esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Kendall Rank Correlation Coefficient  
In order to evaluate the similarity between different transactions, we have used the Kendall rank correlation coefficient. Kendall rank correlation coefficient is a statistic used to measure the ordinal association between two measured quantities. Let \((\mathbf{a}_{1},\mathbf{b}_{1}),\ldots,(\mathbf{a}_{n},\mathbf{b}_{n})\) be a set of observations of the joint random variables \(\mathbf{A}\) and \(\mathbf{B}\), such that all the values of \((\mathbf{a}_{i})\) and \((\mathbf{b}_{i})\) are unique. Any pair of observations \((\mathbf{a}_{i},\mathbf{b}_{i})\) and \((\mathbf{a}_{j},\mathbf{b}_{j})\), where \(i<j\), are said to be concordant if the sort order of \((\mathbf{a}_{i},\mathbf{a}_{j})\) and \((\mathbf{b}_{i},\mathbf{b}_{j})\) agrees: that is, if either both \((\mathbf{a}_{i}>\mathbf{a}_{j})\) and \((\mathbf{b}_{i}>\mathbf{b}_{j})\) holds or both \((\mathbf{a}_{i}<\mathbf{a}_{j})\) and \((\mathbf{b}_{i}<\mathbf{b}_{j})\), otherwise they are said to be discordant. The Kendall \(\tau\) coefficient is defined as  
\[\tau=\frac{n_{c}-n_{d}}{\binom{n}{2}} \tag{3}\]  
where the \(n_{c}\) is the number of concordant pairs, \(n_{d}\) is the number of discordant pairs, and \((\frac{n}{2})\) is the total number of pair combinations. In Kendall rank correlation coefficient, the denominator is the total number of pair combinations, so the coefficient must be in the range \(-1\leq\tau\leq 1\). If the agreement between the two rankings is perfect (i.e., the two rankings are the same), the coefficient has value 1. If the disagreement between the two rankings is perfect (i.e., one ranking is the reverse of the other), the coefficient has value \(-1\). If \(\mathbf{a}\) and \(y\) are independent, then we would expect the coefficient to be approximately zero.
# Beyond 100G: All-Optical Processor for High-Capacity Access\(\sim\)Networks
## 3 Overview of the Proposed All-Optical Processor  
The implementation of PAM4 in 50G and 100G PON significantly increases the demands on DSP due to the higher sensitivity penalties and linearity requirements of PAM4 compared to traditional NRZ modulation. These impairments are handled by stronger DSP equalization techniques, such as feed-forward/decision feedback equalizers (FFE/DFE), or by using machine-learning-based equalization methods, such as deep neural networks (DNN) [33; 34].  
To mitigate the increased computational demands of DSP, we propose integrating an AOP, which can effectively handle the computational needs, reducing latency and power consumption while improving processing performance. The proposed AOP aims to exploit the established low-cost technology of IM-DD PON systems to extend their operation speed at 100 and 200 Gb/s employing current-generation optoelectronic components, increasing their component bandwidth limitations by at least 40%. Moreover, the AOP is considered part of the transmitter at the optical line terminal (OLT), keeping the overall ONU cost and complexity low and comparable to 50G PON systems and rendering the benefits of IMDD technologies up to 200 Gb/s. Figure 1 shows a functional block diagram of the AOP, which stands out due to its unique architecture and dual functionality, enabling it to selectively perform two critical tasks, all-optical equalization and chromatic dispersion compensation, depending on the dominating system impairment, at the dictated operating speed. In the present study, fiber lengths of a maximum of 15 km are considered, rendering linear effects (optical propagation loss, chromatic dispersion, and polarization mode dispersion) the dominating signal impairment mechanism. It is noteworthy that, for future PONscenarios under consideration which target supporting an extended reach of up to 80 km with variable split ratios [Huawei, CableLabs], nonlinear transmission impairments will play a more crucial role that will likely necessitate the adoption of coherent DSP solutions. To this end, the proposed AOP aims to demonstrate the viability of IMDD systems at even higher speeds assisted by means of a machine learning (ML)-assisted algorithms.  
This processor leverages the adaptability of an optical switcher to alternate between tasks. In switch state 0, the AOP functions as an optical equalizer. In switch state 1, the AOP acts as a chromatic dispersion compensation filter. This versatile switching capability enhances the system's adaptability and efficiency.
# Femtosecond Laser Textured Surfaces for Radiative Cooling: Black Metals
## 1 Introduction  
The black appearance of a surface in the visible spectral range can be caused by spectrally broad-band absorption or by the anti-reflective nature of the surface [1, 2]. Diminishing reflectivity \(R\to 0\) in the absence of transmittance \(T\to 0\) (a thick sample) correspondsto the high absorbance \(A\to 1\), in accordance with energy conservation \(A+R+T=1\) (here scattering is accounted for by reflection and absorption). Such anti-reflective black surfaces have great potential for perfect emitter or radiative cooling applications. Radiative cooling relies on the high reflectance of solar irradiation (primarily in the short-wave range of 0.15-2.5 \(\upmu\)m) and high emissivity in the mid-IR range (3-25 \(\upmu\)m). Optical coatings, such as metals and metal oxide layers, have been demonstrated to have significant potential in this area by forming anti-reflective coatings (high emittance) in the atmospheric transparent window (8-13 \(\upmu\)m) [3]. These coatings effectively reflect short-wave solar radiation while emitting mid-IR radiation through the atmospheric window to outer space, thereby cooling the surfaces without energy consumption. However, such emitters or anti-reflective metasurfaces require precise and complex fabrication processes with control of layer thickness, periodicity and material composition [4; 5]. This usually involves advanced high-vacuum manufacturing techniques [6].  
Laser ablation with ultra-short <0.5 ps pulses at high-intensity 0.1-1 PW/cm\({}^{2}\) opens new pathways for material processing by direct energy and momentum deposition. High-temperature and high-pressure phases, as well as novel composite materials, can be generated through ultrafast thermal quenching. Relevant studies, including high-pressure ultrafast dynamics [7], ultrafast photothermal exsolution [8], and ultrafast synthesis via high-temperature shock [9], have been reviewed and discussed. Due to these unique characteristics, femtosecond (fs) laser texturing surpasses conventional surface treatments in both structural and thermal performance. Compared to mechanical or chemical texturing, fs-laser texturing provides precise control over surface microstructures, enabling the optimization of optical absorption properties--an essential factor for radiative cooling. Additionally, fs-laser texturing induces significant microstructural and phase modifications [10], promotes the formation of dense and protective oxide layers that enhance surface durability [11], and increases surface area, roughness, and mechanical stability by facilitating localized martensitic transformations through rapid heating and cooling [12]. These advantages establish fs-laser texturing as a powerful tool for engineering surfaces to enhance radiative cooling.
## 3 Materials and Methods
### Methods
#### 3.2.3 Mobile Application Development (Frontend)  
The mobile application was developed in Kotlin, chosen for its resource efficiency and compatibility with Android. The application utilizes Google Location Services to obtain the user's location. Its primary goal is to capture the user's real-time location, send the coordinates to the API, and receive relevant promotional offers when the user is within 600 m or less of a shopping center. Latitude and longitude coordinates are transmitted to the API via HTTP requests using the Retrofit library. The user interface includes a dynamically displayed share button that appears upon receiving promotions. This button allows users to share offers through social media or messaging applications, leveraging Android Intents to enable seamless integration with multiple platforms.  
API Interaction
* The application makes asynchronous requests to the API using Retrofit to send the coordinates.
* The application receives and displays promotions on the user interface when proximity is detected. The following dependencies were added to the build.gradle file: Google Location Services to track the user's location.  
implementation (\"com.google.android.gms:play-services-location:21.3.0\") Retrofit dependencies for HTTP requests implementation (\"com.squareup.retrofit2:retrofit:2.9.0\") implementation (\"com.squareup.retrofit2:converter-gson:2.6.4\")  
The following permissions were added in the manifest.xml file to allow the application access to the internet and GPS usage:  
<uses-permission android:name=\"android.permission.INTERNET\"/> <uses-permission android:name=\"android.permission.ACCESS_COARSE_LOCATION\"/> <uses-permission android:name=\"android.permission.ACCESS_FINE_LOCATION\"/>
## 3 OFDM Methodology
### DWT Method  
The DWT [53] uses the multi-resolution mechanism to simultaneously represent its signal in both frequency and time domain. Wavelet-based systems use a quadrature mirror filter (QMF) bank, which includes a half-band low pass filter (LPF) with an impulse response and a half-band high pass filter (HPF) with an impulse response that convolves with the signal. In OFDM, the FFT can be replaced by DWT to represent the subcarrier as a multi-resolution signal under low and high filter configurations. The transmission data based on DWT can be represented as:  
\[x_{j,d}=2^{\frac{-j}{2}}\sum_{d=-\infty}^{\infty}x_{n}\Psi(2^{-j}n-d), \tag{3}\]  
where \(d\) is the shift parameter and \(\Psi\) is the mother wavelet, which is constructed in such a way that it may be inverted and the original signal recovered. Then, the IDWT can also present as:  
\[x_{n}=\sum_{d=0}^{\infty}\sum_{j=-\infty}^{\infty}x_{j,d}\Psi(2^{-j}n-d), \tag{4}\]
## 6 Future Research Directions
### Development of a Spatially Explicit Ecohydrological Modeling Framework
#### 6.1.2 Reviewing of the TerrainLab Model [157]  
TerrainLab [194] was adopted concepts from [PERSON] _et al._[208] and it has been tested in the Southern Study Area (SSA) of the BOREAS region to study the spatio-temporal variation of ET and soil moisture [143]. The horizontal boundary of the simulated area is a watershed delineated from a digital elevation model (DEM) where divides between neighboring watersheds are identified. Vertically, the simulation extends from the saturated zone in the soil to the top of vegetation canopy. Within a watershed, the forest ecosystem is divided into basic spatial units, or pixels in remote sensing. Each pixel is treated as a unique vegetation-soil system, except for the ground and runoff water exchanges. Basic model simulations of the physical and biological processes are made at the pixel scale. According to the need of simulating hydrological processes, a pixel is vertically divided into five strata, i.e., overstory, understory, litter or moss layer, soil unsaturated zone, and soil saturated zone. Precipitation, solar radiation, topographic parameters, land cover, LAI, and soil properties are the major inputs to the model.  
The soil water balance component of TerrainLab allows for the spatially explicit simulation of topographically driven lateral subsurface flow and its influence on water table depth and soil water content dynamics based on a raster grid DEM [194]. TerrainLab uses the explicit routing scheme of [PERSON] _et al._[208]. In this approach the soil profile is subdivided into an unsaturated and a saturated zone. Saturated hydraulic conductivity is assumed to be depth-dependent, while other hydraulic parameters are considered to be vertically homogenous. However, the assumption of vertical homogeneity of permanent wilting point, field capacity, and porosity represents a major simplification in peat lands. The lateral movement of water between each raster grid cell (pixel) of the modeling domain and its maximum eight neighboring pixels based on a multi-predilection-flow algorithm occurs in the saturated zone, i.e., as groundwater flow. Groundwater follows the local hydraulic gradient (\(3\times 3\) pixels) that is assumed to be approximated by local ground surface slopes [194, 208].
## 2 Results and Discussion
### Inhibitory Activity of MOG APIs in the EAE.
Furthermore, the cyclic analogues were synthesized by amide bridging the Lys side chain at position 41 or 46 with the C terminal and prevent in different degrees the EAE induced by rat MOG\({}_{35-55}\). The Arg at position 41 or 46 was substituted by Lys in order its side chain amine group to be used for the cyclization (amide bond) with the carboxyl group in C terminal. The finding that EAE induced by rat MOG\({}_{35-55}\) can be ameliorated by co-administration of mutated peptides of human epitope MOG\({}_{35-55}\) is very intriguing. The lower antagonist activity of cyclic analogue may be attributed to their lower binding ability to major histocompatibility complex (MHC) due to the restricted conformational freedom. Furthermore, lack of agonist activity in the EAE system and expression of antagonist activity by linear analogues, maybe suggests that the guanidino group of Arg at position 41 and 46 are required for recognition by TCR and that the Arg\({}^{41}\) and Arg\({}^{46}\) are important residues for EAE induction in combination with the hydroxyl group of Tyr\({}^{40}\) and Ser at position 42 (rat sequence) instead of Pro (human sequence). APLs can switch an agonist peptide into an antagonist peptide. Peptides which are involved in the trimolecular complex between the MHC, the antigen (peptide) and the TCR and cause an antagonistic (EAE suppression) effect (ie loss of T cell activation) have loss of main interaction of peptide side chains with the CDR3 loop of the TCR. Mutation of large side chain of the peptide which interact with the TCR to small side chain amino acids (such as Ala) can cause antagonism and thus, inhibit EAE symptoms. Furthermore, APLs can elicit selected effect or functions from T-cells [39-41]. In malaria and in autoimmune diseases APLs can be used to switch an epitope-specific Th1-type CD4\({}^{+}\) T cell response (characterized by production of IFN-\(\gamma\), IL-2) towards a Th2-type T cell response (IL-4, IL-10, TGF\(\beta\)) resulting in disease suppression. The recruitment of distinct populations of suppressor T-cells to the autoimmune target, by activation through different TCR, is also possible. APLs may activate regulatory populations of T-cells able to antagonize the action of encephalitogenic cells by limiting their deleterious effect in the CNS. It would, therefore, be very interesting to evaluate _in vitro_ the synthesized APLs, in order to explore their immunogenicity, in terms of cell proliferation, antibody and cytokine production.
# A Review of Magnetoelectric Composites Based on ZnO Nanostructures
## 3 ZnO Magnetoelectric Composites
### Thin Films
#### 3.1.2 Doped ZnO Thin Films
Figure 5: (**a,b**) Ferroelectric polarization vs. electric fields loops for pure ZnO and Ni-doped ZnO showing reversible polarization for the Ni-doped thin film; (**c–e**) magnetoelectric response of Li-, Ni- and Mg-doped ZnO thin films vs. an external DC magnetic field. Reproduced with permission from [68]. Copyright [2014], Elsevier. As stated above, the un-doped ZnO shows weak reversible polarization. However, in a report by [PERSON] et al. [18], the ZnO nanorods depicted a clear polarization switching at a coercive field of 1400 MV/m. This was explained by the coexistence of two ZnO phases in the nanorod structure as a side-effect of the fabrication process, effectively creating a core-shell architecture. The samples that exhibited ferroelectricity presented an increased volume fraction of the ZnO \(\beta\)-phase in contrast to samples crystallized only in the P6\({}_{3}\)mc space group, thus, ferroelectricity in pure ZnO is a second-order effect rather than an intrinsic property of the material [69]. Therefore, doping strategies in ZnO were actively researched for their effect on the ferroelectric properties [70], especially in the case of ZnO doped with Li, Mg, Ni and Co. Lithium and Cobalt doped ZnO thin films were reported by [PERSON] et al. [71] to exhibit simultaneous ferromagnetic and ferroelectric behavior, which was attributed to the Co\({}^{2+}\) and Li\({}^{+}\) ions occupying the off-center positions in the ZnO crystal. Both effects are defect-related, inducing permanent local electric dipoles in the case of ferroelectricity due to the different ionic radii of Li\({}^{+}\) and Zn\({}^{2+}\) ions. The magnetoelectric properties of the composite were not discussed in the aforementioned study; however, multiferroic composite materials that combine ferromagnetic and ferroelectric properties are highly effective for magnetoelectric applications [72]. In addition, the incorporation of ferromagnetic materials into ZnO thin films was investigated by [PERSON] et al. [73] via Co ion implantation of pulsed-laser-deposited ZnO films. The magnetoelectric coupling was established by the interaction between the electric dipole moments and magnetic moments, notably without strain-mediated effects due to the magnetostriction of the Co nanoparticles. Although the ME coupling was established at high-intensity magnetic fields, such a multiferroic nanocomposite thin film can present an alternative to conventional ME composites and be a possible candidate in magnetoelectric applications.
# SMARTerra, a High-Resolution Decision Support System for Monitoring Plant Pests and Diseases
## 2 Methods
### Back-End  
This section briefly illustrates the entire back-end workflow, from the preprocessing of measured and forecast weather data to the generation of raster files with risk indices and their storage on the CRS4 filesystem. Notably, all analysis components are performed fully automatically and in near real time.
# A Spatial Reconstruction Method of Ionospheric foF2 Based on High Accuracy Surface Modeling Theory
## 4 Discussion  
As shown in Figure 5, we first divided the solar activity epochs into three periods: a high year, moderate year, and low year. Then, the foF2 data for the four seasons of spring,  
Figure 4: The HAS model’s predicted RRMSEs of seven modeling stations at different times changed with the number of iterations. Convergence, overfitting, and non-convergence were observed.  
summer, autumn, and winter were selected. Next, the foF2 data were further divided into daytime and nighttime categories. The final composition was assigned to different validation datasets to ensure the coverage of different solar activity levels, seasons, and periods, thus ensuring the comprehensiveness and representativeness of the validation.  
To provide a clearer explanation of the statistical variations in the HAS method's foF2 predictions, relatively high, moderate, and low solar activity periods were classified based on the monthly average sunspot number as shown in Figure 6a, where a sunspot number greater than 100 is a period of high solar activity, a sunspot number in the range of [10, 100] is a period of moderate solar activity, and a sunspot number less than 10 is a period of low solar activity [30].  
Figure 5: Validation data were divided based on solar activity epochs, seasons, and local time.  
Figure 6: Solar activity epochs, seasons, and their corresponding predicted RRMSEs. (**a**) Monthly mean sunspot numbers. (**b**) Predicted RRMSEs for different solar activity epochs. (**c**) Predicted RRMSEs for different seasons.  
Figure 5(a) shows the variation in the monthly mean of sunspot numbers during 2013-2018, with the high solar activity period mainly concentrated in 2014, the medium solar activity period was mainly distributed in 2013 and 2015-2017, and the low solar activity period was mainly concentrated in 2018. Figure 5(b) demonstrates the predicted RRMSEs for different solar activity periods. In different solar activity periods, the RRMSEs which were poorly predicted were higher in spring and summer, and the RRMSEs which were more accurately predicted were lower in winter. As a whole, the high solar activity periods predicted the foF2 better than the other solar activity periods, and the prediction was better in winter. The predicted RRMSEs for different seasons can be seen in Figure 5(c). Specifically, the RRMSEs for spring 2014 and winter 2018 were relatively high at most time points, indicating relatively large prediction errors. The RRMSEs for summer 2016 and autumn 2018 were usually relatively low, indicating relatively good predictions. Figure 6 shows the distribution of RRMSEs under different solar activity periods and seasons, which provides an important basis for analyzing the foF2 prediction model.
## 3 Empirical Results
### Heterogeneity Analysis
#### 3.5.2 Regional Heterogeneity Analysis
This may be due to the fact that the economic and technological development in the eastern region is more advanced, and its spillover effect on the central and western regions is more significant. However, environmental regulation shows no spatial spillover effects on agricultural green development in the east-middle region, east-west region and middle-west region. Furthermore, the interaction term between environmental regulation and financial support for agriculture on agricultural green development shows significance only in the east-middle region.