esa-sceva/satcom-chunk-collection huggingface dataset:
## Verification of the New Approach
### Experiments for the Verification of Reliability
#### 3.4.3 Results and Discussion
But, as the scale decreased, especially when the scale was 1/10, the snow tended to only accumulate at the foot of the windward side, and the snowdrift away from the windward side disappeared. As for the snow distribution near the leeward side, when the scale was 1/1 and 1/2, the experiments could precisely reproduce the snow distribution, even though the overall value of the coefficient decreased. When the scale was 1/4 and 1/10, the results gave a lower estimate of the snow accumulation, but for the snow distribution away from the leeward side, the results showed better agreement with the field observation both in value and shape. Snow distribution along the lateral direction is shown in Figure 14b. Except when the scale was 1/10, the snow distribution showed a U-shaped distribution for both field observation and scale experiments. For the 1/10 scale experiment, a similar distribution was found, but the location where the minimum value occurred was much closer to the lateral side. This phenomenon indicates that the erosion effect was excessively decreased. In summary, considering both the shape of the snowdrift and snow distribution coefficient, except when the scale was 1/10, the results of scale experiments reproduce the field observation accurately, suggesting that the method is reliable. Especially, it is known that the relationship between friction velocity \(u\), and threshold friction velocity \(u_{*t}\) is of great significance in the study of the snowdrift. From the analysis of the similarity parameters in Table 4, it could be derived that a too-small value of the parameter \(u_{*}/u_{*t}\) may adversely affect the accuracy of reproduction. Therefore, it is recommended to limit the minimum value of the parameter \(u_{*}/u_{*t}\) for scale experiments. Figure 15 shows the Pearson correlation coefficients of the distribution coefficient between the prototype and scale experiments along the measured line defined as the front, rear, left, and right. It could be concluded from Figure 15 that the results are of poor quality when the value of the parameter \(u_{*}/u_{*t}\) is lower. Assuming that the experiment results are accepted when the Pearson correlation coefficient is higher than 0.6, the lower limit values of the parameter \(u_{*}/u_{*t}\) are 0.353 and 0.287, respectively, when the field observation or 1/1 scale experiment is chosen as the prototype. At present, the result of the field observation is affected by the adverse effects of the unstable wind field and the quantitative assessment of the effect is still unclear. So, when giving a suggestion, the analysis based on treating the 1/1 scale experiment as a prototype should be more respected and, finally, 0.3 is suggested to be the lower limit value of parameter \(u_{*}/u_{*t}\) in the present study. Figure 15: Pearson correlation coefficients of the distribution coefficient between the prototype and scale experiments.
# Radio Frequency Fingerprint Identification for 5G Mobile Devices Using DCTF and Deep Learning
## 2 RFF Extraction Based on a 5G NR PRACH Preamble
### Signal Preprocessing  
Signal preprocessing involves time synchronization, CFO and phase offset (PHO) estimation and compensation, channel estimation and channel equalization, as shown in Figure 3.  
1. _Time synchronization_. The starting point of the received baseband signal \(y\) must be well located to extract the PRACH signal properly. In this paper, we determine the starting point through the repeatability of CP in the signal such that \[D=\underset{i}{arg\max}\left\{\sum_{j=0}^{L_{CP}-1}y(i+j)y^{*}(i+j+L_{inter}) \right\},i=1,\cdots,L\] (4) where \(L_{CP}\) and \(L_{inter}\) represent the theoretical CP length and the signal length without CP, respectively, and \(L\) is the difference between the received signal length and the theoretical signal length.
2. _CFO estimation and compensation_. CFO is caused by the difference between oscillator frequencies at the transmitter and receiver. It is also estimated based on the CP of the PRACH signal such that \[\Delta f=\frac{\sum_{k=1}^{L_{CP}}angle(y(k)y^{*}(k+L_{inter}))}{2\pi L_{CP}L_ {inter}}R_{s}\] (5) where \(angle(\cdot)\) refers to the phase angle and \(R_{s}\) is the sampling frequency of the received signal. The CFO-compensated signal is given by \[y^{\prime}(k)=y(k)e^{-\frac{2\pi\alpha/k}{R_{s}}}.\] (6)
3. _PHO estimation and compensation_. The phase offset can be estimated as follows: \[\phi=angle(\frac{1}{L_{CP}+L_{inter}}\sum_{k=1}^{L_{CP}+L_{inter}}y(k)\cdot x_{ l}^{*}(k))\] (7) where \(x_{l}(k)\) represents the corresponding standard PRACH signal. The PHO can be compensated by \[y^{\prime\prime}(k)=y^{\prime}(k)e^{-j\phi}.\] (8)
4. _Channel estimation and equalization_. In our experiment, the UE worked in the 5G NR n78 frequency band, and hence the PRACH signals were affected by multipath channel fading. Least squares (LS) channel estimation and zero forcing (ZF) equalization techniques were applied to the signal to mitigate the influence of channel fading. The channel estimation can be expressed as [15] \[\hat{H}=\frac{y^{\prime\prime}}{x}\] (9)  
Figure 3: Signal preprocessing flowchart.  
where \(x\) is the standard transmit signal. In our study, we selected the first and last 200 samples of the signal for channel estimation.  
The ZF equalization mitigated the channel distortion for RFF extraction. The equalized signal can be expressed as  
\[y^{\prime\prime\prime}=\frac{y^{\prime\prime}}{\widehat{H}}. \tag{10}\]  
A segment of the waveform after preprocessing is presented in Figure 4. The original received signal and the standard signal are also illustrated for comparison purposes. It can be observed that the preprocessed signal was quite similar to the standard signal.  
The power spectra of the signals before and after preprocessing are illustrated in Figure 5. It can be observed that thanks to the signal preprocessing, especially the channel equalization, the frequency domain distortions were mitigated.
## 5 Simulation Study  
Although the use of other network simulator tools such as ns-3, OMNeT++ or OPNET is possible, all of the designs and simulations in this work are done in ns-2. This is because the three OLSR link metrics, OLSR-ETX, OLSR-ML and OLSR-MD, are implemented in ns-2. The inclusion of these link metrics enabled the extension of OLSR to support multiple metrics during routing decisions, as well as to send traffic through multiple paths based on their QoS requirements. This scheme provides better reliability than a single path.
# The Car-Following Model and Its Applications in the V2X Environment: A Historical Review
## 3 Research on Car-Following Behavior in the V2X Environment
### Modelling the Car-Following Behavior
#### 3.2.2 Information of Multiple Preceding Vehicles
Unlike [PERSON]'s approach, [PERSON] et al. imputed the dual headway into two optimal velocity functions rather than the single one used in [PERSON]'s model. Recently, [PERSON] et al. proposed an extended model by introducing the mixed maximum velocity of the two preceding vehicles [52]. The results of the above works reveal that the motion state information, such as position, velocity, acceleration, and the ones calculated by those of preceding vehicles in the current lane can effectively assist the drivers with optimizing their car-following behavior and thus contribute to the traffic flow in the V2X environment. For the preceding vehicles in the current lane, the more preceding vehicles incorporated, the better the optimization effects exerted. However, these positive impacts conform to the marginal effect. It was verified that incorporating two preceding vehicles could produce considerable positive effects.
## 4 Predictions for Compressive Strength Development of HVFA UhPC
### Calibration of the Proposed Models  
Comparisons of measured compressive strength at different ages and the predictions using the proposed models are made by introducing the statistical values. The mean (\(\gamma_{m}\)) and standard deviation (\(\gamma_{s}\)) of the ratios (\(\gamma\)) between the experiments and predictions are summarized in Table 5. As the unreliability of the equations specified in _fib_ 2010 model [27] is ascertained in Figure 7, the present comparisons focus on examining the validity of the proposed models. For the UHPC specimens cured under standard condition, the values of \(\gamma_{m}\) and \(\gamma_{s}\) range between 0.95 and 0.99 and 0.10 and 0.15, respectively. The corresponding values for the UHPC specimens cured under heat curing are from 1.01 to 1.09 and 0.11 and 0.12, respectively. Note that the proposed equation for 28-day compressive strength  
Figure 9: Modeling of \(S_{I}\) in Equation (1) to predict compressive strength development.  
of UHPC gives a relatively good accuracy, although a slight overestimation tendency was observed for UHPC specimens cured under heat curing. In addition, the predictions are in good agreement with test results measured at early ages of 3 and 7 days and long-term age of 180 days. While very few studies are available to predict the compressive strength of UHPC, the proposed models have a good potential to assess reliably the compressive strength development of UHPC with different fly ash contents and cured under different conditions.
## 4 Experimental Results and Discussion  
In this section, we experimentally evaluate the effectiveness of mitigation measures using three security metrics--the network centrality measure, vulnerability score, and attack impact metrics--to analyze the impacts of vulnerabilities on the security state of satellite networks. The experiments focus on the key threat vectors (\(tv_{13}\), \(tv_{23}\), \(tv_{123\text{-}MEO}\), and \(tv_{123\text{-}LEO}\)) with three mitigation strategies applied. First, OS patches target host vulnerabilities at critical nodes, such as the _GCS_, \(GW_{1}\), and _IoT_, by implementing operating-system-level updates. Second, the protocol patches address vulnerabilities in protocols, including the TCP/IP and CCSDS, to enhance the security of data transmission paths. Third, Creating Filtering Rules (CFR) leverages real-time packet filtering technologies, such as Suricata [54] and Snort [55], deployed at the _GCS_, \(GW_{1}\), and \(GW_{2}\) to detect and block malicious packets along the key network paths.
## 2 Theoretical Background  
### Impact Load Due to Discordance between Wheel and Rail  
The impact load applied to the track from a running train is assumed to be caused by the vehicle becoming unstuck from rail momentarily. In fact, a wheel seldom comes unstuck from the rail while the train is running; this is more likely to occur when sleepers are floating, railpads are not aligned, or track irregularities occur. The wheel is not separated from the rail in the transition zone of this paper. However, a variable of wheel floating is considered to express a general principle of the impact load. Figure 1 shows the wheel separated from the rail.  
When the wheel runs on the rail normally, a deflection occurs due to an upward spring mass, and can be expressed as Equation (1):  
\[\Delta y=\frac{\left(\frac{m_{1}}{2}+\frac{m}{2}\right)}{k_{1}}g \tag{1}\]  
where \(\Delta y\): Axle spring deflection; \(m_{1}\): Spring-to-spring mass; \(m\): 1/2 of car body mass; \(k_{1}\): Spring coefficient of primary axle; \(g\): Acceleration of gravity.  
When a wheel that is separated from the rail drops on the track, a downward force is generated by wheel gravity, and an axle spring restoration force occurs, which can be expressed by Equation (2):  
\[F=k_{1}\Delta y+m_{0}g \tag{2}\]  
where \(F\): Force to wheel (primary axle); \(m_{0}\): Unsprung mass including wheel (primary axle).  
Equation (3) can be obtained by applying Equation (1) to Equation (2).  
\[F=\left(m_{0}+\frac{m_{1}}{2}+\frac{m}{2}\right)g \tag{3}\]  
In Equation (3), the wheel is in the state to receive a converted acceleration of gravity, as described by Equation (4):  
\[\mu=\frac{F}{m_{0}}=\frac{m_{0}+\frac{m_{1}}{2}+\frac{m}{2}}{m_{0}}g \tag{4}\]  
where \(\mu\) refers to the converted acceleration of gravity. The dynamic model representing the floating wheel over the rail is as expressed by Figure 2, and the equation of motion in this dynamic model is represented by Equation (5).  
\[m_{0}\frac{d^{2}y}{dx^{2}}+2c\frac{dy}{dx}+2 ky=m_{0}\mu \tag{5}\]  
Figure 1: Wheel floating above the rail.
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 2 UAV Technology in Wireless Communication
### UAV Communication Network  
The IEEE 802.11 Wireless Local Area Network (WLAN) and radio technology both conduct command and control activities for most commercial UAVs. However, due to the UAV's speed and fluctuating altitudes, IEEE 802.11 is unable to meet the required conditions. Command and control activities can be accomplished in a non-licensed range;  
Figure 1: Details of the task confronting administrative bodies and the activity required for a comprehensive UTM framework.  
however, numerous security and reliability issues would arise. Cellular networks are the only option. Cellular networks are stable, secure, and capable of covering wide areas with acceptable data speeds. However, they are not designed to support flying devices despite substantial standardization efforts. The most pressing issues continue to be interference and radio coverage. Certain limits must be met when a cellular network is linked with a UAV to improve coverage and capacity. UAVs are used as relays or mobile BSs to enhance coverage, connectivity, and capacity. RAN are also simple to install in regions where no established network architecture is available. This implementation is a configuration style that can be set up in the event of a disaster to avoid investing time and money on new infrastructure. It is also beneficial for increasing capacity and coverage during significantly crowded gatherings, such as concerts and sporting events [2; 36; 37; 38].
## 2 Materials and Methods
### Data for Training and Validation
#### 2.1.2 sEMG  
The dataset [7] named sEMG is a collection of four-channel surface electromyography signals from 40 participants, aimed at studying human-computer interaction. The data include various hand gestures, such as rest, wrist extension, flexion, and grip. The data were collected from four forearm muscles during ten distinct hand gestures simulations using the BIOPAC MP36 device and Ag/AgCl surface bipolar electrodes were sampled at a frequency of 512 Hz. The dataset is useful for research into recognition, classification, and predictive modeling in EMG-based hand movement control systems. It also serves as a reference for artificial intelligence models, particularly deep learning, detecting gesture-related electromyography signals. The dataset is recommended for benchmarking existing datasets and validating machine learning and deep learning models.
## 3 Results and Discussion
### Mortar Injections  
As intakes were expected to be high due to the existence of voids or low consolidated zones between 16 m and 21 m, mortar injections were performed to reach a minimum pressure of 20 bars or until injecting 500 L of mortar per every 0.5 m.  
Mortar consumption for the first injection phase (December 2016), performed on the street, reached an average value of 200 L/m. The combination of such low-mobility mortar columns [1; 28] with the installation of high-tensile strength geogrids [34] created a reinforced membrane at the top of the injections, which turned into the foundation of the street granular layers. That ensured the future stability of the street, which did not show any evidence of significant settlements during the next four years.  
Mortar consumption for the second injection phase (December 2018), performed at the embankment, showed higher values than the previous ones. Here, some points reach consumptions of more than 400 L/m in the closest row of injections towards the river. That values are exceptionally high and indicate the presence of voids in the ground. In addition, half of the mortar intakes, around 35 kL, were concentrated in the stratum identified as the origin of sinkholes, i.e., between 14 m and 21 m in depth. Conversely, a reduced intake was observed in the rest of the rows.  
That suggests the most problematic areas are those next to the Ebro River. Thus, both fluctuations in the river level (which means fluctuations of the water table) and the underground inflows coming from the old surficial ravines (today closed) are the most plausible factors conditioning sinkholes in the area under study. There is no evidence that the origin of the sinkhole phenomenon is produced by the water discharge through the alluvial cover, as is the case in other case studies [23]. In the present case study, the Ebro River is the main actor.  
Mortar injections showed to be effective in controlling the sinkhole phenomenon. Precision leveling was carried out since the treated area and its surroundings showed that the surface was stable. Both in the embankment and in the street, no deformations were recorded that indicate any subsidence or sinking process is still active.
### Motivation  
For global path planning, it is necessary to choose a suitable environment model, an optimization criterion, and a reasonable path finding method [9]. However, since there are a large number of for example logistics and care facilities, such a recognition of the environment must be as completely or partially automated as possible. An initial model in the form of a map can be created by for example building a virtual 3D model [10] scanning the environment with a laser [3] using 2D images [11] or directly predefined maps [12]. Based on this map, the environment has to consider the respective tasks as well as the starting position of the robots and the obstacles. If an environment is available, paths for individual robots can be derived [9]. Based on these paths, process times, clearance widths and environmental influences can be embedded, which can lead to a digital twin to control the individual robots.  
In particular, finding possible paths in complex maps to accomplish several tasks is one of the most widespread issues in the robotic domain. In principle, both local and global or coupled local and global path planning can be used [9]. In global path planning, all conditions are already known in the beginning, while in local path planning, conditions such as the robot's position and the map of the environment have to be created first. For care and logistics robots, it is necessary to develop trajectories that respond quickly to changing environments, which furthermore lead also to expectable behavior of the robots. For example, if a robot travels down the center of a hospital corridor, it may be more easily recognized than if it cuts corners and curves.  
This work focuses on pathfinding in a dynamically changing environment reducing the possible paths to a subset of topologically centered paths. Therefore, a method for the automated reduction in the 2D and 3D environment to 1D curves is proposed. Therefore, a 2D or 3D map represented as an image is considered, which contains the position of the tasks and the initial robot positions. By using the skeletonization method from [13], such a map is reduced to the 1D curves, which serves as the main data structure (graph) for the digital twin. This approach is applied in this work on virtually generated 2D maps, 3D maps and 2D real world maps generated by a vacuum cleaner (I-Robot). In the following section, a overview of several common path algorithms and concepts are discussed. Furthermore a more detailed overview on applying skeletonization for the reduction in the environment is given, followed by the generation of digital twins in several environments using existing paths.
# Advances and Future Perspective on Detection Technology of Human Norovirus
## 2 Detection Techniques  
### Morphological Methods  
In the early days (the 1970s-1980s), the clinical diagnosis methods for HuNoVs primarily included electron microscopy (EM) and immunoelectron microscopy (IEM) [27; 28]. The limit of detection (LOD) for EM is high (>\(10^{6}\) virus particles/g sample). Thus, collecting samples (faeces or vomit) was necessary for testing during the acute phase of the disease [29]. Considering that the morphological characteristics of HuNoVs were not evident under the electron microscope, observation of viral morphological characteristics had strong subjectivity. Therefore, the detection rates and accuracy were low. IEM used the convalescent patients' serum as antibodies to specifically capture HuNoV particles before identification. This method significantly improved the accuracy and sensitivity compared with EM. Although IEM was a sensitive and specific serological technology, its high technical requirements, the need to match the serum and the expensive equipment hindered its application in clinical testing.
# A Novel Alternating \(\mu\)-Law Companding Algorithm for PAPR Reduction in OFDM Systems
## 5 Simulation Results and Complexity Analysis
### Computational Complexity Analysis and Performance Discussion  
#### 5.2.1 Complexity Expression Analysis  
In this subsection, we evaluate the computational complexity of other schemes and the proposed schemes regarding the number of complex multiplications and additions (CMAs) required [14]. We enumerate the needed parameters in Table 1 for a more explicit description, such as \(N\) is the number of subcarriers, etc. In addition, since the \(\mu\)-law scheme uses logarithm operations, the complexity of logarithm operations can be expressed as the order of \(O(\log(\cdot))\) in this paper. Next, we derive and describe the complex multiplications and additions (CMAs) expressions required for each scheme in the following.  
Regarding the original \(\mu\)-law scheme and the modified \(\mu\)-law scheme, the OFDM signal is compressed or expanded through the corresponding expanding equation. Therefore, the former requires a total of multiplications and additions of approximately \((N^{2}+4N)\) and \((N^{2}+1)\) CMAs, respectively, as well as \((N+1)\) logarithm operations, and the latter involves a sum of \((N^{2}+6N)\) multiplications, \((N^{2}+2N-1)\) additions, and \((N+1)\) logarithm operations.  
As for the ITM, the scheme is segmentally companding using a threshold value and two different \(\mu\) values. An offset is introduced in the second section to achieve a smooth transition from \(\mu_{1}\) to \(\mu_{2}\). Finally, it is multiplied by a normalization factor \(K\), which is approximately the square root of the ratio of the two signal powers. Therefore, approximately \((N^{2}+7N)\) multiplications, \((N^{2}+7N-2)\) additions, and \((N+1)\) logarithm operations are required.  
For the SULC scheme, each tone component is subjected to companding one by one, sequentially, and the elements with better PAPR are selected and retained until each tone component is completed. Therefore, it needs \((3N^{2}+9N+3)\) multiplications, \((3N^{2}+N-2)\) additions, and \((N+1)\) logarithm operations. As for the proposed AULC scheme, which divides the input signal of length \(N\) into two groups. The first group has \(\beta_{rough}\) tone components, which execute parallel companding the same as the original \(\mu\)-law, and the remaining \((N-\beta_{rough})\) tone components only partially implement the SULC scheme. In total, the process requires roughly \(\left[1.32N^{2}+3.6N+3+\beta_{rough}(0.32\beta_{rough}-0.64N+0.4)\right]\) multiplications, \(\left[1.32N^{2}-0.2N-2+\beta_{rough}(0.32\beta_{rough}-0.64N+0.2)\right]\) additions, and \((0.4N+1+0.6\beta_{rough})\) logarithm operations. For convenience of reference, we provide the analytical expression of the total number of CMAs required for the above schemes in Table 3.