esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing the Parameters of Long Short-Term Memory Networks Using the Bees Algorithm
## 2 Long Short-Term Memory (LSTM)
### Gaps and Open Issues  
One of the most important challenges in training the LSTM is reducing overfitting [13], which is the case where the model fits well enough for training data and performs poorly in validation and testing data so that it is not able to generalize to unseen data [14]. This issue can be overcome by adding a dropout layer [15; 16], trying more optimal regularization values [17], reducing the number of epochs, or augmenting the datasets [13].  
The exploding and vanishing gradient is a problem that occurs in RNNs when the model stops learning after a specific epoch, and the LSTM addresses this issue [9]. However, the optimum weight change can be improved further by having a more optimum learning rate as the gradient is multiplied by the learning rate, which results in the optimum set of weights [18]. Thus, having a more optimum learning rate means more optimum updates for the network weight [11].  
Figure 1: The basic structure of the LSTM [9].  
Figure 2: Intuitive diagram of the LSTM [8].  
Furthermore, improving the performance of the LSTM network is an ongoing challenge [19], as there is no approach found yet that develops the best architecture. However, adopting nature-inspired algorithms for optimizing the LSTM parameters may develop the model performance, as it reduces the need for human input when assigning the parameters [20]. The following section will present state-of-the-art studies on using nature-inspired algorithms to optimize LSTM parameters.
# Online Distributed User Association for Heterogeneous Radio Access Network
## 6 Performance Evaluation
### Jain's Fairness  
Jain's fairness index of UEs' received data rate in a network shows the overall fairness. The fairness index becomes 1 when all UEs receive the same rate. Figure 9(a),b shows the Jain's fairness index of UEs' received data rate for uniform and non-uniform deployment respectively. As expected, the Jain's fairness index for _max-SINR_ in uniform deployment is 0.1, which is very low. Unlike _max-SINR_, our proposed schemes perform better. The fairness index nearly equals to 0.6 for all the proposed schemes. The same outcome can be observed in the non-uniform UE deployment scenario.  
Figure 8: Normalized minimum rate for different association schemes in different deployment scenarios.  
Figure 9: Normalized sum rate for different association schemes in different deployment scenarios.  
User mobility is an unavoidable condition in a network. A network needs to perform well in a mobility environment. We considered Jain's fairness index for the UE share in different schemes to show our performance on the mobility. A network is said to be balanced in a mobility environment if the fairness index is high in different time instances. We monitored the network for ten time instances and noted the fairness. We adopted a random walk for the UEs. Figure (a)a shows the Jain's fairness index in a mobility environment for uniform UE deployment. As soon as the UE changes its location and finds a better BS (higher SINR), it changes its serving BS. This type of association sometimes gets worse if all the UE moves closer to the macro BS. There is stability in _d-choices load1_ and _d-choices load2_ schemes. Each time the UE encounters a different load environment, it tries to balance the load of the network. For non-uniform UE deployment, the UEs are restricted to moving around the BS. Figure (b)b shows the Jain's fairness for non-uniform deployment. Both _d-choices_ schemes continue to show good results, and there is slight poor performance by the _Prob. schemes_, but it is better than _max-SINR_.  
In Figure (a)a,b, we show a comparison of the performance of our proposed schemes with the optimal value. For the comparison, we consider the sum log-utility parameter. It is evident that the sum utility of our proposed schemes show near-optimal performance for both uniform and non-uniform deployment, which ensures the effectiveness of our proposed schemes.  
Figure 11: [PERSON]’s fairness for different association schemes in different deployment scenarios.  
Figure 10: [PERSON]’s fairness index of UE rate for different association schemes in different deployment scenarios.
## 4 Discussion
### Superiority of Time Series NDVI Approach
#### 4.2.2 Effect of Terrain Factors on SOC Prediction
In particular, remote sensing images require high temporal and spatial resolutions, which cause immense difficulty to high-precision digital soil mapping.
## 4 Results
### Correlation between GPP and the Variables
It indicates a factor near 1 that can be said to be closely related to the presence of groundwater. As shown in Table 4, the most important variable affecting groundwater productivity (both T and SPC values) when applying the BRT and RF models is land cover. Conversely, the least influential variable in the case of both models is plan curvature. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multicolumn{2}{c}{**Boosted Regression Trees**} & \multicolumn{2}{c}{**Random Forest**} \\ \cline{2-5}  & \(\mathbf{T\geq 2.61}\) & \(\mathbf{SPC\geq 4.88}\) & \(\mathbf{T\geq 2.61}\) & \(\mathbf{SPC\geq 4.88}\) \\ \hline Land cover & 1.000000 & 1.00000 & 1.00000 & 0.823951 \\ \hline Relative slope position & 0.250930 & 0.690807 & 0.139181 & 0.698339 \\ \hline Hydraulic slope gradient & 0.282250 & 0.509170 & 0.231625 & 0.723901 \\ \hline Depth of groundwater & 0.176912 & 0.484824 & 0.121499 & 0.970446 \\ \hline Slope gradient & 0.297003 & 0.480254 & 0.302406 & 0.677954 \\ \hline Distance from channel network & 0.259162 & 0.457526 & 0.109057 & 0.845124 \\ \hline Hydrogeology & 0.371401 & 0.455671 & 0.113639 & 0.843345 \\ \hline Topographic Wetness Index (TWI) & 0.217793 & 0.349910 & 0.255783 & 0.642187 \\ \hline LS-factor & 0.251877 & 0.323731 & 0.250476 & 0.850150 \\ \hline Terrain Ruggedness Index (TRI) & 0.332975 & 0.311433 & 0.162339 & 0.723926 \\ \hline Distance from fault & 0.170614 & 0.302349 & 0.523101 & 0.933228 \\ \hline Soil texture & 0.181788 & 0.301434 & 0.556208 & 1.000000 \\ \hline Drainage basin & 0.169170 & 0.253417 & 0.149756 & 0.841819 \\ \hline Line density & 0.101006 & 0.214975 & 0.260951 & 0.612850 \\ \hline Distance from lineament & 0.114429 & 0.201170 & 0.174736 & 0.403119 \\ \hline Convergence index & 0.061917 & 0.135385 & 0.155390 & 0.347651 \\ \hline Valley depth & 0.155604 & 0.133019 & 0.070290 & 0.532714 \\ \hline Plan curvature & 0.052066 & 0.060235 & 0.134918 & 0.392855 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Predictor of importance factor of the Boosted Regression Tree (BRT), Random Forest (RF) models.
# Urban Plants Classification Using Deep-Learning Methodology: A Case Study on a New Dataset
###### Abstract  
Plant classification requires the eye of an expert in botanics when the subtle differences in stem or petals differentiate between different species. Hence, an accurate automatic plant classification might be of great assistance to a person who studies agriculture, travels, or explores rare species. This paper focuses on a specific task of urban plants classification. The possible practical application of this work is a tool which assists people, growing plants at home, to recognize new species and to provide the relevant caring instructions. Because urban species are barely covered by the benchmark datasets, these species cannot be accurately recognized by the state-of-the-art pre-trained classification models. This paper introduces a new dataset, Urban Planter, for plant species classification with 1500 images categorized into 15 categories. The dataset contains 15 urban species, which can be grown at home in any climate (mostly desert) and are barely covered by existing datasets. We performed an extensive analysis of this dataset, aimed at answering the following research questions: (1) Does the Urban Planter dataset provide enough information to train accurate deep learning models? (2) Can pre-trained classification models be successfully applied on Urban Planter, and is the pre-training on ImageNet beneficial in comparison to the pre-training on a much smaller but more relevant dataset? (3) Does two-step transfer learning further improve the classification accuracy? We report the results of experiments designed to answer these questions. In addition, we provide the link to the installation code of the alpha version and the demo video of the web app for urban plants classification based on the best evaluated model. To conclude, our contribution is three-fold: (1) We introduce a new dataset of urban plant images; (2) We report the results of an extensive case study with several state-of-the-art deep networks and different configurations for transfer learning; (3) We provide a web application based on the best evaluated model. In addition, we believe that, by extending our dataset in the future to eatable plants and assisting people to grow food at home, our research contributes to achieve the United Nations' 2030 Agenda for Sustainable Development.  
Keywords:plant classification; deep-learning neural networks; transfer learning; plants dataset +
Footnote †: journal: _Acticle_
# A Deep Learning Model for Detecting Diabetic Retinopathy Stages with Discrete Wavelet Transform
## 2 Literature Review
On the Kaggle dataset, their strategy outperforms other cutting-edge techniques with an accuracy of 88.3%. Transfer learning has been utilized in the domain of DR to overcome the limitation of having little annotated data available to train deep learning models. The researchers utilized AlexNet, InceptionV3, VGG16, DenseNet169, VGG19, and ResNet50, trained on large datasets such as ImageNet, as feature extractors for DR pictures [36; 37; 38; 39; 40]. To achieve a highly precise DR classification, the pre-existing models were adjusted and optimized using a labeled dataset specifically designed for DR, such as the APTOS, EyePACS, and Messidor datasets. [PERSON] et al. [29] investigated a transfer learning method for assessing DR without needing a source. The researchers employed a pretrained CNN model and adjusted its parameters on a limited number of retinal pictures without needing more source data. The evaluation uses the EyePACS and Messidor datasets, surpassing other cutting-edge methodologies in accuracy and sensitivity for assessing DR.  
Data augmentation is widely regarded as the predominant technique for addressing the problem of imbalanced data in image classification tasks [41]. It involves creating new data components from current data to enhance the data artificially. Image augmentation techniques, such as cropping, rotating, zooming, vertical and horizontal flip, width shift, and rescaling, can modify the pictures [42]. [PERSON] [43] found an enhanced quality of transfer learning in DR classification using a data augmentation technique. Different datasets and CLAHE techniques were employed by [PERSON] et al. [44] for detecting DR. The images were preprocessed using the DWT method. According to their study, the individual datasets performed better than the combined ones. [PERSON] et al. [45] utilized a wavelet transform with a shifted window model on the APTOS dataset. Compared to the 98% accuracy of the binary classification, the multi-class classification has reached only 86%. Images were segmented by [PERSON] et al. [46], who combined the wavelet assessment, supervised classifier likelihoods, adaptive threshold methods, and morphology-based approaches. Different retinal diseases were monitored by [PERSON] et al. [47] by incorporating DWT in image processing. Another study by [PERSON] et al. [48] applied DWT with different machine learning classifiers, such as k-nearest neighbors (KNN) and support vector machine (SVM) models. They used Messidor data with binary classification. The challenges of DR detection include proper image processing methods and accurate model development to detect different datasets [49]. Most of the studies employed a single image set for training and testing. The common performance measures used to analyze the data are accuracy, recall or sensitivity, and precision [50]. Table 1 describes the inferences from previous DR-related studies.
## 2 Experimental Procedure
### Protocol Description  
We followed a four-week protocol which included a pre-test, three training sessions, and a post-test for the non-native participants (see Appendix A to see the content of the tests). Native speakers recorded the speech training corpus for the kASR system. At the beginning, the non-native subjects took part in the pre-test session individually in a quiet testing room. The utterances were recorded with a microphone and an audio recorder (the procedure was the same for the post-test). All the students took the pre-test under the sole supervision of a member of the research team. They were asked to read aloud the 28 minimal pairs administered via a sheet of paper with no time limitation ([[https://github.com/eca-simm/minimal-pairs-jaranol-eses-jpjp](https://github.com/eca-simm/minimal-pairs-jaranol-eses-jpjp)]([https://github.com/eca-simm/minimal-pairs-jaranol-eses-jpjp](https://github.com/eca-simm/minimal-pairs-jaranol-eses-jpjp)), accessed on 27 June 2021). The pairs came from 7 contrasts containing Spanish consonant sounds considered the most difficult to perceive and produce by native Japanese speakers (see more details in [19]): [9]-[f], [9]-[s], [fu]-[xu], [1]-[r], [1]-[r], [r]-[rr], and [fl]-[fr]. Students were free to repeat each contrast as many times as they wished if they thought they might have mispronounced them.  
From the same 7 contrasts, a total of 84 minimal pairs ([[https://github.com/eca-simm/minimal-pairs-jaranol-eses-jpjp](https://github.com/eca-simm/minimal-pairs-jaranol-eses-jpjp)]([https://github.com/eca-simm/minimal-pairs-jaranol-eses-jpjp](https://github.com/eca-simm/minimal-pairs-jaranol-eses-jpjp)), accessed on 27 June 2021) were presented to the experimental and in-classroom group participants in 7 lessons across three training sessions. The minimal pairs were carefully selected by experts considering the gASR limitations (homophones, word-frequency, very short words, and out-of-context words, in a similar process as in [8]). The lessons were included in the CAPT tool for the experimental group and during the class sessions for the in-classroom group (12 minimal pairs per lesson, 2 lessons per session, except for the last session that included 3 lessons; see more details about the training activities in [19]). The training protocol sessions were carried out during students' course lectures in the classroom, in which a minimal pair was practiced in each lesson (blocked practice) and most phonemes were practiced again in later sessions (spaced practice). Regarding the sounds practiced in each session, in the first one, sounds [fu]-[xu] and [1]-[r] were contrasted, then [l]-[r] and [r]-[rr], and the last session involved the sounds [fl]-[fr], [9]-[f], and [9]-[s]. Finally, subjects of the placebo group did not participate in the training sessions. They were supposed to take the pre-test and post-test and obtainresults without significant differences. All participants were awarded with a diploma and a reward after completing all stages of the experiment.
## 4 Application in Photoacoustic Detection  
A practical application research of the interference system was implemented. Figure 8 shows the schematic diagram of the application of the photoacoustic detection. The schematic diagram consists  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(I_{1}\) & \(I_{1}\)+\(I_{2}\) & \(\sqrt{I_{2}}\) & \
\begin{tabular}{c} **Output** \\ **Signal** \\ \end{tabular} &
\begin{tabular}{c} **Normalized** \\ **Result** \\ \end{tabular} \\ \hline
0.09 & 0.18 & 0.30 & 0.56 & 1.86 \\
0.09 & 0.15 & 0.22 & 0.42 & 1.91 \\
0.09 & 0.11 & 0.14 & 0.27 & 1.92 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The list of measured and calculated results, in V.  
Figure 7: The time-domain and frequency-domain waveforms of the experimental signals: the (**a**) excitation signal; (**b**) excitation spectrum; (**c**) output signal; and (**d**) output signal spectrum.  
of two parts: an excitation part and detection part. In the excitation part, a Q-switched Nd:YAG laser (Continuum, Surelite I-20) was used to excite the acoustic pressure waves within the sample. It emits a train of 8-ns duration pulses at a 532-nm wavelength, with a repetition rate of 20 Hz and a maximum energy of 160 mJ per pulse. The excitation beam was coupled to the liquid-core fiber through a lens, and it illuminated the sample. The detection part is same as that shown in Figure 3. The detection beam from the self-designed probe, which was focused onto the sample surface and phase-shifted by the acoustic pressure waves, was recoupled into the same probe. The outputs of PD1 and PD3 passed through an oscilloscope with a sampling frequency of 100 MHz.  
The sample was made by mixing and heating 95 mL deionized water, 2 g agar powder and 3 mL 20%-Intralipid. As the photoacoustic wave source, a hair was embedded in the sample. Figure 9 shows the detected displacement due to a photoacoustic wave, which is related to the photoacoustic pressure. The depth of the hair can be obtained through further analyses. If a point-by-point scanning mechanism is implemented, a three-dimensional image can be reconstructed. Note that the optical absorption and scattering coefficients that were made here of the sample are similar to those of biological tissues. Consequently, the proposed experimental system has an application value in biomedical photoacoustic imaging. The biggest advantage of the proposed experimental system is that it can detect an acoustic wave pressure with a non-contact style, which is very useful for trauma and internal biological tissue detection. In addition, the experimental system has the advantages of electromagnetic immunity, a wide bandwidth and a focal point with an optical focus size.
## 3 Test Results
### The Results of the Middle Cases  
The data of all the middle void specimens are summarized and presented in Figure 7. Among them, only the specimen of Case 1 did not experience failure, while the remaining specimens all suffered from failure. Case 5 exhibited the smallest ultimate stress, while Case 2 exhibited the largest ultimate stress. Overall, the ultimate stress appears to be inversely proportional to the angle. Except for Case 5, which exhibited a period of minimal deformation before failure, all the specimen curves were linear, with very small differences  
Figure 6: Calibration point distances for each stage of Case 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stage (N)** & **Stress** & **Mean Length** & **Displacement** & **Strain** \\ \hline
0 & 6.94 & 77.02 & 0 & 0.00 \\
200 & 6.95 & 78.37 & 1.35 & 1.75 \\
400 & 13.9 & 79.13 & 2.11 & 2.75 \\
600 & 20.84 & 79.78 & 2.75 & 3.58 \\
800 & 27.79 & 80.78 & 3.76 & 4.88 \\
1000 & 34.73 & 81.94 & 4.91 & 6.38 \\
1200 & 41.68 & 83.15 & 6.13 & 7.96 \\
1400 & 48.63 & 84.10 & 7.07 & 9.18 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The displacement (mm) and stress (MPa) vs. strain (%) data for Case 3.  
in the slope. Cases 2-4 maintained consistency before failure, and the difference in ultimate stress at failure was not significant. At the point of failure, the strain ranged from 0.07 to 0.09 for all specimens.  
The specific data of Figure 7 are shown in Table 6. The stress-strain curves are approximately linear, allowing us to analyze the slope of the stress-strain curve to determine the elastic modulus of the membrane material tested in the experiment, which is approximately 417.3 MPa. This value is close to the elastic modulus of the membrane material (425.0 MPa) obtained from previous data, which were obtained from the company. Combining the above data, it can be concluded that when the edge of the membrane material is damaged, it has a reliable load-bearing capacity within a strain range of 0.0-0.1; when the middle of the membrane material is damaged, it has a reliable load-bearing capacity within a strain range of 0.00-0.07. The data indicate that as the angle of the void increases, the load-bearing capacity of the membrane material significantly decreases, and the ultimate stress slightly decreases. This may be because the increasing angle of the void gradually reduces the effective width of the membrane material, thereby weakening its load-bearing capacity. Additionally, the reduction in the effective width may lead to the damage of the fiber fabric, thereby reducing the overall stiffness and tear resistance of the membrane material.
## 6 Network Layer for WVSN  
The proposed network layer provides two services: _single-path routing protocol_ and _dropping scheme_. Proposed routing protocol finds a single-path adapted with nodes status (energy and topology) and data importance for video transmission. Dropping scheme is a policy for reducing bandwidth and energy consumption of nodes without compromising the quality of services such as video quality. This scheme works by dropping low priority packets with regard to node-Sink distance. Single-path video routing is provided using inter-layer/inter-node command messages which are received from transport layers and negotiating with remote node. Dropping scheme is provided by using information embedded in data packets in compression sub-application layer and routing protocol like priority level of the data and hierarchy level of the capture node.
# Reliability and Efficiency of Metamodel for Numerical Back Analysis of Tunnel Excavation
## 1 Introduction
In the present study, the reliability of the metamodel was confirmed according to the number of values of each dependent variable using five ground parameters as the dependent variables and vertical displacement of the tunnel crown as the independent variable. The five ground parameters are as follows: unit weight (\(\gamma\)); uniaxial compressive strength (UCS); material constant m; geological strength index (GSI); and coefficient of lateral pressure (K). In addition, the priorities of the parameters used in the metamodel were confirmed through global sensitivity analysis (GSA).
# FMT: Unsupervised RGB-D Point Cloud Registration via Fusion Feature Matching with Transformer
## 3 Materials and Methods
### Differentiable Rendering  
The objective of this step is to render RGB-D images from the aligned point cloud, thereby generating supervisory signals based on photometric and geometric consistency. The key idea is that if the estimated pose is correct, the rendered image should align with the input image. This is formally expressed as follows:  
\[\begin{cases}1/Z^{s}K\hat{T}^{s}p_{xyz}^{s}=[u^{t},v^{t},d^{t}]^{T}\Longrightarrow Depth_{R}^{t}\\ 1/Z^{t}K\hat{T}^{-1}P_{xyz}^{t}=[u^{s},v^{s},d^{s}]^{T}\Longrightarrow Depth _{R}^{s}\\ 1/Z^{s}K\hat{T}^{s}p_{xyz|RGB}^{s}=[u^{t},v^{t},d^{t}]^{T}|RGB\Longrightarrow RGB _{R}^{t}\\ 1/Z^{t}K\hat{T}^{-1}P_{xyz|RGB}^{t}=[u^{s},v^{s},d^{s}]^{T}|RGB\Longrightarrow RGB _{R}^{s}\end{cases} \tag{4}\]  
where \(K\) denotes the intrinsics of camera, and \(Depth_{R}^{t},Depth_{R}^{s},RGB_{R}^{t},RGB_{R}^{s}\) represent the rendered target depth image, source depth image, target RGB image, and source RGB image, respectively. \(u,v,d\) represent the horizontal position, vertical position, and depth value of a pixel, respectively.