esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discussion
The drone dataset experiments in Section 2.6.2 do not show this behaviour, but this is due to the lack of complexity in the dataset. On the other hand, the benchmark on the SKAB dataset in Section 2.6.4 shows this behaviour clearly, due to a significantly increased number of features, signal length, and complex signal behaviour. In addition to this, by comparing the results in Tables 4 and 7, the models using Capsules are clearly able to train more effectively, and in some cases perform slightly better on the validation set in comparison to the training set, showing strong generalisation ability. Since the generalisation ability of a NN is directly correlated with the training performance, there is clear evidence to say that the Capsule directly contributes to the strong training performance of the proposed model, and reduces the overfitting generally encountered when training autoencoders. In comparison to recent state-of-the-art approaches, the proposed method overcomes the limitations found in each work. For example, the use of Capsules instead of traditional Convolutional layers used in [34] is shown empirically on the SKAB dataset to improve performance, and reduce overfitting without additional measures. Furthermore, the multi-channel input architecture is rarely used in anomaly detection tasks, and not used in any of the literature reviewed in the present work. However, it is empirically proven in all experiments in the present work that by using this approach, the model is able to learn the features more effectively when using the LSTM univariate encoders and Capsules for univariate decoding and multivariate spatial feature learning. With the proposed approach, there is potential to use heterogeneous input data with minimal modifications to the model architecture.
## 4 Performance Analysis of the Generated Models in Virtual Environments
### Discussion  
Analysing the data with the OVR Metrics Tool, we have determined that, for 3D models with characteristics similar to ours (polygons, texture size, etc.), an acceptable limit could be 72 objects (1,792,944 polygons) of low-poly models from Luma AI. Beyond this limit, the frame rate falls below 60 FPS and presents worrying complexity spikes, which severely impact the fluidity and performance of all the VR headsets used. For example, in scenes with 72 low-poly objects, the frame rate dropped to an average of 56.21 FPS on the Meta Quest 2, 60.16 FPS on the Meta Quest Pro and 62.60 FPS on the Meta Quest 3, indicating that performance starts to degrade significantly beyond this point.  
In the case of medium-poly models, reasonable results were obtained with up to 4 objects (599,600 polygons). When the number of medium-poly objects increased to 12, the total polygon count was similar to that of 72 low-poly objects, but the performance suffered more severely. Specifically, the average FPS dropped to 29.61 on the Meta Quest 2, 30.62 on the Meta Quest Pro and 48.08 on the Meta Quest 3, making these configurations less feasible for a smooth VR experience.  
Lastly, the performance offered by the high-poly models was very low, so their use is discouraged. For instance, high-poly scenes resulted in frame rates as low as 9.30 FPS on the Meta Quest 2, 12.40 FPS on the Meta Quest Pro and 15.97 FPS on the Meta Quest 3. These results clearly indicate that high-poly models are not suitable for standalone VR shopping environments due to their detrimental impact on performance.  
Conversely, low-poly models are deemed appropriate depending on the hardware currently available in VR headsets, as they allow greater scalability by adding more objects without severely impacting performance up to a limit of 72 objects. These could be suitable for large VR shopping spaces that simulate environments such as supermarkets or shopping centres. While the use of medium-poly models is very limited, they could be used in simpler spaces with fewer objects. Despite the visual quality of medium-poly models being slightly higher (approximately 5% improvement), the performance trade-off makes them less viable for extensive use.  
Finally, we discuss some limitations of these 3D models for their use in VR shopping environments. The tests performed completely isolated the 3D models; therefore, other aspects such as the mesh complexity of the virtual environment are not taken into account. It would be interesting to further research the effects of other optimisation techniques such as Levels of Detail (LoD) considering both the environment and the products. In order to obtain a better performance, an optimisation of the 3D model can be made in 3D software tools like Blender. Even though it could be beneficial, it would require people with technical expertise in the field in order to obtain improved results, which might not be possible in the circumstances of small businesses.
## 4 Results and Analysis
### Robustness of the CL-Transformer Classification Model Framework to Noise  
To assess whether the CL-Transformer model trained with Patch-Mix mixed noise can better handle noise in real-world scenarios, this paper conducts evaluation experiments on the UrbanSound8K dataset. First, we apply noise processing to the UrbanSound8K dataset, adding AWGNs with SNRs of 0 dB and 10 dB to a total of 8732 audio samples. Secondly, we extract the Fbank features of the audio as input for the CL-Transformer model. The experimental results are shown in Figure 8 and Table 7.  
The confusion matrices reveal that the innovative audio classification framework attains remarkable classification accuracies of 88.65% and 91.28% on the UrbanSound8K dataset, when subjected to AWGNs with SNRs of 0 dB and 10 dB, respectively. The model's performance surpasses the classification accuracy of some models on the UrbanSound8K dataset without background noise. The experiments demonstrate that our proposed CL-Transformer model framework significantly improves model performance, generalization, and robustness in noisy urban environments. Additionally, it verifies the feasibility of applying this classification framework in real-world scenarios.
# Efficient Data Delivery Scheme for Large-Scale Microservices in Distributed Cloud Environment
## 4 Simulation Scenarios and Results
### Assessment of the Changing Data Locality Rates  
This simulation scenario was implemented with the following settings to evaluate the impact of microservice event channel subscription locality on the data delivery performance of the compared approaches. Two hundred brokers were instantiated, with their coordinates chosen randomly from the coordinate pool. Every broker was in charge of traffic forwarding for 200 event channel topics, with microservice clients subscribing to the channels from a pool of 1000 topics in a distribution that followed [PERSON]'s law. Furthermore, for each broker the local subscription rate (LSR) was changed from 0.2 to 0.8 in steps of 0.2. A higher LSR means that more microservice clients of that broker share local event channels.  
Figures 5 and 6 show the simulation results of the scenario in terms of the ADL and AFT, respectively. Although the PSECTO scheme was better than the proposed model in ADL, the scheme achieved the best AFT. When the LSR was increased from 0.2 to 0.8 (indicating that microservice clients are more interested in local events), the ADL of the proposed scheme decreased from 57.04 ms to 43.43 ms, a 23.87% improvement in terms of ADL. In addition, the AFT of the proposed scheme was on average approximately 86.73% that of PubSubCoord-like (8.26% versus 9.52%). The reason for this is that the proposed method can take advantage of common event channels among the microservice clients of involved brokers to reduce the relayed traffic of the corresponding brokers by clustering them into proper groups. In contrast, the PubSubCoord-like scheme cannot take full advantage of the high data locality rates, as it does not cluster similar edge brokers together. This explains why it experienced the worst performance in terms of ADL and AFT in this scenario.  
Figure 4: Effect of differing number of topics on forwarding traffic.
## 3 Results  
### Sentinel-1 SAR-based Flood Mapping  
Interferometric coherence change analyses were applied to 11 Sentinel-1 descending orbit SAR images to map sections of Houston city and its surrounding areas that have recurrently been affected by severe weather-induced flooding. The proposed approach to map flood risk areas was initially developed based on the analyses applied to Sentinel-1 datasets acquired before and after the Tax Day storm and Hurricane Harvey events (Table 1). The accuracy of the proposed model in identifying flood-prone areas was tested using Sentinel-1 datasets from the recent (2019) flood event over the study area induced by Tropical Storm Imelda (Table 1).  
The following section is dedicated to analyzing the results of detecting and mapping floods over the study area in the earlier (2016 and 2017) flood events followed by the recent (2019) event. Moreover, attempts made to validate the Sentinel-1 SAR-based flood mapping method using different datasets and techniques are also discussed.
# GW-DC: A Deep Clustering Model Leveraging Two-Dimensional Image Transformation and Enhancement
## 3 Experiment and Results
### Conversion Result of Two-Dimensional Images  
The time-series were converted into four kinds of two-dimensional images, including grayscale images, RP images, MTF images, and GADF images. The clustering results of the next stage were used to determine the best image feature representation method. The six public datasets were converted into four kinds of two-dimensional images, and wavelet transform was applied to enhance the obtained images. Then, the proposed DC network was used to cluster according to the enhanced images. The clustering index NMI (\(\%\)) is shown in Table 2.  
The CBF and SonyAIBORobotSurface2 datasets had the best clustering results according to the RP images, only 4% and 2% higher than that for GADF images, respectively. The BME dataset had the best clustering effect according to the grayscale images and GADF images, and their results were 91.18%. The other datasets had the best clustering results according to GADF images. Comprehensively, the GADF representation method had a better representation effect, and this method was selected for image feature representation.  
The time-series of six datasets were transformed into two-dimensional images by the GADF, and the obtained images were denoised and enhanced by the wavelet transform method. To visualize the effect of image enhancement, the original two-dimensional images (shown in Line 1) and the enhanced images (shown in Line 2) of six datasets were displayed, as shown in Figure 10a-f.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Image Type** & **BME** & **CBF** & **SmoothSubspace** & **SonyABR-obotSface2** & **SyntheticControl** & **UMD** \\ \hline Grayscale & 91.18 & 44.32 & 22.89 & 23.50 & 26.26 & 41.50 \\ RP & 33.14 & 58.30 & 39.13 & 27.12 & 65.63 & 33.93 \\ MTF & 38.54 & 53.26 & 12.74 & 21.88 & 51.08 & 12.29 \\ GADF & 91.18 & 54.77 & 42.43 & 25.74 & 71.45 & 46.53 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The clustering results according to four kinds of images.  
Ten images in each dataset were randomly selected for comparative display. In the enhanced images, the blurred noise points were reduced, and the contour edge was clearer  
Figure 10: The comparison of image enhancement effects of six datasets.  
than the original images. The texture features of the images were enhanced by the wavelet transform method.
# Segmenting Urban Scene Imagery in Real Time Using an Efficient UNet-like Transformer
## 2 Methods
### Decoder
#### 2.2.2 Spatial Position Transformation Operation  
The window partition operation in the Swin Transformer is computation-friendly; however, its receptive field is limited in the windows, which has an adverse impact on the model's global information modeling ability. As a result, it limits the model's potential for some tasks that require high-resolution images, such as semantic segmentation and instance segmentation. Figure 3 illustrates the feature information flow based on window self-attention. We found that the output of a window is only related to the window itself, which hinders the cross-window information interaction and weakens the model's feature representation capability. As a remedy, we performed a position transformation operation to maintain the cross-window information interaction, which can be integrated into the window-based multi-head self-attention (WMSA) mechanism, while only introducing a small computational overhead. After that, a spatial alignment operation was used to restore the feature's original spatial position, which is the inverse of the position transformation operation. In addition, multi-head self-attention based on shifted windows uses the shifted window operation to maintain the information interaction between neighboring windows, which is effective but adds a significant computational cost. In this study, inspired by the work of [PERSON] [37], we used a depth-wise separable convolution between the MLP module and GLIM to improve the connection in adjacent windows. As a result, the model can take into account the connection between neighboring windows and cross-windows simultaneously, enhancing its potential to model global context and feature representation.
## 3 Results
### Effect of Carum carvi on In Vivo Antioxidant and Hepatoprotective Activities
#### 3.2.5. Effect of _Carum carvi_ on Acetaminophen-Induced TNF-\(\alpha\) Levels  
The concentration of TNF-alpha, a cytokine marker of systemic inflammation, was monitored in rat hepatic tissues treated with acetaminophen for 1 week. Figure 4 indicates that acetaminophen caused an increase in hepatic TNF-alpha levels. Treatment for the same period with the extract of _Carum carvi_ tangibly decreased the TNF-alpha levels compared to those obtained in the acetaminophen-treated group.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline  & \multicolumn{2}{c}{**Aspartate Aminotransferase**} & \multicolumn{2}{c}{**Alanine Aminotransferase**} & \multicolumn{2}{c}{**Lactate Dehydrogenase (LDH)**} \\  & **(AST or) (IU/L)** & & **(ALT or SGPT) (IU/L)** & & **(nmol NADH oxidised/min/mg protein)** \\ \hline  & Mean & SEM & Mean & SEM & Mean & SEM \\ \hline Control & 47.39 & 3.44 & 10.36 & 0.9 & 165.04 & 16.3 \\ \hline Acetaminophen & 76.5 & 5.8 \({}^{\text{a}}\) & 19.3 & 2.3 \({}^{\text{a}}\) & 268.3 \({}^{\text{a}}\) & 23.5 \\ \hline _Carum carvi_ D1 + Acetaminophen & 62.1 & 5.4 ** & 14.6 \({}^{\text{*}}\) & 1.33 & 208.5 *** & 24.6 \\ \hline _Carum carvi_ D2 + Acetaminophen & 50.47 & 6.8 *** & 11.5 ** & 1.14 & 174.2 *** & 19.8 \\ \hline Only _Carum carvi_ D2 & 47.07 & 3.8 & 10.2 & 1.6 & 169.8 & 21.1 \\ \hline \end{tabular}  
* The mean \(\pm\) SE (n = 6) is represented by each value; \({}^{\text{a}}\)_p_ < 0.001 compared to control group. \({}^{\text{*}}\)_p_ < 0.05, ** \(p\) < 0.01, and *** \(p\) < 0.001 compared to acetaminophen-treated group.  
\end{table}
Table 4: Effect of _Carum carvi_ treatment on restoring the acetaminophen-induced elevation in serum liver toxicity markers.
## 1 Introduction
### S2 Radiometric Requirements  
The Sentinel-2 Mission Requirements Document (MRD) [2] states that for the applications covered by this mission, the radiometric accuracy at top of atmosphere (TOA) has to be not worse than 3% (goal) to 5% (threshold). For inter-band radiometric calibration, 3% accuracy is also required.  
These requirements allow the definition of error bounds for the band reconstruction tasks that we assess in this work. For TOA reflectances, we can aim for the 3% reconstruction error. In terms of surface reflectance, the accuracy of the MACCS-ATCOR Joint Algorithm (MAJA) processor [3,4] is 0.01 (not in %, but in reflectance values), and we can use this value as the requirement.  
Given the fact that there are other errors in the measure (geometric registration between bands, Modulation Transfer Function (MTF) differences, etc.), achieving these error bounds can be considered rather ambitious.  
Other approaches to define the reconstruction requirements could be used. For instance, [5] presents a radiometric uncertainty tool which allows estimation of the radiometric uncertainty associated with each pixel of a Sentinel-2 image in the TOA images provided by ESA. The approach integrates all the errors from the TOA reflectance to the L1C product, and typical values are greater than 10% for open sea, 5% to 15% in rice fields covered by water and 2% to 4% for land areas. We see that the 3% specification is very strict.
## 5 Results and Analysis
### Consistency and Repeatability  
We estimated the overall reliability of the results by evaluating them at a confidence level of 95% or higher, using the acceptable internal consistency indicator. [PERSON]'s alpha showed an overall reliability of 99.64%, confirming the high reliability of the collected data. Figures 8 and 9 show the frequency values within the deviation range of the SOIC packages on a set of histograms with the variance dispersion correlation at 25% of the total deviation range.  
The histogram analysis of Figure 8 revealed a Gaussian curve with a center frequency trend and uniform dispersion around most results. Notably, dielectric fluid FR3 exhibited a higher center alignment frequency for SOP16 packages, while SOP24 packages showed the lowest center alignment frequency.  
Figure 8: Histogram with a normal curve of SOIC packages on dielectric fluids.  
Figure 7: Boxplot of X and Y axes SOIC packages deviation on dielectric fluid.  
Examining the dispersion matrix plot in Figure 9, we observed linear correlations between deviations in both negative and positive directions. Median and mode results for each sample set consistently equaled zero. To assess data consistency, we determined the sample arithmetic mean, maximum, and minimum deviation values and sampled standard dispersion around the mean, standard deviation, median, and mode for each set of samples, thereby reducing limitations.  
We observed that the SOP24 packages had the lowest misalignment values at the minimum and maximum end points relative to the SOP16 and SOP20 packages. However, their standard deviations recorded in tests on all dielectric fluids were the largest. We also observed that SOP16 packages had the highest maximum and minimum misalignment values for the DPMS dielectric fluid and the highest misalignment range in all tested dielectrics. The testing results of the SOP20 packages were compared and recorded as the lowest standard deviation for the FR3 dielectric fluid.
## 3 Models for Wind Turbine Wakes under Stable Conditions
### Modeling of Wind Turbine
#### 3.1.2 Actuator Disk Model Based on Blade Element Method  
In the BEM-based actuator disk model, the rotor plane consists of \(N\) actuator lines and each of the actuator lines is split into \(M\) element sections (Figure 1). The element section collects the local velocity and the rotor speed \(\Omega\) to calculate the element force and applies this force in the neighbor cells of the element section. The reference velocity is firstly assessed from the disk-averaged velocity and is then applied to evaluate the rotor speed.  
By transforming the local velocity at the blade element into polar velocity components (\(u_{r}\), \(u_{\theta}\), and \(u_{n}\)), the force of the blade element is  
\[\overrightarrow{\Delta F}=\frac{B\Delta\theta}{4\pi r}\rho u_{rel}^{2}c(C_{L} \overrightarrow{e_{L}}+C_{D}\overrightarrow{e_{D}})\Delta r \tag{18}\]  
where \(B\) is the number of the blades, \(c\) is the chord length, and \(\Delta r\) is the length of the element section. The drag coefficient of the element section, \(C_{D}\), and its lift coefficient, \(C_{L}\), which are functions of the attack angle \(\alpha\), are estimated from XFOIL [26] and then corrected by three-dimensional rotational effects of the blades based on [PERSON] et al. [27]. According to Figure 1, \(\alpha=\phi-(\beta+\gamma)\), where \(\phi=\arctan\left[u_{n}/(\Omega r+u_{\theta})\right]\) is the flow angle, \(\beta\) is the blade installation angle, and \(\gamma\) is the pitch angle.  
The element force is distributed across neighbor cells. The force added to a cell is calculated by  
\[\overrightarrow{\Delta F_{\text{cell}}}=\sum_{i}^{N\cdot M}\frac{1}{s^{3} \pi^{3/2}}\exp\left(-\frac{s_{i}^{2}}{s^{2}}\right)\overrightarrow{\Delta F _{i}}F_{\text{tip}}F_{\text{hub}}\Delta V_{\text{cell}} \tag{19}\]  
where \(s_{i}\) the distance of the \(i\)-th element to the cell and \(s\) is the cut-off length scale that takes a value between 2 and 3 cell sizes [28]. \(F_{\text{tip}}\) and \(F_{\text{hub}}\) are the Prandtl tip loss and hub loss functions [29]:  
\[F_{\text{tip}}=\frac{2}{\pi}\arccos\left[\exp\left(\frac{B(R-r)}{2r\sin\phi} \right)\right] \tag{20}\]  
\[F_{\text{hub}}=\frac{2}{\pi}\arccos\left[\exp\left(\frac{B(r-R_{\text{hub}})} {2r\sin\phi}\right)\right] \tag{21}\]  
where \(R\) is the rotor radius, \(R_{\text{hub}}\) is the hub radius, and \(r\) is the radial distance of the element to the rotor center.  
Figure 1: Schemtaics of the Blade Element Theory (BEM)-based actuator disk model.
## 2 Materials and Methods
### Log-Cosh Variational Auto Encoder
Therefore, we use the log-cosh function in place of the logarithmic reconstruction loss term in Equation (1) (i.e., the first portion). The reconstruction loss is concentrated closer to the origin when the logarithmic hyperbolic cosine function is used, avoiding excessive penalization when the reconstruction error is high. Equation (3) provides a clear formula for the logarithmic hyperbolic cosine function. \[f(x)=\log(\cosh(x))=\log\frac{e^{x}+e^{-x}}{2} \tag{3}\]  
To efficiently handle this issue and consider different loss regions, we propose using a log-cosh loss function, as shown in Equation (4). \(X\) and \(\overline{X}\) are the input vector and reconstruction vector, respectively. \[L_{\text{log-cosh}}=\sum_{i=1}^{n}\log(\cosh(X_{i}-\overline{X}_{i}))=\sum_{i= 1}^{n}\log\frac{e^{X_{i}-\overline{X}_{i}}+e^{-(X_{i}-\overline{X}_{i})}}{2} \tag{4}\]  
In our LVAE technique, the log-cosh function replaces the logarithmic reconstruction loss term (the first part) of Equation (1), reconstructing the loss. Equation (5) demonstrates the final loss function we create. \[Loss=\sum_{i=1}^{n}\log(\cosh(X_{i}-\overline{X}_{i}))-D_{KL}[q(Z|X)||p(Z)] \tag{5}\]  
In order to generate novel and unknown types of attack data, we introduce an enhanced VAE model that uses log-cosh as the reconstruction loss function. The generated data are then used to evaluate the model's ability to detect unknown attacks. Unlike conventional VAEs, we use Equation (5) as the loss function for the data generation phase. The input data are first mapped to the latent space feature variable, Z, using the encoder, which is then fed into the trained decoder to reconstruct the data. Finally, the classification stage is employed to classify the newly generated unknown types of attack data. Algorithm 1 outlines the training process for the LVAE method to generate new or unknown types of attack data, where \(x_{i}\) represents the \(i\)th sample of the input dataset, and \(\overline{x}_{i}\) represents the \(i\)th sample of the output dataset.