esa-sceva/satcom-chunk-collection huggingface dataset:
# Error Analysis of Integrated Absorbance for TDLAS in a Nonuniform Flow Field
## 3 Results and Analysis
### Effect of the Line-Shape Function  
The analysis presented in Section 2 revealed that the line-shape function directly affects the fitting result. For the Voigt line shape, in addition to the mentioned 916 algorithm and Fadf algorithm approximations, the linear approximation method proposed by [PERSON] is widely used [37]. In the situation that the line damping parameter is too large or too small, researchers usually choose a Gaussian or Lorentz line shape to fit the absorption signal. Although there are many attempts of using different fitting algorithms used, the evaluation of fitting algorithms is beyond the scope of this work. We believe that the four methods above would well represent the influence of the fitting algorithm on the error. Figure 6 shows the calculated error distribution when using these four line-shape fitting functions for \(n=2\), \(A_{1}/A_{2}=1\), \(0.25\leqslant u\leqslant 4\), and \(0.1\leqslant y\leqslant 10\).  
Figure 6: Distribution of the absolute value of the IA error calculated using the four fitting functions: (**a**) Gaussian Fit, (**b**) Whiting Fit, (**c**) Fadf Fit, (**d**) Lorentz Fit.  
The error distributions in the four cloud images are basically symmetrical across the \(u=1\) axis. This symmetry arises because the two normalized coefficient ratios \(u\) and \(1/u\) are analytically equivalent when the IA ratio is 1. The calculation result supports the conclusion to the analysis in Section 2.2.3.  
The Fadf algorithm can be used as a representative of the Voigt line-shape high-precision approximation algorithm. The error is small (\(R<0.01\)) when the flow field is relatively uniform (\(u\to 1\)) and large (\(R>0.05\)) when the flow field is very uneven (\(u>2\) or \(u<0.5\)). Most of the errors obtained when using the Whiting approximation function, as a linear approximation method, are greater than 0.01, even in a uniform situation, and the error can exceed 0.05. The error obtained when using the numerical approximation algorithm is closer to that for the Voigt theoretical model than that for the linear approximation method.  
A comparison of the Gaussian, Lorentz, and Fadf fitting results reveals that in the area \(y<0.25\), the error generated by the Gaussian fitting is closer to but larger than the error generated by the Voigt fitting. In the area in which \(y>1\) and \(R>0.01\), the error generated by the Lorentz fitting is basically the same as that generated by the Voigt fitting, and there is no black noise on the Lorentz fitting error distribution graph. It is reasonably inferred that in this area, the fitting results obtained using the Lorentz function and the Voigt function should be consistent. The black noise point for the Fadf fit is due to the numerical approximation of the Voigt line-shape by the algorithm. So, the black noise point data for the Fadf fit can be replaced by the fitting result of the Lorentz function.
# Multi-Objective Optimization for EE-SE Tradeoff in Space-Air-Ground Internet of Things Networks
## 3 Problem Solution  
Problem \(\mathcal{P}_{1}\) has two objective functions, including energy efficiency and spectral efficiency. According to [24], because the two objective functions conflict with each other, the MOO problem is hard to solve. The authors in [25] introduced a multi-objective optimization framework for optimizing URLLC design in the presence of decoding complexity constraints. They solved the proposed multi-objective optimization problem through two scalarization methods. The purpose of scalarization is to transform the objective into a single objective function and simplify the problem into a constrained single objective optimization problem. We apply this method to the SAG-IoT network, using the \(\epsilon\)-constraint method to convert multi-objective optimization functions into single objective optimization functions. We retained energy efficiency as the objective function, transformed spectral efficiency into constraint conditions. Therefore, we obtain a single objective optimization problem, which can be described as  
\[\mathcal{P}_{2}: \max_{\mathbf{A},\mathbf{P}_{i},\mathbf{P}_{U},\mathbf{D}}\frac{ \sum_{u}r_{us}}{\sum_{i}p_{i}+\sum_{u}p_{u}+UP_{\text{h}}}\] (12) s.t. \[C1:a_{iu}\in\{0,1\},\quad\forall i,u,\] \[C2:\sum_{i}a_{iu}=K,\quad\forall u,\] \[C3:\sum_{u}a_{iu}\leq 1,\quad\forall i,\] \[C4:0\leq p_{i}\leq p_{\text{imax}},\quad\forall i,\] \[C5:0\leq p_{u}\leq p_{\text{umax}},\quad\forall u,\] \[C6:r_{iu}\geq r_{\text{min}},\quad\forall i,\] \[C7:\sum_{i}r_{iu}\geq r_{us},\quad\forall u,\] \[C8:||\mathbf{d}_{m}^{\text{U}}-\mathbf{d}_{n}^{\text{U}}||\geq d _{\text{min}},\quad\forall m,n\in\mathcal{U},m\
eq n,\] \[C9:\frac{\sum_{u}W\log_{2}\left(1+\frac{p_{u}S_{iu}}{WN_{1}} \right)}{U(W+BK)}\geq\epsilon,\]  
where \(\epsilon\) represents the coefficient of trade off between SE and EE.  
Although converted into a SOO problem, the problem is still difficult to solve because of the non-convex function in fractional form containing integer variables. To solve problem \(\mathcal{P}_{2}\), we can divide the problem into three sub-problems.
## 2 Materials and Methods
### Description of the Proposed Measurement Technique
#### 2.5.3 Extrapolation of the Graduation Characteristic and \"Dead Zones\" of the Algorithm  
A two-dimensional graduation characteristic is a field bound by values of \(d\) from 0 to \(\infty\) and \(\sigma\) from \(\sigma min\) to \(\sigma max\). It should be taken into consideration that in practice measurements outside the specified boundaries are not excluded:  
* Example 1. The measure \(\sigma max\) is taken for copper, \(\sigma\approx 58\) MS/m. If we measure \(d\) on a copper base at low temperature of the test object, the value of \(\sigma\) may exceed the \(\sigma max\).
* Example 2. As was mentioned before, in practice, the curvature of the surface of the test object is a non-excluded stray parameter. When graduating, we use only measures for flat surfaces. If measurements are taken on an object with a concave surface, the \(d\) readings decrease. With a small \(d\) value, the readings may be negative.  
However, such situations should not cause failures in the operation of the algorithm for mathematical processing of AP ECP signals. For this purpose, the graduation characteristic is extrapolated in the directions \(\sigma>\sigma max\), \(\sigma<\sigma min\) and \(d<0\) by using empirically selected polynomial extrapolation functions.  
Descriptions of methods for extrapolating the graduation characteristic and mathematical processing of the signal belonging to the \"dead zone\" of the algorithm are beyond the scope of this article.
# Fully Convolutional Neural Network with Augmented
## 3 Experimental Results, Analysis and Discussion
### Experiment Dataset  
The data set used in the experiment was derived from the \"CCF Satellite Image AI Classification and Recognition Competition\", which is a high-resolution remote sensing image of a region in southern China in 2015, and the data type is aerial imagery. The resolution of the image is sub-meter, which contains three bands of RGB, and the types of features are divided into five categories: background, vegetation, road, building and water body. An example of images in the dataset is shown in Figure 4a, the corresponding category diagram is shown in Figure 4b, and the annotation is given in Figure 4c. The size of the pictures from left to right is \(5664\times 5142\), \(7969\times 7939\) and \(3357\times 6116\), \(4096\times 4096\), respectively. The first three groups of the HRRS images from left to right were selected as the training set and the fourth group was selected as the test set.
# The Ethanol Oxidation Reaction Performance of Carbon-Supported PtRuRh Nanorods
## 4 Conclusions  
In this study, in order to prepare highly effective EOR catalysts, Pt binary and ternary catalysts such as PtRu, PtRh, and PtRuRh have been synthesized. The addition of Ru and/or Rh in Pt can effectively promote the anti-poisoning ability and help the desorptions of adsorbed intermediates, especially for PtRu. Moreover, EOR activities are enhanced significantly through Ru and/or Rh addition, especially ternary PtRuRh with the Pt/Ru ratio of 80/5/15, which both possess the highest I\({}_{06}\) and  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & ECSA\({}_{\rm H}\) & I\({}_{06}\) & I\({}_{\rm max}\) & SA\({}_{06}\) & SA\({}_{\rm max}\) & I\({}_{06\text{-}7200}\) & I\({}_{06\text{-}7200}\) \\  & **(m\({}^{2}\)/g(r\({}_{0}\)))** & \multicolumn{3}{c}{**(A/g(r\({}_{0}\)))**} & **(mA/cm\({}^{2}\)(ECSA\({}_{\rm H}\)))** & \multicolumn{3}{c}{} \\ \hline Pt & 30.8 & 6.6 & 178.8 & 0.15 & 3.57 & 0.83 & 4.1 \\ PtRu & 32.3 & 9.4 & 223.7 & 0.20 & 4.35 & 0.97 & 5.9 \\ PtRh & 33.0 & 10.9 & 214.3 & 0.24 & 4.24 & 1.06 & 5.2 \\ PtRuRh & 60.2 & 29.4 & 603.9 & 0.38 & 7.31 & 1.02 & 15.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: EOR performances of Pt, PtRu, PtRh, and PtRuRh catalysts.  
I06-7200. This can be attributed to the co-existence of RuO\({}_{\rm X}\) and RhO\({}_{\rm X}\) onto surface that not only helps the formations of CO\({}_{2}\) but also the removals of adsorbed intermediates, preventing Pt active sites from poisoning. Based on the above results, we can conclude that the addition of Ru and Rh into Pt catalysts can indeed enhance the activity and long-term performance of EOR. Therefore, carbon-supported PtRuRh NRs can be one of the most promising EOR catalysts.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/11/3923/s1](http://www.mdpi.com/2076-3417/10/11/3923/s1)]([http://www.mdpi.com/2076-3417/10/11/3923/s1](http://www.mdpi.com/2076-3417/10/11/3923/s1)), Figure S1: XRD patterns, Table S1: SEM-EDS and XRD results, Table S2: XPS results, Table S3: CO-stripping results, and Table S4: comparison of EOR activity.  
Conceptualization, T.-H.H. and Y.-R.H.; methodology, T.-H.H.; validation, D.B., and S.L.; writing--original draft preparation, T.-H.H. and Y.-R.H.; writing--review and editing, T.-H.H. and K.-W.W. supervision, K.-W.W.; project administration, K.-W.W.; funding acquisition, K.-W.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ministry of Science and Technology, R.O.C. (MOST 107-2628-E-008-003-MY3, MOST 108-3116-F-008-008, and MOST 108-3116-F-007-001. The authors like to thanks [PERSON] [PERSON] for the funding support (Ministry of Science and Technology, Taiwan (MOST 109-3116-F-007-001 and MOST 106-2112-M-007-016-MY3)).  
The authors declare no conflict of interest.
# Lossless and High-Throughput Congestion Control in Satellite-Based Cloud Platforms +
## 5 Experimental Results and Discussion  
In this section, the CCLF method is extensively evaluated in terms of flow completion time (FCT), network throughput, bandwidth utilization, and packet loss count. Experiments were conducted in both constant-bandwidth and dynamic-bandwidth scenarios to serve different evaluation purposes.
## 2 Basic Theory
### Friction Power  
In the mixed-lubrication regime, hydrodynamic lubrication effects had a significant impact on the friction coefficients. The total friction coefficient \(\mu_{a}\) is determined by combining the body contact friction coefficient \(\mu_{solid}\) with the microscopic EHD friction coefficient \(\mu_{EHL}\), which is expressed as follows:  
\[\mu_{a}=\mu_{solid}+\mu_{EHL}=\mu_{0}\cdot e^{-\sqrt{b/L_{N}}}+c\cdot\gamma_{ c}\cdot L_{N}\cdot(1-e^{-\sqrt{b/L_{N}}}) \tag{7}\]  
where \(\mu_{0}\) is the Coulomb friction coefficient; \(b\) and \(c\) are empirical values, taken as 10,000 and 100, respectively; \(L_{N}\) is the Sommerfeld number; and \(\gamma_{c}\) is the asperity contact ratio [35].  
Because the load is shared by the lubricant and asperities on the surfaces, friction power is also generated by the hydrodynamic shear stress \(\tau_{f}\) amid the fluid and the contact shear stress \(\tau_{a}\) from the metal-metal contact, which is calculated as follows:  
\[\tau_{h}=\eta\frac{\partial u}{\partial h} \tag{8}\]  
\[\tau_{a}=p_{a}\cdot\mu_{a} \tag{9}\]  
The total friction power is generated by the asperity contact shear stress and fluid shear stress in area \(A\) of the bearing bushing, as expressed by Equation (10).  
\[P_{f}=u\iint\limits_{A}(\tau_{h}+\tau_{a})\mathrm{d}x\;\mathrm{d}y \tag{10}\]
# InnCube--A Wireless Satellite Platform to Demonstrate Innovative Technologies
###### Abstract  
A new innovative satellite mission, the InnocubeSat for Education (InnoCube), is addressed. The goal of the mission is to demonstrate \"the wireless satellite\", which replaces the data harness by robust, high-speed, real-time, very short-range radio communications using the SKITH (SKIPTheHarness) technology. This will make InnoCube the first wireless satellite in history. Another technology demonstration is an experimental energy-storing satellite structure that was developed in the previous Wall#E project and might replace conventional battery technology in the future. As a further payload, the hardware for the concept of a software-based solution for receiving signals from Global Navigation Satellite Systems (GNSS) will be developed to enable precise position determination of the CubeSat. Aside from technical goals this work aims to be of use in the teaching of engineering skills and practical sustainable education of students, important technical and scientific publications, and the increase of university skills. This article gives an overview of the overall design of the InnoCube.  
CubeSat; wireless-bus; harness free satellite; satellite technology; CubeSat GNSS; laser ranging; structural battery; dependable software; Rodos +
Footnote †: journal: Aerospace
# Accelerated Simulation of Discrete Event Dynamic Systems via a Multi-Fidelity Modeling Framework +
## 4 Proposed Framework
### Evaluation Index for Simulation Speedup  
The simulation speed improvement obtained by applying the proposed multi-fidelity framework is defined as follows:  
\[Speedup=\frac{T_{High\ fidelity}}{T_{Multi\ fidelity}}=\frac{1}{1-ETR(1-O_{S} \sum_{i=1}^{n}a_{i}r_{i}-O_{E})} \tag{2}\]  
\(ETR\) is the percentage of execution time of the target model in the overall simulation execution time. \(O_{S}\) and \(O_{E}\) are the coefficients for the structure overhead and model exchange overhead in the selection model, respectively. The value of \(O_{S}\) is greater than 1, and \(O_{E}\) has a value between 0 and 1. For each model \(M_{i}\) in the multi-fidelity model, \(a_{i}\) is the ratio of execution time of \(M_{i}\) to the execution time of the target model, and \(r_{i}\) is the percentage at which \(M_{i}\) is simulated in the entire simulation scenario (\(\sum_{i=1}^{n}r_{i}=1\)). That is, \(a_{i}r_{i}\) implies the actual ratio of execution time of \(M_{i}\) in the multi-fidelity model. When \(M_{i}\) is the same as the target model, the value of \(a_{i}\) is 1, and \(r_{i}\) is the percentage of the interest region in the scenario. Figure 12 illustrates the speedup of the proposed framework and the notations in (2). In order to apply the proposed framework more effectively based on (2) (i.e., to maximize the speedup), we first have to select a model with a large \(ETR\) as a target model. This corresponds to the second of the two prerequisites in the first step of the target model selection in our framework. Using a variety of fidelity models through appropriate scenario partitioning can minimize \(\sum_{i=1}^{n}a_{i}r_{i}\), but can lead to increasing the structure overhead \(O_{S}\); thus, we have to reduce the overhead by simplifying the selection function _sf_. In addition, minimizing the frequency of model exchange can increase the speedup by reducing the overhead \(O_{E}\).  
Figure 12: Simulation speedup using the proposed multi-fidelity modeling framework.
# Device Management and Data Transport in IoT Networks Based on Visible Light Communication
## 4 Experimentation and Discussion
### Performance Analysis  
For performance analysis by experimentation, we measured the times required for the VLC data transport and VR handover operations. For each performance metric, 10 experiments are made in the same way, and the min., max., and average values are obtained, as shown in Figures 21 and 22.  
Figure 21 shows the times taken during VLC Data Transport operation. In the figure, the service data request from VR to AA takes 169 ms, maximum. The forwarding of the service data request from AA to PS takes 415 ms, maximum. Such delay includes the time required to make _contentInstance_ of each request. The service data response from PS to AA takes 13 ms, maximum, and service data response forwarding from AA to VR takes 5 ms, maximum. These delays are relatively small, compared to the previous two messages. This is because these two messages do not create _contentInstance_. The overall delay in receiving the service data is a maximum of 589 ms. In this experiment, we used a tiny piece of data. It may take a longer time if larger data is used.  
Figure 22 shows the times taken during the VR handover operation. The VR handover report from VR to AA takes 201 ms, maximum. This operation includes the time to create a HTTP message from VR to AA, and vice versa, and the _contentInstance_ of AA. On the other hand, the VR handover ACK from AA to VR takes 9 ms, maximum. The ACK operation does not create _contentInstance_. The overall handover delay for handover completion is 207 ms, maximum. From the figure, we can also see that the maximum value of handover delay will not exceed 207 ms, because it does not need data.  
Figure 19: Packets captured during the VLC Data Transport operation.  
Figure 20: Packets captured during VR Handover operation.
## Appendix C Predictive Model and Real-Time Data Integration
These notifications should be clear and timely, especially during periods of high flood risk. Messaging services like Twilio can be utilized to deliver SMS, emails, or push notifications. These messages must be tailored to individuals' risk levels and geographic locations, ensuring that critical information reaches the right people promptly. Collaboration with local authorities is vital for integrating the real-time forecasting system into emergency protocols. This includes establishing standard operating procedures (SOPs) for flood response. The SOPs must clearly define the roles and responsibilities of each entity involved, ensuring that everyone knows their specific tasks in the event of a flood. Additionally, SOPs should be regularly updated to reflect best practices and lessons learned from past events. Periodic audits and simulations of flood scenarios should be conducted to test the effectiveness of the SOPs and the alert system. These simulations help identify gaps in procedures and improve coordination among different agencies. Practical exercises must involve all levels of government and relief organizations to ensure that emergency response measures are coordinated and timely. Other important initiatives include training and informational activities for both authorities and the public on using the alert system and responding effectively to emergencies. Training sessions should include practical exercises based on potential flood scenarios. Support these training sessions with informational materials and how-to guides to help users understand the warning system and the safety measures to take in the event of flooding. Continuous feedback from users and local authorities is crucial for ongoing system improvement. This feedback is not just important, but it is also a way for everyone involved to be heard and feel involved in the process. It should be used to identify usability issues and enhance the alert system. Establishing a dedicated communication channel for feedback can facilitate the gathering of valuable information and encourage constructive dialog among all parties involved.
# A Trusted Routing Scheme Using Blockchain and Reinforcement Learning for Wireless Sensor Networks
## 4 Experimental Analysis and Evaluation
### Efficiency
#### 4.3.1 Token Transaction Latency  
We took the transaction packaging time as the evaluation element of the average token transaction latency, which records the elapsed time that miners put the Ethereum token transaction on the blockchain. We recorded the token transaction latency of PoA and PoW blockchain systems with the increase of arrival rate \(\lambda\).  
The experiment results are shown in Figure 8, we can see that the latency of the transaction is relatively stable and does not fluctuate much with the arrival rate \(\lambda\). The average transaction latency of our PoA blockchain system was around 0.29 ms while that of the PoW blockchain system was around 0.52 ms. The results show that our blockchain system based on PoA consensus mechanism can save about 44% of the transaction latency, and obviously, such a token transaction delay is acceptable and has little impact on routing scheduling. It is practical and efficient to use our PoA blockchain system to collect and manage routing scheduling information.  
Figure 7: Average delay of packets with 50% malicious nodes.
# The Basis for Reliability-Based Mechanical Properties of Structural Aluminium Alloys
## 3 Methodology for Data Analysis
### Criteria for the Validation of Appropriate Statistical Distribution  
Prior to conducting a statistical analysis of the mechanical properties of aluminium, material test data were divided into groups regarding relevant properties given in [15]: alloy, temper, product form, and thickness. After the data were divided, a manageable form of quantitative descriptions was established, and the susceptible outliers were discharged.  
Using Vose ModelRisk 6 [32], the results were processed and fitted to continuous distributions. To estimate distribution parameters, maximum likelihood estimation (MLE) was used as this method provides a consistent approach to parameter estimation problems, i.e., it can be developed for a large variety of estimation situations, including missing or censored data. In addition, MLE methods have desirable mathematical and optimality properties as they become minimum variance unbiased estimators as the sample size increases. The ranking of fit for distributions was performed using the [PERSON] information criterion (SIC), Akaike information criterion (AIC) and Hannan-Quinn information criterion (HQIC), and the model fit with the lowest value of deviance according to all three criteria was selected. Information criteria were used for goodness of fit as they penalise overfitting and consider the number of estimated parameters, unlike other ranking criteria.  
Figure 5: Distribution of obtained material test certificates (tensile test results) by tempers.  
Preference was given to lognormal and Weibull distributions as they are recommended for resistance variables in EN 1990 [18] and ISO 2394 [33].  
Cumulative function fractiles relevant for the design purposes (0.1% and 5%) were determined based on fitted distributions. It should be noted that 0.1% and 5% fractiles represent design and characteristic values, respectively. Furthermore, as an additional check, 0.1% and 5% cumulative function fractiles were determined based on distribution type and equations presented in EN 1990 [18], with necessary derivations for certain distributions.