esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Coastal Sea Level Offshore Hong Kong from Jason-2 Altimetry
## 3 Data Sets
### Coastal Altimetry Data Sets  
#### 3.1.1 General Description  
The experimental coastal Jason-2 products analyzed in this study are X-TRACK (CTOH/LEGOS, [6]), ALES (PODAAC, [3]) and PISTACH (CNES, [8]). These data sets are available from the following websites: ftp.legos.obs-mip.fr; [[ftp://podaac.jpl.nasa.gov/allData/coast_alt/L2/ALES](ftp://podaac.jpl.nasa.gov/allData/coast_alt/L2/ALES)]([ftp://podaac.jpl.nasa.gov/allData/coast_alt/L2/ALES](ftp://podaac.jpl.nasa.gov/allData/coast_alt/L2/ALES)) and [[ftp://ftpsedr.cls.fr/pub/oceano/pistach](ftp://ftpsedr.cls.fr/pub/oceano/pistach)]([ftp://ftpsedr.cls.fr/pub/oceano/pistach](ftp://ftpsedr.cls.fr/pub/oceano/pistach)).  
We have to keep in mind the differences between the products considered in this study. X-TRACK is a level 3 (L3) product: using the GDR data and state-of-the-art altimetry corrections, along-track sea level time series projected onto reference tracks (points at same locations for every cycle) are computed at 1-Hz (~6 km along-track resolution). Both a raw and a spatially filtered version (40 km cutoff frequency) of the product are distributed. It is simple to use, and is based on improved geophysical corrections near the coast (see [6] for details) but its current version only includes the standard MLE4 retracker adapted to open ocean conditions. ALES and PISTACH are level 2 (L2, i.e., cycle-by-cycle) products: the measurements are provided at 20-Hz (~0.3 km along-track resolution) and are not projected onto reference tracks, which means that their location varies from one cycle to another (this processing step needs to be done by the user). The ALES and PISTACH products do not include improved geophysical corrections, but provide altimeter range estimates obtained using different retracking algorithms (Section 3.1.2). PISTACH also includes a classification of the altimeter waveforms which can be used to study the type of waveform analyzed. It also indicates if the waveform shape is consistent to the retracker used.
## 3 Numerical Results and Discussion  
In this part of study, the proposed interface-corrected diffuse interface model together with the improved flow field solver was validated by simulating several multiphase problems, including the Laplace law of a static bubble, the evolution of a square bubble, the process of two bubbles merging under the surface tension force, the Rayleigh-Taylor instability, and the evolution of a droplet splashing on a liquid film. The numerical results obtained with the present method were compared with the reference data and the data obtained with the original method.
## 5 Experimental Evaluation
### Uncertainty Estimation  
Figure 16 illustrates the performance of an agent using UCB and uncertainty estimation in a reinforcement learning setting. The x-axis represents the number of episodes, while the y-axis represents the expected reward obtained by the agent. The blue dots represent  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Module**} & \multicolumn{2}{c}{**Accuracy**} & \multicolumn{2}{c}{**Communication Cost**} & \multicolumn{2}{c}{**Energy Consumption**} \\ \cline{2-7}  & **MNIST** & **CIFAR-10** & **MNIST** & **CIFAR-10** & **MNIST** & **CIFAR-10** \\ \hline Fed\_Share & 83.81 \(\pm\) 0.79 \% & 43.63 \(\pm\) 0.81\% & 0.80 & 0.90 & 0.72 & 0.68 \\ \hline Fed\_SGD & 81.88 \(\pm\) 1.12\% & 45.98 \(\pm\) 1.09\% & 0.71 & 0.38 & 0.75 & 0.79 \\ \hline Fed\_AVG & 94.56 \(\pm\) 0.45\% & 50.95 \(\pm\) 0.98\% & 0.32 & 0.61 & 0.86 & 0.81 \\ \hline Fed\_Prox & 94.62 \(\pm\) 0.61\% & 60.37 \(\pm\) 0.85 \% & 0.27 & 0.32 & 0.31 & 0.24 \\ \hline UA-FedRL & 96.87 \(\pm\) 0.22\% & 62.73 \(\pm\) 0.57\% & 0.19 & 0.24 & 0.25 & 0.19 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of different existing federated learning algorithms for Accuracy with 95% confidence interval, communication cost, and energy consumption.  
Figure 15: Comparative analysis of normalized energy consumption across different FL methods on MNIST and CIFAR-10 datasets.  
the rewards obtained by the agent during each episode, while the solid red line represents the expected reward predicted by the UCB algorithm. The shaded pink area around the red line represents the uncertainty estimate of the algorithm.  
Overall, the UCB algorithm performed better as it achieved a higher expected reward in fewer episodes. Moreover, the uncertainty estimate of the UCB algorithm was not large, indicating that the UCB algorithm was more certain about its predictions. Therefore, the UA-FedRL method can help guide the development of more effective reinforcement learning algorithms in the future for FL applications.
# Integrity Monitoring of PPP-RTK Positioning: Part II: LEO Augmentation
## 4 LEO Augmentation
### Horizontal Protection Level
#### 4.4.2 Ambiguity-Fixed HPLs  
With the PAR enabled, the HPLs without and with the LEO augmentation are illustrated in Figure 15 for the two test scenarios as discussed in Section 4.4.1, i.e., for network 1 with quiet multipath and ionospheric conditions, and network 3 with active multipath and ionospheric conditions. The discontinuities in Figure 15b are caused by the different number of resolved ambiguities.  
From Figure 15, it can be observed that the ambiguity-fixed HPLs are generally reduced after adding the LEO augmentation. Figure 16 shows the improvements in the PAR-enabled HPLs after adding the LEO constellation. After reaching the FAR, the improvements generally vary from about 10% to 60%, depending on the constellations used and the measurement/ionospheric environments. It can be observed that the improvements are especially large for poor measurement geometry with the GPS-only scenario.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Constellation**} & \multicolumn{5}{c}{**HPL (m)**} \\ \cline{2-6}  & **30 s** & **60 s** & **180 s** & **300 s** & **1200 s** \\ \hline \multicolumn{6}{c}{Network 1, quiet multipath and ionospheric condition (Category A)} \\ \hline G & 1.10/0.75 & 0.89/0.55 & 0.67/0.25 & 0.60/0.21 & 0.52/0.14 \\ \hline GE & 0.88/0.72 & 0.74/0.56 & 0.58/0.26 & 0.55/0.22 & 0.46/0.14 \\ \hline GC & 0.86/0.74 & 0.74/0.59 & 0.61/0.26 & 0.60/0.23 & 0.51/0.15 \\ \hline GEC & 0.79/0.71 & 0.70/0.58 & 0.58/0.27 & 0.56/0.23 & 0.49/0.15 \\ \hline \multicolumn{6}{c}{Network 3, active multipath and ionospheric condition (Category D)} \\ \hline G & 5.42/3.47 & 4.27/2.57 & 3.06/1.18 & 2.69/1.01 & 2.19/0.67 \\ \hline GE & 4.32/3.41 & 3.56/2.64 & 2.81/1.21 & 2.61/0.99 & 2.03/0.69 \\ \hline GC & 3.95/3.36 & 3.35/2.67 & 2.81/1.26 & 2.71/1.07 & 2.19/0.71 \\ \hline GEC & 3.63/3.22 & 3.14/2.60 & 2.68/1.26 & 2.59/1.02 & 2.12/0.71 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ambiguity-float HPLs at different convergence times. The HPLs without and with LEO augmentation are separated by the slash signs.  
Figure 15: PAR-enabled HPLs without and with the LEO augmentation for (**a**) Network 1 under quiet multipath and ionospheric conditions (Category A); (**b**) Network 3 under active multipath and ionospheric conditions (Category D). The solid lines refer to the case without LEO augmentation, and the dotted lines represent the HPLs with LEO augmentation. Note that the y-scales are different in the two sub-figures.
## 4 Discussion
### Impacts, Limitations, and Future Work  
In this research, we used the M-K test to analyze changing trends, and Pearson correlation analysis was used to study the correlation between drought indices and vegetation GPP. This method is fairly simple to calculate and can be applied to assess and predict the impact of droughts and other extreme climate events on vegetation. Furthermore, we innovatively used two indices, SPEI and VPD, to characterize drought. Although both indices are able to assess the degree of drought, they have different theoretical foundations that result in different conditions characterized by different drought indices. In order to measure the effects of drought on vegetation, the combination of these two indices can comprehensively depict the drought dynamics and effectively reduce the uncertainty induced by each individual index. In this study, the trends and the effects of water deficit on vegetation assessed using the two drought indices are generally consistent, which improves the reliability of the results and strengthens our findings. The results also revealed that drought characterized by SPEI was negatively correlated with vegetation during drought in more areas compared to VPD. The main reason for this phenomenon may be the significant spatial and temporal differences in the effects of temperature and precipitation on drought [87]. SPEI is based on both precipitation and potential evapotranspiration (PET). PET is strongly influenced by temperature and is closely related to VPD. By contrast, VPD is computed without including precipitation. Therefore, further research could be conducted to explore the contribution of temperature and precipitation to drought in different regions of China.  
Nevertheless, this study has some limitations. The first is data accuracy. There were still differences between the GOSIF data and the data measured on site [88]. Because of the influence of soil moisture on light energy use efficiency, GPP, obtained using remote sensing, tends to underestimate the impact of droughts on vegetation [89]. In addition, this study only considered the impact of drought on GPP. Vegetation growth results from a combination of several factors, and may also be affected by other extreme weather events, such as high temperatures, low temperatures, and floods [90]. Thus, the complexity of the causes of drought and the influencing factors on vegetation growth bring certain uncertainty to the results. Despite these shortcomings, the methods and conclusions of this study can be used as a reference for determining the impact of droughts on vegetation. These can be used to take timely and effective measures to prevent and mitigate the adverse effects of droughts on vegetation in relevant areas.
## 5 Conclusions  
In March of 2018 the California Energy Commission mandated that beginning in 2020, all new home and apartment construction must include solar generation. When this level of distributed generation becomes part of the electric grid, ISO are faced with new challenges in terms of frequency and voltage control. Indeed, in Hawaii these issues resulted in a temporary hold on rebates for new residential solar installations.  
In a macrogrid extending over hundreds of square kilometers, integrating a mix of generation conventional power plants, solar, and wind, and functioning as part of a larger interconnect such as ERCOT, there is an inherent inertia that works on the side of the utility. In microgrids, however, this inertia is lacking, and the control problem becomes much more difficult to solve when in islanded mode [4; 7]. There are also different temporal scales involved. Optimal day-ahead scheduling and control of a microgrid is a distinct problem from hour-ahead control to ensure frequency and voltage do not vary outside prescribed limits. Using equipment such as the OP4500 RT-LAB/RCP/HIL real-time  
Figure 24: (a) Skylmager/image with dust on enclosure, (b) after cleaning it.  
power grid digital simulator by Opal RT, we hope to use Hardware-in-the-Loop equipment to analyze the microgrid at JBSA. PMU measurements also need to be incorporated.  
All-sky imaging technology will be a critical component in the overall solution strategy to predict solar irradiance 15 min ahead, and to take corrective measures during ramp events. It must, however, be fully integrated with NWP and satellite-based approaches for day-ahead load forecasting and optimal control of a microgrid. Optimal use of this technology will encompass a diverse group of specializations, including IoT and edge-computing, cyber-security, machine learning, and image processing.  
For example, the characteristics and statistics of the all-sky imager must be included in the stochastic optimization programming for risk neutral and risk adverse operational control of a microgrid [6]. An holistic R&D approach is required. While a Raspberry Pi is the essence of plug-N-play and it is relatively straightforward to build a Skylmager, integration into the IoT and field deployment will remain an active area of research. Imagers will range the gamut in cost, accuracy, and interoperability. MGMS will integrate forecasts from imagers, NWP, and satellites, as well as hundreds of other meters and devices to solve the microgrid control problem. While physics-based methodology will continue to be important, machine learning and IoT technology will play an increasingly critical role. Development of standards such as OpenFMB for interoperability of thousands of devices will also be a necessary component.
## 3 Methodology
### Resampling of Background Winds  
The ambiguity selection process was applied on the UHR product for 3 NWP models; i.e., ECMWF, MERRA-2 and RAP. The ECMWF and MERRA-2 models provided winds at a global scale, whereas the RAP winds were confined over the North America and adjacent oceanic regions. The ECMWF winds where obtained directly from the ASCAT L2 product and had 12.5-km spatial resolution. The MERRA-2 model global winds (tavgl_2d_ocn_Nx product) were available at 7-km spatial resolution through the UHR process. RAP winds were available at a native 13-km resolution horizontal grid and 3-h temporal resolution, and therefore were resampled at a 7-km prior to ingest them in the ambiguity removal process using the pyresample module [55]. This Python module is a fast and generalized interface to resample (reproject) Earth-observing satellite data, both gridded and swath types, by the implementation of a (custom) KD-tree approach.
# Study on the Residual Strength of Nonlinear Fatigue-Damaged Pipeline Structures
## 1 Introduction
With this model, it is possible to accurately predict the variable amplitude strain life of specimens using a relatively small amount of experimental data that can easily be generated. By introducing the concept of isodamage lines, [PERSON] [26] elaborated on a damage accumulation model that is easy to apply to design, which assumes that each isodamage line converges at the knot point of the stress life curve. Compared with [PERSON]'s rule, [PERSON]'s model provides more accurate predictions. [PERSON] et al. [17] proposed a new nonlinear fatigue damage accumulation model, which is presented by introducing a damage function related to the isodamage curves and remaining life aspects. Compared with [PERSON]'s model, the 3D surface form of the isodamage curve obtained by this model has higher consistency with the experimental results. Many researchers turn to establishing nonlinear fatigue damage accumulation models that are based on damage curves [27], material strength degradation [28], and continuum damage [29; 30]. Among these nonlinear models, the damage curve-based models have attracted much attention due to their computational convenience. For example, [PERSON] [31] proposed a crack propagation equation by observing the cumulative amount of crack length and obtaining the action coefficient of nonlinear cumulative damage under multilevel stress loading by fitting a large amount of test data. [PERSON] [32] released the fixed knee point restriction and proposed a nonlinear fatigue damage model considering the nonfixed knee point in the S-N curve. But it requires fatigue life data corresponding to the fatigue limit of the material. [PERSON] [33] proposed a new nonlinear fatigue damage model. This model does not require the determination of any additional material parameters other than the fatigue strength curve (S-N curve) given in the design standards. As a result, the model can also be applied to structural details for better fatigue life estimation. At present, there is an urgent need for an effective analysis scheme that can calculate the residual strength of nonlinear fatigue-damaged pipelines to obtain reliable operation parameter control methods and realize the service safety assessment of damaged pipelines. In a complete operation process, the coiled tubing must experience at least six times of \"bending-straightening\" elastic-plastic alternating deformation. Coiled tubing is a representative research object in nonlinear fatigue damage due to its special operating conditions [34; 35; 36]. In this study, the nonlinear damage analysis of coiled tubing was carried out, the load-bearing characteristics of internal pressure and tensile resistance were studied, and the residual strength and structural strength characteristics of the nonlinear damaged tubing were analyzed. This study provides an effective analysis method for calculating the strength of pipelines with nonlinear fatigue damage, and the analysis results provide an important basis for service safety assessment and have significant engineering application value.
## 3 Metal-Semiconductor-Metal Diode Fabrication  
The back-to-back MSM diode is studied because of its noncomplex structure and equipment availability. The equipment of the MSM diode fabrication consists of an Al plate, a Cu sheet, and an invented mold.  
The Al and Cu were initially set to be metallic electrodes due to their good conductivity, less oxidation, inexpensive material, and availability. The work functions of Al and Cu are about 4.30 and 4.70 eV, respectively, which explains that the minimum quality of energy is required to release the free electron from the surface of the solid material as introduced by [23].  
Firstly, these electrodes were cleaned with distilled water and then dried at room temperature. The mold, consisting of a lid and a base, was built by a 3D printer to contain a 10 mg indigo blue pigment, as shown in the parametric design and fabricated equipment in Figure 2a,b.  
Figure 2: (a) Parametric model of lid and base; (b) components of MSM diode; (c) MSM layer stacks; (d) MSM diode.  
The Cu electrode was firstly laid in the base pothole, filled with 10 mg of local indigo blue pigment. The Al electrode was then topped, and the lid part was consequently closed. These fabricated layer stacks are now an MSM diode, as illustrated in Figure 2c,d.  
A measurement circuit invented to verify the room temperature electrochemical properties of the fabricated MSM diode consists of a \(-5\) to \(5\) V DC power supply, V\({}_{\mathrm{dc}}\), and a \(1\) k\(\Omega\) load resistance, R\({}_{\mathrm{L}}\). The Al and Cu electrodes in the MSM diode were anode and cathode, respectively. Figure 3a,b demonstrate the circuit of the forward-bias and reverse-bias, respectively. The behavior and electrochemical characteristics of this proposed MSM diode will be described in the next section of results and discussion.
# Routing Schemes in FANETs: A Survey
## 2 Background and Motivating the Need for FANETs
### What Are the Mobility Models in FANETs?
#### 2.4.3 Time-Dependent Mobility Models  
The time-dependent mobility model minimizes acceleration and the drastic changes of direction in order to provide smooth movement (e.g., a smooth turn), which is more realistic [93]. There are three common characteristics of this model. Firstly, nodes are dependent on each other, whereby the vehicular nodes follow one another (including speed and direction) on the road. Secondly, nodes are memory-based, and so, the new speed and direction of the nodes are dependent on their previous speed and direction. Thirdly, nodes can move at any speed within a range.  
* Smooth turn (or realistic model) allows a node to choose a point and to form a circle around it, and then, it moves along the circle [55]. When the node moves towards another randomly chosen destination, it chooses a new point and moves along another circle around the newly chosen point. Nevertheless, there is lack of collision avoidance [94], which explain the need for better collaboration among nodes. This model is suitable for multi-UAV scenario.
## 3 Results and Discussion
### X-ray Diffraction Analysis  
Figure 6 shows the X-ray diffraction (XRD) patterns of multicomponent solid waste cementitious materials at different ages. In addition to the hydration products of the C-S-H gel, the hydration products of each group mainly included AFt, Ca(OH)\({}_{2}\), and some unhydrated C\({}_{2}\)S and C\({}_{3}\)S. At 3 d, the EGC0 group exhibited a diffraction peak of AFm, mainly owing to the occurrence of insufficient SO\({}_{4}\)\({}^{2-}\). However, after EMR incorporation, the concentration of SO\({}_{4}\)\({}^{2-}\) increased, and the AFm diffraction peak disappeared and transformed into an AFt peak. The EGC0 group that was aged for 28 d exhibited significantly weakened Ca(OH)\({}_{2}\) diffraction peak intensity, while the group with the appropriate amount of CS exhibited enhanced Ca(OH)\({}_{2}\) diffraction peak intensity, attributable to the existence of residual Ca(OH)\({}_{2}\) after the incorporation of excessive CS and the Ca(OH)\({}_{2}\) generated during hydration. This situation was more significant in the EGC7-9 group with increasing CS content. However, with the decrease in the CS content, the Ca(OH)\({}_{2}\) diffraction peak intensity of the EGC10-11 group did not decrease, which indicates that the hydration rate of GBFS could be improved under the addition of an appropriate amount of CS. Moreover, after the EMR incorporation, the concentration of dissolved SO\({}_{4}\)\({}^{2-}\) increased, and the reaction between the dissolved SO\({}_{4}\)\({}^{2-}\) and the calcium aluminate produced by GBFS promoted AFt formation. The diffraction peak showed an enhanced trend in the XRD pattern, indicating an increase in AFt generation. Therefore, according to the XRD pattern, the hydration activity of GBFS was effectively stimulated by CS and EMR addition, and more hydration products were generated, which is consistent with the results of the mechanical properties.