esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### GAN-Based Fusion Methods  
In 2019, [PERSON] et al. [27] first introduced GAN into the infrared and visible image fusion (IVIF) task, namely FusionGAN. Although FusionGAN has achieved certain results, there are still some shortcomings: on the one hand, relying only on adversarial training to add additional detailed information may lead to the loss of some information; on the other hand, it ignores the edge information in infrared images, resulting in relatively blurred target edges in the fusion result. To solve these problems, [PERSON] et al. proposed Detail-GAN [28] and designed detail loss and target edge enhancement loss, respectively. However, these two methods failed to adequately consider the texture information of infrared images and the contrast information of visible light images, resulting in the generated fused images being more similar to sharpened infrared images. Subsequently, [PERSON] et al. proposed a fusion network that employs a multiple classifier as discriminators, namely GANMcC [29], thereby endowing the fused images with abundant texture information and significant contrast. [PERSON] et al. proposed Perception-GAN [30], which uses dense blocks in the generator to connect the shallow layer and source images with rich detail features to the deeper layer. [PERSON] et al. proposed an unsupervised continuous learning generative adversarial network UIFGAN [31] for training a single model with memory to achieve multiple image fusion tasks.  
The GAN network with a single discriminator cannot well preserve both infrared pixel intensity information and visible light texture detail information at the same time, and the training is not stable enough, which has certain deficiencies. Therefore, [PERSON] et al. proposed the use of dual discriminators in DdcGAN [11] to increase the evaluation and feedback of the generated image, thereby enhancing the stability of training. The two discriminators act on the generator respectively, enabling the fused image to retain features from both modalities. While preserving the texture information of visible light, it also retains the thermal radiation information of infrared images. Building on this, [PERSON] et al. introduced a multi-scale attention mechanism into GAN, proposing AttentionFGAN [32]. Meanwhile, [PERSON] et al. proposed a semantic-supervised dual discriminator generative adversarial network, SDDGAN [33], which guides the fusion process through an information quantity discriminator block.  
Distinguished from the methods previously mentioned, our DAGANFuse calculates attention weights in both the spatial and channel dimensions on two parallel paths of differential features and fused features, respectively, to measure the activity level of the source images at the same scale. It then effectively integrates cross-scale features through a cross-scale attention mechanism, thereby obtaining high-quality infrared and visible light fused images.
# Weed Detection and Classification with Computer Vision Using a Limited Image Dataset
## 3 Results
### Distance Transformation Features  
Following distance transformation, we arranged the maximal distance transform values in descending order. Given that each distance-transformed image contained a variable number of maxima, it was essential to determine the appropriate number of maxima for comparison. Depending on the scenario, the analysis may require only one data point or multiple data points. In our approach, we assumed that the highest values held greater significance than the lower ones.  
To assess the impact of the number of training images on classification performance, we conducted classification tests using 20 and 60 training images (as detailed in Table 5). Various combinations were explored to evaluate their effectiveness. Below are the results of the experiments.  
As illustrated in Figure 8, the classification accuracy of the second column surpasses that of the first. However, the combination of the first three columns demonstrates promising results. In contrast, as shown in the fourth and fifth columns, with the addition of further combinations, the performance deteriorates. Therefore, it may be prudent to either utilize the second column alone or integrate the first three columns. It appears that the first three columns collectively offer slightly more information than the second column alone. Consequently, we proceeded with the first three largest distance maximum values for further analysis and classification tasks.  
Table 6 presents the classifiers' results using the three largest distance transformation values. Remarkably, even with only 10 images, the classification accuracy reached 61%. On the other hand, with 160 training images, the best precision result was still around 66%.  
Figure 8: Visualization of the optimum distance transformation features. The first, second, and third features together show the optimum.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Columns** & **1** & **1, 2** & **1, 2, 3** & **1, 2, 3, 4** & **1, 2, 3, 4, 5** & **2** & **2, 3** & **1** \\ \hline
**20 training images** & 71.25\% & 77.19\% & 77.5\% & 73.44\% & 72.81\% & 76.88\% & 77.19\% & 71.25\% \\
**60 training images** & 70\% & 75.63\% & 75.63\% & 75.63\% & 73.75\% & 79.38\% & 74.38\% & 70\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Some distance transformation parameter combinations to find the optimum features with 20 and 60 training images (1 is the largest value, 2 is the second largest, and so on).
# Z-OFDM: A New High-Performance Solution for Underwater Acoustic Communication
## 4 Computer Simulation Experiments
### Experimental Results
#### 4.2.2 Wuyuan Bay  
It can be observed from Figure 7(b) that the BER of the communication system decreases significantly with the increase in the refinement factor. At a BER of \(10^{-2}\), when the refinement factor \(D=2\), the Z-OFDM system achieved about 3 dB more gain than the FFT-based demodulation algorithm. When \(D=3\), the gain obtained by the communication system was close to 5 dB. Compared with traditional FFT, the maximum gain obtained by the underwater acoustic OFDM demodulation algorithm based on ZoomFFT can reach 6 dB (\(D=4\)).  
Figure 8: BER of the communication system in this experiment.  
Figure 9: BER over time varies underwater acoustic channel of unit time in Fuxain Lake.  
Figure 10 shows the BER of the Z-OFDM system with varying parameters of the underwater acoustic channel of Wuyuan Bay. In Figure 7, the variability of BER is the same as in Figure 6. With the increase in factor \(D\), the variability of BER of the ZoomFFT decreased. However, due to geological factors, the BER of the communication system under Wuyuan Bay was higher than that under Fuxian Lake, and the variability of BER was more complex.
## 6 Simulation Results
### Economic Analysis through One Year
#### 6.2.1 Battery Degradation  
The summarized number of cycles using the rainflow algorithm are depicted in Figure 20. The cycles are a histogram representing the number of cycles per DoD during an entire year for the Coto Laurel plant under different PRRC methods using the rainflow algorithm. The DoDs were accumulated in intervals of 10%. With this information, it was possible to determine the capacity degradation and the lifetime of the ESS by using (18)-(23) with a temperature of 25 \({}^{\circ}\)C and an estimated end-of-life (EOL) capacity of 70%, according to the manufacturer.  
According to Figure 21, The PDS showed to be the PRRC method that offers the most estimated battery lifetime (118 months), followed by the RS (103 months), the SMA (95 months), the ELES and EMA (91 months), the FOLPF (79 months), and the SOLPF (74 months). This was because the PDS method produced fewer battery cycles for high DoD values. According to Figure 6, the battery life is exponentially affected as the number of cycles of high DoD values increase.  
Figure 20: Histogram representing the number of cycles per each DoD during an entire year for the Coto Laurel plant under different PRRC methods using the rainflow algorithm. The DoDs were accumulated in intervals of 10%.
## 3 Results and Discussion  
### Tensile and Cyclic Test Data  
Experimental data for the SA508 and SA333 alloys were adopted from well-established literature sources to assess the cyclic response of steels at different temperatures [51; 52]. Both materials contain approximately 0.2% carbon and possess a ferrite-pearlite microstructure. Both steels are applied in various applications in high-temperature and high-pressure services, such as steam piping systems. Tensile tests were conducted at a constant strain rate of 0.2%/s on solid hot-rolled cylindrical specimens. For SA508 samples, the specimens had a gauge length of 30 mm and a diameter of 10 mm, while for SA333 samples, the gauge length was 15 mm, and the diameter was 8 mm. Cyclic and ratcheting tests were performed under the stress-controlled conditions using a CRIMS-RPL100 fatigue testing machine for SA508 specimens and an Instron 8862 testing machine for SA333 specimens. The stress rates for cyclic tests in the SA508 and SA333 samples were respectively  
Figure 1: The developed flowchart to address the procedure for ratcheting assessment through the A-V and Chaboche hardening rules in the present study.  
kept at 100 MPa/s and 115 MPa/s. The cyclic tests were conducted through the use of a cylindrical heat oven integrated into the testing machine, preserving operating temperatures over the loading cycles. Figure 2 demonstrates the tensile stress-strain behavior of (a) SA508 and (b) SA333 at different temperatures (298 K, 573 K, 623 K). As the temperature increases, the yields and ultimate tensile strengths reduce. Notably, the strength levels were highest at room temperature (298 K), where the materials displayed limited plastic deformation. At 573 K and 673 K, the occurrence of dynamic strain aging (DSA) temporarily enhanced the work-hardening rate, evident in the intermediate rise in flow stress. However, temperatures beyond 873 K resulted in noticeable softening, as seen by the overall reduction in flow resistance and ductility increase.
# Design of Low-Power ECG Sampling and Compression Circuit
## 3 Proposed Scheme
### CS Module  
#### 3.2.1 Addition  
The binary matrix converts the compression process into a selective addition operation. We design an approximate addition calculation to improve circuit performance by sacrificing a little accuracy, as illustrated in Figure 4. We approximate the \(n\)-bit addend selected by the measurement matrix. The high (\(nm\)) bits of the addend remain unchanged, and the low \(m\) bits are approximated while a carry is generated.  
The approximate addend is expressed by Equation (6):  
\[\left\{\begin{array}{l}\text{b}_{n-1}{}^{*}=\text{b}_{n-1}\\ \vdots\\ \text{b}_{m}{}^{*}=\text{b}_{m}\\ \text{c}=\text{b}_{m-1}\cdot\text{b}_{m-2}\\ \text{b}_{m-1}{}^{*}=\text{b}_{m-1}\cdot\text{b}_{m-2}\\ \text{b}_{m-2}{}^{*}=\overline{\text{b}_{m-1}}\cdot\text{b}_{m-2}\\ \vdots\\ \text{b}_{3}{}^{*}=\text{b}_{3}\cdot\overline{\text{b}_{2}}\\ \text{b}_{2}{}^{*}=\overline{\text{b}_{3}}\cdot\text{b}_{2}\\ \text{b}_{1}{}^{*}=0\\ \text{b}_{0}{}^{*}=0\end{array}\right. \tag{6}\]  
The bits of the high and low parts in the approximate addend are not fixed, and the more bits of the low part, the larger the error. Therefore, the bits of the low part must be determined according to the accuracy requirements of the circuit. Considering the low  
Figure 4: The approximate addition calculation.  
4 bits as an example, the generated approximate addend and corresponding error are reported in Table 2.  
The mathematical expectation of the resulting error is 0.25, as calculated in Equation (7), which is too small to ignore for binary data.  
\[\begin{array}{ll}E(\text{error})&=(+1)\times\frac{2}{16}+(+2)\times\frac{2}{ 16}+(+3)\times\frac{2}{16}+(+4)\times\frac{1}{16}\\ &+(-3)\times\frac{1}{16}+(-2)\times\frac{2}{16}+(-1)\times\frac{2}{16}+0\times \frac{3}{16}=0.25\end{array} \tag{7}\]  
Furthermore, the more weighted bits are calculated accurately, and the less weighted bits are calculated approximately. It can be seen from Table 2 that there are only three values for the low 4 bits after approximation, which are 0000, 1000, and 0100, with probabilities of 1/2, 1/4, and 1/4, respectively. Therefore, the approximate accumulation sum of the low 4 bits is as Equation (8):  
\[{y_{q}}^{*}(\text{L})=\left(8\times\frac{1}{4}+4\times\frac{1}{4}\right)\times \left(\Phi_{q,1}+\Phi_{q,2}+\ldots+\Phi_{q,N}\right),\;q\in[1,M] \tag{8}\]  
The accurate calculation of the high part is realized in the CS module, and the approximate calculation of the low part is completed in the remote server. When two results are added, the compressed data are obtained.
# A Novel Deep Dense Block-Based Model for Detecting Alzheimer's Disease
## 3 Materials and Methods
### Dataset and Pre-Processing  
In this study, we gathered data from the ADNI, a worldwide research endeavor that actively promotes the study, analysis, and refinement of treatments for AD to arrest its progression. The ADNI datasets encompass datasets of various modalities, which can be beneficial to researchers in various ways for the early detection of AD. By providing standardized datasets, ADNI offers a means for researchers to conduct coherent research and disseminate compatible data to their counterparts worldwide. The TBM dataset used in this study consisted of 170 nifti files presented in nifti format and was obtained using the standard protocol presented on the ADNI Site. It consists of 28 AD subjects [mean age: 75.0 \(\pm\) 5.0 years, 16 males (M)/12 females (F)], 88 patients with MCI [mean age: 73.8 \(\pm\) 5.4 years, 47 (M)/41 (F)], and 54 CN subjects [mean age: 74.4 \(\pm\) 5.5 years, 30 (M)/24 (F)]. Table 1 presents the demographic characteristics of the participants.  
By converting volumetric 3D data into 2D representations, complex spatial information contained within the nifti format can be effectively extracted and leveraged using ML models. This conversion process facilitates the application of CNNs, which are used to learn hierarchical features from 2D image input. The 3D TBM images were uploaded to Google Colab. Subsequently, all the 3D TBM images were read using the Nibabel library in Python. First, the height and width of each nifti file are saved. Axial images were then sliced at 5-pixel intervals, starting from the 48 th pixel and covering only the hippocampus and temporal lobe up to the 115 th pixel. Consequently, 12 pieces of 2D axial brain slices were obtained for each subject. Considering that the hippocampus and temporal lobe are the regions most affected by AD, a pixel range of 48-115 was selected as the region of interest [42]. The following steps were applied to convert the TBM images from the 3D nifti format to the 2D png format (Figure 3).  
As a result of the procedure shown in Figure 3, 2342 2D brain MR images of the subjects presented in Table 2 were obtained.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Groups** & **CN** & **MCI** & **AD** \\ \hline Number & 54 & 88 & 28 \\ Gender (M/F) & 30/24 & 47/41 & 16/12 \\ Age (mean \(\pm\) std) & 74.4 \(\pm\) 5.5 year & 73.8 \(\pm\) 5.4 years & 75.0 \(\pm\) 5.0 year \\ \hline \hline \end{tabular}
\end{table}
Table 1: Demographic characteristics of the dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Set** & **AD** & **CN** & **MCI** & **Total Scans** \\ \hline Training set & 246 & 491 & 811 & 1548 \\ Validation set & 62 & 123 & 203 & 388 \\ Test set & 84 & 126 & 196 & 406 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distribution of data utilized for training and testing the model.
# A LoRa-Based Linear Sensor Network for Location Data in Underground Mining
## 2 Related Work  
Ad hoc sensor networks have been the subject of a great deal of research in recent years [2]. Linear wireless sensor networks fall within this area as a very specific but important topology where the nodes are located in a linear sequence rather than a mesh. Such networks are sometimes referred to as 'chain type wireless sensor networks' or 'wireless relay networks' [3].  
Most previous work in this area has been concerned with energy efficient transmission. For an emergency network reliability and simplicity of deployment are more important. Relays and tags are unlikely to be in use for very long periods of time--days or possibly weeks rather than months or years. This difference compared previous work has consequences for the design decisions made for the emergency network.  
Wireless data networks for underground mining has also attracted a great deal of attention over the past few years [4; 5; 6; 7]. Primarily wireless networks are used in monitoring mining processes, monitoring location of people and equipment, and for safety systems [8]. Voice communications underground is still heavily reliant on traditional VHF radio. Data networking for underground mining is typically based on IEEE 802.11 using leaky feeder antennae with an optic fiber backhaul [9]. Increasingly, cellular technologies are being considered with LTE a common choice [10].  
However, for an easily deployed emergency system, simpler technologies that can both communicate with end devices and act as its own backhaul are desirable. One of our design decisions is to use LoRa. LoRa is a proprietary technology developed by Semtech [2]. LoRa is a physical layer specification usually implemented in association with the network protocol LoRaWAN. LoRa has attracted some research attention as a mesh and relay technology [11; 12; 13; 14]. However, this has usually been in association with energy efficiency which is of less concern. For an emergency network simplicity of deployment and robustness are more important. It is important to emphasise that we do not use LoRaWAN in our work. LoRaWAN is a protocol that makes use of underlying LoRa transmission capabilities and has found use in many areas but is not a relay technology. In the work described in this paper we used LoRa without LoRaWAN.  
Despite its potential, very little work has been done on the use of LoRa underground. [PERSON] and [PERSON] used LoRa to monitor underground aquducts [13] but no one appears to have explored its possibilities for mining. Possibly this is because of its novelty but also because it is usually implemented in association with LoRaWAN. Deploying it as an ad hoc or linear network technology is still very new. It has been trialled as an actuator network technology for initiating underground detonation of explosives [1; 15].  
Given this, it is not surprising that the use of LoRa underground as an emergency communications technology appears not to have been researched.
## 3 Results and Discussion  
### Numerical Simulation Set-Up  
It is now well known that when the incident water wave enters the non-uniform media area, it would be scattered (or diffracted) by the structures to produce the multiple scattering phenomenon. The transmission properties of water waves through artificial crystals can be obtained from the calculated transmission wave. Three lattice types are considered, i.e., triangular, square and hexagonal lattice arrays. The \(\Gamma K\) or \(\Gamma M\) direction of the lattice structure is set to be parallel to the incident wave side, as shown in Figure 2, where \(\Gamma\), \(M\), and \(K\) are the points of the first Brillouin zone in the irreducible representations of the corresponding symmetry groups. For a concrete example, a triangular lattice with the lattice constant \(a\) is considered. The k-point inside the irreducible Brillouin zone is derived by walking from \(\Gamma\) (0, 0) to \(M\) (0, 0.5) \(\pi/a\) to \(K\) (1/3, \(-1/3\)) \(\pi/a\) and back to \(\Gamma\). Any arbitrary wavevector can be constructed by linear combinations of these basis vectors.  
For our purpose, the most suitable dimensionless units can be defined in terms of the lattice constant \(a\), the water wavelength \(\lambda\) and the wave velocity \(c\). In these numerical results, the frequencies at a specific mode are normalized, and expressed by the basic unit \(f_{n}=a/\lambda\). Using the dimensionless variables, the wave amplitude depends only on two parameters; namely the ratio of \(h_{0}/h\) and the  
Figure 2: First Brillouin zones, irreducible Brillouin zones (red region) and unit cells of the (**a**) triangular; (**b**) square; and (**c**) hexagonal lattice structures.  
ratio of \(r/a\). The wave equation is solved by the plane-wave expansion method as implemented in the open-source MIT Photonic Bands (MPB) software package. Briefly, this method can be used to compute fully vectoral eigenmodes of the wave equations with periodic boundary conditions by preconditioned conjugate-gradient minimization of the block Rayleigh quotient in a plane-wave basis [29]. Here the lattice vector is discretized by a grid of \(N=\{32^{2}\), \(64^{2}\), \(128^{2}\), \(256^{2}\), \(512^{2}\}\) points. Convergence tests have been carefully examined with respect to the basis set size, \(K\) (or \(M\))-point sampling, and aliasing (cell size). The pure computation error for \(N\) above \(128^{2}\) can be expected to be less than 1%.
## 3 Evaluation with Expected Total Costs  
The expected total cost, \(EC_{tot}\), is expressed as the sum of the building cost, \(C_{b}\), countermeasure (CM) cost, \(C_{c}\), and failure cost, \(\text{C}_{f}\).  
\[EC_{tot}=C_{b}+P_{c}C_{c}+P_{f}C_{f} \tag{1}\]  
where \(P_{c}\) is the probability of CM and \(P_{f}\) is the lifetime probability of failure.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & **Measurements Required** & **Types of Piles** & **References** \\ \hline Dynamic Formula & Hammer energy and blow counts & Driven piles & [40] \\ CAPWAP & Stress wave measurements & Driven piles & [41] \\ Empirical Formula & Consumed energy & Vibratory driven piles & [42] \\ Torque-Correlation & Final installation torque & Helical piles & [43] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Typical methods for estimating bearing capacity and required piling data.  
However, it is noted that the formula requires further refinement in reference [44], and we assume that conventional structures have been designed to minimize the expected total cost (Equation (2)).  
\[P_{f;T}=\underset{P_{f}}{\text{argmin}}\left[C_{b}+P_{f}C_{f}\right] \tag{2}\]  
where subscript \(T\) stands for the target value.  
The failure cost can be proportional to the building cost, depending on its impact on the economic activity of the site, and the same can be said for the CM cost. Thereafter, for simplicity, we will normalize the costs by the building cost using the parameters, \(k_{f}\) and \(k_{c}\); that is, \(C_{f}=k_{f}C_{b;T}\) and \(C_{c}=k_{c}C_{b;T}\).