esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Wax Problems and Use of Solvents in the Oil Industry  
Most of the commercial wax inhibitors tested can reduce the deposition of paraffin with low molecular weight (\(\leq\)C\({}_{34}\)), but have little effect on high molecular weight paraffinic wax (C\({}_{35}\)-C\({}_{44}\)) [82]. Wax deposition in oil production flow mainly consists of C\({}_{7}\) paraffin. Paraffin deposition is controlled by temperature, which is why heat application is preferable to manage the paraffin deposition [83; 84]. When the temperature in a system decreases, paraffin in the liquid phase starts precipitating out of the solution as a solid. Wax deposition forms on the wall of the pipe where the temperature gradient is at its highest [3; 85]. Pressure-temperature relationship of wax appearance in both reservoir fluid and stock tank oil (STO) condition is shown by Figure 2.  
The tendency and deposition rate can be adequately predicted by calculating the rate of diffusion of wax molecules onto the wall with the following equation:  
\[\frac{dm}{dt}=\ -\rho D_{m}A\frac{dC}{dr} \tag{6}\]  
where \(m\) is the mass of deposit (kg), \(\rho\) is the density of wax (kg/m\({}^{3}\)), \(D_{m}\) is the molecular diffusion constant (m\({}^{2}\)/s), \(A\) is the deposition area (m\({}^{2}\)), \(C\) is the concentration of wax (%), and r is the radial position (m) [86].  
The radial concentration gradient can be easily calculated if it is broken down into two components by applying the chain rule as shown in the following equation:  
\[\frac{dm}{dt}=\ -\rho D_{m}A\frac{\partial C}{\partial T}\frac{dT}{dr} \tag{7}\]  
where \(T\) is temperature. The concentration gradient can be calculated from the wax concentration predicted by the thermodynamic model for the temperature range [3; 86].  
Additional chemicals considerably affect the process of paraffin wax deposition, where measurements show that wax deposition is rather high for all crude oil [87]. No correlation exists between the temperature and levels of total wax of crude oil [4]. The deposition of paraffin or wax can be called a depositor of carbonaceous material, which is insoluble or dispersed by crude oil under normal conditions [88; 89].  
Figure 2: Pressure–temperature relationship of wax appearance in both reservoir fluid and stock tank oil (STO) condition.  
The effectiveness of the inhibitor must be tested for each application. Tests must be conducted under all possible operating conditions. Chemical inhibitors for prevention of wax include:  
1. Thermodynamic wax inhibitor (TWI)--pressing cloud point, reducing viscosity and pour point, requires a high volume;
2. Depressant/pour point--modifies the wax crystal structure, reduces viscosity, and produces stress, but does not reduce the rate of wax deposition;
3. Dispersants/surfactants coat wax crystals to prevent wax growth and change the wetting characteristics to minimize wax adhesion to the pipe wall or other crystals [3; 90].  
Wax characteristics are outlined in Table 2.
# Optimisation of 2D U-Net Model Components for Automatic Prostate Segmentation on MRI
## 3 Materials and Methods
### Model Architecture  
We use a U-Net architecture [56] for our base model. As discussed in Section 2.1, U-Net has equal downsampling and upsampling layer pairs in the network forming an autoencoder network structure that can be beneficial for organ segmentation. We begin with what we denote as UNet_S, a simplified U-Net model that uses a quarter of the number of feature maps used in the U-Net of [56]. The model architecture is shown in Figure 1.  
The UNet_S consists of \(3\times 3\) convolution and ReLU activation layers in each convolution block (Conv Block), see Figure 2a. For downsampling, a \(2\times 2\) max pooling layer with stride 2 is used. For upsampling, a combination of a nearest-neighbour interpolation and \(2\times 2\) convolution with ReLU activation layers (Interp+Conv) are used. Skip connections concatenate the same resolution feature maps from the encoder to the decoder, but none are within the convolution block. There are 2 drop out layers with drop out rates of 0.5 after the fourth and fifth convolution block. Lastly, a \(1\times 1\) convolution layer with sigmoid activation is used to produce a probability map. We use 6 phases of evaluation to determine the optimised model: downsampling and upsampling component modification, skip connection implementation, drop out rate adjustment, batch normalisation implementation, pooling layer selection, activation function selection and batch normalisation placement. Table 1 shows the main components of each model being compared in this section. Figure 1: UNet_S architecture, the base model.
# End-to-End Classification Network for Ice Sheet Subsurface Targets in Radar Imagery
## 3 Results
### Image Filtering  
We compare the noise suppression effects of three different noise reduction methods on radar images: Lee filtering, anisotropic diffusion, and bilateral filtering. To quantify the noise reduction of each method, we use the equivalent number of looks (ENL) and edge preserving index (EPI) metrics as the primary evaluation criteria. ENL represents the image smoothing effect, and EPI represents the ability of the network to preserve image edges. ENL and EPI are expressed as  
\[ENL=\frac{\overline{\mu}^{2}}{\sigma^{2}} \tag{5}\]  
\[EPI=\frac{\sum\left(\left|p_{s}(i,j)-p_{s}(i+1,j)\right|+\left|p_{s}(i,j)-p_{s }(i,j+1)\right|\right)}{\sum\left(\left|p_{o}(i,j)-p_{o}(i+1,j)\right|+\left|p_ {o}(i,j)-p_{o}(i,j+1)\right|\right)} \tag{6}\]  
where \(\overline{\mu}\) denotes the mean value of the image, \(\sigma^{2}\) denotes the variance of the image, \(p_{s}(i,j)\) denotes the grayscale value of the output image at the point \((i,j)\), and \(p_{o}(i,j)\) denotes the grayscale value of the input image at the point \((i,j)\). A higher value of ENL corresponds to a smoother image; a higher EPI value corresponds to better preserved image edges.  
Using our experimental dataset, we calculate the EPI and ENL values for Lee filtering, anisotropic diffusion, and bilateral filtering [22] (Table 5). Bilateral filtering yields the best image smoothing and edge preservation of the radar images. Figure 6 visualizes the images after different filtering methods: original image (Figure 6a), Lee filtering (Figure 6b), anisotropic diffusion (Figure 6c), and bilateral filtering (Figure 6d). To demonstrate that filtering can improve the classification accuracy of the network, we calculate the OA and Kappa values for network models that had been trained and tested on the original image and the filtered image (Table 6); the network trained and tested with the filtered images results removes more noise from the images, resulting in improved ice subsurface target classification.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Conv Structure** & **OA** & **Kappa** \\ \hline
[3*3 64] & 97.44 & 94.36 \\
[3*3 64]*2 & 97.71 & 95.50 \\
[3*3 64]*3 & 97.54 & 94.52 \\
[1*1 64]+[3*3 64] & 97.63 & 94.80 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different convolution structures after combining features.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Channel** & **OA** & **Kappa** & **Parameters (M)** \\ \hline
(256,48) & 97.66 & 95.44 & 474.8 \\ (128,48) & 97.67 & 95.45 & 404.8 \\ (64,16) & 97.71 & 95.50 & 371.8 \\ (32,8) & 97.66 & 95.39 & 356.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of network classification performance and parameters with different channel combinations.
# Deep Learning-Based Flap Detection System Using Thermographic Images in Plastic Surgery
## 2 Materials and Methods
### Neural Network
#### 2.4.3 Decoding path  
The decoding path contains four R2 CL units that perform the upsampling operation of the output from the unit of the previous layer. With each upsampling operation, the number of feature maps is halved and the size is doubled. In the last layer of the decoding path, the size of the feature map is restored to the original size of the input image. In addition, AER2U-Net uses AGs to readjust the encoder's output features before merging the features at each resolution of the encoder with the corresponding features in the decoder. This module generates a gating signal that controls the importance of features at different locations. AGs gradually suppress the responses of adjacent tissue around the flap without clipping the regions of interest. More precisely, the attention coefficients \(\alpha_{i}\in[0,1]\) for each pixel \(i\) tend to give higher values in target flap regions and lower values in background areas. The output of the attention gate in layer \(l\) is obtained by multiplying element-wise the input feature maps and attention coefficients: \(\hat{x}_{i}^{l}=x_{i}^{l}\cdot\alpha_{i}^{l}\). The attention values are calculated for each pixel vector \(x_{i}^{l}\in\mathbb{R}^{F_{l}}\), where \(F_{l}\) denotes the number of feature maps in layer \(l\). In addition, a gating vector \(g_{i}^{l}\in\mathbb{R}^{F_{g}}\) is used to determine the focus area per pixel. To achieve better performance, the attention coefficient is derived by using additive attention:  
\[Q_{\alpha}^{l} =\Psi^{T}(\sigma_{1}(W_{x}^{T}x_{i}^{l}+W_{g}^{T}g_{i}^{l}+\beta _{g}))+\beta_{\Psi}, \tag{4}\] \[a_{i}^{l} =\sigma_{2}(Q_{\alpha}^{l}), \tag{5}\]  
where \(\sigma_{2}\) corresponds to the sigmoid activation function, \(W_{x}\in\mathbb{R}^{F_{l}\times F_{\text{int}}}\) and \(W_{g}\in\mathbb{R}^{F_{g}\times F_{\text{int}}}\) are linear transformations, calculated by channel-wise \(1\times 1\times 1\) convolutions for the input tensors, and finally, \(\beta_{g}\in\mathbb{R}^{F_{\text{int}}}\) and \(\beta_{\Psi}\in\mathbb{R}\) denote the biases.
# Towards Efficient Wireless Body Area Network Using Two-Way Relay Cooperation
## 4 Simulation, Performance Evaluation and Optimal Packet Size
### Optimal Packet Size
The cooperative links perform well over a range of packet sizes until the performance starts to degrade for larger packets at about 48 cm, where we get optimal packet size of around 1500-1800 bits. At about 50 cm, the optimal packet size becomes 500 bits and single one-way relay link performs better than a two-way relay link for larger packets. Further increase of hop length from 50-60 cm results in a decrease of packet size transmitted with greater EE. At this point, the threshold distances for all three communication modes and effective link lengths for various packet sizes are known, so, based on the observations, a smart switching scheme can be formulated. A hybrid distance-based scheme is presented in the next section to maximize the advantage from a specific communication mode. The conditions of the link remain static for a single transmission but vary afterwards resulting in altered source-destination hop-length. The effect of this quasi-static behavior is catered in terms of posture changes. In the case where hop-length exceeds the effective direct link but relay link is not accessible, coding can be used to further improve the effective link length.
# A Performance Evaluation for Software Defined Networks with P4
## 5 Results and Analysis of Tier 1--Single Type of Traffic Run
### Case Study 4- Content Delivery Network
(**a**) Live video stream in multi-path topology for 600 s. (**b**) Live video stream in grid topology for 600 s. (**c**) Live video stream in the Internet topology for 1200 s. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**a. Network Performance of CDN Transfer in Multi-Path Topology**} \\ \hline \hline  & \multicolumn{2}{c}{**Multi-Path Topology-Single Type of Traffic**} \\  & \multicolumn{2}{c}{**(CDN)**} \\ \cline{2-4}  & **SDN+OvS** & **SDN+P4** & **Improvement** \\ \hline Throughput & \(1.0\times 10^{6}\) bps & \(1.6\times 10^{6}\) bps & 60\% \\ \hline Data transmitted & 667.63 MB & 973.61 MB & 46\% \\ \hline Buffering delay & 8.672 s & 2.7 s & (\(-\)69\%) \\ \hline Jitter & 0.255 ms & 0.164 ms & (\(-\)35.6\%) \\ \hline \multicolumn{4}{c}{**b. Network Performance of CDN Transfer in Grid Topology**} \\ \hline \hline  & \multicolumn{2}{c}{**Grid Topology-Single Type of Traffic**} \\  & \multicolumn{2}{c}{**(CDN)**} \\ \cline{2-4}  & **SDN+OvS** & **SDN+P4** & **Improvement** \\ \hline Throughput & \(0.9\times 10^{6}\) bps & \(1.6\times 10^{6}\) bps & 77\% \\ \hline Data transmitted & 647.63 MB & 973.61 MB & 50\% \\ \hline Buffering delay & 16.9 s & 2.7 s & (\(-\)84\%) \\ \hline Jitter & 0.351 ms & 0.207 ms & (\(-\)41\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Network performance for case study 4—CDN traffic. Figure 9: Jitter of CDN data transmission in case study 4. (**a**) Jitter experienced in CDN Transmission in multi-path topology. (**b**) Jitter experienced in CDN Transmission in grid topology. (**c**) Jitter experienced in CDN Transmission in the Internet topology.
## 5 Experimental Results and Discussion  
In this paper, we compare the performance of PCA, kNN, LOF, AASKR without penalty (AASKR-NP), and AASKR on the TEP simulation data, a benchmark dataset widely used in fault detection research to evaluate the proposed method. Fault detection performance is assessed by comparing type I (false alarm) and type II (miss detection) errors through hypothesis testing. Type I error refers to instances where normal operating conditions are incorrectly classified as faults, indicating the model's tendency to generate false alarms. A low type I error suggests that the model is stable in normal regions and does not frequently trigger unnecessary fault warnings, ensuring operational stability and trustworthiness. Conversely, a type II error occurs when actual faults go undetected, reflecting the model's ability to identify abnormal conditions. A low type II error is essential for ensuring that faults are reliably detected before they cause significant damage or system failures. Type I and type II errors have a trade-off relationship; we averaged these two metrics to evaluate the overall fault detection performance. The second experiment compares the early fault detection times between the conventional and proposed methods for an unplanned shutdown case in a fluidized bed boiler. The fault detection performance for artificially generated faults in TEP was compared. In this experiment, we assessed how much earlier the proposed method can detect fault signs compared to the actual boiler shutdown time. Both experiments were conducted using MATLAB R2021a.
## 4 Cramer-Rao Bound Derivation and Analysis  
CRB is a theoretical lower bound for the covariance of any unbiased estimation. Therefore, it often serves as a benchmark to verify whether an algorithm achieves the optimal performance [36, 37, 38, 39]. In this section, we shall give the CRB of 2D DOA estimation for signals without known waveforms from the previous works and derive the CRB with known waveforms, respectively. According to [24, 39], CRB of 2D DOA estimation for signals without known waveforms and unknown complex amplitudes, which is often referred as unconditional or stochastic CRB, is given by:  
\[\mathbf{CRB}_{u}(\mathbf{\omega})=\frac{\sigma_{v}^{2}}{2N}\Big{[}\text{Re}\Big{\{} \mathbf{H}\odot\mathbf{P}_{+}^{T}\Big{\}}\Big{]}^{-1}\in\mathbb{C}^{2K\times 2K} \tag{21}\]  
where \(\mathbf{\omega}=[\mathbf{\theta}^{T},\mathbf{\varphi}^{T}]^{T}\), \(\mathbf{H}\) is defined as:  
\[\mathbf{H}\triangleq\mathbf{D}^{H}\bigg{[}\mathbf{I}-\mathbf{A}\Big{(}\mathbf{A}^{H}\mathbf{A}\Big{)}^ {-1}\mathbf{A}^{H}\bigg{]}\mathbf{D}\in\mathbb{C}^{2K\times 2K} \tag{22}\]  
\[ \begin{cases}\mathbf{D}\triangleq\big{[}\mathbf{D}_{\theta},\mathbf{D}_{\varphi}\big{]} \in\mathbb{C}^{M\times 2K}\\ \mathbf{D}_{\theta}\triangleq\Big{[}\mathbf{a}^{\prime}_{\theta_{1}}(\theta_{1}, \theta_{1},\varphi_{1}),\mathbf{a}^{\prime}_{\theta_{2}}(\theta_{2},\varphi_{2}),\cdots,\mathbf{a}^{\prime}_{\theta_{k}}(\theta_{K},\varphi_{K})\Big{]}\in\mathbb{ C}^{M\times K}\\ \mathbf{D}_{\varphi}\triangleq\Big{[}\mathbf{a}^{\prime}_{\varphi_{1}}(\theta_{1}, \varphi_{1}),\mathbf{a}^{\prime}_{\varphi_{2}}(\theta_{2},\varphi_{2}),\cdots,\bm {a}^{\prime}_{\varphi_{K}}(\theta_{K},\varphi_{K})\Big{]}\in\mathbb{C}^{M \times K}\end{cases}  \tag{23}\]  
\[ \begin{cases}\mathbf{a}^{\prime}_{\theta}(\theta_{k},\varphi_{k})\triangleq\frac{ \partial\mathbf{a}(\theta_{k},\varphi_{k})}{\partial\theta_{k}}=\bigg{[}\frac{ \partial\mathbf{a}_{1}(\theta_{k},\varphi_{k})}{\partial\theta_{k}},\frac{ \partial\mathbf{a}_{2}(\theta_{k},\varphi_{k})}{\partial\theta_{k}}\cdots,\frac{ \partial\mathbf{a}_{M}(\theta_{k},\varphi_{k})}{\partial\theta_{k}}\bigg{]}^{T} \in\mathbb{C}^{M\times 1}\\ \mathbf{a}^{\prime}_{\varphi}(\theta_{k},\varphi_{k})\triangleq\frac{ \partial\mathbf{a}(\theta_{k},\varphi_{k})}{\partial\varphi_{k}}=\bigg{[}\frac{ \partial\mathbf{a}_{1}(\theta_{k},\varphi_{k})}{\partial\varphi_{k}},\frac{ \partial\mathbf{a}_{2}(\theta_{k},\varphi_{k})}{\partial\varphi_{k}}\cdots,\frac{ \partial\mathbf{a}_{M}(\theta_{k},\varphi_{k})}{\partial\varphi_{k}}\bigg{]}^{T} \in\mathbb{C}^{M\times 1}\end{cases}  \tag{24}\]  
\(\mathbf{P}_{+}\) is defined as:  
\[\mathbf{P}_{+}\triangleq \begin{bmatrix}\mathbf{PA}^{H}\mathbf{R}^{-1}\mathbf{AP}&\mathbf{PA}^{H} \mathbf{R}^{-1}\mathbf{AP}\\ \mathbf{PA}^{H}\mathbf{R}^{-1}\mathbf{AP}&\mathbf{PA}^{H}\mathbf{R}^{-1}\mathbf{AP}\end{bmatrix}
# Trajectory Control Approach for Single-Stage Soft-Switching Grid-Tied Inverters
## 5 Simulation and HIL Test Results  
Hardware-In-the-Loop (HIL) enables real-time testing and validation of control systems using high-level programming in a fast and user-friendly environment. This approach allows validation of control algorithms without a fully constructed physical power stage, significantly reducing risks and costs associated with potential hardware failures. The PLEXIM RT-Box is a cost-effective HIL solution particularly well-suited for this study [23; 24].  
The control algorithm for the single-stage soft-switching grid-tied inverter, using trajectory control and a finite state machine, was initially analyzed through circuit simulation and subsequently verified via HIL experiments. Figure 11 provides an overview of the HIL setup. The plant model, based on the single-stage soft-switching topology, was developed using a GUI-based editor and uploaded to the PLEXIM RT-Box. The control algorithm was coded in C, then uploaded to the TI 28379D digital signal processor, which enabled real-time interaction with the plant model in the RT Box.  
In the HIL plant, the resonance filter voltage, current \(v_{\text{C}},\ i_{L}\), DC side voltage, current \(v_{\text{dC}},i_{dc}\), grid side voltage, and current \(v_{ac},i_{dc}\) were generated by DAC and transmitted to the ADC in TI 28379D. In order to obtain the phase and zero crossing point of the grid voltage, a single-phase phase-locked-loop (PLL) was used [25]. According to the command radius and the measured input values, the DC side FSM and the AC side FSM generate gate signals using ePWM (Enhanced Pulse Width Modulation) modules in the TI 28379D. The input voltage was set to 38 V with power transfer of 340 W and the output bridge is connected to the 120 Vrms, 50 Hz grid with LCL filter. The resonant frequency of the series filter was set to 100 kHz and the switching was designed to operate above the resonant frequency for soft-switching specified in Table 5. In cases where the response of the RT Box HIL plant could not meet the required speed, the resonant frequency and switching frequency were adjusted by the same ratio to emulate the switching sequence and validate the control algorithm.  
Figure 10: AC-side state diagram.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **CASE 1 (P.E. = 1)** & **CASE 2** \\ \hline P & 340 W & 340 W \\ Q & 0 & 150 var \\ Vdc & & 36 V \\ Vgrid & & 120 Vrms \\ fgrid & & 50 Hz \\ Lr & & 3.3 \(\mu\)H \\ Cr & & 750 nF \\ Transformer turns ratio & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Specifications and parameters.  
A power factor correction mode (CASE 1) and reactive power control mode were executed in HIL. The measured waveforms and simulation results are presented in Figures 12 and 13.  
Figure 11: Hardware-In-the-Loop set-up, Controller (TI28379D), Plant (PLEXIM RT—box).  
Figure 12: Waveforms, Case 1.
###### Abstract  
Electro-optic modulators (EOMs) are crucial devices for modern communication enabling high bandwidth optical communication links. Traveling wave electrodes are used to obtain high-speed modulation in these EOMs. We present the electrode design and analysis along with the study of effects of changing orientation on device performance for a thin-film lithium niobate tunable Mach-Zehnder interferometer (MZI) that offers sub-THz bandwidth operations. High velocity and impedance matching with low RF attenuation, high third-order SFDR (\(\sim\)121 dB/Hz\({}^{2/3}\)) and a low half-wave voltage length product (1.74 V.cm) have been achieved for a bandwidth of 136 GHz. High-speed digital modulation using multi-level signal formats (PAM-2, QAM-4 and QAM-16) with low BER for 400 Gbps data has been demonstrated to assess the digital performance of the device.  
integrated optoelectronic; electrode design; thin-film lithium niobate; higher order modulation; optical communication; sub-THz communication; sub-Tbps communication +
Footnote †: journal: _Photonics_  
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