esa-sceva/satcom-chunk-collection huggingface dataset:
# Event-Based Path-Planning and Path-Following
## 6 Conclusions  
In the paper, we developed a two-level control system that allows for the AUV to move along a given reference path in a two-dimensional (2D) environment with obstacles. The key idea behind the developed approach is to use a discrete event system at the top level as a mechanism for detecting situations that require path updates and waypoint management. Two path planning algorithms were developed in order to ensure safe obstacle avoidance and return to the reference path after completing the avoidance maneuver. A digital path-following controller was designed while using the vector Lyapunov function method by minimizing the steady-state positioning errors of the AUV with respect to the moving VT. The simulation results illustrated the performance of the control system proposed.  
The developed approach demonstrates promising results on the plane. In our opinion, the approach has the potential to be extended to the 3D case. Additionally, there is a particular interest in its application to multiple AUVs. We plan to work on these directions in the future.  
**Author Contributions**: [PERSON], [PERSON], and [PERSON] proposed the main idea; [PERSON] supervised the research work; [PERSON] designed the algorithms and conducted simulation; [PERSON] and [PERSON] analyzed the data; [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding**: The event-triggered control system architecture has been developed under support of the Russian Foundation for Basic Research (Projects No. 20-07-00397). The work related to the development of the event-based path planning algorithm for AUVs is supported by the Ministry of Science and Higher Education of the Russian Federation (Grant No. 075-15-2020-787, large scientific project \"Fundamentals, methods and technologies for digital monitoring and forecasting of the environmental situation on the Baikal natural territory\"). The path-following controller has been designed using algorithms and software tools developed under support of the Russian Science Foundation (Project No. 16-11-00053).  
**Conflicts of Interest**: The authors declare no conflict of interest.
# The Role of Electron Trajectories in XUV-Initiated High-Harmonic Generation
###### Abstract  
High-harmonic generation spectroscopy is a powerful tool for ultrafast spectroscopy with intrinsic attosecond time resolution. Its major limitation--the fact that a strong infrared driving pulse is governing the entire generation process--is lifted by extreme ultraviolet (XUV)-initiated high-harmonic generation (HHG). Tunneling ionization is replaced by XUV photoionization, which decouples ionization from recollision. Here we probe the intensity dependence of XUV-initiated HHG and observe strong spectral frequency shifts of the high harmonics. We are able to tune the shift by controlling the instantaneous intensity of the infrared field. We directly access the reciprocal intensity parameter associated with the electron trajectories and identify short and long trajectories. Our findings are supported and analyzed by ab initio calculations and a semiclassical trajectory model. The ability to isolate and control long trajectories in XUV-initiated HHG increases the range of the intrinsic attosecond clock for spectroscopic applications.  
attosecond physics; high-harmonic generation spectroscopy; nonlinear optics Article
# Fast and Robust Object Tracking Using Tracking Failure Detection in Kernelized Correlation Filter
## 3 The Proposed Method
### Prioritization of Search Windows Based on Motion Vectors  
In many practical cases such as watching pedestrians or cars with CCTV, targets show linear movements for a number of frames. It means that movements of targets in the next frame can be predicted by analyzing recent movements of the targets. For this, we analyze movements of the targets by averaging motion vectors of the targets from recent 3 frames as expressed in Equation (6)  
\[\frac{1}{3}\sum_{i=n-3}^{n-1}\left(vx_{i},\ vy_{i}\right) \tag{6}\]  
\(vx_{i}\) and \(vy_{i}\) denote horizontal coordinates changes and vertical coordinates changes of the target in \(i\)th frame and \(n\) denotes current frame number.  
We separate values of motion vectors being analyzed during tracking into 9 regions as shown in Figure 6a. If absolute values of x and y of a motion vector are smaller than 2, it means there are no significant movements so that the motion vector belongs to region number 5. In other cases, each motion vector belongs to each region based on its direction. And each region of motion vector is associated with each search window as shown in Figure 6b. As tracking failure is detected, response map is generated in the search window which is associated with region of current motion vector of the target first, computational load can be reduced by skipping other search windows if the target is re-captured in the preferred search window.  
Figure 5: (**a**) Tracking failure detected as the target disappeared. (**b**) Multiple search windows are deployed in tracking failure handling process.  
Figure 6: (**a**) 9 regions of motion vectors. (**b**) Each search window associated with each region of motion vector.
## 3 Results  
### Realization and Evaluation of the Pre-Test Phase  
#### 3.1.1 Results Obtained in the Iref Stage of the Pre-Test Phase  
As mentioned in Section 2, the Iref stage consists of the realization of three modal tests and subsequent estimations of the modal parameters due to the CMIFs corresponding to the singular value decomposition of FRFs. The plots of the CMIFs, shown in Figure 6, indicate good agreement between the results of the realized modal tests.  
Two peaks around 23 Hz and 27 Hz correspond to the rigid modes (rigid translation in the Z-axis direction and rigid rotation around the Y-axis) caused by the clamping of the specimen to the modal shaker. Since the mode corresponding to the peak around 757 Hz exhibited a high complexity (approximately 0.9), the authors assume that this is not the true mode of the analysed specimen. The result of the analysis of possible modes using the auto MAC from the \(\Gamma^{\text{ref1}}\) measurement is shown in Figure 7. As all the off-diagonal elements are represented by very small numbers approaching zero, the analysis that we carried out led to the estimation of 11 modes which are well separated in frequency.  
The estimated natural frequencies (presented with precision of one decimal point) and damping ratios (presented with precision of three decimal points) of particular modes are presented in Tables 4 and 5.  
The mean course of the CMIF as well as the mean values of both the natural frequencies and damping ratios (lines denoted in Tables 4 and 5 as \(\Gamma^{\text{ref}}\) (mean)), respectively, were further used for comparison with the results obtained in the following stages of the pre-test phase. The mode shapes corresponding to the particular modes are shown in Figure 8.  
Figure 6: Singular curves of CMIF plots obtained from the three experimental modal analyses carried out in the reference \(\Gamma^{\text{ref}}\) stage of the pre-test phase.  
Figure 7: The analysis of the possible modes from the \(\Gamma^{\text{ref}}\) measurement using auto MAC.
# A Method to Determine BeiDou GEO/IGSO Orbital Maneuver Time Periods
## 3 Experimental Validation  
In order to validate the proposed method for detecting the period of orbit maneuvering for GEO and IGSO satellites, the results of orbit maneuvering detection for the period are analyzed.
# Concrete Silos: Failures, Design Issues and Repair/Strengthening Methods
## 2 Failure of Reinforced Concrete Silos
### Failure Due to Foundation/Soil Factors  
Soil Effects  
Poorly drained and less porous soils elevate the risk of crack formation as a result of significant heaving. Silos constructed in areas with clay soils have a greater risk of foundation failure considering that clay has a high water retention capacity, which in turn results in the expansion of the soil once the water freezes [51]. The load-bearing capacity of clay soils can be calculated using the formula in Equation (2), where the symbols \(\mathrm{q}_{a}\), \(\mathrm{N}_{c}\), \(\mathrm{C}\), and \(\mathrm{P}\) denote the permissible bearing capacity, shape factor, the mean shear strength of the soil, footing level overburden pressure [52].  
\[q_{a}\ =\ \frac{C}{F}N_{c}+P \tag{2}\]  
Drained soils have a lower concentration of axial and hoop forces as shown in Figure 2 compared to un-drained soils owing to the presence of excess pore pressure [39]. Based on this evidence, silos constructed in poorly drained silos would require higher structural reinforcement to offset the pressure linked to the bulk solid materials and the hoop and axial forces [39]. Additional risk of foundation emanates from foundation stiffness, which is a risk factor for foundation failure [39]. The risk of failure due to the presence of poor soils and foundation issues can be addressed through the adoption of basic precautionary measures, which include reinforced footing and concrete foundations to offset the circumferential loads at the base of the silos, soil pressure and bending stresses [52]. Alternatively, the silo should be centered on footings to facilitate equal distribution of the load/weight. The building precautions proposed by [PERSON] are consistent with [PERSON], [PERSON], [PERSON], and [PERSON], who proposed the reinforcement of the foundation using RC shell structure [53]. In brief, the soil effects should not be disregarded under any conditions. The design issues in reinforced concrete are discussed in Section 3.
## 5 Use Cases of Attack Detection with Matana
### Platform Setup  
A prototype of MATANA has been implemented on a Xilinx ML605 evaluation board equipped with a Xilinx Virtex-6 FPGA running a Linux kernel 4.15.  
We chose to build our target system with the Chipyard SoC generation framework v1.3 [30], which contains a RISC-V ISA softcore processor Rocket [9], since it is open source, actively maintained, and easily reconfigurable. Rocket is an in-order processor core, we configured it to 64 bit, medium size (with support of virtual memory, no Floating-Point Unit (FPU)), an L1 instruction cache, and an L1 data cache).  
Figure 5 presents the hardware structure of the target system. The hardware part of MATANA is completely integrated in Chipyard (represented in blue color in Figure 5). To ease the integration process, all MATANA-related files have been written in chisel [31], a Scala-based hardware design language used by Chipyard that can be compiled to Verilog code, especially to ease the signals connection inside the target system and be still compatible for different SoC parameters. The probing is mainly done inside the Rocket core to monitor the processor's internal signals, such as the instructions executed, the memory addresses accessed, and some hardware events.  
We also made several choices to ease and increase the accuracy of the evaluation process: (1) The detection logic has been implemented as a userland program for testing purposes, as it allows the test of different software implementations without regenerating the kernel image and rebooting the system; it is intended to be integrated into the Linux kernel after the testing phase in order to benefit from the protection at kernel level. (2) A Linux kernel module has been developed to manage the communications between the hardware and software Detection Module. (3) The Linux kernel has been patched to automatically capture the current PID at context switch.  
The Linux kernel binary was generated based on the freedom-u-sdk project v1.0 [32], version 4.15, with integration of MATANA. The software part of our prototype was compiled with GCC toolchain version 9.2.0 for cross compilation; the toolchain was compiled  
Figure 5: Hardware architecture of MATANA prototype implemented on FPGA.  
with the option rv64 imac since the hardware part of our target system does not have any FPU.
# Enhanced Tuna Detection and Automated Counting Method Utilizing Improved YOLOv7 and ByteTrack
## 4 Experiment and Results  
### Dataset and Evaluation Metrics Introduction  
#### 4.1.1 Dataset Introduction  
The dataset used in this study was obtained from monitoring videos of tuna longline fishing vessels operated by CNFC Overseas Fisheries Co., Ltd. in the South Pacific. A total of 952 images with a resolution of 2560 pixels \(\times\) 1920 pixels were extracted. Among these images, 319 were captured during daytime and 633 during nighttime. They were divided into training, validation, and test sets in a ratio of 6:2:2. After annotating the images using LabelImg software, TXT files were generated containing the class labels and corresponding bounding box coordinates for the foreground objects. The categories for tuna include three species: Yellowfin Tuna, Bigeye Tuna, and SkipJack Tuna, as shown in Figure 10.
# Modelling, Analysis and Performance of a Low Inertia AC-DC Microgrid
## 2 System Model
### Model of Diesel Generator  
#### 2.3.1 Generator Model  
The employed AC microgrid model includes the model of a diesel generator, the governor and the automatic voltage regulator (AVR) dynamics. The synchronous generator is modelled such that \(\omega_{s}\) is the synchronous speed. The deviation of rotor speed from synchronous speed is defined as \(\Delta\omega_{pu}\). \(I_{d},I_{q},I_{o}\) and \(V_{therm},V_{therm},V_{oterm}\) are the stator currents and the stator voltage in \(dq\)0 reference frame, respectively. The stator fluxes in the \(dq\)0 reference frame are defined as \(\psi_{d},\psi_{q},\psi_{o}\). \(E_{d}^{\prime},E_{q}^{\prime},\psi_{d}^{\prime}\) and \(\psi_{q}^{\prime}\) represent per-unitized versions of the rotor fluxes and \(E_{fd}\) is the input field voltage from exciter and \(P_{mech}\) is the input mechanical power from the turbine. The per-unit model constants are listed in Table 1. The equations that model the diesel generator are as follows [31; 32]:  
\[E_{d}^{\prime\prime}=E_{d}^{\prime}\frac{x_{q}^{\prime\prime}-x_{l}}{x_{q}^{ \prime}-x_{l}}+\psi_{d}^{\prime}\frac{x_{q}^{\prime}-x_{q}^{\prime\prime}}{x_{ q}^{\prime}-x_{l}}, \tag{9a}\] \[E_{q}^{\prime\prime}=E_{q}^{\prime}\frac{x_{d}^{\prime\prime}-x_{l}}{x_{d}^{ \prime}-x_{l}}+\psi_{d}^{\prime}\frac{x_{d}^{\prime}-x_{d}^{\prime\prime}}{x_ {d}^{\prime}-x_{l}},\] (9b) \[\psi_{d}=-I_{d}X_{d}^{\prime\prime}+E_{q}^{\prime\prime},\] (9c) \[\psi_{q}=-I_{q}X_{d}^{\prime\prime}-E_{d}^{\prime\prime},\] (9d) \[V_{determ}=E_{d}^{\prime\prime}(1+\Delta\omega_{pu})-R_{s}I_{d}+X_{q}^{\prime \prime}I_{q},\] (9e) \[V_{determ}=E_{q}^{\prime\prime}(1+\Delta\omega_{pu})-R_{s}I_{q}-X_{d}^{\prime \prime}I_{d},\] (9f) \[s\delta=\Delta\omega_{pu}*\omega_{s},\] (9g) \[2 Hs\omega=\frac{Pmech-D\omega}{1+\Delta\omega_{pu}}-(\psi_{d}I_{q}-\psi_{q}I_{d}),\] (9h) \[T_{dq}^{\prime}sE_{q}^{\prime}=E_{fd}-E_{q}^{\prime}-\left(X_{d}-X_{d}^{\prime }\right)*\left(I_{d}-\frac{X_{d}^{\prime}-X_{d}^{\prime\prime}}{\left(X_{d}^{ \prime}-X_{l}\right)^{2}}\left(\psi_{d}^{\prime}+\left(X_{d}^{\prime}-X_{l} \right)I_{d}-E_{q}^{\prime}\right)\right),\] (9i) \[T_{dq}^{\prime\prime}sE_{d}^{\prime}=-E_{q}^{\prime}-\left(X_{q}-X_{q}^{\prime }\right)*\left(I_{q}-\frac{X_{q}^{\prime}-X_{q}^{\prime\prime}}{\left(X_{q}^{ \prime}-X_{l}\right)^{2}}\left(-\psi_{q}^{\prime}+\left(X_{q}^{\prime}-X_{l} \right)I_{q}-E_{d}^{\prime}\right)\right),\] (9j) \[T_{dq}^{\prime\prime}s\psi_{d}^{\prime}=-\psi_{d}^{\prime}-\left(X_{d}^{\prime }-X_{l}\right)I_{d}+E_{q}^{\prime}, \tag{9k}\]  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter** & **Value (pu)** & **Parameter** & **Value (pu)** \\ \hline H & 1.5 & D & 1.33 \\ \(R_{s}\) & 0.0095 & \(X_{d}\) & 2.11 \\ \(X_{d}^{\prime}\) & 0.17 & \(X_{d}^{\prime\prime}\) & 0.13 \\ \(X_{q}^{\prime}\) & 1.56 & \(X_{q}^{\prime}\) & 1.56 \\ \(X_{q}^{\prime\prime}\) & 0.23 & \(x_{l}\) & 0.05 \\ \(T_{\[T_{q^{o}}^{\prime\prime}s\psi_{q}^{\prime}=-\psi_{q}^{\prime}-\left(X_{q}^{\prime}- X_{l}\right)I_{q}+E_{d}^{\prime}, \tag{9}\]
# Pulsed Nanoelectrospray Ionization Boosts Ion Signal in Whole Protein Mass Spectrometry
## 3 Results
### Mechanistic Considerations  
The effects of electrophoretic mobility on the abundances of ions formed in pulsed and direct current nESI was examined for ions with significantly different mobilities. Proteins have electrophoretic mobilities that are substantially lower than Ang. For example, the electrophoretic mobilities of Ang and Myo are 6 \(\times\) 10\({}^{-5}\) cm\({}^{2}\)/Vs and 1.4 \(\times\) 10\({}^{-5}\) cm\({}^{2}\)/Vs [59; 60]. The highest ion signal and \(S/N_{\rm C}\) values for Ang were obtained at a frequency and duty cycle of 250 kHz and 50%. The abundance of the singly positively charged Fe(III)-heme prosthetic (i.e., \(m/z\) 616) also had a maximum abundance and \(S/N_{\rm C}\) at 200 kHz and 50%, which was similar to that of Myo (200 kHz and 60%) formed from the exact same solution and ion emitter tip (Figures 3, 4 and S3). Thus, electrophoretic mobility alone cannot fully account for the enhanced abundance of these ions. There is a lack of a significant electrophoretic entrainment effect in pulsed nESI using nanoscale emitters. In AC ESI with larger bore emitters, the RF amplitudes are higher, volume of the Taylor cone is larger, and polarity of the ESI waveform is fully reversed every period unlike in pulsed nESI, which may contribute to the reduced dependence on electrophoretic mobility.  
The improved performance by use of pulsed high voltage nESI compared to DC nESI can be attributed to at least two factors. First, the use of shorter pulses may result in the formation of smaller sized ESI droplets owing to the lower nESI current. It is well established that larger initial droplets tend to be formed in ESI using higher currents [61]. The use of shorter pulses lowers the ESI current and in principle should result in the formation of smaller initial sized droplets. Such droplets should result in more efficient droplet desolvation and ionisation. Secondly, the use of shorter pulses should reduce charge-charge repulsion in the nESI aerosol plume, which may improve ion transmission through the narrow capillary entrance to the mass spectrometer. This may be beneficial to the detection of lower abundance ions and result in the signal not being linearly dependant on the duty cycle. It is established that in AC ESI using larger emitter tips with higher frequencies can result in a significant narrowing of the Taylor cone angle [50; 51; 52].
## 5 Experiment
### Experimental Results and Analysis
#### 5.4.2 Ablation Experiment  
To further validate the effectiveness of each module in the LGDMGCN model, ablation experiments were conducted on different components, including the Dynamic Multi-Graph Generation module (LGDMGCN \(w/o\) At), Global Spatiotemporal Graph Convolution module (LGDMGCN-Global), Local Spatiotemporal Graph Convolution module (LGDMGCN-Local), Time Attention Mechanism (LGDMGCN \(w/o\) Attn), and External Time Feature (LGDMGCN \(w/o\) Ext). The results are summarized in Table 3.  
According to the results shown in Table 3, removing the dynamic graph generation module from the LGDMGCN model (LGDMGCN \(w/o\) At model) led to an increase in MAE and RMSE, indicating that the dynamic graph generation module positively impacts the model's performance by utilizing multi-source data in both time and space. This suggests that the introduction of this module plays a crucial role in more accurately modeling the spatiotemporal graph. Secondly, removing the local spatiotemporal graph convolution module in the LGDMGCN-Global model resulted in a slight increase in MAE and RMSE. This confirms the effectiveness of local spatial features in capturing local spatial correlations at the station level. Removing the global spatiotemporal graph convolution module in the LGDMGCN-Local model led to an increase in MAE and RMSE, indicating that the global spatiotemporal graph convolution module positively influences the model's performance by utilizing global spatial features of the stations. Comparing the LGDMGCN-Global and LGDMGCN-Local models, it is observed that the global features of station space have a more significant impact on the model's performance compared to local features. The combination of both provides the model with different levels of spatial semantic information, which is beneficial for modeling spatial correlations on different scales.  
On the other hand, removing the time attention mechanism in the LGDMGCN \(w/o\) Attn model and the external time feature in the LGDMGCN \(w/o\) Ext model both resulted in an increase in MAE and RMSE. This indicates that these two modules play a crucial role in modeling temporal relationships and utilizing external features. In the end, the complete LGDMGCN model achieved the best performance in terms of MAE and RMSE, demonstrating the importance of integrating all modules to enhance the overall model performance and further emphasizing the effectiveness of the LGDMGCN model.
# High-Precision Combined Tidal Forecasting Model
## 3 Combined Tidal Forecasting Model Based on Harmonic Analysis and ARIMA-SVR
### Model Checking
#### 3.2.1 Sample Data Preprocessing  
The original waveform was constructed from the real-time signal sets (the tidal series measured from GMT0000 on 1 December 2017 to GMT2300 on 31 December 2017). The original waveform is shown in Figure 3. The waveform construction was based on the Sym8 wavelet basis function and two-layer wavelet decomposition. The threshold was selected using the heursure function, and de-noising was performed with a soft threshold function. The original and de-noised waveforms are compared in Figure 4, and the noise signal is shown in Figure 5. De-noising comparatively flattened the processed waveforms without changing their overall trend (Figure 4). The effectiveness of the de-noising pretreatment was verified in a comparative test (Section 3.2.4). The results show that de-noising improves the accuracy of the sample data.