esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In order to solve the difficulty of precision measurement of small hole diameters with large depth-to-diameter ratios, a new measurement method based on spherical scattering electrical-field probing (SSEP) was developed. A spherical scattering electrical field with identical sensing characteristics in arbitrary spatial directions was formed to convert the micro gap between the probing-ball and the part being measured into an electrical signal. 3D non-contact probing, nanometer resolution, and approximate point probing--which are key properties for high measurement precision and large measurable depth-to-diameter ratios--were achieved. A specially designed hole diameter measuring machine (HDMM) was developed, and key techniques, including laser interferometry for macro displacement measurement of the probe, multi-degree-of-freedom adjustment of hole attitude, and measurement process planning, are described. Experiments were carried out using the HDMM and a probing sensor with a \(\phi\)3-mm probing ball and a 150-mm-long stylus to verify the performance of the probing sensor and the measuring machine. The experimental results indicate that the resolution of the probing sensor was as small as 1 nm, and the expanded uncertainty of measurement result was 0.2 \(\upmu\)m (\(k=2\)) when a \(\phi\)20-mm ring gauge standard was measured.  
Keywords:small hole diameter; depth-to-diameter ratio; spherical scattering electrical-field probing; hole diameter measuring machine +
Footnote †: journal: Applied Physics Letters
## 2 Driving Data Collection  
### Data Collection System  
To obtain the driving data of a hybrid car, a real-time driving monitoring system has been utilized, which can monitor and save various data of the driving car on normal roads, rather than test roads, through wireless communication. With this system, the data of driving cars and driving circumstances can be collected in real-time and control signals from the car and data of the detailed parts can be obtained through the OBD-II diagnosis port.  
Figure 1 shows the configuration and operation concept of the real-time driving monitoring system, which comprises three parts. The first part is a vehicle information-collecting device, which obtains the driving data of cars and parts. The second part is a real-time wireless transmission terminal, which receives the data from the vehicle information-collecting device, transmits the data to the server, and sets monitoring items and devices according to the setting received from the server. The third part is a machine-to-machine (M2M) network platform for collecting the transmitted data and indicating through a domestic mobile communication network and an operation center for saving the driving data and monitoring the driving condition of the test vehicle.  
The data collected from the vehicle are transmitted to the server of the operating institutes after encryption and compression in real-time, through an M2M mobile communication network. The transmitted data are systematically saved in each database of the project according to the vehicles and provide the driving data of cars and parts and analysis of circumstances for user needs.
# FOXS-GSC--Fast Offset Xpath Service with HexagonS Communication
## 4 FOXS-GSC Performance Evaluation
### Traffic Efficiency Evaluation
#### 4.3.1 Methodology  
For this evaluation, we chose the city of Ottawa, Canada. The scenario (Figure 9) was obtained from OpenStreetMap ([[http://www.openstreetmap.org/](http://www.openstreetmap.org/)]([http://www.openstreetmap.org/](http://www.openstreetmap.org/)), ( accessed on 30 May 2023)), and represents an area of 8 km\({}^{2}\) in the downtown region. The Ottawa scenario has 2200 vehicles inserted during the simulation, representing a traffic jam, and 409.42 km of total road length. The mobility model we used was random origin-destination.  
The network parameters were set to 18 Mbit/s at the MAC layer and the transmission power was set to 2.2 mW. Table 4 shows the simulation parameters and values used in this evaluation.  
The routing interval was set to 120 s for all solutions, and three alternative routes were used for FOXS-GSC, PAN3, and CHIMERA to obtain a more appropriate evaluation. This number of alternative routes was chosen because it had the best results for all solutions in the evaluated scenarios.  
The following metrics were used to evaluate the different traffic congestion services:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Map & Ottawa downtown \\ \hline Map Size & 8 km\({}^{2}\) \\ \hline Transmission power & 2.2 mW \\ \hline Communication range & 300 m \\ \hline Bit rate & 18 Mbit/s \\ \hline Beacons & 4 s \\ \hline Route size factor & 25\% \\ \hline Alternative routes (k) & 3 \\ \hline Number of RSUs * & 1, 23, 50 \\ \hline AoK & 3 km \\ \hline Confidence interval & 95\% \\ \hline Interval to request new route & 120 s \\ \hline \end{tabular}  
*Only FOXS-GSC was varied, from 1 to 23 and then 50 RSUs. The other solutions all used 50 RSUs, providing full coverage.  
\end{table}
Table 4: Simulation parameters.  
Figure 9: Topology of the Ottawa scenario.  
* Traveled time: the average travel time from the starting point to the destination of all vehicles.
* Stopped time: the average time spent stuck in traffic jams for all vehicles.
* Average speed: the average speed of all vehicles.
* Traveled distance: the average distance that all vehicles traveled.
* Fuel consumption: the average fuel consumption of all vehicles that traversed the whole route.
* CO\({}_{2}\) emission: the average CO\({}_{2}\) emissions for all vehicles during their trip.
* PTI: the reliability of the ratio of the 95% travel time to the ideal flow on the same path.
* Route compute location: the respective percentages of the location where the route was computed (i.e., vehicle or cloudlet).
# Artificial Intelligence Applications for MEMS-Based Sensors and Manufacturing Process Optimization
## 6 Discussion
### Q2: What Are the Advantages of AI-Based Solutions Compared to State-of-the-Art Solutions?
Insufficient and unreliable data can degrade model performance during implementation. * Data prepossessing is a crucial step to ensure data quality, i.e., the availability of labeled, noise-free data can ensure good training of the AI algorithm for higher accuracy. This can be a time-consuming and challenging task. * More computational power is required compared to the traditional methods. * Some algorithms suffer from over-fitting and bias. * Explainability does not always exist. * Plethora of algorithm availability can be challenging and cumbersome in finding the fitting solution. * Cross-domain knowledge is required. * Infrastructure compatibility poses one of the biggest challenges as sometimes small implementations may need huge changes in the production process, which can be time-consuming. Currently, there exist many data mining process models, such as CRISP-DM (Cross Industry Standard Process for Data Mining) [102], SEMMA (Sample, Explore, Modify, Model, and Assess) [103], KDD (Knowledge Discovery Databases) [104] etc. ; where KDD describes the process and CRISP-DM and SEMMA the implementation. Although these are intensively used in small to large corporations, these methods lack problem clarity, contextualization, multiple reworks, iteration failure, etc. This is why ML-Ops (Machine Learning Operation) is now one of the most suitable and industry-oriented end-to-end solutions for the incorporation of AI [105; 106; 107]. This field is relatively new and thus needs more research work to be standardized.
# Continuous Mobile User Authentication Using Combined Biometric Traits
## 6 Evaluation and Results
### Evaluation Metrics  
For every user, a different HMM is generated individually. Those classifiers return a probability of how likely it is that a specific touch input was generated from this exact model with its internal hidden states and mixtures of the Gaussian distributions. Since a probability alone can not determine if a user's input is valid or not, a threshold for this calculated probability also has to be introduced. This is the limit indicating if an input is authorized, where if the calculated value is below the limit the input is invalid, and if it is larger than the limit the gesture is recognized as being valid. Changing the threshold does have an effect on usability and, more importantly, on security. For example, if the limit is set too low, a lot of invalid gestures may be categorized as authorized input, making the system vulnerable to attacking parties. On the other hand, if the threshold for authentication is set too high, it will have an impact on the usability of the system, because gestures made from a legitimate user, with slight variations to the trained model, will be recognized as invalid input and therefore lock the user out incorrectly.  
The trade-off between security and usability can be expressed with the help of the FAR and False Rejection Rate (FRR). It has to be noted, that the FAR is synonymously used as False Positive Rate (FPR) and also the FRR synonymously as False Negative Rate (FNR) in different papers. The FAR is used to determine the percentage of test samples, incorrectly classified as positive inputs, whereas the FRR describes the percentage of samples, which are incorrectly classified as negative inputs.  
Those percentages are calculated for specific threshold values. To circumvent the need for calculating them for every possible threshold value, the Receiver Operating Characteristic (ROC) curve can be used to display the True Positive Rate (TPR) values against the FPR values for all threshold values in a plot. The \(TPR\) is calculated by subtracting the percentage of the \(FNR\) like \(TPR=1-FNR\). In order to make it easier to compare different models, we use the EER, which is just a special point on the ROC curve, where the FPR is equal to the FNR. The advantage of this final value is that it does not favor one type of error over the other.  
The calculated score for an individual stroke is highly dependent on the length of the gesture sequence. The likelihood of such a gesture belonging to a trained model rises exponentially with respect to the length, as also discussed in [28]. To circumvent this behavior and make all different inputs comparable, the calculated score is normalized according to the length of the sequence.
# Research on Spaceborne Target Detection Based on Yolov5 and Image Compression
## 3 Remote Sensing Target Detection
### Yolov5 Network
#### 3.1.4 Prediction  
In Yolov5, CIOUs_Loss is used to create the loss function of the bounding box. In the post-processing procedure during target detection, screening for many target boxes usually requires NMS operation. The weighted NMS in Yolov5 can improve the recognition of the target to be detected.  
In the official description, there are four versions of the target detection networks, namely Yolov5s, Yolov5m, Yolov5l, and Yolov5x, as shown in Table 2.  
The four structures are used to control the depth and width of the network through the above two parameters, where depth multiple controls the depth of the network and width multiple controls the width of the network. All four networks have the same frame structure, with only the depths and widths differing.
# Experimental Study on the Shear Strength of Silt Treated by Xanthan Gum during the Wetting Process
## 2 Materials and Methods
### Methods  
#### 2.2.1 Sample Preparation  
Soil samples were prepared following the dry method [42], which was commonly used in previous tests. The dried silt powder was fully mixed with dried xanthan gum. Water was then added, and the mixture was stirred evenly. Based on previous experimental studies, the optimal mass ratio (\(m_{lp}/m_{s}\)) of dried XG and dried silt in the water retention characteristics tests was set as 0.0%, 0.5%, 1.0%, 1.5%, and 2.0%, and the optimal mass ratio in the direct shear tests was set as 2.0% [21; 33; 34; 43]. In order to ensure the uniformity of XG-silt, dry soil was stirred and mixed evenly with XG of the corresponding quality. Deionized water was added to form wet soil that avoids the influence of additional chemical components in the water. The soil was then sealed at a constant temperature for 24 h until the water was fully infiltrated. The initial water content of the specimen was 15%, and 92% of the maximum dry density, \(\rho_{\rm d}\) = 1.63 g/cm\({}^{3}\), was selected as the initial dry density. After specimen preparation, a vacuum pump was used to vacuum and saturate the specimen for testing.
## 1 Introduction
Additionally, the hydrodynamic characteristics of the flexible hydrofoil under different motion methods at low frequency (1 Hz) were compared, which provides a reference for the research of hydrofoil devices. This study contributes significantly to advancing the design of flexible hydrofoils by offering practical solutions for real-world applications. The flexible hydrofoils analyzed in this paper are particularly suitable for small-scale hydraulic applications such as improving water circulation in plain river networks, the power optimization of marine probes, pollutant discharge in aquaculture circuits, and pumping in low-head channels. The optimized flexible hydrofoils are more adaptable and ecologically compatible, providing a promising solution to cope with the conditions. Although the proposed solution shows potential in enhancing hydrodynamic performance under ultra-low head conditions, this study does not include a direct comparison of energy consumption between the flexible hydrofoil and traditional pumps or mixers. Future work could incorporate detailed energy efficiency analyses to further validate the benefits of the proposed system. Furthermore, the dimensionless numbers (Reynolds number and Strouhal number) that are required for the defining study are presented in Equations (1) and (2). \[\text{Re}=\frac{\rho\overline{U}c}{\mu}, \tag{1}\]  
\[St\ =\frac{fh_{\text{max}}}{\overline{U}}, \tag{2}\]  
where \(\rho\) = 998.2 kg/m\({}^{3}\) is the density of water at room temperature at 293.15 K; \(\overline{U}\) is the average incoming velocity, where the average incoming velocity generated by the hydrofoil device in this paper was 0.3 m/s; \(c\) is the chord length of the hydrofoil, which was taken to be 1 m in this paper; \(\mu\) = 1.005 \(\times\) 10\({}^{-3}\) Pa\(\cdot\)m\({}^{2}\) is the coefficient of the hydrodynamic viscosity of water at room temperature; \(f\) is the frequency; and \(h_{max}\) is the amplitude of the lifting, which was taken to be half of the chord length (0.5\(c\)).
# A Novel GNSS Interference Detection Method Based on Smoothed Pseudo-Wigner-Hough Transform
###### Abstract  
This paper develops novel Global Navigation Satellite System (GNSS) interference detection methods based on the Hough transform. These methods are realized by incorporating the Hough transform into three Time-Frequency distributions: Wigner-Ville distribution, pseudo -Wigner-Ville distribution and smoothed pseudo-Wigner-Ville distribution. This process results in the corresponding Wigner-Hough transform, pseudo-Wigner-Hough transform and smoothed pseudo-Wigner-Hough transform, which are used in GNSS interference detection to search for local Hough-transformed energy peak in a small limited area within the parameter space. The developed GNSS interference detection methods incorporate a novel concept of zero Hough-transformed energy distribution percentage to analyze the properties of energy concentration and cross-term suppression. The methods are tested with real GPS L1-C/A data collected in the presence of sweep interference. The test results show that the developed methods can deal with the cross-term problem with improved interference detection performance. In particular, the GNSS interference detection performance obtained with the smoothed pseudo-Wigner-Hough transform method is at least double that of the Wigner-Hough transform-based approach; the smoothed pseudo-Wigner-Hough transform-based GNSS interference detection method is improved at least 20% over the pseudo-Wigner-Hough transform-based technique in terms of the zero Hough-transformed energy percentage criteria. Therefore, the proposed smoothed pseudo-Wigner-Hough transform-based method is recommended in the interference detection for GNSS receivers, particularly in challenging electromagnetic environments.  
interference detection; time-frequency (TF) analysis; cross-term; Hough transform (HT); smoothed pseudo-Wigner-Hough transform (SPWHT) +
Footnote †: journal: Sensors  
1
Footnote 1: State Key Laboratory of Satellite Navigation System and Equipment Technology, Zhongshan West Road 589, Shijiazhuanguang 050801, China; [EMAIL_ADDRESS] (B.Y.); [EMAIL_ADDRESS] (J.Y.)  
2
Footnote 2: School of Computer & Information, Hefei University of Technology, Turai Road 193, Hefei 230009, China; [EMAIL_ADDRESS]  
3
Footnote 3: Centre for Transport Studies, Imperial College London, London SW7 2 AZ, UK; [EMAIL_ADDRESS] (M.E.); [EMAIL_ADDRESS] (W.Y.O.)  
40
Footnote 4: Correspondence: [EMAIL_ADDRESS]; Tel./Fax: +86-551-6290-1552
## 3 Simulation Models
### Conduction-Based Model  
This model is based on the following main assumptions: (i) the phase change material is homogenous and isotropic; (ii) the thermo-physical properties of the phase change material are considered to be constant and equal to the average values between the liquid and solid phases; (iii) the volume expansion/reduction during phase change is ignored; (vi) phase change during solidification/melting occurs in a temperature range; (v) negligible convective mechanisms. The energy balance equation is given by:  
\[\rho_{PCM}\ c^{\prime}_{p,PCM}\frac{\partial T}{\partial t}=k_{PCM}\
abla^{2}T \tag{1}\]  
where \(T\) is the temperature, \(t\) is the time variable, \(\rho_{\text{PCM}}\) is the density of the PCM, and \(k_{\text{PCM}}\) is the PCM thermal conductivity.  
The phase change is simulated by means of the effective heat capacity method (EHC). According to EHC, the material effective heat capacity \(c^{\prime}_{p,\text{PCM}}\) is expressed as a function of the latent heat of fusion of the PCM \(L_{h}\) as follows:  
\[c^{\prime}_{p,PCM}=c_{p,PCM}+L_{h}\frac{d\varphi(T)}{dT} \tag{2}\]  
where \(c_{p,\text{PCM}}\) is the average PCM specific heat, and \(\varphi(T)\) is a non-dimensional parameter, which is 0 in the solid phase, 1 in the liquid phase and between 0 and 1 in the transition zone. The latter can be expressed as:  
\[\varphi(T)=\left\{\begin{array}{ll}0,&T<(T_{M}-\Delta T_{M})\\ \frac{T-T_{M}+\Delta T_{M}}{2\Delta T_{M}},&(T_{M}-\Delta T_{M})\leq T\leq(T_{ M}+\Delta T_{M})\\ 1,&T>(T_{M}+\Delta T_{M})\end{array}\right. \tag{3}\]  
where T\({}_{M}\) is the melting temperature, and \(\Delta T_{M}\) is half the temperature phase change range that goes from (\(T_{M}-\Delta T_{M}\)) to (\(T_{M}+\Delta T_{M}\)).
## 4 The Improved Self-Collision Detection Technology
### Determine the Position of Each Approximation Object  
During the movement of the robot, the description of the position and posture of the approximation of each component in the three-dimensional space are carried out during the second key step. The specific structural shape of each component can be described by the spherical element, but the actual position and posture of each component in three-dimensional space cannot be represented by the approximation obtained by the above method, which is only described in the local self-coordinate system. To acquire the actual position of each compartment in three-dimensional space, the traditional method needs to transform the approximation from the local self-coordinate system into the world frame through the homogeneous transformation matrix. The traditional methods and the transformation results can be found in [30; 36].  
However, the traditional method has many defects in the positioning of approximation, and these defects will seriously affect the efficiency of robot expansion detection. For example, (1) to acquire the surface point and the center point of each spherical object, the most time required for the self-collision detection is spent on determining the position of the surface point and the center of the spherical objects by corresponding homogeneous transformation; (2) in the movement of the robot, the positioning of different approximations requires the spatial transformation matrix corresponding to the component at different times to be solved, which increases the complexity of the positioning of approximation; (3) during the positioning of each component, the surface points of each approximation require spatial transformation by the homogeneous matrix of the corresponding component, which will inevitably increase the required time for positioning the entire approximation of the mobile manipulator robot.  
To reduce the complexity of the solution and shorten the required time for the positioning of approximation, we improved the positioning of approximation based on the structure of the mobile manipulator robot. In Section 3, the mathematical model is similar to the actual physical model of the mobile manipulator robot. Therefore, the position of two adjacent key coordinate systems can directly describe the position and shape of the corresponding component in the world frame. The surface point and the center of the spherical objects can be obtained by effectively dispersing the component, which reduces the required time for approximating each component and improves the efficiency of collision detection. The specific steps are described as follows:
# Experimental Study of Erosion Prevention Model by Bio-Cement Sand
## 2 Materials and Methods
### Calculating the Shear Stress  
The initial shear stress generated by water flow cannot be directly quantified; however, the erosion shear stress associated with the water flow can be derived from the velocity of water at a defined depth within the tank. By employing the Reynolds number (\(\text{Re}=v\times d/\mu\)), where \(v\) denotes velocity, \(d\) represents the equivalent diameter of the pipe, and \(\mu\) is the dynamic viscosity coefficient of water--assumed to be 0.01 cm\({}^{2}\)/s--the hydrodynamic state within the tank can be characterized: Re \(<\) 2000 indicates laminar flow, Re \(>\) 4000 signifies turbulent flow, and Re values between 2000 and 4000 correspond to a transitional regime. In instances of turbulent flow, logarithmic equations for velocity distribution along vertical axes in two-dimensional open channel turbulence may be utilized to ascertain frictional velocity.  
\[\frac{u}{u_{*}}=5.5+5.75 lg\left(\frac{yu_{*}}{\mu}\right) \tag{3}\]  
where \(u\) is the hourly average flow rate at the distance from the bed surface \(y\) (0.015 m), \(\mu\) is the motion viscosity coefficient of water and is taken as 0.01 cm\({}^{2}\)/s, and \(u_{*}\) is the frictional velocity.  
Given the frictional flow rate, the erosion shear stress \(\tau\) at the bottom of the tank can be obtained from the following formula:  
\[\tau=\rho u_{*}^{2} \tag{4}\]  
The erosion rate curve is expressed as follows:  
\[E=a\tau^{n} \tag{5}\]  
where \(\tau\) is shear stress (\(\text{N}/\text{m}^{2}\)); \(a\) and \(n\) are undetermined constants.  
In order to judge the starting condition of the sample, the critical shear stress is used as the index to measure whether the sample is starting. The above formula can be converted to  
\[\tau_{c}=\left(\frac{E}{a}\right)^{1/n} \tag{6}\]  
The erosion of 1.0 mm on the sediment surface within 15 min can be considered to reach the starting state [25]. When \(E\) = 0.0001 cm/s, the sediment reaches the critical starting state, and the critical shear stress is \(\tau_{c}\).