esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Windshear Evaluation Method
The position measuring points are written as:  
\[V_{r}=ucos\theta+vsin\theta \tag{1}\]5  
\[V_{r}=ucos\theta+vsin\theta \tag{1}\]6  
The change in wind speed during take-off and landing is the point of focus. \"Windshear\" is exclusively defined as wind change along the glide path. The magnitude of such \"windshear\" will be expressed in terms of velocity difference (in m/s), following the established practice in provision of alerts to aviation users [1; 4]. The windshear usually has the characteristics of sudden and rapid changes. Identifying wind shear with a single head profile is likely to result in missing a report. In this work, the wind speed differences between any two measuring points in the monitoring area are calculated and recorded to evaluate the intensity level of the windshear. Windshear alerting is applied to operations in the airport vicinity within 3 nautical miles (NM) of the point of landing or departure along the intended flight path, as recommended by the ICAO [1]. The monitoring area of runway \(k\) is set as:  
\[V_{r}=ucos\theta+vsin\theta \tag{1}\]7  
where \(m\) is the unit length, which is set as 1.852 km (1 NM). The heading wind change of any two points A and B in the monitoring along the glide path are calculated as  
\[V_{r}=ucos\theta+vsin\theta \tag{1}\]8  
The severity factor \(I_{a,b}\) is used to describe the intensity of the windshear, which is calculated as [34; 35; 36]  
\[V_{r}=ucos\theta+vsin\theta \tag{1}\]9  
\[V_{t}=-u\sin\theta+v\cos\theta \tag{2}\]0  
where \(V_{app}\) and \(L_{a,b}\) are the approach speed of the aircraft and the windshear ramp length, respectively. \(W_{a,b}/L_{a,b}^{1/3}\) is the change rate of wind speed. \(I_{a,b}\) is used to remove the gentle windshear ramp, which changes slowly in length. The filtered windshear ramp is recorded as \(\left[W_{a,b},\left(X_{k}^{a}+X_{k}^{b}\right)/2\right]\). Finally, the distribution of the windshear center on the runway coordinate is obtained. The monitoring area is separated into 7 subareas by 1.852 km. The number of shear ramps in each subarea is counted. When the number of shear centers in the subarea is greater than the specified threshold, a windshear alert message is generated. The windshear alert message contains detailed information, including runway (identification of the runway and specification of approach or departure corridor), character (microburst, wind shear or gust front), magnitude (change in the wind speed, loss or gain), and the subarea [20]. By making full use of the data scanned by PPI, the windshear can be well evaluated and captured. In addition, the error and outlier interference of the wind data can be reduced through the statistics of wind shear in the monitoring area in this work.
# Inversion of Rayleigh Wave Dispersion Curve Extracting from Ambient Noise Based on DNN Architecture
###### Abstract  
The inversion of the Rayleigh wave dispersion curve is a crucial step in obtaining the shear wave velocity (\(V_{S}\)) of near-surface structures. Due to the characteristics of being ill-posed and nonlinear, the existing inversion methods presented low efficiency and ambiguity. To address these challenges, we describe a six-layer deep neural network algorithm for the inversion of 1D \(V_{S}\) from dispersion curves of the fundamental mode Rayleigh surface waves. Our method encompasses several key advancements: (1) we use a finer layer to construct the 1-D \(V_{S}\) model of the subsurface, which can describe a more complex near-surface geology structure; (2) considering the ergodicity and orderliness of strata evolution, the constrained Markov Chain was employed to reconstruct the complex velocity model; (3) we build a practical and complete dispersion curve inversion process. Our model tested the performance using a random synthetic dataset and the influence of different factors, including the number of training samples, learning rate, and the selection of optimal artificial neural network architecture. Finally, the field test dispersion data were used to further verify the method's effectiveness. Our synthetic dataset proved the diversity and rationality of the random \(V_{S}\) model. The results of training and predicting showed higher accuracy and could speed the inversion process (only \(\sim\)15 s), and we proved the important effect of different factors. The outcomes derived from the application of this technique to the measured dispersion data in the Yellow River Delta exhibit a strong correlation with the outcomes obtained from the integration of the very fast simulated annealing method and the downhill simplex method, as well as the statistically derived shear wave velocity data of the sedimentary layers in the Yellow River Delta. From a long-term perspective, our method can provide an alternative for deriving \(V_{S}\) models for complex near-surface structures.  
Rayleigh wave; dispersion curve; inversion; deep learning; deep neural networks +
Footnote †: journal: Medical Imaging
## 3 Results
### Sensitivities of MODIS GPP, ET and WUE to Driving Variables and Model Parameters  
The sensitivities of GPP, ET and WUE almost linearly varied when each of the selected driving variables or model parameters was changed by 10%, 20% and 30%, while other parameters are fixed. Therefore, we only presented the result of sensitivity analysis when we changed the individual driving variable or model parameter by 10%, as illustrated in Figure 6. Overall, annual WUE derived from MODIS GPP and ET was most sensitive to \(\mathrm{LUE_{max}}\) and FPAR at all selected sites. Increasing \(\mathrm{LUE_{max}}\) by 10% led to a 10% increase in both annual GPP and WUE due to the linear relationship between GPP and \(\mathrm{LUE_{max}}\). The ten percent increase in FPAR resulted in an increase of 4.6%-10.2% in WUE at the eight sites, except for the YC cropland site, where FPAR had a similar effect on both GPP and ET. Besides, RH had a great effect of 5.9%-14.2% on the estimation of WUE at the YC cropland site and the three grassland sites, especially for the DX grassland site. Incoming shortwave radiation (\(R_{\mathrm{s}\mathrm{i}}\)) greatly affected GPP and ET with similar sensitivities, so it had little effect on the estimation of WUE.  
Figure 6: Sensitivities of annual GPP, ET and WUE to driving variables and model parameters with their ID numbers listed in Table 1. **(a)** CBS; **(b)** DHS; **(c)** QYZ; **(d)** XSBN; **(e)** YC; **(f)** HBGC; **(g)** NM; **(h)** DX.  
Considering the big differences in LAI between MODIS and observed data at the DHS, XSBN and YC sites, we further conducted the sensitivity analysis by decreasing LAI at DSH and XSBN and increasing LAI at YC to examine how the modeled ET and WUE respond to those changes. The results show that the decrease in LAI by 50% for DHS and XSBN led to a reduction of 0.2% in ET and an increase of 0.2% in WUE, as well as a 200% increase in LAI for YC only caused by a 0.4% increase in ET and a 0.4% decrease in WUE. It indicated that the large error in MODIS LAI has little effect on the estimation of ET and WUE due to its small sensitivity.  
Compared to the original MODIS ET product in 2005, the relative errors of ET in modeled subtropical and tropical forests by the MODIS ET algorithm and site-level meteorological data were reduced from 56% down to 6% for DHS, from 91% down to 48% at QYZ and from 128% down to 98% for XSBN (Table 4).  
Therefore, we inferred that the overestimation of ET and accordingly the underestimation of WUE for QYZ, DHS and XSBN were partly caused by the bias in incoming shortwave radiation (\(R_{\mathrm{si}}\)), relative humidity and VPD. The underestimation of GPP for CBS, QYZ, XSBN and YC may result from undervalued \(LUE_{max}\) for forest and cropland, which also contributed to the underestimation of WUE. Further investigation of the comparison in meteorological variables between the Data Assimilation Office dataset used by the MODIS algorithms and local observations is necessary to identify the dominant error contributors.
# Overview of Distributed Machine Learning Techniques for 6G Networks
## 5 Distributed Machine Learning for 6G
### Communication-Efficient Federated Learning
#### 5.1.1 Compression and Sparsification  
The \"bottleneck\" in the field of communication that is formed owing to the high size of the trained models is one of the most significant issues in the field of distributed learning, especially with regard to wireless channels. Transmission of all these locally trained parameters to the parameter server and repeating this for each iteration of the learning algorithm across a shared wireless channel is a difficult operation for emerging deep neural networks (DNNs), which have millions of training parameters.  
The traditional approach to the problem involves first separating the compression of the DNN parameters from their transmission over the channel. This so-called \"digital\" approach turns all local changes into bits, which are then successfully transferred over the channel, and all decoded reconstructions are then mediated by the parameter server. In the field of ML, the efficient communication strategies proposed to reduce the amount of information (i.e., the number of bits exchanged by the device with the parameter server for each global iteration) have been many. They can be classified into two main groups: sparsification and quantization [50].  
Sparsification aims to convert the updating of the device's d-dimensional model m into its **m*** representation by setting some of its elements to zero. Sparsification can also be thought of as applying a mask vector \(\textbf{M}\,e[0,1]^{d}\) to **m** in such a way that \(\textbf{m}^{*}=\textbf{M}\otimes\textbf{m}\)where the mathematical operator \(\otimes\) represents element-by-element multiplication. We can define the sparsification level of this vector mask as:  
\[\phi\triangleq\|\mathbf{M}\|_{1}/d,\]  
that is, the quantity of its non-zero elements compared to its size. It is important to note that when the \"approximate\" (sparse) model is sent to the parameter server, not all the update values are sent; each device only sends non-null values and its location. Therefore, the lower the sparsification level, the higher the compression level and the lower the communication load. In the distributed learning world, the Top-K sparsification approach is arguably the most popular. At each iteration of this technique, each device creates its own sparsification mask \(\mathbf{M}_{i,f}\) by finding the K values in its local update that have the largest absolute values. The rand-K is a simpler variant that picks a sparsification mask at random from the collection of masks available with sparsification level K. The Top-K technique has been demonstrated to be significantly more effective in terms of precision and convergence speed; nevertheless, its additional complexity can cause the learning process to slow down.
# Predicting Road Crash Severity Using Classifier Models and Crash Hotspots
## 4 Machine Learning Models
### Random Forest (R.F.)  
R.F. is an ensemble learning algorithm that improves its prediction accuracy by stacking a considerable number of classifiers. The classifiers are the D.T., which are grouped, and their grouping is referred to as a forest, whose results are combined, thereby yielding better prediction results. R.F. algorithm is initiated with randomly selecting data points from the complete set, which is then used to construct the D.T.s. Lastly, collective voting is performed for results obtained from each D.T. The outcome class is assigned using the result with the highest votes [72].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **No. of Serious Injuries** & **No. of Deaths** \\ \hline Number of R.T.C.s & 3245 & 756 \\ \hline Average per R.T.C. * & 7 & 4 \\ \hline Total & 22,787 & 3579 \\ \hline Range & 0–46 & 0–13 \\ \hline Severity index = Deaths/No. of R.T.C.s & 0.89 & \\ \hline The annual number of fatal R.T.C.s & 352 & \\ \hline Number of annual fatalities & 1193 & \\ \hline Deaths per 100,000 individuals & 311 & \\ \hline R.T.C.s with fatalities and major injuries each year & 1333 & \\ \hline \hline \end{tabular}  
* Number of serious injuries/number of RTCs with serious injuries; same was used for the average deaths per RTC.  
\end{table}
Table 1: Statistics on serious injuries and fatalities in the study area (adopted from [65]).  
Either the Gini index or entropy index can be used, as shown in Equations (1) and (2), to build nodes within D.T.s, representing the best splicing of data with a maximum distance between its branches [73].  
\[Gini=1-\sum\limits_{i=1}^{C}{(p_{i})}^{2} \tag{1}\]  
\[Entropy=\sum\limits_{i=1}^{C}{-p_{i}*\log_{2}p_{i}} \tag{2}\]  
where \(p_{i}\) is the relative frequency (or proportion) of the observed class and \(C\) represents the number of classes in the dataset. The final R.F. result can be expressed mathematically by Equation (3).  
\[RF=argmax_{je\{1,2,...C\}}\sum\limits_{i=1}^{i}{DT_{i,j}} \tag{3}\]  
where the argmax function represents the max value or majority vote, \(i\) is the D.T. number starting at one and ending with \(i\)th D.T., and \(j\) represents the number of classes available in the outcome feature.  
Due to the incorporation of several D.T.s, the R.F. model can attain high precision values and avoids the overfitting problem. It can also be applied to datasets with gaps. However, the prediction process requires a longer time due to the length of the D.T.s, which also increases its complexity compared to other machine learning techniques, such as K.N.N. [74].
## 3 Results and Discussion
### Effect of Process Parameters
#### 3.1.3 Effect of Extraction Time  
Figure 5 shows the influence of extraction time using ultrasound on the extraction yield of quercetin and rutin. As can be seen, extraction time has a prominent influence on the extraction yield of quercetin and rutin. That is, the extraction yield increased rapidly within the first 30 min. Subsequently, by prolonging extraction time from 30 to 50 min, only a slight increase of the extraction yields of quercetin and rutin was found. A possible reason is that ultrasonic wave initially can disrupt rapidly the cell walls, so there is a larger contact area between sample and solvent, which is helpful to improve extraction yield. However, when extraction time is prolonged, the contact area would be decreased on the inner cell walls due to the increasing distance. As a result, there was no significant change in an extended extraction period. Therefore, 30 min is more suitable for the extraction of quercetin and rutin.
## 5 Challenges and Future Perspectives  
Edible coatings based on polysaccharides or proteins are distinguished by good mechanical strength, but they absorb moisture very quickly and easily. To improve these properties, scientists are developing the concept of hydrogels, which are introduced in biodegradable packaging systems and consist of a hydrophilic polymer that does not dissolve in water. An important aspect is optimising the release rate of active compounds through modifications, using different combinations of biopolymers, or using a binder. In addition, in the development of biodegradable packaging, agricultural waste gains more and more potential [79].  
Numerous studies have been conducted on the impact of the use of edible coatings on maintaining the quality and extending the shelf life of fresh fruit, but efforts are still being made to acquire and introduce new natural coating materials. There is a need for cheap and available film-forming materials, also from waste sources. At the same time, progress in the field of coating methods and the acquisition of new coating materials leads to continuous improvement of their effectiveness without any negative impact on human health. The discovery of the combination of edible coatings and irradiation revealed significant importance in extending the shelf life of fresh fruit, as it maintains the overall quality of postharvest fruit and vegetables but creates distrust among consumers regarding food safety. The postharvest technologies, supplementing the MAP effect, also include the use of silk fibroin coatings, a structural protein comparable to collagen, which has a positive effect on extending the shelf life of the fruit and is environmentally friendly. Unfortunately, due to the risk of residual amounts of metal elements and their level of toxicity, the commercial value of the fruit is reduced and may not be suitable for human consumption [180]. For this reason, further research in this direction is expected. In addition, for economic reasons, innovative coating techniques are still unprofitable at the industrial level, which is why exporters and fruit retailers rarely use innovative methods, sticking to traditional methods. It is still expected to research achieving an extended shelf life of fresh fruit, without interfering with their sensory and nutritional properties, as well as further development of research at the initial stage of development and their application on a larger industrial scale.
## 4 Experiments
### Tracking Performance
#### 4.3.1 Evaluation Results on the MOT17 Datasets
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**MOT17 Dataset**} \\ \hline
**Mode** & **Method** & **MOTA \(\uparrow\)** & **IDF1\(\uparrow\)** & **MT\(\uparrow\)** & **ML\(\downarrow\)** & **FP\(\downarrow\)** & **FN\(\downarrow\)** & **ID Sw.\(\downarrow\)** & **Frag\(\downarrow\)** \\ \hline \multirow{6}{*}{Offline} & jcc [36] & 51.2 & 54.5 & 17.1\% & **35.4\%** & **20148** & 252531 & 2285 & 5798 \\  & eHAF17 [37] & 51.8 & 54.7 & **23.4\%** & 37.9\% & 33212 & 236772 & 1834 & 2739 \\  & SAS\_MOT17 [38] & 44.2 & 57.2 & 16.1\% & 44.3\% & 29473 & 283611 & 1529 & **2644** \\  & eTC17 [39] & 51.9 & 58.1 & 23.1\% & 35.5\% & 36164 & 232783 & 2288 & 3071 \\  & LSST17 [33] & **54.7** & **62.3** & 20.4\% & 40.1\% & 26091 & **228434** & **1243** & 3726 \\ \hline \multirow{6}{*}{Online} & HAM\_SADF17 [2] & 48.3 & 51.1 & 17.1\% & 41.7\% & 20967 & 269038 & **1871** & **3020** \\  & AM\_ADM17 [40] & 48.1 & 52.1 & 13.4\% & 39.7\% & 25061 & 265495 & 2214 & 5027 \\ \cline{1-1}  & Tracktor17 [6] & **53.5** & 52.3 & 19.5\% & 36.6\% & **12201** & **248047** & 2072 & 4611 \\ \cline{1-1}  & MOTDT17 [6] & 50.9 & 52.7 & 17.5\% & **35.7\%** & 24069 & 250768 & 2474 & 5317 \\ \cline{1-1}  & DMAN [34] & 48.2 & 55.7 & 19.3\% & 38.3\% & 26218 & 263608 & 2194 & 6378 \\ \cline{1-1}  & Ours & 48.9 & **57.0** & **21.9\%** & 38.5\% & 32914 & 253059 & 2392 & 4973 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Tracking performance on the MOT17 benchmark dataset. Best in bold. Definitions of abbreviations haven’t been introduced before: ML, FP, FN, ID Sw. and Frag. stand for Most Lost, False Positive, False Negative, Identity Switch and Fragments respectively \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{4}{c}{**Comparison of ID Consistency**} \\ \hline
**Method** & **MOTA\(\uparrow\)** & **IDF1\(\uparrow\)** & **MT\(\downarrow\)** & **ID Sw.\(\downarrow\)** \\ \hline PHD\_GM [42] & 48.8 & 43.2 & 19.2\% & 4407 \\ MTDF17 [41] & 49.6 & 45.2 & 18.9\% & 5567 \\ GMPHDOGM17 [43] & **49.9** & 47.1 & 19.7\% & 3125 \\ HAM\_SADF17 [2] & 48.3 & 51.1 & 17.1\% & **1871** \\ Ours & 48.9 & **57.0** & **21.9\%** & 2392 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of ID consistency with similar MOTA (FP and FN) in MOT17 benchmark dataset.
## 3 Proposed Identification Methods
### Proposed Hybrid Methods
#### 3.5.2 HPSO-QN Single Local Search Method  
In the second method, the HPSO-QN single local search method (SLSM), a stopping criterion is introduced to enhance the exploration of the search space by the PSO algorithm. This criterion allows the PSO algorithm to discover the _gBest_ solution. Once this criterion is met, the _gBest_ solution serves as the starting point for the QN method before moving on to the next PSO iteration. The QN method then performs a local search on this solution to further improve it. The stopping criterion is defined as follows:  
\[\Delta Fcost=\left|\frac{(Fcost(gBest_{k})-Fcost(gBest_{k-1})}{Fcost(gBest_{k-1} )}\right|\times 100,\ \ \ \ \Delta Fcost<F_{th} \tag{20}\]  
Here, \(F_{th}\) represents the user-defined threshold limit. This stopping criterion ensures a balance between exploration and exploitation, facilitating faster convergence and more precise solutions. By refining the PSO-found solution with QN, the chances of discoveringthe true global minimum are increased, resulting in more efficient search space exploration. The pseudo-code of the HPSO-QN SLSM algorithm is presented in Algorithm 4.  
```
0:\(N_{PSO}\), \(k_{max}\), \(x_{min}\), \(x_{max}\), \(w_{min}\), \(w_{max}\), \(\varphi\), \(c_{1}\), and \(c_{2}\)
1: Set \(k=0\), randomly initialize particles' positions \(x_{i}(k)\) and velocities \(v_{i}(k)\)
2:repeat
3: set \(k=k+1\).
4:for each particle \(P_{k}(i)\), \(i=1,2,..N_{PSO}\)do
5: Evaluate \(Fcost(x_{k}(i))\) using Equation (8)
6: Update \(pBest_{k}(i)\) and \(gBest_{k}\) as shown in Algorithm 1
7: Calculate \(v_{k+1}(i)\) and \(x_{k+1}(i)\) using Equations (13) and (10)
8:endfor
9:\(x_{0}=gBest_{k}\)
10:\(QN=fminunc(Fcost(x),x_{0}\),'Algorithm', 'quasi-newton')
11:if\(Fcost(QN)<Fcost(gBest_{k})\)then\(gBest_{k}=QN\)
12:endif
13:until the termination criterion is met
```  
**Algorithm 4** HPSO-QN single local search method.  
```
0:\(N_{PSO}\), \(k_{max}\), \(x_{min}\), \(x_{max}\), \(w_{min}\), \(w_{max}\), \(\varphi\), \(c_{1}\), \(c_{2}\) and \(F_{th}\)
1: Set \(k=0\), randomly initialize particles' positions \(x_{i}(k)\) and velocities \(v_{i}(k)\)
2:repeat
3: set \(k=k+1\).
4:for each particle \(P_{k}(i)\), \(i=1,2,..N_{P}\)do
5: Evaluate \(Fcost(x_{k}(i))\) using Equation (8)
6: Update \(pBest_{k}(i)\) and \(gBest_{k}\) as shown in Algorithm 1
7: Calculate \(v_{k+1}(i)\) and \(x_{k+1}(i)\) using Equations (13) and (10)
8:endfor
9:if\(\Delta Fcost<F_{th}\)then
10:\(x_{0}=gBest_{k}\)
11:\(QN=fminunc(Fcost(x),x_{0}\),'Algorithm', 'quasi-newton')
12:if\(Fcost(QN)<Fcost(gBest_{k})\)then\(gBest_{k}=QN\)
13:endif
14:endif
15:until the termination criterion is met
```  
**Algorithm 5** HPSO-QN multi local search method.