esa-sceva/satcom-chunk-collection huggingface dataset:
# Research and Conceptual Design of Sensor Fusion for Object Detection in Dense Smoke Environments
## 1 Introduction
### Principle of 2D Radial Position Measurement Using FMCW Radar
#### 2.3.1 Range Measurement for an Object Target  
Figure 7a shows the basic operation of the FMCW radar system. The transceiver of the radar system transmits a frequency-modulated signal (\(f_{TX}\)) to an object, and then the receiver of the radar system obtains the reflected signal (\(f_{RX}\)) from the object. The transmitted and received chirp signal can be expressed as  
\[f_{TX}=f_{0}+\frac{B}{t_{chirp}} \tag{9}\]  
\[f_{RX}=f_{0}+\frac{B}{t_{chirp}}(t-\Delta t),\ \Delta t\leq t\leq t_{chirp} \tag{10}\]  
where\(f_{0}\), \(t_{chirp}\), and \(B\) are the starting sweep frequency, frequency sweep time, and frequency sweep bandwidth of the transmitted signal, respectively. The time delay (\(\Delta t\)) caused by the round-trip of the transmitted signal up to reflection by the object is measured as  
\[\Delta t=\frac{2R}{c_{l}} \tag{11}\]  
where \(c_{l}\) is the speed of light in vacuum (\(3\times 10^{8}\) m/s).  
After this, the intermediate frequency (\(\Delta f\)) can be generated using a mixture of the transmitted and received signals. \(\Delta f\) can be used to estimate the range of the object. Now, the distance \(R\) of the object is derived as  
\[R=\frac{c_{l}t_{chirp}\Delta f}{2B} \tag{12}\]  
If there are \(n\) number of multiple objects in front of the FMCW radar system, Equation (12) can be expressed as  
\[R_{i}=\frac{c_{l}t_{chirp}\Delta f_{i}}{2B},\ i=1,2,3,\ldots,n \tag{13}\]  
Figure 7: **(a)** Basic operation of the FMCW radar system, **(b)** Two RX antennas configuration for estimating AoA.
## 4 Numerical Analysis Results and Data Analysis  
### Numerical Analysis Results  
The multi-way factorial design possesses the characteristic that the effect of all factors can be estimated by performing experiments at all combinations of each level [17]. Therefore, in this paper, the multi-way factorial design was used to detect all interaction effects.  
As a result of numerical analysis, the maximum vibration of the lava tube under vehicle load is shown in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Load**} & \multirow{2}{*}{**Total Load (ton)**} & \multicolumn{2}{c}{**Weight Supported**} & \multicolumn{2}{c}{**Weight Supported**} & \multirow{2}{*}{**Load**} & \multirow{2}{*}{**Total Load (ton)**} \\  & & **by Front Wheel** & & & **by Middle Wheel** & \\  & & **0.1 W (kg)** & & **0.4 W (kg)** & \\ \hline DB-24 & 43.2 & 2400 & 9600 & DB-24 & 43.2 \\ \hline
**Weight Supported** & \multirow{2}{*}{**Front Wheel Width**} & \multirow{2}{*}{**Rear Wheel Width**} & \multirow{2}{*}{**Wheel Grounding Width**} & \multirow{2}{*}{**Weight Supported**} & \multirow{2}{*}{**Front Wheel**} \\
**by Rear Wheel** & & & & **by Rear Wheel** & \\
**0.4 W (kg)** & & & & **a (cm)** & **0.4 W (kg)** & \\ \hline
9600 & 12.5 & 50 & 20 & 9600 & 12.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Vehicle load input data.  
Figure 4: Vehicle loading diagram considering time history. (**a**) First vehicle load; (**b**) Twentieth vehicle load; (**c**) Forty-fifth vehicle load; (**d**) Sixtieth vehicle load.  
Table 7 is a multi-way table for joint orientation (A), aspect ratio (B), cover depth (C), and vehicle speed (D). Since the number of factors is four, it can also be called a four-way table.  
If the characteristic values do not satisfy the statistical assumptions, such as normality and homoscedasticity, the average of the characteristic values cannot be the representative value. Therefore, before performing the ANOVA, it was checked whether the statistical assumptions were satisfied. The characteristic values obtained through numerical analysis did not satisfy either normality or equal variance. Accordingly, a log transformation was performed, considering that the characteristics of the data had a positive value less than 1. After transformation, normality and homoscedasticity assumptions were satisfied. Table 8 is a four-way table after natural log transformation.
# Neural Network SNR Prediction for Improved Spectral Efficiency in Land Mobile Satellite Networks
###### Abstract  
The use of satellites to cover remote areas is a promising approach for increasing communication availability and reliability. The satellite resources, however, can be quite costly, and developing ways to optimize their usage is of great interest. Optimizing spectral efficiency while keeping the transmission error rate above a certain threshold represents one of the crucial aspects of resource optimization. This paper provides a novel strategy for adaptive coding and modulation (ACM) employment in land mobile satellite networks. The proposed solution incorporates machine learning techniques to predict channel state information and subsequently increase the overall spectral efficiency of the network. The Digital Video Broadcasting Satellite Second Generation (DVB-S2X) satellite protocol is considered as the use case, and by using the developed channel simulator, this paper performs an evaluation of the proposed machine learning solutions for channels with various characteristics, with a total of 90 different observed channels. The results show that a convolutional neural network with a modified loss function consistently achieves an improvement (over 100% in some scenarios) of spectral efficiency compared to the state-of-the-art ACM implementation while keeping the transmission error rate under 0.01 for single channel evaluation. When observing two channels, an improvement of more than 300% compared to the outdated information spectral efficiency was obtained in multiple scenarios, showing the effectiveness of the proposed approach and allowing optimization of the handover strategy in satellite networks that allow user-centric handover executions.  
l 11 August 2024  
Revised: 4 September 2024  
Accepted: 10 September 2024  
Published: 14 September 2024
## 4 Results  
### Experiment Environment and Dataset  
The pine tree crown segmentation algorithm, the GA algorithm and the SVM classification model training and testing were conducted with the Matlab toolbox based on the SVM framework, libsvm. The experimental operation platform incorporated an Intel(R) Core i7 series CPU, RAM 16.0 GB. In the tree crown segmentation, we used a watershed algorithm to recognize the pine tree crowns from the HI and found that the segmentation accuracy was more than 80% compared with manual visual interpretation using ArcGIS software. Then, we manually delineated the trees that were missed or delineated incorrectly and calculated the spectrum of an individual pine tree by averaging the reflectance of each tree crown. Finally, 56 pine tree samples were extracted and labeled as healthy, early, middle or serious PWD infection stages with the aid of experts. The segmentation and labeling results are shown in Figure 5. In the classification process, the dataset was randomly split into two parts, 35 samples for model training and 21 samples for model testing.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Name** & **Formula** & **Description** \\ \hline TP & True positive & True value is positive, and the number that the model considers is positive. \\ \hline FN & False negative & True value is positive, and the number that the model considers is negative. \\ \hline FP & False positive & True value is negative, and the number the model considers is positive. \\ \hline TN & False negative & True value is negative, and the number that the model considers is negative. \\ \hline User accuracy (UA) & TP/(TP + FP) & The proportion of the correctly predicted number to the total number of a class. \\ \hline Producer accuracy (PA) & TP/(TP + FN) & The proportion of the correctly predicted number to the positive number of a class. \\ \hline Overall accuracy (OA) & (TP + TN)/(TP + TN + FP + FN) & The proportion of total correctly predicted number to the total observed number. \\ \hline Kappa coefficient (KAPPA) & (Po – Pe)/(1 – Pe), where Po – OA, Pe = ((TN + FN) \(\times\) (TN + FP) + (FP + TP ) \(\times\) (FN + TP))/(TN + TP + FN + FP) \(\times\) (FP)’2 & The kappa coefficient is used for consistency testing, and the calculation of the kappa coefficient is based on the confusion matrix. \\ \hline \end{tabular}
\end{table}
Table 2: Accuracy metrics for the classification model.
## 4 Forecasting Methods
### Long Short-Term Memory  
The LSTM model was proposed by [PERSON] and [PERSON] [28] to overcome the vanishing gradients. LSTM model has a similar structure to the RNN model, but each node of the hidden layer unit was replaced with a processing unit named \"_memory cell_\" and then improved with an additional unit \"_forget cell_\" by [PERSON] et al. [29]. To guarantee the pass of the gradient through time without vanishing or exploding, each memory cell contains a recurrent node with a fixed weight. The basic structure of the LSTM cell consists of memory cells and gate units. The LSTM gate units are described as input gate \(i_{t}\), forget gate \(f_{t}\), and output gate \(o_{t}\). In this study, we preferred similar variable names as in [30] to formulate the LSTM model briefly.  
Let \(\{x_{1},x_{2},x_{3},\ldots,x_{n}\}\) represent a similar input sequence, where \(x_{n}\in R^{k}\) represents the \(k\)-dimensional input vector at the \((t+n)\)th time step. The internal state of LSTM \(s_{t}\) interacts with previous hidden state \(h_{t-1}\) and subsequent input sample \(x_{t}\) to decide which internal state vector should be updated, maintained, or deleted based on previous output values and presents the input values. This structure of the LSTM builds temporal connections between memory cell states and maintains it throughout the whole process. Moreover, additional activation functions such as _tanh_ and _sigmoid_ are used in the LSTM memory cell. In the input node \(g_{t}\), both current input sample \(x_{t}\) and recurrent weights from the previous hidden layer are passed through a _tanh_ activation function. The equations of all nodes in a LSTM cell are given by (5) to (10),  
\[g_{t} = \tanh\Bigl{(}w_{g,x}x_{t}+w_{g,h}h_{t-1}+b_{g}\Bigr{)} \tag{5}\] \[f_{t} = \sigma\Bigl{(}w_{f,x}x_{t}+w_{f,h}h_{t-1}+b_{f}\Bigr{)}\] (6) \[i_{t} = \sigma(w_{i,x}x_{t}+w_{i,h}h_{t-1}+b_{i})\] (7) \[o_{t} = \sigma(w_{o,x}x_{t}+w_{o,h}h_{t-1}+b_{o})\] (8) \[s_{t} = g_{t}\odot i_{t}+s_{t-1}\odot f_{t}\] (9) \[h_{t} = \tanh(s_{t})\odot o_{t} \tag{10}\]  
where \(w_{(\cdot)}\) and \(b_{(\cdot)}\) represent weight matrices and biases, respectively; \(\odot\) represent element-wise multiplication; and \(\sigma\) symbolizes the _sigmoid_ activation function. While the input gate is responsible for the update procedure of the memory unit, the output gate and the forget gate determine whether the current information will be maintained or erased in the unit. When the input gate takes the value zero, there is no change in the internal state of the LSTM structure and the memory cell preserves the present activation. This process continues along with intermediate time steps as long as both the input and the output gates are closed.
## 3 Numerical Analyses
### Displacement Transfer Ratio for Horizontal Excitation
Figure 14 illustrates the \(T_{r}\) values for various \(\alpha\) values. When \(\alpha\) = 0, that is, a single-column damper, \(f_{x}\) was 0.64 Hz, which is less than \(f_{s}\), so \(T_{r}\) is less than or equal to 1. If \(\alpha\) = 0.1 (\(f_{x}\) = 0.82 Hz), the relative motion was suppressed up to \(f_{s}\) of 0.71 Hz, following which a jump occurred at 0.79 Hz. In the case of \(\alpha\) = 0.2 (\(f_{x}\) = 1.27 Hz) and \(\alpha\) = 0.3 (\(f_{x}\) = 1.72 Hz), \(f_{y}\) was substantially higher than \(f_{s}\), which has little effect on the suppression of \(T_{r}\) after 0.71 Hz. Furthermore, the maximum \(T_{r}\) was smaller in the case of \(\alpha\) = 0.3 than in the case of \(\alpha\) = 0.2. This is because a larger \(\alpha\) results in a larger \(\theta_{o}\) in Equation (2), so that the lateral reaction force acting on the concave surface of the superstructure prevents horizontal movement. Figure 15 illustrates the \(T_{r}\) values with the changes in the damping ratio. In this case, \(f_{s}\) also remained unchanged, while \(T_{r}\) was the same as that of the general viscous damping system. The resonance control of this vibration isolator enables two energy dissipation factors (friction and viscous attenuation) to be used simultaneously.
# Numerical Simulation of Water Migration during Soil Freezing and Its Resulting Characterization
## 3 Modeling Process  
Table 3 is an example from sample b used to determine the main parameters of the numerical model. The physical parameters of ice and water such as density, thermal conductivity \(\lambda\), heat capacity \(C\), and latent heat of phase transition \(L_{f}\) are conventional physical parameters and are taken from the literature [1]. The initial volumetric moisture content is \(\theta_{s}\)--0.58, and the soil density after compaction treatment is--1.74\(\frac{B}{\rho_{\rm w}T_{0}}\); these parameters were obtained experimentally. Initial freezing temperature \(T_{f}\)--0.16\(\frac{C}{C}\) can be obtained by calculating the empirical equations according to the literature [1]. The residual  
moisture volume content \(\theta\)r--0.04 was as obtained from the literature [31]. The specific heat capacity of soil particles \(C_{\mathrm{s}}\) is 0.88 kJ/ (kg.\({}^{\circ}\)C). The thermal conductivity \(\lambda_{s}\) of the soil was converted from the literature and the value is 1.39 \(\frac{1}{\mathrm{kg}^{-1}}\)[32]. Parameters \(a,m,n\) are the VG model parameters obtained from the SWCC characteristic curve fitting, where \(m=1-1/n\), whose concrete mathematical relationship was obtained from the literature [33]. Parameter \(B\) is generally obtained by the \"one-point method\" in the literature [1]; when there is no experimental data, the value can be taken as 0.56 for clay [21]. As shown in Figure 3. Firstly, a corresponding 2D model was constructed based on the dimensions of the experimental sample. Two coefficient partial differential equations were selected for inclusion, defined as the moisture and temperature fields, while the solid mechanics module was chosen to address stress-strain analysis. All parameter names and their values were entered into the Global Definitions from Table 3. Subsequently, variables including names, formulas, and units for volumetric ice content, volumetric water content, thermal conductivity, heat capacity, etc., were defined. Following this, the corresponding dependent variables, source terms, etc., were inputted into the Control Partial Differential Equations module. For the temperature field, the Delicacy boundary conditions were set according to the experimental conditions, with the temperature at the upper boundary initiated at +3 \({}^{\circ}\)C and at the lower boundary set to \(-\)5 \({}^{\circ}\)C. For the moisture field, the initial saturation degree was set at 0.9, based on the experimental conditions, with no seepage allowed at any boundary. In the solid mechanics module, the model was configured with fixed constraints at the bottom, roller support constraints on both sides, and no constraints at the upper boundary. The model was then meshed and solved.
# A Review of Deep Learning-Based Visual Multi-Object Tracking Algorithms for Autonomous Driving
## 6 MOT Algorithm Based on Transformer
### Comparison of Transformer-Based MOT Algorithms  
We provided a thorough synopsis of the transformer-based multi-object tracking algorithm in the previous section. Table 7 compares the fundamental concepts of the algorithm and the encoder-decoder structure.  
The four approaches are not similar. The multi-object tracking method of the preceding transformer structure has been enhanced by TransCenter. According to the author, dealing with dense scenes is not a good fit for tracking methods based on the bounding boxes of the first two algorithms. Therefore, it is suggested to use the object heat map based on the center point to represent the image features, improving the handling of occlusion issues. TransCenter can also globally predict the center points of each object and associate them in the time domain, showing a higher FP-FN balance, thanks to the global characteristics of the transformer. In order to further improve the accuracy and precision of multi-object tracking, TransMOT believes that the present transformer-based methods are ineffective at modeling the spatiotemporal relationship. As a result, a series of sparse weighted graphs are created for modeling.  
The transformer-based multi-object tracking method is still in its early stages. The transformer has an advantage over CNN as a detector since it employs self-attention to gather global contextual data, build remote dependencies on embedding, and extract more potent semantic characteristics. The transformer does, however, have certain drawbacks as well: transformer-based models need huge training datasets to work well, and the tracking performance for small objects is subpar since the self-attention operator's computational complexity rises exponentially as the embedding vector increases and the size of the patchesis constrained. In order to expand the framework to more difficult circumstances, such as multi-scale items, extreme weather conditions, category confusion, etc., the transformer-based multi-object tracking method needs further research.
## 5 Verification with Numerical Simulation  
In this section, we perform numerical simulations by a finite difference computer program FLAC3D to validate the accuracy of the proposed method.  
As shown in Figure 6, only a half model is built due to the symmetry along the tunnel central line. The tunnel diameter and cover depth are 12 m. To guarantee the accuracy of the numerical model, the dimension size should be large enough to wake the influence of the boundary, and the specific dimension is shown in Figure 6. The mechanical parameters of the soil and support pressure are shown in Table 1.  
In this numerical model, the top surface of the model was free, normal velocity on the four vertical surfaces was restricted, and the boundary surface was fixed. Because we focused on the displacement of soil before the tunnel face, a simplified one-step excavation was applied after reaching initial equilibrium, due to gravity. Based on the assumption that the \(V_{Ls}\) and \(V_{Lt}\) were zero, the normal displacement of points on the opening was fixed after excavation. Immediately, the gradient support pressure was applied on the face. We monitored the displacement of the points on the face and surface. The calculation was stopped when the displacements of several points were stable.  
We extracted the maximum displacement on the tunnel face and ground surface in the numerical simulations, and calculated the volume loss at the tunnel face and the ground surface. The comparison is shown in Figure 7.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Value** \\ \hline Buoyant unit weight of soil (\(\mathrm{kN/m^{3}}\)) & 18 \\ Unit weight of water (\(\mathrm{kN/m^{3}}\)) & 10 \\ Elastic modulus (MPa) & 1 \\ Poisson’s ratio & 0.3 \\ Support pressure ratio & 0.85, 0.9, 0.95, 1.00 \\ Slurry weight (\(\mathrm{kN/m^{3}}\)) & 12, 13, 14 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical parameters of soil and support pressure.  
Figure 6: Numerical model.  
Figure 7: Comparison between numerical simulation and proposed method. (**a**) Volume loss at tunnel face \(V_{lf}\), (**b**) volume loss at ground surface \(V_{lg}\).  
The comparison shows that the proposed method has a good agreement with the numerical simulation.
# ADS-B Like UTM Surveillance Using APRS Infrastructure
## 1 Introduction
Mobile communication is the most affordable communication system to adopt for its opening to the public in most areas. 4G/LTE is available to use with UAVs. However, 4G/LTE cannot be guaranteed for those flying above 400 feet. Based on the ADS-B like system concept, the construction of a UAV surveillance infrastructure using multiple communication media is proposed [4; 6]. In addition to 4G/LTE, LoRa, APRS, and XBee are eligible candidates for ADS-B like for UAV [6]. The ADS-B like candidates have different characteristics compared in Table 1.  
[PERSON] introduced a small ADS-B device by \"uavionix\" [13] for UAVs. It uses commercial frequency 1090 MHz and 978 MHz on UAVs. However, radio stations for UAV ADS-B are not designed to provide coverage down to low altitudes [12]. The UAV service providers are not affordable for the construction of a new infrastructure. [PERSON] presented the use of ADS-B for the general categories of commercial air transportation systems [14] by FAA and ICAO. Under very limited ADS-B authorizations, it is difficult to broadly open to all types of UAVs, especially those small ones flying below 400 feet. In this paper, the APRS as an ADS-B like candidate for UTM is well developed. The APRS on-board unit (OBU) is designed and built with an aim towards low-cost, light-weight, high-reliability, and wide-coverage through ground transceiver station (GTS) deployment. APRS GTS is deployed to establish communication gateways to relay radio surveillance data from UAVs onto the UTM cloud. In the preliminary surveys, the APRS GTS covers a 40 km radius range or longer. The OBU design and GTS deployment for APRS, by system integration with proper firmware programming, is the core technology to downlink the message to UTM surveillance. APRS OBU fabrication, firmware programming, and system integration are discussed in detail in this paper. UAV flight tests from different test sites are demonstrated to show the effect of APRS OBU performance on UTM [15]. APRS OBU will be an important redundant system for the complete ADS-B Like solution in UTM.