esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### AnFIS-PSO  
The Matlab codes for running ANFIS-PSO were obtained from [28]. Particle swarm optimization is one of the widely known evolutionary or metaheuristic algorithms that are used to solve complex non-linear problems. It mimics the swarming behavior of creatures (insects, animals, or birds). A swarm of particles is initially randomly generated. In the iterative search for the optimum solution, each particle updates its position based on its prior experience and the experience of its neighbors. The velocity with which the particle flies the search space is also consequently updated until the optimal or global best solution is found. The details of PSO can be found in [31; 35]. For the purpose of this study, the following parameters were used: inertia weight, w = 1, inertia weight damping ratio, wdamp = 0.99, personal learning coefficient, c1 = 1, global learning coefficient, c2 = 2, number of rules = 10, maximum number of iterations = 1000, and the population size or swarm size, nPop = 25. The corresponding ANFIS-PSO results are shown in Figures 12 and 13.  
Figure 12: ANFIS-PSO data plots for training. The red line represents the different stages of the ANFIS-PSO training.  
It is clear from Figures 12 and 13 that the ANFIS-PSO model outputs track very closely the expected or target data for both training and testing. For more insight, the regression plots are displayed in Figure 14.  
It can be observed from Figure 14 that the ANFIS-PSO model performed well for training and testing, with an overall coefficient of correlation, \(R^{2}\) = 0.9994. This performance metric is slightly higher than the one obtained with the ANFIS model, but the computation time was longer.  
Figure 13: ANFIS-PSO data plots for testing. The red line represents the different stages of the ANFIS-PSO training and testing.
# Empowering Adaptive Geolocation-Based Routing for UAV Networks with Reinforcement Learning
## 4 Adaptive GLAN Using Reinforcement Learning
### System Environment
#### 4.3.3 Reward  
The rewards used for learning are as follows:  
\[R(t)=\left\{\begin{array}{l l}R_{link}&\text{if node link is made}\\ R_{AGLAN,t}=H_{time}*R_{arrived}+H_{time}*R_{timeout}&\end{array}\right. \tag{6}\]  
Each episode learns according to the changed position due to the mobility characteristics of the UAV. First, if the data successfully arrive at their destination, a large reward is given. Conversely, if they do not arrive, it is determined that the learned reward value is not suitable for learning and a negative reward is given accordingly. In addition, negative compensation is given when a network link between UAVs is created to reduce the network load. AGLAN's reward is calculated as the weighted product of the hyperparameter \(H_{time}\) and the time component.
# Exploiting Lunar Navigation Constellation for GNC Enhancement in Landing Missions
## 2 Simulation Environment  
The simulation environment used to performed the analyses of this study is described in the following section. In particular, we first present the dynamic environment in which the serviers and the lander are propagated, together with the associated assumptions. Then, we provide an overview of the sensor models used in the proposed navigation strategy.
###### Abstract  
Traffic speed prediction plays a significant role in the intelligent traffic system (ITS). However, due to the complex spatial-temporal correlations of traffic data, it is very challenging to predict traffic speed timely and accurately. The traffic speed renders not only short-term neighboring and multiple long-term periodic dependencies in the temporal dimension but also local and global dependencies in the spatial dimension. To address this problem, we propose a novel deep-learning-based model, Global Spatial-Temporal Graph Convolutional Network (GSTGCN), for urban traffic speed prediction. The model consists of three spatial-temporal components with the same structure and an external component. The three spatial-temporal components are used to model the recent, daily-periodic, and weekly-periodic spatial-temporal correlations of the traffic data, respectively. More specifically, each spatial-temporal component consists of a dynamic temporal module and a global correlated spatial module. The former contains multiple residual blocks which are stacked by dilated casual convolutions, while the latter contains a localized graph convolution and a global correlated mechanism. The external component is used to extract the effect of external factors, such as holidays and weather conditions, on the traffic speed. Experimental results on two real-world traffic datasets have demonstrated that the proposed GSTGCN outperforms the state-of-the-art baselines.  
spatial-temporal dependencies; traffic periodicity; graph convolutional network; traffic speed prediction 2020
## 4 Hybrid Photonic/Millimetre-Wave System and Simulation Details
### Waveform Generation and Reception  
Multi-carrier IFFT based OFDM generation is performed broadly in line with the 5G NR (3 GPP Rel. 15) specifications for mm-wave communications. Overall, 800 active subcarriers modulated with 32-QAM and with a baud rate/frequency spacing of 244 kHz, are used to generate the \(\sim\)1 Gb/s OFDM signal. The first OFDM symbol is designated as a training symbol (TS). A cyclic prefix (CP) of 6.25% of the signal length is added after IFFT processing. Following parallel-to-serial operation, I/Q up-conversion of the baseband OFDM signal to 2 GHz, results in the real-valued IF OFDM spectrum are shown in Figure 3a. This transmit sequence is then over-sampled to match the global simulation sample rate.  
At the IF OFDM demodulator, the IF signal produced at the output of the receivers described in Section 4.1 is re-sampled and digitally down-converted to baseband. CP removal precedes receiver FFT de-modulation. Channel estimation is then performed through comparison of the transmitted and received TS--facilitating single-tap frequency domain equalization (FDE). Finally, EVM is calculated for all received QAM symbols.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**S.No.** & **Property** & **Value** \\ \hline
1. & mmWave Carrier Frequency & 60 GHz \\
2. & Intermediate Frequency & 2 GHz \\
3. & Optical Linewidth & Variable \\
4. & RIN & \(-\)140 dB/Hz \\
5. & Carrier Frequency Offset & 0 Hz \\
6. & System Sample Rate & 200 GSa/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters.  
The 32-APSK ideal signal constellation is shown in Figure 3b. The 32 constellation points are arranged in three concentric circles which, in this case, are equidistant. Then, 32-APSK encoding of a random binary stream is performed at 200 MBaud, resulting in a total spectral bandwidth of 400 MHz (3-dB bandwidth \(=\) 200 MHz). I/Q up-conversion of the time domain signal results in the APSK spectrum shown in Figure 3c. At the receiver, the single-carrier IF APSK signal is re-sampled and down-converted through a digital I/Q mixer. Using the ideal 32-APSK constellation (Figure 3b) as a reference plane, a blind decision directed least mean squares (DD-LMS) algorithm is optionally implemented for phase estimation and correction of the received constellation.  
Table 2 lists the main waveform parameters of interest for both APSK and OFDM signals.
# Multi-Frame Blind Super-Resolution Based on Joint Motion Estimation and Blur Kernel Estimation
## 4 Proposed Multi-Frame Blind Super-Resolution Algorithm
### Motion Estimation  
\(\mathbf{K}\) and \(\mathbf{x}\) are fixed in this motion estimation step, the motion matrix \(\mathbf{F}\) can be computed as  
\[\cdot\mathbf{F}=\underset{\mathbf{F}}{\text{argmax}}p(\mathbf{y_{1}},\mathbf{y_{2}},\ldots, \mathbf{y_{N}}|\mathbf{K},\mathbf{F},\mathbf{x})p(\mathbf{F}), \tag{9}\]  
We adopt the affine transformation model described by the parameter \(\mathbf{\varphi_{n}}=\big{(}a_{1},a_{2},a_{3},a_{4},t_{x},t_{y}\big{)}^{T}\) with the translation \(\mathbf{t}=(t_{x},t_{y})^{T}\) and the parameter \(a_{i}(i=1,2,3,4)\), which represents the rotation, scaling and other changes for the \(nth\) LR image \(\mathbf{y_{n}}\).  
In object motion, the Jacobian matrix is an important part of instantaneous motion. In order to better describe the sub-pixel motion model \(\mathbf{\varphi_{n}}\), we introduce the Jacobian matrix \(\mathbf{J}(\mathbf{\varphi_{n}})\) of motion parameters into the \(nth\) LR image. \(\mathbf{J}(\mathbf{\varphi_{n}})\) can be expounded as  
\[\mathbf{J}(\mathbf{\varphi_{n}})=\mathbf{D}\mathbf{K_{n}}\frac{\partial\mathbf{F_{n}}}{\partial \mathbf{\varphi_{n}}}, \tag{10}\]  
The derivative of \(\mathbf{F_{n}}\) w. r. t. \(\mathbf{\varphi_{n}}\) is computed using the bilinear interpolation [27].  
The motion matrix \(\mathbf{F}_{n}\) describe the sub-pixel motion of the \(nth\) LR image according to the motion parameter \(\mathbf{\varphi}_{n}\) relative to \(\mathbf{x}\). \(\mathbf{F}_{n}\) can be expressed as  
\[\dot{\mathbf{F}_{n}}=\underset{\mathbf{F}_{n}}{argmin}\cdot\parallel\mathbf{J}(\mathbf{\varphi} _{n})^{T}\mathbf{\beta}_{n}\mathbf{J}(\mathbf{\varphi}_{n})-\mathbf{J}(\mathbf{\varphi}_{n})^{T}\bm {\beta}_{n}(\mathbf{D}\mathbf{K}_{n}\mathbf{F}_{n}\mathbf{x}-\mathbf{y}_{n})\parallel_{2}^{2}, \tag{11}\]  
where \(\mathbf{\beta}_{n}\), which is the confidence weight of the \(nth\) LR image, is calculated in the image reconstruction stage. \(\mathbf{J}(\mathbf{\varphi}_{n})\) is the Jacobian matrix of the \(nth\) LR image. We use the Conjugate Gradient (CG) method to work out the Equation (11) in this paper.
## 1 Introduction
However, until now, no review paper is available, which has discussed in detailed mix proportion of different types of GPC. This paper presents the inclusive investigation of GPC and GPM along with providing critical review on different mix design procedures used for any binder-based GPC with highlighted input and output variables considered for mix design procedure. Moreover, this paper focusses on recently published research on the mechanical, durable, and microstructural properties of GPC and GPM. Figure 1 shows the papers analyzed in this review paper published in the different time regimes. On the basis of critical review, this study proposed the factors having the highest and lowest impacts on both fresh and hardened properties of GPC. In addition, as per authors knowledge, for the first time, the nanomaterial-based GPC, 3D printing using GPC, reinforced GPC and Global warming potential (GWP) assessment of GPC are reviewed critically. In this article, a comprehensive review on current investigation of GPC is presented (Figure 1). A detailed explanation of GPC, including its constituents, activators, aluminosilicate precursor, mix design, and the geopolymerization process is presented in Section 2. In Sections 3 and 4, fresh and hardened properties of GPC as well as the factors influencing these properties are discussed in detail. Flow charts have been proposed to show which factors have higher/lower impact on the fresh and hardened properties of GPC. In Section 5, factors affecting properties of GPC are discussed and their respective flow charts are proposed that represents the effect of properties on mechanical behavior of GPC from fresh to hardened state. The effect of the addition of OPC in GPC is elaborated in Section 6. A critical review on reinforced GPC and application of GPC are presented in Sections 7 and 8, respectively. The recent advances in geopolymer technology such as 3D printing, GWP assessment and nanoparticle-based GPC are added in Section 9. Finally, the conclusions and research gaps in GPC are highlighted in Section 10.
# Review of Machining Equipment Reliability Analysis Methods based on Condition Monitoring Technology
## 3 Failure Physical Analysis for Signal Selection
### The Relevance Analysis between Failure Mechanism and Output Signal
#### 3.1.2 Breakage  
Tool breakage typically includes crack formation, expansion, instability, or impact fracture processes. For the monitoring method of tool breakage, many scholars use cutting force [53], machine motor current [54], power [25], vibration [55] and acoustic emission [56] monitoring signals. Among these sensing signals, Acoustic Emission (AE) signals have received extensive attention due to their high sensitivity, anti-interference and easy installation.  
Two characteristics of the AE signal can be used to determine whether a crack is formed, as shown in Figure 4. The first is the stepwise change of the AE signal count rate when the tool state is gradually worn to severe wear. The second is the sudden AE signal caused by the fracture of the tool material, which has a strong energy. Its amplitude is generally high.  
Figure 3: Relationship between external load, output signal, and damage.
## 4 Results
### Application of the WoE-RF Model  
Similarly, the calculated results of the WoE model can be used as input for the RF model. In this study, training of the RF model was implemented by WEKA software. During the analyzing process, the importance of various conditioning factors can be measured quantitatively and ordered by MDA (mean decrease accuracy) and MDG (mean decrease Gini). Generally, MDA is determined during the Out-Of-Bag error calculation phase, while MDG is a measure of how each variable contributes to the homogeneity of the nodes and leaves [104]. The values of the above-mentioned two metrics of the conditioning factors are illustrated in Figure 6, and a larger value of MDA or MDG means a higher importance of the corresponding variable. Accordingly, in terms of MDA, land use is the most critical factor in the RF model, while soil is second in importance only to land use. For the MDG, the importance of elevation was first, followed by rainfall and land use. Finally, based on ArcGIS software, the landslide susceptibility map using the WoE-RF was generated and is shown in Figure 7.
# A Novel Nonlinear Magnetic Equivalent Circuit Model for Magnetic Flux Leakage System
## 3 Designing MEC Model for MFL System  
In the MEC model, permeances are divided into two main types: air and magnetic. The designed MEC model, comprising both air and magnetic permeances, is illustrated in Figure 5a. This model corresponds to the flux paths depicted in Figure 4. It includes permeances for the yoke (\(P_{yoke}\)), the pole region (\(P_{ub}\), \(P_{lb}\), and \(P_{mb}\)), and the specimen (\(P_{sp}\)) as its magnetic elements. Air permeances account for both leakage flux types and flux passing through the air gap. Specifically, they include the winding leakage flux permeance (\(P_{all}\)), upper and lower part permeances of the horizontal leakage flux (\(P_{hl1}\) and \(P_{hl2}\)), and the air gap permeance (\(P_{gap}\)). Figure 5b shows the dimensional parameters of the MFL system. Also, descriptions of these parameters are given in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Symbol** & **Description** \\ \hline \(w_{s}\) & Length of the slot \\ \(l_{g}\) & Length of the air gap \\ \(w_{e}\) & Width of the yoke \\ \(w_{m}\) & Thickness of the specimen \\ \(d_{w}\) & Width of the windings \\ \(w_{w}\) & Height of the windings and yoke \\ \(l_{c}\) & Depth of the yoke and specimen \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the dimensional parameters.  
Figure 4: The defined flux paths to the MFL system.  
Figure 5: (**a**) Designed MEC model. (**b**) Dimensional parameters.
# Molecular Population Genetics of Aspen Mosaic-Associated Virus in Finland and Sweden
## 3 Results and Discussion
### Genetic Diversity and Phylogeny of AsMaV Isolates Based on RNA3
geographical origin with the exception of three isolates E58213 and E58227 from Sweden and E58491 from Finland based on the constructed phylogenetic trees obtained from RNA3. Interestingly, a robust correlation was observed between restrictotypes and phylogenetic groups, whereas no significant correlation was found between the geographical origin of the isolates and restrictotypes. Notably, restrictotypes I and II clustered together in the phylogenetic group R3-G1, restrictotypes III and IV formed a cohesive group in R3-G2, restrictotype V exhibited a close affinity with the Finnish isolates of restrictotype VII in R3-G3, and restrictotype VI demonstrated a close association with the Swedish isolates of restrictotype VII in R3-G4 (Figure 2 and Table 1). Figure 2: Consensus maximum parsimony trees constructed based on the nucleotide sequence of RNA3 (**A**), the open reading frame at nucleotide level 3 (**B**) as well as amino acid sequence of NP  
protein (**C**). Nodes with bootstrap support of less than 70% collapsed. The RNA3 sequences of pigeonpea sterility mosaic virus 2 (PPSMV-2) (_Emaravirus toordali_) (NC_030661), fig mosaic virus (FMV) (_Emaravirus fici_) (NC_029563), and pistacia virus B (PiVB) (_Emaravirus pistaciae_) (MH727574) were used as out-groups in the analyses. The NP protein sequences of PPSMV-2 (YP_009268864), FMV (YP_009237270), and PiVB (QAR18004) were used as out-groups in the amino acid-based tree of NP protein. The geographical origin and accession number of each isolate are presented next to the isolate name, as well as in Supplementary Table S3. Isolates from Sweden and Finland are shown in red and black colors, respectively, and the out-groups are in blue.
## 4 Results  
### Artificial Male Example  
Recall from Section 1 that the data in our artificial data example were first obtained by acquiring a hyperspectral image of a skin mole and then modifying it manually in order to introduce a small  
Figure 3: (**a**) The HSI setup with the Hyperspectral camera. © NOFIMA, Norway. (**b**) The HSI linear push-broom array. © [[https://commons.wikimedia.org/wiki/User.Arbeck](https://commons.wikimedia.org/wiki/User.Arbeck)]([https://commons.wikimedia.org/wiki/User.Arbeck](https://commons.wikimedia.org/wiki/User.Arbeck)).  
distortion that could simulate a change in the mole itself. The HSI system used for the mole example differs from the one described in Section 3 and a detailed description can be found in [14]. In our analysis, we compared the proposed novel technique to the method of [PERSON] et al. described in [7]. When applied in the present context, this method uses a two-sample test to decide if the test sample distribution differs from the distribution under the null hypothesis. We also experimented with principal component analysis (PCA, e.g., [15]). By examining the scatter plots of the most important principal component directions we concluded that PCA is unable to detect the distortion in the data as seen in Figure 4.  
Because of this, we focus on a comparison between the new methodology and the one described in [7]. In general, the method in [7] performed poorly in this challenging situation. This was also reflected through the false positive rate (FPR) reported in Figure 5.  
Figure 4: Plot of the first two principal components (PC) of the original mole spectral signatures (blue) and the spectral signatures of the distorted data (red) (see Figure 1b). Note the complete overlap of the two data sets, save for a small change at around (PC1, PC2) = (5.7, \(-\)2.2).  
Figure 5: (**a**) The false positive rate (FPR), performing a leave-one-out test and using Bonferroni correction to account for multiple testing with the method described in [7]. (**b**) FPR obtained, performing a leave-one-out test and the Hannig-Marron global rejection threshold with the novel method proposed in this article.  
The FPR results obtained by the scale-space methodology for the range are quite impressive. However, it should be noted that we were not able to get similar results for the mean, the median or the standard deviation. This is to be expected because the range is clearly best suited for detecting the type of distortion we introduced to the mole data reported in Figure 1. Still, while the range is the natural parameter to use in this situation, it is also clear that range based inference can be very sensitive to outliers, should the data include them.
# Orbit Determination of Korean GEO Satellite Using Single SLR Sensor
## 5 Conclusions  
In this study, orbit determination (OD) of geostationary Earth orbit (GEO) satellite was performed for strategy development of OD of Korean GK-2B satellite under the limited SLR observations from a single satellite laser ranging (SLR) station. As Geochang station is the only domestic SLR station that can obtain SLR data for GEO satellites, SLR observation data for GK-2B from this station have been generated. Then, the OD was conducted by sequential filtering followed by smoothing process repeatedly for a week-long arc to yield 43.46 m and 24.01 m in 3D RMS errors for the sparse and dense case, respectively. As a validation of the proposed OD strategy, actual SLR observations of Compass-G1 by Changchun SLR station were utilized. The associated orbit overlap errors compared with Compass-G1 final orbit from GFZ AC were 3.747 m, 22.375 m, and 22.406 m, in radial, in-track, and cross-track directions, respectively, and the 3D RMS error was 31.89 m. These OD results imply that the orbit accuracy can remain within 50 m when more than nine SLR observations are available for a single SLR station. Given that the radar-based OD achieved three-sigma position accuracy on the order of 1.5 km [5], and the optical-tracking-based OD precision for GEO satellite was about km-level [6], SLR can be an alternative observation for enhancing OD of GEO satellites.  
In contrast to previous requirements on the OD of GEO, precise OD of GEO satellites becomes more important with the increasing number of Regional Navigation Satellite Systems (RNSSs), because the orbit requirement of navigation satellites is relatively tight compared with that of general GEO satellites for communication/ground monitoring purpose. Because GEO satellites are always visible in the specific ground region, many Regional Navigation Satellite System (RNSS), such as Chinese BeiDou Navigation System (BDS), Indian Regional Navigation Satellite System (IRNSS), and Japanese Quasi-Zenith Satellite System (QZSS), include GEO satellites in their orbit constellation. Korea also plans to construct its own domestic RNSS in the future, and three GEO satellites will be in constellation. SLR is expected to play a more critical role in OD of these GEO navigation satellites and the OD strategy developed in this article is applicable.  
Conceptualization, E.P. and H.-C.L.; Formal analysis, H.O., E.P. and C.P.; Funding acquisition, H.-C.L.; Investigation, H.O. and E.P.; Methodology, H.O., E.P. and C.P.; Project administration, H.-C.L.; Software, H.O.; Supervision, E.P., H.-C.L. and C.P.; Validation, H.O.; Writing--Original draft, H.O.; Writing--Review & editing, E.P and C.P.  
This research was supported by the Geostationary Earth Orbit Korea Multi-Purpose Satellite Development program (Project No. 2011-0030431) supervised by the Ministry of Science and ICT.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Development and Design Process of the Control System Hardware
#### 2.2.4 Angle Sensor  
In the running process of the control system, the operating angle needs to be collected to determine the equipment's operating position. The angle sensor of the CPM machine was used. The sensor transforms the angle values into voltage variations. By inputting the voltage value of the sensor into the ADC unit of the STM32, it is possible to calculate the current operating angle. When the EMG-controlled CPM device is running, it needs to obtain the angle data through the angle sensor.  
The value of the angle sensor can be recalculated through a formula in the program:  
\[\text{Angle}=(\text{ADC Value}/4096\times 3.32-0.64)/0.021 \tag{1}\]  
where 4096 is the ADC resolution, 3.32 is the reference voltage, 0.64 is the voltage value when the angle is 0 degrees, and 0.021 is the voltage value for a one-degree angle.