esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Methods  
### Cascade Model  
In the current research, a linear compressor cascade, which was tested in the Whittle laboratory of the University of Cambridge [26; 27], has been selected for the investigation of the corner separation. In our previous studies, turbulence model modification, flow mechanisms, and flow control for corner separation were conducted in the same cascade [28; 29; 30; 31; 32]. The test rig used in the experiment consisted of five modern PVD (prescribed velocity distribution) blades. Detailed aerodynamic and geometric parameters are listed in Table 1. The inlet Mach number is approximately 0.07 to yield a blade-chord-based Reynolds number Rec = 2.3 \(\times\) 10\({}^{5}\). The inlet free stream turbulence intensity is about 1.5% according to the experiment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Symbol** & **Magnitude** \\ \hline Chord & \(c\) & 151.5 mm \\ Pitch to Chord & \(s/c\) & 0.926 \\ Aspect ratio & \(h/c\) & 1.32 \\ Camber angle & \(\psi\) & 14.7\({}^{\circ}\) \\ Stagger angle & \(\gamma\) & 42\({}^{\circ}\) \\ Reynolds number & \(R_{\rm e}\) & 2.3 \(\times\) 10\({}^{5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Aerodynamic and geometric parameters of the cascade.  
Figure 1: Geometry of the slot configuration.  
Figure 1 shows the geometry of the slot configuration used in this study. Line AB and CD are the boundaries of the slot, respectively. In order to introduce the mainstream flow from the blade pressure side into the slot without additional losses, line AB is set to be tangent to the pressure surface at point A. For the same purpose, line AB is tangent to the suction surface at point B to reduce the mixing losses between flow from the slot and the mainstream flow on the blade suction side. Hence line AB is defined as an S shape. Line CD is a circular arc which is tangent to the blade suction side at point D. The position of the slot is given in Figure 1. Point A is set at 10% axial chord from the leading edge and Point C is set at 35% of the axial chord from the leading edge to yield the slot inlet length \(I_{\text{AC}}=25\%Ca\). Point B is set at 50% of the axial chord from the leading edge and point D is set at 60% of the axial chord from the leading edge to yield the slot outlet length \(I_{\text{BD}}=10\%Ca\). The height of the slot is set at 10% of the whole blade span, which is 0.02 m. In order to guarantee \(y^{+}\) in the slot, a structure mesh is generated and the first mesh near the wall is set the same as the mesh around the blade, which is \(10^{-5}\) m.
## 2 Materials and Methods  
_Materials_: In this study, four different sample systems were used to investigate the effect of tip penetration due to higher excitation frequency in loss tangent AFM. The first set of samples was a Sapphire surface manufactured by Bruker (known as PFQNM-SMPKIT-12M). This surface provided a flat and stiff surface for AFM measurements. The second sample was a blend of Polystyrene (PS) and Polyolefin Elastomer (ethylene-octene copolymer, LDPE), which were spin-coated on a silicon substrate with 1500 RPM for 60 seconds to create a thin film with varying material properties. The PS regions of the sample had elastic modulus numbers around 2 GPa while the copolymer regions had elastic modulus numbers around 0.1 GPa. Therefore, it could provide a clear contrast in material composition studies by AFM. The third sample was a single-polymer thin film. Polystyrene with 33 kDA molecular weight diluted into 2.5 wt% in THF was used, which was spin-coated onto a silicon wafer at 1400 rpm for 60 seconds. We selected a polystyrene of low molecular weight to prepare a sample that displayed time-dependent behavior within the deformational timescale in our studies (previous studies have quantified the dependence of characteristic times on the molecular weight of polystyrene [16]. The humidity was increased to above 60% in order to create islands that could expose the stiff substrate. This could provide a drastic material change in our sample which is suitable for the purpose of this study. The fourth sample was Teflon, more commonly and widely known by the name Polytetrafluoroethylene (PTFE), a fluorocarbon solid. Teflon has one of the lowest coefficients of friction, which is why it is widely used in many different industrial applications.  
_AFM Measurements_: All measurements were operated in AM-AFM mode using MFP3D Origin AFM (Asylum Research, Oxford Instruments, USA) in air conditions (~299 K, relative humidity of ~%30). All the measurements were done using the same type of cantilever to provide consistency in measurements. Between each measurement, the tip of the cantilever was monitored to ensure there was no contamination from previous studies. The most common type of cantilever available commercially for topography and viscoelasticity measurements of soft samples is AC240 TS-R3 manufactured by Olympus. This cantilever had the first eigenmode resonance frequency of f\({}_{1}=72\)kHz and f\({}_{2}=460\) kHz. The tip radius was measured by the manufacturer to be around 7 nm. The nominal spring constant was measured to be 2.3 N/m.
# Deep Anomaly Detection for In-Vehicle Monitoring--An Application-Oriented Review
## 3 Publicly Available Datasets
### Real-World Datasets
#### 3.1.1 Pedestrians and Crowds
The CUHK Avenue dataset [11] is very common in benchmarks, and it was also acquired using a stationary video camera in the CUHK campus avenue. It has 16 training video samples and 21 test video samples. The abnormal behaviour represented in the scenes is connected to human actions, showing people littering items, walking on the grass, and throwing or abandoning objects in the background. However, both datasets possess severe limitations regarding their single-scene representation, lack of abnormality diversity, and amount of sequences. It is desirable to learn an anomaly detection model capable of performing well under multiple scenes and viewing angles. To address these drawbacks, ShanghaiTech [4] was developed, taking advantage of multiple surveillance cameras with different view angles installed at different spots, to capture real events at a university campus. ShanghaiTech has challenging light conditions and camera angles, as Figure 3c,d exemplify. It contains 130 abnormal events and annotations for pixel-level ground truth of abnormal events. Recently, a new dataset, IITBCorridor [60], was proposed to tackle the lack of diversity of abnormal human activity. The videos were captured on the IIT Bombay campus, using a single-camera setup. There is a single scene, consisting of a corridor where walking and standing are considered regular activities. Protesting, fighting, and abandoning suspicious objects were some of the ten enacted activities. These activities extend from a single person to group-level anomalies, and frame-level labels were provided for training and validating models. Additionally, class labels for each abnormal activity were also provided for classification purposes. Another work that follows this path is ADOC [61], with data  
Figure 3: Abnormal frames extracted from widely used datasets for training and benchmarking video anomaly tasks. (**a**) Two bikers amongst the pedestrians in Ped1 [12] dataset. (**b**) Car and biker in a pedestrian walkway in Ped2 [12] dataset. (**c**) A normal frame from ShanghaiTech dataset [4]. (**d**) Two people fighting in ShanghaiTech dataset [4]. acquired from a surveillance camera deployed on a large university campus. The dataset was created from video captured for 24 consecutive hours. The captured video encapsulates varying illumination conditions, with daytime and nighttime sequences, and crowded scenarios with background clutter. The data are annotated with 875 events.
# An Experimental Study of the Leakage Mechanism in Static Seals
## 4 Results and Analysis
### Effect of Sealing Device Parameters on Leak Rate  
In this section, by combining the single-factor comparison experiment with the leak rate prediction model [18], the effects of the three sealing device parameters, i.e., gasket (metal) contact load, fluid pressure, and apparent surface size, on the leak rate were analyzed. Experimental and calculation parameters are shown in Tables 1 and 2.
## 2 Previous Work
### Contributions  
In this paper, we describe the malicious PUE attack in the MCRN comparing the theory, simulations, and experiments in the SDR testbed to establish a system to detect this attack, which, to the best of our knowledge, is the first approach to detect this attack with this method. The selfish attack is described in [5]. In this paper, we focus on a malicious attack using ML. The main contributions of this paper are summarized as follows:  
* Analyze and extract the PU signal behavior in systems with different modulations such as GMSK or OFDM.
* Establish the SNR and entropy as the two dimensions of the SVM.
* Improve energy detection by adding entropy and feature recognition.
* Define and implement the SVM with the learning behavior as an input generating the decision of the presence of a malicious PUE.
* Train and test the SVM with real values from the SDR.
* Predict the results of the malicious PUE signal.
* Model and implement the malicious PUE attack in the MCRN.
* Implement the MCRN testbed and experiment with the SVM learning stage, calculating the SNR and entropy based on the energy.  
The rest of this paper is organized as follows: Section 3 introduces the SVM system model; the testbed for the experiments is described in Section 4; numerical results are presented in Section 5; in Section 6, a discussion takes place; finally, conclusions are drawn in Section 7.
## 3 Methodology
### Design of Data-Computing Engine
#### 3.3.1 Generation of Data-Computing Task  
To execute a data-computing solution, it is first necessary to understand the computing environment requirements for these data-processing tasks, that is, to create data-computing tasks. When the user calls the execution command of the data-computing solution in the workspace, the workspace will transfer the computing solution to the computing engine.  
Firstly, the computing engine will traverse the entire data-computing solution node and collect metadata information of the data service corresponding to each node. The metadata of data resource services usually includes the size of the disk space occupied by the data resource, which can be used as a basis for allocating the size of the hard disk space for it. The metadata of data-processing services usually includes the hardware environment (such as CPU architecture, memory size, etc.) and software environment (such as the requirement to install Python 3.7 environment, etc.) that the execution of the data-processing program depends on. Based on this software and hardware dependency information, the computing engine will search for suitable server nodes from the network (which have already been registered in the computing engine). Server node matching is a complicated process, which needs a specially designed matching algorithm. The matching algorithm will score each server node based on the software and hardware requirements of the current computing task, and provide a list of candidate servers sorted by their scores. If a suitable server cannot be found, the user must manually configure the corresponding execution environment.  
Subsequently, each data-processing service will generate a data-computing task, as shown in Figure 7. The \"serviceld\" attribute of each \"Task\" node represents the data-processing service to be called by the task, and the \"order\" attribute represents the execution order of the task. For example, a value of \"1\" for \"order\" indicates that the task is executed first, and so on. The \"Servers\" Node indicates the server where the current task is running. There may be multiple \"Server\" nodes under the \"Servers\" node (that is,multiple servers meet the execution conditions of the current task). Each \"Server\" node has a \"score\" property that indicates how well the server node meets the computing environment requirements of the current task. Typically, the current computing task will select a server node with a high score as its execution environment. The \"Dependency\" node indicates the software and hardware requirements of the current computing task. For example, each \"Environment\" node under the \"Software\" node indicates the required software environment information. The \"CPU\", \"Memory\", \"HardDisk\", and \"Network\" nodes under the \"Hardware\" node indicate the required hardware information.
# Estimation of Chlorophyll Fluorescence at Different Scales: A Review
## 5 Current Problems and Discussion  
Significant progress has been made in chlorophyll fluorescence remote sensing from the leaf scale to the satellite scale using active and passive remote sensing technology. Today, chlorophyll fluorescence is widely used in research on the correlation with the physiological state of vegetation. Chlorophyll fluorescence detection has achieved good results at the leaf scale and at the canopy scale and has also been used effectively to monitor vegetation water stress [29; 30; 42; 72; 73; 93; 105; 106; 107; 108; 109; 110; 111], ozone stress [66; 112; 113], nitrogen stress [3; 4; 54; 114; 115; 116; 117; 118], pest stress [119; 120], GPP [121; 122; 123; 124; 125; 126; 127; 128], heat stress [125], and crop productivity [126]. Despite many experiments designed to clarify the relationship between chlorophyll fluorescence and vegetation stress, the internal mechanisms of this relationship remain to be explored. For sun-induced chlorophyll fluorescence derived from the satellite data, the atmospheric effects are significant and make retrieval more complicated. The problems associated with the retrieval methods under consideration will be discussed briefly below.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Meaning** \\ \hline \(v_{i}\) & The singular vector \\ \(\omega_{i}\) & The weight of the singular vector \(v_{i}\) \\ SIFTOA & Fluorescence intensity at the top of the atmosphere \\ I & An identity vector of size \(n\) \\ \(n_{p}\) & The number of singular vectors \\ \(F(\omega,F_{s})\) & The radiance at the sensor \\ \hline \hline \end{tabular}
\end{table}
Table 6: Parameter in Equation (23).
## 4 Results and Analysis
### The Impact of Transfer Learning on Model Performance  
The baseline model AST is initially used for sound classification in the AudioSet dataset and then applied to downstream tasks through transfer learning. Therefore, it is necessary to verify whether transfer learning affects the model's performance. The pre-trained AST model is fine-tuned for 50 epochs, while the non-pre-trained AST model is trained from scratch for 200 epochs. Exponential learning rate decay and a warm-up period are used to optimize the training process, with an initial learning rate of \(1\times 10^{-5}\) for the pre-trained AST and \(1\times 10^{-4}\) for the non-pre-trained AST. The experimental results are shown in Table 3.  
Compared to the UrbanSound8K dataset, the ESC-50 dataset contains only 2000 audio samples but has 50 categories, making it difficult for a non-pre-trained AST model to achieve high classification accuracy on the ESC-50 dataset. In contrast, it can achieve a relatively high classification accuracy on the UrbanSound8K dataset, which has only 10 categories. Notably, the pre-trained AST model drastically elevates classification accuracy across both smaller and more diverse datasets, underscoring the inadequacy of relying solely on non-pre-trained models for urban environmental sound classification tasks. However, compared to the AST model pre-trained only on the ImageNet dataset [35], the AST model additionally trained on the AudioSet audio dataset performs better. As Table 3 vividly illustrates mentioned above, harnessing transfer learning by applying this pre-trained model to ESC tasks significantly amplifies the accuracy and capability. It also emphasizes the transformative impact of our novel pre-training and transfer learning methods in enhancing model performance for audio classification tasks.
## 4 Analysis of Experimental Results
### Hydrate Separator Pressure Loss Experiment Results Analysis  
To verify the accuracy of the model, the pressure drop results from the separator's numerical simulation were compared with the experimental data. The experiment used the WLA-3A controller to adjust the inlet pressure and flow rate. As shown in Figure 9, clean water was slowly injected into the separator at a low flow rate until all of the air was expelled and the separator was filled with water. During the experiment, the valve opening was adjusted every 2 min, and the changes in the outlet pressure caused by variations in the inlet pressure and flow rate were recorded. The changes in the separator's outlet pressure are shown in Table 3. As the inlet pressure varied (a-e), different degrees of turbulence were observed in the separator's spiral channel, as seen in Figure 10. In the numerical simulation, velocity boundary conditions were applied, and the inlet velocity in Table 4 was calculated by dividing the experimental flow rate by the separator's inlet area (0.0162 m\({}^{2}\)).  
Figure 8: Comparison between experimental setup and simulation of spiral runner separation.  
Figure 7: Local comparison between simulation and test.  
Due to experimental limitations, the separator could not operate under the actual conditions of solid-state fluidized mining. Therefore, the numerical simulation boundary conditions were set to match the experimental setup (see Table 3) for the calculations, and the simulation results were compared with the experimental data for validation. The pressure drop curves from both the experiment and the simulation are shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Inlet Pressure (MPa)** & **Flow Rate (m\({}^{3}\)/h)** & **Outlet Pressure (MPa)** & **Pressure (MPa)** & **Inlet Speed (m/s)** \\ \hline a & 0.05 & 11.4 & 0.018 & 0.032 & 0.411 \\ b & 0.08 & 13.9 & 0.016 & 0.064 & 0.367 \\ c & 0.11 & 17.5 & 0.018 & 0.092 & 0.301 \\ d & 0.13 & 21.4 & 0.019 & 0.111 & 0.23 \\ e & 0.15 & 24.3 & 0.020 & 0.13 & 0.19 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Separator Inlet and Outlet Pressure Loss Test Results.  
Figure 10: Turbulence intensity phenomena in separators with different inlet pressures.  
Figure 9: Separator flow pressure differential test diagram.
# Assessment of Grassland Degradation on the Tibetan Plateau Based on Multi-Source Data
## 2 Materials and Methods  
### Study Area  
The TP is located in the south west of China (26\({}^{\circ}\)00\({}^{\prime}\)12\({}^{\prime}\)N \(\sim\) 39\({}^{\circ}\)46\({}^{\prime}\)50\({}^{\prime}\)N, 73\({}^{\circ}\)18\({}^{\prime}\)52\({}^{\prime}\)E \(\sim\) 104\({}^{\circ}\)46\({}^{\prime}\)59\({}^{\prime}\)E). As the largest plateau in China and the highest in the world, the TP has an average altitude of over 4000 m and a total area of about 2.5 million km\({}^{2}\) (Figure 2a). The TP is surrounded by many mountain ranges, where Mount Qomolangma is the highest mountain in the world. In addition, the TP and its surrounding areas have the largest number of glaciers outside of the poles [28]. Therefore, the TP is the headwater of many major rivers in Asia such as the Yangtze River Yellow River, etc., and is known as the \"Water Towers in Asia\" (Figure 2b). The climate of TP is complex and diverse, mainly influenced by the interaction of the Indian monsoon and west wind, with limited influence from the East Asian monsoon, characterized by low temperatures, small annual differences in temperature, uneven precipitation, and strong solar radiation [29; 30]. The annual mean temperature on the TP ranges from \(-\)2.2 \({}^{\circ}\)C to 0 \({}^{\circ}\)C, with the lowest temperature of \(-\)13.5 \({}^{\circ}\)C in January and the highest of 9.7 \({}^{\circ}\)C in July [31]. Precipitation is mainly concentrated from June to September, with annual average rainfall between 415 mm and 512 mm and the annual sunshine hours range from 2730 to 2915 h [32]. The TP is the highest plateau ecosystem in the world, including forests, grasslands and shrubland, etc., with nearly three-quarters of its surface covered with various grassland ecosystems (Figure 2b). Meanwhile, these grasslands are important pastures that play an important role in maintaining the ecology and structure of the TP [33].  
Figure 1: Flowchart of this study.  
Figure 2: Information of altitude and grassland on the TP (**a**) location and elevation of the TP (**b**) grassland and water bodies of the TP.
We are able to unambiguously assess the signal-to-noise ratio (\(SNR\)) of each scan, which then becomes the weight with which a scan enters the average of multiple scans. We also observe a clear gap in the values of \(SNR\), and we discard all the scans below a \(SNR\) of 0.45. We are able to generate an average delay scan profile, suitable for further analysis: in our previous work we used it for comparison with theory. Here we argue that the method is sufficiently simple and devoid of human action to be applicable not only in post-analysis, but also for the real-time assessment of the quality of a dataset. correlation; matched filter; pump-probe; free electron laser; data processing; statistical weight +
Footnote †: journal: Journal of Sound and Vibration
# Positioning-Based Uplink Synchronization Method for NB-IoT in LEO Satellite Networks
## 3 Position-Aided Uplink Synchronization Method  
To address the uplink synchronization issue of NB-IoT terminals in LEO satellite networks, this paper proposes a position-aided uplink synchronization method named based on synchronization signals (BOS). The method first detects the NPSS transmitted by the satellite to estimate time delay and Doppler shift observation values. Then, a TDOA equation is constructed using the delay observations and satellite ephemeris to solve for the terminal' s position. Finally, the TA value and Doppler pre-compensation are calculated based on the terminal position and satellite ephemeris, completing uplink synchronization.  
Furthermore, to assess the theoretical performance bound of the proposed algorithm, the CRLB for the positioning algorithm is derived. Based on this, a receiver block diagram is designed for the terminal, and the power consumption of the uplink synchronization process is analyzed under different numbers of visible satellites.