dataesr/scientific-paragraphs-categorization huggingface dataset:
Normality Hybrid Model of the Use Case The initial parameters of the physics-based model are an assumption of the true parameters controlling the operation of a given turbine. Nevertheless, the true value of these parameters can be estimated using an optimization algorithm. The algorithm aims to find the combination of parameter values that minimize the difference between the output of the physics-based model and the measured SCADA data. In this case, the parameters are tuned (or calibrated) using a surrogated optimization algorithm (surrogateopt) in Matlab  [23]. This optimization algorithm is a global solver specially indicated for cases where the objective function is computationally expensive. The algorithm searches for a global minimum of a cost function min x f (x) with multivariate input variable x subject to linear and non-linear constrains, and some finite bounds.
In the real data made available during this study, there are five anomaly cases labelled as overtemperature in the stator winding (Figure  12 ). The failure modelling was validated using data during these five anomaly cases, obtaining results for the estimated stator winding temperatures, as shown in Figure  13 , compared with the real SCADA winding temperature. The MAPE between the real stator winding temperature measured in the SCADA and the value obtained in the simulation using the calibrated model has a value of 11%, with a maximum percentage error of 16% in the worst scenario. This value still has room for improvement if more accurate design data become available for the thermal model.
Synthetic Failure Data Generation in the Use Case A failure model for stator winding overheating was trained with real data from five labelled failures. For this failure mode, four parameters (CIs) were identified: failure or anomaly duration, ambient temperature, nacelle temperature, and wind speed. The failure duration and ambient temperature are assumed to be uniform during the whole duration of the failure. The distribution of these values in the training data is approximated with a kernel density function (KDE) with a Gaussian kernel (Figure  14 ). Continuous line represents the probability density functions of the duration and ambient temperature observed in the failure/anomaly instances from the real SCADA, while cross symbols represent real observations This technique, compared with density estimation by histogram, creates a smooth PDF that does not depend on the choice of binning.
Instead, a Gaussian component is fitted to each data point. The Gaussian kernel is defined by the function: K(x; h) ∝ exp - x 2 2h 2 (20) where the density function estimated at point x of a univariate distribution is: f (x; h) = n -1 n ∑ i=1 K(x -x i ; h) (21) where (x 1 , x 2 ,. , x n ) are independent and identically distributed random samples from such distribution. The bandwidth h is a smoothing parameter that controls the balance between variance and bias in the resulting density function. The resulting Gaussian mixture is a non-parametric estimator of the probability density function able to represent the uncertainty present in a small data sample. In addition, a domain expert can intuitively control the estimator with a bandwidth parameter based on a descriptive analysis of SCADA data and physical properties of the system.
In this paper, we come up with three secure chaos-based stream ciphers, implemented on an FPGA board, for data confidentiality and integrity. To do so, first, we performed the statistical security and hardware metrics of certain discrete chaotic map models, such as the Logistic, Skew-Tent, PWLCM, 3D-Chebyshev map, and 32-bit LFSR, which are the main components of the proposed chaotic generators. Based on the performance analysis collected from the discrete chaotic maps, we then designed, implemented, and analyzed the performance of three proposed robust pseudo-random number generators of chaotic sequences (PRNGs-CS) and their corresponding stream ciphers. The proposed PRNGs-CS are based on the predefined coupling matrix M. The latter achieves a weak mixing of the chaotic maps and a chaotic multiplexing technique or XOR operator for the output function.
However, these days, most of these stream ciphers are weak against various attacks and then can't be used to secure exchanged and stored data  [3]. Likewise, 5G Security uses the NEA3-128 algorithm for privacy and the NIA3-128 algorithm for integrity. Both NEA3-128 and NIA3-128 are based on ZUC stream cipher  [4]. However, many published researchers  [5, 6]  noticed that there are some weaknesses and vulnerabilities in the ZUC algorithm for differential power analysis attacks (SCA), Chosen IV attacks, etc. For this, we use chaos-based stream ciphers, which are very secure against cryptographic attacks because chaotic signals are intrinsically secure and have high nonlinearity  [7]. The security of a stream cipher relies heavily on the pseudo-random number generator (PRNG), which acts as the primary component in the system.
Hardware Metrics We interpret the implementation of the chaotic maps in regard to the resources utilized (area, DSPs), the speed (WNSi, Max. Freq. , throughput), and the efficiency (throughput-toslices ratio). Furthermore, we give the power consumption. Max. Freq. = 1 Ti -W NSi (MHz). ( 7 ) where Ti is the target clock period (ns) used during the implementation run "i" and W NSi is the Worst Negative Slack (ns) of the target clock used during the implementation run "i" and must be positive, and very close to zero. The data Throughput is calculated by Equation  (8). Throughput = N × Max. Freq. (Mbps). ( 8 ) The Efficiency is calculated by using Equation (  9 ). E f f iciency = Throughput Slices (Mbps/Slices). (9) The chaotic systems implementation was carried out on the Xilinx-manufactured PYNQ-Z2 FPGA board.
To implement the chaotic systems, the code was written in VHDL using 32-bit fixed-point data format, then synthesized and implemented using Xilinx Vivado design suite (V. 2017. 2). The Vivado design suite tools are a set of tools provided by Xilinx for designing and implementing digital circuits on FPGA boards. These tools enable the complete design flow from creating the RTL design, to implementation, synthesis, and programming of the FPGA. The Vivado design suite includes a powerful integrated development environment (IDE) that allows users to easily design, debug, and test their designs. It also includes various features such as timing analysis, power analysis, and design exploration, which can help in fine-tuning the design for optimal performance and power consumption. As illustrated in Figure  4 , we present a summary of the various stages of the conception flow that were executed using Vivado for evaluating the metrics of the discrete Skew-Tent map as an example of demonstration.
Therefore, with the exception of the Trivium and ZUC ciphers, the proposed chaos-based stream ciphers have competitive hardware performance when compared to most other chaotic and non-chaotic systems. Nevertheless, since 2007, several kinds of attacks have been launched against the eSTREAM cipher and ZUC stream cipher, revealing several vulnerabilities to cryptanalysis attacks [3,  [41] [42] [43] [44]. Note: Nowadays, promising new fractional chaotic functions have appeared, which seem very robust against cryptographic attacks. We believe they deserve further study to build chaotic fractional systems for data security. The design of such fractional chaotic systems must deal with the trade-offs between safety, performance, and cost.
We then evaluated the cryptographic features of the proposed PRNGs-CS and analyzed their performance on hardware metrics. The achieved results show a high level of security on the one hand and good hardware metrics achieved by the proposed PRNGs-CS on the other hand. We then realized corresponding chaos-based stream ciphers and confirmed their resistance to cryptanalysis attacks. We also evaluated their hardware performance and compared them to several chaotic and non-chaotic systems. All the obtained results demonstrate that the proposed chaos-based stream ciphers are alternative candidates for data confidentiality and integrity.
• The release of the new Kinect Azure motivated us to write a survey on the different fall detection approaches that use Kinect V1 or V2. This survey provides guidelines to researchers interested in developing a healthcare monitoring system for fall detection for the elderly using Kinect technology. The remaining part of this document is organized as follows. Section 2 is devoted to the presentation of related work and the highlighting of our contributions. In Section 3, we describe the principal features of Microsoft Kinect. In Section 4, we discuss the different modules in the life cycle of the Kinect-based fall detection approach. Section 5 presents the various metrics used to measure performance. Section 6 describes public Kinect datasets for fall detection. Finally, our remarks are discussed in Section 7 before concluding and advising on future works in Section 8.
• This article serves as a valuable reference for the development of elderly fall detection applications. We have compiled a comprehensive overview of both older and recent articles spanning from 2011 to 2022. Additionally, we provide in-depth insights into the various components of fall detection approaches, including data acquisition, preprocessing, feature extraction, actuation algorithms, and alert systems. Furthermore, we offer an extensive examination of performance metrics and highlight all publicly available Kinect-based datasets, making this resource indispensable for researchers and practitioners in the field.
Technical Characteristics The Kinect is composed of different sensors that allow visual and sound data to be collected, consolidated, and analyzed; in other words, it offers restitution of what it films and hears within its operating range. It can capture three types of images: RGB, depth, and infrared. We collected the following technical information from Microsoft's official documentation  [32]  and books  [33] [34] [35]. Table  1  presents the technical characteristics of each model. The Kinect V1 includes an RGB camera, a 3D depth sensor (infrared camera and CMOS detector), a three-axis accelerometer, a four-microphone network, and a tilt motor. The Israeli company PrimeSense developed its 3D vision technology. The data are continuously interpreted from the projected structured infrared light, which maps the infrared lightroom and creates a thermal map that identifies the distance between the lens and the scene for each pixel.
The 3D information is obtained by the depth sensor and the infrared emitter. Combining these two technologies makes it possible to obtain accurate data in 3D space even under dark lighting conditions. Version 2 uses the TOF method to generate a complete 3D image of the measured object. This principle makes it possible to measure the distance of a scene from the time required for light to reach the object and return. The depth sensor operates at a resolution of 512 × 424 in 30 fps, and the IR sensor can be used at the same time as the color. The Kinect V2 also includes a high-quality four-microphone linear progressive array that allows useful sound information to be captured in noisy conditions at 48 Khz. The orientation of these sensors allows for the detection of the sound's origin area over a 180°r ange.
Kinect Software Tools At the time of publication, Microsoft initially planned to use the Kinect only for video games. However, Adafruit launched a hacking contest to make Kinect functional on Windows with an increased bonus of up to USD 3000  [36]. Hacker groups have successfully exploited Kinect data on the PC, providing free libraries and unofficial software development kits (SDKs) such as the CL NUI platform, Libfreenect, OpenNi, and PCL. To act against hacker initiatives and open-source libraries, Microsoft launched the first official SDK in July 2011  [37]. The official SDK allows users to develop applications that encourage gesture and voice recognition. It provides direct access to Kinect data. This SDK includes drivers for deploying the Kinect on a computer running the required operating system.
Fall detection techniques based on RGB or depth fluxes do not monitor the parameters of the subjects' joints; instead, they are based on image processing techniques on the frames around the region of interest. Other models use the 3D positions of the body's joints estimated by the Kinect to monitor changes in posture position before, during, and after a fall. In most approaches, volunteers perform scenarios of falls and activities of daily life to make datasets. They operate in experimental configurations that guarantee their safety. Evaluation of approaches in offline mode are carried out with data already prepared. The integration of data collected by older adults is a challenge for researchers. Effectively, we are convinced that real data contributes to reinforcing the realism of fall detection techniques.
Preprocessing The data acquisition module in turn sends the data to the preprocessing unit. This step ensures that the algorithms make the correct decisions. Firstly, we consider this stage crucial to refining and preparing the parameters. Different filters and techniques are used to clean up the signals and accentuate certain information. We found that filtering was widely used in depth data. We will not go into detail about each technique as we aim to identify the broad outlines applied in this step. Indeed, preprocessing refers to all the tasks that generate information as independent as possible of the defects specific to the Kinect and the experimental configurations. (1) In image preprocessing, different algorithms often detect the target person appearing and reappearing in Kinect's field of vision.
The main advantage of this change is that it solves the nonlinear deformation of the pattern. The median, averaging, particle, and Kalman filters are deployed to homogenize and smooth areas. The median filter retains proper contours compared to the others. Framing objects by encompassing geometric shapes is used in particular works. Authors have relied on the coordinates within a predefined coordinate system to delimit the external surface of the objects of interest in the scene and identify their locations. To measure specific geometrical features compared to the ground, researchers have used techniques such as the v-disparity, Hough transformation, and least squares, as well as using them as an alternative to the parameters of the floor plane equation delivered by the SDK. (2) The large quantity and diversity of the dataset require a split step.
Researchers have divided the data into two minimal parts according to predefined objectives. The training data is the largest, being used to train the algorithms and to calculate parameters in the case of thresholding algorithms. The test data are used to verify the performance of the deployed approaches, and must be made up of new data not used when designing and adjusting the model. Usually, the best performance is obtained with a ratio of 70-80% for training and 20-30% for testing  [43, 44].
TP FP No-fall/ADLs FN TN The confusion matrix exposes how the approach is confused when it judges scenarios detected by intelligent devices. It provides an explanation of the types of errors made by the system. Several evaluation measures based on the confusion matrix elements are defined. These are used to quantify the performance from different perspectives  [115]. The total number of observations successfully detected is known as accuracy. It proves the success rate of fall detection approaches. However, it only reflects the reality of the system when working on symmetric datasets, where there is npo disproportion between the number of real situations (i. e. , falls and ADLs). Ac = TP + TN TP + TN + FP + FN (1) The error rate (misclassification rate) represents the ratio of incorrectly detected observations to the total amount of data, or simply one minus the accuracy.
Kinect Datasets for Fall Detection The creation of rich and well annotated datasets is one of the compulsory elements for detecting falls by computer vision. These datasets are made up of video sequences of fall and ADL events. The datasets are intended initially to assess the success of the algorithms. Many researchers have made their datasets public within the scientific community in order to compare more judicious approaches and raise new challenges. Many of the current fall detection benchmark datasets are performed in simulated scenarios  [118]. Table  5  summarizes the public Kinect datasets usable for fall detection, while Table  6  enumerates the types of scenarios and provides the access links for each database. University of Texas at Arlington. This dataset includes 26 reals falls from the two viewpoints and 61 fall-like events; in all, 31,614 depth frames are registered at 320 × 240 pixel resolution.
Experimental scenarios from the two Kinects are not captured at the same moment. In the same experimental setup there are two fall detection benchmark datasets, namely, EDF and OCCU  [119] ; EDF has no occlusion data, while OCCU has occlusion data. Each of the five volunteers made an un-occluded fall from each point of view in eight directions. There are 110 falls and 30 actions that appear to show similar features to those of a fall occurrence. The video sequences are recorded at the same time from both viewpoints. The main characteristic of the OCCU is the presence of occluded falls with the end entirely occluded by a bed. The data are not captured simultaneously from the two Kinect cameras. The SDU fall dataset  [92]  was recorded using one version 1 Kinect camera installed at 1. 5 m height.
This dataset included five ADLs and intentional falls performed by ten young male and female subjects. In addition, there are a total of 1800 video clips collected in randomly experimental conditions, such as carrying or not carrying an object, lighting on or off, varied layout of the room, and changes of position and direction relative to the Kinect camera. The resolution of the datastream was 320 × 240 pixels, and it is stored in an audio-video interleaved format at 30 fps. The SDU FAll server is in service again, and a new access link with the password is available on the Google page of the author. The UR fall detection dataset [120] can be considered the wealthiest dataset in terms of data relating to vision and movement. This dataset comprises 70 event sequences; the measurements were collected using two Kinect cameras and accelerometers.
All RGB and depth images are synchronized with movement data based on the timestamp value. Camera 0 was parallel to the ground at the height of around 1m, while Camera 1 used an in-ceiling configuration 2. 5 m above the ground. Moreover, accelerometers were mounted on the waist or close to the pectorals. Five subjects performed the action of falling (falls from standing position and falls from sitting on the chair) and ADLs (walking, sitting down, lying on the floor, taking an object from or placing it on the floor, tying shoelaces, bending to left of an object and crouching down) in traditional spaces such as an office, classroom, etc. For the fall events, two cameras and accelerometer devices were used. ADLs were recorded only by the camera parallel to the floor with the accelerometer. The TST fall detection dataset v1  [58]  was collected using a Kinect version 1 in top view configuration.
It includes depth frames captured from four volunteers. A total of 20 tests were carried out by subjects aged 26 to 27 years with different heights between 1. 62 and 1. 78 m. The tests were organized into two main groups; the first ten tests include data from two or more people walking in the covered area, and the rest correspond to simulated falls. Moreover, the TST fall detection dataset v2  [61]  provides vision and acceleration samples. In this dataset, eleven healthy volunteers performed eight types of activities three times, resulting in 264 sequences. The young subjects who performed The ADLs and falls actions were aged between 22 and 39 years and measured 1. 62 to 1. 97 m. TSTv2 was captured using a Kinect version 2 with two accelerometers (IMU shimmer) attached to the wrist and waist of the subjects.
Pawel Mazurek et al. [86]  built the IRMTv1 dataset with two synchronized Kinects. This dataset provides the integer numbers matrices representing the distance between the plane parallel to the device lens and the image's scene points across both cameras simultaneously. Two performers simulated 20 different types of falls and 20 non-falls, for a total of 160 sequences of depth images. The length of the recorded data sequence corresponds to 75 frames. Each performer simulated the scenarios at a distance of approximately 1. 5-4 m from each instrument in the observed area.
Potential solutions include utilizing joints in the upper body or those forming the major axis of the tracked person, such as the head joint. Integrating additional features such as speed, acceleration, and inactivity time can effectively enhance the system's performance in detecting falls. Moreover, performing studies in a 2D space imposes constraints on precision and generalization, resulting in loss of information on the third axis. In most of the reviewed approaches, volunteers with varying characteristics (age, sex, height, weight, and health status) simulated activities within diverse environments and experimental setups. Consequently, the collected data used for method development may require greater realism. Integrating real and simulated data encompassing various fall scenarios and activities of daily living (ADLs) into a publicly accessible dataset would significantly benefit future research endeavors.
Conclusions Developing an effective fall detection system necessitates a comprehensive understanding of human movements. Fortunately, advancements in Microsoft Kinect hardware and software have alleviated many computer vision challenges, significantly advancing scientific research in recognizing and comprehending human activities. In this survey, we have provided an extensive overview of various Kinect-based methods for detecting falls in elderly individuals, offering valuable insights into this critical area. Our focus has encompassed Kinect vision technology, life cycle approaches, performance metrics, and publicly available Kinect-based datasets. Significantly, the principles underpinning a Kinect-based fall detection system can be adapted for applications with various sensor types. Such adaptability is crucial to ensure that the model remains robust across changes in viewpoint, scale, inter-class deformation, and occlusion.
The core objective of fall detection approaches is to safeguard the lives and well-being of elderly individuals under surveillance. From a personal and medical perspective, missed and incorrectly detected falls carry more significant consequences than inaccurately detected activities of daily living (ADLs). Thus, a dependable system is characterized by maximization of correct detections (true positives and true negatives) along with minimization of missed and incorrectly detected falls (false negatives) and incorrectly detected ADLs (false positives). To enhance the performance of these fall detection techniques, avenues for improvement include expanding the dataset, refining data preprocessing and denoising techniques, extracting additional meaningful features, and transitioning from analytical decision algorithms to machine or deep learning-based approaches (and vice versa).