esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Consistent Path Congestion Model
### Consistent Path Congestion Model and Its Related Algebra  
Let paths \(i\) (the current path) and \(j\) (the backup path) be the two available paths between the same source and destination routers in a backbone network, and \(L_{i}(t)\), \(L_{j}(t)\) are the latencies of the two paths, respectively, at time \(t\). In this paper, we measure congestion with latency.
# A High Efficiency Multistage Coder for Lossless Audio Compression using OLS+ and CDCCR Method
## 3 Simple Prediction Method with Backward Adaptation  
The basic advantage of adaptation of prediction coefficients is the ability to adjust \(w_{j}\) coefficients to the variability of the signal over time. An additional characteristic, only for backward adaptation, is the lack of the need for initial analysis of the entire signal frame, which must be carried out using, for example, static models (forward adaptation), where in the case of MMSE, one or many systems of equations with \(r\) unknowns must be determined and solved.  
One of the simplest methods of adapting prediction coefficients is Least Mean Square (LMS). In the basic version it is less effective compared to even the RLS, ALCM+, and OLS+ methods discussed in the next section, as it is relatively slow convergence to achieve the results obtained than MMSE with forward adaptation.  
Adaptation of prediction coefficients in LMS is done according to the following relation,  
\[\mathbf{w}(n+1)=\mathbf{w}(n)+\mu\cdot e(n)\cdot\mathbf{x}(n) \tag{3}\]  
where vector \(\mathbf{x}(n)=[x(n-1),x(n-2),\dots,x(n-r)]^{\mathrm{T}}\) contains \(r\) samples of the currently encoded channel. Most often, the vector \(\mathbf{w}(0)\) is initialised as \(\mathbf{w}(0)=[\beta,\,0,\dots,0]\) where \(\beta=0\) or \(\beta=1\). The complexity of this procedure is linear \(O(r)\), therefore it is suitable for building high order predictive models.  
The step size, \(\mu\), is very important here, as it determines the speed of adaptation of the vector coefficients \(\mathbf{w}(n)\) to the current signal characteristics. Proper selection of this value has a significant impact on the final bit average of the encoded audio track and it is difficult to find a universal experimental value, the same problem applies to the value of the prediction order.  
To a large extent, these problems can be solved by introducing an extended version of this method. The standardised version of Normalised LMS (NLMS) [25] has a higher rate of adaptation due to the energy level of the last encoded signal samples. This involves changing the learning factor:  
\[\mu(n)=\frac{\mu}{1+\mathbf{x}^{\mathrm{T}}(n+1)\cdot\mathbf{x}(n+1)}=\frac{ \mu}{1+\sum\limits_{i=0}^{r-1}x^{2}(n-i)}. \tag{4}\]  
In our solution, we proposed to develop Formula (3) by introducing a scaling factor, which is located on the diagonal of the matrix \(\mathbf{C}=\) diagonal ([0.995\({}^{1}\) 0.995\({}^{2}\)... 0.995\({}^{7}\)]), to the formula for adaptation of prediction coefficients by ES-NLMS method (an idea taken from the exponentially weighted step-size (NLMS) [26]), and the value of 0.995 was selected experimentally:  
\[\mathbf{w}_{\mathrm{NLMS}}(n+1)=\mathbf{w}_{\mathrm{NLMS}}(n)+\mu(n)\cdot e(n )\cdot\mathbf{C}\cdot x(n). \tag{5}\]
# Comparative Analysis of Urban and Rural Ecosystems in the Context of Suitability for Honeybees
## 3 Results  
Effect of Urban vs. Rural Locations on the Functional and Biological Characteristics of Bee Colonies  
The traits that differentiated the type of location and showed significantly higher values in urban apiaries were brood count in spring BC1 (_p_\(\leq\) 0.01) and brood grow dynamics BGD (_p_\(\leq\) 0.01). It is probably true that the consequence of a higher brood count in spring in urban apiaries was a significantly higher infestation of colonies with the _Varroa_ in summer (median, respectively, 0.1 and 0.0), as evidenced by the Mann-Whitney U test (_p_\(\leq\) 0.01), as demonstrated in Table 2 and Supplementary Figure S1. The other characteristics did not differ significantly between apiary types, including honey production, although it was 6.1 kg higher in urban apiaries.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Traits or Measurements \({}^{*}\)**} & \multicolumn{2}{c}{**Type of Location**} & \multirow{2}{*}{**Statistical Test \({}^{**}\)**} & \multirow{2}{*}{_p_-Value**} \\ \cline{2-2} \cline{4-4}  & **Urban (Mean \(\pm\) SE)** & & & & \\ \hline B1 & 5.9 \(\pm\) 0.12 & 5.8 \(\pm\) 0.15 & U = 3269, N1 = 87, N2 = 76 & 0.90 \\ B2 & 5.1 \(\pm\) 0.12 & 5.0 \(\pm\) 0.17 & U = 3195, N1 = 87, N2 = 74 & 0.93 \\ Olndex & 0.82 \(\pm\) 0.11 & 0.91 \(\pm\) 0.12 & U = 3195, N1 = 89, N2 = 85 & 0.51 \\ BC0 (dm\({}^{2}\)) & 28.10 \(\pm\) 1.42 & 30.02 \(\pm\) 1.80 & \(t\) = -0.82, df = 159, N1 = 87, N2 = 74 & 0.40 \\ BC1 (dm\({}^{2}\)) & 62.8 \(\pm\) 2.18 & 53.9 \(\pm\) 2.59 & \(t\) = 2.67, df = 158, N1 = 87, N2 = 73 & \(\leq\)0.01 \\ BGD (\%) & 260.8 \(\pm\) 13.83 & 192.3 \(\pm\) 6.90 & U = 2152, N1 = 87, N2 = 73 & \(\leq\)0.01 \\ BC2 (dm\({}^{2}\)) & 62.0 \(\pm\) 1.79 & 63.1 \(\pm\) 2.83 & Welch’s _t_-test = -0.30, df = 1195, & 0.75 \\  & & & & \\ \hline BC3 (dm\({}^{2}\)) & 1.75 \(\pm\) 0.43 & 1.78 \(\pm\) 0.44 & U = 2956, N1 = 86, N2 = 70 & 0.84 \\ SC1 (number) & 8706.4 \(\pm\) 404.6 & 8626.0 \(\pm\) 495.3 & U = 2994, N1 = 87, N2 = 73 & 0.62 \\ SC2 (number) & 14,404.3 \(\pm\) 580.3 & 13,231.6 \(\pm\) 504.5 & U = 2657, N1 = 85, N2 = 70 & 0.25 \\ SC3 (number) & 7074.7 \(\pm\) 200.1 & 7021.0 \(\pm\) 261.8 & U = 2847, N1 = 86, N2 = 70 & 0.56 \\ VInf. (No/10 g of bees) & 0.19 \(\pm\) 0.04 & 0.17 \(\pm\) 0.04 & U = 2875, N1 = 89, N2 = 87 & \(\leq\)0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effects of urban and rural apiary locations on utility and biological characteristics of bee colonies.
## Appendix A
(2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Dynamic analysis and control application of vibration isolation system with magnetic suspension on satellites. _Aerosp. Sci. Technol._**2018**, _75_, 99-114. [CrossRef]
* [PERSON] (2008) [PERSON] Forensic investigation of reaction wheel nutation on isolator. In Proceedings of the 49 th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 16 th AIAA/ASME/AHS Adaptive Structures Conference, 10 th AIAA Non-Deterministic Approaches Conference, 9 th AIAA Gossamer Spacecraft Forum, 4 th AIAA Multidisciplinary Design Optimization Specialists Conference, Schaumburg, IL, USA, 7-10 April 2008; p. 1953. * [PERSON] et al. (2009) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Vibration isolation of precision payloads: A six-axis electromagnetic relaxation isolator. _J. Guid. Control. Dyn._**2009**, _32_, 395-401. [CrossRef]
* [PERSON] et al. (2014) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Design and experiments of an active isolator for satellite micro-vibration. _Chin. J. Aeronaut._**2014**, _27_, 1461-1468. [CrossRef]
* [PERSON] et al. (2007) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Optical payload isolation using the miniature vibration isolation system (MVIS-II). In _Industrial and Commercial Applications of Smart Structures Technologies 2007_; SPIE: St Bellingham, WA, USA, 2007; Volume 6527, pp. 14-26. * [PERSON] et al. (2014) [PERSON]; [PERSON]; [PERSON] The Damping Forced Vibration of Rectangular Stiffened Plates with Elastic Boundary Edges Including Boundary Damping. _Appl. Mech. Mater._**2014**, _577_, 205-208. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON] A novel design of vibration isolator with high and frequency dependent damping characteristics based on large negative poisson's ratio (LNPR) structure. _Mech. Syst. Signal Process._**2023**, _168_, 1-18. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A Review of the recent advances in piezoelectric materials, energy harvester structures, and their applications in analytical chemistry. _Appl. Sci._**2023**, _13_, 1300. [CrossRef]  
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# Detecting Cover Crop End-Of-Season Using VEN\(\upmu\)S and Sentinel-2 Satellite Imagery
## 3 Methods
### WIST Algorithm
#### 3.1.2 Finding the Senescent Period
The conditions for dormancy assured MACD stays in the negative area while NDVI just starts to increase in the SMA time-series. We used the multi-day SMA rather than the single-day NDVI for reducing random noise in the time-series. Even when we employ the multi-day SMA, variations in the time-series may cause multiple dates that satisfy the dormancy rules. In the WIST algorithm, we used the last occurrence as the dormancy date. This is a more conservative method for identifying the senescent period, ensuring that it includes the termination date. Figure 4 illustrates NDVI times-series, EMA, and MACD used to determine the senescence onset, dormancy, and termination dates in a senescent period. The significance of a senescent period (referred to as \"momentum\" hereafter) depends on the downward potential of time-series NDVI. The average momentum during the senescent period (fromsenescence onset to dormancy onset) was computed using the cumulative absolute MACD (light blue area in Figure 4) divided by the number of days (ndays) of the senescent period as follows:  
\[\text{momentum}(\text{s},\text{d})=\sum_{\text{i=s}}^{\text{i=d}}\!\!\big{|} \text{MACD}(\text{i})\big{|}/\text{ndays} \tag{5}\]  
and an adjustable threshold on the downtrend momentum was set to determine if the trend was strong. A smaller threshold can detect a more subtle trend but may contain noise. The setting of the momentum threshold depends on the satellite dataset and cover crop types and is discussed in Section 5. In this study, we used the same threshold (0.01) as in the WISE algorithm. In addition to momentum, we also used the amplitude of the smoothed NDVI time-series (the difference between the maximum and minimum NDVI) over the period of senescence to further confirm a significant downward trend. The search for maximum NDVI included 15 days before the senescence onset since the maximum NDVI may occur prior to onset (see Figure 4). The minimum NDVI was extracted at the dormancy date. In this study, an adjustable threshold for the NDVI amplitude was defined as 0.15. A strong downward trend must be confirmed by the downward momentum of MACD and the amplitude of NDVI during the senescent period.
# Review of Physical Layer Security in Integrated Satellite-Terrestrial Networks
## 6 Conclusions  
The convergence of terrestrial and non-terrestrial networks in ISTNs for 6G presents both exciting possibilities and significant security challenges. Traditional cryptographic methods face limitations in ISTNs due to potential scalability issues and the evolving threat of quantum computing. PLS emerges as a promising alternative, leveraging the inherent physical properties of communication channels for secure communication. This review explores the potential of PLS in ISTNs, examining how channel characteristics like resource allocation, beamforming, cooperative communication, and physical characteristics can be exploited to create a secure communication environment. Additionally, this paper discusses the challenges and future directions associated with implementing PLS in ISTNs, such as channel modeling and reliable estimation. As research on 6G and ISTNs continues to evolve, PLS holds immense potential to ensure secure and reliable communication in this complex network landscape. By harnessing the unique physical properties of the channels and employing advanced signal processing techniques, network designers can create a future where ISTNs offer not only ubiquitous connectivity but also robust security for a wide range of 6G applications.  
The authors have contributed equally. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not available.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Calculation of Leaf Area Index and LSWI  
The LAI-2000 canopy analyzer (LAI-2000, Li-COR, Inc., Lincoln, NE, USA) was used to measure the kiwifruit tree canopy leaf area index (_LAI_). We measured a group of 50-cm intervals along the branch extension direction at 50 cm below the canopy. A total of 10 groups per tree and 8 trees were selected for measurement once a week, and the duration extended to 2 weeks during the stable period of vegetation canopy. The canopy leaf area indexes at different time intervals were interpolated into daily values by spline interpolation. The 8-day MODIS Surface Reflectance product (MODIS 09A1) at 0.5-km spatial resolution was downloaded and used to calculate LSWI (\(LSWI=(\rho_{\text{NIR}}-\rho_{\text{SWIR}})/(\rho_{\text{NIR}}+\rho_{\text{ SWIR}})\)). The missing MODIS data were filled using linear interpolation based on nearby available data [34; 35; 36]. The 8-day \(LSWI\) was interpolated to a daily scale using a spline function [34].
## 3 Results and Discussion
### Point of Isoelectric  
Isoelectric point testing is done qualitatively. Based on Figure 8, precipitation was dominated at pH 5.3-5.8 during the observation time of 30 min. When compared with commercial gelatin, gelatin produced from various types of solvents has almost the same isoelectric point area. For commercial gelatin, a precipitate appears after the reaction has been running for 30 min, whereas for gelatin with various types of solvents, a precipitate occurs after the reaction has been running for 10-30 min. This shows that the isoelectric point with various types of solvents at the time of immersion of beef bones has the same value (\(p<0.05\)). The types of proteins found in gelatin did not change in their peptide bonds, meaning that the composition of positively and negatively charged proteins did not change significantly. At that pH point, the protein from gelatin is easy to precipitate.
## 2 Fundamentals of Ion-Imprinted Polymers
### General Features of the IIP  
Ion imprinted polymers are adsorbent materials capable of recognizing metal ions. These have several advantages over other adsorbents used in solid phase extraction (SPE) due to their high selectivity, relatively low cost of the synthesis reagents, thermal and mechanical stability, and capacity for easy regeneration after using dilute mineral acids in the pre-concentration step. Furthermore, they enable the preconcentration of metal ions over a wide pH range.  
The high selectivity of IIP concerning other adsorbents is explained by the formed cavities (binding sites), which have a specific size, charge, coordination geometry, and coordination number; in addition, to the memory effect, due to the interaction between the template (metal ion) and the functional monomer through electrostatic forces or coordinate covalent bond [34]. However, the limitation of the selective recognition of the metal ion is also directly related to the polymerization methods adopted.  
Figure 7: Chemical structure of the initiators commonly used in the synthesis of IIP, as well as the representation of the production of 2-Cyano-2-propyl radical from 2,2\({}^{\prime}\)-azo-isobutyronitrile (AIBN).  
In Figure 8 can be seen that in the last three decades there has been a significant increase in the use of IIP as adsorbents in preconcentration studies by solid phase extraction (SPE).
## 4 Conclusions and Discussion  
### Conclusions  
This study has meticulously evaluated the dynamics of Net Ecosystem Productivity (NEP) within the specified research area from 2000 to 2020. Employing MODIS NPP data products and an advanced NEP estimation model, coupled with meteorological data integration, we have attained a comprehensive understanding of the NEP spatiotemporal characteristics and dynamic processes in the region. The key findings of our study are as follows:  
(i) Throughout the past two decades, NEP in the research area exhibited a fluctuating yet generally ascending trend. We discerned that areas with lower NEP values were predominantly situated in desert and arid regions, whereas regions such as Ningxia, Hebei, Inner Mongolia, and the northeastern provinces manifested higher NEP values.  
(ii) The analysis from 2000 to 2020 revealed that 32.97% of the regions experienced a significant increase in NEP, while 24.18% exhibited slight increases. Conversely, only 18.87% of the regions showed a notable decline. This pattern indicates a prevalent trend of ecosystem recuperation and enhancement, with areas exhibiting increasing NEP trends outnumbering those with decreasing trends.  
(iii) The study explored the link between NEP and key environmental factors like annual mean temperature and precipitation using partial correlation analysis, revealing negative correlations in Xinjiang, Gansu, and Inner Mongolia, but positive ones in Gansu, Ningxia, Inner Mongolia, and the northeast. Future outlooks suggest a general rise in NEP across most regions. Land-use changes and climate change were identified as major influencers on regional NEP shifts. However, the future of NEP trends remains largely uncertain, emphasizing the critical need for continued research aimed at strengthening ecosystem resilience, maintaining ecological balance, and ensuring sustainable management of resources.  
In conclusion, this extensive study not only illuminates the intricate dynamics of NEP within the study area but also underscores the paramount importance of ongoing research endeavors. Such efforts are crucial in deepening our comprehension of ecosystem processesand in steering effective management strategies for ecological sustainability within the Three Northern Protection Forest Project Area.
## 3 Methodology
### Residual Detail Restoration Network (DRANet)  
For the LL and LH components obtained through horizontal and vertical filtering, this paper proposes a residual detail restoration network (DRANet). In this network, the input consists of the LL and LH components. The input \(X_{rating,L}\) initially extracts shallow features \(F_{sf}^{*}\) by passing through a convolutional layer, and then the shallow features \(F_{sf}^{*}\) are processed through several consecutive channel and spatial attention blocks (CSAB) for deep feature extraction. Ultimately, the product of a network is generated by fusing its superficial and extensive characteristics. The architecture of this linkage is displayed in Figure 8.  
Figure 8: Structure of DRANet.  
Since the LL and LH components contain relatively less information, especially under the influence of rain, the details and background information are often lost. The design of DRANet aims to focus on these components to restore the occluded image details. The residual network structure allows information to propagate through skip connections, which helps preserve the original background information and reduces information loss. By employing residual learning, DRANet can effectively learn the mapping from input to output, improving its ability to restore details. DRANet uses multiple convolutional layers to gradually extract and restore information, making it especially suitable for recovering background details in the LL and LH components. This step-by-step processing approach better addresses the needs of supplementing both, and includes significant alongside whispered features. The lightweight design of DRANet enables it to maintain high restoration performance while reducing computational resource consumption, making it adaptable to efficiency requirements in practical applications.
## 6 Conclusions
However, it must be acknowledged that this study relied on a limited dataset extracted from accident reports, which may have resulted in inaccurate risk coupling effects. Future research should consider establishing a continuous data collection system to incorporate more dynamic data. This, in turn, will enrich the database and broaden the approach for real-time traffic accident risk assessment. Conceptualization, Y.J. and P.G. ; methodology, P.G. ; software, L.L. ; validation, N.C. and L.L. ; formal analysis, Y.J. ; investigation, P.G. ; resources, J.D. ; data curation, P.G. ; writing--original draft preparation, P.G. ; writing--review and editing, Y.J. ; visualization, P.G. ; supervision, Y.J. ; funding acquisition, N.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The raw data supporting the conclusions of this article will be made available by the authors on request. Author [PERSON] was employed by the company Guangzhou Road Research Institute Co., Ltd. Author [PERSON] was employed by the company Shaanxi Transportation Holding Group. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. \begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Risk Factor** & **Risk Type** & **No.** & **Risk Factor** & **Risk Type** \\ \hline
1 & Speeding & Human & 10 & Improper operation & Human \\
2 & Fatigue driving & Human & 11 & Lack of safety awareness & Human \\
3 & Distracted driving & Human & 12 & Failing to place warning signs properly & Human \\
4 & Lack of attention & Human & 13 & Negligence in observation & Human \\
5 & Improper overtaking & Human & 14 & Failure to maintain safe distance & Human \\
6 & Illegal U-turn & Human & 15 & Failure to use lights properly & Human \\
7 & Failure to follow lane rules & Human & 16 & **Failure to report in time** & Human \\
8 & Failure to wear a seatbelt & Human & 17 & **Improper handling** & Human \\
9 & Driving a vehicle not permitted by license & Human & 18 & **Failure to evacuate people properly** & Human \\ \hline
19 & Noncompliant vehicle specifications & Vehicle & 23 & **Abnormal parking** & Vehicle \\
20 & Carrying flammable or explosive goods & Vehicle & 24 & **Tire combustion** & Vehicle \\
21 & Overloading or oversize load & Vehicle & 25 & **Hazardous material leakage** & Vehicle \\
22 & Missing or unclear reflective markings & Vehicle & & \\ \hline
26 & Downhill & Road & 30 & Icy road surface & Road \\
27 & Curved roads & Road & 31 & Road congestion & Road \\
28 & Road debris & Road & 32 & Nighttime & Road \\
29 & Slippery road surface & Road & & \\ \hline
33 & Rainy & Weather & 35 & Foggy & Weather \\
34 & Snowy & Weather & 36 & Low visibility & Weather \\ \hline \end{tabular}
\end{table} 
Table 2: Risk factors of secondary accidents. The bold text highlights risk factors specific to secondary accidents.