esa-sceva/satcom-chunk-collection huggingface dataset:
# Space Habitat Data Centers--For Future Computing
## 2 Background
A computing bottleneck occurs when space vehicles do not have sufficient resources to host external applications. The use of small satellite edge nodes can be used to resolve this bottleneck challenge. However, [PERSON] et al. [44] point out that the availability of computing platforms can enhance data processing. Therefore, an absence of in-orbit computing platforms degrades space data processing. This can be addressed by increasing space segment computing resources. Therefore, space data processing via space computing platforms reduces water footprint, computing platform access latency, and enhances accessible space segment computing resources. Water is suitable for data centre free cooling at the risk of increased water insecurity. It is also important to reduce data transfer latency between small-satellite and computing platforms. The perspective in this paper is that advances in space technology can be used to address these challenges.
## 4 Optimization of ML Models and Results
### Model Performance
#### 4.2.3 Results of Models for Z-PPV Prediction  
To better demonstrate the performance of the first three models for predicting Z-PPV, the results of the prediction of the validation set using GWO-GBR, GA-GBR, and PSO-GBR are shown in Figure 10. Additionally, the parameters of these models are shown in Table S3.  
Figure 10: The prediction performance of Z-PPV: (**a**) the measured and predicted results of GWO-GBR; (**b**) the confidence band and prediction band of GWO-GBR; (**c**) the measured and predicted results of GA-GBR; (**d**) the confidence band and prediction band of GA-GBR; (**e**) the measured and predicted results of PSO-GBR; (**f**) the confidence band and prediction band of PSO-GBR.  
As illustrated in Figure 10a-f, the R\({}^{2}\) of GWO-GBR, GA-GBR, and PSO-GBR were 0.9265, 0.9103, and 0.8956, respectively. The R of GWO-GBR, GA-GBR, and PSO-GBR were 0.9657, 0.9587, and 0.9470, respectively. The RMSE of GWO-GBR, GA-GBR, and PSO-GBR were 0.0476, 0.0525, and 0.0567, respectively. The MAE of GWO-GBR, GA-GBR, and PSO-GBR were 0.0406, 0.0424, and 0.0475, respectively. According to Figures 6g-i and 10b,d,f, the hybrid models had higher R\({}^{2}\) and R and lower RMSE and MAE compared to the standalone models. This indicates that the hybrid model had a higher performance of Z-PPV regression prediction than the standalone model. The evaluation indicators R\({}^{2}\) and R of GWO-GBR were all higher than the other models, and the RMSE and MAE of GWO-GBR were all lower than the other models. This shows that GWO-GBR had the best performance for Z-PPV regression.  
As shown in Figure 10b,d,f, the confidence band and prediction band of GWO-GBR, GA-GBR, and PSO-GBR for Z-PPV prediction were similar. This shows that the uncertainty performance of Z-PPV prediction models was similar [27]. Therefore, the GWO-GBR model had the best performance for Z-PPV regression.
## 5 Research on Mineral Prospecting Prediction
### Target Area Delineation and Engineering Validation
#### 5.4.2 Engineering Validation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **ZK192-11** & **ZK190-7** & **ZK192-1** & **ZK192-17** \\ \hline Elevation (m) & 258.10 & 246.65 & 231.44 & 228.32 \\ Drilling Depth (m) & 458.92 & 514.68 & 643.87 & 781.45 \\ Initial Borehole Inclination (\({}^{\circ}\)) & 85.7 & 81.4 & 82.4 & 81.8 \\ Depth of Ore Encounter (m) & 442.08 & 498.89 & 636.71 & 360.80 \\ True Thickness of Ore Body (m) & 6.73 & 9.17 & 0.81 & 9.96 \\ Ore Body Grade Range (\%) & & 0.05–1.32 & \\ Average Ore Body Grade (\%) & & 0.186 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Borehole parameters for Line L11. Figure 12: Images of the uranium ore hand specimen from borehole ZK192-17, identification photomicrographs of thin-polished sections, and in situ ore microscopic features. (**a**)—Hand specimen of grey cataclastic-micritic calcareous dolomite ore; (**b**)—hand specimen of fine-grained silty sandstone ore; (**c**)—cataclastic texture of the dolomite ore; (**d**)—fine-grained silt texture of the silty sandstone ore; (**e**)—veinlet pitchblende encrusting galena and pyrite within dolomite ore (reflected light) [24]; (**f**)—colloidal pitchblende coexisting with scheelite and galena in silty sandstone ore (backscattered electron image) [24]. Ptc: pitchblende; Py: pyrite; Gn: galena; Sh: scheelite.
###### Abstract  
This study presents the first genome assembly of the freshwater saprobe fungus _Nectriia lugdunensis_ and a comprehensive phylogenomics analysis of the _Nectriaceae_ family, examining genomic traits according to fungal lifestyles. The _Nectriaceae_ family, one of the largest in Hypocreales, includes fungi with significant ecological roles and economic importance as plant pathogens, endophytes, and saprobes. The phylogenomics analysis identified 2684 single-copy orthologs, providing a robust evolutionary framework for the _Nectriaceae_ family. We analyzed the genomic characteristics of 17 _Nectriaceae_ genomes, focusing on their carbohydrate-active enzymes (CAZymes), biosynthetic gene clusters (BGCs), and adaptations to environmental temperatures. Our results highlight the adaptation mechanisms of _N. lugdunensis_, emphasizing its capabilities for plant litter degradation and enzyme activity in varying temperatures. The comparative genomics of different _Nectriaceae_ lifestyles revealed significant differences in genome size, gene content, repetitive elements, and secondary metabolite production. Endophytes exhibited larger genomes, more effector proteins, and BGCs, while plant pathogens had higher thermo-adapted protein counts, suggesting greater resilience to global warming. In contrast, the freshwater saprobe shows less adaptation to warmer temperatures and is important for conservation goals. This study underscores the importance of understanding fungal genomic adaptations to predict ecosystem impacts and conservation targets in the face of climate change.  
Nectriaceae family; fungal lifestyles; global warming; genomic adaptations +
Footnote †: journal: Fungal
###### Abstract  
In this paper, a synthesized zeolite (ZSM-5) is used as an adsorbent to analyze the adsorption phenomenon of carbon dioxide. This investigation, based on the statistical physics treatment, applied the multilayer model with saturation to understand the CO\({}_{2}\) adsorption on four samples, namely M-ZSM-5 (M = Na\({}^{+}\), Mg\({}^{2+}\), Zn\({}^{2+}\), La\({}^{3+}\)), at various temperatures T = 0 \({}^{\circ}\)C, 30 \({}^{\circ}\)C and 60 \({}^{\circ}\)C. The modeling results indicated that CO\({}_{2}\) adsorption occurred via a non-parallel orientation on the ZSM-5 surface. The CO\({}_{2}\) adsorption capacities varied from 26.14 to 28.65 cm\({}^{3}\)/g for Na-ZSM-5, from 25.82 to 27.97 cm\({}^{3}\)/g for Mg-ZSM-5, from 54.82 to 68.63 cm\({}^{3}\)/g for La-ZSM-5 and from 56.53 to 74.72 cm\({}^{3}\)/g for Zn-ZSM-5. Thus, Zn-ZSM-5 exhibits the highest adsorption amount. The analysis of the adsorption energies shows that the adsorption of CO\({}_{2}\) on ZSM-5 zeolite is a physisorption phenomenon that could be controlled thanks to the energy parameters obtained via the numerical findings using the multilayer statistical model. Finally, the distribution of site energy was determined to confirm the physical character of the interactions between adsorbate/adsorbent and the heterogeneity of the zeolite surface.  
CO\({}_{2}\) adsorption; ZSM-5; multilayer model; statistical physics Article
## Appendix A Fluorescence Retrieval
### Description of the Fluorescence Retrieval Method  
Canopy radiance is related to irradiance and canopy fluorescence by the following equation:  
\[L(\lambda)=\frac{\rho(\lambda)}{\pi}\times I(\lambda)+F(\lambda) \tag{1}\]  
where \(I(\lambda)\), \(L(\lambda)\), \(\rho(\lambda)\), and \(F(\lambda)\) are, respectively, solar irradiance, canopy radiance, reflectance, and fluorescence at wavelength \(\lambda\). As reflectance varies smoothly with \(\lambda\), it can be considered as constant over channels bandwidth, and after integration over bandwidth of channel \(u\), Equation (1) becomes:\[L_{u}=\frac{\rho_{u}}{\pi}\times I_{u}+F_{u} \tag{1}\]  
Fluorescence retrieval was performed according to the method described in [32]. In this method (called _n_FLD hereafter, where \(n\) is the number of measuring channels), fluorescence radiance at a given absorption band (SIF687 in the O\({}_{2}\)-B band, SIF760 in the O\({}_{2}\)-A band) is retrieved using a limited number (_n_) of measuring channels in each O\({}_{2}\) band. Specific models of fluorescence and reflectance that apply only in the vicinity of each absorption band are used. The fluorescence spectrum shape is fixed and determined by measurements at leaf level using the LSF device already described in Section 2.2. Hence, the only free parameter for fluorescence is the overall fluorescence intensity. Canopy reflectance is modeled with a polynomial with free coefficients whose degree is linked to the number of measured channels. When two channels are measured, the degree is zero, which means that reflectance is considered as constant and the method is equivalent to the simple FLD (Fraunhofer line discrimination) method [7]. Here we used three channels in the O\({}_{2}\)-A band, and a polynomial of degree one to model the reflectance (i.e., a linear model). In the O\({}_{2}\)-B band, we used four channels and a polynomial of degree two for reflectance (i.e., a quadratic model with three parameters). With these assumptions, the system of equations generated from Equation (1) for all measured channels can be exactly solved.
# Study on Burr Formation and Tool Wear in Drilling CFRP and Its Hybrid Composites
## 3 Results and Discussion
### Thrust Force  
Since the thrust force directly affects the quality of the machined hole in the drilling of polymer composite materials, it is essential to study the generation and mechanism of thrust force. This is because the thrust force plays a key role in determining the degree of delamination damage [44; 46]. Delamination occurs when the thrust exceeds the interlayer bond strength of the laminate under the continuous cutting motion at the hole exit. Therefore, in order to restrain the delamination damage, it has great significance to predict the thrust force at the beginning of the delamination and to control the thrust force during the drilling process. The total thrust force is divided into three components in the drilling of polymer composite materials: (i) thrust force generated by the cutting lips; (ii) thrust force generated by the chisel edge cutting action; (iii) extrusion force generated by the chisel edge extrusion action [47]. Figure 8 illustrates the effects of the number of holes drilled on the thrust force. A total of 300 holes was, respectively, drilled on CFRP and C-AFRP, using an 8 mm diameter carbide drill with a point angle of 90\({}^{\circ}\), a 2000 rpm spindle speed and a 200 mm/min feed rate. As shown in Figure 8, it can be clearly seen that the thrust force produced by drilling CFRP is obviously higher than that of drilling C-AFRP. The average thrust force is 321.76 N for CFRP and 78.51 N for C-AFRP. This shows that different composite materials have a strong influence on the thrust force. In the process of drilling composite materials, the mechanical strength of the composite material in the feed direction depends on the mechanical strength of the resin. On the other hand, as shown in Figure 8, the thrust seems to increase with the number of holes, which is consistent with other research results [48]. This may mainly contribute to tool wear, which increases with the number of holes drilled. In addition, the thrust force is also related to the geometry of the tool, feed rate, spindle speed, and other factors. Therefore, selecting a suitable bit, such as twist drill, properly reducing the feed rate and increasing the spindle speed, is conducive to reducing the thrust force. In particular, the high cutting temperature caused by high-speed drilling softens the epoxy matrix and reduces the thrust force, which is beneficial to the drilling of the polymer composite materials [44].  
Figure 7: Burr formation in drilling of (**a**) CFRP and (**b**) C-AFRP.
## 3 Modified ElGamal-Based Individual Signature  
The proposed technique for constructing an MPS is based on a new variant of ElGamal encryption system [16] to create the individual signature.  
ElGamal is a widely adopted public key cryptographic system designed to secure digital communications [16]. Its security is based on the discrete logarithm, making it a fundamental element of modern encryption methods. ElGamal is commonly used for secure data transmission, digital signatures, and authentication protocols. The strength of its security is influenced by the size of the cryptographic keys, with larger keys providing stronger protection, though they require more computational resources. The ElGamal algorithm operates through key generation, encryption, and decryption processes, utilizing the mathematical properties of large prime numbers and modular arithmetic.  
1. Key Generation  
Pick a prime number, \(p\), select a private key, \(k\), and calculate the public key, \(y\), as illustrated in Equation (1).  
\[y=g^{k}\mod p \tag{1}\]  
where \(g\) is a generator.
2. Signature  
In our modified system, we put \(h\) and \(h^{\prime}\) as presented in Equation (2).  
\[h=Hash(m),h^{\prime}=Hash(h), \tag{2}\]  
To sign the hash of a message, \(m\), the signature function uses private and secret keys defined by the modified system as shown in Equations (3) and (4).  
\[b=y^{\prime}\mod p \tag{3}\]  
where \(r\) is a random number generated for each message.  
\[a=k\times(h\times b-h^{\prime}\times r)\mod(p-1) \tag{4}\]
3. Verification  
To verify a signature \(\sigma=(a,b)\), Verf function uses Equation (5).  
\[y^{h\times b}\mod p\stackrel{{!}}{{=}}g^{a}\times b^{h^{\prime}} \mod p \tag{5}\]  
We have  
\[g^{a}\times b^{h^{\prime}}=g^{k\times(h\times b-h^{\prime}\times r)}\times y^ {h^{\prime}\times r},\]  
\[=g^{k\times h\times b-k\times h^{\prime}\times r}\times g^{k\times h^{\prime} \times r},\]  
\[=g^{k\times h\times b}=y^{h\times b}.\]
###### Abstract  
Flash droughs adversely affect agriculture and ecosystems due to their rapid depletion of soil moisture (SM). However, few studies assessed the impacts of flash droughs on crops, especially in the agricultural regions of southern China. In this study, we investigated flash droughs using crop root zone SM in the main agricultural region of southern China. Additionally, solar-induced chlorophyll fluorescence (SIF) served as a vegetation index to explore the crop response to flash droughs. The results reveal that the SIF exhibited an upward trend from 2001 to 2020 in the study area, indicating the enhanced photosynthetic capacity of crops and subsequent yield improvement. Hotspots of flash drought frequency occurred in the eastern areas of both the upper and lower Yangtze River regions, specifically in areas where the most rapid types of flash droughs were particularly prevalent. The average duration of flash droughs in the southern agricultural region was 6-12 pentads, a sufficiently long duration to significantly hinder crop photosynthesis, resulting in negative SIF standardized anomalies. The area affected by flash droughs in the southern agricultural region presented a downward trend during 2001-2020, with flash droughs of the longest duration in the recent decade, specifically in 2019, 2010, and 2013. The response frequency and time of SIF to flash droughs were >80% and <2 pentads, respectively, indicating that crops in the study area have a high sensitivity to flash droughs. In the northern part of the middle Yangtze River region and the southwestern and southeastern parts of the South China region, the mean values of the standardized anomalies of the SIF were lower than \(-0.5\) during flash droughs, suggesting that crops in these areas were severely affected by flash droughs. During the late summer of 2019, the study area experienced a precipitation shortage coupled with high evapotranspiration capacity. This unfavorable combination of meteorological conditions can quickly lead to a substantial depletion of SM, ultimately triggering flash droughs that can be devastating for crops. Our findings can enhance the understanding of the impacts of flash droughs on crops in agricultural regions, as well as provide early warning signals of flash droughs for farmers to make appropriate mitigation strategies.  
flash droughs; solar-induced chlorophyll fluorescence (SIF); soil moisture; agricultural regions Published: 25 July 2024
## 1 Introduction
Therefore, parallel-cascade truncated Volterra filters [23; 24], tensor decomposition realization [25], diagonalization [26], and subband realization [27; 28; 29; 30; 31] have been proposed as efficient implementations of third-order Volterra filters, respectively. However, in the parallel cascaded truncated Volterra filter, some of the parameters used to design the approximate model of the target third-order Volterra filter are determined by adaptive signal processing, and the accuracy of the approximate model is reduced due to the adaptation error. In addition, the tensor decomposition realization and diagonalization do not take into account the symmetry of the Volterra kernel, which limits the improvement in computational efficiency. Furthermore, in improving computational efficiency in subband realization, there is a problem that it is not guaranteed to reduce computational complexity because it strongly depends on the nonlinearity of the target loudspeaker. In this paper, we propose an efficient realization method for third-order Volterra filters based on singular value decomposition. Using the symmetry of the target third-order Volterra filter, the proposed method decomposes the filter into several 2D filters of different shapes and performs singular value decomposition on each 2D filter. The resulting singular values and their corresponding singular vectors are then used to construct each 2D filter. By removing the filter sections with small singular values, the approximation accuracy of the original third-order Volterra filter is not degraded and the computational efficiency is improved. This paper is organized as follows. In Section 2, a Volterra filter and the linearization system to compensate for nonlinear distortions are explained. Then, the proposed efficient realization method for the third-order Volterra filter is described in Section 3. Simulation results are given in Section 4 to demonstrate the effectiveness of the proposed efficient realization and a conclusion is given in Section 5.
# Automatic Identification of Bridge Vortex-Induced Vibration Using Random Decrement Method
## 2 Presentation of Method
### Character of VIV and Random Vibration  
A bridge at operation stage is usually subjected to ambient dynamic loadings, such as wind and traffic. When the dynamic loadings are random, the induced response will also be random, such as those caused by wind buffeting and passing vehicles. Wind buffeting is a kind of forced vibration caused by natural fluctuation of incoming wind. In some circumstances, the dynamic loading can be caused by periodic excitation such as vortex-shedding forces, and the induced response becomes periodic or even sinusoidal. Wind buffeting is usually not a serious concern due to its small amplitude at common wind velocity, while vortex-induced vibration can cause user discomfort and fatigue problems due to its large vibration amplitude up to 50 cm. The theoretical background of wind buffeting and vortex-induced vibration may be found in Simiu and Scanlan [1].  
The majority of measured structural responses of a bridge at normal operation stage are random responses induced by wind buffeting and passing vehicles. Due to the broad-banded nature of excitation, random response is characterized by multimodal vibration, indicating a number of predominant frequency components in the power spectrum of random vibration. On the other hand, vortex-induced vibration is a kind of resonant vibration that is caused by the periodic shedding and its resulting sinusoidal forces. Therefore, VIV is characterized as single mode vibration for a specific wind velocity. Several distinct differences between random vibration and VIV are highlighted:  
1. Random vibration is caused by the frequent random excitation, such as wind buffeting and passing vehicles. It is always occurring, regardless of the magnitude of wind velocity, while VIV occurs only at a specific wind velocity.
2. Random vibration is characterized by multiple mode responses, while VIV only has a single predominant frequency in the power spectrum.  
Figure 2 showed the two typical segments from the health monitoring system of the Xihoumen Bridge, which corresponds to random vibration and VIV. It is obvious that the VIV signal is close to the harmonic signal, and is usually considerably larger than wind buffeting.  
Figure 1: Flowchart of the proposed method (coefficient of variation-COV, Random decrement technique-RDT, Vortex-induced vibration-VIV).  
Figure 2: Comparison of the measured acceleration responses: (**a**) Random vibration; (**b**)VIV (Vortex-induced vibration-VIV).
## 4 Engineering Example
### Data Preprocessing and Model Settings
#### 4.2.1 Missing Value Interpolation  
Considering the instability of the sensor acquisition device, corrosion monitoring data often have missing values. First of all, the sample data should be processed for missing values. This paper uses linear interpolation along the time dimension to fill in the vacant values, and the relevant calculation method is shown in Equation (7).  
\[x_{t}=x_{i}+\left(\frac{t-t_{i}}{t_{j}-t_{i}}\right).(x_{j}-x_{i}) \tag{7}\]  
where \(t_{i}\), \(t_{j}\) is the start and end moments of the missing data interval; \(x_{i}\) and \(x_{j}\) denote the monitoring data values at moments \(t_{i}\) and \(t_{j}\), respectively; \(x_{t}\) is the missing data compensation value of \(t\) at any moment between \(t_{i}\) and \(t_{j}\) after interpolation.