esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Testing Methods  
#### 2.3.1 Fracture Test  
Three-point bending tests on notched beam specimens were conducted to evaluate the crack mouth opening displacement (CMOD) and fracture toughness (K\({}_{\text{Ic}}\)) of the developed PVA/SiO\({}_{2}\) NPs reinforced fly ash-based geopolymers. Fracture toughness is the ability of a material to resist cracking when subjected to external loading. For each series, at least three pre-cast notched beams with the dimensions of 100 mm \(\times\) 100 mm \(\times\) 450 mm were cast and cured at room temperature. The precast notch in the specimens was formed using a greased steel plate of 3 mm thickness. The steel plate was removed from specimens after 24 h of casting. All the specimens for each series were prepared with a fixed initial notch length to beam height ratio (a\({}_{0}\)/D) of 0.4 and span to depth ratio (S/D) of 4. The test  
\begin{table}
\begin{tabular}{c c} \hline
**Chemical** & **Component (wt.\%)** \\ \hline Al\({}_{2}\)O\({}_{3}\) & 26.56 \\ SiO\({}_{2}\) & 59.23 \\ CaO & 03.40 \\ Fe\({}_{2}\)O\({}_{3}\) & 05.65 \\ K\({}_{2}\)O & 00.65 \\ MgO & 01.40 \\ Na\({}_{2}\)O & 00.72 \\ TiO\({}_{2}\) & 01.30 \\ SO3 & 00.34 \\ Loss on ignition & 00.46 \\ \hline \end{tabular}
\end{table}
Table 1: Chemical composition of fly ash.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Length (mm) & 8 \\ Diameter (\(\upmu\)m) & 39 \\ Young’s modulus (GPa) & 41 \\ Elongation (\%) & 6 \\ Density (g/cm\({}^{3}\)) & 1.3 \\ Strength (MPa) & 1600 \\ \hline \end{tabular}
\end{table}
Table 2: Properties of poly vinyl alcohol (PVA) fiber.  
was performed under a displacement control of 0.2 mm/min using an Instron testing machine with a load capacity of 250 KN. The experiment loading pattern is shown in Figure 1. Clip extensometers, measuring a range of 4 mm, were placed at the bottom of specimens to measure the crack mouth opening displacement (CMOD). The fracture toughness K\({}_{\text{lc}}\) of each specimen is calculated by the following equations [13]:  
\[K_{IC}=\ \frac{3 PS}{2 bw^{2}}\ \sqrt{\pi a}\ f(a/w) \tag{1}\]  
with:  
\[f(a/w)=\ \frac{1.99-\frac{a}{w}\Big{(}1-\frac{a}{w}\Big{)}\Big{(}2.15-3.93\frac{a }{w}+2.7\Big{(}\frac{a}{w}\Big{)}^{2}\Big{)}}{\Big{(}1+2\frac{a}{w}\Big{)} \Big{(}1-\frac{a}{w}\Big{)}^{3/2}} \tag{2}\]
###### Abstract
It was observed that different performance metrics were used to choose the best model. This makes it difficult for field researchers to select the most appropriate candidate for this. Furthermore, the researchers compared three to six ML models without concentrating on the three categories of supervised algorithms, namely neural network, ensemble model, and classical model. In terms of input features, it is obvious that anthropometric measurements were not the only data used in the models produced. Aside from demographic data, lifestyle data, such as smoking, a healthy diet, and physical data, as well as physiological data, such as blood pressure, heart rate, and blood glucose, were used. As self-reporting lifestyle parameters are subjective [17], and there is a need for specialized instruments to acquire physiological data, our study will investigate the use of anthropometric measures along with easily collected demographic data for hypertension prediction using ML models. Furthermore, studies on the associations between anthropometric measures and hypertension in adolescents are relatively limited when compared to adults [4,18]. Therefore, our study intends to fill this research gap. In our previous work [19], we used anthropometric measurements and simple demographic data to develop a one hidden layer of 50 neurons Multilayer Perceptron (MLP) neural network to predict hypertension in adolescents, yielding a sensitivity of 0.41, specificity of 0.91, precision of 0.65, _F1_-score of 0.50, accuracy of 0.76, and AUC of 0.75. In this study, we extend our previous work by investigating the efficacy of thirteen different ML models: Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Naive Bayes, k-Nearest Neighbor, Multilayer Perceptron, Gradient Boosting, XGBoost, LightGBM, CatBoost, AdaBoost, and LogitBoost, from the three supervised ML categories of neural network, ensemble model and classical model for hypertension prediction in adolescents using anthropometric measurements and simple demographic data. In order to tackle the imbalanced data problem, we implement and evaluate the Synthetic Minority Over-sampling Technique (SMOTE) and the combination of SMOTE with under sampling. To the best of our knowledge, this study is the first to look at the efficacy of ML models for hypertension prediction utilizing thirteen distinct algorithms of the three supervised ML categories in adolescents. In this study, we would be investigating if simple anthropometric measurements are viable for hypertension prediction using machine learning models, and what are the effects of the different models on the prediction results? The objectives of the study are two-fold: (a) investigate the feasibility of anthropometric measurements and simple demographic data for hypertension prediction, and (b) implement, evaluate, and analyze the performance of the thirteen different ML models for hypertension prediction in adolescents using easy-to-collect data.
# Field Measurement Study on Dynamic Characteristics of the Shanghai World Financial Center
## 3 Dynamic Response of the SWFC  
### Earthquake-Induced Response  
The measured sensor data on the top 101 floors of the structure were selected for analysis, and the acceleration response time history and motion trajectories in the X and Y directions of the 101 floors of the building are depicted in Figures 4 and 5. From these figures, the east-west (X-direction) and north-south (Y-direction) vibrations of the SWFC are approximately the same under the four earthquakes, indicating that the stiffness of the SWFC is similar in the X-direction and Y-direction. Besides, the vibration amplitude changes abruptly in the first part of the vibration, and the acceleration response increases suddenly and then decays gradually until it flattens out, which may be caused by the fact that the observation site is located within a medium-strong seismic zone within the North China tectonic block, and seismic intensity and peak acceleration decay according to a law [33; 34].  
Figure 4: Time\(-\)history response on F101 of the SWFC.  
Figure 5: Acceleration trajectory on F101 of the SWFC.  
From Figure 4a-d, the maximum acceleration response amplitudes of all 4 earthquakes occur in the X-direction with 1.984, 0.656, 6.573, and 1.027 cm/s\({}^{2}\), respectively, and in the Y-direction, the maximum instantaneous accelerations of the structure are 1.784, 0.474, 5.149, and 0.626 cm/s\({}^{2}\), respectively. Among them, the acceleration amplitude is the largest under the earthquake of E3. The seismic intensity level, measurement distance, and propagation medium are the main reasons affecting the size of the seismic reaction.  
To further investigate the variation law between acceleration amplitude and floor height, based on the measured results, the relationship curves of the peak acceleration response in X and Y directions with floor height under different seismic effects are shown in Figure 6. Figure 6 shows that the change trends under the four earthquakes are essentially the same. That is, with the increase of the floor height, the peak acceleration response in the X and Y directions of the structure first increases to 40F, then decreases to 80, and the growth rate becomes faster above 80F. The test system's maximum acceleration amplitude appears on the 101 st floor. The result indicates that a super-tall building's superstructure is susceptible to vibration excitation, the amplitude of vibration response increases, and the dispersion of acceleration signal increases gradually with the rise in floor height. It is noteworthy that there is no apparent decreasing trend of acceleration peak near 40F of the structure under the earthquake of E4. This may originate from the low seismic intensity level and the close distance of the source from the SWFC (only 299.7 km), and may also come from the fact that the giant frame and core concrete of the SWFC do not contribute to structural nonlinearity.
## 3 Results  
### Numerical Experiments  
In this study, numerical experiments were conducted to evaluate the performance of the three machine learning approaches: support vector machines (SVMs), boosting ensemble decision trees, and long short-term memory (LSTM) networks. The experiments were designed to classify the tool condition into three distinct classes: green, yellow, and red. A total of 75 samples were used, with each class containing 25 samples. All experiments were performed in Matlab 2024a computational environment on a PC with Ubuntu operating system. The machine specification is the same as in previous experiments, and is presented in [5].  
To ensure a robust evaluation of the models, k-fold cross-validation was applied, with \(k=10\) representing the number of folds. This method involves partitioning the data into \(k\) subsets (folds). Each model is trained on \(k-1\) folds and tested on the remaining fold, repeating this process \(k\) times, such that each fold is used exactly once as a test set. The results are then averaged to provide an overall performance metric.  
The dataset for each class was balanced, with 25 samples per class, ensuring that each model had an equal representation of each tool condition during training and testing. Thisbalance is crucial to avoid biased results and ensure that the models can generalize well to new, unseen data.  
For each approach, the following steps were taken:  
1. Data partitioning using k-fold cross-validation.
2. Training the model on \(k-1\) folds and testing on the remaining fold.
3. Repeating the process \(k\) times to ensure each fold is used as a test set.
4. Averaging the performance metrics to obtain an overall evaluation of the model.  
The k-fold cross-validation method provided a comprehensive evaluation of each model, ensuring that the results were reliable and could be generalized to new data. This approach also helped in identifying the strengths and weaknesses of each model in classifying the tool condition based on the given sensor data.
## 7 Downstream Processing of CA
Those are micellar solutions that separate spontaneously in two phases: a micelle-rich phase and a micelle-poor phase, depending on operating conditions (pH, ionic strength, and temperature) [196]. An ATPMS employing the surfactant n-decyltreatheylene oxide showed preferential partition of proteins towards the micelle-rich phase, but if a denaturation step is applied prior to extraction, the 52% of CA is recovered in this phase [196]. ATPMS formed by the non-ionic surfactant Triton X-114 (0.022%) and an anionic one (1%) allowed a CA recovery of 86.3% at 28 \({}^{\circ}\)C [197]. Similarly, ATPMS of dextran sulfate with non-ionic surfactants Triton X-114 or Triton X-100 have been also used for CA purification, yielding CA recoveries of 80 and 50%, respectively [198]. Aqueous CA from fermentation broth or concentrated solutions can also be extracted by simple liquid extraction with organic solvents. The evaluation of organic solvents butyl acetate, ethyl acetate, n-butanol, 2-butanol, and methyl isobutyl ketone showed that CA partition coefficient is related to solvent properties like dielectric constant and species solubility [199]. Although CA distribution coefficients for 2-butanol and n-butanol were found to be higher than for the esters and the methyl-isobutyl ketone; the high solubility of light alcohols in water makes them impractical for further CA precipitation. For extraction purposes is desirable a water-immiscible organic phase, which also reduces the degradation susceptibility of the molecule. Brites and colleagues [199] concluded that ethyl acetate is the best option considering the subsequent precipitation steps required for the formation of clavulanate salts, yielding a CA extraction of 35.6% with partition coefficient of 0.73 in acidic conditions. Furthermore, organic solvent mixtures containing methyl-ethyl ketone/ethyl acetate and methyl-isobutyl ketone/ethyl acetate allow higher extraction levels of CA from fermentation broth. The equivolumetric mixtures of methyl-ethyl ketone/ethyl acetate and ethyl-isobutyl ketone/ethyl acetate led to extraction yields of 50 and 44.7%, respectively, which is approximately 30% higher compared to the obtained for single ethyl acetate extraction [200]. As CA is marketed in salt form, the direct precipitation from aqueous solutions, either fermentation broth or concentrated solutions, has been studied as a purification alternative. CA extracted via organic solvents can be precipitated in two steps: (i) reaction of CA with an aliphatic amine to form a stable CA-amine salt and (ii) precipitation with potassium 2-ethyl hexanoate [201]. However, CA can be precipitated directly from the organic solvent by addition of potassium 2-ethyl hexanoate without requiring the formation of a stable intermediate [202]. However, slightly higher yield was observed when the amine intermediate is formed (72.4%) compared to the direct precipitation method (69.5%) [201; 202].
# Efficient V2V Communications by Clustering-Based Collaborative Caching +
## 4 Simulation Evaluation
### Simulation Results
Figures 20 and 21 show the results for Scenario 2, for which the number of vehicles increases from 100 to 200, and the channel becomes more congested than in Scenario 1. Compared with Figure 14, the cache hit ratio in C-CoCach-Pop is improved. This is because clusters tend to contain more vehicles, and thus, more content can be cached in a cluster by exploiting the increased cache buffer. However, the successful reception ratio is degraded in all methods due to channel congestion. Compared with C-BCCN-Pop, our proposed method C-CoCach-Pop with collaborative caching still reduces the latency and improves the success ratio. Figure 9:1 Average channel usage (Scenario 1, buffer size: 100). Figure 9:2 Distribution of hop count required to retrieve the requested content (Scenario 1, buffer size: 100). Figures 22 and 23 show the results for Scenario 3, for which the number of vehicles is 300. The proposed C-CoCach-Pop method still achieved better results than C-BCCN-Pop for each metric, although the differences get smaller than for the straight road in Scenarios 1 and 2 due to the obstruction of roadside buildings and complex vehicle mobility in the urban area. Finally, we compared our method with an existing method: ECV+ [3]. Figures 24 and 25 compare our method and ECV+ for Scenario 2. Our method achieved better results because (1) ECV+'s probabilistic caching makes it difficult to fill vehicles' cache buffers, and (2) ECV+ does not consider content popularity. Figure 9:3 Success ratio of retrieving requested content with respect to cache buffer size (Scenario 2). Figure 9:4 Median latency for retrieving the requested content with respect to cache buffer size (Scenario 2). Figure 9:5 Success ratio of retrieving requested content with respect to cache buffer size (Scenario 3). Figure 9:6 Median latency for retrieving the requested content with respect to cache buffer size (Scenario 2). Figure 9:7 Median latency for retrieving the requested content with respect to cache buffer size (Scenario 3).
# Overcoming Limitations of LoRa Physical Layer in Image Transmission
## 4 Experimental Method
### Experimental Setup
#### 4.2.2 Receiving Images over LoRa  
The LoRa radio adapter in the receiver node listens for transmitted signals (see Appendix A) and sends a receipt of acknowledgement when they successfully arrive. Once all hexadecimal packages have been received, they are collected into one data variable by the Arduino processor and then sent, through a serial connection, for processing in MATLAB in order to retrieve image data from the hexadecimal package.
# Measurements and Modeling of the Nonlinear Behavior of a Guitar Pickup at Low Frequencies +
## 2 Nonlinear Models  
Study of a nonlinear physical system, such as a guitar pickup, usually begins with a basic model based on a memory-less polynomial representation [11; 12; 13]. As demands for better physical description increase, more and more complicated nonlinear models are proposed and used. The main criteria for these models are low complexity together with a high ability to predict the systems output. If both criteria are fulfilled, the model can be successfully used for simulation.  
When dealing with modeling of nonlinear systems, two different methodological approaches are usually considered. The first approach studies the physical causes of the nonlinear phenomena and the model is derived purely from the physical laws governing the behavior of the system. These laws can then be used for the derivation of a theoretical nonlinear model of the system [14]. In the second approach, little, or even no physical properties are known. The system is said to be a black box and a generic nonlinear model is used for nonlinear system identification. Among many methods available in the literature [19], the class of Volterra-Series models and block-oriented models [20; 21] have drawn a particular attention in the field of audio, musical instruments, and digital effects [18; 22; 23; 24; 25; 26].  
Probably the best known block-oriented nonlinear model is the Hammerstein model combining two elements in series: a static nonlinear system and a linear filter [27; 28; 29]. This kind of model has been, up to today, applied to guitar pickups in [12; 13]. The Hammerstein model is then often preferred because of its simpler structure and lower computational cost.  
On the other hand, the Volterra series representation is usually considered to be more effective but less practical due to the highly complex calculation of multidimensional kernels [30; 31; 32; 33]. A Generalized Hammerstein model [16; 17; 18] represents a very good compromise between the complexity and the efficacy. It is made up of \(N\) parallel branches, with each branch consisting of a linear filter \(G_{n}(f)\)  
Figure 1: Nonlinear system usually used to model nonlinearities of a guitar pickup [12].  
preceded by an \(N\)-th order power static nonlinear function, for \(n=1,N\). The output \(u(t)\) of such a model to any input \(x(t)\) is governed by the following equation  
\[u(t)=\sum_{n=1}^{N}x(t)^{n}*g_{n}(t), \tag{1}\]  
where \(g_{n}(t)\) is the inverse Fourier transform of \(G_{n}(f)\) and where \(*\) stands for convolution. Note that the Generalized Hammerstein model is functionally equivalent to a Chebyshev polynomial-based model [34] successfully applied in e.g., [35; 36].  
To estimate the filters \(G_{n}(f)\), we need to estimate \(m\) Higher Harmonic Frequency Responses (HHFRs) between the input \(x(t)\) and the output \(u(t)\) noted as \(\mathcal{H}_{m}^{(x,u)}(f)\), \(m\) referring to the number of each HHFR. We use a procedure based on the Synchronized Swept-Sine technique [37].
# Learning-Free Text Line Segmentation for Historical Handwritten Documents
###### Abstract  
We present a learning-free method for text line segmentation of historical handwritten document images. This method relies on automatic scale selection together with second derivative of anisotropic Gaussian filters to detect the blob lines that strike through the text lines. Detected blob lines guide an energy minimization procedure to extract the text lines. Historical handwritten documents contain noise, heterogeneous text line heights, skews and touching characters among text lines. Automatic scale selection allows for automatic adaption to the heterogeneous nature of handwritten text lines in case the character height range is correctly estimated. In the extraction phase, the method can accurately split the touching characters among the text lines. We provide results investigating various settings and compare the model with recent learning-free and learning-based methods on the cBAD competition dataset.  
text line segmentation; text line detection; text line extraction; learning-free; historical handwritten documents +
Footnote †: journal: Applied Sciences
# Blockchain Technology for Intelligent Environments
## 3 Noteworthy Blockchain Implementations
### Public/Permissionless Blockchains  
#### 3.1.1 Bitcoin  
Introduced by the pseudonymous [PERSON] in Nakamoto (2016), Bitcoin undoubtedly earns first place in this list, being the seminal blockchain design and implementation, as well as the first successful fully decentralized digital currency.  
Bitcoin's blockchain is used exclusively for transactions in its own cryptocurrency (called _bitcoin_). That is, there is no support for smart contracts or arbitrary applications. Bitcoin does support some elementary scripting; however, the sole purpose of the scripting is limited to providing flexibility in setting the conditions for spending assets, e.g., requiring either a single user's key, or \(n\) users' keys, or \(k\)-out-of-\(n\) users' keys, etc., in order to dispose of assets.  
Bitcoin is an open-source protocol running on its permissionless blockchain. It employs a Proof-of-Work consensus mechanism, based on double SHA-256 reverse hashing. As an explicit design decision, Bitcoin generates one block per 10 min, on average. This is achieved through a _difficulty_ parameter for the Proof-of-Work algorithm, which is automatically adjusted every 2600 blocks to counterbalance global hashrate increase (or decrease), by considering how much faster (or slower)than 10 min the average generation of the last 2600 blocks (about 18 days) was. The incentivization mechanism to attract miners consists of allowing them to deposit to their wallets a predefined amount of newly generated bitcoins for each block they mine. This is also the only way bitcoins are being generated globally.  
There are significant scalability concerns regarding Bitcoin, as its transaction processing rate of ca. 5-7 transactions per second is deemed too low for serving trade at a global scale.
## 2 Study Region and Methodology
### Study Region  
#### 2.1.1 Overview of Jiuzhaigou Valley  
The Jiuzhaigou park is located in the transition zone of the western margin of the Sichuan Basin and the Qinghai-Tibet Plateau, with an area of approximately 720 km\({}^{2}\) and a vegetation coverage rate of 85.5%. The vegetation types include meadows, shrubs, coniferous forests, and broadleaf forests. With the terrain high in the south and low in the north, the elevation varies from \(<\)2000 m above sea level (a.s.l.) to around 4900 m a.s.l. (Figure 2A). The mountains are classified into three categories: high- (\(\geq\)4000 m), high- and medium- (3000 m to 4000 m), and medium- (\(\leq\)3000 m) elevation mountains [23], which contribute to the vegetation stratification. The primeval forest in the park covers an area of 430 km\({}^{2}\) and occupies 63.5% of the park. Therefore, there is a buffer zone with an area of 600 km\({}^{2}\) for ecosystem diversity protection in the park (Figure 2B). In addition, the climate here is monsoon dominated and semi-humid with sufficient rainfall, which is very conducive to the growth of precious vegetation, such as _Kingdonia uniflora_, _Ginkgo biloba_, and _Taxus chinensis_. However, several fractures run through the study region, such as the Tazang Tectonic Belt and Huya Fracture, resulting in undulating topography, staggered valleys, and steep slopes that are easily prone to geological hazards during the rainy season from May to September. For example, four debris flows and severaloseismic landslides occurred from 14 September to 25 September 2017 after the Jiuzhaigou earthquake, and these events affected nearly 130 km\({}^{2}\) of the forest ecosystem [24].  
Figure 1: Workflow of the research.