esa-sceva/satcom-chunk-collection huggingface dataset:
# A Neural Network-Inspired Matrix Formulation of Chemical Kinetics for Acceleration on GPUs
## 2 Methodology  
This section first summarizes the chemical kinetic equations from a matrix-based perspective (Section 2.1), and then discusses the data structure and organization of the matrices used in the GPU computations (Section 2.2). The matrix-based formulations are presented in the language of traditional artificial neural networks (ANNs) where appropriate. Conveying the exact formulation of the source term computation with ANNs is intended to inform the reader that the form of kinetics equations as-is is efficiently described via neural network layers without any need for training or modeling. For this reason, the authors believe the ANN connections can lead to valuable insights regarding the general interpretation and potential design of GPU-optimal chemical mechanisms. Additionally, the neural network connections open pathways for constrained modeling approaches, here called \"approximate ANNs\", which can be designed to reduce the overall computational effort undertaken during the exact source term computation at the cost of perfect accuracy. Although it does not fall into the main scope of this work, additional details on the approximate ANN formulation are provided in Appendix A for the interested reader.  
Of special importance in Section 2.2, and tied to the matrix data structures explained therein, is the distribution of different _reaction types_ present in a given mechanism. In general, different algorithms are required for different reaction types--some allow for matrix formulations and others do not. As such, a characterization of chemical mechanisms based on the distribution of these reaction types is presented to provide a pathway for assessing: (a) how beneficial the matrix representation can be for a particular mechanism; and (b) how the prevalence of specific reaction types can positively or negatively impact the GPU-derived speedup. The main reason for introducing the reaction-type classification is to bring forward the idea that, in the determination of GPU speedup, mechanism species and reaction numbers are not the only factors; the complexity of the individual reactions themselves also plays an important role.
# Explainable Machine Learning for LoRaWAN Link Budget Analysis and Modeling
###### Abstract  
This article explores the convergence of artificial intelligence and its challenges for precise planning of LoRa networks. It examines machine learning algorithms in conjunction with empirically collected data to develop an effective propagation model for LoRaWAN. We propose decoupling feature extraction and regression analysis, which facilitates training data requirements. In our comparative analysis, decision-tree-based gradient boosting achieved the lowest root-mean-squared error of 5.53 dBm. Another advantage of this model is its interpretability, which is exploited to qualitatively observe the governing propagation mechanisms. This approach provides a unique opportunity to practically understand the dependence of signal strength on other variables. The analysis revealed a 1.5 dBm sensitivity improvement as the LoR's spreading factor changed from 7 to 12. The impact of clutter was revealed to be highly non-linear, with high attenuations as clutter increased until a certain point, after which it became ineffective. The outcome of this work leads to a more accurate estimation and a better understanding of the LoRa's propagation. Consequently, mitigating the challenges associated with large-scale and dense LoRaWAN deployments, enabling improved link budget analysis, interference management, quality of service, scalability, and energy efficiency of Internet of Things networks.  
LoRaWAN; Internet of Things; artificial intelligence; machine learning; regression analysis; propagation modeling; link budget analysis +
Footnote †: journal: _Sensors_
# Revealing Development Trends and Key 5G Photonic Technologies Using Patent Analysis
## 2 Literature Review
### The Importance of Photonic Technology to 5G Networks  
Photonic technologies are largely used in optical communication systems and networks due to their unique characteristics in term of bandwidth, immunity to electromagnetic fields, compatibility with optical fiber, and flexibility [18]. Applications involving the use of photonic technologies have thus far been applied extensively in long-distance and metro transportation systems and in passive optical network (PON)-based access systems [16]. In the development of 5G technology, passive optical networks have become an attractive option due to their effective use of fiber resources and point-to-multipoint topology. In addition, 5G wireless communication technology requires each user or object to have an increase in bandwidth of three orders of magnitude (>500 Mb s\({}^{-1}\)). Thus, the satisfaction of requirements in bandwidth and power consumption is urgently needed. Photonic technology is expected to have an increasingly important role in information and communications technology, because fixed high-capacity links are largely based on this technology [18].  
Following the increase of mobile data traffic, the requirements of high-quality network services and user experiences can be met by photonic technology. For example, the application of optical networks as the fronthaul in mobile communication networks has been receiving academic attention [22; 23]. Additionally, the standards for 5G fronthaul optical networks are being discussed. In terms of the development potential of 5G photonic technology, this study mainly focused on photonic technology in 5G and determined its key technologies through patent analysis. Searching for key technologies was performed using network analysis. The following section further illustrates this analysis approach.
# A High-Efficiency Method of Mobile Positioning Based on Commercial Vehicle Operation Data
## 5 Implementation and Evaluation of a Commercial Vehicle Operation System
### Experimental Results and Discussions  
This subsection presents a case study and analyzes three months of CVO data to evaluate the proposed mobile positioning method. From November 2013 to January 2014, 67 OBUs were driven in experimental environments and 18,508 different cells were detected and connected. These OBUs obtained 6,571,550 CVO records and transmitted them to the CVO server for analyses of location information and cellular network signals.  
Figure 5: The architecture of the CVO system.
## 5 Design of the Virtual Resistor
### Steady-State Current of the Wind Turbine after the Fault  
Figure 7 shows the steady-state equivalent circuit diagram of the grid-connected wind turbine system with the virtual resistor control after a short-circuit fault in the rotating dq frame, where the d-axis of the rotating dq frame coincides with the grid voltage vector. Therefore, the grid voltage vector can be expressed as \(\mathrm{E_{gm}}\), and the equivalent voltage vector of the wind turbine is \(\mathrm{U_{gm}cos\delta_{u}+jU_{gm}sin\delta_{u}}\).  
In Figure 7, the voltage equation of the current path 1 is  
\[U_{gm}\cos\delta_{u}+jU_{gm}\sin\delta_{u}=\Big{(}R_{v}+j\omega_{g}L_{f}\Big{)} \big{(}I_{1d}+jI_{1q}\big{)}+R\big{[}\big{(}I_{1d}-I_{2d}\big{)}+j\big{(}I_{1q} -I_{2q}\big{)}\big{]} \tag{10}\]  
In Figure 7, the voltage equation of the current path 2 is  
\[E_{gm}=-\big{(}R_{line}+j\omega_{g}L_{line}\big{)}\big{(}I_{2d}+jI_{2q}\big{)} +R\big{[}\big{(}I_{1d}-I_{2d}\big{)}+j\big{(}I_{1q}-I_{2q}\big{)}\big{]} \tag{11}\]  
According to Equations (10) and (11), the equation for \(\mathrm{I_{1d}}\), \(\mathrm{I_{1q}}\), \(\mathrm{I_{2d}}\), and \(\mathrm{I_{2q}}\) can be derived as  
\[A\big{[}I_{1d}\quad I_{1q}\quad I_{2d}\quad I_{2q}\big{]}^{T}=B \tag{12}\]  
where  
\[A=\left[\begin{array}{cccc}R+R_{v}&-\omega_{g}L_{f}&-R&0\\ \omega_{g}L_{f}&R+R_{v}&0&-R\\ R&0&-R-R_{line}&\omega_{g}L_{line}\\ 0&R&-\omega_{g}L_{line}&-R-R_{line}\end{array}\right] \tag{13}\]  
\[B=\big{[}U_{gm}\cos\delta_{u}\quad U_{gm}\sin\delta_{u}\quad E_{gm}\quad 0 \big{]}^{T} \tag{14}\]  
According to Equation (14), the expression of the current vector, \(\mathrm{I_{12}}\), is obtained as  
\[I_{12}=\big{[}I_{1d}\quad I_{1q}\quad I_{2d}\quad I_{2q}\big{]}^{T}=A^{-1}B \tag{15}\]  
According to Equation (15), the amplitude, \(\mathrm{I1m}\), of the wind turbine's steady-state current after the fault can be expressed as  
\[I_{1m}=\sqrt{{I_{12}(1)}^{2}+{I_{12}(2)}^{2}} \tag{16}\]  
Figure 7: Equivalent circuit diagram of the grid-connected wind turbine system with virtual resistor control in the dq frame after the three-phase short-circuit fault occurs.
## 4 Numerical Simulation Verification and Discussion
### Analysis of Numerical Simulation Results
Figure 12 shows the deformation of the center section of the pipeline caused by the tunnel shield excavation process of the left and right lines. From the figure, we can see that the left and right lines cause the deformation amplitude of the pipeline to increase significantly in the latter half of the section, and the deformation amplitude of the pipeline under the excavation condition of the right line is 3.5 times that of the left line condition. Figure 11: Vertical deformation of pipeline. Figure 12: Vertical deformation in the middle section of the pipeline. In summary, the distance between tubes and tunnels is an important factor affecting the deformation of pipelines. The process of tunnel excavation will lead to a loss of stratum, which disturbs the surrounding soil body and then puts an additional load on the pipeline, affected by the distance between the tube and the tunnel. The additional load suffered by the pipeline has a negative correlation with the distance between tubes and tunnels and a positive correlation with the tunnel excavation distance.
# Dynamic Analysis of Modified Duffing System via Intermittent External Force and Its Application
## 4 Algorithm Design and Information-Encryption Application
### Simulation Experiment  
By taking a gray image as an example, the simulation experiments of the encryption algorithm were verified with Matlab software. The flowchart of chaos-based secure communication is shown in Figure 10. On the sender side, the pseudorandom sequence is generated with the keys in the first, and original information is encrypted by the scrambling encryption in Encryption 1 for \(M\) rounds; then, it encrypted by the second chaos XOR encryption in Encryption 2. Therefore, the encrypted information is transmitted to the receiver via the public channel, and it is recovered by the inverse operations of encryption algorithm with the correct keys.  
If initial conditions are  
\[\begin{cases}(x_{0},\;y_{0},\;t_{0})=(0.1,\;0.2,\;0),\;\omega=1.1,\\ \beta=0.2,\;k_{1}=0.6,\;k_{2}=0.1,\;A=2.1,\;\tau=\frac{2\pi}{3\omega},\;\eta= 0,\\ T=500,\;h=0.001,\;\Delta T=2\pi/\omega,\;\theta=12,\;\gamma=31,\;M=2,\\ t_{i}=\frac{i\cdot 2\pi+2\pi}{3\omega},\;[T_{i},\;T_{i+1}]=\left[\frac{i \cdot 2\pi}{\omega},\;\frac{(i+1)\cdot 2\pi}{\omega}\right],\;\;(i=0,1,2, \cdots)\end{cases} \tag{24}\]  
then the pseudorandom sequences are generated based on the MD system, and they are used to encrypt original image \(\mathbf{I}\) with the encryption algorithm. Simulation results are given in Figure 11, where Figure 11a is original image \(\mathbf{I}\), Figure 11b is encrypted image \(\mathbf{E}_{1}\) after two rounds of scrambling encryption, and Figure 11c is final encrypted image \(\mathbf{E}_{2}\) after chaos scrambling encryption and chaos XOR encryption. Obviously, it is difficult to obtain useful information from encrypted information.  
In the receiver end, one can recover image \(I\) with the same initial conditions and parameters, and the recovered image is given in Figure 12c. However, it cannot be successfully recovered if one of the initial conditions or parameters is incorrect, and the corresponding experiment results are shown in Figure 12a,b.  
Figure 11: (**a**) Original image \(I\). (**b**) Encrypted image \(E_{1}\) after Encryption 1 (scrambling encryption), i.e., original image in (**a**) is encrypted only after two rounds of chaos scrambling encryption. (**c**) Encrypted image \(E_{2}\) after Encryption 2 (chaos XOR encryption), i.e., image in (**b**) is again encrypted by chaos XOR encryption.  
Figure 10: Flowchart of secure chaos-based communication.
## 6 EEG Signal Pre-Processing and Feature Extraction
### EEG Signals Classification  
The core of EEG signal processing is encapsulated in the classification stage, where extracted features are utilized to train machine learning algorithms for pattern recognition. This stage is crucial for categorizing EEG signals into distinct classes based on their characteristics, aiming to interpret the underlying brain activities or states.
## 3 Data Gathering  
In our analysis, we utilize two well-established datasets on cardiac illnesses sourced from the University of California's Irvine Machine Learning Repository, specifically the Cleveland and Statlog datasets [31; 32]. Table 1 details the attributes common to both datasets, with the final attribute serving as an indicator of a person's heart disease status. To gain deeper insights into the feature distribution, we present Figures 1 and 2, which illustrate the relationship between the maximum heart rate and age, as well as the distribution of the remaining 12 features, respectively.  
The Cleveland dataset comprises medical records from individuals who underwent heart disease evaluations at the Cleveland Clinic Foundation in the late 1980s, containing 303 instances, each representing a patient, and encompassing 13 features, including critical factors like age, gender, blood pressure, cholesterol level, chest pain presence, and results from various medical tests. This dataset has played a pivotal role in the development and testing of machine learning algorithms aimed at predicting cardiac disease.  
The Statlog dataset, part of a dataset collection, consists of 270 instances (patients) with 13 attributes, including age, gender, blood pressure, cholesterol, fasting blood sugar, and various electrocardiography (ECG) and exercise stress test readings. Originally sourced from the Cleveland Clinic Foundation, this dataset has been widely employed in studies related to machine learning algorithms for medical diagnosis. The primary objective is to enable physicians to make more informed treatment decisions by accurately identifying patients based on their feature values.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Feature** & **Description** & **Type** & **Values** \\ \hline Age & Age of the patients & Numeric & Years \\ Sex & Gender of the patients & Categorial & M, F \\ Ca & Number of major vessels & Categorial & 0–4 \\ Chol & Serum cholesterol & Numeric & mg/dL \\ Exang & Exercise induced angina & Categorial & Yes = 1, No = 0 \\ Cp & Chest pain type & Categorial & Male = 1, Female = 0 \\ Oldpeak & ST depression induced by exercise relative to rest & Numeric & 0–6.2 \\ Fbs & Fasting blood sugar & Categorial & mg/dL \\ Restecg & Resting electrocardiographic & Categorial & 0, 1, 2 \\ Thal & Normal; Fixed defect; Reversible defect & Categorial & 0, 1, 2, 3 \\ Thalach & Maximum heart rate achieved & Numeric & 71–202 \\ Slope & Slope of the peak exercise ST segment & Categorial & 0, 1, 2 \\ Trestbps & Resting blood pressure & Numeric & 94–200 \\ Num & Heart disease status & Categorial & Yes/No \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the attributes in the datasets.  
Figure 1: Maximum heart rate versus age.  
Figure 2: Distribution of all the features.
# Rheological Approaches of Wheat Flour Dough Enriched with Germinated Soybean and Lentil
## 4 Discussion
### Optimal Addition Levels of SGF and LGF  
The optimization of SGF and LGF addition in wheat flour resulted in an optimal combination of 5.60% SGF, 3.62% LGF, and 90.76% wheat flour. The differences regarding the falling number and rheological properties between the optimal and control sample could be related to the intake of proteins, lipids, carbohydrates, and minerals of the germinated flours and their interactions within the dough matrix [8]. According to the obtained results, both optimal and control flows are considered strong ones, the enriched sample presenting moderate extension properties (P/L > 0.5) and higher \(\alpha\)-amylase activity (lower FN) [48]. Higher dough total CO\({}_{2}\) volume production and volume of the gas retained in the dough at the end of the test of the optimal sample compared to the control could be due to the increased amount of fermentable sugars along with the activation of amylase during germination of legumes, leading to enhanced CO\({}_{2}\) production [14].  
The images obtained of the dough microstructure showed slight differences among the structures of dough samples. The addition of germinated soybean and lentil led to a lower green area and a higher red one compared to the control due to the higher level of protein from the enriched dough. This ratio color change from the dough structure is due to the high level of protein from the LGF and SGF compared with wheat flour which is partially replaced by them. Both dough samples' structures appeared compact, homogenous, in which starch granules were enveloped by proteins, being glued together. No black regionsare present in these images, meaning that LGF and SGF at these addition levels did not affect in a negative way dough structure which presents good rheological characteristics, such as elasticity, gas holding capacity, and extensibility.
## Materials and Methods
### Ground Experiment on Irradiance Sensor Tilt Response  
The accuracy of the AIRS irradiance tilt correction was evaluated by performing a series of tilt movements outdoors during stable illumination conditions. The test was performed both in blue-sky conditions with direct sunshine (solar zenith angle 57.5\({}^{\circ}\)) and in fully clouded diffuse illumination conditions. In the test, the AIRS was tilted well beyond the normal tilt angles to all heading directions in an attempt to catch the full range of tilt angles experienced in typical UAV flights.
## 3 Application of Integrated Transducer Arrays for Generation of Sound Waves
### Experimental Investigations
#### 3.3.3 Propagation of Structural Mechanical Waves  
Mainly, the impact of the time delay between the actuator initialization has been studied. The wavefront for cases with zero time delay and optimal time delay are presented in Figure 14. In this context, an optimal time delay of 24 us was experimentally determined. The difference of 0.6 us between theoretical and experimental delay times (see Section 2) is affected by uncertainties and tolerances of the geometry and the material properties of the experimental setup.  
Figure 14: Out-of-plane velocities at \(t\) = 20 us for different time delays. (**A**) for zero seconds; (**B**) for 24 ms.  
Figure 13: Coordinate system for the microphone measurement (origin at the first transducer of the array).  
It is clearly observable that, while for the zero time delay the wave propagates equally in positive and negative \(y\)-direction, for the case with the optimized time delay, the wave propagates mainly in the positive \(y\)-direction. Additionally, the wave amplitude of the latter case was twice as much as in the first case. In order to generate evidence, that such wavefronts cause a directed sound wave, the signals recorded using the microphone were analyzed.
# Rain Streak Removal for Single Images Using Conditional Generative Adversarial Networks
## 3 Proposed Model
#### Model Overview  
* Generator Network  
In image-to-image translations, it is necessary to map a high-resolution input grid to a high-resolution output grid. Though the input and output images differ in appearance, they share the same underlying structure, and as such, it is necessary to consider this factor when designing the generator architecture. Most previous work used the encoder-decoder network [43] for such scenarios. In encoder-decoder CNN, the input is progressively downsampled until the bottleneck layer, where the process gets reversed and starts to upsample the input data. Convolutional layers use 4 \(\times\) 4 filters and strides with size 2 for downsampling. The same size of kernel is used for transpose convolution operation during upsampling. Each convolution/deconvolution operation is followed by batch normalization and Rectified Linear Unit (ReLU) activation. Weights of the generator are updated depending on the adversarial loss of the discriminator and the \(L_{1}\) loss of the generator. Architecture details are shown in Table 1.  
These networks require all the input information to pass through each of the middle layers. In most of the image-to-image translation problems, it is desirable to share the feature maps across the network since both input and output images represent the same underlying structure. For this purpose, we added a skip connection while following the general configuration of a \"U-Net\" [44]. Skip connections simply concatenate the channels at the ith layer with the channels at the (n-)ith layer. * Discriminator Network  
We adapted PatchGAN architecture [45] for the discriminator, which penalized the structure at the scale of patches. It tried to classify each N \(\times\) N patch as either real or fake. Final output of the discriminator (\(D\)) was calculated by averaging the received responses by running the discriminator convolutionally across the image. In this case, the patch was 30 \(\times\) 30 in size, and each convolutional layer was followed by a ReLU activation and batch normalization. Zero-padding layers were used to preserve the edge details of the input feature maps during the convolution. Discriminator architecture is described in Table 2.
# Numerical Simulation of T Joints Constructed from Hollow Steel Sections
## 2 Experimental Research  
\"T\" connectors made of square hollow sections made from cold-formed S235 JR steel profiles were the subject of the experimental study. A 90-degree angle in the physical model represents the link between the vertical brace element and the truss chord. The length of the brace was 200 mm, while the chord length was 400 mm. A 3 mm thick fillet weld was used to connect the two components.  
There were three samples tested in a quasi-static environment. Using a hydraulic piston on a hydraulic press with a 100-ton load capacity, a static force on the connection was applied. According to EN ISO 6892-1 [32], the test load was delivered within the permitted limits.  
The test was designed to reach a local displacement of 3% of the chord width or height in order to determine the joint's ultimate bearing capacity based on the deformation limit criterion. The experimental configuration is depicted in Figure 1b, wherein the brace was positioned vertically by positioning the chord at a 90-degree angle from the horizontal plane.  
Hydraulic presses with a 50 kN capacity were used to exert axial pressure forces on zonal elements. The 50 kN range HBM U2A dynamometer was used to measure the imported force intensity. A mobile force reader made by TRC pro with the label DIGIDYN was used to measure the force (Figure 1b).  
Three places were simultaneously used to quantify local deformations (displacements): the two side surfaces of the chord (10 mm from the chord top surface) and one in weld (in the center of the chord top flange). An HBM linear variable displacement transducer with a \(\pm\)2 mm measurement range was used to measure displacements. A mobile force reader  
Figure 1: Geometry of T connection and experimental setup: (**a**) geometry; (**b**) experimental setup.  
made by TRC pro (labeled DIGIDYN) was used to obtain the force readings. As a result, a 4 mm maximum displacement value was set. A SPIDER8 data acquisition system was utilized for data recording, with a 10 Hz sampling frequency.  
Having in mind the stress state around the connection, the local strains were monitored with help of measuring tape rosettes (10/120 RY41) with angles of \(\alpha=0^{\circ}\), \(\alpha=+120^{\circ}\), and \(\alpha=-120^{\circ}\), with base length of 10 mm and data \(p=1\times 10^{-6}\), which were produced by Hottinger (Figure 1b).