esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Details
### Plasma Temperature Calculation  
The emission of DBD plasma was spectrally resolved by using optical emission spectroscopy (OES) technique. A high-resolution monochromator (Triax 550, Horiba Jobin Yvon, Palaiseau, France) allowed us to record atomic and molecular emission features of the excited plasma species in the investigated discharge frequency range. The recorded vibrational and rotational spectra were used for plasma temperature calculation. By using the molecular nitrogen spectra, the vibration temperature (T\({}_{vib}\)) was determined. The integrated intensity of the vibrational bands represented in semi-logarithmic coordinates (Boltzmann diagram) as a function of the upper energy level is given by [29; 30]  
\[ln\bigg{(}\frac{I_{\
u^{\prime}\
u^{\prime\prime}}*\lambda}{c*A_{\
u^{\prime} \
u^{\prime\prime}}}\bigg{)}=ln\alpha=f(E_{\
u^{\prime\prime}}-E_{0}), \tag{1}\]  
where \(c\) is the speed of light in vacuum, \(A_{\
u^{\prime}\
u^{\prime\prime}}\) is Einstein coefficient for the considered transition, \(E_{\
u^{\prime\prime}}\) is energy of the final level, and \(E_{0}\) is the energy of the level for \(\
u^{\prime}=0\). The relationship between the slope and the vibrational temperature is given by the following equation:  
\[m_{1}=\frac{1}{k_{b}*T_{vib}} \tag{2}\]  
The intensity of the assigned rotational features of the N2\({}^{+}\) ion bands (band head at 391.44 nm) represented in semi-logarithmic coordinates as a function of the rotation quantum number (equation below) was used for rotational temperature (T\({}_{rot}\)) calculation [29; 30].  
\[ln\bigg{(}\frac{I_{\eta^{\prime\prime}}}{J^{\prime\prime}+1}\bigg{)}=ln\beta =f((J^{\prime\prime}+1)(J^{\prime\prime}+2)), \tag{3}\]  
The relationship between the slope and the rotational temperature is given by the following equation:  
\[m_{2}=\frac{hcb}{k_{B}*T_{rot}}\simeq\frac{3}{T_{rot}(K)} \tag{4}\]  
where \(h\) is [PERSON]'s constant, \(c\) is the speed of light in vacuum, \(k_{B}\) is [PERSON]'s constant, and \(B\) is the rotational constant (\(B=3\) cm\({}^{-1}\)).
## 3 Methodology
### Analytics Based on the Classification Models  
In the training phase of SVM models, to identify attention level and facial emotion, the 'Helen' dataset is used. In the first step, all the facial landmarks of the detected faces of this dataset are standardized into 300 px \(\times\) 400 px size at the preprocessing stage and then converted to features using the same procedure been used in the detection phase according to Table 1. Processed facial features are then used to train two models after labeling each face in the dataset with emotion class and attention level. Tables 2 and 3 show the attention level labels and emotion level labels, respectively. The 'Helen' dataset has 2000 images as training images; only 1150 images were selected considering the relevance to the study. Those selected images were labeled into five emotion classes and two attention levels by three annotators. The image annotation criteria were defined by the research team considering the research scope. Then, a defined rule set was given to the annotators for image annotation. The main rule was to focus on the head pose and the facial features of the presented person in the picture. The average emotion class and attention level for every image were used to train the models. Out of 330 validation images provided in the 'Helen' dataset, only 160 images were used for the validation of the SVM models. These trained models are used in the classification subsystem. Detected classification results are then sent to the DB server under the meeting ID and participants' names in JSON format each time.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Class Name** & **Description** \\ \hline Attended & Participant is paying attention to the meeting. \\ Not Attended & Participant is not paying attention to the meeting. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Attention classes for image labeling.  
Figure 5: Face landmarks and feature representation of two emotions (source: Helen dataset): (a) “angry face” image; (b) “happy face” image.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Class Name** & **Description** \\ \hline Excitement & Participant is showing an excited facial expression. \\ Happy & Participant is showing a happy facial expression. \\ Neutral & Participant is showing a neutral facial expression. \\ Sleepy & Participant is showing a sleepy facial expression. \\ Disgust & Participant is showing a disgusted facial expression. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Emotion classes for image labeling.
## 2 Adaptive Sensor Arrays
### Power Control  
An important benefit derived from beamforming and consequent increase in the SINR is the possibility of reducing the signal transmission power. This improves the energy efficiency of the system and reduces interference between the users, ensuring that each signal is transmitted with the lowest power required to maintain a good quality connection.  
The control of power in sensor arrays is based on a selected quality criterion, which in the present case was the SINR. During this process, the power is reduced in order to satisfy the restrictions shown in Equation (6). Based on papers presented at [8; 9; 10; 11], the updating of the power is given by:\[P_{i}(n+1)=P_{i}(n)\frac{\delta_{i}}{SINR_{i}} \tag{10}\]  
where it can be seen that convergence is achieved when \(SINR_{i}=\delta_{i}\). In the calculation of Equation (10), it is necessary to know the value of the \(SINR_{i}\) in the moving terminal, which can be estimated from the minimum mean square error of the beamforming step (LMS), as described by:  
\[SINR_{i}=\frac{1-min\,E[{\varepsilon_{i}}^{2}(n)]}{min\,E[{\varepsilon_{i}}^{ 2}(n)]} \tag{11}\]  
where \(SINR_{i}\) represents the estimated SINR for each user.  
Rearranging Equation (10) as a function of the minimum mean square error gives the final expression for calculation of the power:  
\[P_{i}(n+1)=\delta_{i}P_{i}(n)\,\frac{min\,E[{\varepsilon_{i}}^{2}(n)]}{1-min\, E[{\varepsilon_{i}}^{2}(n)]} \tag{12}\]
# Modeling the Energy Performance of LoRaWAN
## 4 Modeling LoRaWAN End-Device Current Consumption  
In this section, we present models of crucial LoRaWAN energy performance parameters such as end-device current consumption, end device lifetime, and energy efficiency of data delivery. We assume a class A end-device that periodically transmits an uplink data message (e.g., a notification that carries a sensor reading). In the models, we consider the impact of bit errors. For the sake of tractability and clarity, we assume a uniform BER that refers to the residual BER after application of physical layer error correcting techniques, equivalent to the residual BER that corresponds to message loss rate due to non-ideal link quality. The section is divided in two subsections, which offer the aforementioned models for unacknowledged and acknowledged transmission, i.e., the transmission of unconfirmed and confirmed data messages, respectively. We develop the models for all DRs that are mandatory (i.e., from DR0 to DR5), as well as for DR6.
## 2 Materials and Methods
### Methods  
#### 2.2.1 Resonant Column/Torsional Shearing Device  
The laboratory tests were performed using modified Stokoe's fixed-free type Resonant Column apparatus with Torsional Shear mode (Wykeham Farrance RC/TS, model WF8500). The modified version of the device and new procedure of interpretation of results were presented in recent publication [49]. Standard WF8500 measurement method is based on the external measurement system of deformation in one plane of the specimen's cross-section--at its upper end. In resonant column mode (RC), the device enables dynamic loading with torque causing shear strain in the tested soil specimen. During a test, torque frequency increases abruptly or smoothly in the range of 0-300 Hz. In torsional shear mode (TS), the device generates cyclic, slowly changing torque with a constant frequency in the range of 0.01-50 Hz. The device is equipped with an aluminum base with a steel plinth equipped with a porous stone and pipes for water supply and drainage from triaxial compression cell together with valves. This allows for performing a controlled saturation process and isotropic consolidation. Pressure control is provided by sensors for cell pressure, pore pressure and backpressure. The size of plinth allows for the installation of a cylindrical soil specimen with a base diameter of 70 mm and a height of 140-150 mm. The loading is generated by an electromagnetic system consisting of four coils and neodymium magnets. The magnets are the moving part of the device and can generate a maximum torque of 1.2 Nm and maximum angle of 10 degrees. The control of the electromagnetic drive system is correlated with the automatic acquisition of measurement data from all sensors installed in the device. The device is powered by an external unit and controlled with a dedicated software \"DYNATOR\". Main control parameters in the RC mode are initial and final frequency, time and amplitude of the voltage that produces torsional loading (moment of electrodynamic force). In the TS mode, the control parameters are frequency, voltage amplitude and number of torsion cycles. Static values, characterizing the mechanical properties of the drive head, are altered due to changes of conditions in the laboratory and change with time (equipment components' wear). For these reasons, before each series of tests, the device is calibrated. Figure 7 presents a schematic drawing of the RC/TS device.
# Parameters of Optokinetic Nystagmus Are Influenced by the Nature of a Visual Stimulus
## 3 Results  
### Onset and Offset Time of the OKN  
In a previous study the influence of contrast and spatial frequency of the OKN onset time was already tested, showing a shorter latency with increasing contrast level and suggesting adjusting the stimulus presentation time upon the parameters of the stimulus [24]. Here in the current study, we aimed to replicate this effect for a grating of a higher velocity as well as for other grating types. To get the first insight we show a box plot (Figure 4), containing combined data of all subjects for the three gratings as an example of one spatial frequency. Here the expected trend was confirmed and thus replicated the effect of the contrast-onset relationship for a higher visual stimulus velocity.  
In testing the OKN onset time, the linear mixed-effects model (\(n=1037;R^{2}=0.72\)) revealed significant effects of \(SF\) (F(3,1012) = 35.32; \(p<0.0001\)), color (F(2,1012) = 31.84; \(p<0.0001\)) and contrast (F(5,1012) = 192.97; \(p<0.0001\)) on the the onset time.  
Figure 4: Box plots of the OKN onset time are shown as a function of contrast for the three gratings (luminance, R-G, B-Y) as an example of one spatial frequency (\(SF=0.12\) cpd). The circle and the asterisk indentify outliers, while the circle denotes a “standard out”, considered a value laying outside the 3 rd quartile + 1.5“interquartile range or 1 st quartile - 1.5“interquartile range; and asterisk denotes a “far out” value, considered a value laying outside the 3 rd quartile + 3“interquartile range or 1 st quartile - 3“interquartile range; The respective number is the indentifier (observation in the data set).  
In testing the OKN offset time (box plot shown in Figure 5), the linear mixed-effects model (\(n=1037\); \(R^{2}=0.70\)) revealed significant effects of \(SF\) (F(3,1012) = 48.10; \(p<0.0001\)), color (F(2,1012) = 74.89; \(p<0.0001\)), and contrast (F(5,1012) = 152.48; \(p<0.0001\)) on the offset time.  
At the last point, we report the violation of homoscedasticity of the residuals in cases of testing both, the OKN onset and offset time, as the Brown-Forsythe test revealed statistical significance. Please note that the problem of heteroscedasticity of the residuals is discussed below in the respective section.
# Study on Abnormal Pattern Detection Method for In-Service Bridge Based on Lasso Regression
## 4 Monitoring Data Analysis  
### Temperature Effect Analysis  
Based on the monitoring data from the SHM system of this bridge, this paper compared the temperatures of various components of the girder and arch rib over two consecutive days in different seasons, as illustrated in Figure 2. Specifically, Figure 2a depicts the temperature variations of the bridge during summer, whereas Figure 2b illustrates the temperature fluctuations during winter. From the figures, it can be seen that the temperature distribution of the girder and arch rib is relatively uniform under the influence of solar radiation. This is because the bridge is made of steel, which has fast heat conduction. Among the six temperature measurement points under direct sunlight, except for the temperature of the right side of the girder's top plate and the right side of the arch rib, the overall temperature variation of the measurement points is basically synchronized, and there is a significant temperature difference between day and night.
# Damage Identification Method Using Additional Virtual Mass Based on Damage Sparsity
## 2 Damage Identification Method Based on Additional Virtual Mass and Damage Sparsity
### Additional Virtual Mass Method
\[R_{ji,l}=\frac{\partial\omega_{ji}(\mathbf{\mu},m)}{\partial\mu_{l}}=\frac{\mathbf{ \rho}_{ji}^{T}(\mathbf{\mu},m)K_{i}\mathbf{\varphi}_{ji}(\mathbf{\mu},m)}{2\omega_{ji}(\bm {\mu},m)} \tag{6}\]  
Virtual structures can be constructed by attaching a virtual mass m to \(n\) substructures. The sensitivity of each order frequency to \(n\) substructures is then calculated from the measured first \(p\) orders frequency of each virtual structure and integrated into a sensitivity matrix \(\mathbf{R}\). The integration process is expressed in Equation (7). \[\left\{ \begin{array}{c}\mathbf{R}_{j,l}=\left[R_{j1,l},\ldots,R_{ji,l},\ldots,R_ {jn,l}\right]^{T}\\ \\ \mathbf{R}_{l}=\left[\mathbf{R}_{1,l},\ldots,\mathbf{R}_{j,l},\ldots,\mathbf{R}_{pJ}\right]^{T }\\ \\ \mathbf{R}=\left[\mathbf{R}_{1},\ldots,\mathbf{R}_{l},\ldots,\mathbf{R}_{n}\right]\end{array} \right. \tag{7}\]  
It is assumed that the damage-factor of the damaged structural finite element model is \(\mathbf{\mu}\), and that of the undamaged structural finite element model is \(\mathbf{\mu}_{0}=1\). If we consider the expansion error \(\mathbf{e}_{taylor}\) based on the Taylor series expansion, the model frequency of the actual damaged structure \(\overline{\mathbf{\omega}}(\mathbf{\mu})\) and undamaged model frequency \(\mathbf{\omega}(\mathbf{\mu}_{0})\) have the following approximate linear relationship. \[\overline{\mathbf{\omega}}(\mathbf{\mu})=\mathbf{\omega}(\mathbf{\mu}_{0})+\mathbf{R}\Delta\mathbf{ \mu}+\mathbf{e}_{taylor} \tag{8}\]
# Operation of Water Supply Systems in the Czech Republic--Risk Analysis
## 3 Results and Discussion
### Risk Analysis
Disruption of pipeline joints can also be caused by the corrosion of the material (caused by \"soft water\" in Operator B), in which case it is resolved through the proper regulation (reduction) of pipeline pressures and by dividing the pipeline into pressure zones. The composition of the water itself has a significant impact on corrosion as it is affected by the temperature of the water, its pH, the total number of magnesium and calcium cations (the so-called hardness of the water), the soluble oxygen content, and others. The risk of corrosion mainly concerns groundwater sources and, thus, it is necessary to select appropriate materials to increase the lifetime of the pipes (e.g., plastic, cast iron, etc.). Operator C uses special cameras to diagnose the condition of the pipes. In the area of water distribution, power cuts were also identified as a significant risk as it was assessed as moderate in the water distribution area with a medium probability of incidence. Furthermore, this risk was rated as difficult to predict. Existing and recommended precautions have already been defined in the text above. For Operators A and B, neglected maintenance was identified as another significant risk which is mainly caused by the lack of financial resources for maintenance and the lack of qualified staff. The recommended measures to mitigate this risk are to increase funding for the maintenance of the water network, to train staff, and to define their required qualifications and responsibilities. Operator C owns backup sources of electricity and technical means to ensure an alternative or emergency supply of drinking water to the inhabitants. It has its own backup water sources and interconnected water supply systems and is currently planning to build its own photovoltaic plant. * Limitations of the study  
Of course, the analysis presented here has its limitations. The sample of operators is small, which is the biggest weakness of the analysis. Therefore, the selection of the evaluated operators representing each operating model was carried out by an expert group and the results of the analysis were consulted and confirmed with experts. However, this limitation should not have any impact on the global picture received.
# Detecting Phishing URLs Based on a Deep Learning Approach to Prevent Cyber-Attacks
## 3 Methodology
### 1D CNN Architecture Diagram  
The input of preprocessed data from the datasets used is fed into the model for model training and to verify whether a website URL is a phishing site or not. A detailed diagram of the 1D CNN architecture and the workflow is shown in Figure 6. The architecture consists of the following sections:  
1. Input Data: Clean URLs are provided as input data for preprocessing;
2. Preprocessing: Stop words are removed from the URLs, and tokenization is performed as in Figure 4;
3. Embedding Layers: Data are transferred to the embedding layer, where data dimensioning is performed based on the length of the URL. Suppose that in our model, the length of the URL is 120. Then, 120 dimensions are provided in the embedding layer;
4. Convolutional Blocks: After embedding the layer, the data are entered into the convolutional block, where seven convolutional layers that have 1D-CNN blocks and one ReLU function have been mapped. Feature mapping and feature extraction of the URL are performed in this layer. One after the other, the inputs are passed through each block to filter out the most important features of the URL;
5. Global Max Pooling: After the convolutional layer, global pooling is performed on the URL's features, where the input size of the matrix is taken with the input of the dimension, and the max value is selected for the computation. Then, the URLs are moved to a deep neural architecture, where dropout is applied;
6. Drop Out: This is used to prevent the model from overfitting;
7. Sigmoid: In this step, based on the features identified, the URL is classified as phishing or legitimate.
# BIM-Based Management System for Off-Site Construction Projects
## 3 Requirements Analysis for System Development
### Elements of OSC Project Management
#### 3.2.2 Usability Requirements  
The system functions specific to OSC projects must be intuitive and efficient. The usability requirements constitute information input and output automation, information visualization, and information-based communication. First, the OSC project accompanies considerable information, and the interdependence between the information points is high. Therefore, an input/output automation function must be established to conveniently manage all the information. In most construction projects, people manage the data manually, such as by directly collecting information and creating and printing documents. This management method consumes considerable time and resources, and information update through continuous iteration is inevitable. Manual information management is inefficient, and it is challenging to ensure accuracy owing to human factors. This aspect may limit the success of the OSC projects that rely heavily on information. Therefore, functions capable of automating the information input and output in various ways must be introduced to facilitate efficient information management.  
Second, the key indicators of the project must be visualized to enable their monitoring to facilitate decision making. In general, even if a system has the status for each stage (Section 3.2.1), it is challenging to make information-based decisions if the data cannot be effectively delivered. Accordingly, it is necessary to improve the information usability for each subdivided management element, such as the schedule, component, and cost, as well as the project or phase information, from a holistic perspective. The system can support optimal decision making by effectively placing the interconnected variables on one screen.  
Third, the participants must be able to share the project plan and status through the system and proceed with the project accordingly. Because OSC projects involve a high interrelationship between the preceding and the following tasks, the system needs a function that allows the parties to evaluate and enhance the performance. Information exchange within the system is also essential to minimize damage by responding immediately to any changes. The requirements from this viewpoint can be summarized as follows.  
* Sensing and parametric-design-based data input automation via a mobile reader (e.g., component selection on both two-dimensional (2D) and 3D drawings);
* Visualization for status by project and component;
* Document (i.e., production log, shipment request, invoice, and payment) analysis and templates;
* 2D-3D model linking for DfMA; review of exchange of opinions for each stage of installation-production-transport;
* Sharing of and response capabilities to real-time changes for each stage manager;
* Visualization of progress by management elements (e.g., schedule, component, and cost).
# Nonlinear Vibration Analysis of a Beam with a Breathing Crack
## 4 Finite Element Model of a Beam with a Breathing Crack  
The cracked beam is discretized by one cracked beam element and (\(n\)\(-\)1) non-crack beam elements, as shown in Figure 5, where \(n\) is the number of elements. Each of the non-crack elements has two nodes and three DOFs at each of the nodes, and the cracked beam element has the same node numbers and DOF numbers as the non-crack elements. Based on the assumption that the changes of damping caused by the crack are relatively small compared to the total damping of the cracked beam, the changes of the damping can be neglected. In addition, it is also assumed that the crack has no effect on the mass of the system. Thus, the nonlinear equation of motion of the cracked beam with a breathing crack is expressed as  
\[\mathbf{M}\ddot{\mathbf{U}}+\mathbf{C}\dot{\mathbf{U}}+[\mathbf{K}_{01}- \mathbf{K}_{02}f(t)]\mathbf{U}=\mathbf{F}. \tag{12}\]  
where \(\mathbf{M}\) is the mass matrix of the cracked beam, \(\mathbf{C}\) is the damping matrix of the cracked beam, \(\mathbf{K}_{01}\) is the stiffness matrix of the undamaged beam, and \(\mathbf{K}_{02}\) is the stiffness matrix caused by the crack; \(\mathbf{U}\) is the generalized coordinate vector of the cracked beam, and \(\mathbf{F}\) is the applied force vector.  
Substituting Equation (5) into Equation (12), the dynamic equation of the cracked beam can be written as  
\[\mathbf{M}\ddot{\mathbf{U}}+\mathbf{C}\dot{\mathbf{U}}+(\mathbf{K}_{001}- \mathbf{K}_{t})\mathbf{U}=\mathbf{F}. \tag{13}\]  
Figure 5: Discretization of the beam with the breathing crack.  
where  
\[{\bf K}_{001}={\bf K}_{01}-\frac{a_{0}{\bf K}_{02}}{2},\ {\bf K}_{t}={\bf K}_{02} \sum_{n=1}^{N}k_{n}\cos(n\omega t+\varphi_{n}).\]  
Equation (13) shows that the stiffness matrix \({\bf K}_{t}\) is time-variant due to the repetitive crack opening and closing. Thus, Equation (13) can be regarded as a linear time-varying equation.