esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Theory
### Skeleton Simulation in Multi-Light-Source Scenarios  
In a multiple light source scenario, contour of each human shadow on the ground is decided by two factors:  
1. human contour shape.
2. positional relationship between the light source and the human.  
Since each single shadow on the ground is restricted in a 2D plate, it is impossible to reproduce 3D information from any single shadow image. However, multiple shadows generated by different light sources can carry contour information from multiple 3D view angles, allowing the reproduction of 3D information.  
A 3D voxel model of an object can be simulated from shadows generated by a annular set of light sources [6]. However, out of the laboratory environment, accurate manual arrangement of light source positions is elusive. Thus SSSE is designed to be adaptive to the posterior combination of random light source positions. With two or more shadows generated by different light sources, our method is capable of simulating 3D human skeleton information.  
In a multiple light source scenario shown in Figure 1a, \(Sh_{a}\) and \(Sh_{b}\) are shadows of human body \(M\), generated by light sources \(S_{a}\) and \(S_{b}\) respectively. The scenario is captured by camera \(C\), and the human body \(M\) and shadows \(Sh_{a}\), \(Sh_{b}\) are captured as \(P\), \(Sh_{a}\), \(Sh_{b}\) in the frame, respectively. With the captured frame, 3D position of the certain joint part \(M_{p}\in M\) can be extracted based on the following three steps.
# Hybrid RF/VLC Network Architecture for the Internet of Things
## 5 Implementation  
In this section, the physical implementation of the system is presented. The structure of each access point comprising the network is described first, followed by the global structure of the backbone, and finally the IP gateway.
## 4 Conclusions  
A new sample environment for in situ dynamic light scattering and SANS at the ESS was developed. The entire setup was transferred to MLZ and test measurements were performed. The sample environment was used to successfully measure SANS. The background problem will be solved at the ESS using a smaller distance between the individual sample cells and the end window of the collimation system. The simultaneous operation of the DLS system was proven for both configured scattering angles. The sample environments' capabilities regarding temperature control and measurement were also characterized and fulfill all specifications. The integration of all devices in both EPICS and NICOS was successfully finished, making the sample environment ready for integration and first tests at the ESS.  
Conceptualization, T.H., P.M.-B. and R.v.K.; SANS measurements, A.J.S. and H.F.; software, A.J.S., G.B., T.B. and L.W.; construction of the light scattering setup, A.J.S. and L.W.; construction of the carrier system: A.J.S., S.J., J.R., H.S., A.H., T.B., T.W., L.P.K., M.K., O.L. and L.W.; writing--original draft preparation, L.W. and T.H.; writing--review and editing, A.J.S., S.J., H.S., A.H., T.B., T.H., T.W., J.R., L.P.K., M.K., H.F., O.L., P.M.-B., R.v.K.; visualization, L.W.; supervision, T.H.; project administration, T.H.; funding acquisition, T.H., P.M.-B. and R.v.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the German Federal Ministry for Education and Research (BMBF) within the project \"FlexiProb\" sample environment grant number 05K2016.  
Not applicable.  
The data shown is available on request from the corresponding author.  
Figure 9: Application for controlling and monitoring the fits of the data during the measurements.  
**Acknowledgments:** We gratefully acknowledge the ESS Motion Control Group for help implementing the control of the translation stage. We gratefully acknowledge [PERSON] for varied fruitful discussions, [PERSON] and [PERSON] for help during the SANS test experiments. Furthermore, we thank [PERSON] (HHU, Dusseldorf) for the original version of the software used to operate the FPGA as a correlator. We gratefully acknowledge financial support for the publication costs by the Open Access Publication Fund of Bielefeld University.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Study on the Impact of Low-Frequency Random Loading and Unloading on Silo Structures
## 2 Reinforced Concrete Silo Force Analysis
### Most Unfavorable Loading Model Analysis  
According to the measured pressure fluctuation patterns, in order to determine the most unfavorable loading method, a reinforced concrete silo model with seven different loading methods, as shown in Figure 4, was established using the finite element software Abaqus for stress analysis.  
The model parameters are as follows: the bulk density of the cement raw material \(\gamma=14\) kN/m\({}^{3}\); the calculated height of the stored material \(h_{n}=40\) m; the friction coefficient of the stored material on the silo wall \(\mu=0.58\); and the internal friction angle \(\varphi=30^{\circ}\). The concrete adopts a plastic damage model using a three-dimensional solid-element C3D8R, which is an eight-node first-order linear reduced-integration hexahedral element. The stress and strain evolution behavior of horizontal and vertical steel bars was characterized using a bilinear model. The steel bars were represented using a two-node linear three-dimensional truss element (T3D2). Due to the unique geometric structure of the truss element, this research did not consider the relative slippage between the steel bars and the concrete, adopting a separated embedded model instead. Fixed constraints were applied at the bottom of the silo.  
We applied the same horizontal pressure load according to the seven working conditions shown in Figure 4, and the ratio of the load in the non-unloading area to the load in the unloading area was 1.43. We then conducted a simulation analysis. Seven silo wall tensile damage diagrams of the different unloading areas were obtained, as shown in Figure 8. The stress nephogram of different unloading areas is shown in Figure 9.  
Figure 7: Comparison of measured horizontal pressure of silo wall at elevation of 31.5 m.  
Figure 8: Contour map of the tensile damage to the concrete on the silo wall in different unloading areas: (**a**) unloading areas No. 1 and No. 3; (**b**) unloading areas No. 2 and No. 4; (**c**) unloading areas No. 3 and No. 5; (**d**) unloading areas No. 4 and No. 6; (**e**) unloading areas No. 5 and No. 7; (**f**) unloading areas No. 6 and No. 1; and (**g**) unloading areas No. 7 and No. 2.  
Figure 9: \(Cont.\)  
As can be seen from the figure, when both discharge outlets No. 5 and No. 7 are operating simultaneously, the maximum stress on the silo wall reaches 3.626 MPa. At this point, the local tensile damage is the most severe. This situation poses the greatest threat to the safety of the silo and is also most likely to cause cracking in the silo wall's concrete. Therefore, in practical engineering applications, the unloading method of using both No. 5 and No. 7 as unloading areas simultaneously should be avoided to reduce the possibility of silo wall cracking.
# Generative Modeling for Imbalanced Credit Card Fraud Transaction Detection
## 4 Methodology and Materials
### Proposed Solution  
In this work, our goal is to achieve better results in detecting fraudulent transactions by leveraging oversampling techniques through generative modeling. To this end, we propose several generative models due to their strong ability to simulate the behavior of a dataset and construct similar synthetic datasets. Our solution can be described as follows: first, we apply a Random Scaler to the Amount and Time features to standardize these attributes. Next, we divide the data into training and testing sets, with 70% allocated for training and 30% for testing. After this, we apply an oversampling technique to the training set to enhance the representation of the minority class. We then train our model using the oversampled training data. Finally, we classify the testing set with the trained model and perform extensive evaluations to identify the most effective strategies for detecting fraudulent transactions. Figure 6 and Algorithm 5 show the main steps of the proposed solution.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Importance Score (1–10)** & **Normalized Weight** & **Justification** \\ \hline \hline Accuracy & 7 & 0.117 & \begin{tabular}{c} Reflects the overall performance of the model, but may not fully capture the performance on fraud detection specifically. \\ \end{tabular} \\ \hline Precision & 9 & 0.150 & \begin{tabular}{c} High precision means fewer false positives, which is crucial in fraud detection to avoid wrongly flagging legitimate transactions. \\ \end{tabular} \\ \hline \hline Recall & 10 & 0.167 & \begin{tabular}{c} Very important as high recall ensures that most fraudulent transactions are detected, minimizing missed fraud cases. \\ \end{tabular} \\ \hline G-Mean & 8 & 0.133 & \begin{tabular}{c} Balances performance between classes, important for handling class imbalance in fraud detection. \\ \end{tabular} \\ \hline Specificity & 7 & 0.117 & \
\begin{tabular}{c} Indicates how well the model identifies non-fraudulent transactions; important but slightly less critical than recall. \\ \end{tabular} \\ \hline F-Measure & 9 & 0.150 &
\begin{tabular}{c} It combines precision and recall, providing a balanced view of the model’s ability to detect fraud while minimizing false positives. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Table of metric scores with justifications.
# Research on Secure Community Opportunity Network Based on Trust Model
## 5 Conclusions  
This study introduces a robust Secure Community Trust Model (SCTM) algorithm for opportunistic network routing, premised on a trust-based framework. The foundation of this model lies in the premise that nodes exhibiting commendable forwarding behavior in historical interactions are likely to maintain similar efficacy in future communications. To counteract malicious activities within opportunistic networks, the SCTM algorithm leverages the historical interaction records between nodes and their counterparts within the same local community to compute a trust score. This score facilitates the dynamic segmentation of secure communities via adaptable trust thresholds and decay functions, ensuring message propagation remains within a safeguarded network milieu. To augment message forwarding efficiency, mitigate the transmission load on the network backbone, and prevent network congestion and node failure, the SCTM algorithm employs a constrained replica forwarding tactic. This approach strategically segments message replicas by evaluating node centrality, residual cache capacity, and remaining energy levels, thus optimizing the routing and forwarding process. Simulation outcomes underscore the SCTM algorithm's superiority over comparative algorithms, demonstrating an unmatched transmission success rate of 81% and minimal transmission delays, all while maintaining  
Figure 13: _Overhead_ and malicious nodes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Direct** & **Epidic** & **Spray** & **BubbleRap** & **SCTM** \\ \hline
**deliveryratio (\%)** & 28\% & 40\% & 80\% & 32\% & 90\% \\ \hline
**latency\_avg (s)** & 6339 & 3080 & 3245 & 2559 & 2032 \\ \hline
**overhead** & 0 & 2966 & 11 & 3243 & 669 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison table between SCTM algorithm and existing algorithms.  
low network overhead. Notably, the SCTM algorithm exhibits proficiency in detecting and isolating malicious nodes, thereby safeguarding against hostile intrusions and preserving algorithmic stability. Future investigations will delve into more sophisticated trust models to bolster data transmission security, scrutinize social affiliations among nodes, and develop routing strategies that are simultaneously low latency, energy-efficient, and highly effective.  
Conceptualization, J.L. and B.S.; Methodology, J.L.; Software, B.S.; Validation, J.L. and B.S.; Formal Analysis, J.L.; Investigation, B.S.; Resources, B.S.; Data Curation, B.S.; Writing--Original Draft Preparation, J.L.; Writing--Review and Editing, B.S.; Visualization, J.L.; Supervision, B.S.; Project Administration, B.S.; Funding Acquisition, B.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data that support the findings of this study are available on request from the corresponding author, [PERSON], upon reasonable request.  
The authors declare no conflicts of interest.
## 3 Literature Review
### Physiological Biometrics
#### 3.2.2 Ocular Dynamics
Other studies [90, 91] using ocular biometrics for mobile authentication have utilized the many details of an individual's iris to perform user authentication. In study [90], researchers propose a multi-instance cancellable iris authentication system (MICBTDL). For the enrollment process, the user device captures left and right iris images. Random cross-folding is applied, and the images are transformed before being sent to a CNN for feature extraction. A reference template is generated and sent to a cloud server. The authentication process runs similarly, but the reference template is sent to the cloud server to be verified with an artificial neural network (ANN). To train their model, the MMU and IITD iris databases were utilized, consisting of left and right iris images of 253 subjects in total. Results found that their CNN extraction method outperformed comparison feature extraction techniques, and that MICBTDL achieved a fair performance against comparison studies, with an EER from 0.03 to 0.06% between the two datasets. Another CNN-based iris authentication system is proposed in study [91]. The architecture of their model involves a mask region-based CNN (R-CNN) structure that locates and extracts iris features with regional proposal networks component (RPN). For the recognition process, mobile inception V4 neural network architecture is fine-tuned and executed. The UTiris dataset, consisting of 1540 iris images captured from 79 users in non-constraint conditions, was used to train their model. Results found that their scheme was able to outperform comparison models with a 99.10% authentication accuracy. The study found their model suitable for high-performance mobile devices. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Study** & **Algorithm** & **EER (\%)** \\ \hline
[86] & CNN & 1.32 \\
[87] & CNN & 1.71 \\
[89] & CNN & 4.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of studies with VISOB dataset [86, 87, 89].
## 6 Path-Following Control Laws While Avoiding Collisions
### Control Laws for Approaching a wayPoint  
One discusses how to set the robot's velocity vector \(\mathbf{v}(k)\in\mathcal{R}^{2}\) so that it approaches a wayPoint, while keeping away from colliding with other objects or static map environments. Let \(\mathbf{W}\in\mathcal{R}^{2}\) define the robot's wayPoint, so that the robot arrives at the wayPoint within one sample-step. Under (2), one defines \(v(k)=\|\mathbf{v}(k)\|\).  
The robot's desired heading direction is set as  
\[\mathbf{h}=\frac{\mathbf{W}-\mathbf{q}(k)}{\|\mathbf{W}-\mathbf{q}(k)\|}. \tag{9}\]  
In order to arrive at \(\mathbf{W}\) at sample-step \(k+1\), the desired speed of the robot is  
\[v_{D}=\frac{\|\mathbf{W}-\mathbf{q}(k)\|}{dt}. \tag{10}\]  
In the case where  
\[\frac{\|\mathbf{W}-\mathbf{q}(k)\|}{dt}<s_{max}, \tag{11}\]  
\(\mathbf{W}\in\mathcal{R}^{2}\) is reachable within one sample-step.  
In the case where (11) is satisfied, the robot sets its speed to \(v_{D}=\frac{\|\mathbf{W}-\mathbf{q}(k)\|}{dt}\). In the case where (11) is not satisfied, the robot increases its speed to its maximum speed \(s_{max}\). Hence, the robot's velocity vector (\(\mathbf{v}(k)\) in (2)) is set as  
\[\mathbf{v}_{g}=\frac{min(v_{D},s_{max})*(\mathbf{W}-\mathbf{q}(k))}{\| \mathbf{W}-\mathbf{q}(k)\|}. \tag{12}\]  
Using \(\mathbf{v}_{g}\) in (12), the robot approaches the wayPoint. In (12), \(min(v_{D},s_{max})\) presents the speed command of the robot. Furthermore, \(\frac{(\mathbf{W}-\mathbf{q}(k))}{\|\mathbf{W}-\mathbf{q}(k)\|}\) presents the heading command of the robot.  
In the case where \(\mathbf{v}_{g}\) in (12) satisfies the safety requirement in Section 5.1, \(\mathbf{v}_{g}\) is utilized as the robot's velocity vector \(\mathbf{v}(k)\). However, in the case where \(\mathbf{v}_{g}\) does not satisfy the safety requirement, one utilizes Algorithm 1 to obtain the robot's velocity vector for avoiding collision with other objects or static map environments.
## 5 Results
### Radar Data Set
#### 5.2.1 Detection Rate of the Poisoned Samples  
Figure 12 and the related subfigures show the comparison of the detection rate \(P_{Det}\) for the three attacks (LF, FSP, TFP) and the three different OD algorithms for different values of \(T_{P}\) in the Radar data set. The results are presented for \(S_{P}=16\) and for three different OD algorithms. In comparison to the eWINE data set (where the SDO algorithm was significantly more effective in performance than the other OD algorithms), the difference among OD algorithms is more balanced for the Radar data set. For the LF attack, SDO is clearly superior to IF and OCSVM as shown in Figure 13a, but for the FSP and TFP attacks, each OD algorithm has strengths and weaknesses depending on the value of \(T_{P}\). In particular, Figure 13b shows that for low values of \(T_{P}\) (\(T_{P}\) = 0.1 and 0.2), the IF and OCSVM are slightly better than SDO, while for higher values of \(T_{P}\) (\(T_{P}\) greater than 0.2), SDO is slightly better than IF and significantly better than OCSVM. Thus, a summary of the results of this Radar data set and the eWINE data set seems to indicate that the detection accuracy of each OD algorithm depends on the data set structure and data distributions but SDO has a strong detection performance while also taking into consideration its low computing complexity.
## 2 Results and Discussion
### Volatile Compounds
#### 2.3.3 Aldehydes and Ketones  
The largest number of aldehydes compounds (24) was detected in Clapp, and Packham contained the fewest aldehydes (13) (Figure 3). The concentrations of aldehyde compounds were relatively low in all pears other than acetaldehyde, hexanal and (_E_)-2-hexenal. Acetaldehyde was detected in high concentrations ranged from 4.81 ng/g in D Jules to 23.71 ng/g in longyuanyangli. C\({}_{6}\) compounds (C\({}_{6}\) aldehydes and C\({}_{6}\) alcohols) are regarded as green leaf volatiles and contribute to the herbaceous odour in fruits [14; 15; 16]. In this study, hexanal, (_E_)-2-hexenal and 1-hexanol are dominant C\({}_{6}\) compounds. The concentration of hexanal ranged from 2.07 ng/g in D Jules to 12.46 ng/g in longyuanyangli and the (_E_)-2-hexenal concentration changed from 1.60 ng/g in D Jules to 6.27 ng/g in longyuanyangli. In addition, benzaldehyde was detected in all pears. Benzaldehyde has an almond-like smell and has been previously isolated from green teas [22], lychee [24], and apricot [16]. Figure 4 shows that small proportion ketones were detected. In total, eight ketones were found, but 6-methyl-5-heptene-2-one was the only ketone that presented in all pears. It has been reported that 6-methyl-5-heptene-2-one was present in higher amount in the peel compared to the flesh and was a degradation product of lycopene [16] or \(\alpha\)-farnesene [25; 26]. In addition, 6-methyl-5-heptene-2-one possesses fatty, green, citrus odour [27] and is a common ketone existed in many fruits [16; 24; 28; 29].
## 2 Main Subject
### Proposed Position Estimation System for Active Driving Assistance
#### 2.2.1 UWB-Based Object Estimation Technique
However, since it is difficult to install ANs at both ends of the entrance and exit of the wheelchair getting on/off space, as proposed in this study, the position is estimated by installing two ANs at both ends of the entrance door, as shown in Figure 3. Therefore, this study proposes a position plan consisting two ANs in consideration of the anchor environment mounted at the entrance of the building under the assumption that the position of the ANs and the position of the TN mounted on the wheelchair are known. The \(r_{j}\) in Equation (1) is the distance on the plane and is calculated through the UWB sensor distance \(R_{j}^{2}\), the height \(Z_{A_{j}}\) of AN, and the height \(Z_{M}\) of TN. Equation (2) is the X-coordinate in the plane and Equation (3) is the Y-coordinate in the plane. The coordinates of the wheelchair are calculated based on the obtained TN positions. \[r_{j}=\sqrt{R_{j}^{2}-\left(Z_{A_{j}}-Z_{M}\right)^{2}} \tag{1}\]  
\[X_{TN}=\frac{r_{1}^{2}-r_{2}^{2}+a^{2}}{2a} \tag{2}\]  
Figure 2: Data pre-processing flow chart for removing UWB sensor noise proposed in this study. Estimation of the UWB Direction Angle  
In order to estimate the direction angle, at least two TNs must exist in a wheelchair, and the direction angle of the wheelchair is estimated based on the position of the TN installed on the wheelchair. Figure 4 shows the process of estimating the direction angle based on the position of the TN measured from the two ANs mounted at both ends of the entrance. The relative direction estimation of the wheelchair is performed through Equation (4). \(\theta_{Default}\) is the UWB angle installed in the existing wheelchair and \(\theta_{UWB}\) is the direction  
Figure 4: UWB-based wheelchair direction estimation. Figure 3: UWB-based wheelchair position estimation. (**a**) Wheelchair positioning schematic diagram; (**b**) UWB sensor TN position estimation. angle of the wheelchair. Through this, the direction angle can be estimated from the UWB sensor. \[\theta_{UIWB}=ATAN2\bigg{(}\frac{\left(Y_{2}-Y_{1}\right)}{\left(X_{2}-X_{1} \right)}\bigg{)}+\theta_{Default} \tag{4}\]