esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of True Time Delay Millimeter Wave Beamformers for 5G Multibeam Phased Arrays
## 3 Numerical Results
### Rotman Lens Results  
In this work, the classical Rotman lens architecture with dummy ports is compared with a novel design where the dummy ports are replaced by absorbers. In general, the role of dummy ports is to reduce the reflection of the incident waves at the side walls of the lens. These incident waves are responsible for the appearance of standing waves in the parallel plate region and, consequently, the degradation of lens response. By replacing the dummy ports with absorbing layers, the incident waves on the side walls are efficiently absorbed, improving the characteristics of the lens. Both designs consist of three input ports, and three output ports. Notably, the design rules of the proposed Rotman lens architectures are based on the design equations of [PERSON] and [PERSON] (2018). Aiming at a compact design, a substrate Rogers RT6006 with \(\epsilon_{r}=6.15\), and \(tan\delta=0.0027\) is chosen. All simulations were performed in the range of 27-33 GHz. The absorber material is assumed as the same substrate, but doped with conductive media (e.g., carbon) to increase its conductivity and, in turn, its loss tangent as: \(\epsilon_{r}=6.15\) and \(tan\delta=0.7\). The presented topology serves as a proof of concept and more practical designs with higher port numbers will be investigated in the future.
## 3 Results
### Temporal and Spatial Evolution of Vegetation Net Primary Productivity
#### 3.2.2 Spatial Variation Characteristic  
As shown in Figure 6a and Table 3, changes of vegetation NPP showed an obvious upward trend from 2000 to 2020 in Beijing. The NPP rising area accounted for 86.77% of the total area, showing a ring shape. The extremely significant increase (\(p<0.01\)) accounted for about 70.62% of the total area, mainly distributed in the west of Fangshan District, Mentougou District, north of Changing District, Yanqing District, Huairou District, Miyun District, Pinggu District, Shunyi District, Tongzhou District, Daxing District. Among the above areas, Shunyi District, Tongzhou District, Daxing District and Pinggu District are flat and far from the urban agglomeration, and the land use type is given priority to cropland and grassland. In other areas, the land use type is mainly forest and grassland with high terrain and less negative human disturbance, however, the changes of vegetation NPP of the mountain ridge area belongs to slightly rise and significantly rise. The main types of land use in the stable area are mainly impervious surface and water body which has no vegetation. The decrease of NPP accounted for 0.4% of the total area, among which, the extremely significant decline (\(p<0.01\)) and significant decline (\(0.01\leq p<0.05\)) accounted for about 10.04% of the total area, mainly distributed in the central area of urban agglomeration with flat terrain. Due to the negative influence of social and economic development, human activities and population density, vegetation activity weakened and NPP decreased.  
Figure 5: Temporal variation of vegetation NPP in Beijing.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Change Trend** & **Significance Level** & **Category** & **Area** & **Proportion** \\ \hline \(<\)0 & \(p<0.01\) & Extremely significant decline & 2.14 & 0.01\% \\ \(<\)0 & \(0.01\leq p<0.05\) & Significant decline & 4.57 & 0.03\% \\ \(<\)0 & \(p\geq 0.05\) & Slightly decline & 59.59 & 0.36\% \\ \(=\)0 & & Stable & 2102.27 & 12.82\% \\ \(>\)0 & \(p\geq 0.05\) & Slightly rise & 1092.03 & 6.66\% \\ \(>\)0 & \(0.01\leq p<0.05\) & Significant rise & 1555.27 & 9.49\% \\ \(>\)0 & \(p<0.01\) & Extremely significant rise & 11,577.04 & 70.62\% \\ \hline \hline \end{tabular} Note: Unit of area: km\({}^{2}\).  
\end{table}
Table 3: Statistics of variation trend of vegetation NPP in Beijing from 2000 to 2020.
## 2 Materials and Methods
### Anti-Aflatoxin Activity Measurement  
#### 2.3.1 Comparison of the Expression Levels of Genes Involved in Aflatoxin Synthesis  
_A. flavus_ ATCC 22546 and _A. flavus_ ATCC 9643 were cultured in malt extract and potato dextrose agar media at 25 \({}^{\circ}\)C and 80% humidity in a growth chamber for one week. The _A. flavus_ ATCC 96045 strain was cultured in malt extract agar medium at 31 \({}^{\circ}\)C and 80% humidity for 14 days. Subsequently, the strains were inoculated into a 25 mL liquid medium and incubated at 25 \({}^{\circ}\)C and 180 rpm for five days. After cultivation, fungal mycelia were collected using a strainer (pore diameter: 40 \(\mu\)m; SPL Life Sciences Co., Pocheon, Gyeonggi-do, Republic of Korea) and homogenized by adding liquid nitrogen. After homogenization, RNA was extracted using TRIzol reagent (Thermo Fisher Scientific Inc., Waltham, MA, USA). The concentration and purity of the extracted RNA were determined using a \(\mu\)Drop\({}^{\text{TM}}\) Plate (Thermo Fisher Scientific Inc., Waltham, MA, USA) and visualized via electrophoresis on 2% (\(w/v\)) agarose gel. Thereafter, 2 \(\mu\)g of complementary DNA (cDNA) was synthesized using the extracted RNA and Maxima First Strand cDNA Synthesis Kit (Thermo Fisher Scientific Inc.). The synthesized cDNA (100 ng) was subjected to quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis using the Rotor-Gene SYBR Green PCR Kit (Qiagen, Germany), 18S rRNA was used as a housekeeping gene, and the RNA amounts of _aflC_, _aflD_, _aflE_, _aflG_, _aflK_, _aflL_, _aflO_, _aflP_, _aflQ_, and _aflR_ were quantified. The qRT-PCR was performed in triplicate, and the results were relatively quantified using the 2\({}^{-\Delta\Delta\text{Ct}}\) method. All the primers used in this experiment were synthesized by Genotech Co., Ltd. (Daejeon, Republic of Korea), and they were listed as previously reported in Table 1[15; 16].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \
\begin{tabular}{c} **Gene** \\ **Symbol** \\ \end{tabular} &
\begin{tabular}{c} **Gene Function** \\ \end{tabular} & \multicolumn{2}{c}{**Primer Sequences (5\({}^{\prime}\)-3\({}^{\prime}\))**} \\ \hline \multirow{2}{*}{18S rRNA} & \multirow{2}{*}{Housekeeping gene} & F & ATGGCCGTTCTTAGTTG \\ \cline{3-4}  & & R & GTACAAAAGGGCAGGAGGTA \\ \hline \multirow{2}{*}{_aflC_} & \multirow{2}{*}{Polyketide synthase} & F & ACTGGCAACTGCAAACCCTA \\ \cline{3-4}  & & R & CCAGCCGTTTGATAACACC \\ \hline \multirow{2}{*}{_aflD_} & \multirow{2}{*}{Reductase} & F & CCAACATGCACGACTATGCG \\ \cline{3-4}  & & R & GCCGTGACCTTGTTTCTC \\ \hline \multirow{2}{*}{_aflE_} & \multirow{2}{*}{NOR reductase} & F & CGTCTCACTACTACAAGGCCAG \\ \cline{3-4}  & & R & TCCGATCACTCTTCCACAC \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of primers used for qRT-PCR.
## 3 Methodology
### Hydrological Model  
The Xin'anjiang (XAJ) [34], a conceptual rainfall-runoff model, was adopted for hydrological simulation and forecasting in this study. The XAJ model is easy to use with minimum input data preparation, which has been widely used worldwide [25; 28; 35]. Figure 2 shows the flowchart of the XAJ model. There are four parts of calculation process within the XAJ [35]: (1) The evaporation module, which calculates the evaporation in three soil layers, including an upper layer, a lower layer, and a deep layer, based on the watershed saturation-excess runoff theory. (2) The runoff yielding module, which uses the storage curve to calculate the total runoff based on the concept of runoff formation on depletion of storage. Therefore, the runoff is not generated until soil moisture reaches the filled capacity. (3) The runoff sources partition module, which divides the total runoff into three components (surface runoff, interflow, and groundwater runoff) by using a free water capacity distribution curve. (4) Finally, the runoff concentration module, in which the surface runoff is routed by the unit hydrograph, and the interflow and groundwater flow are routed by the linear reservoir method. A total of 15 parameters need to be calibrated within the XAJ model: four related to evaporation, two related to runoff generation, and nine related to runoff routing. More details can be found in Ren-Jun [34].  
The XAJ model needs basin-averaged precipitation, temperature, and potential evaporation as inputs, which were calculated by using the Thiessen Polygon method based on the collected datasets in this study. It was calibrated during the 2014-2016 period and validated during the 2017 period. During the calibration, the SCE-UA algorithm [36] was used to optimize model parameter sets based on the well-known objective functions of the Nash and Sutcliffe efficiency (NSE) [37] and water volume Relative Error (RE). The NSE and RE are defined as follows:  
\[NSE=1-\frac{\sum\limits_{i=1}^{n}{(Q_{i}^{obs}-Q_{i}^{sim})}}{\sum\limits_{i= 1}^{n}{(Q_{i}^{obs}-\overline{Q_{i}^{obs}})}} \tag{4}\]  
\[RE(\%)=\frac{\sum\limits_{i=1}^{n}{(Q_{i}^{obs}-Q_{i}^{sim})}}{\sum\limits_{i =1}^{n}{(Q_{i}^{obs})}}\times 100 \tag{5}\]  
where \(Q_{i}^{obs}\) represents the daily observed streamflow on the _ith_ day, and \(Q_{i}^{sim}\) represents the simulated value. \(\overline{Q}^{obs}\) represents the average value of all the daily observed streamflow. The NSE ranges from minus infinity to 1 and the RE ranges from 0 to infinity. A larger NSE and a smaller RE represent a better performance.
## 2 Background and Preliminaries
### Federated Learning Algorithm
#### 2.4.2 Federated Averaging (FedAVG)  
Federated averaging (FedAVG) is a communication-efficient algorithm for distributed training with an enormous number of clients [28]. It ensures data privacy and security and maintains data locality by enabling model training without sharing the raw data. It uses one aggregation by the server in each communication round, which significantly reducing the communication cost between the server and clients. Instead of sharing the gradients with the central server, weights tuned to the local model are shared. Finally, the server aggregates the clients' weights (model parameters). The fundamental idea is that clients run multiple updates of model parameters before passing the updated weights to the central server [26]. Algorithm 2 describes the logic of FedAVG.  
```
1:Server executes:
2:Initialize \(w_{0}\)
3:for each round \(t=1,2,\ldots\)do
4:\(m\leftarrow\max(C,K,1)\)
5:\(S_{t}\leftarrow\) (random set of \(m\) clients)
6:for each client \(k\in S_{t}\) in parallel do
7:\(w_{t+1}^{k}\leftarrow\) (ClientUpdate) \((k,w_{t})\)
8:endfor
9:\(w_{t+1}\leftarrow\sum_{k+1}^{K}\frac{n_{k}}{n_{t}}w_{t+1}^{k}\)
10:endfor
11:functionClientUpdate(\((k,w)\)) \(\triangleright\) Run on client \(k\)
12:\(\beta\leftarrow\) (split \(P_{k}\) into batches of size \(B\))
13:for each local epoch \(i=1\) to \(E\)do
14:for each batch \(b\in\beta\)do
15:\(w\gets w-\eta\
abla l(w;b)\)
16:endfor
17:endforreturn\(w\) to server
18:endfunction
```  
**Algorithm 2** Federated averaging. The \(K\) clients are indexed by \(k\); \(B\) is the local minibatch size, \(E\) is the number of local epochs, and \(\eta\) is the learning rate [9]
# Quality Assessment of HDR/WCG Images Using HDR Uniform Color Spaces
## 2 From State-of-the-Art SDR Quality Assessment Metrics to HDR/WCG Quality Assessment Metrics
### Perceptually Uniform Color Spaces
#### 2.1.3 \(J_{z}a_{z}b_{z}\)  
\(J_{z}a_{z}b_{z}\)[22] is a uniform color space allowing to increase the hue uniformity and to predict accurately small and large color differences, while keeping a low computational cost. It is computed from the \(XYZ\) values (with a standard illuminant D65) in five steps:  
* First, the \(XYZ\) values are adjusted to remove a deviation in the blue hue. \[\begin{bmatrix}X^{\prime}\\ Y^{\prime}\end{bmatrix}=\begin{bmatrix}bX\\ gY\end{bmatrix}-\begin{bmatrix}(b-1)Z\\ (g-1)X\end{bmatrix}\] (9) where \(b=1.15\) and \(g=0.66\).
* Second, the \(X^{\prime}Y^{\prime}Z\) values are converted to \(LMS\) values \[\begin{cases}L=&0.41478972\times X^{\prime}\ +0.579999\times Y^{\prime}\quad+0.0146480 \times Z\\ M=&-0.2015100\times X^{\prime}\ +1.120649\times Y^{\prime}\quad+0.0531008 \times Z\\ S=&-0.0166008\times X^{\prime}\ +0.264800\times Y^{\prime}\quad+0.6684799\times Z \end{cases}\] (10)Third, as for \(ICtCp\), the inverse EOTF PQ is applied on each \(LMS\) component \[\begin{cases}L^{\prime}&=EOTF_{PQ}^{-1}(L)\\ M^{\prime}&=EOTF_{PQ}^{-1}(M)\\ S^{\prime}&=EOTF_{PQ}^{-1}(S)\end{cases}\] (11)
* Fourth,the luminance \(I_{z}\) and the chrominance \(a_{z}\) and \(b_{z}\) are calculated \[\begin{cases}I_{z}=&0.5\times L^{\prime}\ +0.5\times M^{\prime}\\ b_{z}=&3.5240000\times L^{\prime}\ -4.0667080\times M^{\prime}\ \ \ \ +0.5427080\times S^{\prime}\\ a_{z}=&0.1990776\times L^{\prime}\ +1.0967990\times M^{\prime}\ \ \ -1.2958750\times S^{\prime}\end{cases}\] (12)
* Finally, to handle the highlight, the luminance is adjusted: \[J_{z}=\frac{(1+d)\times I_{z}}{1+(d\times I_{z})}-d_{0}\] (13) where \(J_{z}\) is the adjusted luminance, \(d=-0.56\) and \(d_{0}\) is a small constant: \(d_{0}=1.6295499532812566\times 10^{-11}\).
# Smart Sensors and Smart Data for Precision Agriculture: A Review
## 2 Types of Smart Sensors in Precision Agriculture
### GPS and Remote Sensing Technologies
The study assesses the potential of Sentinel-2 and Landsat imagery in PA to address production variability within the field, and determines the optimal timing for remote sensing to correlate with durum wheat yield. In the study, NDVI (Normalized Difference Vegetation Index) was compared with yield monitoring data, revealing significant and highly positive linear relationships (ranging from 0.54 to 0.74), explaining most of the within-field variability. Application of remote sensing data with these methods can effectively assess durum wheat yield, describe spatial variability, provide location-specific management, enhance productivity, save time, and offer a potential alternative solution for traditional farming practices. The authors of [100] discuss the challenges associated with Ground Control Points (GCPs) in agricultural remote sensing using UAVs. Conventional stationary GCPs are labor-intensive and time-consuming in large terrain configurations. The authors present an autonomous mobile GCP and a collaborative strategy to improve the efficiency and accuracy of UAV-based data collection. This system's ability to automatically follow the trajectory was demonstrated in preliminary tests, reducing the RMSE (Root Mean Square Error) lateral deviation from 34.3 cm to 15.6 cm. These tests also validated the feasibility of moving reflectance reference panels without affecting pixel values in mosaic images. In field tests, the autonomous mobile GCP successfully collaborated with the UAV in real time, outperforming conventional calibration methods employing stationary GCPs in terms of georeferencing, radiometric calibration, height calibration, and temperature calibration. The authors of [101] address the challenge of accurate georeferencing of orthomo-saics obtained from UAV images, a critical factor for informed decision-making and the implementation of precise actions in crops. The study also assesses the performance of Real-Time Kinematics (RTK) and Virtual RTK during UAV flights, intending to produce high precision orthomosaics applicable to precision farming processes. The results demonstrate a significant reduction in positioning errors, up to 98%, compared with direct GPS georeferencing. The accuracy assessment involves a static survey using the Mallegan Professional DGPS ProMark3 for precise location measurements of seven control points, which are then compared with estimated orthomosaic coordinates.
## 3 Results and Discussion
### Beam Shaping  
The results presented in Figure 3 demonstrated that the output SDoC could be precisely controlled by the proper choice of \(T\). A related application where the Section 2.1 analysis can also be applied is beam shaping.  
The goal in beam shaping is to produce a beam with a desired SD at a desired location--typically, at the focus of a lens or, equivalently, in the far zone of the source. Referring back to Equation (15), propagating \(W^{\text{out}}\) to the far field, and setting \(\boldsymbol{\rho}_{1}=\boldsymbol{\rho}_{2}=\boldsymbol{\rho}\) yields the far-zone SD:  
\[S^{\text{fz}}\left(\boldsymbol{\rho},z\right)\approx\frac{M^{2}}{\lambda^{2} z^{2}}\tilde{S}^{\text{src}}\left(0\right)\tilde{n}^{\text{src}}\left(0 \right)\left|T\left(-f_{2}\frac{\boldsymbol{\rho}}{z}\right)\right|^{2}. \tag{22}\]  
Note that the source takes the shape of the absolute square of the filter--magnified and rotated \(180^{\circ}\)--in the far field. This can be exploited to produce beams of any desired shape.  
As an example, Figure 4 shows the results of a beam shaping simulation. The source (see Equation (20)) and set-up were identical to that described in Section 2.2, only \(T\) was changed to produce a beam with a more complex shape. Figure 2b shows this \(T\).  
Figure 4a,b shows \(S^{\text{out,thy}}\) (obtained from Equation (16)) and \(S^{\text{out,sim}}\), respectively. By design, these SDs should look very similar to those in Figure 3a,b--the differences are due to the different \(T\). Figure 4c shows the absolute value of the pixel-by-pixel difference of Figure 4a,b.  
Figure 4d,e shows the theoretical and simulated far-zone SDs, \(S^{\text{fz,thy}}\) obtained from Equation (22) and \(S^{\text{fz,sim}}\), respectively. The theoretical far-zone SD \(S^{\text{fz,thy}}\) is the desired beam shape. \(S^{\text{fz,sim}}\) was computed from 2000 far-zone propagated instances of \(U^{\text{out}}\) (these propagations were simulated using fast Fourier transforms [29]), which were obtained from 2000 instances of \(U^{\text{src}}\), synthesized in the manner described in [28]. Similarly, Figure 4c,f shows the absolute value of the pixel-by-pixel difference of Figure 4d,e.  
The agreement between the simulated and theoretical SDs is excellent. These results further validate the analysis presented in Section 2.1.  
If one were to physically generate this source, it would start as partially coherent light emitted from a MMF. It would exit the spatial filter as a circular flat-topped beam, as shown in Figure 4a,b. As the beam propagated away from the spatial filter output plane, it would morph into the Celtic cross image shown in Figure 4d,e.
## 3 Design of Fuzzy PID Controller for Servo Precision Press
### Fuzzy Inference Rules  
The core of fuzzy control design is to establish a suitable fuzzy control rule table based on existing technical knowledge and practical operational experience, combined with various states of position error and error change rate, including large, medium, and small.  
(1) The function of the proportional coefficient \(K_{P}\) is to accelerate the response speed of the system, and the output of the proportional controller is proportional to the input error signal.  
Figure 7: Degree of membership function of \(E\).  
(2) The function of the integration coefficient \(K_{I}\) is to eliminate the steady-state error of the system, and the output of the integration controller is proportional to the integration of the input error signal.  
(3) The function of the differential coefficient \(K_{D}\) is to improve the dynamic performance of the system, and the output of the differential controller is directly proportional to the differential of the input error signal.  
The output quantity tuned by fuzzy control rules is a fuzzy set, which needs to be converted into an exact value before output. Using the centroid method, the exact value of the output quantity is calculated based on the membership degree of the output fuzzy subset.  
The formula for solving the fuzzy PID parameters \(K_{P}\), \(K_{I}\), and \(K_{D}\) is as shown in Equation (5).  
\[\left\{\begin{array}{l}K_{p}=f(E,Ec)=\dfrac{\sum\limits_{i=1}^{p}\mu_{i}(E,Ec) K_{pi}}{\sum\limits_{i=1}^{p}\mu_{i}(E,Ec)}\\ K_{I}=f(E,Ec)=\dfrac{\sum\limits_{i=1}^{p}\mu_{i}(E,Ec)K_{li}}{\sum\limits_{i=1 }^{p}\mu_{i}(E,Ec)}\\ K_{D}=f(E,Ec)=\dfrac{\sum\limits_{i=1}^{p}\mu_{i}(E,Ec)K_{Di}}{\sum\limits_{i=1 }^{p}\mu_{i}(E,Ec)}\\ \end{array}\right., \tag{5}\]  
In the formula, \(K_{P}\), \(K_{I}\), and \(K_{D}\) are determined output quantities; \(K_{Pi}\), \(K_{li}\), and \(K_{Di}\) are fuzzy quantities; and \(\mu\) is the fuzzy control membership degree.  
Figure 8 shows the output surfaces diagrams in \(K_{P}\), \(K_{I}\) and \(K_{D}\) from the MATLAB control toolbox.
## 4 Results and Discussion
### EEG Data Results  
Table 1 presents the average accuracies achieved by the proposed classification systems for both the MI and WM databases, for each effective connectivity method studied. For the MI database, in the training-validation stage, the classifier based on TE\({}^{\theta}_{\kappa\alpha}\) features exhibited the highest average performance, closely followed by the one based on GC\({}^{\theta}\). In the testing stage, we observe the same overall accuracy ranking, although a smaller drop in the classification accuracy occurs for TE\({}^{\theta}_{\kappa\alpha}\) than for GC\({}^{\theta}\), which points to a better generalization capacity by the system trained using features extracted through phase TE. For the WM database, the classifier trained from TE\({}^{\theta}_{\kappa\alpha}\) features also displays the highest average accuracy. However, in this case, there is a large gap in performance between the TE\({}^{\theta}_{\kappa\alpha}\)-based classification system and the closest results from an alternative approach. Furthermore, the results in Table 1 show a consistent improvement in performance between the classifiers that use real-valued TE estimates and those that are trained from phase TE values. They also show relatively low accuracies for the classifiers trained using PSI features. We believe the latter can be explained by two factors. First, by definition, the PSI is unable to explicitly detect bidirectional interactions. It measures connectivity in terms of lead/lag relations, which leads to ambiguity regarding the meaning of PSI values close to zero, since they can be the result of either the lack of interaction or evenly balanced bidirectional connections. If the relevant information to discriminate among the conditions of a cognitive paradigm is related to the bidirectionality of interactions, such as those present in WM [50; 51], then the PSI might not be an adequate characterization strategy. Secondly, the PSI, like GC, is a linear measure; its performance degrades for strongly nonlinear phase relationships.  
In the sections below, we detail and further discuss the results obtained for each database.
## 2 Methodology
### Visibility Analysis
#### 2.1.2 Algorithmic Implementation  
The implementation process for the visibility analysis optimization algorithm is as follows:  
1. Employ the visibility analysis algorithm to calculate the visible area of the observation points.
2. Take the center points of each grid in the visible area as the target points.
3. Implement the visibility analysis optimization algorithm to compute the optimization parameter values corresponding to each target point.
4. The visible area obtained by weighting the optimization parameters and their corresponding weights (calculated using the AHP) can quantitatively evaluate tourists' visual experience of sightseeing. Utilize the GIS view function to display the visibility at graded levels from low to high based on the visual experience scores.  
Figure 3: Sensitivity diagram considering the front horizontal view as an example.