esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Examples of Filtering Data of Individual Buoys
The current _i_Quam QC suppresses the original spatial and temporal variability of \(\Delta T_{L4}\). Figure 13e,f show the maps of similar statistics for \(\Delta T_{IS}\) of QL = 5 processed with the QC EXP. These maps are much closer to those shown in Figure 13a,b. We conclude that the QC EXP better preserves the original variability of \(\Delta T_{L4}\). As shown in Figure 7a, the negative difference \(T_{NIGHT}-T_{L4}\) increases and becomes less stable with increased ADS. Recall the CMC SST is largely produced from nighttime SSTs, with a small fraction of those daytime data deemed least affected by the diurnal warming. Given that the criteria for assimilating in situ and satellite SSTs are different in different L4 analyses, it is instructive to estimate the global diurnal cycle in \(\Delta T_{L4}\)'s produced by subtracting different analyses from \(T_{IS}\)'s. Figure 14a shows such global mean diurnal cycles produced from \(T_{IS}\) with QL = 5 determined by the current _i_Quam QC for eleven L4 analyses listed in Table 3. The averaging periods for each \(\Delta T_{L4}\) are listed in the right column. Note that all curves are normalized at zero (i.e., L4-specific global biases subtracted from all curves to center them all at 0 K). The mean ADS in \(\Delta T_{L4}\) is -0.17 K, with maxima and minima varying within \(\sim\)0.03 K between different L4s. Figure 14b re-plots  
Figure 13: (**a**,**c**,**e**) Biases and (**b**,**d**,**f**) SDs of daytime \(\Delta T_{L4}\), sampled between 8 a.m. and 8 p.m. local solar time (LST) by drifting and tropical moored buoys averaged over 2012–2018, produced from \(T_{IS}\) with (**a**,**b**) current _i_Quam QL = 3–5, (**c**,**d**) current _i_Quam QL = 5 only; and (**e**,**f**) EXP QL = 5.  
the same diurnal cycle but now produced from \(T_{IS}\) with the EXP QL = 5. The EXP QC increases global mean ADSs to \(\sim\)0.23 K, \(\sim\)35% larger than with the current \(i\)Quam QC, and mitigates the differences between various L4 analyses. One concludes that the EXP QC better preserves the diurnal variability in the \(T_{IS}\), mitigates the excessive forcing of QL = 5 \(T_{IS}\)'s to the specific L4 analysis, and reconciles the DSs produced with different L4 analyses.
# Experimental Study of Interfacial Bond Properties between CGM and Existing Concrete
## 2 General Conditions of the Experiment  
### Specim Design and Preparation  
#### 2.1.1 Specim Design  
In accordance with the principle that the specimen form should be as close as possible to the shear transfer mode of the joint surface between the repair material and the existing concrete in the reinforced structure, the Z-shaped specimens shown in Figure 1 were designed to conduct a direct shear test on the interface bond performance between CGM and existing concrete by referring to existing shear test data [15; 16; 17; 18]. The uniformity of the shear stress distribution on the bonding interface and the simple operability of the loading test were also comprehensively considered. The interface forms were set as smooth, triangular, and trapezoidal. The smooth interface was used to simulate a flat interface without any roughening in the process of structure repair, and the triangle and trapezoidal interfaces were employed to simulate an uneven interface during structure rehabilitation. Three groups of specimens with triangular interface shapes and concrete compressive strength grades of C35, C45, and C55 were designed to study the effect of existing concrete strength on the interface bond performance. The parameters of the specimens for the direct shear test are listed in Table 1.  
To study the interfacial bond performance in the compressive-shear composite stress state and analyze the influence of different shear angles on the interfacial bond performance of CGM and existing concrete, four groups of specimens with and without shear keys were designed and prepared. The concrete strength grade was C35, and slant shear angle, \(\alpha\), was 30\({}^{\circ}\) and 45\({}^{\circ}\), as illustrated in Figure 2. Two groups of specimens with shear keys using concrete strength grades of C45 and C55 and shear angle \(\alpha\) = 45\({}^{\circ}\) were also designed and prepared to explore the influence of existing concrete strength on the interfacial bond performance. The dimensions of all specimens were 100 mm \(\times\) 100 mm \(\times\) 300 mm, and the dimensions of shear keys were 50 mm \(\times\) 20 mm \(\times\) 100 mm (same thickness as the specimens). The parameters of the specimens for the slant shear test are given in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline Group No. & Concrete Strength & Interface Form & Shear Key & Number of Specimens \\ \hline Z-C35-I & C35 & smooth & none & 3 \\ Z-C35-T & C35 & trapezoidal & none & 3 \\ Z-C35-S & C35 & triangle & none & 3 \\ Z-C45-S & C45 & triangle & none & 3 \\ Z-C55-S & C55 & triangle & none & 3 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of the specimens for the direct shear test.  
Figure 1: Size and configuration of the direct shear specimens (in mm): (a) smooth interface; (b) trapezoidal interface; (c) triangle interface.
## 2 Methodologies
### Transfer Learning  
Transfer learning aims to remedy the learning problem when there are differences between the source and target domains [36]. A domain \(D=\{X,\,P(x)\}\) is made up of feature space \(X\) and their marginal probability distribution \(P(x)\) belonging to \([0,1]\), while a task \(T=\{Y,\,f(x)\}\) is composed of labels \(Y\) and a function \(f(x)\) for predicting them. \(T\) represents the relationship between \(X\) and \(Y\), and \(f(x)\) is regarded as the conditional probability distribution \(Q(y|x)\) that predicts the probability of a label \(y\) for a sample \(x\). The above descriptions can be integrated as follows:  
Figure 1: The YOLOv5s structure.  
\[\left\{\begin{array}{l}P(x)=\frac{\Sigma}{y}P(x,y)=\frac{\Sigma}{y}P(x|y)P(y)\\ T=\{Y,\,f(x)\}\\ f(x)=Q(y|x)=\frac{Q(xy)}{Q(x)}\end{array}\right., \tag{2}\]  
Assuming a labeled source domain \(D_{S}\) and an unlabeled target domain \(D_{T}\), transfer learning is suitable for coping with the condition of \(P_{S}\
eq P_{T}\) and \(Q_{S}\
eq Q_{T}\). It takes advantage of the knowledge implied in \(D_{S}\) and \(T_{S}\) to construct \(f_{T}(x)\) for \(D_{T}\) and \(T_{T}\), thereby reducing distribution differences across domains as well as keeping the learning domain unchanged. Unlike traditional machine-learning methods, transfer learning transfers knowledge from previous tasks to new ones with relatively little training data, rather than learning new tasks from scratch through a large number of data. According to the transfer method, transfer learning mainly covers four categories: instance-based transfer learning, feature-based transfer learning, model-based transfer learning, and relation-based transfer learning. Most notably, due to its ability to reuse the parameters and structure of a model, model-based transfer learning is widely adopted in neural networks.
## 3 Stationary Localization Algorithms
### Distributed Localization Algorithms
#### 3.2.4 Underwater Positioning Scheme (UPS)  
Based on the Time-based Positioning Scheme (TPS) [24] for outdoor WSNs, the authors in [25,26] proposed Underwater Positioning Scheme (UPS). UPS consists of two steps. The first step is to detect the time differences of arrival signals. Then, the time differences are transformed into range differences. As illustrated in Figure 5, \(A\) is the master anchor node, which initiates a beacon signal every \(T\) seconds. \(B\)'s transmission starts after it receives \(A\)'s beacon signal. \(C\)'s transmissions starts after it receives beacon signals of both \(A\) and \(B\). In addition, \(D\)'s transmission starts after it receives beacon signals of \(A\), \(B\) and \(C\). After \(S\) get all the beacon signals, the time difference of arrival signals is calculated and converted to range difference. In the second step, trilateration is performed to localize unknown nodes.  
UPS requires no time synchronization and has low computation overhead. As evidenced by simulation results, UPS has low localization error. It is applicable to both localization and navigation in UWSNs. Moreover, few anchor nodes are needed to perform 3D localization. However, sensor nodes outside the four anchor nodes' communication range cannot be localized. In addition, UPS does not take into account the impact of transmission failures, which is highly likely because underwater acoustic channels are unreliable. If anchor node \(B\) cannot receive the beacon signal of the master anchor node \(A\), it will not send its own beacon signal. Then, \(C\) and \(D\) will keep waiting until receive beacon signal from node \(B\). Therefore, in [27], an Enhanced-UPS (E-UPS) is proposed. On one hand, a time-out value is designed for the maximum waiting time of the anchor node messages. On the other hand, to overcome the limitation of relying on four anchor nodes, E-UPS is extended to use all anchor nodes. It is observed that up to 16% of the network containing anchor nodes is not localizable [28]. Therefore, a Wide Coverage Positioning System (WPS) [28] is proposed to address this limitation using five anchor nodes.
## 2 Data Collection  
### Participants and Protocols  
Six healthy young adults (age 23.5 \(\pm\) 1.5 yrs.; height 172.0 \(\pm\) 7.8 cm; body mass 79.5 \(\pm\) 15.5 kg) were recruited for the current research from the university volunteers. To be included in this study, participants were required to be healthy, capable of walking on the treadmill for 30 min without a break, free from injuries that affect their walking patterns, and have no previous history of injurious falls for at least the past two years. The entire experimental protocol was explained by the researchers, and an informed consent form approved and mandated by the Victoria University Research Ethics Committee was voluntarily signed by the participants prior to participation.  
Gait testing was conducted on the treadmill (AMTI) for 5 min at 4 km/h, which was considered to be the reasonable preferred pace for healthy young individuals [49]. The Vicon Bonita system (Nexus 2.12.1) with 10 cameras was utilized to track reflective markers at 200 Hz, attached to the heel (the proximal end of the shoe) and the toe (the most anterior superior surface of the shoe). Utilising the analytic software (i.e., Visual 3D, C-motion, Oakville, ON, Canada, [[https://c-motion.com/](https://c-motion.com/)]([https://c-motion.com/](https://c-motion.com/))), a low-pass Butterworth filter (6 Hz) was applied to the obtained position data prior to analysis. Based on the kinematic conventions [50], toe-off and heel contact were first computed to define the swing phase. MFC was identified as the local minimum height of the toe during the mid-swing phase, but when the clear local minimum was absent, the alternative definition was applied utilizing the maximum horizontal velocity of the swing toe [51].  
As illustrated in Figure 1, IMU (Nexus, Trident) was attached to the mid-foot section to record various foot-segment-based kinematic data (200 Hz), but for the current study, tri-axial linear accelerations (AccX, AccY, and AccZ) and angular velocities (GyroX, GyroY, and GyroZ) were obtained for machine learning applications. The overall goal of this study was to predict in which category (Table 1) upcoming MFC would be classified based on the five consecutive frames from toe-off comprising 0.025 s kinematic information from toe-off. In other words, algorithms were developed for a single Inertial Measurement Unit (IMU) (see Figure 1) to predict the upcoming MFC height category, as established by the 3D motion capture system. MFC categories employed in the current study are described in Table 1, determined by the previous studies indicating the average MFC for young adults to be about 1.5 cm (R1), slightly above the average up to 2 cm (R2), and minimum risk of tripping (R3) as above 2 cm [20; 21; 52].
# Collision Avoidance Using Finite Control Set Model Predictive Control for Unmanned Surface Vehicle
###### Abstract  
In recent years, with the development of unmanned platforms, unmanned surface vehicles (USV) are attracting more and more attention. Compared to ordinary ships, USV have a smaller volume and faster speed, so their collision avoidance system (CAS) should have better responsiveness and stability. The paper describes a method that is based on finite control set model predictive control (FCS-MPC). A finite control set is generated by more practical control commands: the thruster speed and propulsion angle of the USV. The method is conceptually and computationally simple and yet quite versatile, as it can account for the dynamics of the USV, steering and propulsion system. Based on the theory of FCS-MPC, a safe and fast CAS is proposed, and it is verified in different static and dynamic environments. The real environment model for collision avoidance is established by extracting the environment data from the electronic chart. The result shows that the method is effective and can control the USV to sail safely and quickly in complex real scenarios with multiple dynamic obstacles.  
unmanned surface vehicle; collision avoidance; finite control set; model predictive control Article
# Development of a Submillimetric GNSS-Based Distance Meter for Length Metrology +
## 2 GNSS Data Processing Approach
### Initial Assumptions
Double difference processing usually yields precise vectors whose components \(\Delta X,\Delta Y,\Delta Z\) are subsequently used to compute the distance  
\[D=\sqrt{\Delta X^{2}+\Delta Y^{2}+\Delta Z^{2}} \tag{2}\]  
Fortunately, correlation usually leads to \(\sigma_{D}<\sqrt{\sigma_{\Delta X}^{2}+\sigma_{\Delta Y}^{2}+\sigma_{\Delta Z }^{2}}\) (and even \(\sigma_{D}<\sigma_{\Delta X}\) and \(\sigma_{D}<\sigma_{\Delta Y},\sigma_{D}<\sigma_{\Delta Z}\)) because in the expression  
\[\sigma_{D}^{2} = (\frac{\Delta X}{D})^{2}\sigma_{\Delta X}^{2}+(\frac{\Delta Y}{ D})^{2}\sigma_{\Delta Y}^{2}+(\frac{\Delta Z}{D})^{2}\sigma_{\Delta Z}^{2} \tag{3}\] \[+ 2\frac{\Delta X}{D}\frac{\Delta Y}{D}\sigma_{\Delta X\Delta Y} +2\frac{\Delta X}{D}\frac{\Delta Z}{D}\sigma_{\Delta X\Delta Z}\] \[+ 2\frac{\Delta Y}{D}\frac{\Delta Z}{D}\sigma_{\Delta Y\Delta Z}\]  
the covariances may be positive or negative depending on the relative geometry between the baseline and the observed satellites. Thus, a proper selection of observables may lead to a reduction in the standard error of the obtained GNSS distance. Finally, a multi-constellation approach has clear advantages for length determination if compared to a single-constellation one, i.e., only GPS. A large number of satellites in view allows the inclusion in the system of normal equations of only those double differences whose impact on the baseline is lower than an assumed threshold as well as to eliminate  
Figure 2: Zero-baseline double difference residuals for a pair of multi-constellation Leica GS10 in July 2015.  
those satellites whose phase observables contain a strong multipath error. As an illustration, in the last GNSS campaign that was carried out at the UPV calibration baseline in July 2015, the number of satellites registered in a time span of eight hours was the following: 21 GPS, 19 GLONASS and 4 Galileo. Thus, the inclusion of the new and emerging constellations gives rise to new possibilities for precise GNSS length determination.
## 2 System Model and Problem Formulation
### Request Model  
In this subsection, we consider \(\pi_{n}(t)\) to represent the request generated by vehicle \(n\) at time slot \(t\). We consider that \(\pi_{n}(t)\) can either be a service request or a content request. By  
Figure 2: A geometric relationship diagram.  
denoting \(g_{n,d}(t)=(0,1)\) (\(g_{n,k}(t)=(0,1)\)) as the indicator of whether service \(d\) (content \(k\)) is requested by vehicle \(n\) at time slot \(t\), we express the constraint on vehicle \(n\)'s request as  
\[\sum_{d=1}^{D}g_{n,d}(t)q_{n}(t)+\sum_{k=1}^{K}g_{n,k}(t)(1-q_{n}(t))\leq 1, \forall n\in\mathcal{N},\forall t\in\mathcal{T}, \tag{11}\]  
where \(q_{n}(t)\in\{0,1\}\) denotes the indicator of the request type. If \(q_{n}(t)=1\), \(\pi_{n}(t)\) is a service request. Otherwise, \(\pi_{n}(t)\) is a content request [46].
# Simple and Efficient Non-Contact Method for Measuring the Surface of a Large Aspheric Mirror  
[PERSON]  
1 Institute of Optics  
2. Traditional contact scanning also exhibits some level of static and dynamic friction. The level of friction changes with surface roughness and cleanliness, and it can vary dramatically from grinding to polishing. Friction-wear of the SMR ball will result in obvious surface measurement error. Furthermore, the friction may lead to scratching of the mirror, which is unacceptable, so the scanning method is not an ideal choice for fine polishing or the finished mirror.
3. When the orientation of SMR varies during scanning, the centering error of the SMR will also be the main component of measurement uncertainty. Thus, the measurement process relies on the operator's operational experience.  
To solve the problems, researchers have developed a Laser Tracker Plus system [14]. An air puck carries the SMR with three miniature flexible rubber air bearings that can glide across a polished surface without scratching. The SMR rests directly on the glass while tracker data are recorded. Air pressure is applied to lift the puck and SMR ~1 mm above the surface while the air-bearings remain nearly in contact with the glass. The puck slides to the next position with minimal force and slowly lowers the SMR to the surface as the air bleeds out. This system was established to guide loose abrasive grinding and initial polishing of the off-axis primary mirror segments of the Giant Magellan Telescope (GMT). However, it is too elaborate and less efficient for our purpose.  
An easily reproducible method is demonstrated to solve the above problems without elaborate devices. The proposed method is accurate enough to be useful to anyone fabricating large optics and needing a safe way to assess the values of the geometric parameters and surface errors during grinding and initial polishing, be it for entrance quality control or troubleshooting a malfunctioning optical system.
## 4 Conclusions  
The purpose of this study was to analyze the rheological properties and microscopic mechanisms of asphalt cold replacement solution after adding modifiers, surfactants, diluents, and reinforcing agent, using base asphalt as a reference group. The purpose of composite modification of base asphalt is to improve its performance indicators and provide a reference for the next step of CMA improvement. Based on the research results, we can draw the following conclusions:  
1. A total of 25 sets of experiments were conducted using a combination of 6 factors and 5 levels in an orthogonal experimental plan. The orthogonal experiments were processed using factor-level and range analysis methods to determine the optimal mixing ratio of two types of ACRLs. The optimal dosage of H-ACRL is (SBS/SBR/erucamide/polphosphate/diesel/acrylic ester) (5%/3%/0.9%/1.2%/15%/5%). The optimal dosage of L-ACRL is (SBS/SBR/erucamide/polphosphate/diesel/acrylic ester) (4%/4%/1.5%/0.9%/18%/7%).
2. After adding modifiers, surfactants, diluents, and reinforcing agent to the base asphalt, the penetration, softening point, and ductility of the asphalt increased by more than 25%.
3. Through DSR and BBR experiments, research has shown that the shear deformation resistance and low-temperature flexibility of H-ACRL and L-ACRL were improved compared to the base asphalt. Compared to base asphalt, H-ACRL and L-ACRL had superior high- and low-temperature performance.
4. Through fluorescence microscopy and FTIR experiments, this research has shown that the compatibility between the addition of modifiers, surfactants, diluents, and reinforcing agent and asphalt was still poor, and the addition of PPA did not improve the compatibility between the polymer and asphalt. Comparing before and after aging, it was found that the particle size of polymer particles in asphalt significantly decreased and the number increased, indicating significant degradation of the polymer during the aging process and improved compatibility between the polymer and asphalt.  
Figure 11: AFM phase diagram.  
Figure 12: AFM 3D Morphology diagram.  
5. This research has shown that there was no chemical reaction between the addition of modifiers, surfactants, diluents, and reinforcing agent and asphalt, and the original molecular structure of the base asphalt was not changed. The surface roughness of H-ACRL and L-ACRL was significantly higher than that of base asphalt, and the contact area between asphalt and aggregate increased, resulting in improved adhesion.  
Methodology, Z.Y.; Formal analysis, X.B.; Investigation, Y.X.; Resources, X.L.; Writing-original draft, L.M.; Writing-review & editing, Y.Z.; Supervision, Z.Z.; Funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Natural Science Foundation Project of Xinjiang Uygur Autonomous Region in 2023. And All China Federation of Trade Unions Employee Innovation Subsidy Fund Project.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Numerical Experiments for the Radiation Term in 1D  
### Verification with an Analytical Reference Solution  
Experiment 3. The following function  
\[u^{\text{exact}}(x,t)=te^{x-t} \tag{35}\]  
is a simple exponential analytical solution [28] of the one-dimensional conduction-convection-radiation equation if \(\alpha=1\), \(K=2\), and if the heat source function is \(q(x,\ t)=\sigma t^{4}e^{4x-4t}+e^{x-t}\). The initial \(u^{0}\) function and the time-dependent Dirichlet boundary conditions are calculated straightforwardly by substituting the initial time and boundary space values to the analytical solution (35), respectively. The computational domain is \((t,x)\in[1,\ \ 3]\times[0,\ \ 3]\), while the space step size is \(\Delta x=0.005\). We use an extremely large, non-physical value \(\sigma=10\) for the radiation coefficients in order to obtain information about the behavior of the schemes in general conditions. With this, the largest value of the term \(\sigma\cdot u^{4}\) is above \(1.2\cdot 10^{4}\), which means that the nonlinearity is very strong. The errors as a function of the time step size are presented in Figure 6. As one can see on the left side of the figure, the errors tend to very small numbers for decreasing time step sizes, so the algorithms are verified. However, some of the algorithms are unstable, and for medium time step sizes (around \(h=10^{-2}\)), their markers are missing from the figure. These are the DF, ADE, the LH fully explicit, LH inside, and LH mixed schemes. The OOEH is not literally unstable but it produced errors which are as large as \(10^{3}\), thus it is also not robust in this strongly nonlinear case. The error of the other methods is limited, even for these large step sizes in this experiment, which means they have very good stability properties. The nonlocal treatment of the radiation term with the product of the neighbors performs the best, but the other nonlocal and the pseudo-implicit treatment is also successful. In the case of the 'inside' and'mixed' versions, the non-negative condition (24) helped to maintain the stability, but they are still outperformed by the nonlocal and the pseudo-implicit versions.