esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Model Description and Data Sources
CMFD comprises seven near-surface meteorological variables: 2 m air temperature, surface pressure, near-surface specific humidity, 10 m wind speed, downward shortwave radiation, downward longwave radiation, and precipitation rate [46]. The dataset integrates meteorological observations and remote sensing/reanalysis data. The temporal and spatial resolution of the dataset is 3 h and 0.1\({}^{\circ}\). These remote sensing/reanalysis data include Princeton reanalysis data, GLDAS data, GEWEX-SRB radiation data, and TRMM precipitation data [47; 48]. CMFD is more accurate than other meteorological forcing datasets [49]. Land use data from 2020 were used in the study and were provided by the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) [50]. The workflow of this paper is shown in Figure 2 and detailed data information is shown in Table 1.  
Figure 2: Workflow chart. The measured NPP data collected from the published research are used as ground-truthing data to validate the NPP simulated by CLM 5.0 [51]. In addition, the spatial patterns of NPP simulation from the CLM5.0 were also validated using the NPP calculated from the Carnegie-Ames-Stanford Approach (CASA) model. The CASA model is known for its computational simplicity, operational feasibility, and parameter sharing options. It has greatly facilitated NPP simulations at both global and regional scales [52]. Currently, the CASA model has become one of the most effective methods to study and calculate large-scale NPP [53], it also suits the NPP simulation in the Tibetan Plateau region [54; 55]. Given that the CASA model relies on MODIS NDVI data which starts from the year 2000, this study used the CASA model simulated NPP from the years 2000 to 2018 as validation data.
## 2 Materials and Methods
### Evaluation with Error Analysis  
The accuracy of the projection methods was evaluated by calculating the mean absolute error (MAE) and standard deviation (SD) based on the differences between the PWVs from the ground truth images with full sampling and those obtained using the vertical and radial projection methods. Additionally, a one-way non-parametric analysis of variance (Kruskal-Wallis test) was performed to assess the differences between the ground truth, vertical projection, and radial projection PWVs and further evaluate the feasibility of the proposed technique in an actual MRI environment. Furthermore, Bonferroni correction was applied to the results of all Kruskal-Wallis tests. All statistical analyses were conducted using Jamovi version 2.4.8 ([[https://www.jamovi.org](https://www.jamovi.org)]([https://www.jamovi.org](https://www.jamovi.org)) (accessed on 30 September 2024)) and Dwass-Steel-Critchlow-Figner (DSCF) post-hoc tests.
# Effect of Sowing Dates on Fatty Acids and Phytosterols Patterns of _Carthamus tinctorius_ L.
## 2 Materials and Methods  
### Plant Material, Trial and Environmental Conditions  
Seed oil, fatty acids and sterol contents were studied on one genotype (Arizona) usually used in organic agriculture.  
The experiment was conducted in 2007, in south-western France at the Regional Centre for experimentation under Organic Agriculture, at Auch (32). Field trial was done on a deep limestone clay silt soil with high water-holding capacity. Two sowing dates were used to generate temperature and rainfall variations during seed development. The trial was sown at a density of 300 seeds by m\({}^{2}\) on April 15 th, for the conventional sowing date (CS); and on June 2 nd, for the late sowing date (LS) which was chosen to reach most contrasted conditions between the two sowing dates considering the best combination between the climate (based on the average of 55 years analysis) and farmers practices (sowing date) that is usually done in the South of France for this species. The development of the crop was monitored weekly. A complete randomised block design was used in this experiment with two treatments (two sowing dates) and three replications. Seeds were harvested at beginning (5 th September) and mid-September (16 th September) for the conventional and late sowing date, respectively. Seeds were harvested at maturity when the water content of 20% was reached.  
At harvest, a set of seeds was debulled manually in order to separate almond (embryo) and hull to perform the extraction and measurement of sterol in the each compartment.
## 3 Experiments Evaluation  
In this section, we evaluate the performance of Algorithm 1 extensively on both simulated data and video CS application. For simulated data, we use Equation (1) to generate a series of CS vectors \(v\). Like most CS literature, we use the noiselet operator [15,16] as the measurement operator \(\mathcal{A}\), which compresses an \(n\times 1\) vector to a \(p\times 1\) vector, and we denote \(\rho_{cs}=\dfrac{P}{n}\) as the CS measurement ratio. \(U\) is an \(n\times d\) matrix whose \(d\) columns are realizations of i.i.d. \(\mathcal{N}(0,I_{n})\) random variables that are then orthornomalized. The weight vector \(w\) is a \(d\times 1\) vector whose entries are realizations of i.i.d. \(\mathcal{N}(0,I_{n})\) random variables, which are Gaussian distributed with mean zero and variance 1. The _K-sparse_ signal \(s\) is an \(n\times 1\) vector whose supports are chosen uniformly at random without replacement and we denote \(\rho_{s}=\dfrac{K}{n}\) as the sparsity of the signal. We use relative error to quantify the sparse recovery performance as follows:  
\[RelErr=\dfrac{\left\|\ \hat{s}-s\right\|_{2}}{1+\left\|\ \hat{s}\right\|_{2}} \tag{6}\]  
Note that we manually add \"1\" to the denominator of \(RelErr\) because \(s\) may be _0-sparse_ in our following experiments.  
In all the following experiments, we use Matlab R2010b on a Macbook Pro laptop with a 2.3 GHz Intel Core i5 CPU and 8 GB RAM. We always set the rank of the fixed subspace as \(d=5\), and the ambient dimension of signal is \(n=512\), unless otherwise noted.
## 2 Network Model
 \tag{1}\]  
where \(P_{s}^{\max}\) is the maximum transmit power of any ST in the underlay mode, \(I_{d}\) is the maximum permissible interference level at any PU, \(h_{sp}\) represents the independent and identically distributed exponential fading coefficient between any ST and the tagged PR, \(||sp||\) is the Euclidean distance between any ST and PR and represents the large-scale path loss attenuation between such ST and PR, and \(\alpha\) is the path loss exponent. For any typical PR \(y_{k}^{p}\) located at a distance \(r_{p}\) from its paired PT \(x_{k}^{p}\) assumed to be located at the origin of its coverage region [2; 24], the signal to interference plus noise ratio (\(SINR\)) received at \(y_{k}^{p}\) in time slot \(t\) is given as  
\[SINR_{y_{k}^{p},t}=\frac{P_{p}h_{x_{k}^{p},y_{k}^{p}}r_{p}^{-\alpha}}{\sigma^{ 2}+I_{pp}+I_{sp}}, \tag{2}\]  
where \(P_{p}\) is the fixed transmission power of PT, \(h_{ab}\) is the exponentially distributed channel fading from transmitter \(a\) to receiver \(b\), \(I_{pp}\) is the interference from active PTs except \(x_{k}^{p}\) at the \(y_{k}^{p}\), \(I_{sp}\) is the interference from active STs at the \(y_{k}^{p}\) and \(\sigma^{2}\) is an independent Gaussian thermal noise signal power. Similarly, for any typical SR \(y_{k}^{s}\) located at a distance \(r_{s}\) from its paired ST \(x_{k}^{s}\), the SINR received at \(y_{k}^{s}\) is given as  
\[SINR_{y_{k}^{p},t}=\frac{P_{s}h_{x_{k}^{p},y_{k}^{p}}r_{s}^{-\alpha}}{\sigma^{ 2}+I_{ss}+I_{ps}}, \tag{3}\]  
where \(I_{ss}\) is the interference from active STs except \(x_{k}^{s}\) at the \(y_{k}^{s}\) and \(I_{ps}\) is the interference from active PTs at the \(y_{k}^{s}\). Next, we obtained the analyses for the success probability and conditional success probability in both primary and secondary networks following the network description provided in this section.
# Correcting for Mobile X-Band Weather Radar Tilt Using Solar Interference
## 6 Discussion and Conclusions
### Review of the Analysis Methods and Results  
The two analysis techniques applied here (a 2D Gaussian model and a sinusoidal tilt model) complement one another through the introduction of quality-of-fit metrics allowing them to be applied to shorter time periods. The methods are of particular relevance to mobile research radars, which are often deployed for short-duration research projects and are more likely to be deployed on less-stable structures than long-term, static operational weather radar. The combination of techniques is especially important given the relatively high latitude of the radar (greater than 50\({}^{\circ}\)), which introduces additional uncertainty into the tilt methodology of [PERSON] et al. (2016) due to the concentration of solar hits within a narrow azimuth range (southerly in this case) and the sinusoidal process of the sun through the sky at lower elevations during this period. Figure 4 clearly shows these artefacts between azimuths of 165\({}^{\circ}\) and 195\({}^{\circ}\) where the sun is at a low enough elevation to generate a collection of solar hits at the same radar-recorded elevation angle. It is particularly in this context that alternative sources of information are useful: in this case, the additional QPE verification described in Section 5 can be used to assess the plausibility of the derived corrections relative to raingauge observations. In the case of NXPol-1 and the RAiN-E datasets, the improvement in percentage bias and MAPE of the radar QPE for the majority of catchments across the region provides external confidence that the corrections are accurate. In the context of tilt corrections, where QPE variation largely results from changes in partial beam blockage correction, the use of catchment average precipitation rather than gauge point estimates is preferable as it provides a degree of averaging that compensates for the highly spatially variation and non-linear nature of beam blockage change, the varying locations of gauges, and the known variability in radar for point observation comparisons.  
While the corrections can be deemed plausible following verification, determining the exact cause of the tilt variation, particularly during Period 8 where no manual intervention occurred, is not possible. The most likely explanation is a settling of the scaffold construction through time, possibly compounded by changes in soil conditions. Other possibilities are movement of the radar's own legs during such an extended deployment without intervention or, less-likely, differential heating of the radar and containers, leading to movement of the system. While deployment on a concrete foundation would be preferable for future field campaigns, the techniques presented here, along with the following recommendations,would make it easier to manage such an uncertain, soft-ground deployment in the future. In all cases, regular monitoring and site visits would be recommended.
## 3 Technology
### Current Research Challenges
#### 3.7.4 Non-Time Invariant Detector Response  
TG SPAD measurements are particularly complex also due to the need to cope with a non-time-invariant detector response. Indeed, some regions of the gate windows are affected by localized oscillations of the bias voltages or electrical disturbances on the SPAD/dummy signals entering the fast comparator. Typically, these are mainly localized close to the rising edge of the gating window [31]. Thus, a realistic modeling of the full detector behavior in a TD-DO system, which is fundamental to understand the effect of key bottlenecks of a TG detector like the diffusion tail, is more difficult due to impossibility of applying the convolution theorem. A first solution was proposed in Ref. [110] with the generation of the so-called \"spread matrix\", which is a discrete representation of the system behavior, assessing the temporal spread of detected photons by acquiring them at different delays from the laser pulse injection time, at very fine steps of, e.g., 100 ps. In this way, it is possible to characterize how the real photon arrival times on the detector are spread when photons are classified by the system at a given measured time.
## 3 Results
### Morphology of PCFs  
After optimising the ideal size for the coagulation bath (dH\({}_{2}\)O), the next step was to produce four different PCFs incorporating PEGs (600 and 1000) into the core. The four PCFs were subjected to Bright-field microscopy analysis to evaluate the morphology and  
Figure 5: Comparison of the indices of the characteristic peaks of F\({}_{uni}\) and F\({}_{hol}\) for the three coagulation bath lengths (0.50, 1.00, and 1.25) m.  
uniformity of the core-sheath structure. Figure 6 shows the morphologies, considering both the PEG variation and the ejection speed in the core.  
In a study by [PERSON] et al. [33], co-axial fibres containing PEG encapsulated in a polymer, forming a core-shell structure, showed a well-defined morphology and efficient encapsulation. When analysing the micrographs presented in Figure 6, an initial observation is that the cores formed are narrow. Although the ejection speed of the PEGs does not directly influence the formation of the core size of the PCFs, this behaviour may be related to the configuration of the needle (co-axial) used during production, which has a calibre of 11 G for the ejection of the CA (Mn 50,000) and 21 G for the PEGs (600 and 1000). It is known that co-axial needles with larger calibre numbers have narrower diameters, which explains the formation of smaller cores. In addition, these dimensions may favour interactions and molecular bonds between the solutions since they have similar functional groups [26], allowing PEG to be incorporated into the CA since the higher molecular weight (Mn) of the CA contributes to its expansion [14].  
In addition, it was observed that the core formed by the two PEGs was well-defined and visible despite having narrow dimensions. This can be explained by the solubility of PEGs in dH\({}_{2}\)O in contrast to the insolubility of CA in the same aqueous medium. Rapid coagulation occurs when the CA solution comes into contact with the coagulation bath composed of dH\({}_{2}\)O, forming a protective sheath [26].  
Finally, a striking feature of the PCFs was their flattened structure. This may be attributable to the low viscosity of the CA polymer solution due to its low concentration (10 wt.%). In addition, as mentioned above, molecular interactions can occur between the CA and PEG solutions, and due to the solubility of PEG in dH\({}_{2}\)O, some of it can be lost by diffusion. In comparison, as reported in the literature, PCFs produced with CA solutions of higher concentration and viscosity tend to exhibit a rounder and thicker morphology [14; 26; 34].
# Distribution Grid Stability--Influence of Inertia Moment of Synchronous Machines
## 1 Introduction  
The idea of grid voltage having a coherent sinusoidal course with constant frequency does not correspond with the reality of phenomena occurring in the distribution grid. Accurate measurements disclose oscillations of grid frequency and an instantaneous phase of distribution grid voltage. Grid frequency oscillation has a random noise trend because it is only the response of an energy system to a casually changing daily demand energy diagram [1]. This effect is particularly significant in microgrids [2; 3; 4]. The issue of network stability has been studied in a number of other works (e.g., [5]).  
The phenomenon of grid oscillations may be described in terms of statistical dynamics using the power spectral density \(S_{\omega}(\Omega)\) of a distribution grid's angular frequency fluctuation. An example of this function characterizing the frequency spectrum of grid frequency deviations is shown in Figure 1.  
A spectrum of grid frequency deviations has two components: (i) A slow component for \(\Omega<2\)s\({}^{-1}\) (  
If a synchronous machine driving inertial masses is connected to the grid, the mechanical movement of a machine shaft must follow frequency and phase changes of a voltage phasor of the grid. A shaft follows the instantaneous grid voltage phase with an angle deviation, producing a load angle \(\beta\) in a synchronous machine. The instantaneous power consumed or supplied by a machine to a grid depends on the magnitude and orientation of the load angle \(\beta\). The greater the inertial masses on the shaft are in comparison to the size of a synchronous machine, the greater the dynamic deviation of the following fast grid phase changes and load angle oscillations will be [6].  
Since an electromechanical system (a synchronous machine and its inertial masses) has very low oscillation dumps, the load angles, excited by distribution grid frequency fluctuations, have a harmonic swinging character. In addition, they are accompanied by an undesirable overflow of energy between the grid and the inertial masses on the shaft.
## 2 System Identification Methods
### Adaptive Neuro-Fuzzy Inference System  
The simple definition of a ANFIS model is a hybrid method that can construct an input-output mapping set of fuzzy \"if-then\" rules with appropriate membership functions to design the model input-output data pairs [1; 23]. The ANFIS system is a nonlinear learning model that utilizes the least-square as well as back-propagation methods to train the fuzzy inference systems membership functions [3; 24]. Moreover, the ANFIS is combined of natural language description of fuzzy systems and the learning properties of neural-networks [24]. Figure 1 demonstrates the ANFIS model structure of two inputs with five layers. It includes two adaptive layers which are distributed between the premise part and the consequent part and three fixed layers [23; 24]. [PERSON] et al. [3] represents a simplified process of ANFIS for the two or more inputs parameters as follows:  
The rules of Takagi-Sugeno fuzzy model were used as [3]:  
\[R_{j}:\text{if }u^{1}\text{ is }P_{1j}\ldots\;\ldots\text{ and }u^{i}\text{ is }P_{ij},\text{ then }z=f_{i}\left(u^{1},\ldots\;\
eg,u^{i}\right),\;j=1,2, \ldots,N \tag{3}\]  
where \(R_{j}\) is the \(j\)th fuzzy rules, \(N\) is the fuzzy rules number, \(P_{ij}\) are fuzzy sets centered at the \(j\)th operating point, and \(u^{i}\) are premise variable or inputs values. \(Z\) is a linear function (\(f_{i}\)) of the equation of the consequent part. The \(R_{j}\) subsystems are integrated by combining the regions of each local subsystem using the interpolation method of fuzzy as a nonlinear system. As shown in Figure 1, the ANFIS model is divided into five layers. The output of layer one can be presented in form:  
\[F^{1,j}=u_{P_{ij}}(u^{i}) \tag{4}\]where \(u_{P_{ij}}(u^{i})\) is the membership function (MF) of \(u^{i}\) in \(P_{ij}\). This MF is applied for each input of layer one.  
The output of layer two represents the firing strength of the rule:  
\[F^{2,j}=w_{j}=u_{P_{ij}}(u^{1})\times u_{P_{ij}}(u^{2})\times\ldots\times u_{P_{ ij}}(u^{i}) \tag{5}\]  
Taking a ration of the layers two in layer three to normalize the outputs of layer two as follows:  
\[F^{3,j}=\ddot{w}_{j}=\frac{w_{j}}{\sum_{i=1}^{N}w_{i}} \tag{6}\]  
Layer four applies a node function to the normalized firing strength and the output can be expressed as:  
\[F^{4,j}=\ddot{w}_{j}f_{j} \tag{7}\]  
The consequent parameters of function \(f_{i}\) are adjustable in this layer and can be used to tune the outputs of that layer. Therefore, the model output (\(y_{s}\)) (fifth layer outputs) summates the layer inputs as:  
\[F^{5,j}=y_{s}=\sum_{i=1}^{N}\ddot{w}_{i}f_{i} \tag{8}\]
## 2 Background
### 6 LoWPAN  
The 6 LoWPAN working group of the IETF has defined an adaptation layer for sending IPv6 packets over IEEE 802.15.4. The goal of 6 LoWPAN is to reduce the sizes of IPv6 packets to make them fit in 127 byte IEEE 802.15.4 frames. The 6 LoWPAN proposal consists of a header compression scheme, a fragmentation scheme, and a method for framing IPv6 link local addresses into IEEE 802.15.4 networks [5,6]. The proposal also specifies enhanced scalabilities and mobility of sensor networks. The challenge to 6 LoWPAN lies in the sizable differences between an IPv6 network and an IEEE 802.15.4 network. The IPv6 network defines a maximum transmission unit as 1,280 bytes, whereas the IEEE 802.15.4 frame size is 127 octets. Therefore, the adaptation layer between the IP layer and the MAC layer must transport IPv6 packets over IEEE 802.15.4 links. The adaptation layer is responsible for fragmentation, reassembly, header compression and decompression, mesh routing, and addressing for packet delivery under the mesh topology. The 6 LoWPAN protocol supports a scheme to compress the IPv6 header from 40 bytes to 2 bytes [7,14].
## 4 Experiment and Results
### Optimization Results  
In this part, a tourist is provided with the optimal scheme of routes, including the initial and adjusted routes, as an example of the optimization results. The tourist's preference weights \([\alpha_{n},\alpha_{c},\alpha_{c}]\) are [0.25, 0.5, 0.25]. The objective importance weights \([w_{C},w_{V},w_{D}]\) are [0.5, 0.3, 0.2]. The time budget is 8:00-18:30. The departure and final arrival locations are (121.62E, 38.90N) and (121.33E, 38.83N), respectively.
# Corrosion Susceptibility and Microhardness of Al-Ni Alloys with Different Grain Structures
## 3 Results and Discussion
### Correlation between Electrochemical Parameters and Microhardness  
Figure 19 correlates the values of polarization resistance, R\({}_{\rm p}\), with microhardness, HV, for both types of grain structures (columnar and equiaxed). It is observed that the higher the HV, the lower the value of R\({}_{\rm p}\) for hypoeutectic (Al-1 wt. %Ni and Al-3 wt. %Ni) and eutectic (Al-5.7 wt. %Ni) alloys, and the trend for the hypereutectic (Al-8 wt. %Ni) alloy changes.  
This different behavior can be justified because the Al\({}_{3}\)Ni intermetallic could play a dual role in the corrosion of an Al-Ni alloy. It can act as a barrier against corrosion or as a galvanic cathode that accelerates the corrosion process. The role that will dominate the corrosion process will depend largely on the quantity and distribution of intermetallic particles. This is in accordance with what was proposed by [PERSON] et al. [16].