esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning Approach for Arm Fracture Detection Based on an Improved YOLOv8 Algorithm
## 1 Introduction
Integrating attention mechanisms into YOLO models helps distinguish between relevant and irrelevant features, boosting performance on small, easy-to-miss objects and improving detection accuracy. Additionally, the HA mechanism does not use a transformer. Instead, it combines two types of attention mechanisms: channel attention and spatial attention. Channel attention emphasizes the important channels in the feature maps, improving the model's ability to capture critical features across different channels. On the other hand, spatial attention highlights significant spatial locations, enhancing the network's sensitivity to essential regions within the image. The HA attention mechanism improves CNNs' overall performance and accuracy, particularly in complex object detection tasks such as those handled by the YOLO series. These mechanisms enhance feature representations in CNNs by focusing only on the relevant channels and spatial locations. In this paper, we modify the YOLOv8 model architecture to integrate the HA mechanism, which combines spatial and channel attention and improves the model's performance in detecting fractures in the hand on the FracAtlas dataset [32]. We compare the performance of the YOLOv8 models with our modified YOLOv8-HA. The model's hybrid approach enhances its ability to focus on the input data's most informative spatial regions and relevant feature channels. The remaining sections of this paper are structured as follows: Section 2 describes the model architecture in detail. Section 3 describes the methods for our study in detail. Section 4 presents the experimental results and discusses how to analyze them. Section 5 summarizes the conclusions.
# High-Tolerance Grating Couplers for Vertical Backside Coupling
###### Abstract
To integrate PICs onto a fiber tip, a perfectly vertical backside-coupling GC based on a binary blazed grating is proposed [17], enabling direct integration of optical fiber and the PICs. Because the optimization of such a 3D complex structure requires high computational power and simulation times, we employed an inverse design approach by using particle swarm optimization (PSO) algorithms to optimize our devices [21]. After optimization, the CE is increased to 40% at the central wavelength of 1550 nm, with a minimum feature size over 200 nm. Our designed structure is well adapted to FOSs applications while maintaining a good fiber-to-chip CE, exhibiting high fabrication and misalignment error tolerance and an easily fabricable structure (only a single etching step required). Combined with the ability to efficiently transfer photonics chips onto fiber facets, the GC can be applied to fiber sensors with applications in minimally invasive surgery, industrial sensing and bio-sensing.
# Estimation of Distribution Algorithms with Fuzzy Sampling for Stochastic Programming Problems
## 5 Stochastic Programming Applications
### The LANDS Problem
#### 5.3.1 The Mathematical Formulation of the LANDS Problem  
The formal description of the LANDS Problem can be defined as follows [77; 79]. The required values for parameters and constants are also expressed.  
\[\begin{array}{ll}i&\mbox{The power plant type ($i=1,\ldots,4$).}\\ j&\mbox{The operating mode ($j=1,\ldots,3$).}\\ x_{i}&\mbox{The capacity of power plant $i$ (decision variable).}\\ y_{ij}&\mbox{The operating level of power plant $i$ in mode $j$.}\\ c_{i}&\mbox{The unit cost of capacity installed for plant type $i$, $c=[10.0,7.0,16.0,6.0]$.}\\ h_{ij}&\mbox{The unit cost of operating level of power plant $i$ in mode $j$,}\\ h&=\left[\begin{array}{ccc}40.0&24.0&4.0\\ 45.0&27.0&4.5\\ 32.0&19.2&3.2\\ 55.0&33.0&5.5\\ \end{array}\right].\\ m&\mbox{The minimum total installed capacity $m=12.0$.}\\ b&\mbox{The available budget for capacity installment, $b=120.0$.}\\ d_{j}&\mbox{Random power demands in mode $j$, $d=[\varepsilon,3.0,2.0]$,}\\ &\mbox{where $\varepsilon$ has values 3.0, 5.0, or 7.0 with probability 0.3, 0.4, and 0.3, respectively.}\\ \end{array}\]  
Therefore, the object function for the LANDS Problem [77; 79] can be expressed as  
\[f(x,v)=\min\left(\sum_{i,t}c_{i}x_{i}+\mathbb{E}[\sum_{i,j}h_{ij}y_{ij}]\right), \tag{15}\]  
Figure 5: The FSEDA and ASEDA performance for the KANDW3 Problem.
# Allocating Small Transporters to Large Jobs
## 6 The General Transporter Allocation Problem
### Heuristics to Compute the Pareto Frontier
#### 6.2.2 Binary Search Heuristics  
In the following, we develop new heuristics by replacing the resolution of program \((\rho\mathrm{MIP})\) with greedy heuristics in the binary search framework from \((\mathrm{BS.MIP}g)\).  
The program \((\rho\mathrm{MIP})\) consists of minimizing the number of used transporters for a fixed makespan. Hence, Algorithm \((\mathrm{BS.MIP}g)\) computes a solution, minimizing the number of used transporters for different makespan values. This is exactly what Algorithm \((\mathrm{GR}c)\) does. Therefore, combining this algorithm with a binary search will not yield any better result.  
On the other hand, Algorithms \((\mathrm{SGJ})\) and \((\mathrm{GR}i)\) focus on the idle time per period. The former looks for a combination of transporters saturating a job, while the latter aims at minimizing the maximum idle time per period. In the following, we slightly modify program \((\rho\mathrm{MIP})\) to look like an instance to \((\mathrm{sTAP})\) and use our greedy algorithms.  
The program \((\rho\mathrm{MIP})\) only differs from a saturated transporter allocation program by the factor \(1/\big{(}1-\rho(j,C)\big{)}\) in Equation (31). Namely, for every job \(j\):  
\[\frac{1}{1-\rho(j,C)}\cdot\sum_{r}\big{[}\overline{X}_{r,j}\cdot\kappa(r) \big{]}-\max_{r}\big{[}\overline{X}_{r,j}\cdot\kappa(r)\big{]}\geq\delta(j).\]  
We rewrite Equations (31) as:  
\[\sum_{r}\big{[}\overline{X}_{r,j}\cdot\kappa(r)\big{]}-\big{[}1- \rho(j,C)\big{]}\cdot\max_{r}\big{[}\overline{X}_{r,j}\cdot\kappa(r)\big{]} \geq\big{[}1-\rho(j,C)\big{]}\cdot\delta(j)\] \[\sum_{r}\big{[}\overline{X}_{r,j}\cdot\kappa(r)\big{]}-\max_{r} \big{[}\overline{X}_{r,j}\cdot\kappa(r)\big{]}\geq \big{[}1-\rho(j,C)\big{]}\cdot\delta(j)\] \[-\rho(j,C)\cdot\max_{r}\big{[}\overline{X}_{r,j}\cdot\kappa(r) \big{]}.\]  
Since the maximum capacity assigned to a job is bounded by the maximum capacity over all transporters \(\kappa_{\max}\), the following constraint is more restrictive:  
\[\sum_{r}\big{[}\overline{X}_{r,j}\cdot\kappa(r)\big{]}-\max_{r}\big{[} \overline{X}_{r,j}\cdot\kappa(r)\big{]}\geq\big{[}1-\rho(j,C)\big{]}\cdot \delta(j)-\rho(j,C)\cdot\kappa_{\max}. \tag{33}\]  
This more restrictive constraint corresponds to a modified instance of \((\mathrm{sTAP})\), with a return time of \(\big{[}1-\rho(j,C)\big{]}\cdot\delta(j)-\rho(j,C)\cdot\kappa_{\max}\) instead of \(\delta(j)\) for job \(j\). We call \((r\mathrm{MIP})\) the mixed integer program obtained by replacing Equation (31) with Equations (33), modeling a saturated transporter assignment problem.  
We can use this modified framework with any of our greedy heuristics to approximate the Pareto frontier. We denote by \((\mathrm{BS.GJ})\) and \((\mathrm{BS.GR}i)\) the binary search-based heuristics using Algorithms \((\mathrm{SGJ})\) and \((\mathrm{SGR}i)\) respectively to solve \((r\mathrm{MIP})\).
## 3 Results and Discussion
It was found that for the riboflavin it is \(11^{\circ}+/-2^{\circ}\), while for the R6G it is \(33^{\circ}+/-3^{\circ}\) on a glass substrate, and \(27^{\circ}+/-3^{\circ}\) and \(45^{\circ}+/+5^{\circ}\) on gold nanoislands substrate, respectively. It can be seen that while the contact angle increases for both cases on the SERS substrate, the relation is similar to that of the pure glass substrate. The contact angle values could be connected with the structure of the molecule and its PSA as well. The examination of the structure of the two analyte molecules shows that riboflavin has several hydroxyl groups (-OH) in its structure, with which it can form larger secondary bonds (H-bond and/or Van der Waals bond) with the gold surface. While the side chains of the rhodamine 6g molecule contains a methyl group (-CH3) in its structure, which could not bond so easily to the Au surface. As a consequence, there is a difference in the orientation of the two analytes on the gold surface, and this has also been supported by earlier studies. It was found that rhodamine 6G molecule adsorbs on the silver surface with the xanthene plane lying parallel and both the tail of the phenyl group and the ethylamine groups pointing up from the surface [40]. In another hand, from model calculations of different orientations and characteristic bands intensities observed in SERS spectra was evident that riboflavin molecules were adsorbed on the surface via aromatic ring, heterocyclic rings II and III (N10 and O2) and heterocyclic rings II and III in the case of gold, silver, and copper, respectively [33]. The results of the contact angle measurements are in good agreement with the PSA of these materials; a larger contact angle means that the material is more hydrophobic, and the PSA value is lower, as has also been shown earlier in another study [41]. As a result of this investigation, it was found that the polarity of the molecule influences the SERS process, as it affects the absorption of the substrate. As a consequence of the results, during the investigation of the SERS effect, not only the parameters of the nanostructure (material, shape, size, gap, and the peak of the plasmon wavelength) and the wavelength of the laser excitation should be considered [12], but the type of the studied analyte, and its adsorption on the substrate as well. It was shown in our study that different analytes have different adsorption, which affects which excitation wavelength will cause the highest SERS enhancement. Previously, there were no examples of systematic studies of the effect of the analytes on the SERS process. Rhodamine 6G and riboflavin have both been used as analyte molecules in several cases to investigate the optimization of different production of SERS substrates. Furthermore, using the optimized substrates, the detection limit is determined for both analytes [4; 23; 33; 42; 43; 44; 45].
# Noise-Assessment-Based Screening Method for Remote Photoplethysmography Estimation
## 5 Discussion (and Future Work)
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Person ID**} & \multicolumn{3}{c}{**Decision Tree**} & \multicolumn{3}{c}{**Random Forest**} \\ \cline{2-7}  & **Still** & **Motion** & **Total** & **Still** & **Motion** & **Total** \\ \hline Person 1 & 37.70\% & 79.17\% & 41.98\% & 35.59\% & 42.86\% & 39.68\% \\ Person 2 & 79.22\% & 88.00\% & 64.68\% & 82.26\% & 81.58\% & 61.07\% \\ Person 3 & 91.09\% & 7.14\% & 71.17\% & 91.43\% & 26.67\% & 81.82\% \\ Person 4 & 82.91\% & 50.00\% & 70.65\% & 82.46\% & 4.35\% & 74.54\% \\ Person 5 & 0.00\% & 7.69\% & 25.68\% & 0.00\% & 38.89\% & 4.44\% \\ Person 6 & 90.91\% & 95.24\% & 86.15\% & 88.89\% & 71.43\% & 92.23\% \\ Person 7 & 21.74\% & 66.85\% & 31.72\% & 20.88\% & 67.57\% & 15.84\% \\ Person 8 & 91.18\% & 0.00\% & 71.97\% & 63.11\% & 0.00\% & 94.55\% \\ Person 9 & 72.00\% & 57.89\% & 90.91\% & 70.00\% & 70.59\% & 88.24\% \\ Person 10 & 86.14\% & 78.67\% & 75.49\% & 88.78\% & 68.75\% & 90.48\% \\ Person 11 & 71.62\% & 86.67\% & 56.72\% & 67.69\% & 50.00\% & 53.33\% \\ Person 12 & 93.42\% & 85.25\% & 73.15\% & 90.00\% & 85.45\% & 57.89\% \\ Person 13 & 9.09\% & 7.69\% & 10.47\% & 9.09\% & 7.14\% & 2.35\% \\ Person 14 & 12.82\% & 4.76\% & 19.30\% & 37.10\% & 14.29\% & 14.93\% \\ Person 15 & 20.69\% & 16.67\% & 21.78\% & 18.75\% & 46.67\% & 15.09\% \\ Person 16 & 84.48\% & 54.55\% & 89.07\% & 85.42\% & 60.00\% & 54.86\% \\ Person 17 & 58.14\% & 100.00\% & 34.29\% & 58.14\% & 57.14\% & 49.00\% \\ Person 18 & 92.16\% & 39.13\% & 37.00\% & 59.70\% & 20.00\% & 50.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of proposed methods (decision tree, random forest) in person ID. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Person ID**} & \multicolumn{3}{c}{**SVR**} & \multicolumn{3}{c}{**Transformer**} \\ \cline{2-7}  & **Still** & **Motion** & **Total** & **Still** & **Motion** & **Total** \\ \hline Person 1 & 31.11\% & 50.00\% & 41.30\% & 18.18\% & 45.61\% & 54.55\% \\ Person 2 & 76.09\% & 100.00\% & 77.08\% & 36.36\% & 70.97\% & 87.50\% \\ Person 3 & 84.00\% & 0.00\% & 89.58\% & 91.67\% & 7.02\% & 75.00\% \\ Person 4 & 72.00\% & 46.15\% & 98.00\% & 50.00\%  
verified only in an indoor environment, so verification in outdoor, driving, and fitness environments should be conducted. Finally, although an improvement in accuracy was confirmed via a method with low coverage for the actual total time, there is a limitation in that the heart rate measurement accuracy does not cover the entire section. To overcome this limitation, a method of compensating all the sections based on high-confidence data with low predicted error margins should also be conducted as part of future works.
# Hydrodynamics of an Airlift Column for Aeration in Molten Sulfur
###### Abstract  
The airlift column is a promising technology for the removal of volatile gas from high-viscosity molten sulfur. However, a detailed analysis is lacking on the hydrodynamic properties inside the column, due to the difficulty in flow behavior detection in the opaque molten sulfur. In this work, we adopted the computational fluid dynamics simulation to understand the hydrodynamic behaviors in an airlift column for molten sulfur aeration. In addition, we analyzed the impacts of the superficial gas velocity (\(U_{Gr}\)) and column height on the hydrodynamic characteristics, such as gas holdup, average bubble diameter, and liquid circulation velocity (\(U_{Lr}\)) in the column. The simulation shows that at a constant column height of 15 m, an increase on gas holdup can be obtained with the increase of the superficial gas velocity, while the bubble diameter remains almost constant. Once the superficial gas velocity exceeded 0.333 m/s, the liquid circulation velocity increased slowly. With a variation on the column height from 5 to 25 m, a negligible change on gas holdup, but an obvious increase on liquid circulation velocity and bubble diameter is observed at the given superficial gas velocity of 0.0389 m/s. Furthermore, the simulation shows a similar trend, but with considerably more detailed information, on the relationship between the gas holdup and liquid circulation velocity when compared to the predictions from the Chisti correlation (1988) and an optimized correlation proposed in this work.  
internal loop airlift column; molten sulfur; CFD simulation; gas holdup; bubble diameter; liquid circulation velocity +
Footnote †: journal: Applied Sciences
###### Abstract
The authors describe a framework called HARFT (Hybrid Adaptive Reconfigurable Fault Tolerance) that proposes different operating modes that can be activated depending on the necessary level of fault mitigation against application performances. The framework implements scrubbing techniques and takes advantage of the partial reconfiguration capability of the FPGAs. Additional techniques such as ECC, parity schemes and TMR are also used. The mitigation mechanisms implemented in the processor embedded in the FPGA (Zynq) allows switching from a Symmetric Multi-Processor (SMP) mode to an Asymmetric Multi-Processor (AMP) mode after a reset of the system. The system allows the user to choose a third mode in which one or several Microblaze processors operate in lockstep in the programmable logic, potentially coupled with hardware accelerators. The three modes have been tested in different scenarios with simulated SEEs sent to a Zynq device containing soft processors but no hardware accelerators. The combination of these mitigation techniques leads to the design of complex systems able to adapt themselves to the level of radiation to which they are subjected. The ability to balance between resources usage and operational safety is a key point to reduce the cost of the high-performance FPGA-based nano-satellite board, while maintaining device adaptability and long-term reliability. In this work, we propose a payload computation platform based on a Zynq Ultrascale+ MPSoC FPGA. The platform provides different reconfigurable partitions (RPs) connected with streaming interfaces in order to offer efficient computation acceleration to the application. A memory scrubbing mechanism based on the Soft Error Mitigation Controller provided by Xilinx is integrated into the platform. This scrubber provides information about the level of radiation to which the device is currently subject and, based on this information, the application is automatically and dynamically deployed in two different modes: a \"High-Performance Mode\", where each partition hosts a different hardware co-processor, or in a \"Safe Mode\", where streamed partitions are dynamically reconfigured in order to act as a TMR version of a single hardware core. A Low-Power mode in which RPs are disabled thanks to the DPR is also provided to fulfill nano-satellite application requirements regarding energy-saving constraints. Compared to [13], our platform offers the ability to efficiently integrate reconfigurable hardware accelerators that can take advantage of the streaming structure of the interconnect. It provides a controller for the configuration memory scrubber and upper software layers to handle correctable but also non-correctable soft errors due to radiation. Finally, the platform leverages the heterogeneity provided by the MPSoC using one of the real-time processors to perform both platform monitoring and the fault injection campaign.
# Designing Audio Equalization Filters by Deep Neural Networks
## 3 Proposed Method  
The rationale behind the proposed approach stems from the following reflection--the training of a deep neural network is, in fact, an optimization problem, where a loss term is minimized by the back-propagation of the error through the neural network. This idea is not completely new and shallow neural network have been previously proposed for optimization (see, e.g., References [26; 27]). Deep neural networks, however, were shown in Reference [28] to perform better in optimization tasks, possibly due to their parameter redundancy. In this work thus we propose to exploit deep neural networks for the optimization of equalizing filter coefficients.  
Our approach consists of training a neural network by backpropagation in order to obtain, as output, optimal coefficients that minimize a frequency-domain loss. Each set of impulse responses requires a different training, meaning that the network is not expected to generalize, but rather perform optimization by fitting its weights, differently to common Deep Learning classification and regression tasks. We test a shallow network, that is, a Multilayer Perceptron (MLP), and two deep network architectures: a CNN and a convolutional Autoencoder (AE). In the absence of prior art, we feed the networks with the only available data, that is, the measured impulse responses. The neural networks, in turn, provide filter coefficients that are iteratively optimized to minimize a loss function. In the following we describe the architectures and the respective loss functions. In all cases the loss function contains at least one term based on the distance between the achieved frequency response and the desired curve. In our case, for simplicity, the desired curve is flat and the distance is computed in the frequency range \(\omega_{l}:\omega_{h}\), to be defined according to the use case. In our work we use the Euclidean distance [29; 30] to compute the distance, which was found to converge faster than the \(L_{1}\)-norm [31].
## 3 Principle and Processing Flow of Atmospheric Correction Algorithm
### Atmospheric Correction LUT  
For wide-swath, wide-field multispectral images captured by the MSC, it is not feasible to use a single set of aerosol optical depth (AOD), column water vapor (CWV), viewing geometry, and solar geometry data for atmospheric correction. Instead, atmospheric radiance must be calculated regionally. To improve computational efficiency and avoid repeated calls to the radiative transfer model, an atmospheric correction lookup table (LUT) is pre-constructed. The LUT provides precomputed atmospheric parameters, streamlining the correction process.  
Based on the orbit and overhead pass times of the HJ-2A/B satellites, the ranges for the viewing solar zenith angle (SZA), zenith angle (VZA) and relative azimuth angle (RAA)  
Figure 3: Matching results of AOD and CWV for MSC image. (**a**) Matched AOD distribution. (**b**) AOD distribution after linear interpolation. (**c**) Matched CWV distribution. (**d**) CWV distribution after linear interpolation.  
are set. Additionally, the ranges and step sizes for CWV and the AOD are defined, along with the aerosol model provided by the 6 SV atmospheric radiative transfer model. These parameter settings are detailed in Table 2, where the six aerosol models are as follows: continental model, maritime model, urban model, desertic model, biomass burning model, and stratospheric models.  
For each CCD and spectral band of the MSC, the atmospheric correction LUT is constructed using the 6 SV model. The spectral response functions of each band are incorporated into the simulations to compute the key atmospheric parameters: path radiance (\(L_{path}\)), direct transmittance (\(T_{dir}\)), diffuse transmittance (\(T_{diff}\)), and atmospheric hemispherical albedo (\(S\)). These parameters form the basis of the LUT.  
Atmospheric correction is closely related to the aerosol model, with each model representing an independent LUT. To ensure efficient organization, each LUT is named based on its corresponding spectral band, CCD, and aerosol model. By leveraging precomputed LUTs, atmospheric correction calculations for the MSC images are significantly accelerated, enabling the timely processing of wide-swath satellite data.