esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this paper, we propose a hybrid beamforming architecture with constant phase shifters (CPSs) for uplink cell-free millimetre-wave (mm-Wave) massive multiple-input multiple-output (MIMO) systems based on exploiting antenna selection to reduce power consumption. However, current antenna selection techniques are applied for conventional massive MIMO, not cell-free massive MIMO systems. Therefore, the enormous computational complexity of these techniques to optimally select antennas for cell-free massive MIMO networks is caused by numerous randomly distributed access points (APs) in the service area and their large antennas. The architecture proposed in this work solves this issue by employing a low-complexity matching technique to obtain the optimal number of antennas, chosen based on channel magnitude and by switching off antennas that contribute more to interference power than to desired signal power for each radio frequency (RF) chain at each AP, instead of assuming all RF chains at each AP have the same number of selected antennas. Therefore, an assignment optimization problem based on a bipartite graph is formulated for cell-free mm-Wave massive MIMO system uplinks. Then, the Hungarian method is proposed to solve this problem due to its ability to solve this assignment problem in a polynomial time. Simulated results show that, despite several APs and antennas, the proposed matching approach is more energy-efficient and has lower computational complexity than state-of-the-art schemes.  
mm-Wave communications; hybrid beamforming; cell-free massive MIMO; matching theory; Hungarian algorithm; antenna selection Article
# Application of Thermoelectric Generators for Low-Temperature-Gradient Energy Harvesting
## 2 Basic Principles of Thermoelectric Generators  
Thermoelectric energy harvesting is typically based on thermoelectric generators (TEGs), which, based on the Seebeck effect, directly convert a temperature gradient between their hot and cold ends into electrical energy [4; 22]. As will be detailed in the next subsection, due to the inherent limitations of the thermoelectric conversion process, the efficiency of TEGs is always low, usually below 8-9%, and much less for small temperature gradients, since the efficiency is governed by the Carnot cycle [17].
## 3 Results
### Visual Data Screening of The Multi-Characteristic Permeate Quality and Water Recovery Efficiency
#### 3.1.2 The Nanofiltration Process  
As shown in Figure 5, the boxplot portrayals demonstrated great differences between the behaviors of the six nanofiltration process characteristics. The permeate flux, total phosphorus and NO3- concentration measurements exhibited strong unsymmetrical data scattering, favoring either lower or higher values per case. Tendencies toward a lower permeate flux values were common with ultra- and nanofiltration trials. The datapoint groupings for the total phosphorus and the NO3- concentration measurements did not complement their behaviors in the two investigated processes. Moreover, the boxplots  
Figure 3: Bean plot screening of the seven ultrafiltration process characteristics.  
Figure 2: Boxplot screening of the seven ultrafiltration process characteristics.  
of the permeate flux, turbidity and total nitrogen content data were all characterized by one extreme/outlier point, which could not be overlooked given that it influenced the overall data reduction process by 11%. As shown in Figure 6, the corresponding bean plot data screening conveyed a very vivid picture of the multifaceted behavior that underscored each of the six process characteristics. Only the total nitrogen content dataset could be assumed to behave normally. The permeate flux data were definitely skewed, while the turbidity, the total phosphorus content and NO3- concentration variables appeared polarized, possibly implying a two-mode trend. A weak separation was observed among the ultrafiltration turbidity experiments (Figure 3). The two-peak disposition for the total phosphorus content was milder, as were the NO3- concentration variables in the ultrafiltration dataset. Useful information was gleaned from the QQ plot screening (Figure 7). It became transparent that the permeate flux, the total phosphorus content and the NO3- concentration variables could be affected by the occurrence of outlier/extraneous data points--contributions that collectively ranged from 33% to 56% of the total runs. In spite of the turbidity exhibiting a tight confidence band, there was an extreme data point that likely perturbed the data location and dispersion estimations. On the other hand, the electrical conductivity data possessed substantial variability, as evidenced by its consistently broader QQ plot confidence band.
## 3 Results and Discussion
### Material Characterization
#### 3.1.6 Bright-Field Transmission Electron Microscopy (BFTEM)  
Figure 5 presents the bright field transmission electron microscopy (BFTEM) micrographs for Hap-H3, Hap-H3 750, and Hap-FCF samples.  
In the Hap-H3 sample, with a scale bar of 50 nm, irregular agglomerates of nanoparticles showing a semispherical morphology are observed (Figure 5A) with a particle size of 41.5 \(\pm\) 12.5 nm (Figure 5(A1)). In contrast, the Hap-H3 750 sample (Figure 5(B1)), also shown at 50 nm scale, exhibits agglomerates with a needle-like morphology (Figure 5B) measuring 5.2 \(\pm\) 19.6 nm in width and length, suggests a morphological transformation induced by the thermal treatment.  
The sample loaded with forchlorofenuron (Hap-FCF) at a scale of 50 nm reveals agglomerates that retain the needle-like morphology observed in Hap-H3 750 (Figure 5C). The addition of the phytohormone increases the needle length from 5.2 \(\pm\) 19.6 nm to 6.1 \(\pm\) 31.2 nm (Figure 5(C1)). According to the XRD (Figure 1), the Hap-FCF sample exhibits a 10\({}^{\circ}\) shift to the right in the harmonic signals, accompanied by a reduction in crystallinity. The diffraction signals predominantly display hydroxyapatite, suggesting that FCF is incorporated within the Hap structure, promoting elongation of the needles. The 5X digital zoom for all samples (Figure 5(A3-C3)) confirms the compact hexagonal crystal lattice (highlighted in yellow), which primarily corresponds to hydroxyapatite, as reported in other studies [40] and identified in Figure 1, according to JPCDS No. 09-0432.  
The ring diffraction patterns in Figure 5(A4-C4) indicate the polycrystalline nature of the hydroxyapatite nanoparticles in the Hap-H3 and Hap-H3 750 samples, as well as the polycrystalline composite of hydroxyapatite and forchlorofenuron (Hap-FCF). Each diffraction ring corresponds to a family of crystal planes, representing an interplanar distance [41] characteristic of the compact hexagonal crystal structure. Incorporating the phytohormone, as evidenced by XRD (Figure 1), results in reduced crystallinity, confirmed by the decreased definition in the ring pattern (Figure 5(C4)).
## 8 Experimental Results  
The proposed TSF is validated with an experiment using a three-phase 6/4 SRM with 7.5 kW, 3000 rpm, and 270 V DC-link voltage (shown in Figure 15). A Texas Instruments (TI) TMS320F2812 digital signal processor (DSP) is used as a digital controller, and the clock frequency of TMS320F2812 is 150 MHz. The \(T\)-\(i\)-\(\theta\) characteristics are stored as look-up tables in the DSP. The SRM is driven by a three-phase asymmetric inverter and the motor shaft is coupled with a torque-speed sensor and position sensor. The three-phase asymmetrical inverter uses the six-cell IGBT module of Infineon Corporation. The type of IGBT is FS450R12 KE4 (withstand voltage 1200 V, normal current 450 A). The driving chip of the three-phase bridge uses the 6 SD312 EI (working frequency is up to 100 kHz) produced by CONCEPT Corporation in Switzerland. There is one current sensor on each phase line. A synchronous generator with a resistance box is used as the load.  
Figure 16 shows the measured phase current and instantaneous torque at 3000 rpm at a torque reference of \(T_{r\!f}=1.5\) Nm. For the CCC, the linear TSF, the sinusoidal TSF and the proposed TSF1 (\(r=4\)), the torque ripples are around 184%, 67.8%, 66.7%, and 40.4%, respectively compared to the values of 182.3%, 61.3%, 60.8%, and 36.6% in simulation results shown in Figure 12. And the peak phase currents are 8.9 A, 9.5 A, 9.5 A, and 5.9 A, and the rms phase currents are about 5.6 A, 5.4 A, 5.4 A and 3.9 A, respectively. This indicates that the experimental results agree well with the results of the simulation in terms of current waveforms, torque ripple and torque response under the same operating conditions. Due to a current tracking error, the CCC and the conventional TSFs show much higher torque ripples compared to the TSF1 (\(r=4\)), which achieves better tracking and has a nearly flat output torque, ignoring the torque ripple caused by the inherent current ripple of the hysteresis controller.  
Figure 14: The values of the objective function \(J\) at different weight factors \(w_{f}\). (a) 1000 rpm; (b) 3000 rpm.  
Figure 16: Experimental results (speed = 3000 rpm, \(T_{rsf}\) = 1.5 Nm): (**a**) CCC; (**b**) Linear TSF; (**c**) Sinusoidal TSF; (**d**) TSF1 (\(r\) = 4).  
Figure 15: Experimental setup of the SRM drive system.  
Assuming the total electromagnetic torque \(T\) is periodic when the actuation current is constant or position-periodic, the total torque \(T\) can be extended using a Fourier series as follows:  
\[T=t_{0}+\sum_{k=1}^{\infty}t_{k}\cos\left(kN\omega_{0}+\varphi_{k}\right) \tag{20}\]  
where \(N=3\) for the three phases of the \(6/4\) SRM. Figure 17a-d depicts the Fourier analysis of the experimental results of the total torque shown in Figure 16; the units for the phase and amplitude on the y-axis are rad and Nm, respectively. It is evident that the harmonic component of the proposed TSF1 (\(r=4\)) is the least. Thus, the results of the simulation and experimental prove that the torque-speed performance and the drive efficiency are significantly improved by using the proposed TSF.
## 2 Related Work
However, in the face of a large number of distributed interference sources, a variety of interference targets, and rapid changes in the dynamics of LEO satellites, it is often difficult for such exact algorithms to quickly converge to an exact solution. Besides some exact algorithms, heuristic algorithms are often used for distributed optimisation problems. In [38], a jammer optimisation method based on the Pelican optimisation algorithm is proposed, but the method is prone to fall into local optima. In [39], an artificial bee colony-based jammer allocation algorithm is proposed, but the method is sensitive to the relevant parameter settings, and the accuracy of the results is limited. In [40], a fusion algorithm based on Ant Colony and Q-Learning (Ant-QL), which does not rely on a priori information and enhances adaptability through real-time interaction between the jammer and the target radar, is proposed, but the method has a slow convergence speed, which is not able to meet the real-time requirements on the battlefield. In [41], an evolutionary reinforcement learning-based collaborative jamming resource allocation method is proposed, which uses joint multi-domain information to accomplish the optimisation of the joint allocation of jamming beams and jamming power under the constraint of jamming resources. In [42], a two-stage approach based on cooperative game theory is proposed to efficiently determine the optimal location and size of distributed energy sources to maximise their potential benefits. In [43], a multi-objective microgrid optimal arithmetic problem with different equations and inequality constraints is presented, and the optimisation of this distributed system is carried out using cooperative games. Currently, there are fewer studies on distributed interference optimisation problems using game-based approaches, and this study conducts a related study to fill this gap.
# Prediction of COVID-19 from Chest CT Images Using an Ensemble of Deep Learning Models
###### Abstract  
The novel SARS-CoV-2 virus, responsible for the dangerous pneumonia-type disease, COVID-19, has undoubtedly changed the world by killing at least 3,900,000 people as of June 2021 and compromising the health of millions across the globe. Though the vaccination process has started, in developing countries such as India, the process has not been fully developed. Thereby, a diagnosis of COVID-19 can restrict its spreading and level the pestilence curve. As the quickest indicative choice, a computerized identification framework ought to be carried out to hinder COVID-19 from spreading more. Meanwhile, Computed Tomography (CT) imaging reveals that the attributes of these images for COVID-19 infected patients vary from healthy patients with or without other respiratory diseases, such as pneumonia. This study aims to establish an effective COVID-19 prediction model through chest CT images using efficient transfer learning (TL) models. Initially, we used three standard deep learning (DL) models, namely, VGG-16, ResNet50, and Xception, for the prediction of COVID-19. After that, we proposed a mechanism to combine the above-mentioned pre-trained models for the overall improvement of the prediction capability of the system. The proposed model provides 98.79% classification accuracy and a high \(F_{1}\)-score of 0.99 on the publicly available SARS-CoV-2 CT dataset. The model proposed in this study is effective for the accurate screening of COVID-19 CT scans and, hence, can be a promising supplementary diagnostic tool for the forefront clinical specialists.  
COVID-19; transfer learning; chest CT scan image; ensemble learning Article
## 4 Testing of the Solution
### Simulated AUV-CCS Performed Tests
#### 4.2.3 Automatic Discovery Tests  
In this case, the goal is to test the dynamic discovery mechanism of DDS, which comes in handy in an unreliable environment such as the underwater scenarios. Dynamic discovery means that it is not necessary to specify in advance the IP address of the participants, as it is expected from them to be anywhere (space decoupling) and that they can start, join and leave in any order and at any time (time decoupling). Thus, for space and time decoupling testing, the vehicle is not subscribed to a topic and has not joined the deployment as a participant. The Publish/Subscription Manager publishes locations in DDS every 5 s. The ROS-DDS Proxy of the vehicle joins as participant and subscribes to the topic. The vehicle successfully receives all the previously published locations.
# Change of Lip Curvature Through Extraction and Non-Extraction Orthodontic Treatment
## 1 Introduction
The study identified short facial height and changes in arch width as key risk factors, suggesting that tooth extraction itself may not significantly influence overall changes in soft tissue contours. Even in 3D studies, lip position analysis still relies on linear measurements. The critical factor for assessing lip flattening--changes in contour--has not yet been quantitatively assessed. Therefore, this study introduces novel soft tissue measurements, SAL (A' to Sn'-Ls) depth, SAL (Sn'-A'-Ls) angle, PBL (B' to Li-Pg') depth, and PBL (Pg'-B'-Li) angle as tools to evaluate the lip curvature. The investigation delved into the correlation between these measurements and other measurement parameters. In addition, we conducted an analysis of the alterations in these measurements resulting from extraction and non-extraction orthodontic treatments and contemplated the utility of these measurements in extraction decision-making. The aim of this study was to assess whether new parameters demonstrated more pronounced flattening of the upper and lower lips in the extraction group compared to the non-extraction group. The null hypothesis was that there was no significant difference in lip flattening between the extraction and non-extraction groups. Further, a predictive formula for the upper and lower lips could be developed if a significant correlation was found.
# A Deep-Learning-Based Bearing Fault Diagnosis Using Defect Signature Wavelet Image Visualization
## 4 Methodology Evaluation Results
### Performance Evaluation of DSWI Compared to Vibration Image and Conventional Wavelet Spectrogram
Apart from the comparison, vibration images obtained from the time-domain analysis are not sensitive enough to weak incipient damage that may cause less discriminate information. Thus, the proper processing methods are preferred, which can result in discriminating information with conventional wavelet spectrogram and DSWI. From performances that are based on the other methods of the 2-D converted image of the AE signal, which taken together with results in discriminant patterns form for different types of faults in bearings, the conventional wavelet spectrogram can obtain a classification accuracy of more than 90%. However, the proportion of misclassification among different class types is not equal. Most of the misclassification happens in normal class because the pattern is not sufficiently discriminant. From the classification report, it is clearly observable that the proposed DCNN model with the DSWI input is able to extract and learn the features from the training dataset and classify the features in the testing dataset for the appropriate faulty and healthy conditions.
# Terahertz Single-Pixel Imaging Improved by Using Silicon Wafer with SiO\({}_{2}\) Passivation
## 3 THz Photomodulation Experiments
The experimental data agree well with the theoretical calculation following the Fresnel formulas, where the incidence angle is 25\({}^{\circ}\), and the refractive indices of SiO\({}_{2}\) and silicon at the wavelength of 808 nm are taken to be 1.45 and 3.68, respectively. The optimal thickness of SiO\({}_{2}\) as well as the corresponding minimum optical reflectivity may vary as the incident angle changes. Specifically, for smaller incidence angles, the minimum optical reflectivity can be slightly smaller; for the Brewster angle between the air-SiO\({}_{2}\) interface (\(\theta_{\text{B}}=55.4^{\circ}\)), the optical reflectivity of the CW laser keeps at 13% irrespective of the SiO\({}_{2}\) thickness. The independence of the optical reflectivity on the SiO\({}_{2}\) thickness at \(\theta_{\text{B}}\) is due to full transmission through the air-SiO\({}_{2}\) interface in this scenario, thus the optical reflectivity is exactly that by the SiO\({}_{2}\)-Si interface. Although even lower optical reflectivity at small incidence angles can result in even larger MF, in our experiments the specific angle of 25\({}^{\circ}\) was used due to space limitation of the experimental setup. Figure 3 shows that as the SiO\({}_{2}\) thickness increases, the MF of the THz radiation and the optical reflectivity of the CW laser behave reversely. When the SiO\({}_{2}\) thickness increases from 0 nm to the 148 nm, the optical reflectivity decreases, thus the concentration of generated carriers raises and the THz transmittance reduces since more photons impinge onto the silicon wafer producing carriers. When the SiO\({}_{2}\) thickness further increases, the optical reflectivity increases, reducing the photons that can be absorbed by the silicon wafer, the concentration of generated carriers, and the THz transmittance. These suggest the key role played by the anti-reflection effect of SiO2. However, for samples S1 and S9, both of which have almost the same optical reflectivity, as shown by Figure 3, the former has much smaller terahertz MF than the latter. This is because the dielectric layer added on top of the silicon surface terminates dangling bonds that arise due to the lattice termination, resulting in a reduction in trap density [18]. Therefore, we show that the SiO\({}_{2}\) not only plays a role of chemical passivation, but also acts as an anti-reflection film that enhances the use of light energy [26].
## 2 Materials and Methods
### Problem Modelisation
#### 2.1.1 Network Elements and Parameters
Notice that \(O\) is symmetric,
* \(I_{k_{r}},\forall k\in K\) are the parameters indicating the **instantiation times** of each \(k\)-type VNF. * \(R_{t}=\{r_{1},r_{2},...,r_{|R_{t}|}\}\) is the set of incoming **session requests** set during time-step \(t\). Every request \(r\) is characterized by \(b_{r}\in[0,1]\), the scaled _maximum_ **bitrate**, \(p_{r}\in[0,1]\), the mean scaled **payload**, \(I_{r}\), the **content provider** requested, and \(u_{r}\), the **user cluster** from which \(r\) was originated,
* \(T\) is a fixed parameter indicating the maximum RTT threshold value for the incoming Live-Streaming requests. * \(P^{*}\in\mathbb{M}(|\mathbf{R}|,|N_{H}|)\) is the **optimal processing times** matrix, where \(\rho^{*}_{res,k}\) is the processing-time contribution of the _res_ resource in any \(k\)-type VNF assuming optimal utilization conditions,  
Figure 1: The assumed Service Function Chain composition for every Live-Video Streaming session request. We assume that every incoming session needs for a streamer, a compressor a transcoder and a cache VNF modules. We assume container based virtualization of a vCDN. * \(\Psi\in\mathbb{M}(|\mathbf{R}|,|N_{H}|)\) is the **time degradation** matrix, where \(\psi_{res,k}\) is the parameter representing the _degradation base_ for the _res_ resource in any \(k\)-type VNF,
* \(c^{t}_{res,k,i^{\prime}}\)_vres_\(\in\mathbf{R},\forall k\in K,\forall i\in N_{H}\) are a set of variables indicating the _res_ **resource capacity** of \(f^{k}_{i}\) VNF during \(t\).
# An Efficient 5G Data Plan Approach Based on Partially Distributed Mobility Architecture
## 3 The Proposed 5G Procedure
### Intra-Gateway Handover  
Handover (H.O.) is a necessary procedure that maintains U.E. connectivity during the mobility inside the area of E-UTRAN. Depending on which eNB the U.E attached, the handover procedures are categorized as Intra Gateway Handover (Intra-GW H.O.) and Inter Gateway Handover (Inter-GW H.O). Intra-H.O occurs once the user moves out of the coverage area of the eNB source (eNB-S) and attaches to the target (eNB-T), which is in the same S-GW's domain. The Intra-GW H.O. with X2 support scenario is similar to the LTE  
Figure 4: The data delivery procedure from Mobile host to the Internet in the proposed 5G concept.  
Figure 5: The data delivery procedure from Mobile host to Mobile host in the proposed 5G concept.  
procedure [47] because E-UTRAN functions and procedures are unchanged in the proposed work. However, if both the source eNB and the target eNB belong to different S-GW's domains, the Inter-H.O. procedure shall occur, adjustments to the signaling method may change. In this section with Figure 6, we propose a handover without a gateway relocation procedure to reduce signaling messages.  
In proposed 5G network, all control functions are centralized in C.E., thus, eliminating MME involvement which simplified the H.O procedures even further. When the C.E. receives a path switch request from the target eNB, it allocates the necessary information of gateway (including TEID, QoS), adds parameters of the target eNB, and forwards them to M-GW through a session control message. After that, a Path switch response message, enclosed gateway's information, is sent to the target eNB. A GTP tunnel is created to deliver packets from the target eNB to M-GW.