esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of Feedforward Perceptron Network with LSTM for Solar Cell Radiation Prediction
## 3 Experimental Design
### Description of the ANN Model  
As shown in Figure 6, we created an ANN model for prediction of radiation amounts. Deep ANN was utilized with a structure of 1 input layer of size 8, 2 hidden layers each of size 50, and a single output layer with size 1. Therefore, we utilized only one row to predict the following 48 th row.  
For the model in Figure 6, the tanh function was used as the activation function for the proposed model. Hyperparameter optimization using random grid search was performed on the batch size and the learning rate. Stochastic gradient descent (SGD) was utilized as the optimizing parameter for the deep ANN. The hyperparameters' ranges are specified in the following Table 4.  
Table 4 demonstrates the hyperparameters of the ANN model.
## 2 Materials and Methods--Implementation, Integration, and Testing of the IDEASSat Flight Model
### Delivery, Launch Vehicle Integration, and Launch  
Following re-integration and the second vibration test, permission to ship was granted to the spacecraft team by the launch services provider ISILaunch. IDEASSat was delivered to ISILaunch integration facilities in the Netherlands for deployer integration on 1 December 2020. Due to pandemic travel restrictions, the spacecraft team viewed the integration process remotely, providing input to the launch provider operators when needed.  
At the time of delivery, it was found by ISILaunch operators that the deployable solar panels were warped, potentially due to deficiencies in PCB materials, as shown in Figure 15. The red circle in Figure 15 shows the stopper at the edge of one of the warped solar panels, which was not anodized and would be pressing against the deployment rail of the deployer once inserted. This would increase the risk of cold welding between the stopper and the deployment rail when subjected to vacuum, while also increasing the friction between the stopper and the rail during the deployment process. Both of these factors would increase the risk of unsuccessful deployment.  
Figure 14: The configuration of IDEASSat during the thermal vacuum cycling test.  
Figure 13: TVC test temperature setting.  
The ISISPACE QuadPack was used as the spacecraft deployer for IDEASSat, which has one dynamic rail that slides forward and outward when the deployer door is opened [31]. After discussion between the spacecraft team and ISILaunch engineers, it was suggested that the risk of deployment failure would be reduced if the spacecraft could successfully be induced to move through the application of sufficient force at deployment time. This is because dynamic friction coefficients are generally smaller than static friction coefficients.  
ISILaunch engineers proposed a solution to use the dynamic rail to provide an additional impulse at deployment. Figure 16 shows the orientation of the spacecraft inside the deployer. The red circle indicates the position of the QuadPack dynamic rail. In order to reduce the risk of cold welding and friction, while also providing an initial impulse to the spacecraft upon deployment, the spacecraft was oriented such that stoppers on the solar panel with the largest deflection due to warping were in contact with the dynamic rail of the deployer. Improvised testing on the ground was difficult, since the spring coefficient of the deployer pusher plate was insufficient to counter the additional friction along the deployment rails at 1 g gravity. However, it was verified that the motion of the dynamic rail was sufficient to induce the spacecraft into motion from rest. The solution was therefore considered to be the best available option in terms of risk mitigation, since the return of the spacecraft for solar panel replacement was not possible programmatically.  
Figure 15: The aluminum stopper at the edge of the warped solar panel was deflected by as much as \(1\) cm, increasing the risk of deployment failure due to cold welding or friction.
# Cascaded Frequency Selective Surfaces with Matryoshka Geometry for Ultra-Wideband Bandwidth
###### Abstract  
The purpose of this paper is to present cascaded frequency selective surfaces (FSSs) with matryoshka geometry to increase the effective bandwidth. We carry out an analysis of the influence of the spacing between the surfaces on the FSSs frequency response. The application involves a two-layer cascaded FSS, one as a band-stop filter with a matryoshka geometry and the other as a band-pass filter with inverted or negative matryoshka geometry. With this framework, it is possible to extend an ultra-wideband (UWB) of a bandwidth up to 2 GHz in the 1.8 GHz to 3.8 GHz range with just two layers and an air gap of 12 mm, in addition to a bandwidth of 2 GHz to 3.2 GHz with a smaller 4 mm gap between layers.  
microstrip; FSS; cascade surfaces; matryoshka geometry; UWB; bandwidth +
Footnote †: journal: Applied Physics Letters
## 2 Materials and Methods
### Fluorescein Diacetate (FDA) Analysis  
To evaluate microbial activity, the hydrolysis of fluorescein diacetate (FDA) was measured, according to [PERSON]'s method as modified by [PERSON] and [PERSON] [27; 28].  
FDA can be hydrolyzed by different enzymes present in soil, including protease, lipase, and esterase. The product of this reaction is fluorescein, and its concentration can be measured through spectrophotometric analysis (it has an intense yellow color).  
This method is a simple, fast, and reliable technique to determine overall microbial activity.  
The method requires the use of two solutions:  
* potassium phosphate buffer: 8.7 g-L\({}^{-1}\) of K\({}_{2}\)HPO\({}_{4}\) and 1.3 g-L\({}^{-1}\) of KH\({}_{2}\)PO\({}_{4}\) at pH = 7.6;
* FDA stock solution in acetone: 0.1 g of FDA and 50 mL of acetone.  
From each sample, 2 g of wet soil was taken and mixed into 15 mL of potassium phosphate buffer and 100 \(\upmu\)L of FDA stock solution in acetone. The solution was agitated for 1 h at 50 rpm. Subsequently, to stop the hydrolysis reaction, 15 mL of acetone was added. The samples were centrifuged at 6000 rpm for 5 min, and then filtered through a 1.2 \(\upmu\)m filter to remove possible colloidal particles. Solution  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Salt** & **Concentration** \\ \hline (NH\({}_{4}\))\({}_{2}\)HPO\({}_{4}\) (g-L\({}^{-1}\)) & 11.62 \\ \hline NH\({}_{4}\)NO\({}_{3}\) (g-L\({}^{-1}\)) & 75.97 \\ \hline KH\({}_{2}\)PO\({}_{4}\) (g-L\({}^{-1}\)) & 0.5 \\ \hline K\({}_{2}\)HPO\({}_{4}\)\(\cdot\)H\({}_{2}\)O (g-L\({}^{-1}\)) & 0.5 \\ \hline MgSO\({}_{4}\)\(\cdot\)7H\({}_{2}\)O (g-L\({}^{-1}\)) & 0.008 \\ \hline CuSO\({}_{4}\)\(\cdot\)4H\({}_{2}\)O (g-L\({}^{-1}\)) & 0.002 \\ \hline MnSO\({}_{4}\)\(\cdot\)H\({}_{2}\)O (g-L\({}^{-1}\)) & 0.002 \\ \hline FeSO\({}_{4}\)\(\cdot\)7H\({}_{2}\)O (g-L\({}^{-1}\)) & 0.002 \\ \hline CaCl\({}_{2}\)-2H\({}_{2}\)O (g-L\({}^{-1}\)) & 0.002 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mineral salt medium for bacteria (MSMB) composition.  
absorbance was then measured via spectrophotometric analysis at 490 nm, referenced to the blank that contained only the potassium phosphate solution.  
For each sample, two replicates were done. In this paper, all the results have been reported without error bars and standard deviations, since two replicates cannot support a reliable statistical analysis.  
The amount of FDA hydrolyzed was evaluated using Lambert and Beer's law:  
\[\text{A}=\epsilon\text{-C-1} \tag{1}\]  
where  
\(\text{A}=\log\frac{\text{I}_{0}}{\text{I}_{1}}\), with I\({}_{0}\) initial light intensity and I\({}_{1}\) transmitted light intensity;  
\(\epsilon=\text{molar attenuation coefficient (L\text{-mol}^{-1}\text{-cm}^{-1})}\);  
\(\text{C}=\text{molarity of attenuating species (mol\text{-}L^{-1})}\);  
\(\text{I}=\text{optical path length (cm)}\).  
The calibration line was made with fluorescein samples at known concentration.  
The fluorescein production was monitored from t = 0 to t = 30 days, two to three times a week.
## 4 Discussion
### Prospective Applications of UAV Surveys in the System of Environmental Coastal Monitoring
Prospects for the use of optical UAV-derived data are connected not only with simple recording of violations of water and environmental management rules. Acquisition from ultra-low-height UAV optical surveys and thematic application of such a derived product of digital aerial photogrammetry as photogrammetric dense point clouds and ultra-highly detailed digital surface models (DSMs) raises UAVs to an important basic level within multi-platform monitoring systems. Nevertheless, the cost-efficiencies and time-efficiencies of UAV surveys in coastal areas, the quality of acquired data, and their adequacy for studying highly dynamic objects and processes strongly depend on specific conditions formed in water environments [28]. Acquisition and processing UAV-based data in the course of coastal environmental studies involves some difficulties and restrictions unrelated to survey equipment and principally typical of remote sensing of water bodies: glints, shadows, surface waves, wind speed, and so on. Rapidly changing conditions of irradiance and water transparency are the key limitations of UAV surveys due to the rather long time required to survey even small areas. Consequently, it is critically important to develop and apply special survey techniques ad hoc, taking into account the peculiarities and phenology of objects and sites in study, and to select appropriate flight times carefully to coincide with optimal meteorological and hydrological conditions, especially for repeated multi-temporal UAV-based studies.
# Performance of Time Reversal Based Underwater Target Detection in Shallow Water
## 4 Simulation  
The envelope of the received signal from the finite scatters forms the K-distribution, the shape parameter of the K-distribution is related to the number of scatters, and the scale parameter is determined by the average size of scatters. The K-distribution can be a good approximation because it is easy to apply to various environments and situations depending on shape parameters and scale parameters. Therefore, since the two parameters of the K-distribution can be a physical model of the submarine environment, a clutter channel with a K-distribution is applied by setting shape parameters and scale parameters according to the simulation environment.
## 3 Experiments and Results
### Study of the Applied Classifiers
#### 3.1.3 Ensemble  
Ensemble methods combine the predictions of several base estimators built with a given learning algorithm in order to improve robustness over a single estimator [37]. There are two families of ensemble methods--averaging methods and boosting methods [37]. The basic principle of averaging methodsis to develop several estimators independently and then to average their predictions [37]. In this work an averaging method named random forest (RF) classifier is applied. RF is a classifier consisting of a collection of tree-structured classifiers \([h(x,\theta_{k}),k=1,2,...]\) where \([\theta_{k}]\) denotes independent identically distributed random vectors and each tree casts a unit vote for the most popular class at input \(x\)[39]. In RF algorithm each tree in the ensemble is built from a sample drawn with replacement from the training set [37; 39]. In addition, when splitting a node during the construction of the tree, the split that is chosen is no longer the best split among all features whereas the split that is picked is the best split among a random subset of the features [37; 39]. As a consequence of this randomness, the bias of the forest usually slightly increases but, due to averaging, its variance also decreases, usually more than compensating for the increase in bias, hence yielding an overall better model [37; 39].  
According to [39], for an ensemble of classifiers \(h_{1}(x),...,h_{K}(x)\) and with a training set of random vectors \(Y,X\), the margin function can be defined as:  
\[\mathrm{f}(X,Y)=a_{k}I(h_{k}(X)=Y)-\max_{j\
eq Y}a_{k}I(h_{k}(X)=j) \tag{10}\]  
here \(a_{k}\) denotes the average number of votes at \(X,Y\) and \(I(\cdot)\) is the indicator function. The larger the margin is, the more confidence lies in the classification. According to [39], the generalization error is determined as:  
\[\delta=P_{X,Y}(f(X,Y)<0) \tag{11}\]  
here \(P_{X,Y}\) is the probability over \(X,Y\) space. For RF classifiers, \(h_{k}(X)=h_{k}(X,\theta_{k})\) and according to [31], with the increase in tree numbers it becomes converges to  
\[P_{X,Y}(P_{\theta}(h(X,\theta)=Y)-\max_{j\
eq Y}P_{\theta}(h(X,\theta)=j)<0) \tag{12}\]
# Identifying Earthquakes in Low-Cost Sensor Signals Contaminated with Vehicular Noise
###### Abstract  
The importance of monitoring earthquakes for disaster management, public safety, and scientific research can hardly be overstated. The emergence of low-cost seismic sensors offers potential for widespread deployment due to their affordability. Nevertheless, vehicular noise in low-cost seismic sensors presents as a significant challenge in urban environments where such sensors are often deployed. In order to address these challenges, this work proposes the use of an amalgamated deep neural network constituent of a DNN trained on earthquake signals from professional sensory equipment as well as a DNN trained on vehicular signals from low-cost sensors for the purpose of earthquake identification in signals from low-cost sensors contaminated with vehicular noise. To this end, we present low-cost seismic sensory equipment and three discrete datasets that--when the proposed methodology is applied--are shown to significantly outperform a generic stochastic differential model in terms of effectiveness and efficiency.  
low-cost sensors; deep neural networks; vehicular noise; earthquake measurement; earthquake signal contamination; seismometer +
Footnote †: journal: Applied sciences  
0
Footnote 0: 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 4 Case Studies
### System Description
#### 4.2.2 Mechanism Analysis of the Proposed Incentive  
As previously mentioned, the wind turbine in the proposed model is finally chosen for planning at bus 1 among buses 1, 16, and 23. Meanwhile, the energy storage is finally selected for planning at bus 8 among the three nodes of buses 8, 20, and 24. Among the range of optional buses, the proposed model is capable of selecting the most suitable buses for planning the wind turbine and energy storage. The influence mechanism of the proposed incentive is analyzed below.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{2}{c}{**Planning Results (MW)**} & \multirow{2}{*}{**Total Carbon**} \\ \cline{2-2} \cline{4-4}  & **Wind Turbine** & & **Energy Storage** & \\ \hline Unincentivized case & 0 & 0 & 39,150.4 \\ Proposed incentive & 700 & 499 & 34,718.3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Planning results with no incentive and with the proposed incentive.  
Figure 5: (**a**) Expectation of bus carbon intensity without incentive; (**b**) expectation of bus carbon intensity with the proposed incentive.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Load No.** & **Bus** & **Load Power(MW)** & **Load No.** & **Bus** & **Load Power(MW)** \\ \hline L1 & 3 & 180 & L6 & 9 & 175 \\ L2 & 4 & 74 & L7 & 10 & 195 \\ L3 & 5 & 71 & L8 & 14 & 194 \\ L4 & 6 & 136 & L9 & 15 & 317 \\ L5 & 7 & 125 & L10 & 19 & 181 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Power load reference value.  
For wind turbines, the bus carbon intensities of buses 1, 16, and 23 prior to planning are shown in Figure 6. It can be seen that bus 1 had the highest carbon intensity before planning, which is beneficial for the wind turbine to achieve a higher profit from the proposed incentive mechanism. Therefore, the final priority is to plan a wind turbine at bus 1.  
Regarding energy storage, the bus carbon intensities of buses 8, 20, and 24 prior to planning are shown in Figure 7. The variances in the carbon intensity curves for these three buses are 0.1416, 0.0302, and 0.0009, respectively. Bus 8 has the largest variance; this is also corroborated by the sharpest fluctuation in the carbon intensity curve of bus 8 in Figure 7. The more dramatic the bus carbon intensity fluctuation before planning, the more room there is for energy storage to profit from the proposed carbon intensity incentive mechanism due to its time-shifting characteristic. Assuming a constant bus carbon intensity, it is difficult for energy storage to profit from the carbon market under the proposed incentive mechanism, no matter how much it is charged and discharged. Thus, the energy storage in the proposed model is planned at bus 8.
# The Combined ASTER MODIS Emissivity over Land (CAMEL) Part 2: Uncertainty and Validation
## 2 Data
### Emissivity
#### 2.1.4 Laboratory Validation Data  
We chose three different sites to cover a range of land surface types including barren (Namib quartz sand and Yemen carbonate), snow/ice (Greenland), and mixed vegetation (Tucson, Arizona and the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP)). Validation data for the Namib site consists of sand samples that were collected over the Namib dunes near Sossussvlei and measured in the lab at JPL using a Nicolet spectrometer ([PERSON] et al., 2018; [PERSON] et al., 2018). The snow and ice validation emissivity was taken from 2006 Greenland ice sheet measurements from University of California, Santa Barbara (UCSB) and from [PERSON] (2018). A spline-fit was used to remove high resolution artifacts from the UCSB measurements and data from [PERSON] and [PERSON] (2018) was then used to extend the spectrum to longer wavelengths. Validation data for the ARM SGP site and Tucson, Arizona (AZ) site is based on University of Wisconsin Atmospheric Emitted Radiance Interferometer (AERI) downlooking measurements and coincident airborne data ([PERSON] et al., 2018). Measured AERI emissivities of vegetated and non-vegetated surfaces are weighted and combined using a vegetation index that depends on the day of year ([PERSON] et al., 2018). The Tucson, Arizona lab data is also a linear combination of AERI measurements. For this case the ASTER Normalized Difference Vegetation Index (NDVI) product is used to define the vegetation fraction used to weight the measurements.
## 4 Discussion  
Based on Figures 1-7, the values of MSE contained in Table 1, the CI values contained in Table 2, and the results of the research contained in papers [28; 29; 30; 31; 32], the following observations can be made:  
* All the results obtained in the article regarding the proposed model were compared with the benchmark, i.e., the LC model,
* In all the studied cases, the MSE value for the nGMs model, which took into account the divisions of periods into higher and lower dispersion, is lower than for the LC model,
* As a consequence of the above observation, the respective confidence intervals are narrower, thereby resulting in more accurate forecasts,
* In cases that neither contained higher or lower periods of dispersion nor switchings, the LC model works as a better model, one better suited to the empirical data, the LC model usually fits the empirical data better (\(F_{35},M_{22}\)),
* In cases that neither contained higher or lower periods of dispersion nor switchings, the nGs model fits the empirical data better (\(M_{37}\)),
* In cases of determining periods with significantly smaller and higher dispersion, the proposed method of modelling \(\mu_{x,t}\) reflects the shape of the empirical data better than the LC model and the nGs model,
* The weakness of the proposed nGMs model can be found in the minimum number of observations that uniquely estimate all model parameters in the period with higher dispersion (in the case of the model (52)-(53) and (61)-(62), a minimum of seven observations, this condition is not met by \(M_{37}\)).  
Based on the above observations, the nGMs model is recommended for modelling and forecasting mortality rates in time intervals with a higher dispersion of \(\mu_{x,t}\). The model proposed in this paper provides closer estimates of empirical values than the model based on the Lee-Carter model. The proposed model can be applied, among other things, to estimate life expectancy by age group, taking into account the type of death. This fundamental life insurance factor allows the insurance company to evaluate risks more accurately and thus increase competitiveness in the financial market.
## 4 Discussion
### Salinity Stress
When evaluating these results alongside established knowledge about the adaptability of local populations to challenging conditions, we hypothesize that the local variety may have struggled to fully utilize its potential in pots and controlled environments, or it may not possess inherent resistance to salt stress. A plethora of studies conducted for evaluating salt tolerance of diverse genetic material including landraces of rice, grass pea and alfalfa, respectively, demonstrated that there is a salinity-genotype interaction, indicating that there are diverse responses of the different genotypes (including many local varieties) to salinity stress [100; 101; 102]. In general, the results showed that the commercial variety had a better response to salinity stress compared to the local variety under control conditions. Priming treatments that increased shoot growth parameters, biopriming (T3) or root growth parameters, biopriming/hydropriming (T4), helped the varieties to cope better with stress. Similar conclusions were deduced in a study by [PERSON] et al. [22] in a greenhouse pot experiment, with three local populations and two commercial varieties of _Lathyrus sativus_, where they also found that the commercial variety had a better response to salinity stress, and they set a critical salinity level threshold for grass pea at 120 mM, without any seed priming treatment.