esa-sceva/satcom-chunk-collection huggingface dataset:
# A Single Simulation Platform for Hybrid Photoacoustic and RF-Acoustic Computed Tomography
## 1 Introduction
The fluence distribution is then converted to an initial pressure rise, which is further propagated through the tissue medium and detected by the ultrasound transducers located on the boundary using acoustic simulation tools such as the K-wave toolbox. NIRFast [18] uses the finite element method (FEM), while K-wave uses a spectral-based finite difference method (FDM) to model the propagation of acoustic waves [19]. The goal of the reconstruction problem is then to recover the tissue properties (optical absorption in PACT and conductivity in RACT) given only the sensor data. Groups that use a single simulation platform for both the optical and acoustic propagation needed for the hybrid PACT/RACT technique are less common. Recently, there are studies that use the commercial software COMSOL to solve both propagation problems with a single software package [20; 21]. As an alternative to the commercial software, the simulation system described in this paper uses the open source softwares Gmsh [22] and GetDP [23], often combined under the name ONELAB [24]. ONELAB is an FEM solver, which uses Gmsh for creating the FEM mesh, and GetDP for solving generic partial differential equations (PDEs) with the FEM method. Advantages of using Gmsh include its ability to create user defined meshes, but also having standard interfaces with other commonly used mesh and computer-aided design (CAD) software such as STEP, IGES, and STL. Segmented DICOM images commonly used in MRI can then be converted to a mesh that Gmsh understands, creating realistic phantoms on which to test algorithms. The ability of GetDP to solve generic PDEs allows the user to implement algorithms to solve for optical and acoustic propagation, similar to the combination of NIRFast and K-wave. Both propagation methods, as well as any reconstruction methods, are implemented in the same mesh, with no loss of precision. Since there is less chance of numerical error, a single software platform represents a more accurate approximation of a real-world scenario. Besides PACT with optical sources, we demonstrate that the generality of GetDP allows for sources in the RF regime of the electromagnetic spectrum to also be simulated for RACT. The use of the ONELAB software package allows accurate TACT modeling, in order to develop algorithms for functional imaging of the human body. Although there are experimental studies combining PACT and RACT techniques into one setup, perhaps our study is the first to report a single TACT simulation platform for simulating both PACT and RACT. The rest of the paper is organized as follows: In Section 2, we describe methods and materials. Sections 3.1 and 3.2 show PACT and RACT results on a homogeneous phantom. Sections 3.3 and 3.4 show PACT and RACT results on an approximate breast phantom. Section 4 is a discussion of the results in Section 3, comparing errors in reconstruction between PACT and RACT, and between the two phantoms. Section 5 concludes the paper.
## 3 Sample, Experimental Devices, and Protocols
### X-ray \(\mu\)CT  
The first step of the protocol was to take reference X-ray images of the samples. The X-ray tomograph used to produce the reference images was an X-ray micro-computed tomography (\(\mu\)CT) system (EasyTom XL 150 \"Mechanic Ultra\", RX Solutions, Chavanod, France). The slice thickness of the X-ray cross-section was 30 \(\upmu\)m for the newborn arm phantom, and 50 \(\upmu\)m for the chicken drumstick. The number of projections was 10,080 for the newborn arm phantom, and 4320 for the chicken drumstick. The voltage of the X-ray source was 60 kV for the newborn arm phantom, and 110 kV for the chicken drumstick, and the beam aperture was 43\({}^{\circ}\) for both. The image size was 1840 \(\times\) 1456 pixels\({}^{2}\) (16 bits). No image processing was done to enhance the contrast. The software used to display the images was Fiji(Image)( plugins) [38].  
Figures 2 and 3 show, respectively, the \(\mu\)CT of the newborn arm phantom and of the ex vivo chicken drumstick.
## 5 Results
### Evaluation Metrics
#### 5.1.1 Image Synthesis Metrics  
There are no existing metrics for magnetic field synthesis. In order to objectively demonstrate the superiority of the proposed method, all the experiments were quantitatively evaluated using the mean absolute error (MAE), the structural similarity (SSIM) index, and the peak signal-to-noise ratio (PSNR). The MAE, also known as the L1 loss, is a measurement between two paired matrices. It calculates the sum of absolute errors divided by the matrices' sizes in this study, which are intuitive metrics for similarity measurements. The SSIM index measures the similarity of structural information in two images, where 0 indicates no similarity and 1 indicates total positive similarity. SSIM can be used in physics-based image restoration [34; 35; 36], and a higher SSIM index means that the synthetic fundus image is closer to the real one. PSNR measures the image distortion and noise level between two images. A higher PSNR value indicates a higher image quality.
# Acoustic Emission Based Fault Detection of Substation Power Transformer
## 3 Methods
### Evaluation Criteria  
The fault discrimination problem described in this paper belongs to a typical binary classification problem, and sensitivity is an important evaluation index. However, due to the extremely uneven number of samples in positive and negative categories, simply using sensitivity as an indicator cannot well quantify performance of a classifier. In fact, In the binary classification problem, we should investigate the positive (fault sample) and negative (normal samples) categories at same time. Therefore, the accuracy rates are also discussed in our binary classification experiments.  
Sensitivity (SEN), and accuracy in the binary classification were calculated as averages over the 10 cross-validation cases according to the following formulas:  
\[\text{SEN}=\text{TP}/\text{P} \tag{6}\]  
\[\text{Accuracy}=(\text{TP}+\text{TN})/(\text{TP}+\text{TN}+\text{FP}+\text{ FN}) \tag{7}\]  
Where True positives (TP), true negatives (TN), false positives (FP), and false negatives (FN), are the four different possible outcomes of a single prediction for a two-class case with classes \"1\" (\"yes\") and \"0\" (\"no\"). A false positive is when the outcome is incorrectly classified as \"yes\" (or \"positive\"), when it is in fact \"no\" (or \"negative\"). A false negative is when the outcome is incorrectly classified as negative when it is in fact positive. True positives and true negatives are obviously correct classifications.  
For estimating the overall performance in multi-classification problems, the average SEN of multi-class was used and it can be defined as  
\[\text{SEN}=\frac{\sum_{i=1}^{N}\text{CR}/(\text{CR}+IU)}{N} \tag{8}\]  
Figure 3: Model architecture for the CNN model used as a comparison.  
where  
1. \(N\)--Number of sets for cross validation;
2. _IU_--Incorrectly unrecognized = number of erroneous classifications (which is equal to the sum of the off diagonal entries of the confusion matrix);
3. _CR_--Correctly recognized = (number of elements in the test set number of classes) -- number of erroneous classifications.  
In addition to the sensitivity index, kappa [31] coefficient is also used as the performance evaluation parameter in the muti-classification experiments.  
In this study, the computations were run on an Intel Core i7-6700 3.4 GHz, 32-GB RAM machine. For different classifier and different parameters, there is a big difference in training time. For the CNN, the GPU was used in the training process. Compared with training, test time is more related to actual application. Based on this, we only count the time spent in the test phase. SVM program used in the simulation process is LIBSVM designed by Professor [PERSON] [28] of Taiwan University.
## Results and Discussion
### Wavelengths Selection  
The selection of optimal wavelengths from the spectral data extracted from hyperspectral images is a task that aims to simplify models and is a precursor to the design of an informative multispectral imaging system [35]. The RC represents the contributions of the specific spectral variables to the PLSR calibration model. In this study, by analyzing RC resulting from the preferred multispectral PLSR models based on three spectral units, peaks and valleys attributed to high absolute values for adulteration levels were individually identified. Finally, a total of nine, 10, and 10 wavelengths for R, A, and KM spectra shown in Figure 4 were defined as the most relevant wavelengths in predicting adulteration levels of carrageenanan adulteration in chicken meat. These identified wavelengths make it possible to obtain a simplified model and, further, to establish a simple and low-cost multispectral imaging system. All the three sets of selected effective wavelengths would be compared as a basis to develop the simplified PLSR models.
###### Abstract  
The metabolic activity of fermentative microorganisms plays a critical role in determining the flavor profile of fermented meat products. Modulating carbon and nitrogen sources represents a promising strategy for enhancing product quality. In this study, _Debaryomyces hansenii_ strains isolated from dry-cured ham were assessed in a sterile sausage model to evaluate the effects of different carbon sources (sucrose, corn starch) and nitrogen sources (leucine, soy protein isolate) on colony growth, enzyme activity, and physicochemical properties. These nutritional factors significantly affected the fermentation performance of _D. hansenii_. Corn starch and soy protein isolate increased colony count by 14.94% and 90%, respectively, and enhanced protease activity by 2-fold and 4.5-fold. Both treatments maintained high lipase activity (>50 U/g). Both supplements improved the water-holding capacity and decreased the water activity. Carbon sources reduced the medium pH, whereas nitrogen sources contributed to the maintenance of pH stability. A further analysis indicated that corn starch promoted the accumulation of aldehydes and ketones, which intensified the sourness and suppressed the saltiness. In contrast, soy protein isolate increased the abundance of free amino acids associated with umami and sweetness, and stimulated the formation of esters, ketones, and pyrazines, thereby enhancing flavor richness and umami intensity. Both ingredients also reduced saturated fatty acid levels and increased the unsaturated to saturated fatty acid ratio. Soy protein isolate exhibited a more pronounced effect on _D. hansenii_ fermentation. This study provides a technical reference for enhancing the flavor characteristics of fermented meat products via the adjustment of carbon and nitrogen sources to regulate _D. hansenii_ fermentation.  
Deharyomyces hansenii; fermented sausage; carbon and nitrogen sources; protease activity; free amino acids; free fatty acids; volatile flavor compounds; electronic tongue; fermentation optimization
## 6 Conclusions  
This paper proposed an NDT technique based on LW data and artificial intelligence algorithms to detect weak adhesion defects in an SLJ. Four algorithms were trained and tested with simulated LW data from an FEM developed and experimentally validated. The algorithms were assessed and compared. A total of 20 different levels of weak adhesion could be detected, with overall accuracy between 97% and 99%. The results showed the effectiveness of the proposed nondestructive methods to treat the problem, which, until now, could only be solved with classical destructive mechanical testing. Furthermore, the overall training capability was studied, revealing that the training time for each algorithm could be reduced by almost 60% by altering the algorithm's parameters. The LSTM emerged as the least-suitable, taking over 200 epochs to converge, and the GRU the most-applicable, converging in less than 50 epochs. Regarding the training time, the FNN proved to be more appropriate. Therefore, the FNN and GRU presented the best accuracy and showed  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Evaluation Criteria**} \\ \hline
**Algorithm** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline FNN & 0.99 & 0.99 & 0.99 & 0.99 \\ CNN & 0.975 & 0.97 & 0.98 & 0.97 \\ GRU & 0.99 & 0.99 & 0.99 & 0.99 \\ LSTM & 0.98 & 0.98 & 0.98 & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Algorithms’ evaluation criteria.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & \multicolumn{3}{c}{**Epochs and Total Time**} \\ \hline
**Algorithm** & **Epochs to Converge** & **Time to Converge (min)** & **Total Time (min)** \\ \hline FNN & 100 & 7 & 28.0 \\ CNN & 150 & 4.5 & 12.3 \\ GRU & 35 & 15 & 177.5 \\ LSTM & 150 & 89 & 238.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of the algorithms.  
relatively acceptable convergence times, making them the most-suitable choices. Further work will expand the technique to increase the resolution for detecting weak adhesion, as well as to localize defects in adhesive joints with a larger and more complex geometry.  
Conceptualization, G.M.F.R., L.F.M.D.S. and A.M.L.; methodology, G.M.F.R. and A.M.L.; software, G.M.F.R.; validation, G.M.F.R., L.F.M.D.S., A.M.L. and R.J.C.C.; formal analysis, G.M.F.R., L.F.M.D.S., A.M.L. and R.J.C.C.; investigation, G.M.F.R. and A.M.L.; writing--original draft preparation, G.M.F.R.; writing--review and editing, G.M.F.R., L.F.M.D.S., A.M.L. and R.J.C.C.; supervision, L.F.M.D.S. and A.M.L.; project administration, L.F.M.D.S. and A.M.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 5 Proposed MOEA/D-AMSF Algorithm
### Algorithm Flowchart  
Based on the description of the key mechanisms and steps above, Algorithm 4 shows the specific flowchart of the proposed MOEA/S-AMSF. Before the execution of this algorithm, we sorted all the connection requests according to the minimum bandwidth request priority criterion (aiming at reducing the RBR), and then calculate KSPs for all source-destination-node pairs. Then the routing result is stored in a data structure named \"traffic_routing_matrix\". Finally, the maximum path length (\(L_{max}\)) and _geneLen_ (\(L_{max}+1\)) are calculated according to the \(K\) shortest paths of all the requests, with traffic_routing_matrix and _geneLen_ as the algorithm inputs.  
MOEA/D-AMSF mainly includes three stages of initialization, evolution, and iteration stop judgment. The initialization process consists of four steps. First, set EP to empty for storing the nondominated solutions found in the search process (line 1); Second, calculate the Euclidean distance between any two weight vectors (line 3), and select several nearest subproblems of each subproblem to form its neighborhood (line 6); Third, generate the initial population \(\{x^{1},\ldots,x^{N}\}\) by IPHG algorithm designed in Section 5.3 (line 8), and each individual in it is assigned an appropriate spectrum block by the FF method. Its fitness value \(F(x)=\left(f_{1}(x),f_{2}(x)\right)^{T}\) (line 9) is calculated, where \(f_{1}(x)\) and \(f_{2}(x)\) are the objective functions \(f_{blocking}\) and \(f_{XT}\) defined in Section 3.2, respectively. Finally, the reference point vector is initialized (line 10).  
The evolution _Stage_ two mainly performs crossover, mutation, reference point update, and neighborhood replacement on each individual in turn. Specifically, the adaptive crossover operator in Algorithm 2 and the adaptive bi-layer mutation with repair operator in Algorithm 3 are first used to generate feasible offspring \(y_{m}^{1}\) and \(y_{m}^{2}\) (lines 12 and 13). Second, the FF method is used to allocate the spectrum blocks for the generated two offspring, the fitness values \(F(y_{m}^{1})\) and \(F(y_{m}^{2})\) are calculated, and the offspring with better fitness value is selected as the new solution \(x_{new}^{i}\) of the current subproblem \(i\) (line 14). Third, the reference point is updated by comparing it with the aggregation function value of \(x_{new}^{i}\) (lines 15-19). Next, the neighborhood of subproblem \(i\) is updated by comparing the aggregation function value of \(x_{new}^{i}\) with the old solution in the neighborhood and replacing the old one if \(x_{new}^{i}\) has a better value (lines 20-24). Finally, EP is updated by removing all the solution vectors dominated by \(F(x_{new}^{i})\) from EP and adding \(F(x_{new}^{i})\) to EP if there is no solution vector dominating it in EP (lines 25-28). If the evolution iteration reaches \(G_{max}\), terminate the algorithm and output EP; otherwise, return to _Stage_ two to continue the evolution (lines 30-34).
###### Abstract  
In this work, a silicon nanowire solar cell for efficient light harvesting in the visible and near-infrared regions is introduced. In this structure, the silicon nanowires (SiNWs) are coated with a graphene layer and plasmonic nanoparticles are distributed on the top surface of the silicon substrate layer. The proposed structure is simulated using the finite difference time domain (FDTD) method to determine the performance of the solar cell by calculating the open-circuit voltage, fill factor, short-circuit current density, and power conversion efficiency. The absorbed light energy is compared for different nanoparticle materials, namely Au, Ag, Al, and Cu, and Au NPs give the best performance. Different values of the radius of the Au NP are simulated, namely 30, 40, 50, and 60 nm, to determine the optimum radius, and the effect of excess carrier concentration on the solar cell performance is also tested. The obtained open-circuit voltage is 0.63 V, fill factor is 0.73, short-circuit current density is 41.7 mA/cm\({}^{2}\), and power conversion efficiency is 19.0%. The proposed SiNW solar cell improves the overall efficiency by almost 60%. Furthermore, the effects of the NW length and distance between NWs are also studied in this work. Finally, the distribution of the optical power in different layers along the solar cell and for different solar cell structures is also illustrated in this paper.  
plasmonic nanoparticles; silicon solar cell; graphene; short-circuit current density; open-circuit voltage; power conversion efficiency
## 4 Analytical Model For Optimal Cache Deployment
### Analytical Model
#### 4.2.3 Example of the Analytical Model  
This section provides an example for calculating the value of \(k^{*}\) using the closed-form formula for a case study with 16 AMEN caches (\(n_{access}=16\)) out of 32 AMEN nodes (meaning that \(h_{access}=1.5\)), and a content catalog of \(N=20{,}000\) VoD items with an average VoD item size of \(S_{a}=6\) Gb and a popularity-skew parameter \(\alpha=1\). We consider the available amount of storage capacity to be \(T=120{,}000\) GB; \(h_{metro}\), i.e., the average hop-distance from the MCEN cache, to be 4.5; and \(h_{server}\), i.e., the average hop-distance from the remote server, to be 8. Substituting these values in Equation (9) we get a value of \(k^{*}=614\), meaning that the most-popular 614 items will be stored in AMEN caches. This also means that the AMEN caches will be allocated a storage capacity = 3684 GB (614 items / _times_ 6 Gb) and that the MCEN cache will be allocated a storage capacity = 61{,}056 GB. By repeating the calculation for \(T=160{,}000\) GB, we get a value of \(k^{*}=818\), and therefore, the amounts of storage to be allocated for AMEN and MCEN caches are 4917 GB and 81{,}323 GB, respectively.
# The Firmware Design and Implementation Scheme for C Form-Factor Pluggable Optical Transceiver +
## 2 Related Work
### CFP Transceiver Management Interface  
Figure 1 shows the architecture of CFP transceiver management system, which consists of three interfaces: the management data input/output (MDIO) interface between the host and CFP transceiver, the interface between the MDIO interface block and CFP register, and the interface between the CFP register, nonvolatile memory (NVM) and digital diagnostic monitoring (DDM) system. The MDIO interface is detailed in the IEEE 802.3 Clause 45 standard document [16].
# Three-Dimensional Real-Scene-Enhanced GNSS/Intelligent Vision Surface Deformation Monitoring System
## 5 Deformation Monitoring Visualization Platform  
The platform integrates multi-source monitoring data with a 3D model, using Vue.js and the Cesium framework to develop the user interface and functionalities. System deployment is achieved using SpringBoot and Tomcat, with MySQL serving as the database. The platform features five major functional modules: map base management, spatial analysis tools, multi-source monitoring, intelligent vision, and site selection. These modules enable the remote control of multiple monitoring devices and the unified management of multiple monitoring data. This design ensures effective control and seamless integration of the monitoring system, providing robust support for safety monitoring, early warning, auxiliary analysis, and emergency response operations (Figure 11).