esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Review of Multimodal Analysis in Education
## 3 Methodologies in Multimodal Analysis
### Tools and Frameworks
#### 3.4.2 Detection, Capture and Tracking Tools  
To support MMLA, a wide array of tools has been employed for detecting, capturing, and tracking learners' behaviours and physiological signals. These include integrated platforms like iMotions for synchronising affective, visual, and physiological measures; motion capture devices such as Kinect and LELIKELEN for embodied learning and gesture analysis; and specialised toolkits like MOVES-NL and PyramidApp for collecting skeletal, electrodermal, and behavioural data. Computer vision-based approaches leverage libraries such as OpenCV, OpenFace, and OpenPose, often combined with deep learning architectures like EfficientNetB2 or ResNet-101, to enhance the accuracy of facial expression, posture, and emotion recognition. Additional tools such as SMART Notebooks and VAD enable the monitoring of content interaction and verbal engagement. Table 2 summarises these tools, their purposes, and representative works.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Tool** & **Purpose** & **Works** \\ \hline Collaborative learning mechanism (CLM) & Framework analysing collaborative discussion and coordination in group learning & [40] \\ CoTrack & Collaboration visualisation and analysis & [58; 68; 78; 104] \\ CognitOS & Student attention and emotion monitoring & [87] \\ EDA Explorer & EDA signal analysis & [105] \\ EZ-MMLA & Web-based multimodal data collection & [53] \\ FACT & Multimodal data capture in classrooms & [106] \\ Group Scribbles & Classroom collaboration support & [106] \\ HELP coding framework & Video annotation for disengagement detection & [100] \\ IMotions Lab & Eye-tracking and physiology synchronisation & [58] \\ KNIGHT framework & Personalised learning analytics & [107] \\ LEDLAB & EDA signal decomposition & [64] \\ MetaDash & Dashboard for teacher decision support & [103] \\ Multimodal Data Value Chain (M-DVC) & Conceptual model to process and transform multimodal learning data & [47; 66; 108] \\ MLeAM & Machine learning (ML)-enhanced learning analytics model & [47; 108] \\ Moodoo & Spatial behaviour tracking in classrooms & [109] \\ MTClassroom & Table-top collaborative environment & [106] \\ SNAPP framework & Online engagement tracking & [107] \\ STREAMS & Temporal alignment of multimodal streams & [16; 110] \\ TeachLiVE & Virtual reality classroom simulation & [111] \\ Web-based Programming Grading & Augmented feedback system & [106] \\ Assistant (WPGA) & Writing analytics in social contexts & [112] \\ Writer(s)-Within-Community Model & Real-time feedback via emotion/engagement & [113] \\ \hline \end{tabular}
\end{table}
Table 1: Tools for multimodal analysis and visualisation, their purposes, and references.
## 5 Results and Discussion
### Ecological Effectiveness  
Comparisons between models adjusted to fit remote sensing data and in situ field measurements must take place over approximately similar areas. Since the Gross Primary Productivity (GPP) estimates derived from flux tower Eddy Covariance (EC) measurements are deemed to be representative of an area of approximately 500 m around the tower (an area of 53.5 ha), the FAPAR product is averaged over the subset of 3 by 3 pixels centered on the Skukuza flux tower (an area of 68.1 ha). The corresponding average standard deviation for each date in the remote sensing time series is computed as the square root of the average variance of the nine pixel values. The four models described earlier are then adjusted to this spatially averaged time series, and the results are described in the following subsections.
# Tunable and Switchable Erbium-Doped Fiber Laser Using a Multimode-Fiber Based Filter
## 4 Conclusions  
A tunable and switchable erbium-doped fiber ring laser was proposed and demonstrated experimentally. A MMF-based PC was implemented in the setup, which is responsible for the tuning and switching function of the proposed laser. By properly adjusting the MMF embedded PC, the laser could operate at the single- wavelength mode, and was able to switch to dual-wavelength laser mode with different wavelength spacing. For operating at the single wavelength mode, the proposed laser was continuously tuned with a wavelength range of 9.3 nm, while for operating at the dual-wavelength mode, the first peak wavelength was also continuously tunable from 1560.815 to 1562.054 nm. The stability tests were also performed, which showed good laser stability. The proposed simple strategy with using only one MMF-embedded PC provides a cost-effective approach for laser tuning and switching options, which should find practical applications in fiber-optic sensing and fiber laser technologies.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] conducted the experiments and analyzed the data; [PERSON] wrote the paper.  
This research was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) grant number 288225.  
The authors declare no conflict of interest.  
Figure 5: Characteristics of 15-times repeated scans of the oscillation operations for (**a**) a single wavelength at 1559.03 nm and (**b**) a dual wavelength at 1558.15 and 1565.38 nm; Wavelength and power fluctuation for (**c**) the single wavelength EDFL and (**d**) the dual wavelength EDFL.
## 5 Maintenance
### Prognostics  
Besides estimating RUL and MTTF, prognostics usually involves design or create models that can describe the behaviour of the investigated equipment or component. There are numerous approaches that can be applied in this field, namely Physical model-based, knowledge-based, data-driven, etc., models, among which data-driven methods are now predominant due to the huge amount of available data and emerging machine learning applications. Nonetheless, certain learning techniques such as fuzzy logic can also be applied for knowledge-based predictions [160].  
Regarding the data-driven approach in [161], a Support Vector Machine (SVM) and Restricted Boltzmann Machine (RBM)-based solution is proposed for estimating RUL. In this work, a dataset measured using a vibration sensor was classified with SVM, while RBM was used to enable learning without abnormal data. Such a functional combination is typical within this field, where one technique is applied for classification while the other is used for to overcome unbalanced data. According to [162], SVM is one of the most popular algorithms for classification; however, but statistical algorithms such as Bayesian Networks are still relevant, mostly in uncertain environments or in the case of a small amount of data [163; 164].  
A typical problem in modelling complex systems is the massive state space that makes it difficult to create a model that takes each feature into account. Therefore, reducing complexity is a common task in this field, where auto-encoder-based [165] solutions can be intensively applied. In [166], the authors proposed an Auto-Encoder Gated Recurrent Unit (GRU) for dimension reduction before calculating RUL; in [167], it was applied the same way, but within a framework.  
Since such predictions are mostly based on time-series data, it is usually beneficial to implement systems that can deal with this type of data. Ref. [168] applied Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) for High-speed railway power equipment, due to its powerful prediction ability for time-series. In [169], a combined LTSM-RNN solution was applied in the same manner; however, the study pointed out that it is suitable for only critical systems due its complexity, while for a huge amount of data vanilla-RNN is more suitable.
## 2 Development of LST Retrieval Algorithm
However, the CSW algorithm had less estimation capabilities (_i.e._, low correlation, large bias and RMSE) when there were strong lapse rate conditions, especially under strong inversion and superadiabatic conditions. Figure 3: Scatter plot **(left)** of prescribed land surface temperature (LST) and estimated LST using the database simulated by Moderate Resolution Atmospheric Transmission version 4 (MODTRAN4) for the full range of conditions; histogram **(right)** of the difference between the prescribed LST and estimated LST. Figure 2: Comparison of COMS IR1 and IR2 spectral response functions with those from Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) on Meteosat Second Generation 2 (MSG2). Figure 5 shows the RMSE distribution of the CSW retrieval algorithm according to various impacting factors. SZA, brightness temperature difference (BTD) and emissivity difference also impacted the estimation capabilities of the CSW algorithm in addition to the surface lapse rate (Figure 5a,c-f). A negative and a positive BTD exaggerated the RMSE of the CSW algorithm under strong inversion and superadiabatic conditions, respectively (Figure 5d). A negative BTD can be caused by negative emissivity differences and aerosols [45]. The impacts of the emissivity differences on the estimated LST were very similar to those of the BTD differences, but the intensity of the impacts was less significant (Figure 5e). The impacts of emissivity differences and BTD indicate that the quality of LST data retrieved by the CSW algorithm over regions with large diurnal variations in LST (e.g., desert or semidesert regions) can be significantly deteriorated at certain times of the day, in particular, during early morning hours and at noon. Furthermore, the impacts of SZA were proportional to the SZA and more significant under strong inversion and superadiabatic conditions (Figure 5c). Figure 5 indicates that the quality of the retrieved LST by this algorithm may be poor when there are large lapse rates, SZAs, BTDs and emissivity differences present. However, the impacts of mean emissivity and differences between two thermal channel emissivities on the correlation were relatively weak (data not shown). In general, the estimation capabilities of the CSW algorithm worked reasonably well for the values varying from \(-1\) to \(+4\) K (BTD), \(-2\) to \(+8\) K (LST \(-\) Ta), and \(<\)50\({}^{\circ}\) (SZA). Figure 4: Estimation skills of COMS Split-Window LST retrieval algorithm according to the difference between LST and air temperature. Figure 5: Distribution of RMSE for COMS Split-Window LST algorithm according to different impacting factors: **(a)** surface lapse rate and emissivity difference; **(b)** emissivity of infrared-1 and emissivity difference; **(c)** surface lapse rate and satellite zenith angle; **(d)** surface lapse rate and brightness temperature difference; **(e)** brightness temperature difference and emissivity difference; **(f)** brightness temperature difference and satellite zenith angle.
# A Novel Cascaded Modular Photovoltaic Energy Storage System for Partial Shading Conditions
For these solutions, many products have emerged on the market, such as module integrated converters and smart DC modules. The power optimizer P860/P960, produced by SolarEdge, is applied in PV systems in different structures with a superior efficiency, and the excessive loss of converters becomes the key factor that affects the efficiency of PV systems [24, 25]. Moreover, a hybrid connect structure is also proposed. PV modules are connected in series to increase the voltage level, and each PV module is connected with a DC/DC converter connected in parallel to a DC bus, as shown in Figure 1c. In this scheme, the MPPT of each PV module is realized independently with the DC/DC converter, the same as for the smart DC module. However, the output current of the PV module does not only flow through converters, thus decreasing the power losses compared with the structure in Figure 1b. The DC/DC converter only needs to control the compensation current, and a high DC voltage level and efficiency of about 97% are achieved. However, the DC/DC converters are directly connected to the DC bus, causing high voltage stress [26, 27, 28, 29, 30]. Furthermore, the random variations in the partially shaded conditions may cause the fluctuation in output power of grid-connected PV power generation systems, reducing the reliability and power quality. Nowadays, the power fluctuations are stabilized mainly by energy storage (ES) systems, which are generally composed of several battery modules in series in order to meet the grid-connected voltage level. However, the central ES system with fixed series configuration suffers from the \"barrel effect\", due to the characteristic differences among the battery modules, resulting in a reduction of the effective capacity, thus the additional active equalization or reconfigurable circuits are necessary [31, 32, 33]. To solve the above problems in the existing schemes, a novel cascaded modular PV/ES system is proposed for partial shading conditions. The circuit topology and control strategy of the PV/ES module are proposed to realize the MPPT control for an individual PV module, and an energy optimized strategy is designed to realize the constant active power from the PV/ES system to a grid in a certain time period, improving power conversion and transmission efficiency. Compared with the conventional PV power generation systems, the advantages of the proposed scheme are as follows: (1) Under the distributed structure of PV modules and ES modules, the current and voltage stresses of DC/DC converters can be significantly reduced; (2) each PV module is regulated by an ES module through the DC/DC converter, and the hot spots and hot strings phenomena can be avoided; and (3) the proposed energy optimization strategy for the coordination of all ES modules with their own capacity constraint, realizing the independent MPPT of each PV module and the constant active power between the PV/ES system and grid under the inconsistency of light intensity in partial shading conditions.
# Creating a Diagnostic Assistance System for Diseases in Kampo Medicine
## 2 Materials and Methods
### Feature Selection from 27 Features  
As a pre-processing step for machine learning, the following Equation (7) was used to standardize the acquired features:  
\[X_{stn}=\frac{X-X_{mean}}{X_{std}} \tag{7}\]  
where \(X\) is the feature to be standardized, \(X_{stn}\) is the feature after standardization, \(X_{mean}\) is the mean value of the feature to be standardized, and \(X_{std}\) is the standard deviation of the feature to be standardized.  
Support vector regression (SVR) was used for machine learning [13]. In machine learning, training with ineffective features may reduce the accuracy of estimation compared with training using only the optimal features. This is because generalization performance decreases due to over-learning, and the selection of optimal features plays an important role in improving the diagnostic accuracy of machine learning. We used the step-forward selection (SFS) [14] method to choose the best combination of features to evaluate each disease. SFS selects features using the following procedure: the computer learns and  
Figure 7: Percentage of values evaluated by each of the four Kampo doctors (A, B, C, and D) for each disease after data modulation (blue bars), and without data modulation (orange bars): (**a**) Percentage of each correct value for qi deficiency; (**b**) Percentage of each correct value for blood deficiency; (**c**) Percentage of each correct value for blood stagnation; (**d**) Percentage of each correct value for yin deficiency; (**e**) Percentage of each correct value for fluid stagnation.  
independently evaluates each image using all features, and then chooses the ones with the highest evaluation accuracy; it then adds one of the remaining features to the selected feature and learns and evaluates it. This process was repeated for all features. Features were added until the evaluation accuracy no longer increased.  
The SVR parameters used for training were C = 100, \(\gamma\) = 0.1, and \(\epsilon\) = 0.1. Features that were determined to be optimal for the evaluation of each disease are shown in Table 3. The average of the four evaluators was used as the correct value for learning. Table 4 shows the mean absolute error (MAE) between the values of the correct answers by the Kampo doctors and the estimated values obtained by SVR before and after feature selection. The evaluation accuracy increased when feature selection was used. Thus, the feature selection was appropriate. The MAE can be calculated from Equation (8):  
\[MAE=\frac{1}{n}\sum_{i=0}^{n-1}\lvert y_{i}-\hat{y}_{i}\rvert \tag{8}\]  
where \(n\) is the number of evaluations made, \(y_{i}\) is the \(i\)th evaluation value given by the doctor, and \(\hat{y}_{i}\) is the \(i\)th prediction value.
# LightMFF: A Simple and Efficient Ultra-Lightweight Multi-Focus Image Fusion Network
## 2 Related Works
### End-to-End Deep Learning Methods  
End-to-end deep learning methods for MFF directly learn the mapping from source images to the fused result, bypassing the explicit generation of a decision map. This paradigm gained significant traction around 2020 with the introduction of IFCNN [PERSON] et al. (2017), a general image fusion framework, and U2 Fusion [PERSON] et al. (2017), a unified unsupervised framework that enhanced training flexibility.  
Following these foundational works, subsequent advancements have introduced diverse architectures and learning strategies. In 2021, SDNet [PERSON] et al. (2017) achieved real-time performance through a squeeze-and-decomposition network, while MFF-GAN [PERSON] et al. (2017) leveraged unsupervised generative adversarial networks to improve visual quality. The year 2022 saw the integration of Transformer-based architectures with SwinFusion [27], which effectively captured long-range dependencies and significantly enhanced fusion performance for complex scenes.  
More recent developments have pushed the boundaries of this field. MUFusion [37] introduced a self-evolutionary training formula with a novel memory unit, leveraging intermediate fusion results for supervision. Further explorations include SwinMFF [26] for enhanced pixel-level fidelity and FusionDiff [29], which applies denoising diffusion probabilistic models to MFF. The latest works address specific limitations in the field: DDBFusion [28] improves feature extraction with a dual decomposition approach, and StackMFF [38] extends MFF to image stacks using 3D CNNs and synthesized training data. Additionally, MMAE [39] introduced a mask attention mechanism to filter redundant information, and LFDT-Fusion [40] proposed an efficient latent feature-guided diffusion model that operates in a compressed latent space.  
However, a core challenge for end-to-end methods is their inherent demand for complex networks and substantial computational resources. Unlike decision map-based approaches that decompose the fusion problem, these models must simultaneously perform both feature extraction and image reconstruction, a significantly more complex task. This makes them less suitable for lightweight applications. For instance, Transformer-based approaches like SwinMFF [26] and SwinFusion [27] often require tens of millions of parameters. Similarly, diffusion-based methods such as FusionDiff [29] typically demand extensive computational power due to their iterative nature. Even relatively compact architectures like IFCNN [24] require a considerable number of parameters to maintain fusion quality. This fundamental limitation stems directly from the end-to-end paradigm, where generating a pixel-perfect fusion result from source images is an inherently complex task, posing significant challenges for ultra-lightweight deployments. For resource-constrained scenarios, decision map-based methods offer a more promising direction by decomposing the fusion problem into more manageable sub-tasks.
# Estimating Interference with a Two-Dimensional Viterbi Algorithm for Bit-Patterned Media Recording
## 3 Proposed Model  
Figure 2 shows the proposed detection model. The original data, \(u[k]\in\{0/1\}\), are modulated into the signal, \(a[j,k]\in\{-1/1\}\), and stored in the BPMR channel, **H**. This channel includes 2D interference and Gaussian noise (\(w[j,k]\)). The output of the channel \(y[j,k]\) goes through an equalizer, **F**, to adjust it close to the desired signal, \(d[j,k]\). The parameters of the equalizer, **F**, and the target, **G**, were estimated using the MMSE algorithm during the training period. The output of the equalizer, **F**, is \(z[j,k]\), which is supplied to the estimator (using 2D VA) to achieve the \(ISI[j,k]\) or \(ITI[j,k]\) (depending on the direction mentioned in Section 2.2).
# Estimating Road Segments Using Natural Point Correspondences of GPS Trajectories
## 1 Introduction
### Task  
The process of automated extraction of road networks from trajectories can be divided into two main steps (1) estimation of intersections and (2) estimation of road segments between them. In this paper, we focus on the second part. Thus, the addressed task is defined as follows. We are given a set of trajectories which in the ideal case all belong to one road segment and each trajectory consisting of an ordered set of 2D points which represent the two-dimensional position on the lateral plane. The objective is to determine the shape of the road segment. Based on the assumption that large enough sets of gathered trajectories vary around the true location of the road segment, the objective can be reduced to determine a sensible summary \(C\) of the trajectory set \(X=x_{1},x_{2},...,x_{k}\). This can be achieved by solving the optimization problem:  
\[\text{argmin}_{C}\sum_{i=1}^{k}D(x_{i},C)^{2}\]  
Due to the different number of points in trajectories and no missing information about the correspondences of points among the trajectories the solution for the optimization problem has to be searched in a huge configuration space resulting in at least quadratic run time for an optimal solution. We propose to assume correspondences of the points among the trajectories based on their order and then determine the shape of the road segments by the less complex averaging of the corresponding points. This simple approach reaches good results on the data provided for the contest, has a very good run time behavior and is conceptually simple in that it allows modifications and extensions in a straightforward manner. In this work, we evaluate the performance of our approach and compare it to the results of the averaging competition.  
The remainder of the paper is organized as follows. Related work is presented in Section 2. The details of the method are described in Section 3. Experiments performed are described in Section 4 and conclusions are drawn in Section 5.