esa-sceva/satcom-chunk-collection huggingface dataset:
# A Machine Learning Approach for the Prediction of Thermostable \(\beta\)-Glucosidases
## 3 Materials and Methods
### Data Collection  
I searched the UniProt database [37] for the E.C. Number code of \(\beta\)-glucosidase (E.C. 3.2.1.21). Thus, 76,582 \(\beta\)-glucosidase enzyme sequences were downloaded, 76,362 of which were extracted from TrEMBL (unreviewed) and 220 from Swiss-Prot (reviewed). Then, regular expressions were used to detect thermophilic organisms. To this end, a search for the substring \"therm\" was performed in lines starting with \"\(>\)\" and having the pattern \"OS=\". I hypothesized that this simple strategy could be used to detect thermophilic organisms. For example, the sequence B9K7M5 of the organism _Thermotoga neapolitana_ met these requirements. A total of 1563 sequences were obtained. Finally, I randomly selected 1500 sequences from thermophilic organisms (i.e., I discarded only 63). I also randomly selected another 1500 sequences from likely non-thermophilic organisms (i.e., sequences that did not have the substring \"therm\" in their header). I then split this dataset (\(n\) = 3000) into balanced training (\(n\) = 2000) and testing (\(n\) = 1000) subsets.  
Furthermore, \(\beta\)-glucosidase enzymes of the GH1 family are approximately 400 residues long. A manual analysis of the collected structures revealed that some structures were incomplete, and others were complexed with other structures. Therefore, I removed structures with less than 200 amino acid residues and more than 800 amino acids. Then, I used the AlphaFold Protein Structure Database [38] API to download the three-dimensional structures corresponding to each sequence. However, some structures could not be found in this database (total instances not found: 537 in the training database and 262 in the test database).  
In conclusion, I collected 1717 structures from thermophilic (\(n\) = 890) and non-thermophilic (\(n\) = 827) organisms and divided them into two datasets: training and test (Figure 1). The training dataset has \(n\) = 1134, with 589 positive (thermophilic) and 545 negative (non-thermophilic). The test dataset has \(n\) = 583, with 301 positive (thermophilic) and 282 negative (non-thermophilic).
## 3 Results
### Validation of the FQE Retrieval Method  
For a better evaluation of the performance, the _RMSE_ and squared Pearson correlation coefficient (\(R^{2}\)) at each wavelength were calculated. Figure 6 quantifies the _RMSE_ and \(R^{2}\) for the measured vs. modeled (gray line) and measured vs. improved (blue line with stars) fluorescence for all the species of leaves considered in this study. The leaves are classified into high top (middle) up (down), low top (middle) up (down), high total, and low total groups. The high (low) total here indicates the up- plus down-layer leaves for high (low) tops. From the figure, the _RMSE_ of the improved curve decreases to values lower than 0.001 in the near-infrared region, and the \(R^{2}\) is mostly above 0.9. Thus, the model performs reasonably well.  
Relative to our findings, the results in [PERSON] et al. (2016), in which \(\Phi\)F was maintained at the default value of 0.01 in all simulations, are inversed. The model relies on optical properties retrieved from the reflectance and transmittance to simulate the fluorescence spectra; in their study, 66 leaves of two varieties were measured, and their fluorescence spectra were simulated. Using the values of ChIF at 685 nm and 745 nm, the comparison of \(F_{b}\)/\(E_{tot}\), \(F_{b}\)/\(E_{tot}\), and \(F_{f}\)/\(F_{b}\) at 685 nm and 745 nm showed that the optical efficiency of most leaves could be explained by radiative transfer. For example, the simulated near-infrared \(F_{b}\) increases with the retrieved chlorophyll content (\(C_{ab}\)), while the red \(F_{f}\) decreases due to reabsorption. They considered the re-calibration of the specific emission spectrum ([PERSON] et al., 2017), which allows  
Figure 6: The RMSE and R\({}^{2}\) of the measured vs. modeled (gray line) and measured vs. improved with the FQE inversion code (blue line) simulations for two soybean varieties per wavelength: 1 st and 3 rd row are RMSE and \(R^{2}\) for WT high top (middle) up (down) and total high leaves; 2 nd and 4 th row are RMSE and \(R^{2}\) for MG low top (middle) up (down) and total low leaves. H: high, L: low, T: top, M: middle. The high (low) total indicates all the high (low) top and middle up- and down-layer leaves together.  
a better fit without the need for additional coefficients. However, the \(R^{2}\) in the comparison of the simulated and measured values is less than 0.9. In our study, for better improving the spectral fit of Fluspect, 102 leaves located at the top and middle of the canopy with different chlorophyll contents were selected. By changing the ChlF efficiency and comparing the measured and simulated values via \(F_{b}\)/\(E_{tot}\), \(F_{b}\)/\(E_{tot}\), and \(F_{b}\)/\(F_{b}\) at 685 nm and 745 nm, \(R^{2}\) reached more than 0.9. The results show that the change in radiative transfer is one of the factors causing the optical efficiency, and the FQE parameters are also very important for simulating ChlF.
## 2 Materials and Methods
### Short Overview of Reliability Methods
#### 2.5.1 Analytical First Order Second Moment (FOSM) and Taylor Series  
Analytical FOSM and the Taylor Series methods belong to the FOSM method group. Only the first terms of a Taylor series expansion of the performance function are used. They are used to estimate the expected value (Equation (15)) and variance (Equation (16)) of the performance function (F). If the number of uncertain variables is N, this method requires evaluating N partial derivatives of the performance function [3]. The expressions for expected value and variance are defined as follows:  
\[\mathrm{E[F]}=\mu_{\mathrm{F}}\approx\mathrm{F(X_{1},X_{2},\ldots,X_{n})} \tag{15}\]  
\[\sigma_{\mathrm{F}}^{2}\approx\sum_{i=1}^{n}\left(\frac{\partial\mathrm{F}}{ \partial x_{i}}\right)^{2}\sigma_{x_{1}}^{2} \tag{16}\]  
With calculated mean and variance, the reliability index \(\beta\) can be determined as follows:  
\[\beta=\frac{\mathrm{E[F]}-1}{\sigma_{\mathrm{F}}}\left(\mathrm{F\ is\ normally\ distributed}\right) \tag{17}\]  
\[\beta=\frac{\ln\left(\frac{\mathrm{E[F]}}{\sqrt{1+\sigma_{\mathrm{F}}^{2}}} \right)}{\sqrt{\ln\left(1+\sigma_{\mathrm{F}}^{2}\right)}}\left(\mathrm{F\ is\ longormally\ distributed}\right) \tag{18}\]  
The function F could be any relevant function, but for the purposes of this paper it is the factor of safety. The second-moment estimation (Equation (16)) is simplified in Taylor Series methods. It is assumed that this simplification can result in a large error, especially in cases where the \(\mathrm{F(X_{1},X_{2},\ldots,X_{n})}\) is a mathematically complex function. To estimate the error of FOSM methods without simplification in the second-moment estimation, direct calculation evaluating the partial derivatives with respect to all random variable is performed. In the Taylor Series method, the second-moment is approximated using the following formula:  
\[\sigma_{\mathrm{F}}^{2}\approx=\sum_{i=1}^{n}\left(\frac{\Delta\mathrm{F_{i}}} {2}\right)^  
normal variables, while the limit state function is transformed into standard normal space. The reliability index is then defined as the shortest distance from the origin to the failure surface (\(\text{g}(\text{U})=0\)) in standard normal space (Figure 4a).  
An iterative procedure (Newton-Rapson algorithm), shown in Figure 4b, was used to calculate the reliability index. The iterative procedure was repeated until all three conditions were met in terms of error size: \(\epsilon_{1},\epsilon_{2},\epsilon_{3}<10^{-5}\) (Figure 4b).
# Trackerless 3D Freehand Ultrasound Reconstruction: A Review
###### Abstract  
Two-dimensional ultrasound (2D US) is commonly used in clinical settings for its cost-effectiveness and non-invasiveness, but it is limited by spatial orientation and operator dependency. Three-dimensional ultrasound (3D US) overcomes these limitations by adding a third dimension and enhancing integration with other imaging modalities. Advances in deep learning (DL) have further propelled the viability of freehand image-based 3D reconstruction, broadening clinical applications in intraoperative and point-of-care (POC) settings. This review evaluates state-of-the-art freehand 3D US reconstruction methods that eliminate the need for external tracking devices, focusing on experimental setups, data acquisition strategies, and reconstruction methodologies. PubMed, Scopus, and IEEE Xplore were searched for studies since 2014 following the PRISMA guidelines, excluding those using additional imaging or tracking systems other than inertial measurement units (IMUs). Fourteen eligible studies were analyzed, showing a shift from traditional speckle decorrelation towards DL-based methods, particularly convolutional neural networks (CNNs). Variability in datasets and evaluation methods hindered a comprehensive quantitative comparison, but notable accuracy improvements were observed with IMUs and integration of contextual and temporal information within CNNs. These advancements enhance freehand 3D US reconstruction feasibility, though variability limits definitive conclusions about the most effective methods. Future research should focus on improving precision in complex trajectories and adaptability across clinical scenarios.  
freehand ultrasound; 3D reconstruction; deep learning; inertial measurement units (IMUs) +
Footnote †: journal: Medical Imaging
# Joint Hybrid Beamforming Design for Millimeter Wave Amplify-and-Forward Relay Communication Systems
## 2 System Model
#### Channel Model  
The mmWave channel model in [17; 18; 19; 20] is considered in this paper, which takes the following form:  
Figure 1: HD AF relay communication system.  
\[\mathbf{H}=\sqrt{\frac{N_{\mathrm{T}}N_{\mathrm{R}}}{L}}\sum_{l=1}^{L}\alpha_{l} \mathbf{a}_{r}(\varphi_{l}^{\mathrm{r}})\mathbf{a}_{t}^{H}\left(\varphi_{l}^{ \mathrm{t}}\right), \tag{2}\]  
where \(L\) denotes the number of propagation paths, \(\alpha_{l}\) denotes the complex channel gain of the \(l^{th}\) path. \(\mathbf{a}_{t}(\varphi_{l}^{\mathrm{t}})\) and \(\mathbf{a}_{r}(\varphi_{l}^{\mathrm{r}})\) are the transmitting and receiving array response vectors. \(\varphi_{l}^{\mathrm{t}}\) and \(\varphi_{l}^{\mathrm{r}}\) stand for the elevation angles of departure (AOD) and azimuth angles of arrival (AOA) for the \(l^{th}\) path, respectively. It is worth noting that our proposed algorithms can be applied to any antenna array. For simplicity, the commonly applied uniform linear array (ULA) with \(N\) elements are considered. The array response vector can be given by  
\[\mathbf{a}(\varphi)=\frac{1}{\sqrt{N}}[1,e^{j\frac{2\pi d}{\lambda}d\sin( \varphi)},...,e^{j(N-1)\frac{2\pi d}{\lambda}d\sin(\varphi)}]^{T}, \tag{3}\]  
where \(\lambda\) represents the wavelength and \(d\) stand for the distance between antenna elements, which is assumed to satisfy \(d=\lambda/2\). In this section, both \(\mathbf{H}_{1}\) and \(\mathbf{H}_{2}\) are modelled as (2).
# Using the Hall Effect for Monitoring the Starter Condition in Motor Vehicles
## 2 Analysis of the Magnetic Circuit and Distribution
### Resultant Magnetic Field of the Starter  
In the direct current engine, resultant induction in gap \(B_{\delta x}\) is the sum of induction in the gap from excitation current \(B_{\delta x}\) and induction in the gap from the armature current (from armature reaction) \(B_{\delta ax}\).  
\[B_{\delta x}=B_{\delta x}+B_{\delta ax}. \tag{13}\]  
The resultant induction is smaller under half of the pole piece as a result of magnetic circuit saturation. Figure 4 presents the distribution of the resultant induction.  
Figure 4: Resultant magnetic field of the direct current engine with brushes placed in axis \(q\).  
Figure 3: Magnetic induction \(B_{\delta ax}\) from armature current when the brushes are placed in axis \(q\).  
By simulating the operational wear of the starter sleeve that changes the armature gap, the distribution of the magnetic flux and the magnetic induction in the gap were determined. Figure 5a shows the spread of the magnetic flux for the starter R5 for the gap in a segment of the commutator. Figure 5b shows the distribution of the induction for an armature gap of 1 mm, as well as the damage in the form of the gap in two segments of the commutator and the worn sleeve of the starter R5. Figure 5c shows the distribution of the induction for the efficient starter. The new diagnostic method allows one to clearly identify the degree of the operational wear of the sleeves as well as the damage in the form of interruptions and short-circuits of the rotor windings. The simulation was performed on the real object using the QuickField program. Apart from starter dimensions, data on the type of excitation, the number of starter grooves, and the material magnetization curves for the rotor and stator were introduced to the program. A variable value of the armature gap was introduced, resulting from the wear of the sleeve. The simulation results indicate that the highest values of magnetic induction occur in the four sections of the yoke between the pole shoes and the yoke gap. For an efficient starter (Figure 5c), the maximum induction value is 0.8 T.
## 1 Introduction
#### Objectives of the Paper  
The main objectives of this research paper are outlined as follows:  
1. To conduct a comprehensive background study and present an enlightening literature review focusing on the advancements and challenges in the field of image captioning, particularly emphasizing the context of low-resource languages like Tamil.
2. To propose a novel methodology, titled \"Dynamic Context-Aware Transformer (DCAT)\", aimed at addressing the unique challenges of image captioning in Tamil. The novelty of DCAT lies in its integration of the Vision Transformer (ViT) and the Generative Pre-trained Transformer (GPT-3) with a specialized Context Embedding Layer, offering a unique approach to capture both the visual and contextual nuances of images.
3. To rigorously test and validate the proposed DCAT methodology using various performance metrics such as BLEU score, METEOR score, Contextual Accuracy (CA), and a Composite Evaluation Score (CES), which collectively assess linguistic accuracy, contextual relevance, and overall effectiveness.
4. To compare and contrast the performance of DCAT with existing state-of-the-art techniques in image captioning, such as Neural Image Captioning (NIC), Up-Down Attention, and Object-Semantics Aligned Pre-training (Oscar), providing a clear benchmark of its advancements and contributions to the field.  
The rest of the paper is organized as follows: Section 2 discusses the related works in the field of image captioning and contextual modeling. Section 3 discusses the materials and methods used in this research. Section 4 details the proposed methodology, including the system model, architecture, and workflow of the DCAT model. Section 5 presents the experimental setup, evaluation metrics, and result analysis. Finally, Section 6 concludes the paper and suggests avenues for future research.
# Pose Estimation Utilizing a Gated Recurrent Unit Network for Visual Localization
## 3 Simulation
### Evaluation  
In Figure 10, the accumulated Root Mean Square Errors (RMSEs) concerning yaw angle obtained from the MVO framework employing the proposed method are reduced when comparing with the MVO. The yaw angle obtained from the MVO employing the proposed method is closer to ground truth than the MVO, as shown in enlarged timesteps of Figure 10a corresponding to the cornering section of the KITTI 06 sequence over timesteps 710-730. For the section where the proposed method is applied, the yaw angle from the proposed method is closer to ground truth as depicted in Figure 11. The timesteps over which the green line segments are overlayed correspond to the timesteps when yaw angle changes. In addition, as seen in Figure 12, it is intuitively clear that odometry performance is improved over the trajectories of most sequences in the KITTI dataset. The KITTI evaluation kit [42; 43] is used for the evaluations in this subsection. In Table 1, translation error [44], rotation error [44], Absolute Trajectory Error (ATE) [43], and Relative Pose Error (RPE) [43] are presented for demonstrating the performance improvement achieved with the proposed method. Figure 9: Ground truth trajectory from RTK-GPS (Real-Time Kinematic Global Positioning System) of KITTI 06 sequence; the red trajectory is the section where the yaw angle exceeds the threshold and the green trajectory corresponds to the remaining section where the yaw angle falls below the threshold.
# Generation of Complete SAR Geometric Distortion Maps Based on DEM and Neighbor Gradient Algorithm
## 2 Methodology
### Interaction of the SGD  
For detailed studies of all interactions among the distortions, only five different cases of the interactions are possible:  
* (D1) Interaction of passive layover and foreshortening
* (D2) Interaction of passive layover and active shadow
* (D3) Interaction of passive layover and passive shadow
* (D4) Interaction of passive shadow and foreshortening
* (D5) Interaction of passive shadow and active layover  
They are shown in Figure 2 and can be derived from Equations (1)-(6).  
The correction method for the boundaries of the SGD is based on the imaging sequence, mainly including: (A) For cases (D1) and (D2), the superposition is the passive layover, and the regions of foreshortening and active shadow can be corrected through Equations (1) and (5); (B) In the presence of cases (D2) and (D3) at the same time, the superposition is also defined as passive layover, whereas the un-overlapped passive shadow should be categorized as foreshortening or non-distortion areas; (C) For case (D3), the superimposed part is passive shadow; (D) In the cases of (D4) and (D5), the superimposed part is passive shadow, which can be corrected through Equations (1) and (6); (E) In the presence of (D3) and (D5) simultaneously, the superposition is passive shadow, while the un-overlapped passive layover should be classified as foreshortening or non-distortion areas.  
Through studying the spatial relationship among the SGD, we have a deeper understanding of the formation and characteristics of the SGD. Additionally, the active distortion does not interact with foreshortening or itself. Conversely, the most of overlapping areas are the passive distortion with other distortions or itself, including the active distortion and the foreshortening. The P-NG method presented in this paper cannot only obtain the passive distortion on a pixel-by-pixel basis in the light of the neighbor gradient between active and passive distortion but can also correct the boundaries among the distortions to yield accurate simulation areas.
###### Abstract  
Presently, a green Internet of Things (IoT) based energy aware network plays a significant part in the sensing technology. The development of IoT has a major impact on several application areas such as healthcare, smart city, transportation, etc. The exponential rise in the sensor nodes might result in enhanced energy dissipation. So, the minimization of environmental impact in green media networks is a challenging issue for both researchers and business people. Energy efficiency and security remain crucial in the design of IoT applications. This paper presents a new green energy-efficient routing with DL based anomaly detection (GEER-DLAD) technique for IoT applications. The presented model enables IoT devices to utilize energy effectively in such a way as to increase the network span. The GEER-DLAD technique performs error lossy compression (ELC) technique to lessen the quantity of data communication over the network. In addition, the moth flame swarm optimization (MSO) algorithm is applied for the optimal selection of routes in the network. Besides, DLAD process takes place via the recurrent neural network-long short term memory (RNN-LSTM) model to detect anomalies in the IoT communication networks. A detailed experimental validation process is carried out and the results ensured the betterment of the GEER-DLAD model in terms of energy efficiency and detection performance.  
Internet of Things; deep learning; anomaly detection; energy efficiency; routing Article
## 3 Case Studies
### TE Graph Max Flows Analysis  
Next, we consider an example of constructing maximum entropy transfer flows based on the algorithm for determining the maximum flow in the graph (Figure 19) [38].  
As an example, we consider the transfer entropy graph for the period from 2001 to 2010 (Figure 15).  
Figure 20a-d show the results of calculating the maximum flows between the selected nodes of the graph \(G_{TE}\) for 2001-2010. When designating the graph's vertices, the abbreviations of statistical regions according to the ICAO were used.  
Figure 19: Plot describing the change in the flow of passenger air transport in the world for the period 2001–2010 [37].  
Figure 18: TE graph for 2006–2015.  
The presented results of the analysis can be useful in assessing the development of the communication field between regions over a given period of time.  
For the following statistical regions of the world according to ICAO [35] that were selected, the following abbreviations were applied in Figure 20: AE&S, Eastern and Southern Africa; AW&C, Western and Central Africa; AW, Arab world; CE&B, Central Europe and the Baltics; EA&P, East Asia and the Pacific; E&CA, Europe and Central Asia; EU, European Union; LA&C, Latin America and the Caribbean; ME&NA, Middle East and North Africa; NA, North America; SA, South Asia; SSA, Sub-Saharan Africa; W, World.  
So, we can conclude that the results of the analysis showed that transfer entropy flows between world regions change dynamically depending on the political, economic, social, and technological factors that manifest themselves over a historical period of time.
## 5 Discussion
### SHAP Model Interpretation  
Compared with physical models, machine learning models are often regarded as 'black boxes' because their predictions are based on data-driven training rather than a direct connection to the physical processes underlying landslide occurrences. This lack of interpretability can make building trust in the model's predictions challenging, especially when such predictions are used for critical decision making [60]. SHAP analysis enhances the interpretability of machine learning applied to LSP by highlighting the influence of predisposing factors. In this study, the NDVI and altitude were the most impactful factors, with the NDVI showing the strongest effect. SHAP analysis identified a critical NDVI threshold of 0.8, consistent with FR analysis.  
Moreover, SHAP analysis revealed complex non-linear relationships between the LCFs and the LSP results. The SHAP values for altitude showed a distinct U-shaped pattern, making it clear that landslides occur at some specific altitude within the research area. Both low- and high-altitude regions exhibited high susceptibility. This could be due to a higher proportion of shallow landslides in low-elevation areas, where human activities and engineering interference with the environment affect slope stability.  
Another possibility is that the study area's historical landslide inventory may be biased, as landslides are more likely to be investigated in accessible areas, often near human settlements and lower areas. While this could introduce some bias, it also has the advantage of focusing the LSP results on areas with high exposure to human impact. However, this pattern also highlights certain limitations, suggesting that the results should be viewed as complementary to more traditional geological analyses.