esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Multi-Scale Convolutional Model Based on Multiple Attention for Image Classification
## 2 Related Work
### Multi-Scale Features  
Here we only discuss multi-scale representation of images based on multi-resolution pyramids, which can be divided into two categories: Image pyramid and feature pyramid. Classic pedestrian detection algorithms, such as \"Haar + Adaboost\" and \"HOG + SVM\", use image pyramids to handle multi-scale targets. The image pyramid is a multi-scale representation of a single image. It consists of a series of different resolution versions of the original image. The Gaussian pyramid and the Laplacian pyramid are two common image pyramids, the former for downsampling images and the latter for upsampling images. Image pyramid processing multi-scale representation can achieve optimal results, but its time and space complexities are too high. Therefore, the feature pyramid becomes the protagonist of multi-scale representation.  
U-Net [40] adopts a symmetric encoder-decoder structure to combine high-level features with low-level features to obtain richer multi-scale information. SSD [41] uses feature maps from different convolutional layers for object detection at different scales. FPN [42] employs the feature pyramid to scale the features of different layers, and then performs information fusion. Built on the FPN, PANet [43] adds a bottom-up pyramid to effectively transfer positioning information to other layers. ZigZagNet [44] further improved PANet, allowing different layers to interact to enhance bidirectional multi-scale context information. MSPN [45] and M2 Det [46] have each completed two or even multiple FPN cascades, which proves that a simple superimposed FPN structure can obtain multi-scale features more effectively. UPerNet [47] and Parsing R-CNN [48], respectively, concatenate PPM modules and RoIAlign operations with FPN to obtain rich multi-scale information.  
Most feature pyramid methods represent multi-scale features in a layer-wise manner. Res2 Net [25] learns multi-scale representations from a novel and more granular perspective. It makes a multi-scale representation into each residual block, allowing a single network layer to have different sizes of receptive fields. However, Res2 Net's grouping of residual blocks is random. We attempted to introduce attention mechanisms into the grouping tasks of Res2 Net, so that more important information has the most hierarchical feature representation.
## 3 Comparison of Fetal Heart Rate Monitoring Techniques
### Cardiotocography (CTG)  
Cardiotocography (CTG) is a procedure for fetal heart rate detection introduced by [PERSON] in 1970 [59, 60, 61, 62]. CTG can be carried out both invasively and non-invasively, as shown in Figure 5a [49] and Figure 5b [49]. In invasive CTG, an electrode is directly attached to the scalp of the fetus using a wire through the cervix. In contrast, in non-invasive CTG, electrodes are placed on the abdomen of pregnant women for the fetal heart rate detection. The signal quality of invasive CTG is more accurate but with less comfort and higher risk compared to non-invasive CTG [49, 62].  
Several studies concluded that long-term continuous monitoring using CTG resulted in discomfort to the patients. In addition, a study comprised of pregnant women at 20 weeks of pregnancy showed that the continuous fetal monitoring using CTG could result in an increase in the number of caesarean sections and misidentification of maternal heart rate [61, 62].  
Figure 4: (**a**) A schematic diagram showing the locations of phonogram sensors. Ch1, Ch2, Ch3, and Ch4 are four phonogram sensors. (**b**) Locations of phonocardiogram sensors and fetal ECG electrodes for the cross-validation experiment [52] (Reprinted with permission from ref. [52] (2018, CC BY 4.0)).
# Effective Dereverberation with a Lower Complexity at Presence of the Noise
## 4 Experiments and Evaluation
### Reference Methods  
To demonstrate the effectiveness of the proposed method, the ISCLP and WPE methods that have excellent performance at present are chosen for the comparison.  
Figure 2: Simulation scene of a room considering 20 source locations and 8 microphones [34].  
ISCLP realized in [41] is a type of method for noise reduction and dereverberation by combining the MCLP and GSC. In the ISCLP, the filter coefficients of the MCLP and GSC are simultaneously estimated by the Kalman filter. In the case of an interfering speaker, the ISCLP can effectively eliminate the interfering speaker, but at the same time it also introduces distortion to the target speaker.  
WPE realized in [42] is a type of dereverberation method based on linear prediction and is the most widely used dereverberation algorithm as well. It assumes that the speech signal follows the complex Gaussian distribution with time variance. Its model parameters and linear prediction filter coefficients can be obtained iteratively through maximum likelihood estimation. This algorithm has good suppression effect on reverberation, but it is easily interfered by the noise.
## 2 Formulation of the Proposed BMUA
### The Basics of BMUA  
The Bayesian approach identifies the posterior PDFs of uncertain parameters by integrating the prior information (existing knowledge) with the measured data (new structural information). In other words, the Bayesian approach aims to update the prior PDF using the measured data, resulting in the posterior PDF. The posterior PDF is written as [53]  
\[p(\mathbf{\Omega}|D,C)=\frac{p(D|\mathbf{\Omega},C)\cdot p(\mathbf{\Omega}|C)}{p(D|C)} \tag{3}\]  
where \(C\) is a model class representing patterns of a structural model; \(\mathbf{\Omega}\) is the vector of parameters considered in the Bayesian updating process; \(D\) is the measured data. \(p(\mathbf{\Omega}|C)\) is the prior PDF of \(\mathbf{\Omega}\) depending on engineering judgment, while \(p(D|\mathbf{\Omega},C)\) is called a likelihood function, reflecting the likelihood of observing measured data \(D\) when the model is characterized by parameters \(\mathbf{\Omega}\). The denominator in Equation (3), \(p(D|C)\), is a normalizing constant to ensure the posterior PDF is integrated into unity over parameter space. To simplify Equation (3), the constant term is denoted as \(c_{0}\) in the rest of this paper. \(p(\mathbf{\Omega}|D,C)\) represents the posterior PDF given the measurement and defined model class. In this study, measured data are taken as measured eigenvalue (the square of frequency) and mode shapes for model updating. The vector, \(\mathbf{\Omega}\), is the structural physical parameters, including mass and stiffness parameters. Therefore, Equation (3) is reformulated as  
\[\begin{split} p\Big{(}\lambda,\mathbf{\phi},\mathbf{\Omega}\Big{|}& \hat{\lambda},\mathbf{\psi},C\Big{)}=c_{0}p\Big{(}\hat{\lambda},\mathbf{\psi} \Big{|}\lambda,\mathbf{\phi},\mathbf{\Omega},C\Big{)}\ p(\mathbf{\lambda},\mathbf{\phi}|\mathbf{ \Omega},C)p(\mathbf{\Omega}|C)\\ &=c_{0}p\Big{(}\hat{\lambda},\mathbf{\psi}\Big{|}\lambda,\mathbf{\phi} \Big{)}p(\lambda,\mathbf{\phi}|\mathbf{\Omega},C)p(\mathbf{\Omega}|C)\end{split} \tag{4}\]where \(\lambda\) are the updated eigenvalues; \(\phi\) are the updated mode shapes; \(\Omega\) are certain critical parameters to be updated. \(\lambda\) are the measured eigenvalues; \(\dot{\psi}\) are the measured mode shapes. The MPVs of the updated parameters can be determined by maximizing the posterior PDF. The procedures and formulations are presented in the following subsections.
###### Abstract  
The provisions for out-of-plane stability of steel arch bridges in three major design codes are presented in this paper. By employing an existing steel arch bridge as a model, the influence of bridge type, arch rib to lateral bracing stiffness ratio, rise-to-span ratio, arch rib spacing, and range of lateral bracing arrangements on the out-of-plane critical axial force of the arch rib is studied using FE analysis. The accuracy of the critical axial force provisions is then evaluated against the FE analysis. The results show that the influence of the rise-to-span ratio on critical axial force is generally small. The critical axial force decreases with increasing arch rib spacing when the stiffness ratio is relatively large. A smaller ratio of arch rib length provided with lateral bracing (\(\gamma\)-value) significantly reduces the critical axial force and normalized critical axial force decreases with increasing stiffness ratio. The critical axial force of half-through type arch bridges is lowest when the stiffness ratio is relatively small. A deck-type bridge has a larger critical axial force than a through-type bridge when the stiffness ratio is relatively large, while the results are the opposite when the ratio is small. The different assumptions made in the provisions result in the various parameters having different impacts on the out-of-plane critical axial force in each code, thus affecting code accuracy. Considering the influence of the rise-to-span ratio, ratio of lateral bracing, and arch rib spacing with different stiffness ratios, factors to improve the accuracy of the critical axial force obtained by the three codes are proposed for a practical design process.  
design codes; steel arch bridge; out-of-plane critical axial force; rise-to-span ratio; arch rib spacing; stiffness ratio; bridge type; range of lateral bracing arrangements +
Footnote †: journal: Applied Sciences
# State-of-the-Art Mobile Radiation Detection Systems for Different Scenarios
## 3 Mobile Platforms
### Air-Based Platforms  
Aerial-based mobile detection can be divided into Fixed Wing, rotatory wing (single rotor or multi-rotor) and hybrid vertical take-off and landing (VTOL) fixed-wing aircraft. All these aircraft belong to a group called heavier-than-air platforms. Another important group is the lighter-than-air platforms which includes balloons and blimps (airships). A hybrid airship capable of transporting people and heavy cargo which is expected to spend only one tenth the fuel spent by a helicopter is under development by Lockheed Martin [65].
## 3 Results  
### Student Performance in CBCT vs. AR Learning  
A total of 43 third-year dental students participated in the study, comprising 18 males (41.9%) and 25 females (58.1%). Students were assessed on their knowledge of root canal anatomy using CBCT images and AR models. The total score for each assessment method ranged from 0 to 18, representing the combined score for the three questions mentioned in the methods.  
The results indicated that students scored significantly higher when utilizing the AR model than CBCT images (\(p<0.05\)). The mean difference in scores between the two methods was 3.95 points, representing a 37% improvement in performance with the AR model (Figure 3).  
Furthermore, a detailed analysis of individual tooth questions revealed statistically significant differences in favor of the AR model for five out of six teeth assessed (\(p<0.05\)). These findings are summarized in Table 1.
## 3 Results
#### 3.1.1 Differences in the HLA Antigens Recognized by Sera HLA Abs  
Figure 1A shows the following three levels of sensitization based on the number of anti-HLA-I Abs on both the LS- and LC-SABs tested with two different detection Abs:  
**Group 1** (low sensitization): sera IgG reacting to <10 HLA antigens (n = 6);  
**Group 2** (moderate sensitization): sera IgG reacting to 11-20 HLA antigens (n = 5);  
**Group 3** (high sensitization): sera IgG reacting to >20 HLA antigens (n = 8).  
Figure 1B shows the following two levels of sensitization based on the number of anti-HLA-II Abs:  
**Group 1** (low sensitization): sera IgG reacting to <15 HLA antigens (n = 8);  
**Group 2** (high sensitization): sera IgG reacting to >15 HLA antigens (n = 5).  
The MFI of both HLA-I and HLA-II Abs differed between LS-SAB, LC-SAB, and the detection Ab. The salient findings are as follows:  
1. The number of HLA-I and HLA-II alleles recognized by sera Abs is significantly higher on the LS-SAB than on LC-SAB, with both the detection Abs (numbers in bold in Figure 1);
2. The _FcMonolgG_ recognized a significantly (two-tailed t-test) higher number of Abs than _IgHPolyFab_, for both the HLA-I and II antigens (number in bold in Figure 2), from moderate-to-high sensitization groups;
3. Sera IgG of several patients (2 DM, 6 FG, 10 DM, 12 DM, 19 HN, and 20 HN) did not react with any of the HLA-II antigens, with both the detection-Abs, while these sera reacted moderately (2 DM and 6 FG) or highly (10 DM, 12 DM, 19 HN and 20 HN) with HLA-I antigens.  
Inferences: (1) More HLA antigens are recognized in LS- than in LC-SABs. (2) In moderately or highly sensitized groups, _FcMonolgG_ recognized more HLA alleles than _IgHPolyFab_, for both HLA-I and HLA-II. (3) The HLA sensitization in several ESRD patients may differ between HLA-I and HLA-II antigens.  
Figure 2: _Cont._  
Figure 2: Three groups of serum IgG antibodies in ESRD patients reacting to HLA-I antigens on either LS or LC, or to both SABs, when tested with two different detection Abs (_FcMonolgG_ and _lgHPlydFab_). MFI values are expressed as thousands.  
Group A: Abs binding to both SABs; Group B: Abs binding to LS-SAB only; Group C: Abs binding to LC-SAB only. IgG antibodies recognizing antigens unique to the SABs are excluded. The sera were tested at 1/10 dilution and the cut-off of the normalized/trimmed mean MFI is 500. Sera of patient 8 DM showed no anti-HLA-I Ab reactivity. HLA antigen with @ sign refer to patient's self-antigen recognized by the patient's IgG Ab. *** Two different kinds of Ab reactivity against a particular allele. One kind of Ab binds LS only (B) and another kind binds to LC only (C), suggesting that it may be due Abs binding LS only (B) may be binding to the heavy chain only or open conformers, OC) and another class of Ab binding LC only(C) may be binding to intact HLA (closed conformers, CC). MFI values are indicated in numbes, each number refering to thousands. For example 10 implies ten thousand and 10+ implies 10,500-10,999.
# Effects of Shading on the Growth and Carbon Storage of _Enhalus acoroides_
## 2 Materials and Methods
### Experimental Design and Sample Collection  
The experiment was divided into two phases. The first phase consisted of three and six months of shading, and the second phase consisted of three months of recovering light. _E. acoroides_, located in the intertidal zone with a patchy distribution and basically uniform growth (only _E. acoroides_ was distributed in the area where shading was performed), were selected in the study area on December 2020, and the light environment with reduced water transmittance was simulated by building a shading shelter in situ. The size of the shading shed was 2 m \(\times\) 2 m, and the height of the shed was slightly higher than the plant height of _E. acoroides_ in a completely floating state. The shading material was a black shading net with uniform density, and the shading intensity was manipulated by changing the number of overlapped shading nets and measuring the light intensity directly below the shading net in situ using an underwater irradiance meter (ZDS-10W-2D). The light measurements were carried out in full sunlight at noon, and the results of the measurements are shown in Table 1.  
Based on the light measurements, the shading rates of the two shading treatments were approximated as 60% (single shading) and 90% (double shading), which were labelled as moderate shading (MS) and high shading (HS), respectively, while the full-light treatment (no shading) was used as the control (CK). Three sample plots were selected in the study area, labeled ZG1, ZG2, ZG3. Three light treatments were set up in each sample plot, with three sets of replicates for each treatment, for a total of 27 sample squares (Figure 1). Due to tidal changes, the shading canopy is sometimes exposed to the water, and sometimes completely submerged. To reduce the experimental error caused by wave interference or the attachment of other marine organisms, we visited the sample site regularly every month to replace the shading net.  
Samples were collected at low tide when the _E. acoroides_ was exposed to the surface. The seagrasses were collected in the test plots using 25 cm \(\times\) 25 cm sampling frames directly below the shading shelters. The _E. acoroides_ were dug out from the sampling frames together with the rhizomes, and the sediments were collected using PVC pipes at a depth of 30 cm with intervals of 0-5, 5-10, 10-20 and 20-30 cm.
## 6 Open Challenges, Future Prospects and Research Directions
### Miniaturization of AO Components  
The miniaturization of AO components is essential for integrating these systems into a variety of use cases.  
Compact and Mobile Platforms: Miniaturizing AO components is crucial for integrating these systems into compact and mobile platforms, such as UAVs, satellites, and portable ground stations. Future research will focus on developing smaller, lighter, and more energy-efficient wavefront sensors and DMs without compromising performance. This involves leveraging progress in MEMS technology and nanotechnology to create miniaturized components that can be easily integrated into various platforms.  
Scalable AO Systems: The miniaturization of AO components will also enable the development of scalable AO systems that can be deployed across a broad spectrum of applications, from small handheld devices to large-scale ground stations. Scalable systems will offer greater flexibility and adaptability, making AO technology accessible for diverse FSO communication scenarios.
## 4 Simulation Results and Analysis
There is an optimal divergence angle for the RIS-SHTLC system. This trend of the BER increasing and decreasing with respect to the divergence angle becomes more obvious as the number of RIS elements increases. In this scenario, the increase in the number of RIS elements can effectively improve the system BER performance while increasing the optimal divergence angle. For example, when \(N_{\mathrm{r}}=20\), 40, 60, the optimal divergence angles of the corresponding RIS-SHTLC systems are 40 \(\times\) 10\({}^{-5}\) rad, 55 \(\times\) 10\({}^{-5}\) rad, and 60 \(\times\) 10\({}^{-5}\) rad, respectively. In practical applications, the use of a small divergence angle requires highly accurate beam pointing control. A large divergence angle can be obtained more easily compared to a small one. The SHTLC system using the RIS technology can easily attain the optimal divergence angle; therefore, the system can achieve the optimal performance. Figure 6: Effect of receiving aperture on the performance of RIS-SHTLC system with different numbers of RIS elements.
# Utilization of Quasi-Zenith Satellite System for Navigation of a Robot Combine Harvester
## 4 Discussion
### Static Experiment
The difference between each positioning augmentation system points result and the RTK position points result is termed the bias of each receiver. The mean of the bias values within a period is termed the offset of each receiver [4]. This offset is calculated from the easting and northing coordinates separately. Table 3 summarizes the accuracy analysis in terms of the offset and the RMSE calculated using Equation (3). Unlike the result from 2 DRMS and CEP, these results show that both CLAS and MADOCA have a similar offset of around 0.33 m in the easting coordinate and 0.43 m in the northing coordinate with an RMSE of around 0.55 m. These results imply the notion that the MADOCA positioning augmentation system is as accurate as the CLAS in terms of a constant offset bias. The results from Figure 7 suggest that CLAS might be appropriate for the robot combine harvester running in automatic mode, the bias respect to the reference measured by the RTK is around 0.40 m but it seems to be almost constant during the six hours of the static positioning experiment. Note that CLAS shows a deviation in the northing coordinate of 0.20 m during the first hour; this might be an inherent characteristic of the CLAS positioning augmentation system. In contrast, the oscillation of MADOCA shows the time dependency of this system; although, for the first hour it is not suitable to be used in a robot combine harvester, it might be used after waiting some time. Therefore, the notion that the MADOCA positioning augmentation system is as accurate as the CLAS in terms of a constant offset bias is not correct. This time restriction was considered for the dynamic positioning experiments.
## 2 Method
### Physics-Informed Neural Networks
\begin{split} F(x,t;u,\partial_{x}u,\partial_{t}u,\dots,\lambda )&=0,\quad\forall(x,t)\in U\\ I(x,t_{0},h;u,\partial_{t}u,\dots,\lambda)&=0, \quad\forall(x,t)\in\mathcal{I}\\ B(x,t,g;u,\partial_{x}u,\dots,\lambda)&=0,\quad \forall(x,t)\in\partial U\end{split}  \tag{4}\]  
where \(F\) contains a sequence of differential operators (i.e., \([\partial_{t},\partial_{x},\dots]\)), which represents the residual of the PDEs; \(\lambda\) is the PDEs' parameter vector; \(I\) is the residual form of the initial condition containing a function \(h(x,t)\); and \(B\) is the residual form of the boundary condition containing a function \(g(x,t)\). \(U=\{(x,t)\,|\,x\in\Lambda,t\in[0,T]\}\), \(\partial U=\{(x,t)\,|\,x\in\partial\Lambda,\)\(t\in\,[0,T]\}\), and \(\mathcal{I}=\{(x,t)\,|\,x\in\partial\Lambda,t=0\}\). Figure 1 illustrates the structure of a PINN model. The space coordinates \(x\) and time \(t\) are usually taken as the inputs, and the outputs \(\hat{u}(x,t)\) are used to approximate the true solution \(u(x,t)\) of the PDEs. The differential operators are calculated by automatic differentiation (AD), and then the PDEs' residuals, initial conditions, and boundary conditions are embedded into the loss function of neural networks (Equation (5)):  
\[L(\theta)=W_{F}\mathcal{L}_{F}(\theta)+W_{I}\mathcal{L}_{I}(\theta)+W_{B} \mathcal{L}_{B}(\theta) \tag{5}\]  
With \(\theta\) representing the weights of neural network; \(W_{F}\), \(W_{I}\), and \(W_{B}\) are the weights for various loss terms; and \(\mathcal{L}_{F}\), \(\mathcal{L}_{I}\), and \(\mathcal{L}_{B}\) are the loss functions of PDE (Equation (6)), initial condition (Equation (7)), and boundary condition (Equation (8)), respectively:  
Figure 1: PINN model. \[\mathcal{L}_{F} =\frac{1}{N_{F}}\sum_{i=1}^{N_{F}}\|F(x^{(i)},t^{(i)};\hat{a})\|^{2} \tag{6}\] \[\mathcal{L}_{I} =\frac{1}{N_{I}}\sum_{i=1}^{N_{I}}\|I(x^{(i)},t^{(i)},h^{(i)};\hat{ a})\|^{2}\] (7) \[\mathcal{L}_{B} =\frac{1}{N_{B}}\sum_{i=1}^{N_{B}}\|B(x^{(i)},t^{(i)},g^{(i)};\hat {a})\|^{2} \tag{8}\]  
where \(N_{F}\), \(N_{I}\), and \(N_{B}\) are the sets of collocation points in \(U\), \(\mathcal{I}\), and \(\partial U\); and \(N_{F}\), \(N_{I}\), and \(N_{B}\) denote the number of sampling points. In this manuscript, the total loss function is represented as \(L_{\text{PINN}}(\theta)\). After the formulation of these loss terms, the PINN can be trained using any optimizer, such as Adam, Stochastic Gradient descent, or a Netwon-based method like L-BFGS. In this work, we mainly use Adam [47] and LBFGS [48], which are described in detail in the following sections.
# Overcoming Limitations of LoRa Physical Layer in Image Transmission
## 4 Experimental Method
### Experimental Setup
#### 4.2.3 MATLAB Manipulation of Received Images  
MATLAB is used to help control the process of sending and receiving data through the LoRa adapters and to analyse the quality of the images before and after transfer over the LoRa network, using the _PSNR_ and _MSE_. Prior to transmission, the MATLAB code sends a prompt message asking the user to enter a number from 1 to 6 to trigger transmission. These numbers correspond to the settings for the _SF_ which, in this case, vary from 7 to 12. Following this, the data transfer process described above is initiated. The MATLAB code for triggering data transfer, retrieving received data packets, and saving and displaying the reassembled image is highlighted in the Appendix A (Figure A5).