esa-sceva/satcom-chunk-collection huggingface dataset:
# Gas-Liquid Mixability Study in a Jet-Stirred Tank for Mineral Flotation
## 3 Results and Discussion
### Influence of Aeration Volume on Flow Field
#### 3.2.2 Influence of Charging Volume on Gas Volume Fraction Distribution  
Figure 12 illustrates the variation in the gas volume fraction with height under different aeration rates, while Table 8 provides the mean values, variances, and dimensionless variances of the gas volume fraction. Analysis of the table reveals that as the aeration rate increases, the mean gas volume fraction, variance, and dimensionless variance all exhibit a linear upward trend. This phenomenon does not clearly indicate the optimal aeration rate, but higher aeration rates do improve gas-liquid mixing efficiency. However, excessively high aeration rates result in significant disparities in the gas volume fraction among different points within the reactor, thereby reducing reaction uniformity to some extent. Therefore, considering all parameters, an aeration rate of 0.25 was selected for subsequent experiments.
## 4 SENSED-SG Design and Protocols
### Physical Layer  
There is no doubt that the release of the IEEE 802.15.4 standard for the physical and medium-access layers have paved the way for the expansion of WSN technology in a wide variety of productive fields [49]. This standard has become the de-facto starting point for any low-data-rate low-power consumption wireless system that strives for interoperability. The IEEE 802.15.4 supports three frequency bands, namely: 868 MHz, 915 MHz and 2450 MHz. However, the 2450 MHz band is by far the most widely used because of (i) its higher data-rate (250 Kbps) compared to the other bands (20/40 Kbps respectively) and (ii) its noise-resilient modulation DSSS and O-QPSK [42]. Therefore, it is essential to ensure the adoption of this standard without any additional modification that could leave room for interoperability concerns.
## 8 Evaluation of Feasibility and Applicability of the Reactive Protection Concept
### Scenario and Adversarial Model  
In order to validate the presented reactive protection concept, a real-life scenario derived from the reference IAS architecture (Figure 4) is presented. This scenario is shown in Figure 5. It presents a simplified remote maintenance application case.  
In the presented scenario, there exist two companies: _Company A_ and _Company B_. The automation system is located at the site of _Company A_; this is represented by a Programmable Logic Controller (_PLC_), and two engineering workstations (_Workstation 1_ and _Workstation 2_). A technician located on the site of _Company B_ wishes to perform maintenance on devices located at the _Company A_ site. In order to perform maintenance in a secure manner, a Virtual Private Network (VPN) connection is established between these two sites. The VPN end point on the _Company A_ site is represented by a Bosch Rexroth _IoT Gateway_. As discussed in the previous section, the _IoT Gateway_ contains a VPN  
Figure 5: Simplified Industrial Scenario of a Remote Maintenance Application derived from the reference IAS architecture (Figure 4).  
Client application that allows it to establish a connection to the endpoint located at the _Company B_ site--hence allowing it to establish a direct connection to the computer of the _Technician_. On the other hand, the end point on the _Company B_ site can be any device that is capable of providing the same functionality as a VPN server.  
For this use case, it is assumed that the credentials required to setup the VPN connection have already been provided. It is also assumed that the VPN configurations are protected against unauthorized modifications.  
The adversarial model contemplated for this scenario is comprised of external threats that may propagate through the secure VPN connection towards the automation system located at the site of _Company A_ (e.g., Slammer worm [63]) or misuse from the part of the _Technician_.
## 5 Conclusions
### Nomenclature  
* Concentrating parabolic through CNT
- Carbon nanotube
- Direct absorption solar collector
- Ethylene glycol
- Evacuated tube collectors
- Flat plate solar collectors
- MWCNT Multi-wall carbon nanotube
- Nanofluid based concentrating parabolic solar collectors
- Organic Rankin cycle
- Parabolic trough solar collectors
- Olive leaf extract
- Barley husk
- Nanoparticle volume fraction
- Thermal efficiency enhancement (%)
- Convection heat transfer coefficient (W/m\({}^{2}\)K)
- Pressure drop increase (%)
- Vol. %
- Nanoparticle volume percentage
- wt %
- Nanoparticle weight percentage  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Nanofluid**} & \multicolumn{4}{c}{**Nanoparticle**} & \multicolumn{4}{c}{**Mac. Increase \%**} & \multicolumn{2}{c}{**Commentation**} \\ \cline{3-11}  & & & **Type** & \(\mathbf{\varphi_{max}}\) & **Size (nm)** & \(\mathbf{\eta_{0}}\) & **h** & **AP** & & & & \\ \hline [PERSON] et al. [6] & CFD & Synthetic call & Al\({}_{2}\)O\({}_{3}\) & 5 vol. \% & - & - & -14 & - & - & - & The first study on nanofluid application in PTC. \\ [PERSON] et al. [10] & CFD & Synthetic call & Al\({}_{2}\)O\({}_{3}\) & 5 vol. \% & 10 & - & -14 & - & Temperature increase has negative effect on PTC while utilizes the nanofluid \\ [PERSON] et al. [6] & CFD & Synthetic call & Al\({}_{2}\)O\({}_{3}\) & 5 vol. \% & 10 & - & -36 & - & Proposed an optimized value for \(\sigma\) = 6.5 vol. \\ [PERSON] et al. [6] & CFD & Synthetic call & Al\({}_{2}\)O\({}_{3}\) & 5 vol. \% & 28 & 7.6 & 740 & - & Proposed an optimized value for \(\sigma\) = 6.5 vol. \\ [PERSON] et al. [6] & CFD & Synthetic call & Al\({}_{2}\)O\({}_{3}\) & 6 vol. \% & - & 15 & 38 & - & Recover thermal performance and the value for \(\sigma\) = 6.5 vol. \\ [PERSON] et al. [6] & CFD & Water & Al\({}_{2}\)O\({}_{3}\) & 6 vol. \% & 28 & - & - & - & There is an optimum congestion of the tube that causes minimum generation of entropy. \\ [PERSON] et al. [7] & Model & Synthetic call & Al\({}_{2}\)O\({}_{3}\) & 5 vol. \% & - & - & 18 & - & Increasing the temperature can cause an increase in convection heat transfer coefficient. \\ [PERSON] et al. [8] & CFD & Thermal oil & Al\({}_{2}\)O\({}_{3}\) & 2 vol. \% & 20 & 4.25 & 10.95 & -10 & Combination of
# Accurate Constant Phase Elements Dedicated for Audio Signal Processing
## 3 Approximation of CPE in Frequency Domain and Numerical Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Rp/Cp** & **R\({}_{1}\)/C\({}_{1}\)** & **R\({}_{2}\)/C\({}_{2}\)** & **R\({}_{3}\)/C\({}_{3}\)** & **R\({}_{4}\)/C\({}_{4}\)** & **R\({}_{5}\)/C\({}_{5}\)** & **R\({}_{6}\)/C\({}_{6}\)** & **R\({}_{7}\)/C\({}_{7}\)** \\ \hline
**15** & **k\(\Omega\)** & **5 k\(\Omega\)** & **1.4 k\(\Omega\)** & **366 \(\Omega\)** & **94.6 \(\Omega\)** & **24.4 \(\Omega\)** & **6.8 \(\Omega\)** & **1.7 \(\Omega\)** \\
15 k\(\Omega\)\(\angle\) & 10 k\(\Omega\)\(\parallel\) & 1 k\(\Omega\)\(\angle\) & 5.6 k\(\Omega\)\(\angle\) & 82 \(\Omega\)\(\angle\) & 22 \(\Omega\)\(\angle\) & 6.8 \(\Omega\) & 1.8 \(\Omega\) \\
680 \(\Omega\) & 10 k\(\Omega\) & 390 \(\Omega\) & 390 \(\Omega\) & 12 \(\Omega\) & 2.2 \(\Omega\) & & \\ \hline
**3.6**\(\upmu\)F & **1.8**\(\upmu\)F & **1.5**\(\upmu\)F & **1.3**\(\upmu\)F & **1.1 \(\upmu\)F** & **927 nF** & **783 nF** & **680 nF** \\
3.3 \(\upmu\)F \(\parallel\) & 1.8 \(\upmu\)F & 1.5 \(\upmu\)F & 1.2 \(\upmu\)F \(\parallel\) & 1 \(\upmu\)F \(\parallel\) & 820 nF \(\angle\) & 680 nF \(\parallel\) & 680 nF \\
330 nF & & & 100 nF & 100 nF & 100 nF & 100 nF & 100 nF \\ \hline \hline \end{tabular}
\end{table} 
Table 37: CPE with order \(\alpha=\) 8/9; fully passive ladder RC parallel-series topology of fractal capacitor. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Rp/Cp** & **R\({}_{1}\)/C\({}_{1}\)** & **R\({}_{2}\)/C\({}_{2}\)** & **R\({}_{3}\)/C\({}_{3}\)** & **R\({}_{4}\)/C\({}_{4}\)** & **R\({}_{5}\)/C\({}_{5}\)** & **R\({}_{6}\)/C\({}_{6}\)** & **R\({}_{7}\)/C\({}_{7}\)** \\ \hline
**170 k\(\Omega\)** & **71 k\(\Omega\)** & **21 k\(\Omega\)** & **6.2 k\(\Omega\)** & **1.84 k\(\Omega\)** & **543 \(\Omega\)** & **161 \(\Omega\)** & **48 \(\Omega\)** \\
3.3 M\(\Omega\)\(\parallel\) & 56 k\(\Omega\)\(\angle\) & 470 k\(\Omega\)\(\parallel\) & 5
## 2 Methodology
### Robust Model Based on Additional Observations with Opposite Weight  
Let the residual of the \(i^{th}\) observation be the largest, and if it is defined as one outlier, its contribution can be removed by combining Equation (7) and an additional observation equation, which is same as the \(i^{th}\) observation equation but with the opposite weight. The additional observation can be expressed as  
\[l_{i}=a_{i}X+e_{i},-p_{i} \tag{9}\]  
where \(a_{i}\) is the \(i^{th}\) row vector of the design matrix \(A\), and \(l_{i}\) is the \(i^{th}\) element of the OMC vector \(L\). \(p_{i}\) is the \(i^{th}\) diagonal element of the weight matrix \(P\).  
By combining Equation (7) and Equation (9), the robust model of real-time PPP estimation becomes the following formulas [34]:  
\[\begin{array}{l}N^{-1}_{new}=N^{-1}-\left(N^{-1}a_{i}^{T}\right)\beta(a_{i}N ^{-1})\\ X_{new}=X+\left(N^{-1}a_{i}^{T}\right)\beta(l_{i}-a_{i}X)\\ \beta=\left(1/p_{i}+a_{i}N^{-1}a_{i}^{T}\right)^{-1}\end{array} \tag{10}\]  
Substituting the new estimates \(X_{new}\) into Equation (8), the updated residuals can be expressed as \(V_{new}\). The unit weight STD can be obtained and replaced by  
\[\hat{\sigma}_{STD}=\sqrt{\frac{VT^{T}_{new}PV_{new}}{n-1}} \tag{11}\]  
where the number of observations reduces by one because of the removal of the outlier observation.
# Influence of Structural Parameters on Deformation Behavior of Metal Rhombic Chain Mesh
## 3 Results  
The solidity Sn represents the ratio of wire projection area to the overall netting projection area. In netting structural design, changes in wire diameter and mesh size both lead to variations in netting solidity, directly affecting netting force distribution and deformation. As a crucial parameter in hydrodynamic calculations of netting, the solidity calculation is shown in Equation (10).  
\[S_{n}=\tfrac{2d}{l}-\left(\tfrac{d}{l}\right)^{2} \tag{10}\]  
Pretension involves pre-stretching the metal netting wires, utilizing this pre-stretch to generate static friction force between connected components through compression, thereby transmitting tension and deformation. To investigate the effects of netting solidity and initial pretension FT on netting deformation, numerical simulations were conducted under two loading conditions: concentrated loads of 60 N, 80 N, 100 N, 120 N, and 140 N and distributed loads of 6 N, 8 N, 10 N, 12 N, and 14 N, combined with three initial pretension conditions of 200 N, 250 N, and 300 N. The vertical displacement of the metal diamond chain netting was analyzed for three solidity values, which are shown in Table 3.
# Semantic Segmentation of Brain Tumors Using a Local-Global Attention Model
## 3 Methods
### The Overall Architecture of Swin UNETR  
Swin UNETR [21] is also a U-shaped model. To better understand its design method, we divide its overall architecture into three parts: an encoder, decoder, and feature fusion component.  
The encoder is composed of 3D versions of Swin Transformer blocks. Each block contains two computational units: Window-based Multi-Head Self-Attention (W-MSA) and Shifted Window-based Multi-Head Self-Attention (SW-MSA). Figure 2 displays this structure in detail. W-MSA can be regarded as a large-kernel convolution with fewer parameters, while SW-MSA enhances information interaction between local windows. In order to progressively reduce the spatial dimensions of feature maps while increasing their channel dimensions, a patch merging module has been designed. Different from conventional pooling operations or convolutions with a stride of 2, the patch merging module performs uniform sampling along each spatial dimension and uses a linear layer to fuse the sampled features.  
Figure 1: Overview of LG UNETR architecture.  
Figure 2: The structure of Swin Transformer blocks.  
The decoder is composed of 3D versions of residual blocks. Each block contains two \(3\times 3\times 3\) convolutional layers, with each convolution followed by an instance normalization layer and a Leaky ReLU layer. Figure 3 illustrates this structure. A \(2\times 2\times 2\) deconvolution layer with a stride of 2 is used to increase the resolution of feature maps after each residual block. The feature fusion component also consists of a residual block and a concatenation operation. The residual block aims to convert attention features into convolutional features to ensure consistency in feature types between the encoder and decoder. The concatenation operation is responsible for the feature fusion.
# Machinery Fault Signal Detection with Deep One-Class Classification
## 3 Fault Signal Detection with Deep Support Vector Data Description
In addition, we also do not employ bias terms and bounded activation functions to prevent the hypersphere collapse problem, as suggested by [PERSON] et al. [12]. After the deep SVDD structures are trained, they are finally used to determine whether newly collected signals are faulty or not. To this end, the anomaly score for the new signal \(x_{new}\), denoted as \(s(x_{new})\) and quantified as follows:  
\[s(x_{new})=\left\|\phi\Big{(}x_{new};\psi^{*}\Big{)}-c\right\|^{2}, \tag{8}\]  
where \(\psi^{*}\) are the optimal parameter derived from training deep SVDD, \(\phi\big{(}x_{new};\psi^{*}\big{)}\) is the mapped signal \(x_{new}\) into the \(q\)-dimensional latent feature space. If new signal \(x_{new}\) having large anomaly score is deemed as a fault signals, and an appropriate action should be taken to cope with abnormality on the machinery status. Conversely, for the new signal having a small score, it is classified as a target signal, and any actions are not taken.
## 1 Introduction  
Edges have been defined as significant local variations in image intensity and are considered an essential feature of image analysis. Given the importance of edge detection in image processing, image analysis, image pattern recognition and computer vision techniques, edge detection has continued to be an effective research area and a fundamental step in retrieving information from digital images [1]. There are many edge detection methods that have been developed by several researchers [2, 3, 4]. However, there are some factors that affect the performance of the edge detection process, especially in the case of images with low-intensity differences between adjacent regions. These differences lead to blurring of the boundaries between these regions. Blurred boundaries can affect the accuracy of the edges delineation and make edge detection a challenging task [5]. This paper addresses the problem of detecting edges in images of hot specimens of molten copper that emit thermal radiation since the illumination of the specimen's boundaries can be considered an additional factor affecting image quality [6].  
The aim of the research in this paper is to propose an edge detection method based on the local gradient estimation to define the edges of a metallic droplet images obtained from the THERMO-WET system for high temperature measurements of surface properties and compare the edges to the contour line obtained from the active contour model of the same images [7], and crowdsourcing-based detection [8].  
The studied images show that the edges are noticeably blurred due to the thermal effects. The edge detection method presented in this paper prove to give better results compared to the classic active contour method [9].
## 2 Theoretical Analysis of the Directivity Response  
The following section describes the basis of the frequency-dependent directivity response of flat panel loudspeakers. A numerical simulation model was set up to analyze the frequency-dependent radiation mechanism of individual modes and to derive the different radiation mechanisms of the near-field effect above and below the coincidence frequency \(f_{c}\). Furthermore, the frequency-dependent DI values of several modes were compared. The findings show that the standing wave pattern especially tends to increase off-axis radiation. Therefore, traveling waves or a combination of standing and traveling waves are preferred. The differences in both wave types for the panel displacement and the directivity response are described and visualized.
# Joint Deployment of Sensors and Chargers in Wireless
## 4 Solutions
### Area Discretization  
For sensor deployment, all effective deployment locations of sensor are within the maximum sensing distance \(d(s)\) centered at the Pols, and \(d(s)=\left(\sqrt[6]{-\frac{ln\,c_{th}}{\Lambda}}+(r-r_{e})\right)\). Therefore, a grid discretization of perception area is adopted at each Pol. As shown in Figure 3, each grid square is represented by its upper right corner. If the distance between a grid square and the Pol is less than \(d(s)\), this grid square is a candidate deployment location for sensor, and all candidate deployment locations for sensors form a set \(S^{\prime}\). In order to minimize the discretization error, an integer \(L_{s}=3\) is used as the number of squares along the radius direction, and then the length of each square is:  
\[d_{s}=d(s)*sin\left(tan^{-1}(1/L_{s})\right) \tag{12}\]  
For charger deployment, all effective deployment locations of charger are within the maximum charging distance \(d_{th}\) centered at the sensors. Therefore, a grid discretization of charging area is adopted at each sensor. Similarly, each grid square is represented by its upper right corner, and the length of each square is \(L_{c}\). If the distance between a grid square and the sensor is less than \(d_{th}\), this grid square is a candidate deployment location for charger, and all candidate deployment locations for chargers form a set \(C^{\prime}\).
# The Holistic Perspective of the INCISIVE Project--Artificial Intelligence in Screening Mammography
## 2 Relevance and Challenges  
### Bring Together AI and Medical Experts  
AI has a long tradition in computer science and is gradually changing the landscape of healthcare and biomedical research. The boost that AI offers the medical domain includes the enhancement of clinical diagnosis and decision-making performance, such as support for diagnosing patients, as well as making prognostic prediction or selecting treatments through clinical decision support (CDS) systems [7]. The contribution of AI in the oncology field is notable, due to the use of medical imaging and digital pathology outcomes for patients' stratification and cancer prevention which offer further indications for decode molecular signaling cascade and cancer mechanism [8].  
Additionally, AI improves the care of chronic disease patients, suggesting precision therapies for illnesses with multiple complications, reducing medical errors [7,9]. However, it is worth noting that AI is not fully autonomous and cannot override human involvement. In the above context, healthcare professionals have to serve as knowledgeable and supportive guides and leaders in this process, making correct diagnoses at the highest possible level [10,11].  
However, this venture includes complex challenges that both sides--AI developers and medical experts--have to face properly. From the AI developers' point of view, complex challenges, particularly in the integration, fusion, and mapping of various heterogenous data, still remain. On the other side, clinicians will need to adapt to their new roles as information integrators, interpreters, and patient supporters, and the medical education system will have to provide them with the tools and methods to do so.  
Prior to this, several studies highlighted [12,13] many of the issues that are brought up by clinicians which are relevant to any tool or instruments introduced into a medical context, including the system's accuracy across representative cases, as well as the medical point-of-view represented by the AI assistant (e.g., due to choices in training data or labeling). Tools that came up from the above procedure have to be evaluated from a medical and commercial point of view as well. The main question surrounding who will end up controlling, certifying, or profiting from the application of AI still remains, and therefore there is uncertainty around the balance of regulatory safeguards and market forces to ensure that patients who benefit most must be a high priority.  
To date, related existing research and innovation initiatives are only limited to small-scale demonstrations, without being excessively validated for reproducibility and generalizability, and without exploring large datasets [10]. In order to explore the full potential of AI solutions in cancer imaging, legal and ethical issues are open challenges which should be addressed in the context of the INCISIVE research project.
# Simplified Welch Algorithm for Spectrum Monitoring
## 6 Conclusions  
In this research, a modified version of the Welch algorithm for spectrum monitoring is proposed. The characteristics of this design were thoroughly analyzed and studied. Spectrum monitoring is used in multiple applications, from interference mitigation to research and development in the scientific community. After the mathematical explanation and the presentation of the design itself, the effects of the \(\alpha\) parameter were presented. It can be concluded that with a smaller \(\alpha\) parameter, the system is more responsive and more appropriate for fast hopping frequency signals. On the other hand, with a larger \(\alpha\), the system is more accurate and is more appropriate for precise spectrum monitoring. It is safe to assume that the modified design can reliably and accurately represents any signal type as other commercial devices. From the last results we see that this design can represent low-power signal 5 dB lower than the other standard spectrum analyzers. The proposed algorithm decreases the required space on FPGA and benefiting from its practical design, it can be embedded in SDR or other FPGA core.  
The main consideration for this system is the convergence time based on the selected feedback parameter, which can cause loss of information for fast variant frequency components and large values in the RAM component that can generate the saturation error.  
**Author Contributions:** Conceptualization, M.H.S.; methodology, M.H.S.; writing--original draft preparation, M.H.S.; writing--review and editing, G.G. ([PERSON]), F.I., G.G. ([PERSON]); supervision, R.L.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research is part of the project entitled AVIO-601 in LASESNA Lab (Ecole de Technologie Superieure) named Interference Mitigation in Satellite Communication. It is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), Thales, Telesat, VIGILANT GLOBAL, CRIAQ, and Atem Canada. Special thanks to CMC for providing the required equipment to succeed with this project.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.