esa-sceva/satcom-chunk-collection huggingface dataset:
# Quaternion Codes in MIMO System of Dual-Polarized Antennas +
## 5 Quasi Quaternion Orthogonal Designs  
Unlike the complex domain, for two dual-polarized antennas there exists a QOD of rate 2 and was shown to attain a decoupled decoder [18]. We employ it  
\[\mathbf{P}_{1}=\begin{bmatrix}z_{1}+z_{2}j&z_{3}+z_{4}j\\ z_{2}^{*}-z_{1}^{*}j&-z_{4}^{*}+z_{3}^{*}j\end{bmatrix}, \tag{22}\]  
to construct a higher rate quasi QOD \(\mathbf{Q}_{quasi}\) as follows. By considering an identical code matrix with different symbols  
\[\mathbf{P}_{2}=\begin{bmatrix}z_{5}+z_{6}j&z_{7}+z_{8}j\\ z_{6}^{*}-z_{5}^{*}j&-z_{8}^{*}+z_{7}^{*}j\end{bmatrix}, \tag{23}\]  
following the same line of approach as used for quasi CODs [16], we obtain a quasi QOD  
\[\mathbf{Q}_{quasi}=\begin{bmatrix}\mathbf{P}_{1}&\mathbf{P}_{2}\\ -\mathbf{P}_{2}^{Q}&\mathbf{P}_{1}^{Q}\end{bmatrix}. \tag{24}\]  
Resultantly, we obtain a quasi QOD to be used in the configuration of 4 dual-polarized antennas capable of sending 8 complex symbols in four time slots yielding a code rate of 2, given by  
\[\mathbf{Q}_{quasi}=\begin{bmatrix}z_{1}+z_{2}j&z_{3}+z_{4}j&z_{5}+z_{6}j&z_{ 7}+z_{8}j\\ z_{2}^{*}-z_{1}^{*}j&-z_{4}^{*}+z_{3}^{*}j&z_{6}^{*}-z_{5}^{*}j&-z_{8}^{*}+z_{7 }^{*}j\\ -z_{5}^{*}-z_{6}j&-z_{6}+z_{5}^{*}j&z_{1}^{*}+z_{2}j&z_{2}-z_{1}^{*}j\\ -z_{7}^{*}-z_{8}j&z_{8}-z_{7}^{*}j&z_{3}^{*}+z_{4}j&-z_{4}+z_{3}^{*}j\end{bmatrix}. \tag{25}\]  
The above code \(\mathbf{Q}_{quasi}\) does not satisfy the main quaternion orthogonality condition as  
\[\mathbf{Q}_{quasi}^{Q}\mathbf{Q}_{quasi}\
eq\lambda\mathbf{I}_{4\times 4}, \tag{26}\]  
where \(\lambda=||z_{1}||^{2}+||z_{2}||^{2}+||z_{3}||^{2}+||z_{4}||^{2}+||z_{5}||^{2}+ ||z_{6}||^{2}+||z_{7}||^{2}+||z_{8}||^{2}\). However, the simulation curve for the above code is obtained in the next section.
# Effect of Local Cyclic Loading on Direct Shear Strength Characteristics of Shear-Zone Soil
###### Abstract  
The reservoir landslide in the Three Gorges Reservoir Area (TGRA) often suffers from local cyclic loading scenarios produced by surge waves, groundwater level fluctuation, traffic loading, and seismic activity. However, the effects of local cyclic loading on the shear resistance of the shear-zone of the reservoir landslide are poorly understood. This study experimentally investigates the effects of local cyclic loading on the shear strength and the deformation of shear-zone material using cyclic direct shear tests. A series of cyclic direct shear tests are performed with different normal stresses, cyclic periods, and numbers of cycles. The experimental results indicate that: (1) Compared with monotonic loading, local cyclic loading can significantly decrease the shear stress of shear-zone soil. (2) Shear-zone soil exhibits greater volumetric contraction under local cyclic loading conditions than that under monotonic loading. (3) Under different vertical stresses, the differentiation of shear deformation after 40 cycles of loading was slight and virtually insignificant. The research results reveal the weakening law of the mechanical strength of shear-zone soil under local cyclic loading, which provides a foundation for investigating the mechanism of the reservoir landslide under the fluctuation of water levels.  
monotonic loading; local cyclic loading; shear-zone soil; cyclic direct shear test Article  
Effect of Local Cyclic Loading on Direct Shear Strength Characteristics of Shear-Zone Soil
# Smart Grid Data Management in a Heterogeneous Environment with a Hybrid Load Forecasting Model
## 3 Related Works
### Challenges of Applications in Power Consumption  
#### 3.3.1 Energy Efficiency Monitoring and Management  
Although smart grid and big data analytics can bring a great revolution to the energy sector, it has challenges and constraints that make its employability a complex endeavor. The most immediate constraint is the overhaul of the conventional infrastructure that would require a high cost [37]. Apart from this, the smart grid and big data analytics have other challenges to their application, owing to complex systems [38]. Smart grids use various smart components that work together to form a system. However, these components working under different environmental conditions is challenging as various devices can become damaged under harsh conditions. This situation makes it more difficult for developing countries to monitor and manage energy efficiency adequately.  
In addition to this, security is one of the most significant concerns of smart grids and big data analytics. Smart grids collect huge volumes of data from their consumers stored in databases and other areas, such as cloud platforms, prone to cyber-attacks [26]. Additionally, smart systems can gather different types of data about the consumers that may also include their private information, and this can undermine their privacy. Additionally, trust is an issue in this regard as consumers may not want to equip their houses with smart devices that continuously store and share their information with others, i.e., power managing authorities [30].
## 4 Parametric Investigation
### Friction Coefficient  
The influence of friction coefficient \(\mu\) on the seismic capacity factor \(\lambda^{*}\) was also examined. For both circular and basket-handle buttressed arches, \(B/R_{c}\) was set to 0.8 and \(H\) was set to 3.0 m. The friction coefficient \(\mu\) varied in a range from 0.1 to 1.0, covering most practical engineering materials. The variations of \(\lambda^{*}\) against the friction coefficient \(\mu\) are illustrated in Figure 19.  
As it is shown, for small friction, i.e., \(\mu\leq 0.5\), \(\lambda^{*}\) increases significantly with the increase of \(\mu\). However, after \(\mu\) reaches some thresholds, \(\lambda^{*}\) becomes constant, i.e., it is barely affected by the friction coefficient. This can be attributed to the change of failure modes. If \(\mu\) is below the threshold, the sliding failure will occur, as shown in Figure 20. Once \(\mu\) is beyond the threshold, no sliding occurs, and the hinge failure mode dominates. Therefore, careful choice of the material regarding the friction angle needs to be conducted with respect to the choice of the geometry of the arch.  
Figure 19: Variation of \(\lambda^{*}\) against \(\mu\): (**a**) circular arch; (**b**) basket-handle arch.
## 4 Discussion  
Numerous methods for the isolation of \({}^{55}\)Fe and \({}^{63}\)Ni have been reported in the literature, including precipitation, solvent extraction, and the use of novel resins such as di-isobutyl ketone-based chromatographic media [13; 14]. However, many of these are complex or require highly specific and costly materials. For example:  
* TRU resins, while commercially available, have limited capacities (only \(\sim\)3 mg of Fe).
* Chelex-100 has been used as a pre-concentrator for Fe but lacks selectivity and often requires additional purification steps such as di-isopropyl ether extraction [7].
* Silica-immobilized formyl salicylic acid has a high Fe loading capacity but, again, lacks Fe specificity [17].  
In contrast, the method described in this study is cost-effective, scalable, and can be applied to multiple low-activity samples simultaneously--a critical requirement for nuclear facility decommissioning workflows.  
After ion-exchange separation, Fe and Ni were collected in HCl fractions, which could not be directly analyzed by LSC due to intense coloration, as shown in Figure 8.  
The coloration stems from the chemical state of the ions: Fe\({}^{3+}\) forms yellow to brown hydrolyzed species, contributing to color quenching; Ni\({}^{2+}\) forms green, often hydrated, complexes such as [Ni(H\({}_{2}\)O)\({}_{6}\)]\({}^{2+}\).  
The experimental results confirmed the strong influence of color quenching on detection efficiency.  
For \({}^{55}\)Fe, doubling the sample mass from 0.125 g to 0.25 g reduced efficiency from \(\sim\)21% to \(\sim\)2%, a tenfold drop, due to the increased concentration of the yellow Fe\({}^{3+}\) complex.  
This increase in color quenching significantly extends the acquisition time required to reach the regulatory MDC, making the choice of dissolution acid a crucial factor.  
To mitigate this effect, samples were redissolved in H\({}_{3}\)PO\({}_{4}\), which forms colorless complexes with Fe\({}^{3+}\) and minimizes quenching [15]. While some earlier studies proposed the use of solvent extraction or reducing agents for decolorizing Fe-containing solutions [6; 7], our approach of using H\({}_{3}\)PO\({}_{4}\) provided a practical and efficient alternative. Although [PERSON] [18] noted the potential of H\({}_{3}\)PO\({}_{4}\) for decolorizing Fe\({}^{3+}\), its application for optimizing \({}^{55}\)Fe quantification in low-activity steel samples remains underexplored.  
For \({}^{63}\)Ni, LSC performance was less affected by color quenching.  
Increasing the sample mass from 0.125 g to 0.25 g only reduced the detection efficiency from 54% to 47%.  
This minor decrease implies that most acids can provide acceptable performances for \({}^{63}\)Ni analysis, as previously indicated in [15].  
Figure 8: HCl fractions containing Fe (**left**) and Ni (**right**) after chemical separation.
# Optimal Dispatch Strategy of Virtual Power Plant for Day-Ahead Market Framework
## 3 Problem Description
### Problem Formulation  
A case study is shown in Figure 3 to reveal the optimal dispatch strategy of the VPP with the grid. It consists of a PV system, wind turbine, fuel cell, micro-turbine and battery energy storage system, which represents the power trading with the grid and electricity market. The control strategies used in the VPP can be categorized into three types such as direct control, distributed control and hierarchical control. The direct control method is based on centralized control where decisions concerning VPP control are made in a centralized way. In the distributed control method, the decisions for VPP control are made in a fully decentralized way, and in the hierarchical control method, which is also the intermediary between direct and distributed control, decisions are made with some extent of the decision-making ability of the VPP. In this paper, the direct control strategy is used for the optimal co-ordination of DERs and to represent them as a single unit for the market, DSOs and TSOs. The VPP control center will take the request from individual VPP units based on the limitations and preferences of the owner [54].  
Figure 3: Power trading with the grid and electricity market.  
Figure 2: Day-ahead market strategy of the VPP.  
The cost of generation indicates the sum of the fuel cost of DGs and start-up or shutdown cost [44]. If the power generated by the sources is not enough to meet the demand, the VPP will purchase the power from the grid. The bidding strategy of the VPP with the grid is developed in this paper with the aid of the beetle antenna search (BAS) algorithm using MATLAB. The VPP output depends on the intensity of the solar irradiation and wind speed that depend on environmental factors. These uncertainties create difficulties for energy dispatch. Therefore, the simulation or modeling of the uncertainty is important. The beta distribution and Weibull distribution methods are used in this work for modeling the uncertainties of renewable generation.
## 5 Simulation Results and Analysis  
In this section, we evaluate the EE of the precoding technique based on the WMMSE algorithm in the CF-mMIMO LEO SATCOM downlink, taking into account the signal distortion introduced by the NPAs.
## 3 Results and Discussion
### Loss of \(H_{2(g/\rm aq)}\) by Bacterial Conversion to CH\({}_{4(g)}\) and \(H_{2}S_{(g)}\)
However, the highest CH4(g) concentrations can be found at the contact area of the reservoir rock and the underlying rock, where additional CO2 is delivered by diffusion of the gas in the underlying rock into the reservoir rock. The 1 DRMT modeling results show an average loss of 3.3 mol H2(g/aq) after 30 years in each cell of the reservoir rock (cells 183-850). Over the same period, on average, 0.2 mol L\({}^{-1}\) of gas CH4(g) and 0.005 mol L\({}^{-1}\) of gas H2S(g) are generated and 0.06 mol L\({}^{-1}\) of gas CO2(g) is consumed, which corresponds to the total amount of CO2(g) that was available as injected and residual gas. The changes in gas composition in the reservoir rock in cell 183, located at the contact with the cap rock, with ongoing time are shown in Figure 2a. The mass of lost H2(g/aq) that is not converted to CH4(g) or H2S(g) is bound in aqueous species (OH\({}^{-}\), CH4, HCO3\({}^{-}\), Al(OH)4\({}^{-}\), CaOH\({}^{+}\), CaHCO3\({}^{+}\), MgOH\({}^{+}\), MgHCO3\({}^{+}\), H2, NH4\({}^{+}\), NH3, H2S, HS\({}^{-}\), H4 SiO4, and H3 SiO4\({}^{-}\)) or in mineral phases (see Section 3.2). Figure 2: Changes in gas composition in the reservoir rock with ongoing time in (**a**) reference scenario and influenced by (**b**) increased storage time of 300 years, (**c**) higher pressure and temperature conditions, (**d**) higher kinetic rate constant for BSR of 2.40 \(\times\) 10\({}^{-7}\) mol kgw\({}^{-1}\) s\({}^{-1}\), (**e**) lower kinetic rate constant for BSR of 9.26 \(\times\) 10\({}^{-9}\) mol kgw\({}^{-1}\) s\({}^{-1}\), (**f**) higher kinetic rate constant for methanogenesis of 2.30 \(\times\) 10\({}^{-6}\) mol kgw\({}^{-1}\) s\({}^{-1}\), (**g**) lower kinetic rate constant for methanogenesis of 2.30 \(\times\) 10\({}^{-12}\) mol kgw\({}^{-1}\) s\({}^{-1}\), (**h**) varied stored gas composition (without CO2(g)). The competition between methanogenic microorganisms and sulfate-reducing bacteria is widely discussed in the literature [53; 54; 55; 56] and is not the object of this study. However, in PHREQC, BSR occurs prior to methanogenesis. This reaction order of H2(g/aq) with SO4\({}^{2-}\)(aq) (BSR) and CO2(aq) (methanogenesis) is tested in a separate batch model. It includes H2O(aq), CO2(aq), and SO4\({}^{2-}\)(aq) (in equal amounts) and a sufficient amount of hydrogen. H2(g/aq) reacts primarily with SO4\({}^{2-}\)(aq) to form S\({}^{2-}\)(aq) until the total amount of the initial SO4\({}^{2-}\)(aq) is consumed. This is followed by the reaction of H2(g/aq) with CO2(aq) to CH4(aq). Thereby, the equilibrium constant (log \(K\)) is the crucial value. In a separate model, the log \(K\) value for SO4\({}^{2-}\) (33.65; (6)) is reduced to below the log \(K\) value of CO2 (16.861; (7); data from phreeqc.dat). In this case, the methanogenesis takes place before BSR. \[\mathrm{SO_{4}}^{2-}\mathrm{(aq)}+9\mathrm{H^{+}(aq)}+8\mathrm{e^{-}}=\mathrm{ HS^{-}(aq)}+4\mathrm{H_{2}O} \tag{6}\]  
\[\mathrm{CO_{3}}^{2-}\mathrm{(aq)}+2\mathrm{H^{+}(aq)}=\mathrm{CO_{2(aq)}}+ \mathrm{H_{2}O} \tag{7}\]
# Fake Base Station Detection and Link Routing Defense +
## 7 Implementation and Experimental Results
### Detection Accuracy Performance against Fake Base Station
Because we already achieved a zero false-negative rate, i.e., all the fake base station experimental samples are detected, our threshold selections achieve optimality by yielding a zero false-positive rate. In Figure 7a, the optimal \(\tau\) ratio is equal to 1.09 if \(\tau_{T}\) ratio = \(\tau_{N}\)-ratio, i.e., \(\tau_{T}\) is equal to 897 ms while the optimal \(\tau_{N}\) is equal to 66. Figure 7b,c vary the two threshold parameters unilaterally without the constraint of \(\tau_{T}\) ratio = \(\tau_{N}\) ratio, i.e., we fix one threshold while varying the other. The fixed values are the optimal values in Figure 7a when we jointly vary the detection threshold parameters, i.e., \(\tau_{T}\) = 897 ms and \(\tau_{N}\) = 66. Figure 7b varies \(\tau_{N}\) while fixing \(\tau_{T}\) = 897 ms. Our scheme variant using \(T\) only has a constant error performance value because that scheme only uses \(T\) compared to \(\tau_{T}\) = 897 ms; \(N\) is not used, and varying \(\tau_{N}\) has no effect on this scheme variant. Because \(\tau_{T}\) = 897 ms, our scheme using both \(T\) and \(N\) provides better results than jointly varying \(\tau_{T}\) and \(\tau_{N}\) when \(\tau_{N}\) is less than \(\tau_{N,0}\) = 60. Fixing \(\tau_{T}\) = 897 ms, the optimal \(\tau_{N}\) is equal to 66. When varying \(\tau_{T}\) while fixing \(\tau_{N}\) = 66 in Figure 7c, the optimal \(\tau_{T}\) is equal to 840 ms. This optimal \(\tau_{T}\) is different from and lower than 897, the optimal \(\tau_{T}\) threshold value when jointly varying the detection thresholds with the constraint of having equal \(\tau_{T}\) ratio and \(\tau_{N}\) ratio in Figure 7a. Based on these results, our scheme uses \(\tau_{T}\) = 897 ms and \(\tau_{N}\) = 66 to provide zero errors in our experiment involving legitimate, faulty but legitimate, and fake base stations.
## 3 EKF-GAPF-Based Method for Multipath Parameter Estimation
### Preliminary Identification of Parameters  
The EKF is utilized to provide a reasonable suggested density for PF. The basic steps of EKF include initialization and updating of predictions and measurements. The implementation process of multipath estimation based on EKF is as follows:  
* Initialization: When \(k=0\), the initial state estimate is \(x_{0\,|\,0}\), the initial error covariance matrix is \(P_{0\,|\,0}\), the process noise covariance is \(Q\), and the measurement noise covariance is \(R\).
* Prediction update:  
Figure 3: Flowchart for implementing multipath estimation.
# Integrated Robotic and Network Simulation Method
## 4 A Case-Study Illustrating the Use of the IRoNS Method
### Choosing Solutions
#### 4.2.1 Average Rendezvous  
This is one of the simplest decentralized rendezvous techniques in the literature [37], in which the reference point for each robot is determined as the average value of its neighbor's positions. At each time step \(k\), each robot uses the received information to drive toward the updated average point (5), where \(X_{\mathit{inf}}\) is the position vector reference for robot \(i\), \(X_{j}\) is the positions vector received from neighbor robot \(j\), \(a_{ij}\) is adjacency matrix element that indicates whether there is a communication link between robots \(i\) and \(j\), and \(j\) is an index of _robot_\(i\)'s \(n_{i}\) neighbors.  
\[X_{\mathit{inf}}(k+1)=\frac{\sum_{j=1}^{n_{i}}X_{j}(k)\cdot a_{ij}}{n_{i}} \tag{5}\]
# An Image Processing Method for Extraction of the Stress Wave Reflection Period
## 2 Algorithm for the Automatic Extraction of the Reflection Period Based on Image Processing
### Step 7. Extraction of the Primary Frequency  
This step entails performing FFT on the correlated signal to obtain frequency domain information. Finally, the main frequency of the signal, or the frequency corresponding to the maximum amplitude in the spectrum (except for the zero frequency), is found.
# PTRNet: Global Feature and Local Feature Encoding for Point Cloud Registration
## 3 Experiments  
To verify the effectiveness of our PTRNet model, we conducted comparative experiments on the ModelNet40 dataset [33], which comes from the ModelNet dataset [33] and contains 12,311 meshed CAD models in 40 categories. It is a large dataset released in recent years and is widely used in point cloud processing tasks [16; 21; 25; 34]. Following the experimental setup of PCRNet [21], we randomly selected 5070 transformations with Euler angles in the range [\(-\)45, 45] and translation values in the range [\(-\)1, 1] units. We sampled 1024 points uniformly from the outer surface of each model, with no other features in the input other than (\(x\), \(y\), \(z\)) coordinates.  
We used the mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) between the true and predicted values as metrics. Ideally, all of these error indicators should be zero if the source and template point clouds are perfectly registered. All angle measurements in the results are in degrees. We conducted our experiments on a computer using an Intel Xeon E5-2600 processor running Linux 18.4. We trained all networks to 300 epochs using a batch size of 1 for DCP_V2_MLP and 10 for all others.  
We compared the performance of PTRNet with other recent end-to-end registration methods based on deep learning, iterative PCRNet [21] and DCP_V2_MLP [15], as well as the manual matching algorithm ICP [1]. In addition, we performed ablation experiments called Model-V1 and Model-V2 using Model-V1 to replace offset-attention with self-attention in PTRNet while leaving other parts unchanged. Model-V2 does not add local features to global features; it makes no other changes.
# Analysis of Synchronous Generators' Local Mode Eigenvalues in Modern Power Systems
## 3 Local Mode Analysis of Synchronous Generators without AVR
### Influence of Synchronous Generator's Operating Point on Local Mode Eigenvalues  
#### 3.2.1 Hydro-Type Synchronous Generators  
The influence of the operating point on the synchronous generator's local oscillations can be evaluated by eigenvalue loci analysis. Hydro-type synchronous generators of nominal powers in the range of 9 MVA to 615 MVA, with data in Appendix A, Tables A1-A4, were analyzed first. Dominant conjugate complex eigenvalues of the system matrix of the H-P model (1) were calculated for different operating points. Figures 1-4 show the local mode eigenvalue loci as a function of operating points for synchronous generators with nominal powers 9 MVA, 12 MVA, 158 MVA, and 615 MVA. Active power ranged from 0.0 pu to 1.2 pu at constant reactive power. The calculations were performed at reactive power values from 0.0 pu to 1.2 pu in steps of 0.1 pu. The individual curves correspond to a constant reactive power. Local mode eigenvalues at nominal power \(P=1\) pu are marked in the figures with squares.  
Figure 1: Eigenvalue loci of local mode of 9-MVA hydro-type synchronous generator without AVR.  
Figure 3: Eigenvalue loci of local mode of 158-MVA hydro-type synchronous generator without AVR.  
Figure 2: Eigenvalue loci of local mode of 12-MVA hydro-type synchronous generator without AVR.  
Eigenvalue analysis was carried out for 54 hydro-generators (the figures represent only four of them). It was an interesting discovery that all eigenvalue loci had similar courses. From the obtained loci, four basic conclusions were obtained:  
* An increase in active power results in an increase in local mode damping (decrease in eigenvalues' real part \(\sigma\)).
* An increase in reactive power results in an increase in local mode damped frequency (increase in eigenvalues' imaginary part \(\omega\)),
* Stability high-problematic situations could be operating points with high values of reactive power and low values of active power, which have minimal damping.
* Stability low-problematic situations could be operating points with low values of reactive power and high values of active power, which have maximal damping.