esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research on Image Acquisition System of Lining Cracks
### Shooting System Hardware Analysis
#### 3.1.3 Focal Length Calculation  
When the camera is imaging, the focal length of the lens determines the actual shooting size. The focal length of the required lens is usually calculated by the field of view, working distance, and camera chip size according to Equation (2):  
\[\frac{l}{s}=\frac{d}{f} \tag{2}\]  
where \(l\) is the length of view, \(s\) is the size of the camera sensor, \(d\) is the working distance of the camera, and \(f\) is the focal length.  
When applied in different tunnels, the corresponding working distances of the cameras are different. Therefore, to obtain images with a fixed shooting range, a zoom camera needs to be used to capture images of the tunnel lining.  
Through the statistics of common tunnel width, lane width, and vehicle width, the working distance of the camera in the tunnel is 2.0-7.0 m. According to Equations (3) and (4), the minimum focal length is 9.30 mm, and the maximum is 32.54 mm. Therefore, the H6Z0812M electric zoom lens with a zoom range of 8-48 mm was finally selected.  
\[f_{\text{min}}=\frac{d_{\text{min}}\times s}{l}=\frac{2000\times 6.47}{1392}=9.30 \,\text{mm} \tag{3}\]  
\[f_{\text{min}}=\frac{d_{\text{max}}\times s}{l}=\frac{7000\times 6.47}{1392}=32.54 \,\text{mm} \tag{4}\]  
where \(d_{\text{min}}\) and \(d_{\text{max}}\) are the maximum and minimum working distances, \(f_{\text{min}}\) and \(f_{\text{max}}\) are the maximum and minimum focal length.
## 4 Results and Performance Evaluation of the Proposed and Existing Routing Protocols
### Performance Metrics  
Performance metrics for all compared protocols are defined as follows:  
**Residual Energy:** It describes the difference between the startup nodes' energy and the energy of the nodes utilized during the operation.  
**Network Lifetime:** The total time spent by the operation of the network is referred to as network lifetime.  
**Throughput:** The total number of efficiently transmitted packets at the sink is called throughput.  
**Path-Loss:** The difference between sending and receiving nodes' transmitted and received powers is called path-loss and is measured in decibels (dB).  
**Channel Loss:** channel attenuation is typically expressed in decibels (dB) per unit distance. Attenuation of zero decibels means that the signal is passed without loss; three decibels means that the power of the signal decreases by one-half.
## 3 Complex-Valued Conditional Generative Adversarial Network  
To mitigate the impact of dynamic noise on satellite individual identification, we proposed a satellite individual identification method based on complex-valued conditional generative adversarial network (CC-GAN). The overall architecture of the model is shown in Figure 2, which is mainly composed of two parts: the generator and the discriminator. The model initially employed a specific generator to eliminate noise features in the noisy signals to generate the denoised signals. Subsequently, the denoised signals were fed into the modified discriminator with a classification capability for RFF features, ultimately achieving the function of identifying the identity of individual satellites.
# Research on Fault Diagnosis of Marine Diesel Engines Based on CNN-TCN-ATTENTION
###### Abstract  
In response to the typical fault issues encountered during the operation of marine diesel engines, a fault diagnosis method based on a convolutional neural network (CNN), a temporal convolutional network (TCN), and the attention mechanism (ATTENTION) is proposed, referred to as CNN-TCN-ATTENTION. This method successfully addresses the issue of insufficient feature extraction in previous fault diagnosis algorithms. The CNN is employed to capture the local features of diesel engine faults; the TCN is employed to explore the correlations and temporal dependencies in sequential data, further obtaining global features; and the attention mechanism is introduced to assign different weights to the features, ultimately achieving intelligent fault diagnosis for marine diesel engines. The results of the experiments demonstrate that the CNN-TCN-ATTENTION-based model achieves an accuracy of 100%, showing superior performance compared to the individual CNN, TCN, and CNN-TCN methods. Compared with commonly used algorithms such as Transformer, long short-term memory (LSTM), Gated Recurrent Unit (GRU), and Deep Belief Network (DBN), the proposed method demonstrates significantly higher accuracy. Furthermore, the model maintains an accuracy of over 90% in noise environments such as random noise, Gaussian noise, and salt-and-pepper noise, demonstrating strong diagnostic performance, generalization capability, and noise robustness. This provides a theoretical basis for its practical application in the fault diagnosis of marine diesel engines.  
marine diesel engine; fault diagnosis; convolutional neural network; temporal convolutional network; attention mechanism +
Footnote †: journal: Applied sciences  
Article
# Calibration of a Quartz Tuning Fork as a Sound Detector
## 3 Results
### QTF Sensitivity  
In the next step, we investigate whether the QTF signal is linearly dependent on the sound pressure; if this can be confirmed, a sensitivity will be determined.  
Figure 4 shows the QTF output voltage as a function of medium and high sound pressure levels. Since the same measurements were carried out with a microphone of known sensitivity (see Section 3.1), the result can be presented in terms of sound pressure.  
The coefficient of determination of the linear regression \(R^{2}\) describes how well the model fits with the measured data. With \(R^{2}=99.97\%\), it is in excellent agreement. The sensitivity corresponds to the slope of the graph, which is \(S_{\text{QTF}}(f_{R})=6.4730\frac{\text{mV}}{\text{Pa}}\). This corresponds to a level of \(-43.78\) dB. The QTF sensitivity is therefore a factor of 6.3 higher than that of the measuring microphone.
# Influence of Posture Change on Train Running Safety under Crosswind
## 4 Results
### Running Safety
#### 4.3.2 Wheel Load Reduction Rate  
When a train runs at a high speed, the wheels do not stay horizontal but move up and down due to the vibration, and the wheel weight of the wheelset increase or decrease. Even if the lateral force on the side of the reduced wheel weight is small (or even none), there may be lateral relative displacement with the wheels, which leads to a derailment. Hence, the WLRR was also considered in the evaluation of the running safety in terms of:  
\[\left\{\begin{array}{c}\ WLRR=\frac{\Delta P}{P}\leq 0.8\\ \frac{\Delta P}{P}>0.8,\ \Delta t<0.035\text{s}\end{array}\right. \tag{9}\]  
where \(\Delta P\) is the load reduction in the wheel load, \(P\) is the average static wheel load of the wheelset, and \(\Delta t\) is the maximum overrun duration. The same as Figure 14, Figure 15  
Figure 14: Time-varying \(DC\) values of two wheelsets: (**a**) the first wheelset of the leading car without posture; (**b**) the first wheelset of the leading car with posture; (**c**) the last wheelset of the trailing car without posture; (**d**) the last wheelset of the trailing car without posture.  
shows the time-varying WLRR values of the first wheelset of the leading car and the last wheelset of the trailing car of the train, with and without the given posture. According to the definition of WLRR, its theoretical maximum value is \(+1\), which means that the wheel-rail contact force is \(0\) and the wheel-rail disengagement state is at this time, so the maximum value of WLRR cannot exceed \(1\). However, its lowest value is not \(-1\). In the process of high-speed train operation, if there is irregularity on the rail surface (the rail surface is slightly arched upwards) or a transient aerodynamic impact at a certain moment. The wheels will have a transient impact on the rail, where the impact force can even reach several times the static wheel weight, which is why WLRR can reach a value higher than \(-1\).  
No remarkable difference in the WLRR value could be found when the train with and without a designed posture passed the crosswind region. Their distributions, peak values, and the developing mode were all the same, saying that a slightly given posture cannot significantly influence the performance of WLRR.
# Dual-Probe Near-Field Phaseless Antenna Measurement System on Board a UAV
## 2 System Description
### Dual-Channel Receiver Calibration and Mounting  
The feasibility of the dual-channel receiver was presented as a proof of concept in [12], where the receiver was placed on board the first version of the developed prototype for on-site antenna measurement. However, it was found that the two input channels were unbalanced, resulting in a 3-4 dB difference in the measured radiofrequency (RF) power, which can be due to small differences in the monopole antenna radiation patterns and the RF cables connecting the antennas and the inputs of the receiver. This unbalance in the dual-channel receiver required the implementation of a calibration stage, conducted as follows:  
1. The UAV is manually placed at a known distance (e.g., 1 m) from an omni-directional transmitting antenna (e.g., a monopole antenna), so that the two monopole antennas onboard the UAV are at the same distance from the transmitting antenna, having the same orientation.
2. The signal level measured at each channel is recorded and the unbalance between both channels (\(\Delta\)RF\({}_{1,2}\)(r\({}_{1}\))) is obtained.
3. Steps 1) and 2) can be applied for different distances between the UAV and the omni-directional transmitting antenna, yielding \(\Delta\)RF\({}_{1,2}\)(r\({}_{2}\)), \(\Delta\)RF\({}_{1,2}\)(r\({}_{3}\)),..., \(\Delta\)RF\({}_{1,2}\)(r\({}_{N}\)).
4. Dual-channel unbalance correction factor is estimated as: \(mean\)(\(\Delta\)RF\({}_{1,2}\)(r\({}_{n}\))), n = 1,2,..., N.  
After this calibration stage, the unbalance between the two input channels was reduced to less than 0.5 dB.  
To maximize the distance between the two monopoles placed on board the UAV, different solutions were considered. In [12], two plastic tubes were attached to the UAV frame (Figure 1, [12]). However, it was found that they were not robust enough to avoid vibrations induced by the UAV propellers, which have an impact in the measurements geo-referring accuracy. After several in-flight tests, ad-hoc mounting frames were designed to mitigate the vibrations on the monopole antennas. The mounting frames for the monopole antennas, manufactured using 3D printing, are depicted in Figure 1.  
The fact that both the mounting frames for the monopole antennas and the UAV frame are made or plastic reduces scattering and interferences from the UAV itself. Placing the monopole antennas away from the UAV also contributes to mitigate this issue.
###### Abstract  
In this paper, leaky Lamb wave radiation from a waveguide plate with finite width is investigated to gain a basic understanding of the radiation characteristics of the plate-type waveguide sensor. Although the leaky Lamb wave behavior has already been theoretically revealed, most studies have only dealt with two dimensional radiations of a single leaky Lamb wave mode in an infinitely wide plate, and the effect of the width modes (that are additionally formed by the lateral sides of the plate) on leaky Lamb wave radiation has not been fully addressed. This work aimed to explain the propagation behavior and characteristics of the Lamb waves induced by the existence of the width modes and to reveal their effects on leaky Lamb wave radiation for the performance improvement of the waveguide sensor. To investigate the effect of the width modes in a waveguide plate with finite width, propagation characteristics of the Lamb waves were analyzed by the semi-analytical finite element (SAFE) method. Then, the Lamb wave radiation was computationally modeled on the basis of the analyzed propagation characteristics and was also experimentally measured for comparison. From the modeled and measured results of the leaky radiation beam, it was found that the width modes could affect leaky Lamb wave radiation with the mode superposition and radiation characteristics were significantly changed depending on the wave phase of the superposed modes on the radiation surface.  
leaky Lamb wave; semi-analytical finite element (SAFE); waveguide sensor; finite-width plate; waveguide plate; width modes; spatial beating; Rayleigh-Sommerfeld integral (RSI) Article
# Performance Study and Efficiency Improvement of Ice Slurry Production by Scraped-Surface Method
## 4 Conclusions  
This work investigated the ice-making performance and efficiency improvement of scraped-surface method. Sodium chloride solution was used as a base solution for the ice slurry preparation. The effects of different sodium chloride concentrations, solution flow rates and scraping speeds on the system performance were compared. Furthermore, the efficiency of nanoparticle-enhanced ice slurry production was investigated. The major findings are as follows:  
(1) During the ice-making process, when the temperature dropped to the freezing point, ice crystals were formed on the wall without obvious supercooling degree.  
(2) The power consumption of the scraping system generally accounted for only 5% of the total power consumption of the system, and its proportion was not significant.  
(3) The system COP decreased with increase in the concentration of ice slurry making solution. When the solution concentration increased from 1% to 6%, the system COP decreased by 13.16%.  
(4) The scraping speed of scraper and solution flow rate both affected the system COP to some extent. The optimum scraping speed during ice slurry making was found to be 13 rpm and the optimum solution flow rate was 1.3 m\({}^{3}\)/h. At these operating parameters, the COP of the system reached a maximum of 2.43.  
(5) The addition of nanosilica accelerated the formation of ice slurry and increased the COP of the system. The higher the amount of nano-addition, the greater the improvement in COP of the system. Also, the size of nanosilica did not have a significant effect on COP of the system.  
Conceptualization, X.L. ([PERSON]); Methodology, K.Z., S.L.; Investigation, Y.L., K.Z.; Data curation, K.Z., R.F.; Writing--original draft preparation, X.L. ([PERSON]); Writing--review and editing, X.L. ([PERSON]); Project administration, X.L. ([PERSON])  
This research was funded by the Open Project Program of the State Key Laboratory of Photocatalysis on Energy and Environment (grant number SKLPEE-KF201819), the Program for Young Teacher Education and Research of Fujian Province (grant number JAT170076) and the Fuzhou University Testing Fund of Precious Apparatus (grant number 2018T025).  
The authors declare no conflict of interest.
# Design of Unmanned Road Widths in Open-Pit Mines Based on Offset Reaction Times
## 5 Conclusions  
Based on the research, the following conclusions are presented:  
1. Unmanned vehicles have precise sensing and positioning capabilities, with shorter sensing times and stronger reactive capabilities than human drivers. It is clear that autonomous vehicles can potentially offer a significant advantage over human-drivenvehicles in terms of their capability and efficiency. Analyzing the differences between manned and unmanned roads in open-pit mines, we drew the lateral offset back distance curve for mining dump trucks, using the response times of sensors and drivers as independent variables. This curve showed that the corrective distance for manned driving is greater compared to that of unmanned driving.
2. A model for the dynamics of the lateral offset in vehicles was created to examine the lateral forces on a truck during offset steering. By analyzing the Northern Heavy Industries NTE240 self-driving mining dump truck, it was discovered that the road width of the unilateral transport lane of an unmanned vehicle, which can be optimized for reduction, can be calculated as \(\mathrm{x_{op}}=\mathrm{x^{\prime}}_{\mathrm{max}}-\mathrm{x_{max}}=1743\) mm.
3. Using the Shengli West No. 1 Open-Pit Mine as a case study, this paper optimized the angle of the south slope and calculated the resulting economic benefits for end-slope mining. This optimization resulted in a stripping volume of 3,273,500 m\({}^{3}\), a coal mining volume of 2,496,280 t, and a stripping ratio of 1.31 m\({}^{3}\)/t. The analysis examined the slope stability of the southern section before and after optimization, along with the impact of truck loading. The slope stability of the slope post-mining was 1.231, satisfying the specifications and confirming the benefits of optimization.  
Conceptualization and methodology, L.H.; writing--original draft preparation, P.L.; writing--review and editing, P.L.; supervision, L.H. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (No. 52374144), Natural Science Foundation of Liaoning Province (CN) (No. 2022-KF-23-10), and Xinjiang Uygur Autonomous Region Science and Technology Major Program (No. 2023A01002).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors do not have any conflict of interest with other entities or researchers.
## 1 Introduction
This is to avoid the situation whereby classification results are artificially exaggerated due to test clips being extracted from the same longer recordings as clips used to train classifiers, as exemplified in [23]. A similar phenomenon has been seen in MIR research where classifiers were tested on tracks from the same albums as their training material [24]. The TUT Database [25], used in DCASE challenges since 2016, fulfils this criterion. It features recordings of 15 different acoustic scene classes made across a wide variety of locations, with details provided in order to avoid any crossover in locations between the training and testing sets. This database was recorded using binaural in-ear microphones. The DCASE 2013 AED task [5] used a small set of office recordings made in Ambisonic B-format (though only stereo versions were released as part of the challenge). Since it was intended for AED, this dataset features recordings of office environments only, not the wide range of locations needed for ASC work. The DEMAND database [26] features spatial recordings of six different acoustic scene classes, each recorded over three different locations. This is a substantial amount of data, but potentially still too small a collection for effective classifier training and validation. The recordings were made using a custom-made 16-channel microphone grid, which offers potential for spatial information extraction, though techniques developed using this data might not be generalisable to other microphone setups. This paper introduces EigenScape, a database of fourth-order Ambisonic recordings of a variety of urban and natural acoustic scenes for research into acoustic scene and event detection. The database and associated materials are freely available--see Supplementary Materials for the relevant URLs. The paper is organised as follows: Section 2 covers the technical details of the recording process, provides information on the recorded data itself, and describes the baseline classification used for initial analysis of the database. Section 3 gives detailed results from the baseline classifier and offers some analysis of its behaviour and the implications this has for the dataset. Section 4 offers some additional discussion of the results, details potential further work, and concludes the paper.
## 5 Discussion
### Recognition Performance  
According to Table 3, the minority class of the partial level appeared in ME, MF, HG, CG, and SG, while TA and PG accounted for the minority class at the none level. Theoretically, an imbalanced dataset drives the recognition of the minority class toward the majority class [50]. Based on the results shown in Figure 9 and Table 6, there was no sign of majority-class bias in terms of misclassification of the minority class. As shown in Figure 6, several full levels in ME were misclassified as the minority class of the partial level. Subsequently, the partial level of P17 in ME was misclassified as the none level instead of the full level. This finding infers that the proposed method performed well in addressing the imbalanced condition of the dataset. In contrast, no related research has specifically evaluated performance in the recognition of finger movement impairment level under the condition of an imbalanced dataset [8; 10; 11; 12; 13]. Therefore, the resulting recognition performance represents the actual condition of the FMA.  
In the previous experiment, the data processing method comprised data scaling only [8]. Since the current study employed an improved recognition method, the the results are not comparable with those achieved in the previous study. Consequently, the best F1 score of each impairment level from the cross-validation fold was selected to match with the holdout method of the previous experiment. In addition, this study evaluated recognition performance in inter-subject cross-validation settings and addressed the imbalanced dataset issue, which was not explicitly addressed in the aforementioned related research [10; 11; 12; 13]. Two related studies that investigated finger movement recognition only employed accuracy as an evaluation metric [12; 13]. Considering the shortcomings of the accuracy metric under the condition of an imbalanced dataset, this study only compared the F1 scores with the results of the previous experiment [8].  
Compared to the recall metric, which is suitable for imbalanced datasets, several F1 scores in this study showed higher values due to the contrary nature of precision relative to the F1 score. Consequently, it might not represent the actual performance of the employed machine learning model. According to this metric, the experimental results achieves in this study are superior to those of the previous experiment, as shown in Figure 10. In the previous experiment, the employed features focused on the statistical  
Figure 10: Box plot of F1 scores achieved in the current and previous study.  
and amplitude characteristics of EMG signals [8]. In contrast, this study emphasized the complex characteristics of EMG signals that improve recognition.
## 3 Results and Discussion
### Performance Characterization of Dust Suppressant
#### 3.2.6 Test for Physical and Chemical Properties  
The PH, viscosity, surface tension, and density values of the dust suppressant solution for the optimal formula were measured, the physical and chemical properties of which are shown in Table 10.  
According to the test for physical and chemical properties, the PH value of water is 6.81 and the PH of the dust suppressant is 7.74--near to neutral. The viscosity of water is about 1 mPa\(\cdot\)s while the viscosity of dust suppressant is 10.98 mPa\(\cdot\)s. Therefore, it has a bonding effect that condenses dust particles together and increases the dust particle size and density. It facilitates both settlement and spraying. The surface tension value of dust suppressant is 27.41 mN/m and that of clean water is 71 mN/m. This shows that the surface tension of dust suppressant decreases obviously, which can effectively accelerate the moisturizing rate of dust and the capture of dust. The density value of dust suppressant is 1.041 g/cm\({}^{3}\) and has a small difference from the density value of clean water. Therefore,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Serial Number** & **PH** & **Viscosity (mPa-s)** & **Surface Tension (mN/m)** & **Density (g/cm\({}^{3}\))** \\ \hline
1 & 7.83 & 10.32 & 27.39 & 1.046 \\
2 & 7.76 & 10.97 & 27.49 & 1.037 \\
3 & 7.62 & 11.10 & 27.36 & 1.041 \\ Average & 7.74 & 11.80 & 27.41 & 1.041 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Physical and chemical properties of dust suppressant.  
Figure 9: Variation curves of samples’ water loss rate over time.  
it can be proved that the formula solution can dissolve with water without sinking or floating, and its intuitive appearance is a clear solution.