esa-sceva/satcom-chunk-collection huggingface dataset:
# An All-Mach Number HLLC-Based Scheme for Multi-Phase Flow with Surface Tension
## 2 Mathematical Formulation  
### Governing Equations  
Inviscid immiscible compressible two-phase flow is considered where the governing equations are based on that by [PERSON] and [PERSON] [15]. Here, a one-fluid formulation is derived from their work by defining a cell-average density \(\rho\), pressure \(p\) and a uniform velocity \(\mathbf{u}\). In the absence of gravitational, viscous effects and heat conduction or phase change, these read in compact form as  
\[\frac{\partial\mathbf{U}}{\partial t}+\
abla\cdot\mathbf{F}(\mathbf{U})=\mathbf{S}, \tag{1}\]  
where \(\mathbf{U}=\left[\rho,\rho\mathbf{u},\rho E,\alpha\right]^{T}\) is the set of conserved variables, \(\mathbf{F}\) and \(\mathbf{S}\) are the flux and source vector written, respectively, as  
\[\mathbf{F}(\mathbf{U}) =\begin{bmatrix}\rho\mathbf{u}\\ \rho\mathbf{u}\otimes\mathbf{u}+p\mathbf{I}\\ (\rho E+p)\mathbf{u}\\ \alpha\mathbf{u}\end{bmatrix}, \tag{2a}\] \[\mathbf{S} =\begin{bmatrix}0\\ \sigma\kappa\
abla\alpha\\ \sigma\kappa\
abla\alpha\cdot\mathbf{u}\\ \alpha\
abla\cdot\mathbf{u}\end{bmatrix}. \tag{2b}\]  
In the above, \(\rho,\mathbf{u}\) and \(p\) are, respectively, the fluid density, velocity and pressure. Further, \(\mathbf{I}\) is the identity matrix, and the volume fraction, \(\alpha\), is computed as a cell average of the volume occupied by the tracked phase \(i\) to the volume of the cell (\(\alpha=\frac{\
u_{i}}{\
u_{i}}\)).  
The volume fraction transport equation is expressed in its semi-conservative form as per [PERSON] et al. [44] and by [PERSON] et al. [36]:  
\[\frac{\partial\alpha}{\partial t}+\
abla\cdot(\alpha\mathbf{u})=\alpha\
abla\cdot \mathbf{u}, \tag{3}\]  
where the right-hand-side is referred to as the dilatation term (compression and expansion of the tracked phase). Moreover, surface tension is included as a volumetric force as per the continuum surface force (CSF) method [4] as  
\[\mathbf{f}_{\sigma}=\sigma\kappa\delta_{s}\mathbf{n}\approx\sigma\kappa\
abla\alpha, \tag{4}\]  
with \(\delta_{s}\) the Dirac surface function, \(\mathbf{n}\) the interface-normal, \(\sigma\) the surface tension coefficient, and \(\kappa\) the interface curvature.  
The density \(\rho\) is volumetrically weighted by  
\[\rho=\alpha\rho_{1}+(1-\alpha)\rho_{2}, \tag{5}\]  
with subscripts denoting the liquid and gas, respectively.  
The total energy \(\rho E\) is expressed as the sum of the kinetic and internal energy \(\rho e\):  
\[\rho E=\frac{1}{2}\rho\mathbf{u}\cdot\mathbf{u}+\rho e, \tag{6}\]  
where \(\rho e\) is given by  
\[\rho e=\alpha\rho_{1}e_{1}+(1-\alpha)\rho_{2}e_{2}, \tag{7}\]  
and the internal energy is computed using the stiffened gas equation of state (EOS).  
Finally, normal to the interface, this system is hyperbolic where there are three unique and real eigenvalues (see Appendix B), \(\lambda_{1}=\mathbf{u}\cdot\mathbf{n}-c\), \(\lambda_{2}=\mathbf{u}\cdot\mathbf{n}\) and \(\lambda_{3}=\mathbf{u}\cdot\mathbf{n}+c\) with \(c\) denoting the acoustic velocity. The next subsection details the closure (EOS) for the above system.
###### Abstract
However, these methods are not designed for vowel reduction detection. Since vowel reduction is not included in the pronunciation prior knowledge, the model needs to adjust the degree of dependence on auxiliary information dynamically. In other words, it is expected to use auxiliary information to determine possible vowel reduction positions, and at the same time, to detect whether there is vowel reduction in acoustic information. There are some methods for fusing multi-stream input, such as the direct merging of high-dimensional feature vectors and the hierarchical attention dynamic fusion method used by [20; 21]. However, these methods treat different inputs as independent streams. In our scenario, the prior articulation input and the acoustic signal input are related. Specifically, they are related in content but not completely corresponding in specific pronunciation. For example, there is no vowel reduction information in the pronunciation prior. This paper proposes a novel vowel reduction detection framework that uses prior pronunciation knowledge as auxiliary input. The innovations of our proposed work are as follows:  
* Among the methods for this task, it is the first to perform an E2E framework that is easy to integrate into widely used ASR-based CAPT systems;
* A method of adopting the auxiliary encoder to utilize the prior information of pronunciation is proposed and several prior types of pronunciation are designed;
* A multi-stream mechanism is proposed. It uses the attention mechanism to process the association between the speech signal and the prior knowledge of pronunciation and generates a fusion information stream, which is sent to the back-end with the original coded information streams. Taking advantage of the hierarchical attention mechanism, encoding streams are dynamically weighted and fused to highlight the more informative streams. In the experimental part, we compare our proposed method with the two baselines and the CNN-RNN-CTC method [22]. Then, the effects of the auxiliary encoder and the multi-stream expansion method are analyzed. We also studied the impact of different auxiliary input types on performance. The structure of our paper is designed as follows: Section 2 describes the methods, Section 3 presents the experimental setup, Section 4 discusses the experimental results, Section 5 concludes.
## 3 Asset Localization
### WiFi and Bluetooth Low Energy  
The WiFi standard (IEEE 802.11) provides broadband communication technology, which is widely used in commercial and industrial areas. Without special hardware, WiFi localization solutions usually use fingerprinting techniques. To improve localization accuracy, a variety of trained machine learning models are used. The study in [86] compares the baseline kNN solution to the SVM and Random Forest techniques, while [87] integrated decision trees and naive Bayes methods to the comparison. The papers [88,89] use dense neural networks to learn fingerprint and localization mapping, while [90] utilizes denoising autoencoders to augment the received fingerprints and estimate the accurate location of the asset.  
Bluetooth Low Energy (BLE) provides low-range, low-speed communication using low energy, and is widely used in a variety of areas. The basic localization technique in case of BLE is fingerprinting, and the same methods can be applied to improve the accuracy as in the WiFi case. However, there are works specifically related to BLE. Ref. [91] applies a special data augmentation process to train and evaluate random forest, XGBoost, decision tree and kNN-based algorithms for inferring the position from the RSSI measurements. The augmentation process helps learners to handle RSSI measurements with high fluctuation, thereby providing more accurate predictions. Ref. [92] is interesting as it introduces the applicability of the well-known LDA (Linear Discriminant Analysis) algorithm in fingerprinting. The work compares the LDA to naive Bayes, kNN and SVM techniques, showing an improvement in localization accuracy with a satisfactory execution time. A couple of other fingerprinting-based works can be found [93,94], and the study provided in [95] overviews the methods of fingerprinting in cases of BLE. Since the creation of Bluetooth 5.1, the BLE standard has made direction-of-arrival (DoA) methods available by using antenna arrays. Using the BLE DoA feature, [96] applies a tiny neural network applicable to a constrained device to replace the famous MUSIC (Multiple Signal Classification) algorithm when finding the signal direction.
# Biometric Authentication and Verification for Medical Cyber Physical Systems
## 5 Location Recognition  
It is necessary to discuss the different techniques that help to verify that the correct person is wearing a sensor, specifically biometric and cryptographic. Biometric authentication is the most popular option, with techniques that are capable of implementing an automatic verification of an individual identification by their physiological and behavioral characteristics. These characteristics can be used to measure the physiological or behavioral human patterns. In order to use the biometric techniques, there are several properties that must to be satisfied [16]:  
* Universal: Available to the entire population.
* Distinctive: It should be different between individuals.
* Permanent: It should remain unchanged for a period of time.
* Collectible: It means that the properties should be easy to collect and measure.
* Effective: Sufficient and stable for a period of time.
* Acceptable: The biometric system process has to be fast and accurate, have good memory storage and give a high performance with limited resources.
* Invulnerable: The biometric system should be hard to access or hacked by any fraud attempts.  
The WBAN security system has to ensure that the sensor is on the correct user's body before it can authorize for information transfer. Biometric techniques use the human body's physiological or behavioral characteristics as an authentication identity in order to ensure a high security of the distribution for the cipher key inside the WBAN communications. The detection, collection, and transmission of human body data in the WBAN is sensitive and must be secured. The biometric tools can be implemented to verify the person who is wearing the sensor. If the verification is completed, then it will transmit the data through the WBAN network.
## 5 Thinking modules
### Physical thinking modules  
Adaptive modulation and coding enable robust and spectrally-efficient transmission over time-varying channels. Basing on radio channel measurements, provided by the physical layers, properly combined with information related to the upper layers (in particular, the kind of services/contents to be carried), the innovative algorithms embedded in this module has to _jointly_ select the most proper frequency band, as well as dynamically adapt the modulation, coding, transmission power and data rate (some of these tasks will be performed in strict cooperation with the MAC-Physical cross-layer module), aiming at increasing the supported throughput and, at the same time, at enhancing the Link Availability (_i.e._ at meeting the desired user QoS and security requirements).  
Moreover, this module cooperates with the Access Selection module (see below), for the dynamic selection of the most appropriate access network; for this task, it needs, as input, aggregated metadata which consider the radio channels of all the candidate heterogeneous access networks.
# High Reliability Evaluation and Lifetime Prediction of 50 GHz Athermal AWG Module
## 4 Reliability Test Result for Outdoor Environments Application
Figure 9 shows the 96-channel average data of five AWG modules after the high-temperature storage reliability test at 85 \({}^{\circ}\)C for 2000 h. The center wavelength shift was within \(\pm\)0.060 nm, and the insertion loss variation was within \(\pm\)0.15 dB, which is stable for a long time under a high-temperature environment. Figure 10 shows the 96-channel average data of five AWG modules after the low-temperature storage reliability test at \(-\)40 \({}^{\circ}\)C for 2000 h. The center wavelength shift was within \(\pm\)0.030 nm, and the insertion loss variation was within \(\pm\)0.35 dB, which is stable for a long time under a low-temperature environment. The reliability test results of five items including vibration, temperature cycling, and high-temperature storage tests prove that the 96-channel (50 GHz-spacing) athermal AWG module is highly reliable without failure and ensures its reliability for long-term use in optical communication networks. Figure 9: All channel average result of module after high storage temperature test: (**a**) Center wavelength shift; (**b**) Insertion loss variation; (**c**) Spectrum after high storage temperature test (H.T 1).
# Multiparameter Fusion Decision Routing Algorithm for Energy-Constrained Wireless Sensor Networks
## 4 Simulation Results and Analysis
### Comparison Algorithms
#### 4.2.2 Recent Algorithm: Network Performance with the Numbers of Nodes  
From the perspective of the forwarding region of the next hop, the FAF-EBRP algorithm considers the ratio of the forward energy density of forwarding neighbors in all candidate next hops. Both the large number and the excellent energy condition of optional next hops can make the forward energy density relatively large; therefore, a large ratio indicates that it is more capable of undertaking the task of data forwarding. Second, for the quality of the forwarding communication link, the ratio of the forwarding link weight in all the alternative links is considered.  
The ECBR algorithm evaluates minimum energy consumption to find a path to the sink. This algorithm defines the energy cost with single-hop transmission energy and the node residual energy. The forward neighbor \(j\) is the forwarding node because it has the minimum sum of the energy cost from node \(i\) to it and the energy cost from it to the next hop \(h\).  
In summary, in addition to considering the residual energy, the ECBR algorithm considers the energy of transferring data to the candidate node. The FAF-EBRP algorithm  
Figure 4: (**a**) NL versus different number of nodes; (**b**) AE versus different number of nodes.  
and energy density and considers various factors comprehensively; therefore, it is more successful in balancing energy consumption and shortening the transmission distance. Hence, FAF-EBRP is more advantageous than ECBR in terms of NL, AE, and AH, but not PLR. Of course, the MPFDR algorithm comprehensively considers the remaining energy and forward distance, single-hop transmission energy consumption, queue cache, energy balance, and other indicators to calculate routing optimization parameters. Concurrently, combined with the defined routing strategy, the reliability coefficient of each parameter is dynamically adjusted according to the change of related indicators of the current node and its forward neighbor area. In other words, because each parameter can reflect a different state of the forward neighbor node, our algorithm can coordinate its own decision-making influence according to parameters. Hence, it provides the most effective routing algorithm to improve energy balance, mitigate node congestion, and improve transmission delay. The above results prove that the proposed MPFDR algorithm significantly outperforms FAF-EBRP and ECBR in all four types of network performance indicators.
# mmHSE: A Two-Stage Framework for Human Skeleton Estimation Using mmWave FMCW Radar Signals
## 4 Methodology  
We propose mmHSE, a two-stage framework for human skeleton estimation that leverages cooperative sensing from dual mmWave FMCW radars. As illustrated in Figure 5, the framework comprises four key components forming a complete end-to-end pipeline. First, a data augmentation module applies multi-dimensional enhancement strategies tai  
Figure 4: Radar data processing pipeline.  
lored to the characteristics of mmWave FMCW radar data, improving generalization across diverse environments. Second, a feature extraction module employs a hybrid architecture combining depthwise separable convolutions and self-attention to efficiently encode heterogeneous representations from the two radar inputs. Third, a cross-modal fusion and initialization module uses cross-attention mechanisms to integrate complementary features, producing an initial estimate of skeletal keypoints. Finally, a pose refinement module incorporates human kinematic priors via a graph-based optimization network to enhance both the accuracy and anatomical plausibility of the final skeleton output.
# The Contact Phase in Vehicle-Pedestrian Accident Reconstruction
## 1 Introduction  
Traffic accidents in which vulnerable participants such as cyclists and pedestrians are involved have been studied over time, first with the aim of reconstructing the events, followed by the establishment of empirical or mathematical models which take into account a series of factors regarding the throwing distance of pedestrians. In the specialized literature, extensive studies have been published by many authors, starting from the 1970s. An objective of these analyses consists in determining the throw distance of the pedestrian. In [1; 2; 3; 4; 5], various formulas for determining these distances were proposed depending on various factors, such as the impact speed, the angle of throw from the hood, the height of the center of mass of the pedestrian, the coefficient of friction between the pedestrian and the ground, the mass of the vehicle and the pedestrian, and the deceleration of the vehicle at the time of impact. In [6; 7], three phases of vehicle-pedestrian impact were identified by [PERSON], Hait and Limpert. These are the contact phase, the flight phase (throwing through the air) and the rolling/sliding phase on the ground. According to pedestrian dynamics, five categories of vehicle-pedestrian collisions are described in [8; 9]. These are wrap, forward projection, gender vault, roof vault and somersault collisions [10]. The contact phase, with the duration of the interval between the first impact and the secondary contact, was studied in 2001 by [PERSON] and [PERSON], who proposed the bilinear model regarding the distance traveled by the vehicle and the pedestrian in this stage. This model takes into account the position of the center of mass in relation to the vehicle. In [11], it is considered that this phase lasts approximately 0.2 s, during which it is approximated that the pedestrian travels a distance of 2 m without specifying exactly which element of the body detaches last from the vehicle. In [12], the authors break down the contact phase into two sub-phases, as shown in Figure 1. The first lasts from the initial impact until the pedestrian hits his head/chest against the windshield/hood (secondaryimpact); the second extends over the period that the pedestrian remains in contact with the car after the secondary impact until separation from the vehicle. The model takes into account the last element of the body that detaches from the vehicle, excluding the upper limbs of the pedestrian from the contact analysis. This phase of contact distance influences others; a simple model is presented in Figure 1. It is assumed that the impact between a vehicle and a pedestrian is plastic. In this case, from the conservation of momentum law, the distance that the pedestrian is in contact with vehicle was successively determined in [12].
## 3 Datasets and Analyses
### Analyses of User-Location-App Associations
**: _Users' interests are associated with locations characteristics and can be associated with app usage records._  
To achieve effective information aggregation and representation learning, we propose a representation model via a graph convolutional network to effectively aggregate user-app-location associations from the attributed graph.
# A Proposal for IoT Dynamic Routes Selection Based on Contextual Information
## 4 Performance Assessment and Results
### Statistical Data Analysis  
To indicate the dispersion or variability of the data in terms relative to its mean value, it was used as a measure of dispersion the CVP (Pearson Variation Coefficient) with confidence degree of 95%. The CVP was used because it is a relative measure of variability and it is independent of the unit of measure used, where the observed data unit may be different and its value will not change. The CVP is given by the relation between the standard deviation and the mean referring to the data of the same sample.  
\[\text{CVP}=\frac{\sigma}{X}\cdot 100 \tag{17}\]  
where:  
* CVP is the Pearson Coefficient of Variation
* \(\sigma\) is the standard deviation of the series data
* \(\overline{X}\) is the average of the series data  
For the energy consumed metric, Table 3 shows that DQCA-OF4 presented lower value in the coefficient of variation with 2.15% when compared to DQCA-OF4 with the fuzzy system (8.64%), OF-FL (11.06%), ERAOF (13.89%), MRHOF (19.04%) and OF0 (29.92%). This shows that DQCA-OF4 data, in the metric energy consumed, are more homogeneous when compared to the data of the other objective functions, representing low dispersion around the mean.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Objective Functions** & **Average (J)** & **Standard Deviation (J)** & **Coef. Var. Pearson** \\ \hline DQCA-OF4 & 174 & 3 & 2.15\% \\ DQCA-OF4 (LF) & 151 & 13 & 8.64\% \\ OF-FL & 145 & 16 & 11.06\% \\ ERAOF & 132 & 18 & 13.89\% \\ MRHOF & 126 & 24 & 19.04\% \\ OF0 & 109 & 32 & 29.92\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical Analysis of Energy Consumption.  
Figure 7: Quality of Service (QoS).  
For delay metric, Table 4 shows that DQCA-OF4 and DQCA-OF4(FL) presented similar dispersion represented by CVP. It is possible to observe that the four functions have similar stability. However, analyzing the mean, the DQCA-OF4(FL) was more efficient (see the value 11 in Table 4) when compared to the other objective functions. In this case, the lower the average, the lower the delay. The low standard deviation (shown in Tables 3 and 4) indicates that the data points tend to be close to the average, allowing variability to be expressed between the data.