esa-sceva/satcom-chunk-collection huggingface dataset:
# Single-Channel Blind Source Separation of Spatial Aliasing Signal Based on Stacked-LSTM
Second, accurate separation requires a longer STFT time window, and this requirement increases the minimum delay of the system. Third, it is hard to accurately reconstruct the phase of the source, and an incorrect estimation of the phase will cause the accuracy of the reconstructed signal to reach the upper limit. The time domain method avoids the problems caused by directly calculating the STFT, reduces the time delay, and improves the separation accuracy. Different from the time-frequency domain method, the time-domain separation system usually receives input sequences containing a large number of time steps, which brings challenges to modeling extremely long sequences. Our proposed method solves this problem by cutting and splicing the sequences into 3-D tensors. In the proposed method, firstly, 1-D convolution is used to extract the encoding feature representation of the mixed communication signal as input. Then, the long sequence input is divided into smaller blocks, and the Stacked-LSTM network with the attention mechanism of the SE module is trained to obtain the deep feature mask of the pure signal source. The Hadamard product of the mask of each signal source and the obtained mixed signal coding feature can be used to obtain the coding feature representation of the source signal. Finally, the 1-D convolution is used again to decode the obtained source signal characteristics to obtain the original waveform. The ratio-invariant signal-to-noise ratio is used as the training loss function to measure the accuracy of mask estimation and the accuracy of signal separation. The main contributions of this article are as follows:  
1. A mixed communication signal data set 10-mixC is constructed using the GNURadio platform. The data set includes 10 mixed signals obtained from five types of modulation communication signals. This data set can provide data support for similar research work in the future. 2. Stacked-LSTM method improves SISNR by 10.09-38.17 dB compared with the two classic separation algorithms of ICA and NMF and the three deep learning separation methods of TasNet, Conv-TasNet and Wave-U-Net. 3. This method effectively improves the accuracy of blind source separation of single-channel communication signals, and has better noise robustness. It can achieve single-channel separation of 10 mixed signals such as BPSK-16 QAM, 8 PSK-64 QAM, 8 PSK-PAM4, 64 QAM-PAM4, etc.
## 5 Conclusions  
A sun-oriented spin-stabilized attitude control method using only magnetic control is proposed in this paper, in which the control progress is divided into four phases: initial damping phase, sun-aligned phase, spin-up phase, and spin-stabilized phase. The advantages of the control method proposed in this paper are reflected in three aspects. Firstly, it can realize the sun-oriented attitude in the presence of the shadow zone of the orbit, which overcomes the shortcomings that most of the existing studies can only be applied to the dawn-dusk orbit. Secondly, the proposed method only uses an offset-installed sun sensor with a limited field of view to measure the solar vector, which makes most of the satellites meet the conditions for the application of the method. Thirdly, a detailed control  
Figure 8: Angle between the solar vector and the \(-O_{b}Y_{b}\) axis.  
Figure 7: Control torque outputted by 3-axis MTQs.  
process from the initial damping phase to the spin-stabilized phase is designed in the proposed method, which greatly improves the practicability of the method. The proposed control method is applicable to most Earth-orbit satellites for which the geomagnetic field can provide sufficient attitude control torque and can control the satellite to achieve the sun-oriented attitude from any initial state. The simulation results show that the proposed method effectively ensures the smooth implementation of satellite equipment debugging, energy acquisition, and other flight missions.  
Conceptualization, B.Y. and Z.M.; methodology, B.Y.; software, B.Y. and D.Y.; validation, Z.M. and D.Y.; formal analysis, B.Y.; writing--original draft preparation, B.Y.; writing--review and editing, Z.M. and D.Y.; visualization, B.Y.; project administration, B.Y.; funding acquisition, Z.M. All authors have read and agreed to the published version of the manuscript.  
This work has been supported in part by the Beijing Natural Science Foundation under Grant JQ20013 and in part by the National Natural Science Foundation of China under Grant 61833009.  
The data used to support this study are included within the article.  
The authors declare no conflict of interest.
# Potential Imaging Capability of Optical Coherence Tomography as Dental Optical Probe: A Mini-Review
###### Abstract  
Optical coherence tomography (OCT) has been emerging in the dental field as an alternative diagnostic imaging for \"optical probes\" owing to its micro-meter resolution and non-invasiveness. This review aims to answer the following question: what is the imaging capability of OCT to visualize the subgingival area? Online searches were performed on PubMed and SPIE digital library databases, followed by a manual screening of references listed in relevant studies. The feasibility and imaging performance of OCT to visualize the subgingival area, including the periodontal, peri-implant, and crown margins, are discussed. All of the literature reviewed in this study demonstrated that OCT has the ability to visualize periodontal, including hard and soft tissues, and peri-implant conditions with high resolution. Gingival sulcus depth, periodontal pocket, and calculus deposition can also be depicted. However, clinical evidence that support the imaging capability of OCT as a dental optical probe to visualize subgingival area is lacking. Limited availability, portability, and usability of OCT for clinical experiments in dentistry, particularly for the subgingival area, might be contributed to its limitations. Hence, further development of handheld OCT systems and controlled clinical trials are needed to confirm the imaging capability of OCT reported in this review.  
diagnostic imaging; optical coherence tomography; subgingival +
Footnote †: journal: Medical Imaging  
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###### Abstract  
Deflectometry is an optical technique for determining properties such as power distribution, wavefront, etc., and measurement of the optical properties of an intraocular lens can provide relevant information for clinicians. The aim of the current study was to establish a protocol for measuring lens power maps and profiles of various optical designs of intraocular lenses with a deflectometer based on the phase-shifting Schlieren principle (NIMO TR1504, Lambda-X, Nivelles, Belgium). The results are discussed with respect to accuracy and repeatability, the influence of the use of filters, and whether to consider the intraocular lens as a thin or thick lens.  
intraocular lens; deflectometry; power profile +
Footnote †: journal: Applied  
0000-0002-4300-3300]C. A.  
Although several articles have been published using the NIMO TR1504 to characterize contact lenses (CLs) [7; 8; 9; 10], there are very few publications about its use with IOLs [11; 12; 13]. This is likely due to the differences in conditions, as intraocular lenses are thicker, and have higher powers compared to CLs, which makes measurements with the NIMO TR1504 more challenging.  
One of the controversies surrounding the NIMO TR1504 instrument is that it has been suggested to produce unreliable results for measurements within the central 1 mm diameter of any lens. However, these suggestions were made based on measurements taken with CLs [14], not IOLs.  
In deflectometry, noise can significantly impact power measurements, as power is calculated from the derivatives of the deflection maps. Consequently, any noise in the deflection maps is intensified by the derivative operation. To address this concern, some authors recommend utilizing filters when taking measurements, particularly in the central zone, though only CLs were evaluated in those publications [14]. Given the physical differences between CLs and IOLs, these parameters should also be analyzed for IOLs.  
Due to the questions surrounding the use of the NIMO TR1504 system to measure IOLs and the correct methods for taking these measurements, the current study aimed to identify which measurements taken with this device can be deemed reliable and under what conditions. To that end, three parameters were analyzed: (1) the accuracy and repeatability of the measurements made by the NIMO; (2) the influence of the use of filters on the measurements; (3) and whether considering the intraocular lens as a thin lens or a thick lens affects the quality of the measurement.
## 4 Results and Discussions
This probability was considered with a high level of confidence, more significant than that observation probability obtained by the Jonson Equation (1) (which gives maximum probabilities of 50% [21]). The calculated screening time tends to be longer in the case of static tests (with a small coverage area) and shorter in the case of dynamic shootings (where the coverage area was much larger). According to the theoretical time values, a significantly larger scattering of the smoke cloud (due to the SGD ignition point) has a negative influence on the screening time, with the same mass of aerosols but in a much larger dispersed volume. In experimental testing, the measured screening times were influenced by wind speed. This parameter cannot be considered in the theoretical calculation and was a parameter that can significantly influence the difference between the two values (ST in static and dynamic testing). It was observed that the smallest error was obtained in the case the second set-up, in which the recorded wind speed was the lowest value (\(\upsilon_{v}=0.8\) m/s). The difference between the screening times _MWIR_ and _LWIR_ domains was also obtained during open field experiments, being given by the differences in performance between the observation systems, which operate at different wavelengths. For adjusting the mathematical model (1-11) for a complete SGD the following constant parameters were used: \(V_{sp}=288\) (L/kg), \(k_{c}=0.71\), \(k_{s}=0.479\), \(m_{pc}=170\) g, \(m_{a}=120.7\) g, \(V_{g}=0.049\) m\({}^{3}\), \(V_{s}=3077\) m\({}^{3}\), \(k_{\rm{cx}}=0.002\), \(l_{s}=5\) m and \(CS=9.395\) g/m\({}^{2}\). At the same time, the multiplier number, n, was introduced (for the use of more than one SGD). The average value of the scattering factor of 47.9% was obtained from the analysis of the physical parameters determined by real measurements. Under these conditions the Equation (11) becomes:  
\[ST_{LWIR}=9.395\cdot\frac{R}{d_{c}}\cdot\frac{1}{v(\lambda)}\cdot 0.875\cdot 10^{ -\gamma} \tag{15}\]  
\[ST_{MWIR}=9.395\cdot\frac{R}{d_{c}}\cdot\frac{1}{v(\lambda)}\cdot 0.775\cdot 10^{ -\gamma} \tag{16}\]  
The coverage area of interest was the area at the base of the smoke, making the dispersion of the cloud from low heights more efficient (the dispersed volume of smoke being smaller but with a high density of aerosols). However, the tactical requirements of combat involve the dispersion of a smoke cloud ten times the size of the target in order to cover a military vehicle, for instance.
## 6 Simulation Experiment and Analysis  
### Dataset Description  
To validate the monitoring capability of the proposed algorithm for low-orbit satellite wideband OFDM signals, a simulation dataset was constructed for testing. The dataset configuration is as shown in Table 2. This setup aims to test the robustness of the model in the face of real low-orbit satellite dynamic channel environments. Our objective is to assess the performance of the model in analyzing and recognizing OFDM signals under dynamic channels, and therefore, frequency offsets and time delays were set to better simulate real-world application scenarios.  
To realistically simulate low-orbit satellite OFDM signals, 64 subcarriers are divided into eight users, with each user comprising eight consecutive subcarriers. The modulation scheme for each subcarrier is randomly selected from [BPSK, QPSK, 8 PSK, 16 QAM, and 64 QAM]. Additionally, the Doppler frequency offset of the OFDM signal is caused by the dynamics of the low-orbit satellite, and thus its frequency offset range is set to [\(-100\) KHz, 100 KHz] in this paper. The length of the time delay sampling points is randomly determined within the range of [0, \(N_{cp}\)] points. In this paper, the channel is characterized by Additive White Gaussian Noise (AWGN) signals, with the channel parameters listed in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**OFDM Parameter** & **Value** \\ \hline Number of effective sample points (\(N_{use}\)) & 640 \\ Number of sample points (\(N_{s}\)) & 800 \\ Number of samples in the CP (\(N_{cp}\)) & 160 \\ Number of subcarriers (\(N_{sc}\)) & 64 \\ Bandwidth (\(BW\)) & 200 MHz \\ Oversampling factor (\(\rho\)) & 25 \\ Sampling rate (\(Fs\)) & 5 Gsps \\ Modulation & BPSK QPSK 8 QPSK 16 QAM 64 QAM \\ Pulse shaping & Rectangular pulse \\ Number of Monte Carlo runs & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: OFDM parameter configuration.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Channel Parameter** & **Value** \\ \hline Time delay & [0, 160] \\ Doppler shift & [\(-100\) KHz, 100 KHz] \\ SNR & [\(-3\) dB, 14 dB] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Channel simulation parameter configuration.
## 2 Methodology
### Experimental Investigation  
An experimental study has been carried out to verify the proposed method's feasibility. The concept behind this experiment was to transport a small-scaled object on a harmonically oscillating inclined plane upwards through dynamic dry friction manipulations.  
The principle diagram shown in Figure 3a represents the experimental setup for transporting an object on an inclined plane oscillating in the longitudinal direction implemented through dynamic dry friction manipulation. Figure 3b shows the general view of the experimental setup. The experimental setup consists of an inclined plane (1) and the transportation surface (2) that was mounted on top of a piezoelectric actuator (3) that was used for dry friction dynamic manipulations through bursts of high-frequency vibrations. The moving object was placed on the transportation surface. The roughness of the manipulated surface was around 0.2 \(\upmu\)m. The inclined plane was subjected to low-frequency harmonic oscillations in the longitudinal direction created by an electrodynamic vibrator (ESE 211, VEB Robotron-Meselektronik, Dresden, Germany) (4). A vibration sensor (5) monitored and controlled the harmonic oscillations. A power amplifier (LV-103, Metra Mess- und Frequenztechnik in Radebeul, Radebeul, Germany) (6) was used to amplify the harmonic signals to drive the electrodynamic vibrator. A piezo linear amplifier (EPA-104, Piezo Systems Inc., Cambridge, MA, USA) (7) was used to amplify the signals subjected to the piezoelectric actuator. A waveform generator (DG4202, RIGOL, Beijing, China) (8) was used to generate the signals. The signals were visualized and monitored using a digital oscilloscope (DS1054, RIGOL, Beijing, China) (9). The transportation of the object (\(\phi\)6 \(\times\) 0.7 mm, 0.201 g) was captured and filmed using a camera connected to a PC (10,11). Then, the videos were divided into frames (40 frames/sec) and the moving object's trajectory was analyzed using MATLAB R2023a software.  
The dry friction between the object and the transportation surface was dynamically manipulated by bursts of high-frequency vibrations (5860 Hz) that were periodically excited in a direction perpendicular to the manipulation plane by the piezoelectric actuator (Figure 3a). This frequency was close to the resonance frequency of the system, resulting in the most efficient reduction in the frictional properties. The bursts excited by the piezoelectric actuator were synchronized with respect to the period of the longitudinal oscillations, as shown in Figure 2. Therefore, the \(\lambda\) parameter was controlled by changing the amount of high-frequency waves in a burst and the parameter \(\phi\) was changed by changing the phase shift between the harmonic signal and the signal for the piezoelectric actuator. Figure 4 shows an oscillogram to demonstrate the synchronized signals of piezoelectric actuator excitation and harmonic oscillation.
## 2 Materials and Methods
### Experimental Scheme  
The cold chain was simulated by harvesting fruits into open cardboard boxes, and their loading into vehicles, transportation, and cold storage. The fruits were transported 283 km from the orchard to Zagreb, Croatia by the route presented in Figure 1. During the transport and in subsequent cold storage, real-time monitoring of temperature, relative humidity, CO\({}_{2}\), and GPS for experimental fruit was performed by WSN.  
Firmness, weight loss, total soluble solids (TSS), titratable acid (TA), and sensory properties are important factors of fruit quality and they play significant roles in consumer preferences, thus having a prominent role in fruit quality assessment during storage [36; 37; 38]. Weight loss (WL),  
Figure 1: The transport route used in the experiment.  
SSC, and firmness are the easiest quality indicator among these quality indicators to measure using a handheld penetrometer during the cold chain logistics, while the others need special instruments or environments. Therefore, it is practicable and important to select WL, SSC, and firmness to evaluate the quality of peaches and nectarines in actual cold chain logistics.  
Fruit weight was determined on the same subsamples using analytical balance before transport (the initial weight, IW) and after cold storage (the final fruit weight, FW). Weight loss was calculated from the difference (IW\(-\)FW) and expressed as the percent reduction of IW. Fruit firmness and juice soluble solids concentration (SSC) were measured using ten pieces of fruit from each subsample (i.e., the fruit grown under red net and the control fruit) immediately before the transport and again after the cold storage. Fruit firmness was determined with an Effegi penetrometer (model FT 327, Milano, Italy) fitted with a 7.9-mm diameter plunger. Measurements were taken at four equatorial positions on each fruit at 90\({}^{\circ}\). SSC values of the juice were measured in each fruit with a digital refractometer (Atago, PAL-2, Tokyo, Japan).
# The Real-Time Optimal Attitude Control of Tunnel Boring Machine Based on Reinforcement Learning
## 4 Case Study  
The engineering data used in this paper come from the Xinjiang Yiner Water Supply Phase 2 Project, excavated mainly by the TBM construction method. The total length of the tunnel was 283.27 km, and the tunneling diameter of 7.03 m. According to the analysis of the engineering geological report, the stratum lithology that the tunnel passes through is greater, and the GECs include 2 to 5. The geology was mainly dominated by massive fresh rock mass, and the integrity of the rock mass was high. TBM, developed by China Railway Construction Heavy Industry, was used for tunneling. The length of the main engine was about 23.8 m, and the diameter of the cutter head was 7.03 m. The cutter head split method is a partial split type, with a total of 49 cutters.
# Experimental Design of a Mobile Landing Platform to Assist Aerial Surveys in Fluvial Environments
## 6 Discussion and Conclusions
**Funding:** The research leading to these results has received funding from the RoboCity2030-III-CM project (Robotica aplicaada a la mejora de la calidad de vida de los ciudadanos. fase III; S2013/MIT-2748), funded by Programas de Actividades I+D in Comunidad de Madrid and cofunded by Structural Funds of the EU. **Conflicts of Interest:** The authors declare no conflict of interest. \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Requirement** & **Fulfilled** & **Details** \\ \hline \multirow{3}{*}{Payload} & \multirow{3}{*}{Yes} & The prototype can carry the chosen UAV with a total payload of 2.5 Kg during the tests while meeting all other design requirements. Please refer to Figure 1 where the Ar. Drone (\(\sim\)2 Kg payload) sits on it. Please refer to the supplementary materials for more detailed information. \\ \cline{3-3}  & & Thanks to the low weight (5 Kg) of the vessel handling of the vessel and transportation is simple. If necessary, it can be easily strapped to the back for ease of transportation to study sites without being excessively cumbersome. Please refer to table provided in the supplementary material with detailed weights. \\ \hline Speed & Yes & Constant cruise speed of 1 m/s can be achieved by the vessel. Please refer to Figure 13. \\ \hline \multirow{3}{*}{Autonomy} & \multirow{3}{*}{Yes} & Autonomy of the vessel during continuous use was around 60 min. Since the system does not operate at full thrust continuously the 400 mAh battery achieved almost 60 min of operation during the tests. \\ \cline{3-3}  & & Tele-operation of the vessel is possible and allows remote control of the vessel, either through manual commands or autonomously. Please refer to the video provided in the supplementary material. \\ \hline \multirow{3}{*}{Price} & \multirow{3}{*}{Yes} & Final price of the vessel was less than 500 €, making this USV platform affordable and accessible. Please refer to table provided in the supplementary material with detailed costs. \\ \cline{3-3}  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: System design requirements fulfillment.