esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards a Scalable Software Defined Network-on-Chip for Next Generation Cloud
###### Abstract  
The rapid evolution of Cloud-based services and the growing interest in deep learning (DL)-based applications is putting increasing pressure on hyperscalers and general purpose hardware designers to provide more efficient and scalable systems. Cloud-based infrastructures must consist of more energy efficient components. The evolution must take place from the core of the infrastructure (i.e., data centers (DCs)) to the edges (Edge computing) to adequately support new/future applications. Adaptability/elasticity is one of the features required to increase the performance-to-power ratios. Hardware-based mechanisms have been proposed to support system reconfiguration mostly at the processing elements level, while fewer studies have been carried out regarding scalable, modular interconnected sub-systems. In this paper, we propose a scalable Software Defined Network-on-Chip (SDNoC)-based architecture. Our solution can easily be adapted to support devices ranging from low-power computing nodes placed at the edge of the Cloud to high-performance many-core processors in the Cloud DCs, by leveraging on a modular design approach. The proposed design merges the benefits of hierarchical network-on-chip (NoC) topologies (via fusing the ring and the 2D-mesh topology), with those brought by dynamic reconfiguration (i.e., adaptation). Our proposed interconnect allows for creating different types of virtualised topologies aiming at serving different communication requirements and thus providing better resource partitioning (virtual tiles) for concurrent tasks. To further allow the software layer controlling and monitoring of the NoC subsystem, a few customised instructions supporting a data-driven program execution model (PXM) are added to the processing element's instruction set architecture (ISA). In general, the data-driven programming and execution models are suitable for supporting the DL applications. We also introduce a mechanism to map a high-level programming language embedding concurrent execution models into the basic functionalities offered by our SDNoC for easing the programming of the proposed system. In the reported experiments, we compared our lightweight reconfigurable architecture to a conventional flattened 2D-mesh interconnection subsystem. Results show that our design provides an increment of the data traffic throughput of 9.5% and a reduction of 2.2\(\times\) of the average packet latency, compared to the flattened 2D-mesh topology connecting the same number of processing elements (PEs) (up to 1024 cores). Similarly, power and resource (on FPGA devices) consumption is also low, confirming good scalability of the proposed architecture.  
many-core; software-defined NoC; data-driven
# The Analysis of WJ Distribution as an Extended Gaussian Function: Case Study
## 3 Results and Discussion
### Method 1
#### 3.1.3 Parameter Scaling Relations: Generalization  
This subsection discourses a route to express the general Gaussian function by the WJ distribution. According to the analysis in the precedent section, it is straightforward that the WJ function is capable of representing the standard normal distribution function with the parameter set of \(\alpha_{0}=12.52\), \(\beta_{0}=0.07915\) and \(v_{0}=63.96\). Since the general Gaussian distribution (1a), as previously noted, can be transformed to the standard normal distribution (1b) by the equation \(x=(y-\mu_{GS})/\sigma\), it is likely to formulate the general Gaussian function with arbitrary parameters by a set of specific parameters \(\alpha\), \(\beta\) and \(\
u\) of the WJ function. In fact, we can transform Equations (2) to (12) below by taking advantage of rescaling [41; 42]  
\[f(x,\alpha^{*},\beta^{*},\
u^{*})=\frac{\beta^{*}}{\Gamma(\alpha^{*}/\beta^{* })}e^{-\alpha^{*}(x-\
u^{*})-e^{-\beta^{*}(x-\
u^{*})}} \tag{12}\]  
where \(\alpha^{*}=\alpha/\sigma\), \(\beta^{*}=\beta/\sigma\) and \(\
u^{*}=\mu_{GS}+\
u\sigma\) (In this paper, we assume the parameters \(\alpha\), \(\beta\) and \(\
u\) obtained by fitting the WJ distribution to the standard normal function as the reference, that is, \(\alpha^{*}=\alpha_{0}/\sigma\), \(\beta^{*}=\beta_{0}/\sigma\) and \(\
u^{*}=\mu_{GS}+\sigma_{V0}\)) to accomplish the procedure of fitting the general Gaussian distribution \(y\sim\mathrm{N}(\mu,\sigma)\) by the WJ distribution. The forms of Equations (2) and (12) are essentially the same, but the distinction lies in the scale change of the variables and the interpretation of the parameters. The derivation of Equation (12) has been based on the substitution of \(y=x\sigma+\mu_{GS}\) into Equation (1b), resulting in the formulation of  
\[f(y,\alpha,\beta,\
u)=\frac{\beta/\sigma}{\Gamma(\alpha/\beta)}e^{-\frac{ \alpha}{\beta}(y-\mu_{GS}-\
u\sigma)-e^{-\frac{\beta}{\beta}(y-\mu_{GS}-\
u \sigma)}}\]  
Several other useful relations involved include  
\[{\mu_{WJ}}^{*}+\frac{1}{\beta^{*}}\Psi\left(\frac{\alpha^{*}}{\beta^{*}} \right)=\mu_{GS}+\sigma\mu_{WJ}+\frac{\sigma}{\beta}\Psi\left(\frac{\alpha}{ \beta}\right),\]  
\[\Gamma\left(\frac{\alpha^{*}}{\beta^{*}}\right)=\Gamma\left(\frac{\alpha}{ \beta}\right),\]  
\[\Psi\left(\frac{\alpha^{*}}{\beta^{*}}\right)=\Psi\left(\frac{\alpha}{\beta} \right),\]  
and \({\mu_{WJ}}^{*}=\mu_{GS}+\sigma\mu_{WJ}\).  
In a subsequent section, we shall present explicit examples to illustrate the obtained expressions of the general Gaussian function by the WJ distribution.
###### Abstract  
The implementation of a 0.38 V K-band low-power fully differential low-noise amplifier (LNA) in a 45 nm silicon-on-insulator (SOI) process is presented. The proposed architecture employs a two-stage approach with transformer-based interstage matching networks to minimize circuit area. The proposed LNA covers the frequency range from 20.3 to 24.1 GHz, it achieves a noise figure (_NF_) as low as 2.2 dB, and a gain of 12.9 dB, with a power consumption of 11.7 mW from a 0.38 V DC supply in a very compact area (0.15 mm\({}^{2}\)) excluding pads. Non-linearity simulations show the proposed circuit achieves a \(P_{0 LAB}\) of \(-\)7.3 dBm, and an OIP3 (Output Third Order Intercept) of 7 dBm. The transformers allow improved area use since they are simultaneously used as matching networks, RF choices to bias the active devices, balums at the input and output terminals to convert the single-ended signal into differential mode, and vice versa, and facilitates the interconnection with the upcoming stages. We used a state-of-the-art tool that generates the desired inductances to perform impedance matching for a given frequency and coupling factor value. A comparison with similar works proves the proposed LNA achieves a very low _NF_ and the lowest power consumption reported in a differential circuit.  
silicon on insulator; fully differential; low noise amplifier; transformer-based matching networks; electromagnetic analysis; K-band +
Footnote †: journal: Applied Physics Letters  
**Citation:** [PERSON].;  
[PERSON]; [PERSON]; [PERSON] [PERSON] A 0.38 V Fully Differential K-Band LNA with Transformer-Based Matching Networks. _Appl. Sci._**2023**, 13, 5460. [[https://doi.org/10.3900/app13095460](https://doi.org/10.3900/app13095460)]([https://doi.org/10.3900/app13095460](https://doi.org/10.3900/app13095460))
## 2 Materials and Methods
### Effective Molar Activity Using DFO Chelator  
EMA (GBq/\(\upmu\)mol) of \({}^{89}\)Zr was calculated via titration with DFO and the purified combined \({}^{89}\)Zr-oxalate fractions 1 and 2. Solutions of DFO (0.5 mL in 1.5-mL microcentrifuge tube) at different concentrations (380.6 to 2.3 \(\times\) 10\({}^{\text{-}}\)5 nmol) were prepared via serial dilution. After 1 h incubation time with \({}^{89}\)Zr (0.9 to 1.1 MBq, 50 \(\upmu\)L), the solutions of DFO were quenched while using DTPA (40 \(\upmu\)L, 1 mmol) for 10 min to complex free \({}^{89}\)Zr. After incubation and quenching with DTPA, the EMA was determined by measuring the DFO labelling efficiency with ITLC-SG analysis using DTPA (100 mM) as a mobile phase solvent. \({}^{89}\)Zr-DTPA complex eluted with the solvent front, whilst \({}^{89}\)Zr-DFO complex remained at the origin. ITLC-SG was analyzed using Radio-ITLC scanner. The binding percentages were plotted on a sigmoidal dose-response curve. EC\({}_{50}\) value, which is reflecting the molar concentration at 50%, was calculated by prism 7 software (GraphPad Software Inc, La Jolla, CA, USA). The EMA was calculated as two times the EC\({}_{50}\) value.
# Fiber Bragg Grating-Based Optical Signal Processing: Review and Survey
## 2 Review of Design Methods for Fiber Bragg Gratings
### Approximate Methods  
Before exact analytical solutions to the analysis and synthesis of FBGs were developed (around the 2000s), the parameters of the grating were engineered to achieve the target functionality based on approximations. A well-known approximation to that end is the first-order Born approximation [26], which relies on the use of weak-coupling gratings.
# Stabilization of an Unconventional Large Airship When Hovering
## 2 Modeling
### Dynamics  
In a compact form, the whole dynamic system of the airship can be expressed as follows [26]:  
\[M.\dot{\
u}=\tau+Q_{G} \tag{5}\]  
With \(\
u=\left(\begin{array}{c}\
u_{1}\\ \
u_{2}\end{array}\right)\) as the velocity vector and \(M=\left(\begin{array}{cc}M_{TT}&0\\ 0&M_{RR}\end{array}\right)\) as the mass matrix. \(M_{TT}\) and \(M_{RR}\) are, respectively, the translation and the rotation part of the mass matrix of the airship. We denote by \(M_{\bar{\eta}}\) the components of the \(6\times 6\) matrix \(M\).  
Let us note that this mass matrix comprises the terms of inertia of a solid body, but also the terms of added masses. The acceleration of a moving body in a fluid creates a resistance force exerted by the latter, proportional to the acceleration of the solid. This force can therefore be likened to an increase in the mass of the solid, hence the notion of added mass. The values of these added masses are important and not negligible in the case of submarines or airships [27]. The computation of these added masses can be done thanks to experiments, or analytically as was done, for example, for the MC500 [28].  
In the right side, \(\tau=\left(\begin{array}{c}\tau_{1}\\ \tau_{2}\end{array}\right)\), \(\tau_{1}\), and \(\tau_{2}\) are, respectively, the external forces and torques, including the rotors effects, the weight (m.g), the buoyancy B, the wind Force F\({}_{\text{v}}\), and the aerodynamic lift (F\({}_{\text{L}}\)) and drag (F\({}_{\text{D}}\)). While \(Q_{G}=\left(\begin{array}{cc}-\
u_{2}\wedge\left(M_{TT}\
u_{1}\right)\\ -\
u_{2}\wedge\left(M_{RR}\
u_{2}\right)-\
u_{1}\wedge\left(M_{TT}\
u_{1} \right)\end{array}\right)\) are the gyroscopic forces and torques, we will denote by \(Q_{Gi}\) its components, \(\wedge\) being the vector product.  
When combining with the kinematic relations (4), the global dynamic model in its developed form becomes:  
\[\left\{\begin{array}{c}\dot{\
u}_{0}=c\psi.c\theta.u+(-s\psi.c\phi+c\psi s \phi s\theta)v+(s\psi s\phi+c\psi c\phi s\theta)w\\ \dot{\
u}_{0}=s\psi c\theta.u+(c\psi c\phi+s\psi s\phi s\theta)v+(-c\psi s\phi+s \psi c\phi s\theta)w\\ \dot{z}_{0}=-s\theta.u+s\phi.c\theta.v+c\phi c\theta w\\ \dot{\phi}=p+s\phi.\tan\theta.q+c\phi.\tan\theta.r\\ \dot{\theta}=c\phi.q-s\phi.r\\ \dot{\psi}=\frac{\psi\phi}{c\theta}q+\frac{c\phi}{c\theta}r\\ M_{11}\dot{u}=\alpha_{1}+Q_{G1}+F_{v_{1}}\\ M_{22}\dot{v}=\alpha_{2}+Q_{G2}+F_{v_{2}}\\ M_{33}\dot{w}=\alpha_{3}+Q_{G3}+F_{v_{3}}\\ \left(M_{46}^{2}-M_{66}M_{44}\right)\dot{p}=M_{46}\alpha_{6}+M_{46}Q_{G6}-M_ {66}\alpha_{4}-M_{66}Q_{G4}\\ M_{55}\dot{q}=\alpha_{5}+Q_{G5}\\ \left(M_{46}^{2}-M_{66}M_{44}\right)\dot{r}=M_{46}\alpha_{4}-M_{44}\alpha_{6}+ M_{46}Q_{G4}-M_{44}Q_{G6}\end{array}\right. \tag{6}\]  
where \(F_{vi}\) are the components of the wind applied forces and \(\alpha_{i}\) the components of the control vector \(\alpha\). The different characteristics of the actuators will be developed in detail in Section 4.
## 2 Materials and Methods
### Experimental Setup and Procedure  
Compost-derived wastewater was treated using a hybrid EC process enhanced with zeolite and a magnetic field (ECZ-MAG). Experiments were performed in a laboratory-scale electrochemical reactor equipped with two parallel aluminum electrodes (AA2007 alloy), submerged at a fixed interelectrode distance of 3 cm. The reactor was operated at a constant current density of 0.0182 A/cm\({}^{2}\), with no pH adjustment prior to treatment. The current density was calculated by dividing the applied current by the total active surface area, considering both sides of the fully immersed electrodes. A synthetic zeolite (15 g/L) was added as a sorptive media, and NaCl (0.5 g/L) was added to enhance conductivity. Two mixing speeds (250 rpm and 350 rpm) and three contact times (10, 20, and 30 min) were applied in Series 1, which includes a total of 12 experiments. Series 1 consists of two groups of 6 experiments each. These two groups differ in the presence of a magnet. The first group of experiments was conducted without a magnet, while the second group was conducted with a magnet. To assess the influence of the magnetic field, a cubic-shaped neodymium magnet (NdFeB, 0.55 T) was positioned beneath the reactor in selected experiments. The experimental setup used to evaluate the performance of the ECZ and ECZ-MAG process is presented in Figure 1, while experimental conditions for Series 1 are summarized in Table 1.  
Figure 1: Experimental set-up of ECZ-MAG performance. Note: 1—DC power supply, 2—glass beaker, 3— wastewater, 4—electrode pair, 5—zeolite, 6—magnet, 7—laboratory stirrer.
## 2 Materials and Methods
### Satellite Motion Equations  
According to [PERSON]'s law of universal gravitation, two objects exert a gravitational force on each other, with the magnitude of the force being directly proportional to the product of their masses and inversely proportional to the square of the distance between them. For satellites, the Earth's gravitational force is the dominant central force. However, to fully describe the satellite's motion, other forces must also be considered. While these additional forces are relatively small, they cannot be neglected in precise orbit calculations. The satellite's motion is primarily governed by the Earth's central gravitational force, and the satellite's motion equation is [41]:  
\[\ddot{r}=-\frac{G\dot{M}\dot{r}}{r^{3}}+R \tag{6}\]  
In this expression, \(\dot{\overset{\rightharpoonup}{r}}=(x,y,z)\) represents the satellite's position vector in the inertial reference frame; \(R\) is the total perturbing acceleration acting on the satellite, including both conservative and non-conservative forces. These forces include, for example, the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Input Parameters**} & \multicolumn{4}{c}{**Orbit Accuracy**} \\  & **Radial** & **Normal** & **Tangential** & **Three-Dimensional** \\ \hline
4.0 m\({}^{2}\) & 620 kg & 342.5 & 27.7 & 131.1 & 368.0 \\
6.0 m\({}^{2}\) & 620 kg & 1399.1 & 0.4 & 28.9 & 1399.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Impact of sail area on orbit determination results under atmospheric drag (unit: millimeters).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Atmospheric Density Model** & **Radial** & **Normal** & **Tangential** & **Three-Dimensional** \\ \hline DTM-78 & 5.7 & 1.3 & 4.2 & 7.2 \\ MSISE-90 & 5.7 & 1.4 & 4.2 & 7.2 \\ NRLMSISE-00 & 5.7 & 1.4 & 4.3 & 7.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effect of atmospheric density models on orbit determination accuracy (unit: millimeters).  
Earth's non-spherical gravitational field, atmospheric drag, solid tides, and ocean tides. To simplify numerical computation, this can be converted into a system of first-order differential equations:  
\[\left\{\begin{aligned} \dot{r}=v\\ \dot{v}=-\frac{GM\tau}{r^{3}}+R\end{aligned}\right. \tag{7}\]  
In this equation, V stands for the satellite's velocity vector. Given the initial state vector \(\left(\overset{\rightarrow}{r}\overset{\rightarrow}{o},\overset{\rightarrow}{v} \overset{\rightarrow}{o}\right)\) at time \(t_{0}\) and the forces acting on the satellite, the state vector at the next time \(t\), \(\left(\overset{\rightarrow}{r},\overset{\rightarrow}{v}\overset{ \rightarrow}{t}\right)\) can be determined through numerical integration. As a result, the numerical solution to the satellite's motion equation becomes an initial value problem for an ordinary differential equation.
# Generalized Nonlinear Chirp Scaling Algorithm for High-Resolution Highly Squint SAR Imaging
## 1 Introduction
Section 2 gives the model of the signal acquisition for squint SAR and illustrates the processing method of generalized nonlinear chirp scaling algorithm (GNLCSA); In Section 3, the dot-matrix simulation under high-resolution highly squint SAR configuration is carried out to validate the proposed algorithm; and, a summary is performed in Section 4.
## 4 Discussion
### Interaction between C Turnover and Denitrification  
In agricultural soils, denitrification is often controlled by the availability of readily decomposable organic matter with increasing C availability leading to increased N losses [6; 13; 14; 63; 64]. In contrast, we measured low denitrification derived N fluxes under oxic conditions, and the correlation between oxic N and CO2 fluxes was very weak indicating that denitrification was not directly affected by litter decomposition in our study. Although soil NO3\({}^{-}\) content was high and high litter and SOM turnover confirmed high C\({}_{\text{org}}\) availability, N2O fluxes were very low, indicating that conditions for denitrifying microorganisms were not optimal. [PERSON] et al. (2021) [PERSON] et al. (2021) reported very low N2O + N2 fluxes from an incubation study with the same soil at 60% WFPS and higher fluxes compared to our study for 75 and 85% WFPS. Thus, our incubation conditions with a soil moisture of 70% WFPS may have been too low to promote denitrifying soil microorganisms. With onset of anoxic conditions, N fluxes increased immediately, confirming that high pCO2 was restricting denitrification during the oxic incubation phase. In contrast to our expectations, the microbial respiration of litter and SOM did not promote the formation of litter associated anoxic hotspots for denitrification as high O2 diffusivity limited denitrification [65; 66].  
In contrast, N and CO2 fluxes were highly positively correlated (adj. R\({}^{2}\) = 0.86, \(p\) < 0.001) under anoxic conditions, and cumulative N emissions increased with increasing litter C input (adj. R\({}^{2}\) = 0.51, \(p\) < 0.001) confirming our hypothesis that higher C availability leads to increased gaseous N losses. However, this effect was based on the role of C as energy source for denitrifiers, as the potential O2 consumption by C decomposition was not relevant under anoxic conditions.  
The ratio of denitrification end products N2O/(N2O+N2) decreased with increasing water-extractable C:N ratio of litter (adj. R\({}^{2}\) = 0.73, \(p\) < 0.001) and increasing soil WEOC:NO3\({}^{-}\) ratio at the end of the experiment (adj. R\({}^{2}\) = 0.41, \(p\) < 0.01) confirming that the ratio of available C to oxidized N is a major control of denitrification product stoichiometry [67]. Immobilization of N after addition of maize straw with high C:N ratio restricted N availability leading to higher N2O reduction to N2. However, as soil NO3\({}^{-}\) content was still very high (\(>\) 70 mg NO3\({}^{-}\)-N kg\({}^{-1}\) in all treatments at the end of the incubation experiment), N2O was the dominant end product, as NO3\({}^{-}\) is preferentially used as electron acceptor and high soil NO3\({}^{-}\) content can inhibit the reduction of N2O to N2 [27; 67].
# Before and After: A Multiscale Remote Sensing Assessment of the Sinop Dam, Mato Grosso, Brazil
## 1 Introduction
Furthermore, fish species limited to small creeks (with dense riparian vegetation) which drain into the main river channel are especially vulnerable to environmental changes caused by dams, deforestation, agricultural runoff, and modifications due to road construction [39; 40; 41]. Some hydroelectric dams have also been shown to be less \"green\" than purported to be. Shallow tropical reservoirs are high emitters of CO\({}_{2}\) and methane [42], in some cases surpassing fossil fuel emission in carbon intensity (CO\({}_{2}\) equivalent per MWh) [43]. While carbon dioxide is a well-established contributor to climate change, methane emissions are also of concern because of its greater global warming potential [42]. The Amazon basin is home to 2406 freshwater fish species (as of April 2021), 1402 of them endemic, representing \(\sim\)15% of global freshwater fish diversity [44]. With 66 endemic species, the Tapajos is the fourth most speciose tributary of the Amazon [45; 46]. After large controversial hydroelectric projects on the Madeira (Jirau dam) [38] and Xingu (Belo Monte dam) [47; 48] rivers, the Tapajos River basin has become the latest target for many hydroelectric projects [49]. A number of run-of-the-river dams along its tributaries have been made possible by agricultural expansion and the associated road construction [50]. On the Teles Pires river, one of the main tributaries of the Tapajos, a series of run-of-the-river dams were constructed. The Teles Pires Complex comprises four Hydroelectric Power Plants (HPP): HPP Sao Manoel (700 MW), HPP Teles Pires (1820 MW), HPP Colider (300 MW) and HPP Sinop (401 MW). Herein, we have carried out a multiscale remote sensing analysis to determine the lentic zone of the HPP Sinop reservoir. From high spatial resolution satellite imagery (i.e., Dove PS and RapidEye) we determined the extent of the flooded area of the reservoir and quantify the land cover types that were inundated. The tree canopy height and above ground biomass of the inundated forest were estimated from spaceborne LiDAR data. Atmospheric methane concentration was also assessed post flooding from the S5P/TROPOMI satellite instrument. At a finer scale, we illustrate the utility of UAS based SfM-MVS photogrammetry for fluvial landscape reconstruction at two study sites (pre-inundation). We introduce the utility of virtual reality (VR) visualization of these UAS datasets for scientific investigation and collaboration. While the focus of this study is on one hydroelectric dam project, the methodologies used are widely applicable to other regions and can serve as a guideline for incorporating multi-temporal and multi-scale remotely sensed data into environmental impact assessments, infrastructure monitoring, and ecological and ichthylogic studies.