esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Analysis on Self-Propulsion of a Waterjet-Propelled Ship with Different Propulsion Models
## 2 Numerical Model
### Multiple Reference Frame Model and Sliding Mesh Model  
The multiple reference frame (MRF) model is a steady-state approximation, in which every grid zone can be assigned different rotational and translational speeds. The local reference frame transformation is executed at the interfaces between zones, so that the flow information can be transferred between the adjacent zones. Additionally, an additional correction term for the local reference frame transformation is applied on all surfaces (including the interfaces and wall surfaces) where rotation or translation is modelled. The MRF approach cannot account for the relative motion of a moving zone with respect to adjacent zones, and the mesh remains unchanged during calculations. Therefore, if strong rotor-stator interaction is considered, it is more appropriate to use the transient sliding mesh model instead of the steady MRF model. However, the flow field computed by the steady MRF model can be used as an initial condition for a transient sliding mesh model.  
In the sliding mesh model, the multiple zones are connected by non-conformal interfaces. The non-conformal interface is updated with the grid motion. A particular concern is that the mesh motion must be specified so that regions can remain in contact with each other. For dynamic meshes, the integral form of the conservation equation for a general scalar \(\varphi\) on an arbitrary control volume \(V\)[37] can be written as  
\[\frac{d}{dt}\int\limits_{V}\rho\phi dV+\int\limits_{\partial V}\rho\phi( \overrightarrow{u}-\overrightarrow{u}_{g})\cdot d\overrightarrow{A}=\int \limits_{\partial V}\Gamma\
abla\phi\cdot d\overrightarrow{A}+\int\limits_ {V}S_{\phi}dV, \tag{3}\]  
where \(\rho\) is the fluid density, \(\overrightarrow{u}\) is the flow velocity vector, \(\overrightarrow{u}_{g}\) is the mesh velocity, \(\Gamma\) is the diffusion coefficient, and \(S_{\varphi}\) is the source term of \(\varphi\). Here, \(\partial V\) is used to represent the boundary of \(V\).  
Since the mesh motion in the sliding mesh model is rigid, all cells retain the original shape and volume. In order to satisfy the space conservation law [38], the volume time derivative of \(V\) is computed from the following:  
\[\frac{dV}{dt}=\int\limits_{\partial V}\overrightarrow{u}_{g}\cdot d \overrightarrow{A}=\sum\limits_{j}^{n_{f}}\overrightarrow{u}_{g,j}\cdot d \overrightarrow{A}=0. \tag{4}\]  
Using a first-order backward difference formula, the time derivative term in Equation (3) can be written as follows:  
\[\frac{d}{dt}\int\limits_{V}\rho\phi dV=\frac{\left[\left(\rho\phi\right)^{n+1 }-\left(\rho\phi\right)^{n}\right]V}{\Delta t}. \tag{5}\]  
Figure 5: The mesh of waterjet system.
## 1 Introduction
### State of the Art
#### 1.1.1 Semi Markov Conditional Random Fields  
The first algorithm presented is based on semi-Markov conditional random fields [4]. With its help, tests were performed on databases: CASIA-OLHWDB with Chinese characters, and TUAT Kondate with Japanese characters. Accurate rates were 94.54% and 94.55%. One-step algorithms based on hidden Markov models use the division of lines of text into segments of the same size. These segments are then described and classified. This type of method does not obtain accurate results about the shapes of characters, so the classification itself does not provide satisfactory results. The two-stage approach besides similar partitioning using hidden Markov models, implies the use of a different classifier to recognize segmented text [5; 6]. The presented solution consists of dividing lines of text into smaller fragments. These fragments can then be combined into sub-paths. The maximum length of a sub-path is determined by the variable m, called clique [7]. For the classification itself, the authors use three feature functions: character classification, geometric context and linguistic context. This solution relies heavily on the support of Markov chains. This type of solution, unlike the one presented by the authors in this publication, may have difficulty recognising texts in which the sequence of characters is random, but contains uncommon combinations of characters, such as in names or surnames.
## Acronyms  
\begin{tabular}{l l} Acronyms & Full words \\ TOA & Time-of-arrival \\ JLAS & Joint localization and synchronization \\ UNs & User nodes \\ ANs & Anchor nodes \\ CRLB & Cramer-Rao lower bound \\ RIS & Robust iterative solution \\ RMSE & Root mean square error \\ RF & Radio frequency \\ WSNs & Wireless sensor networks \\ TOF & Time-of-flight \\ IoT & Internet of Things \\ FD & Frequency division \\ CD & Code division \\ TD & Time division \\ WLS & Weighted least squares \\ SDP & Semidefinite programming \\ TSWLS & Two-step weighted least squares \\ MLE & Maximum likelihood estimator \\ GN & Gauss-Newton \\ CFPS & Closed-form projection solution \\ CWLS & Constrained WLS \\ MSE & Mean-square error \\ FG & Factor graph \\ SPA & Sum-product algorithm \\ \end{tabular}
## 4 Experimental Results
### Discussion  
In industrial production environments, the detection of surface defects in industrial product packaging bags is a vital quality control task. However, this endeavor confronts multiple challenges, the chief of which is the marked data imbalance stemming from an overwhelming abundance of samples representing normal packaging contrasted with a dearth of those displaying defects, resulting in a pronounced long-tailed distribution. Such a skewed distribution can predispose deep learning models to overemphasize common patterns during training at the expense of learning rare defect modes, thereby impeding their generalization capabilities. Additionally, the computationally intensive and parameter-heavy C2F module in the YOLOv8 model renders it inefficient and unsuitable for real-time deployment on edge devices with limited computational resources, contravening the practical requirements of industrial settings.  
In response to the aforementioned challenges, the FSNB_YOLOv8 model presents a suite of innovative solutions. Firstly, it employs an interactive data augmentation algorithm based on fuzzy search, which proactively addresses data skewness. This approach intelligently generates and enhances defect samples, expanding the scale of the defect dataset and thereby mitigating data imbalance issues. It enables the model to thoroughly learn diverse defect patterns during training, enhancing its ability to recognize rare defects in actual production scenarios and bolstering the overall generalization performance.  
Secondly, FSNB_YOLOv8 substitutes the backbone network of YOLOv8 with a composite network comprising FasterNet and SPD-Conv, effectively managing computational complexity and network redundancy. This design aims to maintain detection accuracy while significantly reducing the computational cost of the model, rendering it more compatible with the computational constraints of edge devices, and facilitating real-time, efficient deployment in situ.  
Additionally, the model harnesses a Bidirectional Feature Pyramid Network (BiFPN) to enhance its capability to detect defects across various scales. BiFPN adeptly integrates feature information from different hierarchical levels, ensuring that when confronted with defects of varying sizes, the model can capture crucial features, thereby enhancing detection precision and robustness.  
Lastly, the adoption of the NWD loss function improves the model's sensitivity to positional deviations in defects. Compared to conventional loss functions, the NWD loss function more finely quantifies the distance discrepancies between the predicted and ground-truth bounding boxes, enabling the model to more accurately localize defect positions, thereby reducing misclassifications due to localization errors and ultimately augmenting detection accuracy.
# Methodology for CubeSat Debris Collision Avoidance Based on Its Active ADCS System
## 4 Results and Discussion
### Laboratory Test  
In this section, the experimental validation of the EKF algorithm in executing collision avoidance maneuvers using the reaction wheel is performed. An EQM of the CubeSat was used as a prototype, allowing to assess the performance of the algorithm in a controlled environment before deploying the solution to the real operational mission. The focus was on evaluating how effectively the filter, when integrated with the reaction wheel mechanism, can maneuver the spacecraft to avoid potential collisions with space debris.  
Figure 9 displays the nanosatellite based on the successful CubeSat standard, weighing around 2 kg. This CubeSat contains the following typical spacecraft subsystems: Electrical Power, Command and Data Handling, Attitude Determination, and Control and Structure. It features a Wi-Fi communication system allowing connection with a PC, which was used to operate and control the experiments.  
Figure 8: Results for two days after Maneuver BC for all satellites under study.  
The experiments were based on the application of a control law to maneuver and modify the orientation of the CubeSat in order to modify the ballistic coefficient and thus reduce the height, avoiding possible contact with debris. The action must be fast and controlled, so as to avoid over oscillations, having a fast-settling time, and a steady state error tending to zero.  
As Figure 10 displays, the rise time is obtained at 1.6 s after imposing the law control, with a peak overshoot of 14% (in a peak time of 1.9 s), and a settling time of 2.8 s.
## 3 Results
### Assessment of Aquatic Reed Bed Densities  
The first assessment of the vegetation density map revealed an overall accuracy (OA) of 72.7% for the validation compared to field data. The reference points (RP) used for this assessment obtained a 0.46 m horizontal and 0.54 m vertical precision after differential correction. The mean Horizontal and Vertical Dilution of Precision at which all the measurements (RPs) took place was 1.3 and 2.1, respectively. A mean of 17 measurements was recorded for every RP. The Producer's Accuracy (PA) showed that 85.6% of the sparse areas were correctly identified, but according to the User's Accuracy (UA) for the same class, this was true only 68.2% of the time (Table 3). Contrarily, 59.7% (PA) of dense areas were identified as such, with 80.4% (UA) of the classification truly corresponding to that category. The Kappa coefficient for this assessment suggests that the observed classification is 50% better than that which resulted by chance.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Reference Data**} & \multicolumn{4}{c}{**Reference Data**} \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Classified \\ Data \\ \end{tabular} } & \multirow{2}{*}{Dense} & \multirow{2}{*}{Sparse} & \multirow{2}{*}{Total} & User’s Accuracy (\%) & & \multirow{2}{*}{Classified Data} & \multirow{2}{*}{Dense} & \multirow{2}{*}{Sparse} & \multirow{2}{*}{Total} & User’s Accuracy(\%) \\ \hline Dense & 74 & 18 & 92 & 80.4 & Dense & 47 & 3 & 50 & 94 \\ \hline Sparse & 50 & 1.07 & 157 & 68.2 & Sparse & 3 & 97 & 100 & 97 \\ \hline Total & 124 & 125 & 249 & \multicolumn{4}{c}{Total} & 50 & 100 & 50 \\ \hline Producer’s Accuracy (\%) & 59.7 & 85.6 & \multicolumn{4}{c}{
\begin{tabular}{c} Producer’s \\ Accuracy (\%) \\ \end{tabular} } & \multicolumn{4}{c}{94} & 97 & 150 \\ \hline \multicolumn{7}{c}{Overall Accuracy (\%) = 72.7; Kappa = 0.5} & \multicolumn{4}{c}{Overall Accuracy (\%) = 96; Kappa = 0.91} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix for classification assessed against field data (left) and aerial imagery (right).  
Figure 7: Examples of shape similarity measured onsite (grey fill) and derived from LiDAR data (black outline).  
Results differed for the second accuracy assessment of classified density. Very high-resolution aerial imagery (0.1 m/pxl) taken at the same time and during the same flight as the ALS was implemented for this task. An OA of 96% with the Kappa coefficient of 0.91 was obtained from the evaluation (Table 3). High classification accuracy was explained by the unbiased visual interpretative validation applied by the same operator (reed expert) and for all the AOIs represented on RGB data. Both classes (Dense and Sparse) showed a high level of UA and PA, for which the values were 94% and 97% for sparse and dense classes, respectively. An area on the ground will actually be what the classification reveals 94% and 97% of the time. In the same way, the proximity to one of the values from the Kappa coefficient indicates a \"true\" agreement with the classification (Figure 8).
# Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising
## 2 Review on Time-Series Forecasting Studies
### Artificial Neural Network (ANN)
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Domain** & **Author** & **Dataset** & **Model** & **Result** \\ \hline
**Gold** & [PERSON] et al., 2020 [67] & Gold price from investing site, (January 2015 to December 2018) & GDM, RP, SCG, LM, BR, BFGS, OSS & GDM algorithm has the lowest MAPE of 4.06 \\ \hline
**Stock** & [PERSON] et al., 2015 [68] & CEBR3, CSRN3 & ANN & MAE of 0.0009 and 0.0042 for CEBR3 and CSRN3 \\ \hline
**[PERSON] et al., 2017 [69] & Daily stock closing prices from Wanjawa and Lawrence & ANN & RMSE of 0.1830 \\ \hline [PERSON] and [PERSON], 2020 [70] & Colombo stock exchange & ANN, ARIMA & ANN has the lowest MAPE of 0.1783 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: \(Cont.\) \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Domain** & **Author** & **Dataset** & **Model** & **Result** \\ \hline
**Stock** & [PERSON] and [PERSON], 2006 [71] & Yahoo Finance (Aguist 2005 to August 2005) & RNN & Accuracy of 0.9784 \\ \hline
**Stock** & [PERSON] and [PERSON], 2017 [72] & China stock market (2008 to 2015) & RNN, LSTM, MLP & LSTM has the highest performance with an accuracy of 0.473 \\ \hline [PERSON] et al., 2018 [73] & Yunnan Baiyao stock data & LSTM & Accuracy of 50–65\% \\ \hline
**[PERSON] et al., 2018 [74]** & Yahoo Finance (January 1985 to August 2018) & LSTM, ARIMA & ARIMA and LSTM achieved an RMSE of 5.999 and 0.936, respectively \\ \hline
**[PERSON] et al., 2019 [75]** & American Apple stock data of 2008 & LSTM & MAE of 0.155 \\ \hline
**Cryptocurrency** & [PERSON] et al., 2019 [76] & Coin Market Cap website & LSTM, RF, Linear Regression & LSTM has the best performance with an MAE of 0.1518 \\ \hline
**Fuel** & [PERSON] et al., 2018 [77] & Historical data from major metropolitan cities & RNN & Accuracy of above 90\% \\ \hline
**Gold** & S and S, 2020 [78] & World Gold Council & LSTM & RMSE of 7.385 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: RNN time-series forecasting from 2006 to 2020.
## 2 Methodology
### Grasp Pose Estimation  
After completing the registration between the target point cloud and the template point cloud, a homogeneous transformation matrix \(T\) is obtained. This matrix represents the rigid transformation between the two point clouds and is used to align the template point cloud with the target point cloud. Subsequently, a homogeneous transformation matrix is applied to the ideal grasp pose defined on the template, thereby deriving the  
Figure 12: Sample consensus alignment results: (**a**) front view of the sample point cloud; (**b**) side view of the sample point cloud; (**c**) front view of the workpiece point cloud; (**d**) side view of the workpiece point cloud.  
Figure 13: Comparison of ICP, GICP, and SAC + NDT registration results: (**a**) ICP registration front view; (**b**) ICP registration side view; (**c**) GICP registration front view; (**d**) GICP registration side view; (**e**) SAC + NDT registration front view; (**f**) SAC + NDT registration side view.  
corresponding grasp pose for the target point cloud. This process enables the transfer of known grasping strategies from the template to the real object. The transformation matrix is a standard 4 \(\times\) 4 homogeneous matrix, and is expressed as follows:  
\[T=\begin{pmatrix}R&t\\ 0^{T}&1\end{pmatrix}, \tag{5}\]  
where \(R=\begin{bmatrix}r_{ij}\end{bmatrix}\in R^{3\times 3}\), is a rotation matrix, which describes the pose of the object, and \(t=\begin{bmatrix}t_{x},t_{y},t_{z}\end{bmatrix}^{T}\), is a translation vector, which describes the position of the object in 3D space.  
Using this matrix, the predefined ideal grasp pose \(T^{template}_{grap}\) of the template point cloud can be mapped to the actual grasp pose \(T^{target}_{grap}\) of the target point cloud. The computation is as follows:  
\[T^{target}_{grap}=T\cdot T^{template}_{grap}. \tag{6}\]  
The resulting grasp pose is still expressed in the camera coordinate system. Subsequently, it can be further transformed into the robot base coordinate system using the known hand-eye calibration matrix \(T_{cam2 base}\):  
\[T^{base}_{grap}=T_{cam2 base}\cdot T^{target}_{grap}. \tag{7}\]  
Finally, the grasp pose is processed through inverse kinematics based on the robot's DH model to solve for the joint angles. These computed joint values are subsequently used to generate control commands for executing the grasping operation.
## 4 Conclusions  
A CRLH resonant antenna without metallic vias has been designed and experimentally demonstrated to have patch-like radiation in the broadside direction. By utilizing the new CRLH resonator, patch-type radiation can be achieved. The antenna has a low profile and a radiated efficiency of around 87%. The designed antenna has no spurious resonance over a bandwidth of 10 GHz. A passive beamforming array has been designed and fabricated using the Butler matrix and the CRLH antennas. The CRLH antennas are attached to the output ports of the 4 \(\times\) 4 SIW Butler matrix, and the performance of the CRLH antenna and the 4 \(\times\) 4 SIW Butler matrix is verified. It has potential applications in 5G communication, wireless power transfer, and IoT sensing.  
[PERSON] and [PERSON] jointly conceived the idea. [PERSON], [PERSON] and [PERSON] designed the system and built the experimental setup. [PERSON] and [PERSON] performed the experiments. [PERSON], [PERSON] and [PERSON] assisted with the theory. All authors contributed to the discussion of experimental results. [PERSON], [PERSON] and [PERSON] supervised and coordinated all the work. [PERSON] and [PERSON] wrote the manuscript with contributions from all co-authors All authors have read and agreed to the published version of the manuscript.  
This study is supported under the Future Communications Research and Development Programme (FCP) Research Grant-FCP-NTU-RG-2022-013.  
Not applicable.  
We thank [PERSON] for the support in the measurements.  
The authors declare no conflict of interest.
###### Abstract  
This paper deals with the characteristics of partial discharge (PD) in green gas for a grid (g3), which is thought to be a promising eco-friendly alternative to substitute SF\({}_{6}\) used in electrical power facilities and to reduce the greenhouse effect. g3 gas with 4% NOVEC(tm) 4710/96% CO\({}_{2}\) was used and electrode systems including a protrusion on conductor (POC) and a protrusion on enclosure (POE) were fabricated to simulate PD in a gas-insulated structure. PD characteristics in terms of partial discharge inception voltage (PDIV), pulse parameters in time and frequency, and the phase-resolved partial discharge (PRPD) pattern in SF\({}_{6}\) and g3 were compared. From the results, the PDIVs of g3 were 76%-81% of that of SF\({}_{6}\) in the POC and were 78%-84% of that of SF\({}_{6}\) in the POE, depending on the gas pressure. Rising time, pulse width, and relative amplitude in the frequency domain of PD pulses in g3 gas were greater than those in SF\({}_{6}\). In addition, the PRPD patterns indicated that both the average apparent charge and pulse count of PD in g3 were higher compared with those in SF\({}_{6}\). The results from this paper are expected to provide fundamental material for the green manufacturing of gas-insulated power apparatus.  
green gas for grid; eco-friendly insulation gas; SF\({}_{6}\) alternative; partial discharge; discharge characteristics +
Footnote †: journal: Applied Sciences
###### Abstract  
As rock bursts are unavoidable in deep mines and excavations with high in-situ stresses, ground support systems are implemented to manage and mitigate rock bursts. Cable bolts are commonly used as reinforcing elements in ground support systems, which are subject to dynamic loads in burst-prone excavations. To design an efficient cable bolt in burst-prone conditions, shear and energy absorption capacity must be considered. Numerical modelling is an advantageous method of repeatable testing and it is inexpensive and non-destructive. This study develops a statically and dynamically loaded numerical model of a double shear test in ABAQUS/Explicit. A total of 36 static and 576 dynamic tests are carried out, which examine the influence of bolt diameter, steel yield and ultimate strength, dynamic load velocity and dynamic load mass on the displacement, shear force and energy absorption capacity of cable bolts. As bolt diameter and steel yield strength increases, the maximum shear force resisted and bolt displacement increases. Similarly, as the mass and velocity of the dynamic load increases, the amount of energy absorbed by the cable bolt increases. The main novelty of the current research is to suggest a reliable computational tool to investigate the influence of the different key parameters in the cable bolts on the ultimate capacity. The suggested method is a significantly cost-effective technique compared with the experimental investigations.  
cable bolt; dynamic loading; double shear test; rock burst; finite element analysis; finite element modelling; ABAQUS; energy absorption +
Footnote †: journal: Medical  
development of the model is presented, which includes a detailed explanation of all design considerations. The model is calibrated and validated against existing experimental data, and various cable bolt parameters are tested under static and dynamic loading conditions. The results are discussed, and conclusions are drawn consistent with the existing literature. Finally, the limitations of the model are discussed, and further improvements for future work are provided.