esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting Compressive Strength of
## 5 Conclusions  
In addition to creating a new in-house SEM image database to be used for evaluating the model proposed in this paper, we provide a new deep learning model for predicting the compressive strength of CSRE based on SEM images using computer vision and deep learning. The comparative results of the experiments conducted on the SEM image database to predict the compressive strength of CSRE samples show that the proposed DCNN model outperforms other traditional computer vision methods, obtaining reasonable prediction results and comparatively low RMSE. Therefore, the method can be recommended for practical use as time, and cost-saving tool, as well as the environment protecting one, if applied instead of the current strength-testing machines.  
Despite the reasonable performance of the proposed DCNN, the overall prediction accuracy process is still imperfect because it is the error threshold dependent. Such imperfect performance may be attributed to the number of SEM images used to train the deep features. This deficit can be alleviated by obtaining more SEM images of a higher number of CSRE samples to feed the DCNN. Higher prediction accuracy is expected then. Moreover, using the image enhancement techniques at a preprocessing stage may enhance the prediction results. It is considered to verify these statements in future analysis. Prospective future works will also include the use of DCNN to solve other problems described in [46; 47; 48]. It is expected to overcome the problem of prediction speed. It should increase with the use of more efficient indexing techniques, such as presented in [49; 50; 51].  
**Author Contributions:** Conceptualization, H.A. and P.N.; methodology, A.S.T. and P.N.; software, A.S.T. and A.H.; validation, A.S.T., P.N. and A.H.; formal analysis, A.H. and K.A.; investigation, H.A., A.H., A.S.T., P.N. and J.K.; resources, P.N. and H.A.; data creation, P.N. and J.K.; writing--original draft preparation, A.S.T., P.N., K.A. and A.H.; writing--review and editing, A.H., K.A., and H.A.; visualization, P.N. and A.S.T.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This research was supported by the Cryo-SEM laboratory, the National Multidisciplinary Laboratory of Functional Nanomaterials NanoFun project no. POIG.02.02.00-00-025/09 at the Faculty of Geology, University of Warsaw. [PERSON] would like to thank the Tempus Public Foundation for sponsoring his Ph.D. study. Also, [PERSON]'s work falls under the project EFOP-3.6.3-VEKOP-16-2017-00001 (Talent Management in Autonomous Vehicle Control Technologies) and is supported by the Hungarian Government, and co-financed by the European Social Fund. Moreover, the authors would like to thank the Institute for Computer Science and Control (SZTAKI) for providing an advanced server for training the proposed model.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Simultaneous Coded Plane-Wave Imaging Using an Advanced Ultrasound Forward Model
## Appendix B Establishment of the General Forward Model  
To overcome some emission/ reception artifacts, it is also possible to apply a spatial apodization function to the elements of the probe at emission/ reception [59, 60]. This consists of applying a weight \(w_{j}\) on the excitation signal (\(a_{j}(t)\)) for each element \(j\), and a weight \(w_{i}\) on the received signal at each element \(i\). Let us consider that \(N_{el}\) elements of the probe are active in emission. Under these conditions, the total signal \(y_{i}(t)\) received by an element \(i\) can be approximated by the sum of the signals \(y_{ji}(t)\). We obtain:  
\[y_{i}(t)=w_{j}h_{i}(t)*\sum\limits_{j=1}^{N_{el}}w_{j}h_{j}(t)*a_{j}(t)*\delta (t-t_{j})*g_{ji}(t) \tag{3}\]Assuming that the excitation signal is the same for all \(j\) (\(a(t)=a_{j}(t)\), \(\forall j\)) and that the elements have the same IR \(h(t)\) in emission and reception, we can rewrite Equation (A3) as follows [61]:  
\[y_{i}(t) =w_{i}e(t)*g_{i}(t)\] (A4) \[\text{with: }\begin{cases}\quad e(t)=a(t)*h(t)*h(t)\\ g_{i}(t)=\sum\limits_{j=1}^{N_{\text{el}}}w_{j}\delta(t-t_{j})*g_{j\bar{i}}(t) \end{cases}\]  
As can be seen, the signal \(g_{i}(t)\) depends on the emission tapering, emission delays, TRF, probe and medium geometry, and medium TRF. Using Equation (A1), the discrete expression of Equation (A4) becomes:  
\[\begin{split}\mathbf{y}_{i}=w_{i}\mathbf{E}\left[\sum\limits_{j=1 }^{N_{\text{el}}}w_{j}\mathbf{C}_{j\bar{i}}\right]\boldsymbol{\gamma}=w_{i} \mathbf{EC}_{i}\boldsymbol{\gamma}=\boldsymbol{\Lambda}_{i}\boldsymbol{\gamma} \\ \text{with: }\begin{cases}\mathbf{C}_{i}=\sum\limits_{j=1}^{N_{ \text{el}}}w_{j}\mathbf{C}_{j\bar{i}}\\ \boldsymbol{\Lambda}_{i}=w_{i}\mathbf{EC}_{i}\end{cases}\end{split}\] (A5)
# Neural Network-Based Bilingual Lexicon Induction for Indonesian Ethnic Languages
## 3 Materials and Methods
### Long Short-Term Memory (LSTM)  
Long short-term memory (LSTM) is an upgraded recurrent neural network (RNN) that is used to overcome the problem of vanishing and exploding gradients [18]. LSTM addresses the problem of long-term RNN reliance, where RNNs are unable to predict input data stored in long-term memory but can make more accurate predictions based on current information. The LSTM architecture can store large amounts of data for lengthy periods of time. They are applied to time-series data processing, forecasting, and categorization. Memory cells and gate units are key components of the LSTM architecture. Forget gate, input gate, and output gate are the three types of gates in an LSTM. Figure 1 illustrates the structure of the LSTM model. The library used was tf.keras.layers.LSTM.  
Cell memory tracks the dependencies between components in the input sequence. New values that enter the cell state are handled by the input gate. The LSTM unit utilizes a forget gate to select the value that remains in the cell state. The value in the cell state that remains will be sent to the output gate, where the LSTM activation function, also known as the logistic sigmoid function, will be used to start the calculation. The tanh and sigma symbols represent the types of activation functions employed in the neural network's training layers.  
A sigmoid gate, which restricts how much information may pass through, and allows information to flow through it unmodified, is another essential feature of LSTM. The outputs of the sigmoid layer, which vary from zero to one, specify how much of each component should be permitted to pass. The Equation (2) that controls the LSTM flow is as follows:  
\[f_{t}=\sigma(w_{f}\cdot[h_{t-1},x_{t}]+b_{f} \tag{2}\]  
\[i_{t}=\sigma(w_{i}\cdot[h_{t-1},x_{t}]+b_{i}\]  
\[C_{t}=\tanh(w_{c}\cdot[h_{t-1},x_{t}]+b_{c}\]  
\[C_{t}=f_{t}\times C_{t-1}+i_{t}\star C_{t}\]  
\[o_{t}=\sigma(w_{o}\cdot[h_{t-1},x_{t}]+b_{o}\]  
\[h_{t}=o_{t}\times\tanh C_{t}\]  
Figure 1: Unit structure of the LSTM.  
where \(o_{t}\) : at time \(t\), output gate \(i_{t}\) : at time \(t\), input gate \(h_{t}\) : output at time \(t\) \(f_{t}\) : forget gate, at time \(t\) \(x_{t}\) : input at time \(t\) \(\sigma\) : sigmoid function \(C_{t}\) : the state of the cell at time \(t\) \(w_{o}\), \(w_{f}\), \(w_{i}\), \(w_{c}\) : weights that have been trained \(b_{c},b_{i},b_{f}\) : trained biases
## 3 Results
### Sloping Layer Model  
To evaluate the performance of the Step-by-Step Inversion Algorithm in inclined geological structures, as well as its weak dependence on the initial model, a sloping layer model was constructed. In this second model, the velocities increase from the shallow to deeper layers, following (2.5, 3.5, 4.5) km/s, respectively. The synthetic dataset was computed using a VSP (Vertical Seismic Profile) observation system via forward modeling, and the orthogonal parameters are consistent with model I. The initial model had velocities (2, 3, 4) km/s, with significant discrepancies between the initial and true models. This was to validate the weak dependency of the Step-by-Step Inversion Algorithm on the initial model, demonstrating that accurate inversion is still achievable despite large errors in the initial model. The inversion employed the Adam optimization algorithm with identical parameter settings. The results of the second model and its inversion are shown in Figure 10.  
As can be observed in Figure 10, despite the substantial error in the initial model in the context of a sloping layer model, the Step-by-Step Inversion Algorithm exhibited superior performance overall. This suggests that the algorithm has a lower dependency on the initial model and is capable of achieving reasonably accurate inversions even when the initial model has large discrepancies. In contrast, the traditional Sum-of-Losses Inversion performed poorly, indicating a higher dependency on the initial model. Velocity profiles at depths of 150 m, 500 m, and 800 m were selected for comparison and are displayed in Figures 11-13, respectively.  
Based on the cross-sectional velocity comparison plots, it can be observed that, even with significant errors in the initial model, the Step-by-Step Inversion Algorithm was able to more accurately invert velocities at various depths, including 150 m, 500 m, and 800 m, demonstrating superior precision in the results.  
Figure 9: Comparison of 800 m deep velocity profiles of Homogeneous Layer Model.  
Figure 11: Comparison of 150 m deep velocity profiles of Sloping Layer Model.  
Figure 10: Sloping layer model and inversion results. (**a**) True model. (**b**) Total loss inversion. (**c**) Time by time inversion.
# CNN-Based Multi-Factor Authentication System for Mobile Devices Using Faces and Passwords
## 2 Related Works
On the other hand, the MFA System with MMCNN proposed in this study has a four-layer CNN structure which is suitable for use in mobile devices, and is implemented as a multi-modality system such that both image and text can be used as factors. In addition, as it converts text passwords into secure images using the secure password transformation (SPT) algorithm, it takes data security into account. Therefore, the proposed system can be considered as a system with superior functions to those of existing CNN-based MFA systems. Figure 1: Two types of authentication process: (**a**) parallel multi-input validation and (**b**) sequential multi-input validation. In this study, a lightweight CNN that can be used in mobile devices is proposed. MobileNet [50] and ShuffleNet [51] have been proposed for use in mobile devices; however, MobileNet has a 28-layer structure and ShuffleNet has an 11-layer structure; thus, they are considered to have too many layers. Alexnet [52], which was proposed in the early stages of CNN development, has a simple structure of eight layers; therefore, based on this structure, a four-layer structure suitable for mobile devices was further proposed. Meanwhile, research on capturing text information through images has been conducted. [PERSON] et al. [53] proposed a safe user authentication system which converts key-stroke dynamic behavioral data into a 3D image, which is learned with a CNN and utilized as an authentication factor along with the password. [PERSON] et al. [54] suggested a two-level quick-response (2 LQR) code that has public and private levels for private messages or authentication. [PERSON] et al. [55] proposed a visual cryptographic scheme (VCS) technique that represents the one-time password (OTP) text as an image, then creates three encrypted shares with the image. When decrypting the shares, XOR operations are performed on the three shares to restore the image with the original OTP text. In this study, a safe text-to-image conversion algorithm is proposed, called the Secure Password Transformation algorithm, which hides the location of pixels containing text information, unlike existing methods.
# Investigation of Aerosol Climatology and Long-Range Transport of Aerosols over Pokhara, Nepal
###### Abstract  
This study presents the spectral monthly and seasonal variation of aerosol optical depth (\(\tau_{\text{AOD}}\)), single scattering albedo (SSA), and aerosol absorption optical depth (AAOD) between 2010 and 2018 obtained from the Aerosol Robotic Network (AERONET) over Pokhara, Nepal. The analysis of these column-integrated aerosol optical data suggests significant monthly and seasonal variability of aerosol physical and optical properties. The pre-monsoon season (March to May) has the highest observed \(\tau_{\text{AOD}}(0.75\pm 0.15)\), followed by winter (December to February, \(0.47\pm 0.12\)), post-monsoon (October and November, \(0.39\pm 0.08\)), and monsoon seasons (June to September, \(0.27\pm 0.13\)), indicating seasonal aerosol loading over Pokhara. The variability of Angstrom parameters, \(\alpha\), and \(\beta\), were computed from the linear fit line in the logarithmic scale of spectral \(\tau_{\text{AOD}}\), and used to analyze the aerosol physical characteristics such as particle size and aerosol loading. The curvature of spectral \(\tau_{\text{AOD}}\), \(\alpha^{\prime}\), computed from the second-order polynomial fit, reveals the domination by fine mode aerosol particles in the post-monsoon and winter seasons, with coarse mode dominating in monsoon, and both modes contributing in the pre-monsoon. Analysis of air mass back trajectories and observation of fire spots along with aerosol optical data and aerosol size spectra suggest the presence of mixed types of transboundary aerosols, such as biomass, urban-industrial, and dust aerosols in the atmospheric column over Pokhara.  
aerosol climatology; spectral aerosol optical depth; single scattering albedo; aerosol absorption optical depth; Angstrom parameters; turbidity; long range transportation; back trajectory; aerosol absorption exponent +
Footnote †: journal: _arxiv.org/abs/1208.00022_
# Aroma Characteristics of Lavender Extract and Essential Oil from _Lavandula angustifolia_ Mill.
###### Abstract  
Lavender and its products have excellent flavor properties. However, most studies focus on the aroma profiles of laverander essential oil (LEO). The volatiles in lavernder extracts (LEs), either in volatile compositions or their odor characteristics, have rarely been reported. In this study, the odor characteristics of LEs and LEO were comprehensively investigated by gas chromatography-mass spectrometry (GC-MS), coupled with sensory evaluation and principal chemical analysis (PCA). In addition, the extraction conditions of lavernder extracts from infloreses of _Lavandula angustifolia_ Mill. were optimized. Under the optimal conditions of extraction, twice with 95% edible ethanol as the solvent, the LEs tended to contain the higher intensity of characteristic floral, herbal and clove-like odors as well as higher scores of overall assessment and higher amounts of linalool, linalool oxides I and II, linaly acetate, lavandulyal acetate and total volatiles than LEO. PCA analysis showed that there were significant differences on the odor characteristics between LEO and LEs. The LEO, which was produced by steam distillation with a yield of 2.21%, had the lower intensity of floral, clove-like, medicine-like, pine-like and hay notes, a lower score of overall assessment and lower levels of linalool oxides I and II, linaly acetate, lavandulyal acetate and total volatiles compared with LEs, whereas the relative contents of linalool and camphor in LEO were significantly higher than that in LEs. Furthermore, the earthy, green and watery odors were only found in LEO. Concerning the odor characteristics and volatile compositions, the LEs had better odor properties than LEO. These results provided a theoretical basis for the industrial preparation of lavernder-related products.  
lavender (_Lavandula angustifolia_ Mill.); extracts and essential oils; volatiles; gas chromatography-mass spectrometry (GC-MS); sensory evaluation; odor characteristics
# Glass Defect Detection with Improved Data Augmentation under Total Reflection Lighting
###### Abstract  
To address the technical challenge of identifying tiny defects, especially dust and point defects, on mobile phone flat glass, an automatic optical inspection system is established. The system investigates algorithms including imaging principles, target detection models, data augmentation, foreground segmentation, and image fusion. The system builds an automatic optical inspection platform to collect glass defect samples. It illuminates the glass samples with a combined total reflection-grazing light source, collects the defect sample data, segments the background and defects of the collected data, generates the defect mask, and extracts the complete defects of the cell phone flat glass. The system then seamlessly integrates the extracted defects with a flawless background using Poisson editing and outputs the location information of the defects and the label output to automatically generate the dataset. The deep learning network YOLOv5 works as the core algorithm framework, into which the Constructive Block Attention Module and the small target detection layer are specifically added to enhance the capability of the model to detect small defects. According to the experimental results, the combined lighting effectively improves the precision of detecting dust and bright spots. Additionally, with the adoption of novel data augmentation techniques, the enhanced YOLOv5 model is capable of effectively addressing the challenges posed by inefficient sample data and non-uniform distribution, thus mitigating network generalization issues. Furthermore, this data augmentation approach facilitates the rapid adaptation of the same detection tasks to diverse environmental scenarios, enabling the expedited and efficient deployment of the model across various industrial settings. The mean average precision (MAP) of the optimal model in the validation set reached 98.36%, 2.62% higher than that of the original YOLOv5. In addition, its false acceptance rate (FAR) is 1.27%, its false rejection rate (FRR) was 2.47%, its detection speed was 64 fps, and its correct detection rate in the validation set was 98.75%, which meets the current industrial detection requirements by and large. In this way, this paper achieved the automated inspection of mobile phone flat glass with high robustness, high precision, and a low false acceptance rate and false rejection rate, significantly reducing material losses in the factories and the likelihood of error occurrence in follow-on products. This method can be applied to the multi-scale and multi-type detection of glass defects.  
mobile phone flat glass; total reflection-grazing incidence combined lighting; defect detection; YOLOv5; entropy segmentation; image cloning; Poisson image editing; Convolutional Block Attention Module (CBAM) +
Footnote †: journal: Applied Sciences
## 4 Application and Discussion of the Methodology
### Discussion of the Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**No.** & **MCSs** & **FP** & **FV-I Values** & **Rank** & **No.** & **MCSs** & **FP** & **FV-I Values** & **Rank** \\ \hline
1 & BE1-BE6 & \(2.0175\times 10^{-6}\) & \(1.9372\times 10^{-4}\) & 17 & 12 & BE10-BE11 & \(1.2053\times 10^{-4}\) & \(1.1573\times 10^{-2}\) & 4 \\
2 & BE1-BE7 & \(2.1669\times 10^{-5}\) & \(2.0806\times 10^{-3}\) & 13 & 13 & BE10-BE13 & \(4.6402\times 10^{-5}\) & \(4.4554\times 10^{-3}\) & 11 \\
3 & BE2-BE6 & \(9.8842\times 10^{-6}\) & \(9.4906\times 10^{-4}\) & 14 & 14 & BE10-BE15 & \(6.5372\times 10^{-5}\) & \(6.2768\times 10^{-3}\) & 9 \\
4 & BE2-BE7 & \(1.06158\times 10^{-4}\) & \(1.0193\times 10^{-2}\) & 5 & 15 & BE17-BE5-BE16 & \(6.8436\times 10^{-8}\) & \(6.5711\times 10^{-6}\) & 21 \\
5 & BE3-BE6 & \(3.9883\times 10^{-7}\) & \(3.8294\times 10^{-5}\) & 18 & 16 & BE18-BE5-BE16 & \(6.8929\times 10^{-8}\) & \(6.6184\times 10^{-6}\) & 20 \\
6 & BE3-BE7 & \(4.2835\times 10^{-6}\) & \(4.1129\times 10^{-4}\) & 16 & 17 & BE19-BE5-BE16 & \(3.0887\times 10^{-8}\) & \(2.9657\times 10^{-6}\) & 22 \\
7 & BE4-BE6 & \(6.1166\times 10^{-6}\) & \(5.8730\times 10^{-4}\) & 15 & 18 & BE20-BE5-BE16 & \(2.8065\times 10^{-7}\) & \(2.6947\times 10^{-5}\) & 19 \\
8 & BE4-BE7 & \(6.5693\times 10^{-5}\) & \(6.3077\times 10^{-3}\) & 8 & 19 & BE12-BE5 & \(1.8227\times 10^{-4}\) & \(1.7501\times 10^{-2}\) & 3 \\
9 & BE9-BE11 & \(1.0326\times 10^{-4}\) & \(9.9153\times 10^{-3}\) & 6 & 20 & B12-B10 & \(7.1894\times 10^{-5}\) & \(6.9031\times 10^{-3}\) & 7 \\
10 & BE9-BE13 & \(3.9755\times 10^{-5}\) & \(3.8172\times 10^{-3}\) & 12 & 21 & BE14 & \(4.4128\times 10^{-4}\) & \(4.2371\times 10^{-2}\) & 2 \\
11 & BE9-BE15 & \(5.6008\times 10^{-5}\) & \(5.3777\times 10^{-3}\) & 10 & 22 & BE8 & \(9.0842\times 10^{-3}\) & \(8.7224\times 10^{-1}\) & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: FP and FV-I values of MCSs. and the risk of accidents. At the same time, paralysis in the operator's mind and taking things for granted will inevitably lead to accidents and even loss of life [71]. Assuming that measures are taken to eliminate the inappropriate items of BE12, the incidence of TE will be reduced to 1.0163 \(\times\) 10\({}^{-2}\), and the risk will be reduced by 2.42% compared to the original. Similar analysis can be performed for other MCSs relations. Figure 6 shows the percentage value of the accident risk that can be reduced by eliminating any BE. This method is used to find out all possible causes of EMAs, as well as the most critical factors and weakest links that affect the reliability of the system so that these critical events can be paid attention to in daily operations in order to manage and control risks more effectively. Although the factors that caused the accident cannot be entirely eliminated through the FFTA inspection and evaluation, the risk of the accident can be minimized by taking preventive measures.
# Monocular Camera-Based Robotic Pick-and-Place in Fusion Applications
## 3 Proposed Method
### Reward Setting  
In a RL method, the reward need to be carefully designed. An appropriate reward design can keep the agent's 'curiosity' and guide it to the final goal. In our work, we design a variety of rewards according to the task process, as shown in Algorithm 3.  
```
1: Getting points \(P_{a},P_{b},\ldots,P_{e}\) from Figure 3, \(P_{k}(x_{k},y_{k},z_{k})\) is the 3D coordinates of point \(k\in\{a,b,c,d,e\}\), \(D_{ab}=\sqrt{\left(x_{a}-x_{b}\right)^{2}+\left(y_{a}-y_{b}\right)^{2}+\left( z_{a}-z_{b}\right)^{2}}\), \(X_{ab}=x_{a}-x_{b}\), and so on for \(Y_{ab}\) and \(Z_{ab}\), \(tanh(x)=(e^{x}-e^{-x})/(e^{x}+e^{-x})\), \(S_{a}\) and \(S_{b}\) are the side length of white cube and purple cube.
2: At the grasping stage: \(r_{dis}=1-tanh(\alpha(D_{ac}+D_{ad}+3D_{ae}))\), \(\alpha\) is the weight.
3:\(lifted=clip(((Z_{a}-Z_{t}-\frac{S_{a}}{2})/S_{b}),0,1)\), where \(Z_{t}\) is the height of the table, \(r_{lif}=lifted\).
4:\(Ali_{ab}=\sqrt{\left(x_{a}-x_{b}\right)^{2}+\left(y_{a}-y_{b}\right)^{2}+\left( z_{a}-z_{b}+\frac{S_{a}+S_{b}}{2}\right)^{2}}\)
5:if\(lifted>0.5\)then
6:\(r_{ali}=1-tanh(\gamma AIi_{ab})\), \(\gamma\) is the weight;
7:else
8:\(r_{ali}=0\)
9:endif
10:\(r_{dis}=max(r_{dis},r_{ali})\)
11:if\(\sqrt{X_{ab}^{2}+Y_{ab}^{2}}<0.02\) and \(|z_{a}-S_{b}-S_{a}/2|<0.01\)then
12:\(r_{sta}=clip(D_{ae},0,1)\)
13:else
14:\(r_{sta}=0\)
15:endif
16:\(r_{dis}=max(r_{dis},r_{sta})\)
17:The final reward \(R=w_{1}r_{dis}+w_{2}r_{lif}+w_{3}r_{ali}+w_{4}r_{sta},w_{1},\ldots,w_{4}\) are the weights.
```  
**Algorithm 3** Reward setting.
# Fuzzy Logic in Selection of Maritime Search and Rescue Units
## 4 Case Study
### Stage 3: The computational test and the results
In this case (Figure 6) we can notice that Vessel 5 and Vessel 2 which were at the top of the previous ranking are now much lower. The evaluation is based on fewer criteria and weaker ships are now are higher ranked. Vessel 1 and Vessel 3 are cargo ships with medium speed and medium superstructure height. In the case of a large number of alternatives (e.g., several dozen ships in the vicinity of the distress), this can affect the quickness of the selection, but also the effectiveness of the decision.