esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this paper, we utilize information captured from airborne geo-referenced color images and noisy airborne LiDAR data to fuse the complementary information and accurately detect traffic signs. Our designed method includes three major steps: (1) road extraction, (2) traffic sign candidate detection, and (3) traffic sign classification. Six joint local features are seamlessly incorporated in the aggregation optimization model to accurately identify the road region in color images. Histogram of height information is utilized along the road region in the LiDAR data mapped by the image to global coordinate projection to detect traffic sign candidates. Local deep features are also incorporated in the sparse representation-based optimization model to accurately identify traffic sign candidates in color images mapped by the global to image coordinate projection. Both qualitative and quantitative results show the effectiveness of the proposed method to detect traffic signs. Some of the important findings are summarized as follows:  
* Using the complementary information from color images and airborne LiDAR data improves the accuracy of traffic sign detection.
* Extracting road regions is an essential initial step, which significantly reduces the search space for traffic signs to improve the detection efficiency.
* Representing local deep features in a sparse representation-based local-embedded optimization model helps to capture the local structure of traffic signs for more accurate classification.  
With the advent of unmanned aerial vehicle (UAV) technology, high-resolution aerial images and LiDAR data will be much more affordable and easily accessible for transportation agencies in the future. Although the current data set was collected with a fixed-wing plane, the methodology developed for the current data set will be readily transferable to any UAV-based data collection platform.  
**Author Contributions:** Supervision, Project Administration, Resources: [PERSON] and X.Q.; Conceptualization and Software: M.J, Z.S. and X.Q.; Data Curation: Z.S.; Original Draft Preparation: M.J., Z.S. and X.Q.; Review and Editing: M.J, Z.S. and X.Q. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research has been funded by Utah Department of Transportation.  
**Informed Consent Statement:** Not applicable.  
**Acknowledgments:** The study was sponsored by Utah Department of Transportation. The views expressed are those of the authors and do not reflect the official policy or position of the project's sponsors.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# New Advances and Challenges of Fall Detection Systems: A Survey
## 2 The Related Work on Prior Surveys  
In this section, we discuss prior surveys on the topics of fall detection briefly. Fall detection cannot be considered as a brand new hot field, since it has been explored in the last two decades and has evolved rapidly. Several researchers have reviewed and assessed the prior research work of fall detection, and presented the ideas, theories, and the significant literature published from various perspectives before 2014.  
The first review paper on fall detection was proposed by [PERSON] et al. [5] in 2008. Two years later, [PERSON] et al. [8] reported existing fall detection methods that have been found effective by others, as well as documented the findings of their experiments, the combination of which would assist in the progression towards a safe, unobtrusive monitoring system for independent seniors. In 2013, [PERSON] et al. [6] gave a survey on different systems for fall detection and their underlying algorithms, and divided fall detection approaches into three main categories: wearable device based, ambience device based and vision based. These approaches were summarized and compared with each other and a conclusion was derived with some discussions on possible future work. In the same year, [PERSON] et al. [2] reviewed the fall detection systems in an extensive literature review of fall detection systems, including comparisons among various kinds of studies. It aimed to serve as a reference for both clinicians and biomedical engineers planning or conducting field investigations. Some challenges, issues and trends in fall detection were identified after the review. In 2014, [PERSON] et al. [9] thoroughly reviewed and qualitatively compared fall detection systems and fall prediction systems, in terms of design issues and other parameters. In 2014, [PERSON] et al. [7] presented a survey focusing on smartphone-based solutions for fall detection and prevention.  
The research work aiming at fall detection has increased dramatically in recent years. There are thousands of papers related to fall detection published after 2014. However, there is no updated survey paper to review the great changes and the tendency of this field in this period. According to a search using Google Scholar, we carefully selected twenty influential papers that have been cited at a high level in this field since 2014, all of which have been cited above 30 times. We hope that this work could reflect the development and the trend for fall detection via analyzing these representative research works.
# Technologies Trend towards 5G Network for Smart Health-Care Using IoT: A Review
## 3 Scenarios for 5G Network and Its Requirements
### Enhanced Mobile Broadband (EMB)  
The essential requirements of this scenario are high-level throughput and superior traffic capacities. For example, in [61], following requirements are specified; in urban and suburban areas the user-experienced data rate is 100 Mbps and in hotspots, it is up to 1 Gbps ; the highest data rate is 20 Gbps ; and, the capacity is equivalent to 10 Mbps/m\({}^{2}\). The main purpose of the scenario is to enhance the traffic capacity and data rate in the network.
###### Abstract  
The fifth-generation mobile network presents a wide range of services which have different requirements in terms of performance, bandwidth, reliability, and latency. The legacy networks are not capable to handle these diverse services with the same physical infrastructure. In this way, network virtualization presents a reliable solution named network slicing that supports service heterogeneity and provides differentiated resources to each service. Network slicing enables network operators to create multiple logical networks over a common physical infrastructure. In this research article, we have designed and implemented an intent-based network slicing system that can slice and manage the core network and radio access network (RAN) resources efficiently. It is an automated system, where users just need to provide higher-level network configurations in the form of intents/contracts for a network slice, and in return, our system deploys and configures the requested resources accordingly. Further, our system grants the automation of the network configurations process and reduces the manual effort. It has an intent-based networking (IBN) tool which can control, manage, and monitor the network slice resources properly. Moreover, a deep learning model, the generative adversarial neural network (GAN), has been used for the management of network resources. Several tests have been carried out with our system by creating three slices, which shows better performance in terms of bandwidth and latency.  
5G networks; E2E network slicing; service orchestration; intent based networking; software defined networking; network function virtualization +
Footnote †: journal: Electronics Electronics
# Effective Feature Selection Methods to Detect IoT DDoS Attack in 5G Core Network
## 3 Background
### Feature Selection  
Feature selection is an important technique for the efficient learning and operation of ML models. It is used to reduce overfitting and learning time as well as to improve accuracy. Feature selection is classified as unsupervised or supervised according to the value of y (label). Supervised feature selection selects features based on the label and is largely divided into three types of methods, i.e., filter, wrapper, and embedded methods [4].  
**Filter Method.** A filter method uses a statistical measurement technique to determine the influence and correlation of a feature, as opposed to that of the best feature subset. Hence, each feature is considered an independent variable. This method had the fastest speed in terms of time complexity. Some examples include feature importance and statistical methods.  
**Wrapper Method.** A wrapper method determines a subset with the optimal usefulness by comparing subset combinations of all features in a specific ML algorithm. Therefore, the more features that exist, the higher the number of computations involved; thus, the method has the slowest speed in terms of time complexity. However, this improves the performance of the model because it determines the best feature subset. When the purpose of the ML algorithm is classification, the subsets are evaluated and compared based on their accuracy, precision, recall, and F1-score. Examples include recursive feature elimination (RFE), recursive feature elimination (RFECV), and sequential feature selection (SFS).  
**Embedded Method.** An embedded method is used to learn features that contribute to the accuracy of the model. In terms of time complexity, this approach is between the wrapper and filter methods. Examples of embedded methods include LASSO, elastic net, and ridge regression.  
In this study, feature selection was applied to find a small number of feature combinations (5 or 10) that maintained or increased the 5G malicious packet detection rate compared with a scenario in which all 55 features were used. The feature selection methods used in the experiment were the filter (feature importance) and wrapper (RFE, RFECV, SFS) methods. Feature importance is a method that uses the importance value provided by a tree-based model. RFE repeats the training using all features by removing features with the lowest importance until a user-specified number of features remains (the default is half of all features). RFECV derives the best feature subset using the same process as RFE. However, unlike RFE, the number of features can be unspecified, and cross-validation is possible. SFS finds the best feature subset by adding or removing features using the forward or backward method. SFS must specify the number of features for selection, and cross-validation is also possible.  
Figure 2: The 5G network flow—Uplink (Adapted with permission from Ref. [36]. 2019, Netmanias).
# Study on Ring Deformation and Contact Characteristics of Thin-Walled Bearing for RV Reducer
## 2 Theoretical Analysis
### Analysis of the Carrying Capacity  
Elliptical truncation is not negligible in bearing performance analysis, and there are few studies on the effect of ring deformation on elliptical truncation [26]. The elliptical truncation is used as the judging condition of the carrying capacity of bearings in this paper, and the influences of radial force and geometric parameters on the axial carrying capacity of bearings are analyzed under rigid ring and flexible ring models.  
Figure 5 illustrates the geometric relationship between the ball-raceway contact ellipse and the raceway rib. Under the action of axial force, the contact ellipse moves toward the raceway rib until elliptical truncation occurs. The expression for the angle \(\phi_{i/o}\) associated with the larger rib of the raceway is  
\[\phi_{i/o}=\arccos\left(1-\frac{h_{i/o}}{r_{i/o}}\right) \tag{27}\]  
Figure 4: Flowchart of the solution process for the simulation model.  
where \(h_{ij,o}\) represents the large rib height of the inner or outer raceway; \(r_{ij,o}\) represents the inner or outer raceway groove curvature radius. The expression for the angle \(\varphi_{ai/oj}\) associated with the semimajor axis of the contact ellipse is  
\[\varphi_{ai/o}=\arcsin\left(\frac{a_{ij/o}}{r_{i/o}}\right) \tag{28}\]  
where \(a\) is the semimajor axis of the contact ellipse; the calculation method can be referred to the reference [10].  
The ellipse truncation rate is usually used to characterize the degree of the ellipse truncation. The expression of the ellipse truncation rate is as follows:  
\[R_{ij/oj}=\frac{\alpha_{ij/oj}+\varphi_{ai/oj}-\phi_{i/o}}{2\varphi_{ai/oj}} \times 100\% \tag{29}\]  
where \(\alpha_{ij,o}\) is the ball-raceway contact angle; the contact ellipse is truncated when \(R_{ij/oj}>0\). However, the contact ellipse is not truncated when \(R_{ij/oj}<0\). In addition, when \(R_{ij/oj}=0\), the contact ellipse is not truncated, and the axial force at this time is the axial carrying capacity of the bearing. It is worth noting that the ring deformation can cause a change in \(\alpha_{ij/oj}\), which affects the carrying capacity.
# Online Determination of Boron Isotope Ratio in Boron Trifluoride by Infrared Spectroscopy
## 3 Results and Discussion
### Investigation of the Enriched BF\({}_{3}\) Gas by IR  
Figure 7 shows the IR spectrograms for an enriched \({}^{10}\)BF\({}_{3}\) sample and an enriched \({}^{11}\)BF\({}_{3}\) sample. It is clearly observed in Figure 7A that when the concentration of \({}^{10}\)B decreased, the absorption peaks of \(A_{10}\) were reduced and the \(A_{11}\) peaks increased. Similarly, the absorption peaks of \(A_{11}\) were reduced and the \(A_{10}\) peaks were increased in Figure 7B. The calculation results for the samples in Figure 7A,B were 0.0120 and 0.7536, respectively. All of the results were in good agreement with the ICP-MS as a reference standard (the isotope ratios for Figure 7A,B were 0.0102 and 0.7546, respectively) for each sample, which proved the validity of the IR method. A series of enriched BF\({}_{3}\) samples, with their isotopic ratios determined by IR, are listed in Table 5, with the mean and standard deviation obtained from 10 determinations. By comparing the results of the IR method and the ICP-MS method, the errors of measurement increased slightly when determining the ratio of high abundance samples. This could be attributed to the decline in the signal-noise ratio (SNR), which was caused by the decrease in the absorbance value.  
Figure 6: Influence of pressure and temperature on the isotope ratio of \({}^{10}\)B/\({}^{11}\)B. (**A**) Influence of pressure; (**B**) Influence of temperature.  
Figure 7: IR spectrograms of enriched BF3. (**A**) Spectrogram of an enriched \({}^{11}\)BF\({}_{3}\) sample; (**B**) Spectrogram of an enriched \({}^{10}\)BF\({}_{3}\) sample.
# Energy-Efficient Partial LDPC Decoding for NAND Flash-Based Storage Systems
## 3 Proposed Partial Decoding of LDPC Codes  
The partial decoding of an LDPC code is newly introduced to provide various error-correcting capabilities, which can be adaptively applied according to the channel condition. The decoding strength is adjusted by changing the number of check nodes to be used for decoding. The number of check nodes relevant to a variable node is called the column degree of the H-matrix. Since each variable node collects the local messages come from the connected check nodes, the LDPC decoding works normally with some check nodes removed. Therefore, the error-correcting capability can be adjusted by changing the column degree.
## 4 Experiment and Its Simulation Environment
### Vehicle Motion Control  
Upon completion of the vehicle motion decision process, the lower control layer primarily governs the vehicle's longitudinal and lateral movements via proportional and proportional-differential controllers [27]. The longitudinal motion is managed by the proportional controller with acceleration, which is mathematically defined as Equation (30), where \(\dot{v}\) denotes the vehicle's present longitudinal acceleration, \(\
u\) signifies the vehicle's current speed, \(\
u_{r}\) is the set speed, and \(K_{p}\) represents the controller's proportional gain:  
\[\dot{v}=K_{p}(\
u_{r}-\
u) \tag{30}\]  
In the context of lateral movement, the proportional-derivative controller governs the vehicle's lateral position. Its expression is characterized as presented in Equations (31) and (32), where \(v_{lat,r}\) is the lateral velocity command, \(K_{p,lat}\) is the position gain, and \(\Delta_{lat}\) is the lateral position of the vehicle relative to the centerline of the lane:  
\[v_{lat,r}=-K_{p,lat}\Delta_{lat} \tag{31}\]  
\[\Delta\psi_{r}=\arcsin(\frac{v_{lat,r}}{\
u}) \tag{32}\]  
In this scenario, all environmental vehicles realize their behavior and motion management through the above two-layer control framework.
# A Pilot Study of Stacked Autoencoders for Ship Mode Classification
###### Abstract  
With the evolution of the shipping market, artificial intelligence research using ship data is being actively conducted. Smart ships and reducing ship greenhouse gas emissions are among the most actively researched topics in the maritime transport industry. Owing to the massive advances in information and communications technology, the internet of things, and big data technologies, smart ships have emerged as a very promising proposition. Numerous methodologies and network architectures can smoothly collect data from ships that are currently in operation, as is currently done in research on reducing ship fuel consumption by deep learning or conventional methods. Many extensive studies of stacked autoencoders have been carried out in the past few years. However, prior studies have not addressed the development of algorithms or deep learning-based models to classify the operating states of ships. In this paper, we propose for the first time a deep learning-based stacked autoencoder model that can classify the operating state of a ship broadly into the categories of At Sea, Stand By, and In Port, using actual ship power load data. In order to maximize the model's performance, the stacked autoencoder architecture, number of hidden layers, and number of neurons contained in each layer were measured by performance metrics such as true positive rate, false positive rate, Matthews correlation coefficient, and accuracy. It was found that the model's performance was not always improved by increasing its complexity, so the feasibility of developing and utilizing an efficient model was verified by comparing it to real data. The best-performing model had a (5-128) structure with latent layer size 9. It achieved a true positive rate of 0.9035, a false positive rate of 0.0541, a Matthews correlation coefficient of 0.9054, and an accuracy of 0.9612, clearly demonstrating that deep learning can be used to analyze ship operating modes.  
ship mode; autoencoder; ship mode classification; deep-learning model +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Mass and Geometry Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Resin** & **15\%** & **25\%** & **35\%** \\ \hline \multirow{4}{*}{0 mth} & average thickness \(\pm\) standard & \multirow{2}{*}{2.09 \(\pm\) 0.03} & \multirow{2}{*}{2.10 \(\pm\) 0.02} & \multirow{2}{*}{2.13 \(\pm\) 0.01} & \multirow{2}{*}{2.13 \(\pm\) 0.01} \\  & deviation (mm) & & & & \\  & range (mm) & 0.12 & 0.09 & 0.06 & 0.05 \\  & average thickness \(\pm\) standard & \multirow{2}{*}{2.07 \(\pm\) 0.02} & \multirow{2}{*}{2.05 \(\pm\) 0.01} & \multirow{2}{*}{1.99 \(\pm\) 0.02} & \multirow{2}{*}{-} \\
1 mth & deviation (mm) & & & & \\  & range (mm) & 0.05 & 0.06 & 0.11 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average thickness of samples and values range measured right after manufacturing (0 mth) and 1 month later (1 mth). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Resin** & **15\%** & **25\%** & **35\%** \\ \hline \multirow{4}{*}{0 mth} & average mass \(\pm\) standard & \multirow{2}{*}{2.04 \(\pm\) 0.03} & \multirow{2}{*}{1.97 \(\pm\) 0.02} & \multirow{2}{*}{1.85 \(\pm\) 0.02} & \multirow{2}{*}{1.50 \(\pm\) 0.03} \\  & deviation (g) & & & & \\  & range (g) & 0.11 & 0.08 & 0.08 & 0.09 \\  & average mass \(\pm\) standard & \multirow{2}{*}{1.87 \(\pm\) 0.04} & \multirow{2}{*}{1.82 \(\pm\) 0.01} & \multirow{2}{*}{1.63 \(\pm\) 0.01} & \multirow{2}{*}{1.34 \(\pm\) 0.01} \\
1 mth & deviation (g) & & & & \\  & range (g) & 0.11 & 0.05 & 0.03 & 0.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average mass of samples and values range measured right after manufacturing (0 mth) and 1 month later (1 mth). and its subsequent evaporation. The phenomena described may also have affected the reduction in the dimensions of the samples after one month.
## 3 Remote Sensing Challenges and Solutions to Increase Agricultural Productivity
### Data Issues
#### 3.1.1 Lack of Quality and Quantity, and Inconsistency, in Ground Data  
Agricultural monitoring systems in many countries still rely on traditional approaches (i.e., ground-based) for data collection and reporting. Such data are costly, time-consuming and prone to large errors due to incomplete ground observations and subjectivity, and often do not exist or are inaccessible in many ASA regions [162; 163; 164]. In Sub-Saharan African regions, crop data are generally incomplete and of low quality [26]. In addition, the low quality of labelling in the reference dataset may also influence mapping accuracy [165; 166; 167], and low accuracy of species identification may arise due to lack of skill in the data collectors [168; 169]. Nevertheless, such data are used widely to support supervised classification and yield modelling based on remote sensing data. Furthermore, errors and biases might come from the scale mismatch between the sensor spatial resolution and cropland patterns, particularly for smallholder-dominated systems [60; 170]. Although there have been substantial advances in RS, collecting the ground data required to calibrate and validate RS algorithms across large spatial areas and temporal scales remains a major constraint. Low quality and unrepresentative samples in the ground data could result in substantial uncertainties in the RS-based prediction models. This is challenging when mapping continuous variables such as yield at the farm-scale level, where the calibration data often are unreliable or non-existent.