esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Chip Testing Platform  
#### 2.1. PCB Design  
The PCB was designed with the open-source electronics design automation suite KICAD using a 250 \(\upmu\)m thick REF32 RT/duroid(r) 5880 substrate [32; 33], commonly used for high-frequency design applications [34; 35; 36; 37; 38]. Table 1 shows the value of the different parameters used for calculating the length, width and losses of the microstrip and the results obtained using the equations described in [29], where \(\epsilon_{r}\) represents the dielectric constant, \(\tan(\delta)\) is the dissipation factor, \(\mu_{0}\) is the vacuum magnetic permeability, \(\sigma\) is the copper electrical conductivity, and \(\Phi\) is the phase difference across the microstrip.  
Figures 3 and 4 show the schematic and the layout of the PCB. It depicts how all the wire bonds and microstrips are placed to interconnect the PCB, the integrated circuit (IC), SMA connectors and the RFIN, RFOUT, VDD and GND signals.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Substrate** & & **Requirements** & & **Results** & \\ \hline
**Parameter** & **Value** & **Parameter** & **Value** & **Parameter** & **Value** \\ \hline Laminate & REF32 RT/duroid® 5880 & Frequency (GHz) & 40 & Dielectric losses \(\alpha_{d}\) (dB) & 0.086 \\ \(\epsilon_{r}\) & 2.2 & Z, Impedance (\(\Omega\)) & 50 & Conductor losses \(\alpha_{c}\) (dB) & 0.00022 \\ \(\tan(\delta)\) & 0.0009 & \(\Phi\) (°) & 90 & Length, L (mm) & 23.22 \\ d (mm) & 0.25 & & & Width, W (mm) & 0.75 \\ \(\mu_{0}\) (Tm/A) & \(4.7\times 10^{-7}\) & & & & \\ \(\sigma\) (S/m) & \(5.8\times 10^{7}\) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Microstrip parameters.  
Figure 3: Schematic of the implemented PCB.  
Figure 2: Schematic of the attenuator.  
Figure 5a illustrates the manufactured PCB, including the chip, connectors and wire bonds. In Figure 5b, a magnified view of the wire bonds between the attenuator and the PCB is shown.
## 3 Results and Discussion  
### Biophysical Variables versus Agricultural Drought  
The average R values for the relationships of the agricultural drought index with GPP and the LAI for the predominant month are given in Table 1. The results of the correlation analysis between GPP and the agricultural drought index (Table 1) show that the months of the reproductive stage (April, May and June) of cereals are predominant in the provinces of all the regions considered. Moreover, the average R value (resulting from averaging the R values of the critical month of the provinces belonging to the region studied) shows a clear difference between the Spanish and German regions, presenting a direct relationship between SM anomalies and GPP in the Spanish regions (0.50 in CL and 0.29 in CM) and an inverse relationship in the German regions (\(-0.57\), \(-0.44\) and \(-0.48\) in BY, NS and NW, respectively). These results are consistent with those of [94], which show that in times of drought, in areas where soil water content is high, an increase in plant GPP is promoted, as occurred in this study in the regions of Germany. Thus, moderate drying of wet soil causes an increase in the carboxylation capacity of plants [94]. Several studies have reported improved vegetation conditions during drought in wet regions [95; 96]. In contrast, in areas where the water content is below a threshold, GPP decreases under drought conditions, as is the case in regions of Spain. Vegetation in arid regions reacts quickly to SM deficiency, which would imply greater drought stress in vegetation, as this is a limiting factor of vegetation functioning in water-limited environments [97].  
Regarding the relationship between the LAI and agricultural drought index (Table 1), it is observed that in Spanish regions, the predominant months correspond to the reproductive stage of the plant, as in the results obtained with GPP. In the German regions, July is the predominant month, and the mean R values (0.16, 0.57 and 0.49 in BY, NS and NW, respectively) are lower than those in the Spanish regions (0.69 in both regions, CL and CM). Both cereal variables, GPP and the LAI, show different behavior in the regions of the two countries, in accordance with the fact that one has water-limited and the other energy-limited conditions. In BY, NS and NW, there is an inverse relationship between SM anomalies and GPP and a direct relationship with the LAI. Conversely, in CM and CL, there is a direct relationship in both cases. The results are consistent with those obtained by the authors of [98], who demonstrated decoupling between the LAI and GPP as aridity decreases. Hence, areas in water-limited environments, such as CM and CL, showed a stronger link between GPP and the LAI. In contrast, in the BY, NS and NW regions, under energy-limited conditions, a decreased LAI in drought years could facilitate carbon uptake by smaller leaves and consequently enhance GPP [98].
## 4 Computational Results and Discussion
### Trapping Characteristics  
Figures 5-8 displays streamline structural changes for different of fluid index \(n_{i}\), Weissenberg number \(W_{e}\), electroosmotic parameter \(m_{e}\), and Helmholtz-Smoluchowski velocity \(U_{HS}\). Figure 5a-d depict streamline structure for increasing values of \(W_{e}\). It is seen that the size of enclosed bolus increases with rise in \(W_{e}\). Since, \(W_{e}\) is directly proportional to velocity, so no. of trapped bolus increases as velocity increases. Similarly, Figure 6a-d visualize the streamline structure for different values of \(n_{i}\). It is observed that the size of trapped bolus decreases with rise in \(n_{i}\). Figure 7a-d show that the number of trapped bolus grows with rise in electroosmotic parameter \(m_{e}\). It inspects that as the \(m_{e}\) increases, the tapping bolus strongly appears in EDL. Figure 8a-d depict streamline structure for different values of \(U_{HS}\). It demonstrates that the accumulation of streamline decreases with increase in \(U_{HS}\). Since \(U_{HS}\) is directly proportional to electric field. Thus, stronger \(U_{HS}\) means stronger the external electric field, which strongly effects the bolus formation.  
Figure 5: \(Cont\).  
Figure 6: Streamline distribution for (**a**) \(n_{i}=0.389\); (**b**) \(n_{i}=0.589\); (**c**) \(n_{i}=0.789\); (**d**) \(n_{i}=1.0\).  
Figure 8: \(Cont\).
###### Abstract  
_Arthrobacter_ sp. strain SRS-W-1-2016 was isolated on high concentrations of uranium (U) from the Savannah River Site (SRS) that remains co-contaminated by radionuclides, heavy metals, and organics. SRS is located on the northeast bank of the Savannah River (South Carolina, USA), which is a U.S. Department of Energy (DOE) managed ecosystem left historically contaminated from decades of nuclear weapons production activities. Predominant contaminants within the impacted SRS environment include U and Nickel (Ni), both of which can be transformed microbially into less toxic forms via metal complexation mechanisms. Strain SRS-W-1-2016 was isolated from the uraniferous SRS soils on high concentrations of U (4200 uM) and Ni (8500 uM), but rapid growth was observed at much lower concentrations of 500 uM U and 1000 uM Ni, respectively. Microcosm studies established with strain SRS-W-1-2016 revealed a rapid decline in the concentration of spiked U such that it was almost undetectable in the supernatant by 72 h of incubation. Conversely, Ni concentrations remained unchanged, suggesting that the strain removed U but not Ni under the tested conditions. To obtain a deeper understanding of the metabolic potential, a draft genome sequence of strain SRS-W-1-2016 was obtained at a coverage of 90\(\times\), assembling into 93 contigs with an N50 contig length of 92,788 bases. The genomic size of strain SRS-W-1-2016 was found to be 4,564,701 bases with a total number of 4327 putative genes. An in-depth, genome-wide comparison between strain SRS-W-1-2016 and its four closest taxonomic relatives revealed 1159 distinct genes, representing 26.7% of its total genome; many associating with metal resistance proteins (e.g., for cadmium, cobalt, and zinc), transporter proteins, stress proteins, cytochromes, and drug resistance functions. Additionally, several gene homologues coding for resistance to metals were identified in the strain, such as outer membrane efflux pump proteins, peptide/nickel transport substrate and ATP-binding proteins, a high-affinity nickel-transport protein, and the _spoT_ gene, which was recently implicated in bacterial resistance towards U. Detailed genome mining analysis of strain SRS-W-1-2016 also revealed the presence of a plethora of secondary metabolite biosynthetic gene clusters likely facilitating resistance to antibiotics, biocides, and metals. Additionally, several gene homologous for the well-known oxygenase enzyme system were also identified, potentially functioning to generate energy via the breakdown of organic compounds and thus enabling the successful colonization and natural attenuation of contaminants by _Arthrobacter_ sp. SRS-W-1-2016 at the SRS site.  
keywords: bioremediation; uranium; nickel; whole genome sequencing; resistance; comparative genomics; _Arthrobacter_ +
Footnote †: journal: Journal of chemical and chemical sciences
# 6G Visible Providing Advanced Weather Services for Autonomous Driving
## 6 Predictive Modeling of Weather-Related Crashes  
Vehicular crashes present inherent spatial and temporal trends, and the ability to explain crash risk patterns in southwestern Finland has been a key focus in the first stages of this 6G Visible task. The output of road crash modeling allows traffic authorities to implement targeted safety interventions when, and where, they are needed. By initially developing an explanatory model, we can better grasp the overall crash mechanism and its determinants, which lays the necessary foundation for the subsequent implementation of optimal route planning in terms of safety.  
To build the model, crash data are obtained from the public data of Finnish Transport Infrastructure Agency for the years 2017-2021 inclusive, and the model uses forecasted MEPS data as explanatory variables. The timescale of the analysis is at the hourly resolution to account for the heterogeneity in road traffic density that exists throughout a given day. The spatial extent of the study includes the regions of Uusimaa and Varsinais-Suomi, and a grid consisting of smaller spatial cells based on fixed latitude and longitude increments is overlaid on top of these regions to explicitly define the spatial unit, as illustrated in Figure 7. A spatiotemporal join is then conducted to determine whether a crash has precisely occurred in each cell at a given hour.  
A spatiotemporal generalized additive model (GAM) with a logit link function was used to assign fitted probabilities of a car crash occurring at a combination of hourly and spatial units. GAMs are a powerful modeling tool for spatiotemporal analyses; they can uncover non-linear functional relationships between the predictor variables and the response, and their construction using a rich variety of different spline functions makes them flexible to capture trends in space and time [19]. The advantage of modeling probabilities is that they have a direct interpretation in terms of the crash risk level.  
Henceforth, future directions involve extending this baseline explanatory model into one that can generate predictions within a 24 h window. A major task when developing  
Figure 7: Defining the spatial unit: splitting the regions into grid cells, with crash point locations during the study duration shown as blue dots.  
this predictive model will involve assessing the accuracy of its predictions. Ultimately, we seek a collaboration in a data-driven decision theory framework between the road user and AI, whereby users can be informed about imminent weather-related driving risks on the road when planning a route between two points. To accomplish this, we will first aggregate the data into larger clusters that represent various combinations of meteorological parameters which are set to fixed and well-defined values to create an overall weather profile. Then, we will integrate the output of the predictive model into the knowledge graph construction, illustrating how the crash risk level depends on different temporal, spatial, and weather characteristics.
## 3 Experimental Results and Analysis
### Experiment Setup
#### 3.1.2 Noise Setting  
In real-world hyperspectral imaging tasks, data are frequently affected by various types of noise, such as Gaussian noise, impulse noise, dead pixels, stripe noise, and others. To simulate a complex noise environment, this study defines five composite noise types, referred to as Case 1 to Case 5, as outlined below:  
Case 1: non-i.i.d. Gaussian noise. Each spectral band is contaminated by Gaussian noise with zero mean and varying intensity, where the noise standard deviation ranges from 10 to 70, simulating uneven responses across sensor bands.  
Case 2: Gaussian + stripe noise. In addition to the non-i.i.d. Gaussian noise in Case 1, stripe noise is introduced into certain spectral bands. In these affected bands, about 5-15% of pixel columns are contaminated, creating stripes along the scanning direction to simulate sensor calibration errors.  
Case 3: Gaussian + dead pixel noise. Each band still suffers from the non-i.i.d. Gaussian noise as in Case 1. Additionally, dead pixel noise is randomly introduced into one-third of the spectral bands. In the affected bands, about 5-15% of columns exhibit dead pixel interference, forming noticeable vertical stripes.  
Case 4: Gaussian + impulse noise. Building on Case 1, impulse noise (salt-and-pepper noise) is further added to simulate discrete noise generated during sensor operation or data transmission. Impulse noise affects one-third of the bands randomly, with 10-70% of pixels contaminated, appearing as random white or black dots in the image.  
Case 5: mixed noise. This case represents the most complex noise scenario, where each spectral band is contaminated by at least one type of noise from Cases 1-4, comprehensively simulating various noise disturbances that may occur in real hyperspectral images.
## 3 Results and Discussion
### Pearson's Correlation Coefficients for Explanatory Variables  
The Pearson's correlation coefficients between the explanatory variables were determined prior to creating the ANN model, as is shown in Table 7. The correlation coefficients between the input parameters were relatively low, as is seen in Table 5. There was just a slight negative correlation, with a coefficient value of r = \(-0.559\), between the working field criterion and the horizontal force that was estimated by Equation (1) when using the new--Fi-parameter. This was due to the working field criterion being constituted of five variables (tractor power, soil bulk density, tillage depth, tillage speed, and implement width), all of which exhibited a strong effect on the horizontal force requirements of tillage implements, as was shown in previous studies [55; 56; 57; 58; 59]. Additionally, we observed the moderate positive correlation, with a coefficient value of r = 0.544, between the soil texture norm and horizontal force estimated by Equation (1). However, the positive correlation (r = 0.347, Table 5)--between the initial soil moisture content and the horizontal force estimated by Equation (1) when using the new--Fi-parameter--indicated that that moisture content had a direct relation with the horizontal force estimated by Equation (1). This same trend was observed in previous studies [57; 58]. However, in other research papers [55; 67; 68; 69; 70; 71; 72], the inverse relationship between the initial soil moisture content and horizontal force was observed. The drop in horizontal force caused by increased soil moisture content can be attributable to both the change in soil resistance and the decrease in soil failure force [67].  
Soil texture has a clear effect of a draft force; however, when compared to sandy soil, [PERSON] et al. [73] found that a cultivator's horizontal force increased by around 30% when working in clayey soil. Additionally, according to [PERSON] et al. [74], sandy loamy soil has the greatest values and coarse sand soil have the lowest values of horizontal force with a broad sweep plow. The study's findings show that increasing the working field criterion causes the modified horizontal force to decrease, while increasing the soil texture norm and initial soil moisture content causes the modified horizontal force to grow.
## 2 Network Structure and Fault/Event Model
### Fault Model  
Various types of faults may occur in sensor networks. Among others we focus on faults in sensor readings, due to malfunctioning sensors and noise. Some communication faults may also be covered as long as they can be modeled as faults in sensor readings.  
Faults are assumed to occur in any nodes in the sensor network with the same probability. Each sensor node is assumed to know the range of normal readings. For clarity, we define \"normal readings\" to be the acceptable sensor data in the case of no-event. Any readings outside the normal range are named \"unusual readings\" for convenience. In other words, correct sensor readings in an event region are also called \"unusual readings\". Hence each sensor node can make a binary decision on its own sensor reading, where a \"1\" indicates an unusual reading. Nodes in an event region will report a 1, although the range of sensor readings cannot be well defined, unless the nodes are faulty or some errors affect the correct readings.  
Sensor readings of a faulty sensor node may lie in any range, including the normal and event ranges. Both permanent, transient, and intermittent faults are included in our fault model. Faults exhibiting errors for some extended periods of time may also be covered without modifying the proposed scheme. Transient faults are assumed to occur randomly and independently with the same probability. In the case of a permanent fault, both stuck-at-0 (normal) and stuck-at-1 (unusual) are assumed to occur with the same probability. In other words, sensor nodes with a stuck-at-0 fault always measure normal data, and they thus report a 0 even if they are in an event region.
# W-IFL: An Improved Maximum Power Point Control Model to Promote Renewable-Powered Vehicles
## 1 Introduction
The W-IFL adopts the neural-fuzzy structure to achieve adaptively online control of the MPP with high performance. In particular, the W-IFL has the following two contributions. * Compared to existing ANN methods, the wavelet network avoids the subjective selection of hidden layer numbers when processing discrete-time data, which notably reduces the oscillation at the MPP by 50.0%, and efficiently improves the training efficiency and prediction accuracy by 85.7% and 67.7%, respectively;
* Improved fuzzy rules can, accordingly, outperform other control models in terms of tracking speed and accuracy, with average improvements of 55.3% and 62.3%, respectively. In general, the proposed W-IFL model can efficiently and effectively optimize the current low utilization of converting solar power to electricity, thus providing a blueprint for endurance-limited vehicles powered by renewable energy, which can significantly promote their development. The overall structure is divided into five sections. Section 2 introduces related solutions and emerging challenges. Sections 3 and 4 elaborate on the methodology, application, and evaluation of the proposed W-IFL. Finally, Section 5 summarizes this study and sketches future research directions.
Headspace Solid-Phase Microextraction in combination with Gas Chromatography-Mass-Spectrometry (HS-SPME-GC-MS) has been used for the volatile profiling of different raspberry cultivars. This technique is sensitive, is solvent-free in the extraction step, requires minimal sample preparation, and can be automated [4; 5]. However, the extraction of volatiles is a complex process affected by different variables (i.e., the extraction temperature and time or sample weight), so the use of a statistical technique, such as response surface methodology (RSM), is often required to optimise the extraction conditions. Recently, RSM was successfully applied to optimise the extraction process for volatile compounds in blackberry fruits [6], but its specific use for volatile extraction in raspberry has not been reported yet.  
Despite its benefits, the raspberry is a delicate and highly perishable fruit. Over-ripening, excessive softening and pathogen attack, mainly by the necrotroph _Botrytis cinerea_, are the leading causes of raspberry fruit postharvest losses [7]. Thus, R&D programmes try to develop new cultivars with higher-quality fruits that will lead to profitability for the farmer and to an increase in the demand from the consumer [8]. In particular, the novel \"Adelita\" cultivar was obtained as a result of a patented invention. Consequently, \"Adelita\" was selected due to being a cultivar available all year, with an abundant production of attractive large red-coloured fruits, with a uniform conical shape, very long shelf life and slightly acidic but sweet flavour [8]. These characteristics mean that the \"Adelita\" cultivar is constantly gaining market share on all continents.  
The aim of the present work was the development of a low-cost and non-laborious procedure for differentiating \"Adelita\" from other raspberry cultivars. To this end, morphological data--such as the fruit length, equatorial diameter, fruit mass and firmness--were determined. Besides, the TPC; the antioxidant capacity as determined with the ferric-reducing antioxidant power (FRAP), 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2-azinobis (3-ethylbenzothazoline-6-sulfonic acid) diammonium salt (ABTS) methods; and the main target volatile compounds obtained by HS-SPME-GC-MS were also included. Finally, multivariate data analysis was used for the optimisation of the volatile extraction process and to detect differences between samples.
## 2 Materials and Methods
### Numerical Simulation Model Scenarios  
The numerical simulation scenarios first select 11 groups corresponding to different water blocking rates varying between 0.3 and 0.8. Then, four influencing factors, the area ratio, inclination ratio, inflow velocity, and water temperature stratification ratio, are considered under three different water blocking rates (0.6, 0.7, 0.8). When one influencing factor varies, the others remain constant. Area ratios from 1.0 to 1.5 are tested via six groups. Inclination ratios between 0 and 1.0 are tested via 11 groups. Inflow velocities from 0.025 to 0.2 m/s are tested through eight groups. Water temperature stratification ratios from 0 to 1.0 are tested via 11 groups. In total, there are 119 numerical simulation scenarios, and the details are summarized in Table 3.