esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of the Effect of Temperature on the Sound Transmission Loss of a Curved Plate
## 2 Theoretical Model
### Thermal Stress Analysis  
Assuming that the curved plate is stress-free at temperature \(T_{0}\), the stress state of the plate changes when the temperature changes to \(T\) due to the resulting thermal stress. In the direction along the radius, the relationship between density and temperature, according to [PERSON]'s law of heat conduction, is as follows:  
\[\rho C\frac{\partial T}{\partial t}=\kappa\frac{\partial^{2}T}{\partial R^{2}}, \tag{1}\]  
where \(R\) is the thickness direction, \(t\) is the time, \(\rho\) is the density of the medium, \(C\) is the specific heat coefficient, and \(\kappa\) is the heat transfer coefficient. Assuming that the inner concave side of the plate has a temperature \(T_{2}\), and the outer convex side of the plate has a temperature \(T_{1}\), the temperature boundary condition is as follows:  
\[R=R_{0}:\quad T=T_{2}, \tag{2}\]  
Figure 1: Schematic of the structure of the acoustic transmission arc plate in the temperature field.  
\[R=R_{0}+h:\;\;T=T_{1}. \tag{3}\]  
According to Equations (2) and (3), one solution of Equation (1) is  
\[T(R)=\frac{T_{1}-T_{2}}{h}(R-R_{0})+\frac{T_{1}+T_{2}}{2}. \tag{4}\]  
The thermoelasticity of the plate can be described by a planar stress condition, which assumes a uniform variation in temperature with the thickness of the thin plate. The two-dimensional stress is a function of the variables \(L\) and \(\theta\). The radial stress is zero [28]. The stress-displacement relationship in the temperature field can be expressed as follows:  
\[\sigma_{\theta}=\frac{E}{1-\
u^{2}}[(\varepsilon_{\theta}+\
u\varepsilon_{L} )-\alpha(1+\
u)(T-T_{0})], \tag{5}\]  
\[\sigma_{L}=\frac{E}{1-\
u^{2}}[(\varepsilon_{L}+\
u\varepsilon_{\theta})- \alpha(1+\
u)(T-T_{0})], \tag{6}\]  
\[\tau_{dL}=\frac{E}{1+\
u}\varepsilon_{dL}, \tag{7}\]  
where \(\alpha\) is the coefficient of thermal expansion of the material, \(E\) is [PERSON]'s modulus, and \(\
u\) is [PERSON]'s ratio.  
Assuming that the thermal environment leads to a uniform increase in the in-plane temperature of the plate, the thermal load here does not cause any in-plane displacement, given the boundary conditions. Therefore, Equations (5)-(7) can be simplified as follows:  
\[\sigma_{\theta}=-\frac{E\alpha T(z)}{1-\
u}, \tag{8}\]  
\[\sigma_{L}=-\frac{E\alpha T(z)}{1-\
u}, \tag{9}\]  
\[\tau_{\theta L}=0, \tag{10}\]  
By integrating Equations (8)-(10) in the thickness direction, the thermal stress can be determined as follows:  
\[\left\{\begin{array}{c}N_{\theta}\\ N_{L}\\ N_{\theta L}\end{array}\right\}=\int_{-\frac{1}{2}}^{+\frac{h}{2}}\left\{ \begin{array}{c}\sigma_{\theta}\\ \sigma_{L}\\ \tau_{\theta L}\end{array}\right\}dR=\left\{\begin{array}{c}-\frac{\alpha E (T_{1}+T_{2})h}{2(1-\
u)}\\ -\frac{\alpha E(T_{1}+T_{2})h}{2(1-\
u)}\\ 0\end{array}\right\}, \tag{11}\]  
where \(N_{\theta}\), \(N_{L}\), and \(N_{\theta L}\) are the thermal stresses in the three directions.
## 3 Methodology
### BLCC Creation
#### 3.2.2 Bottom Level: Review-Based \(g\)-Chart for Performance Monitoring
A UCL in the \(\pm 3\) standard deviation method tends to be inadequate for a skewed geometric distribution, which results in an excessively high false alarm rate for the UCL in comparison to that for a  
Figure 4: Data transformation for constructing a \(g\)-chart. normal distribution [47; 48; 52; 53]. The probability limit method sets the UCL and LCL by considering the probability that a normal distribution has a point outside either the UCL (+3 standard deviation) or LCL (-3 standard deviation), that is, approximately 0.00135 each. Thus, for a geometric distribution with parameter \(q_{i}\), the UCL is set at the 99.865 th percentile (=\((1-0.00135)\times 100\)) of the geometric distribution, whereas the LCL is set at the 0.135 th percentile. The CL is set at the 50 th percentile or median. The inverse cumulative distribution function (INVCDF) for a geometric distribution with parameter \(q_{i}\) yields the control limits, and 1 is subtracted from the resulting INVCDF values because the proposed method considers the number between two successive events, rather the number until the next event. \[\text{CL}_{i}=INVCDF(0.5)-1 \tag{9}\]  
\[\text{UCL}_{i}=INVCDF(0.99865)-1 \tag{10}\]  
\[\text{LCL}_{i}=max(INVCDF(0.00135)-1,0) \tag{11}\]  
For the interpretation of \(g\)-charts, it should be noted that an increase in the number of non-negative reviews between negative reviews corresponds to decreased complaints or improved performance. In addition, a greater CL implies fewer complaints for an attribute on average in a time period, indicating a better performance of the attribute. The four test rules in Table 4 can also be used in this step to judge whether the complaint rate or performance level is stable. Additionally, the Benneyan test is conducted to detect an abnormally high complaint rate. Because the LCL of \(g\)-charts is zero in most cases, plots beneath the LCL are not possible, given the non-negative nature of \"number-between\" data. Additional information on the Benneyan test can be found elsewhere [47; 48; 54].
## 1 Introduction
In [44], stable \(V/f\) control with high-efficiency control is compared against sensorless FOC. Sensorless vector control in [44] is composed of two estimation algorithms. The first is the saliency-based method with HF frequency injection for low-speed operation. In the high-speed region, a back-EMF-based method is used. However, the employed algorithms are not further specified in the paper. \(V/f\) control including stabilizing loop is discussed in [43]. In [45], a high-efficiency \(V/f\) control loop used a single DC shunt current measurement. Other authors in [46; 47] used different types of maximum torque per ampere (MTPA) methods based on imaginary power factor calculation suitable for a \(V/f\) controlled PMSM drive. The control \(V/f\) algorithms based on unity power factor (UPF) calculation are simulated and experimentally verified in [48; 49]. A similar technique is applied and compared with FOC in [50; 51], where active flux observer and MTPA are used. Stable \(V/f\) control with UPF was compared to back-EMF-based sensorless FOC in [52], supported by simulation analysis. In [53], the MTPA method, field-weakening, maximum torque per voltage (MTPV), and energy optimal \(V/f\) control are proposed. \(V/f\) control with MTPA for SPMSM based on HF voltage signal injection is presented in [54]. This paper proposes a novel constant power factor loop in the \(V/f\) control strategy with stabilization. The proposed \(V/f\) control does not require information about PMSM parameters and is robust against its variations. These parameters vary during motor operation, e.g., with the magnetic core saturation, temperature, and skin effect. Furthermore, the proposed \(V/f\) control structure does not require the direct calculation of phase shift between current and voltage, as employed in [48; 49]. Instead, the phase shift can be controlled directly in the voltage vector reference frame \(d_{v}q_{v}\) based on the demanded power factor. The proposed strategy is then compared to the traditional sensorless FOC with Luenberger-type back-EMF observer, which can be designed based on the machine model. Both strategies are deeply analyzed, mathematically described, and compared within the paper. At the end of the paper, these sensorless strategies are supported by experimental verification with the traction PMSM designed for golf cart applications, and the pros and cons of both techniques are discussed.
## 2 Materials and Methods
### Satellite Images  
The goal of this research was to monitor events over 40 years, but also to be as easy to reproduce as possible; therefore, satellite images were chosen accordingly. To cover a time span from 1984 to the present day, it was an obvious choice to rely on Landsat satellites, and specifically Landsat 5, 7 and 8. Some Landsat 5 images were also acquired as replacements for some Landsat 7 images that were too problematic to use due to the Scan Line Corrector (SLC) failure. For more recent years (from 2015 to 2019) Sentinel-2 images were acquired. The result was an average of 36 images for each area, but because it was necessary to produce Normalised Difference Water Index (NDWI) values for the year before the completion date of the Hamrin Dam (1981), one Landsat 2 image was acquired as well, only for that area (Table 1). All images were taken at the Top of Atmosphere (TOA) reflectance (for details, see Section 2.4).  
The monthly interannual analysis for 2018 was carried out on 12 Sentinel-2 images for each area, for a total of 36 images.  
To obtain a visual confirmation of the results from the pixel analysis of the NDWI images, it seemed more convenient to use higher resolution imagery, at least for this last stage. A good compromise between cost-effective and good resolution imagery is provided by Planetscope images, distributed by Planet at no cost after a European Space Agency (ESA) Third Party Mission (TPM) application. The Planetscope satellite constellation is formed of more than 120 individual satellites (named DOVE) that cover the globe daily at a 3-3.7 m Ground Sampling Distance (GSD). More information is available at: [[https://www.planet.com/products/](https://www.planet.com/products/)]([https://www.planet.com/products/](https://www.planet.com/products/)) (last accessed on 14 December 2020). Access to Planetscope images was granted for the entire year of 2018 and limited within the three study areas indicated above. Between specific close-up scenes and monthly snapshots, 46 Planescope Ortho Scene (Level 3B) images were gathered through the Planet Explorer portal (Figure S1, the portal is accessible at [[https://www.planet.com/explorer](https://www.planet.com/explorer)]([https://www.planet.com/explorer](https://www.planet.com/explorer)) (last accessed on 14 December 2020)). For previous years, Sentinel-2 or pansharpened Landsat 8 images were used instead.
# Forklift Tracking: Industry 4.0 Implementation in Large-Scale Warehouses through UWB Sensor Fusion
## 4 Experimental Analysis
### Effect of Initial Uncertainty  
This section aims at investigating whether the proposed UKF-based tracking algorithm is robust with respect to errors in the initialization of the filter. We performed an analysis of the method performance by artificially introducing an error on the initial state \(\mathbf{s_{k}}\), namely by changing the initial error covariance matrix \(\mathbf{P_{0}}\) according to the same values reported in Table 1. For each configuration, we ran the UKF and we observed the mean distance error \(\tilde{\varepsilon}_{d}\) when the forklift performed the test-case trajectory IX. The results are depicted in Figure 15. As it can be noticed, as well as in real situations, the UKF performance is not  
Figure 14: Mean distance error \(\varepsilon_{d}\) in function of the forklift speed \(v\) by analyzing the test case trajectories I-VI.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test Case** & \(\tilde{\varepsilon}_{\mathbf{d_{lwn}}}\) **(m)** & \(\tilde{\varepsilon}_{\mathbf{d_{lwn+OTS}}}\) **(m)** & \(\tilde{\varepsilon}_{\mathbf{d_{lux}}}\) **(m)** & \(\tilde{\varepsilon}_{\mathbf{\theta_{lwn}}}\) **(rad)** & \(\tilde{\varepsilon}_{\mathbf{\theta_{lwall+OTS}}}\) **(rad)** & \(\tilde{\varepsilon}_{\mathbf{\theta_{lux}}}\) **(rad)** \\ \hline IV & 3.84 & 2.81 & **2.13** & \(-\)0.04 & \(-\)0.04 & **0.02** \\ IX & 1.77 & 2.43 & **1.17** & 0.06 & 0.06 & **0.01** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mean distance localization errors for test cases IV and IX in Table 2.  
influenced by the initial error uncertainty. The trajectory reconstruction with the onboard sensors only, instead, is greatly influenced by the initial uncertainty error, as typical for dead-reckoning approaches.
# Implementation of an IoT Based Radar Sensor Network for Wastewater Management
## 3 Implementation and Results
### SPS--Site B
#### 3.1.3 Effect of Radar Accuracy on SPS Well Diameters  
It is important to note here the effect of radar accuracy on flow measurements. Even with an accuracy of \(\pm\)2 mm, this accuracy can cause a significant flow error for larger diameter SPS wells. As shown in Table 1 below, Site C's well diameter of 2.54 m can cause up to an approximate 10 L/s flow error per second of level samples.  
An exponential weighted moving average sample smoothing is employed to minimise these errors.
# A Coupling System for Prediction of Physiological Parameters in an Immersed Condition
## 5 Conclusions  
This paper establishes a coupling system between a thermal manikin and a thermal response model of the human body, and verifies the accuracy and reliability of the coupling system with experimental data from subjects. The validation experiments involved air and cold water environments, as well as the wearing of two different styles of life preservers, and the heat production, core temperature, and skin temperature values obtained from the coupling system and subject experiments were compared. The following conclusions were drawn:  
1. The simulated values of the coupled system were compared with the experimental measurements of the subjects, and the statistical analysis based on the paired samples t-test and RMSD indicated that the coupled system could predict typical physiological parameters such as core temperature, mean skin temperature, and local skin temperature with a relatively high accuracy. Therefore, the developed coupled system can be used for physiological parameter prediction.
2. The established coupling system makes use of the advantages of the thermal manikin and the human thermal model, so that the manikin can be actively adjusted according to the changes in the environment, which is closer to the actual situation than the traditional thermal manikin that only characterizes the warmth of the garment with the thermal resistance of the garment, and at the same time compensates for the defects of the human thermal model that cannot be simulated in the complex environment, which enhances the applicability of the human thermal model to satisfy the needs of life preserver testing and research and development.
3. By comparing the core and skin temperature values obtained from different life preservers worn by dummies in the same environment, the thermal protectiveness of different life preservers can be evaluated, and on this basis, it can also be used for human thermal comfort and cold shock assessment.  
Conceptualization, Z.W. and R.Y.; methodology, R.Y.; formal analysis, Z.W.; investigation, C.Z.; resources, X.Q.; data curation, C.Z.; writing--original draft preparation, Z.W.; writing--review and editing, Z.W.; visualization, Z.W.; supervision, X.Q.; project administration, Y.S.; funding acquisition, R.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by [National Natural Science Foundation of China] grant number [No. U1933111] and [Tianjin Research Innovation Project for Postgraduate Students] grant number [No. 2021 YJSOE2006].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data will be made available on request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Methodology
### Raw Data Processing
#### 3.2.1 Raw Data Noise Reduction Processing  
A total of 29,783 geochemical samples were collected from 29 map sheets of 1:200,000 hydrogeological sediment surveys in the Beishan area of Gansu Province. Each sample underwent analysis for 39 geochemical elements. Given that these data were collected by various units over different years, significant systematic errors exist for the same element across different map sheets. To enhance the accuracy of the preferred target area, it is essential to conduct noise reduction, including determining the lower limit of anomalies and frame-by-frame adjustment on the original data.  
To ascertain the lower threshold of anomalies within the raw data, the research team employed the concept of \"machine vision\" and independently devised the \"linear approximation\" method and algorithmic model software. This enabled them to accurately compute the lower limit of positive anomalies and the upper limit of negative anomalies  
Figure 2: Research flow diagram.  
for each element within each map frame of the study area simultaneously, significantly enhancing the precision and efficiency of calculating critical parameters. The \"geochemical data sub-frame adjustment software\" was utilized to simultaneously adjust the 39 elements measured in the 1:200,000-scale water system sediment survey in the study area to regional background reference. Additionally, the \"geophysical data sub-frame adjustment software\" was employed to conduct sub-frame adjustment processing on aeromagnetic data from various periods, survey areas, and scales across the entire region, ensuring the aeromagnetic data were standardized within the scope of the research area.
###### Abstract  
The Multi-Parameter Wireless Sensing (MPwise) system is an innovative instrumental design that allows different sensor types to be combined with relatively high-performance computing and communications components. These units, which incorporate off-the-shelf components, can undertake complex information integration and processing tasks at the individual unit or node level (when used in a network), allowing the establishment of networks that are linked by advanced, robust and rapid communications routing and network topologies. The system (and its predecessors) was originally designed for earthquake risk mitigation, including earthquake early warning (EEW), rapid response actions, structural health monitoring, and site-effect characterization. For EEW, MPwise units are capable of on-site, decentralized, independent analysis of the recorded ground motion and based on this, may issue an appropriate warning, either by the unit itself or transmitted throughout a network by dedicated alarming procedures. The multi-sensor capabilities of the system allow it to be instrumented with standard strong- and weak-motion sensors, broadband sensors, MEMS (namely accelerometers), cameras, temperature and humidity sensors, and GNSS receivers. In this work, the MPwise hardware, software and communications schema are described, as well as an overview of its possible applications. While focusing on earthquake risk mitigation actions, the aim in the future is to expand its capabilities towards a more multi-hazard and risk mitigation role. Overall, MPwise offers considerable flexibility and has great potential in contributing to natural hazard risk mitigation.  
natural hazard and risk; EEW; multi-hazard/risk; rapid response; monitoring 2017  
1  
[PERSON] 1  
Helmholtz Center Potsdam--GFZ German Research Centre for Geosciences, Helmholtzstrasse 7, 14467 Potsdam, Germany; [EMAIL_ADDRESS] (K.F.); [EMAIL_ADDRESS] (M.P.); parolaigaffz-potsdam.de (S.P.); [EMAIL_ADDRESS] (M.P.); [EMAIL_ADDRESS] (S.M.)  
2
## 2 System-Level Design Optimization
### System-Level Optimization Methods
#### 2.2.2 Multi-Level Optimization Method
While it is true that certain applications predominantly operate under dynamic conditions, the inclusion of steady-state analysis can offer valuable insight into key performance aspects, such as efficiency, losses, and thermal behavior, which are often not fully captured in transient simulations. Furthermore, steady-state  
Figure 4: Multi-level optimization method framework. evaluations are typically computationally efficient, enabling their integration into the design and optimization processes without imposing significant additional complexity. Reference [31] focuses on the design of an SRM using a non-linear electromagnetic analysis model combined with circuit theory for an EV application. The proposed model is validated through the FEM, and random variables are introduced to define the wind resistance coefficient, rolling resistance coefficient, and slope in order to simulate various road conditions in the dynamic operation of an EV. The torque, efficiency, and torque ripple are optimized using single-objective optimization carried out by the GA. The torque ripple and controller efficiency are optimized by adjusting the turn-on, \(\theta_{on}\), and turn-off, \(\theta_{off}\), angles. This approach qualifies as an MLOM, since the parameters are optimized in successive steps rather than simultaneously. Typically, control optimization is performed under dynamic conditions. However, the study reports a very high torque ripple coefficient of approximately 1.55, which translates to 155%, while the efficiency remains below 90%. Additionally, the computational time of the method is not discussed. In the previous references, with both the SLOM and MLOM, the optimization process utilizes the entire solution search space, even if some solutions are neither feasible nor exploitable by the optimization algorithms. This approach imposes a computational burden on the optimization algorithm in terms of processing power and time. Therefore, these solutions and the search space should be reduced accordingly.
## 3 Results and Discussion  
### Initial Premises: High Antimicrobial Response of the Nanoparticlesized ZnO  
The particle size of the micrometric ZnO is modified by a chemical process and then, the obtained material is thermally treated at 500 \({}^{\circ}\)C for a short time, 5 min, in the air. (The reader can find more information about the ZnO synthesis in the Material and Methods section) The obtained ZnO is structurally characterized by X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR). The XRD pattern of obtained ZnO shows a single crystalline structure (Figure S2a (Supplementary Materials)). The position and intensity of the diffraction peaks match the hexagonal wurtzite structure ZnO (JCPDS Card No. 36-1451). The FTIR data (Figure S2b (Supplementary Materials)) displays a strong absorption IR band at 437 cm\({}^{-1}\), which is ascribed to vibration Zn-O modes [27; 28]. Other IR bands relate to traces of thermal treatment. At 3470 cm\({}^{-1}\), the broad IR band is assigned to the O-H stretching mode (\(\
u\)(OH)) of the hydroxyl group of absorbed water. The IR bands observed between 1630 and 860 cm\({}^{-1}\) are associated with stretching modes of carbonate group, coming from remains of thermal treatment [29]. Therefore, structural characterization confirms the synthesis of ZnO. Additionally, we carry out a morphological characterization of synthesized ZnO by FE-SEM (Figure S2c (Supplementary Materials)), which shows nanoparticles' agglomerates with a heterogeneous distribution and irregular forms. The image analysis of the nanoparticles displays the average size to 56 \(\pm\) 8 nm (Figure S2c (Supplementary Materials)). Note that ZnO is nanoparticles without a defined organization. Specifically, Figure S2d (Supplementary Materials) allows observing clearly and unequivocally the nanometric character of the obtained ZnO. A close study of a particle is shown in Figure S2e (Supplementary Materials). The determined lattice spacing of \(\sim\)1.630 A can be attributed to the ZnO (110) plane according to the JCPDS Card No. 79-0206.  
It should be noted that recently we have been evidenced an excellent antimicrobial response of the nanoparticulated ZnO against multidrug-resistant organisms (MDROs), which strongly depends on the crystalline defects of ZnO [30]. In the next section, we go a step further and will demonstrate that the high antimicrobial activity can be used for the development of an inorganic composite as a paint preservative for antifungal applications