esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Vertical Shaft Technology and Application in Soft Soil for Urban Underground Space
## 4 Technological Advancements in Mechanized Systems
### Blind Shaft Boring
#### 4.2.2 Shaft Boring Road Header (SBR)  
The shaft boring road header (SBR) is developed for the mechanized sinking of blind shafts in soft to medium-hard rocks with a compressive strength of up to 120 MPa. The SBR is equipped with a road header boom and a rotating cutting drum for efficient excavation of soft to medium-hard consolidated rock. The excavated material from the bottom of the deep shaft is drawn and pneumatically transported to intermediate containers, from which it is cyclically hoisted to the surface using typical tubes. The SBR machine enables sinking of shafts with diameters of 7 to 12 m to a depth of approximately 1000 m, without the need for a pilot hole.  
In 2018, for the first time in Europe, the Nezhinsky Mine in Belarus utilized this mechanized technology to sink two large-diameter service and production shafts, each with a diameter of 8 m, to depths of 697 and 750 m, respectively [51].  
Due to the limitations of the cutting devices employed, neither the Herrenknecht SBR excavator nor the Russian or Polish shaft excavators are suited for excavation in hard rock. The European shaft borers and SBR machines utilize pencil point tools for rock excavation, constrained by rock strength. To address this limitation, Herrenknecht, in collaboration with Rio Tinto under the \"Mine of the Future\" program, developed an alternative shaft excavation machine known as the SBM. Subsequently, the shaft boring cutterhead (SBC), another shaft excavator, was developed. Both the SBM and SBC are equipped with disc cutters, contrasting with the pencil bits used in the SBR [52]. In comparison to conventional shaft excavation, the SBR is safely operated from elevated working decks, ensuring no personnel are present at the bottom of the shaft during mechanized excavation. Overall, the SBR design establishes new safety standards for personnel.
# Sequence-to-Sequence Video Prediction by Learning Hierarchical Representations
## 2 Related Work  
A number of video prediction approaches [6; 7; 19; 27; 28; 29; 30; 31; 32; 33] have been proposed in recent years. The work of [4] introduced the sequence to sequence LSTM model originated from language modeling [34; 35] in order to predict video frames. [PERSON] et al. [3] extended the LSTM to ConvLSTM by introducing the convolutional operation in the recurrent transitions to better capture the spatiotemporal correlations for precipitation nowcasting. [PERSON] et al. [36] found transforming pixels from the previous frames is able to better capture object motion and produce more accurate video predictions and developed an unsupervised action-conditioned video prediction model that estimates the distribution over pixel motion from previous frames. [PERSON] et al. [37] extended the ConvLSTM to build neural networks using predictive coding to predict future frames. MCnet [16] and DRnet [15] proposed decomposition-based approaches such that, each frame is decomposed into content and motion, and then is fed into separate encoders. Other methods [38; 39; 40] consider modelling the individual dynamics of decomposed objects, and propose to decompose frames into those of separated objects, and to model the motion of each object. The work of [17; 18] proposed to use high-level features (e.g., human joint landmarks) to model the dynamics of motion, while the method by [19] focuses on predicting high-level features. PredRNN++ [41] proposed Causal LSTMs with cascaded dual memories to model short-term video. [PERSON] et al. [42] proposed to generate video frames from a single semantic map. [PERSON] et al. [43] developed a point-to-point network which generates intermediate frames given the start and end frames.  
Besides the methods mentioned above, the GAN framework has been increasingly used in video generation or prediction [44; 45; 46; 47]. The work of [5] applied the adversarial training method to a multi-scale architecture to obtain sharp frames for prediction. A dual-motion GAN framework is proposed in [48] by fusing the future-flow and future-frame. The work of [49] combined GAN framework and variational autoencoder to get the stochastic adversarial video prediction model. In this paper, we present a novel hierarchical architecture for video prediction which exploits the hierarchical structures of spatiotemporal features.
# Frequency Comb-Based WDM Transmission Systems Enabling Joint Signal Processing
## 4 Joint Digital Signal Processing Schemes for Carrier Recovery
### Algorithms for Joint Carrier Recovery  
The carrier recovery stage of the DSP chain is typically divided into a coarse frequency offset estimation part and a fine carrier phase estimation part. The coarse frequency offset estimation can be performed relatively seldom since the frequency offset varies only slowly. In contrast, the fine phase estimation needs to be able to track rapid phase fluctuations originating from the phase noise of the transmitter and LO laser. This makes it more demanding, both in terms of computational complexity and performance. Therefore, most of the focus of joint carrier recovery schemes is on the fine phase tracking. Carrier recovery can be performed either in a blind fashion or aided by pilot symbols. Here, we focus on blind methods. Further discussion on pilot-aided joint phase noise estimation can be found in [105; 106].  
There are two main strategies to joint phase tracking, illustrated in Figure 6, that have different objectives and benefits. The first scheme is best described as master-slave phase tracking and aims to share the computational resources needed for phase estimation between the several channels that share the same phase-noise. As the name implies, the phase estimated from a master channel is applied to the other, slave channels. The second strategy can be described as a joint-estimation scheme, where the phase is estimated from all the channels, and the additional phase information is used to improve the phase estimation. The two strategies are described in more detail below.  
Figure 6: Principle of (**a**) master-slave phase recovery and (**b**) joint phase estimation.
## 1 Introduction
The key contributions of this study include the following:  
* Development of a digital FM-ACSK communication system featuring a modified Chua circuit chaos generator, error-feedback chaotic synchronization, and an FM-ACSK modulation scheme, all tailored for FPGA hardware implementation. * Creation of a precise and verified Simulink mathematical model for the digital components of the communication system. * Successful implementation and testing of the FPGA-based communication system, demonstrating practical feasibility and advancing the state of chaotic secure communication systems. The remainder of the paper is organized as follows. Section 2 provides a detailed description of the FM-ACSK communication system, including its architecture and operational principles, as well as the FPGA implementation. Section 3 discusses the experimental setup for the system's study and the results of the tests, demonstrating the system's noise performance and robustness. In Section 4, the system's performance and security aspects are discussed in a broader context, along with key conclusions and directions for future research. Finally, Section 5 provides brief conclusions.
###### Abstract  
Short-term load forecasting (STLF) plays a pivotal role in the electricity industry because it helps reduce, generate, and operate costs by balancing supply and demand. Recently, the challenge in STLF has been the load variation that occurs in each period, day, and seasonality. This work proposes the bagging ensemble combining two machine learning (ML) models--linear regression (LR) and support vector regression (SVR). For comparative analysis, the performance of the proposed model is evaluated and compared with three advanced deep learning (DL) models, namely, the deep neural network (DNN), long short-term memory (LSTM), and hybrid convolutional neural network (CNN)+LSTM models. These models are trained and tested on the data collected from the Electricity Generating Authority of Thailand (EGAT) with four different input features. The forecasting performance is measured considering mean absolute percentage error (_MAPE_), mean absolute error (_MAE_), and mean squared error (_MSE_) parameters. Using several input features, experimental results show that the integrated model provides better accuracy than others. Therefore, it can be revealed that our approach could improve accuracy using different data in different forecasting fields.  
accuracy; bagging ensemble; deep learning; machine learning; short-term load forecasting Article
## 5 Conclusions  
This paper addresses the challenges of low signal-to-noise ratio and significant time-varying Doppler effects in mobile underwater acoustic communication by proposing a CCF-MSS reception scheme. By utilizing correlation cost factors to assess the accuracy of Doppler estimation, the method performs symbol-by-symbol Doppler estimation to achieve dynamic CFO compensation and addresses the issue of energy reduction caused by signals with significant CFO deviations passing through band-limited filters. The proposed method does not require complex speed estimation and significantly enhances Doppler tolerance. Simulation analyses and sea trial data confirm that CCF-MSS exhibits superior resistance to Doppler frequency shifts and noise compared to the FDE-MSS, CDE-MSS, and WDE-MSS reception methods used for comparison across various motion modes. Additionally, it demonstrates high computational efficiency, making it suitable for real-time underwater acoustic communication. In future work, we plan to conduct additional experiments in mobile underwater acoustic communication, to validate the robustness of the proposed system, and to extend single-to-single system communication to single-to-multiple system communication.  
Conceptualization, [PERSON] ([PERSON]) and S.H.; methodology, [PERSON] ([PERSON]); software, Y.H. ([PERSON]); validation, H.Z., S.H. and Y.H. ([PERSON]); formal analysis, J.X.; investigation, G.Y.; resources, S.H.; data curation, [PERSON] ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, [PERSON] ([PERSON]); visualization, H.Z.; supervision, S.H.; project administration, S.H.; funding acquisition, S.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors do not have permission to share data.  
The authors declare no conflicts of interest.
# Fall Detection Approaches for Monitoring Elderly HealthCare Using Kinect Technology: A Survey
## 4 Kinect-Based Fall Detection Approaches
### Data Acquisition  
Data acquisition is the most critical step for all fall detection methods. In the case of a vision-based system, it consists of continuously tracking the person in the scene. First, the camera acquires the predefined data flows, then the processing unit analyzes them. Kinect allows for the proper surveillance of older adults in controlled environments. This technology, with its different smart sensors, encourages the differentiation of humans from other objects in the scene. The three generations of Kinect can record and hear in real time during both day and night. They provide four types of vision streams, namely, color, depth, infrared, skeleton, and sound signals. The skeleton class was available in version 1, and has been replaced by the body class in version 2. Depth and skeleton data ideally respond to ethical issues related to privacy, as they prevent any identification of the people being monitored. Fall detection techniques based on RGB or depth fluxes do not monitor the parameters of the subjects' joints; instead, they are based on image processing techniques on the frames around the region of interest. Other models use the 3D positions of the body's joints estimated by the Kinect to monitor changes in posture position before, during, and after a fall.  
Figure 2: The hierarchy of joints provided by V1 (**a**), V2 (**b**), and Azure Kinect (**c**) [32,41].  
In most approaches, volunteers perform scenarios of falls and activities of daily life to make datasets. They operate in experimental configurations that guarantee their safety. Evaluation of approaches in offline mode are carried out with data already prepared. The integration of data collected by older adults is a challenge for researchers. Effectively, we are convinced that real data contributes to reinforcing the realism of fall detection techniques. Researchers adapt the data acquisition conditions, scenarios, and characteristics necessary to ensure that their perform algorithms successfully.
## 2 Description of the Experiment
### Assumptions and Mathematical Model for Rate-Based Model  
Assumptions for the rate-based approach according to [PERSON] et al. (2016) are summarized below:  
* The reaction is quick and occurs in the liquid film;
* The absorption column is supposed to be adiabatic;
* The interfacial surface area is identical for both heat and mass transfer;
* The liquid-side heat transfer resistance is small compared to the gas phase, and the interface temperature is, therefore, identical as the bulk temperature;
* The type of flow is plug flow, and concentration and temperature change in the radial direction is negligible;
* Both liquid and gas phases are formally discussed as ideal mixtures
## 8 Limitations in the Use of Essential Oils  
* Comprehensive studies are scarce on the toxicity of EOs, particularly concerning pets and animals, leading to a lack of robust evidence on their potential risks and benefits.
* The effects of EOs can vary significantly among different species of animals. This variability introduces complexity in establishing standardized dosages and safety guidelines applicable across diverse animal groups.
* Determining the appropriate dosage and application methods of EOs for animals is challenging due to factors like body weight, metabolism, and individual sensitivities.
* The chemical composition of EOs could vary based on factors like plant source, extraction method, and storage conditions. This lack of standardization poses challenges in predicting their precise effects on animals and requires careful consideration of each oil's unique properties.
* Some EOs, even those derived from plants, can pose risks to animals. For instance, ingestion of tea tree oil has led to intoxication in both humans and animals, demonstrating the importance of informed usage.
## 3 Numerical Modeling and Validation
### Numerical Model Validation  
The accuracy of the numerical model was preliminarily verified through comparative analysis with the V-D theoretical curve. The statistical evaluation of the Root Mean Square Error (RMSE) and Coefficient of Determination (\(R^{2}\)) between numerical results and theoretical predictions is summarized in Table 3. The simulated data were subjected to normalization processing, and the comparative results between numerical simulations and the V-D theoretical method are presented in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Categories of Geological Parameters** & \(P\) (kg/m\({}^{3}\)) & \(\sigma_{\text{ci}}\) (MPa) & \(GSI\) & \(m_{\text{i}}\) \\ \hline Condition A & 2.5 \(\times\) 10\({}^{3}\) & 35 & 50 & 20 \\ Condition B & 2.3 \(\times\) 10\({}^{3}\) & 15 & 30 & 20 \\ Condition C & 2.1 \(\times\) 10\({}^{3}\) & 5 & 15 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter value table for numerical simulation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Condition** & **Description of Rock Mass Characteristics** & **The Recommended Value of D** \\ \hline \multirow{4}{*}{1} & Tunnel boring machines cause minimal & \\  & disturbance to the surrounding rock during & \\  & excavation or blasting based on excellent & \\  & quality control & \\  & Mechanical or manual (non-blasting) excavation of & \\
2 & poor-quality rock masses causes minimal & 0 \\  & disturbance to the surrounding rock & \\
3 & The squeezing leads to the uplift of the tunnel & \\  & bottom and requires the installation of an invert & \\  & Poor-quality blasting results in severe local & \\
4 & damage of 2 to 3 m in the surrounding rock of hard & \\  & rock tunnels & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Recommended values of the rock mass disturbance coefficient D in tunnel engineering.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Categories of Geological Parameters** & **RMSE** & \(\mathbf{R^{2}}\) \\ \hline Condition A & 0.027 & 0.996 \\ Condition B & 0.030 & 0.996 \\ Condition C & 0.033 & 0.995 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical table of RMSE and \(R^{2}\) for each condition.
## 2 Methodology
### Optimization of ANN by Chaos Game Optimization  
The optimization of ANNs using CGO for predicting blast-induced ground vibration involves fine-tuning the parameters and structure of the neural network to enhance its performance in predicting blast-induced ground vibration. The optimization process is implemented as follows:  
Figure 2: Updating the positions of the temporary triangles [42]. (**a**) The first seed in the search space; (**b**) The second seed in the search space; (**c**) The third seed in the search space; (**d**) The fourth seed in the search space.  
Step 1. Initialization: The optimization process begins by initializing the weights and biases of the ANN model. This involves setting up the initial positions \(X_{i}\) of the solution candidates within the predefined search space, which is often represented as a Sierpinski triangle. - Step 2. Search loop: The main search loop of the CGO algorithm is executed to adjust the positions of the solution candidates iteratively. Each candidate solution corresponds to a specific set of weights and biases of the ANN model. - Step 3. Objective function: The performance of each candidate solution (weights and biases) is evaluated using an objective function, which measures how well the ANN model performs in regards to the given task or problem. In this study, RMSE was used as the objective function for the optimization process. - Step 4. Position updating: During each iteration of the search loop, the positions of the weights and biases are updated based on the CGO algorithm. This involves moving the candidates towards better regions of the search space to improve the performance of the ANN model. - Step 5. Exploration and exploitation: The movement limitation factor, \(\alpha_{i}\), is used to balance between exploration (searching for new regions of the search space) and exploitation (exploiting known promising regions). This factor helps control the exploration-exploitation trade-off during the optimization process. - Step 6. Convergence: The optimization process continues until a stopping criterion is met, such as a maximum number of iterations or achieving a satisfactory level of performance. At this point, the best weights and biases found during the optimization process are selected as the final solution. - Step 7. Performance evaluation: Finally, the performance of the optimized ANN is evaluated on a separate validation dataset to ensure that it generalizes well to unseen data and performs effectively in real-world scenarios.  
The framework of the CGO-ANN model for predicting blast-induced ground vibration is introduced in Figure 3.  
Figure 3: Framework of the CGO-ANN model for predicting blast-induced ground vibration.
# Shape Optimization of an Open Photoacoustic Resonator
## 3 Results and Discussion
### Verification  
The optimized resonator is manufactured for an experimental verification of the results. The production tolerance is 0.01 mm. A schematic of the resonator cross-section is shown in Figure 10.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Optimization Variables** & **Range (mm)** \\ \hline Resonance cylinder length \(L_{r}\) & 1–8 \\ Resonance cylinder position \(D_{r}\) & 6–8 \\ Cavity cylinder length \(L_{c}\) & 16.9–17.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Range of optimization variables.  
Figure 8: Relative PA signal of the combined sweep of \(L_{c}\) and \(D_{r}\) (**a**) along with a contour plot (**b**). The color scale gives the values of \(\xi\).  
Figure 9: (**a**): Absolute pressure distribution inside the optimized resonator at 49.2 kHz. (**b**): Relative PA signal of the optimized resonator around 49.2 kHz.  
Additionally, the frequency response of the optimized resonator is calculated using the VT-PML, VT-BEM and AME-PML approaches. The AME-PML simulation is performed with frequency intervals of 10 Hz. The VT-PML and VT-BEM simulations are performed at intervals of 300 Hz and fitted with a cubic spline. The difference in the increments is because the simulation time of the AME-PML is shorter than that of the VT-PML and VT-BEM approach [21]. In order to account for the production limits, the resonator dimensions are rounded 0.01 mm. The three simulation plots are shown in Figure 11 together with the experimental results.  
Experiment and simulations consistently show that the resonance with a peak at 49 kHz has by far the highest amplitude. All simulations predict similar spectral features and show a slight shift of the peak frequency from 49.2 kHz to 49.0 kHz and a decrease of \(\varsigma\) from 7.23 to 4.59. This difference can be attributed to the rounded resonator dimensions, representing the production uncertainties. The new resonator represents nevertheless a significant improvement over the reference resonator.  
The strongest resonance in both the measurements and the simulations spans a wide frequency range. The measured resonance frequency equals 48.3 kHz and is thus lower than the simulation results (49 kHz). This might be attributed to PA signal leakage at the microphone mount. This effect is not considered in the numerical models.  
The weak resonances, recognizable in the simulations, are hard to identify in the measurement due to noise. The measured amplitude of the optimized resonator is 2.34 times larger than the amplitude of the reference resonator as seen in Figure 12.  
Figure 11: Frequency response of the experiment against the simulations of the optimized resonator. The blue, red and green plots represent the VT-PML, VT-BEM and AME-PML approach, respectively. The blue dashed line shows the experimental measurement.  
Figure 10: Schematic of the cross-section of the optimized resonator, dimensions in mm. The dashed lines represent the cylindrical symmetry axes.