esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Derivation of 3D Array Factor
### Results of Array Factor Calculation through Simulations  
We verified the 3D array factor by simulating a situation of uniform amplitude feeding based on the proposed equation for the 3D array factor. In the form of a single element, we designed a cavity-backed patch antenna operating at 10 GHz.  
The designed cavity-backed patch antenna is shown in Figure 6a. RF-35 (dielectric constant = 3.5) from Taconic with a thickness of 0.51 mm was used as a substrate for the antenna. The substrate is 15 mm on the x-axis and 15 mm on the y-axis. The cavity is set to metal with a total length of 7 mm in the z-direction. The cavity is 1.5 mm thickness in the x- and y-direction and 2 mm thickness in the z-direction. The inside of the metal is set to air. Coupling feeding was employed [18; 19; 20]. The cavity-backed patch antenna (Figure 6a) has a patch size of 7 mm on the x-axis and 7 mm on the y-axis, and the thickness of the cavity layer is 7 mm. The bandwidth of reflection coefficient below \(-10\) dB is 9.242-10.81 GHz, as shown in Figure 6b. The element pattern for the cavity-backed patch antenna is shown in Figure 6c. The broadside gain of the single radiation element is approximately 5.479 dBi.  
Figure 7 shows the shape of an \(8\times 8\) random grid array using the cavity-backed patch antenna as a single radiation element. The element spacing was calculated as the distance between patch centers. The element spacing in the x- and y-directions, dx and dy, respectively, were randomly set to 0.5\(\lambda\)-0.56\(\lambda\) (15-16.8 mm), and that in the z-direction dz was set to 0-0.2\(\lambda\) (0-6 mm). The calculated array factor scanned at \(\phi\) = 0\({}^{\circ}\), \(\theta\) = 30\({}^{\circ}\) under uniform amplitude feeding was compared with the radiation pattern obtained from EM simulations.  
Figure 6: Cavity-backed patch antenna: (**a**) element shape, (**b**) S-parameters, (**c**) \(\phi\) = 0\({}^{\circ}\) radiation pattern and (**d**) \(\phi\) = 90\({}^{\circ}\) radiation pattern.  
Figure 8 shows the array factor and radiation pattern at 10 GHz when the shape of the conformal array is as shown in Figure 7. Figure 8a shows the 3D array factor calculated using MATLAB, and Figure 8b shows the radiation pattern of the conformal array simulated by substituting the phase obtained from Equation (11). The patterns shown in Figure 8a,b are very similar. However, Figure 8a shows only the calculated array factor, and Figure 8b show the array factor in Figure 8a and the element pattern in Figure 6c, so that the reduction is reflected in the gain of \(\theta=-90^{\circ}\) and \(\theta=90^{\circ}\).  
These confirm that performance prediction and control in the case of 3D and random arrangement can be achieved using the proposed 3D array factor for uniform amplitude feeding (Equation (11)) by calculating an array factor for an arbitrary array and adjusting the element pattern to adjust the array pattern.
## 2 Dataset and Study Area  
### Dataset  
This study uses eight types of data (Table 1), all from the growing season (March to October), including ET and GPP data required for WUE estimation, as well as related driving factor data. These driving factors include the normalized difference vegetation index (NDVI), vapor pressure deficit (VPD), shortwave radiation (SRAD), maximum temperature (TMAX), the Palmer drought severity index (PDSI), and precipitation (PRE) data. ET and GPP data are sourced from the Penman-Monteith-Leuning Evapotranspiration V2 (PML_V2) (poles.tpdc.ac.cn, accessed on 8 July 2024), with a spatial resolution of 500 m and a temporal resolution of 8 days. The NDVI is sourced from the United States Geological Survey (USGS, lpdaac.usgs.gov, accessed on 8 July 2024), with a spatial resolution of 500 m and a temporal resolution of 16 days. Other driving factor data are from TerraClimate (www.climatologylab.org, accessed on 8 July 2024), with a spatial resolution of 4 km and a temporal resolution of 1 month. The annual GPP and ET data are obtained by summing the data, while the annual driving factor data are obtained by averaging data. Due to the different data sources, the spatial resolutions vary. In this study, a 500 m resolution is used for analyzing WUE. However, for the driver analysis, the WUE data are resampled to 4 km resolution to ensure consistency with the resolution of the driving factors, thereby maintaining uniformity in resolution. The study period spans from 2003 to 2020.
## 4 Information Update and Fusion
### UAV Location Map  
The UAV location map records the positions of UAVs and can be represented as follows:  
\[\mathcal{L}_{i,t}=\left[\mathcal{L}_{i,t}^{x,y}\right]_{L_{x}\times L_{y}}, \tag{21}\]  
where \(\mathcal{L}_{i,t}\) indicates whether a UAV is in the cell \(G_{x,y}\) at timestep \(t\) in the understanding of UAV \(U_{i}\). \(\mathcal{L}_{i,t}\) is defined as a binary variable, namely \(\mathcal{L}_{i,t}=1\) means that there is a UAV in the cell and \(\mathcal{L}_{i,t}=0\) means that there is no UAV.  
The UAV location map \(\mathcal{L}_{i,t}\) is updated in two stages. The map \(\mathcal{L}_{i,t}\) of UAV \(U_{i}\) is first updated by the location information of its neighbors within its communicaiton range at each timestep \(t\), which can be represented as  
\[\mathcal{L}_{i,t}^{x,y}=\left\{\begin{array}{ll}1,&\text{if }\mathbf{\mu}_{j,t}=(x,y) \text{ and }U_{j}\in\mathbb{N}_{i,0}\\ 0,&\text{otherwise }.\end{array}\right. \tag{22}\]  
Then, the UAV location maps from the neighboring UAVs can be integrated. The information fusion mechanism of the UAV location map can be represented as follows:  
\[\mathcal{L}_{i,t}^{x,y}=\mathcal{L}_{j,t}^{x,y},\text{ if }\mathcal{L}_{j,t}^{x,y} >\mathcal{L}_{i,t}^{x,y}\text{ and }U_{j}\in\mathbb{N}_{i,t}. \tag{23}\]
## 2 Problem Definition and Proposed Models
### Artificial Neural Networks  
ANNs are ML tools which work on the concept of biological neurons and their interconnections. ANNs are composed of multiple units in which each unit is called a neuron that takes multiple input signals and converts it to a single output signal with the help of the activation function [20; 21]. Therefore, it can be used to solve classification, pattern recognition, and estimation problems [20; 22; 21]. Interconnections of neurons form a network;in fact, they form a layered structured network, in which the first one is the input layer, the last one is the output layer, and in-between any number of layers are called hidden layers [20; 22], as shown in Figure 5.  
The mathematical function which is repeatedly performed inside ANN is given by Equation (10):  
\[\mathrm{Y}=\sigma\Big{[}\sum_{i=1}^{n}\bigl{(}w_{ij}a_{i}+b_{i}\bigr{)}\Big{]} \tag{10}\]  
where \(\sigma\) denotes the activation function applied to the weighted sum of all inputs from this layer, whereas \(W_{i,j}\) is corresponding weights of this layer. \(a_{i}\) is the input of this layer, and \(b_{i}\) is the bias of this layer. During the \"learning\" phase, the aim is to find the optimal value of \(W_{i,j}\) and \(b_{i}\) parameters for each layer, which is attained by minimizing the cost function called the mean squared error (\(MSE\)) as follows:  
\[MSE=\frac{1}{n}\sum_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2} \tag{11}\]  
where \(y\) represents the actual output (target), and \(\hat{y}\) is the estimated or predicted value by ANN.  
The performance of ANN depends on combination of multiple parameters such as the learning rate, number of neurons, hidden layers, optimization algorithm, and activation function [20]. In this work, ANN is implemented using the same configuration provided by paper [7], so the results from this model can be compared with our proposed regression models, namely, MLR and SVR. The ANN in [7] has three layers: the input, the output layer, and only one hidden layer in-between. Furthermore, the backpropagation algorithm is used for optimization. The activation function used in this work is sigmoid, which is given by Equation (12):  
\[\phi(S)=\frac{1}{1+e^{-S}} \tag{12}\]
## 7 Proposed Framework
### Framework for Application Area 1: Production of Crops and Foods
#### 7.1.1 Step 1: Internet of Things Devices Generate Data  
According to forecasts, the total number of people living on our planet will have increased to 9.6 billion by the year 2050. As a consequence of this, in the agricultural industry, gadgets and sensors that are connected to the Internet of Things are being used so that a growing population can be fed.  
In the context of Internet-of-Things-enabled smart farming:  
* In the building of a system to monitor an agricultural field, sensors play an important role as part of the construction process (soil moisture, temperature, humidity, pH, light, etc.).
* The data generated by sensors and other devices linked to the Internet of Things (IoT) may provide farmers with valuable information that can aid them in making educated decisions on the growth of their crops.
* The information that is collected from Internet of Things devices must be organized before it can be saved into a data storage device.
## 3 Results
### Functional Improvement  
Functional recovery was assessed through measures of movement efficiency, daily activity performance, and return to sports. [PERSON] et al. (2019) [47] found that the group incorporating trunk stabilization exercises experienced superior functional gains compared to those who focused solely on pelvic strengthening. This suggests that interventions targeting core stability contribute to more effective rehabilitation outcomes. [PERSON] et al. (2021) [19] observed that patients in the progressive strengthening and functional training group demonstrated significant improvements in their ability to perform daily activities and sports-related movements, as measured by the International Hip Outcome Tool (iHOT-33). In contrast, the control group, which engaged in stretching and general physical activity, reported lower functional scores.  
[PERSON] et al. (2018) [5] found that, at the two-year follow-up, there were no significant differences in functional improvement between the surgical and physiotherapy groups, further supporting the idea that conservative rehabilitation is a viable alternative to surgery. However, [PERSON] et al. (2021) [29] observed that, after twelve months, the surgical group demonstrated greater improvements in their ability to return to sports and perform activities of daily living (ADLs) compared to those who received only physiotherapy. [PERSON] et al. (2019) [48] found that, while surgery resulted in superior functional improvements, particularly in hip flexion-related activities, no major differences were found in other movement-related functions when compared to structured rehabilitation.  
These results indicate that structured rehabilitation programs, particularly those incorporating progressive strengthening and functional training, can significantly improve functional performance. While surgery may accelerate functional recovery in the short term, long-term differences between the two approaches are not substantial.
# WRTU-16T: Write-Enhanced Low-Power Radiation-Tolerant SRAM for Space Applications
## 3 Implementation Results and Analysis
### Soft Error Robustness Analysis  
Qc is the minimum amount of charge required to flip logic at a sensitive node in an SRAM when a transient current is injected. In other words, Qc means the total charge necessary to invert the stored logic state from logic '1' to logic '0' [13]. Qc is calculated using the following integral expression:  
\[Q_{c}=\int_{0}^{t_{flip}}I(t)dt \tag{1}\]  
\[I(t)=I_{0}(e^{-\frac{1}{\tau_{0}}}-e^{-\frac{1}{\tau_{0}}}) \tag{2}\]  
\(I(t)\) means the transient current, \(t_{flip}\) is the point at which the stored data is inverted, and \(I_{0}\) is the peak amplitude of the current pulse. Time constants of \(\tau_{a}\) = 200 ps and \(\tau_{b}\) = 50 ps are commonly used to model ion trajectory formation and charge collection, respectively.  
The WRTU-16T achieves high Qc through several design features. First, there are only two sensitive nodes, Q and S0, and these nodes are sufficiently physically separated to  
Figure 11: Comparison of HPWR with other SRAMs: (**a**) at VDD = 1 V, (**b**) at various VDD levels from 0.9 V to 1.1 V.  
reduce the possibility of SEMNU. Fewer and the wider spacing of sensitive nodes inherently reduces mutual interference, which increases Qc. Second, the use of cross-coupled inverters and feedback loops enables fast data recovery after an SEU event, which reduces the bit inversion probability and enhances charge tolerance. Third, the WRTU-16T uses eight NMOS transistors to suppress charge sharing, which would otherwise reduce the amount of charge required to invert the node voltage. By limiting charge sharing, more charges must be injected to disturb the stored data, thus increasing Qc and improving soft error resistance. The Qc value and recovery process under SEU conditions are shown in Figures 12 and 13. The WRTU-16T maintained data integrity even when more than 100 fC of charge was injected into the sensitive nodes Q and QB.
## 2 Materials and Methods  
### Materials  
Gallium nitrate (Ga(NO\({}_{3}\))\({}_{3}\cdot\)xH\({}_{2}\)O) and Trisodium Hexachlororhodate (Na\({}_{3}\)RhCl\({}_{6}\cdot\)xH\({}_{2}\)O) were purchased from Aladdin and Alfa Aesar Companies, respectively. Zinc nitrate (Zn(NO\({}_{3}\))\({}_{2}\cdot\)6H\({}_{2}\)O), \(N\),\(N\)-dimethylformamide (DMF), and chromic nitrate (Cr(NO\({}_{3}\))\({}_{3}\cdot\)9H\({}_{2}\)O) were provided by the company of Sinopharm; and polyacrylamide(PAN, Mw = 150,000 g\(\cdot\)moL\({}^{-1}\)) and methyl alcohol (CH\({}_{3}\)OH) were obtained from Macklin. All the chemicals were of analytical grade and were used without any purification. Deionized (DI) water was used throughout.  
Synthesis of Ga(NO\({}_{3}\))\({}_{3}\cdot\)Zn(NO\({}_{3}\))\({}_{3}\)/PAN Nanofibers and GaN:ZnO Solid Solutions  
The 10 wt % solution of PAN (Mw = 150,000 g\(\cdot\)moL\({}^{-1}\)) was prepared by dissolving it in \(N\),\(N\)-dimethylformamide (DMF). An appropriate amount of Ga(NO\({}_{3}\))\({}_{3}\cdot\)xH\({}_{2}\)O and Zn(NO\({}_{3}\))\({}_{2}\cdot\)6H\({}_{2}\)O with a mole ratio of Ga to Zn = 1:1 was mixed into the as-prepared PAN solution with a weight ratio of PAN to salt of 4:1. Then, it was vigorously stirred at an ambient temperature for 5 h with the purpose of forming a homogeneous and translucent precursor solution. After, the solution was delivered by syringe with a flow rate of 0.6 mL\(\cdot\)h\({}^{-1}\) and using a high voltage of 28.5 kV, and the distance between the needle tip and the collector was 15 cm. Herein, the obtained Ga(NO\({}_{3}\))\({}_{3}\cdot\)Zn(NO\({}_{3}\))\({}_{3}\)/PAN nanofiber was preserved in a desiccator at room temperature.  
The as-spun composite nanofibers were nitridded at temperatures of 750, 850, and 950 \({}^{\circ}\)C for 3 h with a heating rate of 5 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\) in a flow of 150 sccm NH\({}_{3}\), respectively, and then the as-nitridded samples were calcined at 400 \({}^{\circ}\)C for 2 h with a cooling rate of 5 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\) and continuously at 500 \({}^{\circ}\)C for 3 h in the air atmosphere. Three samples of GaN:ZnO solid solutions synthesized at different nitriding temperatures were denoted as S\({}_{750}\), S\({}_{850}\), and S\({}_{950}\), respectively.
# Parallelizing the Computation of Grid Resistance to Measure the Strength of Skyline Tuples
## 6 Related Work
As for robustness, the indicator called the _exclusive volume_ refers to the measure of volume in the dominance region of a tuple that is not part of the dominance regions of any other tuples in the dataset; this indicator is computed as an instance of the so-called hypervolume contribution problem, which has also been studied extensively and is #P-hard to solve exactly [40; 41]. Finally, _grid resistance_ is themain indicator of robustness, which we thoroughly analyzed in this paper. The notion of _stability_[42] is akin to robustness in the sense that it still tries to measure how large perturbations can be tolerated to preserve the top-\(k\) tuples of a ranking, although the focus is on attribute values in the scoring function and not on tuple values, as is the case for grid resistance. To the best of our knowledge, apart from the sketchy sequential pattern given in the seminal paper [20], there is no prior work on the computation of the grid resistance, particularly in a parallelized setting. We also observe that both skylines and ranking queries are commonly included as typical parts of complex data preparation pipelines for subsequent processing based, e.g., on machine learning or clustering algorithms [43; 44]. In this respect, an approach similar to ours can be leveraged to improve the data preparation and to assess the robustness of the data collected by heterogeneous sources like RFID [45; 46].
## 4 Discussion
### Understanding the Strength-Ductility Response in the Composite  
The cracked Al\({}_{3}\)BC phase (Figure 6b) supports its brittleness (Figure 3c). Therefore, the fracture morphology suggests that the large CG/clusters might be the origin of the failure in these composites. Traces of pull-out (indicating interfacial debonding, suggesting Al\({}_{3}\)BC particles might have reached their strength limit) and tear ridges (indicating stress concentration occurring around these particles) (Figure 6b,c) suggest Al\({}_{3}\)BC bears significant load during deformation and stress would concentrate around these particles. Brittle FG/CG-Al\({}_{3}\)BC (Figure 6c) initiates cracks or deboning from the matrix, which ultimately leads to the formation of primary cracks (Figure 6a). In addition, Figure 6c reveals some micron-sized AlB\({}_{2}\) particles fractured with smooth surfaces, while some particles remain intact but deboned from the matrix, supporting the brittleness claims. Figure 6d shows that while the primary crack propagates along interfaces, a few irregular secondary cracks initiate within the FG-Al matrix and penetrate further, suggesting some resistance to fracture. These secondary cracks (Figure 6d, yellow arrow) are ultimately bridged and deflected by the surrounding FG-Al matrix, delaying the fracture. This highlights the potential role of the FG-Al matrix in enhancing fracture resistance. As the Al\({}_{3}\)BC phase has a high-volume fraction in the composite, the inner unit stress facilitates load transfer to the adjacent units (\(n=0.4112\)) to coordinate the deformation, likely contributing to early yielding of as-fabricated Al\({}_{3}\)BC/Al composites.  
Figure 6: Scanning electron microscopy images taken on the fracture surface of the composite after the compression test showed (**a**) the primary cracks traveling along the Al\({}_{3}\)BC network with a few secondary cracks penetrating the matrix. (**b**) The presence of cracked/broken Al\({}_{3}\)BC while strongly bonded FG-Al grains tightly surrounded by matrix (white circle). (**c**) The presence of dimples (red arrow) and tear ridges (yellow arrow), along with cleavage planes, shows the mixed mode of fracture. (**d**) Shielded dense and fine microcracks in the FG-Al matrix enable Al\({}_{3}\)BC/Al to efficiently dissipate the deformation energy (yellow arrow).  
Continued loading promotes crack bridging through superplastic flow, maintaining a global strain hardening (\(n=0.3048\)) and resulting in adequate ductility (17%). A high-volume fraction of brittle and hard Al\({}_{3}\)BC (\(V_{f}^{Al_{3}BC}=65\)\(\pm\) 0.1 wt.%) transfers load to the adjacent unit to coordinate the deformation, ultimately improving strength and ductility. However, a very high amount of Al\({}_{3}\)BC (\(V_{f}^{Al_{3}BC}=65\)\(\pm\) 0.1 wt.%) leads to an increase in the probability of primary crack formation and causes the early fracture in the compression test. Such a distinctive microstructure arrangement is beneficial to strengthening the composite, which is favorable to the strength and ductility of the composite.
## 3 System Performance Analysis
### System Throughput Maximization  
The IoT network capacity is a crucial factor. The authors analyzed and obtained an ergodic sum rate as shown in [38] (Equation (28)). The authors then obtained the sum of devices' bit-rate threshold as shown in [38] (Equation (29)). In contrast, this study aims to prove that devices' bit-rate threshold improves network capacity and enhances secrecy performance as well. In the considered IoT network as shown in Figure 1, the target SINR \(\gamma\) has belonged to the IoT devices' bit-rate threshold \(\mathcal{R}\). From expression OP at IoT device \(D_{n}\) as shown in (45) and expression IP at eavesdropper \(E\) as shown in (52), this study obtained the maximum of achievable bit-rate threshold \(\mathcal{R}_{n}\) for IoT device \(D_{n}\) (\(n>1\)) as follows:  
\[\mathcal{R}_{n>1}=\frac{1}{2}\text{log}_{2}\left(1+\frac{\alpha_{n}}{\sum_{j= 1}^{n-1}\alpha_{j}}\right) \tag{53}\]  
By substituting (53) into expressions (45) and (52), the OP at IoT devices and IP at eavesdropper \(E\) always tend to one with any SNR \(\rho\) since expression (53) given ceiling bit-rate threshold of IoT device \(D_{n}\). It means that IoT devices' pre-defined bit-rate threshold cannot tend to equal or greater than (53). On the other hand, expression (53) provides achievable maximum data rates for IoT devices in a heterogeneous manner. It means that the IoT devices are served with different throughput leading to unfairness between IoT devices. Therefore, this paper proposes to unify a data rate threshold for all IoT devices as follows:  
\[\mathcal{R}=\min_{1<n\leq N}\{\mathcal{R}_{n}\}-v, \tag{54}\]where \(v\) factor deals with tolerance for \(v\simeq 0\). The expression (54) is to ensure all IoT devices could decode their own message successfully and reach the same throughput while SNR \(\rho\to\infty\).
## 5 Research Challenges and Future Directions  
### New Network Scenarios  
Both the terrestrial mobile system and the mobile satellite system were two-dimensional network designs in the past. Each of these systems was independent of the other. With 6G, there is an ability to seamlessly communicate over shorter distances via direct connections to nearby devices, medium orbit and low orbit satellite mobile communications, and terrestrial wireless mobile communications [143]. Furthermore, 6G also includes new computation, navigation, and sensing technology. Consequently, 6G will develop a new three-dimensional (3D) core network architecture capable of integrating these technologies and providing ubiquitous worldwide coverage of high-speed mobile communications through the use of intelligent mobility management and control techniques [144]. A number of obstacles must be overcome before this 3D core network design can be made widely accessible, even though it has the potential to overcome conventional coverage constraints and ultimately create extraordinary universal coverage. Consequently, developing and implementing resource management and optimization methods regarding the routing protocol, mobility support, and multiple access is crucial. A redesigned network architecture is required for scheduling.  
Various techniques have been suggested in recent years, such as carrier aggregation, mmWaves, intelligent reflecting surfaces, and Terahertz. New HetNets may emerge from combining these methods with HetNets, adding complexity to the RA problems. The predictable direction of HetNet development in 6G will solve this issue by combining the upcoming technology with HetNets [145]. For instance, an intelligent reflecting surface-aided HetNet may increase capacity while decreasing energy usage. However, finding a solution to the issue is difficult because of the linked transmit power and the phase shift that intelligent reflecting surface causes. An essential research objective is figuring out how to create a network model with a low level of complexity and a high degree of adaptability, with the end goal of achieving even greater gains in network performance.