esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
In the SM system, to estimate the transmitted bits, the receiver needs to use joint ML detection for both the activated transmit antenna and the \(M\)-ary modulated symbols. This joint detector is computationally complex, especially for the SM system with a large number of antennas. Near-ML low-complexity detectors such as those summarized in Reference [24] or that for the index modulation in the frequency domain [25] are more favorable for practical implementation. In this paper, as we are interested in analyzing the effect of the RSI due to the FD mode on the system performance, we assume that the receivers of both R and D can perfectly estimate the transmitted antenna index of the respective transmitters for the ML detection. From (2) and (3), we can calculate the instantaneous signal-to-interference-plus-noise-ratios (SINRs) of \(\text{S}-\text{R}\) and \(\text{R}-\text{D}\) links as follows  
\[\gamma_{\text{R}}=\frac{R_{\text{S}}\|\mathbf{h}_{i}^{\text{R}}\|^{2}}{\sigma^ {2}_{\text{RSI}}+\sigma^{2}}=\|\mathbf{h}_{i}^{\text{R}}\|^{2}\tilde{\gamma}_{ \text{R}}, \tag{4}\]  
\[\gamma_{\text{D}}=\frac{P_{\text{R}}\|\mathbf{h}_{j}^{\text{D}}\|^{2}}{\sigma^ {2}}=\|\mathbf{h}_{j}^{\text{D}}\|^{2}\tilde{\gamma}_{\text{D}}, \tag{5}\]  
where \(\tilde{\gamma}_{\text{R}}=\frac{R_{\text{S}}}{\sigma^{2}_{\text{RSI}}+\sigma^ {2}}\) and \(\tilde{\gamma}_{\text{D}}=\frac{P_{\text{R}}}{\sigma^{2}}\) denote the average SINR at R and the average signal-to-noise-ratio (SNR) at D, respectively. Since the relay node uses the DF protocol, the instantaneous end-to-end SINR of the considered system is defined as  
\[\gamma_{\text{e2e}}=\min(\gamma_{\text{R}},\gamma_{\text{D}}). \tag{6}\]  
where \(\gamma_{\text{R}}\) and \(\gamma_{\text{D}}\) are respectively the instantaneous SINRs at R and D.
## 3 D2 WaVe: Design Rationale and System Architecture
### Video Frames Authentication Scheme
#### 3.3.3 Data Retrieval  
This stage focuses on querying video data and meta-information from the DDS system and verifying data integrity. As Figure 5a shows, a data user calls smart contract functions to query a sequential list of \(TX\) and its proof \(MT\_root\). The data user can use information in \(TX\) to retrieve a list of metadata \(M^{{}^{\prime}}\) from IPFS storage. To verify the integrity of a video segment containing \(F^{{}^{\prime}}\), he/she can easily reconstruct a Merkle tree of digest \(D^{{}^{\prime}}\) and calculate its root hash \(MT\_root^{{}^{\prime}}\). Any modification on the sequential order of \(M^{{}^{\prime}}\) or content of \(M^{{}^{\prime}}_{i}\) will lead to a different root hash value \(MT\_root\) of the Merkle tree. Thus, data integrity of the entire \(M^{{}^{\prime}}\) can be efficiently verified by comparing \(MT\_root^{{}^{\prime}}\) with proof \(MT\_root(M^{{}^{\prime}})\) recorded on the distributed ledger. If verification on \(M^{{}^{\prime}}\) passed, the data user can retrieve watermarked frames \(F^{{}^{\prime}}\) from the DDS system by using CIDs saved in \(M^{{}^{\prime}}\). As IPFS relies on a P2P overlay network to discover all available locations from which objects can be retrieved, and this can dramatically reduce the impact of availability issues in centralized storage systems (e.g., network congestion or single node failure). Finally, the data owner can verify the integrity of each \(F^{{}^{\prime}}_{i}\) by using its meta-information.
## 2 Extraction Techniques
### Microextraction by Packed Sorbent (MEPS)
#### 2.2.1 MEPS Influencing Parameters  
##### 2.2.1.1 Sampling  
Biological fluids, like urine, blood and plasma, are complex samples and should be processed accordingly to optimize the extraction of the target analytes by favoring a better interaction between sample analytes and the sorbent [35]. This involves the dilution of the sample (to reduce the sample viscosity), pH adjustment (to reduce the ionization of weak acids and bases for reversed-phase extraction), deproteination (with previous protein precipitation, for instance) and sample loading speed adjustment (an option in semi-automatic and automatic MEPS (reviewed in [9,35]).
# Estimating Global Gross Primary Production Using an Improved MODIS Leaf Area Index Dataset
## 4 Discussion  
### Environmental Characteristics of Flux Towers Suitable for Validating GPP  
The flux sites we selected for comparison are characterized by relatively homogeneous land surface types, with minimal heterogeneity or topographical differences in the surrounding areas [75]. This uniformity helped to minimize potential errors caused by surface complexity when comparing lower-resolution global data with higher-resolution flux tower data [76]. In addition, the uniformity of terrain, vegetation types, and climatic conditions around the flux sites improved the feasibility and reliability of the comparison [77]. This analysis allowed us to more effectively assess the applicability and accuracy of global datasets in specific regions, providing a solid foundation for data model refinement and optimization [78].  
In this study, we compared the environmental variables of the flux sites with those of the surrounding areas within a radius of 5 km, focusing on the distribution of elevation, leaf area index (LAI), and vegetation types. We compared the elevation of the flux sites with the average elevation in the surrounding 5 km and found that a correlation coefficient of 98.5% and statistical significance of \(p<0.01\) indicate a high degree of consistency (Figure 7A). We also compared the average annual LAI of the site with the average annual LAI of the area within 5 km, which resulted in a correlation coefficient of 98.5% and statistical significance of \(p<0.01\), also indicating a strong similarity (Figure 7B). We calculated the ratio of vegetation types at the site to vegetation types within a 5 km radius of the site (Figure 7C). The results showed that the average proportions for CRO, DBF, DNF, EBF, ENF, GRA, MF, OSH, SAV, WET, and WSA were 0.92, 0.81, 0.80, 0.90, 0.78, 0.92, 0.89, 0.90, 0.97, 0.90, and 0.86, respectively. This indicates that the flux site data adequately represent their spatial environment [79; 80]. These results confirm the reliability of the flux site data and provide important evidence for its application in large-scale environmental studies [81]. These consistent environmental characteristics indicate that the climatic and ecological conditions in the region are relatively stable, which helps to minimize the fluctuations in meteorological data caused by terrain variations and ensure more reliable data collection at the flux towers.  
Figure 6: Spatial pattern of global GPP (KgC m\({}^{-2}\) year\({}^{-1}\)) trend simulated by (**A**) the proposed model, (**B**) BEPS, (**C**) MODIS, and (**D**) VPM.
# Noises in Double-Differenced GNSS Observations
## 4 Results
### Autocorrelation Function
As for the carrier phase observations, a constant value of \(\tau=1\) s was obtained; in the graph, only the summary for the code observations is presented. The obtained results of the autocorrelation time for the code observations for the adopted threshold \(\rho_{t}<0.20\) show significant differences in the temporal correlation of the GNSS measurement noise. The GPS C2W and C5Q observations show the highest temporal correlation, with an average time lag of 17-18 s and spread reaching 40 s. The lowest temporal correlation values and their highest repeatability were obtained for the GPS C1C and Galileo C7Q observations. The presented results lead to the conclusion that a double-differenced GNSS observation time series for a 1 Hz measurement rate can only be treated as not being correlated to time for carrier phase observations. For code observations, such an assumption can be made for observations with an interval of more  
Figure 7: Autocorrelation time lag for code observations (weekly solution). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**System/**} & \multicolumn{2}{c}{**Time Lag [sec]**} \\ \cline{3-4}  & **Observation Type** & **Code** & **Phase** \\ \hline  & 1C & \(\leq\)1 & \(\leq\)1 \\ GPS & 2L & 5 & \(\leq\)1 \\  & 2W & 18 & \(\leq\)1 \\  & 5Q & 17 & \(\leq\)1 \\ \hline  & 1C & 2 & \(\leq\)1 \\ GLONASS & 1P & 4 & \(\leq\)1 \\  & 2C & 4 & \(\leq\)1 \\  & 2P & 4 & \(\leq\)1 \\ \hline  & 1C & 2 & \(\leq\)1 \\ Galileo & 5Q & 5 & \(\leq\)1 \\  & 7Q & 2 & \(\leq\)1 \\  & 8Q & 6 & \(\leq\)1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Autocorrelation time lag for the analysis of a GNSS system for \(\rho_{t}<0.20\). than 20 s. Otherwise, for data with a higher measurement interval, correct data processing requires the observation time correlation to be considered in the stochastic model.
## 3 Test Results and Analysis
### The Load and Displacement of Characteristic Points  
Beam-column connections require good ductility to ensure that the beam and column ends have sufficient deformation capacity. In this research, the ductility of the specimen was represented using the ductility coefficient (\(\mu\)):  
\[\mu=\Delta_{\text{u}}/\Delta_{\text{y}} \tag{1}\]  
where \(\Delta_{\text{u}}\) is ultimate displacement, and \(\Delta_{\text{y}}\) is yield displacement. The yield point is determined by geometrical construction [33].  
Compared with the XJ, the assembled specimens' ductility increased by 53%, 102%, 74%, and 42%, respectively, as shown in Table 8. The concrete structure's brittleness was significantly improved due to MRPC's better tensile strength and tensile hardening characteristics. As shown in Table 7, the ductility of assembled connections using end-bent steel fibers was higher. SFZP-2 entered the yield stage faster than SFZP-3, indicating that the hybrid effect formed by short and dense steel fibers better-delayed crack development during early loading. However, SFZP-2's ultimate displacement was larger, indicating that the longer steel fiber delayed the duration of being pulled out during later loading, thereby inhibiting damage and enhancing the specimen's ductility. Although SF-4's fiber length was relatively long, SFZP-4's ductility was the worst because micro steel fibers could form a disorderly distribution system in the matrix and work better together with PP fibers.  
Figure 14: Skeleton curves. (**a**) Left beam. (**b**) Right beam.  
The use of MRPC in the critical cast-in-place regions can improve the ductility of assembled beam-column connections and reduce the number of reinforcements in the critical regions, making construction easier.
## 2 Literature Review  
### Identification of Factors Affecting Damping Ratio  
For the correct creation of the damping model, parameters affecting this soil property have to be designated. In factors determining the dynamic properties of soil, we can distinguish two main groups. The first is connected with loading condition, for example, effective stress, strain amplitude, strain frequency (strain speed), numbers of loading cycles, or stress history (\(OCR\)). The second group concerns physical soil parameters, like soil type, size, and shape of grains or void ratio [10]. Over the years, a lot of research was conducted to find the most important factors [11; 12; 13; 14].  
In Table 1, the influence of different parameters on the damping ratios of cohesive soil is summarized. According to [12], the most important factors are strain amplitude, effective stress, void ratio, and numbers of loading cycles. Based on [12], one of the first equations for the damping ratio of saturated cohesive soil was presented in [15]:  
\[D=D_{MAX}(1-G/G_{MAX}) \tag{1}\]  
\[D_{MAX}=31-(3+0.03f)\sigma_{0}{}^{1/2}+1.5f^{1/2}-1.5(logN) \tag{2}\]  
where:_f_--frequency of loading, \(\sigma_{0}\)--effective stress, \(N\)--number of loading cycle, and \(G/G_{MAX}\)--normalized shear modulus.  
Further research marginalized the impact of void ratio but showed much more influence of soil type and plasticity on the damping ratio [13; 14]. However, an investigation conducted by [13] shows that the damping ratio could stay constant with increasing confining pressure. Moreover, they indicated that damping decreased with the increasing void ratio, which proved to have much influence on damping characteristics. A number of loading cycles were another difference between [13] and [12; 14]. In [13], \(N\) had no significant effect on damping for moderate numbers; however, [12; 14] presented the number of loading cycles as a very important factor for the mentioned dynamic parameter. Each of the referenced publications [12; 13; 14] showed small or no influence \(OCR\) on \(D\).
## 2 Materials and Methods
### GLONASS L1 OF  
The RF front-end must support the 1602 MHz frequency, while the IF to baseband frequency down-conversion stage must also support the several FDMA channels, allowing them to be seamlessly tracked, independently from their different frequency offset. In fact, this frequency offset is pre-determined and associated with each PRN, relieving the universal channel from this signal type management.  
Because of HW design limitations, a correlator spacing of \(\pm 31\) samples is used during acquisition, which roughly corresponds to \(\pm 4\) chip. In cold acquisition, because two satellites share the same RF offset, the navigation data must be decoded (no encryption) to corroborate that the expected satellite is effectively being tracked. This information is not available in every 2 s long string, and may thus require longer decoding to find out.  
BeiDou B1-I  
This last signal, from the BeiDou Phase II constellation requires an RF front-end capable of processing 1561.098 MHz, with a 2046 Mchip/s. The initial correlator spacing is set to \(\pm 14\) samples during acquisition. The 11-bit un-encoded preamble may be used to synchronize onto the message frame, once the 10-bit secondary code has been wiped out.
## 4 Results and Discussion
### Evaluation  
The number of activated users in the process of information diffusion in social networks is used as the evaluation standard for the UPI-LT model described in this paper to conduct the simulation experiment of information diffusion so as to verify that the model proposed in this paper can promote the impact of information on users and effectively expand the scope of information diffusion.
## 1 Introduction  
The estimated cost of fixing an error during the requirements phase has been estimated as up to 1500 times cheaper than fixing an error in other phases of the project life-cycle [1]. One of the approaches proposed for the reduction and early detection of errors in the requirements is the use of controlled natural languages (CNLs) for writing requirements [2, 3].  
A CNL should read like natural language, such that it is accessible to all or most stakeholders. However, according to a formal grammar, there is always one interpretation for a given sentence [4]. Hence, with a CNL, it is possible to remove ambiguity and, through appropriate support tools, the reduction and early detection of errors in the requirements. CNLs, nevertheless, are not without limitations. They require readers, but especially writers, to know the grammar and learn to express themselves using that grammar in a way that does not impair their creativity [5]. Moreover, formal grammars can easily become complex, and the implementation of support tools demand a significant effort. Lastly, unanticipated constructions or patterns cannot be handled or have to be handled in unexpected and cumbersome ways. These limitations lead to trade-offs (e.g., comprehensive grammar vs. available support tools), which ultimately result in CNLs that are either limited in scope or not very useful. In the end, the CNLs that are found in the literature have at least one of the following limitations: not suitable or very limited for requirements specifications, no runtime code generation support, or no balance between natural and technical modes of expression.  
In this article, we present REX (the implementation used for this article can be found at: [[https://github.com/b4795/REX](https://github.com/b4795/REX)]([https://github.com/b4795/REX](https://github.com/b4795/REX)), accessed on 24 June 2022), a CNL which distinguishes itself by having a small but comprehensive set of rules, and thus, a grammar that is simple, easy to understand, and easy to support. Despite the small set of rules, it is a general-purpose language because it can be extended by the users to suit their particular needs through patterns. In this article, code generation from REX is also demonstrated. Codegeneration (as well as the generation of any other artifacts) is possible because of the CNL's formal grammar, bringing a number of benefits to add to those already mentioned.  
This article is structured as follows. Section 2 provides some background and reviews of related work. Section 3 presents REX through four simple examples of increasing complexity, while Section 4 describes the implementation of support tools. This article ends with some conclusions and additional remarks in Section 5.
## 4 The Analytical Performance of Uplink CDM-Serving Satellites with a Saturated Amplifier
In Figure 12, we compare the analytical curves obtained using Theorem 1, Equations (3), (4), and (6), with those generated through simulation. The results show perfect agreement for various numbers of satellites served. This confirms that the theoretical analysis is correct. Now, we compare the exact and approximated expressions (Equations (5) and (7)) from Theorem 1. The results are shown in Figure 13 (spreading factor \(M=63\)), Figure 14 (\(M=100\)), and Figure 15 (\(M=150\)). Although the exact expression shows perfect agreement, there are small differences in this case. When \(M=63\), the approximated \(P_{b}(e)\) in Equations (5) and (7) actually underestimates the true value of \(P_{b}(e)\), as shown in Figure 13, especially when the number of satellites, \(N_{S}\), is small. However, the difference between the two formulas reduces when \(M\) increases to \(100\), as shown in Figure 10:2 Moreover, the approximate formula is significantly simplified and provides valuable insights into certain phenomena related to the behaviors of different numbers of satellites, as we will explore in the next section. Regarding the choice of sequences in our analysis, when dealing with a large number of satellites (thousands), pseudo-random sequences are typically preferred over algebraic sequences, like Gold sequences. However, if the pseudo-random sequences exhibit good properties, the results remain consistent. Figure 10:3 Comparison between exact theoretical (lines) and simulated (circles) CDM bit error probabilities for a saturated HPA and random spreading codes with spreading factor \(M=100\), for \(N_{S}=2,6,11,21\) satellites. Figure 10:4 Comparison between exact theoretical (lines) and approximate (dashed lines) CDM bit error probabilities for a saturated HPA with random spreading codes, with spreading factor \(M=100\), for \(N_{S}=2,3,4,5,6,7,8,15,20\) satellites. Figure 10:5 Comparison between exact theoretical (lines) and approximate (dashed lines) CDM bit error probabilities for a saturated HPA with random spreading codes, with spreading factor \(M=63\), for \(N_{S}=2,3,4,5,6,7,8,15,20\) satellites.
# Multi-Mode Lithium-Ion Battery Balancing Circuit Based on Forward Converter with Resonant Reset
## 3 Analysis and Verification
### Influencing Factors of Balancing Performance
#### 3.3.1 Effect of Converter Turn Ratios  
According to (31)-(33), balancing performances are impacted by the forward converter turn ratio. In this section, the three turn ratios are analyzed for different cases. The three-cell-to-three-cell balancing mode is chosen and the switching period is selected as T\({}_{\text{S}}\) = 40 \(\upmu\)s to develop the circuit simulation. The relationships between different converter turn ratios and balancing performances are shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c} \hline
**T\({}_{\text{S}}\)/\(\upmu\)s** & **Theory/\%** & **Simulation/\%** \\ \hline
25 & 44 & 55 \\
30 & 53 & 64 \\
35 & 60 & 70 \\
40 & 65 & 75 \\
45 & 69 & 78 \\
50 & 72 & 80 \\ \hline \end{tabular}
\end{table}
Table 2: The duty cycle of MOSFETs to achieve ZVS in different switching cycles.  
From Figure 7, when the turn ratios are enlarged, the input power \(\rm P_{in}\) and output power \(\rm P_{out}\) of the balancing main circuit are increased, and the balancing time is shortened. However, the balancing efficiency is not affected. In principle, the turn ratio of the forward converter should be set larger to achieve higher power energy transmission. However, when the turn ratio of the forward converters iare enlarged, the voltage error of the cells is increased. Therefore, with high-power balancing, more moderate turn ratios are set. Based on the data in Figure 7, the turn ratio of the forward converters is chosen to be 1:2 in this paper.
## 1 Introduction
Furthermore, he also derived the theoretical model of a tracking error distribution of a heliostat based on integral geometry and a geometric probability method [15]. [PERSON] et al. [16] established an elevation-azimuth double-axis tracking model in which the azimuth axis tilt, the non-orthogonal of the elevation-azimuth axis, and the non-parallel degree of the mirror surface plane to the elevation axis were considered. Furthermore, the elevation-azimuth double-axis tracking model is improved  
Figure 1: The 38 kW solar dish Stirling thermal power (SDSP) generation system developed by the authors [7]. (**a**) The physical diagram of the SDC system. (**b**) The geometric parameters of the concentrator [9]. and a tracking model of six angular parameters (inclination angle, inclination direction of azimuth axis, the bias angle between elevation axis and azimuth axis, the zero angle position errors of the elevation axis and azimuth axis, and the canting angle of the mirror surface plane to the elevation axis) is established, and the effectiveness of the model is verified by experimental data [17]. The research on shafting tilt error mainly focuses on the field of large telescopes and precision instruments (such as laser trackers). [PERSON] et al. [18] investigated the tilt of the ASTE 10 m antenna azimuth axis under the influence of thermal and wind loads. [PERSON] et al. [19] established the mathematical model of pointing errors and assembly errors of mechanical parts of optical imagers, and established the simulation model of pointing errors based on the Monte Carlo methods and obtained the distribution of pointing errors before and after assembly error correction. [PERSON] et al. [20] derived a quantitative relationship between antenna elevation angle, azimuth angle, and antenna pointing error to correct the pointing error caused by reflector deformation. Some of the researchers' work is not described in detail, and their main contributions are listed in Table 1.  
In summary, some scholars have conducted tracking error-related studies in different fields. However, there are few studies related to the variation law of the tracking performance of the SDC system in terms of initial tracking position, operation area, and long-term operation under the influence of azimuth tilt errors. Therefore, this paper focuses on the tracking performance of the SDC system under the influence of an azimuth axis tilt error. The influence of radial angle and tangential angle of the azimuth axis on the tracking performance of the SDC system is studied in detail. Under the influence of an azimuth axis tilt error, the variation law of tracking performance of the SDC system in initial tracking position, operation area, and the whole year operation period are analyzed. The results of this paper can provide theoretical guidance for the future installation and layout of SDC systems.