esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Burned Severity and Quantification of Burn Area for Each Land Use/Cover Class  
The 10-m Sentinel-2 constellation with its combined potential 5-day revisiting period has become attractive for time-sensitive events such as burned-area mapping. The Sentinel-2 datasets usable for vegetation mapping were extracted and processed through the JavaScript code editor in the Google Earth Engine platform. All the images we used were near cloud-free. We filtered imagery dates predating (16 October) and postdating (12 November) the fire event to enable the computation of normalized burned ratio (NBR; Key and Benson [61])--the most widely used index for the mapping of burned areas. NBR is calculated as the difference between the near-infrared (NIR) and shortwave near-infrared (SWIR) bands over their sum (Equation (1)). The basis of the NBR is that fire alters vegetation so that its removal and replacement by charcoal spectrally reduces the NIR and increases the SWIR signals [64].  
\[\text{NBR}=\frac{\text{NIR}-\text{SWIR}}{\text{NIR}+\text{SWIR}} \tag{1}\]  
Using NBR as defined in Equation (1), we calculated the dNBR as illustrated in Equation (2). The dNBR has become a standard fire severity measurement [60] as it is a criterion to understand burned severity levels [66]. The dNBR ranges from -2 to +2 with high positive values representing severely burned areas. The severity levels proposed by [PERSON] and [PERSON] [64] are listed in Table 1.  
\[\text{dNBR}=\text{NBR}_{\text{prefire}}-\text{NBR}_{\text{postfire}} \tag{2}\]  
Figure 1: Location of the study area in the Western Cape province, South Africa.  
We also adjusted the dates corresponding to the fire record and used the SWIR (B12, 1290 nm), NIR (B08, 842 nm) and red (B04, 665 nm) bands to display active fire and smoke. Here, we highlight the prominence of Sentinel-2 data for active fire detection as a verification tool, particularly fire incidents that span five days or more.  
The South African Land Cover Classification for 2018 was used to extract polygons for each class within the study area in Environmental Systems Research Institute (ESRI)'s ArcMap environment. The polygon for each class was ingested into the GEE, after which burned severity was calculated. Polygons with large geometry were split to enable the computation of burned areas.
## 3 FCS-MPC Algorithm for Boost Converters
### Solutions to the Non-Minimum Phase Issue  
To enhance controller performance, additional measures must be incorporated to handle the non-minimum phase dynamics. One approach involves modifying the cost function to include terms that anticipate the initial undershoot and adjust the control actions accordingly. Apart from that, using long-horizon FCS-MPC can solve the non-minimum phase issue; however, this is not the focus of this study for two main reasons. First, it is difficult to determine the number of horizons needed to guarantee the stability of the controller, as it would vary with each application. [PERSON] et al. (2018) propose a machine learning-based algorithm to provide the needed horizon in each case. The other reason is the complexity of implementing long-horizon FCS-MPC algorithms.  
Recognizing the limitations of direct voltage control, the focus shifted toward regulating the inductor current to improve the system's response. A well-known method is to use a double-loop control strategy, integrating auxiliary controllers such as PI controllers to adapt the reference current for the MPC controller based on the error in the output voltage [18; 19; 20]. This approach allows the MPC controller to regulate only the inductor current, thus avoiding the non-minimum phase issue.  
Figure 3 shows the block diagram of such a system. While this approach addresses the non-minimum phase issue in FCS-MPC, it introduces slowness due to the PI controller's slower timescale for setting the reference current. This lag compromises the fast dynamic response of FCS-MPC, diminishing its quick decision making and adaptability, resulting in suboptimal performance in scenarios requiring rapid adaptation and precise voltage regulation.
## 5 Conclusions  
We used the data from geostationary orbit satellites to capture the daily fluctuations in GPP and the diurnal patterns of photosynthesis in subtropical Chinese fir forest ecosystems. By comparison with field-measured flux data, the accuracy of these data has been validated. Using ten minutes resolution geostationary satellite data to identify midday depression provides a better understanding of the diurnal variation of photosynthetic rates and their response to different climatic events at regional and continental scales. Our methodological framework has shown a better fitting of data. Furthermore, there were significant changes in the annual occurrence of midday depression. The highest number of midday depression occurrences was identified in 2022 (126 times) with the highest average temperature (29.1 \({}^{\circ}\)C). A higher incidence of midday depression occurred in summer and autumn, with 48 and 34 occurrences, respectively. In contrast, the number of midday depression occurrences was lower during the colder seasons of spring and winter. Compound drought, heat, and drought events induced increases in midday depression at 74.3%, 66.0%, and 47.5%. Heat events had the strongest correlation with midday depression compared to other climatic events. Heat stress is the primary cause of the suppression of plant photosynthesis at midday. Trees are more likely to adopt midday depression as an adaptive strategy during compound drought and heat events. This study can inform forest management and contribute to improvements in earth system models.  
Conceptualization, Q.X. and X.Z.; methodology, Q.X. and K.C.; software, Q.X. and J.L.; validation, Q.X., K.C. and X.Z.; formal analysis, K.C.; investigation, Q.X. and T.L.; data curation, Q.X. and Y.W.; writing--original draft preparation, Q.X.; writing--review and editing, K.C., T.L., and X.Z.; visualization, Q.X.; supervision, X.Z.; project administration, X.Z.; funding acquisition, T.L. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Joint Fund for Regional Innovation and Development of the National Science Foundation (U22A20570), the Science and Technology Innovation Program of Hunan Province of China (2022 RC4027), the National Natural Science Foundation of China (42201064), and the Natural Science Foundation of Hunan Province of China (2023J40440).  
Data Availability Statement: Data will be made available on request.  
The authors declare no conflicts of interest.
# Machine Learning Algorithms: An Experimental Evaluation for Decision Support Systems
## 3 Machine Learning Algorithms and Techniques
### Spectral Clustering  
Spectral Clustering is a clustering unsupervised algorithm which reduces complex multidimensional datasets into clusters of similar data in rarer dimensions [27]. It makes no assumptions about the form of the clusters. In contrast to the K-Means technique, which assumes that the points assigned to a cluster are spherical about the cluster center, Spectral Clustering helps to create more accurate clusters and can correctly cluster observations that actually belong to the same cluster. However, these are farther off than observations in other clusters due to the dimension reduction.
# Research on Key Technologies of 260 \({}^{\circ}\)C/210 MPa Ultra-Deep Logging Equipment
## 3 Research on Key Technologies of Ultra-High-Temperature and High-Pressure Logging
### 260 \({}^{\circ}\)C/210 MPa Low Power Consumption High-Integration Circuit System
#### 3.2.3 Thermal Insulation System Design  
To achieve a 260 \({}^{\circ}\)C ultra-high temperature circuit system in downhole logging instruments, given that the existing electronic components have temperature ratings below 210 \({}^{\circ}\)C, a combination of low-power-consumption circuits and high-power thermal insulation is necessary. Currently, downhole instrument thermal insulation bottles include independent thermal insulation bottles and pressure-bearing thermal insulation bottles. Independent thermal insulation bottles are compact, flexible in design, relatively mature, and frequently used. They typically come in single-end and double-end opening designs. Pressure-bearing thermal insulation bottles are used less frequently in logging instruments. Although they allow the further utilization of instrument cavity space, the weld points present certain unreliability in high-temperature and high-pressure environments, necessitating overall process improvement. At the same time, in order to increase the working time of the thermos in high-temperature environments, insulation paint is sprayed on the inside of the thermos to prevent external heat from entering the interior of the thermos. This series of instruments adopts a double-end-opening independent thermal insulation bottle design, as shown in Figure 11. The efficiency of the heat absorber depends on the composition of the heat-absorbing material, the material of the connectors, the vacuum effect, and the environment to be tested. The length of the heat absorber correlates with the power of the circuit board: the higher the power and the longer the required working time, the longer the heat absorber needs to be.  
To ensure the quality of the logging data (related to the circuit driving of acoustic and electrical instruments) and construction safety, while also ensuring the reliable operation of  
Figure 11: Double-end-opening thermal insulation bottle structure.  
Figure 10: Reuse of acquisition and processing circuit systems.  
the downhole instrument circuit for a long time, the heat-absorbing agent capacity of the instrument should not be too much (which would increase the length of the instrument) nor too little (leading to a short insulation time). Through theoretical calculations and experimental testing, when the ambient temperature is 260 \({}^{\circ}\)C and the circuit power loss is 5 watts, to ensure 30 h of continuous operation of the instrument with the temperature inside the thermos not exceeding 175 \({}^{\circ}\)C, using the metallic heat-absorbing agent, the required capacity of the heat-absorbing agent is approximately 2.5 L. Following this principle, the structure of the thermos can be designed according to the specific needs and working durations to meet the actual logging requirements for ultra-deep wells.
## 2 Materials and Methods
### Data Sources and Preprocessing
#### 2.2.3 Soil Datasets  
The soil datasets used in this study were obtained from field sampling and laboratory measurements. To account for diverse land use types, soil surface samples were collected from the study area during the summer of 2023 at a density of approximately one sample per square kilometer (Figure 2). The \"S\" method was employed for sampling within each plot. Sampling was conducted using stainless steel soil augers at a depth of 20 cm. GPS coordinates were taken as the central point, with other sub-sampling locations radiating around it. Five subsamples were collected from each sampling point and combined into a single composite sample, ensuring uniformity in mining location, depth, and quantity. A total of 72 samples were collected, with each sampling point precisely positioned. The soil samples were then sealed in bags and transported to the laboratory for processing and analysis. The measured nutrients and methods are as follows: soil pH was determined using the potential method [39]; total nitrogen (TN) using the semi-micro Kelvin method [40]; total phosphorus (TP) using acid digestion-molybdenum antimony resistance colorimetry [41]; organic matter (SOM) using potassium dichromate oxidation-spectrophotometry [42]; alkali-hydrolyzed nitrogen (AN) using the diffusion method [43]; and available phosphorus (AP) using sodium bicarbonate leaching--molybdenum antimony resistance spectrophotometry [44].  
Figure 2: Layout of the sampling points in the study area.  
Logarithmic transformation was performed on each soil dataset to approximate normal distribution characteristics, followed by Kriging interpolation [45]. Different models were employed to interpolate various soil datasets. The results indicate that the average error (ME) for each soil parameter is close to 0, suggesting unbiased predictions with good accuracy. The root mean square standardized error (RMSSE) for each parameter is approximately 1, indicating that the prediction standard deviation is reasonably accurate. Additionally, the root mean square error (RMSE) of each soil nutrient is generally close to the average standard error (ASE), implying that the predicted values have minimal deviation from the measured values. This indicates that the interpolation results possess a certain level of precision (Table 2). The interpolation results are presented in Figure 3.
# Experimental Study of the Volumetric Error Effect on the Resulting Working Accuracy--Roundness
## 2 Experimental Setup  
### The Strategy of the Experiment  
The experiment setup is shown in Figure 1. The experiment was divided into two parts. In the first part, a three-axis machine tool was measured with its basic setup, i.e., without software compensation of the geometric errors of the machine. In the second setup, volumetric accuracy was compensated for; no other geometric error compensations were used. It is necessary to monitor the surrounding environment throughout the measurement process. Monitoring was carried out according to the procedure described in [20].  
In both parts of the experiment, circular interpolation measurements were performed with the Ballbar QC20-w, RENISHAW (DBB) for machining without DBB1 compensations and for machining with DBB2 volumetric compensations, according to ISO 230-4 [31]. Volumetric accuracy was measured with the LaserTRACER, ETALON (LTc) without LTc1 (Figure 1--part of the calibration) compensations and with LTc2 (Figure 1--part of the certification) volumetric compensations activated. The next step was the machining of the test workpiece (WP) without software WP1  
Figure 1: Experiment setup.  
and with WP2 volumetric compensations activated. In the last step of each cycle, an inspection of shape accuracy (roundness) was performed using the Talyrond 595S from Taylor Hobson (TH), again for the workpiece without activated software TH1 compensations and with TH2 volumetric compensations activated.
## 3 Proposed Mechanism
### Path Update (PU)
The reverse path appended in the PUMR creates a new TB, and CC assigns a new channel. We illustrate the path update operation with an example shown in Figure 2. Suppose that a channel is overloaded and is only being used by \(tb_{3}\). The sink first sends a unicast PUM to node 5, which is the CDN of \(tb_{3}\). Node 5 has two upstream nodes (_i.e._, 13 and 14), which means that two branches join in this node. It decides to create a new path for one of the branches. It randomly selects node 13 and sends a PUM with the type value set to 2. Therefore, node 13 becomes the PUMR generator and broadcasts PUMR in different channels to find leaf nodes with a hop count to the sink that is not greater than node 13's hop count to the sink. Node 4 is such a node, and the PUMR is eventually forwarded by node 4 to the sink. The reverse path appended in the PUMR creates the path from sink to the PUMR generator and creates a new TB. Node 4 becomes the CDN of the new TB, since it is one-hop away from the sink. The new paths from nodes 13, 22 and 23, now go through node 4 to reach the sink. Finally, the sink initiates a control message that enables all of the nodes in the new TB to change their channel. We discuss the channel changing procedure in the next sub-section. Figure 2: The path update module creates new paths for nodes 13, 22 and 23, by creating a new TB through node 4, and thus, divides the overloaded \(tb_{3}\) of Figure 1 into two TBs.
# Can Vegetation Removal Successfully Restore Coastal Dune Biodiversity?
## 2 Materials and Methods  
### Study Site  
The Nizzanim ITER is an on-going collaborative project established in 2004 to monitor plants, arthropods, small mammals and reptiles [26]. The study was conducted at the Nizzanim ITER site in Nizzanim Dunes Nature Reserve (NDNR), Israel (31\({}^{\circ}\)42\({}^{\prime}\)-31\({}^{\circ}\)44\({}^{\prime}\)N, 34\({}^{\circ}\)35\({}^{\prime}\)-34\({}^{\circ}\)36\({}^{\prime}\)E) covering an area of 20 km\({}^{2}\) along the Mediterranean coastline. Annual average temperature is 20 \({}^{\circ}\)C, and rainfall is 400-500 mm per annum, falling mainly during winter (November-April). The common wind direction is south-west with a very low Drift Potential Index [70].  
Sand dunes in NDNR are a globally distinct ecosystem situated at the intersection between desert and Mediterranean coastal dune systems [69]. The edaphic conditions and the physiognomy of the sand dunes enable numerous Saharan taxa to range northward in spite of the temperate climate that favors Mediterranean species [75], creating an unusual and highly diverse assemblage compared to other Mediterranean coastal systems [76]. Three distinct fixation states of dunes are recognized in NDNR, each state with its own characteristic species diversity and composition [26; 77]. Following the declaration of the reserve, livestock grazing and wood harvesting by local communities (predominantly Bedouin) were prohibited and rapid dune fixation occurred, increasing the shrub cover from 4% to 20% of the landscape over a period of 60 years [27; 72; 78].  
The Nizzanim ITER site consists of mobile, semi-fixed, and fixed dunes separated by inter-dune depressions [79]. The three fixation states can be categorized based on the gradient of perennial percentage vegetation cover (PPC), aeolian sand movement, and visual indicators such as dune geographic structure, plant distribution, dominant perennial species and soil color [27; 56; 79; 80; 81].  
Mobile dunes have 5-15% PPC, mainly _Ammophila arenaria_, and make up approximately 20% of the reserve [27]. Wind-driven shifting sands result in frequent burial and exposure of perennial plants on mobile dunes. Semi-fixed dunes have 16-30% PPC, are dominated by wormwood (_Artemesia monosperna_) and desert broom (_Retama raetam_), and cover approximately 70% of the area. Lastly, fixed dunes vary between 30-50% PPC, and compose approximately 10% of the area of the reserve.
## 2 System Description
### Geostationary Earth Orbit (GEO)  
Geostationary Earth orbit (GEO) refers to the circular orbit located approximately 35,786 km above the Earth's equator, where satellites revolve around the planet at the same rotational speed as the Earth. This unique positioning allows GEO satellites to maintain a fixed point in the sky relative to the surface, making them ideal for continuous coverage. Due to their stationary appearance and wide coverage footprint, GEO satellites are frequently used in fixed satellite services (FSS). However, this stationary nature also makes them more susceptible to prolonged interference from terrestrial systems, particularly those operating at similar frequencies or with high power densities, such as MVDS transmit  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **MVDS** & **GEO Satellite** \\ \hline Primary Use & Delivery of television and & Communication links, \\  & multimedia content & broadcasting, weather \\  & & monitoring \\ Operating Frequency & Ku-band (10.75–14.5 GHz) & Ku-band (extensively used) \\ Coverage Area & Local or regional, up to & Wide area; can cover \\  & 50 km from a transmitter & continents \\ Mobility & Fixed terrestrial & Fixed position relative to a \\  & transmitters & point on Earth \\ Antenna Characteristics & Directive antennas with & Large, fixed antennas on \\  & potential side lobes & the ground \\ Potential Interference & GEO satellites (terrestrial & MVDS (signal reception \\  & Source & transmissions) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between MVDS and GEO satellite systems.  
Figure 1: (**a**) Interference scenario between the GEO satellite and MVDS transmitter. (**b**) Locations of the satellite and MVDS systems: the experimental region, Beirut (Zoom in 1), and the MVDS system (Zoom in 2). (**c**) Block diagram showing important parameters for this study.  
ters. GEO satellite systems receive uplink signals in the Ku-band (13.75-14.75 GHz) from ground-based Earth stations (ES) in Lebanon. After amplification and down conversion to the Ku-band downlink frequency (10.75-12.75 GHz), the satellite retransmits these signals to the Earth.
## 3 Results and Discussion
### Level 3 coSIF Predictions and Quantified Uncertainties  
Next, we consider the spatial characteristics of coSIF across \(D\), the North American region of interest. Figure 6 shows the de-noised and gap-filled coSIF data product in July 2021, with SIF predictions given at a 0.05-degree resolution in the top panel, and the corresponding RMSPEs (i.e., prediction standard errors) in the bottom panel. The spatial predictions show that coSIF is prominent across the eastern half of the region, particularly in the North American Corn Belt, and less so across the western half of the region. The striped  
Figure 5: Solid dots show the empirical (cross-) semivariograms and dashed lines show the corresponding fitted models. **Top panels**: Semivariograms for standardized SIF residuals (February, April, July, October) and standardized XCO\({}_{2}\) residuals (March, May, August, November) in 2021. **Bottom panels**: The corresponding cross-semivariograms. Column titles give the estimated cross-correlation coefficient (\(\rho_{12}\)) and cross-correlation length (\(\ell_{12}\)) for each model. Chordal distance is shown on the horizontal axes (in km).  
diagonal pattern of the prediction standard errors are representative of the OCO-2 orbit tracks (e.g., Figure 3). This is expected from the local behavior of cokriging, which leads to smaller prediction standard errors at grid locations in areas where the data are dense and larger prediction standard errors at grid locations where the data are sparse or non-existent.  
The observed seasonal variation of SIF in Figure 1 is reproduced in the coSIF data product. When comparing Figure 6 to similar figures for different months (given in Figures S1-S3 in the Supplementary Material), coSIF predictions are lower in the spring (April), fall (October) and winter (February), and higher in the summer (July). This seasonal variation is also reflected in the prediction standard errors, which scale with the coSIF predictions; there is more variability in the residuals given by Equation (5) in the summer (July), so the prediction standard errors are larger. Still, for all months considered here, the coSIF RMSPEs are on average about four times smaller than the gridded measurement-error standard deviations \(\{\sigma_{e,1}\}_{l}\) obtained from the Level 2 Lite files as described for Equation (1) (see Table S1 in the Supplementary Material for uncertainty reduction estimates in all four months).  
Figure 6: The coSIF data product for July 2021 at 0.05-degree resolution. **Top**: The coSIF predictions given by Equation (13). **Bottom**: The corresponding root-mean-squared prediction errors given by Equation (14). All units are W m\({}^{-2}\) sr\({}^{-1}\)\(\upmu\)m\({}^{-1}\). The color white indicates the absence of predictions over large bodies of water.
# Stability-Aware Geographic Routing in Energy Harvesting Wireless Sensor Networks
## 3 System Model  
We considered EH-WSNs consisting of N energy harvesting sensors nodes randomly distributed in a two-dimensional area a \(\times\) a. Each sensor node is equipped with omnidirectional antennas and has a certain radio transmission range R. The energy model and wireless link model used in this paper are presented in Sections 3.1 and 3.2, respectively.  
A successful packet reception between two nodes, \(v_{i}\) and \(v_{j}\), is influenced by wireless channel conditions and channel conflict and is modeled on a \(PRR\).