esa-sceva/satcom-chunk-collection huggingface dataset:
# Resource Allocation of UAV-Assisted IoT Node Secure Communication System
## 3 Proposed Optimization Algorithm
### Optimal Transmit Power of UAV  
With the given feasible UAV trajectory \(\mathbf{Q}\) and PSR, the UAV transmit power optimization problem \(P_{1,1}\) is given as  
\[\begin{split} P_{2}:\max_{\mathbf{P}}&\sum_{n=1}^{N }\left[R_{SD}[n]-\max_{k\in K}R_{SE,k}[n]\right]\\ &\text{s.t. }\text{C1},\text{C4},\text{C5},\text{C6}\text{ and }\text{C8}\end{split} \tag{10}\]  
Let \(a_{n}=\frac{\xi[n]\tilde{\beta}}{\delta^{2}\left(\left\|\mathbf{q}[n]-\mathbf{ w}_{SD}\right\|^{2}+H^{2}\right)^{\frac{1}{2}}}\), \(b_{n,k}=\frac{\xi[n]\tilde{\beta}}{\delta^{2}\left(\left\|\mathbf{q}[n]- \mathbf{w}_{SE,k}\right\|^{2}+H^{2}\right)^{\frac{1}{2}}}\). Then, the objective function is simplified to  
\[R_{\text{sec}}[n]=\log_{2}(1+a_{n}p[n])-\log_{2}(1+b_{n,k^{*}}p[n]) \tag{11}\]  
where \(b_{n,k^{*}}=\arg\max_{k\in K}b_{n,k}\). Obviously, if \(b_{n,k^{*}}>a_{n}\), it is necessary to let the optimized power \(p^{opt}[n]=0\) to prevent information leakage. Additionally, if \(b_{n,k^{*}}\leq a_{n}\), the information sent by the UAV to \(DN\) will not be decoded. Therefore, \(P_{2}\) becomes a convex optimization problem, which satisfies [PERSON]'s condition and could be solved by the Lagrange duality method. Then the solution of \(P_{2}\) is given as  
\[p_{\text{opt}}\left[n\right]=\begin{cases}\min(p^{*}[n],P_{\text{max}})&a_{n} \geq b_{n,k^{*}}\\ 0&a_{n}<b_{n,k^{*}}\end{cases} \tag{12}\]  
The closed-form expression of the optimized power is  
\[p^{*}[n]=\sqrt{\left(\frac{1}{2b_{n,k^{*}}}-\frac{1}{2a_{n}}\right)^{2}+\frac {1}{\ln 2r_{n,k^{*}}}\left(\frac{1}{b_{n,k^{*}}}-\frac{1}{a_{n}}\right)}-\left( \frac{1}{2b_{n,k^{*}}}+\frac{1}{2a_{n}}\right) \tag{13}\]  
where \(r_{n,k^{*}}=-\frac{\eta}{N_{0}^{2}[n]}(1-\xi[n])\delta^{2}(\omega_{n}a_{n}+f_ {n,k^{*}}b_{n,k^{*}})\frac{\mu_{n}}{N}+\frac{\lambda_{n,k^{*}}\eta b_{n,k^{*} }\delta^{2}}{N}\), and \(\mu_{n}\geq 0\), \(\omega_{n}\geq 0\), \(\lambda_{n,k^{*}}\geq 0\), \(f_{n,k^{*}}\geq 0\) are the the Lagrange multipliers associated with the constraints C5, C6, C8, and C1, respectively. Finally, we update the Lagrange multipliers with the subgradient method as follows: y  
\[\begin{split}\lambda_{k}(i)&=\left[\lambda_{k}(i-1 )-\tau_{1}\left(E_{sen}-\frac{1}{N}\sum_{n=1}^{N}E_{IT,k}[n]\right)\right]^{+},\forall k\\ \mu(i)&=\left[\mu(i-1)-\tau_{2}\left(\overline{P}- \frac{1}{N}\sum_{n=1}^{N}p[n]\right)\right]^{+}\\ \omega(i)&=\left[\omega(i-1)-\tau_{3}\left(\frac{1}{ N}\sum_{n=1}^{N}E_{SD}[n]-E_{\text{min}}\right)\right]^{+}\\ f(i)_{k}&=\left[f_{k}(i-1)-\tau_{4}\left(\frac{1}{ N}\sum_{n=1}^{N}E_{EH,k}[n]-E_{\text{min}}\right)\right]^{+},\forall k \end{split} \tag{14}\]
## 5 Application to a Specific Case Study: ITER Divertor Remote Handling System
### ITER Environment
Virtual reality (VR): The VR software GUIs at ITER RH possess the capability to receive external commands via an interface port. Certain commands trigger script files, referred to as VR internal commands, which enable adjustments to the VR configuration and display. These script files are pre-programmed within the VR models and are also stored within them. 4. Input devices: Comprising a joystick and haptic arms. The area where the haptic arms are usually located will be used for exoskeleton/VR and new technologies. 5. Operation management systems (OMSs): The OMS software tools are a component of the RH software suite within the high-level control system (HLCS). It establishes communication with various other components of the HLCS, primarily situated within or near the remote handling control room (RHCR). The primary goal of the OMS involves the planning and execution of task procedures. These procedures involve the entire sequence of manual actions necessary to carry out tasks; in the end, the OMS tool essentially drives the remote handling process. 6. The RH database (RHDB): This constitutes an integral component of the OMS, along with the OMS task builder and the OMS task executor. It encompasses a comprehensive range of RH task information, covering the following: * Super-tasks, sub-tasks, methods, and steps. * Instances of RH tools and RH equipment (cross-referenced within super-tasks, sub-tasks, methods, and steps). * Associations with external multimedia files (referenced within super-tasks, sub-tasks, methods, and steps). * Records of task logs (inclusive of annotations and links generated during task execution). * Profiles of RH users and OMS login credentials, with varying permission tiers. 7. Mixed reality: Designs an interactive experience in which the sensor readings are combined with HMI-generated content. The content can span multiple sensory modalities, including visual, auditory, haptic, somatosensory, and olfactory. Moreover, other techniques, such as augmented reality, are designed as a system that incorporates three basic features: a combination of real and virtual worlds, real-time interaction, and accurate 3D registration of virtual and real objects. 8. Data analysis with artificial intelligence: Incorporating user perception and operator fatigue into the application model operation involves integrating computer vision techniques to effectively direct the operator's attention to the ongoing tasks. Additionally, to enhance configurability and usability for the operator, artificial intelligence (AI) is integrated into the general application. This requires further investigation into determining optimal operator inputs to train the AI model, including factors such as work hours, RH device activity, task development duration, and more. All these functionalities will be performed under the operator's supervision, allowing manual intervention at any time to restore the system to a safe state.
## 2 Data Description  
### Solar-Terrestrial Space Environmental Parameters  
The OMNI database is a standardized collection of data on the space environment around the Earth, including solar activity and geomagnetic measurements [39; 40]. The database was compiled from measurements made by multiple satellites, including ACE, WIND, IMP-8, Geotail, and ISEE-3 [40; 41]. The database is available in two parts, Low-Resolution OMNI (LRO) and High-Resolution OMNI (HRO), with a high level of reliability and temporal resolution that can reach up to 1 min [42; 43]. In this study, we used hourly data from the OMNI2 database for four key parameters of the space environment around the Earth during 18-23 November 2003, including the solar wind speed (V\({}_{\mathrm{sm}}\)), the north-south component of the interplanetary magnetic field (IMF B\({}_{2}\)), the magnetic storm index (Dst index), and the auroral electrojet index (AE index). Detailed descriptions of the dataset and download instructions can be found at [[https://omniweb.gsfc.nasa.gov/omniweb](https://omniweb.gsfc.nasa.gov/omniweb)]([https://omniweb.gsfc.nasa.gov/omniweb](https://omniweb.gsfc.nasa.gov/omniweb)) (accessed on 27 August 2003).
# Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery
We then use that CNN to estimate coconut canopy densities both before and after the passage of typhoon Goni. By analysing differences between the two maps we quantify the number of trees that were damaged by the typhoon. As expected, our results show that the largest damages have occurred near the location where typhoon Goni first made landfall. We also demonstrate that our method, based on supervised, class-specific tree densities produces more plausible maps than alternative methods based on unsupervised change detection. Furthermore, we validate our method in selected regions with very high resolution (VHR) images, where individual trees are discernible and the presence or absence of damages can be visually ascertained. This remainder paper is organised as follows. In Section 2 we discuss some related work regarding disaster response and change detection methods for remote sensing applications. In Section 3 we introduce the dataset used in this work and describe in detail the area of interest. In Section 4 we introduce the proposed active learning method for change detection as well as several baselines used for comparison. In Section 5 we evaluate the performance of the proposed method compare it with other baselines. In Section 6 we discuss the strengths as well as the limitations of the proposed method. Finally, conclusions are provided in Section 7.
# Unsupervised Offline Changepoint Detection Ensembles
## 2 Materials and Methods
### Numerical Experiment
#### 2.2.1 Benchmarks  
Tennessee Eastman Process (TEP) Benchmark  
The original paper [30] presented a model of the industrial chemical process named Tennessee Eastman Process and model-based TEP simulator for data generation. The most widely used benchmark based on TEP is presented in [31]. The benchmark consists of 22 datasets, 21 of which (Fault 1-21) contain faults and 1 (Fault 0) is a fault-free. All datasets have training (500 samples) and testing (960 samples) parts: training part has healthy state observations, testing part begins right after training, and contains faults which appear after 8 h since the training part. Each dataset has 52 features or observation variables with a 3 min sampling rate for most of all. Description of the process and related aspects, graphical diagram of the TEP, simulation code, benchmark simulation conditions, benchmark downloading links are presented in [31] and references therein. We do not use the Fault 0 (fault-free) dataset in our experiment due to the unsupervised manner of our approaches. The example of the time series representing a single feature in the dataset is shown in Figure 3. It is worth noting that we applied Z-normalization to the data before the experiment running because of the different scales of the separate features.  
Skoltech Anomaly Benchmark (SKAB)  
SKAB [32] is designed for evaluating algorithms for anomaly detection. SKAB includes 34 datasets and Python modules for algorithms' evaluation. Each dataset represents a multivariate time series collected from the sensors installed on the testbed. All instances are labeled for evaluating the results of solving both outlier and changepoint detection problems. The data and detailed description of the SKAB are available at [[https://github.com/waico/SKAB](https://github.com/waico/SKAB)]([https://github.com/waico/SKAB](https://github.com/waico/SKAB)) (accessed on 8 May 2021). For SKAB, we have also used the Z-normalization procedure because the data are differently scaled as well.
# Research on Safety Prediction of Sector Traffic Operation Based on a Long Short Term Memory Model
## 5 Conclusions  
In this paper, we address the problem of conflict prediction in a sector. Different prediction models are proposed based on the data set of conflicts that is extracted from historical ADS-B data.  
This paper makes two contributions to the field of traffic safety prediction in aviation. Firstly, LSTM prediction models are proposed for forecasting the number of conflicts, and SVR prediction model and RR prediction model are given for the purpose of comparison. The results show that LSTM model does have comparatively higher practical value for conflict prediction, which could capture the variation and the uncertainty of the traffic conflicts in the en-route airspace. Secondly, traffic basic parameters are added into the LSTM prediction model as new input variables with the purpose of the potential improvements for model performance and comprehensive analysis of the relative predictive power of  
Figure 15: Values of Correlation coefficient with time delay.  
traffic parameters. Thus, a more accurate prediction model for conflicts forecasting is achieved with the addition of all traffic parameters, and the ranking results of traffic parameters according to their contributions in conflicts forecasting are presented.  
All proposed methods are applied to a real data set of AR01 airspace in Guangzhou. The prediction results show that the LSTM model has the highest prediction accuracy under limited input conditions, with an MAE of 0.3901. With the addition of traffic parameters, the improved prediction model shows an acceptable result with error reduced by 3% and stronger stability. Among all the models with single traffic parameter, we found that volume is a key factor in improving prediction accuracy and the model with density shows a great advantage in improving model stability. Further steps will include detailed analysis for the relationship between other traffic parameters and safety issues under different time scales, as a variation in time scales will change the values of traffic parameter and traffic status.  
Conceptualization, W.L. and H.Z.; methodology, W.L. and L.Y.; software, W.L. and J.W.; validation, W.L. and J.W.; formal analysis, W.L.; investigation, L.Y.; resources, L.Y. and H.Z.; data curation, L.Y. and J.W.; writing--original draft preparation, W.L.; writing--review and editing, W.L., L.Y., and J.W.; visualization, W.L.; supervision, H.Z.; project administration, H.Z.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China under Grant No. 2018 YFE0208700.  
Not applicable.  
The data presented in this study cannot be shared because of the confidentiality issues.  
The author would like to extend their thanks to reviewers and editors for helping to improve this paper.  
The authors declare no conflict of interest.
## 3 The Model and Result
### [PERSON]'s Problem  
[PERSON] purchases the product when two conditions are satisfied simultaneously: First, [PERSON] incurs a non-negative surplus by purchasing the product (the surplus constraint). Second, in that moment, [PERSON] can afford the product (_i.e._, the price is not greater than [PERSON]'s budget, the budget constraint). The friend's initial valuation of the product is denoted as \(v\). [PERSON]'s surplus from purchasing the product (denoted by \(u_{R}\) ) is then given by \(u_{R}=v+I-p\)[14]. Thus, the surplus constraint is  
\[v+I-p\geq 0 \tag{1}\]  
The budget constraint is  
\[p\leq c \tag{2}\]  
Combining both constraints indicates that \(p\leq\min[c,v+I]\) is required for a successful referral. However, Friend R's budget information \(c\) is private. This information structure adds two layers of complexity for the firm in designing RRPs: the firm must provide sufficient incentive for [PERSON] to induce a larger \(I\); nevertheless, the firm must remain careful about pricing, because both Friend R and [PERSON] are basing their decisions on the market price.  
### [PERSON]'s Problem  
In our model setup, [PERSON] serves as a sales agent in disseminating the product information to Friend R. Unable to observe Friend R's budget information, [PERSON] instead uses a Bernoulli distribution to estimate it. We assume that [PERSON] believes that Friend R has a budget of \(H\) with the probability \(q\) (\(q\in[0,1]\)) and a budget of \(L\) (\(L<H\)) with the probability \(1-q\)[1]. Observing the market price \(p\), [PERSON] forms his or her own belief regarding whether [PERSON] will purchase the product. Denote [PERSON]'s action as the binary variable \(\delta\) and as \(\delta=1(=0)\) when he or she chooses (not) to adopt. Then, the estimation of \(Pr(\delta=1)\) by [PERSON] is given by  
\[Pr(\delta=1)=Pr(p\leq c)\cdot\chi \tag{3}\]  
in which  
\[Pr(p\leq c)=\begin{cases}0,p\geq H\\ q,p\in[L,H),\chi=\begin{cases}0,p\leq v+I\\ 1,p>v+I\end{cases}\end{cases} \tag{4}\]  
where \(\chi\) is an indicator of whether Equation (1) holds.  
Given the estimation in Equation (3), a risk-neutral customer's expected surplus of the referral (denoted by \(u_{S}\)) can be computed by \(E(u_{S})=Pr(\delta=1)\cdot B-\beta I\), where \(B\) denotes the referral reward from the firm and \(\beta I\) denotes the cost associated with the efforts of Customer S to increase the product value \(I\) for Friend R. We consider \(\beta\in[0,1]\) to focus on a reasonable range of efficiency in the communication between Customer S and Friend R [14]. The problem for Customer S, therefore, is to find the optimal \(I\) to maximize \(E(u_{S})\).  
\[\max_{I}E(u_{S})=Pr(p\leq c)\cdot\chi B-\beta I, \tag{5}\]_Future Internet_**2015**, 7**  
\[\text{s.t.}\;Pr(p\leq c)=\begin{cases}0,p\geq H\\ q,p\in[L,H),\chi=\begin{cases}0,p\leq v+l\\ 1,p>v+l\end{cases}\text{, and }I\geq 0.\end{cases} \tag{6}\]
# Modelling and Measuring Trust in Human-Robot Collaboration
## 4 Results
### Factors Affecting Dispositional Trust  
We utilized information from a concise affinity survey during the participants' orientation to identify potential factors influencing their natural inclination to trust in a human-robot environment. These data were collected before any interaction with the designed experiment, ensuring they remain unaffected by procedural biases in the experiment and reflect the individuals' intrinsic perspectives on trust in robot collaboration.  
Additionally, each participant provided demographic information, including gender, age, and the technological intensity of their work role, along with a self-assessment of their trust in robots. At this stage, we investigated potential relationships between these variables and the ex-ante self-assessed trust in robots.  
Figure 4 presents descriptive charts (histogram, probability density function--PDF--, cumulative distribution function--CDF--, and boxplot chart) illustrating dispositional trust across three dimensions of interest: gender (male versus female), age (juniors--younger than 40 years old--versus seniors--older than 40 years old), and work profile (highly technical versus non-technical).  
Figure 4 highlights differences among the analyzed demographic groups. For instance, both the histogram and PDF reveal a noteworthy peak at the 70-80% trust level for females, and a similar peak is observed for juniors. This pattern is absent in their respective counterparts (males and seniors). The boxplot chart also indicates that senior individuals exhibit a slightly superior mean trust level compared to their younger counterparts.  
To validate these perceptions, and considering the non-normal distribution of data, we conducted a Mann-Whitney U test to check for disparities in the data distribution of the different gender, age, and role collectives. Table 2 presents the results of the tests, indicating that, while differences may exist, demographics alone are not significant enough to explain such disparities.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Demographic** & **Segments** & **Statistic** & **Test** & \(p\)**-Value** \\ \hline Gender & Female (X0)—Male (X1) & 322.0 & X0 \textgreater{} X1 & 0.5265 \\  & & X0 \textless{} X1 & 0.4810 \\ \hline Age & Junior (X0)\(-\)Senior (X1) & 247.5 & X0 \textgreater{} X1 & 0.8685 \\  & & X0 \textless{} X1 & 0.1357 \\ \hline Role & Non-Technical (X0)\(-\)Technical (X1) & 372.0 & X0 \textgreater{} X1 & 0.1570 \\  & & X0 \textless{} X1 & 0.8475 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the Mann–Whitney U-test for dispositional trust between different demographics.
# Development Trend of Electronic Nose Technology in Closed Cabins Gas Detection: A Review
## 4 Application of Electronic Nose in Gas Detection of Closed Cabins
### Gas Detection in Aircraft Cabin  
The gas composition in the aircraft is complex, and both hazardous gases and warning gases need to be accurately detected. The volume of the aircraft is much larger than that of the vehicle, so the gas detection in the aircraft cabin needs to consider the location of the gas sensor. [PERSON], [PERSON] (2007) [26] used a computational fluid dynamics (CFD) program to investigate how to detect cabin air with a limited number of sensors, first obtaining the air velocity and air temperature distributions obtained from a two-aisle aircraft cockpit model and later validated the CFD program using experimental data of air pollutants simulated by example gases. By assuming different pollutant release rates and sensor sensitivities, it was found that the optimal location for the sensor was in the middle of the ceiling. Similarly, [PERSON] (2008) [27] used the commercial CFD program FLUENT to study the influence of seat type and pollution source location on the placement and response of pollutant detection sensors in the cabin of dual-aisle commercial aircraft. The results show that the number of sensors is linearly related to the detection speed. However, these two studies did not investigate the relationship between different cabins and sensor locations, and subsequently, [PERSON] (2021) [28] proposed a model to study the association between a large number of low-cost sensors and their possible mounting points, using a new continuous space cooperative positioning technique to locate the optimal sensor location, and improved the association with the likelihood ascent search algorithm.  
We should also be aware of the harmful gases in aircraft cabins. The presence of tricresyl phosphate (TCP) in the cabin and cabin air poses a serious threat to flight safety and the health of passengers in the cabin. [PERSON] (2013) [24] studied the application of poly (3,4-ethyldioxythiophene)-poly (styrene-sulfonate) (PEDOT:PSS) as an antifouling modification agent for glassy carbon electrodes in electrochemical sensing systems. The amphiphilicity of sodium polystyrene sulfonate (NaPSS) is helpful in reducing electrode contamination and can be used for the quantitative determination of gas TCP. The better application is in the aircraft cabin.  
As can be seen from the above review section, the application of an electronic nose in the aircraft cabin pays more attention to the placement of sensors.