esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A localization system is one of the basic requirements for coal mines. Ultra-wideband (UWB), as a technology with broad application prospects, is considered to have great potential in the absence of satellite signals, especially in the underground mine environment, as it can provide rescue assistance. However, state-of-the-art UWB position systems in coal mines cannot efficiently differ the line-of-sight from all communication links, which results in deterioration of the localization accuracy. In this paper, we propose a LOS/NLOS classification method based on a deep learning algorithm. Specifically, we utilize the Generative Adversarial Networks (GAN) to generate diagnostic data for frame transmission under non-line-of-sight (NLOS) condition. Then, a Convolutional Neural Network (CNN) is adopted to identify the NLOS communication. Finally, the trilateral centroid positioning algorithm (TCPA) based on ranging data is used to verify the effect of our method for a localization system in coal mines. Field experiments show that our method can accurately differ the LOS/NLOS with the accuracy of 91.19%. The TCPA algorithm with our method can obtain 3.11% improvement compared with the scenario without using our method.  
Ultra-Wide-Band; non-line-of-sight identification; generative adversarial network; convolutional neural network; trilateral location +
Footnote †: journal: Applied  
1
Footnote 1: email: [EMAIL_ADDRESS]  
Footnote †: journal: Applied
# Grouting Process Simulation Based on 3D Fracture Network Considering Fluid-Structure Interaction
## 2 Comparison Analysis
### Parameter Analysis  
The parameters affecting the grouting process include grouting pressure, water-cement ratio and elastic modulus of rock mass. In order to discuss the influence of different parameters on the grouting process, the diffusion length of fracture 1-1724 at 20 s is analyzed at different grouting pressures (0.5 MPa, 1.0 MPa, 1.5 MPa) and different water-cement ratio grouts (0.7, 1.0, 2.0), and the maximum displacement of the rock mass is analyzed at different grouting pressure (0.5 MPa, 1.0 MPa, 1.5 MPa) and different elastic modulus (4 GPa, 10 GPa, 20 GPa, 40 GPa). The results are shown in Figures 14 and 15, respectively.  
Figure 14: The grout diffusion length at different grouting pressures and different water–cement ratios (20 s).  
Figure 13: Vectorgraph of fracture displacement.  
Figure 15: The maximum displacement of rock mass at different grouting pressures and different elastic modulus.  
As shown in Figure 14, under the same grouting pressure, the grout diffusion length increases with the water-cement ratio due to the larger the water-cement ratio, the smaller the density, and the yield stress and plastic viscosity of the grout increasing with the decrease of density, which results in a faster grout flow rate. In addition, in the case of the same grout water-cement ratio, the grout diffusion length increases as the grouting pressure increases. Furthermore, as shown in Figure 15, under the same grouting pressure, with the increase in elastic module, the maximum displacement of the rock mass decreases, and with the increase in grouting pressure, the maximum displacement of the rock mass increases accordingly. The maximum displacement under 1.5 MPa is small with 0.1391 mm, indicating that the grouting pressure can be appropriately increased to 1.5 MPa to improve the grouting efficiency without causing adverse conditions.
## 2 Related Work  
The trade-off between the energy consumption and the accuracy of localization algorithms was first addressed in [19], which proposed a localization algorithm in which each node can choose between different methods to measure the distances to their neighbors, each one with different accuracy and different energy consumption. This algorithm enables to know the amount of energy needed to obtain a given accuracy, or the accuracy that is achieved with a given energy consumption. As mentioned in Section 1, in wireless ad hoc and sensor networks distances are usually estimated from some parameter of the radio signal, as the RSS or the TOA. In practice, in a given network, only one of these parameters is used to estimate the distances between the nodes, so in order to reduce energy consumption, it would be valuable to find a transmission strategy that minimizes the energy consumption for a given accuracy and for a given distance estimation technique.  
This idea is followed by some object-tracking systems, as [15; 16; 17; 18], where the frequency of the localization is modified to trade higher energy consumption for better localization accuracy. Their general mechanisms consist in detecting or predicting the mobility pattern of the tracked target, and then dynamically adjusting the localization frequency. These methods are based on the assumption that the localization accuracy deteriorates with the target's mobility level due to a delay error produced by the movement of the object between the instant in which the RSS or TOA is measured and the instant in which the position is calculated. However, they do not consider that for a given localization frequency, the accuracy of the RSS or TOA measurements can also be traded for energy consumption.  
In our previous work [20], we propose a communication scheme for RSS-based localization that minimizes the energy that is consumed during the measurement of the RSS while maintaining a given accuracy in this measurement. In [21] we extend this idea to TOA-based localization. The sensitivity of this strategy to errors in the distance estimation is evaluated in [22]. This paper builds on the strategy proposed in these previous works in order to gain a deeper insight into the energy consumption-localization accuracy trade-off. In particular, w.r.t. our previous work, we now provide a theoretical framework, showing that if all the involved parameters are expressed as a function of the signal to noise ratio, an optimal solution can be found in closed form. The advantages of this new formulation are twofold: (i) we capture the rationale of energy consumption by identifying its fundamental elements and their dependencies, and (ii) we achieve a considerable reduction in the computational complexity, as the closed-form solution avoids using a full search in the space of tunable parameters. Furthermore, we also extend previous work to take into account the effects of real propagation conditions, in order that the final strategy achieves the desired accuracy in the distance estimation even in real environments with shadowing and packet losses.
## 3 Results
Furthermore, a linear relationship was observed between the annual fuel gain and the optimum insulation thickness. As the insulation thickness increased, the annual fuel gain also increased. It is also seen that there is a linear relationship between the annual fuel gain and the optimum insulation thickness, and as the insulation thickness increases, the annual fuel gain also increases. For each wall type, two different wall thicknesses were considered. Increasing wall thickness decreased the optimum insulation thickness. The lowest insulation thickness was observed in the Ytong wall type, while the highest optimum insulation thickness was observed in the concrete wall type. The highest gain is 7.77 USD/m\({}^{2}\) year using electricity as the fuel and EPS as the insulation material in a 15 cm thick concrete wall. The lowest gain is obtained by using 15 cm Ytong wall and rock wool as the insulation material. In addition, in Hatay province, it is not economical to insulate if coal is used as the fuel and 15 cm Ytong wall is used as the wall material. In other words, there is no need for insulation if 15 cm thick Ytong wall material is used as the wall material. A similar situation occurs when XPS and rock wool are used as the insulation material for 19 cm holed brick wall. In the case of a 15 cm thick concrete wall with rock wool as the insulation material, the highest insulation cost for electricity use is 6.60 USD/m\({}^{2}\), while the cost for coal use is 0.85 USD/m\({}^{2}\). Figure 5: Interval plot of (**a**) optimum insulation thickness values (m) and (**b**) annual financial gains (USD/m\({}^{2}\)) with rock wool insulation material for Hatay. A detailed analysis of the payback periods for insulation costs reveals that using a 15 cm thick concrete wall and EPS as the insulation material results in an average payback period of 0.86 years when natural gas is used as the fuel, 0.85 years when electricity is used, and 1.66 years when coal is used. Additionally, an analysis of the annual fuel savings indicates that, when electricity is the fuel type, the resulting savings are significantly higher compared to other fuel types. The calculations were repeated for Adhyaman province, which was affected by the earthquake and is located in the second degree-day region of Turkey. The optimum insulation thicknesses and annual financial gains in the case of electricity, natural gas, and coal use for five different walls when XPS is used as the insulation material are given in Figure 6. Moreover, optimum insulation thickness values and financial gains with different insulation materials are given in Table 4. It is seen that the graph obtained for Adhyaman province is similar to the graph given for Hatay province in Figure 3.  
According to Figure 6, when electricity or natural gas is used as the fuel, the optimum insulation thicknesses are close to each other, and the smallest insulation thickness is obtained when coal is used. For the 15 cm concrete wall type, the optimum insulation thicknesses are calculated as 0.0399 m, 0.0157 m, and 0.0394 m, respectively, when electricity, coal
## 3 Proposed Method
### Achieving CVD Compensation with ALCC-Glasses  
We ignore the light decrease from the monitor to the scene camera, and assume that the scene camera is observing the scene directly. The environmental light is separated into two rays, but they may not be precisely 50/50. The ray toward the user-perspective camera will then be decreased by the LCD panel and HMD lenses. We use function \(\mathbf{f}_{decay}\) to represent all of these changes from the scene image, \(\mathbf{I}_{S}\), to the user-perspective image, \(\mathbf{I}_{U}\) (Figure 4), including the difference of the two rays coming from the beam-splitter. We can control part of the decrease by changing the display image, \(\mathbf{I}_{L}\), on the LCD to adjust the transmittance of the LCD. The relationship of \(\mathbf{I}_{L}\) and transmittance is represented by \(\mathbf{f}_{subtraction}\) (Figure 5). Finally, the environmental light will combine with the light emitted by the OST-HMD. When we display an image on the OST-HMD, denoted as \(\mathbf{I}_{H}\), the color is distorted for many reasons; this distortion is accounted for by \(\mathbf{f}_{distortion}\) (Figure 6). With the \(\mathbf{I}_{S}\) observed by the scene camera and \(\mathbf{I}_{U}\) observed by the user, we have  
\[\mathbf{I}_{U}=\mathbf{f}_{subtraction}(\mathbf{I}_{L})\cdot\mathbf{f}_{decay}(\mathbf{I}_{S})+ \mathbf{f}_{distortion}(\mathbf{I}_{H}). \tag{2}\]  
With the three functions in Equation (2) are known, we can compute \(\mathbf{I}_{L}\) and \(\mathbf{I}_{H}\), which are being displayed by the LCD and the OST-HMD, respectively, using the inverse mapping of the functions. In this subsection, we will first explain how to predict \(\mathbf{f}_{subtraction}\), \(\mathbf{f}_{decay}\), and \(\mathbf{f}_{distortion}\) through experiments, and then explain how to control the overlay enabling CVD users to observe a compensated image through the OST-HMD.  
Figure 4: \(\mathbf{f}_{decay}\): The relationship between \(\mathbf{I}_{S}\) and \(\mathbf{I}_{U}\) when the LCD was set to highest transmittance and the OST-HMD had no output.  
Figure 5: \(\mathbf{f}_{subtraction}\): How much the LCD decreased the incoming light beyond \(\mathbf{f}_{decay}\).
## 2 Materials and Methods
### Theory of Threat Modeling
#### 2.4.7 Formal Verification
The termination of model checking is guaranteed by the finiteness of the model (there is a finite number of states). There are various approaches to model checking. They depend on various technical issues, such as the choice of the language that is used in expressing the system specifications and the representation of the system. Automatic model checking is ideal. If the model was selected properly, the required properties can be verified in one pass. Moreover, available tools for model-checking provide a trace for each error path in the model. The model checker will either terminate with the answer true, indicating the property is satisfied, or give a counterexample that shows why the property is not satisfied. Model checking is not suitable for very large systems, as the state space generally grows exponentially with the size of the transition system. This problem is referred to as state explosion problem. Another drawback of model checking is that it operates only on models, and the actual implementation might drift from the correct implementation of the model, so the verified model is not equivalent to the actual implementation. Theorem proving is a pure mathematical or logical approach where the verification problem is described as a theorem in a formal theory. A formal theory is a language in which formulas are written, a set of axioms are developed, and a set of inference rules are used for proving. Theorems can be proved with rules and axioms. A desired property is satisfied if a proof can be constructed from the system axioms and inference rules. Security properties are all aspects related to the confidentiality of system data. Security policies including authenticity, authorization, secrecy, integrity, freshness, and fair exchange [42] are enforced within a system when security must be verified. Formal verification for security properties verification is an efficient technique to ensuring the security measures are satisfied in various system components and in different design stages [43]. In this work, we used formal verification as part of the evaluation of the proposed algorithm, through ensuring that the security measures hold after applying the algorithm. We used B-method to describe a complete abstract model of the case study presented in this work. Altier B tool was used to verify the consistency of the described model and to verify surface attack scenarios on the optimized model. In the next subsection, we briefly introduce the B-method.
## 3 Results
### Relax Levels  
Additional results include the relax levels after each condition. The question \"How relaxed did you feel while experiencing the seat?\" was rated on a seven-point Likert scale (Figure 8). The position with highest relax level was the flat condition, followed by the  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Main Effect of Condition**} & \multicolumn{4}{c}{**Paired Comparisons**} \\  & & \multicolumn{4}{c}{**Two Way Repeated Measures ANOVA**} & \multicolumn{4}{c}{**\(t\)-Test**} \\ \cline{3-10}
**Dependent Variables** & & \multicolumn{4}{c}{**Seat Position**} & \multicolumn{2}{c}{**Time**} & \multicolumn{2}{c}{**Interaction**} & **Up vs. Rc** & **Up vs. FI** & **Rc vs. FI** \\ \cline{3-10}  & & **F** & **\(p\)** & **F** & **\(p\)** & **F** & **\(p\)** & & \\ \hline \multirow{4}{*}{COMFORT} & General & 0.04 & 0.961 & 0.94 & 0.377 & 3.11 & 0.045 \({}^{\star}\) & 0.721 & 0.754 & 1 \\  & Head & 4.51 & 0.050 & 0.42 & 0.662 & 0.26 & 0.899 & 0.738 & 0.001 \({}^{\star}\) & 0.143 \\ \cline{1-1}  & Back & 2.20 & 0.140 & 0.38 & 0.686 & 1.99 & 0.170 & 0.038 \({}^{\star}\) & 0.138 & 0.533 \\ \cline{1-1}  & Buttocks & 0.72 & 0.502 & 0.42 & 0.662 & 0.88 & 0.483 & 0.213 & 0.651 & 0.416 \\ \cline{1-1}  & Legs & 3.91 & 0.039 \({}^{\star}\) & \textless{}0.01 & 1 & 0.39 & 0.813 & 0.411 & 0.008 \({}^{\star}\) & \textless{}0.001 \({}^{\star}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: ANOVA and \(t\)-test paired comparison for dependent variables related to comfort and relax level in the seat for the upright (Up), reclined (Rc) and flat (FI) conditions. \({}^{\star}\) Symbol indicates a significant effect (\(p\)\(<\) 0.05).  
Figure 6: Boxplot with different body part comfort ratings for the upright, reclined and flat seat conditions over time. The diamond shapes indicate the mean and circles indicate outliers.  
reclined position and the upright position. When further analyzed, we found significant differences between the upright position and both the reclined and flat conditions.
# A Distributed Real-Time Monitoring Scheme for Air Pressure Stream Data Based on Kafka
## 4 Experimental Results and Analysis
### Throughput Comparison between Kafka and ActiveMQ
Compared to Kafka, the performance of ActiveMQ's producer may be limited when dealing with large-scale, high-speed data streams owing to the QoS mechanism and message confirmation method during message transmission. Conversely, Kafka has a scalable storage layer and higher transmission and storage efficiency, can adapt to different scales and load requirements, and maintains high performance even when dealing with numerous data sources [37]. Therefore, considering Kafka's throughput, performance, and scalability and its ability to adapt to different application scenarios, Kafka can bring higher performance, better scalability, and more reliable message delivery capabilities to the system as the messaging middleware.
# 5G Physical Layer-Based Procedure to Support Time-Sensitive Networking
## 5 Using The 5G Physical Layer Signal for TSC Synchronization
The main advantages of using the proposed TLL circuit are:  
* High Precision: The primary advantage of this TLL is its ability to achieve extremely high synchronization precision, down to 1 ns in specific scenarios [15]. This level of precision is critical for meeting the stringent timing requirements of 5G networks. * Time Domain Operation: Unlike traditional Phase-Locked Loops (PLLs) that work in the phase domain, the TLL operates in the time domain. This makes it well-suited for applications where precise timing alignment is more important than phase alignment. * Mitigation of Timing Variations: The TLL can correct for signal arrival-time variations due to signal propagation delays and clock drift; it monitors and adjusts the clock to mitigate these variations, ensuring reliable synchronization. Figure 6: Use of slot indication signals for UE clock synchronization. Figure 7: TLL circuit feedback for phase alignment and synchronization.
## 4 Experimental Results and Analysis
### CIC-IDS2017 Results and Analysis
This scenario arises due to the high demand for the model to detect Infiltration, leading to some instances of Infiltration being incorrectly classified as normal traffic. CBF-IDS exhibits outstanding performance in terms of recall and F1 score, achieving remarkable improvements of 200.00% and 107.72% compared to CBC-IDS, and improvements of 19.99% and 10.78% compared to CBA-IDS, respectively. Figure 9 illustrates the clear advantage of CBF-IDS in terms of Web Attack detection performance metrics, with precision, recall, and F1 score attaining 98.61%, 97.71%, and 98.16%, respectively. Compared to CBC-IDS, CBF-IDS achieves significant improvements of 44.17%, 8.12%, and 26.06% for precision, recall, and F1 score, respectively. Similarly, compared to CBA-IDS, CBF-IDS exhibits improvements of 35.12%, 2.41%, and 18.69% for precision, recall, and F1 score, respectively.
# Morphological Analysis of a Collapsing Cavitation Bubble near a Solid Wall with Complex Geometry
## 4 Morphological Analysis Method Exerted on the Cavitation Bubble Collapse Model
### Evolution of Bubble Profiles
#### 4.2.3 Dimensionless Position Offset Parameter  
In this subsection, the effect of the position offset parameter on the collapsing bubble is discussed. Five offset parameters are selected, and the corresponding morphological metric curves of the Minkowski functionals are obtained under two different fractal dimensions. The position offset parameter influences the deformation speed of the collapsing bubble (Figure 15), causing the curves of the Minkowski functionals to clearly differ from \(t=550\) ts. It is apparent that the deformation speed increases with \(\lambda\). The retarding effect of the solid wall on the collapsing bubble is more suitable with the vapor bubble in the lower region of the liquid (\(1.4<\lambda\leq 1.5\)), making the interval between the first and second collapses more obvious.  
Figure 15I,II demonstrates that the absence of the second collapse is related to \(\lambda\) and the geometric characteristics of the solid wall. The profile evolution of the collapsing bubble illustrates that the bubble deformation process is significantly affected by the solid wall. There is a significant difference between the shrinkage speeds of the left and right bubble walls because of the geometric characteristics of the solid wall, determining the size of the bubble at the first collapse. When the bubble at the first collapse is extremely small, the corresponding second collapse disappears during the entire process. Therefore, it is  
Figure 14: Jet velocity and boundary length vs. time under the same fractal dimension: (**a**) jet velocity and (**b**) boundary length.  
Figure 13: Morphological analysis of the collapsing bubble under the same fractal dimension: (**a**) fractional area \(F\), (**b**) boundary length \(U\), and (**c**) Euler characteristic \(X\).  
necessary to comprehensively consider various parameters that affect the deformation of bubbles to reduce the instability of the collapse process.
# Hybrid NOMA Protocol with Relay Adaptive AF/DF Collaboration and Its Modeling Analysis in NB-IoT
###### Abstract  
In order to meet the demand for large-scale device access in Narrowband Internet of Things (NB-IoT) and to overcome the problem that some resources are wasted due to the use of single decode-and-forward (DF) or amplify-and-forward (AF) collaboration in traditional collaborative communication, this paper introduces a non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) hybrid access method into the NB-IoT and proposes a new Hybrid NOMA transmission protocol with relay adaptively selectable collaboration, which is represented as an algorithm. Based on this protocol, we classify the whole transmission process into five states after deriving the outage probability of each link. We then consider the system as a discrete-time Markov model, the closed expressions of the system outage probability and throughput are derived based on the system steady-state probability. In order to improve the system's reliability, we further optimize the above protocol by allowing the source node to retransmit the unsuccessful received superimposed signals a limited number of times. Numerical results and simulations show that the outage probability is lower when multiple retransmissions are possible. The proposed relay adaptive collaborative hybrid NOMA transmission protocol has advantages over the pure OMA transmission mode.  
adaptive relay; non-orthogonal multiple access; orthogonal multiple access; decode-and-forward; amplify-and-forward; discrete-time Markov model +
Footnote †: journal: Electronics Letters  
18 June 2023  
Revised: 19 July 2023  
Accepted: 23 July 2023  
Published: 25 July 2023