esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of a Coherent Dual-Beam Array Feed Network for Aperiodic Concentric Ring Antennas
## 2 Problem Formulation
### Coherent BFN Model  
In this paper, a coherent feeding network with dual-beam shaping and steering capabilities is used in combination with an ACRA in our numeric experimentation. The feeding system relies on the working principles of coherently radiating periodic structures implemented as a beamforming network (also known for its acronym as C-BFN). This feeding network, which combines the redirecting beam control and the power distribution network, uses a unit cell periodically and alternately as a power combiner and divider to lead the signals from the input ports to the radiators. In a C-BFN, each input port obeys an in-phase excitation law (true time delay) and the output ports have a Gaussian-shape amplitude distribution [26; 27]. Usually, the topology of the coherent network is built by using unit cells along with consecutive layers, as is illustrated in Figure 2. This specific example shows the signal propagation from three different input ports to the shared outputs. In this feeding system,  
Figure 1: General layout of a concentric circular ring array (CCRA).  
the power throughout the network shows a binomial excitation behavior [15]. The modular arrangement uses the unit cell as a key piece of the network, facilitating the use of distinct topologies for specific design needs. A C-BFN should guarantee an in-phase performance at the output, no matter its periodicity. Practical research of the feeding system relies on implementing unit cells using different multi-port devices [15; 26; 28].  
As shown in Figure 2, a three-port component is implemented for power division and combination, and its behavior can be described as follows [15]:  
\[[\vec{s}]_{\text{cell}}=\begin{bmatrix}0&j/\sqrt{2}&j/\sqrt{2}\\ j/\sqrt{2}&0&0\\ j/\sqrt{2}&0&0\end{bmatrix}. \tag{3}\]  
This [PERSON]'s matrix ensures isolation among the inputs with a matching behavior that can be observed at the ports. Assuming transmit mode, a transfer matrix can be applied for assessing the final output of the coherent feeding system, as follows [29]:  
\[[\vec{a}_{m+1}]_{(m+1,1)}=[T_{m}]_{(m+1,m)}\cdot[\vec{a}_{m}]_{(m,1)}, \tag{4}\]  
where \([\vec{a}_{m+1}]\) represents the output of the coherent network using one layer. The above can be extended for more layers if necessary. On the other hand, \([\vec{a}_{m}]\) is the excitation input. In both cases, \(m\) represents the feeding ports.  
The transfer matrix for the coherent network is expressed as follows:  
\[[T_{m}]=\begin{bmatrix}\sqrt{2}/2&0&0&\cdots&0\\ 1/2&1/2&0&\cdots&0\\ 0&1/2&1/2&&\vdots\\ 0&0&\ddots&\ddots&0\\ \vdots&\vdots&&1/2&1/2\\ 0&0&\cdots&0&\sqrt{2}/2\end{bmatrix}_{(N,M)}. \tag{5}\]  
In the transfer matrix, its coefficients are related to the unit cell, as can be observed in (3), in which the rows denote power combination and the columns represent power division. Consequently, it is possible to synthesize this feeding system with a complete antenna system, constructing an architecture that relies on Figure 2 and evaluating (4) and (5).
# A Software-Defined Radio Platform for Teaching Beamforming Principles
## 2 System Architecture  
The beamforming platform was designed to combine commercial of-the-shelf RF products, which are typically accessible in any telecommunication laboratory. The aim of this approach is to develop students' competencies in integrating SDR, traditional RF equipment, and open software, offering them hands-on experience in using the customized beamforming platform.
## 3 Discussion  
### Proteome Profiling Revealed SRPs Contain Almost all NRB-Related Proteins  
Many proteins involved in the NRB process have been identified by proteomics-based technologies from different latex components [12; 13; 14; 15; 16; 17; 18; 19]. However, both the identified number of proteins and the quantification of target proteins are limited, probably due to the lack of assembled high-quality rubber tree genomes and high-resolution mass spectrometry data. Recently, more proteomics studies of rubber particles have been performed, and more NRB-related enzymes, including AACT, hydroxymethylglutaryl coenzyme A synthase (HMGS), mevalonate kinase (MEVK), farnesyl pyrophosphate/diphosphate synthase (FADS), geranylgeranyl pyrophosphate synthase (GGPS), etc., have been identified as membrane-bound/attached proteins in purified rubber particles [16; 17; 18; 19; 20; 21]. In a previous proteomic study, 186 proteins were positively identified from rubber particles, containing several isoforms of REF, SRPP, and cis-prenyl transferase (CPT) [20]. After the removal of the abundant REF and SRPP bands, 137 proteins, including 115 unique proteins, were further identified [18]. The protein profiles of LRPs and SRPs resulted in 53 DAPs, containing 22 gene products, such as SRPP, REF, HMGS, HSP70, and phospholipase D [16]. Interaction network analysis of rubber particle proteomics has revealed that the protein complexes of HRT1, REF, and HRT1-REF bridging protein (HRBP) might play crucial roles as NRB machinery [18], and these proteins are associated with the endoplasmic reticulum [22].  
Our 2-DE gel-based proteomics study resulted in more than 100 abundant proteins from rubber particles [23; 24], which is similar with previous reports [16; 18; 20]. In the past decade, only 313 proteins were identified from the Para rubber tree, and 1208 proteins from all rubber-producing plants [25]. Recently, more than 1600 proteins were identified from rubber total latex by iTRAQ [12], and 1839 unique proteins were determined by LC MS/MS from the whole translated draft _Hewea_ genome [19]. Our SRPs proteomics data produced more than 2000 unique proteins (Figure 4A, Table S3), which covers almost all the previously identified proteins. Pathway analysis (Figure 6) revealed that almost all proteins involved in NRB could be detected in SRPs.
# A Survey on Bias in Deep NLP
## 4 Discussion
### Translation  
Previously, we have seen XWEAT for the detection of bias in languages other than English. Although there are also alternatives that work with multiple languages, one of the areas of study is what occurs when translating a text, such as the work of [PERSON] and [PERSON] [41], which seeks and mitigates the bias in English-Spanish translations with the three versions of GloVe previously discussed (base, gender neutral, hard-debiased).  
Not only has translation bias been studied in open models, but also the bias in final products such as Google translator, Microsoft translator, Amazon translator, among others, has been evaluated in the study of [PERSON] et al. [76].  
[PERSON] and [PERSON] [66] pose a mismatch when using Google Translator for translating from languages such as Hungarian with neutral gender into English. The inferred gender does not proportionally represent the actual distribution of workers when making inferences about professions, using Google Translator. This same mismatch appears in Google Translator in more languages, such as Hungarian, Chinese, Yoruba and others when translated into English. In this case, [73] shows a very strong correlation between the science, technology, engineering and mathematics (STEM) family of academic disciplines and men.  
According to [PERSON] [85], Google has fixed the problem it had with Google Translator inferring gender when translating from non-gendered languages into English. In the Google AI blog, [PERSON] [86] develops the first approach to the problem. This solution was put into production in 2018. They trained a CNN with human categorized examples and further divided the training set into three chunks, one for masculine another feminine and another for neutral. To the sentences of each chunk they added in front a token of the type \"<2 MALE>\". Therefore, \"<2 MALE> O bitr doktor\" would translate to \"HE is a doctor\". Allowing this to use all 3 prefixes with the user input to give an unbiased response. This resulted in a recall of 60%.  
The next approach would come in 2020. [PERSON] [87] would firstly translate the phrase obviating the gender and secondly, would look for occurrences of the translated phrase from the same query but with the complementary gender. If only the gender changes when compared to the original translation, the phrase is returned to the user on both genders.
## 4 Experimental Verification  
### Experimental Setup  
The laboratory mock-up of the suspension bridge (in Figure 10) was used to verify the capability of the modal identification methods [37]. A voice coil vibrator was used to excite the structure (band-limited white noise). Prior to vibration measurements, the data acquisition system was established, which involved four single-axial accelerometers, positioned to measure vertical accelerations. The position of the accelerometers is illustrated in Figure 11a. The output data were obtained by repeated measurements, which covered all positions on the deck (Figure 11b). The sample setting was the same as Section 3.1. The modal parameters of the bridge were estimated by CP and GED.
## 3 The Proposed Approach
### The Proposed Approach  
The proposed approach generates test cases based on the following steps:  
1. [label=0]
2. Obtain a cause-effect graph of the procedure according to the INF specification;
3. The logical expression of the desired test part is obtained by assigning the cause logical variables according to the cause-effect graph;
4. An order of the logical variables is determined to construct an OBDD, which is then simplified using the deletion and merging rules to obtain an ROBDD. A node element in the ROBDD contains a variable, a left child node, and a right child node;
5. Determine the number of internal nodes in the ROBDD and denote it with \(k\);
6. Initialize the number of particles (\(N\)), individual learning factor (\(c_{1}\)), population learning factor (\(c_{2}\)), inertia weight (\(\omega\)), improvement rate (\(\alpha\)), and maximum number of iterations (\(N_{g}\)). The position and the fitness value of the historical best for each individual particle are set to 0, and the position and the fitness value of the historical best for the entire population are set to 0;
7. The particle positions (\(X_{i}\)) and velocities (\(V_{i}\)) are initialized according to \(k\) and Table 1, both of which are \(m\times d\) matrices. Each particle position represents \(m\) test cases, where each test case contains \(d\) inputs, and the matrix elements are randomly assigned as 0 or 1. Each particle velocity is an \(m\times d\) matrix of numbers randomly generated between 0 and 1;
8. Update the inertia weights according to Equation (11) and iterate the velocity according to Equation (8), where \(r_{1}\) and \(r_{2}\) are \(m\times d\) matrices of numbers randomly generated between 0 and 1. The iteration process of the elements in each particle position matrix is as follows: if the absolute value of a position element determined from the velocity matrix is greater than a number randomly generated between 0 and 1, the position element is flipped;
9. Calculate the improvement rate (\(\alpha_{t}\)) according to Equation (12) and the fitness function according to Equation (10) and update the historical best fitness value for each individual and its position and the global historical best fitness value and its position.  
Figure 6: A flowchart of the improved PSO algorithm.  
If \(\text{Fit}(X_{i})>\text{Fit}(P_{\textit{lbest}})\), replace the individual optimal position (\(P_{\textit{lbest}}\)) with \(X_{i}\). If \(\text{Fit}(X_{i})>\text{Fit}(P_{\textit{best}})\), replace the population optimal position (\(P_{\textit{best}}\)) with \(X_{i}\);
* Repeat steps 2 and 3 until the coverage of the generated test cases into pairs of combination tests reaches 100%;
* Output the optimal test case set (TS).
# A New GNSS-Derived Water Vapor Tomography Method Based on Optimized Voxel for Large GNSS Network
## 2 Data and Methods
### 3D WV Productions by GNSS Observations  
The 2D PWV product has limited applications in meteorology. [PERSON] et al. (2018) have proposed the 3D WV product through a tomographic algorithm in Hawaii based on GNSS observations. In the algorithm, the slant wet delay (SWD) is the key parameter, and is obtained by restoring the ZWD to the GNSS ray propagation direction. We use the following equation to calculate the SWD of each signal,  
\[\text{SWD}\ =\ \text{ZWD}\cdot M_{w}(E)+M_{\Delta}(E)\cdot(G_{NS}\cos A\ +G_{WE}\sin A)+R, \tag{4}\]  
where \(E\) and \(A\) are the elevation angle and azimuth angle of the satellite; \(M_{w}(E)\) and \(M_{\Delta}(E)\) are the wet mapping function and gradient mapping function; \(G_{NS}\)\(G_{WE}\) are the horizontal gradients in the north-south and east-west directions, respectively; and \(R\) is a residual term. We can then obtain the slant WV (SWV) by  
\[\text{SWV}_{i}^{m}\ =\ \prod\times\text{SWD}_{i}^{m}, \tag{5}\]  
where \(m\) and \(i\) are the GNSS station serial number and satellite serial number, respectively. \(\prod\) is obtained by Equation (3).  
After the retrieval of the SWV of each signal, the objects covered by the GNSS stations need to be divided into a finite number of voxels. Owing to these voxels, an integral signal is discretized into several line segments. Therefore, the complete SWV carried by a ray is divided into several parts by voxels; each part represents the moisture content of its voxel. Through a large number of such rays, the 3D moisture field in the objects can be reconstructed. The above process can be described by  
\[\text{SWV}^{m}\ =\ \sum{}_{ijk}\left(A_{ijk}^{m}\cdot X_{ijk}\right), \tag{6}\]  
where \(\text{SWV}^{m}\) is the SWV of the effective signal \(m\) passing through the top boundary of the objective area. Only such signals can be used for the next processing, because the SWVs carried by them are completely included in the study area. Notably, differing from Equation (5), the \(m\) refers to the serial number of an effective signal whose quantity was determined by both GNSS stations and satellites. \(A_{ijk}^{m}\) is the intercept of the signal \(m\) in voxel \((i,j,k)\). \(X_{ijk}\) is the corresponding WV density of the voxel. Based on the effective signal with the SWV at an epoch, we obtain the observation equation,  
\[Ax\ =\ d, \tag{7}\]  
where \(A\) is the coefficient matrix composed of the signal intercept, \(d\) is the column vector of all the effective SWV observations, and \(x\) is a column vector of unknown parameters that represent the WV density in each voxel. Overall, the 3D WV product can be obtained by solving the linear Equation (7) according to the GNSS observations.
# Comparison of CS-Based Channel Estimation for Millimeter Wave Massive MIMO Systems
## 6 Conclusions  
In this paper, with a unified massive MIMO framework, we overview the CS-based mmWave channel estimation algorithms, namely convex relaxation algorithms, greedy iteration algorithms and Bayesian inference algorithms. The properties of three categories of algorithms are presented and how the estimation quality is affected by the sparseness, pilot symbols, angle quantization and transmitter's hardware impairment is also compared. Acquiring a tradeoff between the performance and computation complexity, the greedy iterative algorithm SP outperforms others at the low SNR region but the BCS algorithm is the best at the high SNR range. Furthermore, it is possible to extend our work to other sparse signal recovery problems as the three categories of algorithms are common methods.  
[PERSON] and [PERSON] conceived the model; [PERSON] performed the performance analysis and simulation results; [PERSON] and [PERSON] analyzed the numerical and simulation; [PERSON] wrote the paper; and [PERSON] and [PERSON] provided some suggestions and revised the paper.  
This work was supported by the National Natural Science Foundation of China (Nos. 61771487 and 61471393).  
The authors declare no conflict of interest.
## 3 Case Study
### Evaluation Metrics  
This study evaluated the model performance using \(RMSE\), \(MAE\), and the regression coefficient (\(R^{2}\)). The calculation formula is as follows:  
\[RMSE=\sqrt{\frac{1}{m}*\sum_{i=1}^{m}\left(y^{(i)}-\hat{y}^{(i)}\right)^{2}} \tag{8}\]  
\[MAE=\frac{1}{m}*\sum_{i=1}^{m}\left|y^{(i)}-\hat{y}^{(i)}\right| \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Item** & \(\beta\) **= 0** & \(\beta\) **= 0.1** & \(\beta\) **= 0.2** & \(\beta\) **= 0.3** & \(\beta\) **= 0.4** & \(\beta\) **= 0.5** & \(\beta\) **= 0.6** & \(\beta\) **= 0.7** & \(\beta\) **= 0.8** & \(\beta\) **= 0.9** & \(\beta\) **= 1** \\ \hline RMSE & 0.263 & 0.262 & 0.263 & 0.28 & 0.269 & 0.269 & 0.27 & 0.272 & 0.269 & 0.275 & 0.27 \\ MAE & 0.176 & 0.174 & 0.175 & 0.183 & 0.179 & 0.178 & 0.178 & 0.181 & 0.178 & 0.182 & 0.186 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Prediction accuracy for various values of the weight coefficient.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Hidden Layers** & **Neurons/Units** & **Activation Function** & **Optimizer** & **Learning Rate** & **Iterations/Epochs** & **Other Settings** \\ \hline BP & \(1\) & 100 & \begin{tabular}{c} Hyperbolic \\ tangent \\ function (Tanh) \\ \end{tabular} & [PERSON] & 0.01 & 1000 & \begin{tabular}{c} Training function: \\ Levenberg– \\ Marquardt; early stopping: \\ error \(c\)\(\times\)\(1\times\)\(10^{-4}\) or \\ validation fail \(>\) 20 \\ \end{tabular} \\ \hline LSTM & \(1\) (LSTM layer) & 200 & \begin{tabular}{c} Rectified linear \\ unit (ReLU) \\ \end{tabular} & [PERSON] & 0.01 (initial) & 100 & \begin{tabular}{c} Input steps: 5; output \\ step: 1; \\ regularization: 0.01; \\ LR decay every 30 \\ epochs by 0.2 \\ \end{tabular} \\ \hline ISVR & – & – & RBF kernel & – & – & – & – & \begin{tabular}{c} \(c\) \(c\) and \(\gamma\) via grid \\ search: \(c\)\(\in\)\([1,20]\) and \\ \(\gamma\)\(\in\)\([0.10,10]\); 5-fold \\ \end{tabular} \\ \hline SSA-ISVR & – & – & RBF kernel & SSA & – & \
\begin{tabular}{c} \(20\) (SSA \\ iterations) \\ \end{tabular} &
\begin{tabular}{c} \(c\) \(c\) \(\in\)\([1,20]\) and \\ \(\gamma\)\(\in\)\([0.01,10]\); \\ population size: 100; \\ fitness: CV MAE \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model architectures and training parameters.  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{m}{(y^{(i)}-\hat{y}^{(i)})}^{2}}{\sum\limits_{i=1 }^{m}{(y^{(i)}-\overline{y})}^{2}} \tag{10}\]  
where \(m\) is the number of samples and \(i=1,2,3,\ldots,m\).
# Deep Neural Network and Long Short-Term Memory for Electric Power Load Forecasting
## 4 Case Studies and Discussion
### Performance Evaluation Metrics  
Four widely used performance metrics (i.e., MAE, RMSE, CVRMSE [61], R2, and MAPE) were adopted to assess the prediction accuracy of the proposed methods.  
\[\text{MAE}=\frac{1}{n}\times\sum_{i=1}^{n}\left(\left|y_{i}-\hat{y}_{i}\right| \right) \tag{3}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\times\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i} \right)^{2}} \tag{4}\]  
\[\text{CVRMSE}=\frac{\text{RMSE}}{\overline{y}_{i}}\times 100 \tag{5}\]  
\[\text{R}^{2}=1-\frac{\text{SSE}}{\text{SST}}=1-\frac{\sum_{i=1}^{n}\left(y_{ i}-\overline{y}_{i}\right)^{2}}{\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}} \tag{6}\]  
\[\text{MAPE}=\frac{100}{n}\times\sum_{i=1}^{n}\left|\frac{y_{i}-\hat{y}_{i}}{y_ {i}}\right| \tag{7}\]  
In Equations (3)-(7), \(y_{i}\) identifies the actual value of sample \(i\), \(\hat{y}_{i}\) identifies the predicted value of sample \(i\), \(n\) is the testing data, \(\overline{y}_{i}\) indicates the mean of the predicted values, SSE denotes the residual sum of squares, and SST represents the total sum of squares.  
Finally, the calculation time required to perform the computation process for the proposed method was also adopted to evaluate the proposed methods.
# Clustering for Lifetime Enhancement in Wireless Sensor Networks
## 4 Simulation Results
### Second Scenario: The Base Station (BS) Is Located at the Center of the Area (100, 100)
Table 5 presents the values for the FDN, MDN, and LDN parameters. Figure 19: Topology with three zones—nodes within 83 m transmit individually, those between 83 and 123 m transmit in pairs, and nodes beyond 123 m transmit in trios. The base station (BS) is located at (100, 100). Figure 20: Lifetime for individual nodes, Cluster-2, and Cluster-3 topologies with BS at (100, 100). Figure 21 demonstrates that partitioning the WSN according to our study improves its performance. The individual node topology is the weakest, but when the WSN is divided into two zones (Figure 18), its lifetime increases significantly. Adding a third zone for nodes beyond 123 m, organized into trios, further enhances the lifetime (Figure 21). According to Table 3, the FDN, which represents the lifetime, improves from 330 rounds for the individual topology to 510 and 628 rounds for the Cluster-2 and Cluster-3 topologies, respectively. However, as mentioned earlier, throughput represents the number of packets sent to the BS. It is related to the number of individual nodes, pairs, and trios in the WSN and their lifetimes. Considering the number of individuals, pairs, and trios in the WSN, it is clear that the individual topology has the highest value, followed by pairs and trios. However, lifetime also plays a crucial role, which explains why Cluster-3 has a higher throughput value than Cluster-2 (see Figure 15). The individual topology does not show a significant difference compared to the other topologies. This can be explained by the short area, where individual nodes dominate, and the lack of long distances that would otherwise challenge this topology. Ultimately, these values vary depending on the distance to the BS and the area dimensions. Figure 21: Throughput for individual nodes, Cluster-2, and Cluster-3 topologies with BS at (100, 100). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Topology** & **FDN** & **MDN** & **LDN** \\ \hline Individual & 330 & 1142 & 2495 \\ \hline Cluster-2 & 510 & 1113 & 2495 \\ \hline Cluster-3 & 628 & 1140 & 2495 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: FDN, MDN, LDN for individual nodes, Cluster-2, and Cluster-3 topologies.
# Soriasis: From Pathogenesis to Pharmacological and Nano-Technological-Based Therapeutics
## 5 Nanotechnological Approaches for Psoriasis
### Lipid-Based Nanoparticles
#### 5.2.2 Lipospheres  
Lipospheres are lipid-based nanoparticles, which are produced by a stabilization of an aquaphobic core by coating phospholipid molecules on its surface. This structure provides some features to lipospheres that could not be compared with other lipid-based nanoparticles, such as its low costs, an increased stability, easy production, controlled release rate, and a good dispersion rate in aqueous media [141]. Lipospheres have been used in different administration routes, such as oral, intravenous, or transdermal, with good results in the treatment of some diseases, including psoriasis.  
In psoriasis treatment, [PERSON] et al. used tacrolimus and curcumin loaded in a liposphere gel formulation and studied its effectiveness in psoriasis patients compared to a control formulation of both drugs in a solution [142]. Lipospheres of 47 nm were produced and incorporated to topical gel formulation. Furthermore, there was a study of distribution of the formulation, observing an improvement on the permeation in the different layers of the skin in mouse models. In addition, there was a study of the effectivity of the treatment compared to the use of the curcumin and tacrolimus solution, being observed as an enhanced performance and histopathological improvement with the lipospheres formulation. Moreover, there was a notable decrease in the levels of TNF-\(\alpha\), IL-22, and IL-17 compared to actual treatment, suggesting that the use of lipospheres could be interesting in the development of new treatments for psoriasis.