esa-sceva/satcom-chunk-collection huggingface dataset:
# Progress of the Computer-Generated Holography Based on Deep Learning
## 3 Network Structures
### ResNet  
ResNet improves the transmission efficiency of information by adding a shortcut connection to the nonlinear convolution layer. Many \"highway networks\" present shortcut connections with gating functions, while the identity shortcuts of ResNet are parameter-free. Thus, the shortcuts are never closed and all information can pass through the whole network [44; 45; 46]. The ResNet reformulates the layers as learning residual functions with reference to the layer inputs. The networks are easier to optimize and can gain high accuracy from considerably increased depth. It is like U-Net and adds the calculation of some residual blocks. Suppose that in a depth network we can expect a nonlinear element convolution layer \(f(x;\theta)\) to approximate an objective function \(h\), which can be divided into two parts, the identity function \(x\) and residual function \(h(x)-x\). Then we can define it as  
\[h(x)=x+(h(x)-x) \tag{2}\]  
According to the general approximation principle, a nonlinear element composed of a neural network has enough ability to approximate the original objective function or residual function while the latter is better to be learned [46]. Thus, the original optimization problem can be transformed into the idea that we make the nonlinear element \(f(x;\theta)\) approximate the residual function \(h(x)-x\) and \(f(x;\theta)+x\) approximate \(h(x)\). A simple  
Figure 2: Structure of the U–Net neural network.  
unit of the ResNet is shown in Figure 3. The typical residual element is composed of multiple convolution layers with equal width and a direct edge across the layer. Moreover, the output is obtained after the activation by ReLU.  
ResNet is a deep neural network composed of many residual blocks in series. It solves the gradient vanishing problem when the network depth increases to a certain extent. It can make the feedforward or feedback propagation algorithm go smoothly and the structure is simpler. The increase in identity mapping cannot reduce the performance of the network. Thus, the ResNet is suitable for the CGH generation after being trained.
# Utilization of Quasi-Zenith Satellite System for Navigation of a Robot Combine Harvester
## 2 Materials and Methods
### Performance Evaluation of Robot Combine Harvester  
In order to verify the performance of a robot combine harvester utilizing the CLAS and MADOCA for navigation, tests were conducted at the experimental farm of Hokkaido University by using the robot combine harvester equipped with the Magellan MJ-3008-GM4-QZS receiver. The robot combine harvester ran in automatic mode following four paths of the predesigned navigation map. This navigation map consisted of a set position points in latitude and longitude coordinates. The robot combine harvester traveled in the experimental farm by using the CLAS and MADOCA for navigation in each run. This experiment was performed to verify the possibility of utilization of both positioning augmentation systems for a robot combine harvester navigation.  
The next experiment was performed by using the robot combine harvester equipped with both GNSS receivers. For this experiment, both receivers were tested together using one antenna connected via a splitter. The robot combine harvester traveled in the experimental farm one straight path with a constant velocity of 0.8 m/s by using the RTK positioning augmentation system for navigation in automatic mode, while the outputs of both receivers were logged in a PC at the rate of 10 Hz. The RMSE of the lateral deviation respect to the predesigned navigation map points was evaluated for the three different positioning augmentation systems. A predicted position based on the begin and end points of the predesigned navigation map was used to estimate the RMSE; this means that the straight path contained in the navigation map works as the target position, and the position points logged by each different positioning augmentation system will have some lateral deviation from this target position. The RMSE of this lateral deviation is calculated using Equation (3).  
The next test is used to evaluate the performance of the CLAS and MADOCA augmentation systems for the robot combine harvester during the harvesting path. Similar to the procedure followed during the static conditions, the performance evaluation of a robot combine harvester experiment took 6 h by activation the CLAS and MADOCA from 10:00 to 16:00 (UTC+9 Time) on different dates to evaluate the time dependency of each system. Then, the robot combine harvester ran in automatic mode using the CLAS and MADOCA as inputs of the navigation controller at different days. The robot combine harvester ran along the same two paths of the predesigned navigation map with two-hours intervals of time between each experimental run. The first run was performed immediately after the CLAS and MADOCA were activated. The predesigned navigation map was created by using the RTK positioning augmentation system. The output from the RTK was also logged during the running for evaluation of the CLAS and MADOCA.
## 3 Results and Discussion
### Performance Characterizes  
Turbidity, polydispersity index, zeta potential, contact angle, and partial size distribution were measured and presented in Figure 7. Nano/SiO\({}_{2}\)/n film recorded the highest turbidity 0.41 due to the presence of nisin, while CH film recorded the lowest, which might be due to the steric hindrance and sufficient electrostatic, Figure 7a. Polydispersity index of the novel films ranged from 0.41 in nano/SiO\({}_{2}\) film to 0.68 CH film, Figure 7a. [PERSON] and [PERSON] [42] reported that the distribution is regarded as broad when the polydispersity index \(\geq\)0.5, while the ideal formulation is regarded as monodispersed for \(\leq\)0.3 [42]. The zeta potential is known as the repulsion degree among the adjacent particles in a dispersion [43]. The zeta potential trend for the novel films indicated a positive overall load. The zeta potential (\(>\)30 mv) is considered stable due to enough repulsive force present for avoiding the aggregations among particles [42]. According to Figure 7b, nano/SiO\({}_{2}\) and nano/SiO\({}_{2}\)/n films reported that the nanoparticles showed high stability, 44.17 and 46.11 mv, respectively. The higher zeta potential value may be attributed to the higher number of NH\({}^{+3}\) groups. That can clarify the increase in the nano/SiO\({}_{2}\) with the combination of chitosan ratio reporting high zeta potential value [44]. It is known that the wetting property is an essential factor in adhesion, and wettability applications, which is influenced by the various materials [41]. The measured reactions of the contact angle among the novel films are illustrated in Figure 7b. CH film recorded 78.56, while the addition of nano-silica decreases the hydrophobicity of the film surface by 80.07\({}^{\circ}\); the addition of nisin reported better hydrophobicity than bothCH and nano/SiO\({}_{2}\) films. Changes in contact angle could be due to the attachment of the selected nano-molecules and nisin. The partial size distribution is one of the mainly significant parameters because it evaluates the destination and application of the formulated bioccomposite [43,44]. [PERSON] et al. [45] reported that nanoparticles are considered as solid particles with a range size from 10 nm to 1000 nm. Figure 7 presents the partial size distribution of the novel films which ranged from 567.86 nm to 2511.43 nm. The results reported a decrease in the response by increasing the nano-molecules and nisin ratio. Chitosan/nano/SiO\({}_{2}\) ratio is a vital factor during the ionic gelation, crosslinked particles, and agglomeration, that might describe the detected trend [45,46].  
Figure 7: _Cont._
## 3 Conventional Offline Motion Preparation
### Relevant Types of Spline  
Many works give an overview of different spline types, e.g., [10; 11; 12; 13]. Each spline type has specific properties. Therefore, they are suitable in different ways for use in offline motion preparation.  
Two types of spline have properties that make them interesting for offline motion preparation. B-splines are widely used in computer graphics and computer-aided design. With B-splines, generating continuous motions with scalable continuity is possible. Many helpful functions are available, e.g., insert or remove nodes [11]. With monomial splines, specifying derivatives at the segment boundaries can also achieve scalable continuity (Hermite interpolation). Monomial splines are much easier to implement than B-splines with the same accuracy [17]. Converting B-splines to the monomial base is easily possible by using a base transform.  
Due to the properties and the requirements mentioned in Section 2.1 some widely used spline types cannot be considered for offline motion preparation. Therefore, they are not taken into account in this work. The widely used cubic splines (e.g., [13]) are not evaluated, since preliminary investigations in [18] have shown that higher order splines (e.g., quantic Hermite splines) can be used much more flexibly with about the same number of coefficients. Akima splines [19] are a type of spline often used for online interpolation in control systems. Due to their lack of C\({}^{2}\) continuity, they do not fulfill the given requirements and are not discussed as well. Furthermore, computationally more expensive approaches such as e.g., non-uniform rational basis spline (NURBS) [11] are not considered, because they do not meet the given requirements for online processing, see [6].  
In this work, B-splines and monomial splines are evaluated together. Both spline types have different advantages and disadvantages, but comparable approximation properties [17; 20]. On the one hand, univariate B-splines of degree 3 are used in the work to realize the required continuity. On the other hand, quintic splines are used, where C\({}^{2}\) continuity is achieved by specifying the 0 th to 2 nd derivative at the segment borders.
## 3 Results
### U\({}_{max}\)-XCOM A/P and M/L  
The U\({}_{MAX}\)-XCOM A/P and M/L measures were not different between limbs, so right and left limb data were combined for both CON and PD groups. There were no significant differences between groups for U\({}_{MAX}\)-XCOM M/L at FDS, MS, and SDS and U\({}_{MAX}\)-XCOM A/P at SDS (Tables 4 and 5). At FDS, there was a marginally significant differences (\(p\) = 0.046) between groups as well as at MS (\(p\) = 0.020); however, there were no significant differences (\(p\) < 0.0028). In the A/P direction, for U\({}_{MAX}\)-XCOM, the effect size for FDS was moderate, and in MS and SDS, it was large, but this may be due to random variation when considering the larger \(p\)-values (Table 4).  
Both groups demonstrated similarly shaped U\({}_{MAX}\)-XCOM A/P and M/L curves throughout the gait cycle (Figure 10). At FDS, the U\({}_{MAX}\) was anterior to the XCOM. From FDS to SDS, the XCOM moved anteriorly, causing the U\({}_{MAX}\) to move posteriorly in relation to the XCOM. Also, from FDS to SDS, the distance between the U\({}_{MAX}\) and the XCOM decreased following FDS and then increased as the groups progressed to MS and SDS. At SDS, this distance again decreased. The U\({}_{MAX}\) remained lateral to the XCOM, and the distance between the U\({}_{MAX}\) and the XCOM increased from FDS to SDS.  
Figure 10: U\({}_{MAX}\)—COM A/P and M/L distance throughout the stance phase (in mm along the y-axis) of a right limb gait cycle from a representative PD (dashed line) and CON (solid line) participant. A positive value indicates that the U\({}_{MAX}\) is anterior to the XCOM and a negative value indicates that the U\({}_{MAX}\) is medial to the XCOM.  
Figure 9: COP–XCOM A/P and M/L distance throughout the stance phase (in mm along the y-axis) of a right limb gait cycle from a representative PD (dashed line) and CON (solid line) participant. A positive value indicates that the COP is anterior to the XCOM and a negative value indicates that the COP is posterior to the XCOM; in the mediolateral direction positive indicates that the COP is lateral to the XCOM and a negative value indicates that the COP is medial to the XCOM.
## 4 Experiment
### Suitability Verification  
Our multi-feature interaction meta-path aggregation heterogeneous graph neural network (MIMA) is designed to enhance the interpretability of recommendations by integrating node attributes and meta-path information. This approach allows for a more transparent understanding of the factors influencing each recommendation.  
By incorporating attribute-enhanced embeddings and meta-path aggregation, MIMA not only improves recommendation accuracy but also provides a clear explanation for its suggestions. This ensures that our model's recommendations are both effective and interpretable.  
An example is the KuaiRec dataset, which is a full-exposure dataset for recommender systems that was jointly produced by a team from CSU and the Computer Science Depart  
Figure 8: (**a**) The result of the BPR loss function changing with training time. (**b**) The result of the MF loss function value changing with training time.  
Figure 9: The impact of the length of the meta-path on the results.  
ment of Racer. We also used this dataset in our experiments and did not include it in the main text because of the small reference baseline. As shown below, you can see that our MIMA works well on other datasets as well.  
Figure 10, which illustrates the precision and recall curves from our experiments using the KuaiRec dataset, underscores the interpretability of the MIMA model. The KuaiRec dataset, with its full-exposure nature and a denseness of up to 99.6%, provides a rich heterogeneous graph with 30 user and 56 video features. Our use of this dataset yielded a recall rate exceeding 40%, demonstrating MIMA's effectiveness for general heterogeneous graphs and its ability to produce highly interpretable recommendation results.  
Potential applications in various domains: The adaptability of the MIMA extends beyond entertainment recommendations. For example, in healthcare, the MIMA could be used to recommend treatments or medications by analyzing patient histories and the medical literature. In finance, it could suggest investment opportunities by considering an investor's portfolio and market trends. Similarly, in social networks, the MIMA could recommend friends or content based on user interactions and preferences. These applications showcase the broader impact and adaptability of the MIMA algorithm.  
In summary, the algorithm proposed in this paper can make full use of user and item attribute information to improve recommendation accuracy. Meanwhile, the experimental results also verify that attribute information has a great impact on recommendation results. Therefore, the multi-feature interaction meta-path aggregation heterogeneous graph neural network is feasible and efficient for recommendations.
# A Machine Learning Method with Hybrid Feature Selection for Improved Credit Card Fraud Detection
## 5 Results and Discussion
### Performance of the ELM Classifier with Filter, Wrapper, and Hybrid Feature Selection Methods  
The ELM's performance without feature selection is compared with instances where the ELM classifier is coupled with the filter, wrapper, and hybrid feature-selection techniques. Firstly, the performance of the ELM without feature selection is recorded. Secondly, the credit card features and their IG values are ranked by the IG-based filter technique.  
The standard deviation [54] of the IG values is calculated and used as the threshold value to select the most informative features. From Table 2, the standard deviation is 0.145. Hence, information gain values above 0.145 are chosen as the essential features employed for training the machine learning model. The features with IG values below 0.145 are removed. Therefore, the top 21 attributes are chosen by the filter approach as the most important features, while the following attributes are removed: \(V8,V19,V24,V23,V26,V13,V25,V15,\) and \(V22\).  
Thirdly, the GA wrapper coupled with the ELM classifier (GAW) is trained, and its performance is recorded. Lastly, the top 21 features selected by the IG technique are used as input to the GAW. The performance from the four scenarios is tabulated in Table 2, i.e., ELM without feature selection, filter-based IG-ELM, wrapper-based GAW, and the hybrid IG-GAW method. In addition, Figure 2 shows the ROC curves and AUC of the various models. Meanwhile, the complete feature set and the features selected by the various feature-selection methods are tabulated in Table 3.  
Table 2 and Figure 2 show that the proposed hybrid IG-GAW obtained the highest sensitivity, specificity, and AUC values of 0.997, 0.994, and 0.990, respectively, outperforming the ELM, the IG-ELM, and IG-GAW.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Population size & 50 \\ Number of generations & 100 \\ Crossover rate & 0.6 \\ Mutation rate & 0.01 \\ Fitness function & G-mean \\ Stopping criteria & Max number of generations \\ Type of mutation & Uniform mutation \\ Type of crossover & Single point \\ Parent selection method & Tournament selection \\ Tournament size & 2 \\ \hline \end{tabular}
\end{table}
Table 1: GA parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Classifier** & **Sensitivity** & **Specificity** & **AUC** & **G-Mean** \\ \hline ELM & 0.881 & 0.904 & 0.900 & 0.892 \\ IG-ELM & 0.936 & 0.960 & 0.940 & 0.947 \\ GAW & 0.949 & 0.962 & 0.950 & 0.955 \\ IG-GAW & 0.997 & 0.994 & 0.990 & 0.994 \\ \hline \end{tabular}
\end{table}
Table 2: Performance of the ELM classifier, filter, wrapper, and hybrid feature-selection methods.
# Validation and Parametric Study of a Soil-Structure Interaction Nonlinear Simplified Model
## 4 Parametric Study of the Simplified Model
### Input Seismic Waves
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Story**} & \multicolumn{3}{c}{**Peak Velocities in (cm/s) under Seismic Waves**} & \multicolumn{3}{c}{**Relative Difference with EL Wave (\%)**} \\ \cline{2-9}  & **EL Wave** & **Kobe Wave** & **Taft Wave** & **TS Wave** & **Kobe Wave** & **Taft Wave** & **TS Wave** \\ \hline
6 & 0.311 & 0.216 & 0.169 & 0.178 & 30.50 & 45.72 & 42.83 \\
5 & 0.284 & 0.205 & 0.156 & 0.165 & 14.37 & 45.20 & 42.06 \\
4 & 0.239 & 0.183 & 0.133 & 0.140 & 18.11 & 44.51 & 41.57 \\
3 & 0.182 & 0.149 & 0.107 & 0.108 & 22.73 & 41.22 & 40.76 \\
2 & 0.121 & 0.106 & 0.077 & 0.074 & 26.27 & 36.36 & 39.03 \\
1 & 0.061 & 0.055 & 0.040 & 0.038 & 28.21 & 33.80 & 37.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 26: Peak velocities of each story of the superstructure (for input 0.1 g). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Story**} & \multicolumn{3}{c}{**Peak Velocities in (cm/s) under Seismic Waves**} & \multicolumn{3}{c}{**Relative Difference with EL Wave (\%)**} \\ \cline{2-9}  & **EL Wave** & **Kobe Wave** & **Taft Wave** & **TS Wave** & **Kobe Wave** & **Taft Wave** & **TS Wave** \\ \hline
6 & 2.500 & 1.995 & 1.451 & 1.401 & 20.17 & 41.95 & 43.94 \\
5 & 2.363 & 1.870 & 1.351 & 1.311 & 23.65 & 42.81 & 44.49 \\
4 & 2.091 & 1.632 & 1.163 & 1.147 & 23.17 & 44.38 & 45.16 \\
3 & 1.704 & 1.300 & 0.908 & 0.935 & 21.42 & 46.70 & 45.12 \\
2 & 1.209 & 0.901 & 0.612 & 0.675 & 18.67 & 49.37 & 44.19 \\
1 & 0.630 & 0.459 & 0.303 & 0.360 & 15.80 & 51.94 & 42.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 25: Peak displacements of each story of the superstructure (for input 0.1 g).
# An Aspect-Oriented Approach to Time-Constrained Strategies in Smart City IoT Applications
## 4 Benefits of Applying Our Approach in the Context of a Smart City
In contrast with a full firmware update, which may require the transmission of a full image over the network, our approach transmits only those components of a strategy for which the WoV constraints determine an update is necessary (Figure 6). Figure 6: Comparison of the firmware-update and aspect-oriented approach to managing the time validity of strategies, showing the lower overhead in the aspect-oriented approach to reach the same goal state. A reduction in communications usage between IoT devices improves both their overall energy usage and service life [42; 43]. Thus, by reducing the communications overhead associated with strategy updates at the scale of an entire smart city, both cost and environmental savings can be achieved.
## 4 Applications
### Estimation of Stay Cable Forces at Queensferrry Crossing  
During the construction of Queensferrry Crossing (QFC), a cable-stayed bridge over the Firth of Forth near Edinburgh, vibration measurements were taken on stay cables to determine their tension forces. The cable forces were also available directly from stressing operations during the installation of the cables, thus serving as a reference for the results determined by smartphones as well as the RPi-based measurement system. A brief summary of the investigations will be given here. For further details, the reader is referred to [6].  
In a preliminary test, the accelerations were recorded under different manual excitations at selected stay cables. The extracted frequency spectra revealed that several modes could be identified, while the symmetric modes of vibration were more prominent. The fundamental frequency was not found due to the nature of the manual excitation near the lower cable anchorage. The frequencies that could be identified with the smartphone and the RPi measurement system agreed well with differences for higher modes typically being less than 1%. It should be noted that, even under small acceleration amplitudes, frequencies could be identified.  
In a second test, the influence of the excitation was investigated with manual excitations compared to ambient vibrations that were mainly induced by wind. Acceleration measurements were taken at different stay cables by two different smartphones. The natural frequencies were extracted, and the cable force was determined by employing a non-linear force-frequency relationship that was obtained by numerical simulations. The comparison to the reference force showed that calculated forces differed by no more than 1% and were on average within 0.5% of the references, which was within the expected accuracy of the reference forces.  
Further vibration measurements were conducted at several other stay cables under ambient excitation. Thereby, the external sensor MPU-6050 was connected to the RPi, and the smartphone was used as a central control unit. This setup was chosen because it features both sufficiently high resolution and a sampling rate necessary for the validation under ambient vibrations. The measurements showed that at least 10 natural frequencies could be identified in each of the measurements for the investigated stay cables. Again, the cable forces were identified applying the non-linear force-frequency relationship, and a good agreement with the reference cable forces was found. The predicted cable forces were within the limit of a 1.5% maximum and less the 1% on average. To sum up, the RPi-based system and a specific smartphone (Sony Xperia Z5) were able to identify frequencies with sufficient accuracy even under ambient wind excitations.