esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analysis of Results Obtained from Conventional Ensemble Approaches  
To justify the effectiveness of the proposed ensemble approach, some popular conventional ensemble learning approaches, such as majority voting [42], average probability [43], and weighted average probability (WAP) [44], are also implemented on our data (RSNA-2022) to combine the predictions from the chosen base learners. Each base model predicts a class for an image in majority voting ensemble learning, and the final class is determined by the maximum number of votes obtained from each base model. This approach's primary drawback is that it treats all base models equally, even if one model is more confident about its prediction than another. In the average probability approach, a predicted probability is assigned to each class by all base models. These probabilities are then averaged over all models, and the final prediction is made for the class with the highest average probability. The major drawback of this approach is the final prediction becomes weak after averaging. For example, consider two models predicting the same image as fractured, but with vastly different probabilities, 0.8 and 0.2. Averaging these predictions would result in a probability of 0.5, potentially diluting the strength of the final prediction. In WAP ensemble learning, the predicted probability obtained from each base learner is considered as a weight of the respective model, and the final class is obtained as a weighted average of the predicted probabilities. Although this method is better than the previous two, the weight calculation often depends on the performance on validation data, making it less adaptable to unseen data. The results obtained from these traditional approaches are compared with the proposed model, which is listed in Table 3.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Ensemble Method** & _Acc (\%)_ & _Spec (\%)_ & _Sens (\%)_ & _F1 (\%)_ \\ \hline Majority voting & 89.17 & 83.2 & 88.05 & 85.03 \\ Average probability & 91.27 & 90.06 & 91.1 & 90.06 \\ WAP & 92.3 & 90.56 & 92.72 & 91.11 \\
**Proposed** & **94.62** & **93.51** & **95.29** & **93.16** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of results obtained from proposed method with traditional ensemble learning methods.  
The proposed method outperforms all the existing conventional ensemble approaches. Although the WAP method performs well, our method yields a slight improvement over WAP across all the performance measures. The weights in the WAP are calculated based on a chosen function. However, in the proposed probabilistic-based ensemble approach, the weights are calculated based on the Bayesian framework, which yields more accurate predictions.
## 4 Future Research Directions for Smart Sensing Technologies in Civil Engineering  
Multi-Modal Sensor Fusion and Hybrid Sensing Architectures  
Future research should focus on integrating multi-modal sensor networks (e.g., combining fiber optics, wireless sensors, and radars) to achieve comprehensive structural health monitoring. For instance, fiber-optic sensors excel in long-term strain and temperature monitoring, while wireless sensors provide flexibility in dynamic load detection. Hybrid systems leveraging AI-driven data fusion algorithms (e.g., deep learning for anomaly detection) could enhance accuracy and reduce false alarms. A priority is to develop low-power, high-sensitivity sensor nodes capable of cross-modal data synchronization in harsh environments (e.g., underwater tunnels or extreme climates).  
Self-Powered and Energy-Harvesting Sensors  
Advancing self-sustaining sensing systems is critical for large-scale infrastructure monitoring. Current battery-dependent wireless sensors face limitations in longevity and maintenance costs. Research should explore energy harvesting from ambient sources (e.g., piezoelectric materials for vibration energy conversion and solar-powered sensors for surface structures). For example, bio-inspired materials like cellulose nanofiber composites could enable biodegradable sensors with integrated energy storage. Additionally, optimizing energy-efficient communication protocols (e.g., LoRaWAN for long-range transmission) is essential.  
To address latency and bandwidth constraints in real-time monitoring, edge computing frameworks must be prioritized. Embedding AI models directly on sensor nodes (e.g., TinyML for crack detection) can reduce reliance on centralized cloud systems. Future studies should investigate lightweight algorithms for edge devices, such as compressed neural networks tailored for structural data. Distributed intelligence also requires robust fault-tolerant mechanisms to ensure reliability in sensor networks.
## 2 Materials and Methods
### Chemicals and Reagents  
The standards used in this study were supplied from manufacturers specializing in standard compounds: compounds **1** (CAS No. 24512-62-7, \(\geq\)98.0%), **2** (CAS No. 6020-1804, \(\geq\)98.0%), **6** (CAS No. 551-15-5, 99.6%), **7** (CAS No. 1135-24-6, 98.0%), **12** (CAS No. 21967-41-9, 98.0%), and **17** (CAS No. 1405-86-3, 99.4%) from Fujifilm Wako Pure Chemical Co., Ltd. (Osaka, Japan); compound **3** (CAS No. 80681-45-4, 99.4%) from ChemNorm Biotech Co., Ltd. (Wuhan, China); compounds **4** (CAS No. 633-65-8, 98.0%) and **15** (CAS No. 491-67-8, 98.0%) from KGaA (Darmstadt, Germany); compounds **5** (CAS No. 74639-14-8, \(\geq\)98.0%), **11** (CAS No. 7770-78-7, 99.4%), and **16** (CAS No. 20362-31-6, 98.1%) from Shanghai Sunny Biotech Co., Ltd. (Shanghai, China); compounds **8** (CAS No. 14259-46-2, 99.5%), **9** (CAS No. 84272-85-5, 99.9%), **10** (CAS No. 520-26-3, 98.6%), and **18** (CAS No. 89-82-7, 99.3%) from Biopurify Phytochemicals (Chengdu, China); compound **13** (CAS No. 2643-85-8, 98.0%) from ChemFaces Biochemical Co., Ltd. (Wuhan, China); and compound **14** (CAS No. 51059-44-0, 98.2%) from Tauto Biotech (Shanghai, China). HPLC-grade acetonitrile, water, and trifluoroacetic acid (\(\geq\)99.0%), and ACS regent-grade formic acid (\(\geq\)98.0%), glacial acetic acid (\(\geq\)100.0%), and phosphoric acid (85.0%) were used in the mobile phase, and they were purchased from JT Baker (Phillipsburg, NJ, USA), Merck KGaA (Darmstadt, Germany), and Fujifilim Wako Pure Chemical Co., Ltd. (Osaka, Japan), respectively. Methanol and dimethyl sulfoxide (99.7%) used to prepare the test and standard solutions were purchased from JT Baker (Phillipsburg, NJ, USA) and Merck KGaA (Darmstadt, Germany), respectively.
## 4 Results and Discussion
### Applying Dual Influential Models
#### 4.1.1 Model Assuming Linear Isotherms for Modifier Adsorption  
Method DIM-Lin-kc applied Equation (6) for data fitting. Equation (6) was derived based on the assumption that the loading of modifier in the stationary phase is proportional to the modifier concentration in the mobile phase. This assumption was applied as a first attempt, intended to obtain the simplest model with the least number of fitting parameters, although it is known that methanol adsorption on porous silica in sc-CO\({}_{2}\) does not follow a linear isotherm across the whole range of methanol fraction [26].  
The results of the fitting for retention factors of phenol on silicas are demonstrated in Figure 2, and those for other solutes on SIL-60 are in Figure 3. The model fittings were all successful in the sense that either the chi-square minimization reached the tolerance of \(10^{-9}\) or the chi-square no longer changed. The adjusted R\({}^{2}\) (adj. R\({}^{2}\)) for all cases were in the range of 0.74-0.94, and the relative standard errors (RSEs) of the fitting parameter \(A\) and \(k_{0}\) were in the range of 7-16%. However, it can be observed from the figures that the model fittings diverge from the experimental data at low concentrations of methanol. This leads to the underestimations of the \(k_{0}\) values (y-intercept) in all cases regardless of physical properties of stationary phase (Figure 2) and the solute nature (Figure 3). These observations point to inadequacy of the model assumptions: as expected, the use of linear isotherms to describe the modifier adsorption on porous silica materials is an oversimplification and cannot be recommended for estimation of \(k_{0}\).  
Note that Equation (6), derived in this work, has a certain similarity to the common retention model for reversed phase chromatography, where the logarithm of the retention factor of a solute is linearly related to the organic solvent fraction and the intercept is the logarithm of the retention factor in pure water as mobile phase [27]. This simple model was found to be not accurate enough for certain systems. Efforts were made to improve the model by introducing quadratic terms to the right-hand side [28]. Another natural way, however, is to apply non-linear adsorption isotherms.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Number of Fitting Parameters** & **Range of R\({}^{2}\)** & **Experimental Efforts** \\ \hline DIM-Lin-kc & 2 (\(A\) and \(k_{0}\)) & 0.74–0.94 & Low \\ DIM-Lan-kc & 3 (\(B\), \(\alpha\), and \(k_{0}\)) & 0.83–1.00 & High \\ DIM-Lan-kc & 4 (\(B\), \(\alpha\), \(k_{0}\) and \(K_{eq,L}\)) & 0.87–1.00 & Low \\ DIM-Lan-kcK’ & 3 (\(B\), \(\alpha\), and \(k_{0}\)) & 0.84–1.00 & Low \\ MRM-Lan-kcK & 3 (\(k_{0}\), \(k_{c}\), \(M\)) & (0.86) *-1.00 & High \\ MRM-Lan-kcKM & 2 (\(k_{0}\), \(k_{c}\)) & 0.69–1.00 & Low \\ \hline \hline \end{tabular}  
* 0.86 is the lowest R\({}^{2}\) among those with successful fitting. However, several fittings failed by this method.  
\end{table}
Table 3: Comparison of methods based on DIMs an MRMs.
## 2 Basic Model Assumptions in Conventional Thin Film Optics
### Material Description
#### 2.2.2 Commonly Used Dispersion Models
In the Tauc-Lorentz model, the imaginary part of the dielectric function of a single oscillator model is merged together with the Tauc edge [30; 31; 32] to generate the imaginary part of the dielectric function of the Tauc-Lorentz model according to [20; 21], leading to Equation (8):  
\[\text{Im}\varepsilon(\omega)\propto\frac{\big{(}\hbar\omega-E_{g}\big{)}^{2} \theta\big{(}\hbar\omega-E_{g}\big{)}+\big{(}\hbar\omega+E_{g}\big{)}^{2} \theta\big{(}-\hbar\omega-E_{g}\big{)}}{\omega\Big{[}\big{(}\omega_{0}^{2}- \omega^{2}\big{)}^{2}+4\omega^{2}\gamma^{2}\Big{]}} \tag{8}\]  
Here, \(E_{g}\) is the absorption edge described in terms of the Tauc optical gap. Compared to the usual writing, Equation (8) is generalized to negative frequency arguments in order to comply with Equation (3). The corresponding real part is calculated in terms of a Kramers-Kronig relation (see later point Section 2.2.3). Explicit expressions can be found, for example, in [20; 33]. The introduction of the Tauc gap in amorphous semiconductor optics is connected to the assumption of a constant (frequency-independent) transition matrix element of the momentum operator. If, on the contrary, constancy of the electric dipole moment operator is presumed, one arrives at the Cody description of light absorption in the vicinity of the absorption gap, which results in the definition of the Cody gap [35]. With corresponding modifications in Equation (8), the Cody-Lorentz model may be formulated [22]. To our knowledge, the Tauc-Lorentz model is more frequently used in practice than its counterpart, the Cody-Lorentz relation. In the Forouhi-Bloomer model (we restrict here on the version for amorphous solids [23]), explicit expressions for \(n\) and \(K\) are derived according to the following:  
\[ \begin{split} K(\omega)&=\frac{A\big{(}\omega- \omega_{gap}\big{)}^{2}}{\omega^{2}-B\omega+C}\\ n(\omega)&=n_{\infty}+\frac{B_{0}\omega+C_{0}}{ \omega^{2}-B\omega+C}\end{split}  \tag{9}\]  
The \(A\)-, \(B\)-, and \(C\)-values are constants. For uniformity reasons, Equation (9) is written here in terms of the angular frequency, which is in contrast to the original publication where the photon energy was used. What appears rather strange is the asymptotic behavior for \(K(\omega\rightarrow\infty)\to A\
eq 0\). This has been recognized early and gave rise to corresponding criticism [20]. Nevertheless, the model is frequently applied to spectra analysis in the vicinity of the fundamental absorption edge.
## 3 Results and Discussion  
### Optical Microscopy  
The microscopic images of the Control and the metal-treated cultures on Day 7 using a 10\(\times\) objective lens are presented in Figure 1. The linear morphology of the filaments is noted, instead of a helical one. Although the morphology relates to cultivation conditions--especially with high illumination during laboratory or industrial cultivation [52]-this morphology of whole cultures of various _Arthospira_ (_Spirulina_) species was previously reported and discussed [53] (and referenced within). In brief, straight filaments are usually reported in laboratory conditions and this morphology has been assigned to mutations associated with cultivation conditions that are under stress, as the linear form presents greater survivability. Straight filaments were also reported in wild cultures [53], making this morphology widely known and accepted [54].  
The images of Figure 1 (10\(\times\) lens) and Figure 2 (60\(\times\) lens) show the effect of the different metals (Cu, Cd, Ni, Pb, Zn) and their mixtures (Mix) on the cells in the three concentrations, in comparison with the Control culture [12]. The control cells have normal morphology (Figure 2a), while the treatment with metals, even at low concentrations (1 ppm), causes severe structural changes and reduced cell viability. The increase in concentration increases the cellular damage from 5 ppm to 10 ppm, with drastic changes taking place in the case of samples Ni, Pb, and Cu and the multi-MT Mix cultures. These include low cell density, fragmentation of cells (Figure 2b), structural and pigmentary degradation (Figure 2c), and possible cellular degeneration or the presence of protoplasts (Figure 2d). Therefore, the results highlight the toxicity induced by heavy metals, which is enhanced when combined.  
Figure 1: Optical microscopy images from all cultures as collected with \(\times\)10 objective lens on Day 7 of cultivation.  
The changes observed include the loss of membrane integrity, which is manifested through disruption in the lipid bilayer, hence the uncontrolled flow and leakage of the cellular contents (Figure 2). Necridial cells occur due to the destruction of intercalary cells. This mechanism also leads to the fragmentation of the filaments that can lead to multiplication [53]. The cytoplasmic content becomes fragmented and the organelles and mitochondria swollen, thus indicating oxidative stress. Also, the nucleus usually appears deformed and shows chromatin condensation as a hallmark of apoptosis. Moreover, the cytoskeleton, which provides mechanical properties to the cell and transports intracellular materials, seems to disorganize to such an extent that cells lose their shape and become functionally impaired.  
These structural changes agree with those in the literature [19], where it is stated that one of the primary targets for many toxicants is the cell membrane; the latter plays a vital role in maintaining cellular homeostasis and regulating the entry and exit of substances across the cells [53]. These membrane disruptions often promote organelle dysfunction, energy depletion, and eventually cell death.
## 6 Discussion
### Implications for Language Learning  
The successful integration of the Data-Driven Personalized Learning Model (DDPLM) within platforms like Edmodo and Duolingo has profound implications for language learning. The empirical evidence gathered through these case studies provides a convincing argument for the widespread adoption of big-data-driven personalization in educational settings. Here, we explore the broader implications of this research for language education.  
The application of DDPLM showcases the potential for enhancing learner autonomy by providing learners with tools that adapt to their unique learning styles and paces. This customization level helps remove common language learning barriers, such as frustration and loss of motivation, which are often the result of a one-size-fits-all approach. By making the learning process more responsive to individual needs, students can take greater control over their learning, potentially leading to higher achievement and satisfaction.  
The findings from this study advocate for a shift in educational paradigms from traditional, teacher-centered models to learner-centered approaches enabled by technology [49]. This shift supports more effective learning and empowers educators to act more as facilitators of learning rather than direct instructors. Such a transformation could lead to significant changes in curriculum design, instructional strategies, and the role of the teacher in the digital age.  
The ability of DDPLM to integrate seamlessly into existing platforms like Edmodo and Duolingo suggests that similar models could be adopted across a wide range of educational technologies without necessitating major overhauls of current systems. This compatibility with existing infrastructures makes it feasible for institutions to adopt personalized learning solutions without substantial investments in new technologies.  
The use of big data analytics in education, as demonstrated by DDPLM, encourages a data-informed approach to decision-making in curriculum planning and management. Educators and administrators are equipped with precise tools to continually assess and enhance the effectiveness of language teaching methodologies based on actual learner data.  
The implications of this research extend beyond mere academic success; they herald a transformative shift in how language education is conceived and delivered. Emphasizing personalized learning supported by big data aligns with contemporary educational needs and prepares learners more effectively for a globalized world where language skills are increasingly crucial. Figure 4 illustrates the global distribution of language learning plat form adoption, with color gradients indicating varying levels of engagement and market penetration.
## 2 Classical Topologies
### Coupling Factor  
One of the concerns in wireless transfer is to evaluate the behavior of the system through variations in the coupling factor (\(\kappa\)). The coils are subjected to vertical and horizontal displacements in these applications. In relation to implantable devices, the situation is even more complex. In addition to the coils' alignment unpredictability, the system is exposed to a medium of organic tissues and liquids of different compositions.  
Figure 6 shows the input current behavior from the maximum misalignment (\(\kappa=0\)) to situations where the coupling coefficient is about 20% above the nominal condition. For topologies with series compensation in the transmitter side, as the coupling factor \(\kappa\) decreases, the input current increases considerably. Therefore, unless a strict primary current control is implemented, the system will be driven to incompatible current levels, making the implementation unfeasible. On the other hand, the PS and PP topologies are less sensitive to reductions in the coupling factor since the input current decreases with the distance between the coils. This characteristic makes them strong candidates for applications in which control simplicity and good stability are needed.
# Learning an Efficient Convolution Neural Network for Pansharpening
## 4 Experiment
### Experimental Results and Analysis  
#### 4.2.1 Reduced Scale Experiment  
In these experiments, the MS and PAN images were down-sampled by following [PERSON]'s protocol to yield the reduced scale pairs. In this case, we fused the degraded pairs and regarded the original MS as the ground truth. We merge our model on 100 images from WorldView-2 and IKONOS, and took that of WorldView-2 for detailed demonstration. Apart from the proposed CNN model, a number of state-of-the-art methods were also simulated for visual and quantitative evaluation. Specifically, we chose band-dependent spatial-detail (BDSD) [32], nonlinear intensity-hue-saturation (NIHS) [33], induction scaling technique based (Indusion) model [34], nonlinear multiresolution analysis (NMRA) [35], \(\ell 1/2\) gradient based (\(\ell 1/2\)) model [36], PNN [13], and MSDCNN [12] for comparison. Among them, BDSD and NIHS belong to component substitution branch, Indusion and NMRA are MRA-based methods, and \(\ell 1/2\) is guided by model-based optimization. For the deep-learning-based methods, PNN and MSDCNN are considered to be the main competitor of the proposed model.  
Given the fact that the MS image of WorldView-2 contained eight spectral bands, we display the results composed of red, green and blue spectral bands (RGB spectral results) of one group in Figure 6 for visualization. To highlight the differences, we display the residual images in Figure 7 for a better visual inspection. For the numeric assessment, we employed the universal image quality index averaged over the bands (Q) [37], eight-band extension of Q (Q8) [38], spatial correlation coefficient (SCC) [39], Erreur Relative Globale Adimensionnelle de Synthse (ERGAS) [26], spectral angle mapper (SAM) [40], and feed-forward computation time for evaluation. The numeric indicators of simulated experiments are listed in Table 1.  
Figure 6: Results of the reduced scale experiment on an area extracted from a WorldView-2 image. (a) LRMS; (b) BDSD; (c) NIHS; (d) Indusion; (e) NMRA; (f) \(\ell 1/2\); (g) PNN; (h) MSDCNN; (i) Proposed; (j) Ground Truth.  
As we can observe from Figure 6, BDSB, NIHS, Indusion, and NMRA impressively improved the spatial details with the cost of introducing different levels of spectra distortions. In contrast, \(\ell 1/2\) preserved precise spectral information, but the spatial components were rarely sharpened. Compared with the traditional pansharpening algorithms, the CNN-based methods tended to produce more satisfactory results. The proposed model effectively improved spatial information without introducing noticeable spectral distortions, while MSDCNN and PNN exhibit spectral distortions in specific regions (water basin). All these observations are also supported by the residual images displayed in Figure 7.