esa-sceva/satcom-chunk-collection huggingface dataset:
# Impacts of Compound Hot-Dry Events on Vegetation Productivity over Northern East Asia
## 4 Discussion
### Uncertainties and Limitations of This Study  
The identification of CHD events poses a significant challenge due to the varying criteria for drought and heat across diverse regions and ecosystems. In this study, a 30-day SPI was used as a drought indicator, with a threshold of SPI \(<-0.5\) defining dry events. However, it is crucial to recognize that the SPI, being primarily a meteorological drought index based on precipitation, may not fully capture drought events that exert physiological impacts on vegetation for certain ecosystems. For instance, in northern ecosystems, even when the SPI is below \(-0.5\), the soil moisture content may still ensure normal physiological activity of vegetation, thus not significantly influencing GPP. Consequently, the definition of dry events used in this study may limit the assessment of drought impacts on GPP. Multiple factors should be considered to enhance the accuracy of evaluating such impacts, including regional and ecosystem characteristics, and appropriate indicators and thresholds should be selected.  
Furthermore, the outcomes of our study may be influenced by the choice of model employed. In particular, CLM5 can barely reproduce the observed anomalous GPP in the southern Yablonov Mountains and the Daxinganling region, which are mostly covered by coniferous forests. Thus, the results discussing forested areas in Section 3 need more certainty. Also, we did not consider feedback from land to the atmosphere. For example, the large decrease in soil moisture did not affect VPD (Figures S2a and S3a), but real-world processes are complex. Land-atmosphere feedback from dry soil is vital for the occurrence of heatwaves and atmospheric droughts [57; 58], which, if not considered, may leave some uncertainty in our results.  
It is important to emphasize that our study focuses exclusively on natural vegetation and needs to comprehensively examine the impact of extreme climate events on crop growth. Nonetheless, the vital role of crops in ensuring global food security and sustainable agriculture cannot be overstated. Future research endeavors will prioritize exploring how crops respond and adapt to extreme climate events.
## 3 Results  
### Sociodemographic Data, Response Rate, Etiology, and Amputation Level  
See Table 1. A total of 164 patients underwent 199 major amputations in the three-year observation time. The male-female ratio was 72% vs. 28% at a mean age 68 years (range: 19 to 97 years). Trauma vs. nontrauma ratio was 12% vs. 88%.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Items** & **Total (N = 164)** & **\%** & **Total (N = 27)** & **\%** \\ \hline Gender & & & & & \\  & Male & 118 & 72\% & 20 & 74\% \\  & Female & 46 & 28\% & 7 & 26\% \\ Age (mean, range) & & 68 years (19–97) & & 65 years (38–82) & \\  & Male & 67 years (19–90) & & & \\  & Female & 70 years (38–92) & & & \\ Traumatic amputation & 20 & 12\% & 7 & 26\% \\ Nontraumatic amputation & 144 & 88\% & 20 & 74\% \\  & Foot & 25 & 17\% & 2 & 7\% \\  & BKA & 46 & 31\% & 13 & 48\% \\  & Knee & 25 & 17\% & 5 & 19\% \\  & AKA & 46 & 31\% & 3 & 11\% \\  & Bilateral & 11 & 7\% & 2 & 7\% \\ Mortality & & 45 & 27\% & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sociodemographic data, N = 164, total study population. N = 27 active participants (PAD = peripheral arterial disease, BKA = below-knee amputation, AKA = above-knee amputation).  
At a minimum of two years to a maximum of five years after their major limb amputation, the questionnaire was sent out to 119 patients. Out of 164 patients, 45 (27%) died within five years after amputation. A total of 49 out of 127 patients (30%) refused to participate in the study. We were not able to reach 45 patients (27%). Potential reasons could include movement, nondocumented death, no reason, etc. Finally, 27 patients (17%) took part in the survey (Scheme 1).  
The mean age of the 27 participants was 65 years (range: 38 to 82 years) with a gender distribution of 3:1 (men/women). Two patients received an amputation of the hind foot (7%), 13 (48%) received a below-knee amputation (BKA), 5 (19%) underwent a knee disarticulation, and 3 (11%) received an above-knee amputation (AKA). Two participants (7%) were bilateral amputees. Amputation due to trauma occurred in seven patients (25%) and was, for all, the final consequence of a traffic accident.
# In-Vitro Simulation of the Blood Flow in an Axisymmetric Abdominal Aortic Aneurysm
## 4 Conclusions  
An in-vitro study of a fusiform AAA was performed using image analysis. We were able to recover the flow patterns observed in correspondence of both the healthy vessel and the aneurysm and to investigate the WSS distribution for varying _Re._ In particular, the recirculation zone, which is known to play a fundamental role in the deposition events associated with aneurysms, is found to move downstream, and this displacement is found to increase with _Re._ Results show very low values of WSS in correspondence of the aneurys cavity and a sharp peak in correspondence of the reattachment point. The peak value is found to decrease with Re and to equal about twice the upstream value. Compared to PIV, the particle tracking technique allows for a larger spatial resolution and an increased dynamic range as it is able to detect regions very close to the walls [29]. We believe that this aspect makes HLPT particularly suited for the computation of WSS in this context. Moreover, when WSS is estimated in in-vitro models through velocity measurements, ambiguities in the velocity vectors detection may arise close to the wall due to the light scattering in correspondence of the liquid-solid interface. We were able to avoid this issue via a proper design of the test section. This study is to be considered as a preliminary stage for the realization of an in-vitro physiological model, including both the wall elasticity and flow time variability, suitable to investigate the correlation of the local hemodynamic characteristics (velocity and vorticity distributions, pressure and wall shear stress fields, transport topology, turbulence) with the abdominal aortic aneurysm progression and the rupture location. In perspective, the particle trajectories provided as an output by this technique may prove particularly suited to investigate blood cell transport [35] and to quantify these phenomena using a Lagrangian approach [32]. The velocity data used to support the findings of this study are available from the corresponding author upon request. The following are available online at [[http://www.mdpi.com/2076-3417/9/21/4560/s1](http://www.mdpi.com/2076-3417/9/21/4560/s1)]([http://www.mdpi.com/2076-3417/9/21/4560/s1](http://www.mdpi.com/2076-3417/9/21/4560/s1)), Video S1: movie_Re1100.avi, Video S2: movie_Re2650.avi. conceptualization, S.E., M.M. and M.A.B. ; data curation, M.M. ; funding acquisition, S.E. and M.A.B. ; investigation, S.E. and M.M. ; methodology, S.E., M.M. and M.A.B. ; writing--original draft, S.E., M.M. and M.A.B. ; writing--review and editing, S.E. and M.M. The financial support provided by Sapienza University of Rome research projects (Grant n. RP116155033 CC02F and RM11816436229621) is gratefully acknowledged. The authors thank [PERSON] and [PERSON] for the useful support provided during the experimental campaign. The authors declare no conflict of interest. List of Symbols
# Shear Wave Velocity Estimation Using the Real-Time Curve Tracing Method in Ultrasound Elastography
## 3 Phantom Experiments  
### Experimental Environment  
The excitation and acquisition of the shear wave are realized by the Verasonics ultrasonic experimental system (America), and its model is Vantage 256. The L11-4V ultrasonic linear array probe is used in the experiment, and its center frequency is 6.25 MHz. We use an ultrafast plane wave imaging technique to image the propagation of the resulting transient plane shear waves. Other parameters of the system are shown in Table 1.  
Four experiments are conducted in a 040 GSE ultrasound phantom with a background elasticity of 30 kPa by the supersonic shear imaging method. The selected experimental areas are shown in Figure 1, in which the red rectangle represents the region of interest for each experiment, and the dotted box in the red rectangle is the range for velocity estimation. In other words, the dotted box is considered to be the main propagation area of the right shear wave in each group of experiments, which is also our targeted imaging area.  
In Figure 1a, a larger solid black circle and a smaller solid black circle on the right side of the region of interest represent the anechoic stepped cylinders. The circles with different decibels on the lower side of the region of interest represent grayscale target groups, ranging from \(-9\) dB to \(+6\) dB. In the region of interest of Figure 1b-d, there are inclusions with a diameter of 6 mm, which are different from the background elasticity, and their elasticity is 10 kPa, 40 kPa and 60 kPa, respectively. In these figures, the small white dots far smaller than the size of the inclusions are the target points, and the white circle without markings is a hyperechoic region of the same size as the other grayscale target groups.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Acquisition Parameter** & **Title 3** \\ \hline Wavelength (\(\lambda\)) & 0.2464 mm \\ Acoustic velocity (m/s) & 1540 \\ Excitation frequency (Hz) & \(6.25\times 10^{6}\) \\ Excitation duration (cycles) & 1200 \\ Number of array elements used in acoustic radiation force generation & 32 \\ Four focal points using supersonic shear imaging technology (\(\lambda\)) & 20,40,60,80 \\ Transverse range of region of interest (\(\lambda\)) & \(-20\)–\(80\) \\ Transverse resolution of beamforming (\(\lambda\)) & 0.25 \\ Longitudinal range of region of interest (\(\lambda\)) & 15–95 \\ Longitudinal resolution of beamforming (\(\lambda\)) & 0.5 \\ Excitation voltage (V) & 50 \\ Channel data sampling frequency (Hz) & \(25\times 10^{6}\) \\ Each frame acquisition interval (us) & 100 \\ Capture shear wave frame number & 70 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Multi-focus excitation acquisition parameters.
# Full-Field Strain Determination for Additively Manufactured Parts Using Radial Basis Functions
## 2 Surface Strain Determination
### Application of Radial Basis Functions for Strain Determination  
To determine the principal stretches (or strains), the tangent vectors \(\vec{a}_{\alpha}=\partial\hat{\vec{x}}/\partial\Theta^{\alpha}\) are needed in Equation (1), i.e.,  
\[\mathbf{a}_{\alpha}^{(n)}=\frac{\partial\hat{\mathbf{x}}(\mathbf{\Theta},t_{n} )}{\partial\Theta^{\alpha}}=\sum_{j=1}^{3}\Biggl{(}\sum_{k=1}^{n_{\mathrm{cp}} }\frac{\mathrm{d}\hat{n}}{\mathrm{d}\rho}\frac{\partial\hat{p}_{k}}{\partial \Theta^{\alpha}}B_{kj}^{(n)}+\sum_{l=1}^{3}\frac{\partial\hat{n}_{l}}{ \partial\Theta^{\alpha}}D_{lj}^{(n)}\Biggr{)}\mathbf{e}_{j},\quad\mathbf{a}_{ \alpha}^{(n)}\in\mathbb{R}^{3}. \tag{28}\]  
The derivatives \(\hat{n}^{\prime}(\rho)\) are compiled in Table 1, and  
\[\frac{\partial\hat{p}_{k}}{\partial\Theta^{\alpha}}=\frac{\Theta^{\alpha}- \Theta_{k}^{\alpha}}{\|\mathbf{\Theta}-\mathbf{\Theta}_{k}\|}. \tag{29}\]  
For the initial state, this reads  
\[\mathbf{A}_{\alpha}=\mathbf{a}_{\alpha}^{(0)}=\sum_{j=1}^{3}\Biggl{(}\sum_{k= 1}^{n_{\mathrm{cp}}}\frac{\mathrm{d}\hat{n}}{\mathrm{d}\rho}\frac{\partial \hat{p}_{k}}{\partial\Theta^{\alpha}}B_{kj}^{(0)}+\sum_{l=1}^{3}\frac{ \partial\hat{n}_{l}}{\partial\Theta^{\alpha}}D_{lj}^{(0)}\Biggr{)}\mathbf{e}_{ j},\quad\mathbf{A}_{\alpha}\in\mathbb{R}^{3} \tag{30}\]  
which is required for the matrix of metric coefficients, \(A_{\alpha\beta}=\mathbf{A}_{\alpha}^{T}\mathbf{A}_{\beta}\), to calculate the gradient vectors \(\mathbf{A}^{\alpha}\). This allows the calculation of the deformation gradient (1) and, accordingly, the principal stretches (10).
## Abbreviations  
* Perspective-n-Points ESA European Space Agency ADR Active Debris Removal OOS On-Orbit Servicing LIDAR Light Detection and Ranging SPEC2019 Satellite Pose Estimation Challenge Competition in 2019 SPEED Spacecraft Pose Estimation Dataset TOF Time of Flight SLSN Satellite Localization Network LRN Landmark Regression Network MBConv Mobile Inverted Bottleneck Convolution SENet Squeeze-and-Excitation Network PE Positional Encoding SA Self-Attention MHA Multi-Head Attention FFN Feed-Forward Network LN Layer Normalization Add & Norm Residual Connection and Layer Normalization Blocks
# Transmission Condition Monitoring of 3D Printers Based on the Echo State Network
## 2 Methodologies
### The Concept of \(Esn\)
Suppose that the training sample (\(u(t)\), \(t\) = 1, 2,..., \(p\)) is added to the reservoir through the input connection matrix \(\mathbf{w}_{in}\). The reservoir states \(x(t)\) and reservoir output \(z(t)\) are sequentially calculated according to Equations (12) and (13), respectively. In the \(j\)-th time, matrix \(\mathbf{B}\) and \(\mathbf{T}\) can be constituted in Equation (17)  
\[\mathbf{B}\ =\left[ \begin{array}{cccc}x_{1}(j)&x_{2}(j)&\ldots&x_{m}(j)\\ x_{1}(j+1)&x_{2}(j+1)&\ldots&x_{m}(j+1)\\ \cdot&\cdot&\cdot&\cdot\\ \cdot&\cdot&\cdot&\cdot\\ x_{1}(p)&x_{2}(p)&\ldots&x_{m}(p)\end{array} \right]\ \mathbf{T}\ =\ \left[ \begin{array}{c}z(j)\\ z(j+1)\\ \cdot\\ \cdot\\ \cdot\\ z(p)\end{array} \right] \tag{17}\]  
where \(x_{m}(p)\) is the state of the \(m\) reservoir neurons at the \(p\)-th time, and \(z(p)\) represents reservoir output at the \(p\)-th time. The weight calculation can be performed according to Equation (18) based on the collection of state matrix \(\mathbf{B}\) and reservoir output \(\mathbf{T}\). \[\mathrm{z}(k)\approx\hat{z}(k)=\sum_{i=1}^{L}\mathbf{w}_{out(i)}x_{i}(k), \tag{18}\]where \(\hat{z}(k)\) represents predicted reservoir output. The objective function can be expressed in Equation (19) to satisfy the minimum variance of the system. \[\min\frac{1}{p-j+1}\sum_{k=j}^{p}y(k)-\sum_{i=1}^{L}\mathbf{w}_{out(i)}x_{i}(k) ^{2}, \tag{19}\]  
Correspondingly, it can be reduced using Equation (20). \[\mathbf{w}_{out}=\left(\mathbf{B}^{-1}\times\mathbf{T}\right)^{\mathrm{T}}. \tag{20}\]  
Therefore, the ESN model can be built according to the aforementioned methods with a simple design and implementation.
# A Survey on Hardware Accelerators for Large Language Models
## 3 FPGA-Based Accelerators
### Column-Balanced Block-Wise Pruning  
In 2021, [PERSON] et al. presented a novel scheme for accelerating Transformer networks using column-balanced block-wise pruning [13]. Column-balanced block-wise pruning combines the key features of both bank-balanced pruning and block-wise pruning. Column-balanced block-wise pruning ranks the blocks' L2 norm by each column to obtain the pruning thresholds and prune blocks for each column.  
By combining the compressed sparse bank (CSB) format and the block-compressed sparse row (BCSR) format, a compressed sparse column block (CSCB) is formed. A specialized process element (PE) is then introduced for the sparse matrix multiplication accelerator, and multiple PEs can be used to increase the accelerator throughput.  
The proposed framework was implemented on different hardware platforms (Intel i5-5257U (2.7 GHZ) CPU, NVIDIA Jetson TX2 GPU, and Xilinx Alveo U200 FPGA) for a further comparison of latency and throughput. The experimental results showed that the FPGA platform achieves an 11\(\times\) speedup compared to the CPU platform and a 2\(\times\) speedup compared to the GPU platform.
# A Packet Collision Reduction Based on Reselection for LTE V2X Mode 4
###### Abstract  
Vehicle to Everything (V2X) is a technology that includes communication between the vehicles and everything such as Vehicle to Vehicle (V2V), Vehicle to Infrastructure (V2I), and Vehicle to Pedestrian (V2P). Long Term Evolution (LTE) V2X based on LTE supports a sidelink communication in which User Equipment (UE) communicates with each other. In Mode 4 of the sidelink communication, the UE autonomously selects a radio resource that is not expected to be used by other UEs based on sensing information. However, a resource can be selected by simultaneous UEs and packet collisions occur because of the periodic resource reselection. In this paper, we propose two resource selection methods for the reselection using information originally included in the control information. Through computer simulations, we show that the proposed methods can improve the packet reception rate without requiring restrictions such as additional information. The main strength of this method is that it effectively utilizes the information contained in Resource Reservation Interval (RRI), which is used in the Semi-Persistent Scheduling wireless resource allocation algorithm. In this research, the value of RRI, which has been used in standards, is utilized to improve performance while maintaining compatibility. Since our method is designed under conditions that maintain compatibility with existing standards, it may or may not have a significant effect, but it does not degrade performance.  
V2X; V2V; LTE V2X; sidelink; Mode 4; SPS Article  
A Packet Collision Reduction Based on Reselection for LTE V2X Mode 4  
doi:10.3390/app1217873  
Academic Editors [PERSON], [PERSON] and [PERSON]  
Received: 29 July 2022  
Accepted: 26 August 2022  
Published: 31 August 2022  
**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
# Fabric Defect Detection Using L0 Gradient Minimization and Fuzzy C-Means
## 3 Methods
### Fuzzy C-Means Clustering Algorithm (FCM)  
The output results via LGM are obtained in the previous section. We can easily process the defect information, which becomes more obvious. Then the FCM algorithm is applied to segment the defects.  
The FCM algorithm [28; 29] is a fuzzy and unsupervised clustering algorithm. Its classification capacity is flexible and simple to implement. The FCM algorithm defines the objective function represented the sum of squares of the weighted distances from each pixel in the target image to each cluster center, which is given by:  
\[J(M,V)=\sum_{j=1}^{c}\sum_{i=1}^{n}\left[\mu_{j}(x_{i})^{b}\right]\|x_{i}-m_{j }\|^{2} \tag{2}\]  
where \(c\) is the number of clusters, \(n\) is the number of pixels in the image, \(\mu_{j}(x_{i})\) is the membership degree of the \(i\)-th pixel belonging to the \(j\)-th class, normally \(0\leq\mu_{j}(x_{i})\leq 1\) and \(\sum\limits_{j=1}^{c}\mu_{j}(x_{i})=1\), \(b\) is a preset fuzzy factor which controls the degree of blur, normally \(b\geq 1\), and \(\|x_{i}-m_{j}\|^{2}\) represents the Euclidean distance from the \(i\)-th pixel to the \(j\)-th class.  
Figure 6: Results of fabric defect based on LGM.  
The physical meaning of the objective function \(J\) represents the sum of squares of the weighted distances from each pixel in the target image to each cluster center. When the Euclidean-distance-weighted value of each pixel-point in the target image to the cluster center is the minimum, and the Euclidean distance from other cluster centers is as large as possible. The basic principle of the FCM algorithm is to find a set of suitable clustering centers and the membership matrix, such that the objective function \(J\) takes the minimum value \(\min\) (\(J\)).  
When calculating the minimum value of the objective function, it is necessary to continuously update the membership matrix and the cluster center according to Equations (3) and (4) until the minimum value is obtained:  
\[m_{j}=\frac{\sum\limits_{i=1}^{n}{\left[{\mu_{j}(x_{i})}\right]^{b}}x_{i}}{ \sum\limits_{i=1}^{n}{\left[{\mu_{j}(x_{i})}\right]^{b}}} \tag{3}\]  
\[\mu_{j}(x_{i})=\frac{{\left({1/{\left\|{x_{i}-m_{j}}\right\|^{2}}}\right)^{1/ \left({b-1}\right)}}}{\sum\limits_{k=1}^{c}{\left({1/{\left\|{x_{i}-m_{k}} \right\|^{2}}}\right)^{1/\left({b-1}\right)}}} \tag{4}\]  
When the objective function obtains the minimum value, the membership degree is retained, and each pixel of the target image is clustered. In the iterative process, the appropriate iteration termination condition should be selected correctly, otherwise, it cannot obtain the ideal segmentation result. When the FCM algorithm is convergent, it can achieve the different clustering centers which are the clustering center of defects information and the clustering center of the normal fabric information.
## 3 Data  
### HJ-1B/IRS Data  
The \"Environmental and disaster monitoring and forecasting with a small satellite constellation\" [23] comprises two small optical satellites (HJ-1A and HJ-1B) that were launched in 2008. The sensor of HJ-1B/IRS consists of four bands, _i.e._, Near Infrared band (NIR, 0.75-1.10 microns), Shortwave Infrared band (SWIR, 1.55-1.75 microns), Middlewave Infrared band (MWIR, 3.50-3.90 microns) and Longwave Infrared band (LWIR, 10.5-12.5 microns). The spatial resolution of the NIR, SWIR and MWIR bands is 150 m and that of the LWIR band is 300 m. HJ-1B/IRS can provide global coverage every two days, and the revisit frequency is between the values of the MODIS and Landsat TM frequencies. The SRF of L WIR of HJ-1B/IRS is shown in Figure 1. Although HJ-1B/CCD suffers from a lens anomaly due to sensor design and atmospheric conditions [24], no lens anomalies have been found in HJ-1B/IRS data yet.  
The HJ-1B/IRS data used in this paper cover 13 cloud-free days in 2012: 19 and 30 June; 8, 23 and 27 July; 14, 15, 22, and 29 August; and 2, 6, 14 and 17 September. The results are then compared with the ground data.
# An Algorithm to Retrieve Precipitable Water Vapor from Sentinel-2 Data
## 4 Discussion
### Error Analysis of the Algorithm
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Level**} & \multirow{2}{*}{**AOT**} & **Sensor Zenith** & **Solar Zenith** & **Elevation** & **NDVI** \\  & & **Angle (\({}^{\circ}\))** & **Angle (\({}^{\circ}\))** & **(m)** & **NDVI** \\ \hline
1 & 0.050–0.064 & 167.822–169.327 & 23.062–23.979 & 391–912 & 0–0.173 \\
2 & 0.064–0.079 & 169.327–170.580 & 23.979–24.569 & 912–1074 & 0.173–0.249 \\
3 & 0.079–0.099 & 170.580–171.875 & 24.569–25.118 & 1074–1200 & 0.249–0.325 \\
4 & 0.099–0.115 & 171.875–173.253 & 25.118–25.647 & 1200–1316 & 0.325–0.412 \\
5 & 0.115–0.133 & 173.253–174.631 & 25.647–26.177 & 1316–1439 & 0.412–0.505 \\
6 & 0.133–0.151 & 174.631–176.010 & 26.177–26.726 & 1439–1583 & 0.505–0.602 \\
7 & 0.151–0.160 & 176.010–177.346 & 26.726–27.316 & 1583–1780 & 0.602–0.703 \\
8 & 0.160–0.183 & 177.346–178.516 & 27.316–28.273 & 1780–2807 & 0.703–0.884 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Grading table of AOT, sensor zenith angle, solar zenith angle, elevation, and NDVI. Figure 9: The reflectance ratios of B9/B8A mixed with bare soil and plants in different proportions.
## 8 Experimental Results
### Case 1  
In this first case, we deal with the planning and pursuit of a given desired trajectory in the Cartesian plane. During the passage of the robot equipped with a perspective camera, from an initial situation to a final situation, the target object does not leave the field of vision during the enslavement. The problem posed is then described in Figure 12. The desired trajectories are described by Equations (25) and (26). The coordinates of the initial position in the Cartesian coordinate system are given by: \((x_{i},y_{i},\theta_{i})=(0\;\text{m},0\;\text{m},0.5404\;\text{rad})\). The coordinates of the four corresponding points in the image plane are given by: \(P_{l}=\begin{pmatrix}402&402&602&602\\ 408&605&605&408\end{pmatrix}\). The final position in the Cartesian coordinate system is: \(\left(x_{f},y_{f},\theta_{f}\right)=(1.5\;\text{m},0.1\;\text{m},0\;\text{rad})\). The coordinates of the four corresponding points in the image plane are given by: \(P_{f}=\begin{pmatrix}291&291&577&577\\ 384&652&652&384\end{pmatrix}\). Pole placement is fixed by the following parameters: \(k_{3}^{x}=3\), \(k_{2}^{x}=8\), \(k_{1}^{x}=3\), \(k_{0}^{x}=8\), \(k_{3}^{y}=3\), \(k_{2}^{y}=8\), \(k_{1}^{y}=3\), \(