esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deep Convolutional Neural Network for Detecting Volcanic Thermal Anomalies from Satellite Images
## 3 Method
### Three-Step Approach
For each run of the SqueezeNet model, we used 120 images for the train set (60% of the volcanic dataset),  
Figure 2: Flow chart of the three-step approach for the study. 20 images for the validation set (10% of the volcanic dataset) and 60 images for the test set (30% of the volcanic dataset). We used such a small amount of training data to verify the reliability and robustness of the model in transferring knowledge to a new domain (with transfer learning). We fed the train and validation sets to the SqueezeNet 1.0 model in Google Colab, applying the transfer learning approach, running the model with a learning rate alpha of 0.001, a batch size of 25 and 5 epochs for the fine-tuning [2]. The number of 5 epochs was chosen as a good trade-off between complexity of the algorithm and goodness of the model, because a higher number of epochs did not improve the performance much compared to the complexity. Finally, we ran the trained SqueezeNet model a total of 10 times over the test subset (30% of the volcanic dataset). An ensemble strategy, combining the 10 trained SqueezeNet models, was adopted to enhance the generalization capabilities; that is, the final model outcome is provided by the outcome most voted upon by the 10 runs of the trained model. Ensemble methods use multiple models to achieve better predictive performances than could be obtained from any of the individual models alone [55]. The ensemble result obtained combining the results of the ten SqueezeNet test runs is used as the output for each image classified. The goodness of the classification for the test set is estimated using different model performance indices, i.e., the accuracy index defined as in Equation (1), the precision index as in Equation (2), the recall index as in Equation (3) and the F1 score as in Equation (4), then calculated as percentages [56]:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{1}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{3}\]  
\[\text{F1 score}=\frac{\text{2}\bullet\text{TP}}{\text{2}\bullet\text{TP}+ \text{FP}+\text{FN}}=\text{2}\bullet\frac{\text{Precision}\bullet\text{ Recall}}{\text{Precision}+\text{Recall}} \tag{4}\]  
where TP (True Positives) are the images predicted to belong to class 1 that actually do belong to that class, TN (True Negatives) are the images predicted to belong to class 0 that actually do belong to that class, FP (False Positives) are the images incorrectly predicted to belong to class 1 that actually do belong to class 0, and FN (False Negatives) are the images incorrectly predicted to belong to class 0 that actually belong to class 1. The total number of images in the test set is given by the sum of TP, TN, FP and FN.
## 4 SoilWorks Numerical Simulation
### Analysis Methods and Models  
The selection of material constitutive models has a significant impact on the analysis results, such as material behavior, stress, and strain. SoilWorks provides various geotechnical constitutive models for users to choose from in order to simulate the stress-strain behavior of soils.  
The Mohr-Coulomb model is commonly used to simulate geotechnical materials. Figure 5a shows the idealized elastic-plastic characteristics. Under this assumption, reliable results can be obtained in general geotechnical nonlinear analyses; therefore, in this study, the Mohr-Coulomb model was also used for relevant numerical analysis work.  
The Mohr-Coulomb yield criterion has two limitations in simulating soil material behavior. The first limitation is the assumption that the second principal stress has no effect on the yield, which does not match the experimental results. The second limitation is that the meridians of the Mohr circle and the yield envelope are straight lines, and the strength parameters do not vary with the confining pressure or pore water pressure (see Figure 5b); therefore, this constitutive model is more accurate when there is minimal variation in the confining pressure but loses accuracy when there is significant variation. Additionally, the yield surface has corners, leading to errors in numerical analysis; however, this criterion provides good accuracy under commonly encountered confining pressures and has become the most commonly adopted failure criterion, effectively solving most numerical analysis problems in geotechnical engineering.  
The soil material parameters required for the Mohr-Coulomb model include the elastic modulus, Poisson's ratio, cohesion, the friction angle, the dilation angle, the bulk modulus, and the elastic shear modulus, totaling seven parameters.
## 4 Experiment
### Analysis of the Exercises' Data  
Twenty-three students, 11 from the first and 12 from the second year, completed 496 exercises in total (Figure 8). Each exercise consisted of two rhythmic sequences that could be answered multiple times. In total, the students correctly answered 837 sequences. The first-year students averaged 24.5 sequences and the second-year 26 sequences.  
The rhythmic dictation application is organized into 16 difficulty levels. The student had to complete at least 12 exercises at the current level in order to advance to a higher level. When starting the application, the student could choose which level to start at from the subset of levels they already achieved. 39% (seven students) of the students remained at the first level (level 11), because they did not complete enough exercises to pass onto the next level, while others moved to higher levels. During the evaluation, only one student reached all of the rhythmic levels available.  
Figure 6: Graph of the application’s usage frequency per day. The second and third meetings are marked individually for each test group. The engagement the day after the meeting (# of exercises) was initially low after the meeting with both the first- and the second-year students. However, it increased after the second meeting.  
We also observed the time needed to complete the individual exercises. In their first exercises, the students needed more time than in later repetitions (Figure 8)--the average time gradually decreased with the number of exercises played. With increasing difficulty levels, we noticed that the number of event deletions increased. The number of dictation plays remained steady across all levels of difficulty. The number of attempts to solve also remained steady, with the exception of level 11 (1/16 difficulty level), where the sequences were trivial for the conservatory-level students to solve. Figure 9 shows a subset of players who played at the most common levels.  
Figure 8: The number of responses (sequences solved) per levels (left), and the average number of seconds spent per exercise in first ten consecutive exercises for first three levels.  
Figure 7: Initial responses to individual questions about the students’ behaviour and experience while using the application, and the responses after one month of application’s use. The most significant change is in the students’ perception of the organ sound. No student reported still using pen/paper while using the rhythmic application.  
The gathered data confirmed our assumption that some of the observed values, such as time spent, decreased over time, while others remained steady due to the increasing difficulty of exercises. The students' engagement in out-of-class use gradually increased, which we consider to be a confirmation of the application's usefulness--students liked the interface and found it easy to use--as well as gamification elements, which, through the student challenge, brought competitiveness into interaction between students.
## 2 Materials and Methods  
The supporting electrolyte (0.1 M NaOH) was prepared using high-purity sodium hydroxide pellets (MERCK) and ultrapure water (18.2 M\(\Omega\) cm resistivity), supplied by a Millipore Direct-Q3 UV system (Darmstadt, Germany). Acetone with a purity exceeding 99% was sourced from Sigma-Aldrich (Poznan, Poland).  
All electrochemical measurements were conducted in a conventional three-electrode setup, using a custom-made Pyrex glass cell, consisting of an 80 mL main compartment and two side (reference- and counter-electrode) compartments, connected to the central section via glass tubings. The electrolyte was thoroughly purged with argon 6.0 grade prior to running each series of experiments and continuous Ar blanket was maintained over the cell's main section throughout the measurements in order to prevent its oxygen contamination. The working electrode (WE) was a polycrystalline nickel wire (0.5 mm diameter, 99.99% purity, Sigma-Aldrich) with an exposed area of 1.76 cm\({}^{2}\). A reversible hydrogen electrode (RHE), fabricated from 1.0 mm diameter palladium wire (99.99% purity, Sigma-Aldrich), served as the reference electrode. A coiled platinum wire (1.0 mm diameter, 99.9998% purity, Johnson Matthey, Chicago, USA) was employed as the counter electrode (CE). Before conducting the experiments, the nickel electrode was thoroughly washed in ultrapure water and was additionally activated in 0.1 M NaOH solution by cathodic polarization at 20 mA for 300 s in order to remove any spontaneously formed Ni oxide layering. By means of a dedicated holder, the working electrode was then mounted vertically and immersed to a defined geometric surface area. Acetone stock solutions were freshly prepared in 0.1 M NaOH by serial dilutions from a concentrated acetone-water mixture, using volumetric flasks and micropipettes to ensure accurate, low-concentration acetone dosing (1.0 \(\times\) 10\({}^{-7}\) to 1.0 \(\times\) 10\({}^{-3}\) M).  
The PGSTAT302N AUTOLAB Electrochemical System (nLab, Warsaw, Poland) was utilized for all electrochemical experiments. The experimental approach included both cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The EIS spectra were recorded for the frequency range of 100 kHz to 1 Hz, with an output _ac._ signal set at 5 mV. Data acquisition and equipment control were managed via the NOVA 2.1.8. software package. For each applied potential, EIS measurements were repeated three times to ensure consistency with observed variability, typically remaining below 10%. The impedance spectra were fitted by means of a complex, non-linear, least squares immittance fitting program, LEVM 6 [17]. Post-measurement _iR_ compensation was applied, based on the recorded _ac._ impedance spectra.
## 4 Methodology  
Figure 2 shows the proposed approach for damage assessment based on social network data. Initially, pre-processing was done to remove irrelevant data as well as convert the tweets' text into a comprehensible structure for the computer. Then, the three algorithms of Naive Bayes, SVM, and deep learning were used to classify the messages. Then, classification was evaluated using accuracy, precision, recall, and F-score metrics. To understand the message propagation behavior in the study area, temporal analysis based on classified messages was performed. In addition, the variability of spatial topic concentrations in three classification algorithms after the earthquake were examined using LQ. Then, the damage map based on the results of the classification of the three algorithms into three spatial units of city, county, and 10 \(\times\) 10 km grids, wascreated. For validation, a FEMA HAZUS loss model was used. HAZUS is a standardized model for evaluating losses from earthquake and other crises [38]. In this regard, confusion matrix metrics, [PERSON]'s rho, Pearson correlation, and [PERSON]'s tau were used to assess our approach damage map with the FEMA HAZUS loss model ([[https://www.conservation.ca.gov/cgs/earthquake-loss-estimation](https://www.conservation.ca.gov/cgs/earthquake-loss-estimation)]([https://www.conservation.ca.gov/cgs/earthquake-loss-estimation](https://www.conservation.ca.gov/cgs/earthquake-loss-estimation)), accessed on 16 October 2019).  
Figure 1: Description of Napa earthquake study area with tweet locations 7 days before the earthquake (**left**) and 7 days after the earthquake (**right**).
# Edge and Cloud Computing in Smart Cities
## 2 Architectural Models for Edge and Cloud Computing in Smart Cities
### Fully Distributed Edge-Cloud Architecture
The total system latency \(T_{\text{sys}}\) in a fully distributed edge-cloud network is expressed as  
\[T_{\text{sys}}=\sum_{\tau\in\mathcal{T}}\left(T_{E_{j}}+\frac{d_{E_{j},E_{k} }}{B_{E_{j},E_{k}}}+T_{C}\delta_{\tau}\right), \tag{13}\]  
where \(d_{E_{j},E_{k}}\) represents the distance between two edge nodes, \(B_{E_{j},E_{k}}\) is the available bandwidth for communication, and \(\delta_{\tau}\) is an indicator function, where \(\delta_{\tau}=1\) if the task is processed in the cloud, and \(\delta_{\tau}=0\) if processed at the edge [38; 39]. The distributed nature of this architecture enhances fault tolerance. If an edge node \(E_{j}\) fails, its workload is reallocated to neighboring nodes without interrupting service. The failure probability of a task execution in this model is given by  
\[P_{f}=1-\prod_{j=1}^{K}(1-p_{E_{j}}), \tag{14}\]  
where \(p_{E_{j}}\) represents the failure probability of node \(E_{j}\). As the number of cooperative nodes increases, the probability of successful execution improves [40; 41]. Energy efficiency is a crucial consideration in fully distributed architectures, especially where edge nodes operate on limited power. The total energy consumption of the system is given by \[E_{\text{total}}=\sum_{j=1}^{K}\left(P_{E_{j}}T_{E_{j}}+P_{\text{comm}}T_{\text{comm }}(E_{j},E_{k})+P_{\text{C}}T_{\text{C}}\delta_{\tau}\right), \tag{15}\]  
where \(P_{E_{j}}\) and \(P_{\text{comm}}\) are the power consumption rates for computation and communication, respectively. Efficient scheduling strategies, such as reinforcement learning (RL)-based task allocation, can optimize energy efficiency by dynamically adjusting resource usage [42; 43]. The fully distributed edge-cloud architecture removes the limitations of hierarchical computing, ensuring adaptability to variations in workload, network congestion, and node availability. This cooperative processing framework maximizes resource utilization and minimizes latency while maintaining service reliability. However, the model introduces challenges such as increased synchronization complexity and the need for consensus mechanisms to maintain consistency across edge nodes [44; 45].
# Optimal Design and Control of MMC STATCOM for Improving Power Quality Indicators
## 7 Results and Discussion
### Nonlinear Load Case Study
The grid side voltage and current of the MMC STATCOM are shown in Figure 17. As shown from Figure 17, when the set-point of the reference reactive power changed at 0.15 s, the current drawn from the grid steps from lagging the grid voltage by 90 degrees to leading the voltage by 90 degrees. The control system of the STATCOM introduces a rapid reaction for modifying the terminal voltage of the inverter in order to produce 10 MVAr of reactive power (capacitive mode). The load current and output voltage are represented in Figure 18.  
The active and reactive power with the connection of the MMC STATCOM is presented in Figure 19a,b respectively. From the figure, a high dynamic performance with acceptable raise time has been achieved for the reactive power variation. Moreover, the three-phase voltage of the converters in this case of study is shown in Figure 20.  
Figure 19: _Cont._  
Figure 18: The load side voltage and current.
## 3 Results and Discussion
### IDEAL Test
#### 3.1.3 Effect of NMAS on the IDEAL Test Result  
As it can be seen from Figure 11, the \(G_{\text{initial}}\) value of sample 3 is bigger than that of sample 7, and the \(G_{\text{initial}}\) value of sample 5 is bigger than that of sample 9 which indicates that the nominal maximum aggregate size is negatively correlated with the initial-cracking resistance of mixtures. As it can be seen from Figure 12, the \(CT_{\text{index}}\) of sample 3 is bigger than that of sample 7, and the \(CT_{\text{index}}\) value of sample 5 is bigger than that of sample 9 which means that the nominal maximum aggregate size is positively related with the crack propagation rate of asphalt mixtures. This is mainly because the asphalt mixtures with larger NMAS have lower optimum asphalt content, which is not conducive to crack resistance. The asphalt content is very essential in the crack resistance of asphalt mixtures because asphalt act as a binder inside the asphalt mixtures. The asphalt helps to enhance the crack resistance because it is a kind of viscous material. Therefore, asphalt mixtures having larger NMAS have lower anti-cracking ability.  
Figure 11: Effect of NMAS on \(G_{\text{initial}}\):  
Figure 10: Effect of gradation type on \(CT_{\text{index}}\).
## 5 Evaluation Results and Discussion
### Semi-Supervised ASR Performance for Spanish  
The evaluation results achieved for the Spanish language are summarized in Table 7 at CER and WER levels. Firstly, it is worth noting that, even when we used less than one hour of PWA transcribed speech, we were able to achieve performances of 25.8 of CER and 49.8 of WER on the test set using the most simple greedy search decoding. These results were further improved by integrating audio from healthy control speakers and the _Large LM_ trained with million of words to rescore and enhance the initial recognition hypothesis. If we consider the challenge of the task and the previous benchmarks of English and Cantonese ASR systems, which were trained with up to 50\(x\) more hours of transcribed speech, these results can be considered very competitive and promising. Moreover, these results are, to the best of our knowledge, the first benchmark of aphasic speech recognition published for Spanish.  
The best initial results with the Spanish AM models trained with the _PWA acoustic set_ were reached by fine-tuning the pre-trained model for 100 epochs, achieving a CER 25.8 and a WER of 49.8. However, previous results in English demonstrated that augmenting the training dataset with data from healthy controls improved the overall ASR performance. In this manner, the Spanish model trained with the _Mixed acoustic set_ improved the WER performance at around 10% when finetuning the pre-trained model for 200 epochs. Once again, this approach showed that using semi-supervised methods on clinical data scarcity domains together with non-pathological data augmentation results in a very promising and interesting strategy.  
Finally, the best performance for this language was achieved through a beam search decoding with the external _Large LM_ model. Once again, the special symbols _FLR_, _SPN_, _BRTH_ and _LAU_ were discarded during the evaluation since these symbols were not covered in the generic texts. Following this strategy, we achieved a 24.8 of CER and a 42.8 of WER on the test set. These results differs with the English subset where the external _Large LM_ did not improve the results at all. This may be due to the fact that the Spanish AM, fine-tuned with much fewer data, did not learn special symbols properly. As a result, they could be removed during evaluation without a negative impact on the performance.
## 2 Energy Management and Review of Surveys
### EM Definition  
The energy management system (EMS) is an information and control system that provides the necessary functionality, ensuring that both the generation and distribution systems supply energy at minimized operational costs [28]. It is an important procedure to achieve a stable and economic operation of MGs through some optimization techniques that manage and coordinate the dispatchable distributed generators (DGs), energy storage systems, demand responses, and other applied strategies to optimize the objective function (s) of an MG [8; 29; 30; 31]. It is also responsible for maximizing the utilization of the RES, considering their relevant uncertainties [8; 32].  
An important point to note is that despite the many definitions of this concept, three sub-systems of power, control, and telecommunication play a fundamental role. The sub-systems' full coordination and integration are the basis for the development from MGs to smart grids (SGs) and, as a result, smart cities (SCs) and smart societies (SSs) [33].
###### Abstract  
The electromagnetic method is widely used in mineral exploration, geothermal resource exploration, and deep earth structure detection. To improve the resolution and positioning accuracy of magnetotelluric surveys for targets beneath cover, it is essential to utilize forward modeling. This approach allows for a better understanding of the capabilities and limitations of MT in resolving features of different scales. In this paper, we employ forward modeling using the finite element method to simulate a series of continuous fault zones ranging from 100 m to 400 m in width, with varying lengths and dips. These fault zones represent conductive fluid pathways that could be associated with different scales and depths. The forward modeling provides the necessary data and method testing to assess the effectiveness of MT surveys in identifying and resolving such features. Our findings demonstrate that a station spacing of 400 m is optimal for resolving fault zones of various widths. For narrower faults (approximately 100 m), extending the survey line to 12 km or more significantly improves the deep structural inversion of the bounding faults, while an 8 km survey line suffices for wider faults (400 m). However, the vertical extent of these features is less well constrained, with deeper faults appearing broader and inversion depths being notably shallower than actual depths. These results highlight the need for careful interpretation of inversion anomalies, especially when supplementary data from other geophysical methods are lacking. Misinterpretation can lead to inaccurate resource assessments and exploration potential.  
forward modeling; magnetotelluric; finite element method; NLCG inversion; geothermal exploration +
Footnote †: journal: Applied Sciences  
[PERSON]; [PERSON] Study of the Impact of Acquisition Parameters on Fault Feature Identification Based on Magnetotelluric Modeling.