esa-sceva/satcom-chunk-collection huggingface dataset:
# Resonant X-ray Emission Spectroscopy with a SASE Beam
## 2 Materials and Methods  
The experiment was performed at the X-ray Pump-Probe (XPP) instrument (SLAC National Accelerator Laboratory, Menlo Park, CA, USA) [13] of the Linac Coherent Light Source (LCLS) [14]. Four percent wt. aqueous solution of iron (III) oxide nanoparticles circulated in the form of a 200 \(\upmu\)m-thick jet was irradiated with 35 fs-short (FWHM) pulses of 1.29 \(\times\) 10\({}^{12}\) photons at the repetition rate of 120 Hz. The beam spot on the sample had an area of 500 \(\upmu\)m\({}^{2}\). The nanoparticles' average size was 30 nm and polypropylene glycol agent was added to the solution to prevent foaming. The mean photon energy was varied around the Fe K-edge ionization threshold (7123 eV) by ramping the electron beam energy up and down at the frequency of 5 Hz.  
The spectrum of each incident SASE pulse was measured non-invasively with high energy resolution using a transmissive spectrometer [15] equipped with a thin bent Si(400) crystal and an ORCA detector composed of \(2048\times 2048\) square pixels with a side length of 6.5 \(\upmu\)m. The spectrometer was adjusted to cover the energy range from 7090 to 7170 eV, which was broad enough to catch the full spectral envelope of each XFEL pulse accounting also for the XFEL jitter, and was more than sufficient for the incident photon energy range in the RXES maps. The average energy bandwidth was 52 meV/pixel, which was sufficient to resolve single spikes in the SASE spectra. The Fe K\(\beta\) XES spectra were acquired by means of a [PERSON] spectrometer [16] composed of a cylindrically bent InSb(444) crystal and a CSPAD-140k detector [17] equipped with a matrix of \(388\times 370\) pixels, each having the dimensions of \(110\times 110\)\(\upmu\)m\({}^{2}\). The [PERSON] spectrometer's average energy bandwidth was 0.5 eV/pixel, and the expected energy resolution was 0.7 eV.  
The RXES planes reconstruction is based on a matrix formalism, where for the number of SASE pulses \(k\), the set of the measured emission spectra \(S_{kn}\), with \(n\) denoting the number of points in a single emission spectrum, is described with the following matrix equation [10]:  
\[S_{kn}=J_{kn}\cdot R_{mn}, \tag{1}\]  
with the set of the incident pulses' spectra \(J_{kn}\), where \(m\) is the number of points in a single incident spectrum (\(k>m\)). In the above equation, the matrix \(R_{mn}\) is the RXES plane, i.e., representation of emission probability for incident photon energy indexed with \(m\) and for emission energy indexed with \(n\). The matrices \(\mathbf{S}\) and \(\mathbf{J}\) are observables and the RXES plane \(\mathbf{R}\) can be determined using the Moore-Penrose pseudoinverse matrix \(\mathbf{J}_{km}{}^{+}\)[10]:  
\[\mathbf{R}_{mn}=\mathbf{J}_{km}{}^{+}\mathbf{S}_{kn}. \tag{2}\]
## 2 Fundamental Theory
### Analysis Process of the Wave Method
\begin{bmatrix}e^{-ik_{i}x}&0\\ 0&e^{-ik_{i}x}\end{bmatrix} \); the transmission and reflection matrices of \(\mathbf{T}_{i}^{+}\), \(\mathbf{T}_{i}^{-}\), \(\mathbf{R}_{i}^{-}\) and \(\mathbf{R}_{i}^{+}\) of the \(i\)th interface are  
\[ \begin{array}{l}\mathbf{T}_{i}^{+}=\frac{2}{\Lambda_{i}}\Bigg{[}\begin{array}[ ]{cc}(1+\gamma_{i})(1+\beta_{i})&-(1-\gamma_{i})(1-i\beta_{i})\\ -(1-\gamma_{i})(1+i\beta_{i})&(1+\gamma_{i})(1+\beta_{i})\end{array}\Bigg{]}, \\ \mathbf{T}_{i}^{-}=\frac{2\gamma_{i}\beta_{i}}{\Lambda_{i}}\Bigg{[}\begin{array}[ ]{cc}(1+\gamma_{i})(1+\beta_{i})&(\beta_{i}-i)(1-\gamma_{i})\\ (i+\beta_{i})(1-\gamma_{i})&(1+\gamma_{i})(1+\beta_{i})\end{array}\Bigg{]}, \\ \mathbf{R}_{i}^{-}=\frac{1}{\Lambda_{i}}\Bigg{[}\begin{array}{cc}-i\beta_{i}( \gamma_{i}-1)^{2}-2\gamma_{i}\Big{(}1-{\beta_{i}}^{2}\Big{)}&-(1+i)\beta_{i}( 1-\gamma_{i}^{2})\\ -(1-i)\beta_{i}\big{(}1-\gamma_{i}^{2}\big{)}&i\beta_{i}(\gamma_{i}-1)^{2}-2 \gamma_{i}\Big{(}1-{\beta_{i}}^{2}\Big{)}\end{array}\Bigg{]},\\ \mathbf{R}_{i}^{+}=\frac{1}{\Lambda_{i}}\Bigg{[}\begin{array}{cc}2\gamma_{i} \Big{(}1-{\beta_{i}}^{2}\Big{)}-i\beta_{i}(\gamma_{i}-1)^{2}&(1+i)\beta_{i} \big{(}1-\gamma_{i}^{2}\big{)}\\ (1-i)\beta_{i}\big{(}1-\gamma_{i}^{2}\big{)}&i\beta_{i}(\gamma_{i}-1)^{2}+2 \gamma_{i}\Big{(}1-{\beta_{i}}^{2}\Big{)}\end{array}\Bigg{]}\end{array} ,\]  
where the coefficients \(\Delta_{i}\), \(\beta_{i}\) and \(\gamma_{i}\) are  
\[ \begin{array}{l}\Delta_{i}=\beta_{i}(\gamma_{i}+1)^{2}+2\gamma_{i}\Big{(}1+{ \beta_{i}}^{2}\Big{)},\ \ \beta_{i}=\frac{k_{i+1}}{k_{i}}\\ \gamma_{i}=\left\{\begin{array}{cc}\frac{E_{2}I_{i+1}k_{i+1}^{2}}{E_{1}I_{i+ 1}^{2}}\ (x<x_{0})\\ \frac{[E_{2}I_{i+1}k_{i+1}^{2}+E_{1}H_{k_{i+1}}+\eta_{1}H_{k_{i+1}}(i\omega)^{ 4}]k_{i+1}^{2}}{[E_{2}H_{k_{i+1}}H_{k_{i+1}}+\eta_{1}H_{k_{i}}(i\omega)^{4}]k_{ i+1}^{2}}\ (x_{0}\leq x\leq L)\end{array}\right.,\end{array} \]  
The vibration wave is reflected back from the right end, and the equivalent transmission matrix \(\mathbf{R}_{0}\) of the entire ABH structure is obtained when the right end of the ABH structure is not connected to any structure, as shown in Figure 6.  
Figure 6: Equivalent diagram of ABH structure. The equivalent reflection matrix \(R\) can be obtained by  
\[\xi_{0}^{-}=R_{0}\xi_{0}^{+}, \tag{15}\]  
where \(R_{0}\) is the equivalent reflection matrix. \[R_{0}=R_{0}^{+}+T_{0}^{-}f_{1}R_{1}f_{1}\big{(}I-R_{0}^{-}f_{1}R_{1}f_{1}\big{)}^ {-1}T_{0}^{-}= \begin{bmatrix}R_{01}&R_{02}\\ R_{03}&R_{04}\end{bmatrix} ,\]  
where \(R_{1}\) is iteratively calculated using the following equation:  
\[R_{i-k}=R_{i-k}^{+}+T_{i-b}^{-}f_{i-k+1}R_{i-k+1}f_{i-k+1}\Big{(}I-R_{i-b}^{-} f_{i-k+1}R_{i-k+1}f_{i-k+1}\Big{)}^{-1}T_{i-k^{\prime}}^{-}\;\;k=1,2,\cdots,i.\]  
According to Reference [27], the fixed boundary matrix \(R\) used in this paper is \( \begin{bmatrix}-i&-1-i\\ -1+i&i\end{bmatrix} \), and the free boundary matrix \(R\) used is \( \begin{bmatrix}-i&1+i\\ 1-i&i\end{bmatrix} \).
## 2 Results  
### Clinical Characteristics of Individuals with Diabetes with or without Complications  
The clinical characteristics of D+Comps and D-Comps are presented in Table 1. The two study groups were well matched with no significant difference in age, duration of diabetes, BMI, HbA\({}_{1c}\), or lipid profile. Individuals with D+Comps showed a trend towards worse kidney function, although this difference was not significant. They also had lower diastolic blood pressure, and more of them were on insulin therapy (both \(p<0.05\)). All individuals in D+Comps exhibited retinopathy. Among them, three had only microvascular complications, while another three had both microvascular and macrovascular complications, as detailed in Supplementary Table S1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **D+Comps (\(n=\delta\))** & **D–Comps (\(n=\delta\))** & \(p\)**-Value** \\ \hline Age (years) & \(67.5\pm 18.3\) & \(73.2\pm 5.2\) & ns \\ Men/women (\(n\)) & \(4/2\) & \(3/3\) & ns \\ Duration of diabetes (years) & \(26.9\pm 5.3\) & \(22.0\pm 5.9\) & ns \\ BMI (kg/m\({}^{2}\)) & \(26.8\pm 4.0\) & \(30.5\pm 5.9\) & ns \\ HbA\({}_{1c}\) (mmol/mol) & \(64\) (\(55\)–\(66\)) & \(61\) (\(40\)–\(64\)) & ns \\ HbA\({}_{1c}\) (\(\%\)NGSP units) & \(8.0\) (\(7.2\)–\(8.2\)) & \(7.7\) (\(5.8\)–\(8.0\)) & ns \\ Total cholesterol (mmol/L) & \(3.7\pm 0.6\) & \(4.3\pm 1.0\) & ns \\ HDL\(\leftarrow\) (mmol/L) & \(1.4\pm 0.4\) & \(1.4\pm 0.1\) & ns \\ LDL\(\leftarrow\) (mmol/L) & \(1.7\pm 0.5\) & \(2.3\pm 0.9\) & ns \\ Triglycerides (mmol/L) & \(1.4\pm 0.2\) & \(1.6\pm 0.8\) & ns \\ Serum creatinine (mmol/L) & \(142.0\pm 0.83\) (\(16.8\)) & \(67.4\) (\(48.6\)–\(7.5\)) & ns \\ U Alb (mg/24 hrs) & \(36\) (\(19\)–\(212\)) & \(8.6\) (\(5.1\)–\(14.15\)) & ns \\ UACR (mg/mol) & \(4\) (\(2\)–\(1\)–\(12.5\)) & \(1.2\) (\(0.8\)–\(5.6\)) & ns \\ eGFR (mL/min.\(1.73\) m\({}^{2}\)) & \(59.8\) (\(40.1\)–\(97.6\)) & \(84\) (\(66.3\)–\(91.3\)) & ns \\ Systolic BP (mmHg) & \(130\) (\(121\)–\(143\)) & \(134\) (\(120\)–\(146\)) & ns \\ Dastolic BP (mmHg) & \(64\pm 7\) & \(76\pm 9\) & \(0.03\) \\ Medications & & & \\ Statin (\(n\)) & \(5\) & \(5\) & ns \\ Aspirin (\(n\)) & \(2\) & \(3\) & ns \\ Insulin (\(n\)) & \(5\) & \(1\) & \(0.01\) \\ Glucose-lowering therapy (\(n\)) & \(2\) & & \\ \hline \hline \end{tabular} Results are expressed as mean \(\pm\) SD or median with interquartile range (IQR) or proportion. Significance was accepted at \(p<0.05\) and no significance indicated by ns. U Alb, urinary albumin; UACR, urinary albumin/creatinine ratio; eGFR, estimated glomerular filtration rate; BP, blood pressure.  
\end{table}
Table 1: Clinical characteristics of individuals with diabetes with and without complications.
## 2 Materials and Methods
### EBSD Data Collection  
The crystallographic texture of samples C, D and E was analysed using a JOEL JSM-7001 SEM (JOEL Ltd., Akishima, Japan) that was equipped with an EBDS detector system for EBSD analysis. A grid area of 755 um by 980 um on the polished sample surfaces was scanned for the analysis of texture. The orientation distributions of the scanned samples were acquired in the form of Euler angles and stored as (.cft) files for post-processing. Post-processing of the obtained EBSD data was conducted in MTEX toolbox 5.3.2, which is a MATLAB-based toolbox used for post-processing of EBSD data. Maps of the orientation and misorientation distribution were acquired for the three forms of LPBF Ti6 Al4V(ELI) alloy. The statistical distribution of the Schmid factor for the dominant slip systems of the \(\alpha\)-phase in the three different forms of the alloy was obtained and presented in this study with consideration of the direction of applied quasi-static load. The average width of the \(\alpha\)-laths in each microstructure was estimated from maps of grain distribution using the line intercept method as described in the ASTM E112-13 standard [30].
## 3 Results
### Results of Classification through Machine Learning  
Data on the x, y, and z axes of the GRF are classified through the machine learning algorithm. Table 2 shows the accuracy, sensitivity, and specificity obtained through five-fold cross-validation. The accuracy is how close a given set of measurements are to their actual ground true value. Comparatively, the results show a high performance. The sensitivity, the probability of a patient conditioned on being an actual patient, is averaged for each algorithm to obtain 0.63 in SVM, 0.72 in LDA, 0.69 in KNN, and 0.67 in random forest. The specificity, the probability of a healthy person conditioned on an actual healthy person, is averaged to obtain 0.87 in SVM, 0.77 in LDA, 0.83 in KNN, and 0.91 in random forest.  
The classification results showed good performance, only possible with a one-step gait. However, the classification results did not tell us at which stage patients' gait differs from that of healthy people.  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Model** & **GRF** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline SVM & x & 0.785 & 0.700 & 0.826 \\  & y & 0.741 & 0.588 & 0.814 \\  & z & 0.850 & 0.600 & 0.970 \\ \hline LDA & x & 0.753 & 0.738 & 0.760 \\  & y & 0.733 & 0.688 & 0.754 \\  & z & 0.773 & 0.725 & 0.796 \\ \hline KNN & x & 0.753 & 0.700 & 0.778 \\  & y & 0.733 & 0.625 & 0.784 \\  & z & 0.874 & 0.755 & 0.922 \\ \hline Random Forest & x & 0.818 & 0.662 & 0.892 \\  & y & 0.810 & 0.637 & 0.892 \\  & z & 0.862 & 0.700 & 0.940 \\ \hline \end{tabular}
\end{table}
Table 2: Human subject classification results through machine learning.  
Figure 5: Results of measured time-series data using the force plate. Data from the stance phase in the gait cycle are measured during the one-step gait cycle, and those of the swing phase are not recorded in the experiment. The normalized time for 0\(\sim\)25% expresses the initial contact, 25\(\sim\)80% expresses the midstance, and 80\(\sim\)100% expresses the propulsion.
## 2 Materials and Methods
### Object Based Image Analysis (OBIA) Procedures  
The images were segmented and classified using the nearest neighbor (NN) method with vegetation, soil, and shadow samples. The OBIA workflow is shown in Figure 3. This shows the hierarchy level for the whole image, which is then narrowed down to the vegetation, before being dividing it into two classes in level 3. In level 4, there are four types of weed.  
The image segmentation started at the scale of 40, using the \"multiresolution segmentation\" algorithm, based on the color (spectral) and shape or the spatial criterion (Figure 4). In this study, the spectral and shape values were weighted based on their homogeneity. The shape value was 0.1 because the overall shape of the weeds and sorghum was only ten percent of the total, which led to inaccurate segmentation. The color value (\(\mathbf{w}_{colour}\)) was 0.5 because the spectral bands of the weeds and sorghum were similar, with only slight differences. The compactness values indicated different shapes and smoothness weights, showing variability in terms of features. The parameters values were flexible because they were based on trial and error.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Spectral Region** & **Bands** & **Sources** \\ \hline NIR & 850 nm & MCA 6, LDA \\ Red-edge & 750, 730, * 720 and * 710 nm & MCA 6, * LDA \\ Red & 680 nm & MCA 6, LDA \\ Green & 560 nm & MCA 6, LDA \\ Blue & 440 nm & MCA 6, LDA \\ \hline \hline \end{tabular}  
* For the red-edge region, 720 and 710 nm bands were taken from LDA and MCA.  
\end{table}
Table 2: The eight bands selected from MCA 6 and LDA.  
The objects were classified according to soil (SO) and vegetation, then the vegetation was segmented on a scale of 1 to 10. In fact, the vegetation was divided into sorghum and weeds. Then, both sorghum and weeds were reclassified. Using the \"enclosed by class\" function, sorghum was merged while weeds were split into different classes. This rule set can be slightly modified to run other images and data.  
The confusion matrix was used to assess the accuracy before its adoption as a normal procedure [19; 20; 21; 22]. The KHAT statistic was computed based on [23] as:  
\[\frac{N\sum_{i=1}^{r}xii-\sum_{i=1}^{r}(xi+\ *\ x+i)}{N\sum_{i=1}^{r}(xi+\ *\ x+i)} \tag{1}\]  
where \(r\) is the number of rows in the matrix; \(x_{ii}\) is the number of observations of row \(i\) and column \(i\); \(x_{i+}\) and \(x_{i+}\) are the marginal total of row \(i\) and column \(i\), respectively; \(N\) is the total number of observations.  
Figure 4: Process tree for the weeds according to eCognition Developer. Adapted from [14].  
Figure 3: Hierarchy of weed discrimination classification. Adapted from [14].
# Parameter Analysis and Optimization of Annular Jet Pump Based on Kriging Model
## 5 Results and Discussion
### The Effect of \(m\) on Jet Pump  
It can be concluded from the response surface analysis that \(m\) is very important for improving the efficiency of jet pumps. There have been many studies on other parameters rather than \(m\). Figure 9 shows streamlines from the suction chamber to the inlet of the throat with different area ratios \(m=1.15\), \(2.11\), \(4\), \(7.18\) with \(\alpha=26.6^{\circ}\), \(\beta=4.12^{\circ}\), and \(q=0.6\). A symmetrical vortex structure appears near the axis from the end of suction chamber to the inlet of throat at \(m=7.18\). This pair of vortices leads to the increase in friction resistance. Figure 10 shows the axial velocity profile of the throat inlet in order to further study this phenomenon.  
As shown in Figure 10, the jet expands towards the center in the suction chamber. As \(m\) decreases, the jet expands deeper into the suction chamber. When the area ratio is \(7.18\), the jet fails to extend to the center and forms a back flow zone at the center. The smaller the area ratio is, the better the mixing effect of the jet at the outlet of the suction chamber is.  
Figure 11 shows the annular jet pump velocity distribution with the above four area ratios. The velocity gradient of the high-speed jet and the sucked second fluid in the suction chamber is relatively big, and the degree of mixing of the two fluids is low. Because the jet expands unilaterally, the jet velocity is high in the suction chamber near the side wall of the throat, and the velocity gradient is big. As the area ratio decreases, the energy loss of the jet increases in the suction chamber and the throat. Therefore, a low area ratio leads to a small inlet of primary fluid and high friction resistance and reduces the efficiency of the annular jet pump.  
Figure 9: Streamlines from the suction chamber to the inlet of throat.
## 1 Introduction
Indeed, the contribution and novelty of this proposal include the processing of different signals through different domain analyses, that is, the time-domain and frequency-domain. In fact, an important aspect of this approach is the calculation of statistical features that are obtained to characterize the acquired vibration and stator current signals; the estimation of statistical features in different domain analyses leads to a high-performance signal characterization. Additionally, the fusion of vibrations with stator current information is carried out by subjecting their characteristic statistical features to a dimensionality reduction procedure. Thereby, the dimensionality reduction is achieved by the LDA, which is classic technique that leads to achieve the maximum linear separation between multiple classes and allows the visual representation into a 2D space. Finally, the automatic diagnosis and fault severity identification is performed through a classical NN-based classifier. The proposed method is validated under a complete set of experimental data that was acquired from a self-designed test bench. Moreover, the effectiveness of this proposal is proved since six different bearing conditions are effectively diagnosed and identified. The assessed conditions comprise a healthy condition and five different severities of fault that appear in the outer race of a bearing with a damage of 1 mm, 2 mm, 3 mm, 4 mm, and 5 mm, respectively.
# Action Recognition via Adaptive Semi-Supervised Feature Analysis
## 4 Proposed Approach
### Formulation  
To leverage the multiple-feature correlation, \(n\) training sample points \(\mathbb{X}=[X_{1},...,X_{n}]\in\mathbb{R}^{d\times n}\) are defined from the underlying Grassmannian manifold, where \(X_{i}\in\mathbb{R}^{d\times 1}\). We aim to uncover a new manifold while preserving the local geometry of data points, that is, \(\alpha:X_{i}\to F_{i}\). Since we should demonstrate data distribution on the manifold, a predicted label matrix \(\mathbb{F}=[F_{1},...,F_{n}]\in\mathbb{R}^{n\times n}\) is defined, where the predicted vector of the \(i\)-th datum \(X_{i}\in\mathbb{X}\) is \(F_{i}\in\mathbb{R}^{n\times 1}\).  
We assume that a similarity measurement of data points in the manifold subspace is available through a Grassmannian kernel [31]\(k_{ij}=\langle X_{i},X_{j}\rangle\). By confining the solution to a linear function, that is, \(\alpha_{i}=\sum_{j=1}^{n}a_{ij}X_{j}\), we define the prediction function \(f\) as \(f(X_{i})=F_{i}=(\langle\alpha_{1},X_{i}\rangle,\langle\alpha_{2},X_{i}\rangle,...,\langle\alpha_{r},X_{i}\rangle)^{T}\). By denoting \(A_{l}=(a_{l1},...,a_{ln})^{T}\) and \(K_{i}=(k_{11},...,k_{in})^{T}\), it can be shown that \(\langle\alpha_{l},X_{i}\rangle=A_{l}^{T}K_{i}\), and thus, \(f(\mathbb{X})=\mathbb{F}=\mathbb{A}^{T}\mathbb{K}\approx\mathbb{Y}\), where \(\mathbb{A}=[A_{1}|A_{2}|...|A_{r}]\) and \(\mathbb{K}=[K_{1}|K_{2}|...|K_{n}]\). As mentioned in [42], the performance of the least-square loss function is comparable to hinge loss or logistic loss. This is associated with its diagonal matrix \(\mathbb{Y}=[Y_{1},...,Y_{n}]\in\left\{0,1\right\}^{n\times n}\), where \(Y_{i}\in\left\{0,1\right\}^{n\times 1}\) is the label matrix. We employ least-squares regression to solve the following optimization problem, then obtain the projection matrix \(\mathbb{A}\):  
\[\min_{\mathbb{A}}\|\mathbb{A}^{T}\mathbb{K}-\mathbb{Y}\|_{F}^{2}+\eta\| \mathbb{A}^{T}\|_{F}^{2}, \tag{1}\]  
Figure 2: An illustration of our method. (**a**) Video sets can be represented in \(\mathbb{R}^{D}\). We can use the principal angles between them to compare two actions. (**b**) Data points on the Grassmannian manifold \(M\) can be described as linear subspaces in \(\mathbb{R}^{D}\). When points on the manifold have a proper geodesic distance, the video-set-matching problem may be converted to a points distance measurement problem. (**c**) By employing a proper Grassmannian kernel, data points can be mapped into another Grassmannian manifold \(M^{\prime}\) where the same actions become closer while different actions are well separated.  
where \(\eta\) is the regularization parameter. \(\|\cdot\|_{F}^{2}\) denotes the Frobenius norm. \(\|\mathbb{A}^{T}\|_{F}^{2}\) controls the model complexity to prevent overfitting.
# Review of Recent Phased Arrays for Millimeter-Wave Wireless Communication
## 2 Millimeter-Wave Antenna Array for 5G Communication
### Millimeter-Wave Phased Arrays for 5G Access Terminals  
In mm-wave cellular networks, backhaul systems and BSs will generally be deployed in crowdy environment on the poles, beacons, and building tops. The combined data to or from the multiple users will be transmitted with less delay from the mm-wave BS to the central hub through various mm-wave wireless or optical channels. The communication range for the outdoor mm-wave access link would be larger when compared to indoor mm-wave communication links, but the power consumption of the user end terminals cannot be increased. Because of the high signal attenuation at mm-wave frequencies, the effective communication distance of mm-wave systems is limited when compared to microwave signals. To reduce the increased path loss in outdoor environments, and owing to the user mobility, a suitable beamforming technique is required for implementation in cellular communication access links in the mm-wave frequency band. This can be realized by using the mm-wave BS equipped with high-gain phased arrays, where there is a relaxation of size and power consumption requirements. Phased arrays at millimeter-wave frequencies present a high-data-rate communication solution using high bandwidth and directional links between the BS and MS terminals.