esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating Secure Methodology for Photo Sharing in Online Social Networks
## 6 Implementation  
There are two phases for our system: the registration phase and posting phase, as explained previously in Section 4. In this section, we describe the implementation of our application.  
Firstly, the registration page was designed in flutter language, in which general data such as name, age and gender will be recorded. This is followed by a live picture of the person. When the user presses the sign-up button, it checks that all information has been filled in and that the photo has been taken. The live picture will be sent to be input into a code written in Python and uploaded to a Google cloud server for execution. Python code contains two operations for registration process the first is to verify that there is a face in the image, followed by the second step. It will compare the face in the image with the faces stored in the firebase when the output from the comparison process is 0 which mean no face similar. Then all this information will be passed on and kept in the firebase and the recording process is completed.  
Second, the photo posting page was designed in flutter language. Two options were added, either by taking a photo from the camera or choosing from the photo library. The selection is carried out by clicking on one of the two buttons. The image will be sent as input to Python function that uploaded to the Google cloud server for execution. Python function has two operations, the first is to check whether there is a face in the image and the number of faces in the image. Afterwards, the faces of the image will be compared to the faces stored in Firebase. When the result of the comparison process is 0 which mean no face similar, the image is saved to the firebase database and posted. However, if the result of the comparison process showed that the face matches one of the faces stored in the firebase, the information of the corresponding face will be restored, the image will be saved in the firebase. Then send a notification to the owner of the corresponding face containing the image and about the possibility of publishing or not.  
Thirdly, the notifications are shown to the user if an image contains their face. The notification includes the image to be published, as well as two buttons for acceptance or rejection, Photo publisher name.  
Fourthly, the profile page, that is, the person's personal page. It is designed to show the pictures he posted, and if the pictures contain other people, they do not appear until they have approved. Figure A1, which is added in the appendix, shows some screenshots of our application.
# Group Authentication Scheme for Neighbourhood Area Networks (NANs) in Smart Grids
## 2 Group Authentication  
The significance of encryption of cryptography in smart grids remains a highly-discussed and debatable topic. There is a need for hiding information, which is present in the form of both data and messages, in order to improve the business productivity of smart grids. Researchers have used different techniques in order to secure data in the smart grids in order to maintain the confidentiality, integrity, and authenticity of data, which have been discussed in the subsequent sections. In this section, the existing group authentication schemes are examined to identify how they are applicable in the context of the SG NAN.
# Performance Evaluation of Classification Algorithms to Detect Bee Swarming Events Using Sound
## 4 k-NN, SVM, and U-Net CNN Algorithms Evaluation
### Experimental Scenario
For every six hours of raw data recordings, 983 Mbytes of data are stored, almost 2 GB per hive per day. The evaluation experiments were performed in five beehives for five months capturing twelve swarming events and receiving a dataset for swarming events of 0.48 TBytes and 0.64 TBytes of non-swarming events.
## 4 HAR Datasets and Experiment Settings
### Sliding Window Segmentation Signal Processing  
Continuous data signals gathered by sensors in practical applications are frequently plagued by noise and abrupt interference, which can dramatically reduce measurement accuracy. In the process of human activity recognition, the data of the sensor will present certain gait characteristics when the human body performs repetitive unit actions. After noise reduction via sliding window, the smoothness of the data time series is preserved and the motion characteristics are highlighted, as shown in Figure 9. The implementation of a sliding window denoising approach improves the accuracy and efficiency of the process.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **Action Categories** & **Sample Size** & **Sensor Type** & **Acquisition Location** \\ \hline \multirow{4}{*}{PAMAP2 Dataset} & \multirow{4}{*}{18} & \multirow{4}{*}{65,052} & inertial measurement units, & \multirow{4}{*}{Ankle} \\  & & & acceleration sensors, & \\  & & & magnetometer, & \\  & & & gyroscope & \\ \hline \multirow{4}{*}{MultiportGAM Dataset} & \multirow{4}{*}{19} & \multirow{4}{*}{11,034} & \multirow{4}{*}{11,034} \\  & & & & Torso \\ \cline{1-1}  & & & & Left Arm \\ \cline{1-1}  & & & magnetometer, spirometer & Right Arm \\ \cline{1-1}  & & & Left Leg \\ \cline{1-1}  & & & Right Leg \\ \hline \end{tabular}
\end{table}
Table 2: Comprehensive human activity recognition datasets with sensor information.  
Figure 8: MultiportGAM dataset for sensors-based tester.  
To identify the optimal sliding window size and sampling frequency values, noise reduction is applied to the specific torso data adopted by the tester. The reason for adopting the torso data here is that when the human body performs periodic activities, the torso differs from the rest of the body in typical general gait characteristics. At the same time, in this study, the sliding window size was varied, ranging from 1 to 9 s. The sampling frequency was adjusted within the range of 15 to 30 Hz, with a step size of 5 Hz. The average accuracy was used as the scoring Index to solve the optimal value of the sliding window size and sampling frequency, and the evaluation scores of different machine learning algorithms were obtained as shown in Figure 10.  
From the results, it can be found that the different algorithms have a peak at a window size of 5 s and a frequency of 25 Hz which indicates that choosing one particular algorithm  
Figure 10: Accuracy scores obtained with different sliding window sizes, sampling frequencies, and machine learning algorithms.  
Figure 9: Exercising within a sample 5 s on a stepper acceleration time-domain diagram; (**a**) description of the original data before sliding window denoising processing; (**b**) description of the data after sliding window denoising processing.  
over another does not make a difference. Also, an accuracy of 0.92-0.99 is good enough for our purposes. Therefore, these values will be used for further experiments and discussion.
## 3 Results and Discussion
### On-Road Measurement of NH\({}_{3}\) and N\({}_{2}\)O Emissions
As shown in Figure 8, the NH\({}_{3}\) concentrations increased after approximately 300 s of constant idle. The MEXA-ONE-QL-NX and OBS-ONE-XL, both measuring at 10 Hz, both reproduced the same kind of profile measured during the on-road test, suggesting that the vehicle presents this behavior during idle. The SESAM followed the same overall emission profile; however, its emissions did not show the highly changing concentrations. This is possibly due to its measurement frequency and the cell's volume. Beside the particular case related to idle operation, NH\({}_{3}\) emissions took place during the TWC light-off and during the acceleration events, with most peaks taking place during the motorway operation. This is in line with what has been reported for on-road operation of CNG light-commercial vehicles (LCV) and other positive ignition engines using TWC [22, 33]. However, the concentrations of NH\({}_{3}\) measured on the road from the tested HD-CNG were substantially lower than those reported for the CNG LCV [22]. This could have resulted from the average 10 ppm limit that the HD has to meet in the Euro VI regulatory test performed in the laboratory, while the Euro 6 LCVs did not have an NH\({}_{3}\) limit yet.
The methodology combined advanced data pre-processing techniques, correlation analysis between conditioning factors and observed events, and explainable AI methods (SHAP) to assess the contribution of each variable to the final predictions. The rest of the paper is organized as follows: Section 2 describes the study area, data sources, and the methodology adopted for flood and landslide susceptibility mapping using the XGBoost model. The geospatial conditioning factors and the inventory data are also detailed. Section 3 presents the results of the susceptibility analyses, including model performance metrics and an interpretability analysis based on SHAP values, which highlight the relative importance of each input variable. Section 4 discusses the findings in the context of previous literature, outlining the strengths, limitations, and practical implications. Conclusions are provided at the end.
## 3 Materials and Methods
### Attack Methods
#### 3.2.2 Black-Box Attack Methods  
A black-box attack method produces adversarial malware, directly manipulating the binary Windows PE malware without considering its (embedding) feature representation and operating in a gradient-free setting. In particular, it observes the output of a query formulated to an attacked machine learning model by ignoring either what the model parameters are or how the model works to achieve its decisions. The authors of [8] surveyed several black-box adversarial attacks, illustrating a taxonomy of these methods. This taxonomy was formulated while accounting for the attack strategies, which may range from reinforcement learning, randomization and evolutionary methods to Generative Adversarial Network methods. In the following, we briefly describe GAMMA, which is an efficient attack method whose implementation is publicly available in the secml-malware library.  
**GAMMA**. This black-box attack method [15] uses an evolutionary algorithm that injects an adversarial perturbation into the Windows PE malware file. This method solves an optimisation problem by resorting to a penalty term to minimise the evasion probability, as well as the size of the binary content that is added to the PE file. The injected content is extracted from goodware binary files instead of being produced randomly. To find the optimised benign content, GAMMA selects benign content iteratively and optimises the selection and size of goodware-originated content using the selection, crossover and mutation functions. As in [15], we used the section evasion formulation of GAMMA, which resorts to the section injection operation to extract sections from goodware files and inject them as a new section into the produced adversarial malware file. In addition, a new section entry is added to the Section Table of the adversarial file. Notably, the authors of [15] showed that, although this operation changes both the byte distribution and the structure of the binary file, it preserves the code functionality by design.
## 2 Descriptions of DUT and HPEMs  
### HPEM Coupling of RF Front-End Circuits  
In practice, the wireless communication equipment is generally located inside the shielded cabins, and the antenna is illuminated by an approximated ideal plane wave in free space. As illustrated in Figure 1, the RF channel of the wireless equipment is the main coupling channel for HPEM pulse propagation. The pulse energy coupled from the antenna is propagated step by step from RF components to the sensitive modules and results in damage or destruction to the certain module, e.g., low noise amplifier [18].  
Hence, regarding to the wireless system with the pyramid horn antenna, how to analyze the conducted response along the RF channel is the main issue for the HPEM risk analysis considered in this paper. Actually, when evaluating the electromagnetic risk of an electric system, it is often hard to obtain the detailed circuit structure parameters of the equipment due to commercial privacy. It is a practical solution to analysis the EM response by EM response modeling based on the measured data.  
For an actual wireless system with an L band, a pyramid horn antenna (Figure 2a) and a RF micro-strip band pass filter (BPF) (Figure 2b) are selected to construct a simplified linear RF receiving channel neglecting the nonlinear modules such as protectors. The EM coupling response along this simplified channel will be modeled and analyzed based on the measured data according to commercial EM simulation and SPICE modeling methods.  
Figure 1: EMP coupling on wireless RF front-end circuits.  
Figure 2: Wireless RF front-end circuits: (**a**) Pyramid horn antenna; (**b**) micro-strip band pass filter.  
As illustrated in Figure 2a, the antenna basic parameters are set as: the length of the horn is 270 mm, the length of the coaxial waveguide is 134 mm, the front opening side area of the antenna is 205 mm \(\times\)150 mm and the impedance of the antenna port is 50 ohm.
###### Abstract  
Digital surface models (DSMs) derived from spaceborne and airborne sensors enable the monitoring of the vertical structures for forests in large areas. Nevertheless, due to the lack of an objective performance assessment for this task, it is difficult to select the most appropriate data source for DSM generation. In order to fill this gap, this paper performs change detection analysis including forest decrease and tree growth. The accuracy of the DSMs is evaluated by comparison with measured tree heights from inventory plots (field data). In addition, the DSMs are compared with LiDAR data to perform a pixel-wise quality assessment. DSMs from four different satellite stereo sensors (ALOS/PRISM, Cartosat-1, RapidEye and WorldView-2), one satellite InSAR sensor (TanDEM-X), two aerial stereo camera systems (HRSC and UltraCam) and two airborne laser scanning datasets with different point densities are adopted for the comparison. The case study is a complex central European temperate forest close to Traunstein in Bavaria, Germany. As a major experimental result, the quality of the DSM is found to be robust to variations in image resolution, especially when the forest density is high. The forest decrease results confirm that besides aerial photogrammetry data, very high resolution satellite data, such as WorldView-2, can deliver results with comparable quality as the ones derived from LiDAR, followed by TanDEM-X and Cartosat DSMs. The quality of the DSMs derived from ALOS and Rapid-Eye data is lower, but the main changes are still correctly highlighted. Moreover, the vertical tree growth and their relationship with tree height are analyzed. The major tree height in the study site is between 15 and 30 m and the periodic annual increments (PAIs) are in the range of 0.30-0.50 m.  
Keywords:DSM; stereo imagery; TanDEM-X; LiDAR; forest heights; change detection +
Footnote †: journal: Remote Sensing, 2017
# Analysis and Visualization of Production Bottlenecks as Part of a Digital Twin in Industrial IoT
## 1 Introduction
This also underlines the importance of the third key characteristic: the bidirectional data exchange. The new system can be improved making use of the feedback from customers and the problems in product operation. Similarly, in later phases, the analysis and integration of data is of paramount importance in order to improve production. There are a lot of possible ways to improve production lines to produce items more intelligently and more quickly. A few examples are shop floor routing, avoidance of task delay and the optimization of setup times. Simplifying this very complex problem is one of the most important factors in terms of efficiency. A key factor in smart manufacturing is the throughput. Machines are principally responsible for throughput and are the first elements which come to mind when thinking about improving this parameter. The machine which has the biggest impact on limiting the speed of production is known as a bottleneck machine. This knowledge is of great interest for manufacturing engineers and enables one to increase throughput by improving this worst element. At the beginning of this century, [PERSON] et al. [12] presented the idea of the Active Period Method. This method uses active and inactive time periods of production systems to determine the overall bottleneck. The goal of this paper is a combination of a deeper investigation of the Active Period Method, an investigation of real life industrial data sets, a systematic consideration of crucial aspects and a sensible application of this method to real life data. Furthermore, the objective is the development of a heatmap visualization, serving as input to the optimization of the overall production line. Finally, the presented approaches are embedded into an overall concept of a self-learning DT. Consequently, the paper is structured in the following manner. Section 2 reports the results of an in-depth literature review. Based on these results, the Active Period Method is discussed and validated in Section 3. An appropriate visualization technique for the results of this method is presented in Section 4. Based on the application to real life data, Section 5 explains the concept of a self-learning DT. The paper concludes with a summary of results and an outlook to future research activities.
# Linear and Nonlinear Modelling of the Usable Area of Buildings with Multi-Pitched Roofs
## 2 Materials and Methods
The nonlinear model, considered to be optimal in terms of fit, achieved a coefficient of determination at a level similar to that of the linear model (95%). Despite a similar level of fit, the nonlinear model required the removal of more cases. The need to remove more outliers in the nonlinear model indicated its lower stability compared with the linear model. Similar actions were performed for the dataset of existing buildings located in Koszalin. In the next stage, the value of the modelled usable area of the buildings was estimated for observations that were not part of the model training dataset. The prediction results were summarized using two measures: \(MAE\) (mean absolute error) and \(RMSE\) (root mean squared error) given by the formulas:  
\[\mathbf{MAE}=\frac{1}{N}\mathbf{\sum}_{i=1}^{N}\left|\mathbf{y}_{i}-\hat{\mathbf{y}}_{i}\right| \tag{3}\]  
and  
\[\mathbf{RMSE}=\sqrt{MSE}=\sqrt{\frac{1}{N}\mathbf{\sum}_{i=1}^{N}\left(\mathbf{y}_{i}- \hat{\mathbf{y}}_{i}\right)^{2}} \tag{4}\]  
where \(MSE\) is mean squared error, \(N\) is the number of observations in the validation sample (estimates), \(y_{i}\) is the observed value of dependent variable for the \(i\)-th object, and \(\hat{y}_{i}\) is the estimated value (prediction). As an addition to the models described above, we have also performed linear regression with regularization called LASSO regression [42; 43]. This method is useful in two situations: when we want to reduce the number of the explanatory variables and/or when we plan to avoid overfitting. The regularization in LASSO is based on a penalty function added to the regression equation and is obtained through minimizing the formula \[\sum_{i}\left(y_{i}-\beta_{0}-\sum_{j}\beta_{j}\kappa_{ij}\right)^{2}+\lambda\sum_{ j}\left|\beta_{j}\right| \tag{5}\]  
where \(x_{ij}\) is the value of \(j\)-th independent variable observed for the \(i\)-th object, \(\beta_{0}\) and \(\beta_{j}\) are the coefficients of the model, and \(\lambda\) is the regularization (penalty) parameter. The higher the penalty \(\lambda\), the faster the method shrinks and removes certain parameters from the model. The result is a linear model with some coefficients set to zero. Such models have proven to perform better than unconstrained linear models on numerous occasions in predictive modelling. Since the method aims at avoiding overfitting, the \(R^{2}\) statistics are usually lower than obtained for models estimated with the unconstrained least squares. Thus, we do not calculate \(R^{2}\) as a measure of fit. Instead, we use the _MAE_ and _RMSE_ measures to compare the prediction results obtained from the regularized models and from the regular ones.
## 2 Materials and Methods
### Performance Evaluations of Detecting Cardiac Structural Abnormalities  
To conduct performance evaluations of detecting cardiac structural abnormalities, we used 104 sets of the sequential 20 video frames of cross-sections around a 4 CV (Heart) and around a three-vessel trachea view (3 VTV) (Vessels), acquired from 40 normal and 14 CHD cases. They were randomly assigned into 10 videos for validation and 42 videos for testing in both the normal and CHD datasets. In SONO, the abnormality score was calculated using the total number of well-detected substructures among the eight selected substructures (crux, ventricular septum, right atrium, tricuspid valve, right ventricle, left atrium, mitral valve, and left ventricle) for Heart, and the four selected substructures (pulmonary artery, ascending aorta, superior vena cava, and ductus arteriosus) for Vessels in each set. These selected substructures were important checkpoints for fetal cardiac ultrasound screening; in particular, the guidelines recommended that the evaluation should be done around each standardized transverse scanning plane [17; 18]. We defined,  
\[\text{abnormality score for Heart}\ =\ 1-\frac{1}{T}\sum\limits_{t=1}^{T}\frac{ \text{h}(t)}{8}\] \[\text{abnormality score for Vessels}\ =\ 1-\frac{1}{T}\sum\limits_{t=1}^{T}\frac{ \text{v}(t)}{4}.\]  
The abnormality score ranged from 0 to 1 where \(t\) represented the frame number and \(T\) represented the maximum number. In this study, we focused on the abovementioned 20 video frames (\(T=20\)) and calculated the abnormality scores in Heart and Vessels. h(\(t\)) and v(\(t\)) represented the total number of substructures with the probability \(\geq\)0.01 in each frame. Then, we compared the accuracy of detecting cardiac structural abnormalities in SONO, using 191 videos of 83 normal cases for training, with other conventional anomaly detection algorithms for general images, such as a typical convolutional autoencoder (ConvAE) for a frame (ConvAE-1 frame), ConvAE [21], AE + global feature [22], and anomaly detection with generative adversarial networks (AnoGAN) [23], for which the training data consisted of 668 videos of 309 normal cases. ConvAE and AE + global feature directly applied to video analyses; however, the other methods were originally intended for still image analyses. The reconstruction errors for each individual frame were used. We assumed that the minimum value of reconstruction errors reflected the background noise and calculated an abnormality score for each method using the range between the maximum and minimum value of reconstruction errors. The code has been uploaded to GitHub ([[https://github.com/rafcc/2020-prenatal-sono](https://github.com/rafcc/2020-prenatal-sono)]([https://github.com/rafcc/2020-prenatal-sono](https://github.com/rafcc/2020-prenatal-sono))).