esa-sceva/satcom-chunk-collection huggingface dataset:
# XCO\({}_{2}\) Fusion Algorithm Based on Multi-Source Greenhouse Gas Satellites and CarbonTracker
## 2 Materials
### TCCON Data  
TCCON (Total Carbon Column Observing Network) sites adopt ground-based Fourier Transform Spectrometers (FTS), and can detect spectral data in the range of 4000-9000 cm\({}^{-1}\) in solar radiation [29]. Among them, there are two CO\({}_{2}\) weak absorption bands, 6220 cm\({}^{-1}\) and 6339 cm\({}^{-1}\). Compared with satellite detection data, the column concentration data of CO\({}_{2}\), CH\({}_{4}\), N\({}_{2}\)O, HF, CO, and H\({}_{2}\)O from TCCON are more accurate because the retrieval method can effectively avoid the error impact caused by aerosols and cirrus clouds. Therefore, the XCO\({}_{2}\) data products measured by TCCON have been confirmed as the verification standard of satellite remote sensing data [30; 31; 32; 33; 34; 35]. The main sites of its site selection are still concentrated in North America and Europe, with few sites in East Asia and Oceania. The TCCON data used in this paper is the GGG2014 version, which were obtained from the TCCON Data Archive (https:// TCCON.ornl.gov accessed on 1 December 2021). When the solar zenith angle is below 82\({}^{\circ}\), its XCO\({}_{2}\) error is less than 0.25%. The information of TCCON sites used in this paper is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **OCO-2** & **GOSAT** & **CarbonTracker** \\ \hline Start date & 6 September 2014 & 21 April 2009 & 1 January 2000 \\ End date & up to now & up to now & up to now \\ Spatial resolution & 2.25 km \(\times\) 1.29 km & 10.5 km \(\times\) 10.5 km & 2\({}^{\circ}\times\) 3\({}^{\circ}\) \\ Time resolution & 16 days & 3 days & 1 day \\ \hline \end{tabular}
\end{table}
Table 1: Data Introduction.  
The XCO\({}_{2}\) products used in this paper are OCO2_L2_Lite_FP. 10R retrieved by the ACOS algorithm, GOSAT ACOS_L2_Lite_FP_9r, and CT2019B covering the period from 2017 to 2018. Due to the coverage of clouds, the scattering and absorption of aerosols in the atmosphere, the spacing of satellite orbits, and the limitations of the inversion algorithm, many observation results are determined as bad-quality data. Therefore, data screening was carried out according to the ACOS use guide, and only the data with \"xc0_quality_flag\" equal to 1 were used to eliminate the data the quality of which does not meet the requirements of OCO-2 and GOSAT [30].
# Sigma Routing Metric for RPL Protocol
## 4 Proposed SIGMA-ETX Metric
### General Scheme  
In an RPL environment, the DODAG root sends a DIO message that is propagated and received by every node in the network. Every node in the DODAG sends a DAO message (to the root) with relevant information to create the network topology (See Figure 4).  
To calculate SIGMA-ETX, some fields are added to the DAO messages containing the ETX values of the sender node and its neighbors. This information is transported from the sending node to the DODAG root.  
With the topology information obtained from the DAO messages, control messages can be sent from the root of the RPL instance, by explicitly specifying in each message what will be the hops that each message will take. Two optional blocks were added to the option fields (See Figure 5) of the RPL DAO messages:  
RPL_OPTION_PATH_LENGTH:  
Contains a single 32-bit field indicating the length of the path from the DAO source to the root of the RPL instance.  
RPL_OPTION_PATH_MEMBER:  
Contains the prefix of the IPv6 address of a single node in the path from the DAO source to the root of the RPL instance, and the ETX value of that individual hop.  
The root uses a new option in the DAO-ACK messages to relay the calculated SIGMA-ETX values to the nodes. The DAO-ACK message has a similar structure as the DAO message, allowing extra options to be appended as needed.  
As Figure 6 shows, the nodes calculate their ETX values with respect to their neighbors, and then send the DAO messages to the DODAG root. Once this information reaches the root, it is used to calculate the SIGMA values for each path, so that the preferred routes can be identified.  
Figure 4: DAO Format.
# Enhanced Scalability and Security in Blockchain-Based Transportation Systems for Mass Gatherings
## 4 Results and Findings
### Statistical Validation of Performance Metrics  
To validate our results, we conducted a one-way Analysis of Variance (ANOVA) test, examining throughput (TPS) and latency (in seconds) across various scenarios. The ANOVA test was used to determine whether the actual difference between our system and the reference systems was statistically significant.  
1. Hypothesis Formulation We define the two null and alternative hypotheses for throughput and latency as follows:
2. _The difference in performance across the different systems is not significant._
3. _At least one system differs significantly from the others._
4. ANOVA Test for Throughput (TPS) We collected throughput values for the baseline, State Channels, and Rollups in our system and reference systems. For ANOVA [41; 42], the test statistic was calculated using Equation (15) [30; 43]: \[F=\frac{Between-group\ Variance}{Within-group\ Variance}\] (15)  
Equation (9) shows the proportion of variance between different system performances relative to the variance within each system's results. A more significant F value indicates a higher likelihood that at least one group's mean differs from the others, rather than the difference being due to random variation.  
Using the given throughput values, we compute the following:  
Mean throughput for the baseline \(\overline{X}_{baseline}=770.0\) Mean throughput for the State Channels \(\overline{X}_{statchannels}=2393.8\) Mean throughput for Rollups \(\overline{X}_{rollups}=3283.3\) Overall Mean: \(\overline{X}_{overall}=2149.07\) The results yielded an F-statistic of 45.23 (\(p<0.001\)), indicating significant differences in throughput.
4. ANOVA Test for Latency (s) Similarly, we calculated the latency values in different situations. The ANOVA formula that is still being applied to the latency values is as follows:  
* Mean latency for baseline \(\overline{X}_{baseline}=5.56\)
* Mean latency for State Channels \(\overline{X}_{statchannels}=2.91\)
* Mean latency for Rollups \(\overline{X}_{rollups}=1.98\)
* Overall Mean: \(\overline{X}_{overall}=3.48\) The calculated F-stat for latency was 62.87, \(p<0.001\), confirming that newer approaches reduce latency.  
Additionally, the ANOVA results revealed that the throughput and latency across the scenarios were statistically significant. This verifies that our system outperforms the reference systems, particularly under the configuration of State Channels and Rollups. This confirms the efficiency of our approach in improving blockchain transaction processing efficiency.
# Integrating PubMed Label Hierarchy Knowledge into a Complex Hierarchical Deep Neural Network +
## 1 Introduction
In addition, to remove the hierarchical inconsistencies of the label set associated with each training sample, where some labels of the hierarchy could not be included [12], we also defined a methodology able to overcome this problem, expanding the label set following the hierarchy and adding the missing labels. We also evaluated the behavior and performances of the proposed HDNN approach applied to the PubMed MeSH indexing task, extending previous experiments presented in [22] and further demonstrating its effectiveness. In particular, this paper extends the previous results, first by including and testing the BioBERT model [18] as the initial layer of the proposed architecture for text encoding. BioBERT is a BERT-based architecture pretrained on a large biomedical domain document collection extracted from PubMed, which has been successfully adopted in many complex biomedical domain NLP tasks. Moreover, we more deeply analyzed the complexity of the algorithm, in particular with respect to the distribution of the number of nodes and the corresponding children nodes. Finally, we extended the number of experiments performed to further investigate the behavior of the proposed architecture. The main novelty of the proposed approach is the development of a technique specifically devoted to incorporating hierarchical innate knowledge into the neural network architecture, able to dynamically adapt to any label set and capable of dealing with huge label numbers, such as the PubMed MeSH. As mentioned above, the most recent best-performing approaches for large-scale PubMed indexing integrate more methodologies and techniques. Therefore, the proposed HDNN, combined with other approaches, can further improve the results obtained in this complex task. This paper extends and improves the research previously presented in [22], first integrating the BioBERT model [18] (a BERT model pretrained on a very large scientific biomedical article collection extracted from PubMed) within the HDNN architecture; moreover, it extends and more deeply investigates the preliminary experiments previously presented. Finally, it provides a more accurate analysis of the performances of the proposed architecture, especially in terms of the depth of the label hierarchy. This paper is structured as follows: The next Section 2 is devoted to a review of the most recent related works in the fields of integrating external knowledge into DNNs, hierarchical text classification, and PubMed large-scale indexing. In Section 3, the details of the methodology are described, and in Section 4, the experiments and results are illustrated and then discussed. Section 5 concludes the paper and presents some possible future works.
# Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks
## 6 Results  
### Preretinal Space Segmentation Accuracy  
We conducted multiple experiments using several networks to segment the preretinal space and the retina in the OCT images. This section presents a qualitative and quantitative comparison of preretinal space segmentation with a graph search approach and four DNN methods. Specifically, UNet, LFUNet, Attention UNet, and ReLayNet networks were used to obtain precise segmentation masks. Table 4 presents baseline results for a whole dataset, without any additional strategies described in this paper. The Dice and MAE (SD) metrics show how accurate the performed segmentation is.  
As can be noticed, all neural networks perform better than the graph search-based method, and from CNN, the UNet has the best performance in all segmented areas and borders. On the other hand, the ReLayNet gives the worst results, which may be explained by a relatively lower number of features compared to other architectures. Additionally, the preretinal space boundary of PCV and the image classes it separates (i.e., Vitreous and Preretinal Space) have worse accuracy than the clearly defined ILM and RPE borders and the two image regions they define. This confirms the difficulty of determining preretinal space boundary due to similar pixel intensities of this region to the vitreous part.
## 2 The Voltage Regulation Model  
### The Model of OLTC  
As traditional voltage regulation equipment, the OLTC can adjust the tap position to realize the voltage regulation. The inner control of the OLTC is shown in Figure 1.  
If the difference between the voltage reference and the distribution network voltage exceeds the dead band, the OLTC will change a tap. Each variation of a tap has a voltage  
Figure 1: The inner control of OLTC.  
change at the secondary side of the transformer. The action of the OLTC has a time delay in the voltage regulation service [15]. The voltage variation can be expressed as:  
\[U_{\text{DN}}(t)=U_{\text{DN}}(t-\Delta t)+\Delta n_{\text{OLTC}}(t)\cdot U_{ \text{tap}} \tag{1}\]  
\[U_{\text{DN}}(t)=[U_{1}(t),\;U_{2}(t),\;\ldots,\;U_{n}(t)] \tag{2}\]  
where \(U_{\text{DN}}(t)\) and \(U_{\text{DN}}(t-\Delta t)\) are the voltage array of the distribution network at the current time and previous time. \(\Delta n_{\text{OLTC}}(t)\) is the variable number of taps. \(U_{\text{tap}}\) is the voltage variation in each tap. \(U_{\text{tap}}=0.0125\), and \(n\) is the number of nodes.
###### Abstract  
Agriculture plays a crucial role as a carbon sink in the atmosphere, contributing to a climate-neutral economy, which requires a comprehensive understanding of Earth's complex biogeochemical processes. This study aims to quantify, for the first time, Gross Primary Productivity (GPP) and ecosystem water use efficiency (eWUE) in almond orchards during their vegetative phase. The study was conducted over six growing seasons (2017-2022) across two drip-irrigated commercial almond groves located in Albacete, SE Spain. Eddy covariance flux tower systems were used to measure Net Ecosystem Exchange (NEE) and evapotranspiration (ET), which were then used to calculate GPP and eWUE. A novel approach was developed to estimate eWUE by integrating the Normalized Difference Vegetation Index (NDVI), reference ET, and air temperature. The results show similar almond orchard carbon-fixing capacity rates to those of other natural and agro-ecosystems. Seasonal and interannual variability in GPP and eWUE were observed. The NDVI-ET combination proved to be effective for GPP estimations (regression coefficient of 0.78). Maximum carbon-fixing values were observed at ET values of around 4-5 mm/d. In addition, a novel method was developed to estimate eWUE from NDVI, reference ET and air temperature (RMSE of 0.38 g-C/kg\(\cdot\)H\({}_{2}\)O). This study highlights the carbon capture potential of almond orchards during their vegetative phase and introduces a novel approach for eWUE monitoring, with the intention of underscoring their significance in a climate change context and to encourage further research.  
eddy covariance; carbon uptake; mitigation; vegetation index Article
## 2 Current Status of GNSS Integrity Service
### Current Status of BDS-3 Integrity Services  
The BDS-3 constellation provides fundamental integrity services with all the operational satellites, including GEO, IGSO, and MEO satellites. In addition, it offers SBAS integrity services with the GEO satellites within the BDSBAS. The fundamental integrity service of the BDS-3 is able to meet the ICAO certification requirements for non-precision approaches (NPAs). Furthermore, the BDSBAS can meet the ICAO certification requirements for APV-I. Currently, China is working actively on passing these certifications [22]. Figure 1 illustrates how the BDS-3 provides the fundamental integrity services and the SBAS integrity services.
## 1 Introduction  
The application of industrial manipulators can largely eliminate human error, boost product quality, and increase productivity [1; 2]. Numerous researchers have sought to develop control methods capable of ensuring that the manipulator follows a desired trajectory. [PERSON] et al. [3] proposed a saturated PID controller to deal with motor failure in mechanical arms resulting from excessive input torque. [PERSON] et al. [4] proposed a static neural network controller that uses a novel Gaussian basis function to facilitate adaptive tracking control. Their controller switches between a neural network controller and a robust proportional derivative (PD) controller. [PERSON] et al. [5] developed an SMC-based position tracking method based on a high-order dynamics model followed by a fuzzy neural network to simulate the original sliding mode control for stability analysis. [PERSON] et al. [6] developed a novel composite control scheme that introduces uncertainties into the dynamic modeling of a manipulator and then uses a novel dead zone compensation method to attenuate uncertainties and the corresponding nonlinearities. [PERSON] et al. [7] proposed an adaptive neural network controller to ensure control over the tracking of uncertain n-links manipulators with full-state constraints. [PERSON] et al. [8] achieved trajectory tracking control in a system that combines the advantages of a sliding mode controller with those of a backstepping controller. [PERSON] et al. [9] proposed an adaptive bias radial basis neural network controller with local and global bias to improve approximation accuracy in dynamic models with significant bias. When the input deviates from the approximation domain due to excessive payload, a neural network controller degenerates into a PID controller to return the system to its original state. Note, however, that most previous approaches to manipulator control systems considered a limited number of interference sources and dealt with them as a whole.  
Our objective in the current paper was to deal with the above issues from two perspectives. First, we established an enhanced second-order dynamic model of the manipulator, which considers payload-related issues as well as those pertaining to model uncertainties, friction, external disturbances, and backlash. We then developed a composite controller based on NFFSMC, which deals with the two classes of interference (payload and other), respectively, using ARBNN and TDE. The proposed control strategy enhances accuracy while reducing the influence of variations in interferences. It also reduces dependence on parameter adjustment and does not require an interference upper bound.  
The remainder of this paper is organized as follows: Section 2 introduces the process of designing our enhanced dynamic model of the dual-axis manipulator. Section 3 outlines our advanced control strategy and stability analysis. Section 4 outlines the simulations used to assess the performance of the proposed algorithm. Conclusions are presented in Section 5.
## 4 Performance Evaluation
### Analysis of End-to-End Delay
#### 4.2.4 **b. Optimal Number of BSs with SSCBT**  
To verify the \(N\) values analyzed in Section 4.2, we conducted simulations with S2 and S3 in Table 4. We examined SSCBT with varying \(N\) from two through five with CW fixed to three from Section 4.2.2(a). Referring to Figure 10, the increase of PDR almost stopped at \(N=4\) after reaching over 99% for both S2 and S3. This implies that SSCBT allowed the high degree of parallelism since \(N\) is much less than the number of nodes at the same level. \(N=4\) accords very well with the analytical value computed by Equation (13) (even though \(N=5\) for S3 analytically).  
link breakages affect energy consumption a little. Furthermore, in RSBP and SSMAb, only an internal node rebroadcasts a message. Thus, the change in link condition may increase or decrease the number of internal nodes; however, the ratio of internal nodes will remain almost same on average. Thus, it is obvious that the decrease of \(K\) does not increase AEC. Similarly, in DPFNI, a node rebroadcasts a message depending on its broadcast probability which is affected by its connectivity as explained in the Introduction. Thus, it still does not increase AEC noticeably, ether.
## 3 Results and Discussion
### Durability Properties
#### 3.4.1 Dry Abrasion Resistance  
Figure 19a illustrates the variation in the abrasion resistance coefficient (ARC) as a function of Juncus fiber content. The higher the ARC of the block, the greater its durability performance in terms of abrasion resistance. Three specimens were tested for each formulation. As expected, the increase in cement content leads to an increase in the ARC, meaning that the stabilization has a positive influence on the abrasion resistance. The block with a high content of fibers (0.2%) and a low content of cement (4%) had the lowest value of ARC, 2.432 cm\({}^{2}\cdot\)g\({}^{-1}\), whereas the highest value was 8.182 cm\({}^{2}\cdot\)g\({}^{-1}\), corresponding to the block a 12% cement content without fibers. For the same content of cement, it can be noticed that the addition of JF slightly decreases the abrasion resistance. This can be due to the morphology of Juncus fibers, characterized by a smooth external surface, contributing to weakened bonding between the fibers and the matrix. It can be concluded that the higher the compressive strength, the better the durability of the block. Figure 19b displays the samples after being subjected to abrasion tests.  
Figure 19: (**a**) Effect of fiber content on the abrasion coefficient of CEBs stabilized with different percentages of cement, (**b**) CEBs after abrasion test.
# New Quality Control Algorithm Based on GNSS Sensing Data for a Bridge Health Monitoring System
## 3 Bridge Experiment
### Introduction
#### 3.1.2 Test Description & Test-Bed  
- _Test Description_  
To conduct this research, GNSS data was collected through an array of six receivers for 24 h from 09:00 on 25 March 2010. Data was received at a sampling rate of 1 Hz (1 s). The antenna used at the reference  
Figure 4: Three-dimensional coordinates of the point of MSWE. (**a**) Horizontal coordinate at MSWE including outliers; (**b**) Vertical coordinate at MSWE including outliers.  
Figure 5: Three-dimensional coordinates of the point of MSEA. (**a**) Horizontal coordinate at MSEA excluded outlier; (**b**) Vertical coordinate at MSEA excluded outlier.  
station and rovers was a Zephyr Geodetic Model 2 produced by Trimble Navigation, Ltd., (Sunnyvale, CA, USA, Figure 6 left). Receivers installed on the bridge as a rover were identical, _i.e._, the same model, namely a Trimble NetR5, produced by Trimble Navigation, Ltd., (Sunnyvale, CA, USA, Figure 6, right). These data were initially received at the GNSS receiver before being transmitted to the SeoHae Bridge management office through a wireless modem. The positioning methodology employed for processing GPS data from bridge is single baseline data processing which is commonly used for BHMS based GNSS. Details are provided in [PERSON] _et al._ [10].