esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
\[H_{CF-LVAD}=k\,n_{CF-LVAD}^{2}-R_{CF-LVAD}Q_{CF-LVAD}-L_{CF-LVAD}\frac{dQ_{CF-LVAD} }{dt}+H_{rec} \tag{12}\]  
\[R_{CF-LVAD}=R_{1}n_{CF-LVAD}+R_{2}Q_{CF-LVAD} \tag{13}\]  
Here, \(H_{CF-LVAD}\) and \(Q_{CF-LVAD}\) represent the pressure head and flow rate, \(R_{1}\) and \(R_{2}\) are resistances related to friction and incidence losses, \(n_{CF-LVAD}\) is the continuous-flow VAD operating speed, k is the blood flow source term, and \(L_{CF-LVAD}\) and \(H_{rec}\) are the inertance and part-load recirculation in the pump. Heartware VAD, HM2 and HM3 devices were simulated using the same equation with different parameter values as given in [18]. A detailed description of the continuous-flow VAD model can be found in [18]. Continuous-flow VAD support was simulated by modifying (8) and (10) as given below. \[\frac{dr_{lv}}{dt}=\frac{3(Q_{mv}-Q_{av}-Q_{CF-LVAD})}{4\pi K_{lv}l_{lv}}\left( \frac{3V_{lv}}{2\pi K_{lv}l_{lv}}\right)^{-1/2} \tag{14}\]  
\[\frac{dp_{av}}{dt}=\frac{Q_{av}+Q_{CF-LVAD}-Q_{ao}}{C_{ao}} \tag{15}\]  
The electrical analogue of the heart chambers, and the circulatory system used to simulate the cardiovascular system and continuous-flow VAD support, has been provided in Figure 1.  
Operating speed of the continuous-flow VADs was adjusted to simulate the same cardiac output as in the healthy cardiovascular system model and with consideration of the operating range and clinical data. Operating speeds between 2120 rpm and 2600 rpm have been reported for the Heartware HVAD device in children [19; 20]. In this study, the operating speed of Heartware HVAD was set to 2600 rpm. Reported average HM2 device operating speed in patients with body surface area <1.5 m\({}^{2}\) was 8524 \(\pm\) 4166 rpm [21]. In this study, the operating speed of HM2 was set to 8700 rpm. Operating speeds around 5000 rpm and 5200 rpm have been reported for HM3 device in children diagnosed with DCM [22; 23]. In this study, the operating speed of HM3 was set to 5200 rpm. Lavare cycle in Heartware HVAD was set by decreasing the pump operating speed by 200 rpm for 2 s and increasing by 400 rpm in the following 1 s over each 60 s cycle [24]. Artificial pulse in the HM3 device was simulated by decreasing the pump speed by 2000 rpm for 0.15 s, then  
Figure 1: Electrical analogue of the cardiovascular system model. R, resistance; L, inertance; C, compliance; p, pressure; MV, mitral valve; AV, aortic valve; TV, tricuspid valve; PV, pulmonary valve; la, left atrium; lv, left ventricle; ra, right atrium; rv, right ventricle; ao, aorta; as, systemic arterioles; vs, systemic veins; po, pulmonary artery; ap, pulmonary arterioles; vp, pulmonary veins. increasing it by 4000 rpm for 0.20 s, before operating HM3 at the set speed over each 2 s cycle [25]. The heart rate was kept at 80 bpm in all the simulations. The simulations were performed using MATLAB Simulink R2017a. The set of equations was solved using the ode15s solver. The maximum step size was \(10^{-3}\) s and the relative tolerance was set to \(10^{-3}\).
# Emerging Technologies for Advanced Power Electronics and Machine Design in Electric Drives
## 2 Topologies of Electric Drives
### Other Electric Drive Topologies
#### 2.4.1 Open-End Winding Drives  
This is basically a classic three-phase machine with access to both sides of the windings (Figure 11) [106]. The machine is fed by two three-phase inverters (or three single-phase full-bridge inverters). The main advantages are partially reduced power of each inverter's leg, the possibility of operation in case of single phase faults, and the possibility of reducing or eliminating the common-mode voltage [107, 108, 109].  
Open-end winding drives improve the utilization of the DC voltage, so that the motor can achieve higher speeds at rated current for a given DC link voltage. This, in turn, leads to an increase in the power density of the system. The open-end winding drives can also have more than three phases, which further improves the fault tolerance of the drive [110].  
Figure 10: Dual 3-phase PMSM with concentrated windings having 12 slots and 10 poles. Four different winding layouts are possible: (**a**) Highly coupled winding layout (HWL), (**b**) Multisector winding layout (MWL), (**c**) Symmetric winding layout (SWL), and (**d**) Asymmetric winding layout (AWL). Letters a/b/c/d/e/f designate magnetic axes of respective phases.
# The Role of AI-Based Chatbots in Public Health Emergencies: A Narrative Review
## 5 Chatbots as Statistical Consultants in Infectious Disease Settings: Potential and Reliability  
In the field of infectious disease management, chatbots with advanced artificial intelligence, such as ChatGPT, are being studied not only as tools for disseminating information, but also to assist in data analysis and decision making. These artificial intelligence systems have the potential to guide public health officials, researchers, and even less experienced users through the complex process of selecting appropriate statistical techniques for epidemic data analysis. With the increasing use of large-scale data collection during epidemics, chatbots could provide accessible real-time advice on which statistical methods to employ based on the characteristics of the available data. However, the effectiveness and reliability of chatbots in this advisory role depend on several factors, including the accuracy of their recommendations, their interpretability, and their potential limitations.
## 2 Mathematical Formulation of the 2L-VRPSC  
### Problem Description  
In the 2L-VRPSC, there is a central depot, several vehicles and customers, where the presence or absence of a customer is probabilistic. To save time and resources consumed for re-routing, distribution companies usually adopt a pre-optimization strategy; namely, the distribution companies follow the pre-designed routes and directly skip the customer that does not require service and move on to the next customer. Therefore, the purpose of the 2L-VRPSC is to reasonably arrange the routes and customer service order to meet the loading constraints for the purpose of minimizing the total expected cost of the final scheduling plan.  
Therein, the assumptions of the 2L-VRPSC are listed as follows: (i) the demand of every customer is known beforehand, but the presence or absence of a customer is uncertain; (ii) the geographical information of the customers and the depot is known, and all nodes in the distribution network are completely connected; and (iii) the attributes of homogeneous vehicles are known.
## 2 Materials and Methods
### Synchronization Validation
\tag{8}\]  
Additionally, in modal expansion, the matrix \(\mathbf{Y}\), which contains the aligned signals with dimensions \(N\times M\), representing the time history records for each sensor, is expressed as  
\[\mathbf{Y}=\Phi\mathbf{q}[k]. \tag{9}\]  
Each element \(Y_{j,\text{aligned}}[k]\) represents the aligned signal value at discrete time index \(k\) for sensor \(j\). The correlation matrix of the responses matrix \(\mathbf{Y}\), i.e., \(\mathbf{R_{YY}}[\tau]\) is calculated as  
\[\mathbf{R_{YY}}[\tau]=\mathbb{E}[\mathbf{Y}^{T}\mathbf{Y}]=\Phi\mathbf{R_{qq} }[\tau]\Phi^{T}, \tag{10}\]  
where \(\mathbf{Y}^{T}\) denotes the transpose of the responses matrix \(\mathbf{Y}\). The Power Spectral Density (PSD) matrix \(\mathbf{S}_{YY}\) obtained by performing the Fast Fourier Transform (FFT), is given by  
\[\mathbf{S_{YY}}[\omega]=\text{PSD}(\mathbf{R_{YY}}[\tau])=\Phi\mathbf{S_{qq} }[\omega]\Phi^{T}. \tag{11}\]  
The Singular Value Decomposition (SVD) of the PSD matrix at a specific radian frequency \(\omega\) can be expressed as  
\[\mathbf{S_{YY}}[\omega]=\mathbf{U}\mathbf{\Sigma}\mathbf{V}^{T}, \tag{12}\]  
where \(\mathbf{S_{YY}}\) is the PSD matrix, \(\mathbf{U}\) and \(\mathbf{V}^{T}\) are the left and right singular vectors matrices, and \(\mathbf{\Sigma}\) is the diagonal matrix of singular values (arranged in descending order). The singular values relate to modal responses, enabling the definition of spectra for equivalent SDOF systems with matching modal parameters. The resulting matrices, i.e., \(\mathbf{U}\), \(\mathbf{\Sigma}\), and \(\mathbf{V}^{T}\), provide valuable information about the dominant modes and their corresponding magnitudes. This simplifies complex MDOF systems by representing them with simpler SDOF systems, facilitating analysis and understanding of the system's behavior at the given frequency. By conducting these experiments, the proposed synchronous wireless sensor network framework and the application of FDD in SHM can be thoroughly evaluated and validated, providing valuable insights for future research and practical implementations in the field.
# Priority Pricing for Efficient Resource Usage of Mobile Internet Access
## 6 Conclusions  
This study investigates the practicality of using equilibrium analysis to address the price selection problem. Results showed that certain conditions that are needed to produce a stationary equilibrium may not hold in real networks; consequently, the system may oscillate instead of converging on the equilibrium. I demonstrated that when such conditions are not met, the system oscillates among a number of states without reaching a static equilibrium. I also showed that the price vectors computed by the equilibrium methods are suboptimal when the system fails to converge on a static equilibrium. As an alternative, I developed a new price selection method that does not rely on an equilibrium analysis. My findings suggest a need for more realistic modeling of the system dynamics to solve the price selection problem.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \((\mathbf{P_{B}},\mathbf{K},\mathbf{v_{1}},\mathbf{\mu})\) & **Equilibrium Method** & **Heuristic Search** \\ \hline (0.5, 10, 1, 317.7) & \(P_{1}=0.5\), \(\mathcal{R}=172.7\), \(\mathcal{R}_{theory}=172.7\) & \(P_{1}=0.5\), \(\mathcal{R}=172.7\) \\ (0.5, 10, 3, 351.0) & \(P_{1}=0.5\), \(\mathcal{R}=154.0\), \(\mathcal{R}_{theory}=154.0\) & \(P_{1}=0.5\), \(\mathcal{R}=154.0\) \\ (1.0, 20, 1, 380.0) & \(P_{1}=1.0\), \(\mathcal{R}=361.0\), \(\mathcal{R}_{theory}=361.0\) & \(P_{1}=1.0\), \(\mathcal{R}=361.0\) \\ (1.0, 20, 3, 365.4) & \(P_{1}=1.0\), \(\mathcal{R}=333.5\), \(\mathcal{R}_{theory}=333.7\) & \(P_{1}=1.0\), \(\mathcal{R}=333.5\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Large capacity networks with frequent arrival rate broadcast (\(F=100\)).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \((\mathbf{P_{B}},\mathbf{K},\mathbf{v_{1}},\mathbf{\mu})\) & **Equilibrium Method** & **Heuristic Search** \\ \hline (1.5, 10, 1, 39.0) & \(P_{1}=1.5\), \(\mathcal{R}=37.0\), \(\mathcal{R}_{theory}=51.3\) & \(P_{1}=[1.7\)–\(2.0]\), \(\mathcal{R}=48.4\) \\ (1.5, 10, 3, 35.0) & \(P_{1}=1.5\), \(\mathcal{R}=41.2\), \(\mathcal{R}_{theory}=41.4\) & \(P_{1}=[1.5\)–\(1.95]\), \(\mathcal{R}=41.2\) \\ (2.0, 20, 1, 86.4) & \(P_{1}=2.0\), \(\mathcal{R}=155.3\), \(\mathcal{R}_{theory}=172.7\) & \(P_{1}=[1.05\)–\(2.25]\), \(\mathcal{R}=155.3\) \\ (2.0, 20, 3, 77.0) & \(P_{1}=2.0\), \(\mathcal{R}=111.0\), \(\mathcal{R}_{theory}=154.0\) & \(P_{1}=[2.25\)–\(2.65]\), \(\mathcal{R}=145.3\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Small capacity networks with infrequent arrival rate broadcast (\(F=5\)).  
In future investigations, the multi-criteria decision analysis and simulated annealing search for the price selection problem would be worthy research topics.  
This research received no external funding.  
The author declares no conflict of interest.
## 2 Named Data Networking and Its Role in IoT Smart Environments  
### NDN in a Nutshell  
NDN is an Information Centric Networking (ICN) architecture that turns content into a first-class network citizen [5; 12; 13]. Unlike the current Internet, where communication is based on named hosts, in NDN, each content is uniquely named and secured at the network layer. Routing and forwarding operations are directly based on content names, and in-network caching is natively enabled. Together with other innovative paradigms, like edge and fog computing, NDN has been recognized as a fundamental pillar of the future Internet [14] that can offer highly scalable and efficient content dissemination in different network scenarios, ranging from the wired Internet to wireless ad hoc environments and IoT systems. Indeed, NDN defines a connectionless communication model extremely suited to IoT environments.  
Basically, NDN communication leverages only two packet types: the _Interest_, used to request contents, and the _Data_, used to carry the content. Both packets carry a hierarchical name that uniquely identifies a piece of data. Names are in the form of Uniform Resource Locators (URLs), and they can be human-readable and user-friendly. Name prefixes are distributed by the network routing protocols to enable the reachability of contents.  
Three main communication actors can be identified: (i) the _consumer_, which sends Interests to request contents; (ii) the original _producer_, which originates the data packets; and (iii) the _content router_, which forwards interests and data packets and may cache the latter to satisfy future requests. Data packets are signed at the time of creation by their producers. This allows to decouple the trust in the data from the trust in the nodes that may cache them. In the following, producers and cachers of contents are both referred to as providers.  
Being a networking solution, NDN operates at two levels: the data plane and the control plane, as detailed in the following subsections.
## 4 Current Evidence and Applications
### Deep Learning in Imaging Analysis  
Deep learning has revolutionized imaging analysis in orthopedics, with algorithms capable of detecting and classifying fractures, assessing bone quality, and evaluating joint health. [PERSON] et al. reported that a deep learning algorithm could detect and classify wrist fractures on radiographs with performance comparable to expert radiologists [39]. The algorithm utilized convolutional neural networks trained on a large dataset of annotated images, enabling it to identify subtle features indicative of fractures. The implementation of such tools in clinical practice can enhance diagnostic accuracy, reduce interpretation times, and support less experienced clinicians in making accurate diagnoses. In pediatric patients, the assessment of growth plates and bone development is critical. [PERSON] et al. developed a deep learning system to automate the evaluation of bone age using hand radiographs [40]. The system demonstrated high accuracy and consistency compared to manual assessments by pediatric radiologists. Automated bone age assessment can streamline workflows, reduce variability, and provide objective measurements essential for managing growth disorders and planning treatments that may affect skeletal development. Furthermore, AI algorithms have been explored for analyzing growth plates to detect abnormalities that could contribute to complications like fracture overgrowth. By segmenting and quantifying features of the growth plate on imaging studies, AI can assist in identifying early signs of altered growth activity, potentially guiding interventions to prevent overgrowth.
## 3 Results
### Elastography Results  
Following qualitative real-time elastography, each nodule was automatically given a Ueno score of one through five. Ueno 1 scores were only found in cystic lesions, which were excluded from the study. In the following table (Table 5), the total occurrence for each Ueno score is shown as well as the number of malignant and benign nodules as confirmed by later investigations with histopathological results or a combination of mammography and MRI.  
Lastly, semiquantitative real-time elastography was performed, and Fat-to-Lesion Ratio (FLR) values were automatically given for each nodule. The FLR values were split into three groups by likelihood of malignancy, as described earlier in Section 2.2 and can be seen in the following table (Table 6). Additionally, the total occurrence per group is shown alongside malignant and benign occurrences as confirmed by later investigations using histopathological results or a combination of mammography and MRI.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Benign** & **Malignant** & **Total** \\ \hline
**BIRADS 3** & 839 & 19 & 858 \\
**BIRADS 4** & 464 & 50 & 514 \\
**BIRADS 5** & 12 & 21 & 33 \\ \hline \end{tabular}
\end{table}
Table 4: Conventional breast ultrasound Breast Imaging Reporting and Data System (BIRADS) scores and malignancies (1405 nodules).  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Se [\%]** & **Sp [\%]** & **PPV [\%]** & **NPV [\%]** & **ACC [\%]** \\ \hline
**Inhomogeneous** & 47.31 & 83.77 & 17.12 & 95.73 & 0.81 \\
**Calcifications** & 20.43 & 98.48 & 48.72 & 94.58 & 0.93 \\
**Intense hypecobgenic** & 24.73 & 89.33 & 14.11 & 94.36 & 0.85 \\
**III-defined borders** & 27.96 & 94.82 & 27.66 & 94.89 & 0.90 \\
**Spiculate and anfractuous margins** & 6.45 & 99.54 & 50.00 & 93.75 & 0.93 \\
**Taller than wide** & 11.83 & 97.87 & 28.21 & 94.00 & 0.92 \\
**Tortuous internal vascularity** & 53.76 & 81.17 & 16.95 & 96.12 & 0.97 \\
**Posterior acoustic shadowing** & 4.30 & 98.93 & 30.77 & 93.58 & 0.93 \\
**Altered lymph nodes with no evidence of hilum,** & 11.83 & 98.93 & 44.00 & 94.06 & 0.94 \\
**eccentric hilum or cortical edema** & & & & & \\ \hline \end{tabular}
\end{table}
Table 3: Conventional breast ultrasound malignancy markers—diagnostic power (1405 nodules)
# Prototype of Augmented Reality Technology for Orthodontic Bracket Positioning: An In Vivo Study
## 2 Materials and Methods
### Method for Applying the AR-Assisted Bracket Navigation System
#### 2.3.2 Bracket Bonding Navigation Module  
In a limited space, the bracket bonding navigation module mapped the bracket position data (planned prior to the operation) to real-time images, during which the tooth contour and FACC information acquired by the FACC model was used, the problem of optical distortion was considered, and scale-ruler-based proportional measurement conversions were performed. The module could avoid operational interference and inconvenience caused by the use of positioning two-dimensional barcode; the module managed to transfer the bracket coordinates from a virtual space to actual space.  
During scale conversion, the researchers had to identify the immovable hard tissues for the system to calculate the ratio of pixel to actual distance (Figure 4). To do so, they obtained the mean of the left-to-target and right-to-target FACC distances for each tooth (Figure 5). The present study regarded the FACC of neighboring teeth as the reference points because the FACC distance is less likely to be prone to cumulative errors caused by gingival changes and recession, missing information, and dental crowding. Furthermore, said mean could reduce unilateral measurement errors. Conversion was performed using the following equations:  
\[\frac{P_{B}}{D_{B}}=\frac{1}{2}\bigg{(}\frac{P_{r}}{D_{r}}+\frac{P_{l}}{D_{l} }\bigg{)}\]  
\[P_{B}=D_{B}\times\frac{P_{r}D_{l}+P_{l}D_{r}}{2D_{r}D_{l}}\]where \(F\) denotes the FACC, \(B\) the bracket, \(D\) the distance, \(P\) the pixel, \(r\) the right tooth, and \(l\) the left tooth. The distance between the FACC of the target tooth and that of the right tooth is represented by \(Pr\) pixels (\(Dr\) mm), whereas the FACC of the target tooth and that of the left tooth are \(Pl\) pixels (\(Dl\) mm). The distance between the bracket and incisor line is denoted by \(PB\) pixels (\(DB\) mm).
# Two-Level Congestion Control Mechanism (2 LCCM) for Information-Centric Networking
## 5 Evaluation
### Performance of Receiver-Driven BBR Algorithm  
To verify the performance of the above receiver-driven BBR congestion control algorithm, we compare the BBR algorithm with the Reno algorithm. The experiment was ran on two Ubuntu 16.04 hosts running the SEANet prototype system, the congestion control module of this system supports both BBR and Reno algorithms. The network environment was set to 100 Mbps of bandwidth and 10 ms of RTT, and the packet loss rate was 0, 0.1%, 1%, and 2%, respectively. The size of the chunk was 100 MB and the experiment results are shown in Figure 9.  
From Figure 9, we can figure out that the difference in the transmission end time is not much when the packet loss rate is 0, 0.1% and 1% for the BBR algorithm. This is because that BBR does not use packet loss as a direct judgment factor affecting the transmission rate. However, with the increasing impact brought by packet loss, the throughout rate starts to become significantly fluctuating, and the transmission end time for the BBR increases from about 9.5 s to about 10.8 s when the packet loss rate varies from 1% to 2%. As the packet loss rate grows, the difference in transmission end time becomes bigger, and this is more evident for the Reno algorithm. When the packet loss rate is 2%, the transmission end time for the Reno is about 18.2 s, while that is 10.8 s for the BBR. The performance improvement of BBR over Reno is about 40.6%.  
Figure 8: The rate changes over time with and without replica switching.  
As shown in Figure 10, it can be further seen that the Reno algorithm significantly changes the transmission performance as the packet loss rate increases. When the packet loss rate is greater than 1%, the transmission end time increases significantly, and the waveform is clearly unstable. This is because Reno is a loss-based algorithm, and each packet loss event causes the congestion window to be halved, thus resulting in lower bandwidth utilization.  
Figure 10: The performance of the Reno algorithm under different packet loss rates.  
Figure 9: Performance comparison of BBR and Reno.  
The experimental results show that the loss-based Reno algorithm causes a sharp drop in throughput rate as the packet loss rate grows, making the bandwidth utilization low. In contrast, the BDP-based BBR algorithm is insensitive to packet loss and makes the throughput rate reach almost 93% of the bandwidth even the packet loss rate is not zero, which accomplishes high bandwidth utilization.  
In conclusion, this experimental result proves that using the BDP-based BBR congestion control algorithm has superior performance than using the loss-based Reno congestion control algorithm.
## 2 Materials and Methods
### Preparation of Polypyrrole-chitosan-BaFe\({}_{2}\)O\({}_{4}\)
#### 2.2.2 Synthesis of PPy-Chi-BaFe\({}_{2}\)O\({}_{4}\) Composite Layer  
The PPy-Chi-BaFe\({}_{2}\)O\({}_{4}\) was synthesized using the electrodeposition method. The gold-coated glass slide was prepared using a sputtering coating method, and the pyrrole was polymerized on the surface of the gold layer in the presence of chitosan and BaFe\({}_{2}\)O\({}_{4}\) using the cyclic voltammetry method.  
To prepare the electrolyte solution for the electrodeposition of PPy-Chi-BaFe\({}_{2}\)O\({}_{4}\), 0.2 g of chitosan powder was dissolved in 50 ml aqueous solution of 0.3M oxal acid, and it was stirred 4 h prior to its use for the preparation of the layer. The monomer solution contains mixed electrolytes in a 0.1 M phosphate buffer solution (pH 7.2), 0.1 M pyrrole monomer, 0.1 M LiClO\({}_{4}\) as a dopant, 0.1 M chitosan, and 0.05 g of BaFe\({}_{2}\)O\({}_{4}\). The electrolyte was stirred during electrodeposition to make sure there is no precipitation. The working electrode, reference electrode, and counter electrode were a gold-coated glass slide, saturated calomel electrodes (SCE), and platinum electrode, respectively. The polymerization of pyrrole was carried out at the potential range of 0 V to 0.8 V in the presence of chitosan and BaFe\({}_{2}\)O\({}_{4}\) nanoparticles at different cycles from 1 to 50. The prepared samples were put in the oven for 2 h to dry the layers. The PPy-Chi-BaFe\({}_{2}\)O\({}_{4}\) composite layers were characterized using the FT-IR spectrometer (model: NEXUS), XRD spectrometer (WITec, Alpha 300R with Shimadzu diffractometer: model XRD6000 and Cu, Ka (0.154 nm)), and the FE-SEM (NOVA NANOSEM 230)