esa-sceva/satcom-chunk-collection huggingface dataset:
### Yield Parameters  
A statistically significant effect (\(p<0.05\)) of different Zn formulations on yield parameters of wheat under field conditions compared to the control was observed (Figure 3). The plots treated with 1.5% bio-activated Zn coated urea showed 15.75 tons ha\({}^{-1}\) biomass, which was 4% more as compared to the control (13.47 tons ha\({}^{-1}\) biomass), Figure 4a. The treatments with 1.5% Zn coated and 1.5% Zn blended urea showed 14.83 and 15.13 tons ha\({}^{-1}\) biomass, respectively and both were about 2% more as compared to the control. In comparison to the recommended ZnSO\({}_{4}\) (14.5 tons ha\({}^{-1}\) biomass), the application of 1.5% bio-activated Zn coated urea caused about 0.6% increase in biomass. A similar trend was recorded regarding the grain yield of wheat, Figure 4b. The application of 1.5% bio-activated Zn coated urea caused an increase of 6.2 and 4.1% in biomass as compared to 1.5% Zn coated and 1.5% Zn blended urea, respectively.  
The data regarding grain yield (tons ha\({}^{-1}\)) of wheat showed that a statistically significant increase with the application of different Zn formulations, Figure 4b. The application of 1.5% bio-activated Zn coated urea yielded 5.60 tons ha\({}^{-1}\) grains and this was 33% increase as compared to the control (3.42 tons ha\({}^{-1}\) grains). Almost similar results were obtained with the application of 1.5% Zn coated and 1.5% bio-activated Zn coated urea applied. With the application of 1.5% Zn blended urea (4.67 tons ha\({}^{-1}\) grains), about 28% increase in grain yield was observed as compared to the control. About 3.5% increase was observed with the application of recommended Zn as ZnSO\({}_{4}\) (3.92 tons ha\({}^{-1}\) grains) compared to the control treatment. The application of 1.5% bio-activated Zn coated urea caused an increase of 9.8 and 19.6% in grain yield as compared to 1.5% Zn coated and 1.5% Zn blended urea, respectively.  
Figure 4: _Cont._  
Data presented in Figure 4c showed that different Zn formulations have a statistically significant effect (\(p<0.05\)) on the 100-grain weight of wheat. With the application of recommended Zn as ZnSO\({}_{4}\) (42.19 g), 19% increase in 100-grain weight was observed compared to the control (34.79 g). Application of 1.5% bio-activated Zn coated urea resulted in the maximum (44.87 g) 100-grain weight compared to all other treatments. The treatments with 1.5% Zn coated (42.47 g) and 1.5% Zn blended urea (39.25 g) showed 18.6 and 11% increase in 100-grain weight as compared to the control (no Zn). The application of 1.5% bio-activated Zn coated urea showed 6 and 12.5% increase as compared to 1.5% Zn coated and 1.5% Zn blended urea, respectively. The alone use of ZSB (38.81 g) caused 11% increase in 100-grain weight with respect to the control. The application of 1.5% bio-activated Zn coated urea caused an increase of 5.6 and 14.3% in 100-grain weight as compared to 1.5% Zn coated and 1.5% Zn blended urea, respectively.
## 3 Experimental Section
### Load-Deflection Curves
That means increasing the ultimate load by 67.3 kN with an increasing rate of 18.76% and decreasing the ultimate load deflection by 1.06 mm with a decreasing rate of 2.88%, as shown in Table 5.1 The (P\({}_{\rm cr}\)/P\({}_{\rm u}\)) ratio for this specimen was 35.32%, which was lower than the control beam, as shown in Table 5.2  
For specimen GF-2 HA, with a harped tendon profile layout and eccentricity at the end (\(\mathrm{e_{e}}\)) = 0, the load-deflection curve is observed in Figure 10i. Its elastic stage was higher than the control beam; the first crack appeared in the concrete at 151.03 kN, and the deflection was 1.20 mm. It had a higher ultimate load of 434 kN but a lower deflection of 35.00 mm at the ultimate load. This means increasing the ultimate load by 75.3 kN with an increasing rate of 20.99% and decreasing the ultimate load deflection by 1.8 mm with a decreasing rate of 4.89%, as shown in Table 5.3 The (\(\mathrm{P_{cr}}\)/\(\mathrm{P_{u}}\)) ratio for this specimen was 34.80%, which was lower than the control beam, as shown in Table 5.4  
For specimen GF-3 HA, with a harped tendon profile layout and eccentricity at the end (\(\mathrm{e_{e}}\)) = \(-\)80 mm, the load-deflection curve was observed in Figure 10j. Although its elastic stage is higher than the control beam, the first cracks appeared in the concrete at 139.02 kN with a deflection of 1.20 mm, and it showed a higher ultimate load of 409.00 kN and a greater deflection of 37.90 mm at ultimate load. This means increasing the ultimate load by 50.3 kN with an increasing rate of 14.02% and increasing the ultimate load deflection by 1.1 mm with an increasing rate of 3%, as shown in Table 5.5 The (\(\mathrm{P_{cr}}\)/\(\mathrm{P_{u}}\)) ratio for this specimen was 33.99%, which was lower than the control beam, as shown in Table 5.6  
In the summary that compares all specimens with the control beam, as illustrated in Figure 10k-m, the differences in ultimate loads and deflections at these loads were attributed to the layout of the tendon profiles (trapezoidal, parabolic, and harped) and the end anchorage of the tendons. The bending moment at each section is determined by the product of the prestressing force and eccentricity of the tendon profile. Beams with zero eccentricity at the end anchorages demonstrated a greater load capacity than those with end eccentricity. The greater load capacity is because beams with zero eccentricity experience uniform compression stress only without any tension stress at the supports, resulting in a more uniform distribution of the applied load across the cross-section compared to the beams with eccentric tendons.
# Joint Hybrid Beamforming Design for Millimeter Wave Amplify-and-Forward Relay Communication Systems
## 6 Conclusions  
We have investigated the joint HBF design for mmWave AF relay communication systems in this paper. Specifically, the optimization-based HBF design for HD relay systems with perfect CSI has been addressed. We have designed an alternating optimization algorithm based on MO, where the modified MSE was adopted as the optimization objective. Simulation results have shown that the performance prevailed other HBF algorithms in the literature. Moreover, we have further extended the scenario to more general cases with channel estimation errors, where the relay system was operated in FD mode. A robust HBF design has been proposed, with SI mitigated by the NP based algorithm. It has been demonstrated via numerical results that the proposed scheme had an improved robustness against SI.  
Conceptualization, J.Z. and H.W.; methodology, J.Z. and D.J.; validation, H.W., B.L. and Y.Z; formal analysis, B.L. and H.Y.; investigation, Y.Z. and X.L.; writing--original draft preparation, J.Z. and Y.Z.; writing--review and editing, H.W and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData is contained within the article.  
The authors declare no conflicts of interest.  
Figure 9: SE versus SNR with different \(\sigma_{e}^{2}\).
## 4 Discussion  
The iterative phase retrieval method based on a changing curved illumination function was compared to the well-established digital off-axis holography. It was found that the resolution obtained could compete with digital off-axis holography and the phase sensitivity even outperformed digital off-axis holography. Due to this property, the iterative phase retrieval method was then applied to renal tissue discrimination. Preliminary results have been shown, which demonstrate its potential for intraoperative tumor diagnosis. Discrimination parameters could be derived from the increased scattering and phase delay of tumorous tissue in comparison to healthy tissue. At the moment, paraffin sections have been investigated, but future research will focus on the application of cryosections, which are quicker to prepare and enable the preservation of the tissue as close as possible to its natural state. Further investigations will involve tissue samples from the transition region between cancer and normal tissue, which medically is likewise very important for removing the entire malign tumor. Moreover, other tissue malignancies will be investigated in order to generalize the findings obtained. The results obtained will be compared with the gold standard in histopathology using stained samples and other imaging techniques.  
This work was funded by the industry on campus project IoC105 \"Intraoperative model-based identification of tissue properties using a multimodal and multiscale approach\" and the H2020 European Project HOLO (TWINN 687328). The work conducted on human tissue has been performed in the scope of the ethics application number 154/2018 BO2 listed at the Eberhard Karls Universitat Tubingen.  
[PERSON] developed the idea and wrote the major part of the paper, [PERSON] arranged and conducted the experiment with the help of [PERSON] and D.C., [PERSON] supplied the medical samples, provided medical expertise and helped writing the paper, [PERSON] and [PERSON] applied for the funding and managed the project.  
This research was funded by the Industry on Campus project IoC105 under the title \"Intraoperative model based identification of tissue properties using a multimodal and multiscale approach\"  
The authors declare no conflicts of interest.
## 2 Material and Methods
### Economic Evaluation  
The present study includes also an economic evaluation of the examined units. The capital cost of the unit can be found according to the next equation:\[C_{0}=K_{\rm col}\cdot A_{\rm col}+K_{\rm tank}\cdot V_{\rm tank} \tag{12}\]  
The tank specific cost (K\({}_{\rm tank}\)) is selected at 1000 \(\rm\upxi/m^{3}\)[30; 31], while the specific cost of the collectors (K\({}_{\rm col}\)) is chosen at 120 \(\rm\upxi/m^{2}\) for the Simple FPC, at 150 \(\rm\upxi/m^{2}\) for the Advanced FPC, and at 250 \(\rm\upxi/m^{2}\) for the ETC [30; 31].  
In this work, the discount factor (r) is assumed to be 5%, the project lifetime (M) at 25 years, and the electricity cost (K\({}_{\rm el}\)) for the auxiliary source at 0.20 \(\rm\upxi/kWh\). The yearly economic gain by the use of the solar system is calculated as below:  
\[\rm CF_{gain}=K_{\rm el}\cdot L_{u,sol} \tag{13}\]  
Practically, this gain indicates the money that would have been spent on the auxiliary system operation for covering the demand if there was not the solar system.  
The first introduced parameter for the economic analysis is the simple payback period (SPP) which is calculated as the ratio of the capital cost of the investment to the yearly economic gain by the use of the solar system:  
\[\rm SPP=\frac{C_{0}}{CF_{gain}}. \tag{14}\]  
The next financial index is the life cycle cost (LCC) of the unit which takes into consideration the total money that will be spent for covering the DHW demand for all the project life.  
\[\rm LCC=C_{0}+\sum_{i=1}^{M}\frac{L_{aux}\cdot K_{\rm el}}{\left(1+r\right)^{i}} \tag{15}\]  
The goal is the minimization of both the (SPP) and the (LCC) in order to achieve optimal economic behavior. The (LCC) is assumed to be a better index than (SPP) because it takes into consideration all the life of the project and it is able to indicate future gains after the payback period. On the other hand, the (SPP) is a usual index in order to evaluate quickly investment with relatively low capital cost. Both indexes are important and thus these are both included in the present work.
# Inferring Urban Social Networks from Publicly Available Data
## 3 Impact of System Parameters  
To sort out the role of the different system parameters in shaping our urban social network, we resort to an extensive experimental campaign focused on three Italian cities of different size: Florence (363,060 residents), Viterbo (66,598), and Sabaudia (21,274). For the sake of readability, the results for Viterbo and Sabaudia are reported in Appendices C and D, respectively.  
For each city, we selected the administrative boundaries from OpenStreetMap and filtered the population inside the city shape as described in Section 2.1. For Sabaudia and Viterbo, the smallest boundary available is the municipality which encompasses a large rural area with small urban agglomerates around the city. For Florence, we were able to select the actual city boundaries which do not include neighboring areas. For each city, we extracted the age labels based on ISTAT data aggregated at the provincial level. We then built the households as described in Section 2.1. The obtained territories are depicted in Figure 1 (and Figure A1 in Appendix A). An overview of the population and the territory is reported in Table 3 for all cities. For further details, see Appendix A.  
In this section, we focus on a few basic properties of the network and on the design choices needed to achieve them. As a side result, we show that any attempt to significantly simplify our network model downgrades the resulting social graph, especially in terms of heterogeneity and connectivity. It is worth it to underline that, to account for variance, hereafter any configuration is evaluated by considering the results of 10 independent runs.
# A Vulnerable Environment Study in Karst Regions between 1991 and 2017: A Bibliometric Analysis
## 3 Results and Discussion
### Future Research
#### 3.7.1 Evolution of Research Topics
The main keywords appearing in 1991-1995 were \"kast\", \"evolution\", \"system\", \"basin\", \"limestone\", \"environment\", \"origin\", \"carbonate\", \"community\", \"Spain\", \"dissolution\", \"deposit\", \"groundwater\", \"flow\", \"water\", \"aquiter\", \"sediment\", \"precipitation\", \"ecology\", \"rock\", \"hydrology\", \"stream\", \"France\", \"ecosystem\", \"morphology\", etc. The research focuses were on the carbonate geochemical reaction, surface and groundwater environment, ecosystem, etc. However, in 1995, the emergence of keywords \"isotope\", \"stratigraphy\", \"carbonate platform\", etc. marked the transformation of research in the field of vulnerable karst environment and signified the application of new research methods in this field. The keywords appearing in 1996-2000 were \"speleothem\", \"diagenesis\", \"cave\", \"geochemistry\", \"sinkhole\", \"climate\", \"transport\", \"pattern\", \"contamination\", \"soil\", \"diversity\", \"conservation\", \"Croatia\", \"water quality\", \"area\", \"China\", \"spring\", \"dynamics\", \"subsidence\", \"river\", \"stable  
Figure 16: Keywords with the strongest citation bursts in research topics year by year. isotope\", etc., which indicated that the subject area had been gradually expanded to cave environment, biodiversity, environment protection and pollution, regional research, application of isotopes, etc. Meanwhile, soil erosion, biodiversity and environmental evolution had also attracted attention. From 2001 to 2009, the emergence of keywords \"paleoclimate\", \"karst aquifer\", \" biodiversity\", \"GIS\", \"USA\", \"karst hydrology\", \"record\", \"temperature\", \"impact\", \"organic matter\", \"management\", \"climate change\", \"variability\", etc. indicated that the study of vulnerable karst environment tended to climate change, hydrological environment, soil environment, biodiversity, and the application of GIS technology. Keywords emerged from 2010 to 2017 including \"region\", \"speleogenesis\", \"vegetation\", \"carbon isotope\", \"land use\", \"carbonate aquifer\", \"rocky desertification\", \"Southwest China\", etc. This suggests that in recent years, the research hotspots of vulnerable karst environment migrated towards ecological reconstruction and restoration, land use, karst rocky desertification, and studies of southwestern China. Through the analysis of the evolution of the research topics, it can be found that since 1991, the research topics of vulnerable karst environment have been gradually refined and transferred from water environment research to cave environment, and ecological environment research, and then gradually reached the fields of ecological restoration and reconstruction and karst rocky desertification.
# Predicting Output Power for Nearshore Wave Energy Harvesting
## 5 Results and Discussion
### Wave Tank Experiment
#### 5.2.2 Output Power Estimation  
To predict the output power, it is necessary to apply classification and regression steps after preprocessing the input data up to the hyperparameter tuning, as shown in Figure 5. For this experiment, we segmented the data into a group of 72 samples; the number of training and testing samples were 111 and 48, respectively, as noted in the previous section.  
The experiment was also designed to identify the best predictor for waves among six types of predictors, which are grouped into two. The first group, containing three types, used ANN, RF and SVM algorithms as regression models. The second group, containing the remaining three types, were the prediction models in which the regressor was preceded by the classifier. For instance, the predictor based on ANN performed classifications and then chose the ANN regression model depending on the result of the classification. In Figure 9, we denote it as C + ANN. The other types can be expressed as C + RF and C + SVR (support vector regressor). These notations hold the same for the other figures in the manuscript. The performances of the proposed systems were analyzed using the mean absolute error (MAE), mean squared error (MSE) and R\({}^{2}\) metrics.  
Regardless of the metrics used, it is clear that the second group, which used a classifier and a regressor together, outperformed the first group without a classifier, as shown in Figure 9. The result of the first group indicates that SVR is the best in two metrics and ANN in one metric, and RF shows the least favorable result. Among the second group, all of the algorithms resulted in a similar amount of error with a slight difference. The predictor based on the SVR model (C + SVR) had the highest R  
Figure 8: Performance evaluation on different window sizes for prediction.  
Figure 9: Comparison of predictors based on: (**a**) MSE; (**b**) MAE; (**c**) R\({}^{2}\).  
score and the minimum MSE and MAE error values. We selected this model as the prediction model for our proposed approach.
## 2 Related Research
### Camera-Based Knee Joint Angle Estimation  
Currently, 3D motion analysis is commonly used for knee joint angle measurements. Systems like VICON utilize infrared cameras and reflective markers to accurately capture joint coordinates [16]. [PERSON] et al. used optical motion capture systems to estimate knee joint angles for optimizing ACL injury assessment and rehabilitation [17]. [PERSON] et al. analyzed knee valgus moments and ACL injury risk mechanisms during non-contact side steps using similar systems [18]. However, these methods are costly, time-consuming, and restricted to laboratory settings.  
Two-dimensional motion analysis, which has been gaining attention recently, offers simplicity, a low cost, and a short execution time, making it suitable for large-scale screenings. [PERSON] estimated knee joint angles during running using digital video cameras [19]. However, 2D analysis can misinterpret hip rotation or lower limb alignment as knee valgus, limiting accuracy. Both 2D and 3D analyses rely on cameras, restricting angle estimation to the camera's field of view, and are thus unsuitable for estimating angles during matches or practices.
## 1 Introduction  
Current automatic recognition systems that contain all three phases (detection, extraction and classification) work with different methods. One of the currently most used methods is the application of neural networks [1, 2].  
A neural network is a series of algorithms that try to recognize certain relationships between data through a process based on how the human brain works. They can also process and adapt foreign input data, learn to classify them and adapt their own process based on them [3].  
The advantage of neural networks is that they can be used not only in one phase but in essentially all three phases. However, they are mostly used in the last phase--classification--when the amount of input data based on a trained set (dataset) can very accurately determine (classify) what the output will be [4, 5, 6, 7].  
There are several tools that make working with neural networks easier. They provide certain predefined algorithms that allow users to build their own neural network relatively quickly and without deep mathematical knowledge [8, 9, 10]. In this paper, we focus on comparing the models created using the most used libraries: EmguCV (a subset of OpenCV), ML.NET and Tensorflow.NET. The Related Work section is focused on an overview of research on the results achieved, depending on the design to the application of neural networks and selected algorithms that multilayer neural networks use in the classification and re-learning phase. The Experiment Preparation section describes the technologies that were used to create the individual models of neural networks. The procedures of data extraction and pre-preparation are described here, together with the creation, training and testing of individual created models of neural networks. In the Discussion section and the evaluation of results from experiments, we present the achieved results and the overall comparison of models.
## 4 High Attenuation Units and Level Shifter Structure Design
### Level Shifter Structure  
The inability to directly utilize positive voltages for on/off control of the transistor has shown poor digital compatibility for GaAs processes. Previous GaAs DSA designs have used off-chip serial peripheral interface (SPI) chips/devices to control the gate signals, which have the following drawbacks that have not yet been addressed: (1) additional gold bonding wires are required, which may lead to deterioration of RF performance and consistency; and (2) 6-bit DSAs demand 6-bit complementary controlled voltages, which means that there are a total of twelve off-chip power supply lines, twelve DC probes of 0/\(-\)5 V and corresponding on-chip pads for complexing the T/R modules, which dramatically increases the chip area and testing difficulty. In addition, the design of on-chip logic circuits should take into account both power consumption and cost. More complex GaAs-based serial-to-parallel (S/P) conversion modules, for example, have not been employed since their integration is much less than that of CMOS processes. In addition, p-HEMT transistors have, theoretically, gate parasitic diodes, which result in input logic thresholds that are sensitive to temperature (i.e., logic functions may fail at high temperatures), making the S/P modules difficult to be verified. In order to avoid the aforementioned disadvantages, this paper proposes an on-chip level-shifting circuit based on an integrated direct-coupled  
Figure 11: Simulation results for the proposed 4 dB, 8 dB and 16 dB attenuation structures (**a**) attenuation error; (**b**) \(S_{21}\); and (**c**) phase error.  
Figure 10: Analyzed (**a**) relative attenuation and (**b**) phase variation of the 4 dB \(\pi\)-type unit with various series inductances.  
field-effect transistor logic (DCFL) structure with a compact layout of 450 \(\upmu\)m \(\times\) 350 \(\upmu\)m, whose circuit schematic and layout are shown in Figure 12a,b, respectively.  
The level shifter circuit is composed of voltage converters and inverters, which consists of E/D mode transistors (with opposite threshold voltage-temperature curves) to reduce temperature sensitivity and improve circuit stability. In addition, the diodes are formed by connecting the source and drain of the E-mode transistors. With the reference voltage \(V_{ref}\) fixed at \(-5\) V, the circuit converts the incoming \(0/5\) V pulses into a pair of complementary \(-5/0\) V outputs simultaneously (i.e., \(V_{out1}\) and \(V_{out2}\)), thus enabling positive voltage control of the ATT and saving half of the DC power lines and probes. The corresponding time domain transient simulation is illustrated in Figure 13.