esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning Based Inversion of Locally Anisotropic Weld Properties from Ultrasonic Array Data
###### Abstract  
The ability to reliably detect and characterise defects embedded in austenitic steel welds depends on prior knowledge of microstructural descriptors, such as the orientations of the weld's locally anisotropic grain structure. These orientations are usually unknown but it has been shown recently that they can be estimated from ultrasonic scattered wave data. However, conventional algorithms used for solving this inverse problem incur a significant computational cost. In this paper, we propose a framework which uses deep neural networks (DNNs) to reconstruct crystallographic orientations in a welded material from ultrasonic travel time data, in real-time. Acquiring the large amount of training data required for DNNs experimentally is practically infeasible for this problem, therefore a model based training approach is investigated instead, where a simple and efficient analytical method for modelling ultrasonic wave travel times through given weld geometries is implemented. The proposed method is validated by testing the trained networks on data arising from sophisticated finite element simulations of wave propagation through weld microstructures. The trained deep neural network predicts grain orientations to within 3\({}^{\circ}\) and in near real-time (0.04 s), presenting a significant step towards realising real-time, accurate characterisation of weld microstructures from ultrasonic non-destructive measurements. The subsequent improvement in defect imaging is then demonstrated via use of the DNN predicted crystallographic orientations to correct the delay laws on which the total focusing method imaging algorithm is based. An improvement of up to 5.3 dB in the signal-to-noise ratio is achieved.  
deep neural networks; welds; anisotropy; ultrasonic non-destructive evaluation; tomography; total focusing method +
Footnote †: journal: Applied Sciences
## 1 Introduction  
Due to the improvement in life expectancy, heart surgery has become increasingly common among elderly population. However, due to the frailty and comorbidities of these patients, perioperative complications are frequent. Moreover, the impact of anaest and analgesic drugs play an important role on the perioperative course [1].  
Post-operative pain is mainly related to median surgical sterotomy; it is intense and difficult to control, especially in the immediate few hours after surgery [2]. It requires high doses of opiates that may lead to different side effects, such as nausea, vomiting, respiratory issues and postoperative delirium. Furthermore, high anaesthetic drug administration can cause a delay in extubating time and weaning from mechanical ventilation. Moreover, sympathetic stimulus and chest pain may induce an important reduction in thoracic excursion, finally facilitating pulmonary infections [3].  
Recently, many thoracic fascial plane blocks have been proven to be a valid option in controlling pain in cardiac, thoracic and breast surgery [4; 5; 6; 7].  
The sternal region is innervated by the anterior branches of intercostal nerves, which arise from the anterior branches of the spinal nerves from T1 to T11 [8]. Parastermal block is a recent block which targets these branches due to the injection of local anaesthetic between the pectoral major and the intercostal muscles, proximal to the sternum. To obtain a good analgesic cover, the block needs to be performed bilaterally with a spread from the II to the VI intercostal spaces.  
Several studies have investigated the parasternal block in cardiac patients trying to understand its impact on intra- and post- operative pain [9; 10; 11; 12; 13]; however, most of the studies are retrospective or based on a small number of patients.  
The aim of this prospective study is to assess the efficacy of parasternal block to manage postoperative analgesia, reduce opioid consumption and improve respiratory function in patients undergoing cardiac surgery.
## 4 Discussion
Virtual reality is a new approach to delivering therapy that targets auditory hypersensitivity experienced by autistic young people, and the development of cheaper consumer-grade VR devices now allows for this form of therapy to be carried out within a home environment. In the case of SoundFields, parents were able to choose to deliver the therapy at a time most appropriate to their child and lifestyle. Furthermore, parents and participants found the software intuitive and easy to use and throughout the study received limited instructions from the researchers. With this in mind, home-based interventions such as SoundFields could provide an accessible option that can be conducted with limited training, potentially having a positive impact upon healthcare systems that have difficulties implementing behavioural therapy due to a lack of resources, time, and specialist training [25; 26]. Despite the positive results, there are a number of considerations to improve SoundFields and its validation as a digital intervention. Firstly, as shown in Figure 6, tracked voluntary interactions indicated that several participants reduced the exposure level of the target stimuli over the experimental period, which may have impacted the effectiveness of the therapy. Therefore, it should be considered to remove all ability for children to control intervention parameters. Instead, it is suggested that, after initial selection of exposure level, these could be set to increase at consistent intervals to improve desensitisation. Further options could include to allow parents an override function should they notice their children struggling with the exposure to target sounds. Although the majority of participants and their parents stated that it had a positive impact, the correlation between SRE and the qualitative feedback was not always consistent. This highlights the requirement for an improved assessment of auditory hypersensitivity. Firstly, parental feedback indicates that they sometimes have a different perspective on the impact of the intervention from their child. Therefore, a formalised questionnaire, such as the Parent Stress Index, could provide evidence of any habituation towards real-world auditory stimuli that would normally cause emotional distress. Secondly, biometric measurements could scaffold the primary outcome measurements and parental questionnaires, bypassing any social, communicative, and cognitive impairments. Similar approaches have been utilised in the validation of noise-cancelling headphones in aiding to reduce negative reactions to environmental auditory stimuli.
# YOLO-SE: Improved YOLOv8 for Remote Sensing Object Detection and Recognition
## 3 Materials and Method
### Sppfe  
The SPPF module in YOLOv8 has demonstrated its advantages in enhancing model performance through multi-scale feature fusion, particularly in certain scenarios. However, we must acknowledge that the SPPF module may have limitations in complex backgrounds and situations involving variations in target scales. This is because it still lacks a fine-grained mechanism to focus on task-critical regions.  
Figure 4: The architecture of SEF.  
Figure 3: The architecture of SEConv2d.  
To address the limitations of the SPPF module and enhance feature extraction capabilities, we introduce the Efficient Multi-Scale Attention (EMA) mechanism [44], which dynamically adjusts the weights in the feature maps based on the importance of each region in an adaptive manner. The EMA attention mechanism is employed to retain information on each channel while reducing computational costs. We achieve this by restructuring a portion of the channels into the batch dimension and grouping the channel dimensions into multiple sub-features, ensuring an even distribution of spatial semantic features within each feature group. This approach helps maintain channel-wise information while minimizing computational expenses. This allows the module to focus on task-critical regions, making it more targeted in complex scenes. The structure of SPPFE is depicted in Figure 5, and we incorporate the EMA attention mechanism into this module. The SPPEF module not only performs multi-scale feature fusion but also finely adjusts features at each scale, effectively capturing information at different scales. This enhancement significantly improves the model's ability to detect small objects.
## 2 Materials and Methods
### Methodology
#### 2.2.2 Hydrological Modeling  
The hydrological model (HEC-HMS) has been extensively used to simulate rainfall-runoff processes [48]. In this model, adequate precipitation in a basin is defined by the characteristics of previously connected surfaces. The flow from the surface and channels consists of a form of direct runoff in the stream. The deficit and constant loss method based on the Snyder unit hydrograph as a transfer method suggests better estimations of the combination of soil moisture accounting (SMA) and deficit and constant based on the Clark and Snyder unit hydrograph (UH) [49]. Figure 4 shows the steps involved in this study. The streamflow hydrograph (\(Q\)) is derived by the interaction of the precipitation gradually increasing (\(P\)) with the unit hydrograph required (\(U\)) as follows:  
\[Q_{n}=\sum_{m=1}^{n<M}P_{m}U_{n-m+1} \tag{1}\]  
where \(m\) varies between 1 and \(n\).  
The transformation of additional precipitation into direct runoff can be obtained using the Snyder unit hydrograph method. For the standard case, the Snyder formula for the peak flow can be expressed as follows:  
\[Q_{P}=\frac{2.75\cdot C_{P}\cdot A}{T_{r}*5.5}, \tag{2}\]  
where \(T_{r}*5.5\) is basin lag, \(Q_{P}\) is the unit hydrograph peak flow of the standard UH corresponding to 1 cm of effective rainfall (m\({}^{3}\cdot s^{-1}\)), \(A\) is the catchment area (km\({}^{2}\)), and \(C_{P}\) is the empirical coefficient relating the triangular time base to lag.  
Figure 4: Flowchart of the developed coupled models, SRTM: shuttle radar topography mission.  
The catchment lag (hour) can be calculated as follows:  
\[T_{p}=CC_{t}(LL_{c})^{0.3} \tag{3}\]  
where \(C\) is a conversion constant 0.75 (ISI), \(C_{t}\) is a coefficient derived from gauged watersheds, \(L\) is the length of the stream from the outlet to the upstream divide, and \(L_{c}\) is the length along the mainstream from the outlet to the point nearest to the watershed centroid.  
The HEC-HMS model can generate a simulation for forecasting river runoff from historical precipitation data, which can be obtained from different methods, including field measurements, numerical weather predictions, and satellite images. To achieve a reasonable prediction performance, the parameters in (1), (2), and (3) need to be tuned accurately according to the watershed characteristics and the input data in the calibration phase. In this study, the standard lag and the peaking coefficient ranged from 2.98 to 23.66 and 0.5 to 0.75, respectively.
## 3 Design of Composite Control Method
### Optimized Design of Disturbance Observer  
It can be seen in Figure 11 that in the low-frequency band, \(W_{\rm T0}(s)\) is not completely close to the system uncertainty \(\Delta(s)\). This implies that the low-frequency disturbance suppression capability does not meet the ultra-high pointing stability requirements of the telescope pointing system. The reason for this lies in the presence of nonlinear friction; the \(H_{\infty}\) controller cannot completely suppress the influence of nonlinear friction torque. To enhance the telescope pointing system's ability to suppress nonlinear friction torque, a disturbance observer (DOB) was introduced to the inner loop of the control system. Additionally, to relax the constraints of the feedback controller and enhance the system's disturbance rejection capability, the \(H_{\infty}\) norm was utilized to optimize the filter \(Q(s)\) of the DOB system.
## 4 Result
### Comparative Experiment of Different Algorithms  
To demonstrate the good performance of the YOLO algorithm on the task of lane line species recognition, this section cites one two-stage algorithm, Faster R-CNN, and two one-stage algorithms, SSD [39] and RetinaNet [40], as comparison networks. These are the current mainstream target recognition algorithms. These algorithms have been applied by many researchers in various task scenarios. The above algorithms are applied to the lane line pattern recognition dataset for training and testing. And the results of YOLOv5 algorithm are compared with the results of the above algorithms. The results are shown in Table 8.  
As can be seen from the table, the detection speed of Faster R-CNN is too slow and bulky. This is not suitable for application and deployment in ADAS. Compared with Faster R-CNN, the SSD and RetinaNet algorithms improve the detection speed, but the average accuracy is lower, which is still difficult for meeting the needs of ADAS. The YOLOv5 algorithm is superior to the above three algorithms in terms of average accuracy and detection speed. On this basis, the improved YOLOv5 algorithm proposed in this paper has further improved in each performance. This algorithm is fully applicable to the working requirements of ADAS.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Model** & **[EMAIL_ADDRESS] (\%)** & **Parameters (M)** & **FPS (frame-s\({}^{-1}\))** \\ \hline YOLOv5s + ECA + GIoU & 94.3 & 7.2 & 84.7 \\ YOLOv5s + ECA + CIoU & 95.0 & 7.2 & 87.7 \\ YOLOv5s + ECA + EIoU & 94.8 & 7.2 & 89.3 \\ YOLOv5s + ECA + SIoU & 95.4 & 7.2 & 85.5 \\ \hline \end{tabular}
\end{table}
Table 6: Comparison of results using different loss functions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**YOLOv5s** & **C3\_Faster** & **ECA** & **SIoU** & **[EMAIL_ADDRESS]** & **Parameters** & **FPS** & **Volume** \\  & & & & **(\%)** & **(M)** & **(frame-s\({}^{-1}\))** & **(MB)** \\ \hline \(\surd\) & & & & 92.1 & 7.1 & 91.3 & 13.8 \\ \(\surd\) & \(\surd\) & & & 91.9 & 5.8 & 99.0 & 11.4 \\ \(\surd\) & & \(\surd\) & & 94.3 & 7.2 & 84.7 & 13.9 \\ \(\surd\) &
# Research on Chinese Nested Entity Recognition Based on IDCNNLR and GlobalPointer
## 4 Experiment
### Ablation Experiments
#### 4.5.1 Multi-Head Attention Mechanism  
In the comparison between BERT-ATT-IDCNNLR-ATTR-GlobalPointer and BERT-IDCNNLR-GlobalPointer, on the CMeEE dataset, the model achieved a 0.745% improvement in \(F1\) score, while on the CLUENER2020 dataset, the model achieved a 3.234% improvement in \(F1\) score.  
In the comparison between BERT-ATT-IDCNNLR-ATTR-GlobalPointer and BERT-ATT-IDCNNLR-GlobalPointer, on the CMeEE dataset, the model achieved a 0.978% improvement in \(F1\) score, while on the CLUENER2020 dataset, the model achieved a 3.387% improvement in \(F1\) score.  
The experiments indicate that the multi-head attention mechanism improves the performance of the model when placed at different positions in various combination models. The  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**CMeEE**} & \multicolumn{3}{c}{**CLUENER020**} \\ \hline
**Compare Models** & **P\%** & **R\%** & **F1\%** & **P\%** & **R\%** & **F1\%** \\ \hline BERT-ATT-IDCNNLR-ATTR-GlobalPointer & **71.276** & 69.127 & **69.617** & 77.325 & **81.407** & **79.285** \\ BERT-ATT-IDCNNLR-ATTR-GlobalPointer & 66.944 & **71.577** & 68.654 & 78.218 & 79.836 & 78.987 \\ BERT-IDCNNLR-GlobalPointer & 70.530 & 68.634 & 68.872 & 74.338 & 77.964 & 76.050 \\ BERT-ATT-IDCNNLR-GlobalPointer & 69.385 & 69.162 & 68.639 & 71.790 & 80.585 & 75.897 \\ BERT-ATT-IDCNNLR-GlobalPointer & 68.223 & 69.677 & 68.355 & 73.138 & 76.036 & 73.635 \\ BERT-ATT-GlobalPointer & 70.079 & 68.952 & 68.844 & 76.894 & 78.997 & 77.906 \\ BERT-ATT-IDCNN-ATTR-GlobalPointer & 68.558 & 71.046 & 69.240 & 77.643 & 80.852 & 79.169 \\ GlobalPointer & 69.405 & 69.032 & 68.605 & 77.034 & 78.519 & 77.736 \\ BERT-ATT-GlobalPointer & 68.692 & 69.586 & 68.634 & 76.434 & 76.733 & 75.910 \\ BERT-ATT-IDCNNNL-ATTR-GlobalPointer & 69.942 & 68.097 & 68.386 & 77.613 & 80.088 & 78.801 \\ BERT-ATT-IDCNNNL-GlobalPointer & NULL & NULL & NULL & 77.397 & 81.070 & 79.156 \\ BERT-IDCNNNR-GlobalPointer & 70.786 & 67.673 & 68.538 & 73.806 & 78.574 & 76.056 \\ BERT-IDCNNNL-ATTR-GlobalPointer & 68.796 & 69.265 & 68.498 & 78.307 & 79.955 & 79.087 \\ BERT-IDCNNNL-GlobalPointer & 69.635 & 69.938 & 69.255 & **80.247** & 77.794 & 78.964 \\ BERT-ATT-IDCNNNR-GlobalPointer & 68.223 & 69.677 & 68.355 & 72.983 & 79.063 & 75.851 \\ BERT-IDCNNLR-ATTR-GlobalPointer & 70.059 & 68.638 & 68.775 & 78.042 & 80.619 & 79.251 \\ GlobalPointer & 67.613 & 71.822 & 69.143 & 75.558 & 75.104 & 74.714 \\ BERT-ATT-IDCNNNR-ATTR-GlobalPointer & 68.381 & 66.853 & 66.953 & 77.587 & 80.533 & 78.986 \\ \hline BERT-IDCNNLR-GlobalPointer(without RePO) & 69.667 & 66.246 & 67.261 & 73.300 & 78.271 & 75.621 \\ BERT-GlobalPointer(without RePO) & 69.852 & 65.694 & 67.029 & 78.123 & 78.695 & 78.379 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Twenty ablation experiments of the present model.  
multi-head attention mechanism performs noise reduction on the data, highlighting important information in the features, and enhancing the model's recognition performance.
# Communication System Based on Magnetic Coils for Underwater Vehicles
## 3 Description of the Communication System
### Transmitter System
#### 3.1.2 Carrier Signal Generation  
A Schmitt trigger circuit was used to build a sinusoidal signal that will serve as the carrier signal. This configuration offers variable frequency thanks to the built-in potentiometer trimpot trimmer 3296 W of 100 k\(\Omega\) and logic gate 74\(LS\)132. This signal has an amplitude of 2.5 V and a frequency of 600 kHz. The carrier signal can be seen in Figure 6.
###### Abstract  
The combination of multiple Global Navigation Satellite Systems (GNSSs) may improve the performance of time and frequency transfers by increasing the number of available satellites and improving the time dilution of precision. However, the receiver clock estimation is easily affected by the inappropriate weight of multi-GNSSs due to the different characteristics of individual GNSS signals as well as the outliers from observations. Thus, we utilised a robust Helmert variance component estimation (RVCE) approach to determine the appropriate weights of different GNSS observations, and to control for the influence of outliers in these observation in multi-GNSS time and frequency transfer. In order to validate the effectiveness of this approach, four time links were employed. Compared to traditional solutions, the mean improvement of smoothed residuals is 3.43% using the RVCE approach. With respect to the frequency stability of the time links, the RVCE solution outperforms the traditional solution, particularly in the short-term, and the mean improvement is markedly high at 14.89%.  
time and frequency transfer; multi-GNSS; robust estimation; Helmert variance component 2018 18 2878 10.3390/s18092878
# Performance Evaluation of Multi-UAV Network Applied to Scanning Rocket Impact Area +
## 3 Strategies for Scanning the Impact Area
### Scanning Strategy Using Multi-UAV System with Area Decomposition  
When the communication between the base station and the farthest node becomes unreliable due to distance, the solution is to subdivide the scanning area into subareas and assign each one of them a UAV, thus establishing a mesh network among the aircrafts. This allows the information to jump from one node to another until it reaches the destination, for example, the base station. Figure 5 represents the aforementioned method, which leads the system range to increase. Although the aircraft is distant, it can be coordinated in a synchronous flight formation when using a strategy that scans subareas with the same size. The size of each subarea must be defined so that each UAV has a reliable communication link with its neighboring units.  
A relevant aspect of this application is that obstacles do not exist within the scanning area since the aircrafts travel over the sea, thus simplifying the trajectory planning. When one of the nodes fails, areas that have already been scanned should not be rescanned. In the case of a node failure, the method proposed by [PERSON] and [PERSON] [45] could be adopted to subdivide the unscanned area into new subareas, also taking into account the spaces that have been scanned so far. Yet there is the need for an algorithm capable of forwarding data packets among all UAVs in the system to the base station in a reliable manner, thus ensuring that the communication remains established.
## 5 Discussion  
Change detection is the process of detecting land cover changes in a specific geographical area. However, diverse types of land cover changes are affected by varying environmental factors, geographical variability, and various sensors. The domain adaptation transfers knowledge between domains, increasing the robustness of trained models by domain shifting, enabling the generalization of models. The problem of generalization for the practical application of change detection can seriously impair the operability of deep learning-based classification approaches in real-world applications given the high demand for data [32; 33]. Moreover, the proposed method incurs a higher computational burden compared with attention U-Net, owing to additional processes of bilateral dissimilarity encoding and DAG. A shallow network may be developed in future work to address the complexity issues.
# Crustal Structure of Hainan Island and Surrounding Seabed Based on High-Resolution Airborne Gravity
## 3 Materials and Methods  
### Data Source  
The airborne gravity survey was conducted by the China Aero Geophysical Survey & Remote Sensing Center for Natural Resources from 2016 to 2019 using an integrated airborne gravity survey. The Cessna 208 B survey aircraft (reented from China Aviation Industry General Aircraft Co., Ltd., Zhuhai, China) was equipped with a GT-2A gravimeter (JSC STC Gravimetric Technologies, Moscow, Russia) and DGPS location system (ProFlex800 GPS, Ashtech Company, Sunnyvale, CA, USA). The main survey grid over Hainan Island was flown with 2 km survey line spacing and tie lines every 10 km. The airborne gravity data were obtained at an average GPS altitude of 600 m, with better resolution to reveal the crustal structure. The internal accuracy of the mean square deviation of the airborne gravimetric measurement for a repeated line test is around 0.6 mGal [39; 40]. The airborne free-air gravity anomaly was calculated after field data preprocessing and basic correction, such as instrument drift and the effect of the EotvOs's indoor processing; then, the Bouguer gravity data were obtained by removing the terrain effects of the seabed and topography using the Terrain Correction modules of the Oasis Montaj software (8.4.1 version) platform produced by Geosoft Inc. of Toronto, ON, Canada, using 1030 kg/m\({}^{3}\) for seawater and 2670 kg/m\({}^{3}\) for rock.