esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results
### Experimental Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sender** & **Receiver** & **Phase** & **Communication Cost** \\ \hline Function owner & \(CSP_{1}\) & Preparation & \(2\cdot(m\times d)\cdot\mathbb{Z}_{N^{2}}^{*}\) (Once for all) \\ Function owner & The public & Preparation & \((m+d)\cdot\mathbb{Z}_{q}\) (Once for all) \\ Function owner & \(CSP_{1}\) & Preparation & \(1\cdot\mathbb{Z}_{N^{2}}+1\cdot\mathbb{Z}_{N^{2}}^{*}\) \\ Function owner & \(CSP_{2}\) & Preparation & \(1\cdot\mathbb{Z}_{N^{2}}\) \\ Request User & \(CSP_{1}\) & Preparation & \(d\cdot(R_{q})^{2}\) \\ \(CSP_{1}\) & Function owner & Preparation & Permission Message \\ \(CSP_{1}\) & \(CSP_{2}\) & Computation & \(2\cdot(m\times d)\cdot\mathbb{Z}_{N^{2}}^{*}+d\cdot(R_{q})^{2}\) \\ \(CSP_{1}\) & Request User & Computation & \(m\cdot(R_{q})^{2}\) \\ \(CSP_{1}\) & The public & Verification & \((d+m)\cdot(R_{q})^{2}\) \\ \(CSP_{2}\) & \(CSP_{1}\) & Computation & \(m\cdot(R_{q})^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Communication Costs. Figure 6: The time cost for the request user. Figure 7: The time cost for verification. Compared with [9], our scheme achieves both function privacy and input privacy. In terms of computational complexity, our scheme is similar to their second scheme with only input privacy for the cloud server side, but we have two servers, which requires twice the computational overhead. At the same time, the two servers need to communicate with each other, which incurs additional communication overhead, but these additional costs provide privacy for the function.
# Design of Tracking, Telemetry, Command (TT&C) and Data Transmission Integrated Signal in TDD Mode
## 2 The Modulation System for the BOCcos and APSK Integrated Signal  
In this section, we will give a signal model of an integrated signal and briefly introduce the concept of modulation and demodulation.
# Generation of a Virtual Cohort of Patients for in Silico Trials of Acute Ischemic Stroke Treatments
## 4 Discussion
For instance, uniform sampling of the principal values within the training set range would allow a broader coverage of the possible anatomy configurations, increasing the number of virtual anatomies with admissible geometric parameters but less represented in the training dataset. Following the generation of vascular centerlines and diameters, a method for the automatic 3D reconstruction of the virtual anatomies was illustrated. More generally, the proposed 3D reconstruction method allows for the automatic reconstruction of patient-specific cerebral vessels starting from centerlines and diameters obtained from the segmentation of clinical images. The accuracy of the reconstruction was evaluated through a comparison with an STL model directly obtained after the segmentation of CT scans of a patient-like 3D-printed cerebrovascular phantom. The results indicated that the obtained CAD model replicates the STL model with good accuracy. Since the reconstruction method is built from the centerline and the associated diameters, circular lumen sections were assumed. In reality, non-circular and irregular sections could be observed. However, the relative distance calculated between CAD--with circular sections--and STL--with real non-circular sections--was less than 19%. The main mismatches were observed in vascular portions with critical shapes, e.g., tracts of high curvature, or at the extremes of the models, where segmentation artifacts were more frequent. In this regard, the accuracy of the vascular reconstruction was compared with the provided STL model to assess the overall agreement of the vascular shapes. Ultimately, the accuracy of the patient-specific vascular model will always depend on the resolution of the clinical imaging techniques.
## 5 Results
### The Proposed Mechanisms
#### 5.3.1 Parametrization  
We first conducted experiments to define the interest packet forwarding probability (\(p\)) used by PIF and ReCIF+PIF. We varied this probability from \(p=0.1\) to \(p=0.9\) considering 30 consumers sending interest at a rate of 50 interest/s. Figure 7 shows the results. We observed that the number  
Figure 6: Default NDN vs. TCP/IP with OLSR: content delivery rate and average delay. (**a**) The percentage of content packets delivered by the TCP/IP stack with the Optimized Link State Routing Protocol (OLSR) and by default Named-Data Networking (NDN). (**b**) The average delay of content packets delivered by TCP/IP stack with OLSR and by default NDN.  
of content packets delivered was the highest when \(p=0.2\) or \(p=0.3\). On the other hand, PIF with \(p=0.2\) provided a lower average delivery delay and overhead than PIF with \(p=0.3\). Thus, we defined PIF nd ReCIF+PIF as having \(p=0.2\) for the experiments presented in the next sections.
## 2 Materials and Methods
### Measurements  
The participants were seated upright in the sealed booth with no external air intake. The fNIRS sensors (NIRS, OBELAB Inc., Kangnam-gu, Seoul, Republic of Korea) were attached 1 cm above the inner corner of each eyebrow to measure the COS and Hb concentrations. Additionally, the sensors for a patient monitor (Bionics, Chuncheon, Republic of Korea) were connected to the right index finger to measure the heart rate (HR) and percutaneous oxygen saturation (SpO\({}_{2}\)) throughout the experiment.  
Before the fNIRS measurement, several calibration processes are performed to ensure accurate signal acquisition and analysis. These include gain calibration to optimize each sensor and detector, motion calibration using a gyroscope to correct for signal distortions caused by movement, and digital signal processing for a precise analysis of the collected light signals to measure hemodynamic changes. The calibrations are individually tailored to account for variations in head shape, skin tone, and other personal factors, ensuring reliable and accurate monitoring of cerebral oxygen saturation and blood flow dynamics. All these steps are executed by simply clicking the calibration button in the program. In contrast, patient monitors do not require individual calibration, as their sensors and measurement techniques are designed to automatically adjust for anatomical, skin tone, or other personal variations. This universal design eliminates the need for manual adjustments, making them reliable and efficient tools for real-time monitoring across diverse clinical settings.  
The COS and Hb concentrations were measured for 7 min under each condition using fNIRS, with measurements repeated four times for each condition using the same method [Figure 2]. The results were saved using dedicated software (NirsitOn software Version 1.2.5, OBELAB Inc., Kangnam-gu, Seoul, Republic of Korea). HR and SpO\({}_{2}\), measured with the patient monitor, were recorded separately for 7 min under each condition. Data from the first 2 min of each measurement were excluded from the analysis to allow the sensor to stabilize after calibration and allow participants to inhale the air inside the booth, ensuring consistent brain function responses to the changed concentrations. Data collected over the subsequent 5 min were analyzed.
## 3 Materials and Methods
### Autoregressive Integrated Moving Average (ARIMA) Model  
ARIMA is a popular and successful classical statistical time series forecasting method based on past data [14; 25; 26]. This model is designed to capture trends, cycles, and fluctuations based on observations over time [27]. It is typically expressed as ARIMA(\(p\), \(d\), \(q\)), where \(p\), \(d\), and \(q\) denote the orders of autoregressive (AR), difference (I), and moving average (MA) components, respectively. The time series in the ARIMA model should be stationary. If the time series is not stationary, a differencing operation is applied, and d indicates the number of times this differencing operation is performed. The Augmented Dickey-Fuller (ADF) test is used to assess the stationarity of the time series. The ADF test determines the presence of a unit root in a time series, checking whether the series is stationary. If a time series is not stationary, the differencing operation is continued to make the series stationary, allowing the ARIMA model to be applied more effectively.  
In the Box-Jenkins approach, four steps are followed to model the ARIMA process. In the first step, the model is identified; in the second step, parameter estimation and selection are performed; in the third step, model verification is conducted; and finally, in the fourth step, predictions are made.  
The autocorrelation function (ACF) and partial autocorrelation function (PACF) plots are used to determine the q and \(p\) values of the ARIMA model. However, it may not always be possible to make accurate observations on the graph. Therefore, the most suitable ARIMA model can also be determined using Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Corrected Akaike Information Criterion (AICc) values. Additionally, strictly adhering to AIC, BIC, and AICc criteria may not always yield accurate results. Therefore, to determine the most suitable model for the dataset, it is recommended to assess ARIMA models on the test set.  
The formulas for AIC, BIC, and AICs statistical metrics are given in Equations (1)-(3), respectively.  
\[AIC=-2\text{log}(ML)+2k \tag{1}\]  
\[BIC=-2\text{log}(ML)+klog(n) \tag{2}\]  
\[AICc=-2\text{log}(ML)+2k+\frac{2k(k+1)}{n-k-1} \tag{3}\]  
Here, \(k\) represents the count of estimated parameters within a specific model, \(n\) denotes the size of the sample, and log(ML) stands for the maximized log-likelihood function tailored for the suggested model.
# Recent Advances in the Development of Mammalian Geranylgeranyl Diphosphate Synthase Inhibitors
## 6 GGDPS Expression  
Several studies have suggested that there is an association between GGDPS expression and disease pathology. [PERSON] et al. examined GGDPS expression in hepatocellular carcinoma specimens. Compared with specimens that were within 2 cm or 5 cm from the tumor margin, levels of GGDPS (mRNA and protein) were higher in the tumor tissues [67]. These studies also suggested a correlation between the GGDPS protein level and the presence of cirrhosis, tumor stage, and early recurrence. GGDPS has been identified as a target gene of early growth response 1 (EGR-1) [68]. In a mouse model of cigarette smoke exposure, GGDPS and EGR-1 levels were increased in lung epithelial cells and knockdown of GGDPS resulted in a decrease in the inflammatory response induced by cigarette smoke exposure [68]. This group subsequently postulated that increased expression of GGDPS results in enhanced geranylgeranylation of Ras proteins, which in turn, leads to activation of the mitogen-activated protein kinase (MAPK) pathway [69]. Similar findings were reported in a model of insulin resistance in which hyperinsulin induced MAPK activity in adipocytes in an EGR-1-dependent manner. Knockdown of GGDPS led to increased insulin sensitivity [70]. Thus, these studies suggest that pursuit of GGDPS inhibition as a therapeutic strategy may be of interest in disease settings characterized by GGDPS overexpression.
# Aerodynamic Analyses of Airfoils Using Machine Learning as an Alternative to RANS Simulation
## 3 Proposed Methodology  
### Architecture of the BPNN for the Prediction of Aerodynamic Coefficients  
The basic building blocks of the proposed methodology can be seen in Figure 3. The BPNN was used as a regression analysis tool to find the coefficients of lift and drag of the airfoils. First of all, the dataset that consisted of the 440 cases that were used in this study was obtained from performing numerical simulations in the Ansys Fluent software using the RANS-based Spalart-Allmaras turbulence model on four different NACA series airfoils, including NACA 0012, NACA 2415, NACA 23024 and NACA 24112. Each of the selected airfoils was simulated with 10 different Reynolds numbers in the range of 0.5 to 5 million.  
The dataset that was collected from the numerical simulations contained four input parameters, namely, airfoil name, flow Reynolds number, Mach number and airfoil angle of attack, whereas the two output parameters were coefficient of drag (C\({}_{\text{D}}\)) and coefficient of lift (C\({}_{\text{L}}\)). The dataset was then divided into two subsets: 70% of the data were used as training data for the network, while the other 30% were reserved for testing and validation. The neural network was then trained using the back-propagation algorithm on the training dataset. The training was continued until the achievement of the stopping criteria, after which the network response was checked using the testing subset. The stopping criteria were set to be attained when the best validation performance was achieved, i.e., when the validation error reached its minimum value.  
The architecture of the BPNN that was used in this study, with four input parameters, ten neurons in the hidden layer (best performance case) and two outputs, is shown in Figure 4.
The flexural failure of beams made of UHPC with coarse aggregate failed in a ductile manner, and it was found out that the maximum aggregate size of 5 or 8 mm has no significant impact on its structural behavior. At the same time, the longitudinal reinforcement ratios control the stiffness and ultimate strength of the beam [23]. Using recycled aggregate to produce sustainable ultra-high-performance concrete (sUHPC) can further enhance the sustainability of the material. Recycled aggregates have been used for paving blocks to enhance sustainability [20]. It was proved that using RCA can reduce the strength of the concrete block, and adding GGBS can help partially recover the strength loss. Introducing recycled aggregate in UHPC material can help reduce both the usage of cement and virgin aggregate. Optimization in design is another aspect of precast deck panels, which can lead to improved service. One way to achieve this is to use prestressing. This combination of high-strength or fiber reinforced concrete with prestressing has excellent potential in bridge deck applications because not only can it decrease the self-weight of the deck system by reducing the thickness, but also offersother advantages such as less maintenance, improved durability, reduced crack width, and high tensile strength. Limited research is available on the application of combining prestressing with the application of sustainable high strength fiber reinforced concrete in the development of bridge deck panels. With this background, the primary objective of this research was to develop and characterize different configurations of full-depth precast prestressed high strength concrete--with and without fibers--bridge deck panels, and partial-depth precast sustainable ultra-high performance concrete (sUHPC)-normal/recycled aggregate concrete bridge deck panels. The collective efforts aimed to explore different optimization methods considering reduced self-weight and increased sustainability while focusing first on their conformation to the existing design standards.
# Evaluating the Real-World NOx Emission from a China VI Heavy-Duty Diesel Vehicle
## 2 Experiments and Materials
### Portable Emissions Measurement System (PEMS)  
AVL-M.O.V.E-PEMS (Figure 2) consists of tailpipe attachment, heated exhaust lines, exhaust flow meter (EFM), exhaust gas analyzers used to measure concentrations of gaseous emissions (including carbon monoxide (CO), carbon dioxide (CO\({}_{2}\)), total hydrocarbon (THC), nitrogen monoxide (NO) and nitrogen dioxide (NO\({}_{2}\)), etc.), PM module, PN module, a global positioning system (GPS) from which we can get vehicle speed, latitude, longitude and altitude, sensors for measuring ambient temperature and humidity, charger, system control, E-box, etc. NOx concentration is calculated by the sum of NO and NO\({}_{2}\) concentration. The electrical power needed for the PEMS operation (DC 22-28 V) is supplied by two external batteries.  
The PEMS uses flame ionization detection (FID) for THC measurement, non-dispersive infrared (NDIR) for CO and CO\({}_{2}\) measurements, non-dispersive ultra-violet (NDUV) for NO, NO\({}_{2}\) measurement. The EFM uses a pitot tube based on Bernoulli's principle to calculate mass flow on the basis of airflow differential pressure measurement. The measurement principle and measurement range of gaseous emissions are shown in Table 2.  
All emissions are measured on a wet basis, so that no corrections are required for the analysis. The PEMS is warmed up for at least 1.5 h, then zeroed and spanned with calibration gas before the test.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type of Engine** & **XX13600-60** \\ \hline Type of Vehicle & Long–Haul \\ Year of production & 2019 \\ Engine rated power & 441 kW \\ Reference Torque & 3000 Nm \\ WHTC Cycle Work & 38.72 kWh \\ Emission standard & China VI (step B) \\ Aftertreatment System & DOC + DPF + SCR + ASC \\ Gross vehicle weight & kg \\ Payload & 50\% \\ Category of vehicle & N3 \\ Fuel & China VI Standard \\ DEF & Adblue (32.5\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of vehicle, engine, aftertreatment, fuel and DEF specifications.  
Figure 1: (**a**) Tested vehicle; (**b**) aftertreatment configuration (picture of real products); (**c**) NOx sensors location and aftertreatment configuration (schematic plot).
# Theoretical Framework (Module) for Short-Sea Shipping System Evaluation
## 3 Theoretical Basis of the Evaluation of Short-Sea Shipping Processes  
### Steps of SSS Research Methodology  
The following stages of the SSS system research methodology were applied to conduct the study (Figure 1). After conducting a literature review, a mathematical model of the SSS system was developed.  
Theoretical calculations of cargo transportation using SSS and other transportation methods were performed. The dispersion and maximum distribution methods [23] were used to design the SSS system, using data obtained during experiments. The maximum distribution method could be applied when at least five measurements were made.  
SSS, which includes cargo transportation between ports and the possibility of transshipment, can be graphically represented in the following diagram (Figure 2).  
A case study was conducted to verify the theoretical calculations and practical application of the presented methodology. Based on the established assumptions, cargo transportation simulations using SSS and the road transport systems were performed. Experiments were performed based on cargo transportation data from shipping lines and road transportation between selected countries.  
Figure 1: The steps of the SSS research methodology.
# Research on ECG Signal Classification Based on Hybrid Residual Network
###### Abstract  
Arrhythmia detection in electrocardiogram (ECG) signals is essential for monitoring cardiovascular health. Current automated arrhythmia classification methods frequently encounter difficulties in detecting multiple cardiac abnormalities, particularly when dealing with imbalanced datasets. This paper proposes a novel deep learning approach for the detection and classification of arrhythmias in ECG signals using a Hybrid Residual Network (Hybrid ResNet). Our method employs a Hybrid Residual Network architecture that integrates standard convolution, depthwise separable convolution, and residual connections to enhance the feature extraction efficiency and classification accuracy. To guarantee superior input signals, we preprocess the ECG signals by removing baseline drift with a high-pass Butterworth filter, denoising via discrete wavelet transform, and segmenting heartbeat cycles through R-peak detection. Additionally, we rectify the class imbalance in the MIT-BIH Arrhythmia Database by applying the Synthetic Minority Oversampling Technique (SMOTE), therefore enhancing the model's ability to detect infrequent arrhythmia types. The suggested system achieves a classification accuracy of 99.09% on the MIT-BIH dataset, surpassing conventional convolutional neural networks and other state-of-the-art methodologies. Compared to existing approaches, our strategy exhibits superior effectiveness and robustness in managing diverse irregular heartbeats and arrhythmias.  
electrocardiogram (ECG); hybrid residual network (Hybrid ResNet); depthwise separable convolution; data preprocessing; arrhythmia classification
## 2 Theory
### Contribution of Attenuation, Random and Scattered Coincidences
#### 2.2.2 Random and Scattered Coincidences  
In presence of an expected rate of random coincidences \(r\) and of scattered coincidences \(s\), Equation (2) becomes  
\[\lambda_{j}^{k+1}=\
icefrac{{\lambda_{j}^{k}}}{{\eta_{j}}}\sum_{i}c_{i,j}\frac{ y_{i}}{\sum_{\xi}c_{i,\xi}\lambda_{\xi}^{k}+r_{i}+s_{i}} \tag{11}\]  
Therefore, Equation (9) still holds by considering the contribution of scatter and random coincidences to \(y_{i}\).  
It can be seen that, the higher is the fraction of random and scattered coincidences, the slower is the convergence rate. In TOF, random coincidences are uniformly distributed over time bins. Pixel convergence is, therefore, influenced not by the whole random amount, but only by the fraction \(\
icefrac{{D_{eff}}}{{FOV}}\) happening within \(D_{eff}\).  
TOF greatly reduces the randoms impact on MLEM convergence. On the other side, since the scatter coincidence profile over time bins follows that of emission coincidences [11], the scatter impact on convergence is not reduced with TOF. Furthermore, as the scatter fraction greatly increases with object dimension, the previously observed TOF convergence rate independence from background dimension \(D\) is no longer accurate.
# Design and Analysis of High-Capacity MIMO System in Line-of-Sight Communication
###### Abstract  
The phase of the channel matrix elements has a significant impact on channel capacity in a mobile multiple-input multiple-output (MIMO) communication system, notably in line-of-sight (LoS) communication. In this paper, the general expression for the phase of the channel matrix at maximum channel capacity is determined. Moreover, the optimal antenna configuration of the \(2\times 2\) and \(3\times 3\) transceiver antenna array is realized for LoS communication, providing methods for \(n\times n\) optimal antenna placement, which can be used in short-range LoS communication and non-scattering environment communication, such as coupling train communication and inter-satellite communication. Simulation results show that the \(2\times 2\) rectangular antenna array is more suitable for the communication of coupling trains, while the \(3\times 3\) circular arc antenna array is more suitable for virtual coupling trains according to antenna configurations. Moreover, the \(2\times 2\) antenna rectangular configuration proposed in this paper has reached the optimal channel in inter-satellite communication, which lays a foundation for the deployment of communication systems.  
LoS; MIMO; channel capacity; antenna configuration +
Footnote †: journal: Sensors  
1
Footnote 1: Key Laboratory of Specialy Piber Optics and Optical Access Networks, Shanghai University, Shanghai 200444, China; [EMAIL_ADDRESS] (K.J.); [EMAIL_ADDRESS] (Y.J.) CASCO Signal Ltd., Shanghai 20071, China; [EMAIL_ADDRESS] 1