esa-sceva/satcom-chunk-collection huggingface dataset:
# SARFNet: Selective Layer and Axial Receptive Field Network for Multimodal Brain Tumor Segmentation
## 3 Methodology
### Axial Atrous Spatial Pyramid Pooling Module (AAM)  
The image in the bottleneck layer contains considerable details, which plays an important role in image restoration during upsampling. The ordinary convolution affects the final restoration effect due to the lack of global information when dealing with detailed features. Axial attention [60] using self-attention can establish global dependence and expand the receptive field of the image on each axial feature. Its receptive field is largerthan the convolution, and more contextual information can be obtained. However, the axial attention mechanism cannot effectively use prior knowledge of the scale invariance, translation invariance, or feature locality of the image, which leads to the inability to construct an accurate local relationship in some cases. ASPP [61] uses features with different expansion sampling rates generated by atrous convolution, so that the features contain multiscale receptive fields. It includes multiscale local information and retains the properties of ordinary convolution. The above two attention mechanisms are fused to create the axial atrous spatial pyramid pooling module (AAM) shown in Figure 4. It uses axial attention to extract global information and retains the characteristics of ordinary convolution and multiscale receptive fields. It can be represented as follows:  
\[Z=conv_{1\times 1\times 1}(cat(atrous(AP(self\_attn(self\_atten(x,w),h)), \theta))) \tag{7}\]  
where \(x\) is the input feature \(x\in R^{C\times H\times W\times D};Z\) is the result processed by the AAM module; \(Conv_{1\times 1\times 1}\) denotes convolution with a kernel of \(1\times 1\times 1\); \(Atrous\) is expressed as an atrous convolution operation with a rate of \(\theta,\theta\epsilon\{1,2,3,4\};AP\) represents the average pooling; and \(self\_attn\) describes the self-attention mechanism. Here, parameter \(h\) represents the column, and parameter \(w\) represents the row. Self-attention can be defined as follows:  
\[Z=\alpha\times torch.bmm\Big{(}Conv(x),sigmoid\Big{(}torch.bmm\Big{(}Conv(x), Conv(x)^{T}\Big{)}\Big{)}\Big{)}+x \tag{8}\]  
where \(Z\) denotes the output result of the module, \(x\) denotes the input feature, and \(Conv\) represents convolution with a kernel of \(1\times 1\times 1\). \(\alpha\) indicates learning parameters, which can learn suitable parameter values through model training. The \(torch.bmm\) operation is defined as a matrix product.
# A Membrane Modified with Nitrogen-Doped TiO2/Graphene Oxide for Improved Photocatalytic Performance
## 3 Results and Discussion
### The Photocatalytic Degradation Properties of the Modified Membranes  
The photocatalytic activity of the PVDF, TiO\({}_{2}\)/PVDF, NT/PVDF, TG/PVDF, NTG/PVDF membranes was evaluated by the degradation of MB under darkness and ultraviolet/sunlight illumination. Figure 9a shows that when the adsorption capacity of the membranes was measured in the dark, all membranes had almost the same adsorption capacity for MB, while the TG/PVDF and NTG/PVDF membranes had a slightly higher absorption capacity due to the participation of GO. The GO structure provided a high specific surface area, resulting in good adsorption properties. However, the number of GO and TiO\({}_{2}\) nanoparticles supported on the surface of the membrane was small so there was no significant influence on the removal of the MB solution by adsorption. The PVDF membrane had a very low MB removal rate under ultraviolet light and sunlight, indicating that the PVDF membrane itself had almost no photocatalytic performance. After modification by nanomaterials, the membranes exhibited an enhanced photocatalytic activity under ultraviolet light, but when exposed to sunlight, the TiO\({}_{2}\)/PVDF membrane only showed a slight MB degradation  
Figure 8: The relative flux of the methylene blue (MB) solution (**a**) in the dark; (**b**) ultraviolet and (**c**) sunlight conditions.  
compared to the NT/PVDF, TG/PVDF and NTG/PVDF membranes. This was because TiO\({}_{2}\) could only be photocatalyzed by ultraviolet irradiation. Compared to the TiO\({}_{2}\)/PVDF membranes, the NT/PVDF and TG/PVDF membranes exhibited higher photocatalytic efficiency because nitrogen doping significantly increased the absorption of visible light in the photocatalysts and the high charge mobility of the GO nanosheets was beneficial to the Electron transfer and separation. Compared to the NT/PVDF and TG/PVDF membranes, the NTG/PVDF membranes had higher photocatalytic activity under ultraviolet light and sunlight, with MB degradation rates reaching 86.5% and 80.6%, respectively. The addition of GO could act as a semiconductor photosensitizer, shifting the Fermi level of the catalyst in the positive direction and enhancing the absorption of visible light. Due to the excellent electrical conductivity of GO, electrons were transported at a high speed, reducing the recombination rate of the electrons and holes [17]. The effect of N doping on the activity of the catalyst significantly improved with the addition of GO. This was because the doping of N significantly reduced the forbidden bandwidth of the catalyst and enhanced its response to visible light.
## 3 Results
### Mechanical Properties
#### 3.4.2 Tensile Test  
Tensile tests, conducted at room temperature using a crosshead speed of 0.3 mm/min on six as-built tensile specimens (three built in the vertical and three built in the horizontal orientation) showed the existence of anisotropy in the different building orientations. From Figure 10, it was observed that there was only a marginal difference in the ultimate tensile strength (UTS) and the yield strength (YS) between specimens built in the vertical orientation and specimens built in the horizontal orientation. UTS and YS of the vertical orientation-built specimen were 739 MPa and 531 MPa, respectively, compared to 731 MPa and 567 MPa of horizontal orientation specimen. However, a significant difference in the percent elongation between specimens built in both orientations was observed in Figure 9. The vertical orientation-built specimen had a significantly higher elongation of 20% compared to 16% in the horizontal orientation-built specimen. The LPBF as-built specimens were far superior in UTS and YS compared to UTS (450 MPa) and YS (170 MPa) of as-cast material [40]. The elongation of both as-built specimens (vertical and horizontal orientation) fell short of elongation of the as-cast material (25%) [40]. From Table 4, post heat treatment of tensile specimens at 1000 \({}^{\circ}\)C for 1 h showed a deterioration of strength properties of the alloy. PHT specimens built in the vertical orientation recorded a UTS of 674 MPa, a 9% reduction compared to the as-built state. The YS also deteriorated by 23% (408 MPa) compared to the as-built state. Similarly, for horizontally built specimens, post heat treatment at 1000 \({}^{\circ}\)C for 1 h recorded a UTS of 665 MPa and YS of 417 MPa, representing a reduction of 9% and 26%, respectively, as compared to the as-built state.  
\begin{table}
\begin{tabular}{l l} \hline \multicolumn{1}{c}{**Material: G-NiCu30 Nb**} & \multicolumn{1}{c}{**HV\({}_{0.2}\)**} \\ \hline As-Cast & 179 \\ LPBF as-built cube (ys =80 μm) & 247 \\ LPBF as-built + PHT (ys = 80 μm) & 221 \\ LPBF as-built cube (ys = 140 μm) & 244 \\ LPBF as-built + PHT (ys = 140 μm) & 210 \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of microhardness of as-cast, as-built, and as-built + PHT cubes.  
Figure 9: Microhardness of as-built, post heat treated (PHT), and as-cast material compared.
## 1 Introduction
The shift in the energy between the incident and scattered photons corresponds to that of the different vibrational modes of the molecules, and the spectrum of inelastically scattered light provides a molecular fingerprint unique to each substance. This spectral information allows for the precise identification and differentiation of biochemical changes associated with various biological conditions, including hepatitis infections [9]. Its non-destructive nature, minimal sample preparation, and ability to analyze biofluids, cells, and tissues in real time make it particularly suitable for studying aqueous biological samples such as blood serum, plasma, and nucleic acid extracts [10]. A range of variations in RS have been developed over the past few decades, including surface-enhanced Raman spectroscopy (SERS) [11], tip-enhanced Raman spectroscopy (TERS) [12], Raman microscopy [13], and confocal Raman microscopy, amongstothers [14]. For a more detailed discussion on the workings of RS and its variations, see the previous work [15; 16; 17]. A systematic literature search of Web of Science was conducted using the search terms \"Raman\" and \"hepatitis\", with no time constraint. The results were screened to retain only diagnostic works and were then sorted according to the keywords \"HAV\" to \"HEV\" and then \"Plasma\", \"Serum\", \"HCR\", \"PCR\", \"Extracted\", \"SERS\", \"Centrifugal\", \"Membrane\", \"Filter Device\", \"microfluidic\", \"Immunoassay\", \"Biosensor\", and \"Dried\". Different approaches to examining and demonstrating the potential of Raman spectroscopy as a diagnostic tool for hepatitis have been explored. An analysis of the results reveals five different approaches based on body fluid sampling for the detection of hepatitis, including the use of blood plasma [18; 19; 20], blood serum [11; 21; 22], filtered portions of the blood using filtration/centrifugal devices or membranes [22; 23; 24], extracted RNA [25; 26], and nucleic acid amplification, like PCR and HCR products [27; 28; 29]. Based on the experimental setups and methodologies, again, different approaches have been studied, including Raman spectroscopy (RS) [18; 20; 30; 31], surface-enhanced Raman spectroscopy (SERS) [11; 21; 32; 33], Raman microscopy (RM) [34], biosensors/microfluidic devices [19; 25; 35], and immunoassay- and antigen-based detection methodologies [19; 36; 37; 38]. Another important part of diagnostics using spectroscopy is the data analysis/data mining techniques [39]. Raman spectroscopy provides complex spectral data with small variations that are difficult to distinguish visually. Reliable interpretation of and deeper insights into the spectral results require preprocessing of the data and the use of various multivariate data modeling and data mining techniques [11; 34; 40; 41]. In this review, advances in the integration of Raman spectroscopy along with multivariate chemometric-based data analysis techniques into hepatitis diagnosis will be explored to evaluate the clinical competency and current standing of Raman spectroscopy for hepatitis diagnosis.
# Dual-Tracer PET Image Separation by Deep Learning: A Simulation Study
## 2 Materials and Methods
### Deep Learning Dual-Tracer Separation  
In deep learning for dual-tracer signal separation, the designed network is usually trained to simultaneously infer the separated images of each tracer, which can be considered as a multi-task learning problem [44; 45; 46]. Our proposed architecture is based on the hard parameter sharing approach commonly employed in multi-task learning [51], where a common space representation is shared for all tasks, with additional layers that are specific to each task. Since all tasks are learned simultaneously, the hard parameter sharing helps our model generalise better and reduce the risk of overfitting [52]. Specifically, our network is based on a CED architecture, but it contains two decoders. The specifics of the tailored CED architecture are shown in Figure 2. It consists of repeated applications of (1) double \(3\times 3\) convolutional layers, (2) a batch normalisation (BN) layer, (3) a PReLU layer, (4) max-pooling for down-sampling, (5) a 2D transposed convolution layer for up-sampling, (6) a \(1\times 1\) convolutional layer, and (7) a ReLU layer. The number of trainable parameters for this 2D CED is around 2.2 million.  
The inputs to the CED are dynamic dual-tracer MLEM images reconstructed from a sinogram sequence obtained during a single PET scan with a time-delayed interval \(\Delta t\) between tracer injections. The dynamic dual-tracer MLEM images, i.e., dual-tracer TACs of each voxel, are mapped to a latent representation via a series of 2D convolutions, batch-normalisation, non-linearities, and down-sampling in the encoder of the CED. The correlations between each frame of the dynamic dual-tracer images are encoded in this latent representation during training and allow prediction of the separated static single-tracer images. The two decoders of the CED separate and decode the latent representation to output static images corresponding to each tracer (in blue for \([^{18}\mathrm{F}]\)FDG and yellow for \([^{11}\mathrm{C}]\)MET). A ReLU layer was added prior to the output in order to introduce the non-negative constraint on the separated images.  
As the network outputs contain the static images of two individual tracers, the mean squared error (MSE) loss function is applied to both the static single-tracer images and can be written as  
\[\mathcal{L}=\frac{1}{N_{S}}\sum_{s=1}^{N_{S}}||y_{s}^{\mathrm{FDG}}-y_{s,\mathrm{Label}}^{\mathrm{FDG}}||^{2}+\frac{1}{N_{S}}\sum_{s=1}^{N_{S}}||y_{s}^{\mathrm{MET}}-y_{s, \mathrm{Label}}^{\mathrm{MET}}||^{2}, \tag{3}\]  
where \(N_{S}\) denotes the total number of training pairs, \(y_{s}^{\mathrm{FDG}}\) and \(y_{s}^{\mathrm{MET}}\) are the network's outputs, and \(y_{s,\mathrm{Label}}^{\mathrm{FDG}}\) and \(y_{s,\mathrm{Label}}^{\mathrm{MET}}\) are the label static single-tracer images.
## 5 Discussion
### Experimental Scenarios  
Regarding the scenarios in which experiments take place, there is a great diversity of areas. The spaces range from universities to parking lots, stores, residential buildings, etc. As can be seen in Figure 9, 31 articles used an area of less than 100 m\({}^{2}\), 26 between 100 m\({}^{2}\) and 500 m\({}^{2}\), 13 between 500 m\({}^{2}\) and 1000 m\({}^{2}\), and 21 higher than 1000 m\({}^{2}\). It is important to highlight that in scenarios smaller than 100 m\({}^{2}\), the use of rooms drops to 41%. In the overall studies, this value is 66%. Although it is true that there are experiments in very small spaces, in general this value indicates that precision is usually prioritized over realistic environments. It should be noted that there are articles with more than one experiment and/or simulation, and every area in these papers is counted here.  
Another important aspect of Wi-Fi fingerprinting radio maps is whether they are used in spaces without rooms or with rooms. This simple fact can greatly change the results of a study. Specifically, 103 of the radio maps used have rooms in their experimentation space, 53 were in spaces without any rooms (spaces without walls), and there are 5 papers that do not indicate any such parameter. Finally, it should be noted, again, that most of the works are performed with the focus on obtaining the best results, and not on performing experiments in a realistic environment. For example, changes in RSSI signals are not significant in experiments of short duration and in very delimited spaces.
# Drone-Based Vibration Monitoring and Assessment of Structures
## 4 Experimental Validation and Results  
In order to validate the developed system (drone, sensor package, damage detection algorithm), we created a laboratory experimental testbed and designed experiments to collect and analyze data. The goal was to show the feasibility of the drone-based deployment and retrieval of our sensor package, as well as the extraction of damage information from the collected vibration data.
# Spot Weld Inspections Using Active Thermography
## 2 Materials and Methods
### Excitation Methods
#### 2.1.1 Light Heating  
Two halogen lamps of 2 kW each are used as a heating source. These lamps are from the brand Edevis and can be found online as the OTvis system. The lamps are mounted on a tripod and placed on either side of the thermal camera. The measurement setup for the light heating experiments can be found in Figure 2, and the according parameter values are discussed in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Distance camera—sample ‘L1’ [m] & 0.44 \\ Distance lamp—lamp ‘L2’ [m] & 0.88 \\ Distance lamp—sample ‘L3’ [m] & 0.44 \\ Angle lamp—camera [\({}^{\circ}\)] & 45 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters used during light heating measurements. A representation of the parameters can be found in Figure 2.  
Figure 1: Nine samples, divided into two series. The A-series consists of four samples with a thickness of 2.5 mm (1–4) and sample 5A is 3.15 mm thick. The B-series only consists of four samples with a thickness of 2.5 mm. The spot weld highlighted by the blue cross is the inspected spot weld for each sample.
## 6 Polynomial Law  
This section discusses the calibration of polynomial empirical laws (see Equation (5)) in accordance with the procedure described in Sections 2 and 3. Additionally, for this law more than \(10^{6}\) conversion models have been established by minimizing the value of the Equation (9) and by varying the number \(m\) of the considered data between 3 and 24. In Figure 8 the coefficients of the calibrated polynomial laws are plotted for four different values of \(m\) (i.e., \(m=8\), 11, 15, 18). The plots would indicate that all the coefficients lie on a plane, and consequently, a relationship between such coefficients may be established.  
Figure 6: Relationship between the coefficients of the calibrated exponential empirical laws (\(a_{e}\), \(b_{e}\)) and the coefficient of variation of rebound index \(Cov_{R}\): (**a**) \(m=8\); (**b**) \(m=11\); (**c**) \(m=15\), (**d**) \(m=18\).  
Figure 7: RMSE (Equation (10)) evaluated for the exponential conversion models vs. coefficient of variation of the in-situ concrete strength \(Cov_{fc}\) and coefficient of variation of the rebound index \(Cov_{R}\): (**a**) \(m=15\); (**b**) \(m=18\).  
Figure 10: Relationship between the coefficients of the calibrated polynomial empirical laws \(a_{p}\), \(b_{p}\), \(c_{p}\) and the coefficients of variation for \(m=18\): (**a**) plot \(a_{p}\) vs. \(b_{p}\) and the coefficient of variation of the concrete strength \(Cov_{K}\); (**b**) plot \(a_{p}\) vs. \(b_{p}\) and the coefficient of variation of the rebound index \(Cov_{K}\).  
Figure 9: Relationship between the coefficients of the calibrated polynomial empirical laws \(a_{p}\), \(b_{p}\), \(c_{p}\) and the coefficients of variation for \(m=15\): (**a**) plot \(a_{p}\) vs. \(b_{p}\) and the coefficient of variation of the concrete strength \(Cov_{K}\); (**b**) plot \(a_{p}\) vs. \(b_{p}\) and the coefficient of variation of the rebound index \(Cov_{K}\); (**c**) plot \(a_{p}\) vs. \(c_{p}\) and the coefficient of variation of the concrete strength \(Cov_{K}\); (**d**) plot \(a_{p}\) vs. \(c_{p}\) and the coefficient of variation of the concrete strength \(Cov_{K}\); (**d**) plot \(a_{p}\) vs. \(c_{p}\) and the coefficient of variation of the rebound index \(Cov_{K}\).  
Figure 11 shows the RMSE evaluated for the polynomial law calibrated for two values of \(m\) (i.e., \(m=15\) and \(18\)). Similar trends have been obtained for the other values of \(m\). The graphs show that as the value of \(m\) increases, as in the laws discussed above, the range of variability of the RMSE decreases, and although for higher values of the coefficient of variation of the concrete strength, the RMSE reaches high values and no relationship can be established.
## 4 Vibration Displacement of the Atomizing Sheet
### Vibration Displacement of the Atomizing Sheet in the Existing Theory
#### 4.2.2 Vibration Theory of the Atomizing Sheet  
According to the research content in Section 3, the first several modes of resonant frequency (including the node diameter) are theoretically analyzed in this section when two boundary conditions are set.  
According to Reference [29], when the frequency ratio is \(\alpha_{mn}=1\), the circular thin plate enters the \(n\)th-order resonance state. In period \(T\), when \(t=T/4\), the vibration displacement of each point on the circular thin plate reaches the maximum value, which is:  
\[w_{mn}(r,\theta,T/4)=\sum_{m=1}^{\infty}\sum_{n=1}^{\infty}F_{mn}\phi_{mn}(r, \theta)/\left[K_{mn}\sqrt{\left(1-\omega_{mn}^{2}\right)^{2}+\left(2\zeta_{mn} \sigma_{mn}\right)^{2}}\right] \tag{15}\]  
Equation (15) can be applied to peripheral fixed support and peripheral simply supported.  
The relevant parameters of the atomizing sheet studied in this paper are shown in Table 1. The natural frequency constants, parameters, and mode functions of the circular thin plates under different boundary conditions are substituted into Equation (15). Then, \(r=0:0.1:a\) and \(\theta=(0:0.02:2)\times\pi\) are set, and the maximum vibration displacement of the atomizing sheet \(w_{mmmax}(r,\theta,T/4)\) under this vibration mode is obtained using MATLAB software for numerical calculation, as shown in Figure 17. The maximum vibration displacement of the first several-order vibration modes when the atomizing sheet with peripheral fixed support and peripheral simply supported are shown as the red and black solid lines, respectively. When the vibration mode only has nodal circles, the vibration displacement is marked with dotted lines. The resonant frequency corresponds to the vibration mode one by one. The vibration mode is from B00 to B15, and the resonant frequency also increases. The specific value can be obtained from Equation (8), which is: \(\omega_{mms}=\frac{\lambda_{mn}^{2}}{a^{2}}\sqrt{\frac{D}{\rho t}}\).  
As shown in Figure 17, the maximum vibration displacement of the atomizing sheet fluctuates with the increase in the resonant frequency under any boundary conditions. When the vibration mode only has the nodal circle, with the increase in the resonant frequency, the maximum vibration displacement is only 0.98%, which is a slight increase under the condition of peripheral simply supported, and only 0.27% under the condition of peripheral fixed support. However, the maximum positive displacement in the original paper increases by 77.12% and even reaches 221.05%, which is unreasonable.