esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Spatial Time Domain Reflectometry
### Spatial Time Domain Reflectometry Sensor Development  
For the purpose of measuring natural porous media on a large scale, conventional TDR has its limitations, especially concerning the measurement of spatially distributed moisture, as probes are usually constructed as small three-pin or two-pin probes, which can only give a point measurement. Spatial TDR was developed by SMG at the Karlsruher Institute of Technology (KIT), Karlsruhe, Germany, to enable moisture profile measurements [17; 18; 19; 37]. A flat ribbon cable consisting of three-line copper wire covered with polyethylene insulation is usually used for this purpose. An example of the spatial TDR sensor is given in Figure 2a. The corresponding conceptual model of the electrical circuit of a TDR transmission line and capacitance model are separately shown in Figure 2b,c. The electrical circuit of an infinitesimal section along the cable can be treated as the electrical circuit plotted in Figure 2b. The electrical design of the sensor was developed by [PERSON], [PERSON] et al. [18].  
For such a infinitesimal section, information on the surrounding soil and coating material properties can be extracted from the conductance (\(G\)) and capacitance (\(C\)) using inverse modeling of the telegraph equation [19; 38; 39; 40; 41]. For instance, one version developed by [PERSON] [5] is selected to conduct the inverse analysis in this study. Before inverse modeling, some assumptions and set-up conditions have to be achieved:  
Figure 1: **(a)** Schematic description of Time Domain Reflectometry (TDR) device (pulse generator for sending a signal and oscilloscope for receiving) and TDR sensor plugged into a bulk of soil; **(b)** TDR waveform for saturated sand with the travel time determined by tangent method (reflection coefficient is dimensionless).  
1. The resistance is assumed to be constant at a value of zero for lower frequencies (<10\({}^{4}\) Hz), because, in practical cases, dielectric losses are much higher than resistance losses, with the exception of long sensors buried into a nearly lossless material such as snow [18];
2. The inductance is assumed to be constant (\(L_{0}\)) for lower frequencies (<10\({}^{4}\) Hz), because only the external inductance remains at the highest frequency (10\({}^{6}\)-10\({}^{9}\) Hz), and a transition frequency around 100 kHz ensures the insignificant influence of the inductance increase at a low-frequency range within the time window of TDR measurement [18];
3. The conductance and capacitance depend on the surrounding moist sandy soil and are assumed to be independent of frequency for lower frequencies (<10\({}^{5}\) Hz) [17];
4. The performance of the flat ribbon cable sensor is very sensitive to the installation in accordance with the 3-D electromagnetic modeling analysis [37] because the air-filled gap of 0.25 mm on both sides of the flat ribbon cable causes significant underestimation of moisture content, while a water-filled gap leads to overestimation [17; 36].
# Radio Frequency Fingerprint Identification for 5G Mobile Devices Using DCTF and Deep Learning
## 2 RFF Extraction Based on a 5G NR PRACH Preamble  
In this section, we first introduce the formula for the 5G PRACH preamble and then present the process of DCTF extraction based on the 5G PRACH preamble.
## 1 Introduction
To date, the only feasible method is the compaction of powders or nanopowders at lower temperatures in the presence of a binder. However, in the case of nanoparticles, the presence of the binder can reduce quite a lot of the saturation flux density because of the decreased phase purity, consequently reducing the energy product, the (BH)\({}_{\rm max}\) of the \(\alpha^{\prime\prime}\)-Fe\({}_{16}\)N\({}_{2}\) magnets. In addition, due to their small size, \(\alpha^{\prime\prime}\)-Fe\({}_{16}\)N\({}_{2}\) nanoparticles are highly susceptible to oxidation. These problems can be mitigated by the use of powders, for which the binder-magnetic material ratio would be lower than in the approach based on nanoparticles. For this study, \(\alpha^{\prime\prime}\)-Fe\({}_{16}\)N\({}_{2}\) powders were prepared via two methods: (i) the mechanical ball milling of iron-based materials with a source of solid nitrogen (ammonium nitrate [30,32,33]) and (ii) by keeping the iron powders up to 660-670 \({}^{\circ}\)C in ammonia-hydrogen mixtures and rapidly quenching them in water with a long consecutive tempering at 120-150 \({}^{\circ}\)C [34] (a method inspired by the pioneering work of [PERSON] [35]). Although preparation via ball milling is a mass preparation method used to prepare powders, the powders obtained via this method showed a high degree of oxidation that contributed to the degradation of the magnetic properties of the resulting particles. In the case of the second approach, (ii), the sample magnetization was about 189 emu/g, lower than the saturation magnetization of iron. Therefore, a mass production method for \(\alpha^{\prime\prime}\)-Fe\({}_{16}\)N\({}_{2}\) powders with good magnetic properties is still lacking, which is why the preparation of permanent magnets based on \(\alpha^{\prime\prime}\)-Fe\({}_{16}\)N\({}_{2}\) is delayed. On the other hand, it is well known one of the most used methods for the mass preparation of powders is the gas atomization method [36,37]. Therefore, in this work, we propose the preparation of Fe-N powders via the gas atomization method and study the influence of some preparation parameters on the morphology and magnetic properties of the obtained Fe-N powders.
## 3 Results
### Canal Configuration Types  
The canal configuration types observed in our study are presented in Table 1. Comparing our results with [PERSON]'s classification, type-I was most frequently detected (26.7%) shadowed closely by type-III (23.4%). The lowest percentage (3.3%) of canal type was observed for types-II, IV, and VI, whereas no type-VIII canals were found. Among the additional canals appreciated in our study, the most common had pattern 1-2-1-3 (6.7%) followed equally by patterns 2-3, 1-2-3, and 2-1-2-1 (Table 1). The representative micro-CT images (that were in line with [PERSON]'s classification) showed the variation in different morphological patterns and canal configurations observed for MSPs in the present study are demonstrated in Figures 1-7. The representative micro-CT images that represented additional canal configurations are presented in Figures 8-11.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vertucci’s Canal Type** & **Pattern** & **Percentage \%** \\ \hline I & 1-1 & 26.7 \\ II & 2-1 & 3.3 \\ III & 1-2-1 & 23.4 \\ IV & 2-2 & 3.3 \\ V & 1-2 & 16.7 \\ VI & 2-1-2 & 3.3 \\ VII & 1-2-1-2 & 6.7 \\ VIII & 3-3 & 0 \\ Additional & 2-3 & 3.3 \\ Additional & 1-2-3 & 3.3 \\ Additional & 2-1-2-1 & 3.3 \\ Additional & 1-2-1-3 & 6.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of the canal configuration of MSPs observed in the study.  
Figure 1: Micro-CT image showing [PERSON]’s type I (1-1) canal configuration.  
Figure 2: Micro-CT image showing [PERSON]’s type II (2-1) canal configuration.  
Figure 4: Micro-CT image showing [PERSON]’s type IV (2-2) canal configuration.  
Figure 5: Micro-CT image showing [PERSON]’s type V (1-2) canal configuration.  
Figure 3: Micro-CT image showing [PERSON]’s type III (1-2-1) canal configuration.  
Figure 8: Micro-CT image showing the additional canal configuration (2-3).  
Figure 6: Micro-CT image showing [PERSON]’s type VI (2-1-2) canal configuration.  
Figure 7: Micro-CT image showing [PERSON]’s type VII (1-2-1-2) canal configuration.  
Figure 11: Micro-CT image showing the additional canal configuration (1-2-1-3).  
Figure 10: Micro-CT image showing the additional canal configuration (2-1-2-1).  
Figure 9: Micro-CT image showing the additional canal configuration (1-2-3).
## 2 Methodology
### Experimental Procedure
#### 2.2.2 Gas Hydrate Inhibition Study  
The HLVE data of CO\({}_{2}\) hydrate was determined through the T-cycle method, which is a study approach using a constant volume thermodynamic cycle. The reactor cells were thoroughly cleaned before the experiment to avoid the effect of any impurities. The 15 mL of sample DES solutions were injected into each cell. The 6-cell reactor was then chilled to the desired experimental temperature. Once the desired temperature was attained, the cells were purged three times and vacuumed to dispose of any miscellaneous gas traces inside them. CO\({}_{2}\) gas was then injected into the cells up to the required pressure range. The range of pressure for experiments of CO\({}_{2}\) + DES gas hydrates was set between the range of 2 and 4 MPa as typically encountered in transmission pipelines. The 6-cell reactor equipped with a 1.8 cm diameter glass ball in each cell was put into a rocking mechanism that provided adequate mixing between gas and liquid phase. The stirring was essential to break the liquid water interface boundary during the hydrate formation. The sudden significant pressure drop observed in the recorded data was the primary indicator for the formation of gas hydrates. When there was no further pressure drop in the system, hydrates were deduced to be formed fully, and the system was heated at a constant rate of 0.25 \({}^{\circ}\)C/min [15]. Figure 1 shows the schematic diagram of the experimental apparatus, which consists of six separate units of identical rocking cells controlled by a built-in programmable logic controller programmed by Dixson FA Engineering Sdn. Bhd (Shah Alam, Selangor, Malaysia). The details of the equipment can be found in previous works in the literature [42; 43].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No** & **Purpose** & **Range (cm\({}^{-1}\))** \\ \hline
1 & Testing for organics and hydrocarbons & 3200–2700 \\
2 & Testing for hydroxyl or amino groups & 3650–3250 \\
3 & Testing for carbonyl compounds & 1850–1650 \\
4 & Testing for unsaturation & 1670–1620 \\
5 & Testing for aromatics & 1615–1495 \\
6 & Testing for multiple bonds & 2300–1990 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Steps to analyze FTIR.
# An Energy-Efficient Routing Algorithm
## 4 Packet Format and Energy Consumption Model
### Energy Consumption Model  
In this paper, a single-hop energy consumption model based on WHOI Micro-Modem is adopted. The attenuation factor [9]\(A(l,f)\) of the underwater acoustic channel for distance \(l\) and frequency \(f\) is expressed as  
\[10\log A(l,f)=k\cdot 10\log l+l\cdot 10\log a(f) \tag{4}\]  
where \(k\) is the energy spreading factor (1 for cylindrical, 1.5 for practical, and 2 for spherical spreading). In the simulation, we set \(k=1.5\).  
The WHOI energy consumption model [10] depends on these factors: receiving power \(P_{r}\), electroacoustic conversion power \(P_{t}^{el}(l)\), and single hop transmission delay \(t_{hop}(L,l)\). The energy consumption is expressed as  
\[E_{hop}(L,l)=t_{hop}(L,l)\cdot\left(P_{r}+P_{t}^{el}\right) \tag{5}\]  
where \(L\) is packet length, \(t_{hop}(L,l)\), is  
\[t_{hop}(L,l)=\frac{L}{\alpha B(L)} \tag{6}\]  
where \(\alpha\) is bandwidth utilization and \(B(L)\) is bandwidth. In Formula (5), \(P_{t}^{el}(l)\) is calculated as  
\[P_{t}^{el}(l)=\frac{P_{t}(l)\cdot 10^{-17.2}}{\eta} \tag{7}\]  
where \(\eta\) is the electronic circuit conversion efficiency and \(P_{t}(l)\) is underwater node transmit power, which is calculated as  
\[P_{t}(l)=B(l)\cdot N(f_{0}(l))\cdot A(l,f_{0}(l))\cdot SNR_{tgt} \tag{8}\]  
where \(f_{0}(l)\) is the optimal transmit frequency, \(N(f_{0}(l))\) and \(A(l,f_{0}(l))\) are the noise and attenuation factor under \(f_{0}(l)\), and \(SNR_{tgt}\) is the required signal-to-noise ratio for receiving data correctly. In addition, it needs to be emphasized that \(P_{r}\) in Formula (5) is not limited by underwater transmission distance, and can be seen as a constant. \(P_{r}\) is only relative to the receiving technology adopted within the underwater sensor nodes. Then, we can take Formula (6)-(8) into (5) and get the energy consumption model.  
\[E_{hop}(L,l)=\frac{L}{\alpha B(l)}\cdot\left(P_{r}+\frac{10^{-17.2}}{\eta^{ \prime}}B(l)N(f_{0}(l))A(l,f_{0}(l))SNR_{tgt}\right) \tag{9}\]  
The values of the parameters used in this paper are shown in Table 1.  
Figure 3: Packet format of the \(i^{th}\) node in compressed sensing clustering uneven-layered and multi-hop routing (CS-CULM).
## 3 Dynamic Analysis of PMSM Based on Rotor Eccentricity
### Co-Simulation Analysis  
Co-simulation is one of the coupled analysis methods used to shorten the analysis time. By storing data such as torque and magnetic flux for each position of the rotor, the time required for dynamic analysis can be shortened, and almost the same results as in coupled analysis can be obtained. Mathematical models of controller and PWM inverter of the PMSM were modeled, and the dynamic analysis was conducted similarly to the coupled analysis.
## 3 Image Preprocessing
### Smoothing and Denoising of Images  
In the process of acquiring and transmitting the image can be affected by noise. This noise causes the image quality to degrade and can mask some of the characteristics of the image, which have the potential to make the analysis of the image difficult. While removing noise from the target image, it is also necessary to retain the details of the original image as much as possible, and the quality of the processing will directly affect the effectiveness and reliability of subsequent image processing and analysis. Commonly used methods for smoothing and denoising include mean filtering, median filtering, bilateral filtering, and Gaussian filtering [22].
# A Synthetic Aperture Radar-Based Robust Satellite Technique (RST) for Timely Mapping of Floods
## 3 Test Cases
### Ouse River--England 2015  
A severe storm affected the norther area of England on 27 December 2015. This storm brought heavy rainfall, with a 24 h total of 168 mm, affecting various areas such as York and Selby in the Yorkshire region [42]. The Ouse River and the Calder and Aire Rivers in West Yorkshire experienced significant flooding (Figure 1), affecting several cities, including Bradford and Leeds. The impact was substantial, with 7574 homes in northern England losing power at 08:00 on December 27. Notably, approximately 5500 of these homes were in the town of Rochdale in Greater Manchester, where a major electricity substation was flooded [43].
# A History of Channel Coding in Aeronautical Mobile Telemetry and Deep-Space Telemetry
## 3 Deep-Space Telemetry
### Viking  
The Viking program sent two missions to Mars, both launched in 1975. Each mission comprised an orbiter and a lander. The lander separated from the orbiter and descended to a soft landing on the Martian surface. In its day, the Viking program was one of the most highly visible NASA programs [25]. It produced the first images of the surface of another plant _from the surface_ of that planet and it was equipped with signs-of-life tests applied to soil samples scooped into test chambers in the lander.  
The telemetry system for the Viking orbiter is shown in Figure 9a. Uncoded engineering data, at a rate of 81/3 or 33 1/3 bits/s, was used to modulate the low-rate subcarrier using PCM/PSK [42]. Science data were transmitted on a high-rate subcarrier using PCM/PSK and could be coded or uncoded. The uncoded data rate was 1, 2, or 4 kbits/s and the coded data rate was 1, 2, 4, 8, or 16 kbits/s [42]. The code was the same (32,6) Reed-Muller code used for the _Mariner_ program described in Section 3.2. The two subcarriers were frequency division multiplexed and the combination was used to phase-modulate an S-band carrier at 2300 MHz [43; 44].  
The telemetry system for the Viking lander is shown in Figure 9b. The configuration shown here is for the direct Mars-to-Earth link. (Each lander was equipped with a 381 MHz UHF link to the orbiter [45; 46]). The lander telemetry system is nearly identical to that of the orbiter, except the subcarrier frequencies and data rates were lower and there was no provision for uncoded transmission of science data. The engineering data rate was 81/3 bits/s and the coded science data rate was 250, 500, or 1000 bits/s [42].
# Study of Data Transfer in a Heterogeneous LoRa-Satellite Network for the Internet of Remote Things
## 4 Gateway Encoding and Packaging Method (GDEP)
The difference in contained utilization between algorithms 1 and 3 is less than 1%. Given the limitations on the delay in sending messages and the resource-intensiveness of the algorithm, such a difference can be neglected. The delay associated with the process of packing depends on the number of open containers and is equal to the number of messages in one container multiplied by the number of open containers. Thus, to reduce the delay, we can only use algorithms with a minimum number of open containers.
## 4 Proposed Solution
### Drl  
Deep reinforcement learning, as an emerging paradigm, is being used to solve decision problems in complex state spaces, is attracting widespread attention from industry and academia, and is being tested for the optimization of UAV trajectories.  
As shown in Figure 2, DRL usually consists of an agent and an interactive environment. The interactive environment includes reward function rules and state transition rules.  
The state-action-reward is a step in the training of DRL. The goal of DRL is to train the agent to take actions to maximize the reward. As shown in Figure 2, the agent obtains state \(s\) from the interactive environment and inputs action \(a\) to be executed to the interactive environment through a neural network. The interactive environment returns the obtained reward \(r\) on the basis of the reward function \(R\) and updates the state to the next state on the basis of the state transition rules.  
DRL typically models a problem as a Markov decision process, which refers to the current state \(s_{t}\), action \(a_{t}\), reward \(r_{t}\), and the next state \(s_{t+1}\) as an ancestor \((s_{t},\,a_{t},\,r_{t},\,s_{t+1})\). Through the continuous cycle of state action reward steps, the agent is fully trained to explore the best strategy to maximize the cumulative reward \(R\), that is, to explore the best strategy to achieve the set goal [32].  
\[R=\sum_{t^{\prime}=t}^{M}\gamma^{t^{\prime}-t}r_{t} \tag{17}\]  
where \(\gamma\in(0,1)\) represents the decay factor, which allows the cumulative reward \(R\) to converge to an upper bound and represents the decreasing influence of future rewards on \(R\).
# Enhancements and Challenges in CoAP--A Survey
## 4 Enhancements in CoAP
### Enhanced CoAP for Interoperability  
The authors in [41] propose a modified version of CoAP to address the issue of unavailability of cross protocol authentication between HTTP and CoAP. The proposed method, WoT-Auth, uses the HTTP Digest Access Authentication (HDAA) method to overcome the issue. HDAA contains a challenge and a response format. The 'WWW-Authenticate' HTTP header is used in transmission of HDAA challenge while 'Authorization' HTTP header is used in encoding of HDAA response. The content format of header includes the list of key-value pairs prefixed with Digest. The user distinguishes between the payload and authentication data using the CoAP 'Options'. Given that the textual format for entries in WWW-Authenticate and Authorization HTTP headers is inefficient in terms of space and parsing them is also more complex, binary format is another choice to opt. However, for each entry a fixed size of field is not sufficient since some entries are of variable length for example realm and username. The CoAP 'options' not only allows variable length entries but also efficient encoding of these values. However, due to the availability of limited option numbers, modification is required in CoAP message format i.e., in CoAP 'options' value. This modification in CoAP is achieved by changing the CoAP 'options' parameter of CoAP message format. The paper proposes embedding of options inside the CoAP options value i.e., the inclusion of sub-options in a single 'options' parameter of message. This enables the implementation of encoding multiple HDAA challenge and response sets in single 'options' value of CoAP message. The proposed flexible encoding scheme is realized by implementing the multiple sub-options in CoAP options parameter in order to fit multiple length variables in single CoAP 'options' value. Each sub-option employs the same encoding scheme as in CoAP 'options'.
# A Background Subtraction Algorithm in Complex Environments Based on Category Entropy Analysis
## 2 Related Work
This method used the efficient features from accelerated segment test (FAST) and represented them using the histogram of oriented gradients (HoG) descriptors to learn the foreground object. Then, the method used the trained SVM to classify which of the feature points obtained from the scene belong to a foreground object. However, methods using machine learning frameworks require a large amount of well-classified data for training, which makes them difficult to apply in a real environment. The key to background subtraction is getting the background image quickly and correctly. However, it is usually impossible to obtain a good initial background in a complex environment such as a dynamic background or a background with prolonged masking in the aforementioned relevant research. Given this context, this study proposes an entropy-based initial background extraction (EBBE) algorithm for the detection of initial backgrounds, as explained below. First, through a color distance classifier, each pixel is clustered and the number of required color categories is automatically increased according to the color change of the scene. Second, the concept of color category entropy is proposed. According to this concept, we can establish a suitable background category by estimating the number of representative color categories at each image position in the dynamic background or camera shake that shows a change of more than two background colors. Third, when the background is masked for a long time, the initial-background convergence time can be dynamically determined via the magnitudes of color category entropy to prevent erroneous detection results due to moving objects being mistaken for a background.