esa-sceva/satcom-chunk-collection huggingface dataset:
# Construction of Geosynthetic-Reinforced Pavements and Evaluation of Their Impacts
## 4 Post-Construction Impact Assessment  
### Light Weight Deflectometer Testing  
A Light Weight Deflectometer (LWD) can be used to measure the stiffness of the subgrade, base, and pavement by releasing and applying loads on the pavement surface. Deflections are measured when a 20 kg weight is dropped from a height of 100 cm. The sudden drop of the weight transmits a stress pulse to the ground, and the deflection can be used to calculate the modulus at the surface. A 300 mm diameter plate was used when performing the testing on the subgrade and granular base. A 200 mm diameter plate was used for the testing on the binder course and surface course. The tests were performed on both the left wheel path (LWP) and the right wheel path (RWP) on the westbound lane. Fewer points were tested on subgrade and base due to the consistent outcomes, while the LWD was performed every 5 m along the lane on both wheel paths on the binder course and surface course. At least three measurements were taken from one location before moving to a new location. Prior to the installation, the stiffness values were shown in Figure 16. The control section had the stiffest subgrade and the geogrid composite section showed the weakest subgrade. The weaker subgrade in the geogrid composite section (Section 3) is consistent with the higher level of pressure experienced on the subgrade (Figure 9) and higher horizontal tensile strain experienced at the bottom of the binder course (Figure 12).  
Figure 16: LWD results tested on compacted subgrade.  
Figure 15: Temperature in each layer during surface course construction.  
The deduced surface moduli after the placement of the asphalt surface course are plotted in Figure 17. From these measurements, the geogrid composite section shows the lowest stiffness measurements which is consistent with weak subgrade measurements as indicated in Figure 16. It can be observed that the fibreglass geogrid section (Section 2) exhibits the highest measurements in surface moduli further indicating the benefit of the fibreglass geogrid within the asphalt layer and its ability to mitigate of the impact construction activities.
## 4 Experiment
### Effects of the Coefficient  
The values of coefficients \(\alpha\) and \(\beta\) in Equation (21) are crucial for controlling the relative influence of two different losses. Therefore, we set different values for \(\alpha\) and \(\beta\) to explore the impact of these parameters on the ACOS task's F1 score. First, we fix the value of \(\beta\) at 1, ensuring that the sum of the loss parameters for the two generation templates equals 1. Then, we vary \(\alpha\) from 0 to 1 in increments of 0.1 to examine the impact of template switching between aspect and opinion terms in templates \(\mathcal{T}_{ACOS}\) and \(\mathcal{T}_{OCSA}\). As shown in Figure 4, regardless of the dataset (Restaurant-ACOS or Laptop-ACOS), the highest F1 score is achieved when \(\alpha=0.5\). This indicates that while the positions of opinion terms and aspect terms differ between the two templates, the semantic information provided by the sequences is equally important.  
For the hyperparameter \(\beta\), we controlled for equal loss parameters of the two templates, such that in Equation (21), \(\alpha=1-\alpha-\beta\). Then, we varied \(\beta\) from 0 to 0.9 in increments of 0.1. Higher values of \(\beta\) increase the model's emphasis on interactive learning information, reducing the constraint on the generation process's loss. As shown in the figure, for both datasets, the highest performance is achieved when \(\alpha=0.4\) and \(\beta=0.2\). This demonstrates that the weighting of the interactive learning templates significantly impacts the experiment. Appropriate interactive learning can aid the information learning process for the two-generation templates. We set \(\alpha\) and \(\beta\) to 0.4 and 0.2 based on the above analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Restaurant-ACOS**} & **Laptop-ACOS** \\ \hline Categories & & 13 & 121 \\ Sentences & & 2286 & 4076 \\ \hline \multirow{6}{*}{Quadruples} & EAEO & 2429 (66.40\%) & 3269 (56.8\%) \\  & EAIO & 530 (14.49\%) & 910 (15.80\%) \\ \cline{1-1}  & IAEO & 350 (9.57\%) & 1237 (21.48\%) \\ \cline{1-1}  & IAIO & 349 (9.54\%) & 342 (5.94\%) \\ \cline{1-1}  & Contain-I & 1229 (33.6\%) & 2489 (43.22\%) \\ \hline \multicolumn{2}{c}{Quadruples/Sentences} & 1.6 & 1.42 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical analysis of Restaurant-ACOS and Laptop-ACOS datasets. EA denotes an explicit aspect term, EO denotes an explicit opinion term, IA denotes an implicit aspect term, and IO denotes an implicit opinion term. Over 33% of quadruples in Restaurant-ACOS include implicit sentiment expressions, and over 43% of quadruples in Laptop-ACOS include implicit sentiment expressions.
# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 5 Machine Learning Models for Identification of the LOS, NLOS, and MP Conditions
### Section Summary  
In summary, RF was the fastest among the three methods for the given dataset in terms of the training time (Figure 4). This was because the training time per sample data in RF only took in the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No. of Neurons in** & **No. of Hidden** & **Mean Accuracy** & **Mean Training Time** & **Mean Test Time** \\
**each Hidden Layers** & **Layers** & **with std (\%)** & **per Sample (ms)** & **per Sample (\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{  }}}}}}}}}}}}\)**)** \\ \hline
50 & 1 & 84.93 \(\pm\) 0.26  
order of microseconds. Meanwhile, SVM and MLP were in the order of several milliseconds depending on the configuration and setup. In terms of test time, SVM performed worst among the three with a test time in the order of milliseconds, while RF and MLP are in the order of microseconds.  
Taking into consideration the evaluated results presented in this section, we established the configurations of three classifiers for further processing in Section 7. The summarized overview is represented in Figure 4. The selected configurations were the radial basis function kernel approach for SVM, 50 independent decision tree estimators for RF, and three hidden fully-connected layers with 100 neurons in each layer for the MLP network.
## 4 Conclusions  
In summary, we present time resolved photoelectron emission in electron coherent excitation and charge migration processes. Simulations were performed on the aligned molecular ion H\({}_{2}^{+}\) by numerically solving the molecular TDSE. The molecule was excited by a linearly polarized XUV pulse, creating a coherent superposition of the ground and excited electronic states. Subsequently, a linearly/circularly polarized soft X-ray attosecond probe pulse was used to ionize the excited molecule. Probing molecular photoelectron momentum distribution displayed an asymmetric structure, which was shown to be dependent on the time delay between the pump and probe pulses, and the polarization and helicity of the probe pulse.  
The asymmetry of the time resolved photoelectron angular and momentum distributions arose from the coherent excitation of the molecule induced by the pump pulse. The dependence of the probing photoelectron emission yield on the time-delay illustrated the evolution of the coherent electron wave packets with time. Our numerical simulations combined with theoretical analysis therefore showed that the time resolved X-ray photoelectron imaging allows monitoring the electron coherence in molecular reaction processes. We showed how the polarization and helicity of the soft X-ray probe pulse influence the probing photoelectron emission, illustrating the property of the coherent superposition state in molecular coherent excitation and charge migration. Relating to new experiments in attosecond time-delayed photoionization of molecules [60], our results pave the way to visualize and manipulate electron motion inside molecules by time-dependent photoelectron spectroscopy imaging, which can be extended to more complex systems and laser control of chemical reactions [61; 62].  
[PERSON]-[PERSON] and [PERSON] conceived and designed the pump-probe photoelectron spectroscopy imaging model; [PERSON]-[PERSON] performed the numerical simulations; and [PERSON]-[PERSON] and [PERSON] analyzed the results and wrote the paper. This research was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Fonds de recherche du Quebec-Nature et technologies (FRQNT). The authors thank Compute Canada for access to massively parallel computer clusters. The authors declare no conflict of interest.
###### Abstract  
We investigated whether Top-of-Atmosphere Shortwave (TOA SW) anisotropy--essential to convert satellite-based instantaneous TOA SW radiance measurements into TOA SW fluxes--is sensitive to cloud-top effective radii and cloud-topped water vapor. Using several years of CERES SSF Edition 4 data--filtered for overcast, horizontally homogeneous, low-level and single-layer clouds of cloud optical thickness 10--as well as broadband radiative transfer simulations, we built refined empirical Angular Distribution Models (ADMs). The ADMs showed that anisotropy fluctuated particularly around the cloud bow and cloud glory (up to 2.9-8.0%) for various effective radii and at highest and lowest viewing zenith angles under varying amounts of cloud-topped moisture (up to 1.3-6.4%). As a result, flux estimates from refined ADMs differed from CERES estimates by up to 20 W m\({}^{-2}\) at particular combinations of viewing and illumination geometry. Applied to CERES cross-track observation of January and July 2007--utilized to generate global radiation budget climatologies for benchmark comparisons with global climate models--we found that such differences between refined and CERES ADMs introduced large-scale biases of 1-2 W m\({}^{-2}\) and on regional levels of up to 10 W m\({}^{-2}\). Such biases could be attributed in part to low cloud-top effective radii (about 8 um) and low cloud-topped water vapor (1.7 kg m\({}^{-2}\)) and in part to an inopportune correlation of viewing and illumination conditions with temporally varying effective radii and cloud-topped moisture, which failed to compensate towards vanishing flux bias. This work may help avoid sampling biases due to discrepancies between individual samples and the median cloud-top effective radii and cloud-top moisture conditions represented in current ADMs.  
CERES; energy budget; broadband radiative transfer simulations; cloud effective radius; upper-tropospheric humidity 2018 9256 10.3390/atmos9070256
## 3 Materials and Methods
### Power Plant Datasets  
In order to form a set of power plants for training our model and running inference, we partnered with Global Energy Monitor (globalenergymonitor.org, accessed on 1 June 2023) to create a complete-as-possible, harmonized inventory of global combustion power plants that are currently operational. This was necessary because no single existing power plant dataset contains all the required information for this work. To use a plant in our ML modeling, we required the following inventory and auxiliary data:  
* An accurate plant location for our satellite imagery.
* The location of FGD flue stacks and NDT cooling towers to focus our models on the relevant signals.
* Attributes of the power plant, including type, fuel, cooling technology, and air pollution control equipment to identify whether the plant is suitable for our models.
* Local weather data to decide whether temperature and humidity are conducive to vapor plume visibility.
* Plant capacity to determine whether the plant is of sufficient size to be modeled and to calculate the generation from the modeled capacity factor. This is used in conjunction with unit operating dates to find the plant capacity on any given date.
* Fuel and prime mover (i.e., steam or gas turbine) type to estimate the emissions factor.  
Figure 2: Overview of the data and models required to estimate CO\({}_{2}\) emissions from fossil-fuel power plants using satellite imagery.  
In addition, plant-level electricity generation data is required for model training. Plants without generation data were used for inference only. We elaborate on each of these data requirements and our sources in Appendix A.
# Statistics on Typhoon Intensity and Rice Damage in Vietnam and the Philippines
###### Abstract  
Typhoons are destructive multi-hazard events. To assess the relationship between typhoon intensity and agricultural loss, there is a need for accurate and standardized information on loss and damage, which is currently lacking. To address this, a database for Vietnam and the Philippines was created to provide aggregated information on the magnitude of rice damage and to highlight the rice-growing areas which were prone to being damaged by typhoons. Our study period was from 1970 to 2018, and we focused on Vietnam and the Philippines as these two countries experience frequent and intense typhoons. As different crops respond differently to wind and rain, we limit our research to a single crop. In this study, we focused on rice as it is a major staple food in Southeast Asia, and rice fields were often damaged by typhoons in the two countries. Of the 829 typhoon events recorded, only 15% of the events resulted in rice damage. The average area of rice damaged per typhoon event ranged from 42,407 ha in Vietnam to 83,571 ha in the Philippines. Meanwhile, the average production loss per typhoon event ranged from 190,227 metric tonnes in the Philippines to 539,150 metric tonnes in Vietnam. The monetary value of rice crops lost was only reported in the Philippines, and this amounted to an average of USS 42 million per typhoon event. There was a weak relationship between landfall wind speed and the three indicators of rice damage, which suggests that rice damage was not primarily due to strong winds. Our results showed that the rice fields in the coastal provinces of Vietnam and the northern parts of the Philippines were more vulnerable to being damaged by typhoons.  
agricultural damage; rice; food security; natural hazard; typhoon 2024 5, 22-37.
## 4 Discussion  
In this study, we have tried to solve the problem of weak correspondence between actual structures and the results of traditional recognition methods by proposing a space target structure polarization interpretation method based on component decomposition and feature extraction. We modified the target polarization domain decomposition process in order to enhance the correlation between the identification results and the actual structure of the target. Validation experiments were conducted using simulation data and anechoic chamber measurement data. The results indicate that the feature vector which integrates parameters from various polarization target decomposition methods can effectively represent the differences among components. Thus, the proposed method can establish a stronger correlation between the recognition results and the actual structures.  
Although our method can directly point out the components of the target, there are also limitations. First, there is less differentiation between components compared to canonical scatterers, and the mechanisms of the different components are made up of the mechanisms of several canonical scatterers. This could be improved by incorporating a more effective neural network algorithm. Second, The acquisition efficiency of polarimetric data is low. In subsequent work, we plan to explore solutions such as data augmentation and data generation to address this challenge. Finally, the proposed method still needs further experimental validation for targets with complex structures and different materials.
## 3 Proposed Approach
### Use Cases  
One of the main advantages of a directional antenna is that it transmits only in a narrow angle, for example 10 degrees, which reduces the possibility of adversary detection. Therefore, we need to design a routing protocol that avoids transmitting in a direction where an adversary aircraft is located, which we call a _forbidden direction_. The forbidden direction is assumed to be aligned with the antenna boresight. By combining the forbidden direction with information about the shape of main lobe and sidelobes of radiation pattern one can specify the associated forbidden sector. The calculation of forbidden direction and sector and, thus, which aircraft we can communicate with in a single-hop, is an important part of the developed routing protocol.  
Figure 3 shows a use case where an adversary aircraft is in the neighborhood. In this case, when [PERSON] wants to communicate with B, [PERSON] is forced to use multi-hop via C in order to reach B without risk of being detected.  
In order to describe the topology control and the routing protocol, we will start with the use cases below. In the evaluation of the protocol, we have more complex use cases (scenarios) as outlined in Section 4.  
* A new aircraft enters the network. This use case can be broken down into (i) two aircraft establishing connection with each other, as in Figure 1, and (ii) a new aircraft enters an existing network with \(N\) aircraft.
* An aircraft loses contact with the network. This use case can be broken down into (i) an aircraft acting as an end node (e.g., \"C\" in Figure 2) loses contact with the other ones in a network of \(N\) aircraft, which does not affect the routing between the other \(N-1\) nodes, and (ii) an aircraft acting as a relay node (e.g., \"B\" in Figure 2) loses contact with the other ones in a network of \(N\) aircraft, which affects the communication and network topology between the other nodes.
* Two friendly aircraft communicating with each other; see Figure 1.
* Three friendly aircraft communicating with each other, using both single-hop routing (A\(\Leftrightarrow\)B and B\(\Leftrightarrow\)C) and multi-hop routing (A\(\Rightarrow\)B\(\Rightarrow\)C); see Figure 2.
* Three friendly aircraft communicating with each other but with an adversary aircraft in the neighborhood. Since the adversary aircraft is in the send direction A\(\Rightarrow\)B, we are forced to use multi-hop routing A\(\Rightarrow\)C\(\Rightarrow\)B when A wants to communicate with B; see Figure 3.
## 3 Results and Discussion  
### In-Plane Shear Properties  
According to a few studies, interleaving increases the toughness but decreases the in-plane modulus and strength of the CFRP composites [26; 27]. Therefore, the in-plane modulus and strength of the buckypaper-interleaved CFRP specimens were examined. Figure 4 shows the in-plane shear moduli and strengths of the specimens. We observed a negligible difference in the in-plane shear modulus and strength between the baseline and buckypaper-interleaved specimens. The in-plane shear strength of the buckypaper-interleaved specimens was 4% lower than that of the baseline specimens. The in-plane shear modulus values of both the baseline and buckypaper-interleaved specimens were similar.  
In general, the in-plane properties of the laminate composite decreased because the fiber volume fraction was reduced due to interleaving. This tendency became more pronounced as the thickness of the interleaved film increased to a certain level [28]. Although only one buckypaper was interleaved, the in-plane property was more sensitive to the fiber volume fraction than the interlaminar property. The in-plane shear strength was slightly lowered due to the increase in the resin-rich area. Therefore, a buckypaper with appropriate thickness and areal weight should be added to reduce the trade-off caused by the swelling of the interleaved film.
# Beam Dynamics Design of a Multi-Ion RFQ for Medical Application
## 2 Basic Principles of the RFQ  
In the RFQ, the beam is focused transversely by RF electric quadrupole fields and accelerated by longitudinal RF electric fields. In the RFQ, the formation of the electric field depends on the electrode  
The defocusing factor \(\Delta\) is defined by:  
\[\Delta=\frac{\pi^{2}qVA}{2m_{0}c^{2}\beta^{2}\gamma}\sin\phi_{\mathrm{s}} \tag{2}\]  
where \(\phi_{s}\) is the synchronous phase, \(\gamma\) is the Lorentz factor, and \(A\) is the acceleration efficiency, which is defined as:  
\[A=\frac{m^{2}-1}{m^{2}I_{0}(ka)+I_{0}(kma)} \tag{3}\]  
where \(m\) is the modulation parameter, \(I_{0}\) is the modified Bessel function of the first kind, \(k\) is the wave number, given by \(k=\frac{2\pi}{\beta A}\).  
For the RFQ, an excessively high surface electric field can lead to breakdown and discharge phenomena, potentially compromising the stable operation of the RFQ cavity. The Kilpatrick factor \(K_{p}\) is commonly used as an empirical criterion to evaluate the risk of RF breakdown [26]. It is defined as follows:  
\[K_{p}=\frac{E_{s\max}}{E_{k}} \tag{4}\]  
where \(E_{s\max}\) is the maximum surface field, \(E_{k}\) is the Kilpatrick limit, which is defined as:  
\[f=1.64E_{k}^{2}\mathrm{e}^{-8.5/E_{k}} \tag{5}\]
# Cross-Domain Fusion Constellation Design of Communication, Navigation and Remote Sensing
###### Abstract
* In order to design the constellation, a constellation design method is proposed to optimize the CDFC based on resource coverage after combining the constellations in different domains. * Through simulation and comparison, the advantages of CDFC in terms of satellite number and cost are verified. This paper is organized as follows. Section 1 introduces the development trend of integrated cross-domain integration at home and abroad, and the research status of constellation design and optimization. Section 2 introduces the integrated constellation design and optimization method based on reverse design and the two-stage algorithm adopted. Section 3 introduces the experimental content and result analysis. Finally, Section 4 gives the conclusion of this paper, and describes the limitations of this study and future research directions.