esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Analysis on the Performance of a Heat Pump Powered by Surface Water Freezing
## 3 Results and Analysis
### Effect of Water Velocity at Evaporator Inlet  
Figure 9a shows the effect of water velocity at the evaporator inlet on the compressor exhaust temperature \(T_{\rm r}\). The higher the inlet water flow rate, the higher \(T_{\rm r}\) is, and its effect increases as the inlet water velocity increases. This is because the higher inlet water velocity contributes to more ice formed and greater heat absorbed by the refrigerant, which results in more refrigerant evaporation and a higher \(T_{\rm r}\). In the same velocity condition, as working time progresses, \(T_{\rm r}\) sharply increases at first. Then, \(T_{\rm r}\) is almost constant after 15 minutes due to approaching a stable state. In the stable state, \(T_{\rm r}\) is about 76.3 \({}^{\circ}\)C, 85.4 \({}^{\circ}\)C, and 104 \({}^{\circ}\)C, corresponding to the inlet water velocities of 0.1 m/s, 0.3 m/s, and 0.5 m/s.  
Figure 9a also shows the effect of inlet water velocity on the isentropic power \(w\) consumed by the compressor. The greater the inlet water velocity, the higher \(w\) is, for  
Figure 9: Effect of inlet water velocity with time (**a**) on \(T_{\rm r}\) and \(w\), (**b**) on \(Q\) and \(COP\).  
the same reason as for \(T_{r}\), and the more evaporation. In the same way, as working time progresses, \(w\) sharply increases at first. Then, \(w\) is almost constant after 15 minutes due to approaching a stable state. When the stable state is reached, \(w\) is about 40.5 kW, 90 kW, and 156 kW, corresponding to the inlet water velocities of 0.1 m/s, 0.3 m/s, and 0.5 m/s.  
Figure 9b shows effects of the inlet water velocity on the heating capacity \(Q\). The higher the inlet water flow rate in the evaporator, the higher \(Q\) is, for the same reason as for \(w\), and the more evaporation. As working time progresses, \(Q\) sharply increases at first. Then, \(Q\) is almost constant after 15 minutes due to approaching a stable state. When the stable state is reached, \(Q\) is about 151 kW, 307 kW, and 457 kW, corresponding to the inlet water velocities of 0.1 m/s, 0.3 m/s, and 0.5 m/s.  
Figure 9b shows effects of the inlet water velocity on the performance of coefficient \(COP\). Although the higher the flow rate, the higher \(Q\) is, \(w\) also has a larger increase, which results in a greater inlet water flow rate in the evaporator, and the smaller the \(COP\) of the system. After stabilization, \(COP\) is about 3.72, 3.39, and 2.94, corresponding to the inlet water velocities of 0.1 m/s, 0.3 m/s, and 0.5 m/s.  
It is indicated once more that the optimal deicing time of the system under the discussed conditions is about 15 minutes.
###### Abstract  
Smart health applications have received significant attention in recent years. Novel applications hold significant promise to overcome many of the inconveniences faced by persons with disabilities throughout daily living. For people with blindness and low vision (BLV), environmental perception is compromised, creating myriad difficulties. Precise localization is still a gap in the field and is critical to safe navigation. Conventional GNSS positioning cannot provide satisfactory performance in urban canyons. 3D mapping-aided (3 DMA) GNSS may serve as an urban GNSS solution, since the availability of 3D city models has widely increased. As a result, this study developed a real-time 3 DMA GNSS-positioning system based on state-of-the-art 3 DMA GNSS algorithms. Shadow matching was integrated with likelihood-based ranging 3 DMA GNSS, generating positioning hypothesis candidates. To increase robustness, the 3 DMA GNSS solution was then optimized with Doppler measurements using factor graph optimization (FGO) in a loosely-coupled fashion. This study also evaluated positioning performance using an advanced wearable system's recorded data in New York City. The real-time forward-processed FGO can provide a root-mean-square error (RMSE) of about 21 m. The RMSE drops to 16 m when the data is post-processed with FGO in a combined direction. Overall results show that the proposed loosely-coupled 3 DMA FGO algorithm can provide a better and more robust positioning performance for the multi-sensor integration approach used by this wearable for persons with BLV.  
localization; navigation; smartphone; GNSS; 3D building models 2  
were selected to evaluate positioning hypothesis candidates' likelihood scores. The position was then integrated with the velocity estimated by Doppler measurements using the FGO as loosely coupled.  
Several experiments were designed in New York City (NYC) to acquire GNSS data during urban commuting (traveling between two defined locations, origin and target). A commercial-grade receiver, u-blox ZED-P9P, was connected to an Nvidia microcomputer, Jetson TX2. The performance of the proposed system was evaluated with the data recorded in a series of trips that took place on the lower east side of NYC (specifically Murray Hill, Manhattan).  
The remainder of this paper is organized as follows: Section 2 is an introduction to the integrated navigation system for pedestrians and existing studies. Section 3 is an introduction to the proposed positioning algorithm. Section 4 contains the designed experiment results and analysis. Finally, a conclusion and future work are presented in Section 5.
## 1 Introduction
In recent years, with the deepening of the research on DT, it has become increasingly apparent that the most significant advantage of the DT is that it can closely and timely reflect the actual service state of physical entities to enhance information-based services in the life cycle of entities [14]. In [15], a digital twin model of lithium-ion batteries was proposed to evaluate the degradation of battery performance. In [16], a ship digital twin model was established based on the data collected by sensors, and the speed loss caused by marine pollution was successfully estimated by the model. Reference [17] proposes a proprietary framework for production line performance prediction based on digital twin technology, which can monitor the performance of the production line when production demand changes suddenly. In [18], a fuselage DT model was established to predict the fracture probability of critical components of the fuselage. In [19], the DT technology was used to model highway bridges for safety risk assessment. The above research proves the great potential of DT in predicting the bad behavior of complex systems [20]. In addition, the characteristic of DT technology is that it can describe the model comprehensively from four aspects: three-dimensional structure, physical characteristics, behavior patterns, and norm restrictions [21]. The electromagnetic coupling process of BTS is a dynamic interactive process, which cannot be fully explained by traditional static model. DT is a good way to overcome this contradiction. Through the DT method, this dynamic process can be fully analyzed and discussed from the different aspects mentioned above. The purpose of this study was to quantitatively analyze the electromagnetic coupling characteristics of the BTS using digital twin technology. In this paper, the electromagnetic coupling process of the BTS was introduced, and the main factors involved in the electromagnetic coupling process were qualitatively analyzed using the fishbone diagram method. Then, according to these sensitive factors, the analytical model or fitting model of each module in the transmission coupling process was established. By combining these modularized models, a digital twin model to quantitatively analyze these sensitive factors was successfully established. Experiments were designed to verify the validity and accuracy of the digital twin model. Finally, the digital twin model was used to quantitatively evaluate the influence of some sensitive parameters on the electromagnetic coupling characteristics of the BTS. The rest of this paper is organized as follows: In Section 2, the electromagnetic coupling process of BTS is reviewed, and the key factors on the electromagnetic coupling process are discussed in the fishbone diagram. Section 3 establishes a high-precision electromagnetic coupling model of the BTS. In Section 4, two experiments are designed to verify the digital twin model. Then, the influence of some sensitive parameters on the electromagnetic coupling process is analyzed quantitatively in Section 5. Section 6 is the discussion and conclusion.
# Validating a 5G-Enabled Neutral Host Framework in City-Wide Deployments
## 6 Validation of Use Cases
### KPIs and Measurement Methodology
#### 6.1.1 User Experienced Data Rate  
The minimum data rate required to ensure a sufficient quality experience (without considering broadcast services) [5]. In this evaluation, the measurement of this KPI is done at the application server, by monitoring the throughput achieved by a single UE that generates traffic towards the server.
###### Abstract  
Acquisition, tracking, and pointing (ATP) is a key technology in free space laser communication that has a characteristically high precision. In this paper, we report the acquisition and tracking of low-Earth-orbit satellites using shipborne ATP and verify the feasibility of establishing optical links between laser communication satellites and ships in the future. In particular, we developed a shipborne ATP system for satellite-to-sea applications in laser communications. We also designed an acquisition strategy for satellite-to-sea laser communication. In addition, a method was proposed for improving shipborne ATP pointing error. We tracked some stars at sea, achieving a pointing accuracy of less than 180\(\upmu\)rad.We then acquired and tracked some low-Earth-orbit satellites at sea, achieving a tracking accuracy of about 20\(\upmu\)rad. The results achieved in this work experimentally demonstrate the feasibility of ATP in satellite-to-sea laser communications.  
satellite-to-sea laser communication; acquisition, tracking and pointing; shipborne ATP; pointing error model +
Footnote †: journal: Applied Soft. 2019, 9, 3940; doi:10.3390/app9183940
# Hybrid-Compliant System for Soft Capture of Uncooperative Space Debris
## 3 Hybrid-Compliant System for a Soft Capture of Space Debris  
The high demand for reliability while capturing uncooperative debris makes the system be designed with compliance in mind first. A soft capture at the impact will ensure that the debris is not pushed away and give enough contact time for the capture. To guarantee this soft capture, this paper proposed a hybrid-compliant system at a conceptual level, with passive and active compliance, while fitting into a CubeSat architecture and considering the capturing of uncooperative box-shaped debris in LEO. Figure 6 presents a conceptual close-up view of the hybrid mechanism proposed for the soft capturing sub-phase. It comprises two crucial parts: the active compliance unit (ACU), with tunable stiffness, and the passive compliance unit (PCU), with a permanent stiffness. Together they form the soft capture Unuit (SCU) of adjustable stiffness of our capturing mechanism.  
Figure 5: General view of a CubeSat-based hybrid-compliant system for soft capture of space debris. It includes the active compliance unit (ACU) and the passive compliance unit (PCU).  
Figure 6: Closeup of the hybrid-compliant system for soft capture of space debris.
## 3 Results and Discussion
### Rapid and Area-Specific Printing of Fn-CaP Microchips onto Human Dentin Surface  
Since the LIFTOP process made it possible to transfer the Fn-CaP film onto hard substrates such as PET, we applied this technique to transfer the Fn-CaP film onto human dentin substrates. Human dentin contains apatite as an inorganic component, which is 70%, in addition to collagen as an organic component. Although human dentin is softer than human enamel (Mohs hardness is 5-6), it is still harder than PDMS. Thus, we employed the LIFTOP process for Fn-CaP printing onto a human dentin surface.  
Figure 5a shows a schematic image of the LIFTOP process for the transfer of the Fn-CaP film to the human dentin substrate (same as Figure 3a except for the receiver substrate). Figure 5b,c shows confocal  
Figure 4: A model for the Fn-CaP film transfer by (**a**) LIFTOP and (**b**) LIFT. A single nanosecond laser pulse was irradiated on (**a**) the Fn-CaP/C/PDMS/PET donor sample through the carbon-coated optical stamp or (**b**) the Fn-CaP/C/PET donor sample through the carbon-coated PET support, leading to strong laser absorption at the carbon sacrificial layer and the subsequent donor film transfer.  
laser scanning microscopic images of the Fn-CaP/C/PDMS/PET donor sample after laser irradiation and of the Fn-CaP microchips printed on the human dentin substrate, respectively. The pulse energy was set at 160 \(\upmu\)J. In Figure 5b, it can be observed that the Fn-CaP donor film was removed at the laser irradiated spots with an average diameter of approximately 80 \(\upmu\)m. The Fn-CaP film thickness was estimated to be approximately 500 nm from the hole depth. Corresponding to the ejection of the donor film, the Fn-CaP microchips were successfully printed on the dentin surface with an average diameter of approximately 80 \(\upmu\)m, as shown in Figure 5c. This shows that LIFTOP is applicable to the additive micropatterning of the brittle Fn-CaP film on the human dentin substrate, and that it would be useful for the tooth surface functionalization. Figure 5d shows the laser pulse energy dependence of the average hole diameter on the donor sample after laser irradiation (red open circles) and the average diameter of the Fn-CaP microchips printed on the human dentin substrate (blue solid circles). The transfer threshold was between 80 and 100 \(\upmu\)J, which was the same with the Fn-apatite donor film formed on the C/PET support, as reported in our previous work [16]. The ablation threshold of the carbon film is reported to be below 50 \(\upmu\)J [16], which is much lower than the transfer threshold in the present LIFTOP process. This difference might correspond to the energy required for cutting and ejecting the film. Both the hole and the transferred microchip sizes increased with the pulse energy.
# A Distributed Indoor Mapping Method Based on Control-Network-Aided SLAM: Scheme and Analysis
## 3 Field Tests and Results
### Evaluation of the Influence of Control Point Density  
In order to assess the influence of the density of control points on the relative and absolute accuracy of the mapping result, we compared the accuracy of the mapping results by adding different numbers of control points distributed evenly to assess the effect of density of control points on the mapping result. The control points in Block 1 are shown in Figure 7. The RMS values of the relative and absolute accuracy are shown in Table 2. These show that the density of control points has a significant impact on the accuracy of the mapping result: there is a trend that as the density of control points increases, the accuracy of the mapping increases accordingly. The relative accuracy and absolute accuracy of the mapping result were increased by 41.86% and 31.19% when the number of control points increased from 4 to 8. However, the improvement in mapping accuracy was not so obvious when the number of control points increased from 8 to 12. The results indicate that the number of control points does have an influence on the accuracy of the mapping result, but when the control points reach a certain number, the accuracy of the mapping result is not increased significantly by adding more control points. In practical applications, we need to reasonably choose the number of control points and control network structure according to the actual situation to achieve reasonable accuracy.
# Unsupervised Anomaly Detection for Time Series Data of Spacecraft Using Multi-Task Learning
## 3 Materials and Method
### Overview  
Hereafter, \(X_{t}=\{x_{1},x_{2},\ldots,x_{M}\}\in\Re^{M}\) indicates M-dimensional data samples at time, and \(X=\{X_{t1},X_{t2},\ldots,X_{tK}\}\in\Re^{M\times K}\) indicates the data acquired over steps. As stated above, this study proposes the MTAD method, which performs unsupervised anomaly detection by simultaneously learning the spatial-temporal patterns of the spacecraft data.  
The overall process of the MTAD method is shown in Figure 3 and can be broadly divided into training and inference processes, as detailed below.  
Training Process: First, the training data are standardized and the mean \(\mu_{train}\) and standard variance \(\delta_{train}\) are obtained. Then, a sliding window is used to obtain the final input data \(X\) and output \(Y\), and the processed data are used to train the multi-task model \(\text{MTAD}_{\text{mt}}\), which incorporates Proxy Tasks 1-4 (detailed in Section 3.3). Finally, the feature vector \(Err(X)\) extracted by \(\text{MTAD}_{\text{mt}}\) is used to train the error evaluation and anomaly detection model, \(\text{MTAD}_{\text{ad}}\), which is implemented by the iForest algorithm.  
Inference Process: Parameters \(\mu_{train}\) and \(\delta_{train}\) are used to standardize the test data \(S\) and are fed into \(\text{MTAD}_{\text{mt}}\) to obtain the feature vector \(Err(S)\). Then, \(Err(S)\) is fed into \(\text{MTAD}_{\text{ad}}\) for anomaly detection.  
The various steps of the training and inference processes are explained in detail in the following subsections.
# A New Methodology for Estimating Surface Albedo in Heterogeneous Areas from Satellite Imagery
## 4 Methods
### Atmospheric Correction Band to Band
#### 4.2.2 V04 Method  
The V04 methodology given in Equation (13) uses a regression function without the restrictions of Equation (12) for estimating the surface albedo, \(\alpha\).  
\[\alpha=0.078+0.076\cdotp_{1}+0.591\cdotp_{2}+1.935\cdotp_{3}-0.492\cdotp_{4}- 0.324\cdotp_{5}+1.816\cdotp_{6}-2.193\cdotp_{7} \tag{13}\]  
This unrestricted methodology helps to better understand the degree of correlation between predictors and surface albedo.
# Tree Based Approaches for Predicting Concrete Carbonation Coefficient
## 2 Tree Based Modelling Techniques  
In the current study, MT, RF and MGGP techniques are grounded on the tree-based approach. Tree-based models use decision trees to present how different input variables can be used to predict a target value and can be used for both classification and regression problems. The basic advantage of tree-based models is that decision trees are easy to understand and interpret, and outcomes can be easily explained. Splitting variables into branches and combining the predictions can aid in maximising information gain and thus obtaining better predictions. The techniques used in the current study are Model Tree with M5 Algorithm (MT), Random Forest (RF) and Multi-Gene Genetic Programming (MGGP).
# An Enhanced Genetic Algorithm for Parameter Estimation of Sinusoidal Signals
## 6 Conclusions  
This work presents an EGA for estimating the parameters of sinusoidal signals. The proposed algorithm takes into account the preservation of population diversity, the balance between convergence speed and accuracy, and the implementation complexity. In contrast to other genetic algorithms, the main features of the EGA are:  
* a prejudice-free selection mechanism for preserving population diversity;
* a TSC operation for enhancing information exchange among individuals and variables;
* An adaptive mutation strategy to avoid premature convergence and stagnation scenarios.  
To evaluate the performance of the proposed method, studies based on benchmark functions are conducted. The results indicate that the EGA not only achieves a higher accuracy and faster convergence speed, but also it provides a good robustness against noise, showing an overwhelming superiority over the SGA, the PSO, the CS, and the CMGA. At last, parameter estimations of real-life sinusoidal signals are performed, which demonstrates the superiority and validity of the proposed algorithm in its entirety.  
The EGA has many issues worth studying further, such as the relationship between time consumption and number of offspring, the applications where it excels and fails, and so on.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Parameter** & **SGA** & **PSO** & **CS** & **CMGA** & **EGA** \\ \hline Offset \((K)\) & 369.8530 & 370.1633 & 370.0567 & 370.1083 & 369.4427 \\ Amplitude \((A)\) & 23.1916 & 23.2972 & 23.3108 & 23.2656 & 22.5831 \\ Frequency \((\omega)\) & 0.0886 & 0.0891 & 0.0874 & 0.875 & 0.0872 \\ Phase \((\varphi)\) & 0.8699 & 0.9205 & 0.9460 & 0.8877 & 0.9457 \\ \hline \(SSR\) & 1.0902 \(\times\) 10\({}^{3}\) & 1.0908 \(\times\) 10\({}^{3}\) & 1.0912 \(\times\) 10\({}^{3}\) & 1.0898 \(\times\) 10\({}^{3}\) & **1.0194 \(\times\) 10\({}^{3}\)** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Estimation results of SGA, PSO, CS, CMGA, and EGA for Circadian Rhythms.  
Figure 8: Plots of Circadian Rhythms data and fitting curve.  
**Author Contributions:** Conceptualization, C.J.; formal analysis, M.L.; software, C.J.; Supervision, C.C.; visualization, P.S.; writing--original draft, C.J. and P.S.; writing--review and editing, P.S. and C.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 UWB Characteristics
### Pulse Shape  
The impulse radio version of UWB technology is commonly known as a pulse-based UWB system. In this system, the UWB pulse, often referred to as a Gaussian doublet, utilizes a square pulse due to its ease of generation through the simple on/off switching of a transistor. However, as previously noted, UWB pulses are typically measured in nanoseconds  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Communication** & **Fractional** & **Band Ratio** \\
**Band** & **Bandwidth \(B_{f}\)** & \(B_{r}(f_{H}/f_{L})\) \\ \hline Narrow-band & \(0.00<B_{f}\leq 0.01\) & \(0.00<B_{r}\leq 1.01\) \\ Wide-band & \(0.01<B_{f}\leq 0.25\) & \(1.01<B_{r}\leq 1.29\) \\ UWB & \(0.25<B_{f}<2.00\) & \(B_{r}\geq 1.29\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Communication band usage scenarios.  
Figure 2: Wireless technologies for IPS comparison in terms of coverage, precision, energy efficiency and cost.  
or picoseconds, and the rapid on/off switching makes the pulse shape non-rectangular but rather helps approximate it as a Gaussian function [17; 27]. The basic modulation methods can be applied for UWB to encode information including pulse position modulation (PPM), burst position modulation (BPM), pulse amplitude modulation (PAM), and on-off keying (OOK) [68; 69]. Generally, higher-order modulations can achieve higher throughput or good spectral efficiencies by enabling more bits to be sent per symbol [68; 69; 70]. PPM is a common method for creating an \(M\)-ary system and is easy to implement but requires very good time resolution to modulate the pulses. In BPPM, the pulses can be sent at the same rate, and the changes of UWB shape depend on the transmitted value [71]. PAM modulation is based on using the pulse amplitude to encode information and allows for the use of an arbitrary number of different pulses. The OOK method can be also considered a particular case of PAM, whereas only a binary set of pulses is allowed. A UWB signal can be represented as a UWB signal transmitted by the help of \(K\) pulses; the pulses are within a period of \(T_{p}\) that consists of certain frames, where each information symbol is considered a UWB signal [62; 63].