esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Flow Control for Low Latency in QUIC
## 4 Evaluation  
We evaluate the performance of the fast autotuning allowance using an ns-3 simulator [18], and compare the results to the static and autotuning allowances [15; 16]. In [15], the static allowance is utilized in seven out of ten representative IETF QUIC implementations, while the autotuning allowance, which is used in quiche, is a flow control mechanism proposed by Google recently. As discussed earlier, we implemented static, autotuning, and fast autotuning allowance in the ns-3 simulator and measured the throughput and transmission time for performance evaluation. We measured the bytes received by the receiver with a tiny buffer for each of the three flow control models. Then, we extended the experiment to evaluate the performance of a four-stream connection with a large buffer. Finally, the transmission delay for each data size was measured through simulations.
## 2 Related Work
### External Measures of Clustering Accuracy  
Besides the SSE or distortion internal cost, the quality of a clustering solution can often also be assessed by some external measure, like the Adjusted Rand Index (ARI) [20; 21], which compares similarity/dissimilarity between an achieved clustering solution and a reference solution (ground truth). In this paper, the Cluster Index (CI) proposed in [22] is used, which was felt to be more sound for capturing the clustering accuracy. CI is best suited to quantify the clustering accuracy of synthetic datasets equipped with ground truth (GT) information (centroids and/or partition labels).  
CI quantifies the degree to which a clustering solution, C, achieved by a certain clustering algorithm is _close_ to GT. First (C \(\rightarrow\) GT), centroids \(\mu_{j}^{\text{C}}\) of C are mapped onto the centroids \(\mu_{i}^{\text{GT}}\) of GT, which, in general, could also be different in numbers. Every centroid of C maps on the particular centroid of GT which has minimal distance from it. As a measure of the dissimilarity in the mapping C \(\rightarrow\) GT, the number of centroids in GT (said \"orphans\") which were not associated with any centroid of C are counted.  
In the second step, GT centroids are mapped onto C centroids (GT \(\rightarrow\) C) and the number of orphans determined in C is counted. The CI(C,GT) value is the maximum number of orphans in the two directions of mapping:  
\[\text{CI}(\text{C},\text{GT})\ =\ \text{max}\ (\#\text{orphans}(\text{C} \rightarrow\text{GT}),\#\text{orphans}(\text{GT}\rightarrow\text{C}))\]  
CI \(\ =\ 0\) characterizes a \"correct\" clustering solution, that is, one where C clusters and GT clusters are structurally very similar to one another. A CI \(>\) 0 indicates the number of centroids incorrectly determined by the clustering algorithm.  
If the dataset comes with partition labels as ground truth [23], the Jaccard distance [9] between partitions (sets of labels or cluster indexes) belonging to the emerged solution and the ground truth solution can be exploited for realizing the mapping and determining the number of orphans in the two mapping directions.  
It is worth noting that, normally, real-world datasets come without ground truth information. However, a \"_golden_\" solution determined using an advanced clustering algorithm can sometimes exist. Also, in these cases (see later in this paper), it becomes possible to check, with the CI, the accuracy of a solution obtained by applying a given clustering algorithm.
# Estimating Maize Yield from 2001 to 2019 in the North China Plain Using a Satellite-Based Method
## 5 Conclusions  
In this study, the EC-LUE model was used to estimate the cumulative values on the gross primary productivity of maize during the growing season in the NCP from 2000 to 2019, based on satellite data and meteorological analysis data. The county-level HI was calibrated by combining the GPP and statistical yield. Then, the GPP was converted into the yield by using the calibrated HI. To evaluate the accuracy of the estimated yield, we compared the estimates and statistics on the county and city scales. The results show that the average growth rate in the total GPP in the summer maize-growing season from 2001 to 2019 was 2.30 Tg C/year, which was directly related to the increase in planting area. The HI has significant heterogeneity in space and little fluctuation in time between 2001 and 2019. Our estimated yield can simulate 64% and 55% of the yield variability on the county and city scale, respectively, and the model captures accurately the inter-annual changes in the maize yield. The county-level and city-level linear regression slopes vary between 0.75-1.08 and 0.6-1.22, respectively, and the MAPE is less than 20% in almost all years for both scales. But the model performance varies from region to region, HB (R\({}^{2}\) = 0.74), HN (R\({}^{2}\) = 0.57), and SD (R\({}^{2}\) = 0.58) outperformed AH (R\({}^{2}\) = 0.29) and JS (R\({}^{2}\) = 0.40) in terms of performance. That is especially true because the combination of the model and the calibrated HI enables estimates on the maize yields from county to province, and for even larger areas. Our study demonstrates that the EC-LUE model can be used to estimate maize yield and highlights the importance of employing a dynamic HI. This study can be considered as a complement to previous studies, demonstrating that the EC-LUE model can be applied to the large-scale estimation of crop yields for various crops, and can contribute to regional and national food production security.  
Conceptualization, L.W.; Data curation, C.H., X.C. and X.G.; Formal analysis, C.H. and X.C.; Funding acquisition, L.W.; Investigation, J.S.; Methodology, L.W., X.C. and X.G.; Project administration, L.W. and J.S.; Writing--original draft, C.H. and X.W. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (41975044, 41925007, 41771360, and 41801021) and the Fundamental Research Funds for National Universities, China University of Geosciences, Wuhan.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We thank the journal's editors and reviewers for their valuable suggestions to improve the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Experimental Methods
### System Configuration
#### 3.1.1 Space-Time Synchronization and Wireless Two-Way Interferometry (Wi-Wi)  
Wi-Wi is a wireless device based on the principles of two-way satellite time and frequency transfer (TWSTFT) technology, allowing precise time synchronization via satellites [25]. Operating within the unlicensed 920 MHz band, Wi-Wi engages in bidirectional packet exchange between devices, enabling precise time synchronization with an accuracy measured in tens of nanoseconds by detecting the carrier phase [26]. Moreover, Wi-Wi possesses the capability to measure distance variations at the scale of millimeters [27]. For our study, we used Wi-Wi with the objective of cross-referencing the logs of two distinct remote mmWave wireless communication devices.  
Figure 4: Configuration of the inter-UAV communication system.  
Figure 3: Photographs of the UAV and onboard equipment utilized in this demonstration.
# An Investigation of CTP Use for Wireless Structural Health Monitoring of Infrastructure
## 4 Conclusions  
In this paper, the robust CTP protocol has been investigated in terms of cooperative communication, with the aim to apply it to an infrastructure SHM task. An infrastructure can be monitored by placing the wireless nodes in a geographic manner and the CTP can take control. Testbed tests have been undertaken and specific links that could be used in terms of cooperation based on the quality of the link have been found. The fact that IEEE 802.15.4 technology can be employed for the SHM task adheres to the necessity of energy efficiency. The CTP, on the other hand, can ensure high transmission reliability; as such, using the transmission power adjustment can optimise the trade-off between energy efficiency and link quality. In this work, the testbed has been attempted to be operated in a pseudo-geographic manner, in order to show cooperative links.  
Our future work includes the addition of a practical cooperative scheme to the CTP protocol, in order to further optimise its performance. Our intuition dictates that an addition  
Figure 7: ETX values of two sets of nodes. Subfigure (**a**) shows trhe ETX value of the first set of cooperative and direct links while subfigure (**b**) the value of the second set of cooperative and direct links.  
of a cooperative packet id may produce even better results. Moreover, the testbed needs to be more accurately utilised, to the point possible due to its topology, in order to make the geographic manner of transmission more accurate. The testbed maintains a certain topology, and we did not take into account the nodes' positions, since they could be affected by many uncontrolled parameters due to their position. This work aims to pave the way towards the cooperative version of CTP. Even though CTP's mechanism changes the parent by default, that does not necessarily give us a cooperative nature. We only know that the best parent selection will be done. The extra field of the packet struct will provide a buyers sellers approach, which will take under consideration the next hop link quality, as well as that a node must transmit to a specific neighbouring node. Moreover, The experiments in this work are preliminary. Lastly, the aim is to setup a wireless network to our premises to better control the geographic nature of the cooperative CTP.  
Conceptualization, E.D.S.; methodology, E.D.S.; software, E.D.S.; validation, [PERSON] and V.K.; formal analysis, E.D.S.; investigation, [PERSON]; resources, E.D.S.; data curation, E.D.S.; writing--original draft preparation, E.D.S.; writing--review and editing, E.D.S. and V.K.; visualization, E.D.S.; supervision, V.K.; project administration, V.K.; funding acquisition, V.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Application of the data can be undertaken after communication with the authors. The authors declare no conflict of interest.
## 3 Results
### I-L Characteristics  
Figure 10 shows the measured I-L characteristics as the current density was increased from 25.5 A/cm\({}^{2}\) to 357.1 A/cm\({}^{2}\). Despite the droop phenomenon because of which luminous efficiency decreases as the current density increases, relatively good characteristics were observed from all chips. Interestingly, as the current density increases, the EQE tended to decrease in the 400-nm chips.  
Figure 10: _Cont._  
Figure 9: VF characteristics of chips at a current density 25.5 A/cm\({}^{2}\).  
Chips fabricated on a PSS substrate displayed better I-L characteristics than those on the sapphire substrate. To verify the results, samples with the structure shown in Figure 2 were grown up to the final InGaN layer of Multi Quantum Well (MQW)s for wavelengths of 385 and 400 nm. These samples were measured for In composition and crystallinity of the light-emitting layer via XRD. Table 1 and Figure 11 show the results of the XRD measurements. The compositions of the luminescent layer (InGaN), were In\({}_{0.08}\)Ga\({}_{0.92}\)N for 385 nm, and In\({}_{0.13}\)Ga\({}_{0.87}\)N for 400 nm. The XRD full width at half maximum (FWHM) (0002) was narrower in the PSS-substrate chips were narrower than the sapphire-substrate chips.  
\begin{table}
\begin{tabular}{c c c} \hline  & **In Composition** & **InGaN XRD FWHM (arcsec)** \\ \hline
385 nm (sapphire) & In\({}_{0.079}\)Ga\({}_{0.921}\)N & 374.4 \\ \hline
385 nm (PSS) & In\({}_{0.081}\)Ga\({}_{0.919}\)N & 360 \\ \hline
400 nm (sapphire) & In\({}_{0.134}\)Ga\({}_{0.866}\)N & 388.8 \\ \hline
400 nm (PSS) & In\({}_{0.134}\)Ga\({}_{0.866}\)N & 360 \\ \hline \end{tabular}
\end{table}
Table 1: Measured XRD of Last-InGaN.  
Figure 10: Measured I–L characteristics. 385-nm LED: (**a**) sapphire substrate (**b**) PSS; 400-nm LED: (**c**) sapphire substrate, (**d**) PSS.  
Figure 12 shows the AFM observations of the InGaN surface (the final layer of MQW). The dislocation density in the 385-nm chip on sapphire was 2.12 \(\times\) 10\({}^{8}\) cm\({}^{-2}\), the dislocation density in the 385-nm chip on PSS was 1.72 \(\times\) 10\({}^{8}\) cm\({}^{-2}\), the dislocation density in the 400-nm chip on SSP was 2.52 \(\times\) 10\({}^{8}\) cm\({}^{-2}\), and the dislocation density in the 400-nm chip on PSS was 1.08 \(\times\) 10\({}^{8}\) cm\({}^{-2}\).
# A Compact Quad-Element UWB-MIMO Antenna System with Parasitic Decoupling Mechanism
## 4 Results and Discussions  
### Induced Surface Currents  
The surface currents at 4 GHz, 6 GHz and 10 GHz are shown in Figure 5. Only one antenna element is made to radiate and others are terminated at matched impedance in order to observe the effect of radiation. There are strong induced surface currents on the non-radiating elements which are caused by coupled fields. Both side and facing elements develop these induced currents. The introduction of decoupling structure helps in suppressing these coupling, in Figure 5a the current plot at 4 GHz is shown and it can be observed that the coupled currents are now visible on the outer section of the decoupling structure which means that these sections resonate at lower
## Appendix A  
According to (A1) the calculation of the classification accuracy \(ACC\) depends on the number of true positives \(TP\), true negatives \(TN\), false positives \(FP\) and false negatives \(FN\).  
\[ACC=\frac{TP+TN}{TP+FP+TN+FN}\] (A1)  
The F1-score is calculated using the positive precision rate \(PP\) and the right positive rate \(RPR\) as described in (A2).  
\[F1=2\frac{PP\cdot RPR}{PP+RPR}\ \ \text{with}\ RPR=\frac{TP}{TP+FN}\ \text{and}\ PP=\frac{TP}{TP+FP}\] (A2)  
In multi-class settings macro-averaging represents the mean of all class-wise evaluation metrics whereas micro-averaging take into account all class samples to calculate an aggregated evaluation metric.
# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 3 CPS Architecture and Applications
### CPS Functional Attributes
#### 3.2.3 Cyber Level (C3)  
This level is the system's central information hub. The information at this level is used to extract additional information about a specific application. For instance, water demand patterns can be further analyzed, and information may be obtained to predict the future pattern of water consumptions. In this analysis, the cyber level algorithms need to be more adaptive and robust [82] to accommodate changed contexts in water demand variations, which may be needed for applications such as water distribution network state estimation, pressure control, and leakage analysis.
## 4 Methodology
### User Spacecraft
#### 4.3.4 JPSS-2  
Lastly, the FDF conducted real-time DOWD OD analysis for the Joint Polar Satellite System-2 (JPSS-2) satellite as part of its early orbit support. The JPSS-2 satellite is a collaborative project between the United States National Oceanic and Atmospheric Administration (NOAA) and NASA, designed for weather forecasting. The purpose of the program is to provide global environmental data, including information on atmospheric temperature and moisture, vegetation cover, sea and land surface temperatures, and ocean color, using polar-orbiting satellites [14].  
The JPSS-2 satellite was launched on 10 November 2022 on an Atlas V 401 rocket from Vandenberg Space Launch Complex 3 (SLC-3E) at Vandenberg Space Force Base in California. It was placed in a polar low-Earth orbit at an altitude of approximately 833 km, an eccentricity of 0.0018, and inclined at 98.8 degrees.  
In this study, the JPSS-2 orbit was reconstructed using an arc length corresponding to 28 JPSS-2 revolutions. The analysis incorporated DOWD measurements obtained on 10 and 11 November 2022. The TDRS-6, -7, -10, and -12 satellites were used to acquire a total of 16 DOWD passes.
## 2 Basic Theory and Calculation Formula
\begin{pmatrix}\left[\vec{J}_{0}^{*}\right]_{l_{l}=0}\\ \left[\vec{J}_{0}^{*}\right]_{l_{l}=N}\\ \left[\vec{J}_{1}^{*}\right]_{l_{l}=N}\\ \end{pmatrix} ,K_{b}= \begin{pmatrix}\left[J_{0}\right]_{l_{l}=0}\\ \left[J_{0}\right]_{l_{l}=N}\\ \left[J_{2}\right]_{l_{l}=0}\\ \left[J_{2}\right]_{l_{l}=N}\end{pmatrix} \] (36a) When the axially moving FGM beam is clamped at one end and simply supported at the other end (CS), the vector form of the boundary condition is, \[K_{b}^{*}= \begin{pmatrix}\left[\vec{J}_{0}^{*}\right]_{l_{l}=0}\\ \left[\vec{J}_{0}^{*}\right]_{l_{l}=N}\\ \left[\vec{J}_{1}^{*}\right]_{l_{l}=0}\\ \left[\vec{J}_{2}\right]_{l_{l}=N}\end{pmatrix} ,K_{b}= \begin{pmatrix}\left[J_ {0}\right]_{l_{l}=0}\\ \left[J_{0}\right]_{l_{l}=N}\\ \left[J_{1}\right]_{l_{l}=0}\\ \left[J_{2}\right]_{l_{l}=N}\end{pmatrix} \] (36b) When the axially moving FGM beam is clamped at both ends (CC), the vector form of the boundary condition is, \[K_{b}^{*}= \begin{pmatrix}\left[\vec{J}_{0}^{*}\right]_{l_{l}=0}\\ \left[\vec{J}_{0}^{*}\right]_{l_{l}=N}\\ \left[\vec{J}_{1}^{*}\right]_{l_{l}=0}\\ \left[\vec{J}_{1}^{*}\right]_{l_{l}=N}\end{pmatrix} ,K_{b}= \begin{pmatrix} \left[J_{0}\right]_{l_{l}=0}\\ \left[J_{0}\right]_{l_{l}=N}\\ \left[\vec{J}_{1}\right]_{l_{l}=0}\\ \left[\vec{J}_{1}\right]_{l_{l}=N}\end{pmatrix}  \tag{36c}\]  
In Equation (36), \(l_{l}=0\) and \(l_{l}=N\). \(\left[\cdots\right]_{l}\) are represented as \(l_{l}\) the row elements of the matrix \(\left[\cdots\right]\). The eigenvalue of a standard generalized algebraic equation set, such as Equation (35), can be solved by orthogonal trigonometric decomposition (**QR**). The method is the most effective and widely used method to solve all the eigenvalues of the matrix, which can obtain several orders of normalized complex frequency \(\Omega\) and the corresponding modal function \(\tilde{W}\big{(}\tilde{z}_{j}\big{)}\) of an axially moving FGM beam at one time.
## 1 Introduction
The worst forest fire in South Korea from 7-15 April 2000 was selected as a case study, and satellite-based vegetation and burn indices and GPP data were used to diagnose both the damage severity and the ecological and physiological recovery levels, depending on the severity level of the forest fire. Further analysis was conducted to minimize the annual variation of meteorological effects. The restoration process was evaluated by comparing the affected areas with an unburned reference area.
# CONCERTS: Coverage Competency-Based Target Search for Heterogeneous Robot Teams
## 4 Methods
### Heterogeneous Clustering  
CONCERTS treats the waypoint assignment problem as a clustering problem. Traditional clustering methods partition a set of data into a predefined number of subgroups, \(K\), to minimize the sum of the distance squared between each data point and the centroid of each cluster. These methods use unsupervised learning to determine cluster centroids. In H-Clustering, \(K\) is equal to the number of teammates and cluster centroids are the positions of each member of the team.  
Then nearest neighbors are computed based on square distance to each centroid and incorporate each robot's _coverage competency_.  
This formulation is flexible to various initial configurations because clusters are centered about robot initial position. When the robots are evenly distributed in the search region, computed waypoints will easily be distributed among the \(K\) agents. However, some situations arise in which robots start next to one another. If two agents with competency gaps begin next to each other, then all the nearby points will be assigned to the more competent teammate. To avoid this situation with only \(K^{\prime}\) clusters, we compute \(K-K^{\prime}\) new clusters when any agent is assigned fewer than some predefined minimum number of waypoints.  
In addition, nearby agents are given a new centroid. After clustering, we obtain the waypoints assigned to each agent (centroid) in \(\mathbb{C}\).  
Let \(\mathcal{W}=\{w_{1},w_{2},\ldots,w_{m}\}\) where \(w_{i}\) is \(i\)-th waypoint extracted from the search map (Algorithm 2) and let \(C=\{c_{1},\ldots,c_{k}\}\) be a set of \(K\) centroids, which correspond to the location of the agents. Suppose \(K^{\prime}\) (\(K^{\prime}\leq K\)) centroids are given as a constraint. The goal is to obtain the remaining number (\(K-K^{\prime}\)) of centroids and to assign each point to the nearest centroid such that sum of weighted 2D Euclidean distance is minimized as follows.  
\[\min_{c_{K^{\prime}+1}\cdots,c_{K}}\sum_{j=1}^{K}\sum_{w\in C_{j}}||\eta_{j}d_ {2}(w,c_{j})|| \tag{6}\]  
where \(\eta_{j}d_{2}(\cdot,\cdot)\) is the weighted distance, which reflects the \(j\)-th robot's sensing capability using the normalized coefficient \(\eta_{j}\) and \(d_{2}(p,q)=\sqrt{(q_{1}-p_{1})^{2}+(q_{2}-p_{2})^{2}}\) denotes 2D Euclidean distance between two arbitrary points, \(p\) and \(q\). We define _coverage competency_ as  
\[\eta_{j}=\frac{SR_{min}}{SR_{j}},\quad\text{where}\quad SR=\text{sensing range}\times\text{moving speed} \tag{7}\]  
where \(SR_{j}\) is the sensing capacity of \(j\)-th agent and \(SR_{min}\) is the minimum value of robot teams, which is the product of a moving speed and sensing range of agent \(j\). Because we normalize this coefficient with \(SR_{min}\), the minimum value of sensing capacity among all agents, we can use it to consider the relative proximity between centroids and points. In this way, we ensure that robots that are fast or have a large FoV are allocated more waypoints than robots that are not.
## 3 Algorithm for Multi-Station Satellite Orbit Optimization in Common-View
### Simulation Annealing Algorithm and Optimization
For Markov chain length intervals from 0 to 60, the algorithm employs a geometric distribution perturbation strategy, using an early increase in perturbation amplitude to escape local optima. For Markov chain length intervals from 60 to 120, a uniform distribution is used, producing values ranging from \(-5\) to 5 to expedite convergence based on the current best solution. For Markov chain length intervals from 120 to 180, the Gaussian distribution perturbation approach was applied to improve search efficiency. Inside the Markov chain length interval of 180 to 200, a uniform distribution is used to achieve a value concentration within the range of \(-0.1\) to 0.1. This enhances the search for optimal solutions. The initial temperature value is higher at the beginning of the optimization process, facilitating a more significant shift in the algorithm's decay and allowing for an exhaustive exploration of the state space. As the temperature decreases, only solutions that minimize the degree of objective degradation or enhance the objective are accepted. Ultimately, the technique reaches its optimization point as the temperature approaches zero and no further decline in the objective is permitted. Figure 3 outlines the specific procedure.