esa-sceva/satcom-chunk-collection huggingface dataset:
# Seeing the Unseen: Real-Time Micro-Expression Recognition with Action Units and GPT-Based Reasoning
## 2 Related Work
### Challenges and Gaps in Existing Research  
Despite the progress seen in Table 1, several limitations continue to hinder the practical deployment of micro-expression systems. These include:  
* Lack of robustness in real-world conditions--most models are sensitive to variations in lighting, occlusion, and camera angles;
* Dataset scarcity and class imbalance--the small size and uneven emotion distribution in datasets like CASME II reduce generalization;* Emotion confusion--visual overlap between pairs like fear and surprise or disgust and sadness lead to misclassification;
* Cultural and individual variation--facial expression interpretation varies significantly across individuals and social contexts;
* Limited contextual integration--many models do not account for linguistic, situational, or multimodal cues that influence emotional meaning [30; 31]. These challenges underscore the importance of integrating contextual awareness and multimodal learning strategies, particularly those offered by LLMs and vision-language models. Our proposed system directly addresses the above limitations through a hybrid architecture that integrates:  
* MediaPipe-based facial landmark tracking for efficient AU detection;
* Expert rule systems grounded in FACS to resolve ambiguous emotion pairs;
* LLM-based classification for robust and context-aware interpretation;
* Weak signal amplification to enhance detection of low-intensity expressions;
* Real-time capabilities validated on CASME II under practical constraints. By leveraging the strengths of psychology, computer vision, and computational linguistics, our research advances the state-of-the-art in real-time, interpretable, and contextually adaptive micro-expression recognition.
# Potential Marker Genes for Predicting Adipogenic Differentiation of Mesenchymal Stromal Cells  
[PERSON]  
1 Natural Science Center for Basic Research and Development, Hiroshima University, Hiroshima 734-8533, Japan 10 Department of Advanced Technology and Development, BML, Inc., Saitama 350-1101, Japan 10 Department of Dental and Medical Biochemistry, Institute of Biomedical & Health Sciences, Hiroshima University, Hiroshima 734-8533, Japan 10 Department of Molecular Biology and Biochemistry, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima 734-8533, Japan 10 Department of Medicalessential. Thus, there is an urgent need for effective strategies to select suitable MSCs. Recent studies have described cell surface markers that predict the potential of MSCs to differentiate into chondrocytes or osteoblasts [8; 9]. However, prediction markers for the adipogenic potential of MSCs have not yet been identified.  
Recently, we identified marker genes to predict the chondrogenic differentiation potential of MSCs [7]. The mRNA levels of the prediction markers before differentiation showed significant correlations with the protein levels of glycosaminoglycan, a chondrogenic marker, in MSC cultures after chondrogenic differentiation. The combined analysis of three marker genes presented an excellent predictive ability for screening MSCs with high differentiation potential for chondrogenesis.  
Here we report potential prediction markers for the selection of potent MSCs for adipogenesis. By comparing expression profiles of 95 genes in 15 undifferentiated MSCs with glycerol-3-phosphate dehydrogenase (GPDH) activities in the same MSCs after adipogenic induction, 22 genes were selected for further analysis. Combined three-dimensional (3D) analysis of mRNA expression of these genes showed the complete separation of highly potent ilium MSCs from jaw MSCs with limited differentiation capacity.
## 3 Methodology
### Component Complementarity Analysis
#### 3.7.2 Analysis of IMSAR  
The IMSAR is designed to fuse multi-scale features from different network levels, focusing on fine-grained details and global context. The module is able to prioritize the most important features at different scales by combining spatial attention and channel attention mechanisms, as shown in Equation (14).  
\[F_{attendad}=F\odot A_{spatial}\odot A_{channel} \tag{14}\]  
where \(\odot\) denotes element-by-element multiplication. Given a feature map \(F\), the attention mechanism computes spatial and channel attention weights \(A_{spatial}\) and \(A_{channel}\), such that important features (e.g., rain traces and details) are preserved and enhanced. The multi-scale approach allows the network to capture features at different resolutions, thus improving the robustness of the network to various rain trace structures.
# Spatial Data Reconstruction via ADMM and Spatial Spline Regression +
## 2 Problem Formulation
When \(\mathbf{w}_{j}\) is available as additional input, we can estimate the spatial density data in square \(j\) by  
\[f(\mathbf{p}_{j})=f^{\prime}(\mathbf{p}_{j})+\mathbf{w}_{j}^{\mathsf{T}}\beta, \tag{5}\]  
where \(f^{\prime}(\mathbf{p})\) is an underlying spatial field functional that preserves local spatial continuity, while \(\mathbf{w}_{j}^{\mathsf{T}}\beta\) is a linear regression part based on the attributes of square \(\mathbf{p}_{j}\) that allows position-specific variation or jumps. In the presence of attributes, we can formulate the constrained spatial sparse recovery problem as  
\[\underset{f^{\prime},\beta}{\text{minimize}} \int_{\Omega}(\
abla^{2}f^{\prime})^{2}\,\mathrm{d}\mathbf{p},\] (6) subject to \[f(\mathbf{p}_{j})=f^{\prime}(\mathbf{p}_{j})+\mathbf{w}_{j}^{ \mathsf{T}}\beta,\quad j=1,\ldots,n,\] \[\mathbf{z}=\mathbf{A}\mathbf{f},\] \[\mathbf{f}\geq 0.\]  
Once we get the spatial field \(f^{\prime}\) and \(\beta\), we can reconstruct \(f(\mathbf{p}_{j})\) for all the squares using (5). For example, we can calculate the cell phone activity at a specific place by the summation of an underlying smooth spatial field \(f^{\prime}(\mathbf{p}_{j})\) and a linear regression of location attributes, where the add-on regression helps to model the jump between two subregions if the two regions are quite different and have distinct functionalities or attributes.
## 4 Experiment
### Dataset  
The Western North Pacific (WNP) Ocean Best Track Data provided by the Joint Typhoon Warning Center (JTWC) [38] were used in this experiment. This dataset has 61,089 records of 2194 tropical cyclones from the years 1945-2017. In each record, there are 6 hourly tropical cyclone landing locations of 0.1 by 0.1 degrees and several meteorological factors, including the minimum sea level pressure (MSLP), the level of tropical cyclone development (TY), the pressure in millibars of the last closed isobar (RADP), the maximum sustained wind speed (VMAX), the wind intensity (kts) for the radii (RAD), the radius of the maximum winds (MRD), the eye diameter (EYE), the radius of the last closed isobar in nm (RRP), gusts (GUSTS), maximum sea levels (MAXSEAS), storm direction (DIR), storm speed (SPEED), storm name (STORMNAME), system depth (Depth), the wave height for the radii defined in SEAS1-SEAS4, and the radius code (SEASCODE). All meteorological factors were one-dimensional points along the track at each timestamp.  
The entire dataset was randomly divided into two sets, where 10% of the tropical cyclone records were used for testing and 90% used for training. Three-fold cross-validation was used, which meant 70% of the training set was used for training and 30% for validation. That meant the training set contained 54,981 tropical cyclone records, and the testing set contained 6108 records. The detailed experiment dataset is shown in Table 1.  
To validate the performance of our proposed AE-GRU method on a real-world tropical cyclone dataset, we chose a number of super typhoons to visualize the predicted tracks, such as Typhoon Meranti, Typhoon Haiyan, Typhoon Tip, and Typhoon Dujuan, of which, Typhoon Haiyan was one of the strongest storms in global oceans for the year 2013. According to the JTWC dataset, it reached the intensity of a Category 5 typhoon (170 kt, 890 hPa) and caused great distress to the cities along its way, resulting in at least 6000 fatalities and up to 10 billion U.S. dollars in damage, affecting many countries including Mainland China, Taiwan Province, the Philippines, and Vietnam [39]. Some example records of Typhoon Haiyan are shown in Table 2. Each row signifies a record that contained the landing locations, the landing timestamp, and some related meteorological factors of this particular typhoon.  
Figure 4: Forward propagation of the GRU layer.
## 2 Methods
### Model Equations in WRF  
The compressible, nonhydrostatic governing equations, i.e., time-averaged Reynolds equations, that govern the mesoscale NWP model are cast in flux forms and can be expressed in a terrain-following hydrostatic pressure vertical coordinate, as in the literature [35; 36]. Modeling of clouds microphysical process plays an important role in mesoscale convective storm simulations. The WRF double-moment 6-class (WDM6) microphysics scheme [37] is applied in this study. It is a bulk parameterization scheme that contains six water species: water vapor, cloud droplets, cloud ice, snow, rain, and graupel. Cloud particles are represented as particle size distribution (PSD) functions and are characterized by their moments in modeling of physical processes. The cloud raindrop size distribution in WDM6 follows the normalized form given by [37]:  
\[n_{X}(D_{X})=N_{X}\frac{\alpha_{X}}{\Gamma(v_{X})}\lambda_{X}^{\alpha_{X}v_{X} }D_{X}^{\alpha_{X}v_{X}-1}\exp[-(\lambda_{X}D_{X})^{\alpha_{X}}], \tag{1}\]  
\[\lambda_{X}=[\frac{\pi}{6}\rho_{w}\frac{\Gamma(v_{X}+3/\alpha_{X})}{\Gamma(v_ {X})}\frac{N_{X}}{\rho_{a}q_{X}}]^{1/3}, \tag{2}\]  
where \(X=C,R\) refers to clouds or rain, respectively; \(\lambda_{X}\) is the corresponding slope parameter; whereas \(v_{X}\) and \(\alpha_{X}\) are the two dispersion parameters.  
The governing equation of the number concentration for each species is expressed as  
\[\frac{\partial N_{X}}{\partial t}=-\mathbf{V}\cdot\
abla_{3}N_{X}-\frac{1}{ \rho_{a}}\frac{\partial}{\partial z}(\rho_{a}N_{X}V_{X})+S_{X}, \tag{3}\]  
Figure 1: Flow chart of the numerical simulation of WDR effects on transmission lines.  
where the first and second terms in the RHS represent the 3D advection and sedimentation for \(X\), respectively. The term \(S_{X}\) represents the source and sink of number concentration for \(X\).  
The number-weighted-mean terminal velocity which is responsible for the sedimentation of the rain number concentration can be obtained by integrating the terminal velocity of rainwater:  
\[\overline{V}_{NR}=\frac{\int V_{R}(D_{R})dN_{DR}}{\int dN_{DR}}=\frac{a_{R}}{ \lambda_{R}^{b_{R}}}\Gamma(2+b_{R})(\frac{\rho_{0}}{\rho_{a}})^{1/2}, \tag{4}\]  
where \(V_{R}(D_{R})\) represents the terminal velocity of rain particles with diameter \(D_{R}\). Based on [PERSON] and Hobbs [38], the expression is given by  
\[V_{R}(D_{R})=a_{R}D_{R}^{b_{R}}(\frac{\rho_{0}}{\rho_{a}})^{1/2}. \tag{5}\]
# A PUF- and Biometric-Based Lightweight Hardware Solution to Increase Security at Sensor Nodes
## 4 Novel Biometric Sensor Nodes Using QFM-Based Fingerprint Features
### Feature Extraction  
Distinctive information is extracted from the smoothed orientations, in the \(c\) windows of \(w\times w\) pixels centered at the \(c\) selected convex cores. The size of the window adequate for the recognition process depends on the size of the fingerprint captures. For most sensors, which capture images of approximately 300 \(\times\) 300 pixels, a suitable window size is 128 \(\times\) 128. It offers a good trade-off between information extracted (smaller windows lose information) and enough pixels inside the ROI (larger windows are more probably outside the ROI).  
In order to compact information, not all the pixels in a distinctive window are considered but a down-sampling by a factor of \(m\) is applied (one pixel is considered out of \(m\) consecutive pixels). After experiments with different fingerprint databases, the most suitable down-sampling factor was found to be 8. Hence, the QFM-based fingerprint feature contains \(w\times w/m=128\times 128/8=16\times 16\) orientations, which means 1024 bits (the 16 possible orientations are coded by 4 bits). If the fingerprints can be acquired with rotations, three smoothed orientation images are evaluated per fingerprint capture, analyzing \(c\) windows from each of them for \(c\) convex core candidates. Hence, the feature vector of one capture can be formed by 3 \(\times\)\(c\) QFM-based features.
# An Improved Differential Quadrature Time Element Method
## 3 Accuracy and Stability
The present improved DQTEM is proved to be highly symplectic (see Table 3), from which one can see Equation (37) is satisfied excellently, especially \(\mathbf{J}_{3}\)-\(\mathbf{J}_{6}\) are rigorously satisfied. This implies the improved DQTEM has high-order accuracy for amplitude and phase, which will be validated below by numerical comparisons. \begin{table}
\begin{tabular}{c c c c c c c} \hline \(\mathbf{n}\) & \(\mathbf{3}\) & \(\mathbf{4}\) & \(\mathbf{5}\) & \(\mathbf{10}\) & \(\mathbf{15}\) & \(\mathbf{30}\) \\ \hline \(0\) \(<wh\leq\) & \(2.828\) & \(2.954\) & \(3.098\) & \(6.282\) & \(12.56\) & \(34.52\) \\ \hline \end{tabular}
\end{table} 
Table 2: The first stable intervals with \(\rho(\mathbf{J})=1\) for different \(n\). Figure 1: Spectral radii for different \(n\) of the improved DQTEM (Differential Quadrature Time Finite Element Method). \begin{table}
\begin{tabular}{c c c c c c} \hline \(\mathbf{(}\mathbf{n}\), max (\(wh\))) & \(\mathbf{abs(J_{1}-1)}\) & \(\mathbf{abs(J_{2}-1)}\) & \(\mathbf{J}_{i}\) (\(i=3\), \(4\), \(5\), \(6\)) & \(\mathbf{abs(J1-1)}\) & \(\mathbf{abs(\rho(J)-1)}\) \\ \hline \((3,2.8284)\) & \(2.3563\times 10^{-7}\) & \(2.3536\times 10^{-7}\) & \(0\) & \(2.6781\times 10^{-7}\) & \(1.5214\times 10^{-9}\) \\ \((4,2.9541)\) & \(5.5475\times 10^{-8}\) & \(5.5475\times 10^{-8}\) & \(0\) & \(6.0547\times 10^{-8}\) & \(2.2148\times 10^{-10}\) \\ \((5,3.0983)\) & \(4.0174\times 10^{-8}\) & \(4.0174\times 10^{-8}\) & \(0\) & \(4.2503\times 10^{-8}\) & \(3.2145\times 10^{-10}\) \\ \((10,6.2827)\) & \(1.0043\times 10^{-10}\) & \(1.0043\times 10^{-10}\) & \(0\) & \(1.5340\times 10^{-10}\) & \(1.0018\times 10^{-11}\) \\ \((15,12.5662)\) & \(2.9468\times 10^{-11}\) & \(2.9468\times 10^{-11}\) & \(0\) & \(3.0976\times 10^{-11}\) & \(4.2154\times 10^{-12}\) \\ \((20,18.8495)\) & \(1.4581\times 10^{-9}\) & \(1.4581\times 10^{-9}\) & \(0\) & \(1.0002\times 10^{-9}\) & \(7.4521\times 10^{-11}\) \\ \((25,28.2741)\) & \(5.1471\times 10^{-8}\) & \(5.1471\times 10^{-8}\) & \(0\) & \(5.9896\times 10^{-9}\) & \(4.2154\times 10^{-10}\) \\ \((30,34.5274)\) & \(9.2145\times 10^{-8}\) & \(9.2145\times 10^{-8}\) & \(0\) & \(9.5474\times 10^{-9}\) & \(6.2148\times 10^{-10}\) \\ \hline \end{tabular} Note: max (\(wh\)) indicates the upper limit of \(wh\) for the first stable interval in Table 2.  
\end{table} 
Table 3: The Precisions of \(\mathbf{J}_{1}\)-\(\mathbf{J}_{6}\), \(\mathbf{J}\)\(\mathbf{1}\) and \(\rho(\mathbf{J})\).
## 4 Network Interference Probability Analysis
### Heterogeneous Node Deployment Probability Analysis  
In Section 4.1, the obtained asymptotic results are based on the assumption that each node has the same transmission range, which is often not the case in practice. In this section, we will analyze the probability of interference-optimality under the assumption that the transmission ranges are different in the network.  
As shown in Figure 10, we assume that the transmission range of node \(u\) is larger than node \(v\). So there are two different positions of node \(v\): (1) as shown in Figure 10a, the distance between node \(u\) and node \(v\) is smaller than \(r\) and larger than \(r/2\), i.e., \(r/2\leq d\leq r\). In this scenario, node \(v\) falls into the coverage area of node \(u\) and the overlapping area is part of node \(v\)'s coverage area. (2) As shown in Figure 10b, the distance between node \(u\) and node \(v\) is smaller than \(r/2\), i.e., \(0\leq d\leq r/2\). In this scenario, the whole coverage area of node \(v\) will be included by node \(u\). The coverage area calculation will be different in these two scenarios.
## 4 Drug Delivery Systems--Applications in Infectious Diseases
### Disrupting Quorum Sensing  
Quorum sensing is the mechanism of chemically mediated inter-bacterial communication. This communication leads to downstream signaling and gene regulation, as a direct response to signals regarding bacterial cell population density. Thus, quorum sensing can modulate the transition from the pro-adhesive status involved in initial colonization to the activation of virulence genes required to induce a clinically significant infection, once a certain critical bacterial population density has been reached.  
In Gram-positive pathogens, such as _S. aureus_, a series of quorum sensing regulators have been described, among which perhaps the best characterized is the accessory gene regulator (agr) system. Specific inhibitors of different types of Agr proteins have been shown to inhibit quorum sensing and decrease virulence in _S. aureus_. For example, the repurposed drug bumetanide specifically binds to AgrA and decreases the expression of virulence genes, including alpha-hemolysin, phenol-soluble modulus and Panton-Valentine leukocidin in vitro, with confirmed in vivo efficacy from a murine dermonecrosis infection model, where ulcer development was significantly reduced [47].  
Ajoene, a naturally occurring sulfur-rich molecule, has been described to inhibit small regulatory RNAs (sRNA) in both _S. aureus_ and _P. aeruginosa_, thus interfering with quorum sensing and downregulating the expression of virulence genes coding for hemolysis and proteases in _S. aureus_ and the _rsmY_ and _rsmZ_ genes in _P. aeruginosa_, which are responsible for the synthesis of virulence factors, polysaccharides and motility factors. Thus, ajoene has been described as a broad-spectrum quorum sensing inhibitor [48].
## Materials and Methods
### Solvent Extraction Experiments
#### 2.2.2 Countercurrent Extraction Mode  
Modeling of constant flow countercurrent extraction was performed according to [PERSON]'s method [PERSON] (1976) with a ratio of 1:2, i.e., one funnel was equal to two theoretical stages of a cascade process. The experimental scheme is presented in Figure 1. Funnels were filled with organic and aqueous solutions and then were shaken. These starting funnels are indicated with superscript \"1\" on the scheme. After phase separation, an aqueous phase from the 5 th funnel (51) was removed (a raffinate, R1) and an aqueous phase from the 4 th funnel (41) was transferred to the 5 th funnel (52), as well as from the 3 rd (31) to the 4 th (42), from the 2 nd (21) to the 3 rd (32) and from the 1 st (11) to the 2 nd (22). Then, a fresh aqueous solution (F, a feeding solution) was added to the first funnel. All funnels were shaken again. After phase separation, in this second step an organic phase from the 1 st funnel (12) was removed (an extract, E1), and an organic phase from the 2 nd funnel (22) was transferred to the 1 st funnel (13), as well as from the 3 rd (32) to the 2 nd (23), from the 4 th (42) to the 3 rd (33) and from the 5 th (52) to the 4 th (43). Then, a fresh organic phase (S, a solvent) was added to the fifth funnel (53). These operations were repeated until a steady state had been reached, so each aqueous (or organic) phase in one funnel contacted two times with another organic (or aqueous) phase every time contact was with a new phase. Thus, five funnels realized a ten-stage countercurrent cascade. The O/A volume ratio was 15:15 mL. Achievement of the steady state was controlled by analysis of the raffinates (R\({}_{i}\), aqueous outputs) coming from the 5 th funnel. It was found for all of the investigated extraction systems that the steady state was reached after passing three cascade volumes; five funnels contained 5 organic and 5 aqueous phases, and at the steady state, we obtained 15 raffinates and 15 extracts. After achievement of the steady state, every odd step of repetition of the \"shaking procedure\" corresponded to stage numbers 1, 3, 5, 7, 9 and every even step to stage numbers 2, 4, 6, 8, 10. For all countercurrent extraction experiments, we analyzed stage numbers 1, 3, 5, 7, 9. Calculated mass balance was not less than 96%.
## 2 On-Site Operation Experiments and Results
### Environmental Data  
Temperature, humidity, and radiation at the testing site from August 2018 to March 2020 were monitored and are shown in Figure 2. The red, blue, and green curves are the mean value curve, upper and lower envelopment lines, respectively. Temperature sinusoidally fluctuates with calendar time, and the maximum value in summer and the minimum value in winter reached 30 \({}^{\circ}\)C and \(-\)20 \({}^{\circ}\)C, respectively. The annual temperature difference reached 50 \({}^{\circ}\)C, as shown in Figure 2a. Humidity varies in a wide range; the maximum value and minimum value of relative humidity reached 100% and 5%, respectively, as shown in Figure 2b. Irradiation varies with seasons as shown in Figure 2c.  
\begin{table}
\begin{tabular}{c c c c c c} \hline No. & Current & Power Coefficient & No. & Current & Power Coefficient \\ \hline
1 & \(I_{\text{max}}\) & 1 & 8 & 0.5\(I_{b}\) & 0.8C \\
2 & \(I_{\text{max}}\) & 0.8C & 9 & 0.5\(I_{b}\) & 0.5L \\
3 & \(I_{\text{max}}\) & 0.5L & 10 & 0.1\(I_{b}\) & 1 \\
4 & \(I_{b}\) & 1 & 11 & 0.1\(I_{b}\) & 0.8C \\
5 & \(I_{b}\) & 0.8C & 12 & 0.1\(I_{b}\) & 0.5L \\
6 & \(I_{b}\) & 0.5L & 13 & 0.05\(I_{b}\) & 1 \\
7 & 0.5\(I_{b}\) & 1 & & & \\ \hline \end{tabular}
\end{table}
Table 1: Electricity meter testing electrical conditions.  
Figure 1: Diagram of on-site operation of smart electricity meters. (**a**) on-site operation process; (**b**) scheme of the experimental set-up.
# Unlocking the Potential of Keyword Extraction: The Need for Access to High-Quality Datasets
## 2 Literature Review
### Challenges in Keyword Extraction Methods (KeyBERT, YAKE, RAKE)
#### 2.4.3 Challenges of the RAKE Model in Keyword Extraction  
Stop word Ambiguity: RAKE uses a list of stop words to identify and remove irrelevant words from the text. However, the definition of stop words can be ambiguous, and what  
Figure 3: Key Phrase detection of RAKE Model.  
may be a stop word in one context may be an essential keyword in another context. This ambiguity can lead to the removal of critical keywords from the text [27; 28; 29].  
Term Frequency-Inverse Document Frequency (TF-IDF) Sensitivity: RAKE uses TF-IDF to assign weights to keywords based on their frequency in the text and their frequency in the document corpus. However, the TF-IDF approach can be sensitive to outliers and may not accurately capture the importance of rare keywords [27].  
Dependency on Punctuation: RAKE uses punctuation to identify phrase boundaries, making it sensitive to the punctuation used in the text [30]. Different types of punctuation may not be equally effective in identifying phrase boundaries, which can lead to errors in keyword extraction.  
Inability to Handle Multi-word Phrases: RAKE may struggle with identifying multi-word phrases or compound words that are important keywords in some contexts. These phrases may not appear in a dictionary or stop-word list, making them difficult to identify using traditional methods [30].  
Sensitivity to Text Length: RAKE's performance can be impacted by the length of the text. Shorter texts may not provide enough information to accurately identify keywords, while longer texts can be computationally expensive and require more processing power. Table 1 compares the challenges faced by the KeyBERT, YAKE, and RAKE models in keyword extraction across five different dimensions. The dimensions include domain-specificity, the need for high-quality data, ambiguity and context dependency, length of text, and difficulty in identifying multi-word phrases. The \"\(\check{\check{\vee}}\)\" indicates that the model faces a particular challenge in that dimension. However, \"\(\check{\times}\)\" indicates that the model is free from that challenge.