esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Agent Systems with Information Sharing: Strategies and Model Checking
## 2 Syntax and Semantics of iMAS  
Inspired by process calculi, we introduce a prototyping language named iMASfor multi-agent systems with information sharing that allows agents to act in parallel and to migrate between distributed locations according to explicit timers. We give the syntax of iMAS in Table 1, where we assume the following:  
* Four sets, _Loc_= {_l,l'_,..._}, _Chun_={_a,b,..._}, _Id_= {_id_,...}, and \(\mathcal{N}=\{N,N^{\prime},\ldots\}\), containing names for location variables or locations, communication channels, recursive definitions, and networks, respectively;
* For each _id_\(\in\)_Id_, a unique process definition _id_(_u_1,...,u_u_u_u_) \(\overset{\text{def}}{=}P_{\text{id}}\) exists;
* Natural number \(t\) is a timeout of actions, integer number \(k\) is a threshold appearing in tests, \(u\) is a variable, \(v\) is a value (integer, string, Boolean), \(f\) is an information field, and \(p\) is either _private_ (to indicate the information accessed only by the agent to which it belongs) or _public_ (to indicate that the information can be accessed by any agent located where the information is available). For example, if, for \(Q\in\)_Id_, its process definition is \(Q(u_{1},\ldots,u_{n})=P\), and for the values \(v_{1},\ldots,v_{n},v^{\prime}_{1},\ldots,v^{\prime}_{n}\), there exists \(1\leq i\leq n\) such that \(v_{i}\
eq v^{\prime}_{i}\), then the agent instances \(Q(v_{1},\ldots,v_{n})\) and \(Q(v^{\prime}_{1},\ldots,v^{\prime}_{n})\) are different. An agent \(A\) is given as a pair \(P\rhd I\), where \(P\) describes how the agent should behave, while \(I\) describes the _private_ information of the agent to which \(P\) has access. An agent \(\text{go}^{t}\)_l_ then _P_\(\rhd I\) stays at its current location for \(t\) units of time, consumes the action \(\text{go}^{t}\)_l_ after the \(t\) units of time have passed, and changes its location by migrating to location \(l\), where it will behave according to \(P\). Since the name \(l\) appearing in migration actions can be a location variable, it can be instantiated during the execution with a location name, thus allowing agents to have a dynamic evolution, as they adapt their behaviors depending on the interactions they have with other agents. An agent _A_\(=a^{\Delta t}!\langle v\rangle\) then \(P\) else _Q_\(\rhd I\) is able to send a value \(v\) on channel \(a\) for at most \(t\) units of time, while an agent _A_\(^{\prime}=a^{\Delta t^{\prime}}? (u)\) then _P_\({}^{\prime}\) else _Q_\({}^{\prime}\rhd I^{\prime}\) is able to receive a value on channel \(a\) for at most _t'_ units of time, and to use that value to instantiate the variable \(u\). The agents \(A\) and _A_\(^{\prime}\) can communicate on channel \(a\) only if they are at the same location. If the agents \(A\) and _A_\(^{\prime}\) communicate, then the variable \(u\) is instantiated by the value \(v\), the two agents remain at the current location, and they will behave according to \(P\) and _P'_ afterwards. If the agents \(A\) and _A_\(^{\prime}\) cannot communicate in the time interval given
## 3 Results and Analysis
### Nanosecond Pressure Profile  
This section investigates the effect of the nanosecond pressure pulse profile on fracture propagation in a cement rock model. A 2D numerical circular model was created of outer diameter of 6 ft (182.88 cm) and a wellbore radius of 3 inches (7.62 cm). The outer boundary conditions of the model are unconfined. The model properties are taken for cement as listed in Table 4.  
Three pressure pulses are applied to the model wellbore. These three cases are selected to examine the effects of the shape of the pressure profile (the pressure peak value and the time extension). In the first case, a pressure pulse of a 2000 MPa peak and a 50- nanoseconds time extent are applied inside the circular wellbore. The pressure ramps exponentially and decays exponentially until it reaches atmospheric pressure. The application of the pulse does not develop fractures in the cement rock model, as shown in Figure 25. The peak of the pressure pulse was increased to high, unrealistic values. However, the fractures that were developed in the model were minor and not adequate for a successful pulse fracturing process.  
\begin{table}
\begin{tabular}{c c c} \hline
**Property** & **Value** & **Unit** \\ \hline Young’s modulus & 33 & GPa \\ Poisson’s ratio & 0.25 & \\ Density & 2300 & kg/m\({}^{3}\) \\ Tensile strength & 2.9 & MPa \\ Shear strength & 1.45 & MPa \\ Friction factor & 0.6 & \\ \hline \end{tabular}
\end{table}
Table 4: Cement properties for nanosecond pressure pulse study ([PERSON] and [PERSON] [31]).  
Figure 24: Major fractures of different thresholds developed in the naturally fractured model.
## 3 Materials and Methods
### Sounding Models
#### 3.5.2. Convolutional Neural Networks  
To enable the CNN models to handle multiple patches, we used a multiple instance framework to combine patches via an attention layer, with the patch size being tuned as a hyperparameter (hyperparameters are discussed in greater detail in Section 3.8). The attention mechanism aggregates the features from all the different patches for a particular image of a plant [33; 34]. Each patch was first encoded with a CNN encoder truncated after a subset of the convolutional blocks. Transfer learning was used to initialize model weights in one of two ways:  
* RESISC: a ResNet50 CNN [45] pre-trained on the RESISC dataset, which consists of aerial RGB images labeled by land cover and scene classes [46]. The RESISC dataset is particularly relevant because it includes a cloud class, enabling the model to capture distinguishing features of clouds--and, likely, plumes. This model uses the RGB channels only.
* BigEarthNet: with a VGG16 CNN [47] pre-trained on the BigEarthNet dataset, which consists of Sentinel-2 images labeled by land cover class [48]. This model uses 10 bands  
Figure 4: Overview of modeling approaches: NDT cooling towers and FGD stacks were cropped from satellite imagery (red frames above) and fed into ML models to classify the power plant’s on/off status and predict the capacity factor. Patch size is selected as detailed in Section 3.5 and may be larger than visualized here. Image from [33].  
from Sentinel-2, excluding the lowest resolution bands of 1, 9, and 10. We were not able to apply this model to PlanetScope but did adapt it for Landsat 8 by matching the band wavelengths as closely as possible and pairing the remaining bands even if the wavelengths are different. While this dataset enabled the model to learn a more diverse set of spectral characteristics, it only contained cloud-free images; our model must learn plume features during fine-tuning.  
After the shared convolutional encoder, we used an attention layer to combine patch features as a weighted sum, with the weights determined by the model itself [49]. A dense layer then made the final prediction. More details about model configuration and training are provided in Appendix B.
## 4 Discussion
### NMR-Based Pore Fractal Modelling
The resulting data, visualized in Figure 14, revealed significant insights regarding the pore size distribution and fractal characteristics. From the analysis depicted in Figure 14, it is evident that the relationship between \(\ln(\varphi_{acc}(T_{2}))\) and \(\ln(T_{2}/T_{2 max})\) for both the fractal dimension of small pores (\(D_{A}\)) and the fractal dimension of all pores (\(D_{T}\)) did not correspond with the regression model provided by Equation (18). This deviation from the expected regression indicates that the fractal characteristics of both small and overall pore distributions in microwave-treated specimens are not well defined. In other words, the distribution and connectivity of these pores do not exhibit clear self-similar fractal patterns, which are typically described by the power-law relationship associated with fractal structures. This suggests a more chaotic or irregular pore structure, likely caused by the heterogeneous heating effects and localized stresses induced by microwave radiation. Conversely, the fractal characteristics of large pores (\(D_{P}\)), representing the larger voids and gaps created as smaller pores merge and expand, showed a much better fit to the fractal model, as shown in Table 7. This suggests that the larger voids within microwave-treated basalt specimens exhibit more pronounced fractal behavior, likely due to the coalescence of smaller cracks and voids into larger, self-similar structures. The overall increase in the macroporous fractal dimension \(D_{P}\) indicates that longer microwave radiation durations and higher microwave power promote the formation of these larger fractal pore structures. This can be attributed to the increased energy input from microwave radiation, which amplifies thermal stresses and drives significant microstructural changes within the rock. These alterations in pore structure largely contribute to the inhomogeneity of pore size distribution in basalt specimens. As pores enlarge or new pores form, especially at specific
# Insights into Native Fermentation Process of Apples (_Malus domestica_) in Low Sodium Conditions
## 3 Results
### Physical and Chemical Analysis
#### 3.1.3 Titractable Acidity  
The tittatable acidity and pH values are the main indicators of microorganism and microbial metabolite increase [26]. The changes in tittatable acidity in the apple pickles during 35 days of storage are shown in Figure 2. It was found that, during the fermentation period, the tittatable acidity of apple pickles increased from the first day until the 35 th day. All pickle samples reached a high level of acidity up to 14 days of fermentation, after which they became relatively stable, showing a slow increase until day 21. The highest value (0.248 \(\pm\) 0.032 g lactic acid/100 g product) of tittatable acidity was registered for the samples fermented with MgCl\({}_{2}\), while the lowest value (0.124 \(\pm\) 0.016 g lactic acid/100 g product) was found for the samples fermented with NaCl. The difference in tittatable acidity may also come from the nature of the cations (Mg\({}^{2+}\) vs. Na\({}^{+}\)) and their interaction with water. It is possible that MgCl\({}_{2}\) inhibits the lactic acid bacteria resulting in a higher tittatable acidity, while the NaCl tends to slow down the lactic acid fermentation process, leading to a lower tittatable acidity.  
Figure 1: Variation of dry matter in apple pickles during fermentation interval. The values on the same line marked with different letters are different in a statistically significant manner (\(p<0.05\)).  
Figure 2: The changes in tittatable acidity in apple pickle fermentation with different brine solutions. The values on the same line marked with different letters are different in a statistically significant manner (\(p<0.05\)).  
From day 21 to day 35 of fermentation, the titratable acidity for all samples increased by 30.5% for the samples fermented in MgCl\({}_{2}\) brine, 35.1% for the samples fermented in KCl brine, and 46% for the samples fermented in NaCl brine. The final titratable acidity level for the samples fermented in NaCl is lower when compared with the samples fermented in MgCl\({}_{2}\) and KCl at the end of the storage interval. The acidity values could be explained by the evolution of lactic bacteria, which registered rapid development in the first 14 days, followed by stagnation and decrease as presented in a previous study [17]. Similar results were reported by [PERSON] et al. [1], who found that after one month of fermentation, the titratable acidity of pickled cucumbers with different brines increased during this period.
## 3 Preliminaries  
### DIRECT Algorithm  
The box-constrained global optimization problem  
\[\min_{\mathbf{x}\in[\mathbf{x}^{\text{low}},\mathbf{x}^{\text{up}}]}f_{i}(\mathbf{x}) \tag{3}\]  
can be addressed by the DIRECT (DIviding RECTangles) algorithm. The DIRECT algorithm, a well-known partitioning-based method, balances local and global searches to efficiently find the global minimum [24; 25; 26; 27].  
It is noteworthy that the linear operator \(\mathcal{T}(\mathbf{x})\) can transform the interval \([\mathbf{x}^{\text{low}},\mathbf{x}^{\text{up}}]\) into a unit hypercube \([0,1]^{m}\):  
\[\mathcal{T}(\mathbf{x})=A(\mathbf{x}-\mathbf{x}^{\text{low}}),\,A=\text{diag}\bigg{(}\frac{ 1}{x_{1}^{\text{up}}-x_{1}^{\text{low}}}\dots,\frac{1}{x_{m}^{\text{up}}-x_{m} ^{\text{low}}}\bigg{)}.\]  
Therefore, the box-constrained problem (3) is simplified as follows:  
\[\min_{\mathbf{x}\in\widetilde{D}}f_{i}(A^{-1}\mathbf{x}+\mathbf{x}^{\text{low}}),\]  
where \(\widetilde{D}\) denotes a unit hypercube defined as \(\widetilde{D}=[0,1]^{m}\). In other words, the search space becomes an \(m\)-dimensional unit hypercube.
## 2 Materials and Methods
### Experimental Procedures  
Homogeneous Photo-Fenton Reactions: The conventional photo-Fenton method, using UV light, Fe\({}^{2+}\) ions, and hydrogen peroxide, was carried out at pH = 3, where Fe\({}^{2+}\) ions  
Figure 1: The experimental setup used for the degradation of Nile Blue dye.  
exhibit their highest solubility, resulting in an enhanced production of hydroxyl radicals, which play a crucial role in the degradation process [18]. Experiments were conducted using hydrogen peroxide at concentrations ranging from 100 to 500 mg L\({}^{-1}\) or varying Fe\({}^{2+}\) concentrations of between 1 and 5 mg L\({}^{-1}\). More specifically, the homogeneous photocatalysis procedure consisted of the following steps: (i) the preparation of the sample with pH adjustment by adding a concentrated solution of hydrochloric acid (HCl, 1M), (ii) the placement of the sample (100 mL) into the reactor and the addition of Fe(II), (iii) the placement of the UV lamp in the reactor, (iv) the addition of oxidants and turning the lamp on (t = 0 min), and (v) taking samples at regular time intervals. On the other hand, for the modified photo-Fenton method, sodium persulfate was used instead of hydrogen peroxide, at concentrations ranging from 100 to 500 mg L\({}^{-1}\).  
Heterogeneous Photo-Fenton Reactions: The concentrations of goethite and Basolite F300 used as catalysts were set at 100 mg L\({}^{-1}\) to enhance the degradation of Nile Blue dye by achieving an optimal balance between providing a sufficient surface area and ensuring proper dispersion. The oxidants were added in different concentration levels ranging between 100 and 500 mg L\({}^{-1}\). Additionally, for the goethite/H\({}_{2}\)O\({}_{2}\) system, experiments with different catalyst concentrations (100-500 mg L\({}^{-1}\)) and different pH values (pH = 3 and pH = 7) were also investigated.  
Control Experiments: Control experiments were also conducted to evaluate (i) the impact of photolysis, using only UV light, (ii) adsorption on the catalysts' surface, by leaving the dye solution in the dark in the presence of the catalyst, and (iii) the photocatalytic efficiency of the catalysts without the addition of oxidants. These control tests allowed for the isolation of each factor's specific contribution to the degradation process.
# A Two-Level Rolling Optimization Model for Real-time Adaptive Signal Control
## 2 Traffic Signal Timing Optimization Algorithm
### Rolling Strategy  
To avoid the effect caused by predicted vehicle arrival errors and to use the newly collected data, the rolling strategy based on DP algorithm was proposed. The algorithm is solved at the end of each time step based on a rolling strategy, as shown in Figure 3.  
From Figure 3, if each optimization horizon includes n time steps, which is related to the length of the prediction horizon, then, the first optimized time horizon will be 1 to n time steps. After the first time step is finished, the second optimized time horizon is 2 to n + 1 time steps. By analogy, when a time step is finished, a new optimization is performed immediately based on the latest forecast data. Therefore, the length of the time step is an important parameter in the rolling strategy. The different time step lengths will be discussed in the case study.
# TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction
###### Abstract  
Changes in ocean temperature over time have important implications for marine ecosystems and global climate change. Marine temperature changes with time and has the features of closeness, period, and trend. This paper analyzes the temporal dependence of marine temperature variation at multiple depths and proposes a new ocean-temperature time-series prediction method based on the temporal dependence parameter matrix fusion of historical observation data. The Temporal Dependence-Based Long Short-Term Memory (LSTM) Networks for Marine Temperature Prediction (TD-LSTM) proves better than other methods while predicting sea-surface temperature (SST) by using Argo data. The performances were good at various depths and different regions.  
long short-term memory (LSTM); temporal dependence; sea surface temperature (SST); prediction +
Footnote †: journal: Sensors
# Enconv1d Model Based on Pseudolite System for Long-Tunnel Positioning
## 3 Methodology
### Model Framework
#### 3.2.2 Spatial Feature Extraction Module
The 1D CNN module within the local information extraction module consists of three convolutional branches (Figure 4), with each branch comprising multiple convolutional layers, batch normalization layers, activation layers, and fully connected layers. The convolutional layers in each branch use 1 \(\times\) 1, 3 \(\times\) 3, and 7 \(\times\) 7 convolutional kernels. The output feature maps are passed through ReLU activation and input into fully connected layers. The local spatial information at three different scales is aggregated and used as input for the encoder. The encoder comprises multi-head attention, feed-forward networks, and residual connections with layer normalization. To reduce model complexity, the number of heads in the multi-head attention was set to 2, with two layers.
## 1 Introduction
Furthermore, in another study published in 2020 [39], the authors concluded that most commercially available methods focus on using non-electroencephalography EEG signals, which is perceived as a major limitation given the inability of these devices to detect all seizures types. Therefore, it is crucial to develop an automated system for the detection of epileptic seizures based on EEG signals. To develop such an automated system, significant further research and experimentation are necessary so as to develop seizure detection methods that are clinically relevant, high-performing, and statistically validated. Therefore, testing the clinical relevance of state-of-the-art findings through the analysis of a large number of EEG data collated from an accurate representation of clinical situations and collected from multiple sites will be necessary. The purpose of this study is to investigate a new data representation model that simultaneously exploits signal oscillatory power in frequency bands and signal complexity as EEG. This representation serves prevailing potent ML algorithms for seizure vs. seizure-free classification. We will rigorously evaluate the proposed EEG data representation features on the publicly available large dataset of Temple University Hospital (TUSZ) collected in clinical settings [40]. The main contribution of this paper can be summarized as follows: (1) An analytical framework for seizure and seizure-free EEG classification is proposed and validated on a new large EEG seizure corpus. (2) The feature significance level is investigated by a univariate data analysis. (3) The performance of complexity measures and oscillatory power is analyzed individually and then combined, as EEG signal representation features, to classify normal and seizure data. (4) The results of four supervised methods are compared: RF, GBDT, SVM, and k-NN. (5) The impact of adding a feature selection step on the performance of classifiers is investigated. (6) The contribution of each channel to the performance of classifiers is examined.
# Conflict Resolution in Mechatronic Collaborative Design Using Category Theory
## 2 Background  
The notion of \"inconsistency management\" was introduced in the first time by [PERSON] et al. [10] in the context of model-driven software engineering. According to [PERSON] et al. [11], the inconsistency can be defined as a conflict or a contradiction between different model elements. This contradiction can take five forms: name, interface, behavioral, interaction and refinement inconsistencies.  
* Name inconsistency occurs between two independent elements with the same name. An example of this inconsistency would be two components named \"sensor\", whereas the first one is an angle sensor and the second is a current sensor.
* Interface inconsistency concerns two elements having mismatching terminologies or values. For example, this inconsistency can be present when a distance \"D\" has two different values in two different models or when an \"electrical connection\" is named \"electrical wiring\" in another model.
* Behavioral inconsistency happens when the behavior of two elements does not match. This kind of inconsistency occurs when a distance is expressed in \"meters\" in a model and in \"kilometers\" in another one for example.
* Interaction inconsistency happens when an element does not respect certain interaction constraints.
* Refinement inconsistency happens when two models of different abstraction levels have different elements in order to fit the corresponding abstraction level. An example of refinement inconsistency would be if, in one model, a \"control unit\" is defined as a whole component, whereas it is defined as a combination of a controller, a signal voltage, and an angle sensor in a more detailed model.  
Interface and interaction inconsistencies are the most popular types addressed by tools and research works. However, behavioral and refinement inconsistencies are the least popular due to the insufficient number of tools capturing and managing these inconsistencies [12]. In our study, we will focus on interaction and interface inconsistencies among different models from different engineering disciplines. Our hypothesis is to solve only the inconsistency, which occurs when two elements present mismatching values or do not respect constraints defined in the system requirements. Name, terminology, behavioral, or refinement inconsistencies are out of the scope of this study.