esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Deep-Learning-Based Approaches  
[PERSON] et al. [18] introduced ERUDITE, a deep neural network approach aimed at optimizing adaptive video streaming controllers to adapt to various parameters, such as video content and bandwidth traces. ERUDITE employs a deep neural network (DNN) as a supervisor to dynamically adjust the controller parameters based on observed bandwidth traces and resulting quality of experience (QoE). In [19], the authors proposed a deep learning approach to predict the qualities of video segments that will be cached, with the goal of enhancing the performance of DASH in a software-defined networking (SDN) domain. They utilized a deep recurrent neural network (LSTM) with three hidden layers to forecast future segments and incorporated SDN technology in their approach. However, when poor bandwidth conditions arise, it can lead to difficulties in downloading segments in real time, which may affect the accuracy of predictions. In [20], the authors presented a video QoE metric for DASH utilizing a combination of a three-dimensional convolutional neural network (3D CNN) and LSTM to extract deep spatial-temporal features that represent the video content. They also incorporated network transmission factors that influence QoE, following the DASH standard. By combining the extracted features, they generated an input parameter vector and used the ridge regression method to establish a mapping between the input parameter vector and the mean opinion score (MOS) value, predicting the final QoE value.  
These studies collectively showcase the application of deep learning techniques, including deep neural networks and recurrent neural networks, in optimizing various aspects of adaptive video streaming to enhance performance and improve the user experience. However, none of these studies consider the perceived change in quality, which can also negatively impact QoE.
# Integration of Directional Antennas in an RSS Fingerprinting-Based Indoor Localization System
## 4 Deployment Cycle
### Site Survey and Calibration Phase
#### 4.1.2 ANN Training
Then, the real input of the MLP was the averaged RSS calculated from the \(N\) beacons transmitted by the tag. This pre-filtering function is the same as the one integrated in the pre-filtering layer of the localization engine. Finally, after the training phase, the overall localization engine is tested, with the trained MLP incorporated into the classifier layer. Tests will be performed in the scenario to estimate the localization error in each defined room. If the error goal has been properly reached during the MLP training process and fits the service quality required in the pre-project phase, then the system will be considered ready to operate in the online stage, and the setup will be over. If not, the site survey will be repeated, to add more fingerprints to the radio map (e.g., repeating measurements in rooms where the localization system is mostly failing), remaking the calibration process.
## 4 Experimental Verification
### Model Training and Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Methods**} & \multicolumn{2}{c}{**The Proposed Method**} & **PP** & **EFDD** & **FSDD** & **SSI-COV** & **SSI-DATA** \\ \hline \multirow{4}{*}{Excitation spectrum 1 (\%)} & 1 st & 0 & 7.63 & 3.56 & 3.56 & 8.73 & 3.56 \\  & 2 nd & 0 & \(-\)1.84 & \(-\)1.36 & \(-\)0.41 & \(-\)1.26 & 4.41 \\  & 3 rd & 0 & \(-\)0.06 & \(-\)0.35 & 2.8 & 1.7 & 0.68 \\  & 4 th & \(-\)0.21 & 0.18 & 0.58 & \(-\)0.17 & 0.23 & 0.50 \\  & 5 th & 0 & 0.31 & 0.12 & 0 & 0.08 & \(-\)1.27 \\ \hline \multirow{4}{*}{Excitation spectrum 2 (\%)} & 1 st & 0 & \(-\)1.05 & 0.06 & 3.56 & - & - \\  & 2 nd & 0 & 0.82 & \(-\)0.08 & 0.31 & 0.69 & 7.11 \\  & 3 rd & 0 & 0.94 & 0.46 & 1.14 & 0.62 & 1.09 \\  & 4 th & \(-\)0.21 & 0.21 & 0.18 & \(-\)0.065 & 0.036 & 0 \\  & 5 th & 0 & 0.37 & \(-\)0.88 & 1.53 & \(-\)0.14 & \(-\)0.52 \\ \hline \multirow{4}{*}{Excitation spectrum 3 (\%)} & 1 st & 0 & - & - & - & - \\  & 2 nd & 0 & 1.39 & \(-\)0.08 & 0.21 & \(-\)2.84 & 10.2 \\ \cline{1-1}  & 3 rd & 0 & 1.72 & 0.87 & 1.69 & 1.06 & 1.51 \\ \cline{1-1}  & 4 th & \(-\)0.21 & 1.26 & 0.18 & 0.15 & 0.23 & 0.21 \\ \cline{1-1}  & 5 th & 0 & 0.37 & 1.01 & 2.02 & \(-\)0.05 & \(-\)0.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The error of different methods for identifying the beam. Based on the analysis results (Figure 15b), the relative errors of the proposed method under white and non-white noises excitation are analyzed, as in Table 4. The results of the proposed method for identifying the rudder surface under different noise excitation are consistent, which shows that the anti-noise interference ability of the method is strong, and it is not limited to the excitation type. Compared with the analysis results, the identification errors of the first four frequencies are all less than 0.2%, so the proposed method has high identification accuracy.
# Hybrid Dilated and Recursive Recurrent Convolution Network for Time-Domain Speech Enhancement
## 3 Model Description
### Recursive Recurrent Speech Enhancement Network (RRSENet)  
The learning process of noisy speech to clean speech can be considered as a sequence learning, where each stage represents the intermediate output of a stage. Therefore, the network can be trained in the same way as a RNN. A GRU module is introduced in RLBlock to form recursive recurrent Block (RRBlock), as shown in Figure 4. Through the RRBlock, the feature dependencies of different stages can be propagated to facilitate the noise removal.  
Compared with RLBlock, RRBlock adds a GRU module before the encoder. The GRU module has a convolution layer, the frame length of input speech is 2048 points, the kernel size of this layer is 11, and the stride is 2. The convolution layer increases the number of channels from 2 to 16, and reduces the input speech frame to 1024 points. The input and output of the first convolution layer in the encoder remains the same in the number of channels and speech frame length, the output is \(16\times 1024\), and the others are consistent with RLBlock. The network trained recursively by using the RRBlock module is named the recursive recurrent speech enhancement network (RRSENet), as shown in Figure 4.  
Figure 3: (**a**) RLSENet: recursive learning speech enhancement network, (**b**) RLBlock: recursive learning block.  
The output of each stage \(t\) in RRSENet can be defined as:  
\[x^{t-0.5}=f_{c}(x^{t-1},y) \tag{4}\]  
\[s^{t}=f_{recurrent}(s^{t-1},x^{t-0.5}) \tag{5}\]  
\[x^{t}=f_{de}(f_{hdm}(f_{en}(s^{t}))) \tag{6}\]  
where, \(f_{c}\) concatenates the output of the previous stage to the original noise signal on the channel, \(f_{recurrent}\) is the loop layer, which takes the intermediate state \(s^{t-1}\) and \(x^{t-0.5}\) as input to the stage \(t-1\), and the loop layer is able to regulate the information flow. Then the intermediate state \(s^{t}\) is input to the encoder \(f_{en}\) for feature extraction of speech information, and then the enhanced speech frames are output after \(f_{hdm}\) and \(f_{de}\).
# Effects of Phase Shift Errors in Recurrence Plot for Rotating Machinery Fault Diagnosis
## 3 Results
### Smoothing Signal
The CRP quantifications parameters for the W-50 and W-30 smoothing are reported in Table 3. It was found that the displacement of the peak maximum from the initial position was six points for the W-50 smooth and four points for the W-30 smooth. This corresponds to the phase shift as a result of the smoothing. The biggest change was found for the length of the longest diagonal line, with a 10.6% difference between the two smoothing processes. The length of the longest vertical line had a difference of about 3% in the two cases. The other parameters remained practically constant. According to the results, it is evident that slight shift phase variations affect the signal precision. Figure 11: Recurrence plot for each peak analyzed with a threshold distance of \(\epsilon=0.10\). (**a**) Gaussian peak; (**b**) smoothed peak for a width of 50 points; (**c**) smoothed peak for a width of 30 points. Figure 10: The phase shifts generated by the smoothing process: (**a**) signals after smoothing; (**b**) normalized and filtered peaks. Finally, another analysis was performed to evaluate the smoothing effect when the sample point number decreases and the results are reported in Figure 12. For this analysis, a noisy Gaussian peak was generated with the following characteristics: 1000, 750, 500, 250, 100, 50, and 25 signal points; 500, 375, 250, 125, 50, 25, and 13 points at the half-width of the peak; peak width of 150, 100, 63, 31, 13, 6, and 3 points; white noise in all cases. The noisy Gaussian peak showed in Figure 8a was smoothed with a triangular smooth of width 50, 40, 27, 15, 7, 5, and 3, with three passes and finally normalized. The phase shift effects were considered for each case. Changing from 1000 to 100 point samples, most parameters decreased by the following percentage: \(RR=97\%\), \(ENTR=62\%\), \(L=62\%\), \(L_{max}=10\%\), \(TT=11\%\) and \(V_{max}=10\%\), while \(DET\) and \(LAM\) remained constant. For samples with less than 100 points, all parameters were drastically affected, and for samples from 50 to 25 points, all parameters were close or equal to zero. These results indicate that sample resolution is critical for an accurate RP quantification and at least 100 points per peak must be considered in the data analysis. Figure 12: RQA quantification for the smoothed Gaussian peaks with different sample points. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **W-50** & **W-30** & **Variation (\%)** \\ \hline \(RR\) & 0.6 & 0.6 & 0 \\ \hline \(DET\) & 1.0 & 1.0 & 0 \\ \hline \(ENTR\) & 5.8 & 5.8 & 0 \\ \hline \(LAM\) & 1.0 & 1.0 & 0 \\ \hline \(L\) & 176.5 & 174.9 & \(-\)0.9 \\ \hline \(L_{max}\) & 435.0 & 389.0 & \(-\)10.6 \\ \hline \(TT\) & 232.1 & 132.6 & 0.2 \\ \hline \(V_{max}\) & 401.0 & 389.0 & \(-\)3.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: RQA quantification for the smoothed Gaussian peaks.
## 1 Introduction  
Frequency response (FR) is a key evaluation criterion for the performance of a microspeaker. Obtaining the FR accurately is of great importance in microspeaker design and practical applications. To measure the FR correctly, finite-size baffles are usually used to shield the front-side radiation of a microspeaker from the effect of the rear-side radiation. Finite-size baffles can significantly influence the FRs of electroacoustic transducers owing to edge diffractions. To obtain the FR of a microspeaker precisely from baffle measurements, the diffraction effects of the measuring baffle should be known beforehand. To standardize the measurement condition for microspeaker industry, the International Electrotechnical Commission (IEC) has specified a standard micro-baffle (SMB) for microspeaker measurements [1], but no further information about the diffraction effects of the SMB has been provided. Therefore, it will be of great instructive value to quantitatively evaluate the SMB diffraction effects for the microspeaker industry.  
Several approaches have been established to investigate the edge diffraction problem [2], such as the Kirchhoff diffraction approximation [3, 4], geometrical theory of diffraction (GTD) [5, 6, 7], Hadden-Pierce solution [8, 9], and Biot-Tolstoy-Medwin (BTM) technique [10, 11, 12]. In the electroacoustic domain, the diffraction effects of the edges of loudspeaker cabinets or measuring baffles have been discussed for decades based on these analytical solutions [13, 14]. The BTM technique is of particular interest because it can be digitalized by a time-sampling technique and applied to first- and higher-order diffractions [15, 16]. The BTM technique is a time-domain solution for edge diffraction originating from the work by [PERSON] and [PERSON] in 1957, where a closed-form impulse response solution for an infinite rigid wedge was presented [10]. [PERSON] et al. extended it to the diffraction problem of finite wedges with a concept of \"discrete Huygens interpretation\" by placing a series of secondary sources along the ridge of the wedge [12]. Based on [PERSON]'s work, [PERSON] et al. improved the BTM technique by deriving analytical directivity functions for the secondary sources and obtained more accurate solutions [17].  
In this study, the SMB diffraction effects on the FRs of microspeakers were investigated based on the extended BTM technique. Two different cases with and without the consideration of the backward diffraction of the SMB were studied, which correspond to the different measurement conditions of microspeaker drivers and closed-box microspeaker modules. Experiments were conducted and showed validity. Based on the theoretical analysis and numerical calculations, normalized FR curves characterizing the SMB diffraction effects on the FR measurements of microspeakers are presented. With the normalized FR curves, one can compensate the FR of a microspeaker measured on the SMB to obtain the results measured on an ideal infinite baffle, and eliminate the diffraction effects caused by the SMB.
# BDCOA: Wavefront Aberration Compensation Using Improved Swarm Intelligence for FSO Communication
## 3 Simulation Results
### Discussion  
This section provides a discussion about the BDCOA-based WA correction in SLAO for FSO communication. The developed BDCOA is analyzed by using different optimization approaches such as MOA, POA, and COA with different population sizes of 20, 30, 40, and 50. This analysis shows that BDCOA outperforms MOA, POA, and COA. For example, the RMS of BDCOA at 500 iterations is 0.12, which is less than MOA, POA, and COA. The developed BDCOA is additionally compared with some existing studies focused on SA-SPGD [27], AMFSPGD [28], and OBAFS [29] for two different scenarios. The reason for the existing studies' poor performance is that they fail to perform effective phase compensation and WA correction in SLAO. The proposed BDCOA discovers the optimum control signals for facilitating phase compensation, which reduces phase distortion and improves the FSO. The integration of BM and DMS in BDCOA are used to avoid the suboptimal solutions and enhance the global searching efficiency during the selection of optimum control signals for DM. This is beneficial for obtaining the WA correction to augment the received signal quality. The decrement in the phase distortion by using the BDCOA for DM reduces the probability of bit errors and further enhances the coupling efficiency. The comparison confirms that BDCOA outperforms the existing examples. BDCOA obtains a high performance over the iterations, confirming its reliability in maintaining the optimum performance in distinct scenarios i.e., a time-varying phenomenon. This confirms that BDCOA is suitable in handling the time-varying turbulence across different wavelengths.
Moving towards the integration of empirical correlations with machine learning algorithms, one paper investigated the use of the support vector machine algorithm to predict soil compaction properties [31]. This approach aimed at significantly reducing the time and effort required in the laboratory, achieving R\({}^{2}\) values of 0.86 and 0.91 for \(w_{opt}\) and \(\rho_{\text{dmax}}\), respectively. Lastly, in a significant stride towards machine learning models, a novel model called \"ComPara2021\" was proposed [17]. This model, based on machine learning, demonstrated superior performance in predicting soil compaction parameters compared to its contemporaries, proving the value of such models in saving both time and cost. Another form of artificial intelligence is genetic programming approaches such as gene expression programming (GEP) and multigene expression programming (MEP). GEP and MEP are advantageous frameworks that integrate the benefits of both genetic programming and genetic algorithms, enabling them to evaluate more complex functions that express the relationship between input and output data [32,33]. The GEP and MEP models have notably demonstrated their effectiveness in predicting a variety of parameters related to compacted soil [15,34,35]. The multivariate adaptive regression splines (MARS) technique, a form of artificial intelligence, offers potential benefits to geotechnical engineers. Besides being adept at tackling prediction problems, MARS can identify key input parameters that significantly influence output parameters. It also allows for the exploration of intricate nonlinear relationships between a response variable and various predictive variables--a crucial aspect in analyzing and designing soil compaction parameters. This technique has seen successful implementations in diverse fields of geotechnical engineering, including ultimate pile bearing capacity [36], the elastic modulus of rocks [37], Slope reliability analysis [38], the compressive strength of soil [39], liquefaction [40], settlement prediction [41], and penetration resistance in clay [42]. Yet, despite its proven effectiveness in these areas, its application in compaction-related studies remains surprisingly limited. In this research, MARS is utilized to estimate the optimal moisture content (\(w_{opt}\)) and maximum dry density (\(\rho_{\text{dmax}}\)) of compacted soil. MARS addresses the limitations of the multiple linear regression (MLR) method, as it does not necessitate an a priori form of equation between dependent and independent variables and can precisely capture complex and nonlinear relationships between inputs and outputs [43]. Unlike techniqueslike the ANN, which often operate as a \"black box,\" MARS can provide an explicit equation linking inputs and outputs [44; 45; 46]. This transparency has led to its frequent application in modeling intricate engineering issues. Finally, MARS successfully mitigates one of the key drawbacks of GEP and MEP, which tend to produce highly nonlinear mathematical relations between input and output data [46].
# COSMIC-2 Mission Summary at Three Years in Orbit
## 2 Mission Status and Milestones
### Payloads  
The primary TGRS payload tracks dual frequency GNSS pseudorange and carrier phase from two pairs of POD and RO antennas facing in the forward and anti-velocity directions. Dual frequency pseudorange, carrier phase, and signal to noise ratio measure  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Flight Model**} & **Current Apogee Orbit Altitude,** & **Orbit Adjustment Dates** \\  & **Inclination, RAAN** & **Orbit Adjustment Dates** \\ \hline
1 & 546 km, 24 deg, 245 deg & 22-07-2019 to 15-08-2019 \\
2 & 547 km, 24 deg, 360 deg & 05-11-2019 to 22-11-2019 and \\  & 21-02-2020 to 23-03-2020 \\
3 & 545 km, 24 deg, 70 deg & 08-06-2020 to 16-07-2020 \\
4 & 542 km, 24 deg, 307 deg & 15-11-2019 to 21-01-2020 and \\  & 543 km, 24 deg, 125 deg & 23-09-2020 to 19-10-2020 \\
6 & 539 km, 24 deg, 184 deg & 10-01-2021 to 03-02-2021 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Current satellite orbit information and adjustment dates (payloads off).  
ments from the POD antennas are used for satellite orbit and clock determination, and to derive TEC, electron density profiles, and scintillation amplitude and phase indices. The RO antennas track dual-frequency open-loop carrier phase, used to derive neutral atmosphere bending angle profiles and associated products. Over the course of the mission the TGRS flight software has been updated to address issues discovered and enhance capabilities. There have been seven updates so far. Their main features and timelines are given in Table 2.  
Two secondary payloads are the Ion Velocity Meter (IVM) and radio frequency beacon (RFB). The IVM measures in-situ plasma density, composition, temperature, and velocity [3; 16]. The tri-band RFB emits signals in the UHF, L, and S bands for reception and processing on the ground to monitor scintillation [4]. Since RFB data must be collected by individual ground stations they are not processed centrally and have thus not been part of the program's formal data releases.
# Phonocardiography Signals Compression with Deep Convolutional Autoencoder for Telecare Applications
## 4 Experiments
### Communication Link Quality Issues  
Besides the segment length, CR, and the fixed-point factors, we further consider the impact of communication link quality on the resulting PRD. We simply model the link quality using the artificially induced bit error rate (BER) from the communication link and compare our proposed method with the conventional DCT method. The evaluation results can be found in Table 7. To have a fair comparison, we set the CR as 15 for the DCT method, so that the resulting PRD is close to 5% in the simulation. As shown in the last row in Table 7, when there is no transmission error, i.e., BER = 0, the DCT method could achieve PRD of 5.48% at CR of 15 while our method could attain PRD of 4.792% at CR of 32. We can observe that the resulting PRD with the DCT compression method exhibits a significant drop when the value of BER is higher than \(10^{-3}\); however, our proposed method shows the smooth performance when the communication link quality becomes noisier. Similar results could also be observed from the QS index. This could imply that we method has a better immunity against transmission errors. Note that the CR for the DCT and our methods are different in Table 7. Thus, we further consider a performance metric \"quality score\" (QS), which is the ratio between the CR and the PRD, i.e., QS = CR/PRD. The QS index is useful when the values of CR for different compression methods are different [44]. When ignoring communication errors, our method could reach a little lower PRD and a significantly higher CR than the DCT-based compression method. \begin{table}
\begin{tabular}{l c c c c} \hline \hline CR & 27 & 30 & 32 & 36 \\ Length for each Compressed Segment & 222 & 200 & 187 & 166 \\ Epoch & 389 & 368 & 362 & 345 \\ PRD & 4.416\% & 4.540\% & 4.601\% & 4.972\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: CR Evaluation at a segment length of 6000. Figure 4: Compressed signals ((**a**) for the time duration of 3 s (segment length of 6000) and (**c**) for the time duration of 4 s (segment length of 8000)) and comparisons between original data with the reconstructed signal ((**b**) for the time duration of 3 s (segment length of 6000) and (**d**) for the time duration of 4 s (segment length of 8000)). Zoom-in plots during 0.2 to 0.6 s for (**b**,**d**) are illustrated in (**e**,**f**), respectively. That is, our proposed method exhibits a higher QS than the conventional DCT method could achieve. However, the computational cost of the DCT method is less than our method. \begin{table}
\begin{tabular}{c c c} \hline
**Word-Length** & **Fractional Length** & **PRD(\%)** \\ \hline Floating-32 & - & 4.6015 \\ Fixed-20 & 17 & 4.6031 \\ Fixed-16 & 13 & 4.6127 \\ Fixed-12 & 9 & 4.6437 \\ Fixed-11 & 8 & 4.6639 \\ Fixed-10 & 7 & 4.7923 \\ Fixed-9 & 6 & 5.2162 \\ Fixed-8 & 5 & 6.1334 \\ \hline \end{tabular}
\end{table} 
Table 6: Word-length evaluation.