esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Model
### Laser Beam Spreading and Broadening  
When the laser beam propagates through the atmosphere, the atmospheric refractive index is randomly fluctuated due to the atmospheric turbulence, resulting in the intensity fluctuations known as scintillation, phase distortion, beam spot wandering, beam spreading and broadening. In particular, the spatial spreading and temporal broadening of laser beams are limiting factors in most applications such as remote sensing and free space optical communications [32]. The effective beam size of a Gaussian beam in a horizontal path is approximated in a far-field region as follows [33]:  
\[W_{eff}^{2}(z)=W^{2}(z)(1+T), \tag{10}\]  
where:  
\[W(z)=W_{0}\sqrt{1+\left(\frac{2z}{kW_{0}^{2}}\right)^{2}}\approx\frac{2z}{kW_ {0}^{2}}=z\theta_{D}, \tag{11}\]  
\[T=4.35k^{7/6}(\Lambda H)^{5/6}\sec^{11/6}(\theta_{z})\int_{h_{\rm s}}^{h_{\rm s }+H}C_{n}^{2}(h)\!\left(1-\frac{h-h_{\rm s}}{H}\right)^{5/3}\!dh, \tag{12}\]  
where \(T\) is the additional parameter corresponding to the beam spreading by the atmospheric turbulence, \(k\) is the optical wavenumber, \(W_{0}\) is the beam size at \(z=0\) (called beam waist), \(\theta_{D}\) is the far-field divergence half-angle, \(\Lambda\) is the Fresnel ratio defined as \(\Lambda=2z/kW^{2}(z)\), \(H\) is the space debris altitude from the station, and \(C_{n}^{2}(h)\) is the refractive-index structure parameter which represents the intensity of atmospheric turbulence.  
Many models have been proposed to predict the behavior of the refractive-index structure parameter. The Hufnagel-Valley model is widely used in satellite optical communications and optical remote sensing because it allows easy variation of day-time and night-time profile by varying various site parameters such as wind speed, isoplanatic angle and altitude [34]. The model is given by  
\[C_{n}^{2}(h)=0.00594\bigg{(}\frac{v}{27}\bigg{)}^{2}\bigg{(}10^{-5}h\bigg{)}^{ 10}\exp\bigg{(}-\frac{h}{1000}\bigg{)}+2.7\times 10^{-16}\exp\bigg{(}-\frac{h}{1500} \bigg{)}+A_{0}\exp\bigg{(}-\frac{h}{100}\bigg{)}, \tag{13}\]  
where \(v\) is the mean square value of the wind speed in \(m/s\), and \(A_{0}\) is the nominal value of the refractive-index structure parameter at the ground in \(m^{-2/3}\).  
The temporal broadening of a laser pulse through the atmosphere is caused physically by two mechanisms: scattering process and beam spot wandering; these are too important to be neglected for high bandwidth signals such as narrow pulses in the range from femtosecond to picosecond [35]. The received pulse width broadened by the atmospheric turbulence is given by [32]  
\[\tau_{R}=\sqrt{\tau_{0}^{2}+8\chi}, \tag{14}\]  
where:  
\[\chi=0.391\Big{(}1+0.171\beta^{2}-0.287\beta^{5/3}\Big{)}\mu\sec\theta_{z}/c ^{2}, \tag{15}\]  
\[\mu=\int_{h_{h}}^{h_{h}+H}C_{n}^{2}(h)[L_{0}(h)]^{5/3}dh, \tag{16}\]  
\[L_{0}(h)=5\bigg{[}1+\bigg{(}\frac{h-7500}{2500}\bigg{)}^{2}\bigg{]}^{-1},\ l_{0}(h)= \beta L_{0}(h), \tag{17}\]  
where \(\beta\) is the inner scale factor, \(0<\beta<1\), and \(l_{0}(h)\) and \(L_{0}(h)\) represent the inner and outer scale sizes as a function of altitude, respectively.
## 3 Method Verification
### Model Experiment  
In order to verify the accuracy of the method established in this paper, a model test was carried out in a tank with an L-shaped wave maker shown in Figure 5. The length of the tank was 44.5 m and the width of the tank was 40 m. Based on factors such as the size of the tank, the wave-making capacity and the connector model size, the scale ratio of the test model was set to 1:50. The stiffness of the model connector in X, Y and Z directions was obtained through scale conversion. It is known that the stiffness of the model connector is relatively small after scale reduction. Therefore, selecting an appropriate material to simulate the stiffness of the model connector was a key challenge in the experiment. In this experiment, an equivalent connector structure was designed as shown in Figure 6, where nylon and steel were used. The main body of the model connector was made of nylon, whereas the pin in the middle was made of steel.  
The connector cannot be directly connected to the platform because of the requirement to satisfy the height from the platform baseline. Connecting the model connectors with the platform model presents another problem. In this experiment, the connector was directly connected to the force sensor, and the force sensor was connected to the measuring  
Figure 5: The model test water tank.  
Figure 6: Design of the model connector.  
beam through the flange to ensure that the measuring structure remained rigid and that measurement error caused by the deformation of the base was prevented. The specific connection way is shown in Figure 7.  
Due to space limitations, only the experimental results of the RAO (Response Amplitude Operator) in \(0^{\circ}\) and \(45^{\circ}\) are compared with the calculated results, as shown in Figure 8. Overall, the theoretical calculation was in good agreement with the test results, and the FMFC model established in this paper accurately reflected the load characteristics of connectors. Specifically, the calculated results were in good agreement with the experimental results at low frequencies, but there were relatively large fluctuations at high frequencies, which may be related to the quality of high frequency waves in the tank. Figure 9 shows the relative error results, which reveals interesting information, including that the relative error of vertical shear was about 15% or lower, such as \(Fz\) in \(0^{\circ}\). However, at some frequency points, the error for horizontal shear in the \(y\) direction was greater than 20%. Through analysis, we found that the reason for this problem was that a soft spring was used to limit the horizontal displacement of the test model in order to prevent the model from drifting with the wave during the experiment. Although the spring stiffness was as small as possible, the tension level of the soft spring still affected the measured horizontal force of the connector in the model test.  
Figure 8: Comparison of the results of connector \(C_{1}\).  
Figure 7: Instrument connection diagram.
# Assessing the Functional Response to Streamside Fencing of Pastoral Waikato Streams, New Zealand
###### Abstract  
In New Zealand, streamside fencing is a well-recognised restoration technique for pastoral waterways. However, the response of stream ecosystem function to fencing is not well quantified. We measured the response to fencing of eight variables describing ecosystem function and 11 variables describing physical habitat and water quality at 11 paired stream sites (fenced and unfenced) over a 30-year timespan. We hypothesised that (1) fencing would improve the state of stream ecosystem health as described by physical, water quality and functional indicators due to riparian re-establishment and (2) time since fencing would increase the degree of change from impacted to less-impacted as described by physical, water quality and functional indicators. We observed high site-to-site variability in both physical and functional metrics. Stream shade was the only measure that showed a significant difference between treatments with higher levels of shade at fenced than unfenced sites. Cotton tensile-strength loss was the only functional measurement that indicated a response to fencing and increased over time since treatment within fenced sites. Our results suggest that stream restoration by fencing follows a complex pathway, over a space-for-time continuum, illustrating the overarching catchment influence at a reach scale. Small-scale (less than 2% of the upstream catchment area) efforts to fence the riparian zones of streams appear to have little effect on ecosystem function. We suggest that repeated measures of structural and functional indicators of ecosystem health are needed to inform robust assessments of stream restoration.  
functional indicators; stream restoration; riparian vegetation; fencing; cotton tensile-strength loss; wood decay; ecosystem metabolism; organic matter transport; catchment restoration; structure-function relationships +
Footnote †: journal:  
Article
## 2 Related Works and Motivation
This limitation is due to the principle of the KCF tracker that traces the target based on the correlation of the appearance and position of the target in the previous frames. Therefore, the presence of challenging scenes that result in a significant mismatch between the appearance of the target and the reference object will lead to tracking failure [10]. In this paper, a robust hand tracking method is proposed which integrates the correlation filter with a correction strategy using the fast object-detection model, the SSD algorithm. With this integration, the tracker can be reinitialized when hand movement is not tracked properly, ensuring consistent and accurate tracking. By detecting the tracked object only during the first frame and when it is lost by the tracker, heavy computational costs of the detector are minimized, leading to an improvement in real-time performance. To assess the performance of the proposed tracker, it is compared with the state-of-the-art KCF tracker in terms of mean average precision (mAP) and frame per second (FPS).
###### Abstract
Although machine learning was used widely in remote sensing research, including lidar data analyses, it has not been applied on the lidar systems in Iceland, partly because this is a rather new approach, and also the performance of Doppler lidars in Iceland was not studied until recently [20; 4]. Whether the machine learning method can perform well on the unique lidar systems in Iceland under the subpolar and volcanic environment is still unknown. Here, we used machine learning algorithms to classify lidar backscatter signals from two Leosphere Doppler wind lidars, with the purpose of aerosol monitoring in Iceland. The sub-polar conditions in Iceland imply several challenges, such as a relatively clean atmosphere with low aerosol concentrations compared to the mid-latitude region, which leads to high noise levels, but also events of intense dust due to wind erosion, volcanic ash clouds, frequent ice clouds and heavy precipitation events [21; 4]. This would be the first time that artificial intelligence is applied on the lidars in Iceland and provides an automatic measurement classification. In this study, we propose two separate steps to solve these problems. In the first step, we used the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm, combined with a conventional method, to identify the noise data. In the second step, we used the Random Forest (RF) method, a well-established machine learning model, to classify the lidar data. Based on our extended experiments in the summer of 2019 (June to August), we show that our method achieves high accuracy and can classify the lidar backscatter in real-time. We will describe the data and methods used in Section 2, present the results in Section 3, discuss the results and give suggestions for future work in Section 4 and summarize the conclusions in Section 5.
## 8 Discussion
### Potentials and the Integration of AI-Enhanced POCUS in LRS
Additionally, by harnessing the power of augmented reality in combination with artificial intelligence, a remote teleguidance system can be designed to assist novice users with probe placement as well as image interpretation. Furthermore, AI can be used for direct quantification of crucial clinical metrics essential for diagnosis [98]. For instance, AI-enhanced POCUS can be used in cardiac applications for left ventricular ejection fraction, vena cava measurements, or blood velocity time integrals [98]. The system's real-time image interpretation and analysis is immensely valuable for remote consultations and monitoring. Ultimately, AI-enhanced POCUS empowers healthcare providers in LRS to embrace telemedicine, allowing them to overcome training and geographical barriers and improve access to specialized care.
## 2 Materials and Methods
### Methods
#### 2.3.3 Trend and Correlation Analysis  
In this study, the following linear equation was used to evaluate the change trends of meteorological factors, VIs, GOSIF, and GPP during the research period:  
\[y=\mathrm{ax}+\mathrm{b} \tag{2}\]  
where \(y\) is the annual trend of each variable; \(x\) is the year; \(\mathrm{a}\) and \(\mathrm{b}\) are the slope and intercept, respectively; a positive value of a indicates an increasing trend, while a negative value indicates a decreasing trend. A significance level (\(p\)) of 0.05 was used for linear trend analysis and the significance level represents the confidence level of the trend value.  
In order to explore the correlation between two indicators, Pearson correlation analysis was used to calculate the correlation coefficient and test the corresponding significance [37]. The specific calculation formula is as follows:  
\[corr(X,Y)=\frac{\sum_{i=1}^{n}\bigl{(}X_{i}-\overline{X}\bigr{)}\bigl{(}Y_{i}- \overline{Y}\bigr{)}}{\sqrt{\sum_{i=1}^{n}\bigl{(}X_{i}-\overline{X}\bigr{)}^ {2}}\sqrt{\sum_{i=1}^{n}\bigl{(}Y_{i}-\overline{Y}\bigr{)}^{2}}} \tag{3}\]  
where \(corr(X,Y)\) is the Pearson correlation coefficient; \(X_{i}\) and \(Y_{i}\) are the \(i\)th number in \(X\) and \(Y\), respectively; \(\overline{X}\) and \(\overline{Y}\) are the mean values of \(X\) and \(Y\), respectively. The Pearson correlation coefficient is between \(-1\) and \(1\).
## 2 System Model and CSI Collection
### CSI Data Collection  
We adopted the Intel 5300 network card, which contains three antennas with omni-directional signal reception characteristics to collect Wi-Fi protocol data (as shown in Figure 1). By replacing the network interface card (NIC) of the Intel 5300 network card on the computer and relying on the network cable for external connection, the uninterrupted observation of the data collection process could be realized. The data collection processing computer was installed with the Linux 802.11n CSI tool [29], which can obtain signal strength and phase information for Wi-Fi data packets at the subcarrier level of the channel of the multi-antenna receiving device and transmit the related data to the computer for processing.  
The CSI data collection process was conducted in an un-manned laboratory during the process. Considering that the Intel 5300 network card cannot connect multiple Wi-Fi APs at the same time, during the data collection process, multiple sets of CSI data were obtained by changing the data collection time and access point (AP) position to analyze the differences in the channel state information in both spatio-temporal and temporal dimensions. The product model of the AP is a TP-LINK AC1900. During the data collection process, we set the usage frequency band of the AP to 2.4 GHz and set the channel to randomly selected, which could well achieve the randomness of the collected data.  
The CSI tool used in this paper can achieve uniform sampling of the 20 MHz bandwidth channel where Wi-Fi is located. For each receiving antenna, 30 uniform frequency domain CSI samples can be obtained. Therefore, 90 sample values were obtained in total for each sampling during the collection process. To fully reflect the dynamic differences of channel state information in the time domain and spatial domain, this paper collected a total of 10 groups of sample data, with 10,000 samples in each group, corresponding to a data collection duration of 100 s, i.e., the sampling frequency of the sample data was 10 ms each time. The data size of each group was around 7.2 MBytes.
# Effect of the Anti-Yaw Damper on Carbody Vertical Vibration and Ride Comfort of Railway Vehicle
## 6 Results of Numerical Simulationsand Discussion
The diagrams in Figure 14 show that, for the velocity of 300 km/h, the lowest value of the ride comfort index in all the carbody reference points is obtained for \(k=5\cdot 10^{7}\) N/m and \(c=5\cdot 10^{5}\) kNs/m. Should the stiffness of the anti-yaw damper is smaller (\(k=5\cdot 10^{6}\) N/m or \(k=1\cdot 10^{7}\) N/m), the lowest value of the ride comfort index is obtained for \(c=1\cdot 10^{5}\) kNs/m. Figure 4:0 Influence of damping and stiffness of the anti-yaw damper upon the ride comfort index in the rear bogie. Figure 4:1 Reduction of the ride comfort indices for \(k=5\cdot 10^{7}\) N/m: (**a**) at the carbody center; (**b**) against the front bogie; (**c**) against the rear bogie. The diagrams in Figure 14 also show that there is an anti-yaw-damping value for which minimum values of the comfort index are obtained. This value depends on the anti-yaw damper stiffness and the position of the carbody reference point (see Table 3). The influence of the mounting angle for the anti-yaw damper, correlated with the vehicle velocity, upon the ride comfort index is featured in Figure 15, as the percentage increase/decrease of the ride comfort indices. The results are obtained for \(k=10^{7}\) N/m, considering the rise of the angle \(\alpha\) from 0 to 10 degrees. As a function of velocity, the carbody reference point and the anti-yaw damping, the increase in the mounting angle has opposite effects on the ride comfort index. The decrease in the ride comfort index, which means the improvement in the ride comfort, is obtained for a large velocity interval for \(c=10^{5}\) Ns/m (diagram a). In this case, this decrease is moderate, of a maximum of 6%. It is observed that the decrease of ride comfort index is higher at the increase in the anti-yaw damping, reaching 15-20%, but the velocity interval narrows down a great deal (diagrams b and c); the ride comfort improves only for the low velocities, of up to 100 km/h. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\(k\) [N/m]} & \multicolumn{2}{c}{**At Carbody Center**} & \multicolumn{2}{c}{**Against the Front Bogie**} & \multicolumn{2}{c}{**Against the Rear Bogie**} \\ \cline{2-7}  & \(c\) [Ns/m] & \(N_{MV_{c}\text{min}}\) & \(c\) [Ns/m] & \(N_{MV_{B_{1}}\text{min}}\) & \(c\) [Ns/m] & \(N_{MV_{B_{2}}\text{min}}\) \\ \hline \(5\cdot 10^{6}\) & \(0.7\cdot 10^{5}\) & 2.00 & \(0.9\cdot 10^{5}\) & 1.83 & \(1.6\cdot 10^{5}\) & 2.26 \\ \(1\cdot 10^{7}\) & \(1.3\cdot 10^{5}\) & 1.82 & \(1.4\cdot 10^{5}\) & 1.74 & \(2.7\cdot 10^{5}\) & 2.11 \\ \(5\cdot 10^{7}\) & \(4.2\cdot 10^{5}\) & 1.45 & \(2.7\cdot 10^{5}\) & 1.60 & \(6.3\cdot 10^{5}\) & 1.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The anti-yaw damping values to minimize the ride comfort index at 300 km/h. Figure 14: Influence of the anti-yaw damping upon the ride comfort index at a velocity of 300 km/h: (**a**) at carbody center; (**b**) against the front bogie; (**c**) against the rear bogie.
## 5 Numerical Simulation Evaluation
According to the proposed sensitive IMF selection criterion, we choose IMF1 as an informative one, and calculate its amplitude envelope and instantaneous frequency following Step 5 of empirical AM-FM decomposition in Section 3 and Step 2 of proposed analysis procedure in Section 4, respectively, for further amplitude and frequency demodulation analysis. Figure 3 shows its envelope spectrum and the Fourier spectrum of instantaneous frequency. The envelope spectrum (Figure 3a) shows a simpler structure than the Fourier spectrum (Figure 1b), since it is free of involute sidebands. Peaks appear at the time-varying transfer path frequency \(f_{\text{t}}\), the gear fault characteristic frequency \(f_{\text{d}}\), as well as their sum and difference combination \(f_{\text{d}}\pm f_{\text{t}}\). This feature is similar to the envelope spectrum of raw signal  
Figure 2: Variational mode decomposition (VMD) results: (**a**) Intrinsic mode function (IMF) 1 waveform; (**b**) IMF1 instantaneous frequency; (**c**) IMF2 waveform; (**d**) IMF2 instantaneous frequency; (**e**) IMF3 waveform; and (**f**) IMF3 instantaneous frequency. (Figure 1c), and shows that the proposed amplitude demodulation analysis of selected IMF is equally effective as conventional envelope spectrum of raw signal. The Fourier spectrum of instantaneous frequency (Figure 3b) exhibits a simpler structure than the envelope spectrum of both raw signal and selected IMF (Figures 1c and 3a). Only one pronounced peak emerges, exactly corresponding to the gear fault characteristic frequency \(f_{\text{d}}\). The proposed frequency demodulation analysis outperforms both Fourier and envelope spectra. It not only overcomes the intricate sideband difficulty inherent with Fourier spectrum, but also removes the irrelevant transfer path frequency existing in envelope spectrum. Most of all, these findings are consistent with the gear fault vibration symptoms expected in amplitude and frequency demodulated spectra [40], demonstrating the effectiveness of proposed amplitude and frequency demodulation analysis method.
# Deep Learning at the Mobile Edge: Opportunities for 5G Networks
## 4 Challenges of DL for 5G Operations at the Mobile Edge
### Caching  
Mobile edge computing can exploit proximity to the user to cache locally relevant data nearby to reduce latency and adapt to surges in popularity for certain content in a region. Employing DL to develop a proactive caching strategy has been shown to improve network efficiency and alleviate demand for radio resources by storing popular content closer to the user than regional datacenters [62; 63]. Effective caching consists of two fundamental steps: predicting content requests using popularity estimates and allocating content among edge nodes.  
The popularity of content influences how often and in which regions of the network users will request the content from the cache. More common content should be cached to avoid delayed retrieval from regional data centers and reduce traffic on the core network. Some studies have used user mobility and behavioral patterns to predict application choices and develop DL caching strategies to anticipate their desired content [64]. Which contents are placed in mobile edge computing caches can be optimized using DL to increase cache hit rate and decrease delays experienced by users. By using popularity models in conjunction with observed requests, an optimal cache placement strategy can be developed using DL technique such as Q-learning [65; 66]; even as users move around, their desired content is more likely to be in a nearby cache.  
Content popularity is necessarily dynamic (based on time of day, cultural events, or trending content) and cache content must be updated with frequency. Partial cache refreshes based on DNN provide online responses to changing popularity [67], and the content of groups of edge nodes within close proximity can be updated through joint optimization to reduce cache redundancy [68].  
In [69], the authors propose an effective approach for collecting globally available resource information through a mobile network architecture based on the SDN. To minimize network latency, they designed an optimal caching strategy that consists of a small-cell cloud and a macro-cell cloud, reducing considerably the latency compared to conventional caching strategies.  
Figure 6 shows the main steps for the predictive caching using ML in mobile edge computing compared with the traditional procedure.
## 6 Numerical Experiments  
To demonstrate the performance of the proposed bi-tuning SSO, three numerical experiments with three different values of N\({}_{\text{var}}\) = 20, 100, and 300 were implemented based on the bi-tuning method mentioned in Section 5.3. Similar published literature to this work could not be found so the experimental results of the bi-tuning SSO could not be compared with any other published contribution. However, the experimental results of the bi-tuning SSO were compared with the state-of-the-art algorithms PSO, GA, and SSO, which are listed as SSO1 in Table 3.