esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on Interface Frictional Characteristics between Sand and Steel Pipe Jacking
## 1 Introduction
It was obtained that the presence of slurry lubrication caused the stress-strain curve to change from strain hardening to strain softening, and the strain softening characteristics were enhanced with the increase of normal stress. [PERSON] et al. [12] carried out a study on the mechanical properties of the pebble soil-concrete and uniform gravel-steel plate interface, with and without slurry lubrication conditions. It was concluded that the particle size, particle gradation and interface roughness of the soil were responsible for the inconsistent shear property patterns at the two different contact interfaces. [PERSON] et al. [13] used a direct shear test to study the friction characteristics between the contact surface of sand and concrete pipe jacking with different particle sizes. The test results showed that with the increase of the sand particle size, the friction resistance between the pipe and soil decreases, and mud can effectively reduce the friction resistance between the pipe and soil. [PERSON] [14] investigated the mechanical properties of the interface of sandy soil and structures under different conditions of mud cake thickness and roughness of the structural interface. It was shown that when the interface was smooth, the stress-strain curve changed from a softening type to a mild softening type and finally to a double-folded type as the thickness of the mud cake increased. [PERSON] et al. [15] designed six different slurry ratios to study the interaction mechanism between a slurry-soil mixture and pipeline, and after pipe jacking grouting, analyzed the shear stress-displacement curve of slurry-soil concrete and the concrete interface under different conditions, and obtained the formula for calculating the shear strength of the slurry-concrete interface considering slurry content. In summary, the current domestic and foreign research on steel pipe jacking wall friction resistance is mostly focused on the bentonite slurry formulation and the establishment and improvement of the pipe soil-pipe slurry contact model. There is little research on the calculation and influencing factors of the friction coefficient between the steel pipe section and the formation and the lubricating slurry. Most of the existing research is also on single specific working conditions, which is lack of representation. Therefore, the friction characteristics of sand and steel pipe jacking with different particle sizes under different normal stresses and different lubrication conditions are studied and analyzed by using the straight shear tester. The shear strength and residual strength of the interface of sand and steel pipe jacking measured by the test are regressed and analyzed, and the friction coefficient of the pipe and soil under different conditions is obtained according to the regression equation.
## 2 Materials and Methods
### Daily EVI2 of Flux Footprint  
The pixels' daily EVI2 trajectories, which were generated from the previous section, were further used to estimate daily EVI2 over the flux footprint. To compute MODIS 250 m EVI2 averages over the flux footprint area, denoted as EVI2\({}_{250\text{m}}\), a maximum of four pixels around the tower were used, and 500 m EVI2 averages (EVI2\({}_{500\text{m}}\)) were computed from the 500 m pixel(s) where the towers are located (Figure 4). Sentinel-2 weighted averages over the flux footprint areas, denoted as EVI2\({}_{10\text{m}}\), were thus computed from 450-5800 pixels depending on the estimated footprint size. Since 10 m Sentinel-2 pixels are much smaller than flux footprints, we calculated the footprint EVI2 more precisely by  
\[EVI2_{10m}=\sum_{i=1}^{n}EVI2_{i}*w_{i}\;, \tag{2}\]  
where \(EVI2_{10m}\) is the footprint EVI2; \(n\) is the number of Sentinel-2 pixels covering the flux footprint; \(EVI2_{i}\) and \(w_{i}\) (0-1) are EVI2 of the pixel \(i\) and the corresponding contribution weight to the total flux.  
Figure 3: Fitted daily EVI2 time-series on a Sentinel-2 pixel (10 m \(\times\) 10 m) at site SE-Lnn (Sentinel-2 tile 33 VUE; row 8891 and column 3190). The black dots represent the raw EVI2 time-series of the pixel. The red and black circles are points marked as vegetation and soil from SCL. The red curve is the fitted daily EVI2 time-series by box constrained separable least squares fitting to double logistic model functions.
## 2 Procedures in Deriving Rainfall Maps from Attenuation  
When the path of CMLs is exposed to rainfall, additional attenuation of the microwave signal occurs accordingly. However, these attenuation increments are not entirely due to scattering and absorption by raindrop particles. Phenomena such as electronic component drift, changes in water vapor, multipath effects, and wet antenna effects can also lead to changes in signal quality. Deriving rainfall estimates from CML attenuation is, therefore, a sophisticated process, which is described in detail in this section.
## 5 Simulation, Experiment, and Comparison
### The Simulation
#### 5.1.3 The Evaluation of Fuzzy Impedance Control  
For evaluating the performance of the fuzzy impedance controller, a testing data is created including 300 records of different values of closing speed, desired force, and object hardness. The testing data does not coincide with the training dataset. The model runs 300 times with the parameters of the testing data. In each time, when the fingers have just touched the object for the first time, the values of \(\Delta F_{e}\), \(\dot{X}_{e}\) are calculated. The fuzzy impedance controller calculates the impedance parameters \(B\) and \(K\). The program measures and records the parameters of the maximum overshoot \(F_{p}\) and the final force error \(F_{fe}\). Table 2 presents the analysis data from 300 tests. Figure 8 is the force monitoring data of some cases in the simulation testing. Section 6 will discuss in detail about this figure.
# Underwater Highly Pressure-Sensitive Fabric Based on Electric-Induced Alignment of Graphene
## 2 Materials and Methods
### Preparation of Superhydrophobic Fabric Sensor  
Preparation of superhydrophobic fabric sensor: the deposition of graphene/PDMS precursor, alignment induced by an electric field, and radio frequency capacitively coupled Ar plasma (RF-CCP) treatment. First, graphene and ethanol solutions were ultrasonically dispersed and exfoliated for 2 h, and the poly(dimethylsiloxane) precursor was then added to ultrasonic and mixed for 1 h (Table S1). The prewashed fabric was dipped in the coating solution for 2 h by ultrasonic impregnation, and the impregnated fabric was then dried and cured in a DC electric field (DC high voltage power supply (SL10P1200 0\(-\)10 kV) purchased from Spellman Electronic Technology Ltd., Hauppauge, NY, USA) (Figure S1) for 2 h. The fabrics after the electric field action were plasma-treated in a radio frequency capacitively coupled plasma (RF-CCP) (Figure S2) reactor (AP-600, North-March, USA) to obtain superhydrophobic conductive polyester fabrics. The fabric pressure sensor was made of a stack of six superhydrophobic conductive fabrics. Both ends of the sensor are encapsulated with conductive copper tape to reduce the effect of contact resistance during testing. The sensor obtains the most stable electrical signal output.
## 3 Requirements for Infrastructure of Service Provision for Cargo Transportation Processes

Table 2: Preliminary frequency evaluation for providing services by scenario’s analysis of the communication networks. Figure 1: Class diagram of main components of the computer-based ontology of cargo transportation process with assessing infrastructure elements. Computers can be bigger, faster, and provide very high-capacity memory devices (terabytes of data), and powerful wireless interfaces, capable of high-speed communication. The conditions of transportation can vary in a large spectrum. The vehicles can move from more slowly until very high speed (160 km/h or more), making it difficult to maintain consistency in coherent V2V communication. However, the existing statistical data on vehicle movements, such as the movement together according to specific patterns or peak time, can help to maintain a link between the mobile automotive groups [21; 22; 23]. Vehicle at any time may be out of communication coverage (wireless fidelity (abbreviated as Wi-Fi), cellular, satellite, etc. ), so the network protocols must be designed so that it can easily connect to the Internet, in normal mode. Despite the many positive, unique features, vehicular network's development is faced with specific challenges, as their primary:  
* Large-scale networks;
* High level of mobility;
* Fragmentation of the network;
* Changing topology;
* Complex communication quality assurance. Vehicular communication networks, in principle, can extend across the road network and cover a considerable amount of network equipment (dynamically accessible for vehicles). The networks are operating in an extremely dynamic environment. In some cases, it may be highly different, for example, in highway speeds can reach up to 300 km/h, in the low-density roads it may be as only about 1-2 transport means per km. On the other hand, the speed of vehicles in urban areas is 50-60 km/h, and the traffic density is relatively high, particularly during the peak periods. Often vehicular communication networks may be fragmented. The dynamic nature of traffic can lead to large gaps between road traffic actors in sparsely populated areas; it can also be created a few isolated clusters of network nodes. Vehicular communication networks' scenarios are highly different from the classic ad-hoc networks since the road traffic actors are moving and constantly changing positions, scenarios are highly dynamic. Furthermore, the network topology changes extremely frequently since the persistent connections and disconnects between network nodes. The degree to which the network is combined depends on two factors: distance among the wireless nodes and the number of connected vehicles [8].
## Methodology
### Characterisation of the Rock Mass  
The geomechanical characterisation of the rock mass prior to the numerical modelling is usually based on the identification and quantification of the ten basic parameters recommended by the International Society for Rock Mechanics (ISRM) [25]. For simplification purposes, to be able to reproduce the behaviour of rock masses at failure in the face of external static loads, there are three fundamental parameters, in addition to the anthropic factors, that condition their response:  
* The rock lithology (in this case, the parameter used is m\({}_{i}\)).
* The quality of the rock mass, identifiable through any of the geomechanical classifications, especially the geological strength index (GSI) [26], which, in general, can be assumed to be equal to the basic rock mass ratio (RMR\({}_{b}\)) of Bieniawskii [27].
* The uniaxial compressive strength of the rock matrix (UCS).  
The representative values of these parameters, used in this study for each type of rock mass, are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rock Mass** & **GSI** & **UCS (MPa)** & **m\({}_{i}\)** \\ \hline VOLCANIC & 25–50–75 & 10–50 & 17 \\ \hline CARBONATE & 25–50–75 & 5–40 & 8 \\ Limestones and Dolomites & 25–50 & 5–20 & 5 \\ \hline EVAPORITIC & & & \\ Gypsum & 25–50 & 5–20 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Geomechanical parameters used according to the type of rock mass.
# Research on Renewable Energy Planning Considering the Flexible Region of the Microgrid
## 3 Model and Solution of Distributed Energy Allocation in the Microgrid under Dynamic Boundary
### Related Component Model of the Microgrid
#### 3.1.3 Photovoltaic Power Generation Model  
The common photovoltaic power generation model [20] is adopted in this paper. The photovoltaic output power \(P_{PV}\) is obtained from the output power, light intensity and ambient temperature under the standard rated conditions (solar irradiance \(G_{STC}\) is 1000 W/m\({}^{2}\), relative atmospheric optical mass is AM1.5, battery temperature \(T_{STC}\) is 25 \({}^{\circ}\)C). The model is as follows:  
\[P_{PV}=P_{STC}\frac{G_{c}}{G_{STC}}[1+K(T_{c}-T_{STC})] \tag{4}\]  
where \(P_{STC}\) is the rated output power of photovoltaic under the standard rated conditions; \(G_{c}\) is the irradiance at the working point; \(k\) is the power temperature coefficient; and \(T_{c}\) is the battery temperature at the working point.
## 3 Experiment
### The Weight for Two Losses  
GSPRnet, which is proposed in this paper, uses the combination of Focal Loss and the Dice coefficient as the overall loss function. In order to illustrate the influence of the weights of Focal Loss and the Dice coefficient in the overall loss on the model performance, we conducted an ablation study.  
Firstly, keeping the weight \(\gamma\) of the Dice coefficient fixed at 1, we varied the weight \(\lambda\) of Focal Loss from 1, increasing by 0.1 each time, to explore the impact of the Focal Loss's proportion in the total loss on the experimental results. As shown in Figure 9a, the model's IoU initially experiences a continuous decline until \(\lambda\) reaches around 1.5, after which it gradually increases. When \(\lambda\) reaches 2, the training IoU reaches a maximum value, but the further increase in \(\lambda\) leads to a subsequent decline. When \(\lambda\) reaches 2.5, the training IoU is even lower than initial level.  
For the Dice coefficient, we fixed the weight \(\lambda\) of Focal Loss at 2, which achieved the best results in experiment, while the weight \(\gamma\) of the Dice coefficient started from 0.5, increasing by 0.1 each time. Figure 9b illustrates the variation trend in the training IoU with the increase in the weight \(\gamma\), with the best result being achieved when \(\gamma\) is 1.  
The experimental results indicate that the model achieves the best performance when the weights of the hyperparameters \(\lambda\) and \(\gamma\) are set to 2 and 1, respectively.
# An Efficient Anomaly Detection System for Crowded Scenes Using Variational Autoencoders
## 4 Experimental Results and Comparisons  
We evaluate the performance of the proposed method mainly on the large scene surveillance dataset: UCSD Anomaly Detection Dataset ([[http://www.svcl.ucsd.edu/projects/anomaly/dataset.htm](http://www.svcl.ucsd.edu/projects/anomaly/dataset.htm)]([http://www.svcl.ucsd.edu/projects/anomaly/dataset.htm](http://www.svcl.ucsd.edu/projects/anomaly/dataset.htm))). The UCSD Anomaly Detection Dataset was acquired with a stationary camera mounted at an elevation, overlooking pedestrian walkways. The crowd density in the walkways was variable, ranging from sparse to very crowded. The UCSD dataset includes two subsets: Ped1 and Ped2. Both contain training set and testing set. Specifically, Ped1 contains 34 training video sequences and 36 testing video sequences. The frame resolution is \(158\times 238\) pixels. In Ped1, people walk towards and away from the camera so that the foreshortening effects occur; Ped2 contains 16 training video sequences and 12 testing video sequences with pedestrian movement parallel to the camera plane. The frame resolution is \(240\times 360\) pixels. All frames in the training set are normal and contain only pedestrians. In addition to normal frames, the testing set contains abnormal frames with bikers, skaters, small carts or people walking in the grass as anomalies. We also make an additional experiment on the ShanghaiTech Campus dataset ([[https://svip-lab.github.io/dataset/campus_dataset.html](https://svip-lab.github.io/dataset/campus_dataset.html)]([https://svip-lab.github.io/dataset/campus_dataset.html](https://svip-lab.github.io/dataset/campus_dataset.html))) to evaluate our method. The ShanghaiTech Campus dataset includes 13 scenes and each scenes are of complex light conditions and camera angles. Each color frame resolution is \(856\times 480\) pixels. The same as the UCSD dataset, all the videos for training are normal and contain only pedestrians and the testing frames for each scene contains abnormal events, such as bikers, people running and fighting.  
All experiments are carried out on a dedicated GPU server with Intel Xeon E5-2620 CPU running at 2.1 GHz, 128 GB of RAM, a Nvidia TITANX GPU and running Ubuntu Mate 16.04. We use the Pytorch library, which is an open-source machine learning library for Python, to implement our anomaly detection architecture.
# Fast Reset Protocol for Superconducting Transmon Qubits
## 3 Results
### Numerical Simulation  
In this subsection, we carry out numerical verification of our reset scheme and analyze the result. Here, we set \(\omega_{q}=2\pi\times 5\) GHz, \(\omega_{r}=2\pi\times 6\) GHz, \(\eta_{c}=-2\pi\times 254\) MHz, \(\eta_{q}=-2\pi\times 254\) MHz [21], \(\kappa_{r}^{-1}=\kappa_{c}^{-1}=50\) ns and \(\kappa_{q}^{-1}=11.5\) us. Obviously, there are three variable parameters in the Hamiltonian expressed by Equation (12)--that is, \(\epsilon\), \(\omega_{d}\) and \(\omega_{c}\). We also consider the resonant coupling of Equation (13)--that is, \(\delta=0\). With respect to the resonant condition \(\delta=0\), we can get \(\omega_{c}\) as a function of \(\omega_{d}\), which is \(\omega_{c}=\omega_{d}+\omega_{q}-\omega_{r}\). Therefore, we can optimize the effect of superconducting qubit reset by adjusting the amplitude \(\epsilon\) and frequency \(\omega_{d}\) of the mixing pulse. We perform a numerical simulation for our superconducting qubit reset scheme by using the Lindblad master equation of  
\[\dot{\rho}_{1}=-i[H,\rho_{1}]+\sum_{i=c,r,q}\Big{\{}\frac{\kappa_{i}}{2} \mathcal{L}(\ket{0}_{i}\bra{1}+2\ket{1}_{i}\bra{2})\Big{\}} \tag{20}\]  
where \(\rho_{1}\) is the density matrix of the considered system, \(\mathcal{L}(\mathcal{A})=2\mathcal{A}\rho_{1}\mathcal{A}^{\dagger}-\mathcal{A}^ {\dagger}\mathcal{A}\rho_{1}-\rho_{1}\mathcal{A}^{\dagger}\mathcal{A}\) for operator \(\mathcal{A}\) and \(\kappa_{i}\) is the corresponding decoherence rate.  
The residual excitation population of a qubit is an important indicator for measuring the superconducting qubit reset scheme. Here, we optimize the residual excitation population of a qubit by adjusting the amplitude \(\epsilon\) and frequency \(\omega_{d}\) of the mixing pulse. By calculating, we have \(\chi_{s}=2\pi\times 11.2\) MHz and \(G=2\pi\times 23.4\) MHz of point A. The frequency \(\Delta_{s}\) is produced by \(\chi_{s}\), which has been canceled by the frequencies \(\omega_{d}\) of the mixing microwave pulse at optimum point A (\(\epsilon=2\pi\times 240\) MHz and \(\omega_{d}=2\pi\times 8.2\) GHz) of Figure 3a. Therefore, the best effect of the superconducting qubit reset at optimization point A, that is, \(\ket{0\epsilon 0}\) state, can be suppressed to 0.04%, as shown in Figure 3b. In addition, the qubit reset time is also an important indicator for measuring the superconducting qubit reset scheme. Thus, we numerically simulate the change in the population of the excited states of the qubit with time. As shown in Figure 3b, we show that the \(\ket{0\epsilon 0}\) can be suppressed to 0.04% within 28 ns (population of the \(\ket{e}\) decay to first minimum value). Meanwhile, our results show that the residual excitation population of the superconducting qubit can be suppressed to 0.1% within 283 ns if photon depletion of the readout resonator is required. Therefore, our approach provides a promising way for the highly efficient superconducting qubit reset from the perspective of qubit residual excitation population and reset time.
# Hyperbolic Asynchronous Method of a Radio Navigation Technique
## 6 Conclusions  
The article presents the concept of an asynchronous method of determining position in a hyperbolic radionavigation system (radiolocation). It describes algorithmically the way of determining the position using an asynchronous approach. Its functional properties have been characterized on the basis of the results of simulation and measurement tests carried out in real operating conditions at sea. The results obtained in this way confirm the practical suitability of the proposed method and its functional properties for special purposes, e.g., defense, because Global Navigation Satellite Systems (GNSS) are seen by terrorists or hostile countries as a high value target. The Volpe Center report contains the following statement [28]: \"During the course of its development for military use and more recent extension to many civilian uses, vulnerabilities of Global Navigation Satellite Systems (GNSS)--in the United States the Global Positioning System (GPS)--have become apparent. The vulnerabilities arise from natural, intentional, and unintentional sources. Increasing civilian and military reliance on GNSS brings with it a vital need to identify the critical vulnerabilities to civilian users, and to develop a plan to mitigate these vulnerabilities.\"  
The presented system is a contribution to the development of independent ground radiolocation systems. It is fully asynchronous. In case of damage or shutdown of one of the stations, the system is fully functional, the only condition is to receive signals from at least three ground stations. It has been developed to be very flexible. It allows the use of more than three ground stations. Placing them in areas of known positions allows for the creation of a grid which will provide readings of coordinates independent from satellite systems. Using more than three reference stations will also increase the precision of calculating the position. It works in both kinds of ground station configurations (triangle deployment and along the coastline as well).  
In the course of designing and building the system, the adequacy of the system for water bodies was focused on. However, the versatility of the proposed solution suggests that the system would work on land as well.  
Conceptualization, R.J.K., [PERSON] ([PERSON]), [PERSON] ([PERSON]); methodology, [PERSON] ([PERSON]), W.S.; software: W.S.; validation, R.K., [PERSON] ([PERSON]), [PERSON] ([PERSON]), W.S.; formal analysis, R.K., W.S.; investigation, W.S.; resources, R.K., [PERSON] ([PERSON]), [PERSON] ([PERSON]), W.S.; data curation, W.S.; writing-original draft preparation, R.K., W.S.; writing-review and editing, [PERSON] ([PERSON]), [PERSON] ([PERSON]); visualization, R.K., W.S.; supervision, R.K.; project administration, W.S.; funding acquisition, R.K., [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
The described research was financed from funds for science in the years 2008-2011 as a development project.  
There is no conflict of Interest.