esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments  
In Section 3, we present our hypothesis that, by allowing a given layer to adjust to abrupt shifts to its input distribution _before_ pruning its channels, the heuristics used to rank order neurons by importance become more effective. Here, we compare our greedy layerwise channel pruning algorithm against alternative pruning schedules and importance measures to demonstrate the increased performance of our approach versus existing baselines. For the purpose of enabling inference acceleration on resource-constrained hardware such as mobile and edge devices, we combine the use of our channel pruning algorithm with moving average statistics-based uniform quantization, as described in Section 4. We evaluate the performance of our algorithms using the following discriminative and generative computer vision tasks:  
1. Image classification with DenseNet121 [40], and MobileNetV2 [41] on CIFAR100 [42]
2. Semantic segmentation with UNet [43], and FRRNet [44] on Cityscape [45]
3. Image style transfer with CycleGAN [46] on Cityscape  
We implement our pruning algorithms using the PyTorch deep learning framework [47] and create a custom neural network pruning module to compute neuron importance estimates and derive binary masks using local buffers3. Example usage of the functions in this repository is provided in Appendix A. For each task, our baselines are built using the existing open-source implementations provided by the respective original authors. To the extent possible, we use the same weight initialization techniques, network architecture, and hyperparameter configurations as reported in the original papers. All models are trained through Nautilus--a distributed research compute infrastructure from the Pacific Research Platform (PRP) [48]. Single-GPU training time for each model ranges from 3 hours (e.g., MobileNetV2 on CIFAR100) to 12 hours (e.g., CycleGAN on Cityscape) depending on the complexity of the task. Furthermore, for the style transfer task, CycleGAN uses a _cycle consistency loss_ that includes two mappings: (1) generator \(G_{\theta}\), parameterized by \(\theta\), maps input image \(\mathbf{x}\) to the target domain \(\mathbf{\hat{x}}\); and (2) generator \(F_{\phi}\), parameterized by \(\phi\), maps \(\mathbf{\hat{x}}\) back to the original image \(\mathbf{x}\). During training, we only prune the forward mapping into the target domain \(G_{\theta}(\mathbf{x})\), as we are only interested in accelerating inference, and the reverse transform is discarded at inference time. Given an input label map, the forward mapping \(G_{\theta}\) of our pruned and quantized CycleGAN model generates realistic 128 \(\times\) 128 images in the first-person driving view, as shown in Figure 6.
# Research on Fault Diagnosis of Rotating Parts Based on Transformer Deep Learning Model
## 3 Results and Discussion
### Limitations of the Proposed Method  
Although the MultiDilatedFormer model performs exceptionally well in the aforementioned fault diagnosis tasks, its use of the multi-head mechanism inherent in the Transformer architecture results in high computational complexity, leading to longer training times. This may pose challenges for real-time diagnostics or in resource-constrained environments. Additionally, the model's performance depends on the quality and diversity of the training data, which may require more data augmentation or denoising methods to enhance stability. In the future, to address the model's computational efficiency issues, further optimizations of the model structure can be explored, such as lightweight Transformer or convolution modules, to reduce both training and inference time. Additionally, considering the practical deployment requirements in edge computing environments, it is advisable to develop a low-complexity version that is more suited for real-time monitoring and diagnosis.
## 5 Summary  
In summary, we focused on the propagation properties of vortex modes carried by the MG beam in an-isotropic oceanic turbulence and developed the wave structure function of beam waves, as well as, we derived the transmitting probability of signal vortex modes of the MG beam. The MG beam with small initial beam width, long wavelength, and small ellipticity parameter has larger ability to obstruct turbulence disturbance. Also, our results reveal that, theoretically, we can improve the information capacity of the communication channel by selecting a suitable semi-cone angle and using the high-order vortex modes of the beam as the information carrier. This result indicates that the MG beam is a beam that is more suitable as the source of the optical communication system than the Laguerre-Gaussian beam [7; 8; 38], because the higher topological charge of the vortex modes carried by the Laguerre-Gaussian beam, the lower the transmittance. In addition, the effect of turbulence on the transmission pattern of vortex carried by MG beam is consistent with that of vortex modes carried by conventional beam, namely: the signal vortex modes carried by MG beam suffers less turbulence disturbance in oceanic turbulence with lower dissipation rate of the mean-squared temperature, larger an-isotropic factor, smaller temperature-salinity contribution ratios, larger inner scale, and higher dissipation rate of kinetic energy per unit mass of fluid. Furthermore, a small-aperture receiver is beneficial to enhancing transmitting probability of the signal vortex modes.  
Conceptualization, Y.Z.; methodology, software and writing-review & editing, Q.L.; formal analysis, Y.Z.; validation, D.Y. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China [Grant No. 61871202] and the Postgraduate Research & Practice Innovation Program of Jiangsu Provence [Grant No. 1142050205206114]. The authors declare no conflict of interest.  
Figure 6: The normalized transmitting probability of the signal OAM modes versus temperature-salinity contribution ratios \(\varpi\), dissipation rate of the mean-squared temperature \(\chi_{t}\) and an-isotropic factor of turbulence \(\zeta\).
# Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium
## 2 Fixed Wireless Access  
FWA systems were first thought of in the 1990s when they were utilised to replace the wiring in conventional telephone systems with the primary goal of lowering installation costs and start-up times [7]. Today they have the same purpose, except that data traffic requirements are much higher, needing larger bit rates over shorter communication distances between nodes and lower implementation costs. To support such needs, FWA systems envision the mmWave frequency bands, including the unlicensed 60 GHz band, as a solution to provide bit rates higher than 1 Gbps, thanks to recent advances in radio technology for these frequencies [8; 9; 10]. With carrier frequencies in the V-band (50-75 GHz) and channel bandwidths of 2.16 GHz, the IEEE 802.11 ad standard defines physical and medium access control (MAC) layer interfaces for short-range, high-throughput systems, enabling SISO (single-input single-output) data rates of up to 2.5 Gbps [11]. In addition, MIMO (multiple-input multiple-output) is supported by its successor, IEEE 802.11 ay [12]. This standard establishes bit rates up to 40 Gbps, and extended transmission distances up to 500 m, thanks to its channel bonding and multi-user MIMO (MuMIMO) capabilities [13]. In addition, the 3 GPP Rel 17 will expand its frequency range 2 (FR2) from 52 to 71 GHz, including this band for 5G deployments [14].  
A typical FWA network deployment is presented in Figure 1, where a fiber broadband city-wide network is connected to the PoP, and the PoP is connected to ENs via a wireless hierarchical backhaul network. Finally, each CPE is connected via the direct 60 GHz link to the ENs to support residential users' connectivity.  
Figure 1: Fixed wireless access network topology in the 60 GHz Band. (PoP = point-of-presence, IWF = inter-working function, CPE = customer premises equipment) [3].
###### Abstract  
This study assesses the risks of glacial lake outburst floods (GLOFs) from moraine sediment dams around Gurudongmar Lake in the Northern Sikikim Himalayas at an elevation of 17,800 feet. It focuses on three moraine sediment dams, analysing the implications of slope failure on the upstream side and the downstream stability under steady seepage conditions, as well as the risks posed by permafrost thawing. Using a comprehensive methodology that includes geotechnical evaluations, remote sensing, and digital elevation models (DEMs), the research employs finite element analysis via PLAXIS2D for the stability assessment. The main findings indicate a stratification of sediment types: the upper layers are loose silty sand, while the lower layers are dense silty sand, with significant variations in shear strength, permeability, and other geotechnical properties. Observations of soliflutions suggest that current permafrost conditions enhance the dams' stability and reduce seepage. However, temperature trends show a warming climate, with the average days below 0 \({}^{\circ}\)C decreasing from 314 (2004-2013) to 305 (2014-2023), indicating potential permafrost thawing. This thawing could increase seepage and destabilise the dams, raising the risk of GLOFs. Numerical simulations reveal that scenarios involving water level rises of 5 and 10 m could lead to significant deformation and reduced safety factors on both the upstream lateral dams and downstream front dams. The study emphasises the urgent need for ongoing monitoring and risk assessment to address the potential hazards associated with GLOFs.  
Gurudongmar Lake; moraine sediment dams; finite element numerical analysis; stability analysis; factor of safety; glacial lake outburst flow (GLOF); permafrost thawing +
Footnote †: journal: Applied sciences  
Article
## 3 3D BEM-DMST Model: Basic Formulation and High-Order Submodels
### Dynamic Stall  
To account for dynamic stall, the code implements multiple semi-empirical models, namely the ones proposed by [PERSON], [PERSON], [PERSON] and [PERSON]; however, the analyses reported in this paper are only conducted with the [PERSON] dynamic stall model, which was specifically formulated for VAWT applications, mounting symmetrical airfoils with 10% chord thickness. The dynamic stall calculation is dependent on the rate of change of the attack angle \(\dot{\alpha}\) which is computed for each time step (any blade position) as a  
Figure 2: Lift and drag coefficients of NACA0021 and DU06W200 at low Reynolds.  
first order backward difference. This may lead to a slight instability when angles of attack vary abruptly in correspondence of \(\theta=180^{\circ}\) (blade in the leeward phase).  
\[\alpha_{ref}=\alpha_{fc}-\Delta\alpha_{ds} \tag{2}\]  
\[\Delta\alpha_{ds}=\gamma K_{1}\sqrt{\frac{c\,\dot{\alpha}}{2W_{rel}}}S_{\dot{ \alpha}} \tag{3}\]  
\[K_{1}=0.75+0.25\,S_{\dot{\alpha}} \tag{4}\]  
\[C_{L}^{dyn}=\left(\frac{\alpha_{fc}-\alpha_{0}}{\alpha_{ref}-\alpha_{0}} \right)\left(C_{L}(\alpha_{ref})-C_{L}(\alpha_{0})\right);\qquad C_{D}^{dyn}=C _{D}(\alpha_{ref}) \tag{5}\]  
[PERSON]'s formulation is summarised in Equations (2)-(5), where \(\gamma\) is a semi-empirical constant, usually ranging between 0.5 and 1.5, assumed to be equal to standard values suggested by [PERSON] [2] for herein BEM analyses; \(S_{\dot{\alpha}}\) is the sign of the derivative of the effective angle of attack; \(\alpha_{0}\) is the zero-lift angle. Besides, Equation (5) extends the original formulation to cambered airfoils, generally featuring a negative zero-lift angle.
# Efficient Data Exchange between WebAssembly Modules
## 5 Results and Discussion
### Performance Analysis
#### 5.1.2 Dynamic Structures Test
SIMD can operate with up to four numbers of \"u32\", so it is possible to create a mask and add four numbers. However, we need to ignore the \"id\". To do so, we use zero, then summed with zero, as exemplified in Figure 25.  
SoA: This performs the same functionality as the other tests but uses a different form of data organization, as demonstrated in Figure 26, where each field becomes a vector. SoA+SIMD: Similar to fixed+SIMD, this uses the SoA data organization structure but manually employs SIMD using the features available in Zig. This function, unlike Fixed+SIMD, allows the addition of four numbers at a time instead of two due to the continuity of the values. Figure 27 demonstrates the implementation of this test. A mask is created, containing four numbers, and then added to another four numbers in a single instruction with the help of SIMD, as illustrated in Figure 28.  
Figure 24: Code implementing the fixed+SIMD test, in Zig. Figure 25: Example of two SIMD lanes being added (fixed+SIMD test). The results of this test coincide with the results of another study [65], thus demonstrating that SoA, even without any manual optimization, offers better performance across all architectures. The SoA+SIMD implementation manages to be even more efficient due to a simplified SIMD implementation. This indicates that this data layout is particularly efficient for the type of computations being measured. This efficiency can be attributed to better cache utilization, reduced memory access overhead, and the ability to leverage SIMD instructions effectively. Notably, this performance improvement was consistent across all tested CPU architectures. An extra observation can be made about PC5, an Azure virtual machine with low computational power, which shows the same performance gains as the others. Furthermore, the SoA test on PC5 takes an average of 290 microseconds, while on PC1, it takes 311 microseconds in the dynamic size test (\"Stride\"). Table 3 summarizes the performance differences, in proportion, relative to the native. Table 4 shows the variation in performance between different runs of the same test, which highlights the performance instability of fixed+SIMD on PC1 and PC2.
## 4 Control Units of the DSTATCOM
The cumulative errors within the control circuit of the DSTATCOM, as described in (30)-(33), are used to define the objective function:  
\[i_{S}^{ds*}=C_{1}\left(V_{S}^{dc*}-V_{S}^{dC}\right) \tag{22}\]0  
\[i_{S}^{ds*}=C_{1}\left(V_{S}^{dc*}-V_{S}^{dC}\right) \tag{22}\]1  
\[i_{S}^{ds*}=C_{1}\left(V_{S}^{dc*}-V_{S}^{dC}\right) \tag{22}\]2  
\[i_{S}^{ds*}=C_{1}\left(V_{S}^{dc*}-V_{S}^{dC}\right) \tag{22}\]3\[i_{S}^{ds*}=C_{1}\left(V_{S}^{dc*}-V_{S}^{dC}\right) \tag{22}\]4  
The proposed fitness function (_OF_) comprises four objectives (_OF\({}_{1}\)_, _OF\({}_{2}\)_, _OF\({}_{3}\)_, and _OF\({}_{4}\)_). The foremost objective (OF\({}_{1}\)) aims to minimize the ITSE of \(V_{S}^{dC}\) in the DSTATCOM (\(e_{Vdc}^{2}\)). Simultaneously, OF\({}_{2}\) focuses on minimizing the ITSE of \(i_{S}^{d}\) (\(e_{ii}^{2}\)), while OF\({}_{3}\) targets the minimization of the ITSE of \(i_{S}^{d}\) (\(e_{ii}^{2}\)). The fourth objective (OF\({}_{4}\)) seeks to reduce the ITSE of \(V_{S}^{mag}\) (\(e_{o\_mag}^{2}\)). The constraints of the problem focus on the upper and lower limits of the controllers' parameters, as well as the tolerated level of Total Harmonic Distortion (THD) in the signal fed into the grid, which is required to remain under 5%. These parameters can be delineated as follows:  
\[i_{S}^{ds*}=C_{1}\left(V_{S}^{dc*}-V_{S}^{dC}\right) \tag{22}\]5  
\[i_{S}^{ds*}=C_{1}\left(V_{S}^{dc*}-V_{S}^{dC}\right) \tag{22}\]6  
\[i_{S}^{ds*}=C_{1}\left(V_{S}^{dc*}-V_{S}^{dC}\right) \tag{22}\]7  
\[i_{S}^{ds*}=C_{1}\left(V_{S}^{dc*}-V_{S}^{dC}\right) \tag{22}\]8  
* Computational Complexity Analysis,  
The control unit of the DSTATCOM consists of four distinct loops: two outer loops for managing voltage and two inner current loops dedicated to current tracking, as shown in \[i_{S}^{ds*}=C_{1}\left(V_{S}^{dc*}-V_{S}^{dC}\right) \tag{22}\]9 This structure is critical for achieving precise and responsive control. As presented in (27) and (28), the PI-PI controller consists of four decision-making variables, while the FOPI-FOPI controller incorporates six control parameters, reflecting its more intricate design due to fractional-order dynamics. The computational complexity of each controller can be determined based on the number of decision-making variables and control parameters involved. The PI-PI controller's complexity can be approximated by O(4k), and \(k\) signifies the number of control loops. This linear relationship indicates that the computational demand scales with the decision variables. Conversely, the FOPI-FOPI controller's complexity is influenced by the need for fractional-order calculations, which typically require more computational resources. This complexity can be expressed by O(6k). The analysis of computational complexity underscores the importance of optimizing both controllers to ensure real-time feasibility in practical applications. While the linear nature of the complexities suggests that the systems can efficiently handle the computations, the specific demands of fractional-order calculations in the FOPI-FOPI controller necessitate careful consideration in algorithm design.
# Systematic Evaluation on Speckle Suppression Methods in Examination of Ultrasound Breast Images
## 4 Experimental Results
### Experiment 3  
In this experiment, the parameters of all filters are set under the radiologists' guidance (Table 4). \(\sigma\) in WF denotes as the standard deviation for degraded function, \(K_{SNR}\) is the noise to signal ratio in WF. \(N\_windowsize\) stands for the filter window in BF.  
As shown in Table 5, it can be seen clearly that the WTF, GLF, and WF can achieve high LRD and low VMR (1/MR). Among these three filter algorithms, the WF and WTF can achieve greater clarity of edge in edge region than the GLF. For a high VMR, the LF, BF, PMD, WJD, and TV can achieve high LRD values, and their order from high to low is BF, LF, PMD, WJD, and TV. That is to say, the BF can perform best in terms of keeping clarity of edge. The performance of LF is secondary, PMD, WJD, and TV follows LF. Two groups of filtered image, including the first benign tumor image and the second malignant tumor image are shown as the example of filtering results in Figures 5 and 6.  
Figure 5: Image filtering results of the first benign tumor image: (**a**) source image; (**b**) Wavelet Transform Filter (WTF) result; (**c**) Gaussian–Laplace Filter (GLF) result; (**d**) Wiener Filter (WF) result; (**e**) Lee Filter (LF) result; (**f**) Bilateral Filter (BF) result; (**g**) PMD result; (**h**) WJD result; and (**i**) Total Variation (TV) result.  
Figure 6: Image filtering results of the second malignant tumor image: (**a**) source image; (**b**) WTF result; (**c**) GLF result; (**d**) WF result; (**e**) LF result; (**f**) BF result; (**g**) PMD result; (**h**) WJD result; and (**i**) TV result.
# Interpretable Optimization: Why and How We Should Explain Optimization Models
## 6 Example Applications
### Energy Systems and Carbon Emission Reduction  
Policymakers and stakeholders in the energy sector must plan long-term infrastructure while balancing economic, environmental, and social objectives. Interpretability is critical in this context, as energy transition plans affect millions of households and require public approval. Investors and policymakers need clear explanations of why specific energy pathways were chosen over others, and grid operators and industry stakeholders require interpretable solutions to manage the trade-offs between cost, reliability, and sustainability.  
Failing to provide clear explanations may lead to hurdles when planned decisions move into the implementation phase. The SuedLink transmission line project in Germany--crucial for distributing wind energy from the north to the south--has experienced considerable delays. Local communities and environmental groups have opposed the project, raising concerns over landscape disruption, impacts on property values, and perceived health risks from electromagnetic fields [113].  
Similarly, proposed wind farms in Italy's Apennine Mountains have faced resistance due to concerns over natural habitat destruction, noise pollution, and visual impacts on significant landscapes, resulting in necessary assessments, negotiations, and project redesigns, which have delayed the planning-to-operation timeline.  
In the UK, the Navitus Bay Wind Park, intended to power approximately 700k homes, was canceled due to concerns over potential harm to tourism, marine life, and the site's natural beauty [114]. [PERSON] and [PERSON] [115] studied several factors influencing the community acceptance of onshore wind farms in Eastern China, showing that insufficient information dissemination was the most significant factor regarding acceptance.  
These examples showcase several issues that are making energy planning increasingly difficult. Stakeholder consensus is key but becoming more difficult to achieve, and, equally important, it is necessary to always plan with alternatives. Different plans can be very similarly effective in terms of their primary outcomes, yet they can have divergent implications, such as in terms of their landscape impact.  
Potential issues in the implementation phase could be mitigated by providing a sensitivity analysis, in which the influence of parameters is studied to enhance the interpretability of the results.
## 4 Experiments and Discussion
### Ablation Study  
In this section, to verify the effectiveness of our proposed attention mechanism, we conductablation experiments on four comparable models and perform analysis. The four ablation experiments include replacing CDAM with two convolutional layers, termed Without_CDAM: replacing CDAM with CBAM, termed CBAM; without the differential feature path, termed Without_DFPM: and only containing the differential feature path, termed Without_FFPM.  
* Qualitative analysis: As can be seen from the results in Figure 5, in the absence of CDAM, the Without_CDAM model not only reduces the brightness of infrared targets but also has the problem of blurred contours. In contrast, adding the CBAM model enables the fused image to retain some infrared target information but loses some texture details. In addition, the results of the Without_DFPM model, the Without_FFPM model, and our model are similar, with no obvious differences. All of them better retain the brightness information of infrared targets and the texture information of visible images.