esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theory: Coherent Multi-Transducer Ultrasound Imaging
Finally, the total beamformed image \(S(Q_{k};\alpha)\) can be obtained by coherently adding the individually beamformed images acquired in a sequence in which both probes transmit:  
\[S(Q_{k};\alpha)=s_{1,1}(Q_{k};\alpha)+s_{1,2}(Q_{k};\alpha)+s_{2,1}(Q_{k};\alpha )+s_{2,2}(Q_{k};\alpha) \tag{3}\]  
In the same way, several PWs transmitted at different angles, \(a=1,\ldots,A\), may be coherently combined to generate an image,  
\[S(Q_{k};A)=\sum_{i=1}^{N}\sum_{j=1}^{N}\sum_{a=1}^{A}s_{i,j}(Q_{k};a_{a}^{i}) \tag{4}\]  
The optimum beamforming parameters that determine Equation (1) (\(\mathcal{P}=\{c,\theta_{1},\mathbf{r}_{1},\theta_{2},\mathbf{r}_{2}\}\)), not a priori accurately known, are calculated by maximizing the cross-correlation of backscattered signals from common point-like targets acquired by individual receive elements, by using gradient based optimization methods,  
\[\mathcal{P}=\arg\max_{\mathcal{P}}\chi(\mathcal{P}) \tag{5}\]  
where the cost function \(\chi(\mathcal{P})\) is defined as follows,  
\[ \begin{split}\chi(\mathcal{P})=&\sum_{k}^{K}\sum_{ h}^{H}\{\text{NCC}(T_{1}R_{1}(h,t_{1,1,h}(Q_{k};\mathcal{P})),T_{2}R_{1}(h,t_{2,1,h} (Q_{k};\mathcal{P})))W_{1,1}W_{2,1}\\ &+\text{NCC}(T_{1}R_{2}(h,t_{1,2,h}(Q_{k};\mathcal{P})),T_{2}R_{ 2}(h,t_{2,2,h}(Q_{k};\mathcal{P})))W_{1,2}W_{2,2}\}\end{split}  \tag{6}\]  
NCC is the normalized cross-correlation, and \(W_{i,j}\) is a weighting factor defined as,  
\[W_{i,j}(\mathcal{P})=\frac{1}{2}+\frac{1}{2H}\sum_{h_{b}\
eq h}^{H}\text{NCC }(T_{i}R_{j}(h;t_{i,j,h}(\mathcal{P})),T_{i}R_{j}(h_{b};t_{i,j,h}(\mathcal{P} ))) \tag{7}\]  
A minimum of two points is needed to solve this trilateration problem in 2D [16]. Although, since the approach relies on a measure of coherence, which may well be more tolerant, previous results suggest that the method may work when there are identifiable prominent local features [10]. On the other hand, isolated point scatterers can be artificially generated by other techniques, for instance by inclusion of microbubble contrast agents [17]. Preliminary results show the potential of microbubbles to optimize the beamforming parameters in CoMTUS, which could be implemented in vivo wherever the FOV contains vasculature [18].
## 4 The COASYM-MAC Protocol
### Tree Formation  
In WSNs, nodes are deployed at random in the monitoring region and report the gathered data to the BS in a converge-cast (many to one) manner [52]. In this subsection, we discuss tree formation topology in detail. Tree formation can be divided into three stages. In the first stage, the distance of each node from the Base Station is calculated and one-hop count nodes are selected. In the second stage,each node selects a temporary parent under various conditions; however, there may be a child node imbalance among parent nodes. Hence, we balance the number of child nodes in the third stage.
# An Architecture for the Performance Management of Smart Healthcare Applications
## 6 Conclusions  
This article presented PRIMUS, an architecture for performance management based on adaptable smart healthcare application requirements and network resource slicing. Different from the literature, PRIMUS adapts network virtual resources and routes according to the s-health demands. Hence, it identifies the applications and configures the network on-the-fly. The results obtained in a virtualized environment, supporting SDN, NFV, and network slicing, show the feasibility to achieve the performance requirements of s-health. PRIMUS achieved latency values less than 10 ms in URLLC slice type (refers to critical care monitoring), and less than 20 ms for eMBB (refers to telemedicine). The required 1 ms of URLLC latency can only be achieved with network physical infrastructure efficient, such as 5G and 6G networks. Hence, this work contributes to the state-of-the-art and research community by implementing a virtualized scenario considering network slicing, network performance management, and critical applications performance requirements, i.e., s-health applications. As future directions, we intend to improve reliability, latency values considering a 5G environment, and PRIMUS scalability with more devices. Therefore, we concluded that network slicing is absolutely necessary to enable the smart healthcare envisioned scenario in 5G and 6G networks. Furthermore, network slicing with virtualization techniques will be key technologies to resolve heterogeneity issues.  
A.V. contributed to the writing--original draft preparation and editing, methodology, results analysis, and project administration; B.H.S. and N.G.P.J. contributed to system implementation, performance evaluation, data curation, editing, and results analysis; A.S. contributed to funding acquisition and editing; M.N. contributed to project administration, editing, and revisions. All authors have read and agreed to the published version of the manuscript.  
This work was supported by CAPES, CNPq, and FAPESP. The grant numbers are CNPq 426701/2018-6, 309129/2017-6 and 432204/2018-0, and FAPESP 2018/23098-0 and 2020/05884-8.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Types of Supplementary Dampers and Their Characteristics
### Viscoelastic Dampers  
Viscoelastic dampers are designed using materials that possess both viscous and elastic characteristics, enabling them to effectively dissipate energy [57]. These dampers function as passive energy dissipation devices, utilizing the distinctive properties of viscoelastic materials to reduce seismic energy and alleviate structural vibrations. Typically, they consist of layers of viscoelastic polymers positioned between steel plates [58]. In the event of an earthquake, the cyclic loading induces deformation in the viscoelastic material, leading to energy dissipation primarily in the form of heat. This mechanism significantly lessens the dynamic response of the structure, resulting in reduced displacements, accelerations, and internal forces [59; 60]. The ability of viscoelastic dampers to effectively manage vibrations across both low and high frequencies makes them suitable for a wide range of structural applications. Their unique combination of damping and stiffness enhancement sets them apart from other damping systems, providing engineers with greater design flexibility and opportunities for performance optimization [61; 62].  
Figure 7: Examples of various approaches to adding viscous dampers [56].  
One of the primary advantages of viscoelastic dampers lies in their linear behavior across a wide range of frequencies and temperatures, which simplifies both analysis and design processes. These dampers exhibit exceptional resistance to fatigue and maintain their functional properties over prolonged durations, thereby ensuring consistent performance throughout the structure's lifespan [63; 64]. Recent advancements in polymer technology have led to the development of high-performance viscoelastic materials that demonstrate enhanced temperature stability and superior energy dissipation characteristics [65; 66]. Additionally, researchers have explored hybrid systems that combine viscoelastic dampers with various damping methods to achieve enhanced performance across a broader spectrum of loading scenarios. As the demand for resilient structures grows, viscoelastic dampers are evolving continuously, offering innovative solutions for seismic protection in both newly constructed and retrofitted buildings [67; 68].
## 7 Design Example and Simulation Results
### Numerical Analysis and Design
Considering that both Option 1 and Option 2 produce the same PV voltage, but Option 2 requires a slower (and cheaper) hardware for the filter implementation, this example adopts Option 2: \(\Omega=9.6\) krad/s, \(k_{1}=0.61\) A/V, \(t_{s}=0.8\) ms. In general, the solution with a lower \(\Omega\) is desirable since a less restrictive slew rate is needed. Finally, the flyback converter will be constructed with a MOSFET and a diode supporting 100 kHz, which requires a hysteresis band with a peak amplitude lower than 0.47 A according to the design equation given in (43). Then, the hysteresis parameter is set to \(H=0.5\) A to provide a safe margin, which ensures a switching frequency lower than 95 kHz. Using detailed circuital simulations, the following subsection validated the SMC and filter performance considering the selected parameters.
## 5 Results and Discussion
### Combustion Characteristics
#### 5.1.2 Heat Release Rate (HRR)  
The HRR indicates the chemical energy of the fuel that is converted into thermal energy. Figure 6 shows the variation of HRR with respect to the angle of the crankshaft for diesel and biodiesel blends. In general, diesel has the highest HRRs for the modes of operation tested, followed by PB5, PB10, PB5 SB5, and PB10 SB5, respectively. It was observed that the maximum HRR was 1.08, 1.14, 1.18, 1.22, and 1.26 J/deg for PB10 SB5, PB5 SB5, PB10, PB5, and diesel in the mode of operation D, respectively. On average, the HRRs of PB5 SB5 and PB10 SB5 decreased by 3.38% and 8.47% compared to PB10 biodiesel. Viscosity is considered the key factor for this behavior because it produces slower combustion, resulting in a reduction in heat release. Similar behavior was obtained in the investigation of Can [41].  
Figure 6: Influence of fuel on heat release rate for (**a**) mode A, (**b**) mode B, (**c**) mode C, and (**d**) mode D.
## 3 Methods
### Segmentation of the Lung Anatomical Structures
#### 3.1.3 Isotropic Interpolation  
Lung CT scans are two-dimensional (2D) cross-sectional images taken from different angles around the body. These slices can be stacked together and reconstructed into a three-dimensional (3D) isotropic image for more comprehensive analysis and visualization. Assume that \(V(x,y,z)\) is 3D data of stacked CT slices with the dimensions \(N_{x}\ \times N_{y}\ \times N_{z}\) (width \(\times\) height \(\times\) thickness) where \(N_{x}\ =\ N_{y}\) (generally it is equal to 512 which is the typical resolution of a 2D CT slice) and \(N_{z}\) is the total number of slices in a single exam. Here, the term \"single exam\" means all of the sequential CT slices for a single patient during one CT scan. Then, the position of a voxel in \(V\) can be expressed by \(V\big{(}x_{p},y_{q},z_{k}\big{)}\) where:  
\[\big{\{}x_{p}\big{\}}_{p=0}^{N_{x}-1},\ \big{\{}y_{q}\big{\}}_{q=0}^{N_{y}-1}, \text{ and }\{z_{k}\}_{k=0}^{N_{z}-1}. \tag{3}\]  
Figure 3: The range of radiological density of the lung structures.  
For the CT scans, we can assume that \(x_{p}=~{}p\Delta~{}x\), \(y_{q}=~{}q\Delta~{}y\), and \(z_{k}=~{}k\Delta~{}z\) where \(\Delta\) denotes the intervals between pixels along each coordinate. In a situation with inevitable noise, the positions of the voxel are defined as  
\[g\Big{(}x_{p},y_{q},z_{k}\Big{)}=~{}V\Big{(}x_{p},y_{q},z_{k}\Big{)}~{}+n\Big{(} x_{p},y_{q},z_{k}\Big{)} \tag{4}\]  
where \(n\Big{(}x_{p},y_{q},z_{k}\Big{)}\) represents the additive noise. After stacking the 2D slices, linear interpolation has to be carried out across the z-axis, because the thickness of the CT slices may vary even in a single exam:  
\[g_{lin}(z)=~{}\Bigg{(}1-\frac{z-z_{k}}{z_{k+1}-z_{k}}\Bigg{)}g(z_{k})+~{}\Bigg{(} \frac{z-z_{k}}{z_{k+1}-z_{k}}\Bigg{)}g(z_{k+1}),~{}z_{k}~{}\leq~{}z~{}~{}\leq ~{}z_{k+1} \tag{5}\]  
where \(z\) is the new slice to be interpolated, and \(z_{k}\) and \(z_{k+1}\) are two adjacent slices of \(g(x,y,z)\). Then, \(g_{lin}(z)\) is the resulting image after linear interpolation.  
Some of the previous CAD methods reconstructed the volumetric structure CT slices at the beginning stage and then detection was performed on the reconstructed volume. Although working on the isotropic image provides comprehensive information about the tumor nodules, it demands high computational effort and time. Conversely, working on 2D data demands less effort but provides insufficient information about the tumor nodules. As an expedient method, we performed segmentation slice-by-slice on 2D data; after that, we reconstructed an isotropic image of 2D segments for further analysis.
## 4 Results
### Characterization
deviation of the size and area of the wedge. For large wedges near \(\tilde{h}/h_{\text{max}}=1\), \(\langle F_{N}\rangle\) increases to \(11\%\), which means that more than the correct number of wedges are found. This can be explained by the fact that the wedge templates are drawn from an interval of \([0.5,0.95]\cdot h_{\text{max}}\). When the templates are consistently smaller than the turbulence wedges, two maxima form in the cross-correlation curve close to the actual position of the wedge: One at the left edge of the wedge, one at the right, which leads to the algorithm detecting two smaller wedges close together instead of one large wedge. Figure 12b shows the mean position error \(\langle\frac{\Delta x}{\tilde{h}}\rangle\) over the relative height. Note that the mean wedge width \(\langle\tilde{w}\rangle\) was calculated for each height parameter instead of averaging over all. For small wedges with \(\tilde{h}/h_{\text{max}}<0.4\), the largest position error of \(\langle\frac{\Delta x}{\tilde{h}}\rangle=10\%\) can be found. However, for wedges with typical heights found in real thermograms (see gray region in Figure 12b), the position error is only around \(1\%\). For large wedges near \(\tilde{h}/h_{\text{max}}=1\), where more than one wedge is detected, the average position error only includes the position of the first found wedge. The position error therefore drops to \(\langle\frac{\Delta x}{\tilde{h}}\rangle=-5\%\) for \(\tilde{h}/h_{\text{max}}=1\), indicating that the first wedge is found slightly left of the actual position. This issue could be solved by using larger templates; however, this wedge size seldom occurs in real thermograms. In Figure 12c, the relative deviation of the height, the width and the area of the wedge is shown over the true relative wedge height. For wedges with \(0.2<\tilde{h}/h_{\text{max}}<0.3\), the algorithm determines larger-than-true areas, which is evident from \(\langle F_{A}\rangle>0\). For wedges larger than \(\tilde{h}/h_{\text{max}}>0.6\), the areas found are consistently smaller than the true areas, which is also apparent in the verification results in Table 2. This can be explained by the intensity gradient in the thermogram, which reaches the intensity value of the wedge at \(\tilde{h}/h_{\text{max}}=0.73\). Therefore, the tips of the wedges in this height range have a CNR close to zero, which interferes with the determination of the wedges' areas.
###### Abstract  
This paper presents a novel digital background calibration technique for timing mismatches in time-interleaved analog-to-digital converters (TIADCs). We reconstruct and eliminate the timing mismatch based on the theory of timing-mismatch-induced spurious signals in the frequency domain. The proposed compensation algorithm utilizes the conjugate property between spurious signals to achieve low complexity. A coarse-fine correction architecture is adopted to eliminate higher-order time-skew errors. A novel feedforward estimation algorithm based on the correlation of adjacent channels is proposed to extract the time-skew errors. Our proposed calibration technique is suitable for an arbitrary number of channels. The simulation results demonstrate that the utilization of the proposed calibration technique yields significant improvements in both the signal-to-noise-and-distortion ratio (SNDR) and spurious-free dynamic range (SFDR). The SNDR and SFDR are improved from 55.82 dB and 56.64 dB to 79.92 dB and 105.98 dB, respectively.  
time-interleaved ADC; time-skew errors; fully digital calibration; feedforward architecture; background +
Footnote †: journal: Electronics Electronics
## 2 Materials and Methods  
### Nano-Satellite System Overview  
The proposed platform is a FPGA-based payload board intended to be embedded in a nano-satellite along with an on-board computer (OBC). From the Mission Control Center (MCC) point of view, the OBC is the entry point for communication with the Earth, especially with the Attitude Determination and Control System (ADCS) sub-module responsible for satellite navigation. Messages for the payload board are transferred by the OBC through a serial link whereas scientific instruments can use dedicated high-speed interfaces with the FPGA board (Figure 1). An example of a nano-satellite is the CubeSat format [14]. In this specification, the different boards that compose such a miniaturized satellite are stacked on top of each other inside multiple 10 cm cubes.
## 2 Materials and Methods
### Clinical Study Protocol and Sample Collection
Vaginal pH was measured using a pH-indicator strip (range of 0.0-6.0; Merck, Darmstadt, Germany). The method of clinical severity scores was modified according to [PERSON] et al. [19]. The score of color, odor, and secretion of vaginal discharge was evaluated by the principal investigators of the clinical trial, and the severity of vaginal itching was evaluated by participants. The color of vaginal discharge was scored from 0 to 4, with 0 being clear, 1 being white, 2 being yellow-white, 3 being yellow-green, and 4 being grey. The odor of vaginal discharge was scored from 0 to 4, with 0 being no odor, 1 being mild, 2 being moderate, 3 being severe, and 4 being very severe. The vaginal secretion was scored from 0 to 2, with 0 being small amount, 1 being moderate amount, and 2 being profuse amount. The vaginal itching was scored from 0 to 4, with 0 being no itch, 1 being mild, 2 being moderate, 3 being severe, and 4 being very severe. RNA Extraction and Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) Analysis of Inflammatory Cytokines  
Total RNA of the obtained cell pellets from the vaginal swab was isolated using TRIzol reagent, according to the manufacturer's instructions (Ambion Life Sciences, Thermo Fisher Scientific, Inc., Waltham, MA, USA). A total of 1 \(\upmu\)g of RNA was reverse-transcribed to complementary DNA (cDNA) using MultiScribe(tm) Reverse Transcriptase (Thermo Fisher Scientific, Inc., Waltham, MA, USA). Expression of inflammatory cytokines was determined by qRT-PCR using the SYBR Green PCR Kit (QIAGEN, Hilden, Germany) and specific primers, at a final concentration of 0.6 \(\upmu\)M. The sequences of specific primers targeting inflammatory cytokines (IL-1, IL-6, and TNF-\(\upalpha\)) were shown in the Supplementary Table S2. Human GAPDH was used as a housekeeping gene control for normalizing the mRNA levels of target genes. The fold changes of the inflammatory cytokines were calculated by the \(2^{-\Delta\Delta\text{Ct}}\) method for which the Ct values of samples collected at the first visit were set as the baseline.
# Modeling of Satellite-to-Underwater Integrated FSO-PON System Using NOMA-VLC
## 1 Introduction
### Motivations  
As discussed above, FSO-VLC is introduced in [14; 15; 16; 17], while FSO-UOWC and RF-FSO systems are presented in [3] and [1], respectively. Moreover, the earth-to-satellite FSO system is analyzed in [18] and FSO/RF link for satellite-to-unmanned aerial vehicles has been investigated in [19]. However, a hybrid PON-FSO model incorporating VLC for long hauls and high bandwidth under the impact of different fiber and water impairments has not been investigated yet for satellite-to-underwater applications. Therefore, in this paper, a hybrid FSO-PON system using NOMA-VLC considering fiber impairments and different types of ocean water has been modeled, investigated and presented.  
Here, the FSO system is utilized to connect satellite-to-earth stations. The proposed PON was designed using integrated TWDM-PON with WDM-OCDMA incorporating an MNZCC code from land-to-underwater for >100 Gbps data rate. Additionally, >100 km fiber transmission distance was also used to enhance the system's security. Also, NOMA-VLC was utilized with red, green and blue (RBG) underwater LEDs to effectively reduce the system cost as well as to provide EMI-free transmission.
## 4 Statistical Methodologies and Analysis Results
### Analysis of Validity  
This research used Confirmatory Factor Analysis to examine validity, and applied Principle Component Analysis to recognize the differences between each construct. The method of examination was a varimax rotation of KMO, as well as the Barlett spherical test; after the testing, the KMO value was 0.676 and the Barlett spherical test value was 670.655, which meant that the questionnaire data was adequate for carrying out factor analysis. Each component eigenvalue would have to be at least 1 for the questions to be combined into a construct which can be classified as one factor. The higher the eigenvalue, the more reliable the results are. By means of 'Principal Component Analysis' and the 'varimax rotation of KMO', five factors were analyzed and had eigenvalues over 1.0; 'Satisfaction' was 2.694, Perceived Playfulness' was 3.239, Perceived Usefulness' was 3.073, 'Perceived Ease of Use' was 3.168, and 'Intention' was 2.866. As seen in Table 4, the axis of rotation and load values were over 1, and the total interpretation of variance was up to 75.196%, meaning that the results have a high validity.
# Analytical TCP Model for Millimeter-Wave 5G NR Systems in Dynamic Human Body Blockage Environment
## 4 Numerical Results
### Accuracy Assessment  
Further, we assess the accuracy of the developed model. As a simulation tool, we have chosen the mmWave 5G NR cellular network module developed at the NYU Wireless group [24], which has been utilized in [10] for empirical TCP performance evaluation in 5G NR systems. The default ns-3 TCP NewReno implementation with all the associated mechanisms was utilized. We did not disable duplicate acknowledgments in this implementation as the simulated topology presumes a single path towards the destination in a wired part of the network, so that packet reordering does not happen. The rationale behind the choice of this topology is to isolate and investigate the effect of blockage on the 5G NR interface. To this aim, Figure 4 shows a comparison between model and simulation data as a function of the distance between 5G NR BS and UE. The link-layer buffer size has been set to 100 KB to reflect our modeling assumptions, which is much smaller compared to the BDP. As one may observe, the model results match well those obtained using the computer simulations across the whole range of considered distances. Notably, the reported behavior remains true for other input system  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Carrier frequency & 28 GHz \\ Bandwidth & 10 MHz \\ Transmit power & 0.2 W \\ Propagation model & 3 GPP UMi [13] \\ BS/UE antenna gains & 14.58 dB, 5.57 dB \\ LoS blockage loss & 20 dB \\
5G NR BS and UE heights, \(h_{A},h_{U}\) & 4 m, 1.5 m \\ Blocker height/radius/velocity, \(h_{B},r_{B},v_{B}\) & 1.7 m, 0.3 m, 1 m/s \\ Spatial blocker density, \(\lambda_{B}\) & 0.1 blockers/m\({}^{2}\) \\ Round-trip time, \(D\) & 50 ms \\ Link-layer buffer size & 100 KB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Default system parameters.  
parameters, including the velocity of UEs, allocated bandwidth, density of blockers in the environment. Thus, in what follows, to demonstrate the effect of these parameters on TCP throughput, we utilize the developed model.