esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Both the average acceleration method and the central differences method were used to discuss numerical instability. Gears have been widely used in industrial applications over the years, taking into account that time-varying variables or time-varying gear mesh stiffness could improve gear system accuracy. [PERSON] and [PERSON] [16] approximated time-varying mesh stiffness by using a constant mesh stiffness with a transmission error excitation. [PERSON] et al. [17] developed a new dynamic model of the spur gear pair which considered the pressure angle and contact ratio as time-varying. [PERSON] et al. [18] discussed the effect of the time-varying friction coefficient on the total effective mesh stiffness for the spur gear pair and indicated that the gear mesh stiffness changed due to the change in direction of the time-varying friction on both sides of the pitch line. A time-varying mesh stiffness model of the helical gear pair, including axial tooth torsional stiffness, tooth bending stiffness, and gear foundation stiffness, was developed by [PERSON] et al. [19]. In an analysis of the spur gear pair dynamics system, [PERSON] et al. [20] considered that both the pressure angle and the backlash change over time. Although some nonlinear factors have been incorporated into the dynamic analysis of the gear system, few studies have considered the time-varying behavior in nonlinear dynamic modeling. There have been some studies on the time-varying meshing of the spur gear pair [18] and helical gear pair [19], the time-varying dynamic characteristics of the spur gear pair [17; 20], and the spur-geared rotor-bearing system [14]. However, the dynamic characteristics of a helical-geared rotor-bearing system with time-varying effects have not been studied. In general, in nonlinear dynamic model analysis of the helical-geared rotor-bearing system, in order to facilitate modeling, previous studies have regarded the gear pair center distance, helix angle, and gear contact ratio as constants. In fact, when the gear pair has translational motion due to rotating shaft deformation, the center distance of the gear pair will change, which will then cause changes in the line of action, helix angle, and contact ratio. This study introduces a novel dynamic model for a helical-geared rotor-bearing system with 3-D (3-dimensional) motion due to rotating shaft deformation, which includes the time-varying dynamic characteristics of the helix angle, transverse pressure angle, gear pair center distance, and total contact ratio.
# Three-Line Microstrip Array for Whole-Body MRI System at 7 T
## 4 Conclusions  
In this work, we proposed the use of a triple-line microstrip in an array configuration for whole-body MRI applications. We first investigated the properties of the triple-line microstrip by performing simulations using tissue phantoms. The proposed array configuration is capable of producing a field intensity slightly higher than that of the BC coil across the whole body, with better uniformity than that produced by the whole-body patch antenna and the whole-body birdcage coil. In terms of SAR, the proposed array exhibited fewer hot spots than the patch antenna, especially in the brain region. The maximum SAR can be observed in the elbow regions, since these are closer to the t-MTL elements.  
Although the proposed array is not perfect, it is a viable design for whole-body RF coil development in MRI.  
This research was funded by the National Research Foundation of Korea grant funded by the Korea government (MSIT) grant number NRF-2019R1G1A1100803 and by a grant (HO16C0004) from Osong innovation center funded by the ministry of health & welfare, the Republic of Korea.  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Mean/Max SAR [mW/kg]**} \\ \hline RF & Eyes & Spinal Cord & White matter & Heart & Stomach \\ transmitter & 102/362 & 8.9/47.8 & 49.6/142 & 9.5/64 & 11.7/7.2 \\ Patch & 8.1/14.0 & 1.2/6.4 & 7.2/17.1 & 1.0/6.2 & 2.1/11.0 \\ t-MTL array & 9.8/14.8 & 7.3/30.7 & 8.6/20.0 & 1.9/6.9 & 5.2/18.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean and maximum SAR.  
Figure 10: The computed \(\mid\)B\({}_{1}\)\({}^{+}\)\(\mid\)-field and SAR maps in axial views, for (**a**) the brain, (**b**) torso, and heart, as well as (**c**) the histogram of the SAR distribution per volume m\({}^{3}\) in the whole human model.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Acknowledgments:** The author would like to thanks to [PERSON] and [PERSON].  
**Conflicts of Interest:** No conflicts of interest.
## 2 Problem Definition
### Demand Response  
An efficient method of consumption-side management is to establish load response programs. These programs are divided into two general types: time-based and incentive-oriented. In this research, the TOU method, which is a time-based program, was utilized. This method offers a three-tariff price based on three categories: peak, medium, and low. By implementing load response programs, the distribution network operator can shift the load from the peak and costly hours to low and cheap hours. It should be noted that subscribers' ability in the load shift is confined to a maximum value [36]. The current work could be extended by considering thermostatically controlled loads [37].
# Elastic-Plastic Threshold and Rational Unloading Level of Rocks
## 1 Introduction
### Evolution Analysis of Strain Energy  
According to the law of conservation of energy, the entire deformation failure process of a rock is associated with energy conversion. Following the law of thermodynamics, energy production is mainly derived from external forces. In the triaxial test, external forces imposed on rock specimens include the axial pressure and confining pressure of the testing equipment. Imposed axial pressure causes axial deformation of rock specimens, while the confining pressure leads to lateral deformation [26; 30]. Therefore, the total energy \(U\) absorbed by rock specimens includes the axial strain energy \(U_{1}\) absorbed by axial deformation and the hoop strain energy \(U_{3}\) consumed by lateral deformation. Then there is:  
\[U=U_{1}+U_{3} \tag{5}\]  
At any time \(t\) during the test, the axial strain energy \(U_{1}\) and the hoop strain energy \(U_{3}\) can be derived from the stress-strain curve integral, that is:  
\[U_{1}=\int_{0}^{\varepsilon_{1(t)}}\sigma_{1}\mathrm{d}\varepsilon_{1} \tag{6}\]  
\[U_{3}\mathrm{=2}\int_{0}^{\varepsilon_{3(t)}}\sigma_{3}\mathrm{d}\varepsilon_{3} \tag{7}\]  
where \(\varepsilon_{1(t)}\big{(}\varepsilon_{3(t)}\big{)}\) is the axial (transverse) strain at any time \(t\).  
According to the concept of definite integral in Equations (6) and (7), the infinitesimal method is adopted to obtain the sum of the area, that is:  
\[U_{1}=\sum_{t=0}^{t_{0}}\frac{1}{2}\big{(}\sigma_{1(t+1)}+\sigma_{1(t)}\big{)} \big{(}\varepsilon_{1(t+1)}-\varepsilon_{1(t)}\big{)} \tag{8}\]  
Figure 6: Evolution curve of strain–axial pressure increment ratio.  
\[U_{3}=\sum_{t=0}^{t_{0}}\Big{(}\sigma_{3(t+1)}+\sigma_{3(t)}\Big{)}\Big{(}\epsilon_ {3(t+1)}-\epsilon_{3(t)}\Big{)} \tag{9}\]  
Combined with the concept of axial pressure increment ratio, the strain energy-axial pressure increment ratio is expressed as:  
\[\frac{\Delta U_{t+1}}{\Delta\sigma_{1(t+1)}}=\frac{U_{t+1}-U_{t}}{\sigma_{1(t+ 1)}-\sigma_{1(t)}} \tag{10}\]  
where \(\Delta U_{t+1}\) is the strain energy increment in the rock interior at time \((t+1)\) and \(U_{t+1}(U_{t})\) is the strain energy in rock interior at time \((t+1)\) (and \(t\)).  
The evolution of strain energy during loading of siltstone specimens and the relationship between the axial pressure increment ratio and stress level are shown in Figure 7.  
The example of axial strain energy (Figure 7) shows a pronounced increase in the rate of change at the 60.17% stress level. In other words, the strain energy-axial pressure increment ratio demonstrates the first pronounced change at this level, which is consistent with the stress level corresponding to the elastic-plastic threshold.  
To sum up, a stress level at the elastic-plastic threshold during the loading process is observed. The parameters associated with the extreme deformation and the first pronounced response to axial pressure changes, together with the parameters for strain and strain energy, can be interpreted as the sign of the occurrence of this threshold.
# Predicting Phubbing Through Machine Learning: A Study of Internet Usage and Health Risks
###### Abstract  
Phubbing, defined as the disruption of social relationships and interactions due to excessive cell phone use, is becoming an increasing concern in modern society. Since one of the primary motivations for cell phone use is internet access, it is crucial to assess the time that individuals spend online to measure the prevalence of phubbing as a social behaviour disorder. This study aimed to better understand and evaluate the phubbing phenomenon by predicting future trends in internet usage using machine learning techniques. Four machine learning models--an artificial neural network (ANN), support vector regression (SVR), random forest (RF) regression, and time series--were employed to predict the average internet usage. Data from 2014 to 2024 were obtained from the World Bank, and cross-validation was used to enhance the reliability and accuracy of the models. All four models were successful in predicting internet usage, with the ANN showing the highest accuracy, followed by SVR, RF, and the time series. According to the data, the average daily time spent online increased from 277 min in 2014 to 417 min in 2024. Projections based on these machine learning models estimate that this figure will rise to 507 min by 2030 and 603 min by 2035. These findings provide valuable insights into the potential risks of increased phubbing behaviours on social interactions and offer a foundation for the exploration of the long-term health implications of excessive internet use. Future research could further examine the effects of phubbing on mental health and develop strategies to mitigate this social behaviour disorder.  
phubbing; health; internet use; addiction; machine learning; communication +
Footnote †: journal: Applied sciences  
Article
## 4 Discussion
### SmartPaddles and Analysis Center  
Several studies mention the advantages of using wearables in swimming and other sports, regardless of the type of variables measured and monitored [10; 35]. However, regardless of whether it is based on IMU's or other sensors, data handling (after data collecting) may not be user-friendly for coaches and swimmers. That is, data often need to be filtered and manipulated to be presented to coaches and swimmers. Indeed, studies using wearables indicate that data often have noise that needs to be filtered out [9; 36]. This process might be time-consuming and not user-friendly for non-experts with data handling such as coaches and swimmers. Therefore, having wearables that can be paired with operating systems that allow the presentation of filtered data to coaches and swimmers is an important step in the training process. As previously mentioned, the set of sensors used in this study is based on a system that includes the sensor units, an application for recording, and an analysis and data storage application for visualization [32]. Consequently, coaches and swimmers can visualize the propulsive force time-series (Figure 3A1,A2), the average of all arm-pulls performed (Figure 3B1,B2), and the time spent in the underwater and recovery phases (Figure 3C1,C2). Moreover, as the sensor units are based on IMU's, the hand's path can also be visualized from several perspectives (Figure 4). This visualization allows both coaches and swimmers to better understand the potential difficulties in their stroke mechanics. Indeed, information about the path of the swimmers' hand is of great importance for improving performance [37].  
Propulsive force in swimming can be experimentally and directly measured based on pressure sensors or IMU's [17; 32; 38]. It can also be indirectly measured by tethered swimming [39]. Despite all the methods, there is still no gold-standard method to measure propulsive force in swimming. Notwithstanding, it should be mentioned that the sensor units used in the present study had a high agreement with a commonly used pressure sensor system [32].
# Multi-Band Unmanned Aerial Vehicle Antenna for Integrated 5G and GNSS Connectivity
## 2 Dual-Port Inverted T-Shaped MIMO Antenna and Analysis
### Mean Effective Gain  
Mean effective gain (MEG) is the diversity metric within multi-port elements, representing the relationship between the power received by the multi-port elements and that received by an isotropic antenna. Theoretically, the MEG value for a multi-port element should fall within the range of \(-3\) dB to \(-12\) dB. This was achieved, as shown in Figure 16.
# Evaluation of Design Parameters on Drying of Kiwi Fruit
## 2 Experimental Study  
### Setup  
A new experimental dryer setup was constructed in order to perform the tests. Figure 1 shows a schematic view of convective type dryer.  
As seen in Figure 1, the setup includes fan, heater, drying cabinet, air flow duct, air inlet and outlet ducts, hygrometer, a digital scale with a measurement range of 0-6100 g with a sensitivity of \(\pm\)0.1 g to measure the mass loss of the product. The digital scale is 30 cm long, 19 cm in wide. A drying stray with a length of 27 cm and a width of 21 cm was manufactured to be connected to the scale and the products to be dried were placed on the tray and the product mass loss was read out at regular intervals. The drying room is in a shape of a rectangular prism with 60 mm length, 40 mm width, 60 mm height, and is made of a steel sheet with a thickness of 3 mm. A hole with a diameter of 120 mm was made at the bottom of the cabinet and connected to the air flow duct. The hot air coming from the flow channel was aerated with the product from below and a 100 mm diameter duct was opened to the side surface of the cabinet to vent the warmed air. The air flow channel, which is connected to the drying room at the bottom, has a length of 2.5 m and is in a shape of a circular diameter of 120 mm, and is made of a steel sheet with a thickness of 3 mm. This channel is insulated with a glass wool with a thickness of 30 mm and the upper part is covered with aluminum tape. The temperature of the kiwi fruit and of the cabinet outlet can be determined by using the T type thermocouple by means of the temperature measurement tools located on the cabinet. The air flow rate was controlled by a radial fan with a power of 0.537 kW. The radial fan has 50 Hz, 220 V and a currency of 2.5 A. The fan used in the system is a constant speed (2800 rpm). The heating system consists of wires with a power of 2000 W, and the drying air temperature required for drying is fixed by means of a variac. The cabinet outlet temperature and the internal temperature of the product are measured by means of a thermometer with a measurement range of \(-\)20 to +80 \({}^{\circ}\)C. The drying air velocity was determined by means of an anemometer with a measurement range of 0.15-3 m/s. The kiwi samples required to be dried were placed on the tray connected to the scale and the mass loss was recorded at 15 min intervals. The measuring instruments developed in this experimental work are showed in Table 2 with photos and technical specifications [19].  
Figure 1: Setup (1. Hygrometer [HTC-1], 2. Precision scale (AVERY BERKEL, Model CC061), 3. Thermometer-Anemometer [KIMO], 4. Outlet, 5. Tray, 6. Chamber, 7. Air flow channel, 8. Heating system, 9. Fan [SB908 AN-22-3], 10. Variac [STACO 3 PN1520B]).
# Machine Learning Applications in Seismology
## 1 Introduction  
The comprehension of earthquakes and natural hazards, including volcanic eruptions and landslides, as well as explosions, through observational data is a pivotal activity within the field of seismology. The rapid advancements in seismogram technology have resulted in the accumulation of extensive seismic datasets, presenting significant opportunities for the exploration of seismicity patterns, the physical processes underlying earthquakes, and the elucidation of earthquake mechanisms [1; 2; 3; 4; 5; 6; 7; 8]. The availability of such large-scale seismic data can substantially enhance data-driven research endeavors in seismology [9; 10; 11; 12; 13]. These data-rich resources can be employed for a variety of analytical and modeling initiatives, thereby assisting seismologists in gaining insights into earthquake mechanisms, forecasting seismic hazards, and formulating strategies for disaster prevention and mitigation.  
Recent progress in seismic data acquisition and processing, particularly through the application of machine learning techniques, has proven beneficial for seismologists in identifying signals or patterns that traditional methodologies may overlook [8; 14]. For instance, the automatic detection of seismic events via models such as PhaseNet [15] streamlines the processing of seismic data [16; 17; 18; 19; 20]. Additionally, the classification of seismic events utilizing convolutional neural networks (CNN) demonstrates greater efficiency compared to conventional feature-based methods [21; 22; 23]. Furthermore, machine learning approaches to earthquake prediction and early warning systems offer alternative strategies for mitigating earthquake hazards [4; 24; 25; 26]. In summary, machine learning methodologies significantly enhance the capabilities of seismologists in processing seismic data and uncovering the physical mechanisms associated with earthquakes [27].  
Over the past two years, we have compiled 15 articles for this Special Issue titled \"Machine Learning Applications in Seismology\". These contributions encompass topics such as seismic inversion, earthquake detection, ground motion simulation, focal mechanism analysis, and earthquake early warning and forecasting systems. The articles underscore the necessity of integrating machine learning into seismological research and provide illustrative examples of its application within the discipline.
## 3 Results and Discussion
### Interpretation of CB Model
#### 3.6.3 Local Interpretation  
In addition to global, the SHAP algorithm can also visualize the local interpretation of the predictive model. It significantly enhances the prediction's transparency, which  
Figure 13: Global SHAP interpretation based on CB model.  
traditional algorithms cannot achieve. In this part, the SHAP algorithm was used to measure the input features' impacts on the output (locally) for two randomly selected specimens based on CB model prediction. Figure 13(a),b provides the force plots of the local interpretation for the specimen (No. 111: G33S-31) tested by [52] and the specimen (No. 297: BM2/1.0 T1) tested by [57]. In these plots, the impact value of each input feature was expressed by length. The feature that positively impacted the prediction was assigned a red color, while the feature that negatively impacted the prediction was assigned a blue color. The mean value of the CB model output (416.10 kN) was identified as a base value for all specimens. For specimen No. 111, the CB model prediction was equal to 107.43 kN and its corresponding experimental value was 107.00 kN. The most significant input features that pushed down the base value were \(b\), \(f_{c}^{\prime}\), \(d\), \(h\), and \(f_{yl}\), while features \(\rho_{l}\) and \(a/d\) tended to boost the base value. For specimen No. 297, the CB model prediction was equal to 698.61 kN and its corresponding experimental value was 700.00 kN. The most significant input features that boosted the base value were \(h\), \(d\), \(b\), \(a/d\), and \(f_{yl}\), while features \(\rho_{l}\) and \(f_{c}^{\prime}\) tended to push down the base value. These findings are consistent with the global interpretation result, where the features \(b\), \(f_{c}^{\prime}\), \(d\), \(h\), and \(\rho_{l}\) have the largest contribution to the CB model prediction. In summary, the local interpretation offers engineers practical benefits when designing or retrofitting RCDBs. For designing, it reveals how these features impact the beam strength and potential failure mechanisms, allowing for an optimized design that achieves the desired SS within a few trials, hence saving time, labor, and materials. For retrofitting, it helps identify weak points in existing RCDBs by showing which features negatively impact the SS, enabling cost-effective strengthening strategies.
## 4 Results and Discussion
Likewise, Figure 15 shows the bit error rate obtained when the communication signal is transmitted between the two phases (in this case, the switching frequency of the DC-DC converter is 10 kHz). The bit error rate drops from 464 to 410 for conventional PWM and from 454 to 427 for RPWM. This is because connecting the modems between two phases assures better balancing with respect to phase-to-ground connection; hence, signal transmission is affected by less interference. Figure 11: Bit error rate: PWM (blue) versus RPWM (orange). Figure 12: Voltage harmonics at the DC-DC converter output: PWM versus RPWM (25 kHz switching frequency) and frequency spectrum of QPSK modulated signal. The x-axis is in logarithmic scale. Figure 14: Bit error rate with coupling/decoupling networks connected between two phases or between one phase and ground (25 kHz switching frequency). Figure 13: Voltage harmonics at the output of the DC-DC converter: PWM versus RPWM (10 kHz switching frequency) and frequency spectrum of QPSK modulated signal. The x-axis is in logarithmic scale. Figure 15: Bit error rate with coupling/decoupling networks connected between two phases or between one phase and ground (10 kHz switching frequency).