esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper presents the design, proof-of-concept implementation, and preliminary performance assessment of an affordable real-time High-Sensitivity (HS) Global Navigation Satellite System (GNSS) receiver. Specifically tailored to capture and track weak Galileo E1b/c signals, this receiver aims to support research endeavors focused on advancing GNSS signal processing algorithms, particularly in scenarios characterized by pronounced signal attenuation. Leveraging System-on-Chip Field-Programmable Gate Array (SoC-FPGA) technology, this design merges the adaptability of Software Defined Radio (SDR) concepts with the the robust hardware processing capabilities of FPGAs. This innovative approach enhances power efficiency compared to conventional designs relying on general-purpose processors, thereby facilitating the development of embedded software-defined receivers. Within this architecture, we implemented a modular GNSS baseband processing engine, offering a versatile platform for the integration of novel algorithms. The proposed receiver undergoes testing with live signals, showcasing its capability to process GNSS signals even in challenging scenarios with a carrier-to-noise density ratio (\(C/N_{0}\)) as low as 20 dB-Hz, while delivering navigation solutions. This work contributes to the advancement of low-cost, high-sensitivity GNSS receivers, providing a valuable tool for researchers engaged in the development, testing, and validation of experimental GNSS signal processing techniques.  
GNSS; FPGA; system on chip; SoC-FPGA; high-sensitivity GNSS receiver; software-defined radio +
Footnote †: journal: _Sensors_**2024**, 24, 1416. [[https://doi.org/10.3390/s24051416](https://doi.org/10.3390/s24051416)]([https://doi.org/10.3390/s24051416](https://doi.org/10.3390/s24051416))
# Computational Methodology for the Development of Wrinkled Tubes by Plastic Deformation
## 2 State-of-the-Art: Wrinkled Tube in Metal Sheets by Forming  
To meet recent demands for manufacturing technologies involving the joining of tubes through the forming of industrial and engineering components, the current state-of-the-art aims to overcome inherent process limitations and optimise manufacturing parameters as well as die geometrical tubes.  
Researcher [PERSON] et al. [28], in 2021, presented a new method for joining sheets to tubes by creating a single-turn flange by compressing the free end of the tube using a die. The deformation of the tube forms a flange that can be pressed against the metal sheet, ensuring the assembly joint without the need for additional surface preparation or machining grooves into the sheet holes.  
Addressing the challenges of traditional tube joining processes, such as welding or the use of pins and bolts, hydroforming was studied in 2022 by [29]. The process involves axial compression that induces buckling/folding in the tube, followed by joining with the sheet. The tube is pressurised with low pressure, along with the application of an axial force that deforms the tube outward and around the sheet. After this step, the buckled region is compressed to create the joint.  
In 2023, one of the innovations in the literature was electromagnetic forming (EMF) joining, especially for lightweight components in the automotive and aerospace industries. The advantage of this process lies in its ability to join dissimilar materials. The paper titled \"Joining by Electromagnetic Forming\" provides a comprehensive review of the EMF process, particularly focusing on its application in joining operations [30].  
Among the challenges related to obtaining tube-to-sheet joints is the occurrence of protrusions or notches near the joints, which can compromise the structural integrity and lead to the deformation of the involved plates. To prevent warping of the sheets during the application of forces, in 2023, the authors in [31] proposed a new joining process by forming and compressing an accessory ring to produce annular compression of the sheet, thus forming high-quality joints with smooth surfaces. In this study, the effects of process parameters such as the height and width of the ring, tube thickness, and flow velocity on the joint behaviour were analysed.  
Further addressing the complexity of parameters, in [32] the authors investigated the interference-fit joining of aluminium alloy tubes with mandrels made of different materials and manufactured under various metallurgical conditions. They discuss the effects of initial clearance, surface roughness, and material properties on the process. These parameters have a direct relationship with the residual stress in the mandrel, the joint area, and the interfacial friction coefficient between the tube and the mandrel.
## 5 Spatially-Resolved Spectroscopic Technique for Optical Property Measurement
### Algorithms and Instrumentation
#### 5.2.1 Inverse Algorithms and Instrumental Design Parameters
The results showed that the error produced by the finite beam with the beam size less than 0.5 mm, compared to the infinitely small beam, was less than 1%, but it increased linearly with the beam size larger than 0.5 mm. In general, a 1-mm light beam introduces about 5% error for estimating \(\mu_{\mathrm{a}}\) and \(\mu_{\mathrm{s}}^{\prime}\) compared to the infinitely small beam. Hence the size of a light beam in the system should be less than 1 mm in order to control the error within 5%. Laboratory tests were performed to examine the actual light beam. Further, MC simulations showed that the optimal minimum source-detector distance depended on the values of \(\mu_{\mathrm{a}}\) and \(\mu_{\mathrm{s}}^{\prime}\), and it ranged from 0.5 to 2 mm, corresponding to 1 to 4 transport mean free paths [mfp\({}^{\prime}\), 1 mpf\({}^{\prime}\)\(=\)\((\mu_{\mathrm{a}}+\mu_{\mathrm{s}}^{\prime})^{-1}\)] [104]. After consideration of the different combinations of \(\mu_{\mathrm{a}}\) and \(\mu_{\mathrm{s}}^{\prime}\), the optimal minimum source-detector distance was recommended to be 1.5 mm and the optimal maximum source-detector distance to be equivalent to 10-20 mfp\({}^{\prime}\) or determined by the minimum SNR.
# A Fast and Lightweight Method with Feature Fusion and Multi-Context for Face Detection
## 3 Methods
### Architecture
#### 3.1.1 Multi-Scale Features  
As mentioned above, we use outputs of several selected convolutional layers to obtain multi-scale features. We first choose output feature maps with different scales produced from the forward process of the network. Using outputs before the 8 th layer will bring large calculations, so we choose three sets of outputs which are produced by conv9, conv13 and conv17, with a scaling step of 2 and strides of 4, 8 and 16.  
Since shallow layers are semantically weak and not suitable for prediction, we then fuse the deep semantically strong features to the shallow high-resolution feature maps by a top-down pathway, as illustrated in Figure 3. First, channels of output feature maps of conv17 are reduced to 256 through  
Figure 3: Overall architecture of our method.  
a 1 \(\times\) 1 convolutional layer. Then, the reduced outputs are bilinearly up-sampled and merged with outputs of conv13 by element-wise summation. The merged feature maps are then merged with the outputs of conv9 in the same way but with channels of 128. After two iterations, we obtain two sets of fused feature maps and in total, three sets of feature maps with semantically strong features at all three scales, called P9, P13 and P17. A 3 \(\times\) 3 convolutional layer is attached after each set of the feature maps with channels of 512, 256 and 128.
## 5 Numerical Results  
### Simulation Setup  
To evaluate the performance of the proposed protocols, this section compares the performance of the two protocols proposed in this paper with the Slotted ALOHA protocol and the IRSA protocol. The program running environment is Python 3.11.5.  
The CAS slot lasts 0.2 ms, and the TTS slot lasts 4 ms. The system runtime is set to 20,000 ms, which is the length of one superframe. The number of pieces of SUE is 220, and the number of ACNs is 1. The traffic load refers to the average number of packets arriving for all pieces of SUE within 4 ms. The polynomial probability distribution of copies is derived from the effective distributions validated in [7]. In their research, a series of optimal probability distributions were obtained using differential evolution algorithms, which balance the trade-off between computational complexity and system throughput. For our simulation, we select a distribution with moderate complexity (avoiding excessive copy numbers that increase the decoding burden) and proven performance: \(\Lambda(x)=0.5x^{2}+0.28x^{3}+0.22x^{8}\). \(N_{C}\) is 200, and \(G^{*}\) is 0.7. The initial access probability \(P_{1}\) is 1, the initial estimated number of users \(M_{1}\) is 220, and the initial estimated arrival rate \(\lambda_{1}\) is 1/22.  
In the simulation, channel utilization is used as the metric to measure the efficiency of the protocol. The channel utilization \(U\) is calculated as \(U=\frac{N_{T}}{N_{T}}\), where \(N_{T}\) is the total number of TTS slots and \(N_{TS}\) is the number of TTS slots with traffic transmission.  
The specific parameters of the simulation process are shown in Table 1.
# Research on Localization Algorithms Based on Acoustic Communication for Underwater Sensor Networks
## 3 Localization Algorithm
### Computation Algorithm
#### 3.1.2 Distributed Technique
The authors describe the particular algorithm in Figure 4. Anchor nodes with more energy can position themselves in each localization period, and they perform the complex mobility prediction algorithm. The localization of ordinary nodes contains the mobility prediction and location estimation. A Scalable Localization scheme with Mobility Prediction (SLMP) is presented in [30]. The localization process is similar with the EMBL. Figure 4: Block diagram of the proposed EMBL algorithm [29]. Figure 3: Polygonal line paths [26]. In this subsection, we have discussed the computation algorithm and the difference between the centralized and distributed techniques. We use some papers to show the different characteristics of the two techniques. Furthermore, we summarize these papers that use the computation algorithm to achieve the positioning in Table 2. We mainly compare the coverage, localization time, accuracy, computational complexity, and energy consumption. It is not suitable to discuss what the best algorithm is when the application scenario is not taken into account. For example, if the power is limited, the reader should choose the localization algorithm with low energy consumption. If high accuracy is required, the reader should choose the localization algorithm with high localization accuracy, but, at the same time, the computational complexity may be high. Thus, according to the following table, the reader can choose the suitable algorithm for doing some application or doing some improvement. The comparisons between the localization algorithm referred to in this subsection and other localization algorithms that are introduced in other subsections will be shown in Section 4.
###### Abstract
The authors, in order to adapt the system to the needs of everyday life as much as possible, tried to achieve the highest possible robustness of the designed algorithm. Hence, the use of mental commands was abandoned in favor of facial expressions, which are more unambiguous. The designed classification system was therefore designed to effectively filter out numerous disturbances in such a way as to isolate and properly assess the signal generated by the examined persons from their facial expressions. The following sections effectively develop and describe this system. Moreover, the system was also expected to accurately indicate when the signal artifacts due to the selected nervous tics occurred and which one occurred. An expert classification system was expected to indicate the occurrence of a particular neural tic in the signal; the details are described in future sections. The designed expert system was therefore based on a solution using artificial intelligence in the form of a neural network. The tests included an analysis of the signal based on its filtering and an observation of the results after the application of power spectral density estimation. In this study, it was decided to use the Emotiv EPOC Flex Gel kit. It is a wireless BCI headset, which consists of 32 electrodes and provides the possibility of a wide application in projects requiring precision [28]. The use of Emotiv EPOC Flex took place, inter alia, in the work on comparing the impact of smoothing filters on the quality of data recorded with the Emotiv EPOC Flex brain-computer interface headset during sound stimulation [10]. It was also used in articles covering research on the use of mental commands in the mobile robot control task in [4; 5]. Further research using this device was presented in the article on a brain computing training system for motor imagery [29] and the publication on EEG-based eye-movement recognition [30; 31; 32; 33]. In addition, the headset was used in studies on the measurement of stress while using VR (virtual reality) [34] technology, as well as performing other [35] activities. The topic of research on the influence of nervous tics on BCI systems is not very well developed. Of course, attention should be paid to numerous studies on the influence of artifacts (mainly visual and blinking) on EEG signal quality and system efficiency. However,the number of studies focusing on topics strictly related to artifacts caused by nervous tics and disturbances in the environment of the examined person is small. The next section features articles and research related to the topics covered in this article.
## 3 Micromechanical Analysis
### Micromechanical Methods
#### 3.2.3 Continuity Conditions  
As mentioned earlier, throughout the representative cell, the continuity conditions of displacements at the interface between the subcells and the adjacent unit cells must be fulfilled. This turn means that the continuity conditions must be satisfied in both \(X_{2}\) and \(X_{3}\) directions within the composite, as described below.  
Inspecting Figure 3, the following conditions hold:  
\[x_{2}^{(f)}=X_{2}^{i}\mp X_{2}^{(f)}=\mp R\cos\theta, \tag{37}\]  
and  
\[x_{2}^{(m)}=X_{2}^{i}\mp X_{2}^{(m)}=\pm\bigg{(}\frac{h}{2}+R-R\cos\theta \bigg{)} \tag{38}\]  
Upon substitution of Equations (37) and (38) into Equation (16) for \(\lambda=f\) and \(\lambda=m\), viz.,  
\[u_{i}^{(f)}=u_{i}^{o(f)}\mp R\cos\theta\xi_{i}^{(f)}+x_{3}^{(f)}\xi_{i}^{(f)}, \tag{39}\]  
Figure 3: The representative cell used for continuity conditions.  
\[u_{i}^{(m)}=u_{i}^{o(m)}\pm\left(\frac{h}{2}+R-R\cos\theta\right)\xi_{i}^{(m)}+x_{3 }^{(m)}\xi_{i}^{(m)}. \tag{40}\]  
where  
\[u_{i}^{o(f)}=u_{i}^{o(f)}(X_{2}^{(f)}), \tag{41}\]  
\[u_{i}^{o(m)}=u_{i}^{o(m)}(X_{2}^{(m)}). \tag{42}\]  
It is necessary to take into account the average of the requirement for continuity of displacements at the interface between the two subcells. So,  
\[\begin{array}{l}\int_{-\pi/2}^{\pi/2}\left[u_{i}^{o(f)}\mp R\cos\theta\xi_{i }^{(f)}+x_{3}^{(f)}\xi_{i}^{(f)}\right]R\cos\theta d\theta=\\ \int_{-\pi/2}^{\pi/2}\left[u_{i}^{o(m)}\pm\left(\frac{h}{2}+R-R\cos\theta \right)\xi_{i}^{(m)}+x_{3}^{(m)}\xi_{i}^{(m)}\right]R\cos\theta d\theta.\end{array} \tag{43}\]  
After some calculus, we have  
\[u_{i}^{o(f)}\Big{(}X_{2}^{(f)}\Big{)}\pm\frac{\pi R}{2}\xi_{i}^{(f)}=u_{i}^{o (m)}\Big{(}X_{2}^{(m)}\Big{)}\mp\left(h+2R-\frac{\pi R}{2}\right)\xi_{i}^{(m)}. \tag{44}\]  
From Equation (44), the continuity conditions of displacements in the \(X_{2}\) direction are determined as follows:  
\[u_{i}^{o(f)}=u_{i}^{o(m)}=u_{i}^{o}. \tag{45}\]  
Equation (45) expresses the continuity of displacement for the two neighboring cells: \(\lambda=f\) and \(\lambda=m\).
# Linear Optics Calibration in a Storage Ring Based on Machine Learning
## 1 Introduction
In addition, researchers from the Institute of High Energy Physics have proposed a new dynamical aperture (DA) prediction method based on machine learning, which can reduce the computational cost of DA tracking by approximately an order of magnitude while maintaining sufficiently high evaluation accuracy [19]. [PERSON] et al. proposed using MLMOGA as a full-fledged replacement for the traditional Tr-MOGA in optimizing 4 GSR lattices [17]. Their research opened up new possibilities for considerable speedups of traditionally lengthy computational processes. In this paper, we explore applying machine learning to linear optics calibration. Such linear optics calibration based on machine learning is a new method. In practice, the LOCO method minimizes the difference between the orbit response matrix of the model and the measured matrix through SVD calculation and multiple iterations. The machine learning model can predict the quadrupole errors directly from the response matrix. Our method provides a new option for linear optics calibration of a storage ring. This paper is organized into the following sections. Section 2 explains the theory of linear optics calibration and describes machine learning theory, including the experimental method, and construction of convolutional neural networks (CNN). Section 3 documents the experimental research on machine learning, including the experimental results and analysis. Finally, Section 4 provides a brief summary of this paper.
## 3 Experimental Analysis
### Component Prediction
#### 3.4.3 Residual Component Prediction Based on GPR  
In the proposed combined method, the residual component is input into the GPR model. The GPR model estimates the potential load distribution based on the residual component and provides predictions for future load residual components. In the prediction of residual components based on GPR, the noise level is set as 0.3, the mean function is set to 0, and the kernel function selected is the Gaussian kernel, as shown in formula (6). The hyperparameters in the Gaussian kernel function are automatically optimized through the maximization of the log marginal likelihood function, as shown in formula (12).  
Figure 9 shows the prediction results of the residual component of the test set load data for the first two weeks (336 h). The GPR model demonstrates its effectiveness in predicting residual components characterized by substantial variability and randomness.  
To further demonstrate the precision of GPR in predicting the residual component, Figure 10 depicts the MAPE values for the residual component of the load data at each moment during the initial two weeks (336 h). It indicates that, with a few exceptions, the MAPE values of the forecast results generally range from 0 to 0.5, signifying a high level of prediction accuracy.  
Figure 8: MAPE values of seasonal component at each timestamp for the previous 336 h.  
Figure 9: Predicted results of residual component for the previous 336 h.  
Based on the aforementioned analysis, the LSTM, CNN, and GPR models all demonstrate excellent performance in predicting their respective components, achieving high accuracy.
###### Abstract  
Most of the tunnel excavation methods involve the use of explosion and vibration techniques that is not feasible in urban areas due to unavoidable production of noise, vibration, and dust. The environmental considerations of tunneling projects in urban areas demand the use of excavation methods in which minimum noise, vibration, and dust is produced. In this study, non-vibrational rock splitting method is introduced that involves the fragmentation of rock segments using a split-wedge system inserted into already drilled boreholes. The main objective of this study is the investigation of important parameters involved in the non-vibrational rock splitting method for improving its efficiency. Discrete element analysis of this method was performed using Particle Flow Code (PFC\({}^{\text{2D}}\)) and the concept of Representative Elementary Volume (REV) was used to simulate intact rocks based on their unconfined compressive strength and modulus ratio concept. Maximum borehole spacing values were obtained using the numerical simulation of rock splitting process in intact rocks. The numerical analysis results show that increased borehole spacing values can be used for all intact rock types in cases of average modulus ratio and high modulus ratio and also that decreasing the borehole depth generally results in the use of increased borehole spacing.  
non-vibrational rock splitting method; discrete element method; representative elementary volume; unconfined compressive strength (UCS); modulus ratio; borehole spacing; borehole depth
## 3 Methods
### Photogrammetry  
For the photogrammetry of the clavicle, 121 photos for fragment 56-186-007-AG-22-122 and 94 for fragment 56-186-007-AG-23-173 were taken in July 2015, using a camera Canon EOS 7D EF100 mm f/2.8 L. The photos have a resolution of 3456 \(\times\) 5184 pixels and have been taken indoors with flash and flash reflector umbrellas. The two bones have been planted head up and head down in Blu Tack adhesive, to get the best possible coverage. The photogrammetry of the clavicle was performed using Agisoft Metashape software (Figure 5) with the characteristics presented in the Table 1. \"Cameras aligned\" is the number of cameras for which the software was able to find position and orientation. \"Tie points\" is the number of points in a digital image that represent the same location in an adjacent image. \"Dense cloud points\" is the number of points in the dense cloud, calculated from the estimated camera positions, and for which program estimates depth information. \"3D model faces\" is the number of polygons in the mesh reconstructed from the dense cloud.  
The texture of the photogrammetry models has a good quality, but fails to provide the detail that metallographic microscopy allows. However, this first 3D digitization offered us a valuation support, and addressed our methodological choices for the rest of the process. With the Autodesk 3 ds Max software, we then removed the 3D Blu Tack (Figure 5).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Bone** & \multicolumn{4}{c}{**56-186-007-AG-22-122**} & \multicolumn{4}{c}{**56-186-007-AG-23-173**} \\ \hline
**Chunk** & **1** & **2** & **3** & **Total** & **1** & **2** & **Total** \\ \hline Cameras aligned & 44/44 & 33/33 & 44/44 & 121/121 & 52/52 & 42/42 & 94/94 \\ Tie points & 4829 & 4999 & 47/66 & 14,594 & 5314 & 5477 & 10/91 \\ Dense cloud points & 3,984,146 & 3,528,415 & 4,087,049 & 11,599,610 & 3,300,123 & 3,746,140 & 7,046,263 \\
3D model faces & 802,323 & 235,800 & 274,280 & 1,312,403 & 221,999 & 250,546 & 472,545 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical specifications of photogrammetry process produced with Agisoft Metashape.  
Figure 5: Photogrammetry (**a**) and 3D cleaning and alignment (**b**).