esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Quantum Message Authentication with Single Qubit Unitary Operation
## 2 Quantum Message Authentication Protocol
### Quantum Message Authentication Phase
In the quantum encryption stage, [PERSON] applies quantum encryption \(\sigma_{i_{A}^{\prime}}\)\(H^{k_{H}^{i}}_{\mu}\) to the QMAC state pair \(\left|\,M\right\rangle_{u}\left|S\right\rangle_{d}\) of Equation (4);  
\[\left|\,M\right\rangle_{u}\Big{[}\otimes_{i=1}^{N}\sigma_{i_{AB}}^{k_{H}^{i} }H^{k_{H}^{i}}\left|\,S\right\rangle_{d}\Big{]}. \tag{5}\]  
Here, \(\left|\,M\right\rangle_{u}\Big{[}\otimes_{i=1}^{N}\sigma_{i_{AB}}^{k_{H}^{i} }H^{k_{H}^{i}}\left|\,S\right\rangle_{d}\Big{]}\) is an encrypted QMAC state pair, and then she sends it to [PERSON]. This quantum encryption is an essential function for verifying that the entity sending the QMAC pair is [PERSON] and for protecting against forgery. The rotation angles \(m_{i}\) and \(s_{1}\) are the elements of the finite discrete variable set. For applying them to real protocols, [PERSON] and [PERSON] must preset the range of the finite discrete variable set and pre-decide how to divide the set range. For example, if [PERSON] and [PERSON] split the rotation angle from \(0^{\circ}\) to \(360^{\circ}\) in intervals of \(10^{\circ}\), then the finite discrete variable set becomes \(\{0^{\circ},10^{\circ},\ 20^{\circ},\ \ldots,\ 350^{\circ}\}\). Here, the size of the discrete variable set is determined by the performance of the experimental apparatus. Therefore, as the performance of experimental apparatus improves, the size of the discrete variable set increases. Increasing the size of the discrete variable set means that the rotation angle can be subdivided, and this can lead to authenticating more information compared with using the four states of the BB84 protocol. On the other hand, If the performance of the experimental apparatus is poor, the size of the discrete variable set decreases. Then, the rotation angle cannot be subdivided, and information that can be authenticated decreases. Additionally, in this situation, if the communication members use the subdivided rotation angles to such an extent that the experimental apparatus cannot distinguish, detecting the malicious behavior of [PERSON] is impossible.
# Novel Techniques for Microbiological Safety in Meat and Fish Industries
## 2 Factors Affecting Microbial spoilage of Meat, Fish and Derived Products
### Interaction Phenomena  
Various physiochemical parameters have important roles in microbial growth due to their interaction with food components. Interactions may be positive or negative depending on various factors such as the type of food, microflora and the additives used during food processing or preservation. Microorganisms also have growth competition in a food product that supports all microbes (bacteria, yeasts, molds), then the bacterial growth is observed prominently. Some of the waste products produced by dominant microbes have the potential to control the growth activities of other microbes.  
Similarly, spoilage microorganisms contribute their role in the inhibition, suppression or elimination of pathogen microorganisms in various food products. Some of the foodborne pathogens that can be suppressed by spoilage microbes are _B. cereus_, _C. jejuni_, _C. botulinum_, _E. coli_, _L. monocytogenes_ and _Salmonella_ spp. Moreover, _Pseudomonas_ and _Shewanella_ _putrefaciens_ and lactic acid bacteria also have the substantial potential to limit the bacterial pathogens' growth activities [17]. Lactic acid bacteria and yeast has a combined effect on _Coliforms_ and _Salmonella_, while yeast also influences lactic acid bacteria [18].  
Hurdle technology is an efficient approach in which various techniques of preservation are combined that have a better effect on microbial/spoilage control activities. Hurdle technology basically builds a barrier in conditions that support microbial growth. These hurdles can be created by varying growth parameters such as temperature, moisture, acidity, redox potential and by using various preservatives to improve microbial stability and sensory characteristics [19].
###### Abstract  
This study aims to numerically analyze the near-field propagation behavior of hazardous and noxious substances (HNSs) and to develop a new metamodel for HNS propagation. Extensive computational fluid dynamics (CFD) simulations were conducted using the ANSYS FLUENT (V. 17.2) code for various HNS spill scenarios. We newly introduced several key parameters, including the streamwise propagation velocity, transverse propagation velocity, and averaged HNS mass fraction. From the results, the advection effect is more dominant with an increase in the current velocity and streamwise propagation velocity, and with a decrease in the transverse propagation velocity. Also, the HNS mass fraction decreases as the current velocity increases with the change of concentration and propagation area. Particularly, a new metamodel of HNS propagation based on the current CFD results was validated by the hidden point test, showing very good fit. We believe this model would make useful predictions under various scenarios without CFD simulations.  
h  
hazardous noxious substance; computational fluid dynamics; Reynolds-averaged Navier-Stokes; metamodel; propagation behavior
## 3 Methods
### The Mathematical Model
#### 3.1.1 Decision Variables  
* \(\text{X}_{ij,v}\): equals 1 if the route from \(i\) to \(j\) is made, otherwise 0
* \(\text{Z}_{i}\): equals 1 if the drop box facility \(i\) is open, otherwise 0
* \(\text{S}_{ij,i}\): equals 1 if the drop box facility \(i\) serving the customer \(j\), otherwise 0
* \(\text{Q}_{i}\): The total amount of demand served in drop box facility \(i\)
* \(\text{a}_{i,v}\): The total amount demand in drop box facility \(i\) carried by the vehicle \(v\)
## 3 Materials and Methods
### Methodology
#### 3.1.2 Simplified Calculation  
When working with this model, although the state equation is effective, it is difficult to further calculate the Kalman filtering process. Therefore, we expand the state equation and carry out some related simplifications:  
\[\mathbf{Z}_{t}=\mathbf{Z}_{0}+\mathbf{\varepsilon}_{1}+\cdots+\mathbf{\varepsilon}_{t}+\mathbf{F}^ {T}\mathbf{\beta}_{1}+\cdots+\mathbf{F}^{T}\mathbf{\beta}_{t}, \tag{6}\]  
\[\mathbf{c}_{t-1}=\mathbf{Z}_{0}+\mathbf{\varepsilon}_{1}+\cdots+\mathbf{\varepsilon}_{t-1}, \tag{7}\]  
\[\mathbf{m}_{t}=\mathbf{\beta}_{1}+\cdots+\mathbf{\beta}_{t}. \tag{8}\]  
Using these definitions, \(\mathbf{Z}_{t}\) can be written as  
\[\mathbf{Z}_{t}=\mathbf{c}_{t-1}+\mathbf{F}^{T}\mathbf{m}_{t}+\mathbf{\varepsilon}_{t}. \tag{9}\]  
By definition, we know that the trend term \(\mathbf{F}^{T}\mathbf{m}_{t}\) provides the mean, and the first component \(\mathbf{c}_{t-1}=\mathbf{c}_{t}-\mathbf{\varepsilon}_{t}\) is a latent variable representing the error. Therefore, we can obtain the same expression as the spatial universal Kriging method:  
\[KrigingEstimate(\mathbf{Z}_{t}-\mathbf{c}_{t-1})=\mathbf{F}^{T}\mathbf{m}_{t}+\mathbf{\varepsilon }_{t}. \tag{10}\]  
In contrast, the effective values we use for the universal Kriging estimate can be expressed as:  
\[\widetilde{\mathbf{Y}}_{t}=\mathbf{Y}_{t}-\mathbf{W}_{t}\widehat{\mathbf{c}}_{t-1}. \tag{11}\]  
It is inevitable that we consider the change of the state equation at different times. Our model is based on the state of the previous value and introduces a universal Kriging estimate; the specific process is shown in Figure 2. As can be seen from the figure, our model is mainly divided into two parts: One is the latent variable \(\mathbf{c}_{t}\) representing the errors, and the other is the trend term \(\mathbf{F}^{T}\mathbf{m}_{t}\). In the model, we use the current observation data to simulate the trend term, and use the method similar to Kalman filter derivation to carry out correction and progressive estimation of latent variable. Therefore, the essence of this model is to update and cyclically estimate the error after removing the known trend term.  
For the sake of consistency, we assume that neither trend term \(\mathbf{F}\) nor Systematic error distribution \(\mathbf{R}_{t}\) changes with the time \(t\). For spatially correlated errors \(\varepsilon_{t}(x)\) at different times, we assume that:  
\[E[\varepsilon_{t_{1}}(x_{a}),\varepsilon_{t_{2}}(x_{b})]=0,t1\
eq t2. \tag{12}\]  
Finally, as an initial condition, we assume that \(\mathbf{Z}_{0}\) is a zero-mean process with a covariance function \(aE[\mathbf{\varepsilon}_{0}\mathbf{\varepsilon}_{0}^{T}]\), in order to keep \(\mathbf{c}_{t}\) consistent over time. Where \(a\) is a known parameter. Thus, we can extrapolate that \(Z_{0}(x)\) is not correlated with \(\varepsilon_{t}(x)\) for all \(t\) from the hypothesis.The next part of this article details the derivation and lists the required leading parameters.
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 4 Future Trend of ML in IVLC
### Intelligent Network Layer
#### 4.2.2 Heterogeneous Network  
6G networks are becoming more and more heterogeneous, composed of different access standards and various network deployment methods. Therefore, it is necessary to consider the indoor and outdoor heterogeneous networking and interconnection issues of visible light communication networks and other communication technologies, for instance, the power line communications, optical fiber access networks, and mobile communications in radio frequency and even higher-frequency bands. Within this complex architecture of the future network, it is difficult for traditional models and algorithms to provide efficient and reliable technical support. AI-driven network technologies have made a series of progress in wireless communication to handle interference coordination and resource scheduling (including power allocation, channel allocation, and access control), which will reduce future wireless resource management costs and improve service quality.  
The traditional network structure is network-centric with quite limited flexibility. The users are passive nodes, and the cell is generally preset to a fixed shape according to the transmission scheme and does not change with the communication traffic. Distributed artificial intelligence can implement a fully user-centric network architecture. By taking advantage of the diversity of user locations and service requirements, virtual amorphous communities can be constructed to provide better services for each user. AI can be used to understand users and perform user prediction, inference, and big data analysis. Moreover, AI can realize self-organizing network operations and management by network edge computing, and eventually form global closed-loop optimization.
## 2 Materials and Methods
### Cough Data Processing  
#### 2.2.1 Audio Pre-Processing  
The cough sounds were stored in.m4a format and were segmented to form different cough entities corresponding to each cough's diagnosis label, using Adobe Audition CS6. The cough sounds were pre-processed to allow for more precise and consistent modeling using Matlab 2017b (MathWorks, MA, United States). This process consisted of detrending, rescaling, and down-sampling of the audio files to 11.025 kHz.
# Design, Manufacturing, and Flight Testing of an Experimental Flying Wing UAV
## 2 Aircraft Conceptual Design
### Matching Plot  
The aerodynamics, propulsion, and flight performance must be determined during the conceptual design process. The preliminary sizing of the aircraft is performed using the matching plot proposed by [PERSON]. In a matching plot, the design problem is solved graphically; in this case, the two design variables are wing loading and power loading. Figure 2 shows a matching plot that satisfies the above performance requirements. The performance requirements are the stall speed, maximum speed, absolute ceiling, and turn radius, as shown in Figure 2. The maximum power loading (minimumpower demand) at the intersection (area under blue solid line (2) and vertical black dashed line (1)) that satisfies all performance requirements simultaneously is 0.5899 Nt/W, and the associated wing loading is 59.3 Nt/m\({}^{2}\).  
The experimental flying wing UAV's performance can be summarized as follows:  
1. The maximum lift coefficient is 1.2 and the stall speed at sea level is 9 m/s. The left area of the vertical black dashed line (1) in Figure 2 satisfies the stall speed.
2. At cruising altitude (100 m), the cruising speed is 1.15 times the maximum lift-to-drag ratio speed and the maximum speed is 1.2 times the cruising speed. The area below the blue solid line (2) in Figure 2 satisfies the requirement of the maximum speed at cruising altitude.
3. The absolute ceiling is 500 m. The absolute ceiling demand is achieved by the area below the red solid line (3) in Figure 2.
4. At cruising altitude (100 m), the turn radius is 15 m at speed of 10 m/s. The area below the purple dashed line (4) in Figure 2 satisfies the requirement of the turning radius at a specific speed (10 m/s) at cruising altitude.
###### Abstract  
Unmanned aerial vehicles (UAVs) significantly shape the evolution of 5G and 6G technologies in India, particularly in reconfiguring communication networks. Through their deployment as base stations or relays, these aerial vehicles substantially enhance communication performance and extend network coverage in areas characterized by high demand and challenging topographies. Accurate modelling of the UAV-to-ground channel is imperative for gaining valuable insights into UAV-assisted communication systems, particularly within India's rapidly expanding metropolitan cities and their diverse topographical complexities. This study proposes an approach to model low-altitude channels in urban areas, offering specific scenarios and tailored solutions to facilitate radio frequency (RF) planning for Indian metropolitan cities. The proposed model leverages the International Telecommunication Union recommendation (ITU-R) for city mapping and utilizes frequency ranges from 1.8 to 6 GHz and altitudes up to 500 m to comprehensively model both line-of-sight (LoS) and non-line-of-sight (NLoS) communications. It employs the uniform theory of diffraction to calculate the additional path loss for non-line-of-sight (NLoS) communication for both vertical and horizontal polarizations. The normal distribution for additional shadowing loss is discerned from simulation results. This study outlined the approach to derive a comprehensive statistical channel model based on the elevation angle and evaluate model parameters at various frequencies and altitudes for both vertical and horizontal polarization. The model was subsequently compared with existing models for validation, showing close alignment. The ease of implementation and practical application of this proposed model render it an invaluable tool for planning and simulating mobile networks in urban areas, thus facilitating the seamless integration of advanced communication technologies in India.  
U  
202569
## 1 Introduction  
Fifth- and sixth-generation (5G/6G) networks promise higher data rates for more connected devices than previously possible on cellular networks. Multiple industries are taking advantage of these benefits, including the energy sector and the applications this enables, by pushing for vastly more connected devices for Smart Grid, Smart Homes, Smart Manufacturing, Smart Cities, and other areas, collectively often referred to as the Internet of Things (IoT). Many of these application domains also intersect with Critical Infrastructure sectors, elevating the importance of longevity and reliability in these connected devices. With their rapidly growing number and the advantages provided by OFDM, such as reduced impact from Intersymbol Interference (ISI), more robust signal equalization, or ease of implementation, OFDM receivers are of vital importance and interest.  
A drawback of these receivers being implemented on resource-constrained devices, however, is two-fold: the relatively high power requirement for signal transmission, and their computational complexity. This paper proposes a deep learning-based architecture for channel estimation, demodulation, and subsequent channel decoding. The motivation of this work is to improve OFDM receiver accuracy and facilitate reliable communications at lower signal quality while progressing towards a fully Machine Learning-based OFDM receiver architecture. Researchers are continuing to propose new applications of ML in 5 Gand 6G implementations [1], with our research showing significant promise for replacing traditional receivers with deep learning architectures.  
Our models utilize a CNN for channel estimation and demodulation and a GNN for LDPC-based channel decoding. CNNs of varying sizes ranging from 200 k to 1.2 M parameters are compared against a traditional receiver's performance, with significant gains achieved by the DL-based approach with respect to the achieved BER. For the GNN, both a 20 k and 80 k parameter model were compared against standard LDPC decoders, showing comparable BER compared to standard belief propagation (BP) and Min-Sum algorithms. Our models are then combined in an end-to-end architecture, which shows significant improvement over the decoded output of a traditional receiver.  
The remainder of the paper is organized as follows: Section 2 will provide a review of related works, and Section 3 outlines the methodology of this work, including subsections for our CNN and GNN model designs. Section 4 presents the results for each model and compares them to traditional approaches. Section 5 outlines our future work, and Section 6 concludes the paper.
## 1 Introduction
The results of the outdoor experimental research presented here confirm the effectiveness and stability of the proposed control architecture using a continuous smooth feedback controller along with a time-optimized controller generating the robot's motion trajectory. In summary, the key contributions can be described as follows:  
* Implementation of a nonlinear controller using transverse functions using a novel tuning scheme: Despite prior theoretical works available in the literature, this method has not been widely adopted or applied in practical robotics, particularly in user-level robots;
* Integration of modern industrial automation components in mobile robotics: Industrial automation elements are incorporated into the control architecture of a nonholonomic robot, enhancing its functionality and bridging the gap between advanced control theory and practical applications in robotics;
* Development of a universal robotic testing platform using the ROS environment: The robot is designed as a versatile platform within the ROS ecosystem, enabling experimentation and validation of advanced control strategies and automation technologies in real-world transport applications.
## 3 Data Acquisition
### Landslide Conditioning Factors
#### 3.2.13 Slope Length-Angle Index (LS)  
The LS index was obtained by summing the slope length (L) and slope angle (S). It was extracted from the DEM using SGAG software. We defined five LS categories using the natural breaks classification method: 0-5.225, 5.226-10.73, 10.74-16.78, 16.79-24.75, and 24.76-70.13 (Figure 3m).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Lithological**} & \multicolumn{1}{c}{**Description**} & **Age Era** & **Age Period** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{Kll1} & Gray and light gray, thick-bedded to & \multirow{3}{*}{MESOZOIC} & Early \\  & & massive, fetid orbitolina bearing & & Cretaceous \\ \hline \multirow{3}{*}{2} & \multirow{3}{*}{Kll2} & Thick-bedded, gray to dark gray, rudist & \multirow{3}{*}{MESOZOIC} & Early \\  & & and orbitolina bearing limestone. & & Cretaceous \\ \hline \multirow{3}{*}{3} & \multirow{3}{*}{Klv,12} & Basaltic and andasitic volcanics, tuff, & \multirow{3}{*}{MESOZOIC} & Early \\  & & volcanic breccia and calcareous shale with & & Cretaceous \\ \hline \multirow{3}{*}{4} & \multirow{3}{*}{K213} & Gray and light gray micritic limestone & \multirow{3}{*}{MESOZOIC} & Late Cretaceous \\  & & and radioarian limestone with calcschiste & & \\ \hline \multirow{3}{*}{5} & \multirow{3}{*}{K2v} & Basalt, andesite, spilitic basaltic and & \multirow{3}{*}{MESOZOIC} & \multirow{3}{*}{Late Cretaceous} \\  & & pyrochastic rocks with developed layering & & \\ \cline{1-1}  & & and climbing. & & \\ \hline \multirow{3}{*}{6} & \multirow{3}{*}{Klv,13} & Spilitic basalt, basalt and andesite lava & \multirow{3}{*}{MESOZOIC} & Early \\  & & with intercalation of red, blue, gray & \multirow{3}{*}{Cretaceous} \\  & limestone, red shale and sandstone. & & \\ \hline \multirow{3}{*}{7} & \multirow{3}{*}{Klv,14} & Tuff, green tuff, andesite and andesite & \multirow{3}{*}{MESOZOIC} & Early \\  & & dacite, shale, limestone and sandstone. & & Cretaceous \\ \hline \multirow{3}{*}{8} & \multirow{3}{*}{Kls} & Purpel to red medium to thick-bedded & \multirow{3}{*}{MESOZOIC} & Early \\  & & sandstone with intercalation of & \multirow{3}{*}{Cretaceous} \\  & & polymictic conglomerate and silty shale. & & \\ \hline \multirow{3}{*}{9} & \multirow{3}{*}{K2 sh} & Black, dark gray, yellow shale, silty shale & \multirow{3}{*}{MESOZOIC} & Late Cretaceous \\  & & and phillitic shale with minor sandstone & \\  & & and micritic limestone intercalations. & & \\  & & (Sanandaj shale) & & \\ \hline \multirow{3}{*}{10} & \multirow{3}{*}{Qal} & Recent alluvium (alluvial channel & \multirow{3}{*}{CENOZOIC} & Quaternary \\  & & deposits). & \\ \hline \multirow{3}{*}{11} & Residential & \multirow{3}{*}{Salavat Saddle} & \multirow{3}{*}{} \\  & area & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Lithological units and its description of the study area.