esa-sceva/satcom-chunk-collection huggingface dataset:
# An Embedded Sensor Node Microcontroller with Crypto-Processors
## 3 Microcontroller Design
### Adc  
Connectivity to external analog sensor sources is provided by an integrated 12-bit analog-to-digital convertor (ADC). The implemented device is a fully differential 100 kSps, 12-bit, successive approximation register ADC with self-calibration capabilities. The sampling rate goes up to 1 MSPs at the nominal supply voltage of 2.5 V. The operation is also possible at a supply voltage of 2 V but both generate results with reduced accuracy. The size of the ADC core is 1.12 mm \(\times\) 1.1 mm.
# Effect of Environmental Parameters on Structural Health Status Assessment Using OMA Techniques
## 8 Correlation of Natural Frequencies with Temperature and Axial Force  
In this section, the effects on the structure of environmental temperature are evaluated, trying to separate frequency variations due to environmental factors from frequency variations due to structural damage.  
Very light levels of damage have been introduced to stress the disturbance on the identification coming from environmental parameters.  
The correction of the effect of temperature is performed in two different ways: firstly, by using ambient temperature regression; then, by using axial force variation directly measured on each beam as the correction input. This second approach will lead to much better results than the correlation with ambient temperature.
# Wastewater Treatment with Bacterial Representatives of the _Thiothrix_ Morphotype
## 2 Types of Wastewater Treatment Plants Where _Thiothrix_ Morphotype Can Occur
### Aerobic Granular Sludge  
As a promising alternative to the wastewater treatment process involving activated sludge, aerobic granular sludge (AGS) has received increasing attention in recent decades. In contrast to flocculated activated sludge, AGS is a granular microbial aggregate with a spatially layered structure (anaerobic/microaerobic/aerobic from inside to outside) that promotes the growth of microorganisms with diverse functionalities in one small unit [57].  
In treating textile wastewater, AGS was shown to be more effective than conventional activated sludge with _Thiothrix_ filaments disappearing from AGS after such treatment, resulting in improved sludge settleability [32]. However, it should be noted that AGS can potentially also face bulking problems due to the proliferation of bacteria similar in morphotype to _Thiothrix_. At the same time, it was shown that in the presence of bacteria of the _Thiothrix_ morphotype (51.4 \(\pm\) 8.3% of the total microbial population), stable aerobic granular sludge can be formed, and the morphology of the granules is largely determined by the operation of the reactor and, to a much lesser extent, by the morphologies of individual cells [44]. Moreover, members of the _Thiothrix_ morphotype can be among the dominant bacteria in filamentous granular sludge (FGS), which is capable of maintaining relatively stable carbon and phosphorus removal efficiency but is challenging in terms of nitrogen removal [43].  
An important factor affecting the granulation and sedimentability of AGS is the food-to-mass ratio (F/M) [58]. As this ratio increases in the first stages of wastewater treatment, the number of bacteria of the _Thiothrix_ morphotype increases significantly, which decreases the efficiency of AGS precipitation. However, the gel-like polysaccharides associated with these bacteria effectively help to preserve the granular biomass, which gradually leads to a decrease in the F/M and the restoration of AGS' sedimentability [58]. The C/N ratio also has an important effect on the characteristics of activated sludge and the development of _Thiothrix_. A decrease in this ratio led to increased competition between different microorganisms, including the dominant bacteria from the genus _Defhuviococcus_ and the _Thiothrix_ morphotype, which reduced the efficiency of the system [59]. Nutrient deficiency, especially nitrogen, also had a negative effect on AGS systems, causing sludge bulking due to the proliferation of the _Thiothrix_ morphotype [60].  
Incorrectly selected conditions often lead to a significant increase in the size of the granules due to an increase in the number of bacteria similar in morphotype to _Thiothrix_ and the disruption of the system [61].
## 5 Conclusions  
This paper proposes a pattern reconfigurable antenna by employing two RF MEMS switches. By changing the two RF MEMS operating modes, the proposed antenna can switch among three kinds of reconfigurable patterns, namely middle (approximately \(0^{\circ}\)), left (approximately \(-20^{\circ}\)), and right (approximately \(20^{\circ}\)). The proposed pattern reconfigurable antenna was analyzed using an active element pattern method and a signal flow diagram. Comparing the measured results with the calculated and simulated results, good agreement was obtained. The proposed pattern reconfigurable antenna can be applied to a 5 th-generation (5G) mobile communication and satellite communication system because of its excellent performance.  
**Acknowledgments: This work was supported by a project of 14 nm technology generation silicon-based novel devices and key crafts research (2015 AA016501).**  
Figure 8: The measured pattern of the proposed antenna using RF MEMS switches. (**a**) In the (0,0) state. (**b**) In the (0,1) state. (**c**) In the (1,0) state. (**d**) In the (1,1) state.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Available Literatures** & **[4]** & **[5]** & **[2]** & **[18]** & **This Study** \\ \hline Reconfigurable means & MEMS switches & MEMS switches & PIN diodes &
\begin{tabular}{c} Tunable Graphene \\ Superstrate \\ \end{tabular} & MEMS switches \\ \hline Antenna type & patch & slot-array & Double Layer patch & Double Layer & patch \\ \hline Operating frequency & 34.8 & 30 & 27.5 & 30 & 35 \\ (GHz) & 60 & 13 & 45 & About 30 & 40 \\ \hline \multirow{2}{*}{Block volume (mm\({}^{3}\))} & About & About & \multirow{2}{*}{\(5.1\times 5.1\times 1.274\)} & \multirow{2}{*}{\(16\times 16\times 10.3\)} & \multirow{2}{*}{\(3.7\times 4.4\times 0.4\)} \\  & 500 \(\times\) 500 \(\times\) 2 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of the proposed pattern reconfigurable antenna with the literature.  
**Author Contributions:** [PERSON] and [PERSON] are the project manager; [PERSON] conceived, designed and performed the experiments; [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
A global, monthly averaged time series of Sun-induced Fluorescence (SiF), spanning January 2007 to June 2015, was derived from Metop-A Global Ozone Monitoring Experiment 2 (GOME-2) spectral measurements. Far-red SiF was retrieved using the filling-in of deep solar Fraunhofer lines and atmospheric absorption bands based on the general methodology described by [PERSON] et al, AMT, 2013. A Principal Component (PC) analysis of spectra over non-vegetated areas was performed to describe the effects of atmospheric absorption. Our implementation (SiF KNMI) is an independent algorithm and differs from the latest implementation of [PERSON] et al, AMT, 2013 (SiF NASA, v26), because we used desert reference areas for determining PCs (as opposed to cloudy ocean and some desert) and a wider fit window that covers water vapour and oxygen absorption bands (as opposed to only Fraunhofer lines). As a consequence, more PCs were needed (35 as opposed to 12). The two time series (SiF KNMI and SiF NASA, v26) correlate well (overall \(R\) of 0.78) except for tropical rain forests. Sensitivity experiments suggest the strong impact of the water vapour absorption band on retrieved SiF values. Furthermore, we evaluated the SiF time series with Gross Primary Productivity (GPP) derived from twelve flux towers in Australia. Correlations for individual towers range from 0.37 to 0.84. They are particularly high for managed biome types. In the de-seasonalized Australian SiF time series, the break of the Millennium Drought during local summer of 2010/2011 is clearly observed.  
sun-induced fluorescence; GOME-2; gross primary productivity; time series; flux towers; principal component analysis; OzFlux +
Footnote †: journal: Remote Sensing, 8, 895
## 4 BP Neural Network and Pearson Correlation Analysis
### Model Setting
#### 4.1.2 Evaluation Indicators  
The evaluation metrics employed include the precision percentage (PP%) and relative error percentage (8), and several standard statistical indicators, such as the sum of square error (E\({}_{\rm SSE}\)), mean absolute error (E\({}_{\rm MAE}\)), mean absolute percentage error (E\({}_{\rm MAP}\)), mean square error (E\({}_{\rm MSE}\)), and root mean square error (E\({}_{\rm RMSE}\)) [28]:  
\[\left\{\begin{array}{l}\rm PP\%=\sum\limits_{i=1}^{S}1(X_{i}-0.05\cdot X_{i }<Y_{i}<X_{i}+0.05\cdot X_{i})/S\\ \rm\delta=\frac{|X_{i}-Y_{i}|}{|X_{i}|}\cdot 100\%\\ \rm E_{\rm SSE}=\sum\limits_{i=1}^{S}(X_{i}-Y_{i})^{2}\\ \rm E_{\rm MAE}=\sum\limits_{i=1}^{S}|(X_{i}-Y_{i})|/S\\ \rm E_{\rm MAP}=\sum\limits_{i=1}^{S}|(X_{i}-Y_{i})/X_{i}|/S\\ \rm E_{\rm MSE}=\sum\limits_{i=1}^{S}(X_{i}-Y_{i})^{2}/S\\ \rm E_{\rm RMSE}=\sqrt{\sum\limits_{i=1}^{S}(X_{i}-Y_{i})^{2}/S}\end{array}\right. \tag{19}\]  
In Equation (19), S denotes the number of datasets; \(X_{i}\) represents the actual value, with its average as \(\overline{X_{i}}\); and \(Y_{i}\) indicates the prediction value, and its average is \(\overline{Y_{i}}\). Below is a concise description of each indicator:  
* The precision percentage (PP%) employs an indicator function that equals 1 if the condition in parentheses is met; otherwise, it is 0;
* The relative error (\(\delta\)) measures the percentage ratio of the absolute difference between \(X_{i}\) and \(Y_{i}\) for the absolute value of \(X_{i}\);
* The square sum of errors (E\({}_{\rm SSE}\)) computes the total errors between \(Y_{i}\) and \(X_{i}\). While easy to calculate, it can be affected by outliers, distorting the overall error distribution;
* The mean absolute error (E\({}_{\rm MAE}\)) calculates the average absolute differences between \(Y_{i}\) and \(X_{i}\). It remains stable against outliers but may miss some distribution details;
* The mean absolute percentage error (E\({}_{\rm MAP}\)) evaluates the prediction error percentage between \(Y_{i}\) and \(X_{i}\). Like E\({}_{\rm MAE}\), it is unaffected by anomalies but can become unstable when the actual values are zero;
* The mean square error (E\({}_{\rm MSE}\)) reflects the average derived from E\({}_{\rm SSE}\). Although more robust than other metrics, it remains sensitive to outliers that might overshadow smaller errors;
* The root mean square error (E\({}_{\rm RMSE}\)), being the square root of E\({}_{\rm MSE}\), enables the measurement of prediction errors across different dimensions on a common scale. Similar to E\({}_{\rm MSE}\), E\({}_{\rm RMSE}\) also shows sensitivity toward larger discrepancies.
## 3 Results
### Familial Aggregation  
Pedigrees in the CHRIS cohort exhibit a peculiar structure, where more than 90% of the participants are found in a single pedigree that horizontally connects extended families covering up to six generations by marry-ins. In particular, we have found 230 pedigrees. The largest pedigree, termed family XL, consisted of 8897 phenotyped participants connected by 9224 non-phenotyped individuals reported as relatives by their phenotyped cohort members. The remaining pedigrees are substantially smaller. The second largest pedigree contains 50 cohort members and, excluding family XL, the set of pedigrees is characterized by a median size of four members.  
Familial aggregation analysis of the 103 cases and 9569 controls gave rise to 80 participants without further affected relatives in their extended families. Family XL contained 98 (95.1%) cases and therefore required usage of the Kinship Sum test to highlight affected individuals instead of computing a pedigree-wide score. We found seven extended families with two cases. We observed a parent-child relationship of cases once and found two pairs of affected siblings. The remaining four pairs of cases involved more remote kin. Finally, we discovered three extended families with three cases each, and one of them was reported with a significant \(p\)-value, \(p\)-adj = 2.94 \(\times\) 10\({}^{-2}\), which corresponds to affected participant III-2 with her affected son (IV-3) and brother (III-3) (Supplementary Figure S2).  
Figure 1: Manhattan plot. Red line indicates genome-wide significance level of 5 \(\times\) 10\({}^{-8}\). X: Chromosome X (not included in the GWAS). See Supplementary Figure S1 for the QQ plot.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Chr** & **POS (GRCh37)** & **REF/ALT** & **rsID** & **MAF** & **MAC** & \(p\)**-value** & **R2** \\ \hline
1 & 164062528 & C/G & rs528903211 & 0.00050 & 10 & \(1.1\times 10^{-8}\) & 0.47 \\ \hline
1 & 164110550 & G/A & rs573729400 & 0.00056 & 9 & \(2.4\times 10^{-8}\) & 0.43 \\ \hline
1 & 165331300 & T/G & rs745582874 & 0.00066 & 13 & \(4.3\times 10^{-8}\) & 0.81 \\ \hline
13 & 101142909 & C/T & rs768476991 & 0.00028 & 5 & \(4.1\times 10^{-8}\) & 0.48 \\ \hline \hline \end{tabular}  
* Chr: chromosome; POS: genomic position; REF/ALT: reference and alternative alleles; MAF: minor allele frequency in CHRIS; MAC: minor allele count in CHRIS; R2: imputation quality index; value is between 0 and 1, with 1 indicating the highest level of certainty of imputation.  
\end{table}
Table 3: AF genome-wide significant variants.
## 2 Materials and Methods
### Statistical Analysis  
For the statistical analysis, we first checked for the normal distribution of the CoP outcome parameters (CoP path length, range of anteroposterior and mediolateral CoP sway) using the Kolmogorov-Smirnov test with Lilliefors correction. Testing of normality failed for the mediolateral CoP sway range in the control (\(p\) = 0.017) and dual-task_A (\(p\) < 0.001) and _B condition (\(p\) = 0.024) for the medium-heel height shoe. Similarly, the normality check failed for that same parameter in the dual-task_A (\(p\) = 0.019) and dual-task_B condition (\(p\) < 0.001) for the high-heeled shoe. However, following visual inspection with quantile-quantile (Q-Q) plots, the mediolateral CoP data in the above situations were normal with slight deviations. One-way ANOVA was used to test the effect of heel height (low, medium, high) on postural balance performance. In the event of a significant heel height effect, post hoc comparisons were performed. A 3 \(\times\) 2 ANOVA with cognitive load (control, dual-task_A, dual-task_B) as a within-subjects factor and heel height (medium, high) as a between-subjects factor was performed on the two-legged CoP parameters. To control for the violation of the sphericity assumption, the Geisser-Greenhouse-adjusted \(F\)-test was used [48]. In case a significant main or interaction effect (cognitive load by heel height) was found, a Bonferroni-corrected multiple comparisons analysis was conducted. Further, a one-way ANOVA was used to check for possible interferences of heel height (medium, high) on cognitive performance between the reference and the dual tasks. To estimate the strength of the potential main or interaction effects on the investigated outcome parameters, [PERSON]'s d with values of d < 0.2, 0.2 \(\leq\) d < 0.8 and d \(\geq\) 0.8 indicating a small, medium or large effect size was calculated [49]. All statistical analyses were performed using SPSS IBM v.21 and the significance level was set at a = 0.05. For the graphical representation of the outcomes, we used boxplots depicting the median and the 5 th to 95 th percentile as whiskers.
# A Novel PCA-Based Method for PRNU Distillation to the Benefit of Source Camera Identification
## 5 Conclusions  
In the past few years, researchers have made great efforts to improve the performance of PRNU-based source camera identification. In this paper, we proposed a scheme to remove interference noise from reference PRNU noise to improve the performance of the SCI scheme. We found that some interference noise related to image content still existed in the reference PRNU even after the periodic interference noise had been removed from the reference PRNU using SEA. We constructed a training sample set using the correlation between local pixels and used PCA to estimate the interference noise. Extensive experimental results showed that our proposed method obtained better ROC curves and Kappa statistics than the prior works. In fact, the running time of the proposed method will significantly increase when the block size is larger than \(1024\times 1024\). In future work, we will try to estimate the interference noise using suitable non-linear distribution data methods to transform the data domain, such as the LDA method.  
Conceptualization, B.M.; methodology, J.L.; software, Y.L.; validation, J.L. and Y.L.; formal analysis, Y.L.; investigation, Y.L.; resources, X.W. and C.Q.; data curation, Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, J.L., Y.L and S.L.; visualization, X.W. and C.Q.; supervision, J.L.; project administration, J.L.; funding acquisition, J.L., C.W. and B.M. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by National Natural Science Foundation of China (No.6227225, No.61872203); National key research and development program of China (No.2021 YFC3340602); Shandong Provincial Natural Science Foundation Innovation and Development Joint Fund (No.ZR202208310038); Shandong Provincial Natural Science Foundation (No.ZR2020 MF054, No.ZR2022 LZH011); Ability Improvement Project of Science and technology SMES in Shandong Province (No.2022 TSGC2485); Project of Jinan Research Leader Studio (No.2020 GXRC056); Project of Jinan introduction of innovation team (No.2019 GXRC031, No.202228016); Project of Jinan City-School Integration Development (No.JNSX2021030).  
Figure 9: The ROC curves with two methods on four camera devices. The Cozzolino2019 method proposed by [PERSON] et al. [14].
# A Blockchain Self-Sovereign Identity for Open Banking Secured by the Customer's Banking Cards
## 6 Demonstrative Implementation
### Key Recovery
#### 6.5.1 Java Card for Banking and Identity Operations  
SSBI introduces the utilization of the customer banking card to store identity secret keys and perform transaction signing to balance security and usability. The majority of banking payment cards support signature implementation for the SECP256K1 curve-based ECDSA, which is compatible with the Veramo platform and Ethereum Blockchain.  
Banking customers commonly use payment smart cards for day-to-day purchases and money withdrawals. This relies on strong industry standards and security rules, such as EMV [48] and PCI-DSS [47], with advanced deployment of strong cryptographic keys and certificates. In addition to usually maintaining a secure PIN code or biometric verification to perform payment transactions, customers trust the issuer (banks) and rely on security rules that control this industry. We extend this level of trust and user experience to apply the financial identity sharing paradigm to open banking by using the same card for private key storage and signing the underlying Blockchain transaction for DID and VC operations, as suggested by [7].  
Figure 8: Sample snapshots present SSBI sub-functionalities to register and _create_DID_ and fill in banking VC details, followed by signing and storing the VC by calling the _Create_VC_ API with the underlying presence of the customer card to perform Ethereum transaction signing.  
Java smart cards are designed to be tamper-resistant and follow Java Card Platform specifications [49], where the developed applet can be installed once and run on several card's virtual machines; this applet is deployed on the card's operating system layer. In contrast to many studies that recommend a hardware wallet [50; 51], we utilize the banking card to manage identity secret keys. This was also proposed by [7], where the applet is installed separately from the banking applet, with programmed functions to initialize master keys, generate sub-keys, and sign transactions securely on the card for DID and VC operations.
# Modeling of Geophysical Flows through GPUFOW
## 3 Sensitivity Analysis
### Impact of the Diagonal Correction  
Evaluation of the impact of the diagonal correction factor to reduce the emplacement bias due to the cell geometry was done by simulating a debris column collapse on a flat surface. The choice to model this as a debris flow was motivated by the intent to eliminate perturbing factors, such as the mass flux rate or the temperature-dependent rheology.  
The material is assumed to have a density of 2300 kg/m\({}^{3}\) and a Bingham rheology with yield strength of 1254 Pa and a consistency index of 30.5 Pa s. The starting condition for the collapse is with all the debris occupying a single cell, with a 4000-m-high column.  
Figure 3 shows the final emplacements for different values of the diagonal correction factor. With factor 0, the flow only happens between cells in the east/west and north/south directions (Von Neumann neighborhood), resulting in the classical diamond shape. With factor 1, the flow also happens in the diagonal direction (Moore neighborhood), and the resulting emplacement preserves a square-like shape even at longer times. By contrast, the ideal coefficient value of \(2^{-6}\) results in a nearly perfectly circular emplacement.
## 2 Methodology
### Integrated Methodology for the Elaboration of Geospatial Exposure Databases
#### 2.3.1 Footprint of Buildings  
The footprints of buildings are the first geospatial element of the GED and can be obtained from different sources. Worldwide coverage is provided by Bing (Microsoft) as open source vector data [58]. Another open alternative source, although not complete, is the building vector cover from OpenStreetMap [52] accessible using OverPass API [61] directly or embedded as a QGIS 3.16.16 plugin. However, the correct delineation at a detailed scale of building footprints is essential to obtain meaningful results in seismic exposure studies, so it is necessary to carry out additional work to \"clean up\" these data.  
In the case study of San Jose, footprints were digitized from 2018 national orthophotograps at 1:1000 scale with a 0.5 m accuracy [62].