esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic UAV Inspection Boosted by Vehicle Collaboration Under Harsh Conditions in the IoT Realm
## 5 Simulation
### Performance Results and Analysis of Algorithms in Dynamic Task Handling Scenarios
#### 5.2.3 Variation in Number of UAVs  
When studying the impact of varying the number of UAVs on algorithm performance, the total number of task points was set to the default value of 2000 and the target area side length was set to 50 km.  
As shown in Figure 11, the time cost decreased rapidly at first as the number of UAVs increased, then slowed down, and gradually leveled off. The figure demonstrates that using multiple UAVs significantly improved efficiency compared to a single UAV, roughly doubling the work efficiency. However, the efficiency gains from adding more UAVs diminished as their number increased. Among the algorithms, the KGTSA performed the best, while the SOA performed slightly worse than the KGTSA.  
As shown in Figure 12, since the number of UAVs did not affect the vehicle route planning, the vehicle travel distance remained almost independent of the number of UAVs. To better optimize the total time cost, a higher number of centroids, i.e., docking anchor points, were set. As a result, the vehicle travel distances for the KGTSA and SOA were slightly higher compared to the other two algorithms.  
As shown in Figure 13, the total UAV flight distance showed almost no direct relationship with the number of UAVs. This was because the UAV route planning results were primarily determined by the clustering of task point locations, and the number of UAVs only affected the allocation of routes among them. As the number of UAVs increased, the UAV route planning did not change, so the total UAV flight distance remained constant. Additionally, the KGTSA outperformed both the HGA and the Greedy Algorithm due to the more optimal UAV route planning and allocation scheme proposed in this study. The KGTSA also slightly outperformed the static SOA as the dynamic optimization adjustments enhanced the efficiency of UAV cooperation.  
Figure 11: System time cost.  
Figure 12: Vehicle driving distance.
# HPC Cloud Architecture to Reduce HPC Workflow Complexity in Containerized Environments
## 2 Related Work
### Container-Based HPC Cloud Solutions  
#### 2.2.1 Shifter  
Shifter is a user-defined container image solution developed by the National Energy Research Scientific Computer Center (NERSC) for HPC services. It has been implemented as a prototype on the Cray supercomputers at NERSC. It allows an HPC system to permit users to run a Docker image efficiently and safely. Shifter's workflow [18] is shown in Figure 2. It provides a user-defined image manager, workload management for resources, and a user interface to distribute containers in an HPC environment and provides HPC services to users. This solution covers various image types, as well as providing great detail for a container-based image management workflow. However, because the batch scheduler used in the traditional HPC field is used without integrating it with the container-based scheduler, the amount of automation for the job workflow is still limited.  
The Swiss National Supercomputing Center (CSCS) has developed a CSCS Shifter [19] based on a NERSC Shifter to work with GPU resources at the Piz Daint supercomputer. A CSCS Shifter can be used by loading the module shifter-ng. To use Shifter commands, users must include the GPU-enabled software stack by loading daint-gpu [19]. As shown in Figure 3, the workload manager can distribute job requests from users, but this can also be a connected runtime service. This architecture not only manages images but also runs containers directly with these converted images. Although this solution implements automation of the job workflow by Shifter Runtime, it still has a dependency issue in that it has to run in its own supercomputer environment and a network performance issue between parallel processing running on multiple nodes.
# Seismic Data Denoising Based on Wavelet Transform and the Residual Neural Network
## 2 Methods and Evaluation Index  
### DWT-Resnet Denoising Principle  
Figure 1 shows the network structure of DWT-Resnet. The noise-bearing seismic data were decomposed using 1D wavelets, ranking the decomposed high-frequency and  
low-frequency coefficients in order to form a dataset conforming to the 1D Resnet input, and then the clean seismic data were taken as the training labels of the Resnet, which were trained for denoising. The trained network was applied to the test set using the same wavelet-based transform and decomposition levels so that the denoising results of noise-bearing seismic data were directly obtained.  
The mathematical principle of the DWT-Resnet method is described next.  
As a transform domain analysis method similar to Fourier transform, wavelet transform separates the effective signal from the noise by doing the inner product of the basis function and signal. For a certain given wavelet \(\Phi(x)\), the wavelet basis function is:  
\[\Phi_{a,b}(x)=\frac{1}{\sqrt{a}}\Phi(\frac{x-b}{a}) \tag{1}\]  
where \(a\) is the scale parameter, \(b\) is the time window parameter, \(a,b\in\mathbb{R}^{+}\). That is, the wavelet basis function \(\Phi_{a,b}(x)\) is the shift and scale transformation of the initial wavelet function \(\Phi(x)\). Therefore, for the signal \(f(x)\), its wavelet transform equation is:  
\[WT(a,b)=\int_{-\infty}^{+\infty}f(x)\overline{\Phi(\frac{x-b}{a})dx} \tag{2}\]  
where \(\overline{\Phi_{a,b}(x)}\) is the complex conjugate of the signal \(\Phi_{a,b}(x)\).  
The seismic data containing noise \(y\), can be regarded as a superposition of clean data and random noise, i.e.,  
\[y=z+v \tag{3}\]  
By taking the seismic data containing noise through the wavelet transform as the input of the neural network, the clean data \(z\) are predicted by building a mapping of \(R(\text{DWT}(y);P)\), i.e.,  
\[R(\text{DWT}(y);P)\approx z \tag{4}\]  
where \(P=\{\text{W},\text{B}\}\) is the parameter of the neural network and \(DWT(y)\) is the result of the noise-containing data after the wavelet transform. The loss function of the neural network is:  
\[l(P)=\sum_{k=1}^{Q}\left\|R(\text{DWT}(y_{k});P)-z_{k}\right\|_{F}^{2} \tag{5}\]  
where \(\text{DWT}(y)\) indicates the wavelet domain noise data \(y_{k}\) acquired by using the one-dimensional (1D) wavelet transform, \(z_{k}\) represents the clean data obtained by feeding \(y_{k}\) into the neural network and training, \(Q\) is the batch size. Normally, the smaller the \(l(P)\), the better the network optimization, and the higher the denoising capability. Since different  
Figure 1: The network structure of DWT-Resnet.  
wavelet bases with different decomposition levels and different layers of Resnet have different effects on the DWT-Resnet method, these factors will be explored in Sections 3 and 4.
# Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera
## 2 Methodology
### Semantic Segmentation Architecture
#### 2.2.1 DeeplabV3+  
The DeepLabv3+ network was invented by the Google Research Team [61], an extended study from DeepLabv1 [62], DeepLabv2 [52], and DeepLabv3 [59]. It is one of the recent state-of-the-art models with many improvements compared to the previous version of DeepLab models. In DeepLabv3+, an effective decoder module was added to recover detailed object boundaries of the segmentation results [61]. In this work, the DeepLabv3+ network was created with weights initialized from a pre-trained ResNet-18 network as a network backbone. ResNet-18 is an efficient network that is well suited for applications with limited processing resources [63].  
DeepLabv3+ applies atrous convolutions for upsampling the output of the last convolution layer. The atrous convolution offers a simple yet effective alternative to expand  
Figure 1: Example of a labeled image. The red region represents the background while the blue region represents waterbodies.  
the field of view of filters without increasing the number of parameters, by adding a rate parameter that corresponds to the stride. It can be expressed as:  
\[y[i]=\sum_{k}x[i+r\cdot k]\cdot w[k] \tag{1}\]  
where \(y[i]\) is the output, \(w[k]\) represents the filter of length \(k\), \(x\) is the input feature map, and \(r\) represents the atrous rate which corresponds to the stride. Another component in DeepLabv3+ is atrous spatial pyramid pooling (ASPP). It applies atrous convolution in parallel as a method of extracting multi-scale information and improving the accuracy [59]. The encoder features were upsampled by a factor of 4 instead of 16 as in DeepLabv3 [59] and then concatenated with the corresponding low-level features from the encoder network that were convolved by 1 \(\times\) 1 convolution. After the concatenation, 3 \(\times\) 3 convolutions were applied followed by upsampling by a factor of 4. Figure 2 illustrates the DeepLabv3+ architecture.
## 4 Principle and Implementation
### Water Segmentation Model
#### 4.2.3 Decoder Design  
The decoder structure in the original SegFormer model simply performs upsampling and linear variation on the four different resolutions of the feature maps obtained from the encoder output, and then feeds them into multiple fully connected layers to obtain the final result. Although this lightweight decoder structure avoids excessive computation, the direct and aggressive upsampling of the encoder output may lead to the loss of detailed information, especially in the positioning of the water level line as addressed in this paper. To fully leverage the semantic and spatial information in the feature maps at different stages of the encoder output, this paper proposes a new decoder that references the encoder structure in the UNet network. The structure is illustrated in Figure 6. The decoder employs a skip-connection architecture to make full use of the semantic and spatial information contained in the feature maps at each stage. Meanwhile, the stepwise upsampling technique used in the skip-connection process enables the decoder to capture the relevant features that were lost in the downsampling process of the encoder.  
Figure 5: Transformer block structure.  
Figure 6: Decoder Module.
## 4 DT Use Cases and Application in Healthcare
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Potential Use Cases** & & **Benefits Gained** \\ \hline \multirow{3}{*}{Clinical Research} & \(\bullet\) & Improve clinical operations and reduce healthcare risks \\  & \(\bullet\) & Enables predictive analysis of therapy outcomes \\ \hline \multirow{6}{*}{Facility \& Operations Design} & \(\bullet\) & Help reviewing operational strategies \\  & \(\bullet\) & Optimize staffing schedules \\  & \(\bullet\) & Accurate bed occupancy predictions \\  & \(\bullet\) & Accurate medical device utilization \\  & \(\bullet\) & Help improve operational efficiency by identifying bottlenecks in workflows \\ \hline \multirow{3}{*}{Diagnosis \& Preventive Treatment} & \(\bullet\) & Can simulate various patient characteristics and replicate how they behave \\  & \(\bullet\) & Enable precision testing of thousands of treatments \\  & \(\bullet\) & Test limitless “what-if” scenarios \\ \hline \multirow{3}{*}{Drug and Vaccine Development} & \(\bullet\) & Help test new drugs to ascertain safety and effectiveness \\  & \(\bullet\) & Can speed up clinical trials in drug research \\  & \(\bullet\) & Help developing and producing new vaccines \\ \hline \multirow{3}{*}{Personalized Medicine} & \(\bullet\) & Offer a DT created for each patient’s body, physiology, and medical history \\  & \(\bullet\) & Help model the best course of treatment for a patient \\  & \(\bullet\) & Can create simulations of new treatments leading to better-informed decisions \\ \hline \multirow{3}{*}{Innovative Process} & \(\bullet\) & Gaining big-picture visibility \\  & \(\bullet\) & Help reimagine innovation processes \\  & \(\bullet\) & Help strategize for many possible futures \\ \hline \multirow{3}{*}{Medical Devices} & \(\bullet\) & Provide real-time visibility into how medical devices are utilized, their performance, and the need for repair \\  & \(\bullet\) & Help with maintenance scheduling for medical devices/equipment during downtime \\  & \(\bullet\) & Can be used in the modeling of personalized and intelligent medical devices \\ \hline \multirow{3}{*}{Medical Training} & \(\bullet\) & Help with surgical training and treatments \\  & \(\bullet\) & Help with medical education, research, and care delivery. \\  & \(\bullet\) & Minimize the usage of cadavers \\ \hline \multirow{3}{*}{Diagnosis and Therapy} & \(\bullet\) & Help surgeons to plan the surgical procedure \\  & \(\bullet\) & Identify the risks associated with the surgery \\ \cline{1-1}  & \(\bullet\) & Preventive treatment \\ \cline{1-1}  & \(\bullet\) & Pre-simulate optimal therapies \\ \cline{1-1}  & \(\bullet\) & Avoid unnecessary interventions \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Potential Use Case and Benefits.
## 4 FTN Detection Scheme in Multipath Channels Based on Neural Network  
The shaping filter withength L-length is expressed as \(\mathbf{p}=\left[p_{1},p_{2},\cdots,p_{L}\right]^{T}\), \(\mathbf{a}\) is expressed as N-length independent equal probability According to the content of the previous section, the following discrete form of the FTN signal vector model can be obtained  
\[\mathbf{s}=\mathbf{Pa} \tag{7}\]  
where \(\mathbf{s}\) represents the transmission symbol sequence transmitted by the transmitting end after shaping by a shaping filter with a period of \(\tau\)T, \(\mathbf{P}=\left[\mathbf{p}_{1},\mathbf{p}_{2},\cdots,\mathbf{p}_{N}\right] \in\mathbb{R}^{L\times N}\) represents the pulse-shaping matrix. After the FTN signal passes through the multipath channel, the received symbol at the receiving end can be equivalently modeled as a process through the nonlinear channel as follows  
\[\mathbf{r}=\mathbf{P}^{*}(\mathbf{Hs}+\mathbf{w})=\mathbf{Ga}+\boldsymbol{\eta} \tag{8}\]  
where \(\mathbf{w}\) is the Gaussian white noise signal, \(\mathbf{j}\) is the colored noise generated after matched filtering, \(\mathbf{H}\) is the channel response matrix of the multipath channel, and it is also a cyclic matrix. \(\mathbf{G}=~{}\mathbf{P}^{*}\mathbf{HP}\) is expressed as the non-linear aliasing of ISI introduced by FTN and multipath channel, the inter-symbol interference matrices introduced by FTN are represented as \(\mathbf{P}^{*}\) and \(\mathbf{P}\), respectively.  
In order to recover the originally transmitted signal from the non-linear interference aliased received signal, two different deepearning models, CNN and RNN, will be used to detect FTN signaling.
## 7 Conclusions  
This study presents a comprehensive urban sound and environmental monitoring system combining a Hierarchical Wireless Acoustic Sensor Network (HWASN) with an end-to-end sound classification model, CNN-CNN-BiLSTM-Attention (CCBA). This integration delivers a stable, scalable, low-cost solution capable of efficient sound sampling and transmission, along with real-time, robust sound classification. A case study conducted in real-world deployment environments demonstrates the system's efficient data aggregation and transmission capabilities, maintaining data integrity even over indirect communication paths, with no packet loss observed. Moreover, the newly proposed CCBA model achieves up to a 71-fold improvement in response time compared to the teacher model, with only a minimal average accuracy loss of 1.7%, thanks to knowledge and feature distillation techniques. When deployed in new environments, CCBA requires minimal additional training, showcasing high classification accuracy and adaptability. In addition, the system integrates additional environmental parameters such as air quality, light intensity, temperature, humidity, and atmospheric pressure, further enhancing its practicality for urban ecological monitoring. The results from the case study highlight the system's significant scalability, stability, and effectiveness in real-world urban deployments. This leads to the expectation that the system can be widely applied in various critical urban scenarios, including noise pollution monitoring, smart city infrastructure, and public safety.  
**Author Contributions:** Conceptualization, B.P., K.I.-K.W. and W.H.A.; methodology, B.P.; software, B.P.; validation, B.P.; formal analysis, B.P.; investigation, B.P.; resources, B.P., K.I.-K.W. and W.H.A.; data curation, B.P.; writing--original draft preparation, B.P.; writing--review and editing, B.P., K.I.-K.W. and W.H.A.; visualization, B.P.; supervision, K.I.-K.W. and W.H.A.; project administration, B.P.; funding acquisition, B.P. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the China Scholarship Council (Grant number: 202208250025).  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** Data will be made available on request.  
The authors wish to acknowledge the contribution of NeSI to the results of this research. New Zealand's national compute and analytics services and team are supported by the New Zealand eScience Infrastructure (NeSI) and funded jointly by NeSI's collaborator institutions and through the Ministry of Business, Innovation and Employment. URL [[http://www.nesi.org.nz](http://www.nesi.org.nz)]([http://www.nesi.org.nz](http://www.nesi.org.nz)).  
The authors declare no conflicts of interest.
# Two-Stage Short-Term Power Load Forecasting Based on SSA-VMD and Feature Selection
## 5 Experiment and Results
### Multi-Step and Iterative Forecasting Approach  
Load forecasting can be classified by the output dimension of the forecasting model into single-step forecasting and multi-step forecasting, with single-step forecasting only outputting the forecasting value for one future time node and multi-step forecasting outputting a multi-dimensional vector containing the forecasting values for multiple time nodes future. The purpose of short-term load forecasting in this paper is to obtain the load values for the next 24 moments. There are direct and iterative forecasting approaches for short-term load forecasting, but considering that multi-step forecasting will cause a lag in the input sequence, there is a lag problem in the characteristics, and this will affect the accuracy of forecasting. Moreover, considering the application scenario of the forecasting model, the load values for the next 24 moments are predicted on the previous day. Therefore, the forecasting process uses an iterative forecasting approach, whereby the predicted value at each time t is obtained, and then the predicted value at time t is added to the feature vector for the next moment at time t + 1 to obtain the predicted value at time t + 1, and so on, to predict the values for the next 24 time points on the next day.
## 2 Proposed Integrated Design  
The proposed integrated design was modeled and simulated by using one of the standard antenna design and simulation tools, Computer Simulation Technology (CST) Studio. The prototype was designed for smartphones or handheld applications (note: 10 board dimensions were assumed for the design). The proposed prototype was designed using RT/Duorid Rogers 5880 material as the substrate. The material has a relative permittivity \(\varepsilon\)r of 2.2 and a dissipation factor-tany of 0.0004. The final optimized design is reported in Figure 1.  
In Figure 1, there are two MIMO configurations of the antennas, one along the length and the other along the width. The MIMO configuration consisting of two antennas along the length (one at the top and the other at the bottom) of the board is designed for the 4G LTE and lower 5G bands applications. In this MIMO configuration, each antenna element is a novel array of two linear elements with a parallel microstrip feeding network. The partial ground structure is used with the array antenna. The dimensions of the ground have been optimized to acquire the required characteristics in the desired band. Similarly, the four antennas MIMO configuration along the width of the board is designed for the 5G mmWave band. Each antenna element in this configuration is designed with the defected ground structure (DGS). The DGS for each element is optimized by empirical parametric analysis, with the objective of radiation pattern and gain enhanc  
Figure 1: Geometry of the proposed integrated design: (**a**) top view; (**b**) back view.  
and MIMO performance characteristics improvement in the second place. The optimized dimensions annotated in Figure 1 are reported in Table 1.  
The proposed antenna design progression is discussed in two parts. In the first subsection, the mid-band MIMO antenna step-by-step designing is discussed. In the second subsection, the mmWave and high-frequency MIMO antenna design process is explained comprehensively.
## 6 Conclusions  
The secrecy performance of a mixed FSO/RF integrated satellite-HAP relaying networks in the presence of an eavesdropper is evaluated. The FSO and RF links are model as Gamma-Gamma distribution and Rican fading distribution, respectively. The exact closed-form expression of the system equivalent end-to-end CDF is derived under different detection schemes. Based on this, the analytical closed form expressions of the concerned system COP, SOP, and SPSC are determined. To obtain more insight about the system performance, the asymptotic analysis of the system SOP is provided. The results shows that the pointing errors, atmospheric turbulence, and shadowing severity significantly degrade the system performance. In addition, the results indicate that heterodyne detection offers the system better performance compared to IM/DD detection. The future work of this paper could be tailored toward the non-orthogonal multiple access (NOMA) for better system spectrum efficiency.  
K.O.O. and P.A.O.; methodology, K.O.O.; software, K.O.O.; validation, K.O.O. and P.A.O.; formal analysis, K.O.O.; writing--original draft preparation, K.O.O.; writing--review and editing, K.O.O. and P.A.O.; supervision, P.A.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Not applicable.  
Figure 9: SPSC performance of the system under shadowing fading severities for heterodyne detection at \(\overline{\tau}_{1}=45\) dB, \(\overline{\tau}_{e}=5\) dB, and \(\xi=6.5\).
# Microalgal Biomass Generation via Electroflotation: A Cost-Effective Dewaring Technology
## 4 Conventional Dewatering Approaches in Microalgal Culture
### Gravity Sedimentation  
Sedimentation is a simple process for solid and liquid separation, compared to other dewatering methods. It works by gravitational forces with low power consumption and low cost [64]. Separation by sedimentation depends on particle density and size, where a greater difference in density provides rapid sedimentation and vice versa for a lower difference in density [2]. The suitability of this method only accounts for the larger particle size of microalgae that are more than 70 \(\upmu\)m including _Spirulina_. However, it is still relatively slow, due to the low specific gravity of algal cells [15; 59]. Generally, coagulation or flocculation is needed before sedimentation to improve the settling of microalgae. This method is not widely used in the industry, though it can concentrate microalgal suspensions up to 1.5% of total suspended solids [64]. Even though the cost associated with this method is low, it requires longer settling time (24 h to 15 days) than other processes and relies on flocculating agents (alum, ferric sulphate) to facilitate the process. Moreover, the cell concentration obtained from gravity sedimentation is still low as well as the energy demand for pumping the slurry (60-65% of cell recovery efficiency) [64].