esa-sceva/satcom-chunk-collection huggingface dataset:
### Data Privacy and Security  
Secure communication is the prime factor for maintaining data privacy and confidentiality in a different kind of IoT and IoE architecture. Regulations linked to data collection, storing in memory, and exchanging details should follow a way that can preserve the user's personal record is referred to as data privacy. Security can be improved using secure key management [20; 21] and physical unclonable function [22]. Figure 7 shows four domains that defines various security concept of IoT-based network [23].  
As IoT is becoming an integrated part of our daily life, the usage of IoT-based devices is increasing rapidly. It is predicted that 70% of devices will be IoT-based devices due to the continuous development of urbanization. CISCO predicted that $14.4 trillion devices will be exploited by 2025 [16]. M2M traffic is increasing,and it is expected that it will be up to 45% of the whole Internet traffic by 2022. Another study shows that by 2025 global economy of IoT-based healthcare applications will contribute about $1.1-$2.5 trillion in growth per annum. It will change the global economy, and $2.7 trillion to $6.2 trillion is the estimated impact by 2025 [2]. By 2025, it is reckoned that 75 billion IoT devices will be present in the global network. In the worldwide network, the growth of IoT devices is  
Figure 6: Generalized IoE Architecture.  
Figure 7: Security Taxonomy.  
attracting attackers to gain access to fulfilling their goals. According to Symantec, since the rapid expansion and development, cyber-criminals progressively target IoT technology. In 2019, cyber attacks increased by 300% compared to 2018, and it was approximately 3 billion attacks [23]. In [24], it is also stated that security is the major challenge of the IoE network. [PERSON] reckons the threats, and risks of IoE in _The Internet in Everything: Freedom and Security in a World With No Off Switch_. Privacy, cyber-physical security, and interoperability politics, economic growth, individual rights, business models, governance are also discussed in the book [25].
## 3 Results
### Comparison of \(\mathrm{SIF_{d}}\) among Vegetation Classes within Biomes
The Pampa had the lowest maximum T\({}_{\rm can}\) and T\({}_{\rm air}\) values of any biome (311K and 308K, respectively; Figures S8 and S9) and T\({}_{\rm can}\) and T\({}_{\rm air}\) values reached lower values in the Pampa biome compared to any other biome. DBF SIF\({}_{\rm d}\) in the Pampa had the strongest relationship with VPD (r = 0.59, \(p<0.01\); Table 1) while all other vegetation classes (EBF, GRA, SAV, WSAV) were most strongly correlated with T\({}_{\rm can}\) (r = 0.52 \(p<0.01\), 0.77 \(p<0.001\), 0.82 \(p<0.001\), 0.83 \(p<0.001\), respectively; Table 1). EBF SIF\({}_{\rm d}\) in the Pampa showed the lowest correlation to VPD relative to T\({}_{\rm can}\) and T\({}_{\rm air}\), the only vegetation class with the weakest relationship to VPD. Note the DBF sample size was the smallest of all the vegetation classes in all biomes of the study (n = 52, resulting in n = 24 for monthly average SIF\({}_{\rm d}\)), which may have influenced the results (Table 1). In the Pantanal, no significant relationships were found for any vegetation class' SIF\({}_{\rm d}\) with T\({}_{\rm can}\), T\({}_{\rm air}\), or VPD. Values of T\({}_{\rm can}\) and T\({}_{\rm air}\) were the highest and the range of VPD values was the widest in the Pantanal of all the biomes (Figures S8-S10). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{_T\({}_{\rm can}\)_} \\ \hline  & **Amazon** & **Atlantic Forest** & **Caatinga** & **Cerrado** & **Pampa** & **Pantanal** \\ \hline DBF SIF\({}_{\rm d}\) & 0.15 & 0.25 & \(-0.51\)* & \(-0.70\)*** & 0.55 ** & \(-0.15\) \\ \hline EBF SIF\({}_{\rm d}\) & 0.03 & **0.71*** & \(-0.69\)*** & \(-0.52\)** & 0.52 ** & \(-0.07\) \\ \hline GRA SIF\({}_{\rm d}\) & \(-0.70\)*** & \(-0.17\) & \(-0.82\)*** & \(-0.58\)*** & \(0.77\)*** & \(-0.09\) \\ \hline SAV SIF\({}_{\rm d}\) & \(-0.35\)* & 0.19 & \(-0.78\)*** & \(-0.60\)*** & 0.82 *** & \(-0.07\) \\ \hline WSAV SIF\({}_{\rm d}\) & \(-0.41\)* & 0.45** & \(-0.74\)*** & \(-0.61\)*** & 0.83 *** & \(-0.04\) \\ \hline  & \multicolumn{6}{c}{_T\({}_{\rm air}\)_} \\ \hline  & **Amazon** & **Atlantic Forest** & **Caatinga** & **Cerrado** & **Pampa** & **Pantanal** \\ \hline DBF SIF\({}_{\rm d}\) & 0.15 & 0.14 & \(-0.37\)* & \(-0.61\)*** & 0.55 ** & \(-0.13\) \\ \hline EBF SIF\({}_{\rm d}\) & 0.03 & **0.74*** & \(-0.57\)*** & \(-0.50\)** & 0.46 ** & \(-0.01\) \\ \hline GRA SIF\({}_{\rm d}\) & \(-0.72\)*** & \(-0.07\) & \(-0.75\)*** & \(-0.52\)** & 0.76 *** & \(-0.06\) \\ \hline SAV SIF\({}_{\rm d}\) & \(-0.35\)* & \(-0.31\) & \(-0.73\)*** & \(-0.56\)*** & 0.79 *** & \(-0.05\) \\ \hline WSAV SIF\({}_{\rm d}\) & \(-0.40\)* & 0.54*** & \(-0.66\)*** & \(-0.55\)*** & 0.82 *** & \(-0.02\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Relationships (r values) between monthly SIF\({}_{\rm d}\) and T\({}_{\rm can}\), T\({}_{\rm air}\), and VPD for vegetation classes in each biome. \(p\)-values indicated by stars: *** = \(p<0.001\), ** = \(p<0.01\), * = \(p<0.05\), no stars indicate non-significant relationships.
## 4 Results and Discussion
### Features Classification Results
In contrast, the VGG-19 model, renowned for its deeper yet uniform architecture, typically demands more excellent computational resources and training time, potentially accounting for its comparatively lower performance in our study. Other studies, such as that conducted by [61], which compared AlexNet with VGG-19, have observed that while VGG-19 can achieve high accuracy, its computational demands and training requirements are notably more substantial. It is worth reiterating that AlexNet's blend of depth and computational efficiency often renders it a more pragmatic selection for large-scale applications. Our research, which confidently affirms this assertion, shows that AlexNet surpasses VGG-19 in pedestrian crash diagram classification, further consolidating its significance. These results underscore the robustness and reliability of the AlexNet model in accurately identifying features within pedestrian crash diagrams, while highlighting its efficiency in terms of computational resources. The superior performance and reduced computational time of the AlexNet model make it a compelling choice for integration into practical applications and further research initiatives. Our study adds significantly to the growing body of evidence supporting AlexNet's utility in various image classification domains. AlexNet's demonstrated superiority in this context underscores its potential for practical applications, making our findings important for researchers, planners, engineers, and agencies. This technology enables them to efficiently extract and analyze features from pedestrian crash diagrams, leading to the development of targeted safety measures and countermeasures. Ultimately, this contributes to the crucial goal of enhanced traffic safety and reduced pedestrian casualties.
# Experimental and Numerical Dynamic Identification of an Aerostatic Electro-Spindle
## 4 Numerical Results
### Damped Natural Frequencies and Damping-Factor Identification
#### 4.7.1 Logarithmic Decrement Method (LDM)  
Although the LSCEM proved to be the most reliable method, for the sake of completeness, the LDM method was also used to identify the main damping factors (with the largest modal constant) and natural frequencies of the numerical signals \(y_{0}\), \(y_{L}\), \(y_{G}\) and \(\theta_{x}\). The LDM was used, considering time intervals \(\Delta T\) of 5 and 10 ms. The resulting values of the damping factors were compared to evaluate the sensitivity of the method with respect to the signal length. The main frequency of each signal was also evaluated by measuring the average period of the oscillations present in the considered time intervals. This frequency can be compared with the peaks visible in the related spectrum. Tables A1-A4 in Appendix A list both the natural frequencies resulting from the FFT analysis and the frequencies and the damping factor of the signal resulting from the logarithmic decrement method. When the oscillation was highly damped (\(p_{s}\) = 0.3 and 0.5 MPa), only a 5 ms time window was considered. The following considerations can be made based on the data analysis:  
* The frequency obtained from the average period of the oscillation is quite similar to one of the two frequencies detected in the FFT spectrum; in particular, the higher frequency is visible only on the rear plane (signal \(y_{L}\)), while the lower frequency is present on the other signals (\(y_{0}\), \(y_{G}\) and \(\theta_{x}\));
* in most cases, the application of LDM to signals with durations of 5 and 10 ms does not influence the results;
* depending on the initial condition, one of the two modes prevails;
* due to the short duration of the vibration at \(p_{s}\) = 0.3 MPa, which is highly damped, the FFT algorithm does not provide a good estimation of the frequency spectra;
* the frequency increases with the supply pressure, while the damping factor decreases;
* by increasing the air gap, the frequency decreases, as well as the damping factor.  
Figure 13: Examples of frequency spectra of signals \(y_{G}\), \(y_{0}\), \(\theta_{x}\) and \(y_{L}\) obtained from the orbit method.
## Results
### Data Collections at High Spatial or Stand Level Resolution
#### 3.1.3 Thermal Data  
Data collected at the ground calibration site with a hand held IRT device and a portable calibration black body verified the accuracy of G-LiHT's surface temperature products (Figure 5). Here differences in radiant energy between morning and mid-day acquisitions along with imaging spectroscopy data were used for ecosystem assessments of Gross Ecosystem Production (GEP) and LUE, discussed in later sections.
## 4 Experiment
### Setup
#### 4.1.3 Baselines  
To compare our model with previous research, we selected several common baselines from matrix factorization, sequence models, and graph models as follows:  
* **FPMC**[6]: FPMC is one of the earliest attempts at next POI recommendation, combining matrix factorization and Markov chains to create personalized transition graphs.
* **PRME**[8]: PRME is a personalized ranking metric embedding method that integrates sequential information, individual preferences, and geographical influence to improve next point-of-interest recommendation performance.
* **LSTM**[20]: LSTM networks are a classical type of RNN architecture specifically designed to address the challenge of learning long-term dependencies, making them particularly effective for sequential data prediction and analysis, such as human mobility data.
* **PLSPL**[16]: PLSPL combines user-specific long- and short-term preferences using a linear combination unit and employs LSTM models to capture the distinct influences of locations and categories.
* **STAN**[15]: STAN utilizes a bi-layer attention architecture to exploit spatio-temporal correlations within user trajectories, enabling interactions between non-adjacent locations and non-consecutive check-ins. It also incorporates personalized item frequency to enhance target recall.
* **GETNext**[23]: GETNext is a novel graph-enhanced transformer model that integrates global transition patterns, user preferences, spatio-temporal context, and time-aware category embeddings into a transformer framework to improve accuracy in predicting the next POI and address the cold-start problem.
* **STHGCN**[25]: STHGCN leverages hypergraphs to capture both intra-user and inter-user trajectory information, combining it with spatio-temporal data using a novel hypergraph transformer; this method demonstrates superior performance and effectively addresses cold-start issues.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Users** & **POIs** & **Category** & **Check-Ins** \\ \hline NYC & 1048 & 4981 & 318 & 103,941 \\ TKY & 2282 & 7833 & 290 & 405,000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic statistics of the NYC and TKY datasets (after data preprocessing).
# Time-Dependent Vehicle Routing Problem with Drones Under Vehicle Restricted Zones and No-Fly Zones
## 6 Numerical Experiments
### Algorithm Validation  
Based on 12 large-scale test cases, comparative experiments are conducted using the hybrid genetic algorithm (HGA), genetic algorithm and variable neighborhood search (GAVNS), and HGAVNS-LM. The HGA parameter settings are consistent with those in [38], while for GAVNS, all parameters are the same as those in HGAVNS-LM except for the learning mechanism. The GAVNS parameter settings are consistent with the initial values of the parameters in this paper. The specific experimental results are shown in Table 3, and the meanings of the metrics can be found in Table 2. According to the results in Table 2, HGAVNS-LM significantly reduces distribution costs, delivery distances, the number of vehicle routes, and the number of drone routes compared to GAVNS and HGA. Additionally, as the customer scale increases, the advantages of HGAVNS-LM become more pronounced. For example, in the C08 case, HGAVNS reduces one vehicle route compared to both GAVNS and HGA, with improvements in distribution costs of 17.8% and 20.9%, respectively. Compared to HGA, the improvement rate of HGAVNS-LM ranges from 1.4% to 17.8%, with an average improvement rate of 4.7%; compared to GAVNS, the improvement rate of HGAVNS-LM ranges from 3.0% to 26.7%, with an average improvement rate of 12.3%, indicating that the learning mechanism enhances the algorithm's solving capability. These results demonstrate that HGAVNS-LM shows strong solving ability across test cases of different sizes and exhibits higher stability and superiority in solving the TDEVDRP-RZ-NFZ.
# Path Optimization of Two-Posture Manipulator of Apple Packing Robots
## 3 Materials and Methods
### Path Optimization Method for the Manipulator of Apple Packing Robots  
#### 3.2.1 Working Principle of Packing Robot  
An apple packing robot is mainly composed of four parts, namely, main control, visual detection, apple tray transmission, and manipulator units. The schematic of the packing robot is shown in Figure 1.  
Figure 1: Apple packing robot: (1) Main control unit, computer (2) Manipulator unit, including motor, manipulator, and end effector (3) Visual detection unit, including camera and positioning mechanism (synchrono wheel) (4) Apple tray transmission unit, conveyor belts.  
Automatic apple packing is shown as follows. First, the fixed grasping and placing points are detected by the visual detection unit. Second, apples on grasping and placing points are detected through image processing methods, and detection results are sent to the main control unit. Third, the main control unit controls the manipulator in transferring apples from the grasping points to the vacant placing points. Grasping points are those positions where apples waiting to be grasped are located after the transportation is completed, and placing points are the grooves on apple trays. The transfer will continue when all grooves on one tray are filled with apples. The tray is then transferred to the fixed position.
# Rolling Bearing Fault Diagnosis Based on Wavelet Packet Transform and Convolutional Neural Network
## 4 Experiment Studies
### Results and Discussion  
There are in total 7200 bearing condition samples. As discussed in Section 3.1, all samples are converted into 1-D time-frequency coefficient vectors of size 64 by using the wavelet packet transform. To form the input of CNN model, the data-to image conversion method is used to convert these coefficient vectors into the gray images with a size of \(8\times 8\). Six types of Gray scale images from six conditions are shown in Figure 5. The main parameters of designed CNN model are presented in Table 2, which includes three trainable convolutional (Conv) layers and two max-pooling layers with two fully connected layers. The performance of the proposed CNN-based fault diagnosis model is evaluated by using ten trials. For each time trial, the training samples, validation samples, and testing samples are randomly selected. Figure 6 shows the confusion matrix of one trial result. Table 3 presents the diagnosis performance of the proposed method in ten trials. From the results, the proposed model presents an excellent diagnostic accuracy of 100% for normal bearing condition, and other fault types exceed 93%.  
From the comparison results, it can be seen that the diagnosis performance of the proposed method is more stable and accurate than that of other methods.
## 2 Light Field Camera Structure and Depth Extraction  
### Pickup Process of Light Field Camera Based on Geometry Optics  
Figure 1 shows the geometrical structure of a light field camera. In this study, the standard plenoptic camera is used as the structure of the light field camera. When the distance between the microlens array (MLA) and main lens is \(a\), the distance from the main lens to the focal plane is represented by \(b_{1}\) where the focal plane of LFC lies at a specific location, not infinity. The microlens diameter is \(l_{p}\) and the focal length of the MLA is \(f_{m}\). The light field information of the object is recorded by the image sensor and MLA, magnified by the main lens. Since the pickup unit is floated on the specific position, it has an advantage that all the objects in the front and rear of focal plane can be photographed. According to the modified \(f\)-number matching condition, the overall size of the image sensor and the MLA are different, and the distance between two optical components is as much as the focal length of the MLA. Because each element is located at a different distance from the main lens, the location and magnification of two elements differ from each other. By using simple geometrical optics, one can determine the magnification value for MLA, and from this, \(b_{1}\) is determined with the lens formula. The distance between the main lens and the magnified sensor is \(b_{2}=(f^{-1}-(a+f_{m})^{-1})^{-1}\). Detailed optical properties of the pickup unit depending on specifications and focal plane of LFC have been previously analyzed [17].
# Financial Fraud Detection Based on Machine Learning: A Systematic Literature Review
## 3 Search Results and Meta-Analysis
### Clustering Based Methods
#### 3.2.4 RQ4 What Are the Gaps and Future Research Direction in Machine-Learning-Based Fraud Detection  
This section attempts to answer RQ4, which aims to present research gaps and future directions of the field. Thus, to determine the limitations and explore the gaps and future work opportunities in this field, we synthesized the reviewed articles as discussed in the following subsections.  
Imbalanced Dataset  
Virtually, most financial transaction datasets comprise millions of transactions, and all of them share a common issue, namely imbalanced datasets [1, 5]. On other hand, the number of fraudulent financial transactions is far fewer than non-fraudulent ones. [33]. This issue is generally caused as a result of the fact that the rate of actual fraud transactions out of all transactions is nominal [2]. The problem of imbalanced data distribution generally affects the efficiency of machine learning models [87]. Therefore, training models for detecting fraudulent activity that is very minimal requires extra consideration.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **Formula** & **References** \\ \hline Accuracy & \(Accuracy=\frac{(TN+TP)}{(TN+TN+TP+TP)}\) & [18, 21, 29, 33, 38, 40, 41, 42, 62, 66, 69, 72, 78, 82, 83, 84, 110, 113, 114, 115, 116, 117] \\ Precision & \(Precision=\frac{TP}{(TP+FP)}\) & [21, 39, 40, 67, 69, 77, 78, 94, 113, 118] \\ Recall/Sensibility/TPR) & \(Recall=\frac{TP}{(TP+FN)}\) & [21, 39, 40, 67, 69, 77, 94, 113, 118] \\ F-measure(F1) & \(F1=2*\frac{Recall*Precision}{(Recall+Precision)}\) & [21, 31, 33, 65, 84, 113] \\ Specificity (TNR) & \(Specificity=1-FP\) & [42, 70, 77, 78, 82, 94, 113] \\ AUC & AUC = the area under ROC curve & [35, 113] \\ Others & & [65, 66, 67, 74, 119] \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance evaluation metrics.  
To address the issue of imbalanced data, some studies applied oversampling approaches [12; 120]. The others attempt to introduce approaches that may effectively work with extremely imbalanced data. For example, [PERSON] et al. [86] and [PERSON] [61] utilized imbalanced and left balancing datasets based on the oversampling method for future work. Additionally, [PERSON] et al. [94] avoided oversampling as it may lead to choice-based sample biases. Essentially, there were no studies that are applied under-sampling methods among the reviewed articles. Moreover, the only applied oversampling method was the SMOTE (Synthetic Minority Oversampling Technique) [121]. Because of this, it could be deduced that future studies could consider employing other oversampling techniques, as well as under-sampling techniques.
## 6 Experimental Results and Analysis
### Experimental Results of the Collection of KMNs
It is well known that results of the collection of KMNs using the proposed framework depended on the change in the position and movement state of the executor of key milestone nodes. Therefore, timing error in the automatic collection of KMNs mainly comes from the extraction accuracy of the spatiotemporal information of the executor. Due to the complex background of the airport ground, it is difficult to improve this accuracy and thus reduce the timing error of KMN collection by only using the spatiotemporal information from the surveillance cameras. Consequently, in future work, it is essential to introduce heterogeneous information from other sensors (e.g., LIDAR, etc.) to improve the extraction accuracy of the spatiotemporal information of the executor, while ensuring the operational safety of the airport ground. Furthermore, it must also be pointed out that several thresholds in the module of collection of KMNs presented in this paper are preset according to the situation of aircraft stands 939 and 734 of Beijing Capital Airport. Since these thresholds are closely related to the distance between the aircraft and the camera, the size of the image resolution, etc., they are not universal and have to be recalibrated manually when the distance between the surveillance camera and the target or when the camera parameters change.