esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Isotope Fractionation  
A dual carbon-bromine isotope analysis was applied for the degradation of both compounds by the four-strain consortium. Significant isotope effects of carbon were observed for both compounds, with no detectable enrichment in bromine. The undetected bromine isotope enrichment implies that DBNPG, as was already shown for TBNPA, is not dehalogenated as the rate-limiting step of the reaction, despite the observable stoichiometric release of bromide to the solution [36; 38]. In addition, DBNPG was not identified during HPLC analysis as a daughter product of TBNPA during biodegradation. TBNPA and DBNPG carbon enrichment factors were determined from the Rayleigh plot, with \(\epsilon\) = \(-\)8.0 \(\pm\) 0.2% for TBNPA and \(\epsilon\) = \(-\)7.0 \(\pm\) 0.4% for DBNPG (Figure 3). Similarly, the carbon and bromine isotope fractionation observed for the two compounds may indicate that the degradation occurs by the analogous pathway. Nevertheless, the slightly lower carbon isotopic effect during DBNPG degradation may be the result of slight masking in transport due to its more hydrophilic nature than TBNPA (2 hydroxyl groups in relation to 1).  
The results suggest that the four-strain consortium is responsible for the degradation in the original enrichment culture while following a similar mechanism and with the same enzymes. This is based on the similarity between the carbon enrichment factors seen in this study and in [PERSON] et al., 2016, in which the original enrichment culture was examined [37]. In the initial enrichment, it was established that TBNPA and DBNPG degradation proceeded by a monooxygenase enzyme. Therefore, it is insinuated that this is the degradation mechanism of the four-strain consortium. Nevertheless, further inquiry to identify the enzyme in the four-strain consortium was not successful.  
Figure 2: Microbial degradation during (**A**) separate (100 mg L\({}^{-1}\) each) and (**B**) concurrent (50 mg L\({}^{-1}\) each) supplement of DBNPG and TBNPA with the four-strain consortium and yeast extract amendment (Table 1). For (**A**) Each point represents one triplicate; (**B**) each point represents 3 replicates with standard deviation error bars.
# A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images
## 3 Results and Discussion
### ResNet Backbone Selection  
In this paper, the SE block and CBAM are sequentially fused with ResNet18, ResNet34, ResNet50, ResNet101 and ResNet152 to implement the network performance test, and the test results are shown in Table 2.  
As can be seen from Table 2, the running time increases incrementally as the number of network layers deepens, but the feature information is also richer, and the model is more effective. For the three performance evaluation metrics of _PA_, _MPA_, and _MloU_, ResNet34 improved by 0.1%, 0.1%, and 0.2%, respectively, over ResNet18; ResNet50 improved by 0.2%, 0.2%, and 0.5%, respectively, over ResNet34. When the networks are stacked to a certain depth, there is no case that the deeper networks are less effective than the shallower ones. Compared with ResNet50, ResNet101 and ResNet152 performance is not further improved. From the above analysis, it is clear that ResNet50 has the highest _PA_, _MPA_ and _MloU_ values with moderate time cost. Therefore, we select ResNet50 as the backbone based on the principle of satisfying the highest accuracy while minimizing the parameters.
## 3 Results and Discussion
### Effect of Carbon Felt Modification  
#### 3.2.1 Physicochemical Characterization of the Unmodified and Modified Carbon Felt Surfaces  
The FTIR spectra of the unmodified carbon felt before and after modification are shown in Figure 1 and confirmed the successful electrode modification. The IR spectrum of LTA zeolite-modified carbon shows new peaks at \(\approx\)460 cm\({}^{-1}\), 575 cm\({}^{-1}\), and 1090 cm\({}^{-1}\) corresponding to vibrational modes bending T-O, double six-rings, and asymmetric stretching vibration of internal tetrahedral T-O-T (with T = Al or Si), respectively, which are the dominating building units in the zeolite structure. Similarly, new peaks appeared at 460 cm\({}^{-1}\), 525 cm\({}^{-1}\), and 1090 cm\({}^{-1}\) in modified bentonite carbon felt spectra, which were attributed to the bending vibration of Si-O-Si, bending vibration of Al-O-Si, and stretching vibration of Si-O, respectively. Additionally, the IR bands between 640 and 950 cm\({}^{-1}\) were due to the bending vibration of T-OH-T (with T = Al or Fe or Mg).  
The modified electrode mass increased by 0.1 and 0.14 g due to the deposition on carbon felt of LTA zeolite and bentonite, respectively.  
Figure 1: FTIR analysis of modified and unmodified carbon felt.  
As observed by ESEM in Figure 2, the unmodified carbon felt was composed of intermingled tubular fibers with an average diameter of 15-20 \(\upmu\)m. The surface of fibers was soft and clean. As a consequence of the carbon felt modification by LTA zeolite and bentonite, the presence of crystalline solid particles was observed, randomly dispersed on the surface of the carbon fibers. More precisely, the ESEM images of LTA zeolite and bentonite-modified carbon felt revealed a particle size ranging from 2 to 4 \(\upmu\)m for elementary crystals forming individual deposits or aggregates non-uniformly distributed around carbon fibers.  
The SSA of the modified carbon felt increased from 6.2 to 13.3 and 16.7 m\({}^{2}\)/g for LTA zeolite and bentonite, respectively (Table S1). This augmentation of the SSA of the electrode material, by a factor greater than 2, was mainly due to the mesoporous and microporous structures of the clay minerals, which can provide more sites for electroactive biofilm and consequently increases current generation [14].  
The carbon felt modification by clay mineral coating also had an effect on the surface hydrophobicity. After coating, the surface of the carbon felt changed completely: from superhydrophobic it became superhydrophilic. The carbon hydrophobicity makes the diffusion of the electrolyte toward the felt more difficult than in a hydrophilic architecture and consequently facilitates the biofilm colonization inside the 3D carbon felt [15].
###### Abstract  
Industrial computed tomography (CT) images reconstructed directly from projection data using the filtered back projection (FBP) method exhibit strong metal artifacts due to factors such as beam hardening, scatter, statistical noise, and deficiencies in the reconstruction algorithms. Traditional correction approaches, confined to either the projection domain or the image domain, fail to fully utilize the rich information embedded in the data. To leverage information from both domains, we propose a joint deep learning framework that integrates UNet and ResNet architectures for the correction of metal artifacts in CT images. Initially, the UNet network is employed to correct the imperfect projection data (sinograms), the output of which serves as the input for the CT image reconstruction unit. Subsequently, the reconstructed CT images are fed into the ResNet, with both networks undergoing a joint training process to optimize image quality. We take the projection data obtained by analytical simulation as the data set. The resulting optimized industrial CT images show a significant reduction in metal artifacts, with the average Peak Signal-to-Noise Ratio (PSNR) reaching 36.13 and the average Structural Similarity Index (SSIM) achieving 0.953. By conducting simultaneous correction in both the projection and image domains, our method effectively harnesses the complementary information from both, exhibiting a marked improvement in correction results over the deep learning-based single-domain corrections. The generalization capability of our proposed method is further verified in ablation experiments and multi-material phantom CT artifact correction.  
Keywords:industrial CT; metal artifact correction; dual-domain; UNet; ResNet; joint framework
# Analysis and Synthesis of Architectures for Automotive Battery Management Systems
## 3 Design of a Distributed BMS and Common BMS Functions
### Balancing  
Since not all cells are equal in their capacity, maximum voltage and charging habits (e.g., internal resistor), the energy of the modules and cells must be balanced during the charging and discharging process (known as active or passive balancing). The single battery cells should maintain the same voltage level in operation. Overcharge and large voltage differences between the single cells should be avoided. Therefore, balancing techniques are established in present systems. Either the cells can be discharged via a resistor during the charging process individually in the passive balancing strategy, or the amount of stored energy is changed between different cells in an active cell balancing strategy. The cell with the lowest charge obtains the charge from a cell or several cells with higher charge. Active balancing is more complicated to implement and also more safety-critical than passive balancing due to the need for different switches. Today's battery cells normally do not have large differences in their parameters after production because they are sorted by measurements according to quality tranches.
## 4 Experimental Results and Discussion
### MNIST Dataset  
MNIST is a small handwritten digit dataset consisting of 60,000 training images and 10,000 test images, each with dimensions of \(28\times 28\) pixels and 10 labels ranging from 0 to 9. For classification, we used a small model called LeNet-5, which comprises three convolutional layers and two fully connected layers [80].  
**Training Process**: To train the model on MNIST, we used one NVIDIA GeForce GTX 1080 Ti GPU. The model was created and trained using the PyTorch library [81], followed by the application of tensor decomposition algorithms using the Tensorly library [82]. We used the Adam optimizer with an initial learning rate of \(3\times 10^{-4}\) and a weight decay of \(1\times 10^{-4}\), along with the ReduceLROnPlateau scheduler, which reduces the learning rate by a factor of 0.001 with a patience of 10 epochs.  
**Results**: After training the model, we decomposed it using the sensitivity method, then fine-tuned the decomposed model for 25-50 epochs using the AdamW optimizer with the same hyperparameters. The model was then binarized using the XNOR-net method, trained for 500 epochs with Adam (learning rate of \(1\times 10^{-4}\) and weight decay of \(1\times 10^{-5}\)), and fine-tuned using the ReduceLROnPlateau scheduler with a patience of 50 epochs.  
Figure 8: Layer connections for a convolution for tensorized BNNs.  
**Discussion**: Table 4 presents a performance comparison of different versions of the LeNet-5 model applied to the MNIST dataset. The full-precision model (FP model) serves as a baseline, while tensorized and binary neural network (BNN) versions demonstrate varying levels of compression and accuracy.  
The FP model achieves the highest accuracy of 99.06% but has the largest parameter size of 0.244 MB. This model represents the best-case scenario in terms of accuracy but is unsuitable for edge devices due to its relatively large size.  
The FP-Tensorized model compresses the parameters by 2.1\(\times\), reducing the model size to 0.116 MB, with only a slight drop in accuracy to 98.75%. While this approach achieves some level of compression, the trade-off between model size and compression is moderate.  
The BNN model, using 1-bit quantization, significantly reduces the parameter size to 0.11 MB, resulting in a 10.8\(\times\) compression ratio. Despite the drastic reduction in size, the accuracy remains competitive, at 99.02%, making BNNs a viable option for edge deployment where efficiency is prioritized.  
Finally, our proposed approach achieves the highest compression ratio of 17.9\(\times\), reducing the model size to 0.067 MB while maintaining an accuracy of 98.73%. This demonstrates that our method can drastically reduce the model size without substantially impacting accuracy. The key innovation lies in combining tensor decomposition with 1-bit quantization, allowing us to achieve extreme compression while preserving performance, especially in resource-constrained environments like edge devices.
## 3 Formulation and Solution of Governing Equations
### Boundary Conditions  
Boundary Conditions for Saturated Soil Layer:  
1. At an infinite horizontal distance, the soil displacement approaches zero, while at r = 0, the displacement remains finite. \[u_{ri}(\infty,\theta,z,t)=u_{\theta i}(\infty,\theta,z,t)=0\] (8) \[u_{ri}(0,\theta,z,t)=u_{\theta i}(0,\theta,z,t)\
eq\infty\] (9)
2. The horizontal displacement of the soil at the base of the rigid foundation is set to zero. \[u_{ri}(r,\theta,L,t)=u_{\theta i}(r,\theta,L,t)=0\] (10)
3. Under the assumption that the vertical displacement of the soil is negligible, the condition of zero surface stress yields the following: \[\left.\frac{\partial u_{ri}}{\partial z}\right|_{z=0}=0\] (11) The conditions at the pile-soil interface are defined as: (1) The pile and soil are in full contact, with no slip or separation at the interface. \[u_{ri}\big{|}_{r=r_{i}}=\left.u_{p}\right|_{r=r_{i}}\cos\theta\] (12) \[\left.u_{\theta i}\right|_{r=r_{i}}=\left.-u_{p}\right|_{r=r_{i}}\sin\theta\] (13)2. The pile-soil interface is impermeable. \[w_{ri}|_{r=r_{i}}=0\] (14) The boundary conditions for the pipe pile are as follows: (1) Boundary Conditions at the Pile Top: \[E_{P}I_{p}\frac{\partial\theta}{\partial z}\bigg{|}_{z=0}=0\] (15) \[k^{\prime}G_{p}A\bigg{(}\theta-\frac{\partial u}{\partial z}\bigg{)}\bigg{|}_ {z=0}=p(t)\] (16) (2) Rigid Support Conditions at the Pile Base: \[u|_{z=L}=0\] (17) \[\theta|_{z=L}=0\] (18)
# A New Method for Detecting Onset and Offset for Singing in Real-Time and Offline Environments
###### Abstract  
This paper introduces a new method for detecting onsets, offsets, and transitions of the notes in real-time solo singing performances. It identifies the onsets and offsets by finding the transitions from one note to another by considering trajectory changes in the fundamental frequencies. The accuracy of our approach is compared with eight well-known algorithms. It was tested with two datasets that contained 130 files of singing. The total duration of the datasets was more than seven hours and had more than 41,000 onset annotations. The analysis metrics used include the Average, the F-Measure Score, and ANOVA. The proposed algorithm was observed to determine onsets and offsets more accurately than the other algorithms. Additionally, unlike the other algorithms, the proposed algorithm can detect the transitions between notes.  
real-time onset detection; singing signal processing; note extraction; singing information retrieval +
Footnote †: journal: Applied  
Article
## 2 Models and Principles
### E_Cbam  
The core of CBAM is that it applies the combination of CAM and SAM to process the input feature map. The SAM can make the neural network pay more attention to the pixel regions that play a decisive role in classification while ignoring the irrelevant regions. Additionally, the CAM takes into account how the feature map's channels relate to one another. Using the CBAM structural principle, we propose the Enhanced Convolutional Block Attention Module (E_CBAM) in this article. In E_CBAM, the DPCI_SE Block is selected as the new CAM, and SK_SAM proposed in this article is selected as the new SAM. The structure comparison of CBAM and E_CBAM is shown in Figure 8.  
Figure 6: Diagram of the overall Selective Kernel process.  
Figure 7: Structure diagram of SAM and SK_SAM. (a) SAM. (b) SK_SAM.  
A dual pooling layer [26] was implemented before the SE Block, and DPCI_SE Block is an enhancement built on the SE Block. A parallel structure made up of an average pooling layer with a max pooling layer is known as a dual pooling layer. Likewise, SE Block should use the GELU activation function in place of ReLU activation. The average pooling layer can retain the global feature information and the max pooling layer can retain the local feature information. The two parts of the feature information are finally summarized through the concatenation operation to obtain richer feature information. Meanwhile, the GELU activation function brings better nonlinear expression ability to SE Block. Experimental results show that DPCI_SE Block is a simple and efficient CAM for WTB damage identification, so it is applied to the E_CBAM proposed in this article.  
In E_CBAM, feature weights are considered in two separate stages. The first stage is the DPCI_SE block, which improves the network performance and interpretation by learning the importance of each channel in the features using global average pooling and fully connected layers after obtaining an adequate feature map through a channel-based attention mechanism, and enhancing the important features in the feature map and reducing the interference of non-important features. The second stage is followed by SK_SAM, which is based on the application of the convolution of selective kernel through a spatial-based attention mechanism that can adaptively learn the weights of the feature map through multi-scale convolution kernels and attention mechanisms to effectively capture features at different scales and focus on important feature regions to improve the differentiation of feature spatial locations and classification performance. The related derivation of the formulas are detailed in [35].
## 3 Results and Discussion
### FTIR-ATR and Granulometry Analyses
This coincides with the average particle size presented in the granulometric curve of Figure 7, where the majority of the CoP particles were measured as less than 1 mm, and they exhibited a tendency to aggregate into larger clusters ranging from 100 to 400 \(\upmu\)m. Similarly to references [13; 33], the cork cells along the axial/tangential section show rectangular cross-sections, with dimensions of around 20-30 \(\upmu\)m \(\times\) 40-50 \(\upmu\)m, and 1 \(\upmu\)m thick walls, approximately. The particle size is relevant for the mechanical properties and the performance of the plant-based filler in the polymer matrix. Figure 6: SEM analyses performed on derived composites with a CoP content of 20%, 30%, and 35%. Concerning the material's microstructure, the surface morphology of the CAG revealed interconnected fibrillated structures, forming agglomerations that entrap chained cells. As depicted in the micrographs at 75\(\times\) magnification in Figure 6, this arrangement of components within the material results in the development of voids and/or cavities within the plastic matrix, significantly impacting the mechanical performance, particularly in terms of elongation and fluidity.
## 2 Materials and Methods
### The Evaluation Indices  
To evaluate the performance of the proposed method, three commonly used indexes, precision (P), recall (R), and F-score (F), are employed. These indices are defined in the following,  
\[P=\frac{TP}{TP+FP} \tag{22}\]  
\[R=\frac{TP}{TP+FN} \tag{23}\]  
\[F=\frac{2\cdot P\cdot R}{TP+R} \tag{24}\]  
where \(TP\) is the total number of true-positive pixels, \(FP\) the total number of false-positive pixels, and \(FN\) is the total number of false-negative pixels.
## 7 Miscellaneous Routes
### Alternative Substrates  
[PERSON], [PERSON] and co-workers demonstrated that silyl end ethers are suitable synthons for aromatic \(\alpha\)-haloketones (Scheme 86) [216]. One \(\alpha\)-bromo- and 12 \(\alpha\)-chloroketones were generated upon the addition of (diacetoxyiodo)benzene (1.5 equiv.) and trimethylsilyl halide (2 equiv.) to the silyl end ether in acetonitrile. 2-bromo-1-phenylethanolamine was isolated in 64% yield and a set of \(\alpha\)-chloroketones were obtained in moderate to excellent yields (55-90%). The procedure tolerated the presence of electron-donating (e.g., Me, OMe, NMe\({}_{2}\)) and -withdrawing (e.g., Cl, Br) substituents on its aromatic substrates. The mechanism as described in Scheme 86 highlighted the initial formation of a ketone intermediate bearing an iodo-leaving group, followed by nucleophilic substitution with the corresponding halide to afford the desired compound.  
[PERSON], [PERSON] and [PERSON] established the synthesis of \(\alpha\)-bromoketones from their corresponding _vic_-1,2-dibromides [217]. As shown in Scheme 87, six haloketones were isolated in moderate to excellent yields. Selectivity issues occurred for the OMe-substituted dibromides, as the brominated arylhydrin was obtained as an important side product. This problem and the relatively long reaction times are this route's main disadvantages.  
\(\alpha\)-Chloroketones were also accessed via the chloroacetylation of arenes (Scheme 88) [218]. This FeIII-catalysed procedure exhibits a low efficacy; however, when highly sterically hindered arenes were used, a remarkable increase in yield was observed.  
[PERSON] and [PERSON] designed the Cu\({}^{1}\) promoted synthesis of \(\alpha\)-haloketones from trichloromethyl carbinols [219]. Fifteen carbinols were efficiently converted upon addition of two equivalents of CuCl and bipyridine in dichloroethane. Subsequent stirring for 3 h under reflux yielded the corresponding \(\alpha\)-chloroketones in 82-96%. This route coped well with the presence of electron-withdrawing (e.g., Cl, Br, NO\({}_{2}\)) and -donating (e.g., Me, OMe) groups on substrates.  
In 2007, [PERSON] et al. reported an HCl/EtOH-based system yielding 15 (hetero)aromatic \(\alpha\)-chloroketones from their enol ethers in 18-100% (Scheme 89) [220], though it should be noted that harsh reaction conditions are required to access the enol ether compounds.  
Enolacetates have also proven to be suitable synthons for \(\alpha\)-haloketones [221]. Electron-poor, electron-rich and sterically hindered enolacetates were efficiently transformed upon the addition of \(N\)-iodosaccharide (Figure 15). Despite the broad scope, only one aromatic \(\alpha\)-iodoketone (i.e., 2-iodo-1-phenylethanolone) was isolated in 56% yield.