esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Comparison between GPP\({}_{\text{OLCI-Fapar}}\) and GPP\({}_{\text{moddis-Fapar}}\)  
Figure 9 describes the temporal dynamics of GPP\({}_{\text{EC}}\), GPP\({}_{\text{MODIS-Fapar}}\), and GPP\({}_{\text{OLCI-Fapar}}\) at the seven sites during 2017 and 2018. Obviously, both GPP\({}_{\text{MODIS-Fapar}}\) and GPP\({}_{\text{OLCI-Fapar}}\) followed a similar variation trend observed in GPP\({}_{\text{EC}}\). Nevertheless, GPP\({}_{\text{MODIS-Fapar}}\) and GPP\({}_{\text{OLCI-Fapar}}\), which overestimated or underestimated GPP\({}_{\text{EC}}\) to varying degrees, were also observed at several sites. For example, a substantial underestimation of GPP at two crop sites during peak growth period and a slight overestimation of GPP at the US-Ton site during growing season were clearly identified. As shown in Figure 10, the relationship between GPP\({}_{\text{MODIS-Fapar}}\) and GPP\({}_{\text{EC}}\) was established. In addition, Tables 3 and 4 show a comparison of GPP\({}_{\text{EC}}\), GPP\({}_{\text{MODIS-Fapar}}\), and GPP\({}_{\text{OLCI-Fapar}}\) for each site across two years. As a result, GPP\({}_{\text{OLCI-Fapar}}\) performed better than GPP\({}_{\text{MODIS-Fapar}}\) at the US-Bi1, Us-WCr, US-KFS, and US-Ton sites, with a higher R\({}^{2}\). With respect to RMSE, the GPP\({}_{\text{MODIS-Fapar}}\) was superior to GPP\({}_{\text{OLCI-Fapar}}\) for most sites, except for the Us-WCr and US-Ton sites. In terms of bias, GPP\({}_{\text{MODIS-Fapar}}\) provided the best performance at most sites, except the US-Ton site. Moreover, the relationship of GPP\({}_{\text{OLCI-Fapar}}\) and GPP\({}_{\text{MODIS-Fapar}}\) with GPP\({}_{\text{EC}}\) in the combined sites was established (Figure 11). Overall, the GPP\({}_{\text{OLCI-Fapar}}\) and GPP\({}_{\text{MODIS-Fapar}}\) produced acceptable GPP estimates, and if only R\({}^{2}\) is considered, the GPP\({}_{\text{OLCI-Fapar}}\) (R\({}^{2}\) = 0.55) performed better than GPP\({}_{\text{MODIS-Fapar}}\) (R\({}^{2}\) = 0.44) across all biomes, demonstrating the applicability of the OLCI FAPAR product for estimating vegetation GPP.  
Figure 11: Scatterplots between the flux tower GPP and the GPP estimated from satellite FAPAR products for all combined data across all sites. Black and red lines represent the GPP derived from the OLCI and MODIS FAPAR respectively. Significance levels: \(p<0.001\).  
Figure 10: Scatterplots between \(\mathrm{GPP_{EC}}\) and \(\mathrm{GPP_{MODIS-FAPAR}}\) for all sites in 2017 and 2018. Significance levels: \(p<0.001\). (**a–g**) represent the sites the same as Figure 2.
## 2 Sequential Modelling Strategy
### Verification and Validation  
An energy analysis has been implemented both for the net and the spacecraft dynamics to validate the results found in this study.  
First, the net's kinetic (\(E_{k}\)) and elastic potential (\(U\)) energy are computed using  
\[E_{k}=\sum_{i=1}^{N_{\text{tot}}}\frac{1}{2}m_{i}\hat{r}_{i}^{2}, \tag{16}\]  
\[U=\sum_{q=1}^{N_{q}}\frac{1}{2}k_{q}(\Delta l)_{q}^{2}, \tag{17}\]  
where \(N_{\text{tot}}\) is the total number of nodes (including bullets) and \(N_{q}\) is the total number of elongated threads with positive elongation \((\Delta l)_{q}\).  
Secondly, the Envisat's kinetic energy is separated into translational (\(E_{k_{k}}\)) and rotational energy (\(E_{k_{k}}\)). As it is possible to separate the translational and rotational dynamics (see Section 2.4), each kinetic energy contribution can also be separated in the same way. The total rotational energy can be written as  
\[E_{\text{tot}}=\frac{1}{2}\hat{\mathbf{\theta}}_{\text{d}}^{T}(t)\mathbf{I}_{ \text{d}}\hat{\mathbf{\theta}}_{\text{d}}(t)-W_{\text{r}}(t), \tag{18}\]  
where the first term is the rotational energy \(E_{k_{r}}\) and \(W_{\text{r}}(t)\) is the rotational work caused by the contact moments \(\mathbf{M}_{C}\).  
Due to the sequential modelling strategy, an overestimation of the effect of the net on the spacecraft dynamics is expected. As Envisat is initially assumed to not move during the net impact and wrapping, the estimated impact forces and moments are overestimated. Furthermore, due to the aforementioned fixation assumption of the spacecraft, the total system (net and Envisat) energy is not expected to be conserved. The results of the verification and validation process can be found in Section 3.5.
## 4 Low Complexity Non-Linear Detection  
The solution in (27) of the proposed signal model (26), although optimal, is not practical for implementation due to its high computational complexity. This section describes a methodology for non-linear detection of linearly precoded data; suitable for the signal model described in (26). The detection process consists of two stages; first, the sorted QR decomposition of the channel matrix \(\mathbf{K}\) is performed. Subsequently, with the help of this decomposition, the proposed non-linear detectors of low computational complexity are executed. Each of the above algorithms is now described.
# Multi-Task Learning for Medical Image Inpainting Based on Organ Boundary Awareness
## 2 Related Works
### Inpainting in General Field
#### 2.1.1 Traditional Approach  
With conventional methods, the algorithms try to find components from the background area, then compute the similarity levels and fill in the hole. It uses the information available in the image containing the deformed part to generate the missing area [9; 10], which provides a simple algorithm, responding relatively well to the inpainting of small areas in case the scene is not too complicated. Conventional methods also do not require a high amount of training data. However, in some cases, such as large or arbitrary-shaped holes possibly with the background of complex structures, these methods can fail to produce a good recovery.
## 3 Experiment
### Import Machine Learning
#### 3.2.1 Logistic Regression  
The evaluation of the diagnostic possibility of obstructive sleep apnea (OSA) using Logistic Regression was implemented as follows.  
\[\hat{\gamma}=\sigma(\text{w}^{\wedge}\text{Tx}+\text{b}) \tag{1}\]  
Here, \"\(\hat{\gamma}\)\" represents the predicted probability, indicating the likelihood of belonging to class 1 (presence of obstructive sleep apnea). \"\(\sigma(\cdot)\)\" denotes the logistic sigmoid function, a nonlinear function that transforms a linear combination into a probability. This function is defined as follows [19; 20].  
\[\sigma(z)=1\ /\ \big{(}1+e^{\wedge}-z\big{)} \tag{2}\]  
In this study, the term \"w\" represents the vector of model weights or regression coefficients, while \"x\" refers to the vector of independent variables or features. The term \"b\" is the model's intercept, also known as bias. The notation \"T\" signifies the transpose of a vector. The regularization strength, denoted as \"C\", inversely affected the model's complexity; a lower \"C\" value meant stronger regularization, which helped to prevent the model from overfitting. By exploring various values for the hyperparameter \"C\" to adjust the regularization strength, this study aimed to mitigate overfitting and enhance the model's performance. The regression coefficients depict the influence of each variable on the log odds, indicating the importance of these variables. The coefficients derived in this study showed the degree to which each feature had a positive or negative effect on the dependent variable, specifically the diagnosis of OSA. The implementation of the code for this analysis is in Algorithm 1.  
```
1:data \(\leftarrow\) LoadData(\"dataFile.csv\")
2:X \(\leftarrow\) data.drop(['data'])
3:Y \(\leftarrow\) data['OSA Diagnosis']
4:Xtrain, Xtest, Ytrain, Ytest \(\leftarrow\) SplitData(X, Y, testSize = 0.2)
5:C values \(\leftarrow\) [0.001, 0.01, 0.1, 1, 5, 10]
6:for each C in C values do
7:logreg \(\leftarrow\) LogisticRegression(C = C)
8:logreg.fit(Xtrain, Ytrain)
9:Ypred \(\leftarrow\) logreg.predict(Xtest)
10:accuracy \(\leftarrow\) CalculateAccuracy(Ytest, Ypred)
11:Print(\"Accuracy for C=C:\", accuracy)
12:endfor
```  
**Algorithm 1** Logistic Regression for OSA Diagnosis
## 3 Results and Discussions
### Taguchi Analysis  
Using an \(S/N\) ratio analysis, using data \(E\) as an example, the larger-the-better value can be determined using Equation (17). The calculated values are shown in Table 6. The results show that the importance of the parameters is B (\(Q_{g}\)) \(>\) A (pH) \(>\) D (\(C_{L}\)) \(>\) C (\(T\)), while the optimum condition is A4B1C2D1. Other analysis data, such as \(R_{A}\), \(K_{G}\), \(\gamma\), and \(\varphi\), can be determined using the same procedure. All of the results are collected in Table 7, showing the different results in an optimum condition and the importance of the parameters. In order to understand the importance of the parameters of the whole system, the ratios of 4, 3, 2, 1, for the sequence 1, 2, 3, and 4, respectively are set. It was found that the pH has highest rations (3.67) and T has lowest rations (1.17). Because of this, the importance of the parameters of the whole system became A \(>\) D \(>\) B \(>\) C. Figure 5 shows the importance of the parameters in a radar chart for whole system and for the individual data.
# Driver Identification and Detection of Drowsiness while Driving
## 2 Related Work
Another approach to deep learning-based driver sleep detection is found in [22], where an efficient approach involving data acquisition and processing, the selection of deep neural network architectures, and performance evaluations of the resulting model is proposed. The methodology employed in [23] is the combination of long-term and long short-term memory CNNs and factored bi-linear features. None of the aforementioned works, although relevant to the subject matter, fully align with the approach adopted in this paper. The objective of this study is to achieve real-time monitoring of a driver's eye status, coupled with the quickest possible authorization of the individual intending to operate the vehicle. This approach is imperative for several reasons. Firstly, the end-user, represented by the driver, is unlikely to tolerate any delay in facial recognition authorization to commence vehicle operation. Moreover, in emergency situations, waiting for facial recognition authentication is impractical. Regarding drowsiness detection, transmitting frames can impede system performance and lead to failures. Thus, frames are dispatched as expeditiously as possible and processed at maximum speed on the server. Consequently, the server hosts the drowsiness analysis. A Jetson Nano processor with only 2 GB of memory incurs computation delays when analyzing camera-captured frames, but it is usable in situations such as a loss of communications with the cloud. Consequently, while the existing literature predominantly discusses identification strategies using ML, this paper's focus lies in implementing a real-time system to ensure swift analysis and minimize errors. Specifically, this includes mitigating the impact of extended frame transmission intervals, which diminishes the system's efficacy in promptly alerting the driver to potential drowsiness. Additionally, it involves optimizing frame analysis speed to prevent delays in frame capture and avoid analyzing outdated frames.
### Problem Statement  
Ensuring secure and efficient data transmission in vehicular ad hoc networks (VANETs) is crucial due to dynamic nodes in a mix of homogenous and heterogeneous VANET envi  
Figure 4: Machine learning models used in VANET routing prediction [27].  
ronments. Packet delays, insecure routes, and intermediary nodes hinder routing efficiency. Mobility patterns and hop count challenges cause delays in data exchange for content sharing. VANETs face attacks like black holes, flooding, and DDoS. In black hole attacks, malicious nodes divert packets from their path, leading to a \"black hole\" effect where packets are discarded. Figure 5 illustrates this issue, impacting communication and causing breakdowns. To address this, we propose a machine learning-based, secure, and efficient routing approach to mitigate malicious node attacks and reduce packet transmission delays, as shown in Figure 6.
## 6 Output Results  
The seismic assessment output results can be presented as a ranked list in the software interface, or can be automatically exported to external software, such as to MS Excel (through the Windows Clipboard) or to Google Earth (through a kml file, as presented in Figure 14).  
Figure 14: Example of the output that can be automatically exported to Google Earth, with individual building score results, for a given earthquake scenario.  
In PERSISTAH software, results can be filtered by country, region, municipality, building typology or soil type, among other options. To rank the seismic risk of each building, the following score is adopted (the score level is proportional to the seismic risk):  
\[score=\frac{100}{\
icefrac{{\omega}}{{S_{e}}}}. \tag{27}\]  
In order to demonstrate the accuracy of the proposed approach, the results obtained with the developed software were compared against the results obtained with a more accurate time-history nonlinear dynamic analysis of a structure (Figure 15) that was analysed in a previous study [52]. A point source (inverse fault) magnitude \(M\) = 6.5 earthquake scenario was considered, with an epicentral distance equal to \(D\) = 13.972 km. The response spectrum was computed for a soft soil site using one of the already implemented GMPEs [53]. Using the proposed optimization process, an EC8-like response spectrum was adjusted to the GMPEs (Figure 16), obtaining \(\alpha_{a}\) = 2.52, \(T_{B}\) = 0.230 s, \(T_{C}\) = 0.496 s and \(T_{D}\) = 1.771 s. Then, seven artificial accelerograms were generated, as proposed in the EC8, so that the mean response spectrum matched the response spectrum obtained with the GMPEs (Figure 17). The ground motion duration was computed using the empirical expression proposed by [PERSON] and [PERSON] [54]. The results obtained with the different methods are presented in Table 2 and show good agreement between the results obtained with the proposed algorithms and the more accurate NDA results.  
More examples of the output interfaces of the developed software are presented in Appendices A and B.
# A Survey on Modeling Languages for Applications Hosted on Cloud-Edge Computing Environments
## 3 Results
### End Devices  
End devices refer to devices that are owned by users, such as mobile devices or computers, and run client-side applications to retrieve information from applications residing in edge-cloud computing environments. While defining end devices is not typically within the scope of edge-cloud computing, there are limited CMLs that support this description. One such example is the application model suggested in [35], which attempts to define the components that synthesize the edge-cloud continuum.  
The deployment model of SMADA-Fog [12] includes a device type referred to as consumer type. According to [PERSON] et al., consumer devices are heterogeneous devices utilized by end users, serving as consumers of the provider's services. These devices encompass a wide range of technologies, including conventional personal desktop computers, smart televisions, laptops, smartphones, mobile robots, IoT devices, and smart home appliances. They have the capacity to support diverse scenarios, ranging from social networking and entertainment to Smart Grid management, healthcare applications, and manufacturing process support.  
In Fog computing, IoT devices play a crucial role as end devices that consist of physical devices equipped with sensors for data collection. The proposed CML of [PERSON] et al. [21; 22] enables the description of these devices. The language includes an entity called IoTDevice, which encompasses two subordinate entities, Sensor and Actuator, used for specifying the sensors and actuators associated with a device. Additionally, the language incorporates a specific connectivity type property to capture the connectivity options such as Ethernet, Wi-Fi, ZigBee (Zonal Intercommunication Global standard), or other types.  
Existing CMLs such as TOSCA, CAMEL, and others could potentially be extended to incorporate descriptions of end devices. The aim of this extension would be to enable cloud platforms to benefit from such descriptions. For instance, a cloud platform could leverage the information about the distance between edge devices and end devices to deploy application components to the edge devices that are closest to the users, thereby optimizing performance and user experience. This type of logic could potentially be expressed in a CML as a specific optimization rule, similar to the other optimization rules.
## 3 Results and Analysis
### Raw Data Processing of Circumferential Stresses  
Since the initial diffracted intensity curves obtained using the X-ray diffraction method are not processed accordingly, the curves have a high degree of fluctuation and contain much prominence. This interferes with the search for the diffracted intensity peaks and their corresponding diffraction angles, and the back-bottom of the diffraction peaks is caused by factors unrelated to the Bragg diffraction used for the measurement, so it is necessary to subtract the back-bottom from the initial diffracted intensity curves and to smooth the data.  
The initial diffracted intensity curves were processed via data deduction back-bottoming and smoothing using Jade 6.0 software. The diffracted intensity curves of the circumferential stress at different azimuth angles for each detection point were obtained as shown in Figures 6a, 7a, 8a, 9a and 10a. The \(x\) axis \(2\theta\) is the diffraction angle and \(\Psi\) denotes the azimuthal angle. The \(y\) axis denotes the diffraction intensity and the diffraction angle separately. The diffraction angle corresponding to the peak value of each diffraction intensity curve at different azimuth angles was taken as the function value, and the fitted straight line between the corresponding diffraction angle and the sine square of the azimuth angle at each detection point was made, as shown in Figures 6-10. At the beginning, the X diffraction pattern was not stable, so the coefficient of determination of the fitted curve was small, as shown in Figure 6b. However, as the experiment proceeded, the X diffraction pattern was gradually stabilized, and the coefficient of determination of the fitted curve was greater than 0.69 and reached 0.97 under the stable conditions, as shown in Figure 9b.  
Figure 6: Raw data of annular stress at detection point I. (**a**) Diffraction intensity profiles at different azimuth angles. (**b**) Straight lines fitting the diffraction angle to the azimuthal angle.  
Figure 7: Raw data of annular stress at detection point II. (**a**) Diffraction intensity profiles at different azimuth angles. (**b**) Straight lines fitting the diffraction angle to the azimuthal angle.  
According to the diffraction angle and azimuth angle sine square of the fitting line, and then using the modulus of elasticity and Poisson's ratio material parameters of the TC4 lug plate samples, the X-ray diffraction method was used to calculate the radial stress main data, which are listed in Table 2.  
Figure 8: Raw data of annular stress at detection point III. (**a**) Diffraction intensity profiles at different azimuth angles. (**b**) Straight lines fitting the diffraction angle to the azimuthal angle.  
Figure 10: Raw data of annular stress at detection point V. (**a**) Diffraction intensity profiles at different azimuth angles. (**b**) Straight lines fitting the diffraction angle to the azimuthal angle.  
Figure 9: Raw data of annular stress at detection point IV. (**a**) Diffraction intensity profiles at different azimuth angles. (**b**) Straight lines fitting the diffraction angle to the azimuthal angle.
## 6 Discussion
### System Complexity and Hyper-Connectivity  
In Section 4, we discussed the four major layers of the system. The optical systems, the QKD driver and interface systems, the QKD control and key management systems, and the key-consuming application. Each layer uses novel hardware and software technologies, which, if deployed on a city-wide scale, would be a substantial undertaking, considering the sizes of most urban centres and the number of vehicles used in them. This trial adds to the existing body of evidence [4; 14] that validates practical V2I technologies, and we encourage further work on integrating quantum-based systems into V2I systems.  
Quantum-based FSO communications for hyperconnected V2I systems may be limited to backhaul connections. This is due to the mobility of vehicles and line-of-sight issues between access points in urban environments [20]. For our trial, the encryption keys were generated from an FSO-QKD system and were used to secure a small V2I network, representing only a small fraction of what would be required for citywide networks. Schemes do exist that would allow for scaling such a QKD system over larger metropolitan areas [13; 42].  
Figure 10: The QKD-secure systems demonstrated to AirQKD project partners and funders with (**A**) the CPM map overlay and camera feed from the research vehicle streamed to the display screen via a cellular router, (**B**) messages flowing in the system, (**C**) the Angoka ZAP status screen, and (**D**), the Angoka gateway system connected to the University’s V2I system via a cellular router.  
The AirQKD V2I testing was conducted on a 2-kilometre campus test route (see Figure 7). While this represents only a small fraction of an urban area compared to potential future citywide V2I systems, it validated the design of the AirQKD-developed technology, but it was not without challenges.