esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Enabling Technologies for THz-FSO Communication
### High-Power THz Sources
These properties make DFG-QCLs highly valuable for future 6G communication, spectroscopy, and secure transmission applications, as they address key challenges in THz signal generation and high-frequency transmission. Photonic mixers: Photonic mixers work on difference frequency generation that effectively generates THz radiation out of optical signals. These mixers are recognized for their potential to produce broadband THz signals, which is important to support high-count data transmission in FSO systems [81; 82]. The wide operating bandwidths offered by photonic mixers make them suitable for use where multi-channel data links are necessary; for instance, in spectroscopy and imaging. The combination of photonic mixers with FSO systems has also improved their spectral efficiency and signal distortion at different atmospheres. Nevertheless, phase coherence remains a technical challenge even when using these UGMs, especially due to variability in the communication process. Free-electron lasers (FELs): THz radiation is generated by the interaction of high-energy electron beams with periodic magnetic fields, which can produce highly coherent and efficient energy FELs. These systems are entirely appropriate for scientific research as well as other applications that must utilize very high levels of power, including material analysis or other imaging processes [10; 22; 73; 28]. Due to their high output power and tunability, FELs are appropriate sources of radiation for use in high-precision applications. However, FELs are inherently bulky, are costly to operate, and require complex maintenance, making them unsuitable for commercial THz-FSO applications. Current research on compact FEL designs intends to overcome these limitations, and recent progress has been made in both their compactness and cost reduction. A comparison of the compactness, power output, and operating efficiency of various THz sources is depicted in Table 11. These technologies are still being further developed to optimize these parameters to fulfill multiple demands of THz-FSO systems. To improve the efficiency of high-power THz sources, efforts are being made toward the identification of integrated systems that include both the characteristics of QCLs and those of photonic mixers [83; 84]. Furthermore, the existing photonic devices are gradually being upgraded to use the latest and best materials like graphene in a bid to increase their wattage efficiency and limit heat production.
# Review of GIS-Based Applications for Mining: Planning, Operation, and Environmental Management
## 6 Discussion  
### Advantages of Using GIS in Mining  
To support mine planning, optimize mine operations, and analyze the environmental impact on the mining site, GIS can be effectively used for the following reasons.  
(1) Effective mine data management in georeferenced, spatial, and attribute data formats: Mine data mainly include drill holes, ore bodies, mineral deposits, pit boundaries, and underground haulage roads, which have spatial characteristics. Therefore, these data can be effectively stored, manipulated, and processed in georeferenced (geographic coordinate-related characteristics), spatial (locational information), and attribute (spatial properties) data formats in the GIS environment. For this purpose, various GIS database frameworks have been established for drill hole data [35; 41], block models of mineral deposits [17], underground haulage road networks [31; 32; 33; 34; 29], underground environmental attributes using ZigBee nodes [37], and underground ventilation networks [44; 45; 46].  
(2) Powerful decision-making support for mine managers: GIS can be used to support decision-making during the various stages of mining development. Examples include ore reserve detection and estimation using spatial 3D query tools [13], visualization of uncertainty in open-pit boundaries [17], and land-use selection after mine closure using multi-criteria decision analysis [70]. Furthermore, mining-induced potential conflicts [24; 25], disasters [47; 53; 54; 55], and hazards [59; 60; 61; 62; 63] can be identified in advance using spatial analysis tools. Finally, all of the data and information are shared with multiple users to discuss optimal decisions through enterprise GIS technology [42].  
(3) Flexibility of multi-parameter considerations: GIS offers numerous data integration tools, map overlapping tools, multi-criteria analysis tools, and mapping algebra functions for multi-parameter consideration. For example, various spatial analysis models have been developed for optimal truck haulage route determination by considering truck movement impact factors [27], multiple-transmitter candidate location selection by considering the possibility of additional mine development and environmental factors [38], and in-pit stormwater pond design by considering the hydrological characteristics of surface drainage systems [50].  
(4) Fast and objective mine site investigation without on-site access: GIS can reduce the time and effort required for detailed mine site investigation by using remote-sensing data and various types of maps (e.g., topographic contour maps, geological maps, hydrological maps, and infrastructure maps). For mine reclamation planning, these methodologies are applied to determine mine-land reclamation activities [70], access mine subsidence and waste erosion [69], and evaluate the visibility impact of mining sites [71]. Consequently, mine investigators do not need to access undeveloped, dangerous, and hazardous areas for a site investigation.
# Development of Ultrasonic Pulsed Plasma Jet Source for Remote Surface Treatment
## 2 Materials and Methods  
### Concept and Construction of the Ultrasonic Pulsed Plasma Jet Source  
Figure 1 shows a conceptual diagram of the supersonic pulsed plasma jet source. First, high-density plasma was generated by supplying low-speed plasma-producing gas to the discharge. Next, the generated high-density plasma was jetted outward with a supersonic pulse jet to transport the active species in the plasma to the target. The gas flow velocity in the pulse jet is much higher than that in the plasma generation process, allowing the dense active species in the plasma to be irradiated to a distant target before they are deactivated [16]. In the case of short distances, more active species can reach the target; thus, a faster treatment is expected. As the flow velocity increased, the discharge power density per flow rate decreased, resulting in a decrease in treatment effectiveness. In addition, the gas consumption increased; therefore, a short pulse method was used. Figure 2 shows the structure of the supersonic pulsed plasma jet. The housing of the plasma device is made of polyacetal, which is an insulator with two gas channels inside. The discharge section consists of a 2 mm thick machinable ceramic plate, which is an insulator sandwiched between two 2 mm thick copper plate electrodes with a 1 mm hole drilled in the center. Plasma gas was supplied from a cylinder through a gas flow meter and check valve to the enclosure. Pulse jets were generated by briefly opening a 0.5 MPa high-pressure gas with a pulse-controlled solenoid valve (K2-100 HA-04-X50-25, Koganei Corp., New Koganei, Tokyo, Japan). We confirmed that nitrogen, carbon dioxide, air, helium, and argon plasmas were stably generated at a plasma gas flow rate of 3 L/min.  
Figure 1: Conceptual diagram of the supersonic pulsed plasma jet: (**a**) continuous plasma jet; (**b**) pulsed plasma jet.
# Non-Volatile Memory Forensic Analysis in Windows 10 IoT Core
## 1 Introduction
### Contributions  
The contributions of this study are as follows:  
* We study the current state of IoT forensics, explaining how the characteristics of the environment affect an investigation, and why traditional forensic techniques cannot provide a functional approach to be applied in this context.
* We present a review of the proposals from the community regarding IoT security and forensics.
* We conduct a forensic examination of the non-volatile memory of the Windows 10 IoT Core operating system. With this contribution, we address the study of a, forensically speaking, unknown operating system, thereby offering guidelines on how its analysis, acquisition, and evaluation should be carried out. This allows investigators to be able to rely on previous work when examining this system, easing the process.
* We list the relevant information that can be retrieved from the storage of Windows 10 IoT Core and which may be useful in a forensic investigation. This serves as a handbook to quickly observe what data can be extracted from the system and where it is located.
* We present a forensic tool to automatize the collection of these artifacts. This provides investigators with an IoT-specific program to properly study the non-volatile memory of Windows 10 IoT Core, instead of having to rely on general tools to perform this task.  
The rest of the paper is organized as follows. Section 2 describes the Windows 10 IoT operating system, Section 3 discusses the related work in IoT security and forensics. An overview on how to perform a forensic analysis on Windows 10 IoT Core is provided in Section 4, and the pieces of evidence found in it are listed in Section 5. A tool to automatize their retrieval is proposed in Section 6. Finally, our conclusions are presented in Section 7.
## 2 Materials and Methods
### Data Sets
#### 2.2.2 Climate Data  
Monthly precipitation and temperature data from 1982 to 2015 were obtained from the Climatic Research Unit Time Series (CRU-TS) data set ([[http://data.ceda.ac.uk/badc/cru/data/cru_ts](http://data.ceda.ac.uk/badc/cru/data/cru_ts)]([http://data.ceda.ac.uk/badc/cru/data/cru_ts](http://data.ceda.ac.uk/badc/cru/data/cru_ts))). The latest version of this database, version 4.03, has been updated to span 1901 to 2018, with 0.5\({}^{\circ}\) spatial resolutions on land areas [35]. It was derived by the interpolation of monthly climate anomalies from extensive networks of weather station observations, and it has also been widely used in climate change-related studies [1; 34].
# All-in-Focused Image Combination in the Frequency Domain Using Light Field Images
## 3 Experimental Results
### All-in-Focused Image Combination  
In this section, the experimental results of the all-in-focused images are presented and evaluated by comparing them with other prominent techniques such as light field software [10], SML [9], DCT-STD [5], DCT-VAR-CV [4], SML-WHV [16], [PERSON]'s method [17], DCT-Sharp-CV [18], DCT-CORR-CV [19], and DCT-SVD-CV [20]. The all-in-focused images of different algorithms are shown in Figures 12-16. From the expanded images in Figure 17, we can easily observe that the results of the light field software, the DCT-STD method, and the DCT-VAR-CV method have lower contrast than those of the SML method, [PERSON]'s method, the DCT-Sharp-CV method, the DCT-CORR-CV method, the DCT-SVD-CV method, and the proposed method. However, it is hard to show the differences from the results of the SML method, [PERSON]'s method, the DCT-Sharp-CV method, the DCT-CORR-CV method, the DCT-SVD-CV method, and the proposed method by subjective evaluation. It seems that there are little differences among the fused images, but the objective performance evaluation can capture their differences precisely. Hence, this paper applies some non-reference fusion metrics, such as the feature mutual information (FMI) metric [21] and Petrovic's metric (QAB/F) [22]. These metrics are calculated without respect to the reference images. The FMI metric measures the amount of information that the fused image contains from the source images, while QAB/F measures the relative amount of edge information that is transferred from the source into the fused image. If FMI or QAB/F indicate a higher value, the fused image performance provides a better result. The comparison results are summarized in Tables 1-5. The proposed method provides outstanding results when we compare it with other comparative methods.  
Figure 11: In-focused regions of the different focal stack images using SML and SUML—Flower’ dataset: (**a**) The input images of different focal lengths; (**b**) SML of stack images; and (**c**) SUML of stack images.
## 3 Digital Image Correlation (DIC) Technology  
Digital image correlation (DIC) technology is a non-contact full-field measurement technology, which determines displacement and deformation based on the correlation of the speckle image on the material surface before and after the deformation occurs. Digital image correlation technology can be used to analyze the deformation characteristics of the rock and soil test models and can then study the whole process of the displacement and failure of soil around a pile. In the present experiment, DIC technology was used to analyze the acquired images. The basic principle is to adopt a cross-correlation method in order to expose objects in different motion states at two times and image them on two different pictures. By dividing the image into square sub-areas of suitable size, it is possible to compare the gray-scale feature values of these sub-areas, identify the correlational relationships between specific sub-areas, calculate the displacement of pixels, and thus, access local displacement field data. After completing the analysis of the entire study area, the overall strain and displacement field distribution information is obtained, which is then visually expressed in the form of images [17; 26].  
We assume that the gray-scale feature functions of the two digital images before and after the deformation are \(f(x,y)\) and \(g(x^{\prime},y^{\prime})\), where, \(x\), \(y\) and \(x^{\prime}\), \(y^{\prime}\) are the positions of a certain pixel before and after the deformation. We use \(u\) and \(v\) to denote the displacement components of the feature point along the \(x\) and \(y\) directions. Then:  
\[x^{\prime}=x+u+u_{x}\Delta x+u_{y}\Delta y \tag{1}\]  
\[y^{\prime}=y+v+v_{x}\Delta x+v_{y}\Delta y \tag{2}\]  
After obtaining the gray distribution of the two sub-areas before and after the deformation, the correlation coefficient is used as the standard for evaluating the similarity between the two sub-areas. The correlation coefficient \(C\) is defined as:  
\[C=\frac{\sum f(x,y)\cdot g(x^{\prime},y^{\prime})}{[\sum f^{2}(x,y)\cdot\sum g ^{2}(x^{\prime},y^{\prime})]^{\frac{1}{2}}} \tag{3}\]  
In the formula, \(f\) and \(g\) are the gray-scale feature values of a certain sub-area before and after deformation. When \(C=1\), the two sub-area are fully correlated; when \(C=0\), the two sub-areas have no correlation.  
Figure 7: Loading and VIC-3D test system.  
Based on the above principles, the built-in DIC analysis software of the VIC-3D non-contact full-field strain measurement system was used to analyze and process the collected images. It was also used to obtain other relevant information, such as the plane displacement field and the displacement of each point.
## 12 The LNSS Self-Spoofing Attack on UUVs  
An LNSS Self-Spoofing Attack on UUVs occurs in situations where the owner of the vehicle wants to hide his/her real position in space for a false one. For example, some UUVs are commercial fishing vessels outside of the allowable area and include their own onboard self-spoofer to indicate the position of the UUV in the allowable fishing area (Figure 14).  
Figure 14: The basic LNSS self-spoofing scheme of UUVs: at some point in time, the self-spoofer determines its position \((x_{s},\,y_{s})\), introducing some delays into each of the satellite channels, introducing some other signal distortion, and transmitting the false position \((x_{s}+\Delta x_{s},\,y_{s}+\Delta y_{s})\) on the same frequency, which aims to deceive GIB receivers and create a false PVT solution. All UUVs in the self-spoofer activation zone are positioned as \(\text{UUV}_{\text{i,false}}=(x_{s}+\Delta x_{s},\,y_{s}+\Delta y_{s})\).  
Figure 13: The basic LNSS meaconing scheme of UUVs: at some point in time, the meaconer determines its position \((x_{s},\,y_{s})\), introducing some delays into each of the satellite channels (the delay is determined by the difference in the GIBs signal paths from the GIBs and from the meaconer to the UUV navigator antenna) and transmitting the false position \((x_{s},\,y_{s})\) on the same frequency of GIBs, which aims to deceive acoustic receivers and create a false PVT solution. All UUVs in the meaconer activation zone are positioned as \(\text{UUV}_{\text{i,false}}=(x_{s},\,y_{s})\).  
From a technical point of view, the LNSS self-spoofing of UUVs is the same spoofing, but here we have an absolutely different way of using spoofing, and if conventional anti-spoofing is based on the choice of technical means to protect oneself from spoofing, then anti-self-spoofing is based on the choice of technical means for attacking this self-spoofing in order to neutralize it, or at least to register the fact of its use by some vehicle including UUV.
# Post-Fire Vegetation Succession and Surface Energy Fluxes Derived from Remote Sensing
## Materials and Methods  
### Study Area  
The Great Xing'an Mountain forest is a component of the Siberian boreal forest, which has been heavily disturbed by fires. This area comprises 29.9% of China's total forests, and its carbon reserves comprise more than one third of China's forest carbon [10]. Historically, the region's fires are characterized by sparse stand-replacing fires over relatively small areas alongside frequent low-intensity surface fires. These fires cycle at 100- to 300-year intervals [33; 34]. The biological diversity in this area means that many species can regenerate after a fire, and this area's unique geographical location both controls the seasonal cycle and limits the time during which vegetation restoration can occur. The primary forest-regeneration species are birches and larches.  
The five forest fires that were analyzed in this study occurred in the study area at almost the same time in June 2000. These fires were located in Northeast China (52\({}^{\circ}\)15\({}^{\prime}\)-53\({}^{\circ}\)33\({}^{\prime}\)N, 121\({}^{\circ}\)51\({}^{\prime}\)-125\({}^{\circ}\)05\({}^{\prime}\)E) along the border of Heilongjiang and Inner Mongolia (Figure 1) and burned more than eighteen thousand hectares of forest at different burn severities. The temporal and spatial extents and heterogeneity of the burn severities from this fire provide an ideal opportunity to study the effects of fire patterns on post-fire succession and their effect on the ecosystem's energy flow. The terrestrial monsoon climate in this region produces short, mild summers (July's average temperature is 16 \({}^{\circ}\)C) and long, severe winters (January's average temperature is \(-\)26.8 \({}^{\circ}\)C). The annual precipitation amount is 461 mm, which is unevenly distributed throughout the year and peaks during summer (i.e., more than 60% occurs from June to August; see Figure 2). The species composition in this region is relatively simple but the forest cover is above 80%. The dominant tree species are larches, which are the southern extension of the eastern Siberian light-confierous forest, and birches, which comprise approximately 10%. Other species include spruces, pines and two species of aspen [10; 35].
# A Multiarmed Bandit Approach for LTE-U/Wi-Fi Coexistence in a Multicell Scenario
## 7 Final Comments and Future Investigation  
In this study, we investigated the use of two Reinforcement Learning algorithms in a multicell scenario involving the coexistence of LTE-U and Wi-Fi networks in an unlicensed band. We developed two approaches, referred to as independent and coordinated MAB, and compared them with a framework based on the well-known Q-Learning algorithm.  
Overall, two main reasons indicate that the independent MAB is the best solution: (i) across our two sets of simulations, this solution improves the aggregated throughput and provides the best fairness among operators; (ii) the independent MAB delivers similar throughput gain compared with Q-Learning, but its implementation involves fewer parameters and more simple calculations, resulting in lower complexity. For example, since there is no need to define states, it allows for a bigger set of possible actions without compromising convergence.  
Finally, despite the solutions being modeled to improve the aggregated throughput, they also enhance user throughput. The independent MAB demonstrated less than 10% of throughput loss for Wi-Fi users, while LTE-U users with the worst performance experienced a gain of over 100%.  
Our further investigations include the coexistence analyses of the proposed solutions with LTE-LAA and NR-U, as well as the conception of RL solutions to Licensed Assisted Access (LAA) (LTE-LAA and NR-U). We also consider investigating Wi-Fi parameters that could also be dynamically configured to improve even further the flexibility of our solution.  
Conceptualization and methodology: I.D.d.R., J.M.d.C.N., S.F.G.N., P.M.d.S. and V.A.d.S.J.; software and data curation: J.M.d.C.N. and S.F.G.N.; validation: J.M.d.C.N., S.F.G.N., P.M.d.S. and V.A.d.S.J.; formal analysis and investigation: J.M.d.C.N. and S.F.G.N.; resources: V.A.d.S.J., A.V.N. and D.V.; writing--original draft preparation: J.M.d.C.N., S.F.G.N. and P.M.d.S.; writing--review, editing and visualization: I.D.d.R., V.A.d.S.J., A.V.N. and D.V. supervision: V.A.d.S.J., A.V.N. and D.V. All authors have read and agreed to the published version of the manuscript.  
This study was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior--Brasil (CAPES)--Finance Code 001 and EFREI Paris.  
Not applicable.  
The proof of concept simulations provided by this paper were supported by the High-Performance Computing Center at UFRN (NPAD/UFRN).  
The authors declare no conflict of interest.