esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling DGNSS Pseudo-Range Correction Messages by Utilizing Satellite Repeat Time
## 5 DGNSS Positioning
### DGPS Positioning  
Figure 7 shows (a) horizontal (north-east direction) errors, and (b) vertical errors from three different positioning schemes. In this figure, the green square refers to standalone positioning errors without PRC, whereas the blue triangle and red dot refer to errors in DGPS positioning with predicted and observed PRCs, respectively. The concentric circles refer to horizontal error magnitudes of 1.0, 3.0, 5.0, and 7.0 m.  
Figure 7: DGPS positioning: (a) horizontal and (b) vertical error without PRC (\(\Box\)), with a predicted PRC (\(\blacktriangle\)), and with the real PRC (\(\blacktriangle\)).  
As shown in Figure 7, the most accurate result was achieved when real PRC data were used. Horizontal RMSE were 1.80 m for positioning without PRC, 1.39 m for positioning with a predicted PRC, and 1.00 m for positioning with the actual PRC. Although RMSE of DGPS positioning with predicted PRC was 0.39 m larger than with observed values, it improved by 0.41 m, compared to positioning without PRC. In terms of precision or standard deviation, the best case was again when the real PRC was used. Although most positioning errors were distributed within 5.0 m when PRC was not used, errors larger than 6.0 m showed up. In contrast, all errors (except some) were within 3.0 m when predicted PRCs were used, and most errors were distributed within a range of 2.0 m, although a bias of about 0.7 m occurred in the southerly direction.  
The vertical errors showed that RMSE without PRC was 9.70 m. However, when predicted and observed PRCs were used, vertical errors are all below 2 m. Vertical error using predicted PRC was 0.57 m larger than with observed values. However, it improved to more than four times that of positioning without PRC. Since the vertical component in the coordinates was closely related to tropospheric delay, vertical errors were significantly reduced due to the effect of tropospheric error correction by PRC [13; 28].
## 5 Multi-Source Fusion Bayesian Compressed Sampling (MSF-BCS)  
### Multi-Source Bayesian Compressive Sampling (MS-BCS)  
In engineering practice, geotechnical investigations provide highly accurate and directly measured intrusive data, however, the data obtained are limited and sparse due to time, cost, and access to the subsurface space. Geophysical data are usually acquired independent of site conditions (e.g., MASW), but indirect, non-intrusive data are obtained [22; 37; 38]. [PERSON] et al. [24] proposed a novel method of multi-source Bayesian compressive sampling to fuse geotechnical and geophysical data for two-dimensional subsurface spatial variability characterization, which fully exploits the advantages of both data. It is worth noting that this method is not limited by soil type, does not require empirical equations between geotechnical and geophysical data, and prior information in the geotechnical domain, and has significant advantages. [PERSON] et al. [24] used maximum likelihood estimation in obtaining the \(\hat{\mathbf{\omega}}^{\rm 2D,m}\) posterior distribution, which may be more effective for simple models and small data sets. However, this method cannot consider the parameter uncertainty and prior information, which may lead to problems, such as over-fitting and under-fitting. Moreover, considering that most current geotechnical engineering models are highly nonlinear, it is difficult to perform integral solutions in high-dimensional parameter spaces [39]. For complex models and large data sets in geotechnical engineering, this study uses Markov Chain Monte Carlo (MCMC) simulation to integrate prior information in the posterior distribution, which can lead to more accurate and comprehensive estimation results. It is further extended to the study of 2D soil liquefaction potential assessment by adaptively performing multi-source fusion sampling with a reduced amount of field measurement works, and this smart sampling strategy will perform the fusion of geotechnical investigation data (CPT measurement data) and geophysical data (MASW), as described in detail below. In the BCS framework, most of the non-trivial 2D basis functions in the two highly correlated datasets are similar, and most of the non-trivial 2D basis functions with relatively large weight values can be common for these two correlated CPT measurement data and MASW data. Therefore, the common hyperparameter \(\mathbf{\alpha}^{(I,II)}\) can be used for \(\mathbf{\hat{F}}^{I}\) and \(\mathbf{\hat{F}}^{II}\)[40]. The cone penetration resistance (\(q_{c}\)) is used as the primary variable and the shear wave velocity (\(V_{s}\)) data obtained by MASW are used as auxiliary variables to improve the sparse data of the primary variable by data fusion. Cross-sections are represented by the matrix \(\mathbf{F}^{m}\), and in the following the superscripts \(m=I\) and \(m=II\) represent geotechnical data (\(q_{c}\)) and geophysical data (\(V_{s}\)), respectively. [PERSON] et al.
# Predicting the Compressive Strength of Concrete Using an RBF-ANN Model
## 7 Conclusions  
The focus of this study is on establishing an RBF-ANN model for predicting the 28-day concrete compressive strength according to the proportioning of the ingredients. The stochastic gradient approach present in [40] is employed for tuning the centers of RBFs and their shape parameters. Using a similar treatment in [41], the stepping parameter in the stochastic gradient approach is treated as a relaxation coefficient which can help to escape from the solution of local minimum error. An RBF in the ANN is analogous to a neuron in the BP-ANN which was employed for the same purpose in the author's previ-ous work [27]. Artificial neural networks with 2-5 RBFs are trained and tested. Each RBF-ANN is trained with 3 runs in which \(2\times 10^{8}\) iterations are processed. After the iteration processes, all the ANNs with the same number of RBFs converge to results that are very close to each other. This implies the convergence is valid. Therefore, the only issue concerning over-fitting is the number of RBFs. Finally, the ANN with just 3 RBFs is chosen for further verification. Data for verification are the same as those used in the author's previous work [27] so they can be compared. The comparisons show that the present model performs better than the author's previous model [27] which was established by using the BP-ANN and has 7 neurons in the hidden layer.  
The centers of the RBFs, their shape parameters, their weights, and the threshold are all listed in this article. With these numbers and using Equations (3), (4), (7), and (13), anyone can predict the compressive strength of concrete according to the concrete mix proportioning on his/her own.  
This research received no external funding.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
All the data used in this paper can be traced back to the cited references.  
The authors declare no conflict of interest.
# Interclass Interference Suppression in Multi-Class Problems
## 4 Design of Interclass Interference Suppression Algorithms
### Construction of Binary Classifier  
The binary-classifier-based approach decomposes multi-class problems into several binary classification problems. OVA and OVO are two commonly used strategies. Firstly, OVA strategy is presented. For a given M-class problem, OVA strategy uses the instances belonging to class \(i(i=1,2,\ldots,M)\) and the instances not belonging to class \(i\) to construct M binary classifiers. In this paper, other classes except class \(i\) are called the negative class of class \(i\), denoted as class \(\vec{i}\). Algorithm 1 shows the detailed procedure of experiments based on OVA in this paper. Secondly, the OVO strategy is presented. For a given M-class problem, OVO strategy uses to construct M(M-1)/2 binary classifiers respectively. Algorithm 2 shows the detailed procedure of experiments based on OVO strategy in this paper. For OVO and OVA method, there are three decision strategies. The first one is based on the voting of classification results. The second one is based on the voting of a certain coefficient. The third one is based on the voting of the support coefficient. For voting based on the support coefficient, the OVA method can use the support coefficient of either the positive class or the negative class. ```
Input: the original dataset Output: M binary classifiers; the corresponding feature subset \(iRedu\), rule set \(iRuleset\) and support coefficient \(iSupp\) of M binary classifiers; the average number of attributes \(aveNum\_Redu\); the overall feature subsets \(used\_Redu\); the overall number of rules \(Num\); average support coefficient \(aveSupp\); the average rule length \(aveLen\); the overall number of rules \(\overline{Num}\) for negative class; average support coefficient \(\overline{aveSupp}\) for negative class and the average rule length \(\overline{aveLen}\) for negative class. Begin \(i=0\); while\(i\leq M\); //\(M\) is the number of classes in multi-class problems 1. Mark all instances not belonging to class \(i\) as class \(\tilde{i}\) and obtain the dataset \(i\_Dataset\) of \(i\)-th classifier. // class \(\tilde{i}\) is denoted as the negative class of class \(i\) here. 2. Obtain the feature subset \(i\_Redu\) of \(i\_Dataset\) via attribute reduction and denote the number of attributes in feature subset as \(iNum\_Redu\). 3. Extract rules from feature subset, and denote the rule set of i-th classifier as \(iRuleset\) 4. Compute the support coefficient of each rule in rule set \(iRuleset\) and denote the set of support coefficients as \(iSupp\). 5. Compute the rule number \(iNum\), the sum of support coefficient \(iSum\_Supp\) and the sum of rule length \(iSum\_Len\) of the class \(i\). 6. Compute the rule number \(\overline{iNum}\), the sum of support coefficient \(\overline{iSum\_Supp}\) and the sum of rule length \(\overline{iSum\_Len}\) of the class \(\tilde{i}\). 7.
# Congestion Analysis of Transport Layer in a Multicell 5G DL Communication System
## 3 Experiments
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Values** \\ \hline Duplex mode & TDD \\ Channel bandwidth & 100 MHz \\ Frequency & 3.5 GHz \\ Type of scenario & Multicell \\ Channel model & UMa without buildings \\ gNB antenna configuration & 64T64R \\ gNB Tx power & 200 W \\ DL MIMO mode & 4 \(\times\) 4 MIMO \\ Beamforming & GoB beamforming \\ Number of UE devices per cell & From 10 to 200 \\ UE speed & 3 km/h \\ Maximum UE power & 23 dB \\ Traffic model & FTP model two: extended 10 \(\times\) 5 MB file with \\ TCP slow-start algorithm \\ Modulation & DL: 256 QAM \\ Simulation length & 30 s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of parameters. \begin{table}
\begin{tabular}{c c} \hline \hline
**Senarios** & **Metrics Studied** \\ \hline Scenario one & PRB utilization and data volume \\ Scenario two & Cell throughput \\ Scenario three & Cell edge throughput \\ Scenario four & User throughput \\ Scenario five & Delay \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Scenarios and metrics investigated.
# Multidisciplinary Optimization of Aircraft Aerodynamics for Distributed Propulsion Configurations
###### Abstract  
The combination of different aerodynamic configurations and propulsion systems, namely, aero-propulsion, affects flight performance differently. These effects are closely related to multidisciplinary collaborative aspects (aerodynamic configuration, propulsion, energy, control systems, etc.) and determine the overall energy consumption of an aircraft. The potential benefits of distributed propulsion (DP) involve propulsive efficiency, energy-saving, and emissions reduction. In particular, wake filling is maximized when the trailing edge of a blended wing body (BWB) is fully covered by propulsion systems that employ boundary layer ingestion (BLI). Nonetheless, the thrust-drag imbalance that frequently arises at the trailing edge, excessive energy consumption, and flow distortions during propulsion remain unsolved challenges. These after-effects imply the complexity of DP systems in multidisciplinary optimization (MDO). To coordinate the different functions of the aero-propulsive configuration, the application of MDO is essential for intellectualized modulate layout, thrust manipulation, and energy efficiency. This paper presents the research challenges of ultra-high-dimensional optimization objectives and design variables in the current literature in aerodynamic configuration integrated DP. The benefits and defects of various coupled conditions and feasible proposals have been listed. Contemporary advanced energy systems, propulsion control, and influential technologies that are energy-saving are discussed. Based on the proposed technical benchmarks and the algorithm of MDO, the propulsive configuration that might affect energy efficiency is summarized. Moreover, suggestions are drawn for forthcoming exploitation and studies.  
distributed propulsion (DP) technology; aerodynamic configuration; boundary layer ingestion (BLI); distortion control; multidisciplinary design optimization (MDO) 2024147781. [[https://doi.org/10.3390/app14177781](https://doi.org/10.3390/app14177781)]([https://doi.org/10.3390/app14177781](https://doi.org/10.3390/app14177781))
# Evaluation of Clumping Effects on the Estimation of Global Terrestrial Evapotranspiration
## 3 Input Data
### Global LAI Product  
The global LAI product in 2015 used in this study was retrieved from the GLOBMAP LAI Version 3 dataset, which includes consistent long-term (1981-2019) global LAI maps at 8 km resolution [37]. The global LAI maps in 2015 were derived from 8-day synthesis MODIS land surface reflectance products (MOD09A1 C6) and the angular information. The red, near-infrared (NIR), and shortwave-infrared (SWIR) reflectance were used for LAI derivation based on the GLOBCARBON LAI algorithm. The cloud pixels of MOD09A1 C6 data were identified by an algorithm of cloud detection [50], and the cloud pixels were filled using the approach of locally adjusted cubic spline capping [51]. The leaf clumping effects had been considered for each pixel with a global CI map at 500 m resolution [52]. This global LAI product had been validated using field measurements of 28 sites and 45 LAI maps of fine resolution over 29 sites which covered major global PFTs [37]. A global LAI map on 4-11 July 2015, is shown as an example in Figure 3.  
Figure 2: The global land cover map in 2013 at 500 m resolution. (1) EBF: Broadleaf Evergreen Forest; (2) DBF: Broadleaf Deciduous Forest; (3) ENF: Needleleaf Evergreen Forest; (4) DNF: Needleleaf Deciduous Forest; (5) MF: Mixed Forest; (6) TO: Tree Open; (7) Shr: Shrub; (8) Herb: Herbaceous; (9) HSTS: Herbaceous with Sparse Tree/Shrub; (10) SV: Sparse vegetation; (11) Cro: Cropland; (12) PF: Paddy field; (13) COVM: Cropland and Other Vegetation Mosaic; (14) Man: Mangrove; (15) Wet: Wetland; (16) BAC: Bare area, consolidated (gravel and rock); (17) BAU: Bare area, unconsolidated (sand); (18) Urb: Urban; (19) SI: Snow or Ice; (20) WB: Water bodies.  
For ET modeling at the one-degree resolution, the global LAI maps at 8 km resolution were aggregated to derive the mean LAI value for each PFT in each one-degree grid. In this approach, each PFT within the one-degree grid is considered, and the LAI related to each specific PFT is averaged from all the fine resolution pixels. This allowed separate ET estimation for each PFT in each one-degree grid. The ET value of each one-degree grid was then calculated as the sum of ET for all PFTs weighted by their areal fractions [49].
## 4 Discussion
For instance, Sentinel-1 passed over the study area following Hurricane Harvey on 5 September, even though the Hurricane concluded on 31 August, and this gap between the event and the SAR acquisition could have missed capturing the standing floodwaters that dissipated by the time of the satellite overpass. Second, the high cut-off empirical value (>45%) set in this study to identify pixels that exhibited a significant decline in coherence with the aim of weeding out false flood detections could have underestimated the spatial extent of the areas that are prone to flood hazards. The geometry and arrangement of the objects and structures in urban areas create a strong backscatter of SAR signals, even in the absence of floodwater on the surface, and, hence, change detection assessments with high threshold values as in the present study may not detect moderate-to-subtle changes in these areas. In addition, the data analysis procedures applied in this study, such as multilooking, that are intended to reduce the inherent noise on SAR signals partly degraded the resolution of the final product (final ground pixel resolution of 80 m), and thus moderate-scale flooded pixels might not have been captured in the final result.
# Adaptive Power-Controlled Depth-Based Routing Protocol for Underwater Wireless Sensor Networks
## 4 Theoretical Analysis and Experimental Results
### Results and Analysis
#### 4.4.5 Influence of the Node Speed on Network Performance  
Due to the influence of water flow, nodes are subject to movement within a certain range, resulting in dynamic changes in the network structure. To address this issue, we propose a localized-globalized integrated strategy. In this section, we will examine the changes in network performance as the flow velocity increases. Furthermore, we will discuss the advantages and additional overhead introduced by the strategy.  
As is shown in Figure 15, when the node speed is low, all protocols exhibit high packet delivery rates. However, as the node speed increases, DBR and EEDBR, which involve a significant amount of redundancy in data transmission, can still ensure packet delivery to the destination node, even if some packets are dropped. However, this comes at the cost of higher energy consumption. In networks using EEDBR, as the node speed increases, the PDR decreases. This is because, compared to DBR, EEDBR limits the number of forwarding nodes. In the case of EAVARP, which employs single-route routing, the PDR significantly decreases with higher node speeds. On the other hand, APCDBRP, which also uses single-route routing, achieves a high PDR due to its localized-globalized integrated strategy. This demonstrates the effectiveness of our proposed strategy in data protection and network recovery.  
The effects of node speed on network end-to-end delay and energy consumption are shown in Figures 16 and 17. In terms of end-to-end delay, DBR and EAVARP are largely unaffected by changes in node speed. However, EEDBR and APCDBRP experience increased end-to-end delay as node speed increases. For EEDBR, this is because the protocol optimizes the packet holding time based on node priority and sets the packet holding time to zero for the best next-hop node. As a result, as node speed increases, the probability of successful forwarding by the best next-hop node and higher-priority nodes decreases, leading to increased end-to-end delay. In the case of APCDBRP, as node speed increases, the probability of successful packet forwarding decreases. When packet forwarding fails, the data protection mechanism is triggered, resulting in packet retransmissions and, consequently, increased end-to-end delay.  
Regarding energy consumption, the presented values represent the average energy consumed per successfully delivered packet. EAVARP exhibits higher energy consumption when node speed is high due to lower packet delivery rates. APCDBRP, with its localized-globalized integrated strategy, experiences increased energy consumption as node speed increases. This is because, at higher node speeds, APCDBRP not only retransmits packets in case of forwarding failures but also sends beacon packets to rebuild routes when the sink node detects a decrease in the number of received packets. However, overall, APCDBRP achieves low energy consumption while maintaining a high packet delivery rate.  
Figure 15: Influence of the node speed on PDR.
## 6 Results, Analysis, and Discussion
The quantities of packets lost were also measured during the experiments, however, in most cases zero packets were found to have been dropped during the communication. The packet loss rate was only found to increase when the network was experiencing a very high load (see Table 12). \begin{table}
\begin{tabular}{c c c} \hline \hline  & **UDP Response** & **TCP Response** \\  & **Time (s)** & **Time (s)** \\ \hline Mean & 1.236800923 & 1.070474602 \\ \hline Variance & 0.068140906 & 0.029979311 \\ \hline Observations & 60 & 60 \\ \hline Hypothesized Mean Difference & 0 & - \\ \hline df & 102 & - \\ \hline t Stat & 4.112987187 & - \\ \hline P (T \textless{}=\textt) one-tail & 3.96196 \(\times\) 10\({}^{-5}\) & - \\ \hline t Critical one-tail & 1.659929976 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Result, Experiment 7. The speed at which the Control Centre detects a DoS attack and activates the TCP phones was also recorded. This is of course influenced by the alert and latency thresholds, which were set to '5' and '0.7', respectively, meaning a DoS attack would be detected after five calls during which the average response time was greater than 0.7. Figure 10 displays a graph showing the average time taken to detect a DoS attack at each strength of attack. The average detection time increases in correlation with the strength of the attack. This is likely due to the mechanism of action, which detects an attack by measuring the response times of a set number of completed calls. Therefore, the longer a call takes to complete, the longer it will take to detect the attack. To combat this, improvements should be made to the detection mechanism that will increase the rate of detection when the response times are high. For example, when the average response time of a phone call is above a certain threshold (e.g., '1 second') the alert level could be increased by two rather than one, reducing the number of completed calls required to trigger the activation of the TCP phones.