esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### Modeling Method  
PLS discriminant analysis (PLS-DA) is a linear classification method based on the PLS regression algorithm, which is suitable for the analysis and prediction of multiple independent variables and strong linearity. It can explain the extracted principal factors from the perspective of the internal nature of data [39,40].  
The support vector machine (SVM) operation becomes complicated when the number of samples is too large, so the least squares support vector machine (LS-SVM) was proposed to address problems of inequality, constraints and quadratic programming in SVM. LS-SVM has a higher precision with a faster speed and smaller random access memory (RAM) usage [41,42]. The solving procedure is presented in Equation (2):  
\[\left\{\begin{array}{l}\min\!Z(w,k)=\frac{1}{2}w^{T}w+\gamma\frac{1}{2}\sum \limits_{j=1}^{l}k_{j}\\ s.t.N_{j}=w^{T}\varphi(x_{j})+m+k_{j}\end{array}\right. \tag{2}\]  
where \(x_{j}\) is the input variables, \(N_{j}\) is the target value, \(\varphi(x_{j})\) is the kernel function, \(k_{j}\) is error variables, \(w\) is weight of size, \(b\) is deviation, and \(\gamma\) is penalty coefficient. Two kinds of kernel functions are used in the paper. These two kernel functions are shown in Equations (3) and (4):  
\[k\big{(}x_{i},x_{j}\big{)}=\mathrm{e}^{-\frac{(x_{i}-x_{j})^{2}}{2\sigma^{2}}} \tag{3}\]  
\[k(x_{i},x_{j})=L(x_{i})\cdot L(x_{j}) \tag{4}\]  
where \(k(x_{i},x_{j})\) is the inner kernel function, \(\sigma\) is the kernel width, \(L(x_{i})\) and \(L(x_{j})\) are mapping functions. Before developing the LS-SVM model, two parameter \(\gamma\) and \(\sigma^{2}\) of radial basis function (RBF_Kernel) and the penalty coefficient \(\gamma\) of linear function must be obtained. The optimal parameter of LS-SVM is to find the best quadratic grid search and leave-one-out cross validation. The parameters determine the learning prediction and generalization performance of the correction model. \(\gamma\) is used to maximize generation performance of model and to minimize model complexity. Nuclear parameter \(\sigma^{2}\) decides the proximity coefficient of the model. The kernel function equation is presented in Equation (5) [43,44]:  
\[y(x)=\sum\limits_{k=1}^{n}a_{k}k(x_{i},x_{j})+b \tag{5}\]  
where \(a_{k}\) is the lag range operator and \(b\) is the statistical deviation. All the preprocessing was performed using Unscrambler 8.00; the pattern recognition and classification algorithms were performed using Matlab(r) R2010a.
# Iterative 2D Tissue Motion Tracking in Ultrafast Ultrasound Imaging
## Materials and Methods
### Evaluation of Motion Estimations  
For each setting, two evaluation metrics for the motion estimation were calculated using 90 kernels distributed in six columns with no overlap laterally and 15 rows with 50% overlap axially. The first evaluation metric was the difference between the set displacement and the estimated displacement for each kernel. The displacements were calculated as the distance moved by each kernel between the start frame and the end frame (0.6 s after start frame) (Figure 2). The second evaluation metric was the difference between the set velocity and the estimated velocity of each kernel. The velocities were estimated as the motion between two consecutive frames 0.4 s after the start of tracking (Figure 2).  
The statistical significance of changes in the mean estimation errors and standard deviations was tested for the initial length of iteration using \(k=1\) as the reference compared to other initial lengths of iteration and cine loops sampled at a low frame rate. The statistical significance of changes in the mean estimation errors was also tested between high frame rate cine loops using \(k=128\) as the initial length of iteration and cine loops sampled at a low frame rate. Significance testing was conducted with \(p<0.05\) as the significance level utilizing the Analysis of Variance (ANOVA) for changes in mean values and the two-sample \(F\)-test for changes in standard deviations. Because the ANOVA was balanced and the changes in the standard deviations were limited, the unequal standard deviations were deemed to have negligible influence on the tests.  
The in vivo measurements were evaluated by visual comparisons of the plotted motion estimations. One comparison was performed between ultrafast ultrasound imaging and conventional ultrasound imaging. The second comparison was performed in the same cine loop using four different positions along the artery wall.
## 2 Materials and Methods: Ten-Koh Mission Description
### Satellite Platform
#### 2.2.2 Comms  
Telecommunications subsystem includes two hot-redundant uplink chains and two cold-redundant downlink chains, with one uplink and one dowlink chain governed by one microcontroller (communications control unit, CCU). The amateur frequency transceivers with flight heritage were supplied by a Japanese manufacturer ([[http://www.nishimusen.co.jp](http://www.nishimusen.co.jp)]([http://www.nishimusen.co.jp](http://www.nishimusen.co.jp))). These are almost completely self-contained RF circuits and only require an external modem, which was implemented with the FX614 IC.  
Both COMM PICs constantly monitor their respective receivers and notify the OBC in case a valid uplink command has been received. The OBC then decides which downlink chain to use in order to send an acknowledgement to the ground-station. Both receivers operate at different frequencies such that the ground control can choose which uplink chain will receive the uplink, and each command specifies which downlink chain the OBC should use. This solution allows flexible in-flight debugging of the system.
## 3 Proposed System Model for Optimal Energy Management
### Demand Side Management Module
#### 3.2.1 Portable Interruptible Load  
Portable interruptible appliances can be interrupted during operation and scheduled for operation at any possible time. This set includes personal computers, microwave ovens, cooking ranges, water pumps, and electric irons. Energy consumption per day \(\varepsilon^{r}_{pi}\) of portable interruptible set of appliances can be determined using Equation (1) taken from [80], where \(A^{r}_{pi}\) is the set of portable interruptible appliances, \(a^{r}_{pi}\) represents one appliance that belongs to \(A^{r}_{pi}\), and \(\lambda^{r}_{pi}\) shows the power rating.  
\[\varepsilon^{r}_{pi}=\sum_{a_{pi^{\prime}}\in A^{r}_{pi}}\left[\sum_{\tau=1}^{ T}\lambda^{r}_{pi}\times\alpha(\tau)\right] \tag{1}\]  
where \(A^{r}_{pi}\) appliances energy cost is mathematically modeled in Equation (3). The state of the appliance, i.e., ON/OFF [01], is represented by \(\alpha(\tau)\) and RTP signal by \(p(\tau)\). The per hour and total operation costs are modeled in Equations (2) and (3), respectively [80].  
\[\sigma^{\tau^{\tau}}_{pi}=\sum_{a_{pi^{\prime}}\in A^{r}_{pi}}\Big{(}\lambda^{ r}_{pi}\times p(\tau)\times\alpha(\tau)\Big{)}\forall\tau=1:T \tag{2}\]  
\[\varsigma^{total^{\tau}}_{pi}=\sum_{a_{pi^{\prime}}\in A^{r}_{pi}}\left[\sum_{ \tau=1}^{T}\lambda^{r}_{pi}\times p(\tau)\times\alpha(\tau)\right] \tag{3}\]  
where \(\varsigma^{total^{\tau}}_{pi}\) shows the total cost of portable interruptible appliances, \(A^{r}_{pi}\) denotes set of portable interruptible appliances, \(\lambda^{r}_{pi}\) shows the power rating, \(p(\tau)\) represents the pricing signal, and \(\alpha(\tau)\) shows the ON/OFF status of appliances.
# Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control
## 4 Results
### Cache Hit Ratio  
The cache hit ratio in this system accommodates the cache requests from the cached vehicles. RSU cache hit ratio also compares the number of requests received from vehicles from respective Zones. ZoR is applied for the cache hit ratio. We have selected 50 vehicles to track for a cache hit ratio compared to the proposed techniques. The graph in Figure 9 shows the adaptability of different techniques adopted through the dynamic nature of the curves, including the main curve. The cache hit ratio of the RPCC, MLCP, and CCMP shows satisfactory results related to the other techniques. We established the review process and defined the effective results based on these techniques.  
On the other hand, the zone-based content pre-caching, i.e., PCZS+PCNS, shows results that received higher cache rates than other techniques. PCZS+PCNS selects the pre-caching zones based on content popularity. The proposed technique shows better results than PCZS+PCNS because its machine learning-based early prediction module effectively pre-caches the content based on accuracy in the prediction. The proposed technique effectively chooses the content pre-caching nodes based on the ZoR-based data  
Figure 8: Correlation map.  
dissemination approach, extending the working and providing accurate prediction results. Thus, the proposed technique improves the cache hit ratio with higher prediction accuracy.
## 3 Results and Discussion
### Characterization of the Zeolite Samples
#### 3.1.2 Electron Paramagnetic Resonance  
EPR spectra of pristine iron-Z-A, P-adsorbed iron-Z-A, and P-desorbed iron-Z-A are exhibited in Figure 2. The EPR spectrum of pristine Iron-Z-A showed a broad and high-intensity signal up to 510.794 a.u. possessing 'g' value = 2.014. In the case of P-adsorbed Iron-Z-A, the broad peak intensity spiked up to 572.753 a.u. and showed 'g' value = 2.015. Similarly, in P-desorbed Iron-Z-A, the peak intensity went up to 723.746 a.u. with 'g' value = 2.010. The 'g' value is \(\sim\)2.0 in all the zeolite species exhibited that the iron in the zeolite was incorporated in the framework in the form of Fe\({}_{\mathrm{x}}\)O\({}_{\mathrm{y}}\) clusters or Fe\({}_{2}\)O\({}_{3}\) nanoparticles. The successive increase in the peak intensity of virgin iron-Z-A, P-adsorbed iron-Z-A, and P-desorbed iron-Z-A suggested the successive exposure of iron species in the zeolite framework due to the adsorption and desorption process. Although there is a change in peak intensity but no significant change in the 'g' value, which proved the firm incorporation of Fe in the zeolite framework. The wide peak intensities in the signals showed the presence of Fe\({}^{3+}\) ions. A very weak intensity shoulder peak in P-adsorbed Iron-Z-A and P-desorbed iron-Z-A can be observed with 'g' value = 4.182 and 4.173, respectively. These new peaks in P-adsorbed and P-desorbed iron-Z-A give evidence for the appearance of new active sites.  
Figure 1: SEM images of (**a**) fresh zeolite-A (Z-A), (**b**) fresh iron-Z-A, (**c**) P-adsorbed iron-Z-A, and (**d**) P-desorbed iron-Z-A respectively.
# A Bipolar Neutrosophic Multi Criteria Decision Making Framework for Professional Selection
###### Abstract  
Professional selection is a significant task for any organization that aims to select the most appropriate candidates to fill well-defined vacancies up. In the recruitment process, various individual characteristics are involved, such as leadership, analytical skills, independent thinking, innovation, stamina and personality, ambiguity and imprecision. It outlines staff contribution and therefore plays a significant part in human resources administration. Additionally, in the era of the Internet of Things and Big Data (IoTBD), professional selection would face several challenges not only to the safe selection and security but also to make wise and prompt decisions especially in the large-scale candidates and criteria from the Cloud. However, the process of professional selection is often led by experience, which contains vague, ambiguous and uncertain decisions. It is therefore necessary to design an efficient decision-making algorithm, which could be further escalated to IoTBD. In this paper, we propose a new hybrid neutrosophic multi criteria decision making (MCDM) framework that employs a collection of neutrosophic analytical network process (ANP), and order preference by similarity to ideal solution (TOPSIS) under bipolar neutrosophic numbers. The MCDM framework is applied for chief executive officer (CEO) selection in a case study at the Elsewedy Electric Group, Egypt. The proposed approach allows us to assemble individual evaluations of the decision makers and therefore perform accurate personnel selection. The outcomes of the proposed method are compared with those of the related works such as weight sum model (WSM), weight product model (WPM), analytical hierarchy process (AHP), multi-objective optimization based on simple ratio analysis (MOORA) and ANP methods to prove and validate the results.  
personnel selection; neutrosophic ANP; neutrosophic TOPSIS; bipolar neutrosophic numbers; chief executive officer +
Footnote †: journal: Applied Sciences
# An Enhanced pix2 pix Dehazing Network with Guided Filter Layer
###### Abstract  
In this paper, we propose an enhanced pix2 pix dehazing network, which generates clear images without relying on a physical scattering model. This network is a generative adversarial network (GAN) which combines multiple guided filter layers. First, the input of hazy images is smoothed to obtain high-frequency features according to different smoothing kernels of the guided filter layer. Then, these features are embedded in higher dimensions of the network and connected with the output of the generator's encoder. Finally, Visual Geometry Group (VGG) features are introduced to serve as a loss function to improve the quality of the texture information restoration and generate better hazy-free images. We conduct experiments on NYU-Depth, I-HAZE and O-HAZE datasets. The enhanced pix2 pix dehazing network we propose produces increases of 1.22 dB in the Peak Signal-to-Noise Ratio (PSNR) and 0.01 in the Structural Similarity Index Metric (SSIM) compared with a second successful comparison method using the indoor test dataset. Extensive experiments demonstrate that the proposed method has good performance for image dehazing.  
pix2 pix; guided filter layer; VGG +
Footnote †: journal: Applied Sciences
## Cooperative Spectrum Sensing Using Eigenvalue Fusion for OFDMA and Other Wideband Signals  
**[PERSON] *, [PERSON] and [PERSON] ***  
National Institute of Telecommunications (Inatel), Av. [PERSON], 510, Santa Rita do  
Sapucai MG 37540-000, Brazil; E-Mail: [EMAIL_ADDRESS]  
* Authors to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] (D.A.G.);  
[EMAIL_ADDRESS] (R.A.A.S.); Tel.: +55-353471-9227; Fax: +55-353471-9314.  
_Received: 31 October 2012; in revised form: 14 December 2012 / Accepted: 19 December 2012 / Published: 7 January 2013_  
**Abstract:** In this paper, we propose a new approach for the detection of OFDMA and other wideband signals in the context of centralized cooperative spectrum sensing for cognitive radio (CR) applications. The approach is based on the eigenvalues of the received signal covariance matrix whose samples are in the frequency domain. Soft combining of the eigenvalues at the fusion center is the main novelty. This combining strategy is applied to variants of four test statistics for binary hypothesis test, namely: the eigenvalue-based generalized likelihood ratio test (GLRT), the maximum-minimum eigenvalue detection (MMED), the maximum eigenvalue detection (MED) and the energy detection (ED). It is shown that the eigenvalue fusion can outperform schemes based on decision fusion and sample fusion. A tradeoff is also established between complexity and volume of data sent to the fusion center in all combining strategies.  
**Keywords:** cognitive radio; eigenvalue fusion; eigenvalue spectrum sensing; wideband spectrum sensing; GLRT; MMED; MED; ED; OFDMA
# Single-Phase, Bidirectional, 7.7 kW Totem Pole On-Board Charging/Discharging Infrastructure
## 4 Result and Discussion  
The study of a 7.7 kW bidirectional charging infrastructure is carried out in MATLAB/Simulink environment using the system specifications listed in Table 1, the designed passive components in Table 2, and the control parameters in the Appendix B. Other than this, for charge and discharge purposes, a customized \(LiFePO_{4}\)-type lithium-ion battery pack of terminal voltage 72 V and current capacity 106 Ah is considered. Furthermore, the effectiveness of the proposed infrastructure is validated in G2V or charging and V2G or discharging mode to fulfil the targeted specifications such as THD, power factor, and efficiency. In this regard, the BDCI has simulated for \(t=4\) s, in which the first \(t=2\) s in G2V mode and V2G mode in another \(t=2\) s. Furthermore, the detailed performances of both modes have been discussed in the following subsections.
## 5 The Sensitivity of the Communication Scheme to Perturbations of System Parameters  
The proposed communication scheme does work well with preselected set of system parameters. Slight changes in parameter values (when the Sender and the Receiver use different parameters) would compromise the communication system. Figure 10 illustrates the sensitivity of the communication system to perturbations; all illustrations represent difference images in the enhanced contrast mode. We set initial parameters to: \(n=20\), \(\tau=20\), \(\
u=0.2\), \(\delta=0.08\), \(a_{0}^{ex}=0.02\), \(a_{0}^{con}=0.02\), where \(a_{0}^{ex}\)--initial value of chaotic logistic map used to select initially excited cells, and \(a_{0}^{con}\)--for transversal connection map.  
Figure 9: The flow chart diagram of the communication algorithm. Original image (**a**); dot-skeleton representation (**b**); initial conditions (**c**); perturbed initial conditions (**d**); cover image (**e**); perturbed self-organizing pattern (**f**); perturbed cover image (**g**); self-organizing pattern (**h**); difference image (**i**).  
The Sender uses all system's parameters as preset in the computational experiment illustrated in Figure 9--but the Receiver uses \(a_{0}^{ex}=0.021\) instead of \(a_{0}^{ex}=0.02\) (Figure 10a). The chaotic Logistic map is sensitive to small perturbations--thus it is natural to expect that the evolving patterns from different initial conditions would result into a different SOP image. Correct initial cell excitation map is essential in order to show the embedded secret (Figure 9a). However, AF model construction allows some perturbations in connection map (Figure 10b), thus different map does not completely brake the results and visual interpretation of the embedded secret is still possible.  
Perturbations can be introduced in the number of iterations \(n\) and cell's refractory period \(\tau\). It can be noted that differences are only visible when \(\tau<n\). Otherwise the model does not reach the breaking point when cells finish their relaxation period. As it can be seen in Figure 10c,d, changes by one time-step does hide most of the embedded image, but if the change is higher, the difference image does not reveal any secret.
# Torque Requirements and the Influence of Pilot Holes on Orthodontic Miniscrew Microdamage
## 3 Results
### Maximum Insertion Torque and Bone Thickness  
There was evidence to suggest that maximum insertion torque differed between thickness groups (\(p\)-value = 0.0001). Keeping pilot hole status constant, maximum insertion torque increased with increasing bone thickness (Figure 4). Maximum insertion torque was on average 3.92 Ncm higher for 2.0 mm bone specimens compared to 1.5 mm bone specimens (95% CI 1.45, 6.39) \(p\) = 0.003), and 5.94 Ncm higher in the 2.5 mm bone compared to the 1.5 mm bone (95% CI [3.47, 8.41] \(p\) < 0.0001). There was little evidence that the maximum insertion torque differed between the 2.0 and 2.5 mm bone block specimens (95% CI (0.45, 4.49) \(p\) = 0.107).
## 4 Evaluation Results
### Medium Scale Prosumer  
For medium-scale prosumer, five clusters representing the peak profiles and five clusters representing the valleys profiles were identified. The centroids of the clusters can be seen in Figure 13.  
Figure 11: TC-S2: best day (SMAPE = 14.9%), with only energy features.  
Figure 12: TC-S3: best day (SMAPE = 11.2%), with energy and temperature as features.  
For the first test case, TC-M1, we have considered only the energy features in the prediction configuration. The training configuration of the models (i.e., splits size of data--60%, 20%, and 20%; number of epochs--over 1000) was similar to the previous evaluation cases. The average SMAPE was about 14%, while the minimum value was 4.8% (see Figure 14).  
We have considered the temperature taken from weather services as a feature to improve the prediction accuracy (i.e., test case TC-M2). With a higher number of epochs, the accuracy was improved, with the average SMAPE being around 12%, and the SMAPE for the best predicted was 4.7% (see Figure 15).  
Figure 14: TC-M1: best predicted day (SMAPE = 4.8%).  
Figure 13: Energy peak and valley cluster centroids.  
Figure 15: TC-M2: best predicted day (SMAPE = 4.7%).  
In the last tested configuration, TC-M3, the temperature sensor was substituted with a humidity sensor, and slightly better results were obtained, with a lower best SMAPE (see Figure 16).
# Research on New Drilling Downlink Communication Based on Anisotropic Magnetoresistive Technology
## 1 Introduction
### Mathematical Model of Magnetic Field Distribution around Downhole Drill String  
In order to further study the magnetic field distribution around the drill string in the new information downlink communication system based on anisotropic magnetoresistance technology, the magnetic field distribution around the downhole drill string is modeled. According to the theory of electromagnetic field and electromagnetic wave, it is known that the constant magnetic field is excited by constant current, which is another special form of the electromagnetic field. Considering the fact that the constant current and sum do not change with time, the basic equation integral form of the constant magnetic field is obtained from [PERSON]'s equations, as shown in Equation (10):  
\[\left\{ \begin{array}{c}\oint_{\mathbb{C}}\overset{\rightarrow}{H}\overset{ \rightarrow}{d}\overset{\rightarrow}{=}\int\overset{\rightarrow}{d} \overset{\rightarrow}{S}\\ \oint_{\mathbb{C}}\overset{\rightarrow}{B}\overset{\rightarrow}{d} \overset{\rightarrow}{S}=0\end{array} \right.. \tag{10}\]  
The differential form is shown in Equation (11):  
Figure 4: Current at any point of bottom hole drill string: (**a**) \(H\) = 500; (**b**) \(H\) = 1000. \[\left\{ \begin{array}{c}\
abla\times\stackrel{{\rightarrow}}{{H}}= \stackrel{{\rightarrow}}{{J}}\\ \
abla\times\stackrel{{\rightarrow}}{{B}}=0\end{array} \right.. \tag{11}\]  
If the medium remains unchanged, Equation (12) can be obtained:  
\[\stackrel{{\rightarrow}}{{B}}=\mu\stackrel{{ \rightarrow}}{{H}}. \tag{12}\]  
Equations (11) and (12) show that the constant magnetic field is passive and rotating, the constant current is the vortex source producing the constant magnetic field, and the magnetic line of force is the closed curve of the cross-chain with source current. There is usually a magnetization surface current on the interface of different magnetic media, and it mutates when it passes through the interface, and the interface satisfies Equation (13):  
\[e_{n}\cdot\left(\stackrel{{\rightarrow}}{{B}}_{1}^{\rightarrow}- \stackrel{{\rightarrow}}{{B}}_{2}^{\rightarrow}\right)=0\ \mbox{or}\ H_{1t}=H_{2t}. \tag{13}\]  
Equation (13) shows that the normal component \(\stackrel{{\rightarrow}}{{B}}\) on the interface is continuous. After the continuity of the normal component on the interface \(\stackrel{{\rightarrow}}{{H}}\) is known. If there is no free surface current (\(\stackrel{{\rightarrow}}{{J}}_{S}^{\rightarrow}=0\)) on the interface, the analysis on the interface \(\stackrel{{\rightarrow}}{{H}}\) shows that the relationship shown in Equation (14) is satisfied:  
\[e_{n}\times\left(\stackrel{{\rightarrow}}{{H}}_{1}^{\rightarrow}- \stackrel{{\rightarrow}}{{H}}_{2}\right)=0\ \mbox{or}\ H_{1t}=H_{2t}. \tag{14}\]  
Equation (15) shows that the tangential component of magnetic field intensity is continuous. To facilitate the subsequent establishment and derivation of the magnetic field intensity model, the potential function is introduced.