esa-sceva/satcom-chunk-collection huggingface dataset:
# Approximate Sensory Data Collection: A Survey
## 1 Introduction
On the other hand, considering that most sensory data are spatially and temporally correlated since the monitored physical world always continuously varies in space and time [5], the exact sensory data collection is not necessary since a sensory data set are highly redundant due to such strong correlation. Therefore, even if exact data collection is not possible for a WSN or an IoT system, most analysis and computation tasks can still be successfully carried out using approximate data sets that only involve partial data, instead of exact data sets which involve all of the data. Thanks to the correlation feature of sensory data, many approximate sensory data collection algorithms have been proposed. In this survey, we classify them into three categories, which are  
Figure 1: The procedure of dealing with sensory data in IoT Systems. the model-based ones, the compressive sensing based ones, and the query-driven ones. This survey summarizes the state of the art of the existing approximate data collection techniques, elaborates their advantages and disadvantages, and points out some challenges and unsolved problems. Finally, we also forecast the research prospects in the future.
# Hardware Acceleration of Satellite Remote Sensing Image Object Detection Based on Channel Pruning
## 2 Related Work
### Difference from Existing Works  
Most prior studies have predominantly focused on using lightweight feature extraction networks or applying pruning techniques independently. Currently, research on the combination of lightweight network architectures with model pruning methods is limited, leaving further scope for reducing model parameters. Furthermore, some outdated CNN models such as SSD, YOLOv2, and YOLOv3 have achieved deployment on the FPGA end, but their detection performance is somewhat inadequate. Conversely, the novel network structure operators of some new models like CenterNet, YOLOv5, and YOLOv7 necessitate substantial modifications to enable deployment on edge devices with restricted operators and instruction sets, significantly impacting development cycles [46]. In summary, we choose YOLOv4 as the reference model and employ MobileNetv3 to lightweight the feature extraction network of YOLOv4. Subsequently, we employ a channel pruning strategy for model pruning and further optimize the model parameters. Finally, to compress the network model further and accomplish deployment on KV260, we utilize Xilinx (San Jose, CA, USA) Vitis AI quantizer based on PTQ (post-training quantization) to quantize the floating-point parameters into fixed-point data before model deployment.
## 4 System Model and Assumptions
### Transaction Model  
In this model, it is assumed that all transactions are uniform in size and complexity. This assumption simplifies the calculations for both throughput and processing, allowing for a clearer understanding of the system's performance. By considering transactions of equal size, we can streamline the analysis without needing to account for variances in transaction types or complexities. Additionally, it is assumed that transactions arrive at a constant rate that either matches or exceeds the desired throughput \((T_{desired})\). This ensures that the system operates consistently at its target conditions without significant fluctuations in transaction flow.
## 3 Wind Turbine Blade Fault Dataset and Fault Diagnosis Model Training  
### Data Collection  
The dataset used in this study was obtained from the ZENODO platform, originating from the Institute of Structural Engineering at ETH Zurich. It consists of fault data for small wind turbine blades. R (healthy state): Normal operating condition. C (simulated icing fault): A mass block (3 \(\times\) 44 g) is added to simulate an icing fault. H and L (crack faults): Cracks are introduced at 17%, 30%, and 50% of the blade's longitudinal axis. Each crack has the following three variations: length 5 cm, 10 cm, or 15 cm. Depth and width: 4 mm and 1.5 mm, respectively. Cracks are set at different positions and lengths to simulate various crack fault scenarios. A summary of the fault settings is presented in Table 2. The training process is presented in Figure 6.  
Experimental tests were conducted under controlled conditions: C-type faults were detected using S22 and S17 sensors. H- and L-type faults, along with the R (healthy) state, were monitored using A2 and S9-1 sensors. All data were collected at a stable temperature of 25 \({}^{\circ}\)C, with white noise used as the excitation source. For each fault condition, 50,000 data samples were collected; 15,000 samples were allocated for testing. The remaining samples were used for training.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Label** & **Description** \\ \hline R & Healthy state \\ C & Mass block added (3 \(\times\) 44 g) to simulate icing fault \\ H & Crack \(l_{1}\)= 10 cm, Crack \(l_{2}\)= 10 cm, Crack \(l_{3}\)= 5 cm \\ L & Crack \(l_{1}\)= 15 cm, Crack \(l_{2}\)= 15 cm, Crack \(l_{3}\)= 15 cm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fault setting overview.
# Low Complexity List Decoding for Polar Codes with Multiple CRC Codes
## 3 Low Complexity SCL Decoding with Multiple CRC Codes
### Optimization of \(J\)-Tuple CRC Positions with Respect to Complexity
```
0:\(\mathcal{A}\), \(\mathbf{p}\), \(\mathbf{k}\)
0:\(\tilde{I}=\left(\tilde{I}_{1},\tilde{I}_{2},\cdots,\tilde{I}_{N}\right)\)
1:\(\tilde{I}_{1}\gets 1\)
2:for\(i=1:N-1\)do
3:\(\tilde{I}_{i+1}\gets f(\mathcal{A},\tilde{I}_{i},i)\);
4:if\(i\) indicates the last parity bit of the \(j\)-th CRC then
5:\(\tilde{I}_{i+1}\gets 1+(\tilde{I}_{i+1}-1)/2^{\eta_{j}}\);
6:endif
7:endfor
8:return I;
```  
**Algorithm 3** Calculation of \(\tilde{I}_{i}\)'s for the basic SCL decoding of the \(J\)-tuple CRC scheme  
When we employ the ID-SCL decoding, the average temporary list size \(\tilde{I}_{i}\) is computed by \(g(\cdot)\) of Algorithm 4 instead of \(f(\cdot)\). Similarly, let \(\tilde{I}_{1}\triangleq 1\) in Algorithm 4. Entire I is obtained by the modified version of Algorithm 3 in which \(f(\cdot)\) at line 3 is replaced with \(g(\cdot)\), and the input of Algorithm 3 is changed to \(\mathbf{p}\), \(\mathbf{k}\), \(\mathcal{A}\) and \(\mathcal{A}_{g}\). In addition, the average complexity of the sorting operation is modified as  
\[\tilde{s}_{i}= \begin{cases}(\tilde{I}_{i}/2)\text{log}_{2}(2\tilde{I}_{i})( \text{log}_{2}(2\tilde{I}_{i})+1),&\text{if }i\in(\mathcal{A}\backslash\mathcal{A}_{g})\text{ and }2\tilde{I}_{i} \geq L,\\ 0,&\text{otherwise},\end{cases}  \tag{7}\]  
where the bitonic sorter is assumed. Eventually, the optimization of CRC positions combined with the ID-SCL decoding is performed by Equation (6) with new I and \(\bar{\mathbf{s}}\) calculated with Algorithm 4 and Equation (7). ```
0:\(\mathcal{A}\), \(\mathcal{A}_{g}\), \(\tilde{I}_{i}\), \(i\)
0:\(\tilde{I}_{i+1}\)
1:if\(i\in(\mathcal{A}\backslash\mathcal{A}_{g})\)then
2:if\(2\tilde{I}_{i}\leq L\)then
3:\(\tilde{I}_{i+1}\gets 2\tilde{I}_{i}\);
4:else
5:\(\tilde{I}_{i+1}\gets L\);
6:endif
7:else
8:\(\tilde{I}_{i+1}\gets I_{i}\);
9:endif
10:return\(\tilde{I}_{i+1}\);
```  
**Algorithm 4** Calculation of \(\tilde{I}_{i+1}\) with \(\mathcal{A}_{g}\) for the ID-SCL decoding, \(g(\mathcal{A},\mathcal{A}_{g},\tilde{I}_{i},i)\)  
In the next section, we compare the proposed and conventional schemes with mixed criteria of complexity and performance. That is, we assess the complexity reduction with only negligible performance degradation.
## 5 Calculation of the Loss Contributions
### AC Losses Generated in the HTS Tapes  
With the purpose of adopting a simple analytical approach for discussion, the well-known [PERSON]'s formula for thin strips is implemented in the proposed tool to compute the AC losses produced in the HTS tapes of the cable [55]. Each tape is approximated as a straight independent conductor having a uniform current distribution over a rectangular cross-section. This can be considered a precautionary choice since, generally, [PERSON]'s model provides an upper limit to the estimated AC losses compared with other approaches [38, 56, 57].  
As shown in Figure 3, it is assumed that the HTS tapes' temperature varies linearly along the longitudinal coordinate of a cable segment \(x\), while the magnetic field distribution in the cross-section can be considered constant. \(T_{HTS}\) is bounded between \(T_{max_{f}}\) and \(T_{max_{m}}\), and for this reason, the function \(Q_{AC}\big{(}T_{max_{f}},T_{max_{m}}\big{)}\) has been made explicit in Equation (30). This temperature variation produces a corresponding trend of the tapes' critical current over a segment. As the AC losses strictly depend on \(I_{c}\), this should be taken into account in the computation. For this reason, the calculation of \(Q_{AC}\) is discretized over some portions of the tape length, corresponding to the sub-segments where the function \(I_{c}\big{(}T_{HTS}(x),\ |B|_{i,j},\ \theta_{i,j}\big{)}\) is applied and considered constant. This procedure has to be implemented for each HTS layer of the cable, as each of them might carry a different current and have a slightly different applied magnetic field. Thus, Equation (31) presents an integral over the HTS tape length of each layer in a cable segment \(L_{lay_{i,j}}\):  
\[Q_{AC}=\sum_{i}^{3}\sum_{j}^{nI_{i}}\int\limits_{0}^{L_{lay_{i,j}}}\left\{ \frac{\mu_{0}\,f}{\pi}\Big{[}I_{c}\Big{(}T_{HTS}(x),\ |B|_{i,j},\ \theta_{i,j}\big{)}\Big{]}^{2}\,\xi_{i,j}(x)dx\right\} \tag{31}\]  
\[L_{lay_{i,j}}=Tapes_{i,j}\frac{d_{stat}}{\big{|}\big{(}\alpha_{i,j}\big{)} \big{|}} \tag{32}\]  
\[\xi_{i,j}(x)=\big{(}1-F_{i,j}(x)\big{)}\text{In}\big{(}1-F_{i,j}(x)\big{)}+ \big{(}1+F_{i,j}(x)\big{)}\text{In}\big{(}1+F_{i,j}(x)\big{)}F_{i,j}^{2}(x) \tag{33}\]  
\[F_{i,j}(x)=\frac{\sqrt{2}\,\big{|}\underline{I_{i,j}}\big{|}}{Tapes_{i,j}\cdot I _{c}\Big{(}T_{HTS}(x),\ |B|_{i,j},\ \theta_{i,j}\Big{)}} \tag{34}\]  
where \(F_{i,j}\) is the ratio between the amplitude of the operating current and the critical current of the \(j\)th layer of the \(i\)th phase. As \(T_{HTS}\) varies along the coordinate \(x\), so do \(I_{c}\) and \(F_{i,j}\) in Equation (34). As described in Section 2.2, for all the tapes of the layers belonging to the \(i\)th phase, the term \(F_{i,j}\) does not exceed the corresponding optimized value \(\sqrt{2}\Big{|}\underline{I_{large_{i,2}}}\Big{|}/\,I_{c}\Big{(}T_{HTS_{max}}, \ |B|_{i,1},\ \theta_{i,1}\Big{)}\).
# Study on the Light Field Regulation of UVC-LED Disinfection for Cold Chain Transportation
## 5 Discussion  
Via comparison from Section 4, it can be seen that the proposed microlens arrays UVC-LED disinfection system achieves light field regulation, which can greatly improve the energy utilization rate and ensure UVC light realizes the disinfection effect of goods under ice in cold chain transportation.  
On the premise of the wide application of UVC for disinfection, aiming at the disinfection demands in cold chain transportation and facing the difficulties caused by ice layer and transportation environment, this paper proposes a uniform UVC light disinfection system, which combines light regulation, optical design and a disinfection system, in order to improve disinfection efficiency, overcome a challenging environment and save energy, providing reliable help for disinfection in cold chain transportation. An additional benefit from the light field regulation of UVC-LED disinfection is that the uniform lighting helps reduce the dose of UVC to 7.92 mJ\(\cdot\)cm\({}^{-2}\) compared to the dose of about 1048 mJ\(\cdot\)cm\({}^{-2}\) in other research, which may help to save a great deal of energy when this technology is widely used. Moreover, the disinfection efficiency can be increased nearly by 30% with the proposed system, achieving a huge increase in efficiency. In conclusion, the results match the initial hypothesis.
## 5 Results and Discussions
### Average Battery Consumption  
Power resource consumption (e.g., battery) is a key in UAV remote sensing as UAVs are highly power constraint. This is measured to study the capacities of the UAV route planning algorithm to move UAVs through cost-effective paths with minimized power consumption to collect and report sensory recordings. Average battery consumption measures the amount of power consumed for the whole the procedure since the routing infrastructure is deployed until the remote sensing mission is finished.  
Figure 5: The average ETE per each visited SP (based on synthesized data of the model generated for this study).  
As Figure 7 depicts, Q-learning outperforms both the benchmarks when SPs are crowded and dense. This is because Q-learning aims to find the best-fitted actions (flight location and direction) according to the current states to maximize the rewards. Thus, the proposed UAV routing algorithm takes the residual energy level and flight distance into account to select minimum cost routes, consequently reducing battery consumption. However, IEMF reduces consumed battery if SPs are rare. This is because IEMF has a limited ability to discover the farm and SP's locations. By this, the UAV leaves the remote sensing mission uncompleted and wrongly returns to the base station even though there are still unvisited SPs on the farm.  
Figure 6: The average number of captured data (based on synthesized data of the model generated for this study).  
Figure 7: The average of consumed battery (based on synthesized data of the model generated for this study).  
TBID underperforms Q-learning and IEMF due to the cost of tree-based infrastructure establishment. This forms a spanning tree to forward the UAV through for remote sensing. This results in increased power consumption, especially when the number of (mobile) SPs is increased.
# Numerical Methods Comparison of Shock-Induced Separation on Transonic Axisymmetric Hump
## 1 Introduction
For the results of RANS, different turbulence models cause 16% difference in the separation length. Regarding WMLES, the separation point was advanced and the length of separation was shorter. In [PERSON]'s [20] work, the explicit filtering LES (EFLES) was applied and stationary ring-like structures were observed in the boundary layer at the apex of the hump, which was considered to associate with turbulence recovery. Meanwhile, the separation point was located at x/c = 0.73, which approximated 0.723 of [PERSON] [19] in the methods of RANS and had 20% of difference by WMLES. And, in the paper, it was observed that the boundary of shock wave near the apex of the hump was away from the tunnel wall, which means there is no influence of shock wave reflection by the tunnel wall in the whole flow field. High-order method LES was applied in a quarter of axisymmetric hump models by [PERSON] [21]. As a result, the separation location at x/c = 0.54 did not match the experiment (x/c = 0.68) well, but the reattachment point at x/c = 1.23 matched well with the experiment (x/c = 1.2). And the simulation resolved the main characteristics of flow field such as the shock with \"\(\lambda\) -structure\". RANS with SST turbulence model [22] and WMLES [23] methods were used in simulations; however, there still exists a little difference in the results of flow field characteristics between experiment and simulations. In the present work, the CFL3D solver (Version 6.7) was used for obtaining the aerodynamic characteristics of the transonic hump experimented by NASA. CFL3D is an open-source solver designed by NASA; the codes can be modified for adding new turbulence models. After version 6.3, CFL3D supported computing the full N-S equation, which was necessary to resolve the SWBILs accurately. The codes of IDDES-SLA were added in the present solver. In this paper, the half model was applied and RANS, URANS, IDDES, and IDDES-SLA methods with the SST turbulence model were used to discuss the accuracy of predictive ability. To validate the numerical methods and grid topology, three grids of different densities were tested, and the results were compared with experimental data. Ref. [20] has already shown that the tunnel wall had no reflective shock. Therefore, in this paper, the far field was used instead of the tunnel device, simplifying the simulation process without significantly reducing accuracy. This paper provides figures, such as \(C_{p},C_{f},x-t\), to analyze the information of the flow field and compare the prediction capacity of four methods and obtained some significant conclusions written in the last section.
# Predictive Modelling for Blasting-Induced Vibrations from Open-Pit Excavations
## 2 Methodology
### Artificial Neural Network  
An ANN is a prediction method based on causes and effects obtained through experience. It can be used as a tool for training, remembering, and analyzing using the computational power of a computer [15]. The network calculates non-linear and complex connections with an input layer, a hidden layer, and an output layer. Each layer has a node for calculation, and their weights and biases act as interlayer connections. The input and output layers consist of causal and result parameters, respectively. The training algorithm of the ANN used in this study was back-propagation, which is the most efficient ANN training algorithm available [16; 17]. In back-propagation, the output values calculated in the forward direction through weights and biases are used to calculate training errors from the true values. Through these errors, weights and biases are corrected to minimize the errors in the reverse direction. These sequences repeat until the errors meet the convergence tolerance or other limit conditions. After the ANN model meets the conditions, it can be used as a prediction model with final weights and biases.  
The ANN requires activation and normalization functions. The former converts the sum of the input signals into the output signal in the nodes of a hidden layer. A non-linear function should be used to determine a non-linear relationship between input and output parameters. Generally, sigmoid, hyperbolic tangent, and rectified linear unit (ReLU) functions, which are non-linear and represented by Equations (1)-(3), respectively, are used as an activation function.  
\[\mathrm{f(x)}=\frac{1}{1+\mathrm{e^{-x}}} \tag{1}\]  
\[\mathrm{f(x)}=\frac{\mathrm{e^{x}-\mathrm{e^{-x}}}}{\mathrm{e^{x}+\mathrm{e^{ -x}}}} \tag{2}\]  
\[\mathrm{f(x)}=\left\{\begin{array}{c}0,\;\mathrm{x}<0\\ \mathrm{x},\;\mathrm{x}\geq 0\end{array}\right. \tag{3}\]  
A normalization function converts all input values which have on different scales into a common scale. It is necessary because the degrees of influence on the output parameter can vary depending on the range of the input parameters. Usually, min-max scaling and standard scaling are used as a normalization function represented by Equations (4) and (5), respectively. In Equation (4), \(\mathrm{x_{Max}}\) and \(\mathrm{x_{min}}\) are the maximum and minimum values for each data type, respectively. In Equation (5), \(\overline{\mathrm{x}}\) and \(\mathrm{S_{x}}\) are the mean and standard deviation values for each data type, respectively.  
\[\mathrm{N(x)}=\frac{\mathrm{x-x_{min}}}{\mathrm{x_{Max}-x_{min}}} \tag{4}\]  
\[\mathrm{N(x)}=\frac{\mathrm{x-\overline{x}}}{\mathrm{S_{x}}} \tag{5}\]  
Figure 1: Three steps of the research process.
## 4 A Brief Discussion of the Literature Review Assessment
### Hybrid Algorithms  
Hybrid Algorithms in GWL modelling represent a sophisticated convergence of multiple approaches and optimisation strategies tailored to address the intricacies of hydrological and hydrogeological systems. Follows a more specialised analysis, elucidating the intricacies and advancements in hybrid methodologies, supplemented with a concrete example:  
* Utilising diverse methods--machine learning-based approaches, such as ANNs, SVMs, and MLP, are among the most prominent methods for predicting GWL. For example, [76] utilised the MLP-2 NSGA-II-MOPSO to predict GWL;
* Hybridisation--combining various methods and employing Hybrid Algorithms to improve prediction accuracy is highly effective. For instance, [3] used the WT-LSSVM and Hybrid Algorithms to achieve more accurate GWL modelling;
* Application of optimisation algorithms--using algorithms such as genetic algorithms and evolutionary strategies to tune model parameters and improve prediction accuracy is highly effective. For example [1], utilised the PSO-ANN method to predict GWL;
* Integrating different data sources--incorporating diverse data sources such as weather and geological data to enhance prediction accuracy is crucial. For example, [82] utilised weather data and sea level information to model GWL;
* Utilisation of hybridisation and model fusion methods--many articles employ hybridisation to combine different approaches, such as ANNs with SVMs or ANNs with physical models like numerical flow models. For example, [80] utilised the AAEO-MLP to predict GWL;
* Incorporating uncertainty analysis--several studies emphasised the importance of uncertainty analysis in GWL modelling. For instance, [79] conducted a study comparing the ORELM with other hybrid methods and found it to be more accurate, efficient, and cost-effective. This underscores the significance of considering uncertainties in model predictions;
* Regional adaptation and validation--many articles focus on specific regions, considering local hydrogeological conditions and characteristics. For example, [83] developed the MLP-WA method to predict GWL, demonstrating its superiority over other hybrid approaches;
* Enhance model performance with novel methods--some studies introduce novel methods to enhance model performance. For instance, [84] proposed the Hybrid GTWANN, which demonstrated robustness and effectiveness in estimating GWL;
* Evaluation of model performance--it is crucial to assess the performance of prediction models rigorously. Studies such as those by [27; 85] compared different models and found that hybrid approaches often outperform standalone methods regarding accuracy and reliability;
* Application of advanced methods for spatiotemporal prediction--several articles focus on the spatiotemporal prediction of GWL using advanced techniques like wavelet analysis and AI. For instance, [88; 89] applied hybrid wavelet-AI methods to predict GWL.