esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results of the Measurements and Analyses
### Tested Device  
The view of the tested electronic module is presented in Figure 4a. There were four such modules with dimensions of 245 mm by 280 mm at their disposal. On each module there were two BLC10G22 LS-240 PVT-type transistors marked as TR_A and two of the type BLC9G22 LS-160 VT marked as TR_B (Figure 4a). These transistors in the power amplifier (PA) circuit are part of the TRX transceiver circuit. They are assembled in the SMT process on a PCB containing 12 layers.  
The temperature measurements on the cases of both the discussed types of transistors were made with the use of a thermographic camera at the power values P \(\leq\) 10 W and P \(\geq\) 20 W. The results of these measurements are thermograms with the temperature distribution along and across the transistor case. An exemplary result of such measurements is shown in Figure 4c, and an exemplary measurement result for P = 21.5 W is presented in Table 1. The used measurement method of temperature distribution on the transistor case and its thermal resistance is described in detail in the paper [27].  
When we study the effect of one quantity on another without fully knowing what equations they might be related to, and there is a relationship between them, we can only estimate the relations between them with some accuracy. In statistics, to find out what relationship exists between data sets, if any, the regression equation is used, among others [39; 40]. There are different types of regression equations. Some of these include exponential linear regression and simple linear regression (to fit your data into an exponential or linear equation). The regression analysis is a method of mathematical ordering that allows you to evaluate which variables actually affect certain parameters and which can be ignored. Ideally, when the variables under investigation are fully interdependent, this will mean that the experimental data will overlap with the results obtained using the regression equation in the plot.  
Figure 4: Tested electronic module (**a**), an example of the distribution of voids tested by the X-ray method (**b**), an example of a thermogram and case temperature distribution for the BLC10G22 LS-240 PVT transistor at the dissipated power P = 21.5 W for the transistor at position 3 in the electronic module described as PCB2 (**c**).
# Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning
## 1 Introduction  
Acute Aortic Syndrome (AAS) encompasses a spectrum of life-threatening conditions, including Acute Aortic Dissection (AAD), Intramural Hematoma (IMH), and Penetrating Aortic Ulcer (PAU) [1]. AAD involves a tear in the inner layer of the aortic wall; IMH entails bleeding within the aortic wall, without a clear tear in the inner layer; and PAU refers to a defect or ulceration in the aortic wall penetrating deeper layers, as indicated in Figure 1 below. Various risk factors, including hypertension and atherosclerosis, are associated with these subtypes [2, 3].  
Figure 1: Anatomy of the aorta and pathogenesis of Acute Aortic Syndrome [3].  
Characterized by defects in the aortic wall, AAS poses the imminent risk of vessel obstruction or rupture, often manifesting through symptoms such as chest pain [3]. High blood pressure and genetic factors are commonly associated with AAS [1]. Symptoms include intense pain, often resembling a heart attack [1; 4]. Prompt diagnosis, typically through imaging techniques, is crucial. A compromised aortic wall can lead to severe complications, underscoring the urgency of addressing AAS [3]. The incidence of AAS in the general population ranges from 3.5 to 6.0 cases per 100,000 patient-years, with higher rates observed among individuals aged 64 to 74 years and those aged 75 years and older [5; 6].  
Diagnosing AAS presents a multifaceted challenge due to its diverse clinical presentations and the absence of standardized diagnostic criteria [3; 7]. This diagnostic uncertainty contributes to delays in treatment, emphasizing the critical need for improved diagnostic approaches [8]. Machine learning, a branch of artificial intelligence, has emerged as a promising tool in healthcare, offering the potential to enhance diagnostic accuracy and patient outcomes [9; 10].  
Recognizing the gap in diagnostic guidance, this paper leverages a comprehensive dataset integrating clinical records from 68 emergency departments across the USA, encompassing medical histories of nearly 150,000 patients from 2021 to 2022. By exploring the utility of machine learning techniques, the study aims to develop predictive models for identifying patients at risk of AAS. Through systematic review and analysis of diagnostic accuracy methodologies, this research seeks to elucidate critical factors influencing AAS diagnosis and facilitate the development of clinical decision support systems. Ultimately, the objective is to expedite AAS diagnosis and improve patient care in emergency settings.
## 6 Conclusions  
In this work, we addressed the problem of balancing a high packet delivery ratio and low energy consumption for UAVs in a sparsely populated network scenario. To resolve the problem, we proposed LECAR, a new DTN-based routing protocol that takes advantage of the mobility model by estimating the location of the destination UAV and approximating the distance to the destination. Along with this estimated distance information, LECAR considers the congestion state of the neighboring UAVs and routes the packet to a UAV that moves closer to the destination and has enough space in its buffer.  
In extensive simulation experiments, LECAR demonstrated a high packet delivery ratio (on average, 27% increase than Spray and Wait) and low energy consumption (on average, 42% decrease than Spray and Wait) compared to the considered routing protocols. Moreover, in maximum cases, LECAR could maintain a single copy per packet at a time in the network. It also ensured low hop counts for routing a packet (on average, 34% less than Spray and Wait). Although it generated a relatively large overhead, the number of transmissions per data packet outweighed the extra overhead and resulted in low energy consumption. These results reveal that LECAR better balances packet delivery ratio and energy consumption considering a sparsely populated FANET scenario. Although LECAR  
Figure 17: Performance comparison of the considered routing protocols regarding consumed energy consumption when the buffer size is (**a**) 25 MB and (**b**) 50 MB.  
is designed considering a specific scenario and mobility model, the key idea can be easily extended and adapted to any other scenario or mobility model.  
In future work, we plan to extend LECAR to significantly reduce the overhead even in a high-density network scenario. We further plan to improve LECAR for minimizing the delay in packet delivery, even for low-density scenarios.  
Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing--original draft preparation, writing--review and editing, and visualization, I.M.; supervision, project administration, and funding acquisition, Y.-Z.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Ministry of Education, 2018R1A6A1A03025109, and was funded by the Korean government, 2019R1A2C1006249.  
Not applicable.  
This research was supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (No. NRF-2018R1A6A1A03025109), and by the National Research Foundation of Korea (NRF) grant funded by the Korean government (No. NRF-2019R1A2C1006249).  
The authors declare
# A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching
## 2 Overview of Feature Detectors and Descriptors
### [PERSON] [22] proposed one of the most commonly used feature detection and description algorithms, named SIFT. SIFT is invariant to scale, rotation, illumination and viewpoint change; therefore, it can also correctly locate key-points in noisy, cluttered and occluded environments. It identifies interest points in two steps. First, a scale space is generated by continuously smoothing the original image using Gaussian filters that ensure scale invariance. Then, the original image is resized to half size, and again smoothing is performed using a Gaussian filter. This process is repeated. In this manner, an image pyramid is formed, with the reference image at the bottom (level 1). Next, it computes the DoG by subtracting two consecutive scales. Second, interest points are identified by examining the \(3\times 3\times 3\) neighborhood of any pixel. The image point at which DoG values attain an extrema among all neighbors is considered as a key-point. A description algorithm was also developed for extracting the descriptor of detected key-points. The descriptor is a position-dependent histogram of local image gradient directions in the neighborhood of the key-point.
## 3 Results and Discussion
### Effect of Aging on the Microstructure of Solder Powder  
Figure 3 displays SEM images of the rapidly solidified eutectic Sn-Pb37 powder after aging at ambient temperature for different numbers of days. Even for aging at room temperature, coarsening of the microstructure is observed. A microstructure with an evident increase in the Pb-rich phase is observed after aging for 40 days. Moreover, some small particles are observed around the large particles in the Pb-rich-phase-coarsened microstructure. This observation indicates that the large Pb-rich phase particles grow at the expense of other particles rather than through a continuous nucleation and growth process. Structures formed during atomization generally have high interfacial areas and nonequilibrium solute concentration, because of the rapid solidification that occurs. This behavior is observed because of the need to reduce the overall energy of the system by decreasing the interfacial area. Sufficient thermal energy is required to reduce the energy of the system through diffusion even at room temperature.  
Figure 4 presents the cross-sectional microstructure of rapidly solidified Sn-Pb37 powder aged for different numbers of days. No microsegregation of the Pb-rich phase was detected during growth. This observation illustrates that the Pb-rich phase in Sn-Pb37 powder coarsened through atom diffusion, with Pb particles becoming fewer in number, more spherical, and much larger during room temperature  
Figure 1: Sn–Pb binary alloy phase diagram.  
Figure 2: Microstructure of eutectic Sn-Pb alloy powder. (**a**) The surface morphology, and (**b**) cross-sectional microstructure.  
aging, indicating that the microstructure of the Sn-Pb37 powder evolves even at room temperature. Because some material properties depend on the material's microstructure, the effect of microstructure coarsening at room temperature should be considered.  
Figure 4: Cross-sectional microstructure of rapidly solidified Sn–Pb37 powder after aging for (**a**) 7, (**b**) 21, (**c**) 29, and (**d**) 40 days.  
Figure 3: Scanning electron microscopy (SEM) images of rapidly solidified eutectic Sn–Pb37 powder after aging for (**a**) 7, (**b**) 21, (**c**) 29, and (**d**) 40 days.
## 3 Analytical Model Calculation and Validation
### Analytical Calculation
#### 3.1.1 Establishment of Equation
Substituting Equation (1) and Equation (4) into Equation (5), we obtain the SOVP equation:  
\[\frac{\partial^{2}W_{\text{a}}}{\partial x^{2}}+\frac{\partial^{2}W_{\text{a} }}{\partial y^{2}}+\frac{\partial^{2}W_{\text{a}}}{\partial z^{2}}=\mu_{0} \sigma\bigg{(}-v_{\text{x}}\frac{\partial W_{\text{a}}}{\partial x}-v_{\text {y}}\frac{\partial W_{\text{a}}}{\partial y}-v_{\text{z}}\frac{\partial W_{ \text{a}}}{\partial z}\bigg{)} \tag{6}\]  
Since the conductivity is zero in the nonconductive regions \(\Omega_{\text{I}}\) and \(\Omega_{\text{III}}\), from Equation (6) the TE potential satisfies the following equation:  
\[ \begin{array}{l}\
abla^{2}W_{\text{a}}^{\text{Ls}}=0\\ \
abla^{2}W_{\text{a}}^{\text{Lr}}=0\\ \
abla^{2}W_{\text{a}}^{\text{III}}=0\end{array}  \tag{7}\]  
and the TE potential in the conductive region \(\Omega_{\text{II}}\) satisfies the following equation:  
\[\
abla^{2}W_{\text{a}}^{\text{II}}=\mu_{0}\sigma\bigg{(}-v_{\text{x}}\frac{ \partial W_{\text{a}}^{\text{II}}}{\partial x}-v_{\text{y}}\frac{\partial W_{ \text{a}}^{\text{II}}}{\partial y}-v_{\text{z}}\frac{\partial W_{\text{a}}^{ \text{II}}}{\partial z}\bigg{)} \tag{8}\]  
where \(W_{\text{a}}^{\text{Ls}}\) and \(W_{\text{a}}^{\text{Lr}}\) are the source and reflected TE potential in the region \(\Omega_{\text{I}}\), respectively [12], \(W_{\text{a}}^{\text{II}}\) is the total TE potential in the region \(\Omega_{\text{II}}\) and \(W_{\text{a}}^{\text{III}}\) is the transmitted TE potential in the region \(\Omega_{\text{III}}\). These potentials are shown in Figure 6.
# Asymmetric Quantum Multicast Network Coding: Asymmetric Optimal Cloning over Quantum Networks
###### Abstract  
Multicasting of quantum states is an essential feature of quantum internet. Since the noncloning theorem prohibits perfect cloning of an unknown quantum state, an appropriate protocol may depend on the purpose of the multicast. In this paper, we treat the multicasting of a single copy of an unknown state over a quantum network with free classical communication. We especially focus on protocols _exactly_ multicasting an asymmetric optimal universal clone. Hence, these protocols are optimal and universal in terms of mean fidelity between input and output states, but the fidelities can depend on target nodes. Among these protocols, a protocol spending smaller communication resources is preferable. Here, we construct such a protocol attaining the min-cut of the network described as follows. Two (three) asymmetric optimal clones of an input state are created at a source node. Then, the state is divided into classical information and a compressed quantum state. The state is sent to two (three) target nodes using the quantum network coding. Finally, the asymmetric clones are reconstructed using LOCC with a small amount of entanglement shared among the target nodes and the classical information sent from the source node.  
quantum information; quantum communication; quantum network; universal cloning; network coding; entanglement +
Footnote †: journal: Applied  
1
Footnote 1: institutetext: Department of Computer Science, Faculty of Informatics, Shizuoka University, Hamamatsu 432-8011, Japan; yu.16 [EMAIL_ADDRESS]
# Dynamic Allocation of SDN Controllers in NFV-Based MEC for the Internet of Vehicles
## 5 Experimental Analysis
### Scalability  
Considering the dynamic aspect of the VANET environment, it is essential to understand the trade-off between the required time to deploy the SDN controllers into the MEC and the effectiveness of the achieved solution for both the exact model and the heuristic. As shown in Figure 9, as the scenario increases from 50 to 150 RSUs, the exact model becomes intractable, as it requires more than \(1.09\times 10^{5}\) ms to obtain the optimum solution. On the other hand, as shown in Figure 10, in a scenario with 15,000 RSUs, the heuristic requires less than \(5.04\times 10^{3}\) ms for \(\beta=1\). In addition, as the parameter \(\beta\) increases to 100, the time reduces to \(3.03\times 10^{2}\) ms. The results show that to decrease the heuristic execution time, it's necessary to adopt \(\beta\) with higher values, aiming at reducing the number of template selection tasks for the controllers. However, in the specific case of \(\beta\) set to 50 and 100 with 15,000 RSUs the values are similar. This fact can be explained by the number of drivers created, considering that more time is required to process the heuristic by creating new drivers than assigning RSUs to existing drivers.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Solution** & \multicolumn{3}{c}{**Number of RSUs**} \\ \hline  & **50** & **100** & **150** \\ \hline Model & 0.02 & 0.01 & 0.38 \\ Alloc-1 & 0.13 & 0.09 & 0.08 \\ Alloc-10 & 0.12 & 0.05 & 0.03 \\ Alloc-50 & 0.49 & 0.27 & 0.24 \\ Alloc-100 & 0.49 & 0.27 & 0.19 \\ \hline \hline \end{tabular}
\end{table}
Table 6: \(\Delta_{Dif}\) for first scenario.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Solution** & \multicolumn{3}{c}{**Number of RSUs**} \\ \hline  & **5000** & **10,000** & **15,000** \\ \hline Alloc-1 & 0.02 & 0.01 & 0.01 \\ Alloc-10 & 0.00 & 0.00 & 0.00 \\ Alloc-50 & 0.00 & 0.00 & 0.00 \\ Alloc-100 & 0.00 & 0.00 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 7: \(\Delta_{Dif}\) for high-density scenarios.
## 5 Software Architecture
### Instrumentation, Sampling and Information Storage  
The instrumentation procedure is executed according to the programmed sampling time, which defines the frequency that the system wakes up, measures the sensors, records  
Figure 8: The general operation of the sensor node.  
Figure 7: The general operation of Gateway node.  
the information and (if necessary) sends the data. In the designed nodes, the instrumentation time was defined as an integer value of the number of samples desired per hour, with a maximum of 12. This process makes the system always instrument in the same minute every hour. The equation to calculate the remaining seconds that the system has to fall into the deep sleep before the next sample is calculated with the following equations.  
Equation (2) calculates the exact minute of the hour when the next sampling will take place.  
\[minute_{sampling}=sampling*int\bigg{(}\frac{minute_{current}}{sampling} \bigg{)}+1 \tag{2}\]  
where sampling is the minutes between measurements programmed in the setup file.  
Equation (3) calculates the remaining seconds to reach the sampling time and, therefore, the time that the node must be in deep-sleep ultra-low power mode before it wakes up.  
\[seconds_{wakeup}=minute_{sampling}*60-minute_{current}*60-second_{current} \tag{3}\]  
Regarding the deviation in the time measurement between sampling periods, a test was performed to measure the time differences when the node was awakened from deep sleep. The average deviation for a period of 900 s (15 min) was 2.7 s, which implies an accuracy of 0.3%. This low accuracy is because the ESP32 does not use the RTC to wake up the device, but an ultra-low power timer. However, the device compares at each wakeup RTC and resets the second's wakeup value for the next sampling, so the timer accuracy value is not too important.  
Analogic sensors are connected to ADC pins integrated into the module. In addition, P16 is used for battery level measurement. UARTs and digital pins are used for digital sensors, as previously written.  
To improve the stability of the sensor readings, a biased average of 20 sensor readings was performed every 25 ms.  
A Python dictionary with seven keys in the format [ID_nodo; Temp; Hum; %CO\({}_{2}\); %Lum; Vbatt; ppm C\({}_{2}\)H\({}_{4}\)] is returned after the measurement procedure is executed.  
These data are temporarily stored in a partition of the device's flash memory. If the communication procedure is not possible due to coverage lacks or other problems, the data are stored together with the time stamp assigned to that dictionary in the SD card.
###### Abstract  
The energy performance of buildings especially public buildings needs to be optimized together with environmental, social and cost performance, which can be achieved by the multiobjective optimization method. The traditional building performance simulation (BPS) based multiobjective optimization is time-consuming and inefficient. Practical projects of complex public building design usually involve many-objective optimization problems in which more than three objectives are considered. Using BPS based multiobjective optimization is not sufficient to solve this kind of design problem. This paper aims to propose an artificial neural network (ANN) based many-objective optimization design method, an architect-friendly integrated workflow has been implemented. The proposed method has been applied on a public library building in Changchun city of China to optimize its Energy Use Intensity (EUI), Spatial Daylight Autonomy (sDA), Useful Daylight Illuminance (UDI) and Building Envelope Cost (BEC). The optimization process has obtained 176 non-dominated solutions. By adopting the selected relative optimal solutions, 1.6\(\times\)10\({}^{5}\)-2.1\(\times\)10\({}^{5}\) kWh energy can be saved per year; sDA value and UDI value can be increased by 8.1%-11.0% and 4.3%-4.7% respectively; BEC can be reduced by \(\forall\)1.2\(\times\)10\({}^{5}\)-2.1\(\times\)10\({}^{5}\) (\(\$1.7\times\)10\({}^{4}\)-3.0\(\times\)10\({}^{4}\)). The optimization time has been greatly shortened in this method and the whole process is highly efficient without manual data conversion between different platforms.  
many-objective optimization; artificial neural network (ANN); evolutionary algorithm; public building design; energy consumption; daylighting; building envelope cost
###### Abstract
In addition, frequency nulls can be moved or avoided with more complicated transmitter structures, such as parallel IM/phase modulation (PM) [26], dual-parallel MZM (DP-MZM) [27], and polarization modulation (PolM) [28]. Going back to the hardware complexity constraint of conventional IM-DD links, 10 km SSMF transmission of 100 G PAM-6/8 signals are achieved with the help of pre-chirping by operating DDMZM at single-arm driven mode [29]. In Reference [30], a differential time skew-based VSB scheme is proposed, which shapes the signal through an inherent asymmetric transfer function without an optical filter. These works offer possibilities to utilize imbalances for combating power fading. In this work, by extending our recent work in Reference [31], we provide a comprehensive investigation of the influence of three kinds of MZM imbalances, including bias deviation, amplitude mismatch, and differential time skew in short-reach DD systems at C-band. The theoretical transfer function considering fiber dispersion and PD detection in the presence of the above imbalances are first derived, respectively. Transmission experiments are carried out by using 34 Gbaud Nyquist PAM-6/8 signals over a dispersion-uncompensated 20 km SSMF link at C-band. After applying Volterra nonlinear equalizer (VNE), the bit error rates (BERs) of PAM-6 and PAM-8 signals achieve the 7% and 20% hard-decision forward error correction (HD-FEC) thresholds of 4.5 \(\times\) 10\({}^{-3}\) and 1.5 \(\times\) 10\({}^{-2}\) after fiber transmission, respectively [32]. In addition, the received optical power (ROP) sensitivities at back-to-back (BTB) and after transmission are measured and compared with respect to bias, amplitude mismatch, and time skew, respectively. The rest of this paper is organized as follows. In Section 2, the theoretical model of imbalances after fiber dispersion is derived. Section 3 introduces the experiment setup and DSP stacks. Section 4 presents and discusses the experimental results. Finally, the conclusions are drawn in Section 5.