esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Implementation Method
### Extraction  
HWPT can decompose a signal into an infinite number of signals with different refined frequencies and extract the signal of interest from these signals. Consequently, the FIV and FIS components were extracted from the measured vibration and sound pressure signals by HWPT. Following is a description of the main steps involved in the extraction process.  
Firstly, the layer number \(j\) of HWPT was determined based on the frequency band detected by the spectrum analysis of CCF between vibration and sound signals. In termsof frequency bandwidth \(B\) and layer number \(j\), the correspondence can be expressed as follows:  
\[B=2^{-j}f_{\rm h}\quad j=0,1,2,3\ldots \tag{9}\]  
where \(f_{\rm h}\) represents the highest analysis frequency. The upper and lower limits \(m\) and \(n\) of the frequency band are defined as follows [27]:  
\[\left\{\begin{array}{ll}m=sB\\ &s=0,1,2\ldots 2^{j}-1\\ &n=(s+1)B\end{array}\right. \tag{10}\]  
where \(s\) is the index of the sub-band.  
Further, after determining the frequency range \((m,n)\) of the \"weak\" FIV, the frequency-domain expression for the harmonic wavelet \(\hat{\psi}_{\rm m,n}\) can be obtained [28]:  
\[\hat{\psi}_{\rm m,n}[(n-m)\omega]=\left\{\begin{array}{ll}1/[(n-m)2\pi]&2\pi m \leqslant\omega<2\pi n\\ 0&\text{other}\end{array}\right. \tag{11}\]  
Then, Fourier transform was conducted on the measured signal \(f(t)\) to obtain its discrete value in the frequency \(\hat{f}(\omega)\). The discrete HWT can be expressed in frequency-domain as follows:  
\[\hat{W}(m,n,\omega)=\hat{f}(\omega)\hat{\overline{\psi}}_{\rm m,n}[(n-m)\omega] \tag{12}\]  
where \(\hat{\overline{\psi}}_{\rm m,n}[(n-m)\omega]\) is the conjugate of \(\hat{\psi}_{\rm m,n}[(n-m)\omega]\).  
By taking the inverse Fourier transform of Equation (12), an equivalent expression of the HWT in the time domain can be expressed as [19]:  
\[W(m,n,k)=(n-m)\int_{-\infty}^{\infty}f(t)\overline{\psi}_{\rm m,n}\bigg{(}t- \frac{k}{n-m}\bigg{)}\text{dt} \tag{13}\]  
where \(k\) is the translation parameter, \(k/(n-m)\) is the translation step of the harmonic wavelet, \(\psi_{\rm m,n}\) is the general expression of harmonic wavelet in the time domain, and \(\overline{\psi}_{\rm m,n}\) is the conjugate of the \(\psi_{\rm m,n}\).  
Finally, the time-domain waveform \(W(m,n,k)\) of the \"weak\" FIV in the detected frequency band \((m,n)\) was obtained by HWPT.
# Comprehensive Study of IoT Vulnerabilities and Countermeasures
## 3 IoT Vulnerability Layers
### Hardware Vulnerabilities
#### 3.1.3 Implants and Hardware Trojans
Device manufacturers should enforce supply chain security protocols to prevent unauthorized hardware modifications at any stage of production or distribution. Tamper-evident enclosures and active intrusion detection mechanisms can alert users to unauthorized physical modifications. Additionally, implementing periodic integrity verification through hardware attestation ensures that any post-deployment alterations are detected before they can compromise system security. Secure boot mechanisms combined with hardware-based root-of-trust validation can further reinforce a device's resistance against implanted threats [81].
## 3 Experimental Results
### Effective Dose (ED) Correlations  
The dataset includes ED values ranging from 0.8 mSv to 24.7 mSv. As shown in Table 5, linear and multiple linear regression models were used to investigate the relationship between ED as an output variable and BMI, weight and age as input variables after excluding up to three outliers.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & & **DLP (mGy.cm)** & & \\ \cline{3-6} \multirow{4}{*}{\(\text{\text{\text{\text{\text{\text{\text{  
In terms of BMI, ED exhibited a positive linear relationship across the given exams as indicated by the evaluation metrics (\(p\)-value less than 0.05). The relationship between ED and weight mirrored that of BMI. As observed earlier, age did not show any linear relationship with ED in any of the given exams.  
Using multiple regression models resulted in a higher correlation with the output variables than when using simple regression in both CT exams. This increase was demonstrated by comparing the R\({}^{2}\), MAPE and \(r\) values of the multiple linear regression models with those of the respective best simple linear regression models.
## 1 Introduction
The primary goal of this research is to develop an innovative method for predicting the effective elastic and thermal properties of composite materials through mathematical curve fitting. The study begins by collecting 3D microstructures containing 200 spherical inclusions with varying volume fractions (10%, 15%, 20%, or 25%) and phase contrasts. These contrasts are quantified by the ratios of [PERSON]'s modulus and thermal conductivity modulus and have values spanning from 10 to 200. Employing finite element analysis, these microstructures are analyzed to estimate the effective bulk, shear, and thermal conductivity moduli. The gathered data form a comprehensive dataset, which is instrumental for constructing accurate mathematical equations that precisely fit the curves of various moduli and that take into account changes in key input parameters: volume fraction and contrast. The process involves a thorough analysis of the data, identification of patterns, and the development of mathematical relationships that accurately reflect the observed behaviors of the moduli. Integrating these equations and acknowledging their interdependencies leads to the creation of a detailed and adaptable 3D model. This model effectively captures the impact of varying the volume fraction and contrast on different moduli, thus providing a complete understanding of the material's response under a variety of conditions. The structure of this article is organized in the following manner: The second section details the methodology for generating 3D microstructures utilizing Digimat (version 5.1.1), which is image generator software. This section also covers the collection of output data via finite element calculations performed with specialized Z-set software (version 8.5). In the third section, we concentrate on the central aim of this study, which involves the construction of a mathematical model. This model leverages curve-fitting functions from the scipy library to make predictions and validate results based on the collected datasets. The final section of the article provides a comprehensive summary of our findings and offers concluding remarks.
## 2 Data and Their Processing  
### Data Sources and Composition  
The dataset used in this study is named \"Electric Motor Temperature\", collected and released by the LEA Laboratory at Paderborn University, Paderborn, Germany [36]. The experimental data were measured directly from a test bench equipped with a 52 kW three-phase automotive traction PMSM. The measurement setup includes the following [36]:  
(1) Thermocouples (type K, class 1) embedded in the stator teeth, winding, yoke, and rotor permanent magnets.  
(2) A telemetry unit to transmit rotor temperature data (average of four permanent magnet surface sensors).  
(3) dSPACE DS1006 MC rapid control prototyping system (dSPACE, Paderborn, Germany) and analog-digital converters (ADCs) for synchronized data acquisition.  
(4) All signals were sampled at 2 Hz over 140 h of operation.  
(5) Preprocessing steps, including exponentially weighted moving averages (EWMAs) and standard deviations (EWMS), were applied to enhance regression features.  
The dataset explicitly reflects real-world PMSM dynamics. Thus, the training of the proposed model on empirical data from a physical test bench ensures alignment with actual thermal behavior. The test bench for the PMSM is shown in Figure 1.  
The dataset is organized in a tabular format, with each record (sample) representing sensor measurements at a specific time point. The dataset includes multiple measurements sessions (sample numbers), and the main variables are listed in Table 1.  
These input features can be categorized into four types. The motor speed and torque are motor state variables. Currents (_i_d, i_q_) and voltages (_u_d, u_q_) in the d/q coordinate system are also motor state variables. Temperature variables include _pm_ (permanent magnet temperature) and _stator_yoke_. However, _pm_ is difficult to monitor, while _stator_yoke_ is relatively easy to monitor. Therefore, _pm_ is chosen as the modeling target, while the other variables serve as modeling features.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable Type** & **Variable** & **Description** \\ \hline \multirow{6}{*}{Input Feature} & _u_q_ & The q-axis component of the voltage \\  & _coulant_ & measured in the dq-coordinate system \\  & _scator\_winding_ & The temperature of the coolant \\  & _u_d_ & The d-axis component of the voltage \\  & _stator\_tooth_ & The temperature of the stator teeth \\  & _motor\_speed_ & The rotational speed of the motor \\  & _i_d_ & The d-axis component of the current \\  & _i_q_ & The q-axis component of the current \\  & _stator\_yoke_ & The temperature of the stator yoke \\ \hline Target & _pm_ & The temperature of the permanent magnet \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key variables and descriptions in each sample.  
Figure 1: A test setup featuring a representative PMSM with comparable specifications [36].
## 5 The Validation of Experiments
### The Sideband Current Harmonics Comparative Test  
In order to validate the precision of the simulation prediction results, the sideband current harmonic spectra of the prototype under steady state operating conditions are verified and analyzed. Figure 17 shows the phase current sideband harmonic spectra of six classical generalized DPWMs before and after MRCFM optimization. It can be seen that DPWMs with fixed carrier frequency modulation have obvious main-order components whose distribution characteristics are basically consistent with the simulation prediction results mentioned above. The peak values of the sideband current harmonics of the optimized DPWMs are significantly reduced. The harmonic components originally concentrated near the carrier frequency are uniformly spread to its surroundings, which shows that the MRCFM has a good harmonic suppression effect. Figure 18 shows the comparison curves between the simulated and measured peak values of the sideband current harmonics of different PWM strategies. It can be seen that the simulated and measured results are very close to each other with an error of no more than 0.004 A, which verifies the accuracy of the numerical prediction model of multi-physics field.  
Figure 16: The PMSM vibration noise test system.  
Figure 17: Experimental comparison of sideband current harmonics before and after MRCFM optimization: (**a**) Fixed carrier frequency modulation; (**b**) Markov chain random carrier frequency modulation.
## 2 Skin and Proximity Effect Losses  
### Skin and Proximity Effect  
The phenomenon where alternating current tends to flow more intensely near the surface of a conductor is known as the _Skin Effect_. This effect is caused by eddy currents generated as a result of a changing magnetic field [13]. In the presence of neighboring conductors carrying AC, their changing magnetic fields further distort the current distribution, leading to the _Proximity Effect_[14].
# Validity of the Enode Sensor and My Jump 3 App for Assessing Countermovement Jump Performance
## 2 Materials and Methods
### Research Tools
#### 2.3.2 My Jump Lab App (My Jump 3)  
The My Jump 3 App (formerly My Jump 2) is a mobile application available for iOS and Android smartphones. For jump assessments, My Jump 3 uses the smartphone's camera and video analysis algorithms to estimate jump heights based on flight times. Users record jumps with the smartphone camera, and the app analyzes the video to calculate key metrics. It supports various jump types, including the countermovement jump (CM), squat jump (S), and drop jump (DJ). Designed with an intuitive interface, the app is accessible to coaches, athletes, and sports scientists without requiring extensive technical expertise. It allows for data storage within the app, with options for exporting data for additional analysis or recordkeeping. The app also provides immediate feedback on jump performance, enabling users to make quick adjustments during training (My Jump Lab Pro). Specifically, the My Jump Lab App (My Jump 3) employs the same methodology as that of My Jump 2, where jump analysis involves marking the takeoff and landing moments on the time axis. According to the manufacturer, the new AI feature in My Jump Lab enhances this process, using computer vision (image recognition) to create a bounding box around the subject captured in the video and calculates their position in pixels during the jump. These pixel data are then converted to centimeters, using the user's body height as a calibration factor. Preliminary testing of the app against a Force Plate showed excellent validity, 0.93 between instruments, highlighting the app's reliability for measuring vertical jump heights [43]. In this study, My Jump 3 was used on an iPhone 14 Pro, which offers a video-recording capacity of 240 frames per second (FPS). The smartphone was positionedon a stand 1.5 m from the participant, at a height of approximately 30 cm, following the app's recommended guidelines [17], which resulted in an observation angle of about 11.5\({}^{\circ}\) from the ground, to ensure stability and minimize recording errors [44]. The app was operated by an experienced observer with expertise as a strength and conditioning (S&C) coach, ensuring adherence to the app's guidelines.
# Transcriptional Analysis of Masson Pine
## 4 Discussion
### Effects of Elevated CO\({}_{2}\) on Masson Pine Hornones and Stomatal Regulation  
In this study, the results showed that except _ZEP_, the expression of each gene in the ABA synthesis pathway tended to decrease with prolonged CO\({}_{2}\) treatment time, especially _NCED_. The catalysis of 9-_cis_-neoxanthin to Xanthoxin by NCED is a key step in the ABA metabolic pathway in plastids [(52)].Therefore, a decrease in the expression of _NCED_ seriously affects the expression of other genes in the ABA metabolic pathway, which in turn affects ABA accumulation in the cell [(53)]. Previous studies have shown that ABA is greatly affected by BR when regulating stomatal opening and guard cell physiological states [(54)]. Elevated CO\({}_{2}\) led to a sharp increase in the expression of key genes in the BR metabolic pathway, such as _CYP_, which would inhibite ABA from binding to ABI (a kind of serine/threonine protein phosphatase), thereby weakening the effect and signal transmission of ABA [(55)].  
For other hormones, the key genes in their synthetic pathway increased to varying degrees at the same stage. Among them, the expression of _GGPS_, the key gene of GA synthesis, continued to up-regulate with prolonged stress. Studies have shown that GA can divide plant hypocotyl epidermal cells, promote stomatal formation, regulate stomatal density [(56)], so as to maintain stomatal conductance and the transpiration rate under the condition of elevated CO\({}_{2}\) concentration. On the other hand, according to our result, the key gene in the JA and SA synthetic pathway, including _AOC_, _LOX_ and _PAL_, remained at a high expression level with prolonged stress. It indicated that the reaction proceeded in the direction of JA and SA synthesis, which would cause stomatal closure [(57; 58; 59)].  
In general, although previous studies have shown that ABA is the most important hormone to control stomatal switching in plants, in Masson pine under CO\({}_{2}\) stress, the expression level of each gene in the ABA synthesis pathway was basically inhibited. However, this did not affect stomatal closure in Masson pine because the genes regulated other hormones that promote stomatal formation or induce its closure were general up-regulated in the same environment.
## 4 Analysis of Measurement Results
### Green Total Factor Productivity and Its Decomposition Trend by Industry
#### 4.2.3 Consumer Goods Manufacturing  
As shown in Table 7, during the period from 2004 to 2017, the GTFP of China's equipment and consumer goods manufacturing increased by an average of 1.30 annually, of which the average annual growth rate of GTC was 3.86%, and the average annual growth rate of GEC was 2.46%. On the whole, the GTFP of China's consumer goods manufacturing industries continues to improve, mainly driven by the GTC. Although the GEC is generally declining, it begins to increase in the 13 th Five-Year Plan.  
From the inside of consumer goods manufacturing (see Figure 5), for GTC, from the 11 th Five-Year to the 13 th Five-Year Plan, seven industries have increased the growth rate of GTC. Among them, printing manufacturing (H11), furniture manufacturing (H9) and apparel manufacturing (H6) have the highest growth rate of GTC. The GTC of printing manufacturing has changed from an average annual decrease of 4.55% to an average annual increase of 7.31%. The growth rate of GTC in furniture manufacturing has changed from an average annual decrease of 6.65% to an average annual increase of 5.01%, and apparel manufacturing has changed from an average annual decrease of 1.59% to an average annual increase of 4.34%. The growth rate of GTC in four industries slowed down. For example, the growth rate of GTC in chemicals manufacturing (H5) dropped from an average annual growth rate of 6.34% to 1.56%.  
As for GEC, from the 11 th Five-Year to the 13 th Five-Year Plan, the growth rate of GEC in tobacco manufacturing (H4) and printing manufacturing (H11) has changed from positive growth to negative growth, and other consumer goods manufacturing industries' GEC growth rate are accelerating.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Mean** & **10 th Five-Year Plan** & **11 th Five-Year Plan** & **12 th Five-Year Plan** & **13 th Five-Year Plan** & **Analysis of Variance (\(p\) Value)** \\ \hline GTFP & 1.0130 & 1.0489 & 1.0045 & 0.9897 & 1.0458 & 0.0070 \\ GTC & 1.0386 & 1.0734 & 1.0141 & 1.0348 & 1.0366 & 0.0064 \\ GEC & 0.9754 & 0.9771 & 0.9905 & 0.9564 & 1.0088 & 0.0096 \\ \hline \hline \end{tabular}
\end{table}
Table 7: GTFP and its decomposition in consumer goods manufacturing.  
Figure 5: Trends of GTC and GEC in consumer goods manufacturing.
## 6 Discussion  
This study presented the results of experiments conducted for the performance evaluation of the deployment of iBeacons for positioning purposes. During robustness testing, positioning accuracy, signal availability and stability were assessed under different environmental conditions. The results of the outdoor robustness test demonstrated that the maximum signal coverage of up to 133 m was relatively consistent, despite different mobile devices were used, and the signal was stable until 80 m. This test also evaluated the stability of the iBeacon signals under different environmental conditions, indicating the presence of trees and nearby vehicle traffic caused an ignorable effect to the iBeacons. The conditions of high temperature, strong wind and pedestrian traffic without blocking the line of sight induced minor discrepancy to the signal. A significant adverse effect on the signals was found when the line of sight between a smart device and the iBeacon was blocked by pedestrians. Thus, preserving the line of sight between the iBeacons and the receiver is the key factor in enabling steady signal availability.  
The performance testing examined various parameters, determined the trade-offs between accuracy and other factors and developed a series of recommendations for indoor BLE iBeacon deployment (Table 4). The results suggest installing iBeacons on the ceiling instead of the wall, as ceiling placement has better geometry and maximises the line of sight by avoiding the obstructions. Although more iBeacons deployed would provide higher positioning accuracy, there is still a concern of cost. Our results recommend an average spacing of 4.4 m for iBeacon deployment, which can balance the positioning accuracy and the number of iBeacons. Appropriate transmission time setting of iBeacon can also reduce the cost of iBeacons, by extending the battery life, as the setting of 417 ms interval outperformed 100 ms and 800 ms in this study. The training phase of iBeacon positioning always involves the collection of fingerprints, which is a labour and time-intensive task. By considering the collection time and outcome, collecting the signal database at the 2 m grid interval with 6 s to 10 s at each point is recommended. The venue test suggests that the existence of electronic devices/equipment and the reflective materials would cause unfavourable positioning performance because of the interference  
Figure 14: CDF comparison of classroom with/without students.  
and multipath effects, but these negative effects are unavoidable and the installation location of iBeacons should be evaluated empirically.
## 3 Seismic Behavior of Steel-Concrete Composite Shear Walls
### Seismic Behavior of Steel-Concrete Composite Shear Walls: Key Findings  
The key findings regarding the seismic behavior of steel-concrete composite shear walls are as follows:  
* Composite shear walls typically demonstrate enhanced strength and stiffness when compared to conventional reinforced concrete or steel-plate shear walls alone. This is due to the combination of materials, such as steel and concrete, which work together to provide increased structural integrity.
* Furthermore, the use of steel and concrete in composite shear walls often results in improved ductility. This means that the structure is able to endure larger displacements without failure during seismic events. This is crucial for ensuring the safety and resilience of the composite building structures during earthquakes.
* Another advantage of composite shear walls is their ability to dissipate energy. This enhanced energy dissipation capability is essential for seismic resistance, as it helps to absorb and dissolve the energy generated by earthquake forces, plummeting the potential for damage.  
* Composite designs also offer the advantage of reduced wall thickness. Compared to traditional reinforced concrete walls, composite designs can often achieve the same level of strength and stiffness with thinner wall sections. This not only saves space but also reduces the amount of material required for construction.
* Additionally, the use of steel elements in composite shear walls can contribute to faster construction. Compared to conventional reinforced concrete systems, the incorporation of steel elements can expedite the construction process, resulting in time and cost savings.
* The presence of steel elements in composite shear walls also improves crack control. Steel helps to control crack formation and propagation in the concrete, enhancing the overall durability and longevity of the structure.
* Composite shear walls generally exhibit improved cyclic performance under cyclic loading conditions, which are representative of seismic actions. This means that they are better able to withstand repeated loading and unloading cycles without significant damage or failure.
* Proper design of composite shear walls can also lead to more predictable and desirable failure modes during extreme seismic events. By carefully considering the design and configuration of the walls, engineers can ensure that the structure behaves in a controlled manner, minimizing the risk of catastrophic failure.
* Furthermore, composite shear walls offer versatility in design. They provide various configuration options, allowing designers to optimize the performance of the walls for specific seismic demands. This flexibility enables engineers to tailor the design to meet the specific requirements of each project.