esa-sceva/satcom-chunk-collection huggingface dataset:
# Two-Stage Fusion-Based Audiovisual Remote Sensing Scene Classification
## 5 Conclusions
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Methods** & **Precision** & **Recall** & **F1-Score** \\ \hline Feature-Level (ADVANCE) [66] & 73.93 \(\pm\) 0.8 & 72.91 \(\pm\) 0.74 & 72.71 \(\pm\) 0.76 \\ Feature-Level (\(\varphi(\cdot)=mean\)) & 79.12 \(\pm\) 0.27 & 78.42 \(\pm\) 0.22 & 79.41 \(\pm\) 0.27 \\ Feature-Level (\(\varphi(\cdot)=max\)) & 83.91 \(\pm\) 0.33 & 83.02 \(\pm\) 0.38 & 83.02 \(\pm\) 0.35 \\ Feature-Level (\(\varphi(\cdot)=sum\)) & 84.05 \(\pm\) 0.30 & 83.56 \(\pm\) 0.39 & 83.72 \(\pm\) 0.42 \\ Feature-Level (\(\varphi(\cdot)=max+sum\)) & 85.21 \(\pm\) 0.98 & 84.76 \(\pm\) 0.46 & 84.74 \(\pm\) 0.31 \\ \hline Decision-Level (\(\phi(\cdot)=mean\)) & 79.96 \(\pm\) 0.52 & 79.75 \(\pm\) 0.61 & 79.93 \(\pm\) 0.49 \\ Decision-Level (\(\phi(\cdot)=max\)) & 87.02 \(\pm\) 0.47 & 87.75 \(\pm\) 0.38 & 87.06 \(\pm\) 0.25 \\ Decision-Level (\(\phi(\cdot)=sum\)) & 86.64 \(\pm\) 0.25 & 86.61 \(\pm\) 0.34 & 86.56 \(\pm\) 0.24 \\ Decision-Level (\(\phi(\cdot)=max+sum\)) & 88.12 \(\pm\) 0.23 & 88.31 \(\pm\) 0.31 & 88.02 \(\pm\) 0.35 \\
**Proposed** & **89.9 \(\pm\) 0.14** & **89.85 \(\pm\) 0.36** & **89.83 \(\pm\) 0.21** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results (%) \(\pm\) STD of the experiments on fusion strategy.
# Memory Model for Morphological Semantics of Visual Stimuli Using Sparse Distributed Representation
## 5 Conclusions  
This paper proposes a novel scheme for modeling memory mechanism in neocortical region of human brain. The proposed scheme models how visual stimuli has connected to memory system and how information in the memory system can be reconstructed during the recall process. We employ sparse distributed representation which imitates a neuron activation model in neocortical region of human brain for memory and recall process.  
We have evaluated the proposed morphological semantic preservation scheme during memory and recall processes. In memory process, external visual stimuli are transformed to SDR from 128-dimension feature vector using encoder part of autoencoder based on CNN model. SDR pattern which represents memorized information from external stimuli can be considered to include semantic information. This paper considers morphological semantics especially. During the recall process, we detect the most similar memory with the given visual stimulus using similarity analysis in the SDR domain. This paper has used MNIST data set for the evaluation of effectiveness of the proposed scheme.  
This paper can be considered as an inter-disciplinary research result. We have been tried to combine memory model from the viewpoint of behaviorism in the traditional cognitive psychology and visual information processing technology using neuron models from neuroscience. In this stage, we can consider 3 kinds of future research directions. The first is improvements of SDR mechanism for embedding semantic information more effectively in the memory process. This includes the fine tuning of feature vector selection scheme instead of vanilla level of autoencoder. The next is extending our scheme for 3-channel color image datasets, as well. After that, we are going to extend embedded information in SDR from morphological level to conceptual level. We consider knowledge construction process for conceptualization.  
All authors have equally contributed conceptualization, implementation, analysis, experiments, result discussions, and writing and revising this paper. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government. [21 ZSI100, Core Technology Research for Self-Improving Integrated Artificial Intelligence System].  
The authors declare no conflict of interest.  
Figure 26: Percentage of dominant memory pattern for each ground-truth label in SDR 20% overlap as a similarity criterion.
###### Abstract  
One primary concern in the field of remote-sensing image processing is the precise classification of hyperspectral images (HSIs). Lately, deep-learning models have demonstrated cutting-edge results in HSI classification. Despite this, researchers continue to study and propose simpler, more robust models. This study presents a novel deep-learning approach, the iterative convolutional neural network (ICNN), which combines spectral-fractal features and classifier probability maps iteratively, aiming to enhance the HSI classification accuracy. Experiments are conducted to prove the accuracy enhancement of the proposed method using HSI benchmark datasets of Indian pine (IP) and the University of Pavia (PU) to evaluate the performance of the proposed technique. The final results show that the proposed approach reaches overall accuracies of 99.16% and 95.5% on the IP and PU datasets, respectively, which are better than some basic methods. Additionally, the end findings demonstrate that greater accuracy levels might be achieved using a primary CNN network that employs the iteration loop than with certain current state-of-the-art spatial-spectral HSI classification techniques.  
Keywords:iterative convolutional neural network; fractal features; hyperspectral image; spatial-spectral features +
Footnote †: journal: Applied
# Wave Propagation in Periodic Metallic Structures with Equilateral Triangular Holes
## 2 Formulation
### Mode Matching
The parameter \(\beta_{ri}(\mathbf{k}_{ps})\) is given by  
\[\beta_{ri}(k_{x,p},k_{y,s},k_{z,ps})\ \ =\ \ \frac{k_{0}^{2}-k_{y,s}^{2}}{k_{z,ps}}\ \widetilde{\varphi}_{x,i} \widetilde{\varphi}_{x,r}^{*}\ +\ \frac{k_{x,p}k_{y,s}}{k_{z,ps}}\ \widetilde{\varphi}_{y,i} \widetilde{\varphi}_{x,r}^{*}\ +\ \frac{k_{0}^{2}-k_{x,p}^{2}}{k_{z,ps}}\ \widetilde{\varphi}_{y,i} \widetilde{\varphi}_{y,r}^{*}\ +\ \frac{k_{x,p}k_{y,s}}{k_{z,ps}}\ \widetilde{\varphi}_{x,i} \widetilde{\varphi}_{y,r}^{*} \tag{8}\]  
where \(k_{z,ps}=\sqrt{\varepsilon_{r}k_{0}^{2}-k_{x,p}^{2}-k_{y,s}^{2}}\) is the vertical wavenumber of the \((p,s)\)-th harmonic, \(\widetilde{\varphi}_{x,i}\) and \(\widetilde{\varphi}_{y,i}\) are the \(x\) and \(y\)-components of the Fourier transform of the \(i\)-th modal function, and the symbol \({}^{*}\) denotes complex conjugate. The \(x\) and \(y\) components of the 2D Fourier transform are written as  
\[\widetilde{\varphi}_{x,i}(k_{x,p},k_{y,s})\ =\int_{x=-\varepsilon/2}^{e/2}\int_{y=0}^{h_{t}} \phi_{x,i}\mathrm{e}^{\mathrm{i}(k_{x,p}x+k_{y,s}y)}\ \mathrm{d}x\mathrm{d}y \tag{9}\]  
\[\widetilde{\varphi}_{y,i}(k_{x,p},k_{y,s})\ =\int_{x=-\varepsilon/2}^{e/2}\int_{y=0}^{h_{t}}\phi_{y,i} \mathrm{e}^{\mathrm{i}(k_{x,p}x+k_{y,s}y)}\ \mathrm{d}x\mathrm{d}y. \tag{10}\]  
Finally, the term \(\widetilde{f}_{ps}\) in (6), namely the vertical spectral function, distinguishes between the mirror-symmetric and glide-symmetric structures [23]. For the mirror-symmetric configuration, it reads  
\[\widetilde{f}_{ps}=\cot\bigl{(}k_{z,ps}\,g/2\bigr{)} \tag{11}\]  
and for the glide-symmetric configuration, it reads  
\[\widetilde{f}_{ps}=\left\{ \begin{aligned} -\tan\bigl{(}k_{z,ps}\,g/2 \bigr{)}\ \,&&\ \ \ p+s\ \ \mathrm{even}\\ \cot\bigl{(}k_{z,ps}\,g/2\bigr{)}\ \,&&\ \ \ p+s\ \ \mathrm{odd}\.\end{aligned} \right. \tag{12}\]
# Visual-Based Localization Using Pictorial Planar Objects in Indoor Environment
## 5 PicPose: A Picture-Based Pose Method
### PicPose Feature Points Extraction  
After a picture has been identified by SSD in an area of a camera view, we need to identify specific feature points for producing the camera pose. To find the pose in the 3D space, at least 4 non-colinear points of a pictorial object need to be extracted from the captured image to match with the corresponding points in the original picture.  
The feature points identification issue is illustrated in Figure 7. The left-hand side of Figure 7a is a full image view captured by a camera, and the right-hand side shows the area of a pictorial object detected by SSD. \(P_{1}\)-\(P_{4}\) show the detected area, inside of which we can find two types of feature points: corner points \(T_{ci}\) and internal feature points \(T_{fi}\). Internal feature points are extracted by feature extraction algorithms and can be anywhere on a picture. Their corresponding feature points in original picture is shown as Figure 7b. In detected picture, each point \(T_{i}\) has one point \(O_{i}\) which has same relative position inoriginal picture with the detected picture. Thus, (\(O_{i}\), \(T_{i}\)) is a matching pair which can map the point frome original picture to camera view. In order to obtain the feature point pairs as acrrcate as possible, two types of feature points extraction methods are discussed below.
# Form-Finding Analysis of the Rail Cable Shifting System of Long-Span Suspension Bridges
## 2 Mechanical Characteristics of the RCS System under the Non-Loading Condition
### Solution Analysis of the System  
Before the tensioning of the rail cable, the longitudinal position \(l_{g}\) of the two anchorage points, the pretension force \(T_{g0}\) and the unstressed cable length of the sling are known. After the installation of the sling, the cable force, line shape, cable tower distance \(l_{z}\), and the height difference \(h_{z}\) can be computed according to actual conditions. On this basis, other unknown variables can be solved by an iterative method. The iterative scheme is shown below:  
1. Assume the horizontal force \(H_{g0}\) and the elevation \(l_{g0}\) at the left support of the rail cable; the horizontal force \(H_{z0}\) at the left support of the main cable, and the increment of the cable force of the sling \(P_{g1}\);
2. Obtain \(H_{gi}\), \(H_{zi}\) from Equations (4) and (11);
3. Substitute them into Equations (10), (13), (7), (8), and (9); then \(V_{g0}\), \(V_{g1}\), \(l_{g1}\), \(h_{g1}\), and \(T_{g1}\) are obtained;
4. From the coordination relation of deformation, \(Y_{g1}\), \(Y_{z1}\) and \(h_{z1}\) are obtained by combing with Equations (15), (17), and (4), respectively;
5. Substitute \(Y_{g1}\), \(Y_{z1}\) and \(h_{z1}\) into Equations (1) and (2); \(V_{z0}\) and \(l_{z1}\) can then be obtained;
6. Substitute \(V_{z0}\) and \(l_{z1}\) into Equations (16), (6), and (13), \(P_{z1}\); \(V_{z1}\) and \(V_{g2}\) can then be obtained;
7. Substitute \(P_{z1}\); \(V_{z1}\) and \(V_{g2}\) the results of step 6 into Equations (2), (1), (10), (7), and (8); \(h_{z2}\), \(l_{z2}\), \(T_{g2}\), \(l_{g2}\), and \(h_{g2}\) can be acquired, respectively;
8. Substitute them into Equations (4), (15), and (17); \(Y_{z2}\), \(Y_{g2}\), and \(P_{g2}\) are then obtained;
9. Following the similar iterative manner, all values of \(l_{gi}\), \(l_{zi}\), \(h_{gi}\) and \(h_{zi}\) are obtained;
10. Check convergence conditions \(\Delta_{z1}=|\sum_{i=1}^{n}l_{zi}|-l_{z}\leq\varepsilon\) (\(\varepsilon=1.0\times 10^{-6}\) is the given error limit), \(\Delta_{z2}=|\sum_{i=1}^{n}h_{zi}|-h_{z}\leq\varepsilon\), \(\Delta_{g1}=|\sum_{i=1}^{m}l_{gi}|-l_{g}\leq\varepsilon\), and \(\Delta_{g2}=|\sum_{i=1}^{m}h_{gi}|-h_{g0}\leq\varepsilon\). If they are satisfied, go to the next step. If not, let \(H_{z0}=H_{z0}+\Delta_{z1}/DH_{z}\), \(P_{g1}=P_{g1}+\Delta_{z2}/DP_{g}\), \(H_{g0}=H_{g0}+\Delta_{g1}/DH_{g}\) and \(h_{g0}=h_{g0}+\Delta_{g2}/DH_{g}\), and return to step 1, iteratively recalculating, where \(DH_{z}\), \(DP_{g}\), \(DH_{g}\), and \(DH_{g}\) are respectively the first derivatives of \(H_{z0}\), \(P_{g1}\), \(H_{g0}\), and \(h_{g0}\)[38].  
Then, all the sling forces and the coordinates of each node are determined. Substituting the results into Equations (9) and (12), the unstressed cable length and the anchorage point elevation of the rail cable can be obtained. A flow diagram for the calculation is shown in Figure 5.
# In-Field Quantum-Protected Control-Based Key Distribution with a Lossy Urban Fiber Link
## 3 Experimental Setup
At [PERSON]’s side, the signal is first passed through a 50 GHz preliminary bandpass filter before entering an erbium-doped fiber amplifier (EDFA), then through a narrowband fiber Bragg grating (FBG) filter, and finally detected by a photodetector. The output from the photodetector is converted into digital data by an analog-to-digital converter (ADC) on [PERSON]’s computer. Optical time-domain reflectometer (OTDR) is connected to the line via an optical switch on [PERSON]’s side. side, while the events highlighted in pink are caused by the connectors on [PERSON]'s side. Since these three connection points are located within controlled users' environments, we assume that [PERSON] cannot exploit the associated losses. From the trace in Figure 2, we estimate the total proportion of local losses intercepted by the adversary, \(r_{\text{E}}\), to be \(20.4\pm 0.2\%\) which corresponds to \(0.993\pm 0.001\,\text{dB}\). The precision of the OTDR is \(10^{-3}\,\text{dB}\). The utilized computational loss determination technique, which particularly implies identifying the start and end points of each local loss feature, is detailed in Section7.1. While the OTDR provides a detailed loss map every 500 sendings of bits (Steps 1-3 of the protocol), real-time loss monitoring is carried out via transmitometry. In this procedure, a sinusoidally modulated signal at frequency 25 MHz with duration 5 ms and power 200 nW is sent once per sending of bits. By comparing the spectral power at the modulation frequency at both the input ([PERSON]'s side) and the output ([PERSON]'s side), the users determine the total loss in the line. From the OTDR reflectogram, they already know the natural losses; subtracting these yields the interceptable loss \(r_{\text{E}}\). Modulating the signal helps mitigate \(1/f\) noise. As detailed in Methods, transmitometry measurements remain accurate as long as system's parameters, namely EDFA's amplification coefficient, remain calibrated. Therefore, before the impact of parameters' drift becomes comparable to the intrinsic power measurement uncertainty, the users must perform a new OTDR measurement and recalibrate transmitometry.
## 4 Discussion  
Our results showed the variation of the retrieval accuracies using several empirical parameters in the model of the data-driven algorithm. Firstly, as shown in Figures 4 and 5, more accurate retrievals can be achieved using wider fitting windows. This is largely because the broader spectral windows may contain more information about the absorption line [32], while reducing the influence of instrumental noise [19]. However, a wide window is not always correlated with an accurate retrieval, which can be explained by the fact that it is more difficult to accurately fit the variation of radiance in a wider window [19]. Secondly, the retrieval accuracy increased first and then decreased with the increase of n\({}_{\text{p}}\), while the optimal retrieval was achieved when n\({}_{\text{p}}\) is 2 or 3. This is easy to understand, for the low-frequency part of the vegetation reflectance spectrum has a fixed smooth shape, which can be well fitted by a 2- or 3-order polynomial in a fitting window narrower than 30 nm [19]. Besides, the accuracy of retrieval first increases and then decreases as n\({}_{\text{SV}}\) increases from 2 to 20. This is because the variance that can be explained by feature vectors increased with n\({}_{\text{SV}}\) at first and gradually saturated [12]. Most of the variance of the training set can be carried out by our selected feature vectors (99.89% of the variance can be explained by the first four feature vectors at far-red band, while the first seven feature vectors at red band  
Figure 10: Fitting (**a**,**c**) radiance spectra and (**b**,**d**) residual error using (**a**,**b**) far-red fitting window (735–758 nm) and (**c**,**d**) red fitting window (682–697 nm) when SIF is fitted and not fitted.  
can explain 99.99% of the variance). The subsequent feature vectors can only explain less than 0.2% of the variance, which mostly comes from the random noise of the instrument.  
It has to be admitted that our set of parameters is only designed for SIFIS-like data and may not apply to other instruments with different spectral characteristics. For satellites with higher spectral resolution (<0.1 nm), it is better to use a narrower fitting window, which only covers the solar Fraunhofer lines [11; 15; 18]. Nevertheless, the reasonable RMSE and the ability of the forward model to fit the radiance spectra indicate that accurate and robust retrievals can be achieved using our optimal parameters at both far-red and red bands. It can also be expected that the optimal combination of empirical parameters in this paper can not only be applied to TECIS-1, but also have the potential of SIF retrieval onboard other satellites to be launched, such as the FLUorescence EXplorer (FLEX) [33] and the Tropospheric Emissions: Monitoring of Pollution (TEMPO) [34] geostationary mission.
## 3 Methodology  
The primary goal of this study is to determine whether smartphone images can yield accurate results for tongue analysis, enabling the provision of a preliminary diagnosis whenever and wherever needed. Tongue images captured by an individual and symptoms mentioned by him or her can aid the medical professional in giving a preliminary line of treatment before directing further detailed investigations. We collaborated with two consulting physicians to collect data over a two-month period using smart phone devices with a camera resolution greater than or equal to 8 megapixels. Analysis and identification of the images to be labelled with single and/or multiple disease labels for each case are performed.  
We classified seven common ailments, each with a significant number of samples, and grouped the remaining diseases diagnosed, with few samples, under a single label, 'others'. The doctor's diagnosis serves as the ground truth for the classification task. The approach used for multilabel classification starts with the initial segmentation of the tongue area from the complete image. Thereafter, a binary classification architecture distinguishes an unhealthy tongue from a healthy one, followed by final disease label prediction using a multi-label classifier model. The workflow for implementing a multi-label classification model is illustrated in Figure 1. Subsequent sub-sections follow, providing brief details of all the steps involved. The strategy for the deep learning classification models used is as follows:  
* Stratified 5-fold cross-validation for disease risk classification.
* Ensemble learning strategy: bagging to reduce variance in data.
* Upsampling of the dataset to set some minimum sample size in each class.
* Extensive real-time data augmentation is needed for training models.
* Class-weighted focal loss to tackle class imbalance.
* Individual training for multi disease classification and disease risk detection.
# An Aquatic Mobile Sensing USV Swarm with a Link Quality-Based Delay Tolerant Network
## 3 Proposed Architecture
### Network Elements
* **LoRa Comm Manager:** This module deals with LoRa technology, since the Wi-Fi is managed inside the DTN operation Processes. * **mOVE:** This module is responsible for implementing the DTN architecture, and also managing the routing algorithm. The embedded Wi-Fi manager is responsible for finding neighbors and storing data until a neighbor is available. * **Sensor Controller:** This module manages all the sensors' drivers, forwards the data to the respective modules, collects the data, and fuses it to filter out noise. * **Thrusters Controller:** This module uses the software from the _UUV Simulator_ for the simulation, and also implements drivers for the real motors in the USV. It takes a waypoint and it is responsible for navigating the USV towards that point. The software and hardware were designed in a modular architecture to give versatility and adaptability to the drone, so that the addition of new functionalities and sensors can be accomplished easily. In this platform, beyond the mobile gateways that allow the data gathering inside the swarm to the infrastructure, there are the infrastructure gateway stations that are connected to a server that collects the data, stores it in a database for the use in other platforms, and processes the data. These stations also have the mOVE and LoRa Comm Manager modules in order to communicate with the mobile network elements and provide the DTN end-to-end path.
## 3 Methodology
### Water-Pumping Subsystems  
In this study, two pumps were used in parallel. The reason behind this choice is that when two or more fixed speed centrifugal pumps are operating in a parallel mode, then the individual curves are combined to form the total pump curve. Installations where several pumps are used in parallel operation are a common practice in particular in transporting water and wastewater applications. In these cases, parallel operation of centrifugal pumps is used as it allows adjusting of flow rate according to demand quantities by switching on and off the necessary pumps units to fulfil the demand requirements of each occasion. In real life operation of pumps in parallel, the system curve will change slightly as the system losses are varying with the square of the flow rate. Therefore, the combined output of two pumps will always be less than twice the flow rate of the single pump.
# Fluorescence Hyperspectral Imaging for Early Diagnosis of Heat-Stressed Ginseng Plants
## 1 Introduction
Ultraviolet or blue illumination is a vital tool for enabling fluorescence applications to assess the chlorophyll composition in plants under stress conditions, in which the ultraviolet excitation radiation generates two broad spectra emissions: blue-green at a range between 400-600 nm, and red and far-red at 600-750 nm [39]. Since the responses of plants' fluorescence characteristics vary depending on the composition of the leaves, it is essential to identify the samples' optimal excitation and emission wavelengths. However, fluorescence-based systems suffer from the limitations of acquiring data under dark conditions [40]. Thus, it is recommended that field-based measurement data acquisition should be performed at night [41; 42]. The overall objective of this study was to investigate the potential fluorescence HSI for the early detection of stressed ginseng plants--specifically, (i) to develop fluorescence HSI for the prediction of heat-stressed ginseng plants based on measured SPAD greenness quantity, (ii) to extract the spectral information from plants and define the optimal wavebands for the further processing of HSI data based on the F-values computed via analysis of variance (ANOVA), and (iii) to visualize and predict chlorophyll composition based on the regression coefficient determined via partial least squares regression (PLSR).