esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
Data relative to values E, L, A, and B for the two groups and the four subgroups are shown in Tables 1-5. According to the data assessed, no statistically significant difference was found between the 40 samples at T0. The 20 samples of group A, exposed to the colorant agent, underwent a color change from T0 to T1, whereas the 20 samples exposed to the physiologic solution did not undergo any significant color change. As regards the E value, a significant increase was found at T1 in the control group, in the ozonized gel group, in the ozonized oil group, as well as at any time in the group submitted to the ozonized oil with regenerative substances. As regards the L value, no significant changes were found at any time between the subgroup, except for the ozonized gel group at T0. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & & **E** & **L** & **A** & **B** \\ \hline Group A—Corsodyl & T0 & 4.61 (3.7; 5.8) & \(-\)4.08 (\(-\)5.6; 4) & \(-\)0.73 (\(-\)1.8; \(-\)0.6) & \(-\)1.04 (\(-\)2.5; \(-\)0.1) \\ \hline  & T1 & 5.99 (5.2; 6.9) & \(-\)5.93 (\(-\)6.9; \(-\)5.2) & \(-\)2.12 (\(-\)3.8; \(-\)1.2) & 0.61 (\(-\)1.6; 2) \\ \hline  & T2 & 4.88 (3.8; 5.8) & \(-\)4.77 (\(-\)5.8; \(-\)3.7) & \(-\)0.39 (\(-\)1.9; 2) & \(-\)1.23 (\(-\)2.5; 0) \\ \hline Group B—Control & T0 & 4.25 (3.6; 5.1) & \(-\)4.21 (\(-\)4.9; \(-\)3.6) & 0.31 (\(-\)1.4; 1.1) & \(-\)0.85 (\(-\)1.8; 1.3) \\ \hline  & T1 & 4.61 (3.8; 5.1) & \(-\)4.61 (\(-\)5.1; \(-\)3.8) & \(-\)0.17 (\(-\)1.3; 1.3) & \(-\)0.62 (\(-\)1.4; 0.5) \\ \hline  & T2 & 5.27 (4.8; 6.1) & \(-\)5.27 (\(-\)6.1; \(-\)4.8) & 0.39 (\(-\)0.5; 1.3) & \(-\)0.04 (\(-\)1.5; 1.3) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Median (min, max) for the values E, L, A, and B for the two groups at each experimental time. \begin{table}
\begin{tabular}{l c c c c c c} \hline  & **Mean** & **SD** & **Min** & **Mdn** & **Max** & **Significance \({}^{*}\)** \\ \hline Control T0 & \(-\)0.32 & 0.11 & \(-\)0.40 & \(-\)0.40 & \(-\)0.20 & A \\ Control T1 & \(-\)2.38 & 1.07 & \(-\)3.80 & \(-\)2.00 & \(-\)1.30 & A \\ Control T2 & \(-\)0.34 & 1.40 & \(-\)1.60 & \(-\)0.80 & 2.00 & A \\ \hline Ozonized Gel T0 & \(-\)0.18 & 0.73 & \(-\)1.00 & \(-\)0.20 & 0.60 & A \\ Ozonized Gel T1 & \(-\)2.08 & 0.40 & \(-\)2.60 & \(-\)2.10 & \(-\)1.60 & A \\ Ozonized Gel T2 & \(-\)0.16 & 1.13 & \(-\)1.90 & 0.40 & 0.70 & A \\ \hline Ozonized Oil T0 & \(-\)0.96 & 0.71 & \(-\)1.70 & \(-\)1.30 & \(-\)0.20 & A \\ Ozonized Oil T1 & \(-\)1.96 & 0.61 & \(-\)2.80 & \(-\)1.90 & \(-\)1.30 & A \\ Ozonized Oil T2 & \(-\)0.74 & 0.89 & \(-\)1.80 & \(-\)0.80 & 0.60 & A \\ \hline Ozonized Oil + Regen T1 & \(-\)1.48 & 0.43 & \(-\)1.80 & \(-\)1.70 & \(-\)0.80 & A \\ Ozonized Oil + Regen T2 & \(-\)2.06 & 0.59 & \(-\)2.60 & \(-\)2.10 & \(-\)1.20 & A \\ Ozonized Oil + Regen T3 & \(-\)0.34 & 0.95 & \(-\)1.60 & 0.10 & 0.50 & A \\ \hline \end{tabular} \({}^{*}\) Different letters show a statistically significant difference between the groups (\(p<0.05\)).  
\end{table} 
Table 4: Median (min, max) for the value A for the two groups at each experimental time.
## 3 Simulation Results and Interpretation  
Several simulations were conducted to analyze the impact of multiple targets on the range and azimuth resolution of radar echoes. During these simulations, each time we change the type of clutter as well as the parameters of each clutter for the two scenarios. In  
Figure 11: Block diagram of a radar echo.  
Figure 12: Gabor coefficients’ probability of density function.  
the first scenario, we simulated two radar targets in a resolution cell (dR, d\(\theta\)) with only a variation in distance. The remaining target parameters were kept constant to demonstrate the range resolution of the proposed detector. In the second scenario, we examined how multiple targets affect radar resolution. In all the scenarios, we compared the performance of our proposed method with that of the normal order statistic constant false alarm rate (OS-CFAR) method. The OS-CFAR method, a reliable technique that [PERSON] [24] proposed, is appropriate for nonhomogeneous environments and is based on order statistics. This process involves ordering the cells of the reference window and selecting the content of the kth cell as an estimate of clutter power. To demonstrate the performance of the studied detector, we conducted several Monte Carlo simulations [25] for the different types of clutter in each scenario.
# Enhancing Chatter Stability for Milling Thin-Walled Blades by Designing Non-Uniform Allowance
## 4 Conclusions  
Targeting the issue of chatter in finishing thin-walled aerospace blade, the proposed process optimizes a non-uniform allowance using the sensitivity to process stiffness for improved surface quality control. The machining chatter of thin-walled parts can be mitigated to a great extent, resulting in a reduction of cutting force fluctuations and vibration. This leads to an improvement of surface roughness of the parts and guarantees a smooth cutting process of thin-walled blades. Therefore, the aforementioned findings infer that the milling process of thin-walled parts can be enhanced. The following conclusions can be drawn.  
(1) The non-uniform allowance structure layout demonstrates favorable qualities for achieving a stable state in the cutting process. This is evidenced by the acquisition process of the dynamic acceleration spectrum. The convergence towards a stable state indicates the effectiveness of the non-uniform allowance distribution.  
(2) The implementation of the non-uniform allowance process method leads to a significant increase in the natural frequency, approximately 100 Hz. This increase indirectly reflects an improvement in the workpiece process stiffness. Moreover, the overall frequency exhibits an upward trend while the vibration amplitude decreases.  
(3) An analysis of the processing surface morphology reveals that the non-uniform allowance process generates superior surface quality compared to uniform transition methods. The resulting surface exhibits smoother morphology, devoid of significant vibration patterns. Additionally, the non-uniform allowance process contributes to a reduction in roughness by nearly 35%.  
The method proposed in this paper through the non-uniform allowance distribution compared with the previous workers has the characteristics of more accurate allowance distribution, more convenient calculation, can be combined with numerical algorithms to quickly achieve the design of edge distribution, without the need for finite element software simulation. Therefore, it has a certain reference significance in actual engineering.  
Y.L.: Methodology, investigation, formal analysis and draft writing. W.T.: Project administration, formal analysis, review, and editing. J.Z.: Project administration, formal analysis, review, and editing. F.D.: Supervision, project administration, and review. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (Grant No. 52075451), the Natural Science Basic Research Program of Shaanxi (Program No. 2023-JC-YB-347), the Aeronautical Science Foundation of China (Grant No. 2019 ZE053008).  
All data and materials used or analyzed during the current study are included in this manuscript.  
The authors declare no conflict of interest.
###### Abstract  
This study presents a process for the quantitative investigation of thinning defects occurring in the containment liner plate (CLP) of a nuclear power plant according to various depths with a combined thermal wave signal and image processing in a lock-in thermography (LIT) technique. For that, a plate sample with a size of 300 \(\times\) 300 mm was produced considering the 6 mm thickness applied to an actual CLP. The sample was designed with nine thinning defects on the back side with defect sizes of 40 \(\times\) 40 mm and varying thinning rates from 10% to 90%. LIT experiments were conducted under various modulation frequency conditions, and phase angle data was calculated and evaluated through four-point method processing. The calculated phase angle was correlated with the defect depth. Then, the phase image was binarized by the Otsu algorithm to evaluate defect detection ability and shape. Furthermore, the accuracy of defect depth assessment was evaluated through third-order polynomial curve fitting. The detectability was analyzed by comparing the number of pixels of the thinning defect in the binarized image and the theoretical calculation. Finally, it was concluded that LIT can be applied for fast thinning defect detection and accurate thinning depth evaluation.  
non-destructive testing and evaluation (NDT&E); containment liner plate (CLP); thinning defect; lock-in thermography (LIT); signal and image processing
# Target Speaker Extraction by Fusing Voiceprint Features
## 4 Conclusions  
In this paper, we explored the effectiveness of voiceprint features for the performance enhancement of speech separation models for the target speaker extraction task. Specifically, two types of voiceprint features were explored: MFCCs and d-vector. In order to integrate the voiceprint features into the magnitude spectrograms used by speaker-independent models, three feature fusion methods were proposed, i.e., Tse-FV(DC), Tse-FV(ECC), and Tse-FV(CEC). In addition, to utilize the fused features, a target speech extraction method was proposed for DPCL and PITNet. Experiments on the simulated dataset generated from the LibriSpeech demonstrated that the integration of the voiceprint feature of the target speaker could effectively improve the performances of speaker-independent models.  
Figure 5: The estimated spectrograms and estimation error diagrams of VoiceFilter model. The \(x\)-axis represents the time frame, the \(y\)-axis represents the frequency, and the unit of color scale is dB. (**a**) The estimated magnitude spectrogram (i) and the estimation error (ii) of the VoiceFilter model integrating MFCCs features using DC fusion method. (**b**) The estimated magnitude spectrogram (**i**) and the estimation error (ii) of the VoiceFilter model integrating MFCCs features using ECC fusion method. (**c**) The estimated magnitude spectrogram (**i**) and the estimation error (ii) of the VoiceFilter model integrating MFCCs features using CEC fusion method. (**d**) The estimated magnitude spectrogram (**i**) and the estimation error (ii) of the VoiceFilter model integrating d-vector features using ECC feature fusion method.  
Besides, the performances of the speech separation models using the MFCC features were generally better than those using the d-vector features. Compared with the d-vector, the MFCCs revealed a great advantage in improving the performances of both speaker-independent and speaker-dependent models.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12168152/s1](https://www.mdpi.com/article/10.3390/app12168152/s1)]([https://www.mdpi.com/article/10.3390/app12168152/s1](https://www.mdpi.com/article/10.3390/app12168152/s1)), Table S1: MOS Evaluation: The quality score varied from 1 (WORST) to 5 (BEST).  
Supervision, Y.S.; writing--original draft, S.C.; writing--review and editing, Y.S. and D.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 61972285, in part by the Natural Science Foundation of Shanghai under Grant 19 ZRI461300, in part by the Shanghai Municipal Science and Technology Major Project under Grant 2021 SHZDZX0100, and in part by the Fundamental Research Funds for the Central Universities.  
Not applicable.
## Model
### Boundary and Initial Conditions  
#### 2.3.1 Boundary Condition  
(a) Surface boundary  
During the drilling process, drillers aim to control the hook load at surface in order to adjust the actual weight on bit (WOB) to the desired value. This is achieved by ensuring that the hook load, which is the difference between the buoyant weight of the drill-string in the axial direction and the friction force and nominal WOB, is equivalent to the desired value. The request for the driller to maintain a constant hook load results in the friction force and nominal WOB being viewed as constant values. This, however, is subject to change due to the heterogeneity of rocks and the stick-slip motion of the drill-string, which causes the friction force and nominal WOB to transform interchangeably. The zero WOB is determined by running downward the drill-string at constant speed and setting the pointer on the weight indicator to zero before the bit reaches bottom of the well. However, the ROP may be very small in drilling processes, and partial drill-string may be in static friction state. In this case, the real WOB may be very small. With that in mind, we adopt static friction of drill-string to calculate hook load. The hook load can be determined through an analysis of the drill-string under static conditions where the frictional coefficient is equal to the static friction coefficient and the nominal WOB remains constant throughout subsequent calculations.  
\[F_{top}=\left(G_{t}-WOB-\Gamma_{f,s}\right)+F_{a}\sin(\omega t) \tag{12}\]  
where \(F_{top}\) is the hook load; \(G_{t}\) is the axial component of gravity of the whole drill-string; \(F_{f,s}\) is the total static friction of drill-string; \(F_{a}\) is the amplitude of exciting force; \(\omega\) is the circular frequency of the excitation.  
(b) Bit boundary  
Generally, a vibrator is typically only required when the ROP is low. Whether the ROP is high or low, the drill-string movement will inevitably experience a stick-slip phenomenon during the slide drilling process, which will cause fluctuations in WOB. Varying WOB will result in varying ROP, which in turn affects the WOB. In 1970, Amoco petroleum exploration and development corporation proposed binary drilling rate model according to a great field of data, and used different bit weight exponent and rotate speed exponent in different hardness stratum. When the stratum changes from soft to hard, bit weight exponent changes from small to big, and rotate speed exponent changes from big to small, and the variation trend of penetration rate changes from fast to slow. The drilling rate model has the following form [31]:  
\[ROP=K\cdot WOB^{a}\cdot n^{b}=K^{\prime}\cdot WOB^{a} \tag{13}\]  
where \(n\) is bit rotate speed; \(a\) and \(b\) are \(WOB\) exponent and bit rotate speed exponent, respectively.
## 5 Discussion
### Discussion of Application  
In our method, the shadow angle is not used to calculate the height of the buildings, but is used to calculate the length of the shadows for classifying the buildings into different height levels, according to the obtained shadow lengths. In practice, we suggest choosing a building with a known height as a reference to assist in height classification and obtaining the approximate height of the building.  
In regards to computational complexity and processing time, these depend on threshold selection and computer performance. Most steps, such as shadow detection, RANSAC, etc., are simple and classical, and could be run automatically. It takes less than 3 min for our experiments to be completed, if the proper threshold is set.  
For the availability and quality of image selection, the proposed method is adapted for high spatial resolution images, with large-scale shadows that could distinguish building in different height levels using a visual interpreter. Both high resolution and shadows are essential in our purposed method. The method shows relatively low sensitivity to environmental factors such as different lighting or weather conditions because the problem could be solved by methods of image enhancement and other ways. For example, as the vegetation in winter is too dark to be accurately detected from the Beijing image, we did not extract vegetation in the experiment. Instead, we eliminated the dark seed-blocks because some dark trees were mistaken as shadows, leading to incorrect seed-blocks, while buildings exhibited a brighter value. If the images is captured under low light conditions, it requires enhancement, such as linear stretching, filtering, etc.  
For the height level, the range of low, middle, and high height in our experiments corresponds to 0-15 m, 15-50 m, and higher than 50 m, respectively. We also suggest classifying the height manually, into fewer or more groups, according to the actual demand, which we also mentioned in regards to the thresholds in this work.
## 4 Thumb Tracking Device Development and Functionality
### Device Components and Their Function  
The structural components of the CMC tracking device were 3D\(-\)printed for ease of manufacturing without the need for specialized equipment. These are the bracelet, pot joint, connector linkage, and thumb ring, as shown in Figure 3. The bracelet is the base of the device and is firmly attached to the human wrist using a Velcro strap. The thumb ring attaches to the MCP knuckle. The pot joint tracks the angular motion of the two\(-\)DOF CMC joint and is shown in Figure 3. The connector linkage connects the pot joint and thumb ring to transfer the CMC joint motion to the pot joint and provides the means for personalization for different thumb sizes.  
The bracelet, pot joint, connector linkage, and the metacarpal (along with the CMC joint FE axis) establish a four\(-\)bar linkage mechanism, as presented in Figure 4. Thus, the CMC joint FE and AA motions are transferred to the pot joint and measured by their respective axis potentiometer. The FE and AA axis rotational potentiometers are attached on the pot joint (model PT6 MV\(-\)103A2020, 0 to 90\({}^{\circ}\) rotation, 0-10 K\(\Omega\) resistance, and 6.3 mm diameter [54]).
## 2 Related Work
### General Protocol Working Principle  
The ACOSAR project's final report [8] gives information on the objectives, project phases, and results. This includes a literature review, dealing with different simulation types, RT systems, communication media, relevant tools, and related projects [9].  
The protocol follows a master-slave principle, including one master and one or more slaves. Distributed Co-Simulation scenarios might consist of HiL systems, test benches, or simulation tools. Both the master and slave require a state machine, which communicate via Protocol Data Units (PDUs). This procedure is based partly on the definition of the Functional Mock-up Interface (FMI), another standard specified by Modelica. The basic elements of the standard are described in the specification document (Version 1.0) [10], particularly with regard to the slave implementation. The configuration of slaves is further described in the Ref. [11].  
In the Ref. [12], the authors gave a short insight into the main design ideas of the DCP (interoperability, integration, compatibility, communication, economy), as well as the tools, protocols, and communication architecture. Three different operating modes and their respective time domains are presented: non-real-time (NRT), soft real-time (SRT), and hard real-time (HRT). The modes can be used depending on the chosen communication protocol. For HRT operation, an EtherCAT connection might be used. The communication can also be realized via TCP/IP (see, for example, the specification document [10]).
## 2 Study Area and Data
### Data
#### 2.2.3 Sample Data
Field visits were scheduled to coincide with phenologically distinctive phases: 1541 plots were recorded in October-December 2021, 1819 in January-March 2022, 4152 in April-June 2022, and 2478 in July-October 2022. This design guarantees that every crop is observed when its spectral signature is most distinctive, gives each class a comparable number of learning samples despite the underlying pixel imbalance, and preserves the real-world spatial mosaic of the study area. To compensate for the limited number of labeled \"non-food-crop\" pixels in southern China and to test cross-regional generalizability, we additionally employed the 2021 Canadian Cropland Dataset (CCD). Although staples dominate the CCD as a whole, it also contains three extensive non-food categories--rapeseed, flax and alfalfa--whose spectral-phenological signatures differ from those of Chinese crops. We extracted 41,672 canola parcels, 12,534 flax parcels, and 9118 alfalfa parcels, retaining only the surrounding pixels of major crops to preserve a realistic landscape context. This experimental design enables us to assess the cross-region and cross-crop-type generalization capability of the proposed method under diverse agro-ecological conditions.
# Multi-Scale Cross-Attention Fusion Network Based on Image Super-Resolution
## 2 Related Works  
### Deep CNN-Based Image Super-Resolution  
Methods based on deep learning have recently been widely used in image super-resolution [15] and have achieved significant advantages over traditional methods. [PERSON] et al. proposed SRCNN [5], the first article to apply a convolutional neural network to the field of image super-resolution. They used a three-layer convolutional neural network to establish an end-to-end mapping SR method between LR images and their corresponding HR images. [PERSON] et al. proposed the VDSR [8] algorithm, which used a deep convolutional neural network and added residual learning to improve the SRCNN network. At the same time, the DRCN [16] algorithm was proposed, which is the first method to introduce recursive learning to realize parameter sharing in SR. Although the initial application of the CNN method can improve the performance of traditional methods, it will increase the computational cost and produce artifacts. Therefore, [PERSON] et al. proposed the FS-RCNN [6] approach to improve computational efficiency by introducing deconvolution in up-sampling. The ESPCN [7] algorithm was suggested by [PERSON] et al., which presents a sub-pixel convolutional layer to upsample the final LR features as HR output to improve the computational performance to achieve a complete end-to-end mapping. Due to the effectiveness of the sub-pixel convolutional layer, the EDSR [9] algorithm also directly uses it for upsampling and removes the BN layer at the same time to increase the amount of network calculation, reduce the model parameters, and improve image performance. [PERSON] et al. proposed the LapSRN [10] algorithm to reduce the amount of network calculation by using a cascade structure to gradually enlarge image reconstruction. [PERSON] et al. proposed the MemNet [17] algorithm, which uses dense blocks for deep networks. [PERSON] et al. proposed the HDRN [18] algorithm, which uses hierarchical thick blocks to reconstruct the image to reduce the amount of calculation brought by the dense residual method. These methods show that deep, residual, and dense connections can improve the network's performance. There are other ways to improve network performance.