esa-sceva/satcom-chunk-collection huggingface dataset:
# Applications of the Open-Source Hardware Arduino Platform in the Mining Industry: A Review
## 3 Arduino in Wearable Systems
### Surrounding Information
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Sensing Items** & **Sensor Name** & **Permissible Limits** & **Threshold Set** \\ \hline \multirow{4}{*}{Gas} & Combustible gas and smoke & MQ-02 & Less than 30\% & 30\% \\ \cline{2-5}  & Methane, Butane, and natural gas & MQ-4 & Less than 10\% & 12\% \\ \cline{2-5}  & Sulfides, Benzene, and Ammonia & MQ-135 & Less than 25\% & 30\% \\ \hline \multirow{2}{*}{Gas} & Temperature & DHT11 & 25–40 \({}^{\circ}\)C & 50 \({}^{\circ}\)C \\ \cline{2-5}  & Humidity & DHT11 & 15–70\% & 70\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Example of permissible limits and threshold values for sensors (modified from [PERSON] [59]). between a worker (belt) and a machine, in addition to a gas sensor (MQ2) and temperature and humidity sensor (DHT11). The ultrasonic sensor-generated high-frequency sound waves and evaluated the echo received by the sensor for distance measurement. It could accurately measure distances ranging from 2 cm to 400 cm, with a resolution of 0.3 cm. The measurement angle was 30\({}^{\circ}\). The experimental results showed that the detection rate by the safety device for a motor car at 3 m was 95%, and that at 2.5 m was 97%. The detection rates at 2, 1.5, 1, and 0.5 m were all found to be 100%. Moreover, an Arduino Nano board (ATMEGA32) connected to the ultrasonic sensor was used to acquire the data and to activate a buzzer on the belt. From these results, it can be concluded that the proposed wearable device can be used for collision avoidance. [PERSON] and [PERSON] [63] developed an IoT-based helmet-type safety management system for monitoring air quality and detecting collisions with uneven roofs in underground mines. A gas sensor (MQ4), temperature and humidity sensor (DHT11), and collision detection vibration sensor (SW420) were connected to an Arduino Nano board within a Wi-Fi wireless sensor network. Using the wireless sensor network, various sensor data with alerts were transmitted to a supervisor at a control station. The levels of temperature and humidity were communicated to the miner by displaying them on a helmet. In the case of gas, a threshold was set, and a buzzer alert was activated if the gas value was beyond the threshold limit value. Additionally, uneven roofs in underground mines can be dangerous to humans, e.g., by causing head injuries. Before a hit on a miner's head occurs, it is essential to detect it. To address this problem, a vibration sensor (SW420) was used. It was a closed-type vibration sensor and provided a clear digital output for currents above 15 mA. The threshold was set by using a comparator with the sensor. In addition, a circuit was built in a proposed helmet for enabling the data to be provided as \"1\" or \"0\" according to the status of wearing the helmet; this was because all of the factors could only be noted if the miners were wearing the helmets.
## 3 Numerical Analyses
### Contact Stability for Design Parameters  
Depending on the vertical natural frequencies and design factors, such as the geometry and friction, breaking of the contact between the superstructure and dome of the spring-viscous damper may occur. That is, if an unstable contact state occurs in a real situation, a momentary collision occurs, and effective performance of the vibration isolation device cannot be achieved. Therefore, a preliminary review should be conducted to predict when unstable contact will occur during the design phase, and the vibration isolation device must be operated while maintaining a stable contact state at all times. This unstable contact state is determined by environmental factors (\(f_{x}\), \(\dot{X}\)) and design factors (\(f_{y}\), \(\mu\), \(\zeta\), \(\alpha\)). Therefore, these factors were analyzed by the numerical simulation, which can serve as very useful data early in the design. Figure 7 presents a stability chart indicating the stable contact area when the design parameters are \(R=0.6\) m, \(\mu=0.02\), \(\zeta=0.3\), and \(\alpha=0.1\). As illustrated in Figure 7, a higher \(f_{y}\) value results in a larger unstable contact area appearing. An increasing \(f_{y}\) value means that the spring becomes stiffer. Therefore, it is advantageous to design the spring to be sufficiently soft.  
However, unstable contact areas occurred around the natural frequencies of the \(x\) direction, where the amplitudes are greater than the static deflection. For example, in Figure 7d, resonance occurs near 0.8 Hz of the excitation frequency, because the natural frequency in the \(x\) direction is 0.8 Hz, as indicated in Figure 5 and Equation (13). Moreover, as the friction coefficient decreased, the unstable  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Dimensions** \\ \hline \(R\) & 0.6 m (0.3–0.9 m) \\ \(\alpha\) & 0.1–0.3 \\ \(f_{y}\) & 2\(\sim\)10 Hz \\ \(\mu\) & 0.005–0.05 \\ \(\zeta\) & 0.3 \\ \hline \end{tabular}
\end{table}
Table 1: Main design parameters.  
Figure 7: Contact stability chart for excitation frequency and acceleration domains, when \(R=0.6\) m, \(\mu=0.02\), \(\zeta=0.3\), and \(\alpha=0.1\) unstable contact zone, stable contact zone): (**a**) \(f_{y}=3\) Hz. (**b**) \(f_{y}=5\) Hz. (**c**) \(f_{y}=7\) Hz. (**d**) \(f_{y}=10\) Hz.  
region increased, as illustrated in Figure 8, and as the friction coefficient increased, the unstable region decreased. Figure 9 presents the changes in the unstable region with respect to the damping ratio: as the damping ratio increased, the stable region increased. The viscous damping coefficient or friction coefficient acts as damping of the system that controls the amplitude at resonance, thereby increasing the stable contact area.  
This analysis reveals under which design parameter conditions unstable contact occurs. Therefore, the design parameters can be determined and reflected in the design so that they are always in stable contact.
# Influence of the Quality of Consumer Headphones in the Perception of Spatial Audio
## 3 Test 1. Sensitivity Disparity between Left-Right Transducers
### Results  
Figure 4a shows the average of the answered angles (for all of the level variation cases) according to the reproduced angle. The average of the answers has a deviation to the left-hand side. This is expected since the variations (0, 1, 2, 4 dB) were always more in favor of the left channel than the right.  
The tendency of this angle deviation to the left can be seen in Figure 4b, considering the level variation applied (0, 1, 2, 4 dB).  
An Analysis of Variance (ANOVA) indicates that the level variation has a very significant influence (\(F\) = 27.338, df = 3, \(p\) < 0.001) over the deviation in the answers.  
If we consider just the central angles used in the experiment (\(0^{\circ}\) and \(65^{\circ}\)), a smaller average deviation can be seen (Figure 5). This leads us to believe that listeners tended to divert the location of the sounds perceived on the sides more, which means that the introduced level variations made the lateral angles disperse more than the central ones.  
Figure 4: (**a**) Average of the answered angles _versus_ reproduced angles (degrees); (**b**) average of the deviation of the answered angles (degrees) _versus_ level variation (dB).  
On the other hand, the influence of the type of sound (timbal, voice, whistle or pink noise) on the deviation in responses can be seen in Figure 6a. Voice and pink noise have lower deviation than timbal and whistle sounds, especially in cases of 0 and 1 dB of deviation. Besides, voice stimuli and pink noise manifest a more separate and clear deviation at varying levels.  
The influence of the type of sound over the deviation of answers is significant (\(F=4.409\), df = 3, \(p=0.004\)) according to an analysis of variance. The sound angle reproduction has a very significant influence (\(F=54.932\), df = 3, \(p<0.001\)) over the deviation of the answers. In Figure 6b, the deviation of the answers for each sound angle reproduction is represented. Angles \(0^{\circ}\) and \(65^{\circ}\) present less deviation to the left. The biggest deviation of the answers corresponds to the angle \(-30^{\circ}\), and it could be due to the fact that it was the only angle on the left side.  
Figure 5: Average deviation of the answered angles (degrees) _versus_ level variation (dB), considering only the angles \(0^{\circ}\) and \(65^{\circ}\).  
Figure 6: Average deviation of the answered angles (degrees) _versus_ the level variation (dB): (**a**) considering the type of sound; (**b**) considering the angle reproduction of sound.
## 2 Related Works
### Bit Error Pattern-Based Interference Classification  
This class of methods does not require the active collection of RSSI samples; rather, the interference classification is based on the analysis of bit error patterns in the packets exchanged during the normal operation of the network. In [32; 33], the authors show that the different interferers, such as IEEE 802.11, Bluetooth and ZigBee, corrupt IEEE 802.15.4 packets, leaving specific error footprints. In addition, the bit error pattern can also be used to reveal the presence of weak links. In particular, [PERSON] et al. [32] propose identification of the interference source by combining (i) the signal strength variations during packet reception, (ii) the link quality indicator (LQI) associated with a packet and (iii) the position of corrupted bytes in the payload. The classification accuracy of the proposed method is 72%, while this result is also IEEE 802.15.4 packet size dependent, since packets with a small payload size are partially overlapped with the interferer, thus carrying a small interference fingerprint. In [34] [PERSON] et al. use forward error correction (FEC) in order to identify the source of bit errors in a received packet (i.e., multipath fading and attenuation, as well as the IEEE 802.11 b/g interference). Therefore, instead of packet retransmissions (which is used in [32]), the FEC method in [34] emerges as an energy-efficient alternative for interference classification, yielding more than 91% classification rate with just one received packet.
## 4 Results and Discussions
### Neural Network Analysis
For the ANN models MLP 3-12-1 and MLP 3-13-1, which were derived from cutting parameters only, the average percentage prediction accuracy of all datasets is approximately 94.9% and 95.4%, respectively. Both models have RMSE values of 4.0%. Although for models MLP 4-15-1 and MLP 4-22-1--which were modeled considering the effect of machining vibration--the average percentage prediction accuracy of all datasets is approximately 96.2% and 96.1%, respectively, and the RMSE values is ~3.0%. The comparison of the performance of four models indicates that surface roughness can be well predicted based on the proposed ANN models. Further comparisons of the RMSE and MAPE values listed in Table 6 indicate that the prediction accuracy can be increased if the spindle vibration in machining was also fed in the prediction models (MLP 4-15-1 and MLP 4-22-1), which have less RMSE values compared with the models based on only the three cutting parameters. It is observed that among the presented neural network models, the model (MLP 4-15-1) with four variables in the input layer and 15 neurons in the hidden layer shows better performance in the predictions of the surface roughness for all testing data as compared with other models. Further, in comparison with the regression models, the neural network modeling based on cutting parameters with machining vibration shows better accuracy in predicting machined surface quality. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**Training Dataset**} & \multicolumn{4}{c}{**Validation Dataset**} & \multicolumn{4}{c}{**Testing Dataset**} \\ \cline{2-10}  & **R** & **RMSE** & **MAPE** & **R** & **RMSE** & **MAPE** & **R** & **RMSE** & **MAPE** \\ \hline ANN Model & \multicolumn{4}{c}{ANN models based on cutting parameters (three input variables)} \\ \hline MLP 3-12-1 & 0.968 & 3.31\% & 4.25\% & 0.929 & 5.82\% & 7.90\% & 0.932 & 4.77\% & 6.86\% \\ MLP 3-13-1 & 0.969 & 3.24\% & 4.21\% & 0.958 & 4.31\% & 5.93\% & 0.947 & 4.14\% & 5.91\% \\ \hline \multicolumn{10}{c}{ANN models based on cutting parameters and vibration level (four input variables)} \\ \hline MLP 4-15-1 & 0.978 & 2.72\% & 3.53\% & 0.973 & 4.02\% & 4.92\% & 0.976 & 2.87\% & 3.96\% \\ MLP 4-22-1 & 0.975 & 2.70\% & 3.56\% & 0.961 & 4.13\% & 5.06\% & 0.976 & 3.02\% & 4.35\% \\ \hline \hline \end{tabular}  
* R: Correlation coefficients; RMSE: Root mean square error; MAPE: Mean absolute percentage error.  
\end{table} 
Table 6: Statistical values of neural network models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**ANN Model**} & \multicolumn{4}{c}{**Sensitivity Levels**} \\ \cline{2-5}  & **Spindle Speed** & **Feed Rate** & **Cutting Depth** & **Vibration Level** \\ \hline MLP 3-12-1 & 29.22 & 12.73 & 12.13 & - \\ MLP 3-13-1 & 28.02 & 24.40 & 11.97 & - \\ MLP 4-15-1 & 71.22 & 16.71 & 12.58 & 31.16 \\ MLP 4-22-1 & 61.68 & 27.94 & 15.20 & 8.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Sensitivity level of the input variables of the selected neural network models.
## 4 Discussion
Additionally, VFI and TVI are angle-independent methods that do not require the beam-to-flow angle to be estimated by hand as it does in e.g., spectral Doppler, which is commonly used in the clinic. 2D VFI has been shown to measure peak systolic velocities (PSV) that agree with magnetic resonance angiography, as opposed to spectral Doppler that overestimates the PSV [36]. 2D VFI only measure two of the three velocity components, thus TVI can be expected to perform comparably or better.
## 2 Materials and Methods
### Measurement Set-Up  
Gait analyses were performed in two gait laboratories (Figure 1). Both laboratories used an optical infrared camera-based motion capturing system provided by one manufacturer (Vicon Motion System Ltd., Oxford, UK).  
The first laboratory (GL1) was equipped with a six-camera system (460 Vcam cameras, Workstation 4.6 build 142 software). The second laboratory (GL2) used a six-camera system (MXF-20 cameras, Nexus 1.3 software). These systems thus differed in their respective configurations, both in terms of analysis software (Workstation vs. Nexus) and camera type (Vcam vs. MXF). Both laboratories applied the same sampling rate (200 Hz) and the same reflective markers (14 mm diameter). Capture space represented the full dimensions (length, width and height) of the motion capture room for GL1 (7 m, 4 m and 3 m) and for GL2 (15.5 m, 8.8 m and 4.8 m). Capture volume was defined as the area within the capture space, for which motion capturing cameras were able to capture the motion task of each  
Figure 1: Data processing chart—clinical gait analysis (CGA) data collected on three healthy test-subjects (\(n=20\) barefoot trials per person). The data were captured using the same measurement protocols in both the first (GL1) and the second laboratories (GL2). Three reliability tests were performed with the datasets from GL1 and GL2. Interobserver reliability was tested for two observers using the CGA data collected at GL2. Observer 1 used the data processing software from GL 1 (Workstation 4.6), which was different to that of Observer 2 (GL2: Nexus 1.3). To assess intraobserver reliability, the same data set was analyzed a second time by observer 1 after a time interval of 12 months, using the CGA data collected at GL2 and the software from GL1. To test between-laboratory intraobserver reliability, the CGA data collected at both sites (GL1 and GL2) were analyzed by Observer 1 using software from GL1.  
subject. The GL1 capture volume was 6 m, 2 m and 2 m. The capture volume for GL2 was 10.0 m, 8.5 m and 2.9 m. Captured marker data were processed and trajectories were labeled using the PlugInGait model under a standardized protocol at both laboratories. All kinematic data were Woltring filtered using a mean squared error setting of 10. These gait data were reduced to 100% of one gait cycle using gait cycle event detection, based on the available force plates (threshold: 20 N). All subsequent measurement conditions were consistent at both laboratories.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CCFD & Co-frequency Co-time Full Duplex \\ ISL & Inter-satellite link \\ GNSS & Global Navigation Satellite System \\ GPS & Global Positioning System \\ URE & User range error \\ ESA & European Space Agency \\ TDMA & Time-division multiple access \\ FTDMA & Frequency-hopping time-division multiple access \\ OQL & Optical quantum links \\ BDS-3 & BeiDou-3 Global Navigation Satellite System \\ PNT & Positioning, Navigation, and Timing \\ MEO & Medium Earth Orbit \\ GEO & Geostationary Earth Orbit \\ TDHD & Time-division half-duplex \\ SOI & Signal of Interest \\ SIS & Self-Interference Signal \\ RF & Radio frequency \\ IF & Intermediate frequency \\ IGSO & Inclined Geosynchronous Orbit \\ SNR & Signal-to-noise ratio \\ BER & Bit Error Rate \\ BDT & BeiDou time \\ PCO & Phase center offset \\ ECI & Earth-Centered Inertial \\ NFO & Nominal frequency offset \\ RAAN & Right Ascension of the Ascending Node \\ \end{tabular}
## 4 Results and Experiments
### Experiment's Establishment  
The models were constructed using TensorFlow 2.17.0 and Keras 3.4.1 on the Kaggle platform 1.6.17. The experimental setup included hardware featuring an Nvidia GeForce RTX 1050 graphics card and Windows 10. During data resampling, only the training set was utilized. The test dataset was kept untouched, reserved exclusively for evaluating the model's performance. Training involved 500 epochs, with each epoch optimizing the model to improve performance and minimize loss, ensuring thorough learning and generalization from the dataset.
## 2 Vibration Characteristics of Idling Vehicles
### Dependence on Number of Occupants  
The chassis of a vehicle can experience different vibrations when idling depending on the number of people inside, though the effect is generally subtle.  
Chassis vibrations are primarily influenced by the engine, transmission, and drive-train while the vehicle is idling. However, the mass of the vehicle, including the number of occupants, can affect how those vibrations are transmitted through the car. As more people enter the car, the total mass of the vehicle increases. The suspension system (which is responsible for absorbing vibrations from the road) has greater moving mass and a subsequent decrease in sprung-mass natural frequency. This can result in slightly altered vibration characteristics, though the changes are usually not large unless there is a significant difference in vehicle mass with increasing number of occupants.  
Additional passengers change the mass distribution of the vehicle. If people are sitting in the front seats, they might add mass to the front suspension and chassis, while passengers sitting in the back seats will affect the rear suspension. This altered distribution can cause vibrations to be transmitted more noticeably through the vehicle's structure.  
More people in the vehicle could also increase the flexing of the vehicle's frame, especially if the car has a lighter chassis or is built with less rigidity. This could cause the chassis to resonate slightly differently, but again, this effect would generally be quite subtle unless the number of occupants is significantly large.  
While idling, the engine speed is low. Idle vibrations tend to be more noticeable at the low-frequency end of the spectrum (\(\sim\)1-2 Hz for typical passenger vehicles), and the presence of additional passengers can slightly change the way those vibrations travel through the chassis and suspension.  
In general, while more passengers will likely have a small effect on the chassis vibrations of a vehicle while idling, this effect is usually minor. The primary sources of vibration (engine, drivetrain, and suspension) still dominate the vehicle's overall vibration profile when idling and changes due to the number of passengers are typically less noticeable compared to those caused by changes in the engine load or road conditions.
## 2 Results  
The profoxydim susceptibility/resistance of samples was tested three weeks after herbicide treatment through growth tests: two resistant biotypes (RES1 and RES2) and one susceptible biotype (SUS) were identified. Following an extensive bibliographic search, we selected twelve candidate genes reported in the literature as being involved in herbicide detoxification in _Echinoclloa_ spp., and analyzed their expression profiles before and after profoxydim treatment.  
By means of an extensive bioinformatic analysis using the psRNATarget: A Plant Small RNA Target Analysis web server [42; 43; 44], on the basis of the nucleotide sequence of each candidate gene, we predicted in silico a set of miRNAs able to target their transcripts. Among these, none had already been identified in _Echinoclloa_ spp. miRNAs with a complementarity degree \(\geq 80\%\) with candidate gene transcripts were selected for analysis in our study. _Ata-miRR66c-5p_ was identified in _Aegilops tauschiti_ Coss., _ath-miRR396b_ and _ath-miRR847_ were identified in _Arabidopsis thaliana_ ([PERSON] [PERSON], _gma-miRR396f_ was identified in _Glycine max_ ([PERSON] [PERSON], _gra-miRR7486c_ and _gra-miRR8759_ were identified in _Gossypium raimondii_ Ulbr., and _osa-miRR395f_, _osa-miRR5538_, _osa-miRR166a-5p_, _osa-miRR66d-5p_, _osa-miRR2099-5p_, and _osa-miRR396a-5p_ were identified in _O. sativa_. In Table 1, the miRNAs and the corresponding target genes analyzed in _E. orgzicola_ are reported.  
The expression profiles of miRNAs and their targets were evaluated in herbicide-susceptible (SUS) and -resistant (RES1 and RES2) _E. orgzicola_ biotypes before (BT) and after (AT) profoxydim treatment.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**miRNA** & **Target Gene** \\ \hline _osa-miRR166a-5p_ & _CYP81A12_ \\ \hline _osa-miRR166d-5p_ & _CYP81A21_ \\ \hline _ath-miRR396b_ & _CYP81A22_ \\ \hline _osa-miRR2099-5p_ & _CYP81A24_ \\ \hline _osa-miRR396a-5p_ & _CYP81A63_ \\ \hline _ath-miRR847_ & _CYP81A6_ \\ \hline _gma-miRR396f_ & _CYP71 AK2_ \\ \hline _ata-miRR166c-5p_ & _CYP72A122_ \\ \hline _gra-miRR7486c_ & _CYP72A254_ \\ \hline _gra-miRR8759_ & _GSTF1_ \\ \hline _osa-miRR395f_ & _EcGST_ \\ \hline _osa-miRR5538_ & _elF4B1_ \\ \hline \hline \end{tabular}
\end{table}
Table 1: miRNAs and the corresponding target genes analyzed in _Echinoclloa_ orgzicola_. In silico miRNA prediction was carried out using the psRNATarget: A Plant Small RNA Target Analysis web server. Optimal alignment between miRNAs and the target gene was investigated by applying the Smith-Waterman algorithm, using the default parameters of the server (Schema V2, 2017 release). Only miRNAs with a high sequence complementarity rate (\(\geq\)80%) with the target gene were considered for the study.