esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
Several authors [18; 19; 20; 21; 22; 24] have analyzed how beach attendance is affected by weather conditions and other aspects such as time, season, etc. The results indicate that beach attendance is affected by the season, day, hour, and meteorological conditions. Our results confirm the same trend.  
Our results regarding the daily temporal distribution of beachgoers are consistent with other beaches along the Mediterranean Sea [18; 19; 20].  
Other authors have used classical econometric models to simulate the seasonality and cyclicality of the processes. In [18], the authors modelled the number of beachgoers as a function of temperature, for temperatures lower than 30 \({}^{\circ}\)C, using the regression coefficient \(R^{2}\) = 0.87 at the Lido of Sete Beach, France. In [21], regression methods are used to determine how the number of beachgoers is affected by the season, day, weather, and ocean conditions (maximum significant wave height, water temperature) in Australian beaches. The authors use ordinal logistic regression to distinguish between three categories of beach visitor numbers: high, moderate, and low. The authors in [19] sought a mathematical expression to model beach attendance. The observed mean number of daily users was adjusted to a time-dependent Fourier polynomial for two beaches in Barcelona. The occupation data for 2002-2004 were averaged to obtain an estimation of the occupation trend for a typical year. This averaged function was projected into a 14-term Fourier polynomial. The Fourier fit obtained and the original time series of the average occupation in a typical year adjusted 74% and 69% of the absolute value of the original time series for the two beaches, respectively. In contrast, our proposed DNN improves these results. It can predict well the number of beach visitors assigned to any of the seven classes or beach occupancy levels and obtains a good performance, with an accuracy of 92.7%.
## 4 Discussion  
To summarize, we have studied numerically the dynamics of a semiconductor laser with optical feedback and sinusoidal current modulation. We simulated the Lang-Kobayashi model to understand why, with small-amplitude modulation, regular 1:1 locked spikes was not observed experimentally. The simulations have allowed us to identify the locking regions in the parameter space (modulation amplitude, modulation frequency). We have found subharmonically locked spikes (2:1 and 3:1) when the modulation is fast enough, and its amplitude is not too large. We have also found 1:1 locked spikes that occur at a higher modulation amplitude and lower modulation frequency, but in this case, the spike timing is more irregular (for subharmonic locking, the normalized standard deviation is \(<\)10\({}^{-2}\), while for harmonic locking, it is \(>\)10\({}^{-2}\)).  
Our interpretation of the physical mechanisms behind these results is the interplay of the laser dynamics near the threshold and the modulation-induced dynamics. While locking phenomena in nonlinear oscillators is well understood and broadly applied, in laser systems, the existence of the threshold introduces additional nonlinear behaviors not observed in conventional, threshold-less oscillators. In the situation considered here, because the laser is near the threshold (where the feedback-induced spiking dynamics occurs), in each modulation cycle, the pump current brings the laser close or below the threshold, and at low modulation frequencies, this occurs for a duration long enough to prevent the generation of fully regular 1:1 locked behavior (due to fluctuations in the timing of the turn-on pulses--the so-called timing jitter). However, when the modulation frequency increases, the intervals during which the pump current is close or below the threshold are shorter, and the current modulation can generate regular 2:1 and 3:1 locked behaviors. It will be interesting, for future work, to analyze how the optical feedback parameters affect the spike timing regularity.  
Conceptualization, methodology, analysis, and writing, J.T.-A. and C.M. All authors have read and agreed to the published version of the manuscript. This research was funded by the Spanish Ministerio de Ciencia, Innovacion y Universidades (PGC2018-099443-B-I00) and the ICREA ACADEMIA program of Generalitat de Catalunya.  
Not applicable.  
Figure 6: Intensity time series displaying (**a**) 1:1 locked spikes (\(f_{mod}=16\) MHz, \(a_{mod}=0.012\)); (**b**) transient spiking dynamics followed by periodic oscillations (\(f_{mod}=6\) MHz, \(a_{mod}=0.006\)).
## 4 Experiments and Analysis
### Experimental Parameter Setting  
The aspect-level sentiment analysis model based on fusion graph neural network proposed in this paper is designed and written using Python programming language. The batch size is set to 8 and the number of model iterations is 100. The dimensional settings for GAT and GCN are established at 500, with a learning rate of \(10^{-5}\).
## 2 Materials and Methods
### Dependency Levels  
In the cohort-generation model, the order in which parameters are computed is critical to ensuring both logical consistency and clinical plausibility. This is especially important when the model incorporates user-defined hard limits or linked equations, as these establish explicit dependencies between parameters. For instance, a parameter like menopause cannot be assigned until both age and sex have been resolved. Similarly, the diagnosis of hypertension might rely on a previously computed value such as systolic blood pressure (SBP).  
To enforce the proper resolution order, a binary dependency matrix is constructed that encodes the relationships among parameters (Appendix C). Each entry \(D_{i,j}\) in this matrix is set to one if parameter \(i\) depends on parameter \(j\) and zero otherwise. This matrix is assembled by parsing each parameter's hard constraints and linking formulas to identify references to other variables. The result is an \(N\times N\) matrix, where \(N\) is the number of parameters in the model and which fully describes the directed dependency structure of the system.  
Once the dependency matrix is defined, the model uses an iterative algorithm to assign a dependency level to each parameter. Parameters that have no dependencies are given Level 1 status and resolved first. Parameters that depend only on those at Level 1 are assigned Level 2. This process continues in sequence, with each parameter assigned the lowest level consistent with its direct and indirect dependencies. The algorithm proceeds until all parameters are ranked. In principle, a parameter could have a dependency level equal to the total number of parameters, depending on the structure of the system. If the algorithm encounters a circular dependency--where two or more parameters form a closed loop of mutual dependence--it halts execution and raises an error.  
With dependency levels assigned, parameters are evaluated sequentially, beginning with those at Level 1 and progressing onward. For example, if age and sex are identified as Level 1 variables, they are computed before SBP, which depends on age and might be placed at Level 2. Hypertension, which may depend on SBP, would then be computed at Level 3. This ordering guarantees that each parameter is resolved only after all the variables it relies on have been assigned valid values.  
After resolving the dependency-resolution process is complete, each parameter's final value is calculated by transforming the correlated Z-score using the following formula:  
\[FinalValue_{i}=\mu+\sigma\cdot Z_{i}^{correlated}\]  
where \(\mu\) is the mean, \(\sigma\) is the standard deviation, and \(Z_{i}^{correlated}\) is the Cholesky-adjusted Z-value for the patient \(i\). This transformation reintroduces the parameter's original scale while preserving the intended correlation structure.
# A Beam Hopping Scheme Based on Adaptive Beam Radius for LEO Satellites
## 4 Presentation and Analysis of Simulation Results
### Comparison of Proposed and Traditional Algorithms with an Increasing Number of Beams  
This simulation is the most interesting one, in my opinion, because it investigated the relationship between overall system throughput and delay with respect to the number of beams, under the traffic distribution introduced in Section 3.7. We still adopt this traffic model in the remaining simulation.  
Figure 13 illustrates the variation in system performance based on the proposed algorithm and the traditional algorithm as the number of beams changes from 10 to 26. It can be observed that as the number of beams increases to 16, there is a certain improvement in system performance, but further increase in the number of beams leads to a bottleneck in system performance due to potential interference.  
Furthermore, it can be noted that with the increase in the number of beams, the improvement in system performance due to beam addition shows a trend of diminishing re-turns, termed as \"diminishing returns\", which is attributed to the increasing restrictions on beam utilization and the resulting potential interference. When the potential interference becomes significant, the addition of beams does not enhance system performance.  
Additionally, by comparing the traditional algorithm with the proposed algorithm, it is evident that when the number of beams is relatively small, the traditional algorithm achieves a maximum performance improvement of approximately 5.19% in system throughput, attributed to the weaker service capability of large beams compared to traditional beams. However, as the number of beams increases, the proposed algorithm achieves superior system throughput performance. Moreover, under any number of beams scenarios, the proposed algorithm achieves at least a 30% improvement in delay optimization.  
Figure 12: Users delay distribution.
###### Abstract  
Insulin-like growth factors (IGFs) plays various roles, including differentiation and mitogenesis, and IGFs are reported to regulate the bone growth and maintenance. This study was performed to analyze the enhancing effects of IGF-2 on osteogenic differentiation and the mineralization of stem cells cultured on deproteinized bovine bone mineral. Stem cell loaded bone graft material was cultured in the presence of the IGF-2 at final concentrations of 10 and 100 ng/mL and the morphology of the cells was observed on Days 1, 3, and 7. The commercially available, two-color assay based on plasma membrane integrity and esterase activity was also used for qualitative analyses on Days 1, 3, and 7. The level of alkaline phosphatase activity and anthraquinone dye assay were used to evaluate osteogenic differentiation on Days 7 and 14. Real-time polymerase chain reaction was applied in order to identify the mRNA expression of BGLAP, Runx2, and \(\beta\)-catenin. The stem cells were well-attached with fibroblast morphology and most of the stem cells produced a high intensity of green fluorescence, indicating that there were live cells on Day 1. The relative cellular viability assay values for IGF-2 groups at 0, 10, and 100 ng/mL on Day 1 were 0.419 \(\pm\) 0.015, 0.427 \(\pm\) 0.013, and 0.500 \(\pm\) 0.030, respectively (\(p\) < 0.05). The absorbance values at 405 nm for alkaline phosphatase activity on Day 7 for IGF-2 at 0, 10, and 100 ng/mL were 2.112 \(\pm\) 0.152, 1.897 \(\pm\) 0.144, and 2.067 \(\pm\) 0.128, respectively (\(p\) > 0.05). The mineralization assay results at Day 7 showed significantly higher values for IGF-2 groups at 10 and 100 ng/mL concentration when compared to the control (\(p\) < 0.05). The application of IGF-2 groups of 10 and 100 ng/mL produced a significant increase of BGLAP. Conclusively, this study indicates that the use of IGF-2 on stem cell loaded bone graft increased cellular viability, Alizarin red staining, and BGLAP expression of stem cells. This report suggests the combined approach of stem cells and IGF-2 with scaffold may have synergistic effects on osteogenesis.  
bone marrow; cell survival; cell differentiation; insulin-like growth factor II; osteogenesis; stem cells
# Experimental Study on Flexural Capacity of Corroded RC Slabs Reinforced with Basalt Fiber Textile
###### Abstract  
This experimental study investigated the flexural performance of corroded reinforced concrete (RC) slabs strengthened with basalt textile-reinforced mortar (BTRM) and basalt fiber-reinforced polymers (BFRP). Ten RC slabs were designed to achieve the expected corrosion levels (8% mass loss for moderate corrosion and 16% mass loss for severe corrosion) by accelerated corrosion methods. Two slabs served as reference specimens, and eight slabs were strengthened with BFRP or BTRM. The specimens were loaded to failure by the four-point bending method. The corrosion ratio, strengthening materials and the number of layers were tested for comparison. The failure modes, flexural capacities, load-deflection curves and deformation performances of the slabs were obtained from experiments. It was found that the use of BTRM layers was more effective in improving the flexural response than the use of the same amount of BFRP layers externally bonded with the corroded RC slabs under a state of serviceability. The results also showed that the strengthening effects of BFRP and BTRM were affected by the initial corrosion ratio and the number of textile layers. In a moderate state of corrosion, the flexural capacities and deflection capacities of RC slabs strengthened by BFRP and BTRM were increased substantially; the flexural capacities were increased by 27.81%-61.85%. In a severe corrosion state, the increase in flexural capacity of strengthened slabs is marginal but the increase in ductility indexes was 18% to 35% compared with the corresponding control slabs. By increasing the number of textile layers from three to five, the increments of the flexural capacity of strengthened slabs are almost doubled. Finally, the calculated results of the flexural capacity of the corroded RC slabs strengthened with BFRP and BTRM were found to be in good agreement with the experimental results.  
reinforced concrete (RC) slabs; accelerated corrosion method; basalt textile-reinforced mortar (BTRM); basalt fiber-reinforced polymers (BFRP); flexural capacity
# Cube of Space Sampling for 3D Model Retrieval
###### Abstract  
Since the number of 3D models is rapidly increasing, extracting better feature descriptors to represent 3D models is very challenging for effective 3D model retrieval. There are some problems in existing 3D model representation approaches. For example, many of them focus on the direct extraction of features or transforming 3D models into 2D images for feature extraction, which cannot effectively represent 3D models. In this paper, we propose a novel 3D model feature representation method that is a kind of voxelization method. It is based on the space-based concept, namely CSS (Cube of Space Sampling). The CSS method uses cube space 3D model sampling to extract global and local features of 3D models. The experiments using the ESB dataset show that the proposed method to extract the voxel-based features can provide better classification accuracy than SVM and comparable retrieval results using the state-of-the-art 3D model feature representation method.  
3D model; 3D object; content-based retrieval; re-sampling; collision detection; similarity match +
Footnote †: journal: Applied Sciences
## 1 Introduction
This is highly labor-intensive and time-consuming work. In addition, since WBMs are classified according to specific defect patterns in keeping with the domain knowledge and judgment of each engineer, there is a problem in that the classification standard is not constant. To address this problem, several studies have been conducted to classify defect patterns of WBMs through automated processes [10]. In a recent study, several researchers applied various machine learning (ML)- and deep learning (DL)-based [11] approaches to WBM analysis to improve the performance of the involved automated processes. To achieve a high automation performance with ML- and DL-based approaches, a sufficient number of WBMs for each defect pattern is required to classify each defect pattern. However, in the semiconductor manufacturing process, the incidence rates for each defect pattern differ. In addition, even if classified as belonging to the same defect pattern, the spatial location, shape, and size of the clustered defects on a given wafer may vary depending on the WBM in which the defect occurs. A new WBM that has not been used for classifier training can degrade the classification accuracy of the classifier, which is one of the reasons it is difficult to improve the performance of WBM defect pattern classifiers through ML and DL techniques. Therefore, achieving a high WBM classification accuracy requires an appropriate WBM augmentation method to solve the data imbalance problem as well as an index to evaluate it. In this paper, we discuss an appropriate augmentation method for improving WBMs' defect pattern classification performance when the data imbalance in the available WBMs data is extreme. To confirm the effectiveness of the proposed method, we used two augmentation models--a deep convolutional generative adversarial network (DCGAN)-based model and a convolutional autoencoder (CAE)-based model--which were trained using real-world data generated during the semiconductor manufacturing process. We performed comparative experiments to assess the classification accuracy and effectiveness of each augmentation method. The classification accuracy according to each defect pattern was calculated from a convolutional neural network (CNN)-based defect pattern classifier. The experimental results showed that the DCGAN-based augmentation method can provide an improved WBM classifier performance compared to the CAE-based augmentation method when augmenting a very small amount of WBM defect data. In the present work, we propose a new index with which to quantify the performance of augmentation methods and evaluate the effectiveness of augmentation methods using this index. Figure 1: Examples of WBM defect patterns. This paper is organized as follows. In Section 2, we present a brief overview of the related works. In Section 3, we introduce the proposed method in detail. In Section 4, we describe the experimental setup and results, and conduct an analysis. In Section 5, we discuss the experimental results. Finally, we conclude this work with a look towards future work in Section 6.
# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 2 Likelihood-Based Modulation Recognition Methods
### Summary of Section 2  
This chapter systematically elaborates on likelihood-based (LB) modulation recognition methods, focusing on their theoretical framework and essential algorithm characteristics. Grounded in Bayesian statistical theory, these methods classify signals by constructing likelihood functions of the received signal under different modulation hypotheses. The theoretical model assumes signal transmission over an AWGN channel, modeling the received signal as a linear superposition of modulation symbols and noise, and deriving the joint likelihood function based on conditional probability density functions. The LB method provides an important theoretical basis for dealing with parameter uncertainty. Despitebeing theoretically optimal within the Bayesian framework, their practical application is limited by computational complexity and the accuracy of parameter estimation.
## 5 Results  
### Models  
The baseline models used in the experiment are listed below.  
**SVM+Wikipedia**: SVM+Wikipedia is the previous SOTA statistical model. It applies SVM with Wikipedia's network of categories and articles to automatically discover new relations and their instances.  
**LSTM and BiLSTM**: LSTM is one of the most potent dynamic classifiers publicly known [38]. Because of the featured memory function of remembering last hidden states, it achieves promising results and is widely used in various NLP tasks. Moreover, BiLSTM improves the token representation by being aware of the conditions from both directions [39], making contextual reasoning available. Additionally, two kinds of representations, GloVe [29] and ELMo [15] are performed separately to ensure a credible model result.  
**Paragraph, Immediate, and PreWin**: These three models are primarily built upon BiLSTM. They simultaneously encode tokens as word embeddings and dependency tags as one-hot vectors (5-10 tokens in general). The difference between them is in the way of picking tokens. Immediate-\(y\) selects \(y\) number of words on the left and right side of the entity as input to the model [40; 41]. The Paragraph model extends the Immediate model by taking the 50 words from the side of each entity as the input to the classifier. The PreWin model relies on a predicate window consisting of a direct vocabulary around the recognized predicate to eliminate noise over a long distance.  
**fastText**: FastText is a tool that computes the word vector and classifies the text without great academic innovation. However, its advantages are obvious. In text classification, fastText can achieve performance similar to the deep network with little training cost.  
**CNN**: The experiment applies the classical CNN model target for the text classification, which is composed of input layer, convolution layer, pooling layer, and softmax layer. Since the whole model adapts to the text (rather than CNN's traditional application: image), some adjustments are made to adapt the NLP task.  
**BiLSTM+Att**: BiLSTM+Att applies an attention layer on the BiLSTM to increase the representation ability.  
**BERT**: BERT is a language model trained on deep Transformer networks which performs NLP tasks well through pre-training and fine-tuning, and achieves the best results in many downstream tasks of NLP [31; 42].
## 5 Evaluation
### Experimental Results
#### 5.1.3 AutomatonCC vs. GreenCC in Real Manufacturing Scenario  
The RAM increase detected in the previous experiment (especially the GreenCC) is the focus of this analysis. In this way, we want to identify if there exists a limit of the ramp up. This is important for a stable operation, because otherwise one risks running out of memory. During our next experiment, both systems have been connected to a real manufacturing stream during a time period of two hours.  
Figure 13 compares the memory peaks of the automaton and the GreenCC. Both graphs show a slightly oscillating behavior at the beginning. This is due to the fact that memory is released and allocated again and again during the checking period. The usage of the GreenCC increases continuously up to about 200 MB. After that, there is a fluctuation around this value. The sharp increase is the result of a long intermediate storage of machine data over a longer period. With the automaton, on the other hand, you can see that memory consumption is significantly reduced. The maximum peak is about 25 MB. The usage of the automaton remains almost constant over the period.  
The CPU comparison shows a relatively similar behavior for both systems. These are shown in Figure 14. Slight oscillations indicate times when the actual validation steps take place. Higher outliers can be explained by several expiring timers at similar time periods.  
Figure 13: Memory usage during 2 h of operation on real manufacturing data.  
Overall, the results reinforce the findings from Sections 5.1.2 and 5.1.3. The higher energy consumption is explained by a higher memory overhead.