esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The integration of AI models into the SEEMP system has demonstrated significant potential for advancing real-time energy management and environmental monitoring in office environments. The system includes real-time data collection, secure transmission, and sophisticated AI-based forecasting, offering an effective solution for optimizing energy consumption and maintaining optimal environmental conditions in office spaces. The edge and cloud AI capabilities in a sensor node monitoring system exemplify the potential for enhanced decision-making, improved user experience, and notable energy savings in IoT environments. Key findings from the comparative analysis of AI models, specifically the hybrid model, LSTM, and GRU, revealed that the hybrid model consistently outperforms the others in accuracy and efficiency. The hybrid model's superior performance is evident across various metrics such as lower MAE and RMSE and higher \(R^{2}\) values in applications such as refrigerator power forecasting, where it achieved an MAE of 2.12%, an RMSE of 7.77%, and an \(R^{2}\) of 93.69%. These results underscore the hybrid model's efficacy and reliability, making it highly suitable for real-time IoT applications in office energy management and environmental monitoring. Additionally, the proposed system is mainly designed for office environments, and further customization and validation would be needed for it to be effectively applied in industrial or residential settings. Another challenge with IoT systems is that they often rely on low-cost sensors, which can introduce noise and affect the model's performance. In future work, the model could be improved by accounting for different types of noise to ensure more accurate predictions in a variety of situations.  
Conceptualization, M.M.R.; Methodology, M.M.R. and M.S.N.; Software, M.M.R.; Validation, M.S.N.; Formal analysis, M.I.J. and M.M.R.; Investigation, M.M.R. and M.I.J.; Data curation, M.I.J.; Writing--original draft, M.M.R. and M.S.N.; Writing--review & editing, M.M.R. and M.I.J.; Visualization, M.M.R.; Supervision, Y.M.J.; Project administration, Y.M.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding authors.  
The authors declare no conflicts of interest.
# Volatile, Microbial, and Sensory Profiles and Consumer Acceptance of Coffee Cascara Kombuchas
## 3 Results and Discussion
### Volatile Organic Compounds
#### 3.3.2 Coffee Cascara Kombuchas
Acetaldehyde was identified in all cascara kombuchas. This is a key product of fermentation and an inevitable component in wine [89]. Acetaldehyde has also been identified in a distilled, fermented coffee pulp beverage [90]. The number and area of alcohols increased from d3 to d9, especially phenylethyl alcohol and ethanol. Even if not identifiable on the olfactory level, ethanol is an important component of the kombucha aromatic profile [72]. Isoamyl alcohol (3-methyl-1-butanol) and 2-methyl-1-butanol, identified in CCB and CCN kombuchas, have also been identified in distilled, fermented coffee pulp beverages [90]. The contribution of acids to the global aroma depends on their concentration range. At low concentrations, acids with six to ten carbons provide a mild and pleasant aroma to wine [91]. The main acid responsible for kombucha sourness is acetic acid. Its concentration tends to increase with fermentation time [42; 72]. Three volatile fatty acids were identified in CCB K and CCN K, decanoic acid, hexanoic acid (caproic acid), and octanoic acid. They have previously been identified during the production of sparkling wine, but only hexanoic and octanoic acids were mentioned as odor-active compounds [71]. Important esters identified during coffee cascara kombucha fermentation were ethyl acetate, isoamyl acetate, ethyl octanoate, ethyl hexanoate, ethyl decanoate, and ethyl isobutyrate. Most of them have also been identified in grape musts fermented by different yeasts [92]. Ethyl acetate was identified in a fermented coffee pulp distillate [90]. Ethyl decanoate and ethyl octanoate have been reported as abundant in ciders [77]. Ethyl isobutyrate, ethyl hexanoate, and isoamyl acetate have been reported as impact compounds in sparkling wine [71]. Regarding ketones, \(\beta\)-damascenone has been described as an aroma-active compound in a Robusta coffee pulp pure [93], while \(\gamma\)-nonalactone has been reported as the main odorant in red wine [88]. Monoterpenes were reported as volatile components of fruits responsible for a wide spectrum of aromas, mostly perceived as very pleasant [85]. Limonene was identified in CCB K d9 and CCN K d0, d3, and d6. Linalool was identified in all coffee cascara infusions and kombuchas. It has been previously identified in coffee cascara infusions [2].
## 2 Project Overview and In-Situ Monitoring
### Geological Conditions  
The Badaling Great Wall station passes through the Jundushan mountains, and there are obvious ground elevation changes. The ground elevation is around 600-1030 m, and the depth of the groundwater level is between 11-77 m. Figure 4 has shown the geology of the super-span tunnel in the Zhangjiakou direction. Complex rock types and several groups of faults and joint systems with different trends have together formed a complex geological environment. The typical physical and mechanical properties of the surrounding rock in the relevant areas have been detailed in Table 2. According to the geological survey data, the ground quality of the surrounding rock was classified as Grade V. Obvious ground settlement and engineering accidents can easily occur when tunneling in the rock of Grade V, as it is considered to be very poor quality. A simplified comparison of the quality of the rock mass between the Chinese classification basic quality (BQ) system and the widely used quality (Q) system has been listed in Table 3 [20].  
Figure 3: The layout and cross section of the super-span tunnel.  
Figure 2: The main structure of the Badaling Great Wall station.
## 3 Results and Discussion
### UV-FSRS Setup with SHBC and SFG-CFWM  
Starting from the fs 800 nm laser amplifier system, we choose to exploit a home-built single-grating-based second harmonic bandwidth compressor to produce a \(\sim\)400 nm, ps pulse [20] as the Raman pump. To conveniently generate an accompanying Raman probe, we rely on the SFG-CFWM method that can be readily tuned by varying the time delay between the two incident pulses or selecting a different sideband on either side of the FP beam (see Figure 1, middle). The wavelength tunability of those sidebands has been discussed in our previous reports [15,17]. Figure 2a shows temporal characterization of the second harmonic bandwidth compressor output at 402 nm with \(\sim\)1.45 ps pulse duration (fwhm). Figure 2b displays the relative spectral position of the narrowband Raman pump and two distinct femtosecond BUMA sidebands from SFG-CFWM processes in a thin BBO crystal, S\({}_{+1}\) and S\({}_{-1}\), which enable the collection of anti-Stokes and Stokes Raman spectrum, respectively. Furthermore, we have demonstrated the SFG-CFWM sidebands spanning a broad UV to visible spectral range from _ca_. 350-490 nm [17], which can be potentially pushed toward shorter wavelengths upon increasing the pump power and/or reducing the incident beam crossing angle [23,24].  
Figure 2: Spectral characterization of the Raman pump and probe pulse pair for FSRS. **(a)** Temporal profile of the narrowband Raman pump measured from optical Kerr effect (OKE). The pulse duration of \(\sim\)1.45 ps is obtained from the fwhm of the Gaussian fit (blue solid curve) to the time-resolved experimental data points (blue open circles). **(b)** Spectra of the SHBC output as the Raman pump in **(a)** (blue) and the first two BUMA sidebands as the Raman probe, S\({}_{+1}\) on the FP side (black) and S\({}_{-1}\) on the WL side (red), respectively.
# Urban PM2.5 Concentration Prediction via Attention-Based CNN-LSTM
## 4 Results and Discussion
### Model Convergence  
After setting appropriate model parameters, it was necessary to verify whether AC-LSTM converges during training. Therefore, the training loss of AC-LSTM model in the one-hour PM\({}_{2.5}\) prediction task was calculated, as shown in Figure 8, and the results were compared with three other deep learning methods (simple RNN, LSTM, and CNN-LSTM). The parameters of all models in Figure 8 were the same, and the mean square error (MSE) after data normalization was used as the loss function for training. It can be seen from Figure 8 that all models converged at epoch = 20. In Figure 8a, after 20 epochs in the one-hour prediction task, the MSE losses of the three models were close, but the MSE loss of the AC-LSTM model was slightly smaller than those of the other two models, LSTM and CNN-LSTM. At epoch = 1, the MSE loss of the simple RNN model in Figure 8b was nearly 100 times greater than that of the three models in Figure 8a. In addition, at epoch = 80, the loss value of the simple RNN model was 0.00154, while none of the other three models in Figure 8a had values greater than 0.0015. Obviously, the three models of LSTM, CNN-LSTM, and AC-LSTM in Figure 8a had better convergence results because of the special memory cell architecture.
## 2 Methods
### The SIA Model  
As mentioned above, we use an SIA model to help define some parameters. In the simple SIA model, the susceptible population may die of natural causes, move to the infected compartment, or remain susceptible. The infected individuals may die from natural causes or infection-related causes, or they may progress to the stage 3 HIV/AIDS compartment. Once an infected individual has progressed to AIDS, they may die from natural causes or from disease-related causes. They may also continue to infect. There is a parameter to adjust the transmission probability per partner for the greater infectiousness of AIDS over HIV.  
Our non-normalized equations for the SIA model are:  
\[\frac{dS}{dt}=G-r\beta\left(\frac{I+\eta A}{N}\right)S-\mu S \tag{1}\]\[\frac{dI}{dt}=r\beta\left(\frac{I+\eta A}{N}\right)S-(\gamma+\
u+\mu)I+\alpha A \tag{2}\]  
\[\frac{dA}{dt}=\gamma I_{tot}-(\alpha+\
u_{a}+\mu)A \tag{3}\]  
We begin our numerical simulations using total numbers in each compartment followed by normalization. Here, \(N\) is the total population, \(G\) is the rate at which people enter the susceptible population, \(r\) is the average number of sexual partners an individual has per year, \(\beta\) is the transmission probability per partner, and \(\eta\) is a modification parameter for those with AIDS and actively infecting susceptible individuals. \(\eta\) is larger than 1, as AIDS is more virulent than HIV. The average death rate by natural causes is given by \(\mu\), \(\gamma\) is the progression rate from HIV into AIDS, \(\
u\) is the HIV-related death rate, \(\
u_{a}\) is the AIDS-related death rate, and \(\alpha\) is the infection rate of AIDS individuals still able to transmit HIV.
## 4 Calibration and Correction of the Additive Phase
### Optical Trap Tests for Phase Calibration  
As a first approximation, calibration of the phase added by the immersion oil is performed by keeping the laser power of the interferometer (\(\lambda_{B}\)) constant, having previously placed the optical trap on the upper face of the slide (glass-air interface), in an area that does not contain paint. Taking as a reference the interferogram phase when the laser that generates the trap is off (0 mW), the power of this IR laser is increased in 5 mW intervals, until reaching 300 mW, with a waiting time of 5 s between increments. In order to apply Equation (4), five interferograms were taken at each power, with a step of \(\pi/2\), and waiting 77 ms between steps; after making a phase measurement at each interval, the reference phase is subtracted from each of these. The result of this experiment is shown in Figure 15, where a profile for the average of phase differences was plotted, observing that the power-phase ratio is linear and uniform throughout the phase map. Thus, knowing the increase in the trap power, it is possible to know the corresponding phase that should be subtracted from the measurements made with the interferometer, since the phase shifting could be considered spatially constant.  
However, as demonstrated in Section 3.1.1, phase distribution may not be constant throughout the field of observation; so, it is advisable to consider such effects when implementing the calibration. For example, Figure 16a shows the phase behavior when applying powers of 110, 140, 175, 205, and 230 mW, in a slide area where there is no sample (i.e., without paint). Likewise, in Figure 16b, the profiles of the gray plane in intersection with the phase maps are plotted, as they are indicated in Figure 16a. Since the behavior between phase and power is linear, only these powers were selected to facilitate visualization. In both figures, it is clear that the additive phase intervals are equispaced, although spatially variable.  
Similarly, Figure 17 show the phase values when the same powers are applied to an area of the slide where a paint sample has been deposited.  
In both characterizations, a positive trend is perceived, which corresponds to the phase-shifting behavior in the respective interferograms. Figure 18 shows the behavior of each trend line and a sample interferogram.
# Battery Life Prediction for Ensuring Robust Operation of IoT Devices in Remote Metering
## 4 Experimental Setup
### Evaluation Metrics  
To evaluate our model's performance, we utilized four metrics: mean absolute error (MAE), root mean square error (RMSE), symmetric mean absolute percentage error (SMAPE), and the coefficient of determination (R\({}^{2}\)). The MAE, as the most intuitive metric, represents the average of the absolute differences between the predicted and actual values, making it effective for evaluating overall prediction accuracy. RMSE, the square root of the  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Stage** & **Order** & **Layers** & **Layers Parameters** \\ \hline \multirow{8}{*}{Input Embedding} & 1 & Xavier Initialization & gain = 1.0 \\  & 2 & Conv1D & Number of filters = 32, Kernel size = 7 \\  & 3 & BatchNorm1D & - \\  & 4 & Activation Function & Tanh \\  & 5 & Conv 1D & Number of filters = 64, Kernel size = 5 \\  & 6 & BatchNorm 1D & - \\  & 7 & Activation Function & Tanh \\  & 8 & Conv 1D & Number of filters = 128, Kernel size = 3 \\  & 9 & BatchNorm 1D & - \\  & 10 & Activation Function & Tanh \\  & 11 & Conv 1D & Number of filters = 128, kernel size = 1 \\  & 12 & BatchNorm 1D & - \\  & 13 & Activation Function & Tanh \\ \hline Encoder & 1 & Encoder layer & Self-attention heads = 4, Encoder blocks = 8 \\ \hline \multirow{4}{*}{Feed Forward} & 1 & Linear & Hidden units = 32, Activation = Gelu \\  & 2 & Drop out & Rate = 0.05 \\  & 3 & Linear & Hidden units = 128 \\ \hline \multirow{4}{*}{Outputs} & 1 & Xavier Initialization & gain = 1.0 \\  & 2 & Conv1D & Number of filters = 64, Kernel size = 3 \\ \cline{1-1}  & 3 & Activation Function & Tanh \\ \cline{1-1}  & 4 & Conv1D & Number of filters = 32, Kernel size = 3 \\ \cline{1-1}  & 5 & Linear & Hidden units = 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperparameters of the C-SDFormer.  
mean squared error, transforms the error metrics into the same units as the actual values and is particularly sensitive to larger errors, highlighting significant deviations. SMAPE evaluates the percentage difference between the predicted and actual values, considering their magnitude, and is less sensitive to outliers, making it suitable for assessing relative errors in balanced datasets. R\({}^{2}\) serves as a measure of the extent to which the dependent variable can be explained by the independent variable, serving as a key indicator of the model's goodness of fit.
## 2 Principles of the Proposed Method
### OSNR Monitoring Based on the IBPSO-Based DNN Trained with AHs  
With the development of evolutionary computation, people gradually combine intelligent optimization algorithm with neural network, and use various optimization methods to train neural network. Because the intelligent optimization algorithm has strong global convergence ability and robustness, it does not need the feature information. Therefore, the combination of the two gives play to the generalization mapping ability of the neural network, and also improves the convergence speed and learning ability of the neural network. Common intelligent optimization algorithms include GA [42], ant colony optimization (ACO) algorithm [43], and PSO. Among them, GA cannot converge effectively in a limited time and ACO needs a long search time and is prone to premature convergence because of its lack of initial information and slow solution speed. As a swarm intelligence optimization algorithm, PSO employs the cooperative mechanism of swarm solutions to produce the optimal solution iteratively. In addition, the concept of PSO is simple and easy to implement, and there are few parameters to be adjusted.  
Figure 2: Constellation diagrams and amplitude histograms with Fourier fitting at three different OSNRs for PM-RZ-QPSK signals.
## 4 Applications of Multimodal Learning for Fault Detection Diagnosis
### Applications in Automotive Systems
#### 4.3.2 Electric Vehicle (EV) Battery Health Monitoring  
Battery management is critical in EVs, as battery performance directly affects range, reliability, and safety. Monitoring systems track parameters like voltage, current, and temperature to assess battery health. Detecting faults early such as overheating, overcharging, or imbalanced cells is essential for avoiding serious failures [74].  
Sensor data from EV batteries allows systems to detect anomalies, such as rapid temperature increases or voltage drops, which may indicate thermal runaway or other critical issues [37]. By combining multiple sensor readings, fault detection becomes more precise and actionable. Machine learning models trained on historical data can predict when a battery is likely to fail, enabling predictive maintenance. These models analyze trends in temperature, charge cycles, and voltage patterns to forecast issues and suggest service interventions before failure occurs [75].
## 1 Introduction
### Review of State-of-the-Art Detection Methods
#### 1.2.5 Learning-Based Detection  
With the advent of deep learning, researchers have proposed using convolutional neural networks (CNNs) or recurrent models to learn spectral features from raw radar signals [9; 10]. These approaches show promise in complex scenarios (e.g., gesture recognition, tracking in clutter), but they require large annotated datasets, generalize poorly across hardware, and introduce latency unsuitable for low-power or real-time systems.
## 3 Smart Factory Use-Case
### Workflow for using the Risk Analysis Component  
To understand how the proposed methods could be used for the execution of risk analysis based on a BPMN model and resource specific information coming from the MES, the workflow from the user perspective is reported here. The workflow starts with the user inserting the high-level description of a process via the MPMS interface. Second, the BPMN is shared with the MES and the RA to obtain the **process definition**. Third, the user inserts an order into the MES and specifies which resources have to be used. Fourth, the RA receives the **process definition** and the **resources, and** the user performs the risk analysis using the obtained information. Finally, the result is sent to the MES and MPMS, which can then trigger the execution. The sequence of the steps is shown in Figure 7.
# Collaborative Unmanned Vehicles for Inspection, Maintenance, and Repairs of Offshore Wind Turbines
## 1 Introduction
Nonetheless, several concept papers and simulation work related to collaborative UAVs for OWT have been presented [24; 25; 26]. In contrast to other previous review work related to the implementation of collaborative UAVs, this paper:  
* Reviews the works that have the potential to be implemented within the OWT IMR operations. * This paper also highlights many strategies that can be implemented as a major enabler toward reaching this goal, including strategies for UAVs to perform complex tasks, achieve better localization and navigation, prolong operation time, and establish effective communication. * Reviews are conducted mostly on related works ranging from 2018 up to 2022. * Summarizes the main findings related to the potential solutions and improvements a collaborative UAVs approach may bring to the unmanned IMR OWT operations. * Briefly discusses the potential failure modes and redundancies for UAVs in the IMR OWT operations and further highlights the importance of exploring redundancies within this area to ensure the reliability, availability, and safety of such collaborative approaches. This paper is structured as follows: Section 2 reviews the state-of-the-art capabilities of current UAVs already being deployed in other sectors, focusing on their potential to be implemented within the OWT sector; Section 3 presents the unmanned collaborative strategies that can be implemented by utilizing multiple types of UVs to perform IMR operations for the OWT; and Section 4 briefly discusses the potential failure modes and redundancies for UVs in the IMR OWT operations. Finally, a conclusion and future directions related to research areas in collaborative UVs are also presented.
# Multi-Scale Aggregation Stereo Matching Network Based on Dense Grouping Atrous Convolution
## 4 Conclusions  
We propose a dense grouping atrous convolution spatial pyramid pooling module. This module uses the atrous convolution of groups and dense connection between convolution layers to form a large receptive field and a feature map containing multi-scale information, which effectively enhances the network's understanding of global information and improves the accuracy of stereo matching. In addition, the cost aggregation is divided into two parts: intra-scale cost aggregation and cross-scale cost aggregation. Through the continuous fusion of information on different scales and through the alignment resolution, we can extract the features with a good recognition degree and effectively reduce the false matching rate of the algorithm in the ill-posed regions of the image.  
For the depth estimation task in autonomous driving, the experiment shows that the method can achieve high accuracy on the scene flow and KITTI 2015 datasets and meets the requirements of the autonomous driving task. Our method is an end-to-end stereo matching method, and most end-to-end stereo matching methods are based on datasets with ground truth, that is, supervised methods. The unsupervised stereo matching method does not rely on ground truth and has a wider range of applications, which will be our focus in subsequent research. Because the dataset needs to be labeled as a supervised method, it requires high labor cost. In addition, there are various scenes in the urban environment, and it is not realistic to comprehensively annotate the collected images. Unsupervised methods can avoid these constraints.  
Conceptualization, Q.Z. and J.Z.; methodology, J.Z.; software, J.Z.; validation, J.Z. and A.D.; formal analysis, Q.Z.; investigation, J.Z.; resources, Q.Z.; data curation, J.Z.; writing--original draft preparation, J.Z.; writing--review and editing, Q.Z.; visualization, J.Z.; supervision, B.G.; project administration, J.Q.; funding acquisition, S.C. All authors have read and agreed to the published version of the manuscript.  
The research is supported by the National Natural Science Foundation of China (Grant No. 11701061): Research on SA algorithm for nonconvex stochastic semidefinite programming, and the scientific research fund project of Liaoning Province (Project No. LJKZ1180).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.