esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Future Climate Changes under Different RCP Scenarios
For the spatial temperature difference, the temperature increases in the basin, especially in the northeastern part, while it decreases in the mountains (Figure 4). The intensity of the positive temperature difference increases from RCP2.6 to RCP2.6 to RCP8.5, while the intensity of the negative temperature difference decreases from RCP2.6 to RCP8.5. Under the RCP2.6 scenario, the temperature declines in almost the entire mountainous region (Figure 4a). The temperature-declined area over the mountains decreases under RCP4.5 (Figure 4b) and only a small area is found with a negative temperature difference in the mountains under RCP8.5 (Figure 4c). Compared to 2006, the regional mean annual temperature in 2055 is slight lower with a difference of \(-0.05\)\({}^{\circ}\)C under RCP2.6, higher with a difference of 0.30 \({}^{\circ}\)C under RCP4.5, and significantly higher with a difference of 0.944 \({}^{\circ}\)C under RCP8.5. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Rate** & \multicolumn{3}{c}{**Precipitation (mm/Decade)**} & \multicolumn{3}{c}{**Temperature (\({}^{\circ}\)C/Decade)**} \\ \hline
**Period** & **2006–2035** & **2036–2055** & **2006–2055** & **2006–2035** & **2036–2055** & **2006–2055** \\ \hline RCP2.6 & 16.2 & 6.4 & 6.5 & 0.30 & 0.12 & 0.22 \\ RCP4.5 & 14.1 & \(-\)4.5 & 9.0 & 0.40 & 0.35 & 0.24 \\ RCP8.5 & 12.2 & 4.2 & 11.8 & 0.42 & 0.30 & 0.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The Mean Rate of Change for Precipitation and Temperature during Different Periods. Figure 2: Temporal variation of regional mean annual precipitation (**a**), temperature (**b**), and CO\({}_{2}\) concentration (**c**) from 2006 to 2055 under RCP2.6, RCP4.5, and RCP8.5. By combining the spatial differences of precipitation and temperature, we can conclude that for the 2006-2055 period, the climate will become warm and dry in the northeast of Tianshan North Slope, while it will become wet and cold in the mountains under the RCP2.6 scenario. Under RCP8.5, the climate will get hot and wet in almost the entire study area. Furthermore, the climate change pattern under RCP4.5 is more complex than the other two RCPs.
## 2 Fundamental Principles
### Measurement Range
#### 2.4.2 Sensitivity Fall-Off  
When using spectrometer based LCI techniques, there is an apparent fall-off in signal strength following a Gaussian rule with increasing OPD between the reference and sample signal leg, even if the reflected signal amplitude is constant. This is caused by the following two factors: finite pixel width and finite spectrometer resolution.
## 4 Discussion
### Clinical Implications  
This study could be useful considering the calculation of SR for developing specific protocols in musculoskeletal tissues for both research and clinical practice by using semi-quantitative methodologies in the future. The most assessed musculoskeletal tissue with elastography is probably the tendon, based on the hypothesis of altered stiffness in the presence of tendon injury [23]. Although a previous study reported patterns in healthy tendon elastography, describing them as a uniformly firm structure or heterogeneous tissue with interwoven longitudinal or spindle-shaped soft tissue strands [24], it is still controversial how the changes in B-mode US with no abnormalities in elastography or changes in elastography with no altered B-mode image [25] should be interpreted.  
In addition, elastography imaging has been used to assess many muscle pathologies, including muscular dystrophy [26] or myositis [27], stiffness differences after exercise [28], or between patients and controls [29]. Furthermore, myofascal trigger points (MTrPs) have been assessed with different US imaging methods, since the manual identification of MTrP shows a poor reliability [30] and imaging techniques to visualize MTrP witch response and changes in the stiffness are needed. Previous studies observing MTrPs using B-mode US were consistent in describing MTrPs as hypoechoic regions [31] and using Doppler US found specific pulsatility index response in MTrPs [32].  
Furthermore, one study conducted by [PERSON] et al. [33] assessed MTrP stiffness by using elastography imaging to quantitatively distinguish MTrP from normal tissue and obtained significant differences between MTrPs and the normal part of the muscle. However, a recent study found no stiffness differences between active and latent MTrPs or control points with MTrPs [12]. The methodology assessed on this study could be applied to calculate the SR between an MTrP control point or two control points (since it showed a good validity and reliability), its correlation with pain pressure thresholds and SR changes after treatment.  
Like in tendons, a previous study reported poor reproducibility of elastography in skeletal muscles [34], probably due to non-standardized contraction/relaxation state of the muscle or image soft tissue anisotropy.
# Small-UAV Radar Imaging System Performance with GPS and CDGPS Based Motion Compensation
## 2 Materials and Methods
### UAV Positioning Techniques
#### 2.2.1 Standalone GPS  
In general, The UAV positioning estimation system A exploits only the information acquired by the on-board GPS receiver (see Figure 2a). Standalone Global Navigation Satellite System (GNSS) indicates the standard positioning service (see chapter 7 in [27], where basic equations can be found), which processes pseudo-range observables to derive in an epoch-wise fashion to estimate the absolute position of the UAV, e.g., in the World Geodetic System 1984 (WGS 84) reference frame. The attention is here focused on GPS due to the available receivers. The absolute positioning accuracy achievable by the standalone GPS receiver, is defined by the specifications provided by the U.S. Department of Defense [28] Loosely speaking, absolute GPS localization errors are estimated by the product of the User Equivalent Range Error (UERE), which is the effective accuracy of the pseudo-range, and, the Horizontal Dilution of Precision (HDOP) and Vertical Dilution of Precision (VDOP). Representative values of horizontal and vertical positioning errors, in case of satisfactory GPS visibility conditions, are, respectively, 3.5 m and 6.6 m [29] However, when reasonably short time flights are considered, several error sources (i.e., broadcast clock, broadcast ephemeris, group delay, ionospheric delay and tropospheric delay) are strongly correlated both in space and time [27] and may introduce positioning error, which results in a slow varying bias. In addition, the use of a proper processing strategy, such as carrier-smoothing [27], allow a reduction of the measurement noise [30], thus improving the GPS performance.
## 3 Mathematical Solution of the Problem
### Numerical Solution of Derived Transcedental Equation  
For the numerical solution of the transcendental Equation (25), a new Python script following the algorithm flow chart displayed in Figure 7 is written. At first, the input dataset is generated by varying the turn angles \(\theta_{S}\), circular arc radii \(R\), and vehicle datum lengths \(d\). For each of the 30 analyzed turn angles \(\theta_{S}\) varying from \(30^{\circ}\) to \(180^{\circ}\), and circular radii to datum length ratios \(R/d\) in the range \(1.0101<R/d<5.0\), unknown values of the substitution \(t\) are calculated. The used ratios \(R/d\) are limited to within a specified range because other combinations of the designed vehicles' datum lengths and radius of the horizontal curves negotiated by these vehicles are not common in road design practice.  
In line with the assumptions in the previous section of the paper, only the calculated values of the substitution \(t\) that meet the condition \(0<t<1\) are plotted as results. A total of 26,400 different combinations of turning angles and ratios \(R/d\) from the input dataset are addressed. Selected parameters in the input dataset and the obtained results of the calculation are shown condensed in Table 1.  
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|} \hline \hline \multicolumn{1}{|c|}{} & \multicolumn{1}{|c|}{} & \multicolumn{1}{|c|}{**Input Parameters**} & \multicolumn{1}{|c|}{**Results**} \\ \hline
**No \({}^{*}\)** & \(\theta_{\rm S}\) [\({}^{\circ}\)] & \(R\) [m] & \(d\) [m] & \(R\)/\(d\) & \(K\) & \(e^{\bf K0s}\) & \(c\) & \(\beta_{\rm max}\) [\({}^{\circ}\)] & \(t\) & \(\beta_{d}\) [\({}^{\circ}\)] \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 1: Input dataset for numerical calculation of transcendental equation and obtained results.  
Figure 7: Flow chart of the algorithm used for the numerical solution of a transcendental equation.  
Numerical solving of the transcendental Equation (25) was conducted by employing Python's SciPy library \"fsolve\" function which is a wrapper around MINPACK's HYBRD and HYBRJ algorithms. HYBRD and HYBRJ are essentially the same, but HYBRD uses forward-difference approximation to compute the jacobian whereas HYBRJ requires the user to provide the jacobian. The purpose of HYBRD is to find a zero of a system of N nonlinear functions in N variables by a modification of the Powell hybrid method [33]. The user must provide a subroutine which calculates the functions.
## 1 Introduction
### Maritime Emissions and Their Effects
#### 1.1.6 Coastal Agricultural Production Exposure  
Agricultural production in Italy is concentrated along the seacoasts as well as in the Po valley. Although the precise proportions cannot be determined, it is clear from Figure 2 that nearly all of the coastal regions, except in the far northwest, are croplands [58].  
The diversity and the quantities of agricultural products are evident in Table 6[59]. Although grapes and olives are, of course, conspicuous, there are many other crops that are also important. Among them, wheat, maize, rice, and soybean crops have all been found to be damaged by BC and ozone emissions in other countries [21]. The damage is caused by a combination of factors: changes in the local and regional patterns of precipitation as a result of climate change; pollution of water supplies by depositions of emissions; and BC falling directly on the leaves of plants [21].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Commodity** & **Tons, in Descending Order, 2018** \\ \hline Grapes & 8,513,643 \\ Wheat & 6,932,943 \\ Maize & 6,179,035 \\ Tomatoes & 5,798,103 \\ Apples & 2,414,921 \\ Olive & 1,877,222 \\ Oranges & 1,522,213 \\ Rice & 1,512,241 \\ Soybeans & 1,138,993 \\ Peaches and nectarines & 1,090,678 \\ Barley & 1,010,328 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Italy’s Agricultural Commodity Production.  
Figure 2: Map of Cropland Areas in Italy.  
Although it is not feasible systematically and precisely to allocate each type of agricultural production to particular areas, it is possible to highlight some of the specialized regions and thereby gain a preliminary understanding of the exposure of agricultural production to the damaging effects of ships' emissions of black carbon [60]. See Figure 3.  
In sum, nearly all the coastal areas of Italy's 7500 km of coasts are important agricultural areas, and for many types of crops.
# Performance of UAV-to-Ground FSO Communications with APD and Pointing Errors
## 3 Symbol Error Rate Calculation
### SER Calculation of Moderate to Strong Links  
From Equations (28) and (33), the PDFs and SER of the SNR for moderate to strong atmospheric turbulence can be derived as  
\[\begin{split} f_{\rm Y}^{\rm GG}(\rm Y)&=\exp\! \left(-\frac{\theta_{FOV}^{2}}{2\sigma_{AoA}^{2}}\right)\!\delta\!\left(\frac{ \sigma_{n}\sqrt{\rm Y}}{P_{t}mG\Re}\right)+\Xi_{1}\!\left[1-\exp\!\left(-\frac{ \theta_{FOV}^{2}}{2\sigma_{AoA}^{2}}\right)\right]\!\sum_{n=0}^{N}\left(c_{1} \!\left(\frac{\sigma_{n}\sqrt{\rm Y}}{P_{t}mG\Re}\right)^{\frac{\pi}{\rm I}-1} \right.\\ &+\left.c_{2}\left(\frac{\sigma_{n}\sqrt{\rm Y}}{P_{t}mG\Re}\right) ^{n+\alpha-1}-c_{3}\left(\frac{\sigma_{n}\sqrt{\rm Y}}{P_{t}mG\Re}\right)^{n+ \beta-1}\right).\end{split} \tag{35}\]  
Again, the SER expression of the UAV-based FSO systems in cases of moderate to strong links can be expressed as\[\text{SER}^{\text{GG}} =2q(M_{I})\int_{0}^{\infty}(Q(A_{I}\sqrt{\Upsilon})\exp(-\frac{\theta _{FOV}^{2}}{2\sigma_{AoA}})\delta(\frac{\sigma_{n}\sqrt{\Upsilon}}{P_{I}mG\Re})\] \[+\Xi_{1}[1-\exp(-\frac{\theta_{FOV}^{2}}{2\sigma_{AoA}^{2}})]\sum _{n=0}^{N}(c_{1}(\frac{\sigma_{n}\sqrt{\Upsilon}}{P_{I}mG\Re})^{\eta-1}+c_{2}( \frac{\sigma_{n}\sqrt{\Upsilon}}{P_{I}mG\Re})^{n+a-1}\] \[-c_{3}(\frac{\sigma_{n}\sqrt{\Upsilon}}{P_{I}mG\Re})^{n+\beta-1} dY+2q(M_{Q})\int_{0}^{\infty}(Q(A_{Q}\sqrt{\Upsilon})\exp(-\frac{\theta_{FOV}^{2}} {2\sigma_{AoA}^{2}})\delta(\frac{\sigma_{n}\sqrt{\Upsilon}}{P_{I}mG\Re})\] \[+\Xi_{1}[1-\exp(-\frac{\theta_{FOV}^{2}}{2\sigma_{AoA}^{2}})]\sum _{n=0}^{N}(c_{1}(\frac{\sigma_{n}\sqrt{\Upsilon}}{P_{I}mG\Re})^{\eta-1}+c_{2 }(\frac{\sigma_{n}\sqrt{\Upsilon}}{P_{I}mG\Re})^{n+a-1}-c_{3}(\frac{\sigma_{n }\sqrt{\Upsilon}}{P_{I}mG\Re})^{n+\beta-1})dY\] \[-4q(M_{I})q(M_{Q})\int_{0}^{\infty}(Q(A_{I}\sqrt{\Upsilon})Q(A_{ Q}\sqrt{\Upsilon})\exp(-\frac{\theta_{FOV}^{2}}{2\sigma_{AoA}^{2}})\delta( \frac{\sigma_{n}\sqrt{\Upsilon}}{P_{I}mG\Re})\] \[+\Xi_{1}[1-\exp(-\frac{\theta_{FOV}^{2}}{2\sigma_{AoA}^{2}})]\sum _{n=0}^{N}(c_{1}(\frac{\sigma_{n}\sqrt{\Upsilon}}{P_{I}mG\Re})^{\eta-1}+c_{2 }(\frac{\sigma_{n}\sqrt{\Upsilon}}{P_{I}mG\Re})^{n+\alpha-1}-c_{3}(\frac{ \sigma_{n}\sqrt{\Upsilon}}{P_{I}mG\Re})^{n+\beta-1})dY.\]
# Research on Seismic Signal Analysis Based on Machine Learning
## 3 Methodology
### A Lightweight Collaborative Learning Model for Seismic Sensing Signal Classification (LCL-SSS)
In order to avoid assigning all samples to a single cluster, the entropy term (the second term in Equation (3) is introduced and the weight coefficient \(\lambda\) is used to adjust its importance. This encourages the prediction results to be evenly distributed on all class sets C, which can realize excessive forced uniform clustering of a large number of classes and can to a certain extent make up for the shortcomings of k-means. This process is realized by dividing the data into different epochs and simulating the cosine annealing curve to adjust the learning rate so that the cluster function of cluster head can iteratively update. So far, the collaborative learning model has been established, and then the cluster accuracy of the model needs to be evaluated. The schematic diagram is as follows.
# A New Lossless DNA Compression Algorithm Based on A Single-Block Encoding Scheme
## 2 Proposed Methodology
### Binary Encoding Scheme  
In this method, the DNA sequences were converted into several binary streams. Firstly, it counted the frequency (Fr) of each nucleotide in the input file and records it in descending order as B1, B2, B3, B4 and B5 occurrences (where B1 was assigned to the most frequent nucleotide and B5 was assigned to the base, which had the smallest count in the input data). Secondly, for N different nucleotide bases in the DNA sequences, N-1 binary files would be created to encode N-1 bases. Each of these files saved the positions of one specific base, i.e., File1 saved all the positions of B1, File2 saved all the positions of B2, etc., by replacing the positions of the base Bi with 1 and replacing the positions of others bases that have smaller frequencies than Bi with 0. The positions of bases with occurrences higher than Bi were ignored, the corresponding pseudocode was provided in Algorithm 1.  
Figure 1: An overview of the proposed algorithm.  
```
**Begin**

_First phase (Binary Encoding Scheme)_

Input: A DNA sequence {b\({}_{1}\) b\({}_{2}\) b\({}_{3}\)......... b\({}_{n}\)}

Output files: File\({}_{1}\), File\({}_{2}\)... File\({}_{n-1}\).

Initialization: File\({}_{i}\) (B\({}_{i}\))= {} Algorithm:

Let B1, B2, B3 and B4 is the descending frequencies order.

For each element of the sequence (j from 1 to n)

if (B\({}_{i}\) = b\({}_{j}\))

Add 1 to File\({}_{i}\)

else

if (B\({}_{i}\)!= b\({}_{j}\) && Fr (B\({}_{i}\))? Fr(b\({}_{j}\)))

Add 0 to File\({}_{i}\)

else

Ignore it and pass to the next base

endif

endif

End.
```  
**Algorithm 1** Binary Encoding Scheme  
It is important to note that the position of the base, which had the smallest occurring number, would be ignored because it could be recovered from the previously generated files.
# ML-AKA: An Authentication Protocol for Non-Standalone 5G-Based C-IoT Networks
## 3 Interfaces and Communication Links for NSA-Based C-IoT Networks  
Providing efficient D2D or IoT communication over a cellular network platform requires various network entities like user equipment (UE), evolved NodeB (eNodeB), and core networks to be interconnected through various interfaces, either through wires or via a wireless medium. Figure 1 shows a block diagram of these entities and their interconnections [15; 16]. Here, the UEs are interconnected via a Wireless LAN (WLAN) with the help of an access point (AP). These APs are interconnected to each other through a base station control by the Evolved Packet Core (EPC) network. The information between UEs and APs is exchanged through a dedicated UPlink/Downlink RF connection, whereas the interconnections between eNodeB and are created with the help of X2 interfaces. The core network performs all the policy, mobility, and authentication-related control and management functions with the use of network entities like Mobility Management Entity (MME) to provide a service gateway (S-GW), packet gateway (P-GW) and Home Subscriber Server (HSS), etc., as shown in Figure 1[17; 18].  
The NSA architecture consists of four different layers. Each layer has some key components and functionality for the end-to-end link establishment. The key network elements of different layers are as follows:
# Scalable Name Lookup for NDN Using Hierarchical Hashing and Patricia Trie
## 4 Proposed Name Lookup Scheme
### Overall Structure of FIB  
The proposed lookup structure consisted of hierarchical hash tables in which each level supported the search structure of name components. Figure 4 depicts the two-level name hierarchy. Each level consisted of several hash tables and their overflow areas. Patricia tries were used for the overflow areas instead of linked lists.  
For a given interest name/com/google/people, the hash value of the first component /com is computed, and then a bucket is determined in the root-level hash table. In this example, unfortunately, several components, which were /com, /edu, and /org, collided in the same bucket and were stored in an overflow area. By using a Patricia trie, the component /com could be found in a leaf node, and it connected to a next level hash table. In the same way, it searched for /google, which was the longest result for the interest name.
# Summarization of Remaining Life Prediction Methods for Special Power Plants
## 3 Future Research Directions
### Dynamic Model-Driven RUL Prediction Methods
#### 3.1.4 RUL Prediction Model for Different Combined Structures  
Considering that there is a series, parallel, or series-parallel mixed structure in the reciprocating motion mechanism of the special power plant, while the load-sharing mechanism is present in the combined structure, the dynamic excitation factors are the same, the physical locations of the mechanisms are adjacent, and the degradation processes of different mechanisms have certain correlations. The dynamic profile of the combined structure in the operation stage will affect the degradation process of each component mechanism, defining the affected dynamic performance degradation parameters as the covariates. Each mechanism in the combined structure is also subject to a shared load or a dynamic profile, and there is a shared covariate. Therefore, the shared covariates are used as a \"bridge\" to connect the degradation models of different mechanisms in order to describe the correlation between the degradation laws of various mechanisms in the combined structure.  
Based on the state space model, a combinatorial structure stochastic degradation model is established considering the shared covariates. Covariates are introduced into the state space to establish a state space model of the combined structural dynamics containing the covariates. Secondly, the degradation of the combined structure at a certain time point is defined as a state, and the degradation model of its dynamic performance is transformed into the state transition equation in the state space model. The unknown parameter estimation model in the dynamic state space model can be constructed based on the particle filter (PF) algorithm. The RUL prediction model of the combined structure is obtained by extrapolating the estimated degradation parameters. Based on this process, RUL prediction models with different combined structures can be studied.
## 1 Introduction
The main contributions of this study are outlined below:  
* An adaptive multi-density (SAMD) prediction model is proposed to solve the dynamic density radius selection problem and improve the accuracy and real-time performance of time-series data prediction. * One essential step is the calculation of the timespan according to real-time electricity prices, which is able to represent the general electricity tendency by mining the key points of different levels of clusters. * When forecasting the time series, the effect of the influencing factors is fully considered, so the multi-core function is used to analyze the influencing factors. * The SAMD model with timespan processing not only deals with the problem of excluding noise points due to the threshold of the set of MITs but also classifies a few of the closest noise points into sparse clusters. A microgrid community is a regional energy management system based on distributed energy technology that allows the balance of power production and consumption in a localized area [14]. The emergence of microgrids makes energy management more flexible and efficient but also increases the complexity of the power system and the volatility of electricity prices. Therefore, the management of a microgrid community relies on high-precision, real-time electricity price forecasts to ensure a stable and economical power supply. The SAMD model proposed in this paper can predict the general trend of electricity prices by separating the time-series dataset into high-quality clusters of high, medium, and low levels and representing them as different kinds of real-time electricity price intervals. This approach can reflect the dynamics of the electricity market more accurately and in real time, thus optimizing new energy utilization, improving the economics of home energy transactions, and enhancing the stability and sustainability of microgrid communities. Figure 1 illustrates energy trading within a microgrid community. The households in this community are assumed to be photovoltaic, have electric vehicles, and are equipped with smart meters and home energy storage batteries. With the popularity of electric vehicles, the provision of services to the grid, homes, and buildings via Vehicle-to-Anything (V2X) technology [15] is also considered in Figure 1.  
The remainder of this paper is organized as follows: Section 2 describes related work. Section 3 proposes a method for timespan processing. Section 4 describes the SAMD model in detail, including its adaptive multi-density radius calculation and multi-kernel support vector machine forecasting. Section 5 provides a description of the experiment and comparative analysis. Finally, the conclusions of this paper are summarized in Section 6.