esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Catalysts Characterization
The Ni dispersion (D%) and the particle size of Ni (_d_) were calculated by Equations (5) and (6), respectively:  
\[D(\%)=\frac{\eta SM}{W}\times 10^{2} \tag{5}\]  
\[d(\mathrm{nm})=\frac{fW}{\rho\eta N_{A}SA}\times 10^{5} \tag{6}\]  
in these equations, \(f\) is the geometric shape factor (6 for spherical particles), \(W\) is the weight percentage of Ni (1.75%), \(\rho\) is the density of Ni (8.9 g/cm\({}^{3}\)); \(\eta\) is the desorbed H\({}_{2}\) (mol/g), \(N_{A}\) is the Avogadro number (6.023 \(\times\) 10\({}^{23}\)), \(S\) is the stoichiometric factor for H\({}_{2}\) chemisorption (metal mole/gas mole = 2), \(A\) is the area occupied by the Ni surface atom (6.51 \(\times\) 10\({}^{-16}\) cm\({}^{2}\)), \(M\) is the atomic mass of Ni (58.69 g/mol). For the calculations of the theoretical hydrogen consumption to be compared with the experimental, the oxidation state of Ni = +2 and that of Ce = +4, was assumed. From the reaction: 20 Ni\({}_{0.05}\)Ce\({}_{0.95}\)O\({}_{1.95}\) + 10.5H\({}_{2(g)}\) = Ni\({}^{0}\) + 9.5 Ce\({}_{2}\)O\({}_{3}\) + 10.5H\({}_{2}\)O\({}_{(g)}\), per gram of catalyst, the mmol of H\({}_{2}\) consumed for Ni is 0.30 mmol H\({}_{2}\)/g, while that required for the reduction of CeO\({}_{2}\) to Ce\({}_{2}\)O\({}_{3}\) is 2.84 mmol H\({}_{2}\)/g. A Tecnai G2 F20 s-twin instrument operating at 200 kV performed transmission electron microscopy (TEM) analysis. Diffuse reflectance infrared Fourier transform spectroscopy (DRIFT) analysis was carried out using a Thermo-electron Corporation Nicolet 4700-FTIR spectrometer coupled to a reaction cell. The reactor unit was embedded with two 6 \(\times\) 90\({}^{\circ}\) off-axis ellipsoidal mirrors arranged to discriminate specularly reflected radiation. Spectra were acquired with 256 scans in the flow of gases controlled by mass flow controllers. The sample was reduced in situ by flowing 50 cm\({}^{3}\) min\({}^{-1}\) of 50% H\({}_{2}\)/He gas mixture, heating up to 500 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C min\({}^{-1}\), and maintaining this temperature for 30 min. The DRIFT spectra of the reduced samples were acquired as background in the 100-500 \({}^{\circ}\)C temperature range at intervals of 100 \({}^{\circ}\)C. The methanation reaction mixture CO\({}_{2}\):H\({}_{2}\):He = 5:20:25 cm\({}^{3}\) min or CO:H\({}_{2}\):He = 5:15:30 cm\({}^{3}\) min\({}^{-1}\) flowed on the sample, and the spectra were recorded in the same temperature range of the backgrounds.
# An Experimental and Numerical Study on Reduction of Generated Axial Force
## 5 Results and Discussion
### GAF Reduction
#### 5.2.1 GAF Reduction by the Model  
GAF estimation with the various design parameters is carried out using the model. Figure 11 shows the GAF at 200 rpm with respect to driving torque. Regardless of the design parameter changes, GAF is proportional to driving torque. As expected from the normal pressure change results, the contact angle and track curvature also affect the GAF. As the normal pressure change results, GAF is proportional to track curvature and inversely proportional to contact angle. Furthermore, GAF is more affected by track curvature than contact angle like normal pressure. With the change of track curvature, GAF at 100 Nm changes from 7.99 to 8.59 N whereas it changes from 8.34 to 8.42 with the contact angle. This GAF change increases as driving torque increases, and eventually it is the most noticeable at 400 Nm. With the change of track curvature, GAF changes from 33.96 to 37.63 N whereas it changes from 37.13 to 37.49 N with the contact angle. At all driving torque, the GAF differences with the track curvature are much larger than the GAF differences with the contact angle.  
Figure 10: Normal pressure changes with track curvature (**a**) Normal pressure change with respect to driving torque (**b**) Normal pressure change at 400 Nm and 200 rpm.  
From the estimation results, the optimized design parameter is +20% contact angle and \(-20\%\) track curvature where the CVJ has the lowest GAF as shown in Figure 11. Figure 12 shows the GAF variation at 400 Nm with respect to contact angle and track curvature. As in Figure 12a, GAF noticeably drops with the decrease of track curvature whereas it slightly rises with the increase of track curvature. On the other hand, contact angle hardly affects GAF as shown in Figure 12b.
## 3 Case Study
### On-Site Measurement Campaign before Damage  
#### 3.2.1 Description of Experiment  
In the framework of this study, a comprehensive survey and measurement campaign was carried out on Span 8 of the Chuong Duong bridge. The campaign included geometric surveys and vibration measurements of the entirety of Span 8. After finishing the geometric survey, we conducted vibration measurements under random stimuli (wind, current, surrounding loads, vehicles crossing the bridge, among others). Eight highly sensitive sensors, ranging in sensitivity from 1054 to 1083 mV/m/s\({}^{2}\), were used. With a sampling frequency of 1651 Hz, each setup's average acquisition time was 30 min. To achieve the vibration modes of the entire span, a measuring grid of 34 points (covering all truss nodes) was established (Figure 4).  
However, due to the limited number of sensors (of which there were eight), the measuring grid has been divided into eight setups. The reference point at node 103 has been selected to link the data from the sensors. Other moving points were located at truss nodes on the bridge (Figure 5). All points defined in the overall measurement grid would thus be fully covered using these setups.  
Figure 4: Placement of measurement points on Span 8 of the Chuong Duong bridge (blue: reference point, red: moving points, in addition to the sensor installation position at the truss node.  
Figure 5: _Cont._In space, the Cartesian coordinate system is used to determine the direction of each measuring point. The X-axis is in the longitudinal direction, the Y-axis is in the bridge's transverse direction, and the Z-axis is vertical with a positive upward direction. Two sensors with X and Y axes were mounted at each bearing. The sensors were positioned on the Y-axis, Z-axis, or both the Y and Z axes at other points. Specifically, the direction of the measuring points is shown in the Table 1.  
The measurement procedure is controlled using a laptop, which also gathers and saves the dynamic responses.  
Figure 5: The measurement setups: (**a**) Setup 1; (**b**) Setup 2; (**c**) Setup 3; (**d**) Setup 4; (**e**) Setup 5; (**f**) Setup 6; (**g**) Setup 7; (**h**) Setup 8.
# Development of Ground Special Vehicle PHM with Case-Based Reason Model
## 3 Module of Weight Optimization
### Basic Steps for Algorithm  
The basic steps of the SA algorithm are developed as a function module, so the main procedure can call the module whenever necessary. The basic steps are given as follows.  
**Step 1.**: Initialize parameters: Temperature T, Temperature decreasing factor \(\Delta t\), current solution \(W=(w_{1},w_{2},\ldots,w_{m})\), \(w_{i}\in(0,1),\ i=1,2,\ldots,m.\), repeat number L, minimum temperature \(T_{min}\).
**Step 2.**: For temperature T, repeat Step 3 to Step 5 for L times.
**Step 3.**: Generate the new solution \(W\) / (Namely, the new weight vector in the feasible solution space is obtained based on the initial weight vector).
**Step 4.**: Substitute the new solution \(W^{\prime}\) into the case-based reasoning process, and calculate the increment \(\Delta F=F(W^{\prime})-F(W)\). Where, \(F(W)\) denotes the objective function of the SA algorithm. The calculation detail of the objective function is explained in Section 3.2.
**Step 5.**: If the increment of objective function is less than zero, the new solution is directly accepted as the current solution. Otherwise, the new solution is accepted as the current solution with probability \(exp\left(-\frac{\Delta F}{F}\right)\).
**Step 6.**: If some termination condition is satisfied, the algorithm will stop, and the approximation of the optimal solution is output. Otherwise, go to Step 7.
**Step 7.**: Decreasing temperature \(T=T\times\Delta t\). If T is less than \(T_{min}\), the algorithm will stop. Otherwise go to Step 2, and continue the cycle.
# Joint Timekeeping of Navigation Satellite Constellation with Inter-Satellite Links
## 3 ISLT Timescale Algorithm  
The function of the ISLT timescale algorithm is to form a synthetic timescale with a more stable frequency by using the satellite clocks and ISL measurements. The computing framework of the KPW timescale was used for the ISLT timescale in this research. Furthermore, optimal weights that minimize the increment of the timescale are proposed and adjusted to the ensemble of clocks.
## 6 Typical Application Cases and Empirical Research
### Implementation Effect Assessment
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Non-Conventional Lane Type** & **Capacity Improvement** & **Delay Reduction** & **Emissions Reduction** & **Reference Studies** \\ \hline \multirow{3}{*}{Contraflow Left-turn Lane (ELL)} & Left-turn capacity increased by 20-30\%; 11.48\% capacity & \multirow{3}{*}{9.63\% average delay reduction after optimization} & Not quantified in reviewed studies & [PERSON] et al. [2015] \\  & increase after optimization & & & \\ \hline \multirow{3}{*}{Exit lanes for Left-turn (EFL)} & 6:1\% capacity increase per & \multirow{3}{*}{1.2\% delay reduction under low volumes; 5:1\% queue length reduction} & Not quantified in reviewed studies & [PERSON] et al. [2010]; [PERSON] et al. [2014] \\  & & increase per 5\% left-turn increase & & & \\ \hline \multirow{3}{*}{Outside Left-turn Lane} & Indirect capacity gains through & Variable effects depending on & \multirow{3}{*}{Not quantified in reviewed studies} & \multirow{3}{*}{[PERSON] et al. [2017]; [PERSON] et al. [2018]} \\  & 21.8\% safety improvement & & & \\ \cline{1-1}  & compared to conventional & & & \\ \cline{1-1}  & intersections & & & \\ \hline \multirow{3}{*}{Tandem Intersections} & 18.6\% intersection capacity increase & \multirow{3}{*}{19.61\% average delay reduction; 20.94\% average queue length reduction; 22.9\% delay reduction (alternative study)} & CO emissions: \(-\)10.93\%; NOx emissions: \(-\)12.97\% & [PERSON] et al. [2014]; [PERSON] et al. [2012] \\ \cline{1-1}  & \multirow{3}{*}{Parallel Flow Intersections} & Up to 70.51\% capacity increase with four-direction & Variable based on configuration scheme & Not quantified in reviewed studies & [PERSON] et al. [2015] \\ \cline{1-1}  & configuration & & & \\ \hline \multirow{3}{*}{Reversible Lanes} & Variable based on demand & \multirow{3}{*}{27.4\% average delay reduction with real-time dynamic control} & VOC, CO, and NOx emissions: \(-\)13.5\% combined reduction & [PERSON] et al. [2015] \\ \cline{1-1}  & & 29.15\% travel time reduction compared to traditional U-turn design & \multirow{3}{*}{66.70\% delay reduction; 100\% reduction in number of stops} & Not quantified in reviewed studies & [PERSON] et al. [2018] \\ \cline{1-1} \cline{2-5}  & Improved utilization efficiency & & & \\ \cline{1-1}  & but 22.86\% saturation flow rate reduction for variable lanes & Variable effects depending on & \multirow{3}{*}{Not quantified in reviewed studies} & \multirow{3}{*}{[PERSON] et al. [2010]} \\ \cline{1-1}  & reduction for variable lanes & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of different non-conventional lane types: effects on traffic delay, capacity, and emissions.
## 1 Introduction
### MAM Protocol  
MAM's protocol consists of many different layers which are characterised by a specific state. In order to illustrate MAM's usage in the proposed design and implementation, it is pertinent to reference the _WOTS_, _MSS_, _NTRU_, _Protobuf3_, and _MAM2_ layers. Winternitz One-Time Signatures (WOTS) layer generates private/public keys and signatures, it verifies a signature and it recovers a public key from a signature. As the layer's name suggests, the signing scheme uses the Winternitz signatures. Merklee-tree Signature Scheme (_MSS_ layer) [9] is responsible for generating \(2^{d}\) signatures of different messages, where d is asserted as d \(\leq\) 20. A Merklee tree is a binary tree where the leaves are the public keys of a corresponding _WOTS_ instance, thus 2d instances, and each tree layer is hashed until the root which stands as the public key of the whole tree. _NTRU_ supports the use of NTRU-style public key encryption [10]. _Protobuf3_ supports the higher-level encoding, decoding and cryptographic processing of the data. In essence, _Protobuf3_ is a language based on the Protocol Buffers Version 2 notation [11]. _MAM2_ layer is responsible for the high-level operations of the protocol, like sending and receiving messages.  
While the full specification and apt description of each algorithm and data structure can be found in the MAM specification, it is crucial to outline certain high-level algorithms. The creation of a data stream by an agent, starts with the creation of a proper channel. When creating a channel, the protocol creates a Merklee tree, where the Merklee Tree Root \(MTR\) = _child_. The inputs of the _CreateChannel_ algorithm are height d (as described above) and a channel name. The output is the _child_. Having generated a channel, the agent can generate an endpoint by running the _CreateEndpoint_ routine, where input is a height d, a channel name and an endpoint name. The output is the _epid_. Finally, in order to broadcast a message, the agent needs to procure a Header data structure comprised of a message_id, a type_id, a session key and a _KEY_ (optional). The session key can be encrypted using the KEY (either using a pre-shared key or a _NTRU public key_). This is crucial as it grants access control to each message, where only the key owner (either having the pre-shared key or the _NTRU private key_)  
Figure 5: Concept diagram of the Masked Authenticated Messaging (MAM) functionality [8].  
will be able to decrypt the session key and access the data packets. A message, finally, has M \(\geq\) 1 data packets, depending on the data size.
# Overview of the Research Status of Intelligent Water Conservancy Technology System
## 4 Application Status  
### Water Security  
The application of modern information technology plays a key role in real-time monitoring, rapid early warning and efficient management of water emergencies and related problems in water safety management. Taking the Sumjin Dam and river water management platform as an example, digital twin technology has been used to achieve dam safety assessment and river stability assessment, and the monitoring and safety management capabilities have been improved by combining a high-performance UAV monitoring system and video analysis system [80]. In terms of optimization of the flood prediction model, the online optimization method driven by knowledge and data fusion has realized dynamic updating of flood feature quantity and intelligent adjustment of the parameter rate, effectively improving forecast accuracy and response speed [81]. The real-time detection method of data quality of intelligent water conservancy digital dual-basin construction based on flow computing solves the problem of insufficient monitoring of data quality, and ensures the reliability and accuracy of monitoring data through the real-time quality detection of the traffic processing system Flink [82]. Through comprehensive data collection and real-time monitoring, the intelligent water conservancy early-warning management system based on digital twins improves the accuracy and timeliness of water conservancy facility management, reduces management risks and saves labor costs, thus effectively avoiding the occurrence of water conservancy facility safety accidents [83].  
Figure 4: Basic construction process of knowledge graph.
# Contribution of Climate Change and Grazing on Carbon Dynamics in Central Asian Pasturelands
## 3 Results
### Contribution of Climate Change and Grazing Effects  
Projections without grazing effects result in higher NEE than projections with grazing effects. Compared with simulations with grazing effects, the NEE is projected to increase to 1.32 \(\pm\) 0.61 g C m\({}^{-2}\) year\({}^{-1}\), which means pasturelands in Central Asia assimilated 0.13 \(\pm\) 0.06 Pg C from 1981-2014. All grassland types have changed to carbon sinks except for MERRA simulation, for which the NEE is projected to be negative. Using CFSR data, the NEE is projected to increase from \(-\)15.42 \(\pm\) 9.73 g C m\({}^{-2}\) year\({}^{-1}\) under the grazing scenario, to 2.29 \(\pm\) 1.18 g C m\({}^{-2}\) year\({}^{-1}\) without grazing. Using ERA data, NEE is projected to substantially increase by 22.72 \(\pm\) 16.18 g C m\({}^{-2}\) year\({}^{-1}\) after eliminating the grazing impact.  
We assessed the relative contribution of climate change and grazing on the NEE for different pastureland types (Figure 7). For the average situation in Central Asia, 100% of carbon release was caused by grazing activities, 6.22% of which was offset by climate change (Figure 7d). Effects of grazing were more profound on TG than on FM and DG (Figure 7a-c). In the TG region, grazing released 35.47 \(\pm\) 7.76 g C m\({}^{-2}\) year\({}^{-1}\) and climate  
Figure 6: Comparison of the spatial patterns of averaged NEE during 1981–2014, simulated by three sets of reanalysis data. DG, Dry Grassland; TG, Temperate Grassland; FM, Forest Meadow.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**NEE (g C m\({}^{-2}\) Year\({}^{-1}\))**} \\ \cline{2-5}
**PFT** & **CFSR** & **ERA** & **MERRA** & **Average** \\ \hline
**DG** & \(-\)9.28 & \(-\)13.32 & \(-\)14.21 & \(-\)12.27 \\
**TG** & \(-\)26.63 & \(-\)38.64 & \(-\)41.13 & \(-\)35.47 \\
**FM** & \(-\)10.34 & \(-\)10.72 & \(-\)15.73 & \(-\)12.26 \\
**CA** & \(-\)15.42 & \(-\)20.89 & \(-\)23.69 & \(-\)20.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Averaged NEE during 1981–2014 under simulations simulated by three sets of reanalysis data. DG, Dry Grassland; TG, Temperate Grassland; FM, Forest Meadow.  
change only offset 4.34%. This situation improves in the DG region, where grazing released 12.27 \(\pm\) 2.63 g C m\({}^{-2}\) year\({}^{-1}\) and climate change increased 12.50% of carbon fixation. As a whole, grazing was the dominant factor for carbon release in Central Asia.
# Variety Identification of Chinese Walnuts Using Hyperspectral Imaging Combined with Chemometrics
## 3 Results and Discussion
### Models Developed Using Feature Wavelengths  
Table 3 displays the variety identification results of the simplified models based on selected wavelengths using three different methods. As shown, PC-PLS-DA model exhibited the worst performance, and the main reason was that a small number of only three variables could hardly contained all the valid information in the identification. When SPA-PLS-DA was considered, not bad classification results were obtained that CCRs equaled to 91.33%, 92.00%, and 89.00% in calibration, cross-validation, and prediction sets, respectively. Our study indicated that variable selection methods for Chinese walnut variety identification obtained different results. Similar results of using different variable selection methods for optimal wavelength selection could be found in the literature [43]. The optimal variable selection methods would be determined and used for identifying the variety. Moreover, previous study related to identify walnut varieties using NIRS showed that only a total classification accuracy of 77.00% \(\pm\) 1.60% was achieved based on models developed by selected wavelengths [5]. This study showed good classification performances using both full spectra and selected wavelengths.  
Figure 4: Wavelengths selection using SPA and CARS algorithms, (**a**) distribution maps of feature wavelengths selected by SPA; (**b**) parameter changes with sampling runs in wavelengths selection by CARS. RMSE: root mean squared error; RMSECV: root mean squared error in cross-validation; SPA: successful projects algorithm; CARS: competitive adaptive reweighted sampling.  
Above all, the PLS-DA model using CARS features achieved best performance with the CCRs of 96.33%, 95.67%, and 91.00% in calibration, cross-validation, and prediction sets. To further assess the performance, the confusion matrix is shown in Table 4. The CARS-PLS-DA model yielded a sensitivity of 93.62%, a specificity of 88.68%, AUC of 0.91, and Kappa coefficient of 0.82 in prediction set. These results suggested that CARS-PLS-DA model had great potential to identify Chinese walnut varieties without any chemical or physical information.
Hence, a circular birdcage RF coil placed in close proximity to an RF shield (less than 5 cm) becomes extremely difficult to implement because of the difficulty in tuning/matching and the poor isolation between ports, which result in poor transmission efficiency. Considering the outline of the average human body, an elliptical birdcage whole-body RF coil can be advantageous for improving the B\({}_{1}\)-field homogeneity and its relative SNR [34]. However, large-size symmetric elliptical birdcage RF coils have lower efficiency than circular birdcage RF coils, which can be improved through geometry optimization to generate an acceptable homogeneous B\({}_{1}\)-field and lower SAR depositions. In this study, a split-leg type asymmetric elliptical birdcage RF coil for whole-body MRI was proposed to generate a homogenous B\({}_{1}\)-field within a larger imaging space horizontally compared with that of a circular birdcage RF coil. An extended RF coil circuit co-simulationapproach was also proposed to calculate the optimal capacitor for the proposed RF coil. Both quadrature (same amplitude, 90\({}^{\circ}\) phase difference) and elliptical (different amplitudes and phase differences) driving approaches were evaluated through EM simulations in this study [35]. To implement the elliptical driving (or multiport driving) approach in an MRI system, more than one RF amplifier is required; however, this is not available in every MRI system. Therefore, we suggested a semi-elliptical driving approach, where the set of vertical (#1, #3) and horizontal (#2, #4) opposite ports had the same amplitude with a 180\({}^{\circ}\) phase difference, and adjacent ports (#1, #2, #3, #4) had different amplitudes with a 60\({}^{\circ}\) phase difference. High-power RF phase shifters and RF attenuators were used to adjust the phase and the power attenuation at the ports, respectively. A practical design of a four-port elliptical high-pass birdcage with asymmetric leg distribution was adopted for narrow-bore MRI (magnet bore inner diameter, 64 cm). EM simulations were performed to evaluate the following parameters: (1) optimal end-ring capacitor value for the desired resonance mode, (2) decoupling between the excitation ports, (3) transmitted RF field (B\({}_{1}\)+-field) uniformity, (4) transmission efficiency, and (5) SAR field distribution.