esa-sceva/satcom-chunk-collection huggingface dataset:
# A Vehicle Velocity Prediction Method with Kinematic Segment Recognition
## 3 Methodology  
### QPSO Model  
The quantum particle swarm optimization (QPSO) algorithm is a particle swarm optimization (PSO) algorithm with quantum behavior improved on the basis of PSO. It uses the wave function to solve the position and velocity of particles in quantum space and obtains the probability density function of a certain position of particles in space by solving the Schrodinger equation. The Monte Carlo stochastic simulation method is used to obtain the particle position equation. In quantum space, the properties of particles satisfying the aggregate state are completely different, and it can be searched in the whole feasible solution space. Therefore, the global search performance of the QPSO algorithm is far better than that of the standard PSO algorithm [30; 31; 32]. The motion state equation of QPSO can be expressed by the following formula [33].  
\[P_{i}^{j}(t)=\varphi P_{i}^{j}(t)+(1-\varphi)G_{i}^{j}(t) \tag{24}\]  
where \(\varphi\)\(\in\)(0,1] is a random number, \(P_{i}^{j}(t)\) is the individual best position of particle \(i\) at time \(t\), \(G_{i}^{j}(t)\) is the global best position of the population at time \(t\), and \(j\) represents the dimension. In the QPSO algorithm, the Monte Carlo strategy is used to randomly generate new points. The particle update formula for particle \(i\) in the round \(t+1\) iteration can be expressed as follows:  
\[Y_{i}^{j}(t+1)=\begin{cases}P_{i}^{j}(t)+\frac{1}{2}\cdot L_{i}^{j}(t)\cdot \ln\left(\frac{1}{u}\right),&u>0.5\\ P_{i}^{j}(t)-\frac{1}{2}\cdot L_{i}^{j}(t)\cdot\ln\left(\frac{1}{u}\right),&u \leq 0.5\end{cases} \tag{25}\]  
where \(u\)\(\in\)(0, 1] is produced by a uniform distribution, \(Y_{i}^{j}(t+1)\) represents the current position of the particle, and \(L_{i}^{j}(t)\) represents the distance between the current position of the particle and the best average best position of the individual, which can be expressed as follows:  
\[L_{i}^{j}(t)=2\cdot\beta\cdot\left|m_{\text{best}}-Y_{i}^{j}(t)\right| \tag{26}\]  
\[\beta=m-(m-n)\cdot\frac{T_{\text{ite}}}{T_{\text{ite,max}}} \tag{27}\]  
\[E_{\text{best}}=\frac{1}{N}\sum_{i=1}^{N}P_{i}^{j}(t) \tag{28}\]  
where \(Y_{i}^{j}(t)\) represents the position of the last iteration of the particle, \(\beta\) is the expansion coefficient, \(E_{\text{best}}\) represents the average optimal position of the particles, \(N\) represents the total number of particles, \(m\) and \(n\) are the expansion coefficient parameters, and \(T_{\text{ite,max}}\) and \(T_{\text{ite}}\) represent the maximum and current iterations of the algorithm, respectively.
## 1 Introduction
Therefore, the goal of this study was to develop a DOI technique that is capable of accurately measuring the 3D broadband optical properties of heterogeneous turbid media without having to employ spectral constraints. Specifically, we sought a technique that provides more accurate analysis of localized buried inhomogeneities, such as tumors, compared to 2D topographic DOI, but without the complexity and long computational times required for typical DOT methods. We restricted the interrogation geometry to reflection-based imaging so as to be applicable to a wide range of noninvasive tissue imaging applications and compact instrument configurations. This work is further motivated by and supports our efforts to create an ultraportable and fast handheld breast imager with few source-detector separations that can be scanned across the tissue to generate high density measurements [26]. As a result, we present a new hyperspectral hybrid FD- and CW-DOI methodology, implemented with a simple probe configuration, that enables broadband 3D optical property recovery without a priori spectral constrains [1]. Because reconstructing each wavelength individually with traditional DOT approaches greatly increases computational time, a limited number of 3D regions were specified from a priori spatial information where optical properties are expected to be different (i.e., the so-called hard spatial constraint). This spatial prior information is intended to be acquired through a separate structural imaging technique, such as ultrasound, which is compatible with reflectance-mode DOI [4; 27]. Furthermore, discrete-wavelength FD measurements were chosen to accurately and directly estimate the tissue optical scattering, which are used to constrain broadband CW measurements. The new hybrid technique allows for reconstruction of the optical properties in heterogeneous media over a wavelength range of 650 to 1000 nm from a simple grid of single source-detector measurements. In this paper, we describe this hybrid DOI technique and study its accuracy using computational modeling and real tissue-simulating phantoms. The computational models simulate an ellipsoidal deep-tissue heterogeneity with optical properties mimicking a human breast tumor. We compare these results to optical properties recovered using conventional topographic DOI, where homogeneous models are employed. We also evaluate the approach experimentally using heterogeneous tissue-simulating phantoms with inclusions that recapitulate a breast tumor.
# Multi-Beam Circular Polarized Reflectarray on Parabolic Reflector by Variable Rotation Technique
## 4 Optimized Design of a Multibeam Parabolic Reflectarray  
Using the parabolic reflector described in Section 2 as a grounded surface, a parabolic reflectarray was designed using the reflectarray element designed in the previous section. The design was implemented using the VRT to match the required phase shift given by (1) and (2) to tilt dual CP focused beams in the specular directions given by \(\theta_{\text{beam,1}}=\ +\)0.28 deg, \(\phi_{\text{beam,1}}=\ 0\)deg and \(\theta_{\text{beam,2}}=\ -\)0.28 deg, \(\phi_{\text{beam,2}}=\ 0\)deg.  
Figure 6: Electromagnetic response of the optimized cell provided by the unit cell at 19.7 GHz as a function of the rotation angle. (**a**) Phase error \(\Delta\varphi\) produced in the 180 deg of difference of phase between the reflection coefficients \(R_{x^{\prime}x^{\prime}}\) and \(R_{y^{\prime}y^{\prime}}\). (**b**) Phases of the reflected electric fields of the co-polar circular polarized waves. (**c**) Amplitude of the reflected electric field of the cross-polar circularly polarized wave for an RHCP incident wave.  
To demonstrate the multi-beam capacity of the designed reflectarray, we simulated the antenna considering three dual CP feed horns: one in the focus and the other two with their \(x\)-coordinates shifted \(\pm 55\) mm. We considered the two circular polarizations for each feed position. For each dual CP feed-horns their H and E plane patterns were modeled as \(\cos^{24}(\theta)\)[24]. With these positions and polarizations of each feed, we obtained the six main spot beams. Figure 7a,b shows the co-polar and cross-polar radiations patterns, respectively. In these figures, solid and dashed lines with the same color correspond to the RHCP and LHCP produced for each feed horn. Note that the six tilted main beams maintained their nice shape. For each feed horn, the desired \(\pm 0.28\)degof tilt of the main beams were achieved with only 0.4 dB of loss in the gain. The cross-polar radiation was 35 dB below the maximum of radiation, in compliance with the typical requirements in satellite antennas for multiple spot beams in the Ka-band.  
The results shown in this section are satisfactory because they fulfill the typical stringent requirements of multi-spot beam satellite antennas [13; 14]. In this case, two beams per feed were obtained using one aperture. According to [13; 14], the final challenge is to obtain four beams per feed in dual band using one aperture. This goal will employ the uses of a four-color scheme for space applications such as direct broadcast satellite service, military communication satellite, and high-speed internet applications. So, the antenna design shown in this section is a required step toward surmounting the final challenge.
## 3 Results and Discussion
### Construction of Quality Score Prediction Model
#### 3.3.4 Assessment of Model Application Performance  
To assess the practical predictive performance of the Jordan-Elman neural network model, 30 unknown samples were evaluated. The results are illustrated in Figure 5.  
As illustrated in Figure 5, the optimal artificial neural network model achieves accurate predictions of the quality scores for 30 unknown samples (Rp\({}^{2}\) = 0.978, RMSEP = 0.373). The prediction results closely align with those of the prediction set, demonstrating that the Jordan-Elman neural network model with the tanh transfer function reliably predicts the quality of YCXY.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Transfer Function**} & \multicolumn{2}{c}{**Calibration Set**} & \multicolumn{2}{c}{**Prediction Set**} \\ \cline{2-5}  & **Rc\({}^{2}\)** & **RMSECV** & **Rp\({}^{2}\)** & **RMSEP** \\ \hline Linear [\(-\)1, 1] & 0.908 & 0.526 & 0.901 & 0.541 \\ logistic & 0.946 & 0.452 & 0.939 & 0.463 \\ tanh & 0.986 & 0.332 & 0.980 & 0.341 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of three transfer functions’ backpropagation artificial neural network.  
Figure 5: Predicted quality scores of unknown YCXY by the best backpropagation artificial neural network model.
# COVID-19 Detection from Chest X-ray Images Based on Deep Learning Techniques
## 3 Proposed Methodologies
### X-ray Diagnosis Using Deep Learning  
X-ray devices are widely used in medical imaging to visualize internal body structures and detect various conditions, such as tumors, lung infections, bone dislocations, and injuries [15]. The process involves passing light or radio waves through the body, and the resulting images provide valuable information for diagnosing and monitoring a range of medical conditions.  
Deep learning has shown remarkable success in various image analysis tasks, including medical imaging. In the context of X-ray diagnosis, deep learning techniques, particularly Convolutional Neural Networks (CNNs), have been leveraged to enhance the accuracy and efficiency of diagnostic procedures. By training CNN models on large datasets of X-ray images, these models can learn to identify patterns and features indicative of specific conditions, allowing for automated and accurate diagnosis.  
One of the significant advantages of using deep learning in X-ray diagnosis is the ability to process large volumes of images quickly and accurately. Deep learning models can analyze X-ray images in real time, providing rapid insights to medical professionals, thereby expediting the diagnostic process and enabling timely interventions.  
Moreover, deep learning models can assist in detecting subtle abnormalities or early-stage conditions that might be challenging for human experts to identify. The ability of deep learning models to learn complex representations from data makes them particularly useful in capturing intricate patterns in X-ray images, enhancing diagnostic accuracy.  
While CT scans offer more precise images of soft tissues and organs, X-rays remain a preferred choice for certain diagnostic scenarios due to their speed, safety, ease of use, and lower radiation exposure [45; 46]. Additionally, the application of deep learning to X-ray diagnosis complements traditional radiological interpretations, providing an additional layer of accuracy and consistency to the diagnostic process.  
The integration of deep learning techniques into X-ray diagnosis holds tremendous potential for improving healthcare outcomes by providing reliable and efficient diagnostic tools for medical professionals.
## 4 Discussion
### Micrographia in Early PD Is More Associated to Shape Complexity than Motor Plan Complexity  
Previous studies report that handwriting produced by PD patients is affected by micrographia, which occurs especially in tasks that escape attention, such as redundant and repetitive handwriting tasks [4]. Accordingly, we expected that tasks involving drawing of repetitive loops should have been more affected by PD micrographia compared to single loops. Conversely, since writing in clockwise condition requires more attention, we expected that micrographia should not occur in executing a novel motor plan.  
Here, we observed that, for simple tasks, the size of strokes drawn by PD patients is significantly smaller than that produced by H subjects, both in free and guided conditions, regardless whether the loops were performed through the habitual or the novel motor plan.  
Conversely, increasing the shape complexity of the task reduces the difference in stroke size between groups compared to simple tasks (measured differences are not significant), suggesting that it tends to reduce the effect of micrographia on PD patients. This result could be due to the characteristic of the shape we used for the complex task, containing repetitive loops, but with different heights. Indeed, we also observed that complex shape tasks led the PD patients to rely more on visual feedback, therefore increasing the attention in executing the task. Noteworthy, increasing the complexity of motor plan involved (from habitual to novel) seems to have small influence on the stroke size in terms of differences between groups, that depends more on the shape complexity of the task. Furthermore, when asking participants to draw the complex shape twice bigger than normal, following a sequence of dots, PD patients tend to increase their stroke size more than H subjects, both for habitual and novel tasks. As a consequence, while on average the strokes drawn by PD patients are smaller than those drawn by H subjects for normal size shapes, strokes drawn by PD patients result bigger than those drawn by H subjects for twice bigger size shapes (although difference in stroke size between groups is not significant). This analysis suggests that the greater increase of stroke size in patients' handwriting is due to the additional attention required for drawing loop shapes twice bigger than normal while following the guiding dots, rather than just the use of guiding dots. Indeed, we did not observe the same phenomenon for guided tasks in other conditions. Taken together, our results suggest that using more complex task (in terms of shapes) and asking patients to force themselves to write bigger than normal (providing also visual cues) could allow to trigger the attentional mechanisms that can be effective for contrasting micrographia. However, further experiments are needed for supporting this claim.
# Red Fox Optimizer with Data-Science-Enabled Microarray Gene Expression Classification Model
## 4 Experimental Validation
### Resulting Analysis of RFODL-MGEC Technique on Colon Tumor Dataset  
Figure 6 demonstrates a set of confusion matrices generated by the RFODL-MGEC model for the test colon tumor dataset. For the entire dataset, the RFODL-MGEC technique categorized 38 images as negative and 21 images as positive. Likewise, for 70% of the training dataset, the RFODL-MGEC approach categorized 27 images as negative and 14 images as positive. Furthermore, with 30% of the testing dataset, the RFODL-MGEC model categorized 11 images as negative and 7 images as positive.  
Table 3 demonstrates a brief classification performance report on the RFODL-MGEC model with the colon tumor dataset. The experimental results indicated that the RFODL-MGEC model demonstrated effective results with the test dataset. For instance, with the entire dataset, the RFODL-MGEC model obtained an average \(accu_{y}\), \(prec_{n}\), \(reca_{l}\), and \(F_{score}\) of 95.16%, 94.37%, 95.23%, and 94.77%, respectively. With 70% of the training dataset, the RFODL-MGEC method attained an average \(accu_{y}\), \(prec_{n}\), \(reca_{l}\), and \(F_{score}\) of 95.35%, 93.75%, 96.55%, and 94.88%, respectively. Additionally, with 30% of the testing dataset, the RFODL-MGEC algorithm obtained an average \(accu_{y}\), \(prec_{n}\), \(reca_{l}\), and \(F_{score}\) of 94.74%, 95.83%, 93.75%, and 94.49%, respectively.  
Figure 5: Loss analysis of RFODL-MGEC technique on prostate cancer dataset.  
Figure 6: Confusion matrices of RFODL-MGEC technique on colon tumor dataset. (**a**) Entire dataset, (**b**) 70% of training dataset, and (**c**) 30% of testing dataset.  
Figure 8 illustrates the training and validation loss inspection of the RFODL-MGEC model on the colon tumor dataset. The figure shows that the RFODL-MGEC approach offered lower training/accuracy loss for the classification process of the test data.
## 6 Conclusions  
The studies reviewed in this survey show that aerial wireless sensor networks (A-WSNs) are an important technological breakthrough for monitoring some oceanographic processes in which it is necessary to achieve high space/time resolutions. Also, the investment cost of deployment of an A-WSN is smaller in terms of both time and money than classic solutions like the ones used in coastal oceanographic observatories, where higher-performance devices are employed (deep-water buoys, autonomous underwater vehicles, gliders, lagrangian buoys, _etc._).  
From this survey it is fair to conclude that the solutions devised are generally ad-hoc ones as their design depends on various influencing factors. These factors include: the characteristics of the marine environment (deep water as opposed to shallows or coastal lagoons, climatic conditions, _etc._), the time-scale of the deployment (from monitoring for a matter of weeks or months to permanent installations), the spatial scope of the deployment (large areas with low spatial resolution as opposed to smaller areas with higher resolution), and the time resolution of data collection (sampling frequencies ranging from minutes or hours to days or weeks). Because of all these different possible scenarios, designs have to be ad-hoc and have to minimize the costs of deployment and maintenance of the network. One important factor in deployment costs is the sensors that are used, which can account for up to 70% of the total initial investment. Reducing the costs of this kind of sensors is therefore a major hurdle that will have to be overcome to enable large-scale implementation of networks of this type. Other important challenges include:  
* Efficient power supply systems to cover the duration of the deployment.
* Components that guarantee appropriate levels of insulation and corrosion-proofing (IP67, IP68, IP69K, _etc._). It will be essential to obtain designs that minimize the number of connectors used, since these are especially sensitive to corrosion in a marine environment.
* Design of buoys with ready access to their components for maintenance and eventual dismantling. This essentially entails replacing power supply systems, replacing or calibration of the sensors used, and dismantling of the system once the monitoring task is concluded.
* Continued improvement of communications systems (antennas and radio modules) so that they are more reliable and guarantee communication between sensor nodes in adverse weather conditions.
* Buoy designs that minimize the impact of the networks deployed on the environments that are monitored. The presence of floating buoys can be a problem in areas with busy sea traffic. Also, buoys can be stolen or vandalized and therefore need to have means of protection (concealed GPS positioning systems, alarms, _etc._).  
The results indicate that A-WSN implementations for oceanographic monitoring are viable. Although research on the use of A-WSNs for oceanographic monitoring is still in its infancy, it points to several exciting challenges which require further interdisciplinary collaboration.
# A 0.18 um CMOS Millimeter Wave Antenna-on-Chip with Artificial Magnetic Conductor Design
###### Abstract  
This paper presents a small-size broadband slot monopole chip antenna for millimeter wave application. Using a 0.18 um CMOS process, through metal_1, the artificial magnetic conductor (AMC) of the metal layer increased the impedance bandwidth of the chip antenna. The additional inverted C branch was used to achieve a better reflection coefficient. By adding an AMC and inverted C branch, the operating frequency of the chip antenna went to 33.8-110 GHz below the reflection coefficient of \(-\)10 dB, and its fractional bandwidth was 103.4%. The maximum gain was \(-\)6.3 dBi at 72 GHz. The overall chip size was 1.2 \(\times\) 1.2 (mm\({}^{2}\)). Through measurement and verification, the proposed antenna reflection coefficient was close to the simulation trend and had better resonance. The frequency range of the chip antenna proposed in this paper covered the 5G NR FR2 band (24.2 GHz-52.6 GHz) and W-band (75 GHz-110 GHz). The proposed chip antenna can be applied to the Internet of Things, Industry 4.0, biomedical electronics, near field sensing and other related fields.  
5G; millimeter wave; antenna-on-chip; CMOS +
Footnote †: journal: Accepted: 31 May 2023
## 4 Propagation Criterion of SRFG  
The derivatives of the cryptographic properties help in determining the propagation characteristics. All derivatives of the combined functions that output from SRFG are linearly equivalent. This is due to the equal number of 0s and 1s in the outputs equating with a hamming weight of \(\frac{u}{2}\). This is fixed for all the outputs of SRFG. The function \(f\) satisfies the propagation criterion of degree \(k\) and order \(m\) if any affine function is obtained from \(f\) with constant \(m\) input bits.  
Let \(f\in B_{N}\) and let \(x,y\in F_{2}^{N}\) be such that \(w(x)=w(y)=\frac{n}{2}\). Then, \(D_{x}f\) and \(D_{y}f\) are linearly equivalent. This signifies a linear permutation \(\mu\) of \(F_{2}^{N}\) such that \(D_{x}f\;=\;  
\[x\mapsto D_{\epsilon_{k}}f(a+y) \tag{10}\]  
The differential functions obtained from SRFG are also symmetric functions of \((n-k)\) variables and weight is \(\frac{\eta}{2}\). Moreover, simplified value vectors and ANF vectors for those differential symmetric are given for all \(0\leq i\leq n-k\) by  
\[g_{gy}(i)=g(i+w(y))\boxplus g(i+k-w(y)) \tag{11}\]  
\[\lambda_{gy}(i)=\otimes_{j\preceq k-wt(y)}\otimes_{j\preceq w(y)}\lambda_{f}( i+j)\boxplus\otimes\lambda_{f}(i+j) \tag{12}\]  
Let \(y\in\bar{V}_{i}\). For any \(z=a+y\) with \(a\in\bar{V}_{i}\), SRFG have the following relation.  
\[w(z)=w(a)+w(y) \tag{13}\]  
\[w(z+\epsilon_{k})=w(a)+w(y+\epsilon_{k})=w(a)+k-w(y) \tag{14}\]  
Thus, \(\forall\ a\in V\),  
\[\begin{split} D_{\epsilon_{k}}f(a+y)=f(a+b)\boxplus f(a+\epsilon _{k}+y)\\ =w(a)+w(y+\epsilon_{k})\\ =w(a)+k-w(y)\\ =g(w(a)+w(y))\boxplus(w(a)+k-w(y))\end{split} \tag{15}\]  
Equation (15) signifies \(g_{gy}\) also follows the symmetric property. This infers that the partial derivatives of \(f\) are also propagated with the propagation features. To calculate simplified ANF vector of \(g_{gy}\), we have decomposed the ANF of \(f\) as given below.  
\[f(a+b)=\otimes_{u\in F_{2}^{n-k}}\otimes_{v\in F_{2}^{k}}\lambda_{u,v}\prod_{ i=1}^{n-k}a_{i}^{u_{i}}\prod_{j=n-k+1}^{n-k}b_{j}^{v_{j}},\forall(a,b)\in(V, \bar{V}) \tag{16}\]  
Then, for any \(y\in\bar{V}\) and \(a\in V\), we have,  
\[\begin{split} g_{gy}(a)=D_{\epsilon_{k}}f(a+y)\\ =\otimes_{u\in F_{2}^{n-k}}\otimes_{v\in F_{2}^{k}}\lambda_{u,v} \prod_{i=1}^{n-k}a_{i}^{u_{i}}\times\prod_{i=1}^{k}(y_{i}\boxplus 1)^{v_{i}} \boxplus\prod_{i=1}^{k}y_{i}^{v_{i}}\end{split} \tag{17}\]  
In Equation (16), \(f(a+b)\) is used to show that if any biased bit vector \(b\) is included in SRFG function \(f\), it does not have any effect on output to disrupt the randomness features. \(\prod_{i=1}^{n-k}a_{i}^{u_{i}}\) and \(\prod_{j=n-k+1}^{n-k}b_{j}^{v_{j}}\) are used for selection of random bit patterns from the set of variables. In Equations (11)-(15), \(g_{gy}(a)\) deals with the derivatives showing that in the presence of any other bit vector \(y\), propagation feature is still preserved.