esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Summary and Discussion
### Challenges and Limitations
#### 5.2.2 Acoustic Emission Sensor Challenges  
* Signal-to-noise ratio--AE sensors are highly sensitive to environmental and mechanical noises. Differentiating genuine acoustic emissions from background noise is nontrivial and requires advanced filtering and denoising algorithms.
* Localization accuracy and sensor placement--The diagnostic effectiveness of the AE testing is influenced by sensor positioning relative to the emission source. Incorrect placement can lead to signal attenuation or misclassification, particularly in multi-material or composite components.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sensor Type**} & **Peak Frequency** & **Operating** & **Application** & **IoT Integration** \\  & **Range** & **Bandwidth** & **Suitability** & **Capability** \\ \hline Narrowband & \multirow{2}{*}{100–300 kHz} & \multirow{2}{*}{Narrow} & Crack initiation, & Moderate (wired \\ Piezoelectric & & & fatigue detection & systems) \\ \hline Broadband & \multirow{2}{*}{100–1000 kHz} & \multirow{2}{*}{Wide} & Corrosion, weld & \multirow{2}{*}{High (supports edge} \\ Piezoelectric & & & defect, delamination & & \\  & & detection & & \\ \hline Wireless AE Node & \multirow{2}{*}{100–500 kHz} & \multirow{2}{*}{Configurable} & \multirow{2}{*}{Remote real-time SHM} & Very High (BLE/Wi-Fi enabled) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of AE sensor types for remote monitoring of port cranes.  
* Interpretation complexity--The acoustic signals generated by microstructural changes can be difficult to interpret without advanced pattern recognition or machine learning tools. False positives and negatives remain a concern in early-stage deployment.
## 4 Experiments
### Comparison with Baselines
#### 4.4.2 Evaluation on CSL  
To demonstrate the robustness and effectiveness of our approach, we compared it with various baseline methods on a Chinese sign language dataset. In Table 6, on the CSL dataset, our model achieved state-of-the-art (SOTA) results, albeit with a marginal improvement of 0.1 points in WER compared to CNN/RNN methods. The results demonstrate that our approach remains competitive in the domain of Chinese sign language recognition. Furthermore, to the best of our knowledge, there are no reported metrics for Transformer-based models on the CSL benchmark. Therefore, we replicate the SL-Transformer model's WER on CLS, as illustrated in the penultimate row of Table 6. Employing the self-attention mechanism, our method reduces the WER by 23.3% compared to SL-Transformer on this benchmark, achieving the state-of-the-art for self-attention models.
# A Review of Vertical Shaft Technology and Application in Soft Soil for Urban Underground Space
## 4 Technological Advancements in Mechanized Systems  
Developments in mechanized techniques have shifted vertical shaft excavation, improving productivity, safety, and feasibility. Mechanized excavation methods enhance overall shaft construction performance by allowing concurrent processes, such as excavation, much removal, and rock support installations. The mechanical excavation tools used are of two main types: cutter bits and disc cutters. The advantages of cutter bits include high-impact loads on the rock surface, lower cutting forces, and kinematics that make them suitable for more flexible excavation geometries. However, when the unconfined compressive strength of rock exceeds the range of 100-120 MPa, rock excavation with cutter bits becomes progressively challenging due to low penetration rates and high bit consumption. Typically, disc cutters penetrate hard ground perpendicularly, operate by rotating over the surface, and can excavate materials with a compressive strength exceeding 300 MPa [31]. For shaft construction, especially in soft UUS, additional challenges arise primarily due to soil instability and groundwater presence. In urban settings, shaft construction must account for confined spaces, existing infrastructure, and potential settlement effects. The use of mechanized systems, including technologies like assembled shaft technology in soft UUS, allows for mechanized excavation, reducing the risk of ground settlement while achieving a balance between excavation and structural safety.  
The shaft serves as ventilation and is a crucial supporting project of the tunnel. To date, shaft engineering has accumulated substantial engineering experience in design methods and construction technologies for diameters less than 8.0 m and depths less than 300 m, employing methods such as drilling and blasting, freezing in soft ground, and reverse shaft techniques. Researchers increasingly recognize the importance of comprehensive matching technologies for mechanized drilling of ultra-large-diameter deep shafts, given the rising number of shaft projects with diameters around 8.0 m and depths exceeding 500 m. A brief review of mechanized methods for ultra-deep shafts is presented, followed by a discussion of assembled shaft technology.
# Real-Time Safety Decision-Making Method for Multirotor Flight Strategies Based on TOPSIS Model
## 3 Experimental Verification
### Data-Driven Multirotor Aircraft Fault Classification Model Construction  
From the 900 groups of experimental sample data, 600 groups of data were selected as training samples to train the failure level classification model of the multirotor based on the random forest algorithm, and the remaining 300 groups of data were used as test samples to verify the performance of the model. To illustrate the performance of the model constructed using the proposed method, five additional models based on SVM, Adaboost, naive Bayes, KNN, and logistic regression were constructed as comparative experiments. For the 300 groups of test data, the accuracy rates of the six models were 97.8%, 95.7%, 95.1%, 97.8%, 94.1%, and 96.8%, respectively. The average confusion matrix of the six models is presented in Figure 3, which represents the proportion of one class to another. The evaluation results of the six models are compared in Table 2.  
From the results, the classification results obtained by the method proposed in this paper had higher accuracy, and the built model had the best comprehensive performance in accuracy and recall rate. According to Figure 2, the experimental results of the classification based on the random forest algorithm show that the correct classification ratio of each category was over 83%, and most of the misclassified samples were in adjacent categories, so the classification robustness is good.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Random forest & 0.978 & 0.9928 & 0.9786 & 0.9856 \\ SVM & 0.957 & 0.9855 & 0.9714 & 0.9784 \\ Adaboost & 0.951 & 0.9852 & 0.95 & 0.9673 \\ Naive Bayes & 0.978 & 1 & 0.9714 & 0.9855 \\ KNN & 0.941 & 0.9645 & 0.9714 & 0.9679 \\ Logistic regression & 0.968 & 0.9786 & 0.9786 & 0.9786 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of evaluation results of the models.
## 5 Conclusions  
This study proposed a method for effectively suppressing the chatter during the turning process of thin-walled cylinders using lightweight TMDs. The major conclusions are summarised as follows:  
1. The chatter can be controlled if the masses or TMDs are attached to the workpiece such that their mounting arrangements do not coincide with the spacing of the nodes of the generated chatter vibration mode.
2. In case the TMDs are attached to the fixed end of the workpiece, the natural frequencies of the TMDs must be adjusted approximately to the chatter frequency for obtaining a sufficient chatter suppression effect.
3. To suppress multiple chatter modes using the TMDs, the TMDs ought to be placed at unequal intervals and at least two attached TMDs should be tuned for each chatter vibration mode.  
Figure 20: Workpiece frequency response without and with TMDs arranged with a regular and an irregular pitch.  
The present study demonstrated the chatter suppression effect in specific mounting arrangements of the TMDs. In order to extend the effectiveness of the proposed method, we are going to further make some experimental verifications, such as evaluation of the reproducibility of the experimental results considering the effects of cutting conditions, machining effect of the workpiece, and tool wear on the chatter suppression effect. Moreover, the development of an analytical model regarding chatter in the finishing process of the external turning of a thin-walled cylindrical workpiece with the TMDs and the derivation of the optimum tuning parameters of the TMDs, including the mounting arrangement, could further enhance the chatter suppression effect.  
Conceptualisation and methodology, Y.N.; software, Y.N. and T.K.; validation, Y.N., H.T. and T.K.; investigation, Y.N., H.T. and T.K.; data curation, Y.N.; writing--original draft preparation, Y.N.; writing--review and editing, Y.N. and H.T.; visualisation, T.K. and Y.N.; supervision, Y.N.; project administration, Y.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to thank [PERSON], an undergraduate student at the Tokyo Institute of Technology, for the experiments and [PERSON], a technical specialist at the Tokyo Institute of Technology, for the technical support. We would like to thank Editage (www.editage.com (accessed on 15 December 2021)) for English language editing services.  
The authors declare no conflict of interest.
# Multi-Wavelength Digital-Phase-Shifting Moire Based on Moire Wavelength
## 2 Method
### Digital-Moire Temporal Phase Unwrapping  
#### 2.3.1 Two-Wavelength Phase-Unwrapping  
For two-wavelength digital-moire temporal phase-unwrapping, two binary grid patterns of different pitch are projected onto the object surface and a single image is captured for each projected pattern. Thus, to perform two-wavelength digital-moire temporal phase-unwrapping, only two captured images are required. Phase-shifted moire patterns (images) are generated by digitally phase shifting a computer-generated binary grid overlaid on the captured images, followed by grid removal, as described in Section 2.1. Phase shift analysis is then applied to compute two wrapped phase maps \(\Phi_{1},\ \Phi_{2}\) with different moire wavelengths \(\lambda_{1}\), \(\lambda_{2}\) (\(\lambda_{2}>\lambda_{1}\)). A moire wavelength is computed for each grid pattern of different pitch, using the method described in Section 2.2. Extraction of the unwrapped phase map \(\varphi(x,y)\) using the moire wavelengths \(\lambda_{1}\) and \(\lambda_{2}\), is now explained.  
From phase maps \(\Phi_{1}\) and \(\Phi_{2}\), an extended continuous phase map \(\Phi_{12}\) with extended wavelength \(\lambda_{12}\), where \(\lambda_{12}=\frac{\lambda_{1}\lambda_{2}}{|\lambda_{1}-\lambda_{2}|}\), is calculated:  
\[\Phi_{12}(x,y)=\left\{\begin{array}{ll}\Phi_{1}(x,y)-\Phi_{2}(x,y),&\Phi_{1 }>\Phi_{2}\\ \Phi_{1}(x,y)-\Phi_{2}(x,y)+2\pi,&\Phi_{1}\leq\Phi_{2}.\end{array}\right. \tag{7}\]  
It is important to note that to apply heterodyne phase unwrapping in digital moire profilometry, the pitches of the two projected grid patterns used to generate the two sets of moire patterns (each set with a different moire wavelength) must be selected such that the beat wavelength \(\lambda_{12}\) is large enough to cover the entire range of the object's depth \(h(x,y)\) (i.e., continuous gray level gradient, no phase ambiguity in \(\Phi_{12}\)). This is quite different from the application of heterodyne phase unwrapping in PSP FPP, where the final beat wavelength covers the full camera field of view. To minimize the phase error caused by noise, \(\Phi_{12}\) can be used as a reference to unwrap the phase map \(\Phi_{1}\), which has the lower wavelength \(\lambda_{1}\), as follows:  
\[\varphi(x,y)=\Phi_{1}(x,y)+(2\pi)Round\left[\frac{\left(\frac{\lambda_{12}}{ \lambda_{1}}\right)\Phi_{12}(x,y)-\Phi_{1}(x,y)}{2\pi}\right], \tag{8}\]  
where \(\varphi(x,y)\) is the unwrapped phase map and \(Round[\ ]\) computes the closest integer value. This high signal-to-noise-ratio (SNR) unwrapped phase map \(\varphi(x,y)\) can then be used during measurement to calculate the object height:  
\[h(x,y)=\frac{\lambda_{1}}{2\pi}\,\varphi(x,y). \tag{9}\]  
Note that if the beat moire wavelength \(\lambda_{12}\) does not cover the full range of depth of the objects being measured, an additional wrapped phase map generated from an additional projected grid pattern (and captured image) can be used, as explained in the following section.
# Predicting Architectural Space Preferences Using EEG-Based Emotion Analysis: A CNN-LSTM Approach
## 2 Literature Review
### Theoretical Background of EEG-Based Emotion Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study (Year)** & **Main Topic** & **EEG Setup** & **Primary Conclusion** \\ \hline [PERSON] et al. (2020) [62] & Natural elements and user perception/ satisfaction & Beta ratio at F7, CP2, FC6, and P3 & Beta ratio linked to task performance; natural settings improved comfort \\ \hline [PERSON] et al. (2017) [19] & Emotional effects of curved interiors & Anterior cingulate cortex, theta, and 128 channels & Curved design induced a positive emotional response, though relatively short-lived \\ \hline [PERSON] (2018) [63] & Ceiling height and its effect on cognition/ wayfinding & F3/F4, Fz (alpha, beta, delta, and theta) & Higher ceilings correlated with better cognition, reduced beta activity \\ \hline [PERSON] et al. (2019) & Comparing emotional responses to virtual versus real environments & Nine channels (alpha, beta, gamma, and theta) & VR elicited emotions similar to real settings; SVM used for classification \\ \hline [PERSON] et al. (2019) [65] & Office design elements and motivation & 14 channels & Artificial neural network achieved \textasci{}85\% accuracy for personal experience classification \\ \hline [PERSON] et al. (2020) & VR chromotherapy and stress relief & Eight channels (gamma/alpha and theta) & VR produced stress relief akin to real therapy; gamma waves served as key biomarker \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Empirical EEG-based studies in the architectural context. As shown in Table 5, EEG can quantitatively measure users' emotional and cognitive reactions in real time, providing a systematic interpretation of how design factors in an architectural environment affect human response [74]. When combined with VR or augmented reality technologies, EEG also facilitates experiments on design scenarios during early phases of construction [64; 72]. Despite the field of neuro-architecture being at a relatively early stage--requiring larger sample sizes and studies across various environmental conditions--integrating EEG throughout the design process can effectively incorporate user emotional factors. This may ultimately advance architectural environments that enhance quality of life [58; 75; 76].
# Advancing TinyML in IoT: A Holistic System-Level Perspective for Resource-Constrained AI
## 6 Future Directions
### Key Insights for TinyML Researchers  
1. Embrace System-Level Co-Design: AI compression alone is insufficient; synergy between algorithms, firmware, and hardware is critical for meaningful speedups and reliability [19; 24].
2. Benchmark Under Real Constraints: True performance cannot be assessed without measuring energy consumption, latency, and memory usage against real application scenarios, especially under harsh or unpredictable conditions [23; 32].
3. Layered Security is Essential: On-device inference helps preserve privacy, but resource-starved devices also require robust encryption, secure enclaves, and trustworthy firmware pipelines to combat tampering or data leakage [12; 24].
4. Adaptation and Specialization: Balancing specialized models (extreme efficiency) with adaptive or multi-task approaches (flexibility) remains an open research question in resource-constrained AI [29; 65].
5. Ethical and Sustainable Deployments: Large-scale IoT expansions demand addressing fairness, bias, environmental impact, and responsible data management, ensuring TinyML's long-term societal benefits [22].
## 2 Study Area and Method
### Model Parameters and Input Data
#### 2.3.6 Forest Age Data  
The InTEC model takes the effects of forest age change on forest productivity into consideration. Therefore, this study sorted out the forest resource inventory sample age information from 2004. Then, 1 km spatial resolution age data from Zhejiang Province was interpolated by a Kriging interpolation method, as shown in Figure 2.
## 3 Experiment Results and Analysis  
### Data and Equipment  
In order to evaluate the method in this paper, the experiments were conducted on servers using 2.6 GHz Intel CPU i7-8700 and 32 GB memory. Here, the image resolution is 1920 \(\times\) 1080 pixels. The true value of visibility for each image to be trained comes from the visibility meter. The visibility monitoring system has collected 6696 images during the data collection period. Images with too low light intensity (e.g., during the dawn and evening), out of focus images and images taken at the instant that the visibility meter feedback with error messages are removed from the database. The final database consists of a total number of 6048 images. We have selected 4536 images randomly as training set and the remaining 1512 images are selected as test set. The distribution of visibility of the database is shown in Table 1 and Figure 9.
## 2 Physical Model and Mathematical Formulation  
Choose x-axis and y-axis in such a way that y-axis is orthogonal to x-axis, shown in Figure 1. Consider \(\tilde{U}(x,t)=\tilde{a}\tilde{x}/(1-\tilde{\lambda}\tilde{t})\) is the non-uniform velocity with moving sheet, where \(a\) represent the stretching/shrinking rate in the direction of x-axis and \(\tilde{\lambda}\tilde{t}\) is non-negative constant having property \(\tilde{\lambda}\tilde{t}<1\). \(\tilde{B}(x)=\tilde{B}_{0}\tilde{x}^{-1/2}\) is the magnetic field along y-direction, where \(\tilde{B}_{0}\
eq 0\) is the magnetic field strength. \(\tilde{T}_{\infty}\) represents free stream temperature, \(\tilde{C}_{\infty}\) is the nano-particle concentration, \(\tilde{n}_{\infty}\) is the microorganism concentration. \(\tilde{T}_{w}(\tilde{x},\tilde{t})\) is the temperature of the sheet such that ([29]):  
\[\tilde{T}_{w}-\tilde{T}_{0}\bigg{(}\frac{\tilde{a}\tilde{x}}{2\tilde{v}(1- \tilde{\lambda}\tilde{t})^{2}}\bigg{)}=\tilde{T}_{\infty}.\]  
Figure 1: Flow geometry. Similarly \(\tilde{C}_{\varpi}(\tilde{x},\tilde{t})\) and \(\tilde{n}_{w}(\tilde{x},\tilde{t})\) are the nano-particles concentrations and microorganism concentrations defined as  
\[\tilde{C}_{w}-\tilde{C}_{0}\Big{(}\frac{\alpha\tilde{x}}{\partial \tilde{z}(1-\lambda\tilde{t})^{2}}\Big{)} =\tilde{C}_{\infty},\] \[\tilde{n}_{w}-\tilde{n}_{0}\Big{(}\frac{\alpha\tilde{x}}{2\tilde {\
u}(1-\lambda\tilde{t})^{2}}\Big{)} =\tilde{n}_{\infty},\]  
where reference temperature represented by \(\tilde{T}_{0}\), reference nano particle concentration is \(\tilde{C}_{0}\) and \(\tilde{n}_{0}\) is the reference concentration of microorganism, respectively.
## 4 Databases and Evaluation Parameters  
### Database  
The High-Resolution Fundus (HRF) Image Database [25] of retinal fundus images and the Oasis brain MRI database [44] of brain MRI were used for validation of the experimental results for the preprocessing steps or proposed image enhancement techniques.There were 18 image pairs of high and low quality images from the HRF database.The Oasis database contained three sets of 38 patients, and each set of patient images contained three planes, comprising a group of 38 images in the axial plane, 38 images in the coronal plane, and 38 images in the sagittal plane. As postprocessing steps and to obtain retinal vessels, we used the Digital Retinal Images for Vessel Extraction (DRIVE) [2] and Structured Analysis of the Retina (STARE) [29] databases for algorithm validations. The DRIVE database contained 40 images, while the STARE database contained 20 images. The main advantage of using these databases is that they have ground truth images for validations, as many researchers have used these databases. These databases allowed us to compare our retinal segmentation methods with existing methods. For the brain MRI, we used the same Oasis database to analyze the postprocessing steps. The output of the postprocessing steps showed the impact of the preprocessing steps on it, and we explain the impact of the preprocessing steps on the postprocessing in Section 5.
###### Abstract  
Many of rural Alaskan communities operate their own, stand-alone electrical microgrids as there is no state-wide power distribution network. Although the fossil fuel-based power generators are the primary energy source in these isolated communities, an increasing number of microgrids have started to diversify their power supply by including renewable wind, hydro and photovoltaic energy sources. The integration and optimization of the multiple energy sources requires a design of a new power management system that can anticipate how much electricity will be drawn by the community and how much electricity will be generated from the renewable sources in order to control the fossil fuel-based generators to meet the community power demand. To address this problem, we designed a hybrid machine learning algorithm to predict community power draw as one module for the next generation microgrid power management system. The algorithm divides the task of a power load prediction into two sub-models: the first temporal model predicts the future weather conditions and the second model is trained to associate the predicted weather conditions with the community power demand. The results illustrate (1) the feasibility of building a machine learning algorithm that uses relatively small data for model training and validation, (2) the ability to predict the near-future community power load for the microgrids operating in the environments with highly dynamic weather conditions and (3) how the integration of multiple low quality future weather conditions produced high accuracy power load prediction.  
Keywords:power load prediction; long short term memory; neural networks; power management systems +
Footnote †: journal: Applied Sciences