esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Centered on the Yangbajing-Yangyi Basin in Tibet, the study leverages the Google Earth Engine (GEE) platform to process long-term Landsat 8 daytime LST data and optimize their spatial and temporal representation. Additionally, ASTER nighttime LST data are used in a multi-perspective strategy that integrates global, local, elevation-based, and fault buffer zone analyses. These results are then fused using Dempster-Shafer (D-S) evidence theory to effectively overcome the limitations of individual datasets. Building on this, stable geothermal anomaly zones are identified through combined day-night analysis, further demonstrating the potential of multi-source data in complex geological environments. The method is validated in known geothermal fields and preliminarily identifies several potential geothermal anomaly zones, providing a scientific basis for the development of high-temperature geothermal resources and offering new perspectives for exploration in the Tibetan Plateau and similar regions. The following sections provide a comprehensive overview of the research process and findings. Section 2 outlines the geological setting of the study area. Section 3 describes the acquisition and preprocessing procedures for multi-source data. Section 4 presents the results of geothermal anomaly extraction using Landsat 8 daytime, ASTER nighttime, and combined daytime-nighttime analyses. It also discusses the use of MODIS data to infer deep-seated heat sources. Section 5 evaluates the effectiveness of data processing improvements and summarizes the study's limitations and directions for future research. Section 6 concludes the study by highlighting its key findings.
## 2 Methodology  
### Experimental Setup  
Inconel 625 samples in the form of hollow cylinders were fabricated by FormAlloy using a FormAlloy L2 metal AM system, seen in Figure 1[80]. Here, we show a general depiction of how the metal AM process functions and how data is observed/recorded. Powder nozzles allow for the flow of the powder stream that contains powder particles. Once the stream comes into contact with the laser beam, the particles melt and are deposited onto the substrate. This process occurs under the surveillance of the MPS camera. The L2 metal AM system is a DED machine that uses a laser as the heat source and metal powder as the feedstock. The L2 system used is equipped with a continuous wave (CW) fiber laser with a maximum output power of 2 kW and a focused spot diameter of 1.2 mm. The system has a maximum build volume of 200 \(\times\) 200 \(\times\) 250 mm. In terms of in situ sensing hardware, a Melt Pool Size (MPS) camera was set in-line with the path of the laser and measures the width of the moving melt pool during the printing process. This provides users with a real-time measurement of the melt pool dynamics.  
Each cylinder has the following features: (1) 1.2 mm wall thickness, (2) 51 mm height, and (3) 13 mm diameter (Figure 2). To generate the tool path a CAD model was imported into a DED tool path generation software and certain parameters (such as laser power, feedrate, and slice/layer thickness) were set. As a baseline, laser power and feedrate were set at the FormAlloy developed values of 225 W and 1000 mm/min, respectively. Each layer thickness was set the same for both samples and a total of 284 layers (0.18 mm individual layer thickness) were generated to manufacture the samples. The software then generates a G-code file which can be uploaded on the FormAlloy L2. The Inconel 625 powder was sourced from Praxair Surface Technologies (product Ni-328-17) with a particle size distribution between 45 and 106 microns [81]. The parts were printed on a steel substrate with a thickness of 6.35 mm. One sample was built in an open loop where the operator was manually adjusting LP, feed rates (scan speeds), and powder disc speed (controls the powder mass flow rate), to complete the build. An MPS sensor was used in this build to record the width of the melt pool during the build process. The waviness found in open loop sample can be attributed to uneven part temperatures as the sample was being built. For the second sample, the MPS sensor was enabled in closed loop mode, which will adjust the LP based on a targeted MPS. This build was able to be completed with minimal user interaction and higher quality.  
Figure 1: Schematic of FormAlloy L2 metal AM system with MPS Camera.
## 3 Discussion
Ultimately, while existing networks require incremental modernization through targeted upgrades, new DH networks can be designed holistically to incorporate cutting-edge technologies and innovative operational strategies. The long-term sustainability of DH systems will depend on the ability to align technical improvements with economic feasibility, ensuring that both existing and newly established networks achieve optimal return temperature reduction and enhanced energy efficiency.
# Regularized Zero-Forcing Dirty Paper Precoding in a High-Throughput Satellite Communication System
## 2 High-Throughput Satellite Communication System Model
### High-Throughput Satellite System Channel Model
Therefore, the rainfall attenuation matrix \(\widetilde{H}\) can be expressed as \(\widetilde{H}=diag\{\sqrt{\xi_{1}}e^{-j\varphi_{1}},\ldots,\sqrt{\xi_{k}}e^{- j\varphi_{k}}\}\), where \(\sqrt{\xi_{k}}e^{-j\varphi_{k}}\) is the rainfall attenuation factor of the \(k\)-th user. Since the feed spacing of a multi-beam antenna is much smaller than the signal propagation distance, it is assumed that the rainfall attenuation factor between different beams and the same user is the same, and \(\varphi\) follows a uniformly distributed phase, that is \(\varphi\sim U(0,2\pi)\). 3 Regularized Zero-Forcing Dirty Paper Precoding of a High-Throughput Satellite Communication System
## 3 Results and Discussion
### Effect of WA Dose on Fluoride Removal  
In Figure 3b, the fluoride removal efficiency and adsorption capacity of the adsorbent are depicted for various WA doses. At a concentration of 3.33 g/L of WA, the fluoride removal percentage reached 79.06%, indicating relatively high removal efficiency. Increasing the WA dose to \(\geq\)6.67 g/L resulted in a fluoride removal percentage exceeding 98%. As the WA dose rose from 3.33 g/L to 16.67 g/L, the fluoride adsorption of WA declined from 118.59 mg/g to 29.79 mg/g, while maintaining a constant fluoride concentration in the solution. This decrease was attributed to the higher doses of WA, which enhanced the availability of unoccupied adsorption sites for binding fluoride [23]. Although there was a reduction in fluoride adsorption capacity, the overall efficiency of fluoride removal by WA increased due to the higher number of adsorption sites [37]. Thus, the optimal WA dose for removing more than 98% of the fluoride from a 500 mg/L fluoride solution was determined to be 6.67 g/L.
# Progress in THz Rectifier Technology: Research and Perspectives
## 4 Perspectives in THz Rectifier Technology for Energy Harvesting Applications
### MIM and MI\({}^{n}\)M Diodes  
Although nanoantenna technology presents significant challenges in terms of DC power requirements, conversion efficiency, parasitic interactions between antennas, development of miniaturization of new materials, and alternatives to noble metals, potential topics are represented by the fabrication and characterization of rectifier diodes operating at THz frequencies.  
The future of this type of diode promises to be intriguing even though they are not currently commercially available at a large scale. This technology, due to its ability to operate at zero-bias, will have a major impact on EH applications. Although the literature indicates the value of 343 THz [48] as the best cut-off frequency, intensive work is still necessary to improve the performance of these nonlinear devices. Single-insulator (MIM) and multiple insulator (MI\({}^{n}\)M) could represent in the near future a possible candidate for rectenna at THz frequencies. However, several issues discussed above are associated with the possible fabrication of these diodes. Currently, the trend focuses on several areas of investigation. The first area of investigation is the manufacturing process, which in the next few years could contribute to overcoming the impedance mismatch antenna-diode. Different combinations of materials could be the second area of investigation to obtain the desired I-V characteristic and high FOMs. The requirement for highly precise manufacturing processes, metal contact deposition, ability to grow or deposit ultrathin oxide layers, and patterning nanoscale devices represent the third area of investigation. Among the various methods of oxides deposited directly on the metal, ALD offers the best quality oxides with very precise thickness control. However, it is a very expensive and time-consuming process. Therefore, other less expensive methods used for this purpose could be native, thermal, anodic, and plasma oxidation. The use of scanning electron microscopy (SEM) and transmission electron microscopy (TEM) allows us to observe the morphology of the deposited oxides [28]. Finally, the last area of investigation is how to improve the coupling efficiency. In this direction, high FOMs with low resistance can be simultaneously obtained by adding a second insulator layer (MI\({}^{2}\)M) or more insulator layers (MI\({}^{n}\)M) [4; 94; 95; 96; 94].
# Towards Automatic Landslide-Quake Identification Using a Random Forest Classifier
## 3 Results and Discussion  
### Performance of the Automatic Classifier Using 24 Attributes  
It is important to note that the use of a confusion matrix may be affected by the number of samples of various classes. The performance of the classification may suffer from an unbalanced proportion between the noise to the others, potentially leading to a biased result. In the study, when calculating the confusion matrix, the number of the three classes (earthquake, landslide, and noise) was deliberately controlled to be the same. Table 3 presents the classifying result of the training dataset from the trained classifier containing 24 attributes. In 214 seismic records of landslide events, 186 records were correctly classified, and 28 records were misclassified as earthquakes or noise. The sensitivity of landslide events was 86.9%. In 209 predicted landslide events classified by the presented classifier, 186 records were real landslide events; however, 19 earthquake events and four noise events were misclassified as landslides. The precision for landslide events was 89.0%. In addition, the sensitivity and precision for earthquake events were 88.8% and 88.4%, respectively; the sensitivity and precision for noise events were 98.1% and 96.3%, respectively. The accuracy of the automatic classifier for that training dataset was 91.3%, and the sensitivity or precision for all three different classes exceeded at least 85%. It is evident that the present automatic classifier succeeded in classifying most of the seismic events in the training dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Identified Class**} \\ \cline{3-4}  & & **A** & **B** \\ \hline \multirow{2}{*}{Manual class} & A & True Positive & False Negative \\ \cline{2-4}  & B & False Positive & True Negative \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix.  
Figure 7: Flowchart of five-fold cross validation.
## 4 Results and Discussion
### Water Absorption Index (WAI)  
WAI values of TGPFs were plotted against BA and CT, as shown in Figure 9. The constant CT and BA content are indicated in the top right corner of the graphs. Figure 9a shows that the points of BA10-200, BA10-400, and BA20-400 are absent because these samples decomposed or dissolved completely after 48h in water due to the solubility of the SS. It can be seen from Figure 9a that WAI values decrease as the bottom ash content increases at all of the curing temperatures. With the increase in the bottom ash content, the foamability of the TGPFs decreased, and the WAI is directly related to the foamability of TGPFs, hence the WAI decreased with increased bottom ash content. At 600 \({}^{\circ}\)C, WAI increases as the bottom ash content increases from 30% to 40%. This behaviour is due to the irregular foaming presented by 40%BA composition when cured at 600 \({}^{\circ}\)C, as discussed earlier, and is in accordance with the decrease in bulk density and increase in foamability as the BA is increased from 30% to 40% at 600 \({}^{\circ}\)C curing temperature, as was mentioned earlier in 4.4. Figure 9a shows that the maximum WAI values are obtained at 500 \({}^{\circ}\)C CT for all the compositions, as can be seen from the 500 \({}^{\circ}\)C curve.  
It is clear from Figure 9b that the WAI values increase as the curing temperature increases from 200 \({}^{\circ}\)C to 500 \({}^{\circ}\)C for all of the compositions. As the temperature increases from 500 \({}^{\circ}\)C to 600 \({}^{\circ}\)C, there  
Figure 8: Effect of curing temperature on (**a**) the bulk density and (**b**) the foamability of TGPFs.  
Figure 9: Effect of (**a**) bottom ash content and (**b**) curing temperature on the water absorption index (WAI) of TGPFs.  
is a decrease in WAI values due to the viscous phase transition from 500 \({}^{\circ}\)C to 600 \({}^{\circ}\)C. Figure 9b shows that WAI values are at the maximum for the 10% BA composition, as can be seen from the curve of the 10% BA composition. The maximum WAI value of 171%can be observed for BA10-500 and the minimum WAI value of 36.68% can be observed for BA40-200.
# Evaluation of Rice Straw Ash as a Pozzolanic Addition in Cementitious Mixtures
## 1 Introduction
SiO\({}_{2}\) was the main oxide present in all the ashes, but its content was higher in RSA. Other oxides present in these ashes were K\({}_{2}\)O and CaO [18]. The microscopic study of RSA revealed that it is heterogeneous in the chemical distribution, depending on the cellular structure that remained after the combustion process [18]. [PERSON] et al. [19] studied concretes with a partial PC replacement with RSA. These authors evaluated different properties, such as density, compressive and flexural strengths, durability (mass loss of concrete immersed in 5% H\({}_{2}\)SO\({}_{4}\) during 32 days) and ultrasonic pulse velocity. From the mechanical point of view, the best results were obtained with a concrete containing 5% RSA, although the durability results were better for the concrete with 15% RSA. [PERSON] et al. [20] studied using RSA as a pozzolanic material in cement mortars, and obtained a 12.5% increase in compressive strength when substituting 10% PC for RSA. Introducing RSA did not severely affect setting times. Therefore, these authors concluded that using RSA as a pozzolanic material was satisfactory, and they suggested a study to improve combustion conditions to obtain better quality ash. In a later work, the same authors [21] analyzed the relationship between the permeability and compressive strength of mortar with PC replacements up to 15%, and indicated that permeability decreased with increased compressive strength within the 5-10% substitution range. At 15% substitution, compressive strength slightly decreased and permeability increased. They also studied RSA obtained at different temperatures (uncontrolled temperature, 400, 600, and 750 \({}^{\circ}\)C) [22] and concluded that the best pozzolanic reactivity was obtained by burning biomass at 600 and 750 \({}^{\circ}\)C.  
[PERSON] et al. [23] analyzed the mechanical properties of lightweight self-compacting concrete by incorporating RSA and cotton stalk ash (CSA). They observed diminished workability, improved mechanical strength and greater structure compactness in the PC samples with replacements up to 10% with both ashes. [PERSON] and [PERSON] [24] studied the effect of replacing PC with RSA, microsilica (MS), and a combination of both materials, to fabricate pavement concrete. They recommended a mixture with 5% RSA and 7.5% MS, which led to 53.6 MPa compressive strength after 28 curing days. This work aims to analyze the reuse, as a pozzolanic material, of the rice straw (RS) collected in the Albuifera (Spain) and to study the rice straw ash (RSA) obtained during outdoor burning, with a self-made burner. RSA was characterized by chemical composition and a mineralogical structure. Finally, various techniques were carried out to analyze ash reactivity: (a) standardized Frattini tests and pH and conductivity measurements for an aqueous suspension of CH/RSA mixtures; (b) thermogravimetric tests on PC/RSA pastes and mechanical tests on PC/RSA mortars.
# Experimental Demonstration of Single-Channel EEG Signal Using 32 x 32 Pixel OLED Screen and Camera
###### Abstract  
Currently, the radiofrequency (RF)-based wireless technology deployed in electroencephalography (EEG) to diagnose brain diseases suffers from frequency spectrum and electromagnetic interference, and might also have adverse effects on the health of patients and equipment used in hospitals, especially in RF-restricted zones like intensive care units (ICUs). Optical wireless communication (OWC), specifically visible light communication (VLC), is featured in 5G network to complement the radiofrequency (RF) technologies due to the fact that huge unlicensed bandwidth and available infrastructure, both indoor and outdoor, reduces the implementation cost. The conventional VLC systems deploy photodiodes as receivers, requiring hardware and infrastructure modifications in addition to smaller field of view (FOV), but the use of cameras reduce the infrastructure cost due to inbuilt filters and a wider FOV coverage gives the ability to scale a larger area. The wider FOV and the movement of camera rotation, without any additional adjustments to maintain the line-of-sight (LOS), allows the patient to be anywhere within the room and FOV. This paper demonstrates a novel healthcare system for EEG using visible light optical camera communication (VL-OCC), where a 32 x 32 pixel OLED screen acts as transmitter and the receiver section consists of several different cameras such as digital single-lens reflex camera (DLSR), android smartphone, and Thorlabs camera. The experiments were performed in LOS deploying on-off keying (OOK) modulation at several distant measurements to determine the system reliability and stability through bit error rate (BER) performance. The proposed system results depict that the DSLR camera outperforms the smartphone and Thorlabs cameras, as it is capable of transmitting an error free bit rate of 2.8 kbps at 30 fps up to 5.5 m.  
visible light optical camera communication; organic light-emitting diode; 5G; healthcare; electroencephalography +
Footnote †: journal: Medical Imaging
# A Low Complexity Persistent Reconnaissance Algorithm for FANET
## 4 Results
### Connectivity  
Figure 8a,b show each algorithm's network connectivity in the iterative process of FANET construction. To facilitate the demonstration, we select the FANET construction process of different algorithms in the same scene, including identical threat PDF and the movement of monitored targets. Due to limited space, only the process where the RUs' number is 10, 14, and 18 is displayed, respectively. The RWP algorithm is listed separately in Figure 8, considering that the value range of this algorithm's communication performance is quite different from others.  
Figure 8: The Network Connectivity fluctuation of algorithms in the iterative process of FANET construction: (**a**) The fluctuation of the RWP algorithm; (**b**) The fluctuation of other algorithms.  
Figure 7: Trajectory of the FANET in a certain time period (Front view).  
RWP is a random movement model. In this model, nodes move randomly without any constraints. The nodes are random in speed, direction of motion, and are independent of each other. However, our reconnaissance targets are not entirely random in the real world. Therefore, after the distance constraint in Equation (6) is converted into a penalty coefficient (Equation (11)), the increase in the penalty term makes the network connectivity of RWP fluctuate randomly in a wide range (shown in Figure 8a). Moreover, the network connectivity of the FANET constructed by RWP does not converge with the iterative process since the nodes' movement in the RWP algorithm has characteristics of randomness and irregularity.  
Figure 8b shows the convergence process of PSO, TARFC, and TA&CRFO's network connectivity. Among them, PSO only focuses on optimizing network connectivity, while TARFC and TA&CRFO also consider threat avoidance during the continuous reconnaissance process. Therefore, PSO is slightly better than TARFC and TA&CRFO in terms of network connectivity. TA&CRFO is a simplified version of TARFC in terms of complexity, but as can be seen from the chart, the performance of network connectivity is comparable to that of TARFC. In addition, as the RU number increases, the network connectivity of those algorithms also increases, and their performance shows a tendency toward convergence.
# Improved Control of Forest Microgrids with Hybrid Complementary Energy Storage
## 3 Hybrid Complementary Energy Storage System
### Predictive Converter Control Method  
Achieving fast smoothing of the system power requires fast converter control, whereas adopting the routine method based on real time value of DC/DC converters may weaken the effect of the self-adaptive droop control due to the lag regulation of current. Therefore, a predictive control method is proposed for the hybrid energy storage system to improve corresponding regulation speed. The converters of the supercapacitor and battery adopt the same Buck/Boost topology, as shown in Figure 5, with the switches \(V_{1}\) and \(V_{2}\) operating complementarily [27]. When the DC voltage in the microgrid is lower than the switching threshold, the energy storage unit discharges, in which condition the converter works in Boost mode. Contrarily, when there is power surplus and the energy storage unit needs to be charged, the converter switches to Buck mode.  
The converter of the energy storage system usually adopts a double closed-loop control structure, with the outer-loop being voltage control and the inner loop current control. Being different from the commonly used PI control method, the predictive control method based on converter model collects the current state variables of the system and calculates the predictive current value at the next moment through the predictive model. The switching action of the converter is then chosen by minimizing the deviation between the predicted current value and the reference one [28]. This method adopts active predictive control instead of passive feedback regulation, which effectively avoids the time lag in traditional PI-based current regulation within the inner loop. Therefore, the predictive method is suitable for the occasion of voltage regulation, which requires high control speed.  
Figure 4: Principle diagram of self-adaptive inertia droop control.  
Figure 5: Topology and working mode of bi-directional DC/DC converter in Boost mode: (**a**) Conduction period of \(V_{2}\); (**b**) Shutdown period of \(V_{2}\).