esa-sceva/satcom-chunk-collection huggingface dataset:
# Flocculation Harvesting Techniques for Microalgae: A Review
## 2 Auto-Flocculation
Similarly, in a _Nammochloropsisulata_ culture at pH 10.4, the self-aggregation efficiency (\(\sim\)90%) of algal cells could be enhanced by the co-precipitation of Ca\({}^{2+}\) and Mg\({}^{2+}\)[38]. Negatively charged algal cells are destabilized due to the presence of oppositely charged metal ions. Under these circumstances, the absolute value of the zeta potential of algal cells decreases while increasing the Van der Waals forces, thereby promoting flocs formation between algal cells [13]. In another study, the auto-flocculation efficiency of _Chlorocoecum_ sp. was improved considerably from 63% to 84% by increasing the ammonium concentration from 10 to 50 mg/L, possibly due to the enhanced extracellular protein secretion at the higher ammonium concentration [42]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Condition** & & **Alga (Cell Density)** & **Optimal Harvesting** & **Ref.** \\ \hline \multirow{4}{*}{Acidic pH} & pH 4.0 & _C. ellipsoidatum_ (4.38 g/L) & 95\% @ 15 min & [31] \\  & pH 4.0 & _C. nivale_ (4.17 g/L) & 94\% @ 15 min & [31] \\  & pH 4.0 & _Scenedesmus_ sp. (6.94 g/L) & 98\% @ 15 min & [31] \\ \hline \multirow{4}{*}{Alkaline pH} & pH 11.5 & _C. muelleri_ \#662 (0.42 g/L) & 100\% @ 30 min & [34] \\  & pH 11.0 & _C. vulgaris_ (0.5 g/L) & 95\% @ 60 min & [35] \\  & pH 12.0 & _Chlorocoecum_ sp. R-AP13 & 94\% @ 10 min & [36] \\  & pH 12.5 & _Etthia_ sp. YC001 (1.2 g/L) & 94\% @ 30 min & [37] \\  & pH 10.4 & _N. oxalate_ (\(2.27\times 10^{5}\) cells/mL) & 90\% @ 10 min & [38] \\  & pH 11.6 & _S. quadricauda_ \#507 (0.54 g/L) & 95\% @ 30 min & [34] \\ \hline Culture aging & 16 days & _S. obliquus_ AS-6-1 (2.25 g/L) & 80\% @ 30 min & [39] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of auto-flocculation techniques for algae harvesting. Auto-flocculation-based harvesting of algae is a potentially low-cost and eco-friendly biomass recovery strategy, because no chemical flocculant is used [8; 37]. Additionally, the medium used can be effectively recycled after harvesting, for subsequent algal cultivations [36]. However, it should be noted that this harvesting process is generally slow and highly species-specific [30; 40].
## 4 Numerical Examples  
### Benchmark Problem: Simple Inversion Analysis Based on the Results of Forward Analysis  
#### 4.1.1 Problem Description  
The validity of the inversion solutions applied in this manuscript is checked by the inversion analysis of a simple model in this section. A two-dimensional construction problem is investigated in this section. The outline and discrete grids of the model can be seen in Figure 3. The bottom of the model is fixed in all directions. The materials of the three construction layers are set as the Duncan-Chang EB model with different material parameters. Material information can be found in Table 1.
# Adaptive Hierarchical Sliding Mode Control for Double-Pendulum Gantry Crane Based on Neural Network
## 3 Controller Design
### Stability Proof  
To prove the stability of the closed-loop system, the following Lyapunov function is chosen:  
\[V=\frac{1}{2}s^{2}+\frac{1}{2\gamma}\widetilde{W}^{T}\widetilde{W} \tag{32}\]  
where \(\gamma\) is the adaptive learning rate. If \(\gamma\) is too large, rapid adaptation occurs but may lead to oscillation, and if too small, slow adaptation occurs that affects control performance (in this design, \(\gamma=800\)).  
Differentiate \(V\):  
\[\dot{V}=s\dot{s}+\frac{1}{\gamma}\widetilde{W}^{T}\dot{W} \tag{33}\]  
Substitute the first derivative of the sliding mode surface into \(\dot{V}\):  
\[\begin{array}{l}\dot{V}=s\dot{s}+\frac{1}{\gamma}\widetilde{W}^{T}\dot{W}\\ =s[M(q)^{-1}(u-C-G)-\ddot{x}_{d}+\lambda_{1}\dot{e}+\beta_{1}(\bar{\theta}_{1}+ \lambda_{2}\dot{\theta}_{1})+\beta  
In this system, for the sliding mode term \(\frac{-\eta|s|}{M}\) and the approximation term \(\frac{-\epsilon s}{M}\), \(|s|\geq s\), with the maximum approximation error \(\varepsilon^{-}\), meaning we get \(|\varepsilon|\leq\varepsilon^{-}\).  
\[-\frac{\eta|s|}{M}-\frac{\epsilon s}{M}\leq-\frac{\eta|s|}{M}-\frac{| \varepsilon||s|}{M} \tag{36}\]  
That is,  
\[-\frac{\eta|s|}{M}-\frac{\epsilon s}{M}\leq-\frac{\eta|s|}{M}+\frac{| \varepsilon||s|}{M}=-\frac{(\eta-\varepsilon)}{M}|s| \tag{37}\]  
To ensure that this term is negative, we need  
\[-\frac{(\eta-\varepsilon^{-})}{M}|s|<0 \tag{38}\]  
When \(\eta>\varepsilon^{-}\) satisfies this, this term is negative. By satisfying the above conditions, the Lyapunov derivative is negative definite, and the closed-loop system achieves stability. The control law parameters are listed in Table 2.  
The approximation error \(\varepsilon\) generally does not exceed 1, and the system meets the above stability requirements. Thus, the Lyapunov function \(\dot{V}\) is monotonically decreasing, and the system state \([e,\dot{e},\theta_{1},\theta_{1},\theta_{2},\theta_{2}]\) asymptotically converges to zero, while the weight estimation error \(\widehat{w}\) remains bounded. The trolley position can asymptotically track the desired trajectory, and the double-pendulum angle stabilizes in the vertical position. The entire closed-loop system achieves asymptotic stability.
# An Improved Confounding Effect Model for Software Defect Prediction
## 3 Confounding Effect Model for Software Defect Prediction
### Traditional Confounding Effect Model  
In the traditional confounding effect model, size is considered a confounder, affecting the predictive ability of software metrics and bringing bias to the prediction results. In the study by [PERSON] et al. [16], a causal diagram was used to describe the confounding effect of size, as shown in Figure 1a. In this figure, variables represent the size, metric, and defect-proneness, respectively. The unidirectional edge represents causal belief, connecting the cause and effect of an event. Bidirectional edges represent general relationships. According to the graph's connectivity, there are paths m-s-d and m-d. It is known from the nature of the causal graph that when m is used to predict d, s will have a confounding effect and bias the results.  
However, according to [PERSON] et al.'s paper [18], there are deeper affecting factors beyond size, as shown in Figure 1b. They concluded that those factors, such as the software development cycle and the number of developers, cause confounding in size and software defects. These factors affect the value of code metrics and are outside the scope of traditional code metrics.
# Development and Characterization of a Flexible Soundproofing Metapanel for Noise Reduction
## 3 Results
### Structure of Flexible Soundproofing Metapanel
#### 3.1.2 Design of Flexible Soundproofing Metapanel
Based on these findings, the final design parameters for the metamaterial panel's unit cell are as follows:  
* Unit cell dimensions: \(L_{\mathrm{x}}=L_{\mathrm{y}}=5.5\) mm, \(L_{\mathrm{z}}=3.5\) mm;
* Frame thickness: \(t_{\mathrm{f}}=0.5\) mm;
* Material: Urethane rubber with a Young's modulus of 6.5 MPa;
* Membrane thickness: \(t_{\mathrm{m}}=0.6\) mm. This configuration optimizes the acoustic performance of the FSM within the desired frequency range while maintaining structural integrity and flexibility. Figure 7 illustrates the vertical displacement of the unit cell when STL peaks occur in the anti-resonance region. Three primary modes (\(\overline{\omega}_{1}\), \(\overline{\omega}_{2}\), \(\overline{\omega}_{3}\)) are observed in this figure, each exhibiting the following significant phenomena: (1) Membrane Vibration: The membrane vibrates in the opposite direction to the incident sound wave (acoustic force). (2) Anti-phase Superposition: The vibrations of the membrane and frame attain opposite phases and superpose. (3) Displacement Cancellation: Due to this anti-phase superposition, the total displacement of the entire unit cell approaches zero. (4) Formation of Anti-resonance Region: Anti-resonance occurs in this frequency region where total displacement is minimized, manifesting as peaks in the STL graph. Figure 6: (**a**) Changes in STL due to variations in material stiffness. (**b**) STL curves for different rubber materials. (**c**) Effect of membrane thickness on STL and bandgap position. This mechanism significantly reduces the transmission of sound wave energy at anti-resonance frequencies, resulting in high acoustic insulation performance. These phenomena observed in each mode (\(\overline{\omega}_{1}\), \(\overline{\omega}_{2}\), \(\overline{\omega}_{3}\)) are crucial factors enabling the metamaterial panel to achieve effective soundproofing performance across a wide frequency range. The interplay of these modes and their associated phenomena contributes to the FSM's ability to attenuate sound effectively across various frequencies, making it a versatile solution for noise reduction applications. This multi-modal behavior is a key feature distinguishing metamaterial-based sound insulation from traditional mass-law-based approaches, offering superior performance in targeted frequency bands while maintaining overall broadband effectiveness.
## 5 Conclusions  
In this article, we constructed a VSP upgoing and downgoing wavefield-separation method utilizing signal knowledge representation. First, our methodology involved establishing a signal knowledge representation system and its acquisition methodology, followed by the construction of a dual convolutional autoencoder knowledge-driven separation framework. By transforming the knowledge representation into constraint loss functions, an effective unsupervised separation of the upward and downward waves was achieved. While this study utilized a dual convolutional autoencoder framework, the proposed signal knowledge representation system and its corresponding constraint losses could potentially be adapted to guide other neural network architectures, such as U-Net, for VSP wavefield separation. Systematic validation experiments on horizontally layered and Marmousi models demonstrate that this method effectively addresses the VSP wavefield separation challenges under complex geological conditions.  
However, this method has several limitations. The accuracy of physical knowledge representation acquisition depends on the precision of the full waveform inversion, whereas the axial dip angles in morphological knowledge representation require manual picking. Furthermore, although the knowledge representation of the propagation directional difference reflects the orthogonality of the upgoing and downgoing waves, their  
Figure 15: Comparison of reconstruction loss convergence for the proposed KGCAE method (red curve) and the dualCAE method (blue curve) during training on the Marmousi model. The x-axis represents training iterations, and the y-axis displays the reconstruction loss value. The KGCAE method demonstrates notably faster convergence and greater stability throughout the training process.  
propagation directions in the actual VSP data are not completely orthogonal. This potentially affects the generalization capability of the model. To address these limitations, future studies should employ more robust inversion algorithms to obtain a more accurate representation of physical knowledge and investigate intelligent feature extraction algorithms for automated morphological feature recognition.  
Conceptualization, C.L.; formal analysis, J.G.; investigation, J.G.; methodology, C.L., L.Q. and J.L.; software, C.L. and L.Q.; validation, J.G.; writing\(-\)original draft, J.L.; writing\(-\)review and editing, J.L. All authors have read and agreed to the published version of the manuscript.  
The research was supported by the National Natural Science Foundation of China (Grant No. 42474168).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
We are grateful to the reviewers for their valuable comments.  
The authors declare no conflicts of interest.
## 1 Introduction
### Electrophysiology and Artificial Intelligence in SCI  
Electrophysiology is critical in SCI diagnosis, classification, and prognosis [25]. Among widely used evoked potential (EP) techniques, SSEPs, MEPs, and nerve conduction studies assess neural pathway integrity [26; 27]. EPs provide objective insights into neurological dysfunction, even when clinical evaluations are challenging [28; 29]. Studies show that SSEPs correlate strongly with SCI outcomes, including walking ability, hand function, and bladder control [30]. Combining clinical assessments (e.g., ASIA scores) with electrophysiological tests enhances diagnostic accuracy and informs treatment strategies. Recent advancements have introduced artificial intelligence (AI) and machine learning (ML) into SCI research. AI leverages large datasets to enhance disease prediction, classification, and treatment planning [31]. ML algorithms use statistical techniques (regression analysis and Bayesian inference) to analyze patient data, improving diagnostic precision. Neural networks have been successfully applied in SCI prognosis, medical imaging, and treatment optimization [6,32]. By integrating AI-driven models with neurophysiological assessments, researchers aim to predict SCI progression and optimize rehabilitation strategies, leading to better patient outcomes.  
This study aimed to identify the best predictors of SCI recovery using ML, examining the importance of electrophysiological recordings among other input parameters for ASIA score determination and motor recovery.
## 3 Results  
### XRD and SEM of the La\({}_{0.8}\)Ca\({}_{0.2}\)CrO\({}_{3}\) Powders  
XRD patterns of La\({}_{0.8}\)Ca\({}_{0.2}\)CrO\({}_{3}\) powders are shown in Figure 1a. The samples were synthesized by both combustion and SSR methods. It can be seen that an almost single perovskite phase was obtained for the powders by the combustion method. However, the sample by the SSR method displayed a stronger intensity of a perovskite phase with a little amount of CaCrO\({}_{4}\) and Cr\({}_{2}\)O\({}_{3}\). The stronger diffraction peaks of the perovskite may have been due to a higher synthesis temperature. Moreover, a slight amount of CaCrO\({}_{4}\) as a second phase could redissolve the perovskite matrix above 1000 \({}^{\circ}\)C and Cr\({}_{2}\)O\({}_{3}\) was considered as incomplete reaction impurity substance. Figure 1b,c shows the SEM micrographs of La\({}_{0.8}\)Ca\({}_{0.2}\)CrO\({}_{3}\) powders synthesized by the two methods. The powder by the combustion method consisted of homogeneous, fine submicron particles (0.1-0.2 \(\upmu\)m) estimated from the SEM micrographs.  
The above results exhibit the advantage of the combustion method in synthesizing La\({}_{0.8}\)Ca\({}_{0.2}\)CrO\({}_{3}\) powders. It may be ascribed to that the combustion method could complex metal ions by means of the organic agent and release a relatively large volume of heat and gases, matching a number of the particular characteristics such as high purity, high surface area and hard agglomerate-free [10; 17]. All the advantages could help the La\({}_{0.8}\)Ca\({}_{0.2}\)CrO\({}_{3}\) ceramics possess superior sinterability and physical properties.
## 2 Shearer Positioning System Based on SINS
### Principle of the Shearer Positioning System Based on SINS
To further improve the accuracy of the positioning system, orbital odometer positioning technology is introduced to correct the SINS displacement signal [40; 41]. The running distance of the shearer on the scraper conveyor is monitored by the travel gear sensor, and the running distance is calculated according to the circumference of the gear travel. The running distance obtained by the odometer is accurate, and the orbital odometer positioning technology is simple, reliable, and easy to implement.
## 1 Introduction
Studies on the properties of aerosols under various weather conditions have also been carried out across Korea. For example, [PERSON] et al. [7] analyzed aerosol physical properties by examining Asian dust and haze in Seoul, whereas [PERSON] et al. [22] compared the changes in the aerosol chemical composition of Asian dust, haze, and mist in Gosan, Jeju. [PERSON] and [PERSON] [23] indirectly revealed the long-distance transport of Asian dust particles by measuring the mass and number concentration according to particle size in Seoul and at the sources. Classifications and comprehension of long-distance transport or locally occurring high-concentration aerosol phenomena are limited when using only ground observation data. To this end, various long-distance transport studies using backward trajectory analysis and satellite observation data have been performed [24; 25; 26]. [PERSON] et al. [27] used the backward trajectory model to classify aerosols transported over long ranges and studied the optical and chemical aerosol properties in each case. [PERSON] et al. [28] classified cases of fog, high wind events, and sea-breezes using ground measurements data from coastal areas in France; however, mist, haze, and Asian dust occur in a complex manner, and identifying their sources remains challenging. In addition, studies on the classification of the high-concentration aerosol phenomena considering their physical characteristics and migration path are currently insufficient. Accordingly, the purpose of this study was to investigate the microphysical properties of aerosols in various weather conditions during the spring season in Busan, South Korea. A classification algorithm for meteorological phenomena occurring in Busan was proposed using the results of aerosol physical properties for each phenomenon. The findings from such research stand to benefit from identifying regional characteristics through the physical properties of aerosols and inferring external factors such as long-distance transport. In addition, statistical arrangement and analysis of the representative characteristics of each weather condition can contribute to the prediction of urban air pollution and corrective judgment by classifying the haze, mist, and Asian dust phenomena based on the physical properties of aerosol using a more objective and scientific method.
## 3 Materials and Methods
### Architecture  
The envisioned architecture is presented in Figure 1, along with the main functions. It was designed to target the following objectives:  
* To gather original data heterogeneous data sources (web, social media);
* To process and analyze input data to form an event classification taxonomy extracting breaking news;
* To create automated alerts to trigger drone applicability;
* To support semi-automated/supervised drone navigation (and tagging) through a set of waypoints (with the notes that a safe landing zone is always needed, predefined, or detected in deployment time, while PIC in the communication range is required at all times);
* To develop user-friendly dashboards for drone control;
* To synchronize aerial footage with ground-captured AV streams (e.g., UGC) and propagate related annotations based on spatiotemporal and semantic metadata (i.e., topic, timestamps, GPS, etc.);
* To investigate AV processing techniques for the detection of points/areas of interest in arbitrary images and video sequences;
* To provide information, training, and support about drone utilization in public based on local regulations and ethical codes;
* To implement semantic processing and management automation for the captured content in batch mode, which could be utilized in combination with the other publishing channels and UGC streams (i.e., for analysis, enhancement, and publishing purposes). The architectural implementation provides a solid framework, including:  
1. A prototype web service with open access to external (third-party) news sources for the detection of breaking news;
2. Pilot software automating aspects of drone-based newsgathering;
3. Content storage and management repository with indexing and retrieval capabilities;
4. On-demand support and training on the new services. All legislation matters were accounted for during project deployment and testing and the associated training sessions. Aiming to address both breaking news detection and event geographical localization, field journalism experience was also taken into account.
# A Novel Spectral-Spatial Classification Method for Hyperspectral Image at Superpixel Level
###### Abstract  
Although superpixel segmentation provides a powerful tool for hyperspectral image (HSI) classification, it is still a challenging problem to classify an HSI at superpixel level because of the characteristics of adaptive size and shape of superpixels. Furthermore, these characteristics of superpixels along with the appearance of noisy pixels makes it difficult to appropriately measure the similarity between two superpixels. Under the assumption that pixels within a superpixel belong to the same class with a high probability, this paper proposes a novel spectral-spatial HSI classification method at superpixel level (SSC-SL). Firstly, a simple linear iterative clustering (SLIC) algorithm is improved by introducing a new similarity and a ranking technique. The improved SLIC, specifically designed for HSI, can straightly segment HSI with arbitrary dimensionality into superpixels, without consulting principal component analysis beforehand. In addition, a superpixel-to-superpixel similarity is newly introduced. The defined similarity is independent of the shape of superpixel, and the influence of noisy pixels on the similarity is weakened. Finally, the classification task is accomplished by labeling each unlabeled superpixel according to the nearest labeled superpixel. In the proposed superpixel-level classification scheme, each superpixel is regarded as a sample. This obviously greatly reduces the data volume to be classified. The experimental results on three real hyperspectral datasets demonstrate the superiority of the proposed spectral-spatial classification method over several comparative state-of-the-art classification approaches, in terms of classification accuracy.  
hyperspectral image; improved SLIC; superpixel; superpixel-to-superpixel similarity; spectral-spatial classification +
Footnote †: journal: Applied Physics Letters  
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