esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study
### Preparatory Steps  
The experiments presented in this section were based on similar work in the literature [6; 53; 54]. These authors performed queries and tests with the operators _objects_, _conj_, _left_, _seq_, and _count_. Table 1 describes and exemplifies these operators using a SQL-like notation created by the authors above:  
* The _object_ operator retrieves the frame of interest within a time window. For instance, the query example selects/finds the frame of an object with the label \"car\" within a time window of 10 s, considering each second is composed of 30 frames. This operator is the most simple one and evaluates only one attribute. * The _conj_ or conjunction operator retrieves the frame of interest, evaluating objects and attributes within the time window. The query example selects the frame in which an object appears with the label \"car\" and another object with the label \"truck\" within a 10 s time window. * The \(left\) operator retrieves the frame of interest, evaluating the spatial relationship \"left\" between objects in the same frame. The query example selects the frame in which an object with the label \"car\" appears at the left of the object with the label \"truck\" within a 10 s time window. * The \(seq\) operator retrieves the frame of interest, evaluating their temporal relationship in a given time window interval. The query example alerts if an object with the label \"car\" and another with the label \"truck\" appear within the same time window of 10 s.
* The \(count\) operator is the most complex than the former ones, and it counts the number of objects, evaluating their temporal occurrence within the same time window interval. The count of objects should be consistent across several frames, denoted by each frame clause. The query example counts any objects that appeared within a time window of 10 s and alerts if the count is greater or equal to 5. * The \(window\) operator also establishes a time interval scope in which the queries are performed based on frame numbers or timestamps. It is often used to limit the time interval to reduce the computational cost of searches. This operator restricts the time window size of the entire query. Below, we briefly explain each _Rules_ required per query. * The operator _objects_ require one _Rule_ per target object/attribute. * Both operators, _left_ and _conj_, require one _Rule_ for each pair of target objects. * Three _Rules_ are needed for the _seq_ operator. _Rule_ (a) counts the number of times the objects appear within the time window. _Rule_ (b) fires when the _seq_ count is reached. _Rule_ (c) resets the count when the _seq_' _Rule_ fires. * The operator \(count\) also requires three _Rules_. _Rule_ (a) counts the number of objects in the same frame. _Rule_ (b) checks if the count is consistent for each frame and fires when it matches the number of objects in the time window. _Rule_ (c) resets the count operator when the \(count\)' _Rule_ fires.
## 3 Materials and Methods  
### Dataset Collection  
This study investigates the need for data security and privacy protection by selecting multiple representative datasets for experimentation to validate the effectiveness and applicability of the proposed method. To fully demonstrate the versatility of the method, the experiments cover a range of domains, including image classification, object detection, and time-series data analysis. The specific datasets chosen for each domain are highly representative in terms of data size and task complexity. The following provides a detailed description of the datasets used in the experiments and their respective application scenarios. In the image classification domain, the MNIST-USPS, BreakHis, and CelebA datasets were selected, as shown in Figure 1. The MNIST-USPS dataset is a classic dataset in the field of handwritten digit classification, where the MNIST dataset contains approximately 60,000 training images and 10,000 test images, while the USPS dataset contains 9298 images. These two datasets are widely used in low-resolution handwriting recognition tasks and provide an ideal scenario for testing the basic classification ability of the algorithm. Meanwhile, the CelebA dataset is used for complex facial image tasks, including gender classification, age prediction, and multi-attribute label generation. It contains more than 200,000 labeled facial images, each with 40 attributes. The high data complexity and diverse scenarios make it a good reflection of the algorithm's performance in high-dimensional feature spaces. Additionally, in the field of medical image analysis, the BreakHis dataset was chosen. This dataset contains 7909 microscopic tissue slice images of breast tumors, covering benign and malignant samples at magnification levels of 4\(\times\), 10\(\times\), 20\(\times\), and 40\(\times\). The task is to accurately identify tumor malignancy through image classification.  
For time-series data analysis, the study utilized the credit card fraud detection dataset and the IMDb movie review dataset. The credit card fraud detection dataset contains 284,807 credit card transaction records, with 492 labeled fraudulent samples. The task objective is to achieve precise classification of fraudulent transactions through pattern recognition. This type of dataset is highly imbalanced, presenting challenges for the model's robustness and sensitivity when dealing with imbalanced data. The IMDb movie review dataset, on the other hand, is used for sentiment analysis, containing 50,000 labeled text reviews categorized into positive and negative sentiments. This dataset serves as an important basis for testing the algorithm's text comprehension and sentiment classification accuracy. This dataset is widely used in medical diagnostic research, with high image quality and precise labels, providing a feasible validation for the method when processing medical image data. The specific number of datasets used is summarized in Table 2.
## 7 Case Studies of Color-Independent Visual-MIMO
### Wearable Visual MIMO  
With the growth in the wearable device market, various wearable devices have utilized LEDs for wearable interaction. This is an attempt to combine color-space-based visual-MIMO systems with wearable devices in order to extend the use of LEDs in existing wearable devices to user-oriented interaction.  
The design of LED light, which is a practical medium for the transmission of data in a VLC system, must be carefully considered because of its visible characteristics. Most of the existing VLC systems have been based on the use of white light, so the technology should not be simply applied to wearable devices (i.e., fashionable design) as it is. Communication using RGB LEDs, which are used in wearable devices, can be controlled by two parameters: lighting color and brightness. However, for designers, determining the color and brightness of an LED to be embedded in wearable smart fashion is a very important design issue. Thus, because the color and brightness of the light can vary in accordance with the fashion designer's wishes, seamless communication should be guaranteed for all variations of color and brightness. Given these factors, the color independency of GCM is an essential feature for fashion design. Designers can choose the color and brightness for their design, and engineers can use GCM to set the symbol color to match the chosen color and brightness. Figure 12 presents a transceiving block diagram of a GCM-based color-independent visual-MIMO system with the addition of an LED light design stage [26].  
Figure 13 displays a fashion smart module using fasteners based on a color-independent visual-MIMO platform that is currently under development [26]. The LED array embedded in the strapped cuffs and the zipper slider acts as transmitter for the visual-MIMO system, and the data to be transmitted can be controlled using a smartphone connected with Bluetooth. The transmitted data can be received via a separate device (e.g., a smartphone) with a built-in camera.  
The color-independent visual MIMO platform combined with wearable devices is expected to become another means by which users can express their personality in the customized and user-centered wearable device market. For this purpose, the systematic development of software algorithms and hardware design is required for the adaptive application of this technology to various wearable devices in clothing, shoes, and accessories.  
Figure 12: GCM-based visual MIMO transceiving process including the design of LED lights [26].
## 3 Classification
### VRU Devices
#### 3.7.1 Smartphone  
Due to their versatility and ubiquitous nature, smartphones may prove themselves as most widely accepted choice as a VRU device. It can also be seen from Table 1 that 23 out of 28 systems for V2P communication have used a smartphone as a VRU device. Current commercial off-the-shelf (COTS) smartphones already pack the various sensors, such as, accelerometer, GPS, and communication technologies, such as, cellular (LTE/3G), Bluetooth, Wi-Fi etc. By fusing functionalities of these sensors and communication technologies, effective V2P systems may be developed. Smartphones can also provide the necessary functionality for audio-visual and haptic warnings. This may be useful for V2P systems that incorporate warnings for VRUs.
## 4 Design and Development
### Data Structures and File Formats  
#### 4.2.1 User and Policy JSON Inputs  
Blockchain transactions were structured using JSON-formatted payloads for user registration and policy definition. Listing 1 illustrates the structure of a user registration payload, where each user is assigned a unique identifier and a set of descriptive attributes, such as organizational affiliation, role, and clearance level.  
1. [user_id='user01='attributes\":{  
\"role='geologist\",  
\"organization\":\"Orig\",  
\"clearance\":\"Confidential\"  
Similarly, Listing 2 presents a sample policy specification format, mapping resource identifiers (e.g., mining license IDs) to attribute-based access control conditions. These JSON policies are evaluated at runtime during access requests
# HH-NIDS: Heterogeneous Hardware-Based Network Intrusion Detection Framework for IoT Security
## 7 Conclusions  
This paper presents the HH-NIDS framework for a heterogeneous hardware-based implementation of anomaly detection in IoT networks. The framework includes dataset pre-processing, model generation and hardware-based inference on hardware accelerators. The proposed framework is tested with the UNSW-NB15 and IoT-23 datasets using NN models and achieves the highest accuracy of 99.66%. The inference phase is implemented using different approaches on the microcontroller and SoC FPGA. Firstly, the inference phase is implemented on the MAX780000 EVKIT, which is 11.3 and 21.3 times faster than Intel(r) CoreTM i7-9750H 2.6 GHz CPU and NVIDIA GeForce GTX 1650 GPU, respectively; the power drawn was between 17 mW and 18 mW when the NN model is inferring. In addition, the inference phase on SoC FPGA is implemented using HLS and Verilog approaches, achieving the same practical processing speed. However, the pipelined design on the PYNQ-Z2 board with the Xilinx Zynq xc7z020-1 clg400c device has been optimised to run at the on-chip frequency with a fixed 28 clock cycles overhead. The simulation results have reported a speedup of 53.5 times compared to the MAX78000 EVKIT. Future work will  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**System** & **Platform** & **Time (ms)** \\ \hline
[50] & CPU i3 2.4 GHz & 240.14 \\ \hline
[47] & ARM A9 667 MHz & 1458.10 \\ \cline{2-3}  & FPGA accelerator 76 MHz & 9.02 \\ \hline \multirow{2}{*}{HH-NIDS} & **FPGA accelerator 100 MHz (HLS)** & **2.18** \\ \cline{2-3}  & **FPGA accelerator 100 MHz (Verilog)** & **2.24** \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison in inference time on different platforms and between related works.  
consider using FPGA network specialised hardware (e.g., NetFPGA-SUME) for studying high-performance intrusion detection systems.  
Methodology, D.-M.N., A.T., C.P.-Q., N.-T.T. and C.C.M.; Software, D.-M.N.; Validation, D.-M.N. and D.L.; Formal analysis, D.-M.N., C.C.M. and E.P.; Writing--original draft, D.-M.N.; Writing--review & editing, A.T., C.C.M. and E.P.; Supervision, E.P.; Project administration, E.P. All authors have read and agreed to the published version of the manuscript.  
This research is supported in part by a grant from Science Foundation Ireland INSIGHT Centre for Data Analytics (Grant number 12/RC/2289-P2) which is co-funded under the European Regional Development Fund.  
The data presented in this study are available on request from the corresponding author.  
The authors acknowledge the University College Cork (UCC) and Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for supporting this study. We would like also to acknowledge the support from Qualcomm, Analog Devices, AMD/Xilinx and Dell for various parts of this project.  
The authors declare no conflict of interest.
## 2 Current Vector Analysis of IMs with ITF
### Current Vector Pattern Analysis  
Under ITF conditions in the IM, both positive- and negative-sequence currents are generated, even when only a positive-sequence voltage is applied. These sequence currents can be represented as phasors in the complex plane.  
\[I_{ps}=I_{p}e^{i\theta_{p}},\,I_{ns}=I_{n}e^{-i\theta_{n}},\,I_{p}=Y_{pp}V_{ ps},\,I_{n}=Y_{np}V_{ps} \tag{10}\]  
where \(I_{p}\) and \(I_{n}\) are the magnitudes of the positive- and negative-sequence currents, respectively, and \(\theta_{p}\) and \(\theta_{n}\) are the phase angles of the positive- and negative-sequence currents.  
The total current, comprising both positive- and negative-sequence components, can be expressed as follows:  
\[\begin{array}{c}I_{t}=I_{\text{ps}}e^{i\omega_{e}t}+I_{\text{ns}}e^{-i\omega_{ e}t}\\ I_{\alpha}+jI_{\beta}=I_{p}e^{i(\omega_{e}t+\theta_{p})}+I_{n}e^{-j(\omega_{e}t+ \theta_{n})}\\ I_{\alpha}=I_{p}\cos(\omega_{e}t+\theta_{p})+I_{n}\cos(\omega_{e}t+\theta_{n}) \\ I_{\beta}=I_{p}\sin(\omega_{e}t+\theta_{p})-I_{n}\sin(\omega_{e}t+\theta_{n}) \end{array} \tag{11}\]  
From Equation (9), the relationship between the \(\alpha\beta\)-axis current can be expressed as follows:  
\[AI_{\alpha}^{2}+BI_{\alpha}I_{\beta}+CI_{\beta}^{2}=\left(I_{p}^{2}-I_{n}^{2} \right)^{2} \tag{12}\]  
where  
\[\begin{array}{c}A=I_{p}^{2}+I_{n}^{2}-2I_{p}I_{n}\cos\left(\theta_{p}-\theta _{n}\right)\\ B=-4I_{p}I_{n}\sin\left(\theta_{p}-\theta_{n}\right)\\ C=I_{p}^{2}+I_{n}^{2}+2I_{p}I_{n}\cos\left(\theta_{p}-\theta_{n}\right).\end{array} \tag{13}\]  
Equation (12) represents a quadratic form, where the relationship between the \(\alpha\)- and \(\beta\)-axis currents is governed by the coefficients defined in Equation (13). These coefficients in Equation (12) satisfy the following relationships:  
\[B^{2}-4 AC=-4\left(I_{p}^{2}-I_{n}^{2}\right)^{2}<0. \tag{14}\]  
In the quadratic form presented in Equation (12), the \(\alpha\)- and \(\beta\)-axis currents exhibit an elliptical relationship if the condition given in Equation (14) is satisfied. Under healthy operating conditions, Equation (12) simplifies to a circular form, as the negative-sequence current becomes zero according to Equations (8) and (9).  
\[I_{\alpha}^{2}+I_{\beta}^{2}=I_{p}^{2} \tag{15}\]  
Therefore, the current vector pattern can be represented as a circle under healthy conditions and as an ellipse under ITF conditions, as illustrated in Figure 2. Moreover, both the magnitude and phase of the current vector are influenced by the fault conditions. Although the ITF conditions are analyzed using Equations (8), (9) and (12), accurately calculating the admittance between the positive and negative sequences is challenging due to the coupling between the stator and rotor currents. To address this, the next section presents a fault diagnosis approach based on current vector pattern analysis using co-simulation with FEA and circuit simulation under various fault conditions.
## 3 Results and Analysis
### Numerical Simulation and Process Optimization
The extreme values of the coupling errors are \(\pm 4.50\%\) and \(\pm 8.35\%\), respectively, and the extreme deviations are \(9.00\%\) and \(16.70\%\), respectively. This suggests that the pitching motion significantly influences the roll angle, thus prioritizing the adjustment of the pitch layer in error compensation is  
Figure 8: (**a**) Heat map of pitch angle independent errors; (**b**) heat map of roll angle independent errors. necessary. Furthermore, the simulation results show that the effect of the hydraulic cylinder stroke on the coupling error is largest near the start position and small at other positions.
# A Feature Extraction Method Using Auditory Nerve Response for Collapsing Coal-Gangue Recognition
## 4 Experimental Results
### The Result of Analysis  
To further assess the proposed method's performance, receiver operating characteristics (ROC) analysis is applied to demonstrate its superiority. ROC analysis is a classification performance evaluation method based on statistical decision theory. The SVM classification method's performance of coal-gangue signals can be assessed according to receiver operating characteristics (ROC) graphs [23]. In addition, such performance is decided by using 10-fold cross-validation, avoiding the bias produced by selecting a given training or test set. The relative balance between true positive rates (benefits) and false positive rates (costs) are shown in the ROC diagrams represented by two-dimensional diagrams [24]. We obtain the ROC diagram by calculating the parameters of sensitivity (Sen) and specificity (Spec). In this article, sensitivity measures the fraction of real positive samples which have been accurately recognized, such as the proportion of coal samples which have been correctly identified as coal. Specificity is a parameter of real negative samples' proportions which have been accurately identified such as gangue samples' percentages which have been accurately identified as gangue.  
\[Sen =\frac{TP}{TP+FN} \tag{15}\] \[Spe =\frac{TN}{TN+FP} \tag{16}\]  
where, _TP_ and _TN_ indicate the total number of real positive samples and real negative samples identified correctly, respectively. _FN_ and _FP_ indicate the total number of samples mistakenly treated as negative and positive samples, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Kurtosis** & **SEI** & **EEWPC** & **SC** & **MMFCC** & **[PERSON] et al. [13]** & **Proposed Method** \\ \hline Test accuracy & 63.79\% & 46.55\% & 93.10\% & 74.14\% & 81.03\% & 91.38\% & 99.23\% \\ \hline AUC & 0.7667 & 0.4560 & 0.9286 & 0.7600 & 0.8131 & 0.9798 & 1.0000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Different feature extraction methods’ recognition results.  
Figure 12: Receiver operating characteristics (ROC) graphs showing classification performances. (a) [PERSON] et al.’s method [13]; (b) Our proposed method.  
We plot sensitivity and 1-specificity to obtain the ROC diagram, as represented in Figure 12. The advantage of the classifier for recognizing a specific class is demonstrated by its sensitivity to a class of data, and the accuracy of the classifier in not identifying other classes as a particular class can be represented by its 1-specificity to a class of data. Meanwhile, AUC which is the area under the ROC curve is employed to evaluate the detection performance [25], that is, AUC \(\in\) (0,1), which can be used to measure pattern recognition methods' performance. The corresponding method is better with an increasing value of AUC.  
The test accuracy and AUC of the proposed method are 99.23% and 1.00, respectively. As such, the above evaluation parameters of recognition result confirm that the proposed method performs well at identifying coal and gangue.
# Closed-Form Method for Atmospheric Correction (CMAC) of SmallSat Data Using Scene Statistics
## 4 Discussion
are visible for the LaSRC correction including significant remaining haze, a bluish shift for green vegetation (southwest corner) and dark artifacts in the ocean. The CMAC correction is clear, and the ocean is portrayed with the expected deep blue color balance. Recapping the results presented earlier in this paper and with reference to Figures 10 and 16, the restored color is likely close to the actual surface reflectance. Harmonized radiance products from L8/9 and S2 are meant to prepare smallsats for the application of RadTran for AC. The results from this paper call into question the application of RadTran methods to smallsats since the software performance is limited even when applied, using the software designed specifically for S2 and L8/9. As mentioned above, CMAC was developed to work with the four-band VNIR data tested in this paper for application to S2 data. Hence, as shown by the application to L8 in Figure 18, CMAC should also be applicable to smallsats. This was tested with a limited dataset using experimental procedures. As with the L8/9 application, conclusive testing has not yet been completed. However, using the qualitative criteria of correct color balance and image clearing as an indication that the output is close to true surface reflectance, application for smallsat flocks is expected to work as planned. This initial testing was conducted with four-band VNIR Planet Dove data that yielded clear images with correct color balance, confirming that CMAC will work as designed for smallsat application (Figure 19). The major challenge for this application is that smallsat systems will likely contain members launched at different times with different sensors, optics, and designs, thus raising the question of whether each individual smallsat will require its own exclusive calibration. A partial answer was confirmed through analysis of the data from two cohorts bearing file names PS2.SD and PSB.SD, which required separate calibration, but further testing was not possible with the small data set available. Anticipating that  
Figure 18: L8 full tile of the Mexican Gulf Coast from 5 December 2021: (**a**) TOAR, (**b**) Atm-I, (**c**) LaSRC correction and (**d**) CMAC correction. The images were rotated from their collection angle to fit squarely. each smallsat will need individual calibration is likely, so a CMAC calibration workflow has been designed that promises to be both automated and highly precise.