esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Simulation Results
### Mesoscopic Analysis of the Similar Material  
#### 4.4.1 Mesoscopic Contact Force of the Similar Material  
In the discrete element analysis, particles are interconnected via mutual contacts, with local areas influencing the whole and reflecting various mechanical behaviours of mesoscopic material [28]. The contact force is represented by the line of centroid between particles, with a thicker line denoting a greater contact force.  
To clearly demonstrate the contact between fine and coarse particles, this analysis classifies the contact between particles into Green Type I, Red Type II, and Blue Type III, based on the particle size at both ends of the contact. Green Type I contact denotes the contact between coarse particles, Red Type II contact represents the interaction between fine and coarse particles, and Blue Type III contact symbolises the contact between fine particles. These classifications are illustrated in Figure 12.  
Figure 11: Relationship between \(\varphi\) and RFCA.  
Figure 12 displays the contact force distribution of the numerical specimen with varying RFCAs (\(H\) = 0.1, 0.2, and 0.7) under the uniaxial compressive stress of 0.5 MPa. At \(H\) = 0.1, an evident green contact network forms between the particles, with the interaction between the particles primarily governed by the Green Type I contact. The skeleton effect between coarse particles is clear. At \(H\) = 0.2, the number of Red Type II contacts and Blue Type III contacts incrementally increases. The fine particles begin to fill the spaces between the coarse particles, fragmenting the skeleton of coarse particles. Consequently, the skeleton effect between coarse particles lessens. With a further increase in \(H\), the number of Blue Type III contacts rises considerably. The skeleton effect between coarse particles continues to weaken as the material is predominantly filled by fine particles. At \(H\) = 0.7, the contact within the similar material is fundamentally controlled entirely by Blue Type III.
## 2 Materials and Methods
### Method of Analysis
RMSD is a statistical measure commonly used in data analysis and computational modelling to quantify the discrepancy or difference between two distributions of data points. It provides a robust estimate of the average deviation or dispersion between corresponding data points, from the two distributions \(\left(v_{i}^{a}\right)\) and \(\left(w_{i}^{b}\right)\), respectively (Equation (3)). \[\text{RMSD}=\sqrt{\frac{1}{N}\sum_{i}^{n}*\left(v_{i}^{a}-w_{i}^{b}\right)^{2}} \tag{3}\]  
For both metrics, \(v_{i}^{a}\) and \(w_{i}^{b}\) vary following the relationship investigated (MSI vs. OLI, MSI vs. ETM+, OLI vs. ETM+). In the last step, the coefficients obtained for data harmonization were compared with those proposed by [PERSON] et al. [40] for the NASA HLS product and by [PERSON] et al. [39] for the CONUS area. At the time of writing the paper, no harmonization coefficients between the MSI and ETM+ sensors have been released for the NASA HLS product. The Normalized Difference Vegetation Index (NDVI) for the test dataset pairwise was calculated aiming to compare the reflectance's value and assess the effectiveness of the correction. It is computed by normalizing the difference between near-infrared and red spectral reflectance for both, harmonized and non-harmonized data. Moreover, NDVI was calculated by using both uncorrected and corrected reflectance values obtained from the OLS and RMA regression models.
# Attention-Oriented CNN Method for Type 2 Diabetes Prediction
###### Abstract  
Diabetes is caused by insulin deficiency or impaired biological action, and long-term hyperglycemia leads to a variety of tissue damage and dysfunction. Therefore, the early prediction of diabetes and timely intervention and treatment are crucial. This paper proposes a robust framework for the prediction and diagnosis of type 2 diabetes (T2 DM) to aid in diabetes applications in clinical diagnosis. The data-preprocessing stage includes steps such as outlier removal, missing value filling, data standardization, and assigning class weights to ensure the quality and consistency of the data, thereby improving the performance and stability of the model. This experiment used the National Health and Nutrition Examination Survey (NHANES) dataset and the publicly available PIMA Indian dataset (PID). For T2 DM classification, we designed a convolutional neural network (CNN) and proposed a novel attention-oriented convolutional neural network (SECNN) through the channel attention mechanism. To optimize the hyperparameters of the model, we used grid search and K-fold cross-validation methods. In addition, we also comparatively analyzed various machine learning (ML) models such as support vector machine (SVM), logistic regression (LR), decision tree (DT), random forest (RF), and artificial neural network (ANN). Finally, we evaluated the performance of the model using performance evaluation metrics such as precision, recall, F1-Score, accuracy, and AUC. Experimental results show that the SECNN model has an accuracy of 94.12% on the NHANES dataset and an accuracy of 89.47% on the PIMA Indian dataset. SECNN models and CNN models show significant improvements in diabetes prediction performance compared to traditional ML models. The comparative analysis of the SECNN model and the CNN model has significantly improved performance, further verifying the advantages of introducing the channel attention mechanism. The robust diabetes prediction framework proposed in this article establishes an effective foundation for diabetes diagnosis and prediction, and has a positive impact on the development of health management and medical industries.  
diabetes; deep learning; convolutional neural network; machine learning; data preprocessing 2020
## 4 Results and Discussion
### Nodes Distribution  
We model 10-700 mobile nodes to evaluate adaptability in different densities. The node distribution, buffer space and transmission range are shown in Table 2. In this part, we ran 15 times with each simulation setting for each protocol.  
Figure 4 summarizes some key metrics of the simulation results with different density. The results of the delivery rate are shown in Figure 4a. Delivery rates of these three routing protocols are almost the same in low density. With the nodes increasing, our routing protocol gets higher delivery rates than others. Delivery delay means the time for messages to be transformed from source to destination. In our  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Parameter** & \multicolumn{3}{c}{**Value**} \\ \hline Messages & \multicolumn{2}{c}{Generating Interval} & \multicolumn{2}{c}{5 s} \\ \hline  & Grouping & Pedestrains & Automobiles & Trans \\ \cline{2-5}  & Buffer Space & 5 M & 5 M & 50 M \\ \cline{2-5} Nodes & \multirow{4}{*}{Distribution} & Setting1 & 6 & 3 & 3 \\  & & Setting2 & 16 & 8 & 6 \\  & & Setting3 & 30 & 15 & 12 \\  & & Setting4 & 80 & 40 & 24 \\  & & Setting5 & 160 & 80 & 30 \\  & & Setting6 & 260 & 130 & 39 \\  & & Setting7 & 400 & 200 & 45 \\ \hline Bluetooth & Transmission Range & 10 m & 10 m & 10 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters for node distribution.  
Figure 3: Performance comparison under different transmission ranges with two metrics: (**a**) Delivery Rate; (**b**) Overhead; (**c**) Dropped Packets.  
simulations, we take the average delivery delay as a metric of comparison to evaluate performance. In Figure 4b, we can find that Scheduling-PROPHET performs much better than others on average delivery delay at high density. But two traditional routing protocols show more advantages at low density. They use simplex and direct mechanisms to transfer messages, as described in Refs. [5; 7]. That is why they cannot perform well at low density. Overhead refers to how many messages are transmitted successfully in a time unit. As we describe in Section 3.3, we proposed two improved mechanisms to make our routing protocol more efficient. It is easy to see that the proposed routing protocol gets the best performance on overhead and packet drop rate, as shown in Figure 4c,e. It benefits from keeping high delivery predictability. Moreover, Scheduling-PROPHET takes the hop count into consideration, and chooses the shortest path to transmit messages. The result in Figure 4d is validating the effectiveness as Figure 4c.
# Gaze Estimation Based on a Multi-Stream Adaptive Feature Fusion Network
## 4 Experiments
### Results Analysis  
In this section, we further analyze the experimental performance of MSMI-Net on the EyeDiap, MPIIFaceGaze, and Gaze360 datasets and validate the effectiveness of different stream designs and individual modules through ablation experiments. By comparing with other related methods, we further demonstrate the rationality and optimization of the stream design. Specifically, MSMI-Net achieved the best performance on the MPIIFaceGaze dataset and also showed significant performance improvement on the EyeDiap dataset. However, although our network performed excellently on the EyeDiap dataset, it still did not reach the optimal level. Additionally, the performance on the Gaze360 dataset showed a noticeable gap compared to the EyeDiap and MPIIFaceGaze datasets. A further analysis of the feature distributions across the three datasets, as shown in Figure 10, reveals that, compared to the MPIIFaceGaze and EyeDiap datasets, the head shift in the Gaze360 dataset is more significant. Particularly, on the Gaze360 dataset, our model shows a weaker ability to extract gaze features from images with large head shifts compared to images with small head shifts. This gap may stem from the network's limitations in extracting global contextual information, failing to fully capture subtle features under large head pose variations. Therefore, although our network performs well under small head shifts, there is still a need for further improvement in global feature extraction and the robustness of the model in large head shift tasks. This analysis provides valuable insights for future improvements in gaze estimation tasks, especially in enhancing the network's ability to extract features under large head movement.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **MPIIFaceGaze** \\ \hline Without JointCBAM & 4.67 \\ Without AWAM & 4.43 \\ MSMI-Net & 3.85 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation Study of the Fusion Stream Module.
# Technology Identification, Evaluation, Selection, and Optimization of a HALE Solar Aircraft
In addition, changes in climb time are calculated as a function of takeoff start time and aircraft moving distances due to westerlies [10]. An optimization process is very important in the conceptual design phase of a solar aircraft because it can enable maximum aircraft performance [10]. The response surface methodology (RSM) was used as the optimization process [11]. Furthermore, in this study, the technology identification, evaluation, and selection (TIES) method was employed to analyze the solar aircraft conceptual design and sensitivity. The TIES method provides a comprehensive and robust methodology for decision-making in the conceptual aircraft design phase. This method provides the ability to easily assess and balance the effects of various technologies without need of complicated, time-consuming mathematical formulations [12]. The variety of design parameters must be considered during the conceptual design phase of HALE solar aircraft. Therefore, it is necessary to predict and analyze the sensitivity of sizing and design feasibility of a HALE solar aircraft for changes in various combinations of design variables. There are many studies analyzing the possibility of long endurance flight that do not consider those various detailed design variable combinations. So, this study introduced the design of experiment (DOE) to obtain the maximum information from the minimum number of experiments. Using this statistical method, the design possibility of long endurance performance can be identified in advance, thereby the time and cost for the conceptual design of HALE solar aircraft can be saved. The purpose of this study is to present a methodology that can derive optimal design-independent variables and improve aircraft performance using a statistical approach. In this study, the configuration for performance evaluation was referenced to Zephyr S, and configuration and aerodynamic properties were derived using OpenVSP [13] and XFLR5 [14]. For modeling and simulation, Microsoft's Excel, SAS's statistical program JMP [15], and in-house solar aircraft design framework software were used. Design independent variables that have a great influence on design target variables were derived through design of experiment and screening test, and design feasibility and sensitivity between variables were analyzed through response surface method and Monte Carlo simulation. Finally, the optimized design-independent variables were derived using the desirability function of JMP. Based on the optimized design-independent variables, the performance of the newly designed solar aircraft were analyzed and compared with the reference aircraft.
# Robust Visual Localization with Dynamic Uncertainty Management in Omnidirectional SLAM
## 4 Robust Localization within Omnidirectional SLAM
### Observation Model  
The observation model provides measurements that express a motion transformation from the robot's pose to a specific landmark, in terms of SLAM nomenclature. In this approach, and similarly to the explanation introduced in Section 2, the motion transformation can be referred to the comparison between two images, in angular terms (\(\beta,\phi\)). Thus, the motion between two poses of the robot may be recovered in the same manner. Transferring this localization relation into the robot's reference system leads to the following notation, where \(z_{t,n}\) encodes such observation measurements, which already comprises all the presented contributions to come up with a robust localization proposal, as depicted in Section 3.  
\[z_{t,n}=\begin{bmatrix}\phi\\ \beta\end{bmatrix}=\begin{bmatrix}\arctan\left(\frac{y_{n}-y_{t}}{x_{n}-x_{t}} \right)-\theta_{t}\\ \theta_{n}-\theta_{t}\end{bmatrix} \tag{15}\]  
The terms in \(z_{t,n}\) correspond to those associated with the different poses of the observed views in the map, \(x_{n}\), and the current pose of the robot, \(x_{r}\). It is worth noting that such variables are iteratively re-estimated as part of the state vector of the system, \(x_{v}(t)\), in Equation (14).
## 3 Measurement Results
### Measurement Results  
The sine wave input and output and pulse wave input and output results for the buffer operation of the proposed multi-path operational amplifier are shown in Figure 10. The x-axis of (a) is 400-\(\upmu\)s scale, the y-axis is 200-mV scale, and the x-axis of (b) is 2-\(\upmu\)s scale, the y-axis is 50-mV scale. The red wave is the input and the blue wave is the output of the amplifier. Sinusoidal and pulse waves with an amplitude of 100 mV were applied using a 50-pF capacitive load. For the pulse response, the rise time was 1.15 \(\upmu\)s.  
The slew rate measurement results are shown in Figure 11. The red wave is the input and the blue wave is the output of the amplifier. The proposed multi-path operational amplifier exhibits a slew rate of 0.86 V/\(\upmu\)s when a 1 V amplitude pulse wave is applied.  
\[\text{Slew\_Rate}=\frac{\text{dV}_{\text{o}}}{\text{dt}}=\frac{1\text{V}}{1. 15\upmu\text{s}}=0.86\text{V}/\upmu \tag{14}\]  
Figure 10: Measurement results of the buffer operation: (**a**) input sine wave and (**b**) input pulse wave.  
Figure 9: Die photograph and measurement environment of the proposed operational amplifier.  
Figure 12 shows the open loop gain measurement result of the proposed multi-path operational amplifier. The proposed operational amplifier has a gain of 135 dB at 1 Hz and exhibits a first-order frequency response. The proposed operational amplifier has a gain of 50 dB at 10 kHz and a unit gain bandwidth (UGBW) of 3.16 MHz. Compared with the simulation results in Figure 7, similar values are measured.  
Figure 13 shows the common mode rejection ratio (CMRR) and power supply rejection ratio (PSRR) measurements of the proposed multi-path operational amplifier. The proposed operational amplifier has a CMRR of 125 dB at 1 Hz and a PSRR of 100 dB at 1 Hz. The minimum values of the CMRR and PSRR are 69.8 dB and 71.7 dB.  
Figure 11: Measurement results of the slew rate.  
Figure 12: Open loop gain measurement results of the proposed multi-path operational amplifier.  
Figure 13: Measurement results: (**a**) the common mode rejection ratio (CMRR); (**b**) power supply rejection ratio (PSRR).  
Figure 14 shows the results of the input referred offset measurement of the proposed multi-path amplifier. The input referred offset measurement shows that the average input offset is 1.99 \(\upmu\)V and peak-to-peak offset is 3.73 \(\upmu\)V.  
The noise measurement results of the proposed multi-path operational amplifier for a bandwidth of 0.5-200 Hz are shown in Figure 15. Figure 15a shows the input referred noise of the proposed operational amplifier. The measured input referred noise is 47.8 nV/\(\upmu\)Hz at 1 Hz and 10.8 nV/\(\upmu\)Hz at 200 Hz. Figure 15b shows a noise histogram at 200 Hz for 10 samples. The average value of the input referred noise at 200 Hz for the 10 samples is 11.8 nV/\(\upmu\)Hz.
# ISAC towards 6G Satellite-Terrestrial Communications: Principles, Status, and Prospects
## 2 The Development Status and Key Technology Of ISAC
### History of Development  
The development of ISAC originated in the last century. At first, radar and communication systems were two separate systems that developed independently and gradually moved towards integration with the evolution of 5G technology. In the 1860s, [PERSON] et al. [15] combined communication and radar sensing technology for the first time and used radar to transmit pulse code groups, in which part of the pulses in each group were used for information transmission. The authors of [16] proposed the first ISAC scheme using chirp signals. [PERSON] et al. [17; 18; 19] explored how to use orthogonal frequency division multiplexing (OFDM) for waveform parameter design in ISAC. [PERSON] et al. [6; 20; 21; 22] reviewed the overall development of ISAC in recent years. They introduced the process of ISAC from the stage of radar and communication evolving separately to the gradual integration between two systems, ultimately realizing ISAC. At present, universities, enterprises, and standardization organizations have actively participated in the research of ISAC technology, which greatly promotes the development of ISAC.
###### Abstract  
The main aim of this study was to evaluate the influence of heat and surface treatment on the physicochemical properties of samples produced using Direct Metal Sintering incremental technology from Ti64 ELI titanium powder. Two groups of samples were selected for the study: sandblasted and mechanically polished samples. Each group consisted of samples in the initial state and after heat treatment carried out at temperatures of 800 \({}^{\circ}\)C, 910 \({}^{\circ}\)C, and 1020 \({}^{\circ}\)C. The article presents the results of microscopic metallographic observations, wettability and surface topography, hardness, and resistance to pitting corrosion in [PERSON]'s solution, together with microscopic evaluation of the surfaces before and after testing. Based on the test results, both heat and surface treatments were found to alter the functional properties of the printed samples. All the tested samples show hydrophilic properties. Heat treatment at 1020 \({}^{\circ}\)C produces the best resistance to pitting corrosion. This information is important when selecting the mechanical properties of the biomaterial and the physicochemical properties of the surface for a specific type of stabilizer. The choice of appropriate heat treatment and surface treatment of the implant will also depend on the length of time the implant remains in the body.  
additive technology; Ti6 Al4V alloy; heat treatment; grinding and mechanical polishing; personalized implants Article
# Enhancing the 3 GPP V2X Architecture with Information-Centric Networking
## 8 Conclusions  
This paper incentivizes the debate about the adoption of NDN as a networking solution for 3 GPP V2X communications and discusses a set of extensions to make the best of the proposed integration, as summarized in Table 4. In addition to some straightforward enhancements needed to include NDN capabilities in the V2X entities, we propose to augment the eNodeB with the NDN forwarding  
Figure 4: Number of RBs per Interests vs. Interest packet size values, when varying the number of requests issued by the consumers and the Zipf’s parameter \(\alpha\) of our proposal against a baseline non-NDN approach: (**a**) UL (MCS = 11); and (**b**) DL (MCS = 8).  
Figure 5: Average Interest dissemination latency when varying the scheduling periodicity (\(T_{SR}\)): our proposal against a baseline non-NDN approach.  
fabric and data structures, and to extend the radio resources allocation of the eNodeB and make it name-aware.  
The results clearly show that, by letting the eNodeB take local forwarding decisions, the Interest dissemination latency can be significantly reduced. Moreover, the proposed name-based resource allocation succeeds in limiting the redundancy of Interest packets requesting the same named content, with inherent savings in terms of allocated radio resources. The 3 GPP V2X specifications are under progress at the time of writing and are expected to embrace upcoming 5G enhancements. Hence, we strongly argue about the adoption of NDN as a more general networking solution for 3 GPP-based 5G domains, especially in the presence of heterogeneous access technologies, e.g., IEEE 802.11 and cellular interfaces.  
M.A., C.C. and A.M. conceived the idea, organized the work and proposed the solutions. They also conceived and designed the experiments. M.A. and C.C. performed the experiments. All authors analyzed the experiments, and reviewed the writing of the paper, its structure and its intellectual content.  
Acknowledgements.: EURECOM acknowledges the support of its industrial members, namely, BMW Group, IABG, Monaco Telecom, Orange, SAP and Symantec. Conflicts of Interest: The authors declare no conflict of interest.
_Energies_**2014**, \(7\), 3576-3598; doi:10.3390/en7063576  
_energies_  
ISSN **1996-1073**  
www.mdpi.com/journal/energies  
_Article_  
**System for Road Vehicle Energy Optimization Using Real Time Road and Traffic Information**  
**[PERSON] *, [PERSON] and [PERSON]**  
University Institute for Automobile Research (INSIA), Technical University of Madrid (UPM);  
Madrid 28031, Spain; E-Mails: [EMAIL_ADDRESS] (W.C.-M.); [EMAIL_ADDRESS] (S.T.-F.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +34-913-365-300; Fax: +34-913-365-302.  
_Received: 28 March 2014; in revised form: 9 May 2014 / Accepted: 21 May 2014 / Published: 2 June 2014_  
**Abstract:** Nowadays, reducing the energy and fuel consumption of road vehicles is a key issue. Different strategies have been proposed. One of them is to promote Eco-driving behaviour among drivers. Most Eco-driving tips take into account only the road stretch where the vehicle is located. However, larger improvements could be achieved if information from subsequent stretches is used. The main objective of this work is to develop a system to warn the driver in real time of the optimal speed that should be maintained on every road segment in order to optimize the energy used and the fuel consumed while observing a time schedule. The system takes into account the road vertical profile, the fixed and variable speed limits and the traffic information retrieved using V2V and V2I communications. The system has been tested on real road sections with satisfactory results in fuel savings.  
**Keywords:** fuel consumption; quasi-static simulation; minimization; dynamic programming (DP); energy reduction; congestion; vehicle communications