods is that the system in many of those research works used repulsion to oppose the gravitational acceleration and the suspended magnet was constrained so that it could only move in the vertical direction. In this article, the system uses attraction to counteract gravity and the suspended magnet is not constrained in any way, so it is more susceptible to perturbations. Moreover, in this work, we apply several neural control structures and compare the results among them to verify the feasibility of each control structure in the control of the levitation system. Also, some linear classical controllers are developed to give the training data for the neural controllers, as well to enhance the better performance of the neural controllers. In fact, the neural controllers can control the levitation system in a wider-range of operation in contrast with the linear controllers, which only operates on small variations around the linearized point. Furthermore, the proposed neural controllers are implemented in a low-cost hardware, which can be easily built and used for in site laboratory experiments or take-home laboratories. Here, we describe the whole process of implementing a neural controller applied to a magnetic levitation system, as well as some classical controllers for the comparison of the results. Section 2 shows the details of the hardware setup, whereas Section 3 presents the modeling and identification of the levitation system. In Section 4 described the design of the two classical controllers with the objective of providing the training data for the neural controllers. Section 5 describes the implementation and testing of three neural controllerson a real levitation system. Finally, Section 6 draws the main conclusions and addresses perspectives to future developments of the presented work.
# Intermediate Isolation System with Nonlinear Lower Structure and Isolation System
###### Abstract  
This paper reports a study on the Intermediate Isolation System (IIS) applied to existing buildings. This kind of application is particularly suitable when a vertical addition is planned for buildings in seismic zones; in such a case, an isolation system can be placed at the base of the extension to prevent the increase, or, better, to reduce the seismic demand on the existing structure. In previous works, parametric response spectrum analyses have been carried out on lumped mass models by varying the period of the isolation system. As a result, a sort of _IIS design spectrum_ has been derived and used for selecting design solutions for the vertical extension that minimize the overall seismic response. In this paper, the above design indications are assessed in the light of nonlinear time history analyses, accounting for the hysteretic response of the existing structure and the nonlinear behaviour of the isolation system. The IIS configurations are analysed and the results are discussed and compared in terms of peak response. In light of the obtained analysis results, the effectiveness and robustness of IIS applications for vertical extensions are discussed, and design implications are suggested.  
vertical extension; intermediate isolation system; nonconventional TMD; masonry structures; seismic retrofit +
Footnote †: journal: Applied Sciences