esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 5G Radio Propagation Mechanisms
### The 5G Channel Modelling
#### 7.5.1 Deterministic Channel Model  
Deterministic models utilize electromagnetic (EM) wave propagation principles to determine signal strength in any communication scenario. The reflection and diffraction laws are used to estimate the received signal strength at a specific geographical location, which can be specified using a 3D map of the targeted propagation scenario or environment [173]. Even though the deterministic modeling approach predicts signal propagation with high accuracy, it lacks computational efficiency and involves quite complicated processes. The following are some well-known deterministic modeling approaches:  
1. Ray Tracing Model: The ray tracing deterministic approach is based on Geometric Optics (GO), Geometric Theory of Diffraction (GTD), and Uniform Theory of Diffraction (UTD), most of which can be used to simplify and approximate high-frequency electromagnetic propagation. Diffraction paths can be determined using GTD and UTD, while GO is used to determine direct, reflected, and refracted paths [174].
2. Map-Based Model: The map-based channel model is a deterministic modeling approach developed by METIS following extensive radio propagation research [73]. The model uses ray tracing and simplified three-dimensional geometric approaches for the description of the environment and, as such, can capture significant propagation mechanisms, such as scattering, specular reflection, blocking, and diffraction. This modeling approach provides accurate and realistic spatial channel properties and can also be applied for evaluating beamforming and massive MIMO, as well as realistic path loss modeling in V2V and D2D scenarios. To begin, a map is drawn with random objects. Then, diffuse scattering point sources and Tx/Rx locations are defined. Paths are then calculated using path lengths and arrival/departure angles. Shadowing loss, LOS, reflection, diffraction, and scattering are all components of the CIR.
3. Point Cloud Model: A point cloud model is a prediction tool used to characterize the environment with greater precision, comparable to ray tracing. The point cloud model data can be obtained using common methods, such as laser scanning, which produces accurate results and fine object structures. However, point cloud data cannot be used directly in ray tracing tools because no surface representation is available. Cloud points are first filtered to form local surfaces, and neighboring points are found, followed by normal and plane depths. Next, propagation mechanisms such as LOS, specular, and diffuse paths are considered. MPC parameters, such as amplitudes, delays, and angles, are computed. Finally, PDP is calculated from combined paths with bandwidth constraints.
# The Effect of Stick-Slip Friction on the Stability of Nanostructuring Burnishing
## Appendix A  
Consider a beam with a round cross section that is firmly embedded on the left and supported on the right, Figure 3. The length of the beam is \(l\), and it is pressed in at the point \(\xi\) by means of a rotating bearing. We denote the downward deflection of the beam at the point \(\xi\) by \(w(\xi)=A\).  
First, let us estimate the stiffness of the beam at the position \(\xi\). We consider a static or quasi-static deformation of the beam, so that the equilibrium equation reads  
\[EIw^{IV}(x)=0, \tag{1}\]  
which is valid at all points where no forces act on the beam. Here, \(w^{IV}(x)=d^{4}w(x)/dx^{4}\), \(E\) is the elastic modulus of the beam and is \(I=I_{y}=I_{z}=\pi r^{4}/4\) the axial area moment of inertia of the beam, and \(r\) is the radius of the cross section of the beam. The solution of this equation with the boundary and transition conditions  
\[\begin{array}{l}w(0)=0,\ w^{\prime}(0)=0,\ w(\xi-0)=A,\ w(\xi+0)=A,\\ w^{\prime}(\xi-0)=w^{\prime}(\xi+0),\ w^{\prime\prime}(\xi-0)=w^{\prime\prime}( \xi+0)\,w(l)=0,\ w^{\prime\prime}(l)=0.\end{array} \tag{2}\]  
reads  
\[\begin{array}{l}w(x)=A\frac{\left[-3\beta(\xi-2l)x^{2}+\left(\xi^{2}-2\xi l -2l^{2}\right)x^{3}\right]}{\xi^{3}(\xi^{2}-5\xi l+4l^{2})},\ \text{for}\ x\in(0,\xi)\\ w(x)=A\Bigg{[}1+\frac{\left(3\left(\xi^{2}-4\xi l+2l^{2}\right)(x-\xi)+3(\xi-3l )(x-\xi)^{2}+\frac{(\xi-3l)}{(\xi-1)}(x-\xi)^{3}\right)}{\xi(\xi^{2}-5\xi l+4 l^{2})}\Bigg{]},\ \text{for}\ x\in(\xi,l)\end{array} \tag{3}\]  
The elastic potential energy can be calculated as an integral:  
\[U=\frac{EI}{2}\int_{0}^{l}w^{\prime\prime}(x)^{2}dx=\frac{EI}{2}\bigg{(}\int_ {0}^{\xi}w^{\prime\prime}(x)^{2}dx+\int_{\xi}^{l}w^{\prime\prime}(x)^{2}dx \bigg{)}=\frac{1}{2}A^{2}\frac{12 EI^{3}}{\xi^{3}(l-\xi)^{2}(4l-\xi)}. \tag{4}\]  
The coefficient at \(A^{2}/2\) is the effective stiffness \(k(\xi)\) at the point \(\xi\):  
\[k(\xi)=\frac{12 EI^{3}}{\xi^{3}(l-\xi)^{2}(4l-\xi)}=\frac{12 EI}{l^{3}}\frac{1} {(\xi/l)^{3}(1-\xi/l)^{2}(4-\xi/l)}. \tag{5}\]  
The plot of this dependence is shown in Figure 11.
## 7 Discussion
### Open Challenges and Potential Solutions
#### 7.1.3 Data Insufficiency Problem--Low-Rank Matrix Completion  
Through the multi-dimensional scaling (MDS) method, it is possible to construct a Euclidean distance matrix (EDM) to obtain relative positions and then use eigenvalue decomposition to determine the absolute node positions. In this case, a sufficient number of known anchor positions are required to accurately determine the absolute positions of nodes. However, when the wireless communication range is restricted for various reasons, the acquisition of distance information will be limited, and the estimating node positions must rely on a small amount of data. To address this, a matrix completion method has been proposed to reconstruct the incomplete EDM by recovering some entries.  
Since there are infinitely many completion options for generally unknown entries, it is impossible to recover the original matrix from information about a subset of entries. Nevertheless, if the matrix has a low-rank property, the rank of the EDM [47] in a \(k\)-dimensional Euclidean space is at most \(k+2\). Therefore, it can be easily modeled as a low-rank matrix, allowing for the recovery of the partly empty matrix. By employing this approach, it is expected that node positions in scenarios with inadequate distance information can be accurately determined. Thanks to developments in AI methods, particularly NN-based denoising methods such as those in [43, 48, 49, 50] promise to leverage the statistical inference as a novel and potentially more robust alternative to deal with noisy measurements for the EDM-based localization. However, the existing NN-based denoising techniques require computations proportional to the square of the number of entire sensors as the inputting and outputting vectors for NN frameworks. This results from the pairwise nature of generating and measuring the distance between two nodes, entailing a combinatorial approach. Hence, as the number of nodes increases, the complexity grows quadratically. The quadratic increase in the number of computations requires the handling of a substantial amount of training data and the employment of large-size NN models, which may give rise to interesting research topics.
## 4 Simulation Results  
The proposed algorithm was evaluated using CarSim in MATLAB/Simulink. Figure 3 shows the test scenario used in this paper. There was one moving vehicle and one stationary vehicle in front of the host vehicle. After the front moving vehicle changed lanes at the last moment [48], the stationary vehicle was detected as a suddenly appearing obstacle by the CarSim Sensor module of the host vehicle at 5 m ahead the last point to steer (LPS) [4], which was defined as the last location to avoid a collision by steering. The longitudinal vehicle velocity was set at 80 km/h which is the highest longitudinal velocity for testing ESA. Since the stationary vehicle suddenly appeared in front of the host vehicle, the last point to brake (LPB), which was defined as the last location to avoid the collision by braking, was already behind the host vehicle. A single lane-change path generated using TAP was used as the evasive path to avoid the collision. When the host vehicle arrived at the LPS, the AES was trigged automatically.  
The maximum angular rate of the front wheel steering angle was limited to \(42^{\circ}/\)s due to the EPS according to the requirement of the fishhook test [49]. The front wheel steering angle was limited between \(-35^{\circ}\) and \(35^{\circ}\) due to the pinion-rack configuration. Simulation results of the proposed MIMO MPC were compared with those of the SISO MPC [35]. For the first test scenario, the nominal road friction coefficient was 0.9 which was used to obtain the nominal cornering stiffness of the prediction model in MPC. The second test scenario was used to test the robustness of the proposed algorithm with a medium road friction coefficient of 0.7 which is 22% less than the nominal value; i.e., the actual cornering stiffness should be smaller than the nominal values used in the prediction model.  
Figure 3: Test scenario at 80 km/h.
## 2 Related Reviews and Current Contribution  
Several review articles have addressed the problem of epileptic seizure prediction (ESP) using signal processing, ML, and DL techniques. However, these studies differ in scope, emphasis, and depth, and most have not systematically analyzed the role of deep feature learning from temporal and spectral EEG characteristics.  
[PERSON] et al. [17] compared various traditional signal processing methods for seizure detection, including nonlinear time series analysis, entropy measures, and spectral transforms, finding that entropy features could achieve up to 100% accuracy on specific datasets. However, the generalizability of these results across diverse real-world EEG recordings remains questionable. [PERSON] et al. [18] provided a comprehensive overview of ML-based seizure prediction techniques, presenting available code, features, and evaluation metrics, but did not delve into deep learning architectures or the benefits of learned features over hand-engineered ones. [PERSON] et al. [19] focused on recent advancements in portable ESP systems and signal processing pipelines, emphasizing the importance of each stage from preprocessing to classification. While their study introduces trends and challenges in ESP, it does not explore the performance or architecture of DL models such as CNNs and LSTMs. [PERSON] et al. [5] offered a broad review of automatic seizure detection using DL methods across neuroimaging modalities, including EEG and MRI. Although valuable, their work emphasized DL-based rehabilitation and cloud-based systems more than EEG-specific feature extraction for prediction tasks. [PERSON] et al. [20] presented an in-depth review of deep learning methods applied to EEG classification tasks. Their work highlighted the shift from traditional handcrafted feature extraction to end-to-end learning approaches and emphasized key challenges in model interpretability, generalization, and performance across different EEG datasets. The following describes how this review fills this gap:  
1. This review categorizes seizure prediction approaches based on the type of deep features extracted, distinguishing between time-domain, frequency-domain, and hybrid feature representations.
2. Unlike prior reviews that assess generic DL models, this study emphasizes CNN-based architectures, which have shown promising results for capturing spatial and temporal EEG patterns.
3. Finally, this study outlines potential future research directions, particularly focusing on self-supervised learning, Transformer-based models, and interpretable DL approaches for seizure prediction.
# Obstacle Avoidance in a Three-Dimensional Dynamic Environment Based on Fuzzy Dynamic Windows
## 3 Proposed Dynamic Window Approach
### The Candidate Local Goal  
As shown in Figure 4, the candidates for local goal points are generated in a safe fan-shaped area where the agent can fly, and the optimal value of the candidate local goal point is then detected.  
The candidates for local goal points are defined by:  
\[\mathbf{goal}_{local}(i)=(\mathbf{X}(t),\phi_{i}(t),\theta_{i}(t))+R_{s}*(\mathbf{\psi},j \frac{\pi}{mR_{s}},k\frac{\pi}{mR_{s}}) \tag{9}\]  
where \(\mathbf{goal}_{local}(i)\) is the candidates for local points, \(\phi_{i}(t)\) is the azimuth of the candidates for local points. \(\theta_{i}(t)\) is the elevation of the candidates for local points, \(X(t)\) is the position of the agent, and \(R_{S}\) is the safe separation of the agent and obstacles. \(j\), \(k\) and \(m\) are the index and number of the candidate local goals. The larger the value of m is, the more local goal points can be selected, and the better the optimization result of the local goal can get. \(R_{S}\) is defined by:  
\[R_{s}=R_{r}+r_{0}+R_{breaking}(t) \tag{10}\]  
Figure 4: (**a**) Local goals in trajectory planning and (**b**) the position of the local goals.  
Figure 3: Two-level (**a**) velocity space and (**b**) Cartesian space.  
\[R_{\textit{traking}}(t)=\frac{{{v_{t}}^{2}}}{a_{\max}} \tag{11}\]  
\[\mathbf{\eta}=\left[\begin{array}{c}\cos(\phi_{i})cos(\theta_{i})\\ \sin(\phi_{i})cos(\theta_{i})\\ \sin(\theta_{i})\end{array}\right] \tag{12}\]  
where \(R_{r}\) is the agent radius, \(R_{\textit{traking}}\) is the braking distance of the agent, \(v_{t}\) is the current velocity of the agent, and \(a_{\max}\) is the maximum acceleration that the agent can achieve.
# Numerical Fit Modeling for Temperature Mitigation in
## 2 Numerical Fit Model for Arid Regions
### Options for Types of Numerical Models  
The main assumption of a numerical fit model is that the relationship between temperature changes and solar insolation can be expressed with an equation. However, it will be shown that multiple equations are necessary because there are significant differences in the heating and cooling rates between various air masses. Whenever a front or trough moves through an area, it brings air with new properties. It is well known that water vapor is a greenhouse gas because it interacts with longwave infrared ground radiation [24]. Therefore, when the air mass changes, the heating and cooling rates change. However, this can be accounted for by changing the modeling fit parameters for each new air mass as it enters the study area. The revised assumption is that the relationships between temperature changes and solar insolation can be expressed with an equation for a specific air mass. Additionally, it is implied that adding calcite will have no other significant effects on the air mass properties except temperatures.  
The diurnal temperature range (DTR) is an active area of study since global climate change (GCC) has been affecting it [25]. Therefore, much DTR modeling is related to changes in cloud cover at the global scale.  
Diurnal models can be made by a numerical fit of solar radiation and assumed cooling parameters [26]. For example, diurnal temperature variations have been fitted to temperatures month by month in Portland, OR using an exponential equation [27]. Similar to numerical fitting is statistical prediction based on cloud cover. That fitting is the basis for numerical weather prediction [28; 29]. However, a difficulty with using these methods is that the numerical or statistical predictions only work for the range of conditions that are fit. Cloud cover is very common with atmospheric fronts. Consequently, these methods could imply that if there is cloud cover, it is due to a front. The purpose of this section is to model unusual situations of such as calcite plumes, so those methods will not work.  
The diurnal temperature range varies based on factors such as soil condition [30; 31]. Continental areas with dry sandy soil have a higher diurnal temperature change than over water. In mid-latitudes, the difference between the maximum and minimum is often in the range of 10 to 20 \({}^{\circ}\)C.  
Numerical fitting could fit existing data using sine, cosine, exponential, or Fourier series terms [27; 32]. All numerical fits only apply to the conditions that they are fit to. Without modification, these methods are unsuitable to handle predictions of changes due to weather modification. These models have limitations because they do not consider the physics behind the phenomena but only use curve fitting. They do not work well when weather fronts pass through an area, because the heat flow rates change.
## 4 Experimental Setup
### Research Questions  
We formalize our study with the following two research questions:  
_RQ1: Does our proposed SMC feature selection framework using a decision tree classifier achieve better prediction performance than it does with other classifiers?_  
After obtaining the reduced feature set via the feature selection stage, we input crash data with the reserved features into a decision tree classifier to build the model for predicting the crashing fault. This research question evaluates whether the SCM framework, when paired with a decision tree classifier, outperforms its integration with other widely employed classifiers for predicting crashing fault residence.  
_RQ2: Does the MIC-based feature ranking in our proposed feature selection framework perform better than other feature ranking strategies?_  
Our proposed feature selection framework contains two stages, i.e., the SC-based feature clustering and MIC-based feature ranking. According to the description of the framework, the reserved features are largely determined by the ranking results of the features in each cluster. Thus, the feature ranking strategy is important for the reduced feature set, which is related to the prediction performance for crashing fault residence. We designed this second RQ to examine whether the MIC-based feature ranking strategy used in our SCM hybrid feature selection framework performs better than other feature ranking strategies.
## 4 Experiments  
### Datasets and Evaluation Metrics  
#### 4.1.1 Datasets  
Market-1501 [3] is a high-quality pedestrian re-identification dataset composed of 1501 pedestrians captured by six cameras. Among them, the training set has 751 people and contains 12,936 images; the test set has 750 people and contains 19,732 images. It means that each person has 17.2 pieces of training data and 26.3 pieces of test data on average. After adding 500K irrelevant pictures, it is a large-scale pedestrian re-identification dataset with 32,668 + 500K bboxes and 3368 query pictures.  
CUHK03 [29] contains 14,096 pictures from 1467 identities collected by five pairs of cameras. In [29], 100 identities are randomly selected as the test set, and the remaining identities are used as the training set. We use the new protocol proposed in [30] to divide the data set into a training set of 767 identities and a test set of 700 identities.  
MSMT17 [31] is a dataset that is closer to real life scenarios. In four days with different weather, 12 outdoor cameras and three indoor cameras collect videos at three different time periods every day. Then, 126,441 bounding boxes of 4101 identities were obtained, of which the training set contained 32,621 bounding boxes of 1041 pedestrians, and the test set contained 93,820 bounding boxes of 3060 pedestrians. For the test set, 11,659 bounding boxes were randomly selected as the query, and the other 82,161 bounding boxes were used as the gallery. Therefore, this is a more challenging data set due to the fact that it has both indoor and outdoor scene information of different weather and time periods.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 5 BackCom: Wireless Communications
### BackCom Modulation Techniques
#### 5.2.2 Frequency Modulation in BackCom  
FM is used to modify the frequency of carrier signals according to the data present in information signals. This technique helps to carry information to remote receivers by extending the communication range of BackCom having the least interference. Similarly, FSK modulation was proposed to extend the communication range of the coherent receiver in the BiS radio channel [66]. The signal representation of FSK backscattered signals is shown in Figure 20. [PERSON] et al. demonstrated experimentally that FSK could increase coverage in BiS configuration [50].  
Similarly, ambient FM radio waves could be utilized as the signal source for BackCom [96]. This scheme enables BackCom transmissions to be decoded by any FM receiver available in smartphones or cars. The key innovation of this idea is the modulation technique. This technique can transform RF signals to backscattered signals, with simple multiplication operation on RF signals and an additional  
Figure 19: An amplitude shift keying (ASK) or phase shift keying (PSK) modulator for BackCom.  
Figure 18: Amplitude modulation for BackCom.  
operation on audio signals output by the FM receiver. [PERSON] et al. discussed the BackCom design for ambient OFDM carrier signals available in the environment [114]. They further developed a system model for spread spectrum modulation. [PERSON] et al. proved through analysis that the BackCom system can transmit a single symbol per OFDM symbol of the traditional wireless system, and this system can attain better data rates over small distances [115]. However, the desired recipient of backscatter signals is considered to be co-located with the traditional transmitter (lying on the same device).
## 3 Results and Discussion
### Results
#### 3.1.2 Model Evaluation against Observations
\begin{table}
\begin{tabular}{c c} \hline \hline
**Rate** & **g/m2-year** \\ \hline Gross primary production & 935.28 \\ Growth wood & 44.34 \\ Growth foliage & 177.34 \\ Growth fine roots & 217.96 \\
**Total growth (NPP)** & **439.63** \\ \hline Mortality wood & 32.56 \\ Mortality foliage & 176.62 \\ Mortality fine roots & 217.35 \\
**Total mortality** & **426.53** \\ \hline Decomposition from wood & 4.37 \\ Decomposition from foliage & 127.54 \\ Decomposition from fine roots & 185.66 \\
**Total decomposition** & **672.16** \\ \hline Respiration from wood & 39.35 \\ Respiration from foliage & 0.01 \\ Respiration from fine roots & 32.76 \\ Respiration from SOM RZ & 173.98 \\ Respiration from SOM BRZ & 44.16 \\ Respiration heterotrophic & 332.78 \\ Respiration Autotrophic & 495.70 \\
**Total respiration** & **828.48** \\ \hline DOC Production RZ & 17.4 \\ DOC Production BRZ & 4.42 \\
**Total DOC production** & **21.82** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Simulated average annual rates defining the carbon model. The rates in bold are summaries across each of the individual compartments. \begin{table}
\begin{tabular}{c c} \hline \hline
**Rate** & **g/m2-year** \\ \hline DON\_Deposition & 0.04 \\ DIN\_Deposition & 0.03 \\
**Total deposition** & **0.08** \\ \hline Symbiotic fixation allocated to wood & 0 \\ Symbiotic fixation allocated to foliage & 0 \\ Symbiotic fixation allocated to roots & 0.43 \\
**Total symbiotic fixation** & **0.43** \\ \hline Uptake allocated to wood & 0.04 \\ Uptake allocated to foliage & 1.79 \\ Uptake allocated to roots & 1.87 \\
**Total uptake** & **3.70** \\ \hline Mortality wood & 0.06 \\ Mortality foliage (litterfall) & 1.81 \\ Mortality roots & 2.33 \\
**Total turnover** & **4.21** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Simulated average annual rates defining the nitrogen model. The rates in bold are summaries across each of the individual compartments.