esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability Evaluation for Clustered WSNs under Malware Propagation
## 2 Related Work
According to MAC protocols adopted in WSNs, [PERSON] _et al._[42] evaluated a sensor node's reliability under three typical working scenarios including sensor nodes always in active mode, alternating between sleep and active modes on average, and alternating between these modes based on a certain distribution. [PERSON] _et al._[43] evaluated the reliability of energy harvesting sensor nodes and battery-powered sensor nodes as well as develop the corresponding wireless-link-reliability models. [PERSON] _et al._[44] characterized event-driven WSNs according to limited node battery energy and shadowing under channel fading, obtaining reliable data flows in WSNs via wireless link reliability and node energy availability. [PERSON] _et al._[45] analyzed a body sensor node's reliability subject to probabilistic competition between propagation effects and probabilistic failure isolation. [PERSON] _et al._[46] proposed a symbolic ordered-binary-decision-diagram-multicast method to evaluate the reliability of multicast WSNs. [PERSON] _et al._[47] proposed mission-oriented and transmission-paths-based models for evaluating WSNs' transmission reliability. Other measures closely related to reliability include dependability and survivability such as the stochastic-activity-network-based dependability measure [48], the epidemic-theory-based survivability measure [49; 50], survivability analysis using probabilistic model checking [51], and natural-tenacity-based survivability evaluation for mobile WSNs [52]. Unlike this body of work, to the best of our knowledge this work is the first that concentrates on reliability evaluation for clustered WSN under epidemic-malware propagation conditions. While our epidemic model is similar to [14; 17], we further model all state transitions of a sensor node as a CTMC by integrating the malware's infection probability predicted by our strategic malware-infection game into the transition probability. We apply the approach proposed in [53] to compute the MTTF from a Markov process; however, we further find the equation to compute the reliability of a sensor node under epidemic-malware propagation, which is a novel measure. Considering the topology of a typical clustered WSN, we can thus deduce the reliability of a cluster, a route, and a clustered WSN based on reliability theory. In summary, we propose a unified framework for malware-infection and reliability evaluation that integrates both security and reliability properties of a clustered WSN during the evaluation process.
# Adaptive TCP Transmission Adjustment for UAV Network Infrastructure
## 6 Conclusions  
This paper presents the _ASRAN_ for TCP that is suitable for UAV network environment. When a network is deployed through multiple UAVs, transient link instability can occur due to mobility and transient link failure, which in turn triggers frequent changes in network topology. Since transient link instability is not caused by heavy traffic, there is no need to reduce the transmission speed, like the original TCP. In order to cope with an unstable network environment, _ASRAN_ adjusts the TCP parameter to improve the network throughput by recovering unnecessarily reduced throughput when transient instability occurs in the UAV network. As a result, if the _ASRAN_ is applied to UAV, the transmission speed of the overall UAV network can be improved. In addition, since _ASRAN_ uses the Slow-Start algorithm as the basic technique of TCP to increase the throughput, even if there is data loss or buffer overflow, _ASRAN_ can operate in similar way to the original TCP.  
As future work, we will apply _ASRAN_ to multi-path TCP (MPTCP). Our goal is to improve the throughput of each subsocket through _ASRAN_, thereby improving the overall throughput of MPTCP. In addition, we plan to enhance the throughput model of _ASRAN_ when it is applied to MPTCP.  
Conceptualization, J.Y.L.; methodology, J.Y.L.; software, J.Y.L. and W.L.; validation, J.Y.L., H.K. ([PERSON]) and H.K. ([PERSON]); formal analysis, J.Y.L.; investigation, J.Y.L.; resources, H.K. ([PERSON]); data curation, J.Y.L.; writing--original draft preparation, J.Y.L.; writing--review and editing, W.L., H.K. ([PERSON]) and H.K. ([PERSON]); visualization, J.Y.L.; supervision, H.K. ([PERSON]); project administration, H.K. ([PERSON]); funding acquisition, H.K. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was supported by \"Human Resources program in Energy Technology\" of the Korea Institute of Energy Technology Evaluation and Planning(KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea(No. 20174030201820). Preliminary version of this paper appeared at Local Computer Networks (LCN), 2016 IEEE International Conference on. IEEE, 2016 [27].  
The authors declare that they have no conflicts of interest.  
Figure 8: Throughput fluctuation of simulation experiments.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **TCP-NewReno** & _ASRAN_ \\ \hline Average Throughput (Mbits/s) & 5.805 & 6.582 \\ Standard Deviation & 3.692 & 4.094 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average throughput comparison of simulation experiment.
## Abbreviations  
The following abbreviations are used in this manuscript: \begin{tabular}{l l} ANN & Artificial Neural Networks \\ AoA & Angle of Arrival \\ AP & Access Point \\ BPNN & Back Propagation Neural Network \\ CapsNet & Capsule Neural Network \\ CNN & Convolutional Neural Networks \\ CSI & Channel State Information \\ DBN & Deep Belief Network \\ DNN & Deep Neural Networks \\ DQN & Deep Q-Networks \\ DRL & Deep reinforcement learning \\ ELM & Extreme learning machine \\ GNSS & Global Navigation Satellite Systems \\ MLP & Multilayer Perceptron \\ MSE & Mean Squared Error \\ NN & Neural Network \\ PoA & Phase of Arrival \\ RF & Random Forest \\ RMSE & Root Mean Squared Error \\ RNN & Recurrent Neural Networks \\ RSSI & Received Signal Strength Indicator \\ SDA & Stacked Denoising Autoencoders \\ SMN & Single Multiplicative Neuron \\ SNR & Signal-to-Noise Ratio \\ SVM & Support Vector Machines \\ ToF & Time Of Flight \\ VAE & Variational Autoencoder \\ \end{tabular}
## 2 Materials and Methods
### Evaluation of Near Infrared Spectroscopy (NIRS) Approach
#### 2.1.3 Statistical Analysis  
The Opus v. 7.5 software (Bruker Optik GmbH, Germany) was employed for spectral data collection, and FT-NIR spectra were exported to the Unscrambler software (version 10.5.1, Camo, ASA, Oslo, Norway) for chemometrics analysis, calibration, and external validation models. Prediction models were developed using partial least square regression (PLSR) algorithm, considering an independent validation sample set for the chemometrics analysis [24]. In order to obtain the best predictive model, for PLSR, samples were split in two sets: a training set (calibration) with 79% of the samples (315 samples of six fields collected between February and December 2018; day of the year (DOY) 39-135) and a test set, with the rest of the samples (83 samples of eight fields collected between January and February 2019; DOY 10-50) used as an external and independent validation set of the NIRS calibration models. In order to test the model robustness, two different years were considered for calibration and validation sets.  
To find the most accurate model to quantify CP, NDF, and the PQI in pastures, the calibration process was performed on the raw spectra data and after the application of some mathematical algorithms to remove any irrelevant information. Some pre-processing techniques, like standardnormal variate (SNV), normalization and normalization followed by SNV (normalization and SNV) were applied to raw spectra and the best prediction model was selected. Calibration and validation models were developed based on principal components analysis. The quantitative measure for the predictive accuracy from each model was evaluated using coefficient of determination (R\({}^{2}\); an excellent indicator of the accuracy and robustness of a model), root mean square error (RMSE; Equation (2)) for the calibration and external validation data sets, and the residual predictive deviation (RPD; Equation (3)), which corresponds to the ratio between standard deviation (SD) of the results obtained by reference method and the corrected mean error of the prediction of the validation (SEP bias). The value of RPD is usually used as an indicator of the quality of a calibration model. Values of RPD higher than 2, 3, or 5 are recommended, respectively, for screening purposes, prediction, and quality control [25; 26].  
\[\text{RMSE}=\sqrt{\frac{\sum\limits_{\text{i=1}^{n}{(\text{E}_{\text{i}}- \text{M}_{\text{i}})^{2}}}}{\text{n}}} \tag{2}\]  
\[\text{RPD}=\frac{\text{SD}}{\text{RMSE}} \tag{3}\]  
where n is the number of observations and Ei and Mi are the estimated and observed (measured) values, respectively.
## 2 Numerical Model Construction
### Discrete Element Triaxial Test Servo Mechanism  
In triaxial testing, the specimen maintains a stable state of peri-compression during consolidation and shear. In contrast, it is only possible to specify specific velocities for the wall units, not the force or stress properties in numerical simulation. Consequently, controlling the velocity of the wall becomes the sole method for accurately controlling the force exerted by the wall on the internal particles. The velocity of wall movement can be expressed by Equation (6):  
\[\dot{u}^{(w)}=G(\sigma^{measured}-\sigma^{required})=G\Delta\sigma \tag{6}\]  
where \(\dot{u}^{(w)}\) is the wall contraction (or expansion) rate; \(G\) is the servo parameter; \(\sigma^{measured}\) is the real-time stress; \(\sigma^{measured}\) is the target stress; and \(\Delta\sigma\) is the difference between the real-time and target stresses.  
Since the stresses in the wall cannot be detected directly in the software, the stresses can be obtained by measuring the combined external forces on the wall and dividing by the contact area. Equation (7) describes the incremental wall confinement stress \(\Delta\sigma^{(\omega)}\) caused by the confinement motion in one time step.  
\[\Delta\sigma^{(w)}=\frac{k_{n}^{(w)}N_{c}\dot{u}^{(w)}\Delta t}{A} \tag{7}\]  
where \(k_{n}^{(\omega)}\) is the average normal stiffness of the particle-wall contact, \(N_{c}\) is the total number of particles in contact with the wall, \(\dot{u}^{(w)}\Delta t\) is the distance traveled by the loaded wall in one time step, and \(A\) is the wall area.  
The absolute value of the increase in the confining stress caused by wall contraction or expansion was less than the difference between the measured and desired stress to maintain stability. This ensures less change in the confining pressure due to wall movement in each step of the calculation, which is specified as follows:  
\[\left|\Delta\sigma^{(w)}\right|=\left|\frac{k_{n}^{(w)}N_{c}\dot{u}^{(w)} \Delta t}{A}\right|<\alpha|\Delta\sigma| \tag{8}\]  
where \(\alpha\) is the relaxation factor.  
Taking \(G>0\) and \(\Delta\sigma>0\), the servo tuning factor can be obtained as:  
\[G=\frac{\alpha A}{k_{n}^{(w)}N_{c}\Delta t} \tag{9}\]
# Microbes and Parameters Influencing Dark Fermentation for Hydrogen Production
## 4 Bacteria Used for Hydrogen Production During Dark Fermentation
### Enterobacter  
_Enterobacter_ spp. are known to be potential strains for large-scale H\({}_{2}\) production due to their high growth rates, adaptability to environmental conditions, utilization of waste biomass such as feedstock, and resistance to variations in pH, dissolved O\({}_{2}\), and H\({}_{2}\) pressure [120]. Metal nanoparticles have also been used to increase H\({}_{2}\) production by enhancing ferredoxin-oxidoreductase activity [121]. The addition of iron or nickel oxides can help the bacteria produce H\({}_{2}\) by promoting their cell proliferation and enzyme production [122].
# Multi-Scale CNN-Transformer Dual Network for Hyperspectral Compressive Snapshot Reconstruction
## 4 Results
### Experimental Setting  
We use CAVE [5] as the training hyperspectral image set. CAVE has 205 hyperspectral images with a spatial size of \(1024\times 1024\) and 28 spectral bands. The wavelengths of these 28 spectral bands are within the wavelength range of 450-650 nm, and they are 453.3, 457.6, 462.1, 466.8, 471.6, 476.5, 481.6, 486.9, 492.4, 498.0, 503.9, 509.9, 516.2, 522.7, 529.5, 536.5, 543.8, 551.4, 558.6, 567.5, 575.3, 584.3, 594.4, 604.2, 614.4, 625.1, 636.3, and 648.1 nm, respectively. We randomly crop massive sub-images with a spatial size of \(256\times 256\) from the original large-sized image, thereby enhancing the training image set. According to the forward model of CASSI, we can obtain the snapshot measurement with a spatial size of \(256\times 310\), corresponding to each training sample. By performing a reverse dispersion process and backward shift upon the snapshot measurement, we can obtain the reconstruction \(H_{0}\) and take it as the network input. As in [25], 10 scenes are selected from KAIST [26] for testing.  
Furthermore, this paper initialized weights using the method described in [29]. All models were trained for 300 iterations using the Adam [45] optimizer (\(\beta 1=0.9\) and \(\beta 2=0.999\)). We used Adam as the optimizer to train the proposed DualCT-Net, the training was implemented on the PyTorch platform, and CuDNN was used for acceleration. In the network training process, the number of iterations is set to 300, the batch size is set to 5, and the initial learning rate is set to 0.0004, which is halved every 50 epochs.
## 3 Workflow and Validity Analysis of HDE Algorithm
### Center beam approximation error  
The trajectory deviation construction model used in the HDE algorithm, as per Equation (23), assumes center beam approximation. It means that at the same azimuth moment, all targets within the beam irradiation range are constructed according to the trajectory deviation of the beam center target. Such an assumption is based on the following conditions:  
\[\left|\left(\eta+\frac{4\pi}{\lambda}\right)\varphi\big{(}x^{\prime},x_{p},r_{ p}\big{)}+\eta\psi\big{(}x^{\prime},r_{p}\big{)}\right|\ll 1. \tag{37}\]  
Since there is \(\eta\ll\frac{4\pi}{\lambda}\), then Equation (37) requires the following restriction to be satisfied:  
\[\left|\varphi\big{(}x^{\prime},x_{p},r_{p}\big{)}\right|\ll\frac{\lambda}{4 \pi}, \tag{38}\]  
\[\left|\psi\big{(}x^{\prime},r_{p}\big{)}\right|\ll\frac{f}{\Delta f}\frac{ \lambda}{2\pi}. \tag{39}\]  
By substituting the range space-variant trajectory deviation Equation (8) and azimuth space-variant trajectory deviation Equation (7) into conditions Equations (38) and (39), an equivalent substitution of the conditions can be achieved through the limitation of the maximum trajectory deviation \(d_{max}\) according to [33]:  
\[d_{max}\ll\frac{2L^{2}}{\pi\lambda}\cong\frac{L}{\lambda}\frac{L}{2}=\frac{ \text{azimuth resolution}}{\text{azimuth beam width}}, \tag{40}\]  
\[d_{max}\ll\frac{L_{r}}{\lambda}\frac{c}{2\Delta f}=\frac{\text{range resolution}}{\text{range beam width}}, \tag{41}\]  
where \(L=\lambda r_{p}/L_{a}\) is the length of antenna in azimuth, \(L_{r}=\lambda/\theta r_{max}\) is the length of antenna in range, \(\theta r_{max}\) is the beam width in range. These conditions impose limits on the beam width of the SAR.  
In summary, for the typical X-band airborne SAR systems in Table 1, Equation (40) requires that \(d_{max}\ll 66.63\)m, Equation (41) requires that \(d_{max}\ll 12.73\)m. It can be found that the HDE algorithm proposed in this paper is suitable for narrow-beam SAR systems and can construct for large nonlinear-trajectory deviation in jamming modulation.
## 4 Discussion and Conclusions  
With the wider application of GNSS technology in various fields, each navigation satellite system has created a modernization program to upgrade its features. To assess the performance of the next-generation GNSSs, RNSSs, and LEO augmentation constellations, we use the method by [PERSON] et al., which does not require the actual ephemerides, which are not available before the constellation is deployed for full operation. Our analysis showed that the next-generation navigation satellite systems can significantly improve satellite availability and can strengthen geometric distribution. The increased satellite visibility can reduce the blind areas of GNSS services [17], and the better geometrical structure is helpful to reduce initialization periods [44], both of which contribute to high-precision GNSS applications, such as automated driving in urban environments and scientific research in the Arctic region.  
This method can also be used to evaluate the performance of satellite-based augmentation system (SBAS) and other regional systems in development, such as the southern positioning augmentation network (SouthPAN) from Australia and New Zealand.  
Of course, positioning accuracy is not only affected by satellite visibility and DOPs but also by other factors, such as satellite clock stability, which are needed for analysis when the next generation constellations are in full operation.  
In summary, the results of this study will enrich the growing knowledge base of the new generation navigation satellite systems and will provide a basis for constellation selection and design.  
Methodology, H.C.; validation and formal analysis, H.C. and S.Z.; writing--original draft preparation, H.C.; writing--review and editing, H.C. and S.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The National Key Research and Development Program of China, grant number 2016 YFB0501803.  
The authors declare no conflict of interest.
# Intelligent Agents and Causal Inference: Enhancing Decision-Making through Causal Reasoning
## 3 Results and Discussion
### Performance Evaluation of CBOT  
The performance of CBOT was compared to two EBOTs: one utilizing a blind search algorithm (uninformed) and the other an informed search algorithm (A*). Table 3 presents the results from 250 matches in the same testing scenario.  
As expected, the EBOT with the uninformed search algorithm performed poorly compared to others in Figure 7. The CBOT narrowly outperformed the EBOT with A*, primarily because the latter's heuristic calculation included a penalty for being detected while calculating the path, considering it a failure. However, this EBOT's behavior was based on a heuristic calculation that did not consider causal elements.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Agent** & **Logic** & **Successes** & **Failures** & **Success Rate** & **Failure Rate** & **Energy (Success)** & **Energy (Failure)** \\ \hline EBDT-US a & Random search & 75 & 175 & 30 & 70 & 76.39 & 49.5 \\ EBOT-IS b & A* & 198 & 52 & 79.2 & 20.8 & 37.7 & 84.9 \\ CBOT & Causal inference & 205 & 45 & 82 & 18 & 35.4 & 40.8 \\ \hline \hline \end{tabular}  
* EBOT Uninformed Search. b EBOT Informed Search.  
\end{table}
Table 3: Performance evaluation results.  
Figure 6: Effects of the interventions.  
An analysis of the agents' success rate as a function of the GBOT view sensor status is presented in Figure 8. We observe a clear difference between cases in which the view sensor is enabled and cases in which it is disabled. The ability to evade detection is more effective when the enemy agent cannot use its sense of sight to track them when the view sensor is disabled.  
An analysis of the relationship between the energy consumption of the agents and the state of the enemy agent's view sensor is presented in Figure 9. There is no doubt that agents operating in an environment where the view sensor is disabled consume less energy than those operating in environments where the view sensor is enabled. This finding illustrates how the ability to evade visual detection can affect the efficiency with which agents use their energy resources.  
Figure 8: Success rate according to sight sensor.  
Figure 7: Validation results.  
Figure 10 illustrates the relationship between energy consumption and GBOT detection. As can be seen, energy consumption is generally lower in cases in which agents are not detected. This relationship, however, has some outliers that suggest a degree of variation. Accordingly, although there is a correlation between detection and energy consumption, other factors, such as the existence of a listening sensor, could also affect this factor.
## 3 Results  
### Measurements  
The results from the FSS-ES field test at 85 m distance are shown in Tables 6-9. The ping loss is 0%, and the bit error rate (BER) is 10\({}^{-6}\). No BER was obtained, although three carriers were considered in testing the transmitted power (TX) of 5G-BS and were reduced to 100 W. However, the received signal frequency for the new C-band LNB without filter (3.705 GHz), C-band LNB with filters (3836.27 GHz), and extended C-band with several filters (3.705 GHz) were all tested. Also, 5G carrier 1 (full load) and carrier 2 (full download) operated at 100 MHz GB. Carrier 1 (100% load), carrier 2 (100% load), carrier 3 (full download), and the 5G-BS were on at all times. A difference was observed between RF interference (RFI) and RFI\({}^{\text{\text{\textregistered}1}}\), whereas RFI\({}^{\text{\textregistered}1}\) denotes that the link passes but with a high BER.  
Figure 8: Conceptual framework for GRNN topology.