esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Site Evaluations  
Monthly GPP is evaluated in three locations, as shown in Figure 5 and Table 1. In SDDEM, LAIorg and LAImod show two peaks of GPP in May and January, while FLUXNET only shows one peak of GPP in August. In addition, both LAIorg and LAImod overestimate the GPP with respect to the FLUXNET observation with the mean bias (MB) of 5.7 and 4.16 gC m\({}^{-2}\) day\({}^{-1}\), respectively. Yet, LAImod is closer to the FLUXNET than the LAIorg. At ZM-MON, both LAIorg and LAImod capture the monthly variability with respect to the observation. Furthermore, LAImod shows improved performance compared with the LAIorg, as LAIorg has an MB of 1.2 gC m\({}^{-2}\) day\({}^{-1}\), while LAImod shows an MB of 0.75 gC m\({}^{-2}\) day\({}^{-1}\). In particular, the Pearson correlation coefficient (CORR) is improved from 0.41 of LAIorg to 0.67 of LAImod. Finally, at CG-TCH, the models show a poor ability to capture the monthly variability in the GPP compared to FLUXNET. However, LAImod still outperforms LAIorg, because LAImod has an MB of 2.38 gC m\({}^{-2}\) day\({}^{-1}\), while the MB of LAIorg is 5.48 gC m\({}^{-2}\) day\({}^{-1}\). As mentioned already, the mismatch in spatial representation between the model results and Fluxnet data should be noted, yet it still provides qualitative insights into the model performance to some extent.  
Figure 2: The figure shows the spatial pattern of the solar radiation absorbed by the vegetation (SABV; in W m\({}^{-2}\); (**a–c**)), photosynthesis rate (FPSN; in \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\); (**d–f**)), leaf carbon (LEAFC; in gC m\({}^{-2}\); (**g–i**)) and soil temperature of depth 10 cm (ST10; in K; (**j–l**)) over the period of 2001–2010, for the LAIorg (OLD) and LAImod (NEW) simulations and the significant difference (Diff) between the two simulations. The significant difference was calculated using Student’s \(t\)-test with \(\alpha\) = 0.05.  
Figure 3: The figure shows the gross primary production (GPP) over the period of 2001–2010 (in gC m\({}^{-2}\) day\({}^{-1}\)) for: MAM season in the first row (**a–f**); JJA in the second (**g–l**); SON in the third (**m–r**), DJF in the fourth (**s–x**). For each row, LALorg (OLD) is on the left, followed by LALmod (NEW); CARDAMOM is in the third from left, OLD minus CARDAMOM, NEW minus CARDAMOM and the difference between NEW and OLD. Significant model bias is indicated in black dots using Student’s \(t\)-test with \(\alpha=0.05\).  
Figure 4: The figure shows the Net Ecosystem Exchange (NEE) over the period of 2001–2010 (in gC m\({}^{-2}\) day\({}^{-1}\)) for: MAM season in the first row (**a–f**); JJA in the second (**g–l**); SON in the third (**m–r**), DJF in the fourth (**s–x**). For each row, LAIorg (OLD) is on the left, followed by LAImod (NEW); CARDAMOM is in the third from left, OLD minus CARDAMOM, NEW minus CARDAMOM and the difference between NEW and OLD. Significant model bias is indicated in black dots using Student’s \(t\)-test with \(\alpha=0.05\).
# A BDGIM-Based Phase-Smoothed Pseudorange Algorithm for BDS-3 High-Precision Time Transfer
## 2 BDGIM-Based Phase-Smoothed Pseudorange Algorithm
### Classical Phase-Smoothed Pseudorange Principle  
[PERSON] proposed a method of pseudorange smoothing using the phase difference between epochs in 1982 [22], and it essentially combines a high-precision phase and low-precision pseudorange observations. The method not only weakens the pseudorange noise and multipath effect but also avoids fixing the ambiguity, which allows the receiver observations to be utilized to their full potential. That is,  
\[\widetilde{P}_{k}=\omega_{k}P_{k}+(1-\omega_{k})[\widetilde{P}_{k-1}+\lambda( \phi_{k}-\phi_{k-1})] \tag{9}\]  
where \(k\) and \(k-1\) denote the \(k\)th and \(k-1\)th epochs, respectively; \(\omega_{k}\) is the smoothing weight factor; \(\widetilde{P}_{k}\) and \(P_{k}\) represent the smoothed and the original pseudorange observations of the \(k\)th epoch; and \(\phi_{k}\) and \(\phi_{k-1}\) denote the phase observations of the \(k\)th and \(k-1\)th epochs. Since the phase observation noise is substantially less than the pseudorange observation noise, the accuracy of the smoothed pseudorange observation is greatly improved. However, it should be noted that Equation (9) ignores the effect of the ionospheric delay error variation in the \(k\)th and \(k-1\)th epochs, resulting in a less-than-optimal smoothing effect. The classical phase-smoothed pseudorange method usually treats the ionospheric delay variation as zero, in which case, the pseudorange accuracy is not effectively improved when the ionospheric delay variation is more drastic. Therefore, considering the variation in the ionospheric delay between epochs, we re-derived the principle of phase-smoothed pseudorange.
## 3 Results and Discussion
### Suitability Analysis  
Furthermore, in order to demonstrate that the method proposed by this article is suitable and is effective compared to actual measured point cloud data, the experiment involved the collection of data using MLS. The research group scanned a section of the railway. The original point cloud data was processed and extracted to obtain the final classification result, as shown in Figure 3. The centerline cloud data has a plane deviation of less than 5 mm compared with the measured data, and the elevation deviation does not exceed 10 mm, which can reach the accuracy of the method proposed.  
After a one-to-one correspondence between the starting mileage and measured data is established, the comparison of calculations and measurement of curve elements was found and is shown in Table 3, where the suitability and accuracy can be verified. Meanwhile, in order to check the accuracy of the solution element in a more intuitive way, the results of the five main stakes are plotted in Figure 4 along the railway. The red marks are the resurveyed data of the curve elements from the railway administration, and the black marks are the calculated results of the curve elements from the vehicle-borne point cloud data. It was found that the positional deviation between the calculated results and the resurveyed data of the ZH point and the HZ point, and the HY point and YH point, was 5-11 m. The positional deviation of the QZ point that was in the middle of the full curve was 0.12 m. Each of the calculated curve element values is not much different from the actual measured value, which can reach the construction demand.  
In addition, ERROS can quickly calculate the curve of the existing railway curve features and the main stake coordinates and mileage. It takes 5.7 s to calculate the above parameters, which indicates that the proposed method is highly efficient.  
Figure 3: Extraction results of the railway midline. The purple represents the rail top area, the red is the rail side area, and the cyan area is the rail centerline.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Elements** & **Circular Curve Radius** & **First Transition Curve** & **Second Transition Curve** & **First Tangent** & **Second Tangent** & **Full Curve** \\ \hline Calculation & 2010.203 & 137.002 & 128.571 & 190.751 & 186.533 & 376.956 \\ Measurement & 2000 & 140 & 140 & 190 & 190 & 379.80 \\ Deviation & 10.203 & \(-\)2.998 & \(-\)11.429 & 0.751 & \(-\)3.467 & 0.156 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The calculation results compared with the railway measured data (m).
# A Hybrid Scheme for Disaster-Monitoring Applications in Wireless Sensor Networks
### Motivation
This monitoring system must also satisfy the requirements of short delay in alert messages and high throughput of multimedia information for volcano disaster monitoring.
# Thermal Conductivity of Nanofluids: A Review on Prediction Models, Controversies and Challenges
## 3 Parameters That Affect Thermal Conductivity
### Nanofluids
#### 3.3.7 Aggregation  
In recent years, the introduction of carbon nanotubes in NFs has promoted significant research efforts due to their promising enhanced thermal conductivity and, consequently, applications in the cooling and lubricants fields. However, NFs with only a suspension of stand-alone nanotubes do not often exhibit substantially improved thermal conductivity levels. This is likely due to the infrequent physical contacts of the nanotube between each other, as they assume irregular positioning and distribution in the base fluid. To overcome this problem in practice, there are two available ways. One is to increase the volume fraction of the nanotube on the base fluid, which may lead to excessive viscosity of the NF. The other way can be found in the work developed by [70], where the authors proved that the aggregation of oppositely charged NPS (metal oxide and nanotubes) into combined clusters can explain the overall thermal conductivity enhancement. With the proper control of conditions of the suspension itself, it is possible to add fractions of positively charged metal oxide particles on the base fluid that will aggregate on the negatively charged nanotube surface (which is a phenomenon that can be enhanced with the addition of a surfactant) and hence form the aggregation cluster chain along the nanotube by electrostatic attraction.
# Unraveling the Scale Dependency of SIF-Based Phenology: Amplified Trends and Climate Responses
## 2 Data and Methods
### Data
#### 2.2.3. Reanalysis of Climate Datasets  
In this study, the ERA5-Land Daily Aggregated-ECMWF Climate Reanalysis dataset provided by the European Center for Medium-Range Weather Forecasts (ECMWF) was used, which is daily updated with global coverage and a spatial resolution of 0.1\({}^{\circ}\)[50]. In particular, we focused on a range of meteorological variables, including 2m temperature (T2M), 2m dewpoint temperature (D2M), soil temperature (TS), precipitation (PRE), soil water content (SWC), vapor pressure deficit (VPD), and photosynthetically active radiation (PAR) for the mid- to high-latitude deciduous forest region in the Northern Hemisphere from 2008 to 2017. The VPD was calculated using the surface pressure, T2M, and D2M from ERA5, based on the methodology described by [51]. The PAR was derived from the surface solar radiation downwards from ERA5 based on the methodology described by [52]. These variables, which involve temperature, moisture, radiation, and precipitation, are key climatic factors that affect phenological periods by influencing plant growth, development, and physiological processes. For comparison, we integrated hourly climate datasets into eight-day intervals and resampled them to 0.01\({}^{\circ}\) and 0.05\({}^{\circ}\) for response analysis with phenological metrics.
## 2 Materials and Methods
### Rotor Equivalent Wind Speed  
Traditionally, it has been judged that the wind speed at the hub height, which is the center of the rotor disk area, is the average wind speed of the wind passing through the entire rotor disk under the assumption that atmospheric stability is near-neutral, and the wind speed constantly increases with height. However, when atmospheric stability is near-neutral or weakly stable, or for small and medium-sized wind turbines with a rotor disk diameter of less than 50 m, this is a suitable method. As wind turbines have recently become super-sized, the rotor equivalent wind speed is recommended, which can take into account the effect of changes in kinetic energy on power generation [21,22]. This is a principle that considers wind speed at various heights passing through the rotor disk area by horizontally multiple divisions, as shown in Figure 2.  
The rotor equivalent wind speed \(u_{\alpha}\) is defined as shown in Equation (1):  
\[u_{eq}=\sqrt{\sum_{i=1}^{n_{b}}\frac{A_{i}}{A}u_{i}^{3}} \tag{1}\]  
where \(A\) is the total area of the rotor disk, \(A_{i}\) is the area of the \(i\)-th segment of the rotor disk, and \(u_{i}\) is the wind speed at each segment height. That is, \(A_{i}/A\) may represent a partial weighting value with respect to the rotor disk area, and imply the momentum of the entire rotor disk area by multiplying it by the wind speed passing through each sector. \(A_{i}\) can be calculated through Equation (2), which can be easily obtained with the arc cosine function. It is assumed that the radius of the rotor rotation area is \(R\), and the height of the sector is \(h\) when setting the first sector from the top (Figure 3). The length of \(h\) can be calculated  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **Content** \\ \hline Type & Leosphere WindCube \\ Measurement period & 2013.01.01–2014.12.31 \\ Height above MSL 1  [m] \\ Location & Wind speed & 40, 60, 76, 80, 100, 116, 160, 200, 250, 290 \\  & Wind direction & 40, 60, 76, 80, 100, 116, 160, 200, 250, 290 \\ Observation height [m] & Air pressure & 2 \\ Relative humidity & 2 \\ Air temperature & 2 \\ \hline \end{tabular}
\end{table}
Table 2: Key information of the LiDAR in the Anholt offshore wind farm.  
through Equation (3), where H and D mean the hub height and the rotor disk diameter, respectively. The relative ratio of HHWS and REWS can be found from Equation (4).  
\[A_{i}=R^{2}cos^{-1}\left(\frac{R-h}{R}\right)-\left(R-h\right)\sqrt{2 Rh-h^{2}} \tag{2}\]  
\[h_{i}=H+\frac{1}{n_{h}}\left[i-\left(\frac{n_{h}+1}{2}\right)\right]D \tag{3}\]  
\[\frac{V_{REWS}}{V_{HHWS}}=\sqrt[3]{\sum_{i=1}^{n_{h}}\frac{A_{i}}{A}\left[1+ \frac{1}{n_{h}}\left(i-\left(\frac{n_{h}+1}{2}\right)\right)\frac{D}{H}\right] ^{3a}} \tag{4}\]
## 1 Introduction
### State of the Art for Channel Modeling
The model foresees frequency dependent large scale parameters and the number of cluster and rays generated as random variables instead of fixed numbers. Moreover, mmMAGIC supports bandwidths up to 2 GHz and is supposed to meet most of the 5G channel model requirements, however it is a high computational model that makes numerical simulations inefficient. The METIS channel model [30] is able to achieve a more flexible and scalable methodology by providing a map-based model, a stochastic model and their combination: the hybrid model. The map-based model supports frequency ranges up to 70 GHz and uses a simplified 3-D geometric description of a propagation environment and additional randomly generated shadowing objects. The stochastic model (up to 100 GHz) is a GBSM legacy of WINNER strategy. Moreover, METIS proposes a simplified approach to generate spatially consistent LSPs through a sum of sinusoids [31; 32], which results in a lower computational consumption, especially useful for D2D/V2V applications. In [33] we can find the recent MG5 GCM model as a combination of new advanced channel modeling techniques such as array-time evolution, spherical wavefront, etc. This model can meet many 5G channel requirements with a relatively low complexity, although it only takes small-scale fading into account and neglects large-scale fading. Furthermore, parameters for the model for different scenarios need to be extracted from future measurements. A different approach has been introduced in [34], where authors provide a machine learning based framework. In particular, they provide a time-varying channel modeling method by using a neural network to model and predict the path loss together with shadowing, fading and joint small scale parameters. Results from the neural network have been compared with measurements at 26 GHz performed in a microcell. A comprehensive 3 GPP-like mmWave channel model called statistical spatial channel model (SSCM) has been proposed in [35; 36; 37] based on measurements in the range 28-73 GHz [38]. Differently from the 3 GPP-WINNER strategies, they introduce the concepts of time clusters and spatial lobes, which can describe the temporal and spatial statistics independently.
## 3 Preliminaries
### Network Model  
In this paper, the network is assumed to have the following properties:  
(1) A great number of sensor nodes are randomly deployed in a square area. Only one sink is deployed in the area to collect the sensing data from all sensor nodes. All sensor nodes and the sink are stationary once they are deployed.  
(2) Sensor nodes have homogeneous capabilities and limited energy, which is powered by batteries. Thus, both energy efficiency and load balance are important issues.  
(3) The network is organized into clusters, including cluster heads (CHs) and cluster members (CMs). The CM sends sensing data to its CH directly, and the CH forwards the aggregated data to the sink via multi-hop transmission.  
(4) Each node has a maximum transmission range \(R_{max}\) that is determined by its hardware capability. Therefore, the connectivity of the cluster-based topology should be considered.  
(5) Each node has two transmission ranges to control the network topology: the clustering range \(R_{c}\) and the inter-cluster transmission range \(R_{t}\). \(R_{c}\) determines the size of each cluster, while \(R_{t}\) impacts the inter-cluster topology. According to the analysis given in [19], we have \(R_{t}=R_{\max}\) and \(R_{c}\leqslant R_{t}\).  
(6) The distance between any pair of nodes can be estimated by RSSI (received signal strength indicator) or its geographic location.  
In this paper, the energy consumption model is adopted from [10], with the free space channel. The energy consumed for transmitting \(l\)-bit data over distance \(d\) is denoted by,  
\[E_{tx}(l,d)=l*(E_{elec}+\epsilon_{fs}*d^{2}) \tag{1}\]  
and the energy consumed for receiving \(l\)-bit data is:  
\[E_{rx}(l)=l*E_{elec} \tag{2}\]  
where \(E_{elec}\) and \(\epsilon_{fs}\) are the parameters of transmission and reception circuits. Their values are set as \(E_{elec}\) = 50 nJ/bit and \(\epsilon_{fs}\) = 10 pJ/bit/m\({}^{2}\). Besides, the energy for data aggregation is set as \(E_{DA}\) = 5 nJ/bit/signal.
# An Effect of White Space on Traditional Chinese Text-Reading on Smartphones +
## 1 Introduction  
Due to the convenience of smartphones, users use them at any time, place or occasion. The penetration rate of smartphone users is growing rapidly. According to eMarketer's estimates [1], 73.4% of Taiwan's population uses smartphones. Smartphones are no longer cutting-edge communication gadgets but are now necessities in peoples' lives. The Global Views Survey Research Center [2] reported that 46.4 percent of the Taiwan population had reading experiences on e-books and that 27.1 percent of them used a smartphone to read an e-Book. The population reading either e-news or e-books on smartphones should continuously increase due to increasing penetration of smartphones in Taiwan. Moreover, Taiwanese are accustomed to reading articles written in traditional Chinese characters. Therefore, it is necessary to explore effects of traditional Chinese typography on reading performance for Taiwanese readers.  
Traditional Chinese characters have substance and occupy space on the text page. Space occupied by characters is known as positive space; unoccupied or empty space is known as negative space [3], which is called white space in traditional two-dimensional print design. White space is a frequently used term in graphic and web design and applied to graphics or visual communication design. The white space of the typographic design includes the margin, line spacing, and paragraph spacing, etc.  
Many readers without design backgrounds believe that large white space in a text wastes space. However, the white space may be one of the factors improving reading performance [4]. [PERSON] and [PERSON] argued that the white space would not waste space because it provided readers with a relief, which was conducive to visual communication and reading comprehension [4]. [PERSON] proposed typographic tonality, defined as the ratio of the area of positive space to the area of white space [5]. He believed low tonality (i.e., more white space) would increase text legibility and comprehension. Summarily, white space provides relief to readers in reading tedious text and reduces reading loadings.  
However, except for line spacing, the above suggestions of applying white space to typographic design were drawn from practical experiences of design practitioners; they did not offer any scientific evidence to support their claims. Hence, this paper tries to explore the impact of typographical white space on reading performance on smartphones based on scientific evidence.
## 3 Phase Contrast Imaging Techniques on Conventional Systems
### Edge Illumination  
Initially developed as \"coded aperture\" [47] at synchrotrons and adapted to conventional sources [23], it has been called edge illumination (EI) since 2013 [48]. It is based on the observation that by illuminating only the edge of the detector pixels, high sensitivity to phase effects is obtained. The effect obtained is comparable to the ABI with a fine angular selection on the direction of the photons.  
Figure 7 shows a typical edge Illumination set-up. The method uses a pair of masks, one before the sample and the other close to the detector. Those masks are usually made of Au or W with \(\upmu\)m size wide apertures (slits) and tens of \(\upmu\)m periods. Although the  
Figure 6: Example of a Talbot carpet pattern created by phase gratings.  
configuration may appear similar to that of a grating interferometer, the physical principle is different. The spacing between the bars of the grids is wider than for GI, and instead of creating interference patterns, the grid placed in front of the sample (G0) simply splits the beam into thin vertical beamlets. In the absence of the sample, each of those beamlets will hit the center of each column of the detector. A second grid is placed in front of the detector, collimating vertically part of the pixels, reducing their individual fields of view. When the sample is inserted, the rays that are deviated will no longer arrive in the free spaces of G1, changing the intensity received by the detector. Displacing G1 slightly on one side and the other will allow us to retrieve the local refraction of the beam in the direction perpendicular to the grids and the dark-field scattering. The use of such a simple configuration, as opposed to ABI, eliminates the need for a monochromatic beam.  
It has been shown that this technique requires only low spatial and temporal coherence. The method has been successfully implemented on conventional X-ray sources [49].  
Though its set-up is simpler and the requirements in terms of coherence are smaller, EI suffers from some of the same limitations as GI for 3D: dose, acquisition time and difficulty to rotate around the sample/patient. This technique seems better suited for pre-clinical studies than in vivo radiological applications because of the long acquisition time/high dose delivered to the sample, since part of the radiation passing through the sample is stopped by the second slit. Another limitation remains the detection of the unilateral phase gradient only (even if a 2D implementation exists [50], their use remains scarce), and thus has the complexity to perform 3D imaging with this device.