esa-sceva/satcom-chunk-collection huggingface dataset:
# Computational Evaluation of Turbulent Supersonic Jet Impinging on Inclined Plate
## 3 Turbulence Modeling
### Detached-Eddy Simulation  
The DES method represents a more sophisticated hybrid turbulence modeling formulation that can be adopted in conjunction with various RANS models. In this work, the \(k-\omega\) SST-based DES model is actually employed. The \(k-\omega\) SST model was among the different pure RANS models tested in the previous CFD study used as reference [16]. Therein, a correct surface pressure distribution for the impinging jet flow at low incidence (\(\theta=30^{\circ}\)) was attained, due to the proven ability of this model to provide accurate mean flow results, especially for wall-bounded separated flows [18].  
In the current approach, the evolution equations for the two turbulence variables are slightly modified as follows:  
\[\frac{\partial}{\partial t}(\rho k)+\frac{\partial}{\partial x_{j}}\big{(}\rho u _{j}k\big{)}=P_{k}-F_{DES}D_{k}+\frac{\partial}{\partial x_{j}}\Bigg{[}(\mu+ \sigma_{k}\mu_{t})\frac{\partial k}{\partial x_{j}}\Bigg{]}\, \tag{7}\]  
\[\frac{\partial}{\partial t}(\rho\omega)+\frac{\partial}{\partial x_{j}}\big{(} \rho u_{j}\omega\big{)}=P_{\omega}-\beta\rho\omega^{2}+2(1-F_{1})\sigma_{ \omega}\omega\frac{\rho}{\partial x_{j}}\frac{\partial k}{\partial x_{j}}+ \frac{\partial}{\partial x_{j}}\Bigg{[}(\mu+\sigma_{\omega}\mu_{t})\frac{ \partial\omega}{\partial x_{j}}\Bigg{]}\, \tag{8}\]  
with \(F_{DES}\) given by:  
\[F_{\text{DES}}=\text{max}\bigg{[}(1-F_{2})\frac{L_{\text{RANS}}}{C_{\text{DES }}\Delta},1\bigg{]}\, \tag{9}\]  
where the switching functions \(F_{1}\) and \(F_{2}\) are the same as in the standard \(k-\omega\) SST model [18]. In the above definition, \(L_{RANS}=\sqrt{k}/(\beta^{*}\omega)\) represents the resolved turbulence length scale, \(\Delta\) is the largest (linear) dimension of the FV cell, with \(C_{\text{DES}}\) standing for a calibration coefficient.  
In practice, RANS and LES solutions are coupled by introducing a hybrid turbulent length scale, which is modified with respect to the pure RANS approach, where it holds \(D_{k}=\rho k^{3/2}/L_{RANS}\). This way, a single turbulence closure acts as either a sub-grid scale model, where the local grid resolution is fine enough for LES, or a RANS model, where that is not true. The two different regions are actually separated by a transition zone where the eddy-viscosity level is sufficiently low to allow the rapid regeneration of turbulent eddies. Basically, DES aims at the prediction of separated flows in fluids engineering research at a manageable cost. More details about the DES formulation can be found in the original work by [PERSON] [29].
# Performance of Nanostructures within InGaN-Based Multiquantum-Well Light-Emitting Devices
## 2 Experiments  
The samples studied in this work were grown using metalorganic vapor phase epitaxy (MOVPE) on _c_-plane sapphire substrates. The layer structure of the sample with conventional barrier layers in MQWs consisted of a 20-nm-thick GaN buffer layer, a 3-\(\mu\)m-thick n-type GaN layer, an undoped GaN layer possessing five periods of In\({}_{0.18}\)Ga\({}_{0.82}\)N/GaN MQWs, and a 100-nm-thick p-type GaN layer. The thicknesses of the InGaN wells and the GaN barriers in the MQW structures were 2 nm and 11 nm, respectively. For the sample with MQB nanostructures, the layer structure was similar, but only with five periods of In\({}_{0.15}\)Ga\({}_{0.85}\)N/GaN MQWs, and the barrier layer of MQW was replaced with a five-period In\({}_{0.02}\)Ga\({}_{0.98}\)N/GaN (1 nm/1 nm) heterostructure. After growth, the epilayers were characterized through HRXRD. Rocking curves appeared in the (002) reflection produced using the Bede D1 system. The temperature-dependent EL measurements were conducted after mounting the sample in a closed-cycle helium cryostat where the temperature (\(T\)) was varied from 20 to 380 K and using a current source operated from 10 to 100 mA. The luminescence signal was dispersed through a 0.5 m monochromator and detected by using a silicon photodiode by the standard lock-in amplification technique.
# Beamforming Techniques for Resilient Navigation with Small Antenna Arrays +
## 3 Selected Beamforming Techniques  
This section provides a brief description of a set of beamforming techniques that have been selected for their implementation in user terminals with a small number of antennas, and tested later in Section 4. Note that most of them rely on having prior knowledge about the DoA of the LOSS to be spatially filtered. Such knowledge can be obtained by resorting to maximum likelihood estimation [16], spectral estimation methods [17], or, in some applications such as in GNSS, this information can easily be obtained from the satellite ephemeris and the orientation of the user's terminal. In any case, determining the DoA of the LOSS remains out of the scope of the present paper, and it will be assumed to be a priori known henceforth.
## 4 Experiments
### Evaluation  
For the private dataset of the electric power operation site without labeling, the subjective visual effect and the number of human keypoints correctly detected by HRNet (High-Resolution Net) [21] estimation model were used as the evaluation metrics for the superiority of the image enhancement effect. For the labeled SCIE public dataset images, _PSNR_ (peak signal-to-noise ratio) and _SSIM_[22] (structural similarity) were used as the evaluation metrics, which were calculated using MATLAB built-in functions.  
When comparing the quality of a low-light augmented image to a true labeled image, the _PSNR_, an engineering term that describes the ratio of the highest achievable strength of a signal to the power of the destructive noise that influences its representation accuracy, was utilized. The definition of _PSNR_ for an original image \(I\) of size \(m\times n\) and a processed image \(K\) is:  
\[PSNR=10\times\log_{10}\left(\frac{MAX_{I}^{2}}{MSE}\right) \tag{22}\]where the \(MSE\) (mean square error) is:  
\[MSE=\frac{1}{mn}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}[I(i,j)-K(i,j)]^{2} \tag{23}\]  
\(MAX_{I}\) indicates the maximum value of the image point color. Each pixel has a value of 255 when it is represented in 8-bit binary and \(2^{B}-1\) when it is represented in B-bit binary. The higher the \(PSNR\), the less distortion there is and the closer it is to the original image.  
\(SSIM\) is a metric used to compare two pictures. Two images--the real labeled image and the low-light enhanced image--were used to calculate the \(SSIM\). The two photos' \(SSIM\)s were calculated as follows:  
\[SSIM(x,y)=\frac{\left(2\mu_{x}\mu_{y}+c_{1}\right)\left(2\sigma_{xy}+c_{2} \right)}{\left(\mu_{x}^{2}+\mu_{y}^{2}+c_{1}\right)\left(\sigma_{x}^{2}+\sigma _{y}^{2}+c_{2}\right)} \tag{24}\]  
where \(\mu_{x}\) is the mean value of \(x\), \(\sigma_{x}^{2}\) is the variance of \(x\), \(y\) is the same, \(\sigma_{xy}\) is the covariance of \(x\) and \(y\), \(c_{1}=(k_{1}L)^{2}\) and \(c_{2}=(k_{2}L)^{2}\) are constants used to maintain stability, and \(L\) is the dynamic range of the pixel values. \(k_{1}=0.01\) and \(k_{2}=0.03\). The structural similarity had a range from 0 to 1 and the value of the \(SSIM\) was equal to 1 when the two images were exactly alike.  
In order to evaluate the light weight of the model, the number of parameters (Params) and the number of floating-point operations (FLOPs) in the network were used as evaluation metrics.
## 2 Classical Method  
The reference method, as proposed in the SEMI standards [3; 7], is usually applied to a one-point or a three-point support setup. When the wafer's front surface points upward, it is denoted normal orientation, and when the front face points downward, it is denoted inverse orientation. When the wafer is flipped to change from normal to inverse orientation, the flip axis must be chosen carefully so that the relative position of the supports and the wafer remains unchanged (Figure 1). This is achieved by selecting the axis so that the flip operation maintains even symmetry. In a centered one-point support, any axis can maintain even symmetry. However, in a regular three-point support setup, only the axes passing through the center and any of the contact points maintain even symmetry.  
The first step of the reference method is to obtain the normal orientation measurement \(y_{f}(x,y)\), where \(x\) and \(y\) are the wafer plane coordinates. It is assumed that this measurement is the sum of the actual front surface shape of the wafer \(s(x,y)\) and the gravity effect \(g(x,y)\)[7].  
\[y_{f}(x,y)=g_{f}(x,y)+s(x,y) \tag{1}\]  
It is also assumed that the gravity effect is identical regardless of whether the wafer is in normal or inverse orientation; the inverse orientation measurement is denoted \(y_{b}(x,y)\). It must be noted that, to measure the wafer in its inverse orientation, it must be flipped around an axis. Here, it is assumed that the flipping is carried out around the \(y\)-axis, giving formally [7]  
\[y_{b}(x,y)=g(x,y)-s(-x,y) \tag{2}\]  
The operation of flipping the wafer around an arbitrary axis at \(\Phi\) degrees with respect to the \(x\) axis is defined as \(F_{\Phi}\). Therefore, the flipping with respect to the \(y\)-axis as \(F_{90}\) is referred to as  
\[s(-x,y)=F_{90}(s(x,y)) \tag{3}\]  
Considering all these assumptions, the actual wafer shape and the gravity effect can be derived as follows:  
\[s(x,y)=\frac{y_{f}(x,y)-F_{90}(y_{b}(x,y))}{2} \tag{4}\]  
\[g(x,y)=\frac{y_{f}(x,y)+F_{90}(y_{b}(x,y))}{2} \tag{5}\]  
Figure 1: **Left**: layout of a regular three-point support. **Right**: system of coordinates.  
The previous pair of equations are strictly true only when the gravity effect is the same in both directions. From a pure mathematical point of view, it must have even symmetry around the flip axis: \(g(x,y)=F_{90}(g(x,y))\). Considering only the effect of the wafer supports, either a centered one-point support or a three-point support setup, enables the wafer to be flipped while maintaining even symmetry.  
In a real implementation, there are many sources of error that break the needed even-symmetry requirement: the placement error of the supports, the flip axis angle error, the deviation of the wafer perimeter from a perfect circle, thickness variations or mechanical stress resulting from wafer processing, and anisotropy related to the lattice structure of the silicon substrate to list some, even if the silicon wafer is within specifications [5].  
In this work, only two symmetry requirements are considered: the placement error of the supports and the flip axis angle error.
## 5 Results
### Virtual Sensor Fault-Correction Effect
#### 5.2.2 Energy Consumption
The results of evaluating unmet hours and energy consumption indicate the necessity of periodic sensor fault detection using the virtual sensor developed in this study to prevent worsening indoor thermal comfort and increased energy consumption.
## Data and Methods
### ECA&D Surface Observations  
TCC data from both CLARA-A2 and ISCCP-H are compared with the corresponding data from surface observations from 22 stations across Europe, the locations of which are provided in Table 3 and Figure 1. These come from a European dataset compiled within the ECA&D project. ECA&D is a well-established database of daily WMO station observations which are analyzed for WMO Region IV (Europe and Middle East), putting particular emphasis on changes in daily extremes [49]. Twenty-two stations were selected to broadly cover Europe and to provide observations for the full 1984-2012 time period. For the comparisons, monthly mean TCC values were computed from the originally available daily ECA&D data in order to match the temporal resolution of the satellite data. The daily data of each station include either human observations or data from ceilometers. For a number of stations, ceilometers replaced human observations at some point in time. However, this is not the case for all the stations. Finally, the CLARA-A2 and ISCCP-H gridded data were compared against the corresponding ECA&D data from the station contained in the grid cell.
## 3 Results and Discussion
### The Grating Slant \(\Phi\)  
Investigation of the grating constant enables two-dimensional mapping of optical shrinkage in the lateral plane. However, no information on optical shrinkage in the perpendicular direction is provided, although the lateral scan data contains corresponding information in the form of the grating slant parameter \(\Phi\).  
In the case of one-dimensional, plane-wave, transmission type gratings the lattice planes are aligned parallel to each other, perpendicular to the sample surface. Any deviations from this configuration must be linked to local differences in optical shrinkage. As a consequence, lateral mapping of the slant parameter allows for the deduction of the depth relevant information by means of the lattice plane orientation.  
Here, the results from lateral scans show a causal connection between local grating slant and recording intensity as well as exposure duration, respectively, _i.e._, \(\Phi(I,\,t_{\mathrm{exp}})\). With respect to the recording intensity, three different behaviors can be classified, presented in Figure 3.
# Multi-Party Controlled Semi-Quantum Dialogue Protocol Based on Hyperentangled Bell States
## 3 Security Analysis
### Attacks by the Dishonest Controller
#### 3.4.3 Deliberate Disclosure  
When compromised controllers (e.g., [PERSON]\({}_{i}\)) collude with external attacker [PERSON], they may leak operational parameters R to facilitate secret theft. The attack proceeds as follows: [PERSON] intercepts particle sequence \(S_{A}\) sent from [PERSON]\({}_{n}\) to [PERSON], forging a counterfeit sequence \(S_{A}\) to transmit to [PERSON]. If all controllers are untrustworthy and disclose their operations \(\{R_{i}\}\) to [PERSON], she may deduce the initial system state \(\ket{\Phi}_{AB}^{PS}\) through reverse engineering to extract secrets. In Step 2 of the protocol, [PERSON] selects sufficient particles from the remaining \(N-n\) received sequences for security verification. Since [PERSON] learns the system state after sending \(S_{A}\)/, she cannot predict [PERSON]'s chosen measurement basis. This inevitably triggers a QBER exceeding the predefined security threshold, forcing communication termination. As a result, the collusion between the controller and [PERSON] will inevitably be detected.
# Programming Real-Time Sound in Python
## 1 Introduction
### Related Work  
The number of computer music programming languages is notable. Each language differs in terms of abstraction, coding approach, learning curve, portability across architectures, and operating systems. In parallel, all make real-time sound programming easier. In particular, those in the Music-N tradition [13] support dynamic instantiation of graphs of _unit generators_ (UG's), the basic building blocks of a sound synthesis and processing algorithm. Csound [14] structures the code in two parts: an instrument file contains the UG's, while a score file controls them along time through note and other event parameters. Pure Data [15] and Max [16] allow for the visual programming of UG networks and related control messaging. SuperCollider [1] implements a client-server architecture that enables interactive sound synthesis and algorithmic composition through live coding, also interpreting different languages thanks to the flexibility of the client. ChucK supports deterministic concurrency and multiple control rates, providing live coding and enabling live performances [17]. Faust follows the functional programming paradigm, and builds applications and plugins for various real-timesound environments thanks to the automatic translation into C++ code. It also provides a powerful online editor, enabling agile code development [18].  
Existing sound applications in Python mainly focus on the analysis and presentation of audio, as well as on music retrieval [19; 20; 21]. Most such applications have no strict temporal requirements and, hence, are conventionally written for the interpreter: examples of this approach to sound programming can be found in e.g., [22], where latency figures are also detailed. Concerning sound manipulation, computer musicians often rely on the pyro library [23], a client-server architecture that allows combining its UG's together into processing networks. Hence, the abstraction from the signal level is realized through pyro also in Python, making the creation of sound generation and effect chains possible as most sound programming languages do.  
On the other hand, the low-level development of sound algorithms is not trivial when working with UG's, as they encapsulate the signal processing by definition. Additionally, because of the existing excellence in this sound programming paradigm, our paper puts the accent to coding at signal level. Addressing such a level in Python requires to profile and refactor usually few signal processing instructions. The advantages of code refactoring go beyond sound applications; in fact, refactoring can be applied to contexts, including, among others, real-time data collection, systems control, and automation.
### Related Work
Thus, a significant research challenge persists in developing TAS scheduling algorithms that can efficiently generate GCLs for a large number of data flows while ensuring high schedulability and strictly adhering to the demanding low-latency and low-jitter requirements of autonomous driving applications. This paper aims to address this gap by proposing a novel TAS-based scheduling algorithm along with optimization techniques designed for such environments.
# Tamper and Clone-Resistant Authentication Scheme for Medical Image Systems
## 5 Watermarking Analysis and Security Evaluation
### Security Analysis of the Proposed Protocols
#### 5.1.2 Man-in-the-Middle Attack  
In MIMA, an adversary intercepts all exchanged data between a medical device (or a user) and a TA server. The target of the adversary is to eavesdrop and later to deliver false data. Therefore, a successful MIMA is when an adversary can fool a TA server.  
In the proposed protocol.1:  
The MIMA-adversary intercepts the messages in steps 3 and 8. In this case, the adversary can extract the signed watermark \(Z\) from step 8 by using the inverse of the public embedding algorithm.  
To deliver a false message to the TA server, a MIMA-adversary should be able to use the signed watermark \(Z\) again/later, which is equivalent to the fact that there are two watermark images \(WM_{1}\) and \(WM_{2}\) having the same signed watermark \(Z_{1}=Z_{2}\). The size of the key space is \(2^{n}\), so, the probability of such a collision is \(2^{-n}\). Therefore, the proposed protocol.1 of MIS is secure against MIMA.  
Note that the same analysis can be used to prove that the proposed protocol 2 is secure against MIMA.
# Research on the Influence of Turbulent Flow Induced by Dunes on AUVs
###### Abstract
However, the actual seafloor terrain is diverse, and complex flow fields induced by complex seafloor terrains have a profound effect on AUV stability. Certain studies have focused on the effects of complex seafloor terrains on underwater vehicles. [PERSON] [12] simulated towing experiments of AUVs near the seafloor using the CFD method and investigated changes in AUV hydrodynamics in the process of navigating above typical complex terrains such as single inclined slopes, seafloor bedrock terrains, sand wave terrains, and eroded depression terrains. [PERSON] et al. [13] numerically investigated the influence of downward slopes on the hydrodynamic performance of nearby AUVs using the CFD method. [PERSON] et al. [14] conducted water tank experiments and numerical simulations of the Reynolds stress model to study the forces acting on AUVs above a wedge-shaped seafloor. They analyzed hydrodynamic values such as resistance, pressure, and friction coefficients for AUVs under different Reynolds numbers, attack angles, and drift angles, which provided insights for designing and improving AUVs for operations above continental slopes and in estuaries. [PERSON] et al. [15] studied the influence of an inclined seafloor on an autonomous underwater helicopter (AUH) using the CFD method and suggested that an inclined seafloor has a more significant influence on AUV resistance compared to a flat seafloor. In the research on the influence of submarine terrains on underwater vehicles, scholars have mainly considered the state of underwater vehicles in relatively stable flow fields. For flow fields characterized by higher turbulence levels, [PERSON] et al. [16] excited turbulence using grids and investigated its effects on AUV hydrodynamic characteristics. Similarly, [PERSON] et al. [17] explored the influence of rotational flow fields induced by propellers on the changes in hydrodynamic characteristics of AUV. However, these studies primarily focused on the average hydrodynamic characteristics of AUVs, not the turbulent flow induced by complex seafloor terrains over time, which can cause continuous changes in the hydrodynamic characteristics of AUVs, even leading to significant fluctuations in the forces acting on them. Therefore, based on practical engineering considerations, this paper selects common underwater dune terrains, uses the CFD method to simulate turbulent flow near the terrain, and analyzes the hydrodynamic performance and flow field variations in AUVs within the turbulent flow induced by the seafloor terrain, providing a theoretical foundation for AUV maneuverability and control.
# All You Need Is a Few Dots to Label CT Images for Organ Segmentation
## 4 Performance Tests
### Tests On Segmentation Accuracy  
In our case, we focus on making the model show acceptable performance when a few labeled data are given. To verify the performance of our model, we measured the evaluation metrics by varying the number of training data. The x-axis of Figure 7 represents the number of people extracted from labeled images. In this experiment, we used only five CT images per person.  
Figure 7 shows the dice score and Hausdorff distance of the proposed model and baseline models. As illustrated in Figure 7, our model is superior to baseline models in terms of dice score. In particular, our model achieved a 0.6 dice score with training data of only one person, whereas baseline models achieved only about 0.5 dice score.  
However, our model has a higher Hausdorff distance than DeepLab-V3 because segmentation generated by DeepLab-V3 is rarely scattered. In contrast, our method can generate segmentation with some scattered points since our approach exploits the density computed using visual similarity between pixels. If there are points that have similar features with anchor pixels, these points can be classified as part of the target organ, even if these pixels are apart from the target organ (this circumstance can also be found in Figure 4). This drawback can be relieved by carefully picking anchor pixels and thresholding.  
We also compared our model with other semi-automatic algorithms, such as the active contour algorithm and the watershed algorithm. We exploited the true segmentation label to construct informative seed for the watershed algorithm. However, we found that these algorithms poorly detected the region of organs and achieved a dice score too low (0.04 and 0.07, respectively).