esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Verification of Simulations and Experiments
### Simulation Results
#### 4.1.2 Load Analysis  
Owing to the characteristics of the compressor with constant torque characteristic load, the PMSM must maintain an increased efficiency at the rated operating conditions to achieve an efficiency improvement effect. The characteristics of the rated torque of the PMSM for electric compressors used for the cooling of the electric vehicles are compared in Figure 12. At this time, the rated power is 3 kW, and the rated speed is 6000 rpm. Additionally, the characteristics are applicable to the rated load of 4.775 Nm. The peak-to-peak value of torque ripple was calculated to be 2.16 Nm for the initial model, and 1.34 Nm for the optimal model. The torque ripple of the optimal model has improved by 38% compared with the initial model. Based on this analysis, the rated efficiency was 92.0% for the initial model, and 93.5% for the optimal model, thus improving the efficiency by approximately 1.5%.  
Figure 13 shows the flux distributions and magnetic flux densities of the initial and optimal models, respectively. The leakage flux that did not contribute to the output and magnetic saturation at the iron core was also analyzed. Based on the proposed rotor shape optimization process, an increased amount of magnetic flux flowed through the stator's core in the case of the optimal model. In addition, improvements of magnetic flux flow and the effect of the variation of the magnetoresistance were obtained. These improved the back-EMF and reduced the cogging torque, as explained in the previous section. The maximum magnetic flux density of the rotor was 1.9 T for both the initial and the optimal models. This density was not saturated. Correspondingly, it was therefore considered that it was properly designed.  
Figure 11: Comparison of the cogging torque characteristics.  
Figure 12: Comparison of the rated torque characteristics.  
Figure 13: Flux distribution and magnetic flux density: (**a**) Initial model; (**b**) optimal model.
## 3 Architectures in the 5G and LPWAN-IoT and Smart Cities
### IoT Architecture  
Today's IoT applications are geared towards enabling a smarter livelihood for everyone which involves the interconnection among several smart devices for smart cities. The adequate architecture will enable reliable interconnections of various smart devices in our homes, buildings, cities, and industries for the industrial internet of things (IIoT) [34]. Hence, architecture for IoT has been viewed from different perspectives by various IoT designers since there is no specific universally agreed IoT architecture. Different architectures have been advocated by numerous researchers. A study of available IoT architectures is provided in [35; 36; 37]. The general IoT architecture is the layered approach architecture. Its simplest or basic form is a three-layer architecture as shown in Figure 2. This architecture consists of the following [38; 39]:  
* Perception Layer (Layer 1): This is the layer that considers physical devices and sensors that collect data/information about the entity as well as the environment;
* Network Layer (Layer 2): This is where the connections to other things, devices, and services, including the processing of sensor data, are carried out;
* Application Layer (Layer 3): This layer defines the applications where the IoT is to be deployed, including provisioning application services to the users.  
The three-layer architecture is meant only for the basic requirements of IoT. Sophisticated applications and business solutions require more layers in the IoT architecture. For instance, there are four-layer, five-layer, six-layer, and seven-layer architecture. Considering the present-day advanced smart applications due to the emerging technologies and 5G network support, a seven-layer IoT architecture is preferred for many IoT applications. An illustrative of the seven-layer IoT architecture is illustrated in Figure 3.  
Figure 3: Seven-Layer IoT Architecture (adapted from [37]).  
Figure 2: Three-Layer IoT Architecture (adapted from [38]).
# An Automatic Navigation System for Unmanned Surface Vehicles in Realistic Sea Environments
## 6 Simulation Study and Discussion  
The section discusses and analyses the proposed ANS of the with the DTFMS and FCS-MPC in two different environments. The first simulation is to validate the algorithm in a simulation environment, and two different test scenarios are designed: (1) the USV runs in a static environment; and (2) the USV operates in a dynamic obstacle environment. The second simulation is designed to test the algorithm in a realistic sea environment with static and dynamic environments. The modeling of the simulation and real environments has been described in Sections 3 and 5. The parameters of the USV and the algorithm from Table 1 is used in all cases.  
The algorithm is coded in Matlab and simulations are run on the computer with a Core i7 3.6 GHz processor and 4 GB of RAM. The simulation results are given by time-dependent motion sequences. The following symbols and color codes in motion sequence diagram are applied:  
* The start and end points for the USV are marked as blue and red '\(*\)' markers. The end point can be enlarged to a red circular area, and if the USV enters the area, it can be considered to be finished. Moreover, the dynamic target is marked as yellow '\(*\)' marker.
* There are three curves of the USV in the diagram. The magenta curve is the planned path of the DTFMS method for the USV. The blue curve represents the track of the USV up to a final time. The green curves denote the predicted trajectories at the present time. The symbol of the USV is the blue boat form.
* To make the vessels easy to observe, the symbols of both the USV and the target vessels (TVs) are enlarged 15 times in terms of size.
* The curves and boat forms with red, orange, and cyan colors represent the tracks and symbols of TV1, TV2, and TV3, respectively.
###### Abstract  
This study evaluates in vitro cytotoxic and antiproliferative activity on human colon tumor cell line Caco-2 (ATCC-HTB-37) of a standardized (5 mg GAE/mL) ethanolic extract from _Stokesia laevis_ (Sla26), of five polyphenols compounds (reference substances, ref.), namely luteolina-7-O-glucose, luteolin-8-C-glucose, caffeic acid, gentisic acid, and p-aminobenzzoic acid (PABA), as well as of Slae26 combinations with the five reference substances, 1:1 mass rate (GAE, ref.). Cell viability studies (MTS test) have revealed IC\({}_{50}\) values of 36 \(\upmu\)g GAE/mL in the case of Slae26 ethanolic extract, while Slae26 combinations with the five phenolics indicated IC\({}_{50}\) values around 5 \(\upmu\)g GAE/mL. In silico docking studies on the molecular targets human tankyrase 1 (TNKS1) and human tankyrase 2 (TNKS2) in complex with their native ligands, Co-crystallized 3J5A and Co-crystallized FLN, indicated score values of \(-104.15\) and \(-76.97\), respectively; in the series of the reference compounds studied, luteolin-7-O-glucose was revealed with the best score values on both molecular targets (\(-80.49\) and \(-85.17\)), together signifying real antiproliferative potential against human colon cancer of Slae26, of luteolin-7-O-glucose, and of Slae26 combinations with all food-related bioactive compounds tested.  
food bioactive compounds; stokes aster ethanolic extracts; health effects; antiproliferative activity; Caco-2 cells; docking; TNKS1; TNKS2 2021
# Incorporating Spatial Autocorrelation into GPP Estimation Using Eigenvector Spatial Filtering
## 5 Discussion
### Ability of the Three GPP Products to Capture GPP Variations  
To further evaluate the capability of SA-LGBM and other GPP products in capturing GPP variations and their suitability across different vegetation types, we selected representative locations for six distinct vegetation types, which are grasslands (GRA), cryptlands (CRO), evergreen needleleaf forests (ENF), permanent wetlands (WET), deciduous broadleaf forests (DBF), and open shrublands (OSH). We conducted a quantitative assessment of their seasonal changes from 2015 to 2020, as depicted in Figure 12. We ensured that each site possessed quality samples corresponding to the relevant vegetation type. All three products effectively depict the seasonal fluctuations of GPP across sites featuring diverse vegetation types, demonstrating uniformity in general patterns, notably in the timing of GPP peak occurrences and overall variability trends. GPP generally peaks in summer each year and approaches zero in winter. It can be observed that MOD17 exhibits a significant underestimation for sites with high GPP values, such as CRO and GRA types. This is consistent with previous research findings [58; 59]. We found that SA-LGBM has the highest fitting degree with flux site data, effectively capturing vegetation signals across a wide range of GPP. SA-LGBM largely overcomes the underestimation issue of existing GPP products in areas with high vegetation signals [68]. The ability of the three products to detect GPP changes in sparse vegetation is unstable and exhibits significant fluctuations. Overall, SA-LGBM exhibits good adaptability and can capture variations across different geographic regions and vegetation types. Especially in areas with complex vegetation types, the SA-LGBM method accurately reflects subtle changes in GPP, providing more reliable estimation results.  
Figure 12: Evaluation of temporal variations in GPP across six distinct locations characterized by diverse vegetation types.
## 4 Experiments and Analysis
### Sensing Location Determination of the IMU Sensor
In this case, it is difficult to predict CVA with the sensing values; hence, the [2] chest position was excluded. Second, to reduce the number of FHP false positives, the same experiment was performed for a slightly tilted head posture, as shown in Figure 10. This is a common posture that people assume when looking at the bottom of a monitor or desk while working on a computer. In this case, although the CVA decreased slightly, it is not a FHP condition. The participants were then asked to demonstrate the posture shown in Figure 10, and their average \(\theta\) were summarized, as shown in Table 4.  
Because the results presented in Table 4 do not show the FHP, the CVA hardly changed; however, only when a sensor was attached to the [3] back of the neck or [1] forehead did the measured forward tilted angle (\(\theta\)) become similar to the \(\theta\) in the FHP state. That is, if a sensor is attached to the [1] forehead or [3] back of the neck, a state that is not FHP may be mistakenly judged as FHP. Based on these findings, we decided to attach the MPU6050 sensor to the cervical spine, and then the \(\theta\) and CVA of the sensor changed linearly. Table 3 shows that, in the standard posture, the average \(\theta\) of the sensor was \(-40.0^{\circ}\) and the average CVA was \(49.9^{\circ}\). The forward stretching of the neck reduced the \(\theta\) and CVA of the sensor. In the FHP state, the average \(\theta\) of the sensor was \(-59.2^{\circ}\) and the average CVA was \(30.5^{\circ}\). Figure 11 shows this result graphically, where the CVA and \(\theta\) were observed to decrease with the same slope. Therefore, this can be expressed using Equation (2). \[\textit{CVA}\ =\ \textit{Forward tilted angle}\ (\theta)\ +\ 89^{\circ}. \tag{2}\] \begin{table}
\begin{tabular}{l l l l} \hline
**Sensor Location** & **[1] Forehead** & **[3] Back of Neck** & **[4] Cervical Spine** \\ \hline Forward tilted angle (\(\theta\)) of the pose with head slightly tilted (\({}^{\circ}\)) & \(-6.7\) & \(-32.1\) & \(-45.3\) \\ Forward tilted angle (\(\theta\)) of the standard posture (\({}^{\circ}\)) & \(-18.5\) & \(-16.3\) & \(-40.0\) \\ CVA of the pose with head slightly tilted (\({}^{\circ}\)) & \(44.0\) & & \\ CVA of the standard posture (\({}^{\circ}\)) & \(49.9\) & & \\ \hline \end{tabular}
\end{table} 
Table 4: Relationship between the forward tilted angle (\(\theta\)) and CVA with a slight head tilting. Figure 10: Slightly tilted head posture (CVA = 45\({}^{\circ}\)).
## 4 Case Study
### Simulation Analysis  
#### 4.2.1 Market Clearing Analysis  
Figures 5 and 6 show the power clearance situation of the bilateral market and pool market, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **a/(\$/(MW)\({}^{2}\))** & **b/(\$/MW)** & **c/\$** \\ \hline Thermal power generator & 0 & 25 & 0 \\ Wind power generator & 0 & 32.86 & 0 \\ Photovoltaic power generator & 0 & 37.5 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Power producer cost parameters [46].  
Figure 4: Wind and photovoltaic power output curve. (**a**) Wind power output curve; (**b**) Photovoltaic output curve.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(P_{p,\text{min}}\)**(\$/MW)** & \(P_{p,\text{max}}\)**(\$/MW)** & \(P_{b,\text{min}}\)**(\$/MW)** & \(P_{b,\text{max}}\)**(\$/MW)** \\ \hline Thermal power generator & 25 & 100 & 25 & 100 \\ Wind power generator & 33 & 100 & 33 & 100 \\ Photovoltaic power generator & 38 & 100 & 38 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quotation parameters of power producers [47; 48].  
According to Figures 5 and 6, through the hybrid power market mechanism and combined with carbon responsibility incentive measures, the electricity sold by new energy generators in the bilateral market is more than that in the pool market at any time; this phenomenon is especially obvious when the wind (01:00-11:00; 19:00-24:00) and the light is strong (13:00-18:00). In addition, as can be seen from Figure 6, the bid-winning power of new energy generators in the pool market at any time is much less than that of the thermal power generator. However, Figure 5 shows that the electricity sold by new energy generators in the bilateral market is close to the electricity sold by thermal electricity suppliers and even exceeds it at some moments, such as t = 2, 3, 11, 13. From one perspective, with the characteristics of zero carbon intensity, new energy generators guide users to participate in the bilateral market through carbon responsibility and actively choose green electricity consumption. From another perspective, according to the MCP mechanism in the pool market, the thermal power output is stable and the cost is low, which gives it a competitive advantage. In summary, it can be seen that the introduction of bilateral  
Figure 5: Clearing of the bilateral markets.  
Figure 6: Clearing of the pool market.  
transactions provides users with a place to consume new energy and solves the problem of new energy bidding difficulties by market means to a certain extent.
# An Implementation of the HDBSCAN* Clustering Algorithm
## 2 Background and Literature Review  
### Dbscan  
One of the first, and perhaps the most well-known density-based clustering algorithm is DBSCAN [7]. It was first proposed in 1996 and remains a relevant technique for clustering tasks today. DBSCAN stands for density-based spatial clustering of applications with noise. The algorithm requires a rather obscure distance parameter as input, which is used as a threshold for determining the data points that will be assigned to clusters, and those marked as outliers. There is no obvious intuition for establishing the best value for this parameter. As the authors of [3] point out, DBSCAN may have issues when the density of individual clusters in a dataset varies.
## 5 Beamlines  
There is one undulator line for the HX application and another for the SX application. However, there are several end-stations for each undulator line to support various requests from users. For hard X-ray application, there are two end-stations called HEH1 for the pump-probe experiment and HEH2 for the imaging [6]. These two stations are located in tandem. In order to provide HX FEL photons to HEH2, the entire HEH1 stage is able to move in the transverse direction by 1 m. When HX/SX FEL photons emerge from their corresponding undulator system, they pass through various components located in the undulator hall (UH) and the optics hall (OH). Both halls are isolated with proper concrete shielding.  
For the HX case, photons are then allowed to enter the experimental hall (EH) where HEH1 and HEH2 are located. Mirrors and a double crystal monochromator (DCM) are located in UH/OH as well as various collimators and safety shutter for radiation safety. In HEH1, beam position monitors (BPM) and profile intensity monitors (PIM) are installed to measure the position and the intensity of the HX FEL beam. An optical laser/X-ray correlator (OXC) is also installed to measure the offset of photon arrival times in fs accuracy. Two Be compound refractive lenses (Be-CRL) are installed along with three slits to define and optimize the XFEL beam. In the final stage, there is a hexapod diffractometer and 4-circle goniometer to adjust sample's location. Finally, there is a robotic arm to control its position of the detector. The HEH1 beamline is shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Instruments** & **Number** \\ \hline Position Beam Energy & Stripline Beam Position Monitor & 160 \\  & Cavity BPM & 49 \\ Beam Charge & Turbo Integrated Current Transformer (ICT) & 10 \\ Beam Size & Screen Monitor & 54 \\  & Wire Scanner & 9 \\ Bunch Length & Coherent Radiation Monitor & 4 \\  & Transverse Cavity & 3 \\ Arrival Time & Arrival Time Monitor & 10 \\ Beam Loss & Beam Loss Monitor & 26 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Major components of beam diagnostics and their functions.  
HEH2 is designed based on forward scattering geometry, and will be used for the coherent X-ray imaging (CXI) or the serial femtosecond crystallography (SFX) [7]. The XFEL beam is focused to about 2 \(\upmu\)m by the K-B mirror. A wire scanning method was used to measure the focusing beam profiles. A tungsten wire with a diameter of 200 \(\upmu\)m was placed at the focal point. A photo diode detector placed behind the wire was also used to measure the beam intensity during the wire scanning. There are also several diagnostic devices such as Pop-in, Quadrupole BPM (QBPM), and photo-diodes. The HEH2 beamline is shown in Figure 7.  
Figure 6: Inside view of HEH1.  
Figure 7: Inside view of HEH2.  
For the SX beamline, there are also two end-stations: one for coherent diffraction imaging (CDI) or X-ray emission/absorption spectroscopy (XES/XAS), and another for SX resonant scattering.
## Appendix A Decomposition of Left- and Right-Going Waves in a Multi-Reflection System  
A multi-reflection system can be regarded as a superposition of waves propagating in the positive and negative directions of the coordinate \(x\). We call them the right- and left-going waves, respectively. To understand the characteristics of the wave field in the computational domain, decomposing the composite water surface elevation into the right- and left-going waves is often required. The reconstruction of surface elevation by superposition of harmonic waves can be found, for example, in [PERSON] et al. [21; 39; 40; 41]. The wave profile in a multi-reflection system can be expressed by\[\eta(x,t) = \sum_{m\geq 1}A_{I}^{(1,m)}\cos\left[kx-\omega t+\kappa_{I}^{(1,m)}\right] \tag{11}\] \[+\sum_{m\geq 1}A_{R}^{(1,m)}\cos\left[kx+\omega t+\kappa_{R}^{(1,m)}\right]\] \[+\sum_{m\geq 1}\sum_{n\geq 2}A_{I,B}^{(n,m)}\cos\left[nkx-n\omega t +\kappa_{I,B}^{(n,m)}\right]\] \[+\sum_{m\geq 1}\sum_{n\geq 2}A_{R,B}^{(n)}\cos\left[nkx+n\omega t +\kappa_{R,B}^{(n,m)}\right]\] \[+\sum_{m\geq 1}\sum_{n\geq 2}A_{I,F}^{(n,m)}\cos\left[k^{(n)}x-n \omega t+\kappa_{I,F}^{(n,m)}\right]\] \[+\sum_{m\geq 1}\sum_{n\geq 2}A_{R,F}^{(n,m)}\cos\left[k^{(n)}x+n \omega t+\kappa_{R,F}^{(n,m)}\right]\]  
where \(A\) is the amplitude and \(\kappa\) is the phase difference. The subscripts \(I\) and \(R\) denote the incident and reflected waves, respectively. The second subscripts \(B\) and \(F\) denote the bounded and free waves, respectively. The superscript \(n\) denotes the \(n\)th harmonic, and the superscript \(m\) denotes the \(m\)th reflection cycle.  
The waves are decomposed into the incident and reflected waves in terms of the harmonic bound and free waves. The bound waves are in phase-locked modes that travel at the same phase velocity as the first harmonic waves and are bound to them. The free waves propagate at their respective phase velocities, which can be presented by the dispersion relation  
\[(n\omega)^{2}=gk^{(n)}\tanh k^{(n)}h_{0}\,. \tag{12}\]  
Let us verify the reconstruction of the wave motion provided by Equation (11) on an example. Considering the canonical problem shown in Figure 1, together with the use of reflective boundary conditions, we reconstruct the recorded waves on the flat segment at \(x=-4000\) m for a resonant and a non-resonant regimes (see Figure 11). A good agreement is found between the reconstructed and recorded waves. A new component is added in each reflection cycle. A jump appears in the time series of the free-surface elevation when it arrives (see Figure 11b). The jumps can vanish when we smooth the amplitude numerically. As the result shown in Figure 11 proves that Equation (11) can describe the multi-reflection system, we consider that the smoothing process can be ignored.
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Article Reference** & \begin{tabular}{c} **Energy** \\ **Cost** \\ \end{tabular} & \begin{tabular}{c} **Travel** \\ **Time** \\ \end{tabular} & \begin{tabular}{c} **Waiting** \\ **Time** \\ \end{tabular} & \begin{tabular}{c} **Charging** \\ **Location** \\ \end{tabular} & \begin{tabular}{c} **Battery** \\ **Capacity** \\ \end{tabular} & \
\begin{tabular}{c} **Dynamic** \\ **Optimization** \\ \end{tabular} &
\begin{tabular}{c} **Distributed** \\ **Decision** \\ \end{tabular} \\ \hline [PERSON] et al. [17] & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & & \(\times\) & \(\times\) \\ [PERSON] et al. [18] & \(\times\) & & \(\times\) & \(\times\) & \(\times\) & & & \\ [PERSON] et al. [19] & \(\times\) & & & \(\times\) & \(\times\) & & & \\ [PERSON] et al. [22] & \(\times\) & \(\times\) & & \(\times\) & & & \\ [PERSON] et al. [23] & \(\times\) & \(\times\) & & \(\times\) & & & \\ [PERSON] et al. [24] & \(\times\) & \(\times\) & & \(\times\) & \(\times\) & & \\ [PERSON] et al. [25] & & \(\times\) & \(\times\) & & \(\times\) & & \\ [PERSON] et al. [26] & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & & \(\times\) \\ [PERSON] et al. [27] & & \(\times\) & \(\times\) & \(\times\) & & \(\times\) & \(\times\) \\ [PERSON] et al. [28] & & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ [PERSON] et al. [29] & \(\times\) & \(\times\) & & \(\times\) & \(\times\) & & \(\times\) \\ [PERSON] et al. [30] & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ [PERSON] et al. [31] & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ [PERSON] et al. [32] & & & & & \(\times\) & & \\ [PERSON] et al. [33] & & & & & \(\times\) & \(\times\) & \(\times\) \\ [PERSON] et al. [34] & \(\times\) & \(\times\) & & \(\times\) & \(\times\) & & \\ our work in this paper & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics in previous works. based real-time traffic and charging infrastructure service information acquisition, as well as distributed dynamic optimization to adapt to changes in the transportation network. Furthermore, most studies only consider the problem of charging scheduling at fixed charging stations, while only a few studies have examined the dynamic wireless charging problem using energy segments. Thus, this paper aims to address the dynamic path optimization and charging selection problem by incorporating real-time traffic information and the service status of charging infrastructure.