esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Method
### Hardware Implementation and PIL Architecture
However, the Linux operating system is not designed to work with real-time applications and predictive processing [44] unless the RT-Preempt patch is installed on the Raspberry Pi, thus creating a para-virtualized design which allows to use a micro-kernel running in parallel to the standard Linux kernel [45]. To this extent, at this introductory stage, the objective of the work was to evaluate the feasibility of implementing the Motion Planning and Motion Control custom algorithms using low-cost equipment without considering real time constraints, but effectively assessing the overall algorithm efficiency, comparing I/O signals in MIL/PIL configurations. On average, the processor worked for 50% of its total computing power with a maximum RAM memory allocation of about 10%. In accordance with the signals' setup that the aforementioned platform exchanges with the host PC, the numeric data type of each signal has been chosen in order  
Figure 6: PIL test architecture setup, exploiting UDP/IP connection between host PC and real-time target hardware: (1) Speedgoat Baseline Real-Time target machine; (2) target PC monitor; (3) host PC. Figure 7: PIL test architecture setup, exploiting UDP/IP connection between host PC and target hardware: (1) Raspberry Pi 4B board; (2) host PC. to give a sufficient precision for each computational tasks required by the application flashed on the board. Subsequently, the PIL architecture has been configured for the Speedgoat Baseline real-time target machine with hardware specification shown in Table 2. Thus, the model containing the motion planning and motion control running on the Speedgoat machine was validated considering real-time constraints. Motion planning takes a relatively large computational time to generate the search tree expansion and select the path with the shortest distance to the goal position. On the other hand, the motion control must be executed in fast time loops to obtain state feedback, more precise actuator control and trajectory tracking. To this extent, in our implementation, motion planning component is configured to run every 50 ms, while motion controller runs at higher sample rate, i.e., 10 ms, the same sample rate defined for MIL architecture. On average the Motion Planner is executed with a Target Execution Time (TET) of 0.043 s while the Motion Control every 0.008 s.
## 5 Experiments and Results  
To demonstrate the effectiveness of the proposed approach and our design choice for the new FM-ResNet unit, we train encoder-decoder based architecture using 3D Pre-ResNet without and with VAE regularization as well as the proposed FM-Pre-ResNet without and with VAE regularization. Table 2 summarizes an average whole heart segmentation results on CT and MRI images. On CT images, the 3D Pre-ResNet network achieves average WHS segmentation results for DSC, JI, SD, and HD of 87.11%, 80.16%, 1.71 mm, and 24.44 mm, respectively. The addition of VAE at Pre-ResNet segmentation encoders' endpoint improve DSC, JI, SD and HD values for 2.12%, 1.0%, 0.2039 mm, and 2.704 mm, respectively. The 3D FM-Pre-ResNet network achieves DSC, JI, SD, and HD values of 90.03%, 82.14%, 1.43 mm, and 18.82 mm, respectively. Compared to the 3D Pre-ResNet, it achieves improvement in DSC, JI, SD, and HD values of 90.03%, 82.14%, 1.432 mm, and 18.82 mm, respectively. Compared to the 3D Pre-ResNet, it achieves improvement in DSC, JI, SD, and HD values of 2.29%, 1.98%, 0.2789 mm, and 56,307 mm, which means that the proposed FM-PreResNet unit significantly improves significantly. Moreover, the highest DSC, JI, SD, and HD are achieved using 3D FM-Pre-ResNet + VAE network and report values of 89.50%, 80.44%, 1.8599 mm and \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & \multicolumn{4}{c}{**CT**} & \multicolumn{4}{c}{**MRI**} \\ \hline
**Architecture** & **DSC** & **JI** & **SD** & **HD** & **DSC** & **JI** & **SD** & **HD** \\ \hline
3D Pre-ResNet & 0.8711 & 0.8016 & 1.7110 & 24.4421 & 0.8306 & 0.7554 & 5.9201 & 42.5578 \\  & \(\pm\) 0.0721 & \(\pm\) 0.0609 & \(\pm\) 0.4991 & \(\pm\) 17.8355 & \(\pm\) 0.9254 & \(\pm\) 0.0581 & \(\pm\) 0.4421 & \(\pm\) 21.6645 \\ \hline
3D Pre-ResNet + VAE & 0.8923 & 0.8116 & 1.5071 & 21.7381 & 0.8534 & 0.7545 & 3.7701 & 38.8812 \\  & \(\pm\) 0.0209 & \(\pm\) 0.0358 & \(\pm\) 1.407 & \(\pm\) 16.8850 & \(\pm\) 0.0441 & \(\pm\) 0.0583 & \(\pm\) 0.9100 & \(\pm\) 23.5812 \\ \hline
3D FM-Pre-ResNet & 0.9003 & 0.8214 & 1.4321 & 18.8114 & 0.8840 & 0.7855 & 2.4558 & 32.0451 \\  & \(\pm\) 0.0148 & \(\pm\) 0.0271 & \(\pm\) 0.0518 & \(\pm\) 12.4032 & \(\pm\) 0.0701 & \(\pm\) 0.0455 & \(\pm\) 0.7956 & \(\pm\) 17.5508 \\ \hline
**3D FM-Pre-ResNet + VAE** & **0.9039** & **0.8224** & **1.1093** & **1.1093** & **0.8950** & **0.8044** & **1.8599** & **25.6558** \\  & \(\pm\) **0.0517** & \(\pm\) **0.0571** & \(\pm\) **0.0215** & \(\pm\) **12.3737** & \(\pm\) **0.0215** & \(\pm\) **0.0757** & \(\pm\) **0.6740** & \(\pm\) **16.4001** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of an average WHS results in terms of DSC, JI, SD, and HD on different architectures for the CT and MRI testing dataset.
## 4 Time-Domain and Time-Frequency-Domain Feature Extraction Based on the LMD Method  
### LMD Method  
Due to the dynamic working conditions and environmental factors, the vibration signals obtained from the field monitoring are usually nonstationary. The analysis tools for nonstationary signals include wavelet decomposition, the EMD method, and EMD-like methods. LMD [35] adaptively decomposes a series of components that can describe the characteristics of the signal by multiple iterations based on the local characteristics of the signal. Compared with EMD, LMD uses a smoothing method to obtain the local mean function and the envelope estimation function, which can effectively avoid modal mixing, end-point effects, and negative frequency problems of EMD [57]. In this regard, this paper uses LMD to decompose the data. LMD essentially separates the pure frequency modulation signal and the envelope signal from the original signal and multiplies these signals to obtain an instantaneous frequency with the physical significance of PFs; iterations are performed until all PFs are separated out. Subsequently, the time-frequency distribution of the original signal can be obtained. For any signal \(x(t)\), the decomposition process is as follows [35]:  
1. All local extremum points \(N_{i}\) of the original signal \(x(t)\) are calculated and the average \(m_{i}\) of all adjacent local extremum points \(N_{i}\) and \(N_{i+1}\) is derived: \[m_{i}=\frac{N_{i}+N_{i+1}}{2}\] (1)  
\[\begin{array}{c}u_{1}(t)=x(t)-PF_{1}(t)\\ u_{2}(t)=u_{1}(t)-PF_{2}(t)\\ \vdots\\ u_{k}(t)=u_{k-1}(t)-PF_{k}(t)\end{array} \tag{10}\]  
So far, the original signal \(x(t)\) is decomposed into \(k\) component signals and a residual function \(u_{k}(t)\):  
\[x(t)=\sum_{kk=1}^{k}PF_{kk}(t)+u_{k}(t) \tag{11}\]  
As explained above, the decomposition step of LMD is a multi-loop iterative process, first, a pure frequency modulation signal is obtained by iterating through the local extreme of the original signal, and then a PF component is obtained by further iteration, and so on, until all PFs are separated out. The decomposition flow chart of LMD is shown in Figure 4. FM signal in the figure represents the frequency modulation signal.
## 3 Results  
This section comprehends two experiments carried out to evaluate the feasibility and reliability of the interferometric system for satellite tracking. The first one (Section 3.1) compares the quality of the observations of two channels with different signal-to-noise ratio (SNR) levels. Its objective is to assess whether the system is capable of tracking weak signals well below the noise level. The latter (Section 3.2) uses the interferometric observations in order to experimentally retrieve the orbit of the observed satellite, showing the consistency and feasibility of the technique.
# Vertical Correlation and Array Gain Analysis for Vertical Line Array in Deep Water
## 4 Experimental Results
### Correlation and Array Gain in the Shadow Zone
The auxiliary engine of the ship was still working when the ship was stopped in the measurement points. At point A with the horizontal range of 2.4 km, the effect of the auxiliary noise on the marine environment noise cannot be ignored. At point B with 10.3 km, the auxiliary engine noise has a slight effect. We compare the noise correlation between two measurement points, as presented in Figure 25. The noise at point B is basically uncorrelated, and the noise correlation coefficient at point A fluctuates around 0.2. Considering the whole experiment lasting for only two hours, so the changes in the marine environment caused by the time are negligible. The small difference in noise correlation is caused by the auxiliary engine. According to Figures 15a and 23a, the average noise gain of point A and point B is \(-0.678\) dB and \(-0.302\) dB, respectively, with a difference of only 0.376 dB. Therefore, we can conclude that the auxiliary engine noise has little effect on the array gain and can be ignored.
# Experimental and Numerical Investigation of the Mesoscale Size Effect in Notched Woven Composites
## 2 Materials and Methods  
### Material and Mechanical Testing  
The material system studied is a carbon fiber-reinforced epoxy prepreg from Microtex Composite (GG630 T125 12K, 37% resin). The matrix is an E3-150 resin, reinforced with a 2 \(\times\) 2 full carbon fiber fabric. A laminate with a [0/90/0] stacking sequence was manufactured by hand layup and autoclave curing, with an average thickness of 2 mm. Rectangular specimens measuring 250 (l) by 24 (w) mm were cut from the manufactured plate using a CNC machine, and holes were drilled into them. Three different values of hole diameter D were considered: 2, 4, and 8 mm. The central 4-mm diameter was selected to have a width-to-diameter ratio of 6, the value prescribed by open-hole tensile strength testing standard ASTM D5766 [21]. The other two were derived from the first with a factor of 2, picked to remain at a scale where considering the mesoscale analysis undertaken is most meaningful. An overview of the specimen dimensions and nomenclature is given in Table 1.  
Tensile tests were conducted with an Instron 8001 hydraulic universal testing machine with a procedure adapted from the ASTM D3039 standard [22], at a quasi-static displacement rate of 2 mm/min. At least three specimens for each configuration were tested and mean values were considered. The free span of the specimen between the grips measured 145 mm. All tests were recorded with an 8.9 MP stereo camera system, imaging the entire specimen. In order to perform DIC, the specimens were airbrushed with a black-on-white speckle pattern (Figure 1b). Before painting, pictures of the bare specimens on the test fixture were taken (Figure 1a), to be used later to build the FEM model. The speckle images were processed using Correlated Solution's VIC 3D 9.1.6 DIC software, selecting an optimal subset size of 31 px and a step between the subsets of 6 px, which is less than 1/3 of the subset size in accordance with [23]. The load was measured with a 100 kN load cell, while DIC was used to monitor displacement and strains on the specimen surface.
###### Abstract  
The purpose of this study was to investigate the effect of silane treatment of a resin-coated resin-based composite (RBC) base material on its microtensile bond strength (uTBS) to a computer aided-design/computer-aided manufacturing (CAD/CAM) ceramic restorative material. RBC blocks (4 mm \(\times\) 7 mm \(\times\) 10 mm) were prepared, and the adherented surfaces were prepared as follows: no resin coating + no silane treatment (Group I), no resin coating + silane treatment (Group II), resin coating only (Group III), and resin coating + silane treatment (Group IV). The resin coating was applied with Clearfil SE Bond and flowable RBC to the adherented surface. Each treated sample was bonded to a ceramic block using Panavia V5. After storage in 37 \({}^{\circ}\)C water for 1 week, microspecimens were fabricated, and the uTBS was tested. The failure mode of each specimen was determined using stereomicroscopy and scanning electron microscopy. The uTBS of Group II (20.2 \(\pm\) 4.0 MPa) was not significantly different from that of Group I (17.6 \(\pm\) 5.9 MPa) (\(p\) > 0.05), and the uTBS of Group I was significantly smaller than that of Group III (21.3 \(\pm\) 7.2 MPa) (\(p\) < 0.05). The uTBS of Group IV (24.9 \(\pm\) 3.8 MPa) was significantly greater than that of Group III (\(p\) < 0.05). Our findings indicate that silane treatment of the resin-coated surface can enhance the uTBS. Silane treatment of RBC base material was not effective when prepared without resin coating.  
resin coating; silane; surface treatment; microtensile bond strength; base material; CAD/CAM; indirect ceramic restoration +
Footnote †: journal: Applied
## 1 Introduction
[17] constructed a simplified electromechanical coupling model suitable for online fault diagnosis by obtaining the time-varying meshing stiffness under different tooth breakage fault levels, and elucidated the influence of tooth breakage fault severity on key dynamic characteristics, such as the stability of the system's electromagnetic torque output, transmission efficiency, and vibration intensity. In summary, the dynamic response of motors under open-phase fault-tolerant control and the dynamic response of electric drive transmission systems under various fault conditions have been extensively studied. However, the influence of self-fault-tolerant control on the dynamic response characteristics of electric drive systems, such as phase current, electromagnetic torque, and gear mesh forces, under open-phase faults conditions, and the corresponding evolutionary patterns, have not yet been deeply investigated [18]. This lack of research may lead to the specific excitations generated during fault-tolerant control, masking the true vibration characteristics of gear systems, thereby undermining the effectiveness of vibration-based operation condition monitoring and fault diagnosis methods. Therefore, based on the core principles of electromagnetism, [PERSON]'s magnetic circuit law and [PERSON]'s Second Law of Motion, this paper establishes an electromechanical coupling model. This model integrates an EMN model of a DTP-PMSM with the dynamic model of a helical planetary gear transmission system to investigate the vibration characteristics of electric drive transmission systems under self-fault-tolerant control.
# Analyses of GLONASS and GPS+GLONASS Precise Positioning Performance in Different Latitude Regions
## 2 Evaluation Methods
### Accuracy Assessment  
The station coordinates and ZTDs provided by the IGS were used as references to assess the accuracy of DD network solutions and PPP solutions. The Root Mean Square Error (RMSE) of daily coordinate estimates was used as the accuracy assessment indicator,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Processing Strategies** \\ \hline Signal selection & L1 and L2 \\ Sampling rate & 30 s \\ Elevation mask & 3\({}^{\circ}\) \\ Precise products & CODE final precise products \\ Weight for observations & Elevation-dependent weighting \\ Receiver clock & Estimated as white noise \\ Ionosphere & IF combination \\ Troposphere & GPT2, VMF1 \\ Ocean tidal loading & FES2004 \\ DCB & CODE DDs monthly files \\ Antenna center offset and variation & ISG14.txt \\ Processing mode & PPP static in 24 h window \\ Strategy & Forward extended Kalman filter \\ \hline \hline \end{tabular}
\end{table}
Table 1: The processing strategies of PPP.  
Figure 1: DD network processing scheme.  
\[\text{RMSE}_{\text{COORDINATE}}=\sqrt{\frac{\left(\text{COORDINATE}_{\text{estimated}}- \text{COORDINATE}_{\text{IGS}}\right)^{2}}{\text{n}}} \tag{3}\]  
where n is the total number of daily coordinate estimates.  
The tropospheric products from IGS are sampled every 300 s, while, in this paper, the ZTDs estimated interval by DD strategy was one hour, and 30 s by PPP strategy. Thus, the tropospheric estimates or the IGS products needed to be resampled to match the sampling intervals. The current study resampled the IGS tropospheric products to 1 h and the PPP tropospheric to 300 s to coincide with IGS products. The RMSE of ZTD estimates is,  
\[\text{RMSE}_{\text{ZTD}}=\sqrt{\frac{\left(\text{ZTD}_{\text{estimated}}- \text{ZTD}_{\text{IGS}}\right)^{2}}{\text{n}}} \tag{4}\]  
where n is the total number of available ZTD estimates after resampling.
###### Abstract  
This study addresses a lexical ambiguity issue in Sesotho sa Leboa that arises from terms with various meanings, often known as homonyms or polysemous words. When compared to, for instance, European languages, this lexical ambiguity in Sesotho sa Leboa causes computational semantic problems in NLP when trying to identify the lexicon of a language. In other words, it is challenging to determine the proper lexical category and sense of words due to this ambiguity problem. In order to address the issue of polysemy in the Sesotho sa Leboa language, this study set out to create a word sense discrimination (WSD) scheme using a corpus-based hybrid transformer-based architecture and deep learning models. Additionally, the performance of baseline and improved machine learning models for a sequence-based natural language processing (NLP) task was assessed and compared. The baseline models included RNN-LSTM, BiGRU, LSTMLM, DeBERTa, and DistilBERT, with accuracies of 61%, 79%, 74%, 70%, and 64%, respectively. Among these, BiGRU emerged as the strongest performer, leveraging its bidirectional architecture to achieve the highest baseline accuracy. Transformer-based models, such as DeBERTa and DistilBERT, demonstrated moderate performance, with the latter prioritizing efficiency at the cost of accuracy. The enhanced results explored optimization techniques and hybrid model architectures to improve performance. BiGRU, optimized with ADAM, achieved an accuracy of 84%, while BiGRU with attention mechanisms further improved to 85%, showcasing the effectiveness of these enhancements. Hybrid models integrating BiGRU with transformer architectures demonstrated varying results. BiGRU + DeBERTa and BiGRU + ALBERT achieved the highest accuracies of 85% and 84%, respectively, highlighting the complementary strengths of bidirectional context modeling and advanced transformer-based contextual understanding. Conversely, the Hybrid BiGRU + RoBERTa model underperformed, with an accuracy of 70%, indicating potential mismatches in model synergy. These findings highlight how crucial hybridization and optimization are to reaching cutting-edge performance on NLP tasks. According to this study's findings, the most promising approaches for fusing accuracy and efficiency are attention-based BiGRU and BiGRU-transformer hybrids, especially those that incorporate DeBERTa and ALBERT. To further improve speed, future research should concentrate on exploring task-specific optimizations and improving hybrid model integration.  
**Keywords:** natural language processing (NLP); RNN-LSTM; BiGRU; LSTMLM; DeBERTa; DistilBERT; optimization; attention mechanism; hybrid models; transformer architectures; contextual understanding; sequence modeling; ADAM optimizer; bidirectional neural networks; machine learning; performance evaluation
# Multi-Scale Factor Image Super-Resolution Algorithm with Information Distillation Network
###### Abstract  
Deep convolutional neural networks with strong expressive ability have achieved impressive performances in single-image super-resolution algorithms. However, excessive convolutions usually consume high computational cost, which limits the application of super-resolution technology in low computing power devices. Besides, super-resolution of arbitrary scale factor has been ignored for a long time. Most previous researchers have trained a specific network model separately for each factor, and taken the super-resolution of several integer scale factors into consideration. In this paper, we put forward a multi-scale factor network (MFN), which dynamically predicts the weights of the upscale filter by taking the scale factor as input, and generates HR images with corresponding scale factors from the weights. This method is suitable for arbitrary scale factors (integer or non-integer). In addition, we use an information distillation structure to gradually extract multi-scale spatial features. Extensive experiments suggest that the proposed method performs favorably against the state-of-the-art SR algorithms in term of visual quality, PSNR/SSIM evaluation indicators, and model parameters.  
single image super-resolution; information distillation; multi-scale factor network Article
## 3 Results
### Experimental Measurements
#### 3.3.3 Preliminary Evaluation of Calibration Range  
The primary function of the holder is to enable the positioning of the magnet in the vertical direction. The measurements (results in Figure 18) were split into two series for different loadings \(F\) and a constant \(M_{c}\) = 5 Nm. In the second series (curves with smaller markers in the drawing), the increments of adjustment bolt torque were reduced to obtain better positioning than in the first series (larger increments of adjustment bolt torque results in an increase in the magnet inclination angle). The different starting points for (\(M_{r}\) = 0 Nm) are caused by the deformation of the holder under external load \(F\). In each test, in both series, the displacement of the magnet was proportional to the torque \(M_{r}\) and almost linear. The disturbances are probably caused by inaccurate screw tension. Tightening subsequent bolts causes a drop in the tension of previously tightened bolts. However, in the experiment, the previously tensioned screws were not readjusted. A comparison of the graphs in Figure 18 of both series clearly shows that reducing the increments of torque \(M_{r}\) provides better adjustment. Finally, the obtained positioning range was sufficiently higher than the range required in the THz undulator (\(\pm\)0.15 mm).  
Figure 17: Displacement of magnet under external load \(F\): (**a**) with “push-in” magnet; (**b**) with “pull-out” magnet.