esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of an L-Band SAR Microsatellite Antenna for Earth Observation
## 3 Antenna Gain Estimation
### Perfect Parabolic Reflector  
To identify losses introduced by the different elements of the antenna system, the reflector is first optimized for its center frequency, considering a perfect parabolic surface without ribs or feed assembly. A theoretical feed pattern of form \(cos^{n}(\theta)\) was used for the design optimization, with \(-10\) dB edge taper. The feed, located at the paraboloid focal point, spreads spherical waves as \(\frac{1}{r}\), where \(r\) is the distance from the focus to the reflector. These spherical waves are transformed by the paraboloidal reflector into a plane wave propagated to the aperture plane at a constant amplitude.  
A theoretical pattern for a circularly polarized feed [16] can be described by  
\[E(r)=A_{0}e^{j\tau\phi}[\hat{\theta}C_{E}(\theta)+\hat{\phi}j\tau C_{H}(\theta )]\frac{e^{-jk\tau}}{r} \tag{2}\]  
where \(A_{0}\) is a complex constant, \(\tau=+1\) for LHCP or \(\tau=-1\) for RHCP, and  
\[C_{E}(\theta)=C_{H}(\theta)=cos^{n}(\theta) \tag{3}\]  
Figure 6: Conceptual mechanism of wrap-rib deployment and deployment test of wrap-rib reflector.  
for \(0<\theta<\frac{\pi}{2}\) in the E- and H-plane patterns, respectively. The parabolic half-angle \(\theta_{0}\) is defined as \(2\tan(\frac{1}{4\frac{T}{D}})=69.39^{\circ}\). The variable \(n\) relates to the feed illumination taper \(T\) in [dB] and \(\theta_{0}\), given by:  
\[n=\frac{-0.05.T}{log[cos(\frac{\theta_{0}}{2})]} \tag{4}\]  
Ignoring any central blockage, spillover efficiency (\(\eta_{spill}\)) and amplitude taper efficiency (\(\eta_{aper}\)) are calculated as:  
\[\eta_{spill}=1-u^{2(n+1)} \tag{5}\]  
\[\eta_{aper}=\frac{4(n+1)(1-u^{n})^{2}}{n^{2}[1-u^{2(n+1)}]}\cot^{2}\frac{\theta _{0}}{2} \tag{6}\]  
where \(u=cos(\frac{\theta_{0}}{2})\).  
For an ideal \(-10\) dB edge taper feed, amplitude taper and feed illumination efficiency values are \(\eta_{taper}=\eta_{spill}=-0.457\) dBic. As for feed blockage losses, they occur around the boresight of the reflector where the field is shadowed by the feed platform. This blocking causes an increase of the SLL due to the discontinuous aperture distribution and the scattering of the incoming wavefront by the blocking structure. Minimizing central blockage requires smaller feed systems. Effect on cross-polarization, axial ratio and sidelobes are studied by modeling a blocking disk with varying radius placed at the parabolic focal point. To keep sidelobe levels below \(-25\) dB, maximum dimension of the feed platform has to be no larger than \(28.8\) cm (\(BR=8\%\)), with respective \(\eta_{block}=0.11\) dBic. The backfire helical antenna has a cross-sectional dimension of \(8\) cm (\(BR=2.2\%\)), which gives almost negligible blockage losses of \(0.002\) dBic. On the other hand, the taper and spillover losses sum up to \(2.4\) dBic for the simulated pattern of the helical antenna. Subtracting \(\eta_{taper}\), \(\eta_{spill}\) and \(\eta_{block}\) from the maximum antenna gain of \(33.6\) dBic results in peak losses of \(1.7\) dBic for an ideal parabolic reflector.
## 5 Conclusions  
The lithium power battery space equation of state and the observed output equation were constructed in this study by utilizing the Thevenin cell model. Furthermore, the BBOBP-EKF method was developed to evaluate the SoC for batteries. The error of the EKF algorithm was corrected using a BP neural network, and the BBO method was adopted to improve the initial weights and thresholds of the BP neural network. In the end, the BBOBP-EKF method was compared with the BP-EKF algorithm and the EKF algorithm to obtain the SoC estimation error: the proposed method's maximum error was 1% lower than that of the BP-EKF algorithm and 2.4% lower than that of the EKF algorithm, and the minimum error was also reduced, thus demonstrating that the accuracy of battery SoC estimation founded on the BBOBP-EKF method was enhanced compared with that of other algorithms.  
Conceptualization, X.L. and X.Z.; methodology, X.L. and X.Z.; software, X.Z.; validation, X.Z.; formal analysis, X.Z.; investigation, X.L. and X.Z.; resources, X.L.; data curation, X.Z.; writing--original draft preparation, X.Z.; writing--review and editing, X.L. and X.Z.; visualization, X.L.; supervision, X.L. and X.Z.; project administration, X.L.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Minimum Error /\%** & **Maximum Error/\%** & **Mean Absolute Value of Error/\%** & **Error Variance/\%** \\ \hline EKF & 1.6421 \(\times\) 10\({}^{-3}\) & 5.7156 & 1.8099 & 1.1731 \\ BP-EKF & 9.9855 \(\times\) 10\({}^{-4}\) & 4.2849 & 0.8207 & 1.0498 \\ BBOBP-EKF & **1.7112 \(\times\) 10\({}^{-4}\) 2** & **3.2658** & **0.7483** & **0.9443** \\ \hline \hline \end{tabular}  
\({}^{2}\) The values in digital boldface represent better performance compared to other algorithms in the table.  
\end{table}
Table 2: Comparison of error indicators of different algorithms.  
Figure 10: Comparison of absolute values of errors for different algorithms.  
**Funding:** This work was supported by the key Science and Technology Research Program of Chongqing Municipal Education Commission [Grant No.KJQN202200543] and the Chongqing Municipal Graduate Research Innovation Project [CYS23414].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are not disclosed due to confidentiality requirements.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interest or personal relationship that could have appeared to influence the work reported in this paper.
# Towards Accurate Children's Arabic Handwriting Recognition via Deep Learning
## 6 Conclusions and Further Work  
In this paper, we proposed two deep-learning models. We designed a CNN and fine-tuned a VGG-16 pre-trained model for Arabic handwriting character recognition. The models were designed to classify the letters into 29 and 108 classes based on letter shape. The research was carried out using the Hijja dataset for children's handwriting. We evaluated the performances of the CNN and VGG-16 and compared them with each other. We also trained and tested our proposed models using the Arabic Handwritten Character Dataset (AHCD). We then compared the results with the results of approaches from the literature. We selected the best-performing model among them, namely, the CNN model, which was able to outperform state-of-the-art classification algorithms using the Hijja dataset and achieved 99% accuracy. Then, we developed Mutgin, a prototype for children to practice writing and spelling skills, and integrated it with our CNN model. Furthermore, we evaluated the Mutgin application using user acceptance testing to measure three criteria: effectiveness, efficiency, and satisfaction. Our results indicated that the application performed well.  
This research could be extended in many ways. The developed deep learning models could be trained on a combination of datasets to improve performance. They could also be trained to recognize connected letters and words. Such models can be used in writing and dictation applications. In addition, models can be built for the recognition of Arabic calligraphy. In the future, more features can be added to the prototype, such as the ability to share results with parents and teachers, personalization with photos, and support for different platforms. The application of gesture-based handwriting could be investigated in the context of writing applications for children in Arabic and other languages.  
Conceptualization, I.A.-T. and N.A.; methodology, I.A.-T., N.A., A.A.-A. and A.B.D.; software, A.B.D. and N.A.; validation, A.B.D. and N.A.; discussion and results, A.A.-A., I.A.-T. and N.A.; writing--original draft preparation, A.A.-A.; writing--review and editing, A.A.-A., I.A.-T. and N.A.; supervision, I.A.-T. All authors have read and agreed to this version of the manuscript.  
This research project was supported by a grant from the \"Research Center of the Female Scientific and Medical Colleges\", Deanship of Scientific Research, King Saud University.  
Not applicable.  
The authors declare no conflict of interest.
## 3 A Community Anomaly Detection Approach Based on Structural/Attribute Information
### Proposed Community Anomaly Detection Approach
#### 3.2.4 Complexity Analysis  
First, we partition the graph into communities using the Louvain algorithm, which costs \(O(vlogv)\), where \(v\) is the number of nodes in the graph. After that, we calculate the eigenvector centrality that costs \(O(v+e)\), where \(e\) is the number of edges in the graph. Then, we compute the StrAnomaly score with the linear cost with the node number. Next, we define the context using the Laplacian score, which costs \(O(mv^{2})\), where \(m\) is the number of attributes. After that, we compute the AtrrAnomaly score that costs \(O(v^{2})\). Overall, the CAnom algorithm (Algorithm 1) has a computational complexity of \(O(max(mv^{2},v+e))\). The worst case occurs when all the graph nodes are assigned to one community, as a quadric analysis is performed for each community. Therefore, in a real network where the number of communities is high, the algorithm's performance increases.
# Microwave Heat Treatment Induced Changes in Forage Hay Digestibility and Cell Microstructure
## 3 Results  
### Chemical Composition of Microwave Treated Hays  
The control forage nutritive value determined in this study was within the standard range for hays commercially evaluated and reported at the Feed Test division of the Australian Wool Testing Authority (AWTA Limited, Melbourne, VIC, Australia).  
The DM content of hays without MW treatment in this study was between 890 and 930 g/kg (fresh weight basis). In general, the DM content of hays increased with increasing MW treatment time (Table 1). However, no difference was observed in DM content in MW treatment and forage type interaction (\(p\) = 0.197). Similarly, different hays showed no difference in OM, NDF, and ADF change due to MW treatment and forage type interaction (Table 1). However, the effect of MW treatment increased oat hay NDF content at 60 s of treatment (\(p\) = 0.026), and ADF content of canola, pasture and wheat hay increased (\(p\) < 0.001) at 80 s (Table 1).  
Hays used in this study had CP ranging from 70 to181 g/kg without MW treatment (Table 1). There was a significant interaction (\(p\) = 0.007) observed in CP between the MW treatment and forage type (Table 1). The MW treatment in canola hay for 20 s and oat hay for 40 s had increased CP compared with the control, but the other hays had no difference in CP compared to their controls (Table 1).
###### Abstract  
The passivation behavior of steel reinforcements in concrete is significantly influenced by the environment, concrete pore solution, and the passive film formed on the steel surface. The present study used electrochemical methods to successfully characterize the passivation process of steel reinforcements in concrete. The passivation behavior of commonly used HRB400 steel reinforcement material in concrete was studied using various electrochemical parameters quantitatively. As the soaking test time increased, the OCP gradually increased and stabilized after 5 days, indicating that the steel electrode transitioned from an active state to a passive state in the simulated liquid environment of concrete. The steel reinforcement developed a protective passive film that reduced its tendency to corrode. According to EIS, after soaking for one day, the steel electrode showed significant early passivation, indicated by an increase in its ac diameter. The WE arc gradually increased in the first 5 days of immersion, suggesting dynamic passive film formation and development. Beyond 5 days, the passive film stabilized with minimal further changes in its impedance spectrum, indicating carbon steel electrode passivation. The working electrode's impedance increased significantly on the fifth day, and gradually increased slightly after 10 days, indicating comprehensive coverage by the oxide film. Attributed to the growth and development of the oxide film, the electrode resistance reached a relatively stable state after the fifth day. The shift in corrosion potential offers an indication of the level of passivation of the steel reinforcements. The decrease in the anode Tafel slope and increase in the corrosion potential indicate the formation and stabilization of an oxide film on the steel surface, which is beneficial for its long-term durability in concrete structures. By analyzing the OCP, EIS, and dynamic potential polarization curve method data, it is possible to gain insights into the passivation behavior of steel reinforcements in concrete structures. This study aims to provide a basis for optimizing the corrosion protection of steel reinforcements in concrete structures. The significance of this study lies in a deep understanding of the passivation behavior of steel bars in concrete, providing a theoretical basis for improving the durability and lifespan of steel bars in concrete structures.  
steel reinforcement; electrochemical method; passivation process; quantitative characterization 22 November 2023
# Unraveling the Scale Dependency of SIF-Based Phenology: Amplified Trends and Climate Responses
## 3 Results  
### Analysis and Comparison of Phenology Extracted from SIF Data at Different Scales  
Using data from 16 flux stations, we present the statistical results of phenological metrics extraction from three different resolutions of SIF data in Table S2 of the Supporting Information. The phenological results extracted from the GRSIF001 data show that the mean error (ME) at the Start of Growing Season (SOS) is \(-0.16\) days, with a mean absolute error (MAE) of 4.48 days and a root mean square error (RMSE) of 5.39 days. The ME at the End of Growing Season (EOS) is \(-15.49\) days, with an MAE of 15.49 days and an RMSE of 16.99 days. These results are based on 45 phenological data points from 9 research sites. The ME of SOS extracted from GOSIF data is 6.76 days, with an MAE of 9.20 days and an RMSE of 12.60 days. The ME of EOS is \(-21.07\) days, with an MAE of 21.07 days and an RMSE of 22.10 days. These results are based on 42 phenological data points from 7 research sites. The ME of SOS extracted from GOME-2 SIF data is \(-58.48\) days, with an MAE of 58.94 days and an RMSE of 63.00 days. The ME of EOS is \(-42.73\) days, with an MAE of 42.73 days and an RMSE of 45.99 days. These results are based on 48 phenological data points from 8 research sites. These results demonstrate the level of consistency with the phenological data extracted from GPP sites, indicating the capacity of SIF data to track phenological metrics.  
Three different resolutions of SIF data were comprehensively statistically analyzed between 2008 and 2017, and Figure 4 demonstrates the distribution of day-year (DOY) for the phenological metrics SOS and EOS, computed by the SIF products over a year. Overall, the consistency between the phenological metrics calculated based on GRSIF001 and GOSIF is high, with the earliest SOS occurring around day 50 and the latest at approximately day 150, and the earliest EOS occurring around day 210 and the latest at approximately day 280. In contrast, the alignment of phenological metrics derived from GOME-2 SIF with those obtained from the other two sources was weaker, with SOS ranging from as early as day 315 of the prior year to as late as day 100 of the subsequent year, and EOS as early as about day 150 and as late as about day 300. This discrepancy is primarily attributed to the lower spatial resolution of GOME-2 SIF, which limits its ability to capture localized phenological variations and results in reduced consistency compared to the higher-resolution SIF products.
## 4 Different Manners of Production of Green Hydrogen and Derivatives  
### Process of Production of Green Hydrogen  
The Magallanes region, in the southern end of Chile, has positioned itself as a strategic place to produce green hydrogen, a clean fuel for the global energy transition. This process takes advantage of the abundant wind resources in the area and follows several stages [30; 31; 32; 33; 34; 35] (Figure 6):  
* Renewable wind energy generation: Magallanes has exceptionally strong and constant winds, ideal for wind farms. Large wind turbines transform this kinetic energy into renewable, emission-free energy.  
Water electrolysis: the electricity generated feeds electrolyte, equipment that decomposes water molecules (H\({}_{2}\)O), in hydrogen (H\({}_{2}\)) and oxygen (O\({}_{2}\)) by electrolysis. The water used can come from local sources, even desalinated if necessary.
* Storage and transport: The hydrogen produced is compressed or liquefied for storage. Given the remote location of Magallanes, certain options include the following: (i) conversion into ammonia (NH\({}_{3}\)), which is evaluated to facilitate maritime transport; and (ii) export via specialized ships to international markets.
* Uses and applications: Magellan green hydrogen could be (i) used as fuel for heavy industries (mining, maritime transport); (ii) used as raw material for green fertilizers and chemicals; and (iii) exported to Europe and Asia, where demand is growing.
## 2 Related Work  
### Transformer Based E2E Architecture  
The Transformer contains an Encoder network and a Decoder network [21; 22; 23]. Both the Encoder and the Decoder consist of several layers stacked, as shown in Figure 1a,b, respectively. The Encoder transforms the input features \(X=[x_{1},\dots,x_{T}]\) into a sequence of encoded features \(H_{e}=[h_{e,1},\dots,h_{e,T}]\), as follows:  
\[\begin{split}& H_{0}=CNN(X)+PE\\ & H_{i}^{{}^{\prime}}=H_{i}+MHA_{i}(N(H_{i}),N(H_{i}),N(H_{i}))\\ & H_{i+1}=H_{i}^{{}^{\prime}}+FF_{i}(N(H_{i}^{{}^{\prime}})), \end{split} \tag{1}\]  
where \(i=0,\dots,e-1\), \(e\) is the number of encoder layers, \(CNN(\cdot)\) is a convolution network, \(PE\) is positional encoding, \(MHA(\cdot)\) is a multi-head self-attention mechanism, and \(FF_{i}\) is a positionwise fully connected feed-forward network. In this work, layer normalization \(N(\cdot)\) is employed before each sub-layer. The Decoder receives the encoded features \(H_{e}\) and the label sequence \(Y[0:l-1]\) to emit the probabilities of the Decoder output units set \(\mathcal{Y}[l]=[y_{1}^{1},\dots,y_{l}^{\textit{units}}]\) of the \(l\)-th step, and then determine the subsequent \([\hat{y}_{1},\dots,\hat{y}_{l}]\), as Equations (2)-(4):  
\[E_{dec}=Embed(Y[0:l-1]). \tag{2}\]  
\(Embed(\cdot)\) is an embedding layer that transforms a sequence of labels \(Y[0:l-1]\) into a sequence of learnable vectors \(E_{dec}\in\mathbb{R}^{l\times d^{att}}\), and \(d^{att}\) is the dimension of attentions.  
\[\begin{split}& D_{0}=E_{dec}+PE\\ & D_{j}^{{}^{\prime}}=D_{j}+MHA_{j}^{self}(N(D_{j}),N(D_{j}),N(D_{j}) )\\ & D_{j}^{{}^{\prime\prime}}=D_{j}^{{}^{\prime}}+MHA_{j}^{src}(N(D_{ j}^{{}^{\prime}}),H_{e},H_{e})\\ & D_{j+1}=D_{j}^{{}^{\prime\prime}}+FF_{j}(N(D_{j}^{{}^{\prime \prime}})),\end{split} \tag{3}\]  
where \(j=0,\ldots,d-1\), \(d\) is the number of decoder layers.  
\[\begin{split}&[p_{att}(\mathcal{Y}[1]),\ldots,p_{att}(\mathcal{ Y}[l])]=softmax(D_{d}W^{o})\\ &\hat{y}_{f^{\prime}}=\operatorname*{arg\,max}_{y_{f^{\prime}} \in\mathcal{Y}}p_{att}(\mathcal{Y}[l^{{}^{\prime}}])\qquad l^{{}^{\prime}}\in 1,\ldots,l\end{split} \tag{4}\]  
where learnable weight matrices \(W^{o}\in\mathbb{R}^{d^{att}\times d^{units}}\) belong to the output linear layer, and \(d^{units}\) is the number of output units.  
In this work, in the training stage, ground truth sequence \((Y[0:l-1]=[y_{0},\ldots,y_{l-1}])\) is adopted as the input of the embedding layer, while in the decoding stage, predicted sequence \((Y[0:l-1]=[\hat{y}_{0},\ldots,\hat{y}_{l-1}])\) is adopted.
# Evaluating the Flexibility Benefits of Smart Grid Innovations in Transmission Networks
## 3 Use Case Scenario: Battery Integration in the Cypriot Power System  
The main source of flexibility in a power system was always the conventional generators, however during the last years, the Battery Energy Storage Systems (BESS) constitute a viable flexibility resource in the power systems. This is mainly thanks to cost reduction of the battery technology, the need for more environmentally friendly solutions, and the enhanced technical performance of BESS, i.e., fast ramp rates. Demand side management, interconnections with neighboring systems, grid strength and forecasting accuracy can also provide flexibility to the power system [22; 23].  
In this use case, a BESS is considered to be installed to the Cyprus power system along with the existing conventional generation plants. For this purpose, three sizes of 40, 80, 120 MWh were considered. The selection of the above battery capacity is based only on the nominal power of the wind parks. Considering the variation of the wind potential, the examination of various scenarios is demanded. Note that the optimization of the BESS size is not included in the goals of the current work. The use-case has been developed and studied in the context of FLEXITRANSTORE project, where a flexibility adequacy assessment platform has been developed in order to evaluate the flexibility indices of the power systems apart from the generation adequacy indices like LOLE. This will provide a more realistic view of the risks facing the electricity systems in the following years when the generation mix will consist mainly of variable renewables and additional balancing resources will be needed. Additionally, this FLEXITRANSTORE platform and the strategic decision-making method will evaluate the benefits of innovation technology in the power
## 5 Experimental Investigations
### Longitudinal Load-Bearing Behavior of Beams Cast in Several Steps
#### 5.2.1 Specimens and Test Set-Up  
Two tests were simultaneously performed on one beam, as can be seen in Figure 19. The beams were cast in a total of three working steps by Franz Oberndorfer GmbH & Co KG (Gars am Kamp, Austria). First, the bottom plate with a thickness of 70 mm was cast. In the second step, the cross-beam stubs were cast on top of the hardened bottom plate. In the longitudinal direction of the beam, their length was 220 mm. The heights of the cross-beam stubs were 350 mm (beam N) or 230 mm (beam C). In the final step, the top concrete layer was cast. Between each of the casting steps, one day passed. Accordingly, the height of the concrete layer above the cross-beam stubs was 80 mm (beam N) or 200 mm (beam C). This results in a total height \(h\) of 500 mm of the two beams. The total width \(b\) was 600 mm. The length \(l\) of the beams was 8000 mm. In order to obtain a proper bond between the cross-beam stubs and the top layer of concrete, the bottom slab and the top surface of the cross-beam stubs were roughened by raking. In addition, an indented construction joint was created between the lateral surface of the cross-beam stubs and the top layer of concrete, by using trapezoidal strips inside the formwork, according to Eurocode 2 [31].  
Figure 18: Crack pattern at maximum load \(F_{\text{max}}\) above the support (a) S100 AL95 ; (b) S100 AL295; (c) S200 AL95; (d) S200 AL295 (crack opening in mm) [34].  
The shear span-to-effective depth ratios I = a/ d for beam N and beam C were 3.36 and 3.39, respectively. These values were chosen so that bending-shear failure could occur.