esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Active-Passive Reconfigurable Intelligent Surface for Cooperative Transmission Systems
## 2 System Model  
Figure 1 shows an illustration of the hybrid active-passive RIS collaborative transmission system. In this network, there is a single-antenna transmitter (S), a hybrid active-passive RIS, and a single-antenna receiver (D), where HAPR is deployed on the surface of a building between the two ends of the communication. It is impossible to connect the direct link between S and D because of adverse propagation conditions. The hybrid active-passive RIS has \(N\) REs, each consisting of passive load impedance, active load impedance, and two switches, of which Switch 1 controls the passive load impedance and Switch 2 controls the active load impedance. Only one switch can be on at a time. Switch 1 is open and Switch 2 is closed, which means that each element is a passive element and we call HAPR working in passive mode; on the contrary, Switch 1 is closed and switch 2 is open, indicating that each element is an active element and HAPR is working in active mode. The states of Switch 1 and Switch 2 are programmed and controlled by the RIS controller, as well as being intelligently changed in real time according to the channel environment. More specifically, the controller chooses the mode with the greatest SNR to reflect the signal. Assuming CSI for all links is known and available, e.g., channel estimation could be performed by deploying active sensors on the HAPR [26; 27]. Therefore, by modifying the reflection coefficient matrix \(\Theta=diag(a_{1}e^{i\theta_{1}},...a_{n}e^{i\theta_{n}}...,a_{N}e^{i\theta_{N}})\), where \(a_{n}\in(0,a_{max})\) and \(\theta_{n}\in[0,2\pi)\) stand for reflection coefficient and phase shift, respectively, HAPR can automatically rearrange the radio channel based on the available CSI.  
The channel from S to HAPR is marked as \(\widetilde{h}_{S_{m}}\in\mathbb{C}^{N\times 1}\), and that from HAPR to D is denoted as \(\widetilde{h}_{nD}\in\mathbb{C}^{1\times N}\), where \(\widetilde{h}_{S_{m}}=h_{S}e^{i\varphi_{n}}\) and \(\widetilde{h}_{nD}=h_{D}e^{i\phi_{n}}\). \(h_{S}\) and \(h_{D}\) are the amplitudes of the respective channel coefficients, which are mutually independent _Nakagami-m_ random variables (RVs), written as \(h_{S}\sim Nakagami(m_{S},\Omega_{S})\), \(h_{D}\sim Nakagami(m_{D},\Omega_{D})\). And \(\varphi_{n}\) and \(\phi_{n}\) are the phase shifts of the respective channels.
# Underwater Image Mosaic Algorithm Based on Improved Image Registration
## 2 Materials and Methods
### Detection of Feature Points with SIFT
#### 2.2.2 Accurate Keypoint Localization  
Since not all the extremum points solved by the above process are feature points, some points sensitive to noise or poor stability need to be removed by the operation of the keypoint location. In this work, two existed approaches have been used. The scale-space function \(L(x,y,\sigma)\) is expanded at the sampling point \(x=(x,y,\sigma)^{T}\) through Taylor series to delete the extremum point with low contrast [34; 35]. At the same time, the edge effect of \(D(x,y,\sigma)\) is reduced by using the ratio of trace and determinant of the Hessian matrix [36].
## 2 Materials and Methods
### Calibration Process  
A calibration process was performed prior to the animal fluorescence measurements of the miniature tubes. For the calibration process five fluorescent samples were prepared, covering the range of ratios that were measured during the experiment. The samples were placed in sealed cuvettes and the reference phantom was placed above them. The fluorescence energy emitted by these samples was measured at two excitation wavelengths, one for each fluorophore, with the appropriate emission filter. For each measurement, the total number of photons measured by the TCSPC system was used as the fluorescence energy value. A calibration curve of fluorescence energy ratio versus concentration ratio was created based on these measurements.
## 3 Antenna Optimization: the CRO-SL Algorithm
### Substrate Layers Implemented  
Though different search strategies can be defined at the practitioner's discretion, this work adopts a five-substrate construct of the CRO-SL. They are briefly described below:  
1. HS: Mutation using the Harmony Search procedure. Harmony Search [28] is a well-known meta-heuristic based on the how a music orchestra improvises a melody. HS substrate controls the generation of new larvae in one of the following ways: (i) with a probability HMCR in (0, 1) (Harmony Memory Considering Rate), the value of a component of the new solution is drawn uniformly from the same values of the component in other corals of the current reef; and (ii) with a probability PAR in (0, 1) (Pitch Adjusting Rate), where small adjustments are applied to the values of the current solution.
2. DE: Differential Evolution algorithm mutation. This substrate is based on the DE algorithm defined in reference [29]. This approach introduces a differential mechanism for exploring the search space. In this case, new larvae are generated by perturbing the current larva by using a vector of differences between two individuals in the population. This perturbation is defined as \(x^{\prime}{}_{1}=x^{\prime}{}_{1}+F(x^{2}{}_{1}-x^{3}{}_{1})\) (where F stands for a weighting the perturbation amplitude, 0.6 in this case). After this perturbation of the current larva, the perturbed vector \(x^{\prime}\) is in turn combined with an alternative (different) coral in the reef, by means of a classical 2-points crossover, as defined next.
3. 2 Px: Classical two-points crossover. The crossover operator is the most used operator for exploring the search space in evolutionary computation algorithms [30]. It consists of coupling two individuals at random, and then, after choosing two points for the crossover, interchanging the genetic material in between these two points. In the current CRO-SL implementation, one larva to be crossed comes from the 2 Px substrate, whereas the other can be chosen from any part of the reef.
4. GM: Gaussian Mutation. We consider a traditional Gaussian mutation of the form \(x^{\prime}{}_{1}=x_{1}+N_{i}(0,\,\sigma^{2})\), where \(N_{i}(0,\,\sigma^{2})\) is a random number following the Gaussian distribution of 0 mean and variance \(\sigma^{2}\). We introduce a linear decreasing of \(\sigma\) value during the algorithm, from 0.2(A-B) to 0.02(A-B), where [B,A] is the domain search. Note that this procedure produces a stronger mutation in the beginning of the algorithm, and a fine tuning of the search with smaller displacements nearing the end or the algorithm's evolution.
5. SAbM: Strange Attractors-based Mutation. This is a new search operator proposed in reference [31], specifically designed to use fractal geometric patterns in the search of new larvae. Specifically, it is designed to generate structures of non-linear dynamical systems with chaotic behavior [32]. Interested reader may consult reference [31] to obtain more information on this operator.
## 2 Indoor Wireless Positioning System  
Figure 1 presents the block diagram of an UWB indoor wireless positioning system. In the diagram, the green ellipses represent base stations, i.e., anchors, while the gray inverted triangle at the center indicates the mobile tag. Three anchor modules are positioned at the corners of the experimental area by 732 \(\times\) 488 \(\times\) 220 cm and are connected to a computer via a universal asynchronous receiver/transmitter (UART) interface through a TTL-to-USB conversion module. The system performs bidirectional ranging between the tag and the anchors. The measured distances are processed using a Java-based TOA simulation algorithm to estimate the tag's coordinates. The positioning error is then determined by comparing the estimated and actual positions in a defined 3D coordinate system (X, Y, Z).  
Both the anchor and tag modules consist of an Arduino Nano microcontroller connected to an UWB chip via a serial peripheral interface (SPI) bus. The Arduino Nano executes the control program and communicates with the UWB chip to perform ranging tasks. To transfer the ranging data to the computer, the anchor modules use UART to send data to a USB-TTL module, which then communicates with the computer over a USB connection.
# Quantum Circuit Implementation of Multi-Dimensional Non-Linear Lattice Models
## 10 Conclusions  
Quantum-circuit implementations for the evaluation of the non-linear equilibrium distribution function in one-dimensional non-linear lattice models for fluid modeling were presented in detail. A reduced-precision quantum floating-point format was employed with an asymmetric bias optimized for the lattice model considered. For this floating-point representation, the increase in the quantum circuit width and circuit depth with an increasing number of mantissa qubits used was analyzed. The second part of this work dealt with the design of quantum-circuit implementations for the non-linear terms of equilibrium distribution functions in multi-dimensional Lattice Boltzmann models. Specifically, as a first step, the evaluation of the kinetic energy in two-dimensional lattice models was described in detail. The complexity analysis of various quantum circuit designs considered in this study showed that a novel, modular design in which a shift-and-add-based approach directly incorporates contributions from \(u\)- and \(v\)-velocity components leads to the smallest quantum circuit width. Extending the number of mantissa qubits used in the examples (i.e., \(N_{M}=4\)) to a more realistic scale, e.g., \(\geq\)8 (to avoid excessive rounding), the quantum circuit width becomes \(O(100)\) qubits. For the projected availability of future quantum computers, this means that in the near future, meaningful proof-of-concept experiments could be performed. In future work, the extension to quantum-circuit implementations for the collision terms of the full two- and three-dimensional Lattice Boltzmann methods will be presented as the first next step. Then, quantum algorithms for the full two- and three-dimensional Lattice Boltzmann method will be the main research focus.  
This research received no external funding.  
**Data Availability Statement:** More detailed information on the quantum-circuit implementations, as well as data from complexity analysis, is available from the author upon request by email.  
**Acknowledgments:** The author would like to acknowledge funding received from the University of Glasgow supporting this research.  
**Conflicts of Interest:** The author declares no conflict of interest.
# On the Quality of Deep Representations for Kepler Light Curves Using Variational Auto-Encoders
## 2 Materials and Methods
### Extending VRAE to Include Temporal Information
#### 2.2.1 Problem Setup  
Consider a dataset \(D=\{\vec{x}^{(1)},\vec{x}^{(2)},\dots,\vec{x}^{(N)}\}\) of \(N\) input patterns \(\vec{x}\) distributed according to an unknown probability distribution \(p(\vec{x})\). These inputs pattern are vectors of variable length, \(\vec{x}^{(i)}=(x^{(i)}_{1},x^{(i)}_{2},\dots,x^{(i)}_{T_{i}})\), where \(x^{(i)}_{j}\in\mathbb{R}\) represent the \(j\)-th observation of the time series \(\vec{x}^{(i)}\) of length \(T_{i}\). Let \(t^{(i)}_{j}\) be the timestamp when the \(j\)-th observation was obtained for datum \(i\). Furthermore, we define the time interval, or _delta_, for each observation: \(\delta^{(i)}_{j}=t^{(i)}_{j}-t^{(i)}_{j-1}\), with \(\delta^{(i)}_{0}=0\ \forall i\). Let \(s^{(i)}=\text{std}(\vec{x}^{(i)})\) be the standard deviation of the \(i\)-th time series. This paper focuses on transit-shape domain objects over a light curve \(\vec{x}^{(i)}\) in order to recognize patterns of exoplanets orbiting its host star. An example is shown in Figure 1 (the time unit used is explained in Section 3.1).  
Assuming a detrended light curve \(\vec{x}^{(i)}\) (with zero mean), the standard deviation, \(s^{(i)}\in\mathbb{R}_{+}\), expresses the scale magnitude of the variability on flux \(\vec{x}^{(i)}\): \(s^{(i)}=\sqrt{\frac{1}{T_{i}-1}\sum_{j=1}^{T_{i}}\left(x_{j}^{(i)}\right)^{2}}\), which we abbreviate to _F-std_ (Flux standard deviation).
# Temperature Load Mode of Bridge in Permafrost Region of Qinghai-Tibet Railway
## 4 Finite Element Model
### Sunshine Temperature Field Simulation Model  
The geological and climatic conditions of the Qinghai-Tibet Plateau are complex, and the concrete bridge structure exposed to the air must be closely related to the surrounding natural environment. Existing studies show that the temperature change of the bridge  
Figure 11: Longitudinal displacement of beam and rail. (**a**) Width of beam gap. (**b**) Daily variation in beam end displacement. (**c**) Relationship between beam end displacement and temperature change. (**d**) Beam-rail longitudinal relative displacement.  
beam is related to the geographical location, topographic conditions, the orientation of the bridge, season, solar radiation intensity, and temperature change.  
Since the geographic latitude and longitude spanned by the bridge is very small, and the temperature distribution along the length of the bridge is relatively close, theoretically, there is no heat conduction process along the longitudinal direction of the bridge, and the slight change in the temperature difference along the length of the bridge can be ignored. Therefore, the temperature field of bridge structure can be simplified as a 2D transient nonlinear problem on the section. The heat exchange between the bridge and the outside world can be divided into three parts: solar radiation, radiation heat transfer, and convection heat transfer. Based on the thermal analysis module of large-scale general finite element software ANSYS, these three heat exchange actions were transformed into heat flux boundary conditions, and the calculation model of sunshine temperature field distribution of double-piece T-beam bridge of Qinghai-Tibet railway was established, as shown in Figure 12[28].  
According to relevant literature [29; 30; 31; 32], the specific heat capacity of concrete is 920 J/(kg\(\cdot\)K), the thermal conductivity is 1.74 W/(m\({}^{2}\cdot\)K), the unit weight of concrete is 2400 kg/m\({}^{3}\), the radiation absorption coefficient of the concrete surface is 0.65, the average reflectivity of snow covered ground is 0.7, the blackness of the radiation system between the outer surface of T-beam and sky radiation surface and ground is 0.9, and the atmospheric blackness is 0.8.
# A Context-Aware Neural Embedding for Function-Level Vulnerability Detection
## 2 Framework Design
### Contextual Semantic Learning  
The key to identify the potentially vulnerable context is to learn accurate code semantics that correctly represents its contextual meaning. We can add an if statement before the sensitive sink sprintf is called, and the if statement checks the size of the variable testStr (see Figure 2b code sample at line 6). This new code piece would remedy the vulnerability because if the size of variable testStr exceeds the array size, the sink will not be called. Therefore, whether the sink sprintf is vulnerable depends on whether its preceding code context has validation statements or not. Hence, the same token sprintf in different contexts (with or without validation of its parameters) should be represented differently, and being able to recognize these differences is the key to detect vulnerable patterns.  
The techniques such as Word2 Vec capture the meaning of a token based on a relatively small context window. Therefore, we applied the ELMo to produce word representations by learning the representation of each word based on a deep context. The deep context is a broader context learned by combining the internal states of the bidirectional LSTM. This feature enables the ELMo to produce different representations of a word based on the different contexts in which the word is used [15]. For instance, considering the sensitive sink sprintf as the target token shown in Figure 2, ideally, the ELMo should be able to capture both the vulnerable source-sink code context as described in Figure 2a, and the patched source-validation-sink context as shown in Figure 2b.  
To evaluate whether the ELMo can generate different embeddings for a target sink function based on the difference contexts, we constructed three potentially vulnerable functions which do not have any validation for the parameters and three patched versions with _if_ statements for validation of the parameters prior to the functions being called. Figure 3 shows the 2D plot of these function embeddings learned by the ELMo. The t-SNE algorithm was used to map the 1024-dimensional vector representations to a 2D plane. It can be seen that the learned representations of the vulnerable functions represented by the red rectangles are separated with that of the corresponding patched versions, which have _if_ statements for validation. It proves that the contextual information (i.e., with or without  
Figure 2: The different contexts of the target token sprintf (red). (**a**) The potentially vulnerable context (yellow) of target token (red): sprintf. (**b**) The patched context (yellow) of target token (red): sprintf.  
validation) of the code can be learned by the ELMo. This will facilitate the analysis for vulnerability detection.
# Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study
## 2 Literature Review
### Hyper-Parameters
Consequently, it is advisable to slowly raise the number of layers while observing the model's performance accuracy. Further increase in layers might result in additional processing time while the best performance cannot be guaranteed [42]. Number of neurons/units per layers. In addition to network layers, the number of neurons or units in each layer can also significantly impact the performance of the DL algorithm. Similarly, increasing the number of neurons in the hidden layers of the Deep Neural Network or the number of LSTM units in the LSTM layers might cause low detection accuracy and long training time [42]. Therefore, researchers need to fine-tune these values to ensure an effective and efficient DL model for phishing detection without compromising its performance accuracy. Number of kernels. Instead of the number of neurons in the hidden layers of DNN, in CNN architecture, the number of kernels or filters in the convolutional layers can significantly influence the success rate at which phishing websites are detected. Kernels, or filters, are used mainly in CNN models to convolve the input data into numerous feature maps [22]. Different kernels were used in previous research works, ranging from 8 [11] to 512 [42], depending on the number of input features, size of the dataset, or the neural network architecture. Kernel size. Kernel size, or window size, is another parameter that needs to be fine-tuned in CNN models. Kernel size is the size of a one-dimensional window, which is convolved sequentially in the convolutional layers and depends on the number of input features [42]. Different kernel sizes have been utilized in previous studies, with typical values from 1 to 10 [28; 29; 35]. The optimal kernel size that produces the best performance is determined based on the model's loss and accuracy. Optimizer. While training deep neural networks, the loss function or error is calculated to evaluate the effectiveness and efficiency of the DL algorithm. This loss function can be optimized, and the weights can be updated by using optimizers [34; 42]. Popular methods of optimization include Root Mean Square Propagation (RMSProp), Gradient Decent (GD),Adam, AdaGrad, AdaDelta, etc. [26]. GD and Adam are the most common optimization techniques in classification problems to optimize the error and adjust the weights [37]. Activation function. The activation function is represented by a mathematical equation that defines an output of a neuron based on given inputs [13]. It is considered one of the important parameters in DL architectures that determines the training model's output, accuracy, and efficiency. In the neural network, neurons of the same layer usually use the same activation function [6]. Rectified Linear Unit (ReLU), Softmax, sigmoid, and Tanh are examples of frequently-used activation functions for DL models.
## 3 Methodology
### Data Pre-Processing  
Raw sensor data from wearable instruments usually includes noise and variations that can negatively impact HAR model interpretation. This study implemented several pre-processing efforts to improve data quality and enhance model recognition. The pre-processing channel has three prominent phases: data denoising to eliminate undesirable noise from sensor signals, data normalization to regularize input values, and data segmentation employing a sliding window method to formulate sequential data for the deep learning model. These pre-processing phases are essential for providing the HAR system's robustness and reliability, as they support reducing the effects of sensor noise, instrument variations, and temporal inconsistencies in unprocessed data.
## 2 Calculation Methods
### Optical Efficiency of Solar Trough System  
Applying the calculation method of trough solar system we developed before [11], the effective solar radiation on the mirror is represented by \(B_{eff}(\theta_{\perp})\), the solar radiation at one reflection point \(P\) is \(B_{eff}(\theta_{\perp})\). \(\rho\), \(\alpha\), \(\tau\) is reflectivity of mirror, transmittance of glass envelope and absorptivity of vacuum receiver, respectively, \(\rho\tau\alpha\)\(B_{eff}(\theta_{\perp})d\theta_{\perp}\) refers to the amount of solar radiation reflected on the receiver and absorbed by the reflection point \(P\). In addition, when the incident light is not perpendicular to the reflector, a part of the solar rays may not reach the receiver after reflection. The impact of this part on the efficiency is called end loss. The optical efficiency of reflection point \(P\) is:  
\[\eta_{P}(y)=\frac{\int_{-\theta_{0}}^{\theta_{0}}\rho\tau\alpha B_{eff}( \theta_{\perp}d\theta_{\perp})}{I_{in}}*(1-\frac{f_{0}\tan(\theta_{\parallel})* (1+\tan^{2}(\beta/2))}{L}) \tag{1}\]  
The later part of the function is end loss. \(L\) is the length of parabolic mirror, \(f_{0}\) is the focal length, \(\beta\) is the rim angle of the point \(P\), \(I_{in}\) is the incidence solar energy flux which equals to \(DNI_{in}*\cos(\theta_{\parallel})\), \(\theta_{0}\) is maximum angle of solar rays to the receiver.  
Then, the average optical efficiency of the whole system is calculated from the integration of each point of mirror as following:  
\[\eta_{0}=\frac{\int_{R}^{W}\eta_{P}(y)I_{in}dy+\int_{0}^{r_{0}}taI_{in}\cos( \varphi)dy}{I_{in}*W}=\frac{\int_{0}^{r_{0}}\eta_{P}(y)+\int_{0}^{r_{0}}ta \cos(\delta)dy}{W} \tag{2}\]  
The former part of the equation is the contribution of the reflected rays, and the later part of the equation is the contribution of the rays irradiated on the collector directly. \(R\) is the radius of vacuum tube receiver, \(W\) is the width of the parabolic trough mirror, \(\delta\) is the incidence angle of the radiation on the absorption tube which can be calculated from the geometry.  
This method also takes the influence of incidence angle of solar rays on the efficiency of trough solar system into consideration [11].  
\(q_{net}(t)\) is the net energy power in any time which is calculated in following way:  
\[q_{net}(t)=I_{in}(t)\cos(\theta_{\parallel})*\eta_{0}-q_{loss}(t) \tag{3}\]  
The energy loss \(q_{loss}\) is calculated as follows:  
\[q_{loss}=a_{0}+a_{1}T+a_{2}T^{2}+a_{3}T^{3}+DNI*(b_{0}+b_{1}T^{2}) \tag{4}\]