esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Numerical Results
### Simply Supported Cantilevered Beam  
#### 6.1.1 General Data  
Figure 2 illustrates the geometry and loading conditions of the problem under investigation. Taking advantage of structural symmetry, a half-model--as shown in Figure 2--is adopted for the numerical simulations. The computations are carried out using the following parameters:  
- Poisson ratio: \(\
u=0.3\);  
- Number of elements in vertical (\(Y\)) direction: \(2^{6}\);  
- Number of elements in horizontal (\(X\)) direction: \(3\times 2^{6}\);  
- Side length: \(L=2^{6}\) (by this choice a uniform mesh of square elements of unit size is generated);  
- \(P=Q=1\);  
- Volume fraction = 0.5;  
- SIMP penalization factor: \(n=3\);  
- \(p\)-norm parameter = 6;  
- \(\delta=10^{-9}\).  
As for the multi-material characterization, the number of materials is set to \(NM=3\), with corresponding [PERSON]'s moduli \(E_{1}=1\), \(E_{2}=2\), and \(E_{3}=3\). It is worth noting that this configuration is based on the system previously studied in [20], with the sole difference being the extension to a three-material setting in the present work.
# Slotted Circular-Patch MIMO Antenna for 5G Applications at Sub-6 GHz
## 4 A 2-Port MIMO System Using Two Opposite 1 \(\times\) 4 Antenna Arrays
### Measurement Results Discussion of Two 1 \(\times\) 4 Arrays MIMO
#### 4.3.2 MIMO Performance Results of Two \(1\times 4\) Arrays MIMO  
Figure 26 presents the comparison between measured and simulated calculation results of a MIMO of two \(1\times 4\) arrays antenna using Equations (3)-(12) as in Section 3.3.2. CCL can be estimated using Equations (3)-(8). CCL should be less than 0.4-bit/s/Hz to be considered an effective performance. Figure 26a illustrates a comparison of CCL values from both measured data and simulations, showing that they remain under 0.3-bit/s/Hz, particularly at the resonant frequencies.  
ECC can be calculated as a function of S-parameters, as in Equation (9). Its value tends to zero, leading to an effective performance. Figure 26b illustrates that the ECC is less than 0.003, particularly at the resonant frequencies.  
DG is calculated from ECC, as in Equation (10), and a value tending to 10 dB leads to an effective performance. Figure 26c indicates that DGs for both measured and simulated values yield a diversity gain of 10 dB, which contributes to an effective antenna design.  
TARC is the reflected total power to the incident power. TARC can be calculated from Equation (11). A value less than \(-10\) dB leads to an effective performance. Figure 26d indicates that TARC remains below \(-10\) dB, especially at the resonant frequencies.  
MEG considers the received average power compared to the received average power by an isotropic antenna. MEG can be calculated from Equation (12). MEG1 and MEG2 values close to \(-6\) dB result because of bidirectional radiation. The MEG comparison between measured and simulation results is shown in Figure 26e.  
It can be observed that TARC in Figure 26d exhibits higher variability between simulation and measurement compared to other parameters. This is because TARC is highly sensitive to phase changes. In contrast, ECC, MEG, DG, and CCL are mathematically derived from stable quantities of S-parameters. These parameters utilize averaging techniques or statistical models that do not require precise phase control or active excitation. Despite this variability, both the simulated and measured TARC values remain below \(-10\) dB, particularly at the desired frequencies, indicating an effective design.  
Figure 25: The measured and simulated gain of the MIMO of two \(1\times 4\) arrays.
## 4 Results
### Assimilation of SSM In Situ Measurements
By increasing a threshold considered for a good timing detection from 4 to 5 days, TruPosRat scores improved to 0.70, 0.74 and 0.62 for F1, F2 and F3, respectively, instead of 0.59, 0.68 and 0.48 for 4 days. The results of 15-day cumulated amounts over the drip irrigated fields are presented in Figure 4d-f. Similar performance is obtained for both 3-day and 6-day revisit times with reasonable RMSE and bias. Lower statistical metrics are obtained for the 12-day window with an acceptable RMSE. The most notable outlier is highlighted in Figure 4d-f. It corresponds to the underestimation observed at the beginning of the 2017-2018 seasons. The underestimation in December 2018 is noticeable for all the windows while the underestimation in January is only significant for a 12-day window. In a second step, data of D2 during 2017-2018 season are discarded given the high SSM values at the end of the season (especially the 2017-2018 season) due to the very specific growing conditions observed on the field. The results are significantly improved for the three windows as shown in Table 1: the best performance is obtained for a 3-day revisit time but RMSE and bias are still reasonable at 6 days and, to a lesser extent, 12 days. Figure 4: (**a–c**): statistical metrics (Pbias, TruPosRat and IrrigEvntRat) of the flood irrigated fields F1, F2 and F3 for different window lengths: 3-, 6- and 12-day. (**d–f**): statistical metrics (R, RMSE and bias) of the retrieved irrigation versus observed irrigation accumulated by 15 days, over the drip-irrigated fields D1 and D2 both seasons for the three window lengths. Finally, the estimated seasonal amounts are plotted against observation for the three window lengths at Figure 5. While it has been observed that the intra-seasonal dynamic of the retrieved irrigation event may present some scattering with the observations, the seasonal amounts are remarkably well estimated for the three window lengths. At 3 days, the noticeable outlier corresponds to an overestimation over the rainfed field R1 during season 2017-2018 when a false irrigation event is detected. For a 6-day window, the estimation is still accurate with a slight loss of statistical metrics with regards to a 3-day revisit time. In addition to the outlier already observed at 3 days, another one is detected that corresponds to the 2017-2018 season over D1 where several events were missed at the beginning of the season. The estimation accuracy dropped for a 12-day revisit time underlining the difficulty of correctly detecting not only the timing of events but also the amounts of irrigation when the available observation frequency is low.
## 2 Case Study and Data
### DUACS OI Products  
The DUACS system is an operational production of sea level products for the Marine (CMEMS) and Climate (C3S) services of the E.U. Copernicus program, on behalf of the CNES french space agency. Regularly (0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\)) daily gridded products are delivered based on optimal interpolation (OI) of the previously introduced pseudo along-track nadir and wide-swath SWOT SSH data. The DUACS methodology is fully described in [7].
# Transactive Energy to Thwart Load Altering Attacks on Power Distribution Systems
## 3 Automatic Generation Control
### Automatic Generation Control in the Frequency-Domain  
In a power grid, several generators may provide reserve generation to the grid. However, for simplifying the proposed analysis we consider a case where only one generator provides reserve generation to the grid. This generator reacts to the changes in the frequency of the sinusoidal voltages \(\Delta f\) by making a change \(\Delta P_{t}\) in the power injection of the generator into the power grid. The amount of change \(\Delta P_{t}\) in the power injection of the reserve generator depends on the amount of change in the frequency \(\Delta f\) and several design parameters; e.g., the generator's governor speed regulator \(R\), secondary loop gain \(K_{i}\), angular momentum \(M\), damping constant \(D\), time constant \(T_{g}\), and turbine time constant \(T_{t}\). This dependency can be seen from Figure 1 which shows a block diagram of the automatic generation control in the complex frequency domain [36; 37].  
In the block diagram of Figure 1, the relationship between frequency of the sinusoidal voltages and the response of the generator is represented in complex frequency domain \(s\). Although the frequency domain representation of a feedback control system facilitates the design and analysis of the system, the focus of this paper is on the time-domain characteristics of the automatic generation control. Therefore, in the next section the frequency-domain representation of the automatic generation control is transformed to a time-domain representation.
# Optimization Dubins Path of Multiple UAVs for Post-Earthquake Rapid-Assessment
## 2 Problem Modeling  
In this section a formulation of the MURARP is presented. A directed graph \(G=(\mathbf{T}\cup\mathbf{D}\cup\mathbf{D^{\prime}},\mathbf{A})\) is given, where \(\mathbf{T}\) is the potential targets set, \(\mathbf{D}\) is the set of UAV launching depots, and \(\mathbf{D^{\prime}}\) is the set of UAV landing depots. The arc set \(\mathbf{A}\) consists of all possible connections between any two vertices in and all directed arcs connecting all vertices in \(\mathbf{D}\cup\mathbf{D^{\prime}}\) and all vertices in \(\mathbf{T}\). A group of heterogeneous UAVs, represented by \(\mathbf{U}\), launched from their associated launching depots and return to their associated landing depots after visiting a subset of \(\mathbf{T}\). Each UAV is constrained by its endurance. When a UAV accesses a potential target, the profit associated with the weight of the target is successfully obtained with a certain probability. The optimization objective of the MURARP is to maximize the expected profit. Table 1 defines the major notation of relevant sets, indices, parameters, and variables of MURARP.
## 4 Results
### Information Gain Ratio Feature Selection and Equal Width Transformation  
Random Forest's accuracy was improved by 0.7% with better sensitivity and F-measure when the equal width data transformation technique and information gain ratio technique were applied. The CN2 Rule Inducer improved by 0.1% with less sensitivity and better specificity. Interestingly, Classification Tree, Logistic Regression, Naive Bayes, Neural Network and SVM showed no sign of improvement and remained unchanged. kNN's performance was decreased by 3% from the previous example, as shown in Table 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **Sensitivity** & **Specificity** & **F-Measure** \\ \hline Random Forest & 0.876 & 0.981 & 0.883 & 0.930 \\ CN2 Rule Inducer & 0.873 & 0.978 & 0.883 & 0.928 \\ Tree & 0.872 & 0.978 & 0.881 & 0.927 \\ Log. Reg. & 0.870 & 0.992 & 0.871 & 0.928 \\ kNN & 0.864 & 0.961 & 0.886 & 0.922 \\ Naive Bayes & 0.835 & 0.935 & 0.876 & 0.905 \\ Neural Network & 0.835 & 1.00 & 0.835 & 0.910 \\ SVM & 0.825 & 0.949 & 0.857 & 0.901 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Data transformation (equal width), feature selection (information gain).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **Sensitivity** & **Specificity** & **F-Measure** \\ \hline Random Forest & 0.855 & 0.973 & 0.865 & 0.916 \\ Log. Reg. & 0.846 & 0.990 & 0.846 & 0.912 \\ kNN & 0.842 & 0.971 & 0.854 & 0.909 \\ Tree & 0.842 & 0.955 & 0.864 & 0.907 \\ CN2 Rule Inducer & 0.842 & 0.957 & 0.863 & 0.908 \\ SVM & 0.820 & 0.958 & 0.842 & 0.896 \\ Naive Bayes & 0.811 & 0.935 & 0.848 & 0.889 \\ Neural Network & 0.811 & 1.00 & 0.811 & 0.896 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Data transformation (equal width), feature selection (none).
## 2 Structure of the Fenna-Matthews-Olson Light-Harvesting Complex
### Structure of the FMO Complex  
The FMO is a water-soluble protein with a molecular weight of 150,000 Da [7; 8] with a maximum diameter around of 8.3 nm. This rare property has facilitated its crystallization for high resolution spectroscopic studies [8; 12]. Trimer structure of FMO includes monomer sequences working together still with certain independence to gather the light excitation. Each monomer is a \(\sim\)360 amino acid sequence of the complex which folds into a \"bag\" containing eight bacteriochlorophyll \(a\) molecules with an average distance between nearest-neighbor pigments of 12 A [17] (Figure 4). The FMO structure, shown in Figure 5, consists of a homotrimic protein complex, which subunits are related by a 3-fold symmetry axis. The side chains of the amino acids conforming the protein structure interact with one another through Van der Waals forces, dipole-dipole interactions, ions and hydrogen bonding creating structures called _a_-helices, _\(\beta\)_-sheets and random coils resulting in the monomer shown in Figure 5B. The \(\beta\)-sheet ribbons compose the large surface wall of the complex, and its amphipathic nature (possessing both hydrophilic and hydrophobic properties) provides shielding for the non-polar chlorophyll core from the surroundings, while _a_-helices and random coils make up the contact region between subunits, Figure 5A. These nano-structures have an important effect in BChl interactions [14].  
It was initially reported that each of its subunits contains seven bacteriochlorophylls [7], but an eighth BChl molecule was later discovered between subunits, bringing the total number of BChls in the protein to 24 [1; 11; 12; 34]. The established nomenclature for numbering the pigments is still the originally proposed by [PERSON] and [PERSON] [7], to which the eighth BChl was added. Following this nomenclature, BChl 3 and BChl 4 are the nearest to the cytoplasmic membrane while BChl 1, BChl 6 and BChl 8 are closer to the chlorosome baseplate (as seen in Figure 5) [1; 4; 34; 35].  
The reader must remember that the FMO complex is present in all species of GSB, being _C.__tepidum_ and _P.__aestuarii_ the two most widely studied cases. Genetic diversity between species and strains (genetic variants of the species) becomes in slight structural differences of their FMO complex. To fix the analysis, the model calculations performed in this article are based on some experimental reported values for _C.__tepidum_[13] despite we tackle a compared view for _P.__aestuarii_ in the closing section. Section 6 will discuss a deeper analysis on the effects of these structural differences and provide a comparison between FMO complexes among certain species and strains.
## 2 Materials and Methods
### Data Set
\begin{table}
\begin{tabular}{c c c c c} \hline
**Abbreviated** & **Original Features** & **Predictor** & **Data** & **Resolution** \\
**Feature Name** & & **Type** & **Source** & **(km)** \\ \hline DOY & Day of year & Time & & \\ mean temp & Average air temperature at & Meteo & ERA5 & \(\sim\)28 \\  & 2 m height (daily average) & & & \\ min temp & Minimal air temperature at & Meteo & ERA5 & \(\sim\)28 \\  & 2 m height (daily minimum) & Meteo & ERA5 & \(\sim\)28 \\  & \multicolumn{1}{c}{} & & \\ max temp & Maximal air temperature at & Meteo & ERA5 & \(\sim\)28 \\  & 2 m height (daily maximum) & & & \\ dewp temp & Development temperature at 2 m & Meteo & ERA5 & \(\sim\)28 \\  & \multicolumn{1}{c}{} & & \\  & height (daily average) & & & \\ total precip & Total precipitation (daily sums) & Meteo & ERA5 & \(\sim\)28 \\ surface pressure & Surface pressure (daily average) & Meteo & ERA5 & \(\sim\)28 \\ sea level & Mean sea-level pressure (daily average) & Meteo & ERA5 & \(\sim\)28 \\ wind u & Horizontal speed of air moving towards the east, & & & \\  & at a height of 10 m above the & Meteo & ERA5 & \(\sim\)28 \\  & \multicolumn{1}{c}{} & & \\ wind w & Horizontal speed of air moving towards the north. & Meteo & ERA5 & \(\sim\)28 \\  & \multicolumn{1}{c}{} & & \\ EVI & Enhanced vegetation index & & 006 MOD9 GA & \\  & \multicolumn{1}{c}{} & & \\  & \multicolumn{1}{c}{} & & \\  & \multicolumn{1}{c}{} & & \\ NDVI & Normalized difference vegetation index generated & & & \\  & from the near-IR and red bands of each scene. & & & \\ RED & Red surface reflectance (sur refl 801) & MODIS & 006 MOD09 GQ & 0.25 km \\ NIR & NIR surface reflectance (sur refl 802) & MODIS & 006 MOD09 GQ & 0.25 km \\ sur refl b03 & Blue surface reflectance, & MODIS & 006 MOD13Q1 & 0.25 km \\  & \multicolumn{1}{c}{} & & \\ sur refl b07 & MIR surface reflectance, & MODIS & 006 MOD13Q1 & 0.25 km \\  & \multicolumn{1}{c}{} & & \\ view zenith & View zenith angle, 16-day frequency & MODIS & 006 MOD13Q1 & 0.25 km \\ solar zenith & Solar zenith angle, 16-day frequency & MODIS & 006 MOD13Q1 & 0.25 km \\ rel azim & Relative azimuth angle, 16-day frequency & MODIS & 006 MOD13Q1 & 0.25 km \\ lat & Latitude & Spatial & & \\ lon & Longitude & Spatial & & \\ slope & Landform classes created by combining the ALOS CHILI & & & \\  & and ALOS mTPI data sets. & Spatial & ALOS Landform & \\ \hline \end{tabular}
\end{table} 
Table 1: Description of features used as predictors for the phenological phases of olive trees.
## 2 Materials and Methods  
The La-Co co-substituted SrM hexaferrites were prepared by solid-state reaction in air. The precursors used were SrCO\({}_{3}\), Fe\({}_{2}\)O\({}_{3}\), La\({}_{2}\)O\({}_{3}\), and Co\({}_{3}\)O\({}_{4}\) powders (all 99.9% purity) with a nominal composition of Sr\({}_{1-x}\)La\({}_{x}\)Fe\({}_{12-x}\)Co\({}_{x}\)O\({}_{19}\), where \(0.0\leq x\leq 0.4\). The SrCO\({}_{3}\) and Fe\({}_{2}\)O\({}_{3}\) powders were first mixed and ball-milled with zirconia balls in ethanol for 24 h. The resulting powder mixture was then dried in an oven at 70 \({}^{\circ}\)C. The powder mixture was dried and calcined at 1280 \({}^{\circ}\)C for 2 h in air with cation ratios of Sr:Fe = (1\(-\)_x_):(12\(-\)_x_). Subsequently, La2O3 and Co3O4 powders were added to the calcined powder, ball-milled for 24 h in an ethanol solution, and dried again. The dried powders were then peltetized using a uniaxial press (Jeil Hydraulics, Seoul, Republic of Korea) and sintered at 1300 \({}^{\circ}\)C for 2 h in air.  
The densities of the sintered samples were calculated using Archimedes' principle (KERN & SOHN GmbH, Balingen, Germany). Powder X-ray diffraction (XRD) was performed using a Bruker D8 Advance instrument and a Cu K\(\alpha\) radiation source (\(\lambda\) = 0.15406 nm) for phase analysis and lattice parameter calculation. The lattice parameters a and c were calculated using Rietveld refinement. Microstructures of the samples were analyzed using field emission scanning electron microscopy (FE-SEM) with a ZEISS MERLIN Compact instrument (Zeiss, Oberkochen, Germany). To observe grain boundaries, polished samples were thermally etched in a muffle furnace at 1130 \({}^{\circ}\)C for 0.5 h in air. Initial magnetization and magnetic hysteresis (M-H) curves were measured at room temperature using a VSM (VSM-7410, Lakeshore, Westerville, OH, USA) with an applied field sweep within \(\pm\) 26 kOe. Cube-shaped specimens with dimensions of approximately 2.0 \(\times\) 2.0 \(\times\) 2.0 mm were used for these measurements. The \(M_{S}\) and \(H_{a}\) values were determined from the initial magnetization curves, while the \(H_{ci}\) values were obtained from the \(M\)-\(H\) loops.
# Satellite On-Board Change Detection via Auto-Associative Neural Networks
## 3 Results  
Although the procedure previously described was separately applied to the three bands R, G and B, in this section only the results of the R band will be shown. Indeed, this band is recognized to have, in general, greater sensitivity to variations, and the results achieved using the other two bands were less accurate [24].  
In Figure 11, two images extracted from the test set are shown. Figure 11a1,a2 represent an area mainly covered by vegetation. The main changed elements regard the land cover with some variations in buildings and roads. The ground truth is represented at two levels: in Figure 11b, it relies on the OSCD dataset, while, in Figure 11c, it stems from the CVA technique explained in Section 2.2. In both cases, the changed areas are shown in white. Similarly, Figure 11d1,d2,e,f represent another pair of images with the associated ground truth, but, in this latter case, the images show mainly built-up areas.
# BigGRU-Based Adaptive Receiver for Indoor DCO-OFDM Visible Light Communication
## 3 Principles of the BiGRU-Based Receiver  
### BiGRU  
Unlike classification detection based on image processing, the present and past sequential data are interrelated [16]. An RNN retains data sequences with network-hidden states. RNNs can efficiently process sequential data and learn information from previous data during processing [19]. Long short-term memory (LSTM) and GRU have improved RNN models by alleviating the problem due to severe gradient disappearance and gradient explosion; GRU is a simple variant of LSTM [20].  
Figure 2 shows the detailed structures of GRU and LSTM units. Unlike in LSTM, GRU combines the forget and input gates into a single update gate. A GRU unit comprises a reset gate \(r_{t}\) and an update gate \(Z_{t}\)[21]. Under the control of these two gates, the output \(h_{t}\), determined by the current input \(x_{t}\) and the previous state \(h_{t-1}\), is calculated as (8)-(11):  
\[r_{t}=\sigma(W_{r}x_{t}+U_{r}h_{t-1}+b_{r}), \tag{8}\]  
\[z_{t}=\sigma(W_{z}x_{t}+U_{z}h_{t-1}+b_{z}), \tag{9}\]  
\[h_{t}=\tanh[W_{h}x_{t}+U_{h}(r_{t}\odot\widetilde{h}_{t-1})]+b_{h}, \tag{10}\]  
\[h_{t}=(1-z_{t})\odot h_{t-1}+z_{t}\odot\widetilde{h}_{t}, \tag{11}\]  
where \(W\) denotes the weight matrix; \(b\) denotes the bias term; \(\sigma\) is the sigmoid transform; and \(\odot\) is the Hadamard product (obtained by the multiplication of corresponding elements of vectors).  
For the received OFDM signal, the current data are affected by the forward data and the backward sequence. This study considered a model with a bidirectional structure to learn information better from the preceding and following data. Figure 3 shows the BiGRU model structure and the proposed architecture of the BiGRU network for implementing the receiver. The BiGRU model was determined based on the state of the two GRUs in the opposite direction [22].  
Figure 2: (**a**) Detailed structure of an LSTM and (**b**) GRU units.
# A Simple Alternative Method for Preservation of 2-Methylisoborneol in Water Samples
###### Abstract  
2-Methylisoborneol (2-MIB) is one of the most commonly observed taste and odor (T&O) compounds present in drinking water sources. As it is biodegradable, a preservation agent, typically mercury chloride, is needed if the water is not analyzed right after sampling. Since mercury is a toxic metal, an alternative chemical that is cheaper and less toxic is desirable. In this study, two chemicals commonly used in water treatment processes, chlorine (as sodium hypochlorite) and KMnO\({}_{4}\) (potassium permanganate), are studied to determine their feasibility as preservation agents for 2-MIB in water. Preservation experiments were first conducted in deionized water spiked with 2-MIB and with chlorine or permanganate at 4 and 25 \({}^{\circ}\)C. The results indicate that 2-MIB concentrations in the water samples spiked with both chemicals remained almost constant within 14 days for all the tested conditions, suggesting that oxidation and volatilization did not cause the loss of 2-MIB in the system. The experiments were further conducted for three different reservoir water samples with 30-60 ng/L of indulgent 2-MIB. The experimental results demonstrated that preservation with permanganate may have underestimated the 2-MIB concentration in the samples as a result of the formation of manganese dioxide particles in natural water and adsorption of 2-MIB onto the particles. Chlorine was demonstrated to be a good preservation agent for all three tested natural waters since oxidation of 2-MIB was negligible and biodegradation was inhibited. When the residual chlorine concentrations were controlled to be higher than 0.5 mg/L on the final day (day 14) of the experiments, the concentration reduction of 2-MIB became lower than 13% at both of the tested temperatures. The results demonstrated that sodium hypochlorite can be used as an alternative preservation agent for 2-MIB in water before analysis.  
2-methylisoborneol; sodium hypochlorite; preservation; potassium permanganate 2018 15 May 2018 18 May 2018