esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Volatile Constituents from Food Lures by Tephritid Fruit Flies
## 3 Results
### Antennal Sensitivity and DoOR Comparison  
The thirteen compounds detected by fruit flies belonged to five different functional groups, including six esters, three pyrazines, two alcohols, one aldehyde and one terpene (Figure 3). Comparison with the DoOR database revealed that 11 of the 13 compounds are detected by olfactory receptors of _D. melanogaster_ with varying sensitivities (Figure 3). 2,6-dimethylpyrazine has not been reported to induce a response in _D. melanogaster_, while \(\alpha\)-methylbenzyl acetate is not present in the DoOR database.
# A Lithology Recognition Network Based on Attention and Feature Brownian Distance Covariance
## 2 Methodology
### ResNet18 Backbone  
ResNet18 has gained extensive utilization within the domain of computer vision, particularly excelling in tasks such as image classification, target detection, and image segmentation [18]. Its exceptional performance stems from its adept utilization of residual connections, a feature that effectively optimizes the gradient flow during training and mitigates the risk of overfitting [19]. Moreover, the ResNet18 architecture boasts a moderate number of layers and exhibits rapid convergence, making it a well-suited choice for applications demanding real-time processing and high accuracy, such as lithology recognition tasks. Hence, ResNet18 has been selected as the backbone network for this study, with the network structure depicted in Figure 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer Type** & **Output Size** & **Layer Type** & **Output Size** \\ \hline Conv & 64 \(\times\) 128 \(\times\) 128 & Res\_a3 & 256 \(\times\) 16 \(\times\) 16 \\ MaxPool & 64 \(\times\) 64 \(\times\) 64 & Res\_b3 & 256 \(\times\) 16 \(\times\) 16 \\ Res\_a1 & 64 \(\times\) 64 \(\times\) 64 & Improved CBAM & 256 \(\times\) 16 \(\times\) 16 \\ Res\_b1 & 64 \(\times\) 64 \(\times\) 64 & Res\_a4 & 512 \(\times\) 8 \(\times\) 8 \\ Improved CBAM & 64 \(\times\) 64 \(\times\) 64 & Res\_b4 & 512 \(\times\) 8 \(\times\) 8 \\ Res\_a2 & 128 \(\times\) 32 \(\times\) 32 & ConvAvgPool & 512 \(\times\) 1 \(\times\) 1 \\ Res\_b2 & 128 \(\times\) 32 \(\times\) 32 & FBDC & 512 \(\times\) 1 \(\times\) 1 \\ Improved CBAM & 128 \(\times\) 32 \(\times\) 32 & Conv & 6 \(\times\) 1 \(\times\) 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: RImprove network structure parameters.  
Figure 1: The overall architecture diagram of the FC-Res network model.  
The main structure of the ResNet18 network is four stacked residual blocks, and the residual block structure is shown in Figure 2, which connects the input feature information to the output of the residual block through a \(1\times 1\) convolution, which mitigates the gradient disappearance and explosion of the deep network during training. The input network is a 256 \(\times\) 256 three-channel RGB image, which outputs six rock categories after a series of residual blocks as well as pooling operations. The input rock image is feature-extracted and downsampled by a convolutional layer to obtain a feature map of size N \(\times\) 6 \(\times\) 8 \(\times\) 8 (N is a mini-batch size), which is then continued to be downsampled by an 8 \(\times\) 8 adaptive average pooling layer, continuing sampling to obtain an N \(\times\) 6 \(\times\) 1 \(\times\) 1 size feature map, and, finally, the extracted rock features are input into a Softmax classifier to calculate the rock category prediction probability.
## 3 Modeling and Simulations of the Assembly Error Effect on the Stability Accuracy
### Modeling and Simulations of the General Kinematics and Compensation Principle
Therefore, the relationship between the gyroscope sensitive velocities and the compensation velocities are expressed as Equation (8):  
\[\left\{ \begin{array}{c}\dot{\theta}_{a}=-\omega_{ibz}^{p}/c\theta_{p}=- \omega_{gyro\_z}sec\theta_{p}\\ \dot{\theta}_{p}=-\omega_{iby}^{p}=-\omega_{gyro\_y}\end{array} \right., \tag{8}\]  
Based on the analysis above, the general kinematics and compensation principle of the system are simulated in Simulink as shown in Figure 6. In the simulation module of the azimuth kinematics from \(\{b\}\) to \(\{a\}\), e.g., the angular velocity vector of the system base in \(\{i\}\), namely \(\overset{\rightarrow}{\omega}_{ib}\), is set as one of the inputs. The other input is the z-axial component of the azimuth velocity vector \(\overset{\rightarrow}{\omega}_{ia}\), namely \(\omega_{iuz}\). \(\omega_{izz}\) can be obtained by using signals of the motor modules, which will be discussed later. The velocity vector of the azimuth in \(\{i\}\), namely \(\overset{\rightarrow}{\omega}_{ia}\), is set as the output. Based on Equation (4), the transformation matrixes of \(R_{b}^{a}\) are imbedded in the module. The azimuth motor velocity and the rotation angle, namely \(\theta_{a}\) and \(\theta_{a}\), can also be calculated by using inverse operation of Equation (4). Similarly, the simulation module for the pitch kinematics from \(\{a\}\) to \(\{b\}\) can also be interpreted by using Equation (5). Those velocity vectors and parameters will be used for later analysis and modeling. Figure 6: Simulation modules of the System’s General Kinematics and Compensation.
# Finite Element Analysis on Block Shear Mechanism of Lean Duplex Stainless Steel Welded Connections
###### Abstract  
The block shear equations specified in the current AISC specification for structural steel buildings and North American cold-formed steel design specifications are based on research results of carbon steel bolted connections. These equations were found to be inapplicable for the welded connections in the literature. This issue is primarily associated with the use of the incorrect assumption on block shear failure mechanism. The present paper examines the accuracy of various block shear equations available in the design specifications and in the literature. The paper also examines the shear hardening capacity and the level of tensile stress over the critical net area with the results of finite element analysis, in which the fracture simulation is considered. It shows that the block shear capacities of lean duplex stainless steel welded connections can be predicted accurately using tensile stress equal to \(1.25F_{\text{u}}\), as proposed in the literature.  
finite element analysis; welded connection; lean duplex stainless steel; stress triaxiality; block shear fracture
# Underwater Acoustic Target Tracking: A Review
## 2 Related Surveys and Contributions
Many domestic and foreign scholars have thoroughly studied underwater target tracking and numerous papers have been published. Therefore, a survey on underwater target tracking is in need. * We summarize the abundant related knowledge of underwater acoustic target tracking ranging from the acoustic tracking instrument to the process of tracking, which will provide readers with a comprehensive understanding of this research area. * We provide an updated literature review of underwater acoustic target tracking algorithms and most of the papers which focus on a study of the underwater target tracking are included. A comprehensive comparison of these algorithms is provided according to the characteristics of the target such as numbers, dimensions, and maneuvering. The existing review papers concentrate on target tracking algorithms based on WSNs. However, some tracking algorithms cannot be directly applied to underwater target tracking due to the special nature and complexity of the marine environment. Our article is the first paper providing a survey of acoustic tracking algorithms for underwater targets. Meanwhile, tracking instruments are not limited to UWSNs which are variations of WSNs, and TASA and acoustic imaging sensors are also included.
## 3 Numerical Visualization
### Amplitude Mismatch with Differential Driven Mode  
Figure 3a plots the transfer function of differential driven mode versus amplitude mismatch ratio \(k\). The bias phase was fixed at the quadrature point of \(\pi/4\). Here, \(k=100\%\), \(0\%\), and \(-100\%\) correspond to the special cases of pure IM, IM with single-arm driven mode, and pure phase modulation, respectively. We found that the fading frequency gradually increased from 14.0 GHz to 17.1 GHz with \(k=0\%\), and finally got to 19.8 GHz with \(k=-100\%\). Compared to Figure 2a, amplitude mismatch had a wider tunable frequency range than bias deviation in single-arm driven mode. Figure 3b shows the calculated transfer function versus bias induced constant phase at \(k=-30\%\), \(0\%\), and \(30\%\), respectively. It can be confirmed that, when \(k<0\) (solid line), the fading frequency shifting enhanced compared with the single-arm driven mode (dashed line) under the same bias phase, while \(k>0\) (dotted line) lead to suppress the frequency shifting. In other words, to achieve the target transmission bandwidth, amplitude mismatch was more effective to be exploited, leaving the bias deviates less away from the linear modulation region.
## 2 Preliminary Knowledge
### Maximum Mean Difference (MMD)  
Domain adaptation methods based on discrepancy metrics play an important role in transfer learning, where the most widely used discrepancy metric is the maximum mean difference (MMD). Many domain adaptation methods employ the MMD to quantify the distributional divergence between source and target domains, and for further improvement on this basis, a plethora of UDA methods have been developed.  
MMD is utilized to quantify the dissimilarity between two distinct yet related distributions. Given that the data from the source and target domains originate from two different distributions, p and q, respectively, MMD calculates the distance between these distributions within the Reproducing Kernel Hilbert Space (RKHS) [41]. Formally, the MMD is defined as follows:  
\[L_{\text{MMD}}(p,q)\triangleq\left\|E_{p}[\phi(x^{s})]-E_{q}\big{[}\phi\big{(} x^{t}\big{)}\big{]}\right\|_{\mathcal{H}}^{2} \tag{3}\]  
where \(\phi(\cdot)\) is the mapping function from the input features to the RKHS. At this time, the sample features of the source domain and the target domain satisfy the equation \(k\big{(}x^{s},x^{t}\big{)}=\phi\big{(}x^{s}\big{)}\cdot\phi\big{(}x^{t}\big{)}\), \(k\big{(}x^{s},x^{t}\big{)}\) is the kernel function. Theoretically, \(L_{MMD}(p,q)=0\) when \(p\) and \(q\) have the same distribution [41].  
\(L_{MMD}(p,q)\) can be calculated as follows:  
\[\begin{split} L_{\text{MMD}}(p,q)&=\left\|\frac{1}{ n_{s}}\sum\limits_{i=1}^{n_{s}}\phi(x_{i})-\frac{1}{n_{t}}\sum\limits_{j=1}^{n_{t}} \phi\big{(}x_{j}\big{)}\right\|_{\mathcal{H}}^{2}\\ &=\frac{1}{n_{s}^{2}}\sum\limits_{i=1}^{n_{s}}\sum\limits_{j=1}^{ n_{s}}k\Big{(}x_{i}^{s},x_{j}^{s}\Big{)}\\ &+\frac{1}{n_{t}^{2}}\sum\limits_{i=1}^{n_{t}}\sum\limits_{j=1}^{ n_{t}}k\Big{(}x_{i}^{t},x_{j}^{t}\Big{)}\\ &-\frac{2}{n_{t}n_{t}}\sum\limits_{i=1}^{n_{s}}\sum\limits_{j=1}^{ n_{t}}k\Big{(}x_{i}^{s},x_{j}^{t}\Big{)}\end{split} \tag{4}\]  
Figure 3: Structure of domain discriminator.  
In domain adaptation, the feature extractor's parameters are adjusted by reducing the MMD between the source and target domain feature representations. The objective of this process is to make the feature representations of the source domain and target domain close to each other, so that when dealing with data from different domains, the feature extractor can generate universal feature representations, which are also known as domain-invariant features. In this paper, MMD is employed to bridge the marginal distribution gap between the source and target domains, ensuring a global feature alignment.
## 4 Data Collection and Storage
### Cloud Data Store  
The data from IoT module are stored on the cloud for further processing. The cloud has two storages: a simple storage and relational database [22]. The simple storage stores the data-points and the relational records the output of AI algorithms [23].
# DPS Clustering: New Results
## 4 Results: Third Stage of DPS
### Second Part: Final Selection of \(D\)-Clusters  
Thanks to (14)-(16), the search for \(d\)-clusters in the space of \(r\)-connectivity components \(\text{DPS}_{C}(X)\) with respect to distance (13), that is, the formation of the space \(C(\text{DPS}_{C}(X),d)\) is constructive. Each \(d\)-cluster \(C\in C(\text{DPS}_{C}(X),d)\) has two characteristics: internal \(r(C)\) and external \(e(C)\) (17). Based on this information, expert E must decide: is it necessary to combine the spots from \(C\) into a single whole or not?  
Let \(\text{DPS}_{f}(X)=\text{DPS}_{f}(X|\text{E})\) denote the set of maximal \(d\)-clusters in \(\text{DPS}_{C}(X)\), obtained after joining spots by expert E. According to Consequence 1, they are all disjoint.  
Under the condition that \(\text{DPS}_{f}(X)\) is nontrivial in the set \(X(\alpha)\), a partition appears in the general case larger than the partition (5) into \(r\)-connectivity components:  
\[X(\alpha)=\{C:C\in\text{DPS}_{f}(X)\}\vee\{cX(\alpha):\forall C\in\text{DPS}_ {f}(X)\Rightarrow cX(\alpha)\
otin C\} \tag{18}\]  
Let it also denote by \(\text{DPS}_{f}(X)\) and consider it the third and final stage of the DPS algorithm. Full history is presented in Figure 6.  
Thus, for each component \(c_{r}X(\alpha)\), one of three things can happen:  
* \(c_{r}X(\alpha)\in\text{DPS}_{f}(X)\Leftrightarrow\text{component }c_{r}X(\alpha)\) is sufficiently isolated from the rest to be of interest to the expert, it is the only way out of the global entity behind it on \(X\);
* \(c_{r}X(\alpha)\
otin\text{DPS}_{f}(X)\), but \(c_{r}X(\alpha)\subset C\in\text{DPS}_{f}(X)\Leftrightarrow\text{component }c_{r}X(\alpha)\) is part of the \(d\)-cluster \(C\), which is of interest to the expert and in the team represents a global entity in \(X\);
* \(c_{r}X(\alpha)\
otin C\)\(\forall C\in\text{DPS}_{f}(X)\Leftrightarrow\text{any }d\)-cluster containing \(c_{r}X(\alpha)\) is of no interest to the expert: either it is not internally dense enough, or it is externally separable. According to the expert, if \(c_{r}X(\alpha)\) is a fragment of the output of the global entity on \(X\), then it is not clear enough.  
Furthermore, Example 3 will show that all options for \(c_{r}X(\alpha)\) are possible.  
At stage 3.2 (Figure 6), the expert can act in different ways. Let us present the simplest Boolean variant of actions for the formation of \(\text{DPS}_{f}(X)\).
## 1 Introduction
First, we present exact mathematical expressions for the received SNR and outage probabilities of DAS with an arbitrary shadowing correlation coefficient between zero and one. Second, assuming that the correlation of shadowing on DASs is an exponentially decaying function of the inter-separation between neighboring antenna elements, we show the trade-off between macro-diversity gain and increased path-loss. Third, through numerical results and simulation, we show that there exists an optimal inter-element separation that minimizes the outage probability of DAS with two different combining schemes. Figure 1: An example illustration of a distributed antenna system (DAS).
# Technique Evolutions for Microorganism Detection in Complex Samples: A Review
## 2 Current Detection Methods
### Cellular Components Detection and Analytical Methods
#### 2.3.4 Nucleic Acid Amplification Techniques  
This method consists in detecting the presence or the absence of a specific nucleic acid fragment. The targeted nucleic acid is amplified exponentially by repeating DNA polymerization. The most widely used method is the polymerase chain reaction (PCR), in which a thermostable DNA polymerase copies the fragment using nucleotides primers [32]. The result can be analyzed through DNA sequencing, fragment size analysis in gel electrophoresis, or fluorescent-labelled probes. Depending on the method of analysis chosen, the amplification technique may be qualitative, semi-quantitative, or quantitative. False negatives may occur if inhibitors of the DNA polymerase are present. False positives are also prone to happen because of cross-contamination from background DNA. PCR is a proven and robust technique that is currently widely used for the detection of COVID-19 in particular [33; 34].  
PCR has been a reference technique in molecular biology for a long time, and a new technique seems promising: isothermal amplification [35; 36]. This method seems to be robust and allows to amplify nucleic acids in an exponential way at a constant temperature. Isothermal amplification is a technique adapted to the monitoring of pathogens and in situations of low quantities of DNA [37; 38; 39]. We can briefly quote different techniques such as: nucleic acid sequence-based amplification (NASBA), loop-mediated isothermal amplification (LAMP), helicase-dependent amplification (HDA), and recombinase polymerase amplification (RPA).  
These methods do not necessarily differentiate between viable and non-viable microorganisms. Contrary to the methods presented in the previous sections, cellular component analysis requires prior knowledge on the microorganisms. Specific antigen, spectral signature, or a DNA fragment must be used to detect the corresponding pathogen. While this is adapted for identification purposes, multiple specific analyses are necessary for broad-spectrum detection. Cultivation of microorganisms is often necessary to obtain a detectable signal.  
To conclude on the current detection methods, they are routinely used in laboratories as a valuable tool for controlling biological complex medium and ensuring their microbiological safety. However, most of them are considered as slow, results being delivered only after an incubation time up to several days or with too many preparation steps not in accordance with the final use of the sample. Therefore, conventional microbiological controls rarely allow for proactive corrective action. The culture step is usually needed for reasons of sensitivity of the methods. In complex media, a small number of bacteria is hard to detect because the signal-to-noise ratio is low. Recently, innovative detection methods proposed solutions to this issue.
# Estimation of Resonance Frequency for Systems with Contact Using Linear Dynamics Methods
## 6 Results
### Results--Satellite Antenna Structure  
For the satellite antenna during functional tests, a sine sweep test was performed to point out the resonance characteristics. There were two runs of the sine sweep test--before functional tests and after--to investigate whether there was a change in the structure after functional tests. Both of the runs resulted in being very close to each other's response. The resonance frequencies obtained in the test were taken as an object of comparison to numerical simulations and the simplified resonance estimation method. The responses of the sine sweep tests are plotted in Figure 14.  
Figure 12: Comparison of experimental tests with modal analysis and estimated values of resonance frequency for the TW090_1 specimen (m = 7.6).  
Figure 13: Comparison of experimental tests with modal analysis and estimated values of resonance frequency values for TW090_1 specimen (m = 9.7).  
Numerical simulations were performed using the presented method for the numerical model of antenna. In this case, not only the value of resonance frequencies were to be registered but also the mode shapes were recognized and matched together, for simulation with neglected contact and for fixed contact edge case. The differences between the vibration modes were: 0.43% for the first mode, 1.18% for the second, and 0.92% for the third one. Results of the satellite antenna tests are presented in Table 5.
## 2 Basics of CAD systems
### Feature extraction
#### 2.3.3 Image feature extraction  
The goal of the feature extraction is to characterize an object to be recognized by measurements whose values are very similar to those for objects in the same category, but very different from those for objects in different categories [32]. The accuracy of the subsequent classification would depend on the variability in the feature values for objects in the same category (intraclass variability) relative to the difference between feature values for objects in different categories (interclass variability). In addition, features should be invariant to irrelevant transformations of the inputs such as translation, rotation, and scale [32]. However, the problem is how to determine the image features to characterize the objects. Let us consider characterizing MS lesions as a simple example. According to Figure 6, the shapes of the MS lesions look like circles or ellipsoids, and the MS lesions seem to be located near to the lateral ventricles or within white matter regions. However, a majority of false positives would be very small and linear, and be located far from the center of the brain or in gray matter regions. Therefore, as image features to characterize MS lesions, we chose the circularity and the distance between centroids of each candidate and a brain region, which are invariant to translation, rotation, and scale. Note that the distance should be normalized by patient-specific values such as the anterior-posterior distance of each brain region. Figures 8(a) and (b) show the relationships between the circularity and the distance between two centroids of each candidate and a brain region, for true MS regions and false positive regions, respectively [30,31]. These relationships back up the assumption  
Figure 7: Illustrations of: (a) an original T2-weighted image and (b) white matter regions segmented by a level set method [55].  
derived from our observation in Figure 6. Many image features have been designed for characterizing specific lesions or diseases in the neuroradiological CAD system [25-31,41].  
Effective features for detecting specific objects (lesions) should be selected by discussing with radiologists. By taking sufficient time and a careful look at images, CAD researchers should learn how the lesions look like. This activity would be to determine a model for the object such as a spherical shape for aneurysms. From the point of view of reducing false positives, several models for false positives should be determined such as a cylinder shape for vessel. Then, many features based on the models can be chosen such as effective diameter, sphericity, irregularity, slenderness.
# A Voltage Multiplier Circuit Based Quadratic Boost Converter for Energy Storage Application
## 6 Conclusions  
A voltage multiplier circuit based quadratic boost converter has been realized, and a prototype is developed for energy storage application. Comparing the proposed topology with other recently proposed boost and quadratic boost topology shows its better performance in terms of voltage gain and voltage stress across the switch for a wide range of duty cycles. Loss analysis with the converter's thermal model also shows efficiency above 91% for the entire 200 Watt input power operation. The peak efficiency of 94.5% is obtained for an input voltage of 24 Volts. The proposed converter with continuous input current would be a strong candidate for energy storage and renewable energy application.  
Conceptualization, J.A., M.Z. ([PERSON]) and A.S.; Formal analysis, J.A., M.Z. ([PERSON]) and A.S.; Funding acquisition, S.A., M.S., M.Z. ([PERSON]) and M.F.; Investigation, J.A., M.Z. ([PERSON]) and A.S.; Methodology, J.A., M.Z. ([PERSON]), A.S., C.-H.L., S.A., M.S., M.Z. ([PERSON]) and M.F.; Project administration, C.-H.L., S.A., M.S., M.Z. ([PERSON]) and M.F.; Resources, C.-H.L.; Software, C.-H.L.; Supervision, M.Z. ([PERSON]), A.S. and C.-H.L.; Writing--original draft, J.A., M.Z. ([PERSON]) and A.S.; Writing--review and editing, S.A., M.S., M.Z. ([PERSON]) and M.F. All authors have read and agreed to the published version of the manuscript.  
This research received funding from Deputyship for Research & Innovation, \"Ministry of Education\" in Saudi Arabia through the project number IFKSURG-1438-089.  
The authors extend their appreciation to the Deputyship for Research & Innovation, \"Ministry of Education\" in Saudi Arabia for funding this research work through the project number IFKSURG-1438-089.  
The authors declare no conflict of interest.