esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Striped field mice (_Apodemus agarrius_) are the main host for the Hantaan virus (HTNV), the cause of hemorrhagic fever with renal syndrome (HFRS) in central China. It has been shown that host population density is associated with pathogen dynamics and disease risk. Thus, a higher population density of _A. agarrius_ in an area might indicate a higher risk for an HFRS outbreak. Here, we surveyed the _A. agarrius_ population density between 2005 and 2012 on the Weihe Plain, Shaanxi Province, China, and used this monitoring data to examine the relationships between the dynamics of _A. agarrius_ populations and environmental conditions of crop-land, represented by remote sensing based indicators. These included the normalized difference vegetation index, leaf area index, fraction of photosynthetically active radiation absorbed by vegetation, net photosynthesis (PsnNet), gross primary productivity, and land surface temperature. Structural equation modeling (SEM) was applied to detect the possible causal relationship between PsnNet, _A. agarrius_ population density and HFRS risk. The results showed that _A. agarrius_ was the most frequently captured species with a capture rate of 0.9 individuals per hundred trap-nights, during 96 months of trapping in the study area. The risk of HFRS was highly associated with the abundance of _A. agarrius_, with a 1-5-month lag. The breeding season of _A. agarrius_ was also found to coincide with agricultural activity and seasons with high PsnNet. The SEM indicated that PsnNet had an indirect positive effect on HFRS incidence via rodents. In conclusion, the remote sensing-based environmental indicator, PsnNet, was highly correlated with HTNV reservoir population dynamics with a 3-month lag (\(r\) = 0.46, \(p\) < 0.01), and may serve as a predictor of potential HFRS outbreaks.  
Hantaan virus (HTNV); remote sensing; hemorrhagic fever with renal syndrome (HFRS); net photosynthesis (PsnNet); rodent population dynamics
# Radiometric Method for Determining Canopy Stomatal Conductance in Controlled Environments
###### Abstract  
Canopy stomatal conductance is a key physiological factor controlling transpiration from plant canopies, but it is extremely difficult to determine in field environments. The objective of this study was to develop a radiometric method for calculating canopy stomatal conductance for two plant species--wheat and soybean from direct measurements of bulk surface conductance to water vapor and the canopy aerodynamic conductance in controlled-environment chambers. The chamber provides constant net radiation, temperature, humidity, and ventilation rate to the plant canopy. In this method, stepwise changes in chamber CO\({}_{2}\) alter canopy temperature, latent heat, and sensible heat fluxes simultaneously. Sensible heat and the radiometric canopy-to-air temperature difference are computed from direct measurements of net radiation, canopy transpiration, photosynthesis, radiometric temperature, and air temperature. The canopy aerodynamic conductance to the transfer of water vapor is then determined from a plot of sensible heat versus radiometric canopy-to-air temperature difference. Finally, canopy stomatal conductance is calculated from canopy surface and aerodynamic conductances. The canopy aerodynamic conductance was 5.5 mol m\({}^{-2}\) s\({}^{-1}\) in wheat and 2.5 mol m\({}^{-2}\) s\({}^{-1}\) in soybean canopies. At 400 umol\({}^{-1}\) of CO\({}_{2}\) and 86 kPa atmospheric pressure, canopy stomatal conductances were 2.1 mol m\({}^{-2}\) s\({}^{-1}\) for wheat and 1.1 mol m\({}^{-2}\) s\({}^{-1}\) for soybean, comparable to canopy stomatal conductances reported in field studies. This method measures canopy aerodynamic conductance in controlled-environment chambers where the log-wind profile approximation does not apply and provides an improved technique for measuring canopy-level responses of canopy stomatal conductance and the decoupling coefficient. The method was used to determine the response of canopy stomatal conductance to increased CO\({}_{2}\) concentration and to determine the sensitivity of canopy transpiration to changes in canopy stomatal conductance. These responses are useful for improving the prediction of ecosystem-level water fluxes in response to climatic variables.  
c 2019 9 114 10.3390/agronomy9030114 1
# Heart Murmur Quality Detection Using Deep Neural Networks with Attention Mechanism
## 5 Results
### The Effect of Data Augmentation  
The data augmentation was tried following the method in Section 4.5; however, it had minimal impact. The results are shown in Table 8. For the metric of accuracy, there was only a maximum improvement of 0.5% compared to the original data with some metrics decreased, so the data augmentation was not used for segment- and patient-level detection above. It is evident that the traditional data augmentation method is ineffective in mitigating the negative impact of insufficient data volume. Therefore, it is crucial to collect more data in the future to adequately support the training of neural network models.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Murmur Characteristics**} & \multirow{2}{*}{**Accuracy (\%)**} & \multicolumn{3}{c}{**Harsh**} & \multicolumn{3}{c}{**Blowing**} \\ \cline{3-7}  & & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline Not used \({}^{*}\) & 68.8 \(\pm\) 5.0 & 72.4 \(\pm\) 2.6 & **75.9 \(\pm\) 8.9** & **73.9 \(\pm\) 5.5** & 63.8 \(\pm\) 7.5 & 58.6 \(\pm\) 4.7 & 60.8 \(\pm\) 4.4 \\ Grading & 67.1 \(\pm\) 3.4 & 74.7 \(\pm\) 3.4 & 67.3 \(\pm\) 6.9 & 70.6 \(\pm\) 4.2 & 59.0 \(\pm\) 3.9 & 66.9 \(\pm\) 7.1 & 62.4 \(\pm\) 3.7 \\ Pitch & 68.7 \(\pm\) 5.3 & 73.7 \(\pm\) 4.1 & 72.9 \(\pm\) 6.5 & 73.3 \(\pm\) 5.0 & 62.0 \(\pm\) 6.9 & 62.6 \(\pm\) 5.9 & 62.2 \(\pm\) 5.8 \\ Timing & **69.9 \(\pm\) 4.8** & 75.6 \(\pm\) 5.1 & 74.0 \(\pm\) 14.3 & 73.6 \(\pm\) 7.1 & **65.4 \(\pm\) 8.7** & 64.0 \(\pm\) 12.3 & 63.4 \(\pm\) 4.1 \\ Shape & 69.1 \(\pm\) 4.0 & **76.4 \(\pm\) 3.8** & 69.5 \(\pm\) 8.6 & 72.4 \(\pm\) 4.9 & 61.6 \(\pm\) 5.5 & **68.6 \(\pm\) 7.9** & **64.5 \(\pm\) 4.0** \\ \hline \end{tabular} \({}^{*}\) “Not used” means no characteristic information was used.  
\end{table}
Table 7: Model performance when other characteristic information was used.  
Figure 6: The accuracy of different types of PCGs, including different types of “Timing”, “Shape”, “Grading”, and “Pitch”. Specific values for each type of PCG can be found in Table 2.
## 2 Principles of the Proposed ANN Equalizer
In particular, the training data of the advanced training scheme are obtained as follows. We first generate \(\left(M^{N+2K}\right)\) different symbols, including \(M^{2K}\) redundant guard symbols, similar to the case explained in the extensive training scheme. This symbol data stream is then transmitted through the optical-fiber system that adds noise, as well as distortions due to chromatic dispersion. The discretized received signal samples pass through the DSP unit. The samples corresponding to the redundant guard data are discarded (see Figure 1(a)) and the signal samples corresponding to the original training symbols and their transmitted peers are used to formulate the first group of input-output training pairs, as shown in Figure 1(a). To take into account the channel noise, the same stream of data, consisting of \(\left(M^{N+2K}\right)\) distinct symbol patterns, is resent through the fiber system \(n\) times. The same procedures are then repeated at the DSP unit to formulate a total of \(n\) different groups of input-output training pairs, each of which consists of \(\left(N\times M^{N+2K}\right)\) input training vectors and \(\left(1\times M^{N+2K}\right)\) corresponding desired outputs, as shown in Figure 1(b). Note that the desired output vectors in all groups are identical; however, the input training vectors in each group have different values due to randomness of the system noise. The total number of training data needed by the advanced training scheme is \(N_{tr}=n\times M^{N+2K}\). In the form of neural network training data, a total of \(n\times\left(N\times M^{N+2K}\right)\) input training vectors and \(n\times\left(1\times M^{N+2K}\right)\) corresponding desired output vectors are used. Once the input-output training data pairs are determined, the optimization training problem (2) is solved using any well-defined optimization algorithm to obtain the optimal ANN weights \(\mathbf{W}^{n}\). In this work, we specifically use the adaptive moment estimation (Adam) algorithm [33; 34]. The Adam algorithm is one of the most effective stochastic-gradient-based optimization algorithms that is widely used to solve training problems of ANNs and deep neural networks. It shows efficient ability to solve optimization problems with a large number of data and parameters, with little memory requirement. It also offers superior effectiveness to solve problems that include very noisy and sparse gradients, which is the common case in neural network training problems. The Adam algorithm is derived from the calculation of the evolutionary moment. It uses estimations of the first and second moments of the gradient to iteratively update weights of the neural network.
# An On-Orbit Task-Offloading Strategy Based on Satellite Edge Computing
## 4 Algorithm Introduction and Simulation Analysis
### Statistical Analysis  
The simulation findings provided some support for the GABPSO algorithm's superiority. Referring to research [36], a two-way analysis of variance (ANOVA) was used to explore the system cost in relation to each parameter for a more in-depth analysis. This process was used to test the effect of two factors on the dependent variable, consistent with the type of simulation in this study. Firstly, the objective was defined to test whether there was any difference in the scheduling algorithms or high-load satellite proportions at the 0.05 level of standard significance. The calculation parameters are shown in Table 3.  
* Step 1: Null Hypotheses: \(H0^{(1)}\): There is no significant difference in the scheduling algorithms \(H0^{(2)}\): There is no significant difference in the high-load satellite proportions Alternative Hypotheses:  
Figure 8: System cost vs. computational complexity. Figure 9: System cost vs. ISL bandwidth. \(H1^{(1)}\): There is a significant difference in the scheduling algorithms \(H1^{(2)}\): There is a significant difference in the high-load satellite proportions
* Step 2: In this scenario, \(a\) = 4 and \(b\) = 7. At the 0.05 level of significance \(H0^{(1)}\): \(F_{a}((a-1),(a-1)(b-1))=F_{0.05}(3,18)=3.16\) \(H0^{(2)}\): \(F_{a}((b-1),(a-1)(b-1))=F_{0.05}(6,18)=2.66\)
* Step 3: Calculation Total sum of squares: \(SST=\sum_{i=1}^{a}\sum_{j=1}^{b}\left(x_{ij}-\overline{x}\right)^{2}\) = 667,536.11 Variation between rows: \(SSR=\sum_{i=1}^{a}\sum_{j=1}^{b}\left(\overline{x}_{i}-\overline{x}\right)^{2}\) = 195,397.82 Variation between columns: \(SSC=\sum_{i=1}^{a}\sum_{j=1}^{b}\left(\overline{x}_{j}-\overline{x}\right)^{2}\) = 461,881.36 Variation due to error: \(SSE=SST-SSR-SSC\) = 10,256.93 The specific results of the two-way ANOVA analysis are shown in Table 3.
* Step 4: Decision As \([Fr=114.30]>[F_{a}((a-1),(a-1)(b-1))=3.16]\), we can reject the \(H0^{(1)}\) at the 0.05 level of significance. The results of the analysis show that different scheduling algorithms produced significant differences. In addition, as \([Fc=135.09]>[F_{a}((b-1),(a-1)(b-1))=2.66]\), this verifies that different proportions of high-load satellites can also have a significant impact. Similarly, other simulation results were analyzed, and the specific results are shown in Table 4.
# Seamless 5G Multi-Hop Connectivity Architecture and Trials for Maritime Applications
## 3 Maritime Network Architecture and Solutions for Seamless Connectivity
### Single- and Multi-Hop Ship-Shore Connectivity
Option A5 with N3 IWF would provide further convenience regarding access authentication, which is not subject to technical performance evaluations but could be theoretically evaluated. For a 5G modem, the special case of multi-hop connectivity is also shown in Figure 10. In that case, an onboard 5G network on another ship, following any of the splits presented in Figures 7-9 in the previous section, could provide connectivity to the considered ship. The other ship would also have an ABC router, enabling different options to connect to yet another ship or an onshore network. In the case of only one 5G modem with one SIM, this is non-trivial, as the SIM would have to be admitted to the 5G network of another ship and different onshore networks. The corresponding solutions heavily depend on which split option has been selected. In the case where multiple 5G modems are available, one could be used for ship-to-ship multi-hop connectivity while the other one could be used for ship-to-shore connectivity. The ship-to-ship 5G modem would have to be configured to prevent it from connecting to the 5G network on its own ship. The ship-to-shore 5G modem should benefit from seamless cross-border/-MNO service continuity, as discussed in the next section.
## 5 Conclusions  
From the perspective of integrity risk control of potential hazards uncertainty and user algorithm implementation complexity, the concept of constellation dynamic selection is proposed in this paper. The stable and healthy development of four GNSS constellations and the baseline ARAIM user algorithm based on snapshot principle provide the feasibility to dynamically select different constellations to evaluate the GNSS integrity in different locations and different epochs.  
A practical and computationally effective ARAIM method based on constellation dynamic selection is implemented. Two alternative rules are given to select the targeted two constellations with the best vertical geometry performance. Only the two constellations with the minimum VDOP are selected and replaced the other constellations for integrity monitoring. The test results show that the ARAIM worldwide availability can reach 100% under LPV-200 criteria with less than 10% of the computational complexity of ARAIM with four constellations. The proposed method also shows robustness under depleted configurations like satellite outages. Furthermore, the statistics results based on observation stations validated the superiority of the proposed method under current GNSS constellations. A tradeoff is achieved between the high availability and robustness of ARAIM with three or four constellations and the practicability and accessibility of ARAIM with two constellations.  
The proposed method is especially suitable for expanding the application of ARAIM technology in harsh environment. We also noticed the criterions for constellation selection can be improved significantly, just as the increasing research for satellite selection. With the further study of satellite/constellation prior fault probability and performance in space, the ISM details of different constellations will definitely be different. How to improve the dynamic selection criterion with the ISM to guarantee the best integrity is the next work.  
Conceptualization, methodology, validation, writing--original draft preparation, Q.M.; writing--review and editing, Y.Z. and S.L.; visualization, S.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Open Fund of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, grant number 21P01 and the Foundation of Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, China, grant number SEU-MIAN-202101.  
The authors declare no conflict of interest.
## 1 Introduction
Such errors can be avoided in TCPInSAR processing since the TCPs are co-registered only according to their own offsets. To tackle the atmospheric artifacts along the long-span bridge surrounded by sea water, we limit the maximum length of arcs (or point pairs) by utilizing local Delaunay triangulation to construct arcs. Equipped with these processing strategies, TCPInSAR has the potential to monitor the deformation of long-span cross-sea bridges caused by foundation settlement, bridge deck creep, stress relaxation, as well as environmental conditions, such as temperature, wind, tide, and others.
###### Abstract  
The initial stresses have a strong effect on the mechanical behavior of underground rock masses, and the initial stressed rock masses are usually under strong dynamic disturbances such as blasting and earthquakes. The influence mechanism of a blasting excavation on underground rock masses can be revealed by studying the propagation of stress waves in them. In this paper, the improved Mohr-Coulomb elasto-plastic constitutive model of the intact rock considering the initial damage was first established and numerically implemented in Universal Distinct Element Code (UDEC) based on the variation of the experimental stress wave velocity in the initial stressed intact rock, and the feasibility of combining the established rock constitutive model and the BB (Bandis-Barton) model which characterizes the nonlinear deformation of the joints to simulate stress waves across jointed rock masses under initial stress was validated by comparing the numerical and model test results subsequently. Finally, further parameter studies were carried out through the UDEC to investigate the effect of the initial stress, angle, and number of joints on the transmission of the blasting stress wave in the jointed rock mass. The results showed that the initial stress significantly changed the propagation of the stress waves in the jointed rock mass. When the initial stress was small, the transmission coefficients of the stress waves in the jointed rock were vulnerable to be influenced by the variation of the angle and the number of joints, while the effect of the angle and the number of joints on the stress wave propagation gradually weakened as the initial stress increased.  
numerical modeling; blasting stress wave; underground rock masses; initial stress; transmission coefficient +
Footnote †: journal: Applied Sciences
# Hybrid Deep Learning-Based Intrusion Detection System for RPL IoT Networks
## 3 IoTR-DS Dataset Creation and Attacks Modeling
### IoTR-DS Attributes  
As mentioned, instead of using a monitoring and sniffing tool to capture the nodes' traffics, the IoTR-DS is created by utilizing the sensing data packets sent by all nodes to the root. Some of the routing attributes are appended to the UDP (data) packet when they reach the network layer, such as numSentDIO, numSentDIS, numRankChange, and others (see Table 2). The root will also append some attributes of information related to the UDP (data) packet, such as recTime and numUdpRec. According to the literature, these attributes are useful and analyzed by the IDS system to detect abnormal behaviors. Their values might change due to normal network behaviors such as nodes joining the tree, changing their rank or parent, etc. On the other hand, the change can be due to anomaly behaviors in the network, such as attacks. Moreover, a specific attack can make changes to one or more parameters at a certain level. The IDS system's job is to differentiate between normal and abnormal traffic and classify the attack types.  
The packets sent by the nodes are stored in the root as log files, which represent the raw datasets. Table 1 lists the dataset IoTR-DS attributes, where the last one represents the traffic label, whether it is normal traffic or an attack. When the malicious nodes attack, the assumption is that the whole network will be affected during the attack time. Therefore, all traffics are labeled with an attack number during the attack time. The number zero is reserved for labeling normal traffic (i.e., when there are no attacks).
# Pipe Routing with Topology Control for Decentralized and Autonomous UAV Networks +
## 8 Simulation Results and Discussion
### Summary  
We see that both pipe routing and topology control contribute to maintaining a robust set of alternative routes and lead to better routing performance (higher PDR, longer route-up time, and fewer route breaks) in all settings, with TC-Pipe (which uses both) outperforming Pipe, AODV, and ConCov-Pipe. With no pipe region, AODV performs worst; the pipe region allows the other methods (TC-Pipe, Pipe, and ConCov-Pipe) to proactively switch routes within the pipe, leading to fewer route breaks and thus fewer route discoveries. In general, routing and coverage performance is a trade-off [7]. TC-Pipe sees a slight decrease in coverage (\(C_{v}\), \(F\)) compared to Pipe, AODV, and ConCov-Pipe at a lower node density (30 UAVs), but at a higher density, TC-Pipe achieves comparable coverage performance with better PDR.  
The Relay scheme achieves higher PDR than TC-Pipe in single-target settings and some three-target settings (longer routes and lower data rates), because it uses dedicated relay UAVs to establish stable routes. However, it provides the worst coverage performance among the tested schemes, especially for multiple targets with long routes at low UAV density, because significantly fewer UAVs are available to perform area coverage. In both Pipe and TC-Pipe, the PDR and coverage performances gracefully decrease in proportion to the percentage of UAVs failing in the network. At a higher density, this decrease is less than at a lower density, as there are enough UAVs to maintain a robust pipe.
# An Overview of CubeSat Missions and Applications
## 5 CubeSats as Astronomical Exploration and Research Enabling Platforms  
In recent years, CubeSats have emerged as useful platforms for astronomical observation. Their low cost, modularity, and scalability potential provide a suitable candidate for replacing larger and more expensive platforms. They have been used in multiple missions, to investigate various cosmic phenomena, to study exoplanets, interstellar matter, etc. [204]. The main instruments that are utilized for these missions are UV/VIS/IR telescopes, X-ray spectrometers, solar X-ray spectrometers, gamma ray burst detectors, particle detectors, and radio interferometers. The working principles of these instruments and some notable missions will be presented hereafter.