esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The 3D Arbitrary Baseline Algorithm
### The Virtual Elements Based on Virtual Baseline  
The virtual elements are constructed by the actual elements. Essentially, the virtual elements does not exist, which are constructed by the minus between different phase differences. The idea of virtual elements is that more information of the actual elements receive can be used for direction finding. The elements can be arranged more flexibility. If the length of the virtual baseline is short, the number of ambiguous values will be less than that of using actual baseline. Then the computational complexity of the rotation comparison method (It will be introduced in Section 4.3.) can reduce a lot.  
In order to explain how to construct the virtual element in a simple way, we assume that the antennas E and F are placed in X-axis and Y-axis respectively. M is another antenna. The virtual element G and H constructed by E and F are shown in Figure 7. According to the principle of the arbitrary algorithm, the phase differences of M, F and E, F can be written as:  
\[\phi_{\mathrm{FE}}+2k_{4}\pi=\frac{2\pi}{\lambda}[(x_{\mathrm{E}}-x_{\mathrm{ F}})\cdot\cos\beta\cdot\cos\alpha+(y_{\mathrm{E}}-y_{\mathrm{F}})\cdot\cos \beta\cdot\sin\alpha] \tag{18}\]  
\[\phi_{MF}+2k_{5}\pi=\frac{2\pi}{\lambda}[(x_{\mathrm{F}}-x_{\mathrm{M}})\cdot \cos\beta\cdot\cos\alpha+(y_{\mathrm{F}}-y_{\mathrm{M}})\cdot\cos\beta\cdot \sin\alpha] \tag{19}\]  
Subtract Equation (19) from (18) from the left and right simultaneously, the phase difference between G and N is expressed as:  
\[\begin{array}{c}\phi_{\mathrm{GN}}=\phi_{\mathrm{FE}}-\phi_{\mathrm{MF}}+2k_ {6}\pi\\ \qquad=\frac{2\pi}{\lambda}[(x_{\mathrm{E}}+x_{\mathrm{M}}-2x_{\mathrm{F}}) \cdot\cos\beta\cdot\cos\alpha+(y_{\mathrm{E}}+y_{\mathrm{M}}-2y_{\mathrm{F}}) \cdot\cos\beta\cdot\sin\alpha]\end{array} \tag{20}\]  
Equation (20) can be used instead of any two of Equations (11), (12) and (13) to solve Equation (15). Based on the virtual baseline method, all the information received by the elements should be used sufficiently.
## Materials and Methods
### Design and Fabrication of Scaffolds  
A computer-aided-design (CAD) model of a scaffold, with a cuboid shape of 7 \(\times\) 7 \(\times\) 5 mm, was generated using Magics EnvisionTEC (V13, Materialise, Leuven, Belgium), which was then sliced into 31 layers with the Bioplotter RP software (V2.9, EnvisionTEC GmbH, Gladbeck, Germany). The slice thickness was considered as 88% of the strand diameter. VisualMachine software (BP, V2.2, EnvisionTEC GmbH, Gladbeck, Germany) was utilized to control the printing and assign the print parameters for the model (Figure 2). A perpendicular pattern with alternating angles of 0\({}^{\circ}\) and 90\({}^{\circ}\) was used between the two adjacent layers, each layer consisting of strands with a distance of 1 mm. A 3D-BioplotterTM system (EnvisionTEC GmbH, Gladbeck, Germany) was used to fabricate scaffolds by printing alginate solution into the 50 mM CaCl\({}_{2}\) solution to induce crosslinking layer-by-layer. Specifically, the 3% alginate solution was maintained at 10 \({}^{\circ}\)C for 10 min in a low-temperature dispensing head. Alginate was dispensed at 18-20 \({}^{\circ}\)C using a conical needle with the inner diameter of 200 \(\upmu\)m. The scaffolds were printed in a 12-well tissue culture plate coated with PEI, with each well containing 1 mL of 50 mM CaCl\({}_{2}\) to crosslink alginate immediately after dispensing. The pressure was set at 0.2 bar and head speed of 8 mm/s selected during printing. Printing conditions are presentedin Table 1. After fabrication, scaffolds were maintained in the crosslinking solution for a time period sufficient to allow the Ca\({}^{2+}\) ions to penetrate and crosslink the whole structure.  
For assessing the elastic modulus of bulk gel, bulks of alginate were also created on the 3D-BioplotterTM system by employing the procedure and printing conditions similar to the above scaffold fabrication except the zero distance set between two adjacent strands.
# Improving Geotechnical Properties of Expansive Subgrade Using Sugar Cane Molasses and Cement
## 2 Materials
### Compaction Test  
Proctor compaction tests were conducted on both untreated and treated soil in accordance with the guidelines set by ASTM D-698 [17] to ascertain the maximum dry density (MDD) and optimum moisture content (OMC) for each soil specimen. This procedure entails compacting soil samples at a specified moisture level within a 4-inch-diameter mold using standard compaction energy. Initially, the soil underwent air drying and was subsequently divided into 4 to 6 samples, with the moisture content of each sample being adjusted through the incremental addition of water ranging from 3 to 5 percent.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sr. No.** & **Specific Items** & **Unit** & **Measured** \\ \hline
1 & Loss on ignition & \% & 2.05 \\
2 & S\({}_{\text{i}}\)O\({}_{2}\) (silica) & \% & 21.63 \\
3 & Al\({}_{2}\)O\({}_{3}\) (alumina) & \% & 5.52 \\
4 & Fe\({}_{2}\)O\({}_{3}\) (Iron Oxide) & \% & 3.42 \\
5 & CaO (Lime) & \% & 61.45 \\
6 & MgO (Magenesis) & \% & 1.86 \\
7 & S\({}_{0}\)3 (Sulfur Trioxide) & \% & 2.71 \\
8 & C\({}_{3}\)S (ofClinker)-tri silicate & \% & \textgreater{}55.0 \\
9 & C\({}_{2}\)S (of Clinker)—dicalcium silicate & \% & \textgreater{}14 \\
10 & C\({}_{3}\)A (of Clinker)—tricalcium Aluminate & \% & \textless{}8 \\
11 & CAF (of Clinker)-tetracalicium & \% & \textgreater{}10 \\  & alumina-ferrite & \% & 0.45–0.80 \\
12 & Free Lime & \% & 0.45–0.80 \\
13 & Liter Weight & G/Lt & \textgreater{}1310 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical composition of the sample cement.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sr. No** & **Constituents** & **Result** \\ \hline
7 & Moisture (\%) & 23.78 \\
8 & Total Sugar (\%) & 48.46 \\
9 & Invert Sugar (\%) & 10.53 \\
10 & Sulphated ash (\%) & 14.52 \\
11 & Ca (\%) & 1.60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._Subsequently, the soil was uniformly mixed with the desired proportions of molasses, cement, and water. The prepared soil samples were then sealed in polyethylene bags within the laboratory environment for 24 h at ambient temperature (27 \(\pm\) 2 \({}^{\circ}\)C) to achieve water equilibration. Following this, the soil was placed and compacted in the compaction mold in three layers, each subjected to 25 blows per layer utilizing standard compaction energy. Ultimately, the moisture content and dry density of the untreated and treated soil, incorporating varying percentages of molasses and cement, were determined.
###### Abstract  
The increased number of vehicles and poor road conditions in many countries result in slow moving traffic. At low-speeds, riding a motorcycle requires continuous input from a rider to achieve stability, which causes fatigue to the rider. Therefore, in this research, the low-speed stability of a motorcycle is studied using a theoretical and experimental approach to identify the parameters that can reduce the rider's effort. Initially, a linear mathematical model of the motorcycle and rider system is presented; wherein, the equation of motion for the stability of the system in roll direction is derived. The open-loop and closed-loop poles from the equation are calculated to determine the regions for the low-speed stability. Subsequently, experiments are conducted on the motorcycle instrumented with the required sensors, on a straight path at speeds below 10 km/h. The input and output parameters from the experimental data are analyzed using a statistical method. Steering angle and steering torque are the input parameters; roll and yaw angles and their corresponding velocities are the output parameters selected for the analysis. Correlation and lead time between the input and output parameters are compared to identify the parameters useful for the rider to attain the low-speed stability. The results obtained from the experimental analysis validate the mathematical model. In addition, these findings also validate that the input parameters required to control the motorcycle to achieve low-speed stability can be estimated using the identified output parameters.  
Keywords:low-speed stability; balancing; motorcycle dynamics; rider control +
Footnote †: journal: Applied Physics Letters B
# A Gradient-Based Cuckoo Search Algorithm for a Reservoir-Generation Scheduling Problem
## 5 Case Study
### Reservoir Scheduling in Dry Years  
In this section, we evaluate the performance of GCS in dry years. The streamflow of Pingshan station in Figure 11 were chosen as the input of the LHGS model. The parameters and other conditions were also the same as those in Section 5.3.  
Figure 10: Convergence process of the algorithms in the year 1987.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**1997**} & \multicolumn{2}{c}{**1963**} & \multicolumn{2}{c}{**1970**} & \multicolumn{2}{c}{**1981**} \\ \cline{2-9}  & **Mean** & **Std.** & **Mean** & **Std.** & **Mean** & **Std.** & **Mean** & **Std.** \\ \hline IHS & 1966 & 284.55 & 2107 & 29.49 & 2037 & 208.99 & 2065 & 35.92 \\ KGSA & 1953 & 8.57 & 2021 & 204.31 & 1984 & 10.67 & 2007 & 10.42 \\ CS & 2039 & 5.24 & 2140 & 11.24 & 2096 & 12.60 & 2098 & 6.48 \\ GCS & 2105 & 0.38 & 2228 & 0.44 & 2181 & 0.13 & 2159 & 0.37 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of power generation (\(10^{8}\) kWh) in normal years.  
Figure 9: Streamflow of Pingshan station in normal years.  
As in Sections 5.3 and 5.4, GCS performed best in these four methods. The mean values obtained by KGSA are the smallest, while the standard deviations obtained by IHS are the largest. As shown in Table 6, the average power generation obtained by GCS is 199.2, 201.9, 194.4 and 197.7 billion kWh in the four normal years. Compared with IHS, KGSA and CS, the average increases are 5.0%, 7.7% and 3.2%, respectively. Figure 12 also shows that the convergence speed of GCS is the fastest. From Sections 5.3-5.5, it is clear that GCS can effectively obtain satisfactory results in different streamflow input scenarios.
# Multi-Objective Optimization for EE-SE Tradeoff in Space-Air-Ground Internet of Things Networks
## 5 Conclusions and Future Works  
In this paper, we construct a SAG-IoT model, equating the channel between IoT devices and UAVs to a line of sight channel. Due to the limited energy of drones, we focus on energy efficiency, but an increase in energy efficiency can lead to a decrease in spectral efficiency. Therefore, we consider both energy efficiency and spectral efficiency as objective functions to construct optimization problems. We improve the \(\epsilon\)-constraint to transform the multi-objective optimization problem into a single-objective optimization problem with additional constraints. Additionally, we introduce a BCD-based method to jointly optimize sub-channel selection, IoT device transmission power, UAV transmission power, and UAV location deployment to solve the complex single-objective optimization problem. Simulation results demonstrate the convergence of our proposed algorithm. At the end of the paper, we provide simulations to demonstrate the convergence of the proposed algorithm, analyz the effects of different tradeoff coefficients on the energy efficiency and spectral efficiency of the system, and analyz the effects of factors such as UAV flight altitude and maximum launch power on the algorithm performance. However, our SAG-IoT model only includes one satellite, and we should consider the case of multiple satellites in the future. In addition, we only considered the location deployment of UAVs without considering the trajectory. We will carry out further research on this.  
Conceptualization, J.Z.; Methodology, J.Z., Y.M., X.G. and J.Y.; Software, [PERSON] and Y.M.; Validation, J.S.; Formal analysis, Y.M. and X.G.; Investigation, J.Y.; Resources, J.Y. and J.S.; Data curation, J.Z. and X.G.; Writing--original draft, Y.M. and J.Z.; Writing--review & editing, J.S.; Project administration, J.Z. and J.Y.; Funding acquisition, J.Z. and X.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [National Natural Science Foundation of China] grant number [62201054] and [Beijing Nova Program] grant number [Z211100002121161].  
The authors declare no conflict of interest.
# MB-OFDM-UWB Based Wireless Multimedia Sensor Networks for Underground Coalmine: A Survey
## 4 Other Research Issues
### Target Localization  
The sensing model for video sensors in WMSNs is based on directional sensing model which utilizes perspective projection relationship of cameras, which differs remarkably from the omni-directional sensing model for traditional WSNs. Thus, there are significant differences between the target localization algorithms of the two. In recent years, the target localization problem has received considerable attention and research work in the area of WMSNs.  
An object localization scheme for WMSNs, which can be run on a single camera node by utilizing the size of the object on the frame obtained from frame differencing and the monocular vision imaging model, has been proposed in [61]. In [62], the authors have presented a cooperative target localization algorithm which can achieve a good trade-off between energy consumption and accuracy of localization, on the issue of node selection for target localization in WMSNs. [PERSON] et al. [63] has raised a single-node and a double-node target localization algorithm used in simplified calibration scenes, according to the characteristics of multi-mode cooperation in WMSNs. [PERSON] et al. [64] presented a video collaborative localization algorithm which uses miners' lamps as the key feature and the camera vision imaging model, to estimate the real location of miner for coalmine scene. As shown in Figure 6, the localization process is performed collaboratively by three video sensors. The research and experimental work of [64] indicates a new way for localization and target tracking in coalmine. Consequently, the physical appearance and outfit of miners provide extra information for analyzingtheir locations and behaviors, which further help to meet the higher safety standards and satisfy the growing demand for multimedia data. Just as human beings use eyes to track and confirm targets, target localization with WMSNs endows underground mines with \"eyes\". Combining with the UWB localization mentioned in the previous section, monitoring system for underground coalmines can achieve both good location accuracy and intuitive visual information.  
From the existing research work, it can be seen that the study on target localization techniques for WMSNs should be related with specific conditions in the acquiring methods, contents, types and quantity of information. Likewise, the target localization techniques for underground coalmine WMSNs should also take the network structure, characteristics of monitoring scenes, MB-UWB wireless transmission mode, and processing capacity of sensor nodes into consideration, as well as be designed by combining a distributed source coding scheme and a target capturing scheme.
# A Method of Aerial Multi-Modal Image Registration
## 2 Real Scene Acquisition and Generation of Virtual Scene Simulation
### Real Scene Imaging Analysis Based on Infrared Visible Binocular Vision
\(R\) is a rotation matrix with the size of \(3\times 3\), which determines the orientation of the camera. \(t\) is a translation vector with the size of \(3\times 1\), which determines the position of the camera. \((x_{1},y_{1},1)\) is the image coordinate before transformation, and \((x_{2},y_{2},1)\) is the image coordinate after transformation. The expression of homography transformation is expressed by homogeneous coordinates as follows:  
\[\left[ \begin{array}{c}x_{2}\\ y_{2}\\ 1\end{array} \right]=\left[ \begin{array}{ccc}a_{11}&a_{12}&a_{13}\\ a_{21}&a_{22}&a_{23}\\ a_{31}&a_{32}&a_{33}\end{array} \right]\left[ \begin{array}{c}x_{1}\\ y_{1}\\ 1\end{array} \right] \tag{4}\]  
Equivalently,  
\[\left\{ \begin{array}{c}x_{2}=\frac{a_{11}x_{1}+a_{12}y_{1}+a_{13}}{a_{13}x_ {1}+a_{23}y_{1}+a_{23}}\\ y_{2}=\frac{a_{21}x_{1}+a_{22}y_{1}+a_{23}}{a_{31}x_{1}+a_{32}y_{1}+a_{33}} \end{array} \right. \tag{5}\]  
where \(a_{11}\), \(a_{12}\), \(a_{21}\),\(a_{22}\) are scaling and rotation factor; \(a_{13}\), \(a_{23}\) are translation factors in horizontal and vertical directions, respectively, and \(a_{31}\), \(a_{32}\) are affine transformation factors.
## 4 Results Analysis and Discussion
### Discussion
Furthermore, while the study compares multiple machine learning models, the emphasis on only a selection of models may obscure other potentially useful methods that might improve CKD prediction accuracy. Furthermore, the research recognizes the relevance of feature selection; yet, investigating advanced feature selection approaches other than those used may provide different outcomes. The LADTree model's interpretability is promising, but its computational efficiency and resource consumption compared to simpler models like Naive Bayes may pose challenges for practical deployment in clinical settings. These limitations highlight the need for more studies to test and refine the findings over a wide range of datasets, as well as to improve the suggested approach's applicability in realistic circumstances.
# A Data-Driven State Estimation Based on Sample Migration for Low-Observable Distribution Networks
## 4 Case Study
### Robustness to Bad Data  
Affected by operating conditions and environmental factors, bad data inevitably exist in the measurement data input to distribution networks. In order to assess the robustness of the proposed approach, a 20% mixture of Gaussian error is introduced into historical measurement data to emulate the presence of faulty data in actual distribution networks. These augmented data serve as an additional training sample for the pre-training model. We assess the robustness of different algorithms by adding bad data (with an error standard deviation set to 3-10 times that of normal data) to the voltage amplitude measurements at nodes 2, 10, 15, and 22. Figure 6 shows the average absolute error calculated by our algorithm, WLS, and WLAV for nodes with added bad data. In Figure 8, the proposed method demonstrates good robustness in this case.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Measurement** & \multicolumn{2}{c}{**Average Estimation Error/10\({}^{-3}\)**} & \multicolumn{2}{c}{**Maximum Estimation Error/10\({}^{-3}\)**} \\ \cline{2-5}
**Redundancy** & **Voltage Magnitude** & **Voltage Angle** & **Voltage Magnitude** & **Voltage Angle** \\ \hline
1.22 & 0.8286 & 0.9042 & 1.0528 & 1.1077 \\
1.07 & 0.9272 & 1.0296 & 1.9918 & 2.0132 \\
0.87 & 1.0114 & 1.1252 & 2.3543 & 2.2841 \\
0.80 & 2.1522 & 2.2172 & 3.6844 & 3.4546 \\
0.76 & 4.2219 & 4.2904 & 5.7872 & 6.9681 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Estimation results of proposed method under different measurement redundancy.  
Figure 7: Comparison of state estimation results for an actual distribution network system: (**a**) comparison of absolute error of node voltage magnitude; (**b**) comparison of absolute error of node voltage angle.
## 2 Multiuser Hybrid Beamforming System Model
\begin{table}
\begin{tabular}{l l} \hline \hline No. of users & 4 \\ \hline Data streams per user & 1 \\ \hline No. and type of base station antenna & 32, ULA, isotropic, back-baffled \\ \hline No. and type of receive antenna per user & 4, ULA, non-back-baffled \\ \hline Modulation type & 16 QAM \\ \hline Frequency of operation & 28 GHz \\ \hline OFDM FFT length, CP length & 256, 64 \\ \hline Encoder type, code rate (fixed) & Convolutional, \(\frac{1}{3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Transceiver parameter table. Figure 2: System model for data transmission and reception. where \(n_{k}\in C^{N_{M}}\) is the complex additive white Gaussian noise (AWGN) with \(n_{k}\sim CN\big{(}0,\sigma^{2}I_{N_{M}}\big{)}\). This signal \(\tilde{y_{k}}\) is received by the \(k\)th user and is processed by the combiner \(W_{RF_{k}}\in C^{N_{M}}\) to obtain \(y_{k}=W_{RF_{k}}^{H}\tilde{y_{k}}\), i.e.,  
\[y_{k}=W_{RF_{k}}^{H}H_{k}\sum_{n=1}^{M}F_{RF}F_{BB_{k}}s_{n}+W_{RF_{k}}^{H}n_{k}, \tag{4}\]  
where RF combiners \(W_{RF}\) are designed as quantized phase shifter so that \(\big{|}\big{[}W_{RF_{k}}\big{]}_{i}\big{|}^{2}=1/N_{M}\). Achievable rate for the \(k\)th user assuming Gaussian symbol transmission through the channel is given as  
\[R_{k}=\log_{2}\Bigg{|}1+\frac{\frac{P}{M}\Big{|}W_{RF_{k}}^{H}H_{k}F_{RF}F_{BB _{k}}\Big{|}^{2}}{\frac{P}{M}\sum_{n\
eq k}\Big{|}W_{RF_{k}}^{H}H_{k}F_{RF}F_{ BB_{k}}\Big{|}^{2}+\sigma^{2}}\Bigg{|} \tag{5}\]  
Subsequently, the achievable sum rate for the system is given as [9]  
\[\bar{R}=\sum_{k=1}^{M}R_{k}. \tag{6}\]  
In this proposed work, \(F_{RF}\) as the beams of the learned codebook is acquired. \(W_{RF}\) through conventional beam sweeping is obtained, as detailed in Section 3. The acquisition of \(F_{BB}\) occurs in the second step of a two-step procedure, as outlined in [4]. It is important to note that, in this proposed work, the focus is solely on learning \(F_{RF}\). The contribution can also be conceptualized as adaptive beam sectoring that is aware of the environment.
# Three-Dimensional Object Recognition Using Orthogonal Polynomials: An Embedded Kernel Approach
## 5 Conclusions  
Signal preprocessing plays a crucial role in computer vision applications, as it directly enhances the performance of computer vision algorithms. OMs are considered as robust descriptors for signal analysis and recognition. Convolving kernels with the signal and pre-computing OMs increases computational demands. Therefore, this paper introduced a novel mathematical model that embeds the kernel directly into the OPs functions by integrating the processes into a single, streamlined framework. By computing the OMs of smoothed 3D signals directly, the model achieved a substantial reduction in computational overhead while maintaining high accuracy. Our experimental work on 3D objects demonstrated that the proposed algorithm outperforms traditional methods. When the polynomial order is equal to 10, the accuracy improves further to 83.85%, indicating that the proposed work benefits from higher polynomial orders, likely due to its enhanced ability to capture complex features. In the future, we plan to further enhance the performance of the proposed method and apply it to various applications, such as medical imaging and robotics.  
Conceptualization, A.A.M. and A.H.A.-s.; methodology, B.M.M., A.A.M. and S.H.A.; validation, A.A., M.A., A.M.A.-H. and A.H.; investigation, A.H., M.A., A.A., A.M.A.-H. and A.H.A.-s.; resources, B.M.M. and A.M.A.-H.; writing--original draft preparation, B.M.M., S.H.A., M.A., A.H.A.-s. and A.A.M.; writing--review and editing, A.H.A.-s., B.M.M. and A.H.; visualization, A.M.A.-H., S.H.A., A.A. and A.A.M.; supervision, S.H.A.; project administration, S.H.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors would like to thank the University of Baghdad for general help and support.  
The authors declare no conflicts of interest.