esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Volumetric Ultrasound Research Platform with 1024 Parallel Transmit and Receive Channels
###### Abstract  
Volumetric ultrasound imaging is of great importance in many medical fields, especially in cardiology, but also in therapy monitoring applications. For development of new imaging technologies and scanning strategies, it is crucial to be able to use a hardware platform that is as free and flexible as possible and does not restrict the user in his research in any way. For this purpose, multi-channel ultrasound systems are particularly suitable, as they are able to control each individual element of a matrix array without the use of a multiplexer. We set out to develop a fully integrated, compact 1024-channel ultrasound system that provides full access to all transmission parameters and all digitized raw data of each transducer element. For this purpose, we synchronize four research scanners of our latest \"DiPhAS\" ultrasound research system generation, each with 256 parallel channels, all connected to a single PC on whose GPUs the entire signal processing is performed. All components of the system are housed in a compact, movable 19-inch rack. The system is designed as a general-purpose platform for research in volumetric imaging; however, the first-use case will be therapy monitoring by tracking radiation-sensitive ultrasound contrast agents.  
volumetric imaging; 1024 channel ultrasound system; 3D beamforming; matrix array; ultrafast ultrasound imaging; ultrasound system design Article Real-Time Volumetric Ultrasound Research Platform with 1024 Parallel Transmit and Receive Channels [PERSON] et al. (2021), [PERSON] et al. (2021), [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +49-6897-90713-60
# Time Series Visualization and Forecasting from Australian Building and Construction Statistics
## 4 Experiments
### Discussion  
Overall, the performance of the LSTM-based prediction greatly depends on the \(nln\) parameter, as mentioned in Section 4.2.2. The effect of the prediction varies when the model learns different number of past data points. When the LSTM-based model learns more data points (i.e., in our implementation is five), the prediction tends to have a similar performance to the SARIMA-based prediction. The LSTM-based model predicts more accurate on the overall trend. Instead of predicting all seasonal changes and spikes in the time series data, it gives out a smooth trend line, without many twists on the line. The SARIMA model makes predictions on the seasonal and non-seasonal parts, and then integrates them together, while the LSTM-based prediction only learns from the past five  
Figure 10: SARIMA-based prediction results.  
data point, in order to learn the pattern that includes both the non-seasonal and seasonal parts. Therefore, they both end up with a prediction line that tends to become linear and smooth. There are not many spikes on the prediction line for both the SARIMA and LSTM-based models, with \(nln=5\). They both achieve a good performance on the recent data prediction.  
On the other hand, when the LSTM-based model learns less data points from the past, it is obvious that there are ups and downs around the spikes, which means that the LSTM-based model has learned from the recent changes. However, the difference between highest and lowest values is not as big as the original line. The reason for this may be that it is affected by other economic factors. The economic factors converge the changes of the prediction. Compared with the SARIMA model, both of LSTM-based predictions provide good results, and they both are slightly better than the SARIMA model by comparing the RMSE value, even though they predict the results in two different directions.  
However, there are a few improvements can be made on this approach. The first thing is to gain more data, due the fact that some of the data on the ABS website starts recording in recent years, and this cause a big lack of data in training. Besides, since the missing data are all at the beginning, it is not possible to put dummy data in or just put zeros instead; these methods may dramatically affect the accuracy of the model, so there is no way to fill up these missing values. Since the LSTM model cannot take empty data records, the data range is limited by the feature that has the least among all features. If a better dataset can be found, and the other economic factors are recorded completely, the model will have a better performance.  
In later implementations, more features can be added, and only the features with large number of data entries remain for training. Currently, the method uses the fix window size to train the model. Another approach that may improve the model is to train it on all the past data points; this may be helpful to learn both the history data trend and recent changes in the time series data.
# Deep-Learning Based Algorithm for Detecting Targets in Infrared Images
## 4 CMF-3 DLSTM
### Network Structure of CMF-3 DLSTM  
CMF-3 DLSTM target detection model includes four modules, namely trunk network, regional proposal network, ROI pooling layer, and classification network, as shown in Figure 5.  
The trunk network still adopts a 16-layer VGG16 network. The model file saved after pre-training the target detection model Faster R-CNN on the visible light domain dataset Image Net is used for parameter initialization in CMF-3 DLSTM utilizing the idea of transfer learning, and two multiscale feature extraction mechanisms are still used for multiscale feature extraction and feature fusion. The process of generating candidate boxes for regional proposal network RPN is unchanged. It still extracts and screens regional proposal boxes that may contain targets from the feature graph output by the trunk network, and about  
Figure 4: CMF Net target detection results.  
Figure 5: Structure diagram of CMF-3 DLSTM model.  
300 high-scoring regional proposal boxes are screened. The ROI pooling layer mainly generates a fixed-size feature map based on region proposal mapping of candidate box generated by RPN network recommendation for subsequent classification and regression. The classification network primarily uses the multiscale feature map processed by ROI pooling layer mapping and uses the 3D six-way long- and short-term memory network 3 DLSTM, constructed based on BI-LSTM, to extract context information. At the same time, the attention mechanism is used to assign different weights to the context features extracted from other dimensions and directions to achieve the fusion of spatio-temporal features. Finally, the input is given to the classification and location layers for target classification recognition and position correction.
## 2 Conceptual Model and Research Hypotheses
### Social Influence and Perceived Usefulness of EESS  
According to the original TAM model [14], perceived use (PU) represents, along with perceived ease of use (PEOU), the determinants of the intention to use the technology. The direct impact of social influence on perceived use (PU) is demonstrated in several research studies based on the technology acceptance model. [PERSON] and [PERSON] [43] show the significant influence of social influence and subjective norms on perceived usefulness, which is seen as a critical determinant of the acceptance of smart meters by household consumers in a study conducted in Indonesia. Other authors [20] demonstrate the positive effect of social influence on the attitude toward use (ATU), which is a result obtained by the mediation of perceived usefulness (PU) in a study in the Chinese electric vehicle sector. In the smart meter sector, [PERSON] et al. [18] directly analyzes the impact of social influence on behavior intention to use (BIOU), showing the role of this determinant in shaping Brazilian public opinion on accepting these technologies. [PERSON] et al. [44] argues that social influence has a greater impact on the behavioral intention to use than facilitating conditions, and that there is a mediating role of the facilitating conditions in the relationship between social influence and behavioral intention. [PERSON] et al. [45] shows that subjective rules, defined as an indicator of social influence, are positively correlated with the behavioral intention to use technology, while other research [29] shows the positive relationship between social influence and the intention of Jordanian users to adopt smart city technologies. In another piece of TAM-based research, [PERSON] [5] shows that the intention to adopt smart thermostats is positively and significantly influenced by performance expectations, hedonic motivation, and social influence. Therefore, we make the following hypotheses:  
**Hypothesis 3 (H3).**_Social influence (SI), specifically (a), the adoption of EESS at the level of the network of friends, colleagues, or family (SI_FF), and (b), the promotion of the use of EESS in public space (SI_PS), will have positive effects on their perceived usefulness (PU)._  
**Hypothesis 4 (H4).**_PU will mediate the relationship between (a) SI_FF and (b) SI_PS, and the behavioral intention to use EESS (BIOU)._
## 3 Methods
### Decision Tree and Random Forest  
Decision tree (DT) was proposed by [PERSON] [57] in the early 1960s, being a conditional probability distribution defined in feature space and class space to find the purest division method [58]. The composition of decision tree has four elements: (1) decision nodes; (2) scheme branch; (3) status node; and (4) probability branch. Compared with black box classification models such as neural networks, decision trees can be logically well explained [59].  
The random forest (RF) algorithm was developed by [PERSON] in 2001 [60]. As we all know, DT is usually trained by recursively splitting data, but it is easy to overfit. To avoid this problem, RF runs by constructing multiple decision trees simultaneously; then, the prediction is obtained from each sample, and the best solution is finally selected by voting [61]. Compared to ANNs and SVMs, it is more efficient and robust against overfitting and outliers in order to improve the performance, especially when the dataset is large [62; 63]. It is suitable for demonstrating the nonlinear effects of variables, and can learn the complex interactions between variables [60; 64]. The potential of RF in satellite remote sensing data classification has also been verified [64; 65; 66].
## 3 Simulation and Experimental Verification
### Case 1: A Numerical Experiment with Multiple Interferences
(**a**) Mkurtogram; (**b**) the filtered signal obtained by Mkurtogram; (**c**) the SES of (**b**). Figure 3: The time-domain waveform of the simulated signal. (**a**) the repetitive transients generated from outer race fault; (**b**) the interference impulse; (**c**) the cyclostationary noise; (**d**) the combined signal. As a comparison, the FK and the Autogram are applied to analyze the simulated signal, and the results are given in Figures 6 and 7, respectively. As depicted in the Figure 6a, the optimal center frequency \(f_{\text{c}}\) = 7200 Hz and bandwidth \(B_{w}\) = 533.3 Hz can be found, which correspond to the resonant frequency of the added interfering impulse. From the filtered signal displayed in Figure 6b, a prominent large impulse at the time of 0.17 s is found, which indicates that the FK is sensitive to the large disturb impulse. From the SES given in Figure 6c, we cannot find any fault-related fault characteristic frequencies. The Autogram is depicted in Figure 7a, where the center frequency \(f_{\text{c}}\) = 800 Hz and bandwidth \(B_{w}\) = 1600 Hz are obtained. It can be noted that the IFB identified from Figure 7a is the same with result as shown in Figure 5a, which is determined by Mkurtogram based on the gear meshing period \(T_{\text{m}}\). From the SES displayed in Figure 7c, the gear meshing frequency \(f_{\text{m}}\) and its harmonics are found clearly. From this case, we can see that the Autogram fails to avoid the interference of cyclostationary noise. In this case, both FK and Autogram are unable to detect the outer race fault duo to the complex interferences. Figure 5: Case 1: The results of Mkurtogram based on the gear meshing period \(T_{\text{m}}\). (**a**) Mkurtogram; (**b**) the filtered signal; (**c**) the SES of (**b**). \[s(t)=A(t){\sum_{n}}\mathrm{h}(t-n/f_{\mathrm{o}}-\tau) \tag{14}\]0 Case 1: The results of FK (**a**) the FK; (**b**) the filtered signal obtained by FK; (**c**) the SES of (**b**). In this case, the FK fails to detect the bearing fault due to the large interfering impulse. After AC process, the Autogram avoids the interference of tremendous impulse, while it is still disturbed by gear meshing interference. It mainly because the kurtosis indicator can't distinguish whether the repetitive impulses is generated by the bearing defect. However, based on the regular periodic distribution of fault-related peaks in AC domain, a unified representation of the impulsiveness and periodicity is performed by Mkurt. Therefore, the Mkurtogram method has successfully decreased the interference of both the large impulses and cyclostationary noises. By setting different interested input periods, the Mkurtogram can detect the different fault-related peaks in turn.
# A Global Review of PWR Nuclear Power Plants
## 2 International Development of the PWR Design
### American PWR Evolution  
Although the United States also developed the BWR design across different generations, the PWR design has been the one with the highest OP (Figure 3). In the 1950s, PWR design became the preferred choice when developing a nuclear-powered armada. Subsequently, the country's first nuclear power plant, _Shippingport_, with a PWR reactor inside, was the origin of the most powerful nuclear reactor design to date. In fact, its reactor served as a reference to those built and later used in the United States and to those exported by North American companies [3; 18; 19].  
Given the high growth expectations of the US nuclear industry, the companies _Babcock & Wilcox_ (B&W) and _Combustion Engineering_ (CE), initially focused on the manufacture of the main components of Westinghouse's BWR and BWR nuclear reactor designs by General Electric, joined the nuclear race with the development of their own PWR reactor designs [13].  
The different variants of the PWR nuclear reactor design developed from the 1960s by these three US companies are described below: (i) _Westinghouse_, (ii) _Babcock & Wilcox_, and (iii) _Combustion Engineering_.
## 3 Implementation Plan and Theoretical Basis
### Calibration Matrix Calculation  
Image processing and stereo vision often involve four coordinate systems: world coordinate system, robot coordinate system, camera coordinate system, and image (pixel) coordinate system [14]. The image data of the target flange detected by the above sensors are all carried out in the image coordinate system. In order to realize the automatic docking of the loading and offloading arm, the coordinate points in the image coordinate system must be transformed into coordinate points in the world coordinate system. In this paper, the light principle was used to realize the reconstruction of 3D points from a single image; that is, the reconstruction of 3D points was realized by using the intersection of light rays and a known plane. In this paper, the Zhang calibration algorithm [15] was selected to calibrate the internal and external parameters of the camera in combination with the calibration plate. As shown in Figure 6, in one of the images used for calibration, the calibration plate was directly placed near the port of the small crane tube and the global coordinate system was established on the calibration plate.  
According to the principle of eye-to-hand, it is similar to the actual engineering, using the constant transformation relationship between the calibration plate and the loading and offloading arm:  
\[\mathrm{robot}_{\mathrm{end}}^{\mathrm{1}}\mathbf{T}\cdot\mathrm{robot}_{ \mathrm{end}}^{\mathrm{1}}\mathbf{T}\cdot\mathrm{P}_{\mathrm{cam1}}\mathbf{T} =\mathrm{r}_{\mathrm{end}}^{\mathrm{robot2}}\mathbf{T}\cdot\mathrm{c}_{ \mathrm{robot2}}^{\mathrm{cam2}}\mathbf{T}\cdot\mathrm{P}_{\mathrm{cam2}} \mathbf{T} \tag{2}\]  
where \(\mathrm{r}_{\mathrm{end}}^{\mathrm{obot}}\mathbf{T}\) represents the homogeneous transformation matrix from the column to the end of the loading and offloading arm, \(\mathrm{c}_{\mathrm{robot}}^{\mathrm{cam}}\mathbf{T}\) represents the homogeneous transformation matrix from the end of the loading and offloading arm to the camera, \(\mathrm{c}_{\mathrm{cam}}^{\mathrm{p}}\mathbf{T}\) represents the transformation matrix from the camera to the target, robot1 and robot2 represent the samples after the first and second movements of the loading and offloading arm, respectively, and the two samples were compared and calculated. We can rearrange the above formula to give \(\mathbf{A}\cdot\mathbf{X}=\mathbf{X}\cdot\mathbf{B}\)  
\[(\mathrm{r}_{\mathrm{end}}^{\mathrm{robot2}}\mathbf{T}^{-1}\cdot\mathrm{r}_ {\mathrm{end}}^{\mathrm{obot1}}\mathbf{T})\cdot\mathrm{r}_{\mathrm{robot}}^{ \mathrm{cam1}}\mathbf{T}=\mathrm{r}_{\mathrm{robot2}}^{\mathrm{cam2}}\mathbf{ T}\cdot(\mathrm{c}_{\mathrm{cam2}}^{\mathrm{p}}\mathbf{T}\cdot\mathrm{c}_{ \mathrm{cam1}}^{\mathrm{p}}\mathbf{T}^{-1}) \tag{3}\]  
The obtained \(\mathbf{X}\) is the desired calibration matrix.  
Figure 6: Calibration coordinate system transformation.
# Effects of High Toughness and Welding Residual Stress for Unstable Fracture Prevention
## 4 Conclusions  
Brittle fracture avoidance research was conducted using welding consumables with excellent fracture toughness and LTT. The safety against unstable fracture was improved using the effect of mismatching the toughness of two welding consumables via the rainbow welding technique and the effect of a compressive welding residual stress at the brittle crack propagation front with a LTT welding consumable. Based on the experiment results, the following conclusions can be drawn:  
(1) A welding technique (rainbow), which can prevent brittle crack propagation using toughness heterogeneity in welding consumables with different toughness values, was described. The welding consumable application with two different toughness values demonstrated a significantly better effect for 80 mm thickness due to the influence of the welding amount distributed along the thickness. The improved possibility of avoiding brittle fracture due to heterogeneity of toughness was confirmed.  
(2) To change the brittle crack propagation path by distributing the compressive welding residual stress, a LTT welding consumable was applied to the special welding part. The brittle crack propagated straight along the weld joint and, when met with a special welding part to prevent brittle fracture, did not propagate in a straight direction but instead deviated to the base material and was then arrested.  
(3) High-strength thick steel plates, used for the construction of large structures (e.g., ships) have a risk of unstable fracture; thus, it is necessary to prevent such a risk. The results of this study confirmed that the safety of unstable fractures can be improved by applying the rainbow type welding method using large toughness welding consumables and with the technology of generating compressive welding residual stresses via a LTT welding consumable.  
[PERSON], and [PERSON] jointly conceived and designed the experiment, performed the experiment and conducted data analysis. [PERSON], and [PERSON] analyzed the data and plotted the figures, wrote this paper. [PERSON] provided scientific guidance. All authors have read and agreed to the published version of the manuscript.  
Figure 12: Residual stress distribution of low transition temperature welding consumable and commercial welding consumable. (**a**) LTT, 0 mm; (**b**) LTT, 30 mm; (**c**) LTT, 60 mm; (**d**) CWC, 0 mm, (**e**) CWC, 30 mm; (**f**) CWC, 60 mm.  
**Funding:** This study was funded by Chosun University grant number 2020.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# DGBL-YOLOv8s: An Enhanced Object Detection Model for Unmanned Aerial Vehicle Imagery
###### Abstract  
Unmanned aerial vehicle (UAV) imagery often suffers from significant object scale variations, high target density, and varying distances due to shooting conditions and environmental factors, leading to reduced robustness and low detection accuracy in conventional models. To address these issues, this study adopts DGBL-YOLOv8s, an improved object detection model tailored for UAV perspectives based on YOLOv8s. First, a Dilated Wide Residual (DWR) module is introduced to replace the C2f module in the backbone network of YOLOv8, enhancing the model's capability to capture fine-grained features and contextual information. Second, the neck structure is redesigned by incorporating a Global-to-Local Spatial Aggregation (GLSA) module combined with a Bidirectional Feature Pyramid Network (BiFPN), which strengthens feature fusion. Third, a lightweight shared convolution detection head is proposed, incorporating shared convolution and batch normalization techniques. Additionally, to further improve small object detection, a dedicated small-object detection head is introduced. Results from experiments on the VisDrone dataset reveal that DGBL-YOLOv8s enhances detection accuracy by 8.5% relative to the baseline model, alongside a 34.8% reduction in parameter count. The overall performance exceeds most of the current detection models, which confirms the advantages of the proposed improvement.  
YOLOv8; UAV; small object detection; feature fusion; lightweighting +
Footnote †: journal: Applied Sciences  
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