esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methodology
#### 2.2.2 Long Short-Term Memory (LSTM)  
Deep learning is a sort of neural network with several layers, commonly referred to as deep structured learning. These networks do better than standard neural networks at remembering information from prior occurrences. One such device that employs many networks in a loop is a recurrent neural network (RNN). Thanks to networks that are in a closed loop, the information may be kept. Each network in the loop gets information and input from the preceding network, takes the required action, and produces output while sending the information to the next network. Only recent information is requiredby certain applications, but more historical data may be desired by others. The common recurrent neural networks experience learning delays when the gap between the moment of necessity and the required prior information expands. However, a kind of RNN called Long Short-Term Memory (LSTM) Networks [82] can learn such occurrences. These networks have been created expressly to get over the recurrent networks' problem with long-term reliance. The ability to recall information over a long period of time is a strength of LSTMs. The validity of the model may be impacted by extra previous information, hence LSTMs are an obvious option for usage. The four neural network layers that make up a standard LSTM module sometimes referred to as a repeating module, interact in a special way. The LSTM is represented mathematically as follows:  
\[f_{t}=\alpha\left(W_{f}\cdot[h_{t-1},x_{t}]+b_{f}\right) \tag{1}\]  
\[i_{t}=\alpha(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{2}\]  
\[c_{t}=\tanh(W_{c}\cdot[h_{t-1},x_{t}]+b_{c}) \tag{3}\]  
\[o_{t}=\alpha(W_{o}\cdot[h_{t-1},x_{t}]+b_{o}) \tag{4}\]  
\[h_{t}=o_{t}\times\tanh h(c_{t}) \tag{5}\]  
where \(f_{t}\) is the forget gate at time step \(t\), \(i_{t}\) is the input gate at time step \(t\), \(c_{t}\) is the cell gate at time step \(t\), \(o_{t}\) is the output gate at time step \(t\), \(h_{t}\) is the hidden state at time step \(t\), \(h_{t-1}\) is the previous hidden state at time step \(t\), \(o_{t}\) is the output gate at time step \(t\), \(\alpha\) is the activation function, \(W_{f},\ W_{i},\ W_{c},W_{o}\) are the weight of forget gate, input gate, cell gate, output gate respectively and \(b_{f},\ b_{i},\ b_{c},b_{o}\) are bias of forget gate, input gate, cell gate, output gate.
# Evaluation of Machine Learning Assisted Phase Behavior Modelling of Surfactant-Oil-Water Systems
## 3 Results and Discussion
### Machine Learning Model Predictions Beyond Training Data Range  
Figure 9 illustrates the extrapolation capabilities of the cubic SVM model beyond the training dataset's salinity range. As expected, near-range extrapolation shows reasonable accuracy, with the two-phase region growing as salinity conditions deviate from the optimum. The edge of the defined range is at 3%wt. Figure 9 shows the results for near extrapolation for 3.5%wt, whereas Figure 9 shows far extrapolation for salinity 7%wt. As we move further from the trained conditions range, the model begins to exhibit unphysical behavior, such as the re-emergence of a three-phase region in conditions where such behavior is not expected.  
Figure 7: Phase behavior predicted with machine learning and graphical equation-of-state, ternary diagram at varying salinities: (**a**) 0.85%wt, (**b**) 1.15%wt, (**c**) 1.5%wt.  
These results highlight the limitations of machine learning models when applied outside their trained parameter space. While the SVM model performs well within the interpolated region, its predictions become unreliable in far-extrapolated conditions. This outcome emphasizes that machine learning models should be applied cautiously and not expected to predict accurately beyond the range of the training dataset--in this case, salinity conditions beyond the trained optimum.  
Additionally, as shown in Figure 10, reducing the training dataset size from 5000 to 500 data points significantly impacts model performance. With only 500 data points, multiple regions in the compositional space are misclassified, particularly in multiphase areas. This highlights the importance of a sufficiently large dataset for training robust machine learning models. A smaller dataset limits the model's ability to capture the full complexity of phase transitions, leading to less reliable predictions, especially when extrapolating beyond the trained conditions.  
In summary, while the cubic SVM performs well with a large dataset within its trained range, caution must be taken when reducing data size or extrapolating too far beyond the trained conditions, as performance can degrade significantly in such scenarios.  
Figure 8: Fixed water-oil ratio compositional space, fish plot. HLD = In(S/Sopt), Sopt = 1.15%wt.  
Figure 9: Machine learning model extrapolation: (**a**) near extrapolation at 3.5%wt (3%wt is the upper range for tuned data); (**b**) far extrapolation at 7%wt (3%wt is the upper range for tuned data).
# Electrokinetic Properties of 3D-Printed Conductive Lattice Structures
## 1 Introduction
The advantages of incorporating lattice structures in mechanical parts is so obvious [26] that commercial 3D printing software includes this feature. Moreover, strategies for optimizing the lattice geometry specifically for additive manufacturing have been proposed [27]. From the mechanical point of view, a three-dimensional frame of struts just meeting at nodes must have a nodal connectivity \(Z\) greater than five to ensure structural stability [28]. This rule does not hold when the structural elements are intimately welded as they are with 3D printing. In fact, lattices manufactured by 3D printing combine light weight and remarkable mechanical properties [29; 30]. Whereas lattice structures combine many properties of foams, it has been argued that 3D-printed lattice structures may outperform foam materials of the same porosity [31]. A plastic 3D mesh manufactured by FDM can conduct electricity when the polymer filament that feeds the printer has been loaded with nanoscopic conducting inclusions at a concentration above the percolation threshold [11; 20; 32]. Polymer-based resistive lattices have potential applications as 3D stretchable conductors [33]. They are also interesting media for electromagnetic (em) applications owing to their lower conductivity compared to metallic networks. As a consequence, only a small fraction of incoming em radiations is reflected; the rest enters the structure while being progressively absorbed. The shielding structure does not need to be earthed, because the em attenuation is due to power loss produced by Joule heating. Inspired by research works on disordered 2D networks of metallic nanowires [34] or on 3D percolation networks in composite polymers [35], one may infer that  
Figure 1: (**a**) Diamond lattice made of interconnected rods. A small part of the structure is detailed in (**b**). an artificial lattice of conducting elements may offer protection against electrostatic discharges or be efficient to shield electromagnetic perturbations. In the latter case, absorption resonances are expected to happen for wavelengths of the order of, or shorter than, the unit cell size [36]. In the present paper, by combining the theory of resistor networks [37] and classical continuous-medium electrodynamics, a new formulation of the electrokinetic behavior of a conducting lattice is derived. Due to the length scale provided by the lattice parameter, the effective continuous medium that mimics the electrokinetic response of a resistor lattice is characterized by a non-local Ohm's law.
## 2 Related Work  
A sizable amount of research exists for disease diagnosis through retinal fundus images. The research can be categorized in six directions. The first direction is the binary computer-aided diagnosis (CAD) system that is based on retinal blood vessel segmentation [13]. The second direction is binary CAD systems dedicated to identifying age-related macular degeneration (AMD) disease [9]. The third direction is binary retinal CAD systems mainly devoted to segmenting the optic disc (OD) region [3]. The fourth direction includes multi-class CAD systems devoted to grading the progress levels of only DR [14], or only glaucoma [15]. The fifth direction includes binary CAD systems used to classify more than one retinal disease together, such as DR and/or glaucoma and/or AMD. These diseases are identified, however, one at a time, as either existing or not using binary classification [16]. The sixth direction includes multi-class CAD systems applied to classify several retinal diseases together and evaluates them as a multi-classification problem [17]. This direction is the most relevant to the present work, and is therefore reviewed in detail next, with Table 1 summarizing its highlights. An automatic lesion detection system is proposed by [23] for early detection of DR-related lesions. A bag-of-visual-words (BoVW) algorithm is employed to discriminate among different DR lesions using a maximum margin classifier. Despite the robustness of the system, its only purpose is to identify the different types of DR signs rather than grading the DR severity. [PERSON] et al. [18] suggest a binary CAD system for DR detection by experimenting on two datasets of images. The RGB fundus image is transformed into a gray-level image. Out of a total of 840 extracted features, only 670 meaningful features are used. This system leverages a support vector machine (SVM), probabilistic neural network (PNN), and PNN improved by a genetic algorithm (PPN-GA). In spite of the effectiveness of the system, its binary operation is a limitation. Moreover, the system is designed to find whether the pathology is DR positive, without determining the severity grade of the disease. [PERSON] et al. [19] present a CAD system based on nonlinear feature extraction to distinguish between normal and AMD images. The feature vector is encoded and provided to a SVM classifier. The system is tested on three datasets, one private and two public,
## 2 Theoretical Background  
### Gesture-Based User Interface  
The term natural user interface (NUI) was first proposed by [PERSON] in the 1970s, as it was invented to describe an interface that reflects the real world [18]. NUI is a general term used by designers and developers, referring to a UI that has a minimum visual direction and is easy to learn. Unlike traditional graphical UIs and command line interfaces, NUI can be used naturally without traditional input/output devices such as keyboards or mice. Moreover, NUI aims to be natural based on the previous experiences of the user; thus, the user can use a well-designed NUI even without receiving training. In particular, the direct manipulation of virtual objects requires that the system provides representations of objects that behave as if they are real-world objects [19]. Additionally, [PERSON] and [PERSON] [20] demonstrated that using natural controls in games can influence perceived realism (graphics and sound), which, in turn, can predict spatial presence and enjoyment.  
Based on the work of [PERSON] et al. [21], we classify NUIs into five types, as explained in Table 1. The touch interface is probably the most well-known NUI, with billions of smart devices using it. Voice interfaces have gained popularity through smart speakers and other voice-controlled smart home devices. While some commercial BCI products exist, they have not become popular in the consumer market. Finally, eye interfaces can be provided through an eye tracker embedded in VR HMDs (e.g., POVE, HTC VIVE Pro Eye, Varjo XR-4,and Apple Visio Pro). While eye data are typically insufficient as a primary interaction method, they can be used as an auxiliary interaction modality, as well as to personalize the user experience based on the user's visual attention.  
Among the NUIs presented in Table 1, the gesture interface allows intuitive and comfortable interaction with content based on the user's movement and can provide a comfortable and natural interaction with the user because it enables three-dimensional interaction. In addition, compared to other NUIs, various expressions are possible, and they are intuitive and convenient to use [22]. Behavioral factors are more easily acquired, and recognizing the learning context related to the performance of the content is also easy [23]. Considering these points, we chose to use a gesture interface to explore presence in a VR experience.
## 4 Experimental Results
### Ablation Study
#### 4.3.2 Three-Scale Fusion Module (TSFM)  
In the object detection algorithm based on deep learning, the backbone network usually uses down-sampling five times to gradually extract the high-level features of the image (multi-scale feature maps P1-P5), and selects the feature maps of different scales (P3-P5) in the last three layers to be sent to the neck network for fusion. In order to explore  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Setting**} & \multicolumn{4}{c}{**r**} \\ \cline{2-5}  & **t = 20\%** & **t = 30\%** & **t = 50\%** & **t = 99\%** \\ \hline Baseline & 3.11\% & 5.13\% & 10.46\% & 76.28\% \\ +EPA (k = 5) & 3.34\% & 5.42\% & 11.07\% & 89.36\% \\ +EPA (k = 7) & 3.22\% & 5.13\% & 10.66\% & 92.94\% \\ +EPA (k = 9) & 3.22\% & 5.13\% & 10.66\% & 91.14\% \\ +EPA (k = 17) & 3.34\% & 5.27\% & 10.86\% & 90.54\% \\ +EPA (k = 19) & 3.34\% & 5.27\% & 11.07\% & 94.75\% \\ +EPA (k = 21) & 3.11\% & 5.13\% & 10.66\% & 90.54\% \\ +EPA (k = 23) & 3.22\% & 5.27\% & 10.86\% & 89.95\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: This table shows the impact of different k values in environmental perception attention on the effective receptive field of the backbone network of the baseline model through the high contribution area ratio r.  
the impact of the combination of three-scale feature maps on the model, we selected a combination of feature maps of different scales. The experimental results are shown in Table 4.  
We first fused the feature maps of the last three layers (P3-P5) through TSFM and sent them into the neck network. Compared with the baseline model, the improvement was very limited. We analyzed that the baseline model itself used the features of the last three layers of maps and sent them into the neck network for fusion, which would cause information redundancy and duplication. However, the baseline model simply fuses feature maps of adjacent scales multiple times in the neck network, while TSFM can fuse three-scale feature maps at the same time, providing a richer multi-scale feature representation. Then, the P2-P4 layer is fused into the network through TSFM, which effectively improves the model performance. This method introduces the fusion of the lower-level P2 feature map, which can utilize the position information and spatial detailed information in it to help the model better understand the local structure in the image.  
We applied the above two strategies to the baseline model at the same time. Compared with the TSFM (P2-P4) method, although this method obtains a higher [EMAIL_ADDRESS], it does not increase the [EMAIL_ADDRESS]:0.95 indicator, which has stricter requirements on positioning accuracy. This further shows that the information in the low-level feature map helps to improve the positioning accuracy and predict the boundary of the target more accurately.
## 3 Materials and Methods
### Field Surveying
#### 3.3.2 Data Collection  
Traffic CO concentrations were collected from the field by using a low cost Gas Analyzer device model Micro-clip5 (1 ppm resolution). The data were collected continuously in 15-min intervals (recording the 15-min minimum, maximum, and average). The traffic flow data and meteorology information were simultaneously collected using a data logger and GPS device (Garmin GPS etrex 10, Olathe, KS, USA; available at University Putra Malaysia). Figure 3 shows the sampling procedure. The traffic CO analyzer was installed in the location of samples by using a Global Navigation System (GPS), at least 2 m from the road edge. The GPS was used to determine the geographic location of samples and to manually verify the locations using land-use maps. The traffic CO was measured four times a day on weekends and weekdays, in the morning (6.30 a.m. to 8.30 a.m.), afternoon (11.30 a.m. to 1.30 p.m.), evening (6.30 p.m. to 8.30 p.m.), and at night (11 p.m. to 12 midnight). In addition, the traffic data were collected by using digital cameras installed in the road's side in the sample's location. The traffic flow data were classified into several types (the number of cars, the number of heavy vehicles and the number of motorbikes), where the cars were private cars and taxi cars. On the other hand, the heavy vehicles refer to the following: medium truck, heavy truck, super-heavy/special duty truck and buses, while the motorbikes refer to any type of motorbikes. The meteorological data (temperature, humidity, wind speed and wind direction) were collected for two days, including on weekdays and weekends, to examine various scenarios in the study area for accurate and effective study of traffic CO modelling and mapping (e.g., hazard maps, risk maps, and further analysis). Figure 4 shows the data collection procedure adopted in this study.
## 4 Results
### Results of FDM
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Indicator**} & \multicolumn{3}{c}{**Sustainable Development**} & \multicolumn{3}{c}{**Facilities Maintenance**} & \multicolumn{3}{c}{**Market Strategy**} & \multicolumn{3}{c}{**Building Security**} & \multicolumn{3}{c}{**Public Interest**} & \multicolumn{3}{c}{**Financial Affairs**} \\ \cline{2-19}  & **Mid** & **Min** & **Max** & **Mid** & **Min** & **Max** & **Mid** & **Min** & **Max** & **Mid** & **Min** & **Max** & **Mid** & **Min** & **Max** & **Mid** & **Min** & **Max** \\ \hline Min & 5 & 3 & 6 & 5 & 3 & 7 & 4 & 3 & 5 & 5 & 3 & 6 & 5 & 4 & 6 & 5 & 4 & 6 \\ Max & 9 & 8 & 9 & 8 & 7 & 9 & 9 & 7 & 10 & 9 & 8 & 10 & 9 & 8 & 10 & 9 & 8 & 10 & 9 & 7 & 10 \\ Geometric Mean & 6.79 & 5.4 & 7.92 & 7.13 & 5.78 & 8.38 & 6.53 & 5.15 & 7.7 & 7.26 & 5.96 & 8.44 & 6.03 & 4.85 & 7.23 & 6.35 & 5.1 & 7.44 \\ Standard Deviation & 1.04 & 1.29 & 0.75 & 0.95 & 1.13 & 0.72 & 1.05 & 0.96 & 1.13 & 1 & 1.25 & 0.98 & 1.12 & 1.05 & 1.18 & 1.11 & 0.84 & 1.17 \\ Arithmetic Mean & 6.87 & 5.58 & 7.96 & 7.2 & 5.91 & 8.42 & 6.62 & 5.25 & 7.79 & 7.33 & 6.12 & 8.5 & 6.12 & 4.95 & 7.32 & 6.44 & 5.16 & 7.53 \\ G-score & & & & & & 7 & & 6.19 & 7 & 7.09 & & 6.56 & & 6.43 & & 10 \\ Rank & 7 & & & 5 & & & 11 & & 3 & & & & & & & & & & & \\ \hline \multicolumn{19}{c}{**Spatial Function**} & \multicolumn{3}{c}{**Urbana Form**} & \multicolumn{3}{c}{**Environmental Impact**} & \multicolumn{3}{c}{**Building Aschettic**} & \multicolumn{3}{c}{**Intelligent Technology**} & \multicolumn{3}{c}{**Multiple Satisfactions**} \\ \cline{2-19}
**Indicator** & **Mid** & **Min** & **Max** & **Mid** & **Min** & **Max** & **Mid** & **Min** & **Max** & **Mid** & **Min** & **Max** & **Mid** & **Min** & **Max** & **Mid** & **Min** & **Max** \\ \hline Min & 6 & 5 & 7 & 5 & 3 & 6 & 5 & 3 & 6 & 6 & 5 & 7 & 4 & 2 & 5 & 5 & 4 & 6 \\ Max & 9 & 8 & 10 & 9 & 8 & 10 & 9 & 8 & 10 & 9 & 8 & 10 & 8 & 7 & 9 & 8 & 7 & 9 \\ Geometric Mean & 7.37 & 6.24 & 8.42 & 7.17 & 5.8 & 8.35 & 6.94 & 5.6 & 8.25 & 7.16 & 6.07 & 8.34 & 5.73 & 4.37 & 6.75 & 6.32 & 5.05 & 7.54 \\ Standard Deviation & 0.81 & 0.79 & 0.82 & 1.05 & 1.25 & 1.09 & 1.24 & 1.52 & 1.17 & 0.84 & 0.81 & 0.73 & 1.02 & 1.26 & 1.02 & 0.8 & 0.91 & 0.71 \\ Arithmetic Mean & 7.41 & 6.29 & 8.46 & 7.25 & 5.95 & 8.42 & 7.05 & 5.83 & 8.33 & 7.2 & 6.12 & 8.37 & 5.82 & 4.56 & 6.82 & 6.37 & 5.12 & 7.58 \\ G-score & & & & & & 7.03 & & 6.97 & & 7.41 & & & 5.8 & & & & & & & & \\ Rank & 1 & & & 4 & & & 6 & & & & 2 & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A list of the overall experts’ evaluation results of design indicators.
## 4 Description of Different Deployed VPN Solutions
### IPsec/L2 TP Road Warriors Scenario  
The IPsec/L2 TP scenario is one of the most common deployments today. It is used for its excellent compatibility, but being outdated, it should be avoided whenever possible. IPsec in transport mode and NAT with multiple connected clients often suffer from performance and stability issues. Many L2 TP clients are configured using aggressive/PSK mode, which is problematic from a security point of view. The encapsulation of PPP over IPsec can cause MTU problems, and to remedy this, you force the MTU of clients into a range of 1200-1500 KB, depending on the type of WAN connection. For IPsec/L2 TP servers behind  
Figure 5: Generic VPN Deploy Topology.  
NAT, the registry settings on Windows to allow clients to connect have to be changed. The supported clients are many, all Apple iPhone, iPad, Mac OS X, Android, Linux with a command line, and Microsoft Windows. The server has three components to configure: libreswan for IPsec, xl2 tpd for L2 TP, and pppd for PPP. Figure 6 shows the comparison of the average throughput and standard deviation in IKEv1 L2 TP Road Warriors scenario, wired (on the left) and wireless (on the right).  
As seen from the figure, OpenWRT as an Ikev1-L2 TP client turns out to be the best-performing, closely followed by Windows 10, where, however, it is necessary to work at the registry level to enable Diffie-Hellman group 14, 2048 bit. As for the wireless clients, the iPhones are slightly better performing than Android.