esa-sceva/satcom-chunk-collection huggingface dataset:
# Sitting Posture Recognition Systems: Comprehensive Literature Review and Analysis
## 4 Machine Learning in Posture Recognition
[PERSON] [110] proposed a fuzzy logic-based recognition method for high-level processing, acquiring 99% accuracy in 21 videos. The CNN algorithm is used by [PERSON] [126] in a 408-frame training set. The accuracy is 82.86%, which is lower than other proposed algorithms. Convolutional neural networks [84,120,126], and Support Vector Machines [72,122] are the most frequently applied methods in video-based data. Table 10 presents various algorithms used in Kinect camera-based technologies for analyzing sitting postures. The table includes the reference, the algorithm employed, the size of the dataset (measured in frames or videos), and the accuracy percentage where available. Here's a detailed breakdown of each row. The table illustrates the diversity of \begin{table}
\begin{tabular}{c c c} \hline \hline
**Author** & **ML Algorithm** & **Accuracy** \\ \hline \multirow{5}{*}{[138]} & FG1 NN & 97.83\% \\  & NN50 & 95.83\% \\  & WG1 NN & 98.0\% \\  & WG20 NN & 98.08\% \\  & WG30 NN & 98.25\% \\ \hline \multirow{2}{*}{[67]} & Spiking neural network & 88.52\% \\  & Linear Regression & \textgreater{}90\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._ \begin{table}
\begin{tabular}{c c c c} \hline \hline
**ML Algorithm** & **Reference** & **ML Algorithm** & **Reference** \\ \hline Random forest & [58,64,65,68,92,95,96,140] & XGBost & [58] \\ Decision tree & [58,74,92,95,96,120,136,137] & ICA model & [56] \\ KNN & [58,61,74,95,96,101,137] & DCNN & [87] \\ SVM & [68,74,82,96,97,101,137] & DNN & [68] \\ Naive Bayes & [58,68,92,96,101] & TSPN & [87] \\ Multimodal DL & [55] & Logistic Regression & [68] \\ CNN & [59,70,88,94] & K-means & [96] \\ Linear Regression & [68,74,89,92] & Boosting & [96] \\ ANN & [75,80] & Custom & [60] \\ Multilayer perception & [66,92] & Light GBM & [74] \\ Fuzzy logic & [62] & ISOM-based & [90] \\ \hline \hline \end{tabular}
\end{table} 
Table 8: ML algorithms used in sensor-based technology. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Algorithm** & **Data Size** & **Accuracy** \\ \hline
[128] & A multiple-scale spatiotemporal feature extraction model & 700 videos & 93.52\% \\  & based on graph convolutional network (M2 SGCN) & 6922 frames & 99.57\% \\
[118] & Light Convolution Core (LCC) & 720K frames & 97.05\% \\  & Convolutional Block Attention Module (CBAM) & 720K frames & 97.05\% \\
[120] & Convolutional neural networks & 21 videos & 99\% \\
[110] & Fuzzy logic & 800 frames & 89.6\% \\
[72] & Random forest & 21,000 frames & 96.4\% \\
[141] & Convolutional neural networks & 16,300 frames & 97.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Algorithms used in video camera-based technologies. approaches in the field of sitting posture recognition using Kinect cameras, highlighting the importance of algorithm choice and dataset size in achieving high accuracy.
## 3 Materials and Methods
### Self-Attention Mechanism  
The self-attention mechanism, which is inspired by human cognitive processes, is a deep learning technique designed to prioritize critical information while reducing focus on less relevant data, thereby alleviating the issue of information overload caused by excessive network parameters [26]. In this study, we integrated the SA mechanism with BiLSTM to investigate its efficacy in extracting global traffic features at various positions before and after BiLSTM, as well as in uncovering deep feature correlations. Firstly, the input traffic data point \(I\) was multiplied with the corresponding weight matrix to obtain the  
Figure 6: BiLSTM structure.  
query vector \(Q\), key vector \(K\), and value vector \(V\), where \(Q=W_{q}\cdot I\), \(K=W_{k}\cdot I\), and \(V\)\(=W_{v}\cdot I\). Subsequently, Correlation \(A\) between \(Q\) and \(K\) was computed, followed by a softmax normalization to yield \(A^{\prime}\). \(A^{\prime}\) denotes the attentional weight of each query vector for each item of input data, respectively, which is given by the following:  
\[A=\frac{Q\cdot K^{T}}{\sqrt{d_{k}}}, \tag{8}\]  
\[A^{\prime}=softmax(A)=\frac{\exp(A)}{\sum\limits_{k=1}^{n}\exp(A)}. \tag{9}\]  
Finally, Value Vector \(V\) was multiplied with \(A\) to obtain the final output \(O\). The formula is \(O=A^{\prime}\cdot V\). The complete SA mechanism is formulated as follows:  
\[Attention(Q,K,V)=softmax(\frac{Q\cdot K^{T}}{\sqrt{d_{k}}})v. \tag{10}\]  
The augmented emphasis of the SA mechanism on the critical traffic features extracted by the input BiLSTM contributes to the heightened precision of the traffic model predictions.
## 4 Simulation Results and Analysis
### Grid Independence Test  
The melting process of a cylindrical heat exchanger with an outer diameter of 0.3 m and a height of 0.45 m was tested for grid independence. The number of grids was 80,000, 110,000, and 130,000, respectively. The melting curve is shown in Figure 9. In the figure, the number of grids corresponding to the curve s1 is 110,000, the number of grids corresponding to s2 is 80,000, and the number of grids corresponding to s3 is 130,000. It can be clearly seen from Figure 9 that the three curves are very similar, and the time to complete the melting is 2130 s, 2090 s, and 2110 s, respectively, which is very close. The differences between each other are only 1.87%, 0.96%, and 0.95%, respectively. Figure 7 shows the melting characteristics of the cylindrical heat exchanger with an outer diameter of 0.3 m and a height of 0.45 m when the number of grids is 110,000.  
Figure 8: Liquid fraction distribution for the cylindrical heat exchanger with a diameter of 0.07 m and height of 0.95 m.
## 3 Numerical Algorithms and Computational Methodologies
### Coupled Simulations for Core-Fluid, Shell-Fluid, and Connecting Wall  
#### 3.5.1 Initial and Boundary Conditions for Coupled Simulations--Core-Fluid  
As already mentioned for the core fluid system, the initial conditions for temperature and velocity, at the core inlet, are 2 m/s and 300 K, respectively, while the polymer-stresses are initialised as zero. Pressure is assigned a zero-gradient boundary condition. At the core outlet, all fluid variables are assigned a zero-gradient boundary condition with an exception of pressure, which is set to a value of zero. At the pipe walls, no-slip boundary conditions are imposed on the velocity, linear extrapolation boundary conditions are applied to the polymer-stresses, and a zero-gradient boundary condition is employed for the pressure. A coupled temperature-boundary condition is applied at the interface of the core-fluid and the connecting wall. In the OpenFOAM framework, such a boundary condition is built into the _rheoMultiRegionFoam_ solver to ensure continuity of the heat flux across domain interfaces, see [41].
## 2 Experiments
### Experimental Results  
The true stress-strain curves of Inconel 718 superalloy measured by previous isothermal compression tests were shown in Figure 3. As expected, the stress-strain relationship is highly non-linear, and highly susceptible to temperature, strain and strain rate. Via the comparison of one stress-strain curve with another, it can be summarized that the stress level decreases with temperature increase or strain rate decrease. This is due to the following facts. When the strain rate is accelerated, the dislocations participating in deformation in unit time increase, in this situation, deformation is unable to be coordinated duly by dislocation and grain movements, thus, the multiplication of dislocation push the stress value to a higher level. When raising the deformation temperature level, the heat activation increases the average kinetic energy in atoms, and makes atomic diffusion and dislocation movement easier, which induces the decrease in the stress level. As for each stress-strain curve, it shows an initial rapid followed by an increasingly slow work hardening (WH), after which two types of stress-strain evolution exist. In the first case, the stress decreases gradually after a single peak value, an evidence of dynamic recrystallization (DRX) dominated softening, which corresponds to the conditions of 0.01 s\({}^{-1}\) and 1203-1253 K, 0.1 s\({}^{-1}\) and 1153-1253 K, 1 s\({}^{-1}\) and 1153-1303 K and 10 s\({}^{-1}\) and 1203-1303 K. In the second case, the stress approximately keeps a steady state, an evidence of dynamic recovery (DRV) dominated softening, under the conditions of 0.01 s\({}^{-1}\) and 1303-1353 K, 0.1 s\({}^{-1}\) and 1303-1353 K, 1 s\({}^{-1}\) and 1353 K and 10 s\({}^{-1}\) and 1353 K. The metallographs under the conditions of 1203 K and 0.01 s\({}^{-1}\) and 1353 K and 0.01 s\({}^{-1}\) were shown in Figure 4. It could be seen that considerable DRX grains were generated but some initial grains are only stretched instead of transforming into new DRX grains at 1203 K, similarly, a small amount of DRX grains generate along the stretched grain boundaries at 1353 K. So it was concluded that DRV and DRX usually coexist even though one of them may play the dominant role. From the previous descriptions, it can be concluded that the compression flow behaviors of Inconel 718 superalloy are extremely complex and highly non-linear, not only due to the effects of temperature and strain rate on stress level, but also due to the evolution characteristics involving WH, DRX, DRV and even the precipitation and dissolution of \(\gamma^{\prime}\), \(\gamma^{\prime\prime}\) and \(\delta\) phases.  
Figure 2: Experimental equipment and condition of the isothermal compression tests: (**a**) Gleeble 3500 testing machine; (**b**) experimental process.
# A Quality Control Method for High Frequency Radar Data Based on Machine Learning Neural Networks
## 3 Results
### Continuous Anomalies
Figure 9a clearly illustrates how the iterative prediction method significantly enhances the efficiency of the anomaly detection. The trend in F-score variation is influenced by several factors, including the type and quantity of anomalous data, the selected threshold, and the number of iterations. As is shown in Figure 5b, anomalies with a length of 2 predominate within the realm of continuous anomalies. Figure 9b,c further emphasizes the exemplary performance of the iterative prediction model in addressing consecutive anomalies with lengths of 2 and 3. Combining these insights, even though the model encounters challenges when detecting longer consecutive anomalies due to constraints related to the input sequence length, its outstanding performance in detecting consecutive anomalies with lengths of 2 and 3 remains evident in Figure 9d,e. In Figure 9d, the overall curve shifts upward and to the right, indicating an improvement in the model compared to the previous version. In Figure 9e, it can be observed that the iterative prediction model effectively enhances the model's anomaly detection performance, with improved recall rates for both single-point anomalies and consecutive anomalies.
# Minimum Distance Optimization with Chord Edge Growth for High Girth Non-Binary LDPC Codes
## 3 Minimum Distance Optimization Using Progressive Chord Edge Growth  
Based on the high-girth graph [22; 23; 24] and a set of optimized non-zero entries that are associated with a row [15], a method of configuring non-zero entries in the manner of progressive chord edge growth is proposed to increase the minimum distance of NB-LDPC codes. The non-zero elements are selected for each chord edge based on the FRC of the cycles. Only those cycles containing the newly-added chord edges are checked, which effectively reduces the number of cycles that need to perform a FRC check. Furthermore, a simple variant of the proposed method is applied to optimize the NB-LDPC codes with an efficient encoding structure.
# Research on Remaining Useful Life Prediction of Bearings Based on MBCNN-BiLSTM
## 2 Theoretical Background
### LSTM Network  
The recurrent neural network (RNN) is specifically designed to deal with data related to time sequence, which can better deal with the temporally related problems by adding the relationship between front and back temporal order on the basis of fully connected neural networks. However, RNN networks have gradient disappearance and gradient explosion problems while processing long-term data and are not appropriate for long series forecasting. LSTM is a variant of RNN, which can better solve these problems, and its core lies in using cell states to record long-term historical information and manage it through various gate mechanisms. Its structure is illustrated in Figure 2. It mainly includes input gates, forgetting gates, cell states and output gates.  
The Input Gate expressions are as follows:  
\[Z^{i}=\mathit{sigmoid}\left(W_{i}\cdot\left[V\left(W\left(i\right),h^{t-1} \right)\right]+b_{i}\right) \tag{3}\]  
\[Z=\mathit{tanh}\left(W_{c}\cdot\left[V\left(W\left(i\right),h^{t-1}\right) \right]+b_{c}\right) \tag{4}\]  
The expression of the Forget Gate is as follows:  
\[Z^{f}=\mathit{sigmoid}\left(W_{f}\cdot\left[V\left(W\left(i\right),h^{t-1} \right)\right]+b_{f}\right) \tag{5}\]  
The cellular update expressions are as follows:  
\[C^{t}=Z^{f}*C^{t-1}+Z^{i}*Z \tag{6}\]  
\[Z^{o}=\mathit{sigmoid}\left(W_{o}\cdot\left[V\left(W\left(i\right),h^{t-1} \right)\right]+b_{o}\right) \tag{7}\]  
The Output expressions are as follows:  
\[h^{t}=Z^{o}*tanh\left(C^{t}\right) \tag{8}\]  
where \(W_{i}\), \(W_{f}\), \(W_{c}\), \(W_{o}\) denote the weight matrix of the Input Gate, the weight matrix of the Forget Gate, the weight matrix of the current input cell state and the weight matrix of the Output Gate, respectively; \(V\) denotes the merge operation on the matrix; \(b_{i}\), \(b_{f}\), \(b_{c}\), \(b_{o}\) denote the bias term of the Input Gate, the bias term of the Forget Gate, the bias term of the current input cell state and the bias term of the Output Gate, respectively; \(Z^{i}\), \(Z\), \(Z^{f}\), \(Z^{o}\) denote the Input Gate, the current input cell state, Forgetting Gate, and Output Gate, respectively.  
Although the gradient disappearance and gradient explosion issues that arise in RNN can be mitigated in LSTM, its training of the network in prediction always propagates from front to back in the order of time series, with the result that it can only learn the information before the degradation of the current bearing and cannot fully utilize the information after the degradation of the current bearing. Since the current status of bearing degradation is relevant to the previous historical information, as well as being closely relevant to the subsequent information, this paper uses BiLSTM instead of LSTM for the temporal feature extraction of bearing degradation. The structure of the BiLSTM network is illustrated in Figure 3, which can fully exploit the bearing degradation time features and improve the data utilization and prediction accuracy.  
Figure 2: LSTM network structure.
## 2 Methods
### Description of the Time-Gated Long-Range Imaging LiDAR System
After completing this operation, proceed to the subsequent operations in a sequential manner until the entire scene is comprehensively scanned. By means of relative algorithms, one can reconstruct the image. Initial validation results, displaying high-resolution, low-noise indoor imaging at 20 m, are presented in Figure 1d. These imaging targets are, a 1951 USAF resolution, a 5 cm tall dinosaur toy and a 10 cm diameter lens cap with the company name (TAMRON) on it, respectively. The acquisition of data with high SNR is the prerequisite to ensure the normal operation of the system. Therefore, a comprehensive suite of denoising techniques was applied to the current LiDAR system, encompassing temporal, spatial, spectral, and polarization filtering methods, as shown in the probe part of Figure 1a. In detail, the polarization filter works first with the help of polarization beam splitter, where 50% of the solar background illumination from the receiver head is filtered and most of the signal light can get through. Then, the spectral filter works by a narrow bandpass filter (1.2-532 nm), where the solar background illumination outside the 1.2 nm bandwidth is removed. Finally, the temporal filter is used with the range-gated technique. In addition, the spatial filter works using telephoto lenses with a small field of view. With the help of these noise reduction techniques, the noise is reduced to about ten million parts of the original noise. It is worth noting that both lenses can be adjusted to meet different requirements. As depicted in Table 1, the first row presents an imaging result of a building located at a  
Figure 2: Flowchart showing sequence of steps followed for LiDAR data acquisition and processing. distance of 2500 m using the aforementioned setup, while the second row displays the same building captured with another transmitter lens featuring a focal length of 2000 mm and an aperture diameter of 200 mm. Evidently, the result in the second row exhibit higher resolution, revealing more intricate details of windows. Typically, a larger aperture corresponds to a higher resolution, but also entails a smaller field of view and longer distance.
## 4 Challenges and Future Research Areas
### Channel Stationarity  
As shown in Section 3 the mobility of UAVs significantly affects the channel stationarity, thus compromising the WSS characteristics. Non-stationarity is a fundamental characteristic that distinguishes UAV channels from ordinary channels. The inference of wide sense stationary uncorrelated scattering (WSSUS) is often disrupted in A2A and A2S communication. Apart from the geometrical stochastic models presented above, there is no extensive and thorough research available in the literature that assesses the non-stationary characteristics of the A2A and A2S channels. Moreover, the absence of measurements is more than evident. In this context, the assessment of the stationarity intervals will be an intriguing and novel topic in future research, in which the wideband-frequency-selective characteristics of the channels (small-scale fading parameters) will need to be evaluated within these stationary periods. Different metrics can be employed for this purpose, using various propagation scenarios, such as the time-domain power-delay profile (PDP) the correlation coefficient, the correlation matrix distance (CMD), the spectral difference, or spectrum evolutionary methods. Therefore, additional measurement experiments in different environments and different flight scenarios (with variations in altitude, speed, trajectory, and distance) will help to elaborate the stationary characteristics of the A2A and A2S channels further.
# Application of Nanofluids in CO\({}_{2}\) Absorption: A Review
## 2 Nanofluid
### The Method of Nanofluid Preparation
#### 2.1.1 One-Step Synthesis Method  
In the one-step method, nanofluid is directly prepared in the base fluid and there is no need for intermediate phases like storage or drying of nanoparticles and their dispersion in the base fluid [49]. This means, the synthesis and dispersion of nanoparticles take place at the same time and inside the base fluid, this hinders oxidation of nanoparticles. Some technologies like plasma, microwaves, or lasers can help this process. Nanoparticles like Au, Ag and Cu have been successfully synthesized via a single-step method [50]. This process was also used by some researchers for the preparation of nanofluids. To prevent the agglomeration of nanoparticles, dispersion, drying, transportation, and storage are eluded in this method, this will increase the stability of nanofluid and lower the production costs. Generally, since single-step nanofluid preparation is a rather costly method, it is not practical in large-scale production. Thus, special focus has been placed on the single-step chemical process, which is a relatively cost-effective method of nanofluid preparation [49].
## 3 Results
### Mapping LAI for Schiermonnikogo Island  
LAI was mapped for the Island using both RapidEye and Sentinel-2 data. Before producing the maps, masks were produced for saltmarshes, grass and crops using the vegetation map of the Island modified by [86] and employed to mask out the areas occupied by other land cover types (build-up area, water bodies and dunes) in the Rapid-Eye and Sentinel-2 imageries. We intentionally included the agricultural fields in the masked images since the suitability of PROSAIL in mapping LAI of crops has been earlier confirmed [71; 73; 78]. The masked RapidEye and Sentinel-2 images were then used as inputs for the inversion process of the two LUTs. Accordingly, the LAI maps of the island were then retrieved using the best fitting spectra. Figure 8 shows the LAI maps derived from RapidEye and Sentinel-2. Their mean and variance agreed with those observed during the field campaigns (Table 1). Although the spatial distribution of LAI from the two years are comparable, the Sentinel-2 derived map has larger range and shows higher LAI values in comparison to those from RapidEye. Nevertheless, the map produced from RapidEye data (owing to its fine spatial resolution) presents more details on the spatial variation of LAI.
# The ConTAD Project: Detection of Multi-Container Loss for Safety of Navigation +
## 2 Review of the MSC Zoe Accident
### Relevant Findings  
The following findings are deemed pertinent to the development of a container tracking device (CTD) with automatic accident detection capabilities.  
* The accident did not occur in extreme weather conditions. The investigation revealed that the current ship size with beams of 60 m is particularly susceptible to heavy rolling in normal sea states. Consequently, it can be anticipated that further losses of containers will occur.
* It took the crew approximately five hours to notice the container loss. Upon realizing that they were losing cargo, the crew turned the ship's bow against the waves, thus preventing further losses. The accident occurred in darkness and during a storm, conditions that could have contributed to the unnoticed loss of cargo. The implementation of an automatic device for the detection of container loss would allow for prompt action in the event of a loss, thereby reducing the number of containers lost.
* The accident occurred near the coastline, resulting in significant environmental contamination in ecologically sensitive and protected areas. The shallow waters contributedto the occurrence of short wave periods, which were likely a critical factor in this accident. It can be reasonably anticipated that further container losses will occur in similar areas. This underscores the necessity for a fast and effective means of detecting and salvaging lost containers, particularly those containing dangerous goods (DG).
* It took over nine months to locate and salvage 90% of the lost containers. Only 75% of the cargo was recovered [5]. Reducing this time span will result in cost savings and a reduction in pollution following a container loss.
* The precise number of lost containers could only be determined after the ship had been fully unloaded before it went to a shipyard for repairs. Therefore, it took over two weeks to ascertain which containers had been lost at sea. The prompt notification of the loss of containers will serve to reinforce the resilience of supply chains.