esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study of OWA Operators Learned in Convolutional Neural Networks
## 5 Experimental Framework
### CNN Architecture  
We designed a small CNN with the minimal size to host the new layer, composed of two convolutional blocks, each one composed of a convolutional filter followed by a batch normalization stage and a ReLU activation layer. After the convolutional stage, we used a simple dense layer as the output. The architecture is summarized in Table 1. For a review of the usage of the OWA layer on a larger network, refer to [13].  
The choice of the base architecture is again motivated to allow for more repetitions of the experiments. In this case, choosing a minimal architecture also reduces the number of variables involved in the configuration of the OWA layer, allowing us to focus better on the analysis of the learned operators.  
With this base architecture, we can insert the proposed layer between the two convolutional blocks, using the 64 channels of the first block as input and generating a new representation with an increased number of channels to use as input in the second convolutional block. This insertion point is denoted as OWA\({}_{1}\) in Table 1.
### Fouling Removal  
One of the most used fouling removal technique that has been applied to pipelines is the use of chemicals as it can achieve up to 100% removal [12]; however, this is achieved with the addition of harsh implications to the environment. Legislation has recently surfaced to demote the use of chemicals, thus other solutions for fouling removal and prevention must be reviewed and carried out in replacement of chemicals. Various techniques on the market require the halt of production and facilities to carry out defouling due to its invasive approach. The pause in production can amount to profit losses that can become costly. A noninvasive approach would provide added benefit of removal without the halting of facilities.  
Previous work has shown capabilities to remove inner wall pipe fouling noninvasively using ultrasounds [13; 14; 15; 16]. This is carried out by the attachment of a High-Power Ultrasonic Transducer (HPUT) onto the outer wall of a water-filled pipe. The HPUT delivers a high compressional wave through the structure and into the liquid medium to generate acoustic cavitation bubbles. The generated cavitation bubbles produce a fouling removal mechanism due to the implosion of these bubbles, creating high forces, pressure, and temperature that dislodges fouling from the fouled surface.  
Further work is required to validate its capability to achieving long-distance cleaning. To apply this technique onto subsea pipelines, the ultrasonic technique must be marinized for underwater applications.  
This paper begins with the methodology and development of a marinization technique to enclose a 40 kHz high power ultrasonic transducer in Section 2. To validate the marinized transducers, the laboratory methodology is discussed in Section 3. The designed transducer array is validated on a fouled 6.2 m long, Schedule 40, 6-inch diameter carbon steel pipe in Section 4. A field trial is carried out using the ultrasonic technique in Section 5 and a complete system demonstration for underwater applications is shown in Section 6. The paper concludes with the findings and suggests further work in Section 7.
## 5 System Performance
### Impact of Sampling Time  
The data collected from the multi-sensor device were exploited to assess the impact of sampling time. To this purpose, the original series of data, which were acquired with the sampling times reported in Table 1 and denoted as \(x_{\text{m}}\) hereafter, were decimated using various decimation factors. The decimated series were then linearly interpolated (\(x_{\text{i}}\)) to reconstruct series of equal length to the original ones. Subsequently, the maximum absolute error between the original and the interpolated series was evaluated and it is  
Figure 14: Carbon dioxide, TVOCs, PM 2.5, and PM 10 data acquired by the multi-sensor (solid), the commercial device (dashed), and the reference instruments (dashed-dotted lines) during a one-day measurement campaign. Data collected by the PROMET&O device refer to averages values evaluated at every report time.  
shown in Figure 15 for temperature, relative humidity, illuminance, TVOCs, PM 2.5, and carbon dioxide.  
As the sampling time increases, the maximum error also increases for all the considered physical quantities. Such a result is related to the down-sampling effect, meaning that a too-low sampling time may result in inaccurate measurement of transient events.
# Effects of Resonant Electromagnetic Fields on Biofilm Formation in _Pseudomonas aeruginosa_
## 3 Results  
### Planktonic Bacterial Growth Does Not Vary Statistically under Various Magnetic Field Conditions  
In a series of experiments, PAO1 cells in microtiter plates were exposed to either the ambient field (\"ambient\"), a shielded field (\"zero\"), or a specific EMF of 3.9 Hz (\"exposed\"), as described above. In some series, the ambient condition was omitted.  
No difference in absolute growth rate was observed (data not shown). After 20 h of exposure, the planktonic bacterial growth was recorded as OD600 values and normalized to the value for the \"shielded/zero\" condition for that series (set at 100). For individual wells in the microtiter plate, grown under identical conditions, the maximal standard deviation was 14%.  
Figure 2 shows the normalized average planktonic growth values for each of the three experimental conditions. Ambient and exposed plates showed values of 98 and 105, respectively, when normalized to the shielded condition (value 100). The average of several experimental series for each condition showed a consistent standard deviation of 12% in all cases.  
In conclusion, no statistically significant difference could be observed on planktonic bacterial growth between experimental conditions tested in this study.
## 2 3D Thermal Model
### Investigation of Losses (Heat Sources)  
Understanding the power losses associated with an electrical machine, their behavior and distribution are of paramount importance when evaluating machine performance and thermal distribution. The losses in electrical machines can be classified by the spatial distribution of heat sources. The calculations of the copper, core, and mechanical loss for SRM are presented in References [29; 30; 31]. The specific core losses \(P_{Fe}\) in [W/m\({}^{3}\)] can be expressed by the theory of the Bertotti equation [31]. The core loss is distributed into three items:  
\[P_{Fe}=P_{h}+P_{e}+P_{ex}=k_{hf}B^{a}+\frac{\sigma(\pi d)^{2}}{6\rho}f^{2}B^{ 2}+k_{ex}f^{1.5}B^{1.5}, \tag{2}\]  
where \(P_{h}\), \(P_{e}\) and \(P_{ex}\) are hysteresis loss, eddy current loss and excess loss, respectively. The manufacturer of the magnetic sheets provides the value of iron loss in [W/kg] for given values of magnetic flux density (\(B\)) and frequency (\(f\)). Based on curve fitting techniques, the loss coefficients (\(k_{h}\), \(\alpha\) and \(k_{ex}\)) can be identified. These coefficients that are used to compute iron losses are described in Table 3 for ferromagnetic material M19-29 gauge steel. Moreover, the B-H curve and the measured manufacturer's core loss curves of M19-29 gauge steel are shown in Figure 2.  
The ANSYS-Maxwell package was utilised to develop a loss model for the prediction of different components of the 72/48 SRM. To obtain the electromagnetic characteristics of the SRM, 2D FEM is developed. It evaluates the core losses in the SRM fed by an asymmetrical half-bridge power converter circuit and control circuit, which is modelled in SIMPLORER as presented previously [1]. The converter is powered by a 510 Vdc power supply. The current chopper control of phase A is performed with a turn-on and turn-off angle of 3.87\({}^{\circ}\) and 6.37\({}^{\circ}\), respectively, the sequence of phase B and C has been reported previously [1]. The average of the electromagnetic torque is 7.28 kNm obtained at DC current 174 A. The core loss distribution in the stator and rotor at rotor positions 6.28\({}^{\circ}\), rated speed 105 rpm and rated load can be seen in Figure 3a. The copper losses were specified as functions of stator Dc current with winding resistance per phase (0.217 \(\Omega\)), as shown in Figure 3b. In summary, the main losses in different portions of the 72/48 SRM are listed in Table 4.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Description** & **Value** & **Unit** \\ \hline Hysteresis coefficient, \(k_{h}\) & 165.36 & [Ws/ T\({}^{2}\)m\({}^{3}\)] \\ Electric conductivity, \(\sigma\) & 1.96 \(\times\) 10\({}^{6}\) & [S/m] \\ Lamination thickness, \(d\) & 0.35 \(\times\) 10\({}^{-3}\) & [m] \\ Excess loss coefficient, \(k_{ex}\) & 0.517 & [W/T\({}^{1.5}\)s\({}^{-1.5}\)m\({}^{3}\)] \\ Mass density, \(\rho\) & 7800 & [kg/m\({}^{3}\)] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Material coefficients for ferromagnetic M19-29 gauge steel.
# Cooperative Spectrum Sensing Based on Convolutional Neural Networks
## 2 Related Work
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Main Contribution** & **Advantages** \\ \hline
[30] & A STFT-CNN method is proposed for SS based on the short-term Fourier transform (STFT) and CNN. & The signal feature in the frequency domain is considered. \\ \hline
[31] & The application of machine learning (ML) models in cooperative spectrum sensing of cognitive radio networks (CRNs) is considered, including multilayer perceptron (MLP), support vector machine and Naive Bayes. & The sensing complexity is much lower compared with the ML-based schemes. \\ \hline
[32] & A CNN-LSTM detector is proposed, which first uses the CNN to extract the energy correlation features from the covariance matrices generated by the sensing data; then, the series of energy correlation features corresponding to multiple sensing periods are input into the LSTM so that the PU activity pattern can be learned. & The time series features are considered for the possible improvement of the sensing performance. \\ \hline
[33] & The distributed deep reinforcement learning method is adopted to learn the optimal CSS strategy. & The distributed CSS is discussed and analyzed. \\ \hline
[34] & Multiple machine learning-enabled solutions are adopted to tackle the challenges of the complex sensing model in CSS for a non-orthogonal multiple access transmission mechanism, including unsupervised learning algorithms (K-means clustering and Gaussian mixture model) as well as supervised learning algorithms (directed acyclic graph–support vector machine, K-nearest-neighbor and back-propagation neural network). & The sensing accuracy is greatly improved with a moderate complexity. \\ \hline
[35] & Deep reinforcement learning (DRL)-based CSS algorithm is proposed, which is employed to decrease the signaling in the network of SUs. & The sensing accuracy of CSS is improved as far as possible. \\ \hline
[36] & Based on the fact that pulse radar signals can be modeled as a train of rectangular pulses with two amplitude levels, we develop a generalized likelihood ratio test spectrum sensing scheme, as well as its less complex sub-optimal variations, to detect the presence of such signals. & The closed-from solutions can be obtained with a lower complexity. \\ \hline
[37] & A deep compressive spectrum sensing GAN (DCSS-GAN) is proposed, where two neural networks are trained to compete with each other to recover the spectrum from undersampled samples in the time domain. & The sensing performance with the undersampled samples can be greatly improved. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The machine learning-based SS schemes.
## 7 QC-RL-LDPC Codes Construction for the Proposed System  
Numerous methods for the construction of QC-LDPC have already been proposed, and a majority of finite code designs are centered around creating a Tanner graph structure with advantageous features, for example, in [40; 41; 42; 43; 44]. However, in this investigation, in order to design the entire QC-RL-LDPC coding scheme, we integrated the base graph construction by progressive edge growth (PEG, [40]) with protograph optimization for PBRL codes using density evolution [13] and proprietary algorithms to determine CPMs [36]. This allowed us to create a single, comprehensive method that gives both \(\mathbf{H}_{\text{HR}}\) and \(\mathbf{H}_{\text{IR}}\) as a result.  
The proposed approach to the construction of the QC-RL-LDPC can be summarized as in the following steps:  
1. The HRC base graph, that is, a graph corresponding to the \(R\times C\) base matrix \(\mathbf{W}_{\text{HR}}\) indicating nonzero submatrices in \(\mathbf{H}_{\text{HR}}\) in (2), is constructed with an improved PEG algorithm [16]. By row and column permutations, \(\mathbf{W}_{\text{HR}}\) is converted to ALT (almost lower-triangular) form.
2. The rows of the IR code base matrix \(\mathbf{W}_{\text{IR}}\) are determined subsequently with a greedy optimization that uses density evolution with the reciprocal channel approximation, the method described in [13].
3. The parity check matrix of the quasi-cyclic HR code \(\mathbf{H}_{\text{HR}}\) and the IR part \(\mathbf{H}_{\text{IR}}\) are initialized with CPMs with zero shifts in the positions indicated by ones in \(\mathbf{W}_{\text{HR}}\) and \(\mathbf{W}_{\text{IR}}\), respectively.
4. The cyclical shift values \(s_{r,c}\) in \(\mathbf{H}_{\text{HR}}\), together with the matrix \(\mathbf{H}_{\text{IR}}\), at positions corresponding to the values of 1 in \(\mathbf{W}_{\text{HR}}\) and \(\mathbf{W}_{\text{IR}}\) are subsequently modified in the entire structure \(\mathbf{H}\) (9) simultaneously, with an algorithmic search method similar to the one proposed in [36; 45] for LDPC codes. The algorithm seeks a Tanner graph with minimized existence of small cycles with low external connectivity, measured by the extrinsic message degree (EMD) feature.  
The crucial characteristic of the designed algorithm is that after determining base matrices (protographs) for both parts \(\mathbf{H}_{\text{HR}}\) and \(\mathbf{H}_{\text{IR}}\), with appropriate methods previously known for rate-fixed LDPC codes, the whole graph of the QC-RL-LDPC coding scheme is optimized with a joint algorithm.
## 2 Numerical Methods
### Numerical Methods  
FVM (Finite Volume Method) based discretization usually uses advanced reconstruction methods to solve for inviscid fluxes. The AUSM+ scheme obtains mesh interface flux by advancing the calculation based on the reconstruction of the solutions of both sides of the mesh interface. This approach is characterized by small dissipation in the viscous layer and high definition for shock waves. The basic idea of the AUSM+ scheme is to split the inviscid flux term into a convective flux term, and a pressure flux term [20]. The interface flux using this approach is as follows:  
\[F_{1/2,\text{AUSM+}}=c_{\text{f}}\Big{(}M_{\text{L}}^{+}\big{|}_{\beta=1/8} \psi_{\text{L}}+M_{\text{R}}^{-}\big{|}_{\beta=1/8}\psi_{\text{R}}\Big{)}+ \Big{(}P_{\text{L}}^{+}\big{|}_{\alpha=3/16}P_{\text{L}}+P_{\text{R}}^{+}\big{|} _{\alpha=3/16}P_{\text{R}}\Big{)} \tag{4}\]where 1/2 denotes the mesh interface, and the subscripts L or R denote variables on the left and right side of the control volume interface, respectively. The definitions used in this model are given below:  
\(c_{\mathrm{f}}=\min(c_{\mathrm{fL}},c_{\mathrm{fR}})\), where:  
\[c_{\mathrm{fL}}=\left\{\frac{1}{2}\left[\frac{\gamma p_{\mathrm{L}} +\mathbf{B}_{\mathrm{f}}\cdot\mathbf{B}_{\mathrm{L}}}{\rho_{\mathrm{L}}}+ \sqrt{\left(\frac{\gamma p_{\mathrm{L}}+\mathbf{B}_{\mathrm{L}}\cdot\mathbf{B} _{\mathrm{L}}}{\rho_{\mathrm{L}}}\right)^{2}-4\frac{\gamma p_{\mathrm{L}}B_{ \mathrm{nL}}^{2}}{\rho_{\mathrm{L}}^{2}}}\right]\right\}^{\frac{1}{2}};\] \[c_{\mathrm{fR}}=\left\{\frac{1}{2}\left[\frac{\gamma p_{\mathrm{R }}+\mathbf{B}_{\mathrm{R}}\cdot\mathbf{B}_{\mathrm{R}}}{\rho_{\mathrm{L}}}+ \sqrt{\left(\frac{\gamma p_{\mathrm{R}}+\mathbf{B}_{\mathrm{R}}\cdot\mathbf{B} _{\mathrm{R}}}{\rho_{\mathrm{R}}}\right)^{2}-4\frac{\gamma p_{\mathrm{R}}B_{ \mathrm{nR}}^{2}}{\rho_{\mathrm{R}}^{2}}}\right]\right\}^{\frac{1}{2}};\]  
The convective flux term, \(M^{\pm}\), and that of pressure term, \(P^{\pm}\), are respectively assigned as:  
\[M^{\pm}_{\beta=1/8}=\left\{\begin{array}{c}\pm\frac{1}{4}(M\pm 1)^{2} \pm\frac{3}{8}\left(M^{2}-1\right)^{2},\quad\left|M\right|\leq 1;\\ \frac{1}{2}(M\pm\left|M\right|),\quad\left|M\right|>1\end{array};\right.\] \[P^{\pm}_{\alpha=3/16}=\left\{\begin{array}{c}\pm\frac{1}{4}(M \pm 1)^{2}(2\mp M)\pm\frac{3}{16}M\left(M^{2}-1\right)^{2},\quad\left|M\right| \leq 1;\\ \frac{1}{2}(1\pm\mathrm{sgn}(M)),\quad\left|M\right|>1\end{array};\right.\]  
In order to match the high spatial accuracy, the 2 nd-order Runge-Kutta method was applied for temporal discretization. Given the semi-discretization equation is \(\frac{du}{dt}=L_{\mathrm{h}}(u)\) where \(L_{\mathrm{h}}(u)\) denotes spatial discretization operator, the temporal discretization scheme is given by:  
\[\begin{array}{l}u_{j}^{(1)}=u_{j}^{n}+\Delta tL_{h}u_{j}^{n},\\ u_{j}^{n+1}=\frac{1}{2}\left(u_{j}^{n}+u_{j}^{(1)}\right)+\frac{1}{2}\Delta tL _{h}u^{(1)}.\end{array} \tag{5}\]
# Sport-Specific Abdominal Wall Muscle Differences:
###### Abstract  
Aim: This study aims to compare the thickness of abdominal wall muscles--the external oblique (EO), internal oblique (IO), transversus abdominis (TrAb), rectus abdominis (RA), and inter-recti distance (IRD)--between amateur soccer and basketball players using ultrasonography. Methods: This cross-sectional study was conducted with 35 male amateur athletes, including 17 soccer players and 18 basketball players. Ultrasonographic measurements of the EO, IO, TrAb, RA muscles, and IRD were taken while the muscles were in a relaxed state for all the participants in both sides. Results: Significant differences were found in the RA muscle thickness, with basketball players showing a greater mean thickness compared to soccer players. No significant differences were observed in the TrAb, IO, and EO muscles between the two groups. The IRD showed a trend towards larger separation in basketball players, though this was not statistically significant. Conclusions: This study highlights sport-specific adaptations in the RA muscle, likely due to the distinct physical demands of basketball and soccer. The findings underscore the importance of tailored training and rehabilitation programs that consider these morphological differences to enhance performance and reduce injury risks.  
ultrasound imaging; injury prevention; sport; muscle; sports; gender specificity Article
# Remote Sensing in Urban Forestry:
## 2 Remote Sensing Applications in Urban Forests
### Theme 1: Multi-Source Input
#### 2.1.2 Satellite Imagery and Airborne LiDAR
Point-cloud colourisation is a promising technique for remote sensing data fusion. By combining every LiDAR point with spectral information extracted from satellite imagery, the colourised 3D point clouds with NDVI can be employed not only for segmenting individual trees but also for distinguishing trees from their surrounding urban environments. Nonetheless, it should be noted that the estimation accuracy of combining satellite imagery and LiDAR data would be reduced if the fetch interval between the two data sources is relatively long, or if the research objects are understory urban forests.
## 3 Preliminaries and Definition
### Cryptographic Background
#### 3.1.2. Bilinear Pairings  
Let \(\mathbb{G}_{1}\), \(\mathbb{G}_{2}\), and \(\mathbb{G}_{T}\) be three cyclic groups of prime order \(p\). Let \(u\) and \(v\) be generators of \(\mathbb{G}_{1}\) and \(\mathbb{G}_{2}\), respectively. Bilinear map \(e\) is the map \(e:\mathbb{G}_{1}\times\mathbb{G}_{2}\to\mathbb{G}_{T}\) with the following properties.  
1. Bilinearity: For all \(g_{1}\in\mathbb{G}_{1}\), \(g_{2}\in\mathbb{G}_{2}\), and \(a,b\in\mathbb{Z}_{p}^{*}\), \(e(g_{1}^{a},g_{2}^{b})=e(g_{1},g_{2})^{ab}\).
2. Non-degeneracy: \(e(u,v)\
eq 1\).
3. Computability: There is an efficient algorithm to compute \(e(g_{1},g_{2})\) for any \(g_{1}\in\mathbb{G}_{1}\) and \(g_{2}\in\mathbb{G}_{2}\).  
Like many pairing-based cryptographic protocols, our protocol uses a special form of bilinear map called a symmetric pairing where \(\mathbb{G}_{1}=\mathbb{G}_{2}\). For the remainder of the paper, all bilinear pairings are symmetric, and we denote \(\mathbb{G}_{1}=\mathbb{G}_{2}\) by \(\mathbb{G}\).  
Weil pairing [27] or Tate pairing [28] on elliptic curves can be used as an efficiently computable non-degenerate bilinear map.
## 6 Discussion
### Lithology Determination of Pipe Nos. 50 and 110 Kimberlites  
The specific rock properties of the kimberlite in southern Liaoning have not been discussed in detail, while the composition characteristics of phlogopite can restrict the attribution of the kimberlite. In contrast to the Mengyin diamond area situated in the NCC, no discernible evolutionary pattern of tetraferriphlogopite was observed in this investigation [69], while enrichment of both Fe and Al in #50 phlogopite is an identifying characteristic of kimberlites [26; 77]. The behavior of the #50 phlogopite in this study is somewhat similar to that of the Tongo micaceous kimberlite from the Man Craton, West Africa [68], and Aries micaceous kimberlite from the Central Kimberley Basin, Western Australia [23]. For instance, (1) both samples exhibit a high abundance of mica; (2) the majority of phlogopite displays simultaneous Fe and Al enrichment, which is considered a typical characteristic of kimberlites; and (3) no diopside has been observed in the petrographic analysis. The difference is that the carbonate in the Tongo kimberlite is dolomite, while the carbonate in the Liaoning No. 50 pipe kimberlite is calcite. This reflects the special characteristics of the southern Liaoning kimberlite. Because the evolution trend of the #50 phlogopite is similar to that of the Tongo and Aries micaceous kimberlites and there are no obvious lamprophyre features, the No. 50 pipe kimberlite is classified as a micaceous kimberlite. In contrast, the No. 110 pipe kimberlite may exhibit a closer affinity to Group I kimberlite.