esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Backhaul Design as a Graph Optimization Problem
### Mapping C-RAN Resilience Requirements to Graph Invariants
This can be achieved if the Wiener index of the resultant graph is equal to the Wiener index of the original graph subtracted by the transmission of the removed vertex, for any vertex of the graph. Formalizing this idea, the node Wiener impact of a node \(v\) in a 2-connected graph \(G\), denoted as \(\tau_{v}\), is defined as:  
\[\tau_{v}=W(G-v)-W(G)+T(v), \tag{1}\]  
where \(W(G)\) is the Wiener index of the original graph \(G\), \(W(G-v)\) is the Wiener index of the graph obtained by removing \(v\) from \(G\), and \(T(v)\) is the transmission of the node \(v\). Therefore, the Wiener impact can be used as a more robust way of measuring network resilience in 2-connected networks: the lower is the Wiener impact of a node, the lower is the growth of the distances between node pairs in the resultant graph when that node is removed. In our approach, we consider that a network is resilient if it has two disjoint paths of limited length connecting any pair of nodes. For more information on betweenness, Wiener index, and transmission, one can refer to [34].
## 6 Construction and Principals of Smart Home System for New Elders
### Construction of Smart Home System
#### 6.1.2 Basic Network Layer  
High-speed, ubiquitous, highly reliable wired fiber-optic network and wireless network constitute the basic network layer of home devices and environmental facilities. This layer provides the channel to transmit the data collected by the intelligent perception layer to the data processing layer. Its reliability and transmission speed are the key to ensure that the elderly's home life data is transmitted in time and to get effective and precise feedback.
# Broadband Holography via Structured Black Silicon Nano-Antennas
## 4 Conclusions  
In this work, we have demonstrated the feasibility of manufacturing binary amplitude holograms by nanostructuring Si with a controllable pattern of broadband nanoantennas.  
We demonstrated this technique on a large scale, along with the simultaneous ability to manufacture a whole Si wafer and with one photolithography step.  
Different techniques can also be explored within this method to transfer the interference pattern in the photoresist at a higher resolution than photolithography, including nanoimprinting, deep UV photolithography, and electron beam lithography. The intrinsic characteristic of this nanofabrication method is a fast, large-scale, and cost-effective fabrication.  
This approach can also enable the realisation of holograms on flexible Silicon substrate, which can be integrated into wearable and flat electronic devices for optical imaging, data storage, and security. They can also be employed as anti-fake stamps for credit cards and currencies. Remarkably, we show that the broadband nature of the reflectivity properties of both Si and BSi allow the same hologram plate to work at different wavelengths, without observable image reconstruction losses.  
Conceptualization, V.M. and A.F.; methodology, V.M. and M.S.; software V.M. and M.S.; validation, V.M. and M.S.; formal analysis, V.M. and M.S.; investigation, V.M.; resources, A.F.; data curation, M.S.; writing-review and editing, V.M.; visualization, V.M.; supervision, A.F.; project administration, A.F.; funding acquisition, A.F.  
Figure 3: (**a**) Original digital image. (**b**) Four different computer generated holograms interference patterns fabricated on a single silicon wafer. (**c**) Digitally reconstructed image using the ray tracing approach. (**d**) Reconstruction using blue laser \(\lambda=450\) nm illuminating the pattern in the red circle. (**e**) Reconstruction using green laser \(\lambda=532\) nm illuminating the pattern in the red circle. (**f**) Reconstruction using red laser \(\lambda=632\) nm illuminating the pattern in the red circle.  
**Funding:** This research was funded by CFR-KAUST grant number OSR-2016-CRG5-2995 and The APC was funded by KAUST.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Description of the Heat Transport and Distribution Networks Based on Absorption Systems  
### General Description  
Figure 2 shows the considered configurations to transfer recovered heat from industrial processes to final users. The conventional case (Figure 3a) corresponds to the direct use of waste heat at about 70 \({}^{\circ}\)C in a district heating network. In this case, a secondary fluid at about 65 \({}^{\circ}\)C transfers the recovered heat to the final users using heat exchanger HE1 and HE2, with a temperature difference between the supply and return of about 30 \({}^{\circ}\)C. HE1 and HE2 are simple heat exchangers that are able to provide heat at 40 \({}^{\circ}\)C for the buildings connected to the heat distribution network. On the other hand, Figure 2b-d correspond to systems that use absorption technology using two stations, one to upgrade the temperature of the waste heat (SUS) and another one (SDS) to take benefit of this heat to distribute it efficiently to the final users at a lower temperature.  
The working principle of these configurations is similar to that of electricity networks: The waste heat is upgraded at the generation site (industries) in a \"step-up thermal station\" (SUS) and then the heat is transported to the \"step-down thermal substation\" (SDS) from which the hot and chilled water are distributed to the end users. Figure 2b shows the case with an absorption heat transformer as SUS unit and an absorption heat pump as SDS to deliver heat, with both using an additional heat exchanger in a similar configuration as presented in Figures 1 and 2. The heat taken at low temperatures at the SDS units reduces the return temperature of the heat transport circuit down to about 20 \({}^{\circ}\)C. Figure 2c,d show a similar configuration but using an absorption heat pump able to provide heating and cooling simultaneously. The main difference between Figure 2c,d is the requirement or not of a cooling tower in the step-up thermal station to reject heat to the ambient atmosphere. The elimination of the cooling tower provides many benefits: Lower investment and running costs, avoiding the use of additional chemicals, and lower consumption of water, etc. However, in spite of its disadvantages, the integration of a cooling tower could provide an additional degree of freedom to facilitate the system operation. In the following Sections 2.2 and 2.2, the absorption units that could be used in the mentioned thermal substations (SUS and SDS) are described in more detail.  
Figure 3: Dühring scheme of (**a**) the single-effect absorption heat transformer (SEAHT) and (**b**) the double absorption heat transformer Type 3 (DAHT).  
Figure 2: Configurations for the transport and distribution of waste heat: (**a**) Conventional system. (**b**) Absorption-based DH (District Heating) system to supply heat, (**c**) absorption-based DH and DC (District Cooling) system requiring a cooling tower, and (**d**) absorption-based DH and DC system without a cooling tower.
## 4 Results
### Comparison on a Monthly Scale  
The scatter plots (Figure 6) and descriptive statistics of Table 2 display the comparison of GPPs on monthly scale for each selected regions. In region 1 (Figure 6a), MSWEP substantially overestimated precipitation, with a linear regression coefficient of >1. CHIRPS, PERSIANN-CDR and TMPA overestimated monthly precipitation lower than 50 mm/month but underestimated larger precipitation events of >50 mm/month. TMPA was relatively the best performing product followed by CHIRPS and PERSIANN-CDR. In region 2 (Figure 6b), GPPs had a good linear relationship with in-situ, of which TMPA had the closest relationship followed by PERSIANN-CDR, CHIRPS and MSWEP.  
Figure 5: Comparisons of the Empirical Cumulative Distribution Functions (ECDFs) of daily precipitation (with different thresholds: 2.5, 5 and 10 mm/day) for in-situ and GPPs in selected regions. The products with a significantly (95%) similar distribution to the in-situ data based on a Kolmogorov-Smirnov test are given in the respective boxes. (**a–c**), (**d–f**), (**g–i**) and (**j–l**) are equivalent to regions 1, 2, 3 and 4 in Table 1.  
In region 3 (Figure 6c), TMPA results were consistent with in-situ, with a regression coefficient of 0.93, MSWEP, CHIRPS and PERSIANN-CDR overestimated precipitation events of <40 mm, while precipitation events of >70 mm were underestimated by all GPPs. At region 4 (Figure 6d), TMPA and PERSIANN-CDR overestimated and CHIRPS underestimated in-situ precipitation.  
The descriptive statistics in Table 2, suggest that at monthly time scale TMPA had relatively improved linear regression coefficients among all GPPs. The RMSD values varied from region to region, with a relatively larger range of values at region 1 and more prominent large values for PERSIANN-CDR and MSWEP. STD suggests that the standard deviation of GPPs is relatively smaller at region 1, however a slight increase can be seen at region 2 and PERSIANN-CDR systematically overestimated monthly precipitation. The performance of GPPs is relatively improved on monthly time scale, though deviations in precipitation magnitude do exist which could possibly be attributed to regional scale precipitation variability, climate and land surface features.  
Figure 6: Scatterplots of monthly precipitation for in-situ and GPPs at selected locations. (**a–d**) are equivalent to regions 1, 2, 3 and 4 in Table 1.
## 3 Results
### Field Load Test Results  
During the static load tests, once the vehicle passed the starting point at 0 s and was positioned at the center of span S1, a static load was applied for approximately 35-40 s before moving the vehicle to span S2 for load application. As shown in Figure 15, the deflection values for each load case show similar trends. The maximum values measured by the deflection gauges are listed in Tables 6 and 7, where (+) indicates upward deflections and (-) indicates downward deflections. The results of repeated tests were similar, indicating their reliability. The deflection at both ends of the span was the greatest as the vehicle loading position changed, indicating symmetrical behavior of the bridge under the applied load. This demonstrates good lateral load distribution.  
The natural frequency of the bridge, obtained from the dynamic load test, was 6.326 Hz, and the maximum values measured during the static load test are listed in Table 8. Furthermore, the measured deflection response ratios were calculated using the deflection values obtained from the load tests and the reference model used in the structural analysis.  
Figure 15: Deflection graphs of static loading tests for LC1 and LC4: (**a**) Gauges 1 and 2, (**b**) Gauges 3 and 4; LC2 and LC5: (**c**) Gauges 1 and 2, (**d**) Gauges 3 and 4; LC3 and LC6: (**e**) Gauges 1 and 2, (**f**) Gauges 3 and 4.
## A Web-Based Geovisual Analytical System for Climate Studies  
**[PERSON] 1, [PERSON] 1, [PERSON] 1,*, [PERSON] 2, [PERSON] 3, [PERSON] 3, [PERSON] 1, [PERSON] 1, [PERSON] 4 and [PERSON] 1**  
1 Center of Intelligent Spatial Computing for Water/Energy Science, College of Science, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA; E-Mails: [EMAIL_ADDRESS] (M.S.); [EMAIL_ADDRESS] (J.L.); [EMAIL_ADDRESS] (Q.H.); [EMAIL_ADDRESS] (C.X.); [EMAIL_ADDRESS] (Z.L.)  
2 NASA Goddard Institute for Space Studies, and Center for Climate Systems Research, Columbia University, New York, NY 10025, USA; E-Mail: [EMAIL_ADDRESS]  
3 NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA; E-Mails: [EMAIL_ADDRESS] (M.B.); [EMAIL_ADDRESS] (R.C.)  
4 Center for Science and Technology Policy Research, University of Colorado Boulder, 1333 Grandview Avenue, Campus Box 488, Boulder, CO 80309, USA; E-Mail: [EMAIL_ADDRESS]  
*Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-703-993-4742.  
_Received: 10 October 2012; in revised form: 20 November 2012 / Accepted: 10 December 2012 / Published: 14 December 2012_  
**Abstract:** Climate studies involve petabytes of spatiotemporal datasets that are produced and archived at distributed computing resources. Scientists need an intuitive and convenient tool to explore the distributed spatiotemporal data. Geovisual analytical tools have the potential to provide such an intuitive and convenient method for scientists to access climate data, discover the relationships between various climate parameters, and communicate the results across different research communities. However, implementing a geovisual analytical tool for complex climate data in a distributed environment poses several challenges. This paper reports our research and development of a web-based geovisual analytical system to support the analysis of climate data generated by climate model. Using the ModelE developed by the NASA Goddard Institute for Space Studies (GISS) as an example, we demonstrate that the system is able to (1) manage large volume datasets over the Internet; (2) visualize 2D/3D/4D spatiotemporal data; (3) broker various spatiotemporal statistical analyses for climate research; and (4) support interactive data analysis and knowledge discovery. This research also provides an example for managing, disseminating, and analyzing Big Data in the 21 st century.  
keywords:geospatial cyberinfrastructure; big data; spatiotemporal; WebGIS +
Footnote †: journal: Journal of Computer Science
# Radio Frequency Fingerprint Identification for 5G Mobile Devices Using DCTF and Deep Learning
## 2 RFF Extraction Based on a 5G NR PRACH Preamble
### Formula for the 5G PRACH Preamble
To launch the access requirement, the UE randomly selects one of the 64 preamble sequences. Examples of the different PRACH preambles for one cell are illustrated in Figure 2, where the periodic structure can be observed. Moreover, the preamble sequences generated from the same root value (i.e., Figure 2a,b) exhibited high similarity on the waveform. In this paper, we use the term \"_group_\" to refer to the preamble sequences generated from the same root value. In our experiment, each _group_ consisted of four preamble  
Figure 2: Different PRACH preambles of one cell (in-phase channel): (a) preamble index 1; (b) preamble index 2 and (c) preamble index 11.  
sequences (i.e., \(\lfloor L_{ZC}/N_{CS}\rfloor=4\)). Hence, a total of 16 _groups_ were used to generate the PRACH preambles. Detailed gNB parameters are provided in Section 4.
# A Novel Feature Selection Based on VMD and Information Gain for Pipe Blockages
## 4 Experimental Results and Analyses
### Sensitive IMF Selection
As shown in Figure 6, the IMF10 component is selected as the left subtree of the left subtree at the third layer; the IMF6 component is selected as the right subtree of the left subtree at the third layer; the IMF4 component is selected as the left subtree of the right subtree at the third layer, and the IMF2 component is selected as the right subtree of the right subtree at the third layer. The selection process of IMF is shown in Table 2.  
Step 6: After the third layer selection, the maximum information gain is less than the given threshold of 0.5, and the component filtering is terminated and completed. To sum up, the root node of the decision tree is component 1, which has the largest information gain at the first layer. After the second layer selection, component 3 has the largest information gain in the left node, whereas component 11 is the largest in the right node. Meanwhile, the components in the third layer are IMF10, IMF6, IMF4, and IMF2. According to Figure 6, the components selected following the information gain filtering are IMF1, IMF3, IMF11, IMF10, IMF6, IMF4, and IMF2. \begin{table}
\begin{tabular}{c c} \hline \hline
**Number of Layers** & **Screening of Sensitive IMF** \\ \hline The first layer selection IMF & IMF1; \\ The second layer selection IMF & IMF3; IMF11; \\ The second layer selection IMF & IMF10; IMF6; IMF4; IMF2; \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sensitive IMF selection based on information gain. Figure 6: Component diagrams from information gain filtering.
## 3 Water-Jet Propulsion Dynamics Modeling
### A Dynamics Model of Direct Sailing Variable Acceleration Motion
#### 3.2.3 A Dynamics Model of Variable Acceleration Motion  
When the water-jet propeller works in the tail of the submersible, the water absorption of the pump causes the flow velocity of the tail to increase, changing the original flow field and interfering with the fluid dynamics of the hull; the thrust provided by the propulsion pump decreases correspondingly. The differential equation of the submersible's direct acceleration motion can be expressed as follows:  
\[\frac{dV_{s}}{dt}=\frac{T\tau-R}{m}, \tag{9}\]  
where \(m\) is the mass of the submersible, \(V_{s}\) is the submerged speed, \(R\) is the drag of the submersible, \(t\) is the acceleration time of the submersible, and \(T\) is the apparent thrust considering the thrust reduction factor.  
Net thrust  
\[T_{net}=T^{\prime}-R, \tag{11}\]  
Apparent thrust  
\[T^{\prime}=T(1-w), \tag{12}\]  
Submerged drag  
\[R=\frac{1}{2}\rho V_{s}{}^{2}C_{d}S, \tag{13}\]  
where \(S\) is the wetted area of the submersible, \(C_{d}\) is the drag coefficient of the submersible, and \(w\) is the thrust reduction factor. (In order to simplify the calculation, \(w\) value of 0.15 was chosen from the submersible design parameters [30].)  
The relationships of the drag coefficient at different submerged speeds can be obtained from numerical calculations and flume experiments; these are shown in Figure 5. When \(V_{s}\) is more than 2 m/s, the values of drag coefficient no longer change with the increasing of submerged speed, which agree well with the simulation results. The Reynolds number around the submersible is \(2.98\times 10^{6}\) (obtained from Equation (14) at 2 m/s). According to the Nikolay experimental curve [31], the drag coefficient is independent of the Reynolds number in the square of the resistance.  
\[\mathrm{Re}=\frac{V_{s}L}{\
u}, \tag{14}\]  
In the above, \(\mathrm{Re}\) is the Reynolds number of the submersible in the submerged state, \(L\) is the characteristic length of the submersible, and \(\
u\) is the hydrodynamic viscosity of water.  
The direct sailing submerged speed during the acceleration motion process can be derived from Equations (4)-(13), as shown in Equation (15). Thus, the relation between the performance parameters of the pump and the direct sailing submerged speed is established also.  
\[V_{s}=\sqrt{\frac{a}{b}}\mathrm{tanh}(c_{1}\sqrt{ab}+t\sqrt{ab}), \tag{15}\]  
\[a=\frac{Cn_{i}(1-w)}{m}, \tag{16}\]  
Figure 5: Submerged speed–drag coefficient curve.  
\[b=\frac{\rho C_{d}S}{2m}, \tag{17}\]  
Initial conditions: \(C_{I}\) is 0, obtained from Equation (15) at 0 m/s when the time is 0. The numerical solution of the submerged speed with respect to time is input to CFD for unsteady calculation based on the UDF.
# New Approaches in Dynamic Metrics for Lighting Control Systems: A Critical Review
## 5 Discussion of Results
### Useful Daylight Illuminance  
UDI clearly illustrates how the indoor illumination distribution varies significantly with the distance from the window. The distribution also varies significantly depending on the geographic location and room orientation. For example, models located in Seville with a south-facing orientation--such as the one depicted in Figure 5--show acceptable daylight levels that extend toward the rear of the room. This contrasts with other configurations where illuminance levels decline more sharply, underscoring how the \"effective depth\" of daylight penetration is inherently influenced by both climatic and orientational factors.  
Moreover, UDI effectively complements ASE by identifying specific points within the space where daylight levels exceed comfort thresholds. It allows for a more detailed analysis of the spatial distribution of daylight, providing a nuanced understanding of where illuminance levels may become excessive.  
The key aspects to highlight from this section are as follows:  
* UDI highlights the strong influence of distance from the window, location, and orientation on both indoor daylight distribution and potential glare.
* In Seville's south-facing models, acceptable UDI values (within the lower and upper thresholds) extend deeper into the room compared to other cases.
* UDI complements ASE by identifying concrete points where illuminance values exceed the upper threshold, supporting a more precise analysis of potential glare or discomfort.
# A Tunable Microstrip-to-Waveguide Transition for Emergency Satellite Communication Systems
###### Abstract  
A tunable microstrip-to-waveguide transition is proposed for the ground station of emergency satellite communication systems. The proposed transition, consisting of a microstrip, three matched patches, three waveguides, and three metal screws, can not only convert the microstrip's TEM-dominated mode into the waveguide's TE mode within the transmitting frequency band, but also possesses the ability to filter out the amplified noise signals within the receiving frequency band around 12.5 GHz. Importantly, by adjusting the screws' lengths, it is feasible to change the suppression frequency within the receiving frequency band and keep a good match within the transmitting frequency band. The measured results demonstrate that the proposed transition has a return loss of over 10 dB from 14 to 14.5 GHz and an out-of-band suppression of over 20 dB, from 12.25 to 12.7 GHz, with a typical value of \(-48\) dB around 12.55 GHz. This unique feature eliminates the need for additional waveguide filters that prevent the amplified noise signal, thereby contributing to the miniaturization of the ground station.  
microstrip-to-waveguide; tenability; satellite communication 202413390/electronics13224370