esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This study starts from the perspective of time series dynamic analysis and uses the Patsnap global patent information database as the data source. We use the LDA topic model and TF-IDF text vectorization method to comprehensively mine and identify technical topics and keywords in unmanned ships, which provide reference and guidance for technology research and development in the field of unmanned ships. Based on the above research, the findings of this article are as follows. First, the evolution of related technologies in unmanned ships progresses through a budding stage, a phase of rapid development, and is presently in a stable development stage. Second, technological development in unmanned ships encompasses three themes: growth, decline, and hot spots, analyzed based on the intensity of technological themes. Then, the technical themes of unmanned ships are linked within the three stages of development, presenting an evolutionary relationship of splitting, inheritance, and fusion through the content analysis of technical themes. Finally, it was found that unmanned ship technology has more room for development in perception, navigation, control, and power technology by analyzing the evolution of technology topics.  
This study also has some shortcomings. First, data sources are limited. This study only considered patent data in unmanned ships; the data source is relatively singular. The data source can be further expanded in the future, such as science and technology news, academic papers, and science and technology reports. Second, the research scenario is limited. This study only takes the field of unmanned ships as the research object. In future research, verifying the universality of the method in other fields is necessary.  
Conceptualization, Y.L. and X.W.; methodology, J.Y.; software, S.W.; validation, Y.L., X.W. and J.Y.; formal analysis, Y.L.; investigation, J.Y.; resources, J.Y.; data curation, X.W.; writing--original draft preparation, Y.L.; writing--review and editing, Y.L.; visualization, S.W.; supervision, X.W.; project administration, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 72271037 and The APC was funded by 72271037.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# The Applications of MALDI-TOF MS in the Diagnosis of Microbiological Food Contamination
## 5 Future Perspectives
### Integration with Food Quality Management Systems  
The future of MALDI-TOF MS lies not only in technical advancement, but also in its seamless integration into digitalized food quality management systems. Internet of Things (IoT)-enabled instruments, cloud-based spectral storage, and real-time alerting systems will allow for continuous microbiological monitoring across the food supply chain. Spectral metadata could be used to trace microbial signatures across raw materials, production batches, and geographic locations, supporting blockchain-based traceability and predictive risk analytics [25; 32; 41; 146].  
Furthermore, MALDI-TOF MS data can be used to develop microbial risk models and shelf-life prediction algorithms, particularly when linked with environmental, process, and sensory data in artificial intelligence (AI)-driven platforms [30; 120].
# Two-Stage Short-Term Power Load Forecasting Based on SSA-VMD and Feature Selection
## 4 Load Forecasting Methodology  
### Long Short-Term Memory  
The forecasting method proposed in this paper uses a long short-term memory neural network [44]. LSTM networks are commonly used to deal with time series problems. Compared to the traditional Recurrent Neural Network, LSTM adds the gating mechanism to the RNN. There are three gates in the LSTM, the forgetting gate, the input gate and the output gate. The forgetting gate is used to control how much information needs to be forgotten about the internal state at the last moment, the input gate is used to control how much information needs to be saved about the candidate state at the current moment, and the output gate is used to control how much information has to be output from the current cellular memory to the external state (the current hidden layer state). The internal structure is shown in Figure 3.  
In the internal structure of the LSTM, \(f_{t}\) is the forgetting gate, which uses the \(sigmoid\) activation function to control the value between \((0,1)\) by controlling the size of the gate to determine how much previous information is forgotten.  
\[f_{t}=\sigma(W_{f}\cdot[h_{t-1},x_{t}]+b_{f}) \tag{14}\]  
\(i_{t}\) is the input gate, which also uses the \(sigmoid\) activation function. It is used to filter the information about the current candidate state and keep the useful information.  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{15}\]  
Figure 3: The internal structure of the LSTM.  
\(o_{t}\) is the output gate, which also uses the _sigmoid_ activation function. It is used to output the last retained information as the current moment in time of the hidden layer.  
\[o_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o}) \tag{16}\]  
\(\widetilde{c}_{t}\) is the candidate cell memory, determined by the last moment of the hidden layer transitions \(h_{t-1}\) and the current moment of the input \(x_{t}\), and the activation function is the tanh function.  
\[\widetilde{c}_{t}=\tanh(W_{c}\cdot[h_{t-1},x_{t}]+b_{c}) \tag{17}\]  
\(c_{t}\) is the current cellular memory, which is determined by the cell's last moment of memory and candidate state, and is controlled by the forgetting gate and the input gate, which control what data is left behind.  
\[c_{t}=f_{t}\odot c_{t-1}+i_{t}\odot\widetilde{c}_{t} \tag{18}\]  
\(h_{t}\) is the output of the memory cell for the next moment, which is the cryptic state.  
\[h_{t}=o_{t}\odot\tanh(c_{t}) \tag{19}\]  
The LSTM network solves the long-range problems of gradient disappearance and gradient explosion in traditional RNNs through the design of three gates: the forgetting gate, the input gate and the output gate. In the forecasting method proposed in this paper, the high-frequency components obtained from VMD decomposition are fed into the LSTM network to obtain the components' forecasting values, which provide the feature values for the later model fusion.
## 5 Applications of Wireless EM Energy Transfer and Propagation in SCs
### Near and Onboard Living-Tissue Tools in SC Environment  
Onboard tools are utilized in shapes that are portable, detachable, or integrated, which can execute inactive or active actions. Their deeds are placed in living tissues in general urban biodiversity. They can perform a detecting task, e.g., examination, predicting, etc. [20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30], or assistance jobs, e.g., support, invigorating, drug release, etc. [31; 32; 33; 34; 35], as well as serving as scanners involved in image-assisted medical therapies and interventions [36; 37; 38; 39]. These two burdens could be distinct or linked and distantly piloted or self-ruling. Thus, such tools generally require permanent real-time inside and outside elegant interactions. Additionally, their required operational power could be integrated or distantly transmitted [61]. Moreover, remote data communication routines are essential for the intelligent operation of onboard tools using wireless transmission or other remote-sensing strategies, such as satellites for the observation, monitoring, and management of biodiversity in general [62].  
Detecting tools (sensors) are normally non-invasive and portable or detachable, functioning in real-time and permitting continuous observation of the involved tissue, therefore providing appropriate health information to deduce its comprehensive state and, furthermore, an early health picture appraisal. Further personalized concerns related to disruptions of vital functions, such as the rate of respiration, the pressure of fluid circulation, etc., could be identified via detachable smart tools.  
The supporting tools are of two categories implanted in the tissue (for health maintenance and organ stimulation) or containing the tissue (for image-assisted therapeutics). The first are pumps, tissue stimulators, pacemakers, implanted cardioverter defibrillators, etc., while the second are imaging scanners containing living-tissue parts that are concerned with surgical or implanted drug release procedures [36; 39].  
In addition, the above-detailed tasks of observation, prediction, support, stimulation, etc., of onboard tools, also permit the management of post-cue conditions in circumstances of previous disorders. Thus, circumventing transpositions, displacements, relocations, etc., substituting face-to-face care with an integrated connected support approach.
# Improving Hard Rock Materials Cuttability by Hydraulic Fracturing at Mining Working Face
## 1 Introduction
### Discussion of Experimental Results  
The purpose of this section is to investigate the effects of the borehole dip angle and the differential stress on the initiation and propagation of un-directional HFs. The results show that among the seven cases of experiments, only one case creates radial fractures alone (Figure 11), and the other six cases produce both axial fractures and radial fractures. There is a lack of theoretical research on the initiation and propagation of HFs around the borehole during un-directional hydraulic fracturing pretreatment to the hard rock fault at a coal working face. Hence, the experimental results are of great significance to the field-scale application of un-directional hydraulic fracturing.  
The stress direction at the working face is unitive. However, since the differential stress is not the same at different regions of the geological structure, both axial and radial fractures initiate during un-directional hydraulic fracturing. Under the condition of a low differential stress (1400 Pa), the radial fracture develops more quickly, and the size of the radial fracture has little difference from that of the axial fracture in the projection direction. As the differential stress increases, the radial fracture develops slowly and mainly propagates axially. Due to the influence of the differential stress, both axial and radial fractures have a tendency to propagate along the plane perpendicular to the minimum principal stress direction. After the re-orientation distance is more than three times the borehole diameter, the axial and radial fractures will be parallel to each other. When the borehole dip angle is 45\({}^{\circ}\), the radial fractures are more likely to occur. Both axial and radial fractures re-orientate more quickly, compared with the condition that the borehole axis is perpendicular to the maximum principal stress. The hydraulic fractures develop perpendicular to the direction of the minimum principal stress when the fracture re-orientation distance is still within three times the borehole diameter. Under a high differential stress (Figure 11), HFs initiate radially in the sample and only radial fractures occur. Moreover, when the borehole dip angle is 45\({}^{\circ}\), all radial fractures symmetrically initiate and propagate. When the differential stress at a coal working face is constant, a certain dip angle should be considered for arranging a borehole.  
3 Hydraulic Fracturing Application at Guojiawan Coal Mine to Improve Cuttability of Hard Fault Rocks  
According to the initiation and propagation mechanism of un-directional HFs, hydraulic fracturing field tests on pretreating a hard rock fault were carried out at the 51207 working face of Guojiawan Coal Mine, Shaanxi Province.
## 4 AI-Based Solving Methods for V2G Optimization
### Learnheuristics  
Learnheuristic algorithms represent an innovative approach to addressing optimization problems characterized by dynamic environments. The framework combines constructive heuristic methodologies with machine learning algorithms to efficiently predict the target variable and generate promising solutions iteratively. Initially, the framework generates input data for the machine learning module, which is then used iteratively to generate new promising solutions. Within each iteration, multiple runs of a constructive algorithm are performed, and solutions are evaluated. The solution with the best estimated objective value from these runs is selected as the promising solution. This iterative process allows for the generation of high-quality solutions while reducing the computational effort by replacing costly simulations with machine learning predictions [36]. Figure 2 illustrates a general learnheuristic framework addressing the V2G optimization problems. V2G optimization involves dynamically balancing energy supply and demand, where decision costs fluctuate based on factors like charging/discharging schedules, grid conditions, and environmental conditions. Traditional optimization approaches may struggle to handle such dynamic decision costs efficiently, leading to suboptimal solutions or excessive computational overhead. However, the learnheuristic framework's ability to adaptively predict decision costs and generate promising solutions iteratively makes it well suited for addressing V2G optimization challenges. By utilizing machine learning algorithms to predict decision costs and guide the search for optimal charging/discharging schedules, learnheuristics can enhance grid stability, maximize energy utilization efficiency, and minimize costs for both users and utilities in V2G systems. Moreover, the framework's iterative nature allows it to continuously learn and improve its performance over time, making it particularly efficient in dynamic and uncertain environments like V2G systems.
# Pipeline Damage Identification Based on Additional Virtual Masses
## 3 Numerical Simulation
### Excitation and Frequency Response
#### 3.3.2 Frequency Response with Virtual Masses  
To construct the frequency response of the virtual structure with additional virtual masses, FT (Fourier Transform) with exponential window was performed on the noisy simulated measured excitation and responses. The result is used in Equation (4) to obtain the frequency response of the virtual structure G5 for the virtual mass \(m\in[0,15]\) kg. Figure 7 shows the nephogram of the constructed frequency response with respect to the virtual mass. As the mass increases, the first natural frequency decreases. Figure 8 illustrates the changes of the first frequency response after adding 0-8 kg in the middle of the substructure, which are directly calculated by using the frequency with different masses (FEM). Figure 9 shows the change of the first frequency with different masses by substituting the original frequency response into Formula (4). By comparing Figures 8 and 9, we can say that the two are basically identical. Such a case indicates that the frequency response is effective and accurate after constructing additional virtual masses using VDM and does not need to reload the FEM, thereby greatly improving the computational efficiency.  
Figure 6: Frequency response.  
Figure 7: Nephogram of frequency response with respect to virtual mass.  
Similarly, frequencies of 10 virtual structures (Table 4) with the corresponding optimal masses can be calculated by additional virtual mass formula (VDM). Table 5 presents a comparison with FEM.
# Cyberattack on Flight Safety: Detection and Mitigation Using LoRa
### Our Contribution  
This paper presents a LoRa-based solution as an additional layer of protection against cyberattacks on commercial aviation safety. The proposed solution enables the mitigation of cyberattacks in both systems: ADS-B and GNSS. It was implemented as an IoT and sensor network system and tested in a simulation and field experiments. Preliminary results suggested that the concept can also be utilized as an improved sense and avoid system. To the best of our knowledge, this is the first research work to suggest the use of the LoRa 2.4 GHz protocol as a platform for ranging and ad hoc communication for civilian aviation. The presented concept can be implemented as an after-market safety product as it uses globally unlicensed ISM RF communication transceivers.  
This paper is organized as follows: Section 2 introduces the problem of interest. Section 3 presents the flight safety framework including the protocols and technology used in the suggested solution. Section 4 covers the experiments performed using both the simulator and field experiments, and finally, Section 5 concludes this paper and discusses a few possible future work directions.
# The Novel EfficientNet Architecture-Based System and Algorithm to Predict Complex Human Emotions
## 2 Related Work
Additionally, there is a lack of research regarding brainwave frequencies associated with emotions. Research results indicate that EEG signals may not be suitable for emotion recognition due to their susceptibility to noise and background changes [32; 33]. [PERSON] et al. [34] used CNN based on the ResNet architecture to detect emotions through facial expressions and physiological signals, focusing on HRV and ECG. Images of the subjects were captured using a smartphone camera, and their physiological signals were recorded using a custom-made ECG device. The subjects' emotions were categorizedas happy, sad, neutral, fearful, or angry based on analyses of the ECG signals and facial expressions. The method's accuracy, as determined from ECG data, was 68.42%. [PERSON] et al. [35] detected emotions by analyzing both heartbeat signals and facial expressions. They proposed a method to detect four different emotions in players when playing games, based on their heartbeat signals and facial expressions. This method used a bidirectional long- and short-term memory network and a CNN for analysis. As mentioned earlier, there are limited studies that have examined both bio-signals and facial expression image data for classifying emotions. Traditional methods relying solely on facial expression analysis often lead to inaccurate results due to the controllable nature of facial expressions and the difficulty in distinguishing certain emotions based on appearance alone. To address these challenges, we propose a method that integrates both facial expression data and HRV signals. HRV is a reliable physiological indicator that provides information about the balance between sympathetic and parasympathetic nervous system activities. This balance reflects the body's emotional state and response, offering a deeper understanding of emotional regulation that is not accessible through facial expressions alone. By combining these two sources of information, our study aims to enhance the accuracy and reliability of emotion recognition systems. In this study, we used the CNN-based EfficientNet algorithm, specifically the lightest model, B0, for image data recognition [36]. It is trained as an efficient CNN-based model for data classification and prediction, accurately identifying emotions using both facial expression images and bio-signal data. Compared to other algorithms, CNN-based classification requires relatively minimal pre-processing [37; 38].
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 9 Discussion
### Experimentation  
As shown in Figure 5, three main hardware platforms were involved in this study:  
* A Ubotica CogniSAT-XE1 (XE1) [13]: In an EO mission, this is responsible for model inferencing. The experimental results in Table 6 were obtained on this platform.
* An Advantech ARK-1123L [82]: In an EO mission, this is responsible for TTA. The experimental results in Table 7 were obtained on this platform.
* A Linux desktop: In an EO mission, this is limited to model training, but since there were minute differences in ACC and FP rate between a desktop-hosted version and an XE1-hosted version of the same model, the experimental results on ACC and FP rate in Figures 7-11 were obtained on a Linux desktop. Realism was compromised in this setting, but the domain adaptation methods were evaluated and compared on an equal footing.  
No UDA schemes were included in our experimentation because once target-domain data are downlinked, performing UDA using the unlabelled target data _and subsequently_ updating the satellite-borne model partially (rather than fully) using FISH Mask predictably produces lacklustrate results.
# A Multi-Frame PCA-Based Stereo Audio Coding Method
###### Abstract  
With the increasing demand for high quality audio, stereo audio coding has become more and more important. In this paper, a multi-frame coding method based on Principal Component Analysis (PCA) is proposed for the compression of audio signals, including both mono and stereo signals. The PCA-based method makes the input audio spectral coefficients into eigenvectors of covariance matrices and reduces coding bitrate by grouping such eigenvectors into fewer number of vectors. The multi-frame joint technique makes the PCA-based method more efficient and feasible. This paper also proposes a quantization method that utilizes Pyramid Vector Quantization (PVQ) to quantize the PCA matrices proposed in this paper with few bits. Parametric coding algorithms are also employed with PCA to ensure the high efficiency of the proposed audio codec. Subjective listening tests with Multiple Stimuli with Hidden Reference and Anchor (MUSHRA) have shown that the proposed PCA-based coding method is efficient at processing stereo audio.  
Keywords: stereo audio coding; Principal Component Analysis (PCA); multi-frame; Pyramid Vector Quantization (PVQ) +
Footnote †: journal: Applied Sciences