esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Damage-Sensitive Features
#### 2.1.2 Coherence and Correlation  
The magnitude-squared coherence is used to estimate the power transfer between the input and output of a system. For an ideal constant parameter single-input single-output linear system, the coherence will be equal to one. The magnitude-squared coherence is expressed as a function of the power spectral densities:  
\[C_{io}(\omega)=\frac{\left|P_{io}(\omega)\right|^{2}}{P_{ii}(\omega)P_{oo}( \omega)}\,, \tag{5}\]  
where \(C_{io}\) is the magnitude-squared coherence between the signal pair, \(\ddot{x}_{i}\) and \(\ddot{x}_{o}\) at the frequency \(\omega\). \(P_{io}\) denotes the cross-power spectral density between the same sensor pair, whereas \(P_{ii}\) and \(P_{oo}\) refer to the power spectral density of the input and output signal, respectively. In a similar manner to \(TAC\) in Equation (4), a DSF to track the changes in the coherence can be formulated as the colinearity between a healthy reference state and a new state for a frequency range \(\omega_{m}\).  
While coherence as a DSF evaluates the correspondence between two signals in the frequency domain, their correlation can also be estimated in the time domain. The wavelet decomposition offers a time-frequency representation that enables tracking correlation for separate frequency bands. As shown by [PERSON] et al. [75], a decrease in the correlation of the wavelet coefficients outside the frequency range of a particular structural mode may indicate nonlinearity and residual damage. Thus, exploiting the time-frequency representation of the discrete wavelet decomposition, the correlation for a specific frequency bandwidth, \(\omega_{m}\), can be derived using Equation (6):  
\[WCC_{io,m}=1-\frac{\psi_{m}(\ddot{x}_{i})\psi_{m}(\ddot{x}_{o})}{\sqrt{\psi_{m }(\ddot{x}_{i})^{2}}\sqrt{\psi_{m}(\ddot{x}_{o})^{2}}}\,, \tag{6}\]  
where \(\psi_{m}\) is the wavelet coefficient corresponding to the pseudo-frequency (range) \(m\) and \(\ddot{x}_{i}/\dot{x}_{o}\) are the accelerations measured at the input/output DOFs. To reduce sensitivity to noise, \(WCC\) is calculated for a short moving time window, as described in Section 2.2. When the structure behaves linearly, an almost perfect correlation is observed outside the frequency values that correspond to the local structural modes, thus leading to a value of (\(WCC_{io,m}=0\)). When the frequency values of structural modes decrease, then the correlation in the bandwidth below the initial frequency in turn decreases. Thus, this DI is evaluated for a frequency bandwidth corresponding to \(\omega_{m}=[0.5\cdot Tmax_{io,m},0.85\cdot Tmax_{io,m}]\), where \(Tmax_{io,m}\) is the frequency that corresponds to a peak of the transmissibility function (see Equation (2)) and \(m\) designates the mode (or frequency bandwidth) of interest.
# Biodegradable Polymeric Architectures via Reversible Deactivation Radical Polymerizations
## 2 Biodegradable Polymeric Architectures
### Micelles, Vesicles and Capsules  
Polymers have been widely explored for the preparation of varied particles, e.g., micelles, vesicles and capsules, based on the expectation that these particles can be the appropriate reservoirs for controlled drug delivery. The advantage of using these polymer particles as drug carriers over traditional administration of free drugs lies in the increased circulation time in the body as these particles are usually big enough to prevent fast clearance through kidney filtration which has a cut-off molecular weight of 50,000 g\(\cdot\)mol\({}^{-1}\)[27]. Another advantage of polymer particles is called \"stealth-like\" effect which can be observed with the particles smaller than 200 nm or those surface decorated with specific polymers, e.g., poly(ethylene glycol) [92]. This \"stealth-like\" effect will greatly increase the circulation time. Polymer particles are usually prepared by amphiphilic block copolymers, where the  
Figure 8: (**a**) Scheme for biodegradable nanogels synthesized by inverse miniemulsion ATRP; (**b**) Fluorescent images of cells with Ce6-loaded nanogels as a function of incubation time (scale bar 300 \(\upmu\)m). Copyright 2016, Springer.  
hydrophobic block is used to form the core and the hydrophilic block from the corona in polar media. Research has revealed that the morphology of the polymeric particles might be mainly determined by the ratio of hydrophilic segment to the hydrophobic one [93]. Using block copolymers to prepare micelles has been extensively conducted and well-reviewed [8]. The design of polymeric particles with hydrophobic cores is based on the fact that anti-cancer drugs are usually hydrophobic and can be impregnated within the particle cores. Of course, the polymer particles can also be tailored with hydrophilic core and hydrophobic corona when required, mostly by manipulation of the polarity of preparation solvent. Once the drug is impregnated within particles, another issue arises with how to control the drug release. The traditional drug release from the particles is usually controlled by the self-degradation. However, if the polymers are designed as biodegradable, better control or more controlling means can be realized. The preparation of polymeric micelles for drug delivery using RAFT polymerization was reviewed by [PERSON] [7]. In this section, we mainly discuss the preparation of polymer particles that can undergo biodegradation and their potential applications.
## 5 Evaluation  
FBAC has two design goals: (1) to maximize QoE fairness among users and (2) to improve the overall QoE efficiency of users. To accurately verify the performance of FBAC, we deployed a video-on-demand system on the real network as an experimental platform for algorithm evaluation.
## 3 Power Converters Modelling
### DC-DC Full Bridge Converter Average Model  
DC-DC full bridge converters process power from one of the main buses to meet voltage and current requirements for final loads like LED luminaries, personal computers, etc. This topology is an important part of the system because there are several versions at different power ratings in the DCMG. This configuration allows an adequate output voltage regulation, provides isolation between the bus and the final load and gives reliability to the general system. This kind of isolated converter is able to manage a greater power rating than other isolated topologies. DC-DC full bridge converter is a two-stage topology, integrated by an inverter (\(Q_{1}\)-\(Q_{4}\)), a rectifier (\(D_{1}\)-\(D_{4}\)), linked by high-frequency transformer, and a \(LC\) output filter (Figure 3).  
The DC-DC full bridge converter operates with phase shift pulse wide modulation. Two specific pair of power devices in the inverter are defined; \(Q_{1}\) and \(Q_{2}\) are the first pair and \(Q_{3}\) and \(Q_{4}\) are the second pair. The conduction period of time for each pair of MOSFETs must last less than a half of switching period. Diodes \(D_{1}\), \(D_{2}\), \(D_{3}\) y \(D_{4}\) conduct depending on its own polarization state in a specific time. A switching function \(S_{1}\) is defined according to the on and off times in the devices \(Q_{1}\), \(Q_{2}\), \(D_{1}\) and \(D_{2}\) take the zero state and the one state simultaneously. In the same way, \(Q_{3}\), \(Q_{4}\), \(D_{3}\) and \(D_{4}\) synchronize  
Figure 2: Boost converter.  
their on and off state, and the corresponding switching function \(S_{2}\) is assigned to this set; \(S_{1}\) and \(S_{2}\) operate with 180\({}^{\circ}\) phase. In order to obtain the switched model, \(v_{x}\) is defined as the rectifier voltage output. By applying [PERSON]'s laws, equations for switching model in time domain are obtained from the DC-DC full bridge switching equivalent circuit. Then, the average model is obtained by considering the modulating signal, \(u(t)\), as the average of the selected commutation function; the resultant average model is described as:  
\[\frac{dv_{C2}}{dt} =-\frac{1}{R_{o}C_{2}}v_{C2}+\frac{1}{C_{2}}i_{L}\] \[\frac{di_{L}}{dt} =-\frac{1}{L}v_{C2}+\frac{1}{L}2 nuV_{i} \tag{2}\]  
The average model in (2) can be used in any of the following versions: 380 V to 190 V at 1.2 kW to supply electronic loads in each cubicle, 190 V to 48 V at 400 W for supply drivers for LED luminaries. Two versions, 380 V to 190 V at 3.3 kW for power transfer between primary and secondary bus, in the other way, a 190 V to 380 V at 3.3 kW version. For charge-discharge batteries, a 380 V to 250 V at 2.5 kW and 250 V to 380 V at 2.5 kW for ESS at primary bus; finally, two more versions 190 V to 125 V at 1.25 kW and 125 V to 190 V at 1.25 kW for ESS in secondary bus.
## 2 Materials and Methods  
### Sample Collection  
In total, 43 production wells from a certain oil field were used as test samples, including 28 sets of samples collected during the ASP-flooded block (ASP) and 15 sets of samples collected during the water flooding after the ASP-flooded block (WF), respectively. The average effective permeability of the oil reservoirs in this study was 554 MDin the ASP and WF blocks. All of the samples from the production wells were taken from the wellhead. Ten-litre plastic was used to collect the produced fluid samples to ensure that the samples were not contaminated during the sampling process. The fluid in the pipeline had to be completely drained prior to the sample collection. All samples were transported to the laboratory as soon as possible after sampling and the collection of microbial samples and extraction of DNA were carried out as soon as possible after the separation of oil and water. Approximately 1 L of water samples was centrifuged several times at 12,000 rpm for 30 min in order to collect microbial cells [16; 17]. Genomic DNA was extracted from the microbial cells using a bacterial genomic extraction kit (TianGen, China). Amplification with the primers 338F (5\({}^{\prime}\)-ACTCCTACGGGGAGGCACAG-3\({}^{\prime}\)) and 806R (5\({}^{\prime}\)-GACTACHVGGTATCTAATCC-3\({}^{\prime}\)) of the V3-V4 region of the bacterial 165 rRNA gene and high-throughput sequencing were entrusted to Majorbiogroup in Shanghai, China.  
The basic physico-chemical parameters (environmental factors) were provided by the Oil Production Plant. This study may have limitations such as the potential for sampling bias.
## 3 Experiment Results and Discussion
### Dataset
#### 3.1.2 Segmentation Ground Truth  
For the ground truth of a vehicle, the coordinates of a vehicle in a front view provided by the KITTI dataset were converted in the same way as the bounding box label. The predicted results of the network for detecting road regions (LoDNN) [24] were applied to the KITTI dataset in order to  
\begin{table}
\begin{tabular}{c c} \hline \hline \(L_{tot}\) & **Total Loss** \\ \hline \(L_{seg}\) & Segmentation loss (Cross-entropy) \\ \hline \(L_{det}\) & Detection loss \\ \hline \(L_{center\_x}\) & Loss about center x coordinate of bounding box (MSE) \\ \hline \(L_{center\_y}\) & Loss about center y coordinate of bounding box (MSE) \\ \hline \(L_{width}\) & Loss about width coordinate of bounding box (MSE) \\ \hline \(L_{height}\) & Loss about height coordinate of bounding box (MSE) \\ \hline \(lw_{seg}\) & Segmentation loss weight (=1.0) \\ \hline \(lw_{det}\) & Detection loss weight (=0.1) \\ \hline \(N\) & Batch size \\ \hline \(W\) & Width of the Segmentation map \\ \hline \(H\) & Height of the Segmentation map \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notation related to loss function.  
generate the ground truth of the drivable areas. There is a limitation in generating a ground truth that is identical to the actual object, because the resolution of LiDAR decreases as the distance increases. However, we verified that it was possible to generate ground truth in the experiment. In the generated segmentation answer shown in Figure 8, the blue color represents the 'drivable region', the magenta color represents a 'car', the green color represents a 'van', the yellow color represents a 'truck', and the red color represents 'other regions (background)'.
# Quantum Computing for Healthcare: A Review
## 3 Applications of Quantum Computing for Healthcare
### Radiotherapy  
Radiation therapy has been employed for the treatment of cancers; it uses radiation beams to eliminate cancerous cells to stop them from multiplying. However, radiotherapy is a sensitive process which requires highly precise computations to drop the beam on the cancer-causing tissues and avoid any impact on the surrounding healthy body cells. Radiography is performed using highly precise computers and involves a highly precise optimization problem to perform the precise radiography operation, which requires multiple precise and complex simulations to reach an optimal solution. Through quantum computing, running simultaneous simulations and figuring out a plan in an optimal time becomes possible, and hence the spectrum of opportunities is very vast if quantum concepts are employed for simulations.
## 2 Materials and Methods
### US Images  
All US images of the parotid glands were obtained between September 2019 to March 2024 and reviewed for this study. The US images were obtained using an E-cube 8 (Alpinion, Seoul, Republic of Korea) US unit with a high-density linear transducer (3-12 MHz) from  
Figure 1: Schematic diagram illustrating the flow of the study. ROI, region of interest; ICC, intraclass correlation coefficient; ROC, receiver-operating characteristic.  
the patients who complained of intraoral dryness. Two oral and maxillofacial radiologists assessed the parotid glands in the US images with reference to sialography images. US images of normal parotid glands were selected based on the following inclusion criteria (Figure 2a): images whose findings were confirmed by sialography and clinical information, images obtained before sialography or ductal irrigation procedure, and images taken in a vertical position to the superficial lobe showing the largest portion of the parotid gland. The exclusion criteria were (Figure 2b) as follows: images containing significant artifacts, images of [PERSON] syndrome, benign or malignant tumor or any other pathologic lesions of parotid glands, findings that were not consistent with sialography or clinical information, images obtained after sialography or ductal irrigation procedure, and images not taken in a vertical position to the superficial lobe.  
A total of 91 US images (of parotid glands) from 63 patients were included (Table 1): 23 images of male patients and 68 of female patients, with 53 images of adults (<65 years) and 38 of older adults (\(\geq\)65 years). The age classification of adults and older adults is based on the description by the National Institute of Aging in the United States (available from [[https://www.nia.nih.gov](https://www.nia.nih.gov)]([https://www.nia.nih.gov](https://www.nia.nih.gov)), accessed on 24 October 2024).  
\begin{table}
\begin{tabular}{l c c} \hline
**Demographic Characteristics** & **Patients (N)** & **Parotid Glands (N)** \\ \hline Gender & & \\ Male & 17 & 23 \\ Female & 46 & 68 \\ Age & & \\ \(<\)65 years & 39 & 53 \\ \(\geq\)65 years & 24 & 38 \\ \hline Total & 63 & 91 \\ \hline \end{tabular}
\end{table}
Table 1: Demographic characteristics of patients and their parotid glands included in this study.  
Figure 2: Examples of ultrasound images of parotid glands: (**a**) a normal parotid gland with homogeneous echogenicity, included in the study; and (**b**) an inflammatory parotid gland showing heterogeneous echogenicity with numerous hypoechoic foci, excluded from the study.
# A Novel Connectivity-Based LEACH-MEEC Routing Protocol for Mobile Wireless Sensor Network
## 4 Simulation Results and Discussion
### Results Discussion
#### 4.4.3 Packet Delivery Ratio (PDR)  
MWSN nodes suffer packet loss due to mobility that may result in link collapse. There is an inverse association between speed and packet delivery ratio (PDR) [53]. In RPGM, the mobile sensor nodes are grouped firmly together. Hence, the average inter-node distance among group members is much less than other mobility models. A CL in RPGM can easily receive and deliver messages to member nodes and base station, which reduces the chance of link collapse. Other mobility models have random movement may result in link collapse. Figures 8a-d and 9a-d refer to RPGM, RWP, GM and MH datasets with two different speeds, respectively. Figure 8 shows that LEACH-MEEC PDR is comparatively high as compared to LEACH-MAE, Mobile-LEACH and LEACHDistance-M. The high PDR of LEACH-MEECH is due to an optimal selection of CL. It is also observed that the PDR values fluctuates as transmission distance and speed increase. However, the optimal selection of CL helps the mobile sensor nodes to stay connected due to radio radial connectivity, therefore increasing the sensor network availability and minimizing the packet dropped rate. A comparison for PDR in Figure 9 shows LEACH-MEEC outperforms LEACH-MAE, Mobile-LEACH and LEACHDistance-M using four mobility models datasets.  
Figure 7: Remaining energy versus time for 100 nodes, maximum speed 7.5 m/s, (**a**) RPGM Dataset; (**b**) RWP Dataset; (**c**) GM Dataset; (**d**) MH Dataset.  
Figure 8: Packet delivery ratio versus number of nodes, maximum speed 1.5 m/s, (a) RPGM Dataset; (b) RWP Dataset; (c) GM Dataset; (d) MH Dataset.  
Figure 9: _Cont._
## 5 Modeling and Simulating Si-SC Structures
### Optical Modeling and Simulation  
A detailed understanding of light interaction with the Si-SC is essential for optimizing ARC design. The FDTD method was employed to compute the optical absorption spectrum across the relevant wavelength range (\(350\,\mathrm{nm}\) to \(1100\,\mathrm{nm}\)). By considering the electric field intensity \(|E|\) and material permittivity \(img(\epsilon)\), the absorbed power (_Pabs_) was calculated using Equation (5).  
\[\mathbf{Pabs}=-0.5\omega\times|E|^{2}\times img(\epsilon). \tag{5}\]  
Quantum efficiency (QE), defined as the ratio of _Pabs_ to incident power (\(P_{in}\)), was determined for each wavelength using Equation (6).  
\[\mathbf{QE}(\lambda)=\frac{Pabs(\lambda)}{P_{in}(\lambda)}. \tag{6}\]  
To provide spatial resolution of efficiency, local quantum efficiency (QE\({}_{local}\)) was calculated, considering optical transmission and absorption at specific locations within the device:  
\[\mathbf{QE}_{local}(x,y,z)=\text{QE}(\lambda)\times\text{T}_{opt}(x,y,z, \lambda)\times\text{A}_{opt}(x,y,z,\lambda). \tag{7}\]  
The optical generation rate per unit volume, denoted as \(G_{v}(\lambda)\), representing the rate at which electron-hole pairs are generated within a material per unit volume in response to incident photons at a specific wavelength \(\lambda\), was calculated using:  
\[\mathbf{G}_{v}(\lambda)=\text{A}_{opt}(\lambda)\times\text{I}_{ph}(\lambda). \tag{8}\]  
where \(I_{ph}\) represents the incident photon flux at wavelength \(\lambda\).
## 2 Signal Model and Adaptive Clutter Cancellation
Furthermore,  
\[\stackrel{{\rightharpoonup}}{{a}}(\psi)=\frac{1}{\sqrt{K}}[1,\exp( -j\psi),\exp(-j\psi\frac{B_{1,3}}{B_{1,2}}),\ldots,\exp(-j\psi\frac{B_{1,K-1}}{ B_{1,2}}),\exp(-j\psi\frac{B_{1,K}}{B_{1,2}})]^{T} \tag{2}\]  
where \((\bullet)^{T}\) denotes the transpose operator. \(B_{1,i}(i=2,\cdots,K)\) are the along-track baselines between the master and the \(i\)th slave satellite. \(K\) is the number of spaceborne SARs with transmitters and receivers. The adaptive clutter cancellation processor can be expressed as,  
\[\stackrel{{\rightharpoonup}}{{y}}(m,n)=\stackrel{{ \rightharpoonup}}{{W}}(m,n)^{H}\cdot\stackrel{{ \rightharpoonup}}{{Z}}(m,n) \tag{3}\]  
where \(\stackrel{{\rightharpoonup}}{{y}}\) is the output, \(\stackrel{{\rightharpoonup}}{{W}}\) is the weight of the processor, and \((\bullet)^{H}\) denotes the Hermitian transpose. Under some well-known optimal criteria, the processor can be given in a more general form as,  
\[\stackrel{{\rightharpoonup}}{{W}}(m,n)=\gamma R^{-1}(m,n)\cdot \stackrel{{\rightharpoonup}}{{S}}(m,n) \tag{4}\]  
where \(R\) denotes the clutter plus noise covariance matrix. The complex constant \(\gamma\) is selected according to the following criteria. Under linearly constrained minimum expectation power criterion, the normalization constant \(\gamma=1/\sqrt{\stackrel{{\rightharpoonup}}{{S}}(m,n)^{H}R^{-1}(m,n )\stackrel{{\rightharpoonup}}{{S}}(m,n)}\). \(\gamma\) can be arbitrarily chosen for the maximum SCNR criterion. Similar to traditional adaptive beamforming or adaptive clutter cancellation techniques for GMTI, \(R\) is unknown and is usually estimated using the maximum likelihood (ML) technique. Assume that the neighboring \(2K_{m}*2K_{n}\) samples around pixel \((m,n)\) are available, which are free from any moving target signals and identically distributed. Then the sample clutter plus noise matrix of pixel index \((m,n)\) is [15; 22; 23],  
\[\hat{R}(m,n)=\frac{1}{2K_{m}*2K_{n}}\sum_{k_{m}=-K_{m}k_{m}\
eq 0}^{k_{m}=K_{m}} \sum_{k_{m}=-K_{m}k_{m}\
eq 0}^{k_{m}=K_{n}}\left(\stackrel{{ \rightharpoonup}}{{Z}}(m-k_{m},n-k_{n})\cdot\stackrel{{ \rightharpoonup}}{{Z}}(m-k_{m},n-k_{n})^{H}\right) \tag{5}\]  
In practice, \(\stackrel{{\rightharpoonup}}{{a}}(\psi)\) is always unknown and the scanning method is always exploited to get the estimation \(\stackrel{{\rightharpoonup}}{{a}}(\psi)\) for the matched filter, which is a computationally intensive technique. The Equation (4) can be computed as,  
\[\stackrel{{\rightharpoonup}}{{W}}(m,n)=\gamma\hat{R}^{-1}(m,n) \cdot\stackrel{{\rightharpoonup}}{{S}}(m,n) \tag{6}\]  
The adaptive clutter cancellation processor can be expressed as,  
\[\stackrel{{\rightharpoonup}}{{y}}(m,n)=\stackrel{{ \rightharpoonup}}{{W}}(m,n)^{H}\cdot\stackrel{{ \rightharpoonup}}{{Z}}(m,n) \tag{7}\]