esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Teaching-Learning-Based Optimization Algorithm for the Mobile Robot Path Planning Problem
## 4 Proposed ETLBO for MRPP
### Pyramid of ETLBO
#### 4.3.2. Hybrid Offspring Generation Method  
An efficient swarm-based metaheuristic should make full use of both the local and global information on the solutions found. Local information on solutions can be helpful for exploitation, while global information tends to guide the search for exploring promising areas. The evolution of TLBO mainly depends on the global information, and thus TLBO has an obvious advantage in that learner individuals will converge at a single attractor/teacher individual. It can be observed that DE evolves mainly based on the distance and direction information, which are types of local information in nature. In other words, DE has the advantage of not being biased toward any prior defined guide, and such an attribute makes DE able to maintain population diversity and explore local searches.  
The key reason for employing the hybridization of different metaheuristics is that the hybrid approach can make full use of the strengths of each algorithm while simultaneously overcoming the associated limitations. In this regard, instead of employing a single updated learner individual in the TLBO, the proposed ETLBO makes use of the integration of two offspring individuals. The first offspring individual is computed by the TLBO, and the second one is obtained by the DE operators. Detailed mathematical expressions are described as follows.  
(1) Teacher phase  
Given a candidate solution, \(x\), the corresponding offspring individual, \(x^{\prime}\), is calculated by the hybridization of the teacher phase in TLBO and differential learning in DE:  
\[x^{\prime}=u_{1}\cdot x^{TLBO,Teach}+(1-u_{1})\cdot x^{DE,Teach} \tag{13}\]  
where \(x^{TLBO,Teach}\) represents the offspring individual obtained by Equation (4) in TLBO, while \(x^{DE,Teach}\) represents the offspring individual computed by Equations (6)-(11) in DE. The hybridization factor \(u_{1}\) is a random number that lies in the interval [0, 1].  
(2) Learner phase  
Analogous to the teacher phase, the offspring individual \(x^{\prime}\) of a candidate solution \(x\) is generated by taking the following equation:  
\[x^{\prime}=u_{2}\cdot x^{TLBO,Lear}+(1-u_{2})\cdot x^{DE,Learn} \tag{14}\]  
where \(x^{TLBO,Learn}\) represents the offspring individual obtained by Equation (5) in TLBO, while \(x^{DE,Learn}\) is the offspring individual computed by Equations (6)-(11) in DE. The hybridization factor \(u_{2}\) is a random number that lies on interval [0, 1].
# Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters
## 3 Results and Discussions
### Radar Signal Analysis
#### 3.2.2 Relationship between Radar Signal and Soil Roughness  
As noted in the introduction, in the case of bare soils, the soil roughness has a very strong influence on the behavior of radar signals. In the present study, the relationship between radar signals and the parameter Hrms was analyzed. As shown in several preceding studies [16; 34], it is valid, to a good approximation, to take the linear sum of the radar signal contributions produced by roughness and soil moisture. In order to avoid the influence of moisture heterogeneities, the relationship between roughness and radar signals is analyzed for two moisture classes: a class with low moisture values \(\leq\) 10 vol. % and a second class with soil moisture (mv) ranging between 10 vol. % and 20 vol. %. In the database used for the present study, since a very small number of bare soil measurements (8) were found to correspond to the highest moisture values (>20 vol. %), an additional third class, with mv > 20 vol. %, was not considered. Figure 8 shows the relationship between the soil roughness parameter (Hrms) and the S1 signals in the VV and VH polarizations, for each of these two moisture classes. A high correlation, greater than 0.7 in the case of low moisture values (mv < 10 vol. %), is obtained between the radar signal strengths and the roughness parameters. In this moisture class, the VV and VH data have approximately the same behavior, characterized by roughness-induced signal strength variations of approximately 6 dB. It can be seen in Figure 7 that in the high moisture class (10% < mv < 20%) there is just a small number of data points (16 observations), with no reference fields having a value of Hrms smaller than 1 cm. This leads to a lower correlation between roughness and radar signal.  
Figure 6: S1 radar data as a function of soil moisture: (**a**) VV polarization, NDVI < 0.25; (**b**) VH polarization; NDVI < 0.25; (**c**) VV polarization, 0.25 < NDVI < 0.5; (**d**) VH polarization, 0.25 < NDVI < 0.5; (**e**) VV polarization, NDVI > 0.5; (**f**) VH polarization, NDVI > 0.5.
## 3 Lightweight Cryptography for Smart Objects
### Lightweight Cryptographic Hash Functions
#### 3.3.2 Photon  
PHOTON [48] is a hardware-oriented family of cryptographic hash functions designed for constrained devices. PHOTON uses a sponge-like construction [49] as domain extension algorithm and an AES-like primitive as internal unkeyed permutation. A PHOTON instance is defined by its output size (\(64\leq n\leq~{}256\)), its input rate \(r\) and its output rate \(r^{\prime}\) (PHOTON-\(n/r/r^{\prime}\)). The use of sponge functions framework aims at keeping the internal memory usage low. The framework has been extended in order to increase speed when hashing small messages, which is typically inefficient with sponge functions framework.
###### Abstract  
Background and Aims: Previous observational studies found inconsistent associations between serum vitamin D levels and dental caries risk. A dose-response meta-analysis of cross-sectional studies was performed to investigate the association. Methods: To April 2023, the ISI Web of Science, PubMed, Scopus, and Google Scholar databases were searched for published papers. Finally, 13 cross-sectional studies were considered that provided odds ratios (ORs) with 95% confidence intervals (CIs) for dental caries in relation to serum vitamin D levels across all age groups. Two reviewers conducted a thorough screening of the studies, data extraction, bias risk assessment, and evidence quality. A random-effect model was used to assess the pooled estimated odd ratios (with 95% confidence intervals). A weighted mixed-effects dose-response meta-analysis in one stage was carried out. Results: Dental caries was significantly more likely to occur when serum vitamin D levels were low compared to high (OR: 1.41; 95% CI: 1.18, 1.68; GRADE = poor confidence). With a 10 nmol/L increase in serum vitamin D level, linear dose-response analysis showed a significant 3% (OR: 0.97; 95% CI: 0.96, 0.99) decrease in the likelihood of dental caries. Serum vitamin D levels and dental caries were found to be inversely correlated, with a significant dose-response relationship at levels greater than 78 nmol/L. Conclusion: This meta-analysis showed that vitamin D insufficiency was strongly associated with dental caries, and that a 10 nmol/L increase in blood 25(OH)D levels was linked to a 3% decrease in dental caries. However, the findings may have less clinical significance due to the uncertainty of the evidence.  
vitamin D; 25-hydroxy vitamin D; dental caries; systematic review; meta-analysis 2023
# Biomedical Flat and Nested Named Entity Recognition: Methods, Challenges, and Advances
## 6 Discussion  
After the emergence of LLMs, BioNERs have shown significant improvements, with flat NER models achieving around 90% accuracy for most datasets and nested NER models achieving around 80%. However, there are still considerable limitations that hinder their application in real-world environments, particularly in terms of the lack of diverse datasets and generalization across datasets.  
First, lack of data remains a key issue. The scarcity of domain-specific datasets, especially for nested NER tasks, presents a significant challenge. There are only two available corpora for biomedical nested NER, GENIA and BB19, which hinder generalizing the models across different data. While flat NER has more datasets, there is a lack of multi-type corpora. Manually creating large-scale datasets with annotation is labor-intensive and expensive. To overcome these challenges, data augmentation and meta-learning models are actively researched. Data augmentation systems attempt to generate synthetic examples or transform existing data, while the meta-learning models enable effective performance with limited labeled examples. Despite the progress in these areas, the performance of these methods has not yet reached a sufficient level for practical use in real-world applications [100; 101; 102].  
Secondly, generalization across datasets presents another challenge. A model that performs well on one dataset often sees its performance decline when applied to other datasets within the same domain (e.g., NCBI disease to BC5 CDR disease) [71]. This decline is often due to differences in annotation schemes, boundary definitions, and entity types between datasets. Models that struggle to maintain consistent performance across multiple datasets are less reliable for real-world applications. To address this, there is a need for models that can generalize effectively across various datasets and handle these discrepancies.
## 1 Introduction
This study applies drone monitoring technology to the land side of ports, proposing a new drone monitoring path scheme, targeting carbon dioxide emission monitoring of land-based transportation vehicles within port areas. By establishing a new path planning model and its algorithm, and conducting experimental simulations, it aims to provide ports with more accurate and extensive direct carbon dioxide emission data. The contribution of this study can be summarized as follows. First, we propose a novel drone monitoring path planning scheme, which is an innovative path planning solution specifically designed for carbon dioxide emission monitoring on the land side of ports. A framework diagram of the proposed planning scheme is presented, which serves as a potential guideline for progressively improving port emission inventories and formulating environmental enhancement strategies. Second, this paper conducts data analysis on certain aspects involved in the overall scheme, and ultimately selects the initial drone monitoring path plan as a starting point for optimizing the path planning algorithm, resulting in the improved A* path planning algorithm presented. Multiple simulations were conducted, and comparisons made with traditional algorithms, demonstrating the superior performance of the algorithm refined in this study.
# A Study on the Development of a Web Platform for Scalp Diagnosis Using EfficientNet
## 2 Research Backgrounds
### Related Works
Scalp conditions are classified into six categories, with each condition evaluated on a scale from mild to severe across four stages. The model's average accuracy was 65%, with the highest accuracy reaching up to 77% for categories with more data. The EfficientNet-B0 model maintained high diagnostic accuracy despite its lightweight structure, performing with similar success to expensive scalp diagnostic devices. [PERSON] [22] proposed a model to classify seven types of scalp conditions using EfficientNet and transfer learning. The data imbalance problem was improved by oversampling using image augmentation for each state. The experimental results showed that the image augmentation and down-sampling techniques were very effective in increasing accuracy. [PERSON] et al. [23] proposed deep learning models, such as EfficientNet, ResNet, and ViT, to accurately classify the severity of six common scalp conditions. Explainable AI (XAI) techniques, such as Grad-CAM and attention rollout, were applied to visualize the model's prediction process. The experimental results showed that the ViT-B/16 model achieved the highest performance with an accuracy of 78.31%. Based on these results, the \"Scalp Checker (ver.1.0)\" software was developed, enabling users to easily monitor and track their scalp condition. Table 1 summarizes the results of the last five studies, and these various research studies provide solutions to the existing technical challenges in assessing scalp conditions and disease detection, offering valuable information for future research directions. Calibration methods related to accuracy vary across studies, and techniques such as cross-validation, confusion matrix, receiver operating characteristic (ROC) curve, and area under the curve (AUC) analysis have been used.
# Analysis of the Effect of Pulse Length and Magnetic Field Strength on Nonlinear Thomson Scattering
## 3 Results
### Radiated Power of Electrons versus Maximum Radiated Polar Angle  
Figure 7 shows the variation in the maximum radiated power \(dP(t)/d\Omega\) and the maximum radiated polar angle \(\theta_{r}\) with magnetic field strength at different pulse lengths. The pulse lengths \(L=2\lambda_{0},4\lambda_{0},6\lambda_{0},20\lambda_{0}\), respectively, in Figure 7.  
It can be seen from the figure that the maximum radiation polar angle is approximately the same in every plot when the magnetic field intensity \(B_{0}=0.9\). This is because collimation is better with a smaller magnetic field intensity at a larger pulse length. This is because a larger pulse length corresponds to a smaller gradient along the rise. In this way, electrons are affected by the laser pulse for a longer period of time when they reach the focal point of the laser field, leading to greater velocity in the \(Z\) direction when the maximum radiation is emitted, as well as a smaller maximum radiation polar angle.  
When the pulse length is too big, the collimation of radiation decreases as the magnetic field intensity \(B_{0}\) increases. This is because with the increase in \(B_{0}\), the radial radius of the electron also increases, and the acceleration of the electron's circular motion increases. Thus, the velocity in the \(z\)-direction is weakened when the maximum radiation is emitted, which also leads to a decrease in the polar angle of the maximum radiation.  
Figure 7: Maximum radiated power of an electron and the relation between the polar angle of maximum radiated power and magnetic field strength. Where (**a**–**d**) represents the pulse lengths L = 2\(\lambda_{0}\), 4\(\lambda_{0}\), 6\(\lambda_{0}\), 20\(\lambda_{0}\).
# Markov-Embedded Affinity Learning with Connectivity Constraints for Subspace Clustering
## 2 Background
### Markov Transition Probability Matrix  
First, the similarity matrix of the data matrix \(X\) is defined as \(S\), where \(S_{ij}\) represents the similarity between \(x_{i}\) and \(x_{j}\). The details of the construction of matrix S are as follows:  
\[S_{ij}=\begin{cases}1,&x_{j}\in\eta_{K}(x_{i}),\\ 0,&x_{j}\
otin\eta_{K}(x_{i}),\end{cases} \tag{2}\]  
where \(\eta_{K}(x_{i})\) represents the set of \(K\) nearest neighbors of \(x_{i}\). Throughout this paper, without loss of generality, we use the Euclidean distance to measure the distances of samples and set \(K=5\).  
Then, we define \(P\) as the Markov transition probability matrix based on \(S\), and \(P=D^{-1}S\), where \(P_{ij}\) reveals the probability of data point \(x_{i}\) randomly walking to data point \(x_{j}\), and \(D\) is a diagonal matrix with \(D_{ii}=\sum_{j=1}^{n}S_{ij}\). Research has found that there is a close connection between the Markov transition probability matrix and the self-expression [12]. Markov random walks tend to occur primarily within clusters, rarely crossing over to other clusters, which indicates that the Markov transition matrix can also capture the neighbor information of the data. This property has an essential advantage in measuring the relationships of the data. In fact, the Markov transition probability matrix mentioned above preserves the one-step neighbor relationships of the data. However, there are not only one-step neighbor relationships between samples but also higher-order neighbor relationships [13]. These relationships reveal the latent neighbor relationships of the samples, which are essential to fully exploit the comprehensive structures of the data. All types of relationships are visualized in Figure 1.
# ANN Sizing Procedure for the Day-Ahead Output Power Forecast of a PV Plant
## 2 Artificial Neural Networks  
An ANN basically consists of a number of neurons, grouped in different layers, which receive input from all of the neurons in the preceding layer. Each neuron performs a simple nonlinear operation, and the learning capacity is finite due to the number of neurons and links existing among them. The number of neurons in the first input layer is equal to the number of input data, and the number of neurons in the output layer corresponds to the number of output values. Hidden layers are in between, and their neurons can vary in number.  
Choosing the amount of neurons in the layers and the number of layers depends on several factors, as well as the kind problem to be addressed [17]. There are several proposed models to define an optimal network [18], but these methods are not applicable to every area of study. Indeed, the success of a network depends heavily on the experience of the creator [19], and many attempts must be made to reach satisfactory result. Moreover, some limits are imposed by the computational burden. In fact, the more complex the network, the longer the time (hours) needed for data processing.  
This paper describes a heuristic process, by means of a statistical indicators comparison, in order to optimize the settings of a Feed Forward Neural Network (FFNN) for PV forecasting based on weather predictions. The search for the best layout is constrained by computational limitations. For the above mentioned FFNN, the most effective configuration based on the minimum absolute mean error (normalized or weighted) index has been studied. Since the future weather conditions are unknown at the time of prediction, it is impossible to determine a priori which precise network layout will provide the best PV power production forecast.  
It must be pointed out that, during the FFNN training step, both the input (i.e., the historical weather forecasts) and the target (i.e., the historical PV plant output power) datasets employed are just one representative couple of the virtually infinite couples of input and target presented in the historical weather data pool.  
Furthermore, for any given dataset, each network configuration presents an additional element of randomness. In fact, for a specific network layout, the training process is initialized by means of randomly chosen weights and biases; therefore, for the same forecast, the performance is non-unique.  
Hence, to set up the main settings, it is useful to feed the FFNN with the same data that is representative of the majority of the possible mutations incurring in the dataset. A one-year dataset represents a valid data sample, as it spans over periods of different weather conditions. Still, if the network deals with a climatically unusual day forecast, a worsening of the performance should be expected.
# Disaster Recognition and Classification Based on Improved ResNet-50 Neural Network
## 3 Results and Discussion  
Based on extensive theoretical foundations and experimental validation, this study conducted disaster image classification testing using the improved ResNet-50 model. The results from testing a large number of disaster images demonstrate that the model can effectively classify and identify different types of disaster images. Additionally, the model's performance was analyzed using four key evaluation metrics: accuracy, precision, recall, and F\({}_{1}\)-score. We further examined the confusion matrix to assess the model's classification performance and potential biases across different disaster categories. Moreover, by analyzing training and validation loss curves, we evaluated the model's convergence behavior and stability [26]. Finally, a comparative discussion was conducted between the improved ResNet-50 model and the traditional VGG16 network, focusing on classification accuracy and training stability. The following sections provide a detailed analysis and discussion of the model's evaluation and experimental results.
## 3 Signal Demodulation
### PLS-Based Detection Method
#### 3.2.1 Training Process of PLS Regression
The PLS regression algorithm is shown in Algorithm 1. The repeat loop computes the first singular value by the iterative method. We can train the PLS model by assigning the training matrix \(\mathbf{X}_{train}\) and its responses \(\mathbf{Y}_{train}^{T}\) to \(\mathbf{X}\) and \(\mathbf{Y}\), respectively. The element \(\mathbf{Y}_{train}(n,m)\) of \(\mathbf{Y}_{train}\) is 1 if the \(m\)-th received symbol \(\mathbf{u}_{m}^{\prime}\) is matched with the \(n\)-th symbol candidate \(\mathbf{u}_{n}\) and 0 otherwise. For a training matrix having 4 symbol candidates and two received symbols per candidate, \(\mathbf{Y}_{train}\) is as follows:  
\[\mathbf{Y}_{train}=\left( \begin{array}{cccccccc}1&1&0&0&0&0&0&0\\ 0&0&1&1&0&0&0&0\\ 0&0&0&0&1&1&0&0\\ 0&0&0&0&0&1&1\end{array} \right) \tag{18}\]
# Arm Posture Changes and Influences on Hand Controller Interaction Evaluation in Virtual Reality
## 3 Hypotheses Development  
According to the preceding review, we learnt that (a) the handedness feature, biomechanical structure of the arm and its kinematic features were of less concern in hand interaction evaluations and (b) due to the intrinsic fatigue problem in hand operation tasks, two hands are often used alternately in interaction tasks. From this perspective, the left hand interaction and the right hand interaction should be evaluated and compared; however, this was often neglected in earlier research. To fill the knowledge gap, this study compared the left- and the right-hand interaction results, and based on these, summarized the hand choice effect on hand-controller interaction performance.  
In a VR environment, the user performs a bent arm posture to acquire targets that are located at a close distance, but performs a more stretched arm posture to acquire targets at a farther distance relative to the body. In user-perceived fatigue measurement, neurophysiological research revealed that hand interaction in the lower field of the user'svision is more efficient than that in the upper field [38]. [PERSON] and [PERSON] [39] and [PERSON] et al. [40] also found that arm movement in the downward direction is more accurate than that in the upward direction. Based on these, we propose Hypotheses 1 and 2 as follows:  
**Hypothesis 1 (H1).**_In hand-controller interaction, a bent arm posture is more comfortable, which generates a higher interaction accuracy than a stretched arm posture. A bent arm also interacts more persistently than a stretched arm._  
**Hypothesis 2 (H2).**_Hand interaction at a low position, e.g., waist height, is more accurate and persistent than that at the higher position of shoulder or overhead heights._  
Human arms, including both the dominant arm and the non-dominant arm, are bilaterally symmetrical. The biomechanical structure of the arm and its kinematic features determine that arm movement and hand operation in some spaces are more flexible and accustomed than in other spaces [10,11]. Handedness is a general characteristic in human beings, which reflects the motor ability difference between the dominant hand and the non-dominant hand. Handedness is also an individual characteristic in hand-operational behaviors, which implies possible influences on hand-use frequency and hand interaction performance at different positions. Based on these, we raise another two hypotheses:  
**Hypothesis 3 (H3).**_Arm and hand-use choice determines hand-controller interaction performance: the left arm interacts more accurately on the left side than on the right side of the user's body; however, the right arm interacts more accurately on the right side than on the left side._  
**Hypothesis 4 (H4).**_The user-preferred or dominant hand interacts more accurately and persistently than the non-dominant hand._