esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Agricultural Soil-Improving Zeolite for Improving Irrigation Water Quality
## 3 Results and Discussion
### Adsorption Kinetics
#### 3.4.3 Kinetic Models  
As mentioned earlier, kinetic data provide valuable information about sorbent/sorbate interactions and the mechanism governing or dominating the specific sorption process. In this study, the definition of sodium and boron removal kinetics by commercial zeolite was achieved by fitting the experimental results to the three most important kinetic models: pseudo-first-order, pseudo-second-order, and intraparticle diffusion. All models were evaluated based on their regression parameters R\({}^{2}\) to identify their applicability to the studied process. The results are presented in Table 8.  
For both ions, the correlation coefficients were below 0.79 for the intraparticle diffusion models, and in the case of sodium, also for the pseudo-second-order model. This indicated low representativeness and limited applicability for interpreting the experimental data. In other words, in these cases, the models were not suitable for describing the sorption kinetics. However, for both ions, the pseudo-first-order model showed correlation coefficients above 0.94, suggesting sorption kinetics of active site occupation governed by the surface reaction rate--essentially a physical or physisorption process [30, 66]. Additionally, for boron, the pseudo-second-order model also exhibited an R\({}^{2}\) close to unity (0.9349), indicating the influence of chemical sorption or chemisorption as the rate-limiting step in the process's speed [61]. In this case, and accordance with the results obtained by [PERSON] et al. [67], it could indicate that the pseudo-first-order model is valid only in the initial adsorption stage and then transitions to chemisorption. The results obtained for the sorption of both cations on zeolite align with those reported in the literature for different types of zeolites, both natural and synthetic [29, 31, 67, 68, 69].
# AI-Driven Innovations in Software Engineering: A Review of Current Practices and Future Directions
## 3 AI Applications in Software Engineering
### Requirements Analysis
#### 3.1.2 AI-Driven User Stories  
User stories are pivotal components in agile development methodologies, encapsulating user requirements in concise and accessible formats. AI technologies can augment the creation and management of user stories by automatically generating them based on stakeholder inputs or analyzing system usage data [47].  
By employing machine learning models trained on extensive datasets from prior projects, AI systems can predict and formulate user stories that are congruent with business objectives and user expectations. Moreover, these systems can prioritize user stories by evaluating factors such as potential impact, feasibility, and alignment with strategic organizational goals.  
For instance, let us consider a software development team tasked with implementing a new feature. To ensure comprehensive requirement capture, the team integrates AI into their requirements engineering process. After drafting initial requirements, the AI system assists in refining and expanding these into detailed requirements specifications and scenarios. Subsequently, the AI aids in prioritizing the requirements based on criteria such as stakeholder significance, development complexity, and anticipated impact. This application of AI not only streamlines the requirements engineering process but also enhances the accuracy and relevance of the specifications, thereby facilitating a more efficient development workflow.  
Additionally, AI can assist in identifying inconsistencies or dependencies among user stories, thereby facilitating better planning and resource allocation. Machine learning models can analyze historical data to estimate the effort and time required for implementing each user story, enabling more accurate sprint planning and workload distribution.
# Analytical-Numerical Model for Temperature Prediction of a Serpentine Belt Drive System
## 2 Thermal Model
### Overall Structure  
Equations (6), (10), and (25) can be combined to yield the following thermal model for an arbitrary multi-pulley belt drive system:  
\[\left\{\begin{array}{l}\sum\limits_{n=1}^{N}{(1-\xi_{n})P_{fn}+P_{h}=A_{b}h _{b}(T_{b}-T_{a})}\\ A_{ext1}h_{ext1}\big{(}T_{b}-T_{p1}\big{)}=(\xi_{1}-0.5)P_{f1}\\ \vdots\\ A_{exN}h_{extN}\Big{(}T_{b}-T_{pN}\big{)}=(\xi_{N}-0.5)P_{fN}\\ \xi_{1}P_{f1}=S_{1}(\omega_{p1})\Big{(}T_{p1}-T_{a}\Big{)}\\ \vdots\\ \xi_{N}P_{fN}=S_{N}(\omega_{pN})\Big{(}T_{pN}-T_{a}\Big{)}\end{array}\right. \tag{29}\]  
The belt drive system generally has a maximum of 10 pulleys (\(N\leq 10\)) depending on the design and engine requirements. Equation (26) is a system of equations with a maximum of 21 unknown variables (\(T_{p1},\cdots,T_{pN}\), \(\xi_{1},\cdots,\xi_{N}\) and \(T_{b}\)). This study adopts the LU decomposition to solve Equation (29) to obtain the thermal flow information for each component and belt temperature. The calculation time is only a few seconds by using a computer with 12-core 3.4 GHz CPU.  
In addition, the temperature distribution of the \(n\)th pulley in this system, \(\mathbf{T_{pn}}\), can be determined by suitably modifying the referenced numerical solutions. First, the database stores the two sets of \(\{\mathbf{T^{\prime}_{pn}},\Phi^{\prime}_{pn}\}\) under the upper and lower speeds of \(\omega_{pn}\). This model uses the linear interpolation to calculate \(\Phi^{\prime\prime}_{pn}\) and \(\mathbf{T^{\prime}_{pn}}\) at the designed speed \(\omega_{pn}\). Then, the temperature field of the \(n\)th pulley is  
\[\mathbf{T}_{pn}=\xi_{n}P_{fn}/\Phi^{\prime\prime}_{pn}\Big{(}\mathbf{T^{\prime \prime}_{pn}}-T_{a}\Big{)}+T_{a} \tag{30}\]
## 5 Discussion
It provides a cost-effective, scalable, and controllable way to generate large volumes of labeled data for scenarios that might be rare or difficult to capture in real-world measurements. It allows systematic evaluation of different sensor layouts and processing techniques. Future directions include the following:  
* Developing more realistic noise models based on real DAS data analysis. * Incorporating calibration data from controlled experiments to refine VISSIM parameters, geophysical properties, and channel mapping. * Exploring more advanced physics-based models that account for dynamic effects, ground layering, or potentially non-uniform coupling. * Extending the methodology to other transport modes (rail, pedestrians) or event types (incidents, road work).
# Romanian Fake News Detection Using Machine Learning and Transformer-Based Approaches
## 3 Materials and Methods
### Transformer-Based Models  
Advanced deep learning models are frequently used to address complex classification problems, including fake news detection. These models can learn complex data representations due to their deep architectures and ability to model nonlinear relationships [9].  
A Transformer model is a type of deep learning model that was first described in a 2017 paper called \"Attention is All You Need\" by [PERSON], a team at Google Brain, and a group from the University of Toronto [10]. These models have quickly become fundamental in natural language processing (NLP) and have been applied to a wide range of tasks in machine learning and artificial intelligence.  
Transformers are a type of neural network architecture that transforms or changes an input sequence into an output sequence. They achieve this by learning context and tracking relationships between sequence components. The Transformer model uses an internal mathematical representation that identifies the relevance and relationship between the words. It uses that knowledge to generate output.  
Transformer models use an encoder-decoder architecture. The encoder has various layers, with each layer generating encodings about relevant input data before passing on the input data to the next encoder layer.  
The encoders tag each data element, with attention units creating an algebraic map of how each element relates to others. A multi-head attention set of equations calculates all attention queries in parallel. This allows the Transformer model to detect patterns just like humans [11].  
Transformer-based models have become fundamental in the field of artificial intelligence and natural language processing due to their ability to handle data sequences in an efficient and scalable way. Transformers have revolutionized the field, being used not only for natural language but also in areas such as computer vision, image generation, and human-robot interaction [29]. Transformer models have brought about a fundamental change in artificial intelligence, providing a highly efficient way to model data sequences and the complex relationships between their elements.  
We use two Transformer-based models in our approach:  
* BERT-base-multilingual-cased [11]: Pre-trained on a multilingual corpus, including 104 languages from Wikipedia;
* RoBERTa-large [30]: An optimized variant of BERT, pre-trained on a larger dataset including BookCorpus, English Wikipedia, CC-News, OpenWebText, and Stories.  
In contrast to traditional embedding models that assign a single vector to each word (regardless of context), Transformers like BERT and RoBERTa are particularly effective in detecting fake news due to their ability to manage contextual embeddings. This allows them to identify subtle nuances in the language used. By pre-training on large datasets and fine-tuning on specific tasks, these models can provide an accurate assessment of veracity.
## 6 Machine Learning and 5G Security
### The Role of Machine Learning in Enhancing 5G Security
Unsupervised learning algorithms like k-means clustering, Linear Regression, supervised classifier and hierarchical clustering are valuable for anomaly detection, identifying unusual patterns in network data that may signify a security breach [116; 117]. These algorithms do not require labeled data, making them flexible tools for discovering unknown threats. Reinforcement learning, a type of ML where an agent learns to make decisions by interacting with its environment, has shown promise in the area of network intrusion response. By iteratively adjusting its actions based on received rewards or punishments, a reinforcement learning agent can determine the optimal actions to mitigate detected intrusions [118]. Table 11 reports the role of various machine learning techniques in enhancing the security in 5G networks, showing the advantages and limitations of each technique along with the metrics commonly employed for evaluation. Supervised learning is prominently used in applications like IDS and spam filtering. Its high accuracy in identifying known threats and low false-positive rates are notable strengths. However, the technique does necessitate labeled data and is susceptible to overfitting. Typical evaluation metrics include accuracy and the precision-recall curve. Unsupervised learning finds utility in anomaly detection and network traffic clustering. It eliminates the need for labeled data and can unearth previously unidentified threats. Despite these merits, it has lower accuracy compared to supervised learning techniques and incurs high computational costs. Metrics like cluster purity and silhouette score are commonly used for evaluation. Reinforcement learning facilitates adaptive network configurations and policy-based security measures. Its ability to learn optimal policies over time and adapt to evolving network conditions is laudable. However, extensive training data are required, and the complexity of defining appropriate reward functions is a challenge. Cumulative reward and convergence time are key metrics for evaluation. Ensemble methods, often used to enhance IDS capabilities and combine multiple classifiers, are praised for their improved generalization and resistance to overfitting. However, they are computationally demanding and can be complex to interpret. Evaluation is typically conducted using cross-validation scores and the F1 score. Lastly, neural networks, which are used for deep packet inspection and malware identification, are robust in feature representation and excel at capturing complex patterns. These strengths are offset by their resource-intensive nature and risk of overfitting, especially with small datasets. Area Under the Receiver Operating Characteristic (AUROC) and F1 score are standard evaluation metrics.
## 4 Numerical Examples
### Long Span Planar Skeletal Structure
As for the GGW method, the numerical damping can be controlled by changing the parameter \(\alpha\), but its solution attenuates too fast, which will also cause numerical dissipation in the low-frequency mode when filtering out the high-order modes. As a significant improvement to the GGW method, the proposed method can effectively avoid such a problem. To verify the superior accuracy of the proposed method, the responses are computed with the Wilson method, GGW method, and proposed method  
Figure 21: Velocity responses of Point A with different time steps. Figure 20: Displacement responses of Point A with different time steps. As shown in Table 3, the computational time of the Bathe method is shorter than the proposed method when the same time step is used. However, we can find from Figure 23 that the accuracy of the proposed method is better than the Bathe method with the same time step. That is to say, under the conditions of the same accuracy, the proposed method calculates faster with larger time step than Bathe method with smaller time step. For example, in Figure 23, the Bathe method uses 10.186s with \(\Delta t=0.01\) s to obtain results of the same accuracy as the proposed method, which takes 5.039s with \(\Delta t=0.03\) s. From this point of view, the proposed method is sufficiently efficient in both the computational time and accuracy.
## 3 Methodology
### Image Segmentation and Classification  
In order to make the shoreline extraction as automatic as possible, the extraction was accomplished through an object-based image segmentation process using eCognition software. Both radar and optical images were used for image segmentation and classification, including a RADARSAT-2 image acquired on 9 October 2015 and a Landsat 8 image acquired on 28 July 2017. The low water RADARSAT-2 imagery displays the maximum extent of drying areas for the images examined in this study, while the mid-year optical imagery allows visible islands to be extracted for representation on the chart based on mid-year water levels. A multiresolution segmentation algorithm was applied to define image objects by consecutively merging pixels based on a pairwise region merging technique. This is a bottom-up segmentation process that was run in eCognition using several parameters including: scale (determines the size of the objects created), shape (determines to what degree shape influences the segmentation compared to the colour and compactness), and colour and compactness (determine the  
Figure 4: Comparison between **(a)** the relative accuracy of the FQ RADARSAT-2 orthoimages from 8 October 2014 and 7 September 2016, and **(b)** the absolute accuracy of the orthorectified FQ RADARSAT-2 from 8 October 2014 and Canada 1:50,000 topographic data (CanVec data) from 14 March 2016. RADARSAT-2 Data and Products © MacDonald, Dettwiler and Associates Ltd. (2014/2016) – All Rights Reserved. RADARSAT is an official mark of the Canadian Space Agency.  
relative weighting against smoothness) [40]. When executing the process, a scale of 100 was used while and shape and compactness ratios were both applied using a value of 0.5. Each band in the Landsat image was given an image layer weight of 1 and the HH and HV polarizations in the RADARSAT-2 image were given a weight of 5 in order to better define the drying areas.  
A supervised classification was then run to classify each segment as land, drying area, lake or river. Approximately 15 segments were selected from each class as training sites with the mean for each band assigned to all segments. The classification algorithm in eCognition implements the nearest neighbour approach using the assigned training sites, classifying image objects in the domain based on their nearest sample neighbour [40]. The classification process was executed, and minor edits were made to reassign misclassified segments.
## 3 Results
#### 3.1.2 Thermal Comfort  
The survey results show that the mean indoor thermal sensation is far better with the \"glass curtain\" system than without it in winter, spring, and autumn. Without the \"glass curtain\" system, the mean thermal sensation is perceived as slightly cool (\(-1\)), fresh (\(-2\)), and nearly cold (\(-3\)) from mid-November to mid-April. However, with the \"glass curtain\" system closed, the surveys show values between neutral (\(+0\)) and slightly cool (\(-1\)) only from mid-December to mid-March (Figure 8a). In line with these data, the indoor temperatures are dissatisfactory, with PPD values above 20% from the beginning of November to the end of April without the \"glass curtain\" system, and the maximum PPD value exceeds 80%. However, the indoor temperatures are dissatisfactory, with PPD values above 20% only from mid-December to mid-February with the \"glass curtain\" system closed, and the maximum PPD value is 40%. (Figure 8b). The surveys and computer simulations have very similar results, although the computer simulations show larger oscillations because they calculate the thermal sensation for every hour of the day. In summer, the mean indoor thermal sensations are the same in both cases because the \"glass curtain\" remains retracted and opened.  
The surveys also show that the thermal sensation of the occupants is warmer in all the rooms of the dwelling with the \"glass curtain\" system closed. The thermal sensation is perceived as neutral (\(+0\)) to cold (\(-3\)), depending on the orientations of the rooms of the dwelling without the glazed terrace. However, the thermal sensation is perceived as warm (\(+2\)) or slightly warm (\(+1\)) in the rooms close to the glazed terrace and slightly cool (\(-1\)) or fresh (\(-2\)) in the rooms away from the glazed terrace. Consequently, the thermal sensation is dissatisfactory in most rooms of the dwelling without the glazed terrace (Figure 9a), and it is satisfactory in most rooms of the dwelling with the glazed terrace (Figure 9b).  
Figure 8: Results of the thermal comfort surveys and computer simulations of thermal sensation with a “glass curtain” system closed and without it: (**a**) occupants’ thermal sensation and (**b**) percentage of dissatisfied users.
# E2-VINS: An Event-Enhanced Visual-Inertial SLAM Scheme for Dynamic Environments
## 3 Methodology
### Preliminaries of the Event Camera  
The observation of each pixel by the event camera is independent and asynchronous. When the event camera detects a change in the logarithmic brightness intensity at a pixel \(\mathbf{u}_{k}=[x_{k},y_{k}]^{T}\) that exceeds a specified threshold \(C\) (known as contrast sensitivity) [13] at timestamp \(t_{k}\), it generates an event \(e_{k}=(\mathbf{u}_{k},t_{k},p_{k})\):  
\[\Delta I(\mathbf{u}_{k},t_{k})=I(\mathbf{u}_{k},t_{k})-I(\mathbf{u}_{k},t_{k} ^{\prime})=p_{k}C, \tag{1}\]  
Figure 1: The overall pipeline of our proposed E\({}^{2}\)-VINS system. The preprocessing stage includes IMU preintegration and feature tracking of RGB frames. During system initialization, E\({}^{2}\)-VINS performs geometric alignment between IMU measurements and vision-based structure-from-motion procedures while estimating the biases to obtain calibrated IMU measurements. The calibrated data are then applied for IMU-assisted event motion compensation. The compensated events are used to generate event-based dynamicity metrics, which assess the motion dynamics of each pixel. Based on these metrics, visual residuals are adaptively assigned dynamicity weights for different pixels. Finally, E\({}^{2}\)-VINS jointly and iteratively optimizes the system state (camera poses and map points) and the dynamicity weights.  
where \(I\) represents the logarithmic brightness intensity, \(p_{k}\in\{+1,-1\}\) denotes the polarity--indicating whether the brightness change is an increase (brightening) or decrease (darkening)--and \(t_{k}^{\prime}\) is the timestamp of the previous event at pixel \(\mathbf{u}_{k}\).
## 1 Introduction
The proof of concept study conducted in 2018 used the Periprobe RU/AAnalis probe (manufactured by BeachMed, Portalbera, Italy), commonly used for electrostimulation and biofeedback applications, which was adapted to the needs of our tests. It only has two electrodes placed on opposite sides of the probe, allowing only for a bipolar electrode configuration. Even with this configuration, the reported method accuracy was 86.4% (with a sensitivity of 83.3% and a specificity of 94.0% between ONIRY and ultrasound) [45; 46]. As the next step, the ONIRY Probe was designed, allowing for tetrapolar electrode configuration, and used together with the impedance spectroscopy \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Type of** & \multicolumn{5}{c}{**Frequency**} \\
**Incontinence** & **Never** & **Rare** & **Sometimes** & **Often** & **Always** \\ \hline Fixed stool & 0 & 1 & 2 & 3 & 4 \\ Liquid & 0 & 1 & 2 & 3 & 4 \\ Gases & 0 & 1 & 2 & 3 & 4 \\ Use of sanitary & 0 & 1 & 2 & 3 & 4 \\ napkins (diapers) & 0 & 1 & 2 & 3 & 4 \\ Lifestyle change & 0 & 1 & 2 & 3 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Scale of clinical assessment of stool incontinence—Wexner scale [33]. device (ONIRY Meter) for estimation of resistance, reactance, impedance modules, and phase shifts. We then conducted a pilot clinical trial, which aimed to assess the performance and safety of the impedance spectroscopy method. The performance of the ONIRY device was evaluated versus the reference methods (used following the current recommendation in case of suspected obstetric injury [7; 48; 49; 50]), such as physical examination, endoanal ultrasound (e.g., assessment according to OASI classification criteria), and anorectal manometry. The objective of this report is to present results of this study (NCT03769792).