esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### The INGV Cameras Monitoring Network  
The INGV cameras monitoring network at Stromboli volcano in March-April 2020 comprised three fixed instruments, two thermals (SCT and SPCT) and a visual (SQV). Their details are listed in Table 1 and their location is shown in Figure 1. SQV acquires at a frequency of one image every two seconds, SCT one image every second, and SPCT two images every second. The difference in acquisition frequency, as well as in the distance from the crater area and viewing angle, result in a different count of the explosions, depending on the camera taken into consideration. The number of explosions is thought to represent an expression of the magma level within the feeder conduit [19; 39; 49; 50]. We have manually counted the total number of events occurring within the whole crater area using only the thermal cameras (SCT and SPCT), because they allowed a comparison between day and night views. However, the presence of clouds and/or dust may limit or hide visibility, as happened during most of the day on 30 March and in the early h on 1 April 2020. The calculated velocity of PDCs and lava flows along the SdF slope, obtained from the images of the monitoring cameras, are average velocities, because they were calculated dividing the whole travelled path by the time. Some of the images were affected by shadows due to fog, clouds, or ash, resulting in a non-well-defined outline of the features. The cumulative error on these spatial measurements, which was due to the poor quality of some frames, is about 2%, and it was obtained from the number and size of uncertain pixels. All of the times are expressed here as UT.
## 5 Conclusions  
To improve GNSS carrier phase measurement accuracy under strong earthquakes, MEMS accelerometer-aided PLL is proposed. The carrier phase errors of stand-alone PLL and MEMS accelerometer-aided PLL are analyzed based on their error models. Under strong earthquake dynamics, the stand-alone PLLs with a bandwidth narrower than 17 Hz lose lock, but MEMS accelerometer-aided PLLs with a bandwidth of 5 Hz can track a 30 dB-Hz signal with small carrier phase error. Simulated strong earthquake tests are carried out on the simulator and shake table. Test results show that, while the carrier phase errors of stand-alone PLLs are larger than 20\({}^{\circ}\), MEMS accelerometer-aided PLL errors are smaller than 5\({}^{\circ}\). The proposed MEMS accelerometer-aided PLL can effectively improve the GNSS carrier phase measurement accuracy under strong earthquakes. The improvement of the displacement precision could be analyzed in future.  
This work was supported in part by the National Natural Science Foundations of China (No. 41404029), the National Key Research and Development Program of China (No. 2016 YFB0501803), and the National High Technology Research and Develop Program of China (No. 2015 AA124002). The colleagues of the Navigation Group from the GNSS Research Center of Wuhan University who participated in the deeply-coupled system research and testing are thanked for their cooperation. Thanks to [PERSON] for his contributions to the implementation of the GNSS/accelerometer raw data recorder. Furthermore, thanks to [PERSON] for his contributions to the testing.  
[PERSON]: the deeply-coupled system and the MEMS accelerometer-aided PLLs were proposed by him, and the writing of the paper was primarily carried out by him. [PERSON]: the error analysis of the PLLs, the tests and data process were all carried out by him. [PERSON]: he proposed the improved idea, guided the detailed analysis of phase error, and also guided the writing of the paper. [PERSON]: he guided the PLLs testing base on simulator. [PERSON]: the reported research direction was assigned by him, and the structure of this paper was also designed by him.  
The authors declare no conflict of interest.
# Time-Series Prediction of Failures in an Industrial Assembly Line Using Artificial Learning
## 2 Significance and Novelty of the Study  
The existing literature demonstrates the successful application of AL methods for modelling and prediction. However, predictive maintenance studies specifically targeting assembly lines in the aerospace industry remain scarce. The significance and main contributions of the study are outlined as follows:* The configuration data are collected in the aerospace industry. Unlike many other sectors, the safety factor in structural design typically does not exceed 1.2, generally, with some designs even falling below 1.0. This is primarily due to the stringent requirement for lightweight structures. Nonetheless, structural integrity must not be compromised. The assembly phase--being the final stage of production--demands precise component compatibility. Any failure occurring during assembly can halt the entire production line and potentially result in catastrophic in-service damage to the aircraft or spacecraft. Such failures are associated with severe economic consequences.
* The aerospace sector is distinguished by its low-volume, high-precision production and assembly requirements. These unique characteristics necessitate domain-specific attention and pose particular challenges not encountered in other industries.
* Structural damage in aerospace systems may result from various factors, including fatigue, corrosion, manufacturing defects (e.g., voids, inclusions, dislocations), processing flaws (e.g., surface scratches, weld discontinuities, chevron notches, microstructural alterations), mechanical loading (tension/compression, bending, torsion, shear, pressure), impact, wear, creep, friction, hydrogen embrittlement, high temperature, and vibration. While damage tolerance design can mitigate many of these effects, human-induced errors remain difficult to eliminate. Regardless of the underlying cause, early fault prediction can help to prevent such failures.
* Not all AL techniques are suitable for time-series data. Some methods are good at image processing, while others are more effective in modelling. Time-series data demand specific attention due to their unimodal nature and the necessity to incorporate time lags as both input and output for accurate prediction. Furthermore, convergence during the training phase of learning algorithms is not always guaranteed.
* This study not only compares prediction performance but also evaluates different methods with distinct underlying architectures. At the end, the most suitable technique for modelling aerospace assembly-related time series is identified.
* The novelty of this research lies in both its use of domain-specific aerospace assembly data and the implementation of diverse and specialised modelling techniques. Modelling and prediction continue to be an active area of inquiry for both researchers and industry practitioners.  
In conclusion, this study aims to address a notable gap in the current body of knowledge and is expected to contribute significantly to the advancement of the field.
## 2 Measuring Principle
### Measurement Principle of the Cylindrical Capacitance Sensor  
The cylindrical capacitance sensor as shown in Figure 2, is made up of a metal shell, an outer insulating layer, a metal electrode, and an inner insulating layer. The metal electrode is excited by voltage and sealed to insulate the shell and leads to the wire. The metal case is grounded. The fluid flows along the axis in the runner. While ensuring the response strength is measured by the sensor, in order to facilitate the stratified flow of the fluid and increase the measurable range of the fluid, it is necessary to increase the width of the runner so that the thickness of each layer is as small as possible. Moreover, the thinner the internal insulation layer, the wider the response range of the sensor.  
Obviously, the metal electrode, the outer insulating layer, and the metal shell form a capacitance C0 in the cylindrical capacitance sensor. When the oil-water two-phase flow is stratified in the sensor runner, because the density of oil is smaller than that of water, oil is in the upper layer of water. The metal electrode, the inner insulating layer, and the water surface in contact with the inner insulating layer form a capacitance C1. The metal electrode, inner insulating layer, oil, and water surface form another capacitance C2. The total capacitance of the cylindrical capacitance sensor is connected in parallel with the above three parts. It can be seen that the cylindrical capacitance sensor is mainly focused on the fluid in the oil-water stratified state, but it is difficult to guarantee an effect when the fluid is in the non-stratified state.
## 4 Discussion
### Results Analysis  
The test results clearly indicate that the implemented solution can reduce the number of required punctures, as in most cases the physician completed the procedure on the fist attempt when using the system. This leads to a lower likelihood of post-procedural complications, reducing blood loss and the risk of damaging organs, and contributes to faster patient recovery. Importantly, proper training and familiarization with the system is crucial to ensure better results. Participants that took the time to familiarize themselves with how to operate the system obtained better results.  
This study revealed that while utilizing the developed system there was no significant difference in biopsy procedure execution times between less experienced physicians, who demonstrated a 60% improvement, and more experienced ones, who achieved a 48% enhancement. These results indicate that use of our solution is beneficial for operators at any level of expertise. Notably, competitive image guidance techniques such as US are highly dependent on operator experience, whereas in our case it is not especially relevant.  
The accuracy of the procedure is potentially higher when dealing with shallower lesion depths due to the greater risk of angular error. It is imperative for the operator to precisely align the needle with the planned trajectory, as even minor deviations can result in a relatively substantial error when the lesion in located at a greater depth. However, the gathered results indicate exceptional accuracy, with a final location error below 1 cm for lesions at depths above 10 cm, confirming the usefulness of a solution. Furthermore, the repeatability of these results is proven the conducted material collection efficiency test.  
The needle pose estimation accuracy test showcases that while markerless needle tracking is feasible, it requires an individual approach for particular tools in order to overcome issues with distorted point clouds and depth misalignments [39]. When looking straight at the needle, the average displacement error is at most 2.60 mm at a range of 50 cm from the headset. The best accuracy regardless of rotation is achieved when the operator is looking at the needle from about 40 cm, which is the most common case, probably due to the AHAT mode being designed for hand tracking [34]. This study reveals that the needle tracking method is useful for physicians when performing biopsy procedures.
## 4 Discussion
### Anthropometrics and Aerobic Capacity  
Overweightness impacted jumping performance to a large extent (\(p=0.016\), \(\eta_{\text{p}}{}^{2}=0.227\); normal weight vs. overweight). Stepwise regressions were used to quantify the contribution of %BF to physical performance, providing information regarding the specific role of %BF after adjusting for height and body mass. Body height and body mass alone (step 1) were only deterministic for throwing parameters, especially for the running shot (\(r^{2}\)max = 0.52) and aerobic capacity. Together with %BF (step 2), we calculated the largest \(r^{2}\) for the running shot (0.65), jump shot (0.61), medicine ball throw (0.57), and agility T-half test (0.54). The agility T-half test was also the test with the largest increase in \(r^{2}\) (0.53). Even though the results are in line with the previous literature, it was necessary to consider the broader impact of %BF as a continuous variable on performance characteristics. This result is in accordance with studies indicating that a greater %BF is associated with lower aerobic fitness in children and adolescents [39, 40]. This correlation has been relatively well explored in the general youth population [39, 41] but the negative correlation between %BF and VO\({}_{\text{2 max}}\) does not ubiquitously reach the a priori level of significance [42]. In adult male soccer players and youth basketball players, a negative effect of fatness has been reported on parameters of physical fitness, including both anaerobic and aerobic components [10, 11]. In addition, [PERSON] et al. [13] indicated that obesity impacted aerobic performance to a greater extent than anaerobic performance in obese and non-obese school-aged handball players. Accordingly, [PERSON] et al. [43] revealed greater cardiorespiratory fitness and muscle endurance in normal-weight males compared to overweight males aged 12-18 years of age. In a corresponding age group of male adolescents (age range: 13-19 years), [PERSON] et al. [44] demonstrated that overweight participants had lower aerobic fitness in most of the tests assessed. The findings of this investigation corroborate those of [PERSON] et al. [45] and [PERSON] et al. [46] in a sample of male adolescents that were 12-15 years of age.  
The possible differences in playing experience might have influenced the body compositions since some positions tended to have a higher %BF than the other players (pivots vs. wings). However, we did not have a large enough sample size to conduct statistical analysis to confirm this observation. However, it has been previously reported that playing position can influence %BF, as players of different positions are different in terms of body mass, BMI, arm span, and palm length. [PERSON] et al. [2] reported that backs, pivots, and goalkeepers present greater body mass segments than wings, which are the shortest players with the least body mass in German professional male team handball. Due to the low negative associations between %BF and the sprint (15 and 30 m) and agility parameters, these measures need special interpretations concerning the effect of %BF.
###### Abstract  
Recently, some surgeons reported that most scaphoid waist fractures were horizontal oblique and not transverse in orientation. Therefore, this cadaveric study aimed to biomechanically compare fixation strength between central and eccentric screw placements for the solar fixation of this most common scaphoid waist fracture. Eight matched pairs of fresh-frozen forearm cadaver specimens were prepared for testing and randomly assigned to two groups: group I specimens were fixed by screws in a central placement, and group II specimens were fixed by screws in an eccentric placement. Horizontal oblique osteotomy was performed along the scaphoid waist. Then, each specimen was placed under the increasing load of a pneumatically driven plunger. We recorded stiffness, load to failure, and failure mechanisms between the central and eccentric screw placement groups. Stiffness was higher in central screw placement (74.1 N/mm) than in eccentric screw placement (29.39 N/mm). The median loads to failure in groups I and II were 54.14 and 26.22 N, respectively. In this biomechanical model, we demonstrated that central screw placement is superior to eccentric placement in terms of fixation strength. However, further clinical investigation is warranted to evaluate whether the different screw placements for solar approach of horizontal oblique scaphoid fractures affect the clinical outcomes.  
horizontal oblique; scaphoid waist fractures; central screw placement; eccentric screw placement; biomechanical stability +
Footnote †: journal: Applied Sciences
# Efficient CORDIC Iteration Design of LiDAR Sensors' Point-Cloud Map Reconstruction Technology +
## 4 Mobile LiDAR Sensing Circuit and Chip Design
### CORDIC Angle Calculator
#### 4.3.1 CORDIC Horizontal Angle Calculator  
LiDAR is a circular scanner with a horizontal angle of 360 degrees, so the horizontal input angle will be 0 to 360 degrees. There will be two horizontal angles in a block. The first horizontal angle can be determined from the packet information. The second horizontal angle is the horizontal drift angle. The entire packet has a total horizontal angle scan range of 4.8 degrees, and a packet has a total of 12 blocks, so each block has a horizontal angle of 0.4 degrees and a horizontal drift angle of 0.2 degrees.  
The CORDIC horizontal angle calculator is shown in Figure 9. When the 16-bit horizontal angle information is received, it will be compensated first for 17 bits. The \(\mathbf{Y}_{i}\) input information is 0. The \(\mathbf{X}_{i}\) input information is 1. \(\mathbf{\theta}_{i}\) is a fixed comparison angle compared to the horizontal angle of the input. After the information is compared, the signal will be transmitted to shift and subtract the \(\mathbf{X}_{i}\) and \(\mathbf{Y}_{i}\) signals. The most significant bit (MSB) is used to determine whether it is a 2's complement.  
Figure 8: Finite state machine of main controller.  
Figure 9: CORDIC horizontal angle calculator.
## 1 Introduction  
In recent years, e-commerce has entered a new stage of rapid expansion [1; 2; 3]. The development and prosperity of e-commerce mean that it has replaced and eliminated many offline stores. Sales prediction is important to e-commerce. Accurate sales prediction has great significance for enterprises in the coordination of production, procurement, and logistics. Many sales prediction issues are associated with time series prediction. The traditional sales prediction methods are, for example, the parameter prediction method and the Autoregressive Integrated Moving Average (ARIMA) method [4; 5]. Machine learning methods such as Gradient-Boosted Regression Trees (GBRT), Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGBoost) are also used [6; 7; 8]. Traditional methods are mainly developed for offline retailers, and the sales are affected by the weather, season, policies, and passenger flow volume. In contrast, e-commerce sales are mainly associated with activities on e-commerce platforms and network hot spots. For example, the emergence of the COVID-19 pandemic accelerated the process of the decline of traditional offline retailing [9], but e-commerce platforms such as Taobao and Amazon still had large numbers of sales. Hence, there is a specific need to develop sales prediction methods for e-commerce.  
In contrast, e-commerce sales for short-shelf-life products, such as fresh milk, are different from those of other products. Due to the short shelf life of fresh milk, higher precision is required in sales to guide production and inventory. The traditional salesprediction methods such as ARIMA and SARIMA are poor in dealing with this type of problem. Poor sales prediction results in poor inventory plan development and causes extensive waste. To solve the above issues, there is a need to develop a systematic prediction method for short-shelf-life product sales in e-commerce; hence, in this paper, the GRU-LightGBM prediction method is proposed. The method benefits predictions by using GRU to capture timing features and by using LightGBM for multivariable forecasting. The proposed integration is then applied to a case study for its validation and application. The results show that the GRU-LightGBM method has an accurate prediction output and acceptable training speed.  
The structure of this paper is as follows. Section 1 delivers an introduction to the research. Section 2 reviews the literature related to sales prediction methods. In Section 3, the methodology of GRU-LightGBM development is elaborated. In Section 4, the GRU-LightGBM method is applied to a case study for examination. Section 5 provides a comparison of other prediction methods and also presents the conclusions.
# A Semantic Segmentation Dataset and Real-Time Localization Model for Anti-UAV Applications
## 1 Introduction
Therefore, we introduce a novel semantic segmentation dataset for UAV localization, capable of handling small to large UAVs. We hope this dataset will benefit the research community and aid in reducing threats posed by illegal UAV activity. Due to the high-speed nature of UAV movement, detection accuracy alone is not sufficient. Real-time performance is also critical to ensure the system can track and respond to UAVs effectively without excessive computational load. To address this, we also propose a lightweight semantic segmentation model, ThinDyUNet, capable of real-time UAV localization using our VL and IR dataset. We compare ThinDyUNet with several baseline semantic segmentation models retrained on the proposed dataset. The main contributions of this study are summarized as follows:  
1. We introduce a new semantic segmentation dataset for UAV detection. The dataset includes a wide range of UAV types, sizes, and distances, with paired RGB and IR images and corresponding segmentation masks. The dataset is publicly available for unrestricted research use. 2. We propose ThinDyUNet, a lightweight model designed for semantic segmentation-based UAV detection. ThinDyUNet achieves real-time performance with significantly reduced model size, enabling fast inference compared to existing baseline models. Based on the main contributions, this paper mainly proposes a new semantic segmentation dataset for recognizing a small rotary-wing UAV that flies in various environments and consists of two image types: VL and IR images. Since there is no available dataset for anti-UAV using semantic segmentation tasks, this paper becomes the first one to propose it. To initiate the model for the semantic segmentation task using this dataset, we propose a model called ThinDyUNet. By having a proposed model, we hope that it can help to create a benchmark model that can be compared for future research that uses the proposed dataset. Furthermore, we hope that the proposed model can be used as a guide to improve the field of semantic segmentation into a lightweight model and capability for real-time implementation, especially for anti-UAV tasks. This paper is organized as follows. Section 2 reviews existing datasets and models for anti-UAV detection. Section 3 introduces the proposed UAV semantic segmentation dataset. Section 4 details the ThinDyUNet model architecture. Section 5 presents a performance comparison between ThinDyUNet and baseline semantic segmentation models using the proposed dataset. Section 6 concludes the paper.