esa-sceva/satcom-chunk-collection huggingface dataset:
# Parametric Optimization Study of Novel Winglets for Transonic Aircraft Wings
## 3 Methodology  
Airflow is represented as an unsteady, three-dimensional, compressible flow. The governing equations are continuity, x, y, and z-directional Navier-Stokes equations and energy equations. As it would be difficult to solve Navier-Stokes equations due to fluctuating components in the flow field throughout time, a decision was made to use the RANS equation. To solve the closure problem in the RANS equation, the Spalart-Allmaras model is used, as it was created expressly for aerospace applications [31], and it has been proven to perform well for boundary layers subjected to pressure gradients. It simply requires the solution of one transport equation, kinematic turbulent viscosity, which reduces computational complexity. Moreover, the Spalart-Allmaras model [32] has been thoroughly verified for exterior flows and shows good agreement with experimental data in aerospace applications. This study's computational domain and mesh generation are shown in Figures 4 and 5.  
Different computation domain sizes, number of elements, number of inflation layers, and their thickness are tested to obtain accurate results at a fair computational time. The domain size of 150 m \(\times\) 60 m \(\times\) 60 m with tetrahedron elements is finalized for further computation. Near the winglet, 20 numbers of inflation layers with a first-layer element thickness of 0.1 mm and a growth rate of 1.2 are considered to accurately capture the flow characteristics within the boundary layer. A convergence criterion of \(10^{-6}\) is adopted for obtaining accurate results.  
Figure 3: A Schematic of (**a**) twisted, (**b**) canted multi-tip, (**c**) and bird-type swept-back winglets.
###### Abstract  
Alloy 800H stands as one of the few code-qualified materials for fabricating in-core and out-of-core components operating in high-temperature reactors. Welding is a common practice for assembling these components; however, the selection of a suitable filler material is essential for enhancing the high-temperature creep resistance of Alloy 800H weldments in high-temperature applications. In this study, Inconel 625 and Haynes 230 filler materials were used to weld Alloy 800H plates by employing the gas tungsten arc welding technique. The high-temperature tensile and creep rupture properties, microstructural stability, and evolution of the weldments after high-temperature exposure were investigated and compared with those of Alloy 800H. The results show that both weldments exhibit enhanced tensile and creep behavior at 760 \({}^{\circ}\)C. The creep rupture times of the weldments with Inconel 625 filler and Haynes 230 filler materials were about two and three time longer, respectively, than those of Alloy 800H base metal when tested at 80 MPa and 760 \({}^{\circ}\)C. Carbides (MC and M\({}_{23}\)C\({}_{6}\)) were commonly observed in the microstructures of both the weld and base metals in the two weldments after high-temperature creep tests. However, the Inconel 625 filler weldment displayed detrimental \(\delta\) and Laves phases in the fusion zone, and these precipitates could be potential sites for initiating cracks following prolonged high-temperature exposure. This study shows that the weldment with Haynes 230 filler material exhibit better phase stability and creep rupture properties than the one with Inconel 625, suggesting its potential for use as a candidate filler material for Alloy 800H for further investigation. This finding also emphasizes the critical consideration of microstructural evolutions and phase stability in evaluating high-temperature materials and their weldments in high-temperature reactor applications.  
Alloy 800H; Inconel 625; Haynes 230; creep; microstructure; weldment Article
# Recognition of Manual Welding Positions from Depth Hole Image Remotely Sensed by RGB-D Camera
## 3 Depth Image Analysis and Body Joint Identification
### Two-Dimensional Body Joint Identification  
Body joints can be extracted from the restored image where the depth hole is cured. Since the restored image is two-dimensional, we can use various methods for extracting joints from images. In this study, OpenPose [60], a widely used open source, is used. The joint coordinate structure [61] we adapt is shown in the left figure of Figure 12. An example of the body joints extracted corresponding to the structure is illustrated in the right figure.  
Body joints extracted from two-dimensional images cannot always be accurate. The cause of inaccurate results may be an inherent problem of the depth camera or other obstacles such as measurement errors, interference by the measurement environment, and even the incompleteness of the joint extraction module. An incorrect result caused by the camera error is shown in Figure 13b. In this case, it is risky to trust the result of joint information using only a single image. Instead, we set up an assumption that the video stream obtained from the depth camera is continuous and enables a ground truth shown in Figure 13a. Then, we correct the joint coordinates for a frame in question by interpolating the frames before and after the corresponding frame. The interpolation result is shown in Figure 13c, and it is almost consistent with the ground truth.  
Figure 12: Body joints: (**a**) joint structure, and (**b**) an example of joints extracted from a depth image.  
Figure 13: Comparison of ground truth and interpolated two-dimensional body joints: (**a**) ground truth, (**b**) wrong result, and (**c**) interpolated result.
## 4 Experiments
### Evaluation Metrics  
Considering that the remote sensing CD task can be seen as a binary classification task, the precision, recall, F1 score, intersection over union (IoU), and overall accuracy (OA) were selected as the evaluation metrics to quantitatively validate the efficiency of the algorithm presented in this article. These evaluation metrics are always used to measure binary classification models in machine learning. The expressions of these evaluation metrics are listed below:  
\[Precision=\frac{TP}{TP+FP}, \tag{11}\]  
\[Recall=\frac{TP}{TP+FN}, \tag{12}\]  
\[F1=\frac{2}{Recall^{-1}+Precision^{-1}}, \tag{13}\]  
\[IoU=\frac{TP}{TP+FN+FP}, \tag{14}\]  
\[OA=\frac{TP+TN}{TP+TN+FN+FP}. \tag{15}\]  
\(TP\) denotes the sum total of the changed pixels predicted to be changed, \(FP\) denotes the total number of unchanged pixels predicted to be changed, \(TN\) denotes the total number of unchanged pixels predicted to be unchanged, and \(FN\) denotes the total number of changed pixels predicted to be unchanged.
# A Neural-Network-Based Watermarking Method Approximating JPEG Quantization
###### Abstract  
We propose a neural-network-based watermarking method that introduces the quantized activation function that approximates the quantization of JPEG compression. Many neural-network-based watermarking methods have been proposed. Conventional methods have acquired robustness against various attacks by introducing an attack simulation layer between the embedding network and the extraction network. The quantization process of JPEG compression is replaced by the noise addition process in the attack layer of conventional methods. In this paper, we propose a quantized activation function that can simulate the JPEG quantization standard as it is in order to improve the robustness against the JPEG compression. Our quantized activation function consists of several hyperbolic tangent functions and is applied as an activation function for neural networks. Our network was introduced in the attack layer of ReDMark proposed by [PERSON] et al. to compare it with their method. That is, the embedding and extraction networks had the same structure. We compared the usual JPEG compressed images and the images applying the quantized activation function. The results showed that a network with quantized activation functions can approximate JPEG compression with high accuracy. We also compared the bit error rate (BER) of estimated watermarks generated by our network with those generated by ReDMark. We found that our network was able to produce estimated watermarks with lower BERs than those of ReDMark. Therefore, our network outperformed the conventional method with respect to image quality and BER.  
Keywords: watermarking method; neural network; activation function; JPEG compression +
Footnote †: journal: Imaging
# An Analysis of Cloud Security Frameworks, Problems and Proposed Solutions
## 3 Cloud Security Frameworks
### COBIT 5 for Cloud Security
#### 3.1.3 Limitations  
1. Complexity: To implement COBIT 5 for cloud control and assurance, a complete grasp of the framework and its components is required. It can be difficult, particularly for organizations with few resources or technical knowledge. Adequate training and assistance may be required to properly capitalize on the benefits of COBIT 5.
2. Customization Issues: COBIT 5 provides a broad framework that must be customized to an organization's individual needs and cloud environment. Customizing the framework to meet specific needs and cloud service providers may necessitate more effort and skill.
* 3. Tools Dependency: COBIT 5 gives guidelines on control and assurance operations but does not advocate specific tools or technology. Organizations must rely on outside sources or expertise to choose and deploy appropriate cloud control and assurance systems.
* 4. Dynamic Cloud Environment: Cloud environments are ever-changing and dynamic. COBIT 5 may need to be updated and adjusted on a regular basis to accommodate new technology, emerging risks, and changing regulatory requirements. Organizations must stay current in order to keep their control and assurance practices relevant.
# Resistance to Cybersecurity Attacks in a Novel Network for Autonomous Vehicles
## 3 Delay Tolerant Network Protocols
### Black and Grey Hole Epidemic  
As a flooding-based protocol, the Epidemic routing protocol is constantly replicating and transmitting a message to any node that does not yet have a copy of the message [29]. Due to the persistence of the protocol, it has a relatively high delivery probability, but it requires high overheads. As the protocol is relatively effective as a simple algorithm, it is often used as a benchmark to allow comparisons of other protocols. The protocol was modified to allow attacking vehicles to act as both black and grey holes. The adaptations allowed the functional nodes to communicate with attacking nodes without being aware of their state (attacking or not).
## 4 Experimental Study
### Instruments and Measures  
There were two important data collection sources. The first one was the teaching-learning process carried out in class, which may be considered as \"offline data\" or \"real world data\". The second one was the data collected using the monitoring tools provided by the BeingExample platform, mostly information about the students' interactions with the platform, and among them.  
Measures were taken to avoid the Hawthorne effect [39]. Students were not informed that an experiment was taking place, although the teacher was informed, as he had to track extra student data, such us classroom participation and academic performance, based on multiple-choice tests to avoid subjectivity. In order to prevent them from thinking or knowing about the study, the teacher told all the groups that the project would first be done by two groups, and then at a later date by the other group, due to the website workload.  
The most important offline data collected in order to be analyzed were: extra points for participation, student attendance and late arrivals, seating chart for each group, and surveys. Consequently, the data analyzed in this study are closer to social learning analytics [35]. Exam grades ranged from 0 to 100. Furthermore, all grades taken into account for the study were based on multiple-choice tests in order to avoid subjectivity while grading the exams.  
Surveys were used as instruments. Initial surveys were completed during week 0, just before the training, to compare the experimental with the control group, in order to check how homogeneous they were. Both the experimental and control groups completed the surveys. The final survey (week 7) was completed only by the experimental group to collect data on the use of BeingExample, and to see whether any of the questions answered during the initial survey had changed due to the use of the platform. Some of the initial questions were eliminated. A number of new questions were also added.  
Online data tracked by the platform were reduced to three important parameters or indicators: initiative, participation, and communication, as mentioned in the previous section. However, there are numerous possibilities for the nine basic measures taken from BeingExample.  
The data collected during the experiment in some cases did not provide sufficient information to evaluate growth or output differences. Therefore, to validate some of the results obtained, ANOVA (nested) was used for the classroom participation data, in order to answer research question 1.
## 2 Experiment
### Gas Handling and Plasma Generation  
Figure 1 shows a schematic top view of the used experimental setup. Inside a vacuum vessel, a plasma was generated inside a stainless steel cylindrical cavity with an inner radius of \(R=0.033\,\mathrm{m}\) and a height of \(H=0.04\,\mathrm{m}\). The base pressure of the setup was \(1\times 10^{-5}\,\mathrm{Pa}\). In the experiments, argon was used as a background gas and hexamethyldisiloxane (HMDSO, \((\mathrm{CH_{3}})_{3}-\mathrm{Si}-\mathrm{O}-\mathrm{Si}-(\mathrm{CH_{3}})_{3}\)) was used as a reactive precursor gas, with partial pressures of \(p_{\mathrm{Ar}}=5.7\,\mathrm{Pa}\) and \(p_{\mathrm{HMDSO}}=0.8\,\mathrm{Pa}\), respectively. This leads to an HMDSO content of 12.3% and a total pressure of \(p=6.5\,\mathrm{Pa}\). The plasma was generated using a sinusoidal voltage with a frequency of \(f=13.56\,\mathrm{MHz}\) applied to the top wall of the cavity, while the side and bottom are electrically grounded. During the experiments, the plasma power was measured to be \(P=23\,\mathrm{W}\) using an impedance meter (Impedans Octiv Poly).
## 1 Introduction
The generalized regression neural network (GRNN) and the extreme learning machine (ELM) proposed by [PERSON] [13] have a strong nonlinear processing ability to simulate and predict the carbon and water fluxes in terrestrial ecosystems. These models have achieved the highest simulation accuracy in the assessment of the carbon and water fluxes in evergreen coniferous forests and deciduous broad-leaved forests but have given fewer satisfying simulations in cropland ecosystems. [PERSON] [14] compared the performance of three machine learning models based on a backpropagation artificial neural network (BP-ANN), support vector regression (SVR), and random forests (RF) to estimate the ecosystem respiration (Reco) of the grassland ecosystems in northern China. Among the three models, the SVR model had the highest estimation performance followed by the RF model. The BP-ANN model had the lowest estimation performance. At the same time, the research showed that the estimation performance of the three machine learning models in alpine grasslands was better than that in temperate grasslands. [PERSON] [1] used RF, SVR, and an ANN to interpolate the NEE flux sequence of the Zoige alpine wetland ecosystem research station in 2016. The results showed that the simulation ability of the RF algorithm was better than that of the SVR and ANN algorithms. The simulation ability of the three machine learning algorithms was relatively weak at night, sunrise, and sunset during winter and spring. The selection of an interpolation method can cause a difference of \(-\)42 g C\(\cdot\)m\({}^{-2}\) in the annual accumulation of the NEE. Since there is no standard filling method, this study evaluated the performance of the two selected gap filling methods, including the Reddyproc algorithm in R language and an author-developed machine learning algorithm, combined with meteorological factors, including the shortwave downward radiation (RG, WM\({}^{-2}\)), vapor pressure deficit (VPD, hPa), air temperature (Tair, \({}^{\circ}\)C), relative humidity (RH, %), soil temperature (Soil_T, \({}^{\circ}\)C), soil heat flux (Soil_G, WM\({}^{-2}\)), soil moisture content (Soil_WWC, %), and wind velocity (WS, MS\({}^{-1}\)). We constructed two NEE estimation models, discussed the influence of the data and features on each model and the selection of the model parameters, and verified the accuracy of the interpolation results. Our results provide a scientific basis for the standardization of the data processing of the flux observatory/flux network and the establishment of high-quality and representative databases. Meanwhile, it provides a reference and data support for the study of the carbon and water cycles in an alpine wetland of the Qinghai-Tibet Plateau.
# Exploiting Outage and Error Probability
## 3 System Performance Analysis and Our Proposed Cooperative Routing Protocols
### Determination of the Number of Available Relays  
Relays, present in the cooperative region, retransmit the data whenever direct transmission has SNR less than \(\gamma_{0}\). Cooperative region is a common region of S and D's transmission range. Maximum number of retransmissions depends on the number of relays present in the cooperative region. Thus, for \(m\) relays, the maximum allowable retransmissions are also \(m\).  
In this paper, we assume that nodes, \(n\), are distributed randomly over an area, \(X\), with density, \(\rho\). In Figure 2, highlighted region is the overlapping region in which nodes can directly communicate with S and D. In order to find the number of nodes \(n\) in cooperative region, it is required to find area of cooperative region, defined as \(A\). Let the distance between S and D is \(d_{SD}\). The node density is given as:  
\[\rho=\frac{n}{A} \tag{7}\]  
From Appendix A, area of cooperative region with same transmission range, \(r\), is calculated as:  
\[A=2r^{2}\cos^{-1}(\frac{d_{SD}}{2r})-\frac{d_{SD}}{2}\sqrt{4r^{2}-d_{SD}^{2}} \tag{8}\]  
Total number of nodes present in cooperative region is \(\rho\times A\). Since S and D nodes are also included in that area, therefore, total number of retransmission nodes are given as:  
\[m = \rho\times A\ -2 \tag{9}\] \[m = \left[\rho\times\left[r^{2}\cos^{-1}(\frac{d_{SD}}{2r})-\frac{d_{ SD}}{2}\sqrt{4r^{2}-d_{SD}^{2}}\right]\right]-2 \tag{10}\]  
Figure 2: Cooperative region and retransmission nodes.
# An Overview of Key Technologies in Physical Layer Security
## Abbreviations  
The following abbreviations are used in this manuscript:* _An_ _Artificial noise_ AWGN_ _Additive white Gaussian noise_ BER _Bit error rate CSI CSI Channel state information IRS Intelligent reflecting surface ITU International telecommunication Union MIMO Multiple-input multiple-output OFDM Orthogonal frequency division multiplexing PLS Physical layer security QoS Quality of service SINR Signal-to-interference-plus-noise ratio STBC Space-time block code UAV Unmanned aerial vehicle URLLC Ultra-reliable low latency communication WIPT Wireless information and power transfer ZF Zero-forcing
## 2. Medicane Ianos-Induced Landslides  
The first step for understanding the mechanism of the cyclones-induced landslides and to mitigate in near future similar types of catastrophes is to rapidly document them. This can be achieved by immediately organizing a field survey after the event to acquire perishable field data before it disappears. Landslide debris and rockfalls are commonly cleaned out of the road network as part of the recovery operations, but their removal obscures critical information used to analyze the triggers for slope failures. Furthermore, physical processes such as weathering and erosion could additionally alter the landslide-formed landscape after the landslide event, especially concerning small size shallow landslides. Data recovered from landslide studies are essential to understanding landslide mechanisms and the associated risks, which greatly aids in identifying susceptible zones, augmenting emergency response, and mitigating potential property damage and loss of life. This research applies a mixed-method approach, primarily consisting of a desk study followed by a detailed post-event field survey aiming to report the failures in order to compile a landslide-event inventory map.  
In this study, the landslide phenomena triggered in the area of Thessaly and Ionian Islands are presented and preliminary analyzed with the aim to relate their spatial distribution with some basic geological and geomorphological parameters, i.e., geology, aspect, slope and elevation. Although large parts of central and western Greece were affected by Medicare Ianos, Karditsa and Cephalonia island are presented here in detail, as they experienced the most dense and severe landslide phenomena. In order to achieve this, we used information obtained during extensive field work and data provided by rapid satellite imagery mapping captured a few hours and days after the event.  
Satellite-derived precipitation (e.g., Global Precipitation Measurement-GPM) enables monitoring of rainfall amount and patterns in near real-time (Figure 1b), and precipitation maps can be derived for forecasting or study of landslide events such as hurricanes and Medicanes [18; 19]. However, results from Medicare Ianos satellite-derived precipitation showed an inconsistency with ground station measurements, with significantly lower values measured from the satellite sensors [12]. This, in addition to the poor coverage of the affected area (mountainous western Thessaly) by ground stations does not permit us to recreate an accurate map of precipitation during Medicare Ianos.
## 4 Discussion
#### Strengths and Limitations  
Though the study is comprehensive and has detailed analysis, it has a few limitations. First, data collected was self-reported and could not be independently verified through other data sources; thus, a possibility of recall bias might exist. For example, anosognosis (unawareness of one's deficits) is estimated to impact between 25-50% of those with HD [47; 48; 49], which may have contributed to under-reporting of complaints or concerns from HD patients; thus, the true magnitude of effect between HD and GP may be greater than estimated in this study. Second, individuals with HD and HD-CP were recruited from the Rare Patient Voice panel, which might not be representative of the overall HD population. For example, individuals with HD with limited internet access might be underrepresented in our sample, and individuals with greater disease severity might have opted against completing the online survey due to physical and/or cognitive impairment. While we attempted to minimize these potential biases by providing patients with the option of having a care partner assisting (or conducting a telephone-assisted interview in lieu of an online survey), we cannot rule out that those with the greatest burden (i.e., most severe forms of any disease, including HD and PD) may be under-represented in this study. That said, any such biases would not account for the differences observed here (i.e., the inclusion of more severe patients would only have only increased the magnitude of effect reported). Third, the low sample size of the study (specifically in regard to rare patient populations, like those with PD) should be taken into account when interpreting the study results. Fourth, the current study was cross-functional in design, thus causality could not be determined. Finally, while matching and multivariable regressions were conducted to account for imbalances in age and sex distributions between the cohorts, there remains a potential for residual confounding by variables not accounted for in our analyses.