esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Sounding Models  
To estimate power plant generation and CO\({}_{2}\) emissions from satellite imagery at a specific timestamp, our sounding-level models predicted whether a power plant was running or not (on/off) and estimated the capacity factor (generation divided by capacity). We included both sets of models because the on/off task was simpler and could be predicted more accurately, while the regression task was essential for differentiating low from high generation. We multiplied the predicted capacity factor by the capacity to infer generation. As we estimate power plant activity by identifying vapor plumes, focusing our models on the structures that emit these plumes can improve performance. Therefore, we used the annotated NDT cooling tower and FGD stack patches as model inputs, as shown by the red and blue boxes in Figure 1. This helped produce more accurate classification models than a single image of the entire plant, which can have the features of non-power plant activity impacting the accuracy [33]. Two different types of models were trained: gradient-boosted decision trees and convolutional neural networks (CNNs). For each model type, separate models for NDT and FGD were built, as well as for each satellite dataset, for a total of 16 sounding models. Figure 4 illustrates the structure of both model types.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Satellite**} & \multicolumn{2}{c}{**NDT Image Count**} & \multicolumn{2}{c}{**FGD Image Count**} \\  & **Before Filtering** & **After Filtering** & **Before Filtering** & **After Filtering** \\ \hline PlanetScope & 69,533 & 54,136 & 108,038 & 69,462 \\ Sentinel-2 & 18,964 & 15,064 & 30,541 & 18,390 \\ Landsat 8 & 9040 & 7176 & 12,885 & 8235 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Training image count for the years 2015–2022 for each satellite before and after filtering. For PlanetScope, we applied cloud filtering prior to downloading images, so the “Before filtering” column does not include cloudy images. Note that the same image may be used by both the NDT and FGD models since a power plant can have both types of technology.
# An Effective Scheduling Algorithm for Coverage Control in Underwater Acoustic Sensor Network
## 4 Performance Evaluations
### Algorithm Simulation and Analysis
Because it does not apply a node scheduling strategy, this leads to a large number of nodes dying prematurely, which in turn leads to an increase in network vulnerabilities. CMSS and ESACC not only consider network coverage, but also consider the sleep and wake-up mechanisms of redundant nodes. Compared to the CMSS, ESACC can maintain 97% high coverage over a long period of time (from the initial time until 1327 rounds) in its lifetime (3269 rounds). This is because the ESACC algorithm improves the efficiency of network scheduling and reduces the energy loss caused by frequent communication between nodes in the network, so that more nodes with sufficient energy participate in network monitoring tasks, thereby improving network service quality. Figure 10: Relationship between network coverage and running rounds. Figure 9: Relationship between network lifetime and sensing radius \(r_{\rm s}\). Figure 11 is a comparison of the average remaining energy of the network nodes of the ESACC algorithm with CMSS and LEACH-Coverage-U with the change of rounds in the network when the number of nodes fixed at 512, the node sensing radius 20 m, and the number of events 64. It can be seen that, when the number of running rounds is the same, compared with the LEACH-Coverage-U algorithm, the network nodes of CMSS and ESACC have a lower energy gradient and a higher energy utilization rate. This is mainly because the CMSS and ESACC algorithms optimize the network structure, close redundant nodes, and reduce the overall energy consumption of the network. At the same time, ESACC shows better energy utilization than CMSS. This is because, when a dead node appears in the network, based on the minimum set of nodes, the ESACC algorithm seeks the optimal combination by waking up the neighbor nodes of the dead node, making up for the lack of node death, and making the energy consumption of the network balanced, thereby extending the network lifetime.
# Artificial Intelligence Approach for Early Detection of Brain Tumors Using MRI Images
###### Abstract  
Artificial intelligence (AI) is one of the most promising approaches to health innovation. The use of AI in image recognition considerably extends findings beyond the constraints of human sight. The application of AI in medical imaging, which relies on picture interpretation, is beneficial for automatic diagnosis. Diagnostic radiology is evolving from a subjective perceptual talent to a more objective science thanks to AI. Automatic object detection in medical images is an essential AI technology in medicine. The problem of detecting brain tumors at an early stage is well advanced with convolutional neural network (CNN) and deep learning algorithms (DLA). The problem is that those algorithms require a training phase with a big database of more than 500 images and time-consuming with a complex computational and expensive infrastructure. This study proposes a classical automatic segmentation method for detecting brain tumors in the early stage using MRI images. It is based on a multilevel thresholding technique on a harmony search algorithm (HSO); the algorithm was developed to suit MRI brain segmentation, and parameters selection was optimized for the purpose. Multiple thresholds, based on the variance and entropy functions, break the histogram into multiple portions, and different colors are associated with each portion. To eliminate the tiny arises supposed as noise and detect brain tumors, morphological operations followed by a connected component analysis are utilized after segmentation. The brain tumor detection performance is judged using performance parameters such as Accuracy, Dice Coefficient, and Jaccard index. The results are compared to those acquired manually by experts in the field. The results were further compared with different CNN and DLA approaches using Brain Images dataset called the \"BraTS 2017 challenge\". The average Dice Index was used as a performance measure for the comparison. The results of the proposed approach were found to be competitive in accuracy to those obtained by CNN and DLA methods and much better in terms of execution time, computational complexity, and data management.  
artificial intelligence; segmentation; brain tumor; MRI imaging; image processing Article
# Rainfall Estimation from Tropospheric Attenuation Affecting Satellite Links
## 5 Full Procedure for Rain Rate Estimation  
The full procedure to estimate the rain rate consists in the following steps:  
1. measure the instantaneous rain attenuation affecting the satellite link, \(A_{R}(t)\) [dB];
2. obtain the concurrent value of the freezing level, \(h_{0}(t)\) [km]. This quantity can be extracted, for example, from vertical temperature profiles, as explained in Section 3.3;
3. if \(4\leq m\leq 9\)  
\begin{table}
\begin{tabular}{c c c c} \hline  & \multicolumn{2}{c}{_E[\(\epsilon\),(P)]_} & \multicolumn{2}{c}{_RMS[\(\epsilon\),(P)]_} \\ \hline \(\hat{\tau}_{0}(m)\) & 0.97 mm/h & 1.50 mm/h \\ \(\hat{\tau}_{p}(m)\) & 0.62 mm/h & 1.19 mm/h \\ \hline \end{tabular}
\end{table}
Table 4: Mean and RMS values for the comparison between the curves reported in Figure 9.  
Figure 10: Trend of \(\hat{\tau}_{0}(m)\) and \(\beta(m)\) as a function of the month.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \(m\) & 4 & 5 & 6 & 7 & 8 & 9 \\ \hline \(\hat{\tau}_{0}(m)\) & 1.100 & 1.200 & 1.450 & 1.500 & – & 1.000 \\ \(\hat{\tau}_{p}(m)\) & 1.099 & 1.202 & 1.448 & 1.501 & 1.552 & 1.000 \\ \hline \end{tabular}
\end{table}
Table 3: Comparison between different values of \(\tau(m)\).  
Figure 9: Comparison between annual rain rate Complementary Cumulative Distribution Functions (CCDFs).  
1. compute \(\beta(m)\) from (11); 2. evaluate \(\hat{\tau}_{p}(m)\) using (14); 3. derive the equivalent rain height as \[h_{R}(t)=\hat{\tau}(m)h_{0}(t)\ [\text{km}];\] Otherwise: 1. \[h_{R}=h_{0}(t)+h_{BB}(f)\ [\text{km}].\]
4. Calculate the coefficients \(a(h_{R})\), \(b(h_{R})\) and \(c(h_{R})\), using (5), (6), and (7), respectively.
5. Estimate the rain rate as: \[R=a(h_{R})\cdot{A_{R}}^{3}+b(h_{R})\cdot{A_{R}}^{2}+c(h_{R})\cdot{A_{R}}\ [\text{mm}/\text{h}].\] (15)
## 2 Materials and Methods
### Dynamic Bidding of Generators and Traders in Interconnections Subsection
#### 2.1.2 Bidding in Interconnections  
Based on the formation of the offers from the thermal units, in the hourly energy allocation of the hydroelectric units' contribution, whose submitted price offer equals 0 \(\upepsilon\)/MWh, the model is solved again (with the same assumptions for interconnections, whose bids/offers are at the border price for their total capacity) and updated results are collected, including the system's hourly marginal prices at a daily level.  
Next, the model compares each border price with the resulting marginal price (of the second run), according to the following:  
* If the hourly System Marginal Price (SMP) (e.g., 50 \(\upepsilon\)/MWh) > Border price of an interconnection (e.g., 40 \(\upepsilon\)/MWh), then the electricity traders adapt their offers for imports between 40 and 49.999 \(\upepsilon\)/MWh, and for exports between 39.999 and 0 \(\upepsilon\)/MWh.
* If hourly SMP (e.g., 30 \(\upepsilon\)/MWh) < Border price of an interconnection (e.g., 60 \(\upepsilon\)/MWh), then the electricity traders adapt their offers for imports from 60 and (60 + selected coefficient) \(\upepsilon\)/MWh, and for exports between 59.999 and 30 \(\upepsilon\)/MWh.  
In the case of market coupling, such as the expected among the Italian and Greek power systems, bidding in interconnections is treated differently. In the case of market coupling, where power markets are co-optimized, the interconnection (i.e., Italy) is treated as a virtual plant at 500 MW, where bidding for exports from the interconnection (i.e., from Italy and imports to Greece) is set at the defined expected interconnection (Italian) price +0.001, while imports to the interconnection (i.e., to Italy and exports from Greece) are set at the defined Italian price \(-\)0.001.
## 3 Deep Learning
### FNN Architecture  
An FNN uses the Fourier-mapped input as input to a standard neural network:  
\[f(x)=\mathcal{N}(\gamma(x);\theta)\]  
where  
* \(\mathcal{N}(\cdot;\theta)\) is a multi-layer perceptron (MLP).
* \(\theta\) represents the learnable weights and biases.  
A basic architecture looks like  
\[f(x)=W_{2}\cdot\sigma(W_{1}\cdot\gamma(x)+b_{1})+b_{2}\]  
where  
* \(W_{1}\in\mathbb{R}^{h\times 2m}\), \(W_{2}\in\mathbb{R}^{1\times h}\)
* \(b_{1},b_{2}\) are biases
* \(\sigma\) is an activation function (e.g., ReLU)  
The neural network architecture consists of an input layer followed by two hidden layers with 128 and 64 neurons, respectively. Both hidden layers use ReLU activation functions. The output layer employs a sigmoid activation to perform binary classification, distinguishing between seizure and non-seizure instances.  
The model was trained using the Adam optimizer with a binary cross-entropy loss function, a batch size of 32, and over 10 epochs. This configuration was chosen to maintain a balance between computational efficiency and predictive performance.  
Although no explicit regularization methods (such as dropout or L2 regularization) were applied, validation metrics were closely monitored throughout training. This helped ensure the model generalized well and did not exhibit signs of overfitting, particularly given the relatively short training duration.  
Description for EEG signal processing EEG signal characteristics: EEG signals are composed of electrical activities from the brain, reflecting various states such as sleep, alertness, and cognitive engagement. These signals typically exhibit rhythmic patterns across different frequency bands (e.g., delta, theta, alpha, beta, and gamma). Fourier Transform applications: frequency domain analysis: FNNs can leverage Fourier transforms to convert EEG time-series data into the frequency domain. This transformation allows the network to directly analyze different frequency bands, which are essential for interpreting various mental states and conditions. Feature extraction: Through Fourier transformation, the FNN can automatically extract meaningful features related to brain activity, such as power spectral densities for specific frequency bands, without relying heavily on manual feature engineering. Network Architecture: FNNs designed for EEG may include layers that directly apply Fourier transforms, sinusoidal activations, and other elements suited to model periodic functions relevant to brain activities. Some architectures might blend traditional convolutional or recurrent layers with Fourier layers to capture both temporal and frequency-related information. Learning Temporal Dynamics: By integrating temporal dynamics within the framework of Fourier analysis, FNNs can adaptively learn how specific frequency patterns evolve over time, which is crucial for understanding cognitive processes, detecting seizures, or classifying mental states.
## 3 Applications
### Schematic Overview of This Study  
The proposed DRSFO-EES model was applied to EES planning for the BTH region. As shown in Figure 1, the considered EES included several energy-related activities such as heat processing, coke processing, oil refining, electricity conversion, EVs expansion, and energy import. The electricity demands of the BTH region are supplied by local power plants and import from adjacent power grids. The major local electricity conversion technologies include natural gas-based, coal-based, wind and solar power. The vehicle population consists of heavy-duty passenger vehicles (HDVs), light-duty passenger vehicles (LDVs), light duty trucks (LDTs), heavy duty trucks (HDTs), EVs, and \"others\". The DRSFO-EES model was used in this study to determine the air pollutants and greenhouse gases (NOx, SO\({}_{2}\), PM, CO, HC, and CO\({}_{2}\)) emitted from the different energy related-activities in the region. The EES considered a number of uncertainties (such as electricity demand, and many economic parameters expressed as random variables and fuzzy sets) that would affect the optimization scheme.
# Pipe Routing with Topology Control for Decentralized and Autonomous UAV Networks +
## 4 Pipe Routing Scheme
### Description of Routing Metrics  
To choose a long-lifetime, stable and low-congestion route, we use several route information metrics, capturing the route length, interference, expected life of the route and available energy. More specifically, the route length (\(HC_{R}\)) is measured by the hop count (\(HC\)) as the number of links between the target UAV and the BS. The total number of interfering links (\(IL\)) across all UAVs in the route is computed as,  
\[IL_{R}=\sum_{i\in N_{R}}\left(IL_{\phi_{i}}+IL_{\phi_{i}}\right) \tag{3}\]  
where \(N_{R}\) is the set of nodes on route \(R\). \(IL_{\phi_{i}}\) denotes the last \(IL\) value of node \(i\) obtained from its control packet, while \(IL_{\phi_{i}}\) represents the new intra-flow interference links that will emerge at node \(i\) when data transmission begins along the route \(R\). The route lifetime (\(RLT\)) is the estimated time duration after which the route is likely to break; it is the minimum estimated link lifetime (\(LLT\)) over all the links that form the route. A UAV estimates the \(LLT\) of a link with its neighbors using GPS and current trajectory information found in the Hello message from its neighbors. Additional details on computing these metrics can be found in [17].  
In addition to the given route metrics, the residual energy of the route (\(En_{R}\)) is calculated as the minimum energy at all nodes of the route. If \(En_{i}\) is the energy level of node \(i\) in the route \(R\), and \(N_{R}\) is the set of nodes, then  
Figure 3: Modules used in our proposed Pipe and TC-Pipe routing schemes.  
\[En_{R}=\min_{i\in N_{R}}(En_{i}) \tag{4}\]  
UAVs with energy levels below a threshold (\(En_{th}\)) are likely to run out of battery soon, resulting in node failure and causing a route break. Therefore, these UAVs should safely disengage from the route. For this reason, routes with \(En_{R}\leq En_{th}+\delta_{e}\) are not considered during route selection in our scheme. Since the source node receives updated topology information (node energy) every 2 s, we select the value of the tolerance factor \(\delta_{e}\) to be twice the per second energy depletion rate of a UAV to account for the delay. Moreover, preemptive route switching is performed when the current route's \(En_{R}\) falls below the threshold \(En_{th}\).
# Using Markov Random Field and Analytic Hierarchy Process to Account for Interdependent Criteria
## 2 Literature Review
### Ahp
Recent developments in AHP and pairwise comparisons are well illustrated in the selected papers. Ref. [34] study streamlined AHP for complex scenarios, showcasing advancements in methodology simplification. Ref. [35] presented a novel, data-driven fuzzy AHP method, exemplifying the integration of fuzzy logic in environmental decision making. Ref. [36] contribute by evaluating various scaling methods in AHP, enhancing decision accuracy. These papers, together with [37] research on rank reversals in AHP, offer a comprehensive insight into the latest trends and methodologies in AHP and pairwise comparisons. However, a key assumption of AHP is that criteria and alternatives are independent, which may not hold true in real-world decision-making scenarios due to interdependencies between criteria [5; 6]. To address this issue, several approaches have been proposed in the literature, such as the ANP by [7], fuzzy AHP by [38], and hybrid methods that combine AHP with Bayesian networks by [33] or FCMs by [39]. However, these approaches can be challenging to implement or require extensive data, highlighting the need for more efficient and intuitive methods to address criteria interdependencies in AHP.
# An Overview of the IberSpeech-RTVE 2022 Challenges on Speech Technologies
## 3 IberSpeech-RTVE 2022 Evaluation Tasks
### Speaker Diarization and Identity Assignment Challenge
#### 3.2.1 Diarization Scoring  
As in the NIST RT Diarization evaluations ([[https://www.nist.gov/itl/iad/mig/rich-transcription-evaluation](https://www.nist.gov/itl/iad/mig/rich-transcription-evaluation)]([https://www.nist.gov/itl/iad/mig/rich-transcription-evaluation](https://www.nist.gov/itl/iad/mig/rich-transcription-evaluation)) (accessed on 24 July 2023)), to measure the performance of the proposed systems, the diarization error rate (DER) is computed as the fraction of speaker time that is not correctly attributed to that specific speaker. This score is computed over the entire file to be processed, including regions where more than one speaker is present (overlap regions).  
Given the dataset to evaluate \(\Omega\), each document is divided into contiguous segments at all speaker change points found in both the reference and the hypothesis, and the diarization error time for each segment \(n\) is defined as:  
\[E(n)=T(n)\Big{[}\max\Big{(}N_{ref}(n),N_{sys}(n)\Big{)}-N_{Correct}(n)\Big{]} \tag{2}\]  
where \(T(n)\) is the duration of segment \(n\), \(N_{ref}(n)\) is the number of speakers that are present in segment \(n\), \(N_{sys}(n)\) is the number of system speakers that are present in segment \(n\), and \(N_{Correct}(n)\) is the number of reference speakers in segment \(n\) correctly assigned by the diarization system.  
\[DER=\frac{\sum\limits_{n\in\Omega}E(n)}{\sum\limits_{n\in\Omega}\Big{(}T(n)N _{ref}(n)\Big{)}}. \tag{3}\]The diarization error time includes the time that is assigned to the wrong speaker, missed speech time, and false alarm speech time:  
* Speaker error time: The speaker error time is the amount of time that has been assigned to an incorrect speaker.
* Missed speech time: The missed speech time refers to the amount of time that speech is present but not labeled by the diarization system.
* False alarm time: The false alarm time is the amount of time that a speaker has been labeled by the diarization system but is not present.  
Consecutive speech segments of audio labelled with the same speaker identification tag and separated by a non-speech segment less than 2 s long are merged and considered a single segment. A region of 0.25 s around each segment boundary, usually known as the forgiveness collar, is considered. These regions are excluded from the computation of the diarization error in order to take into account both inconsistent human annotations and the uncertainty about when a speaker turn begins or ends.
## 2 Materials and Methods  
### Environmental and Fishery Data  
The habitat environment of Antarctic krill is affected by environmental factors such as sea surface temperature (SST), sea surface height (SSH), chlorophyll (CHL), sea ice concentration (SIC), salinity (SSS), and distance from the shore (GLD) [6], as well as [3; 4; 5]. Therefore, in this paper, marine remote sensing data were downloaded from the official website of the Copernicus Marine Environment Monitoring Service ([[https://data.marine.copernicus.eu/](https://data.marine.copernicus.eu/)]([https://data.marine.copernicus.eu/](https://data.marine.copernicus.eu/)) accessed on 22 May 2025) and the Global Marine Environmental Dataset (GMED--Download Data Layers). To ensure data matching, ArcGIS 10.6 was used to uniformly adjust the spatial resolution of the environmental data to 0.083\({}^{\circ}\)\(\times\) 0.083\({}^{\circ}\).  
The monthly catch production data of Norwegian krill vessels during the fishing seasons from 2021 to 2023 were sourced from the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR). This dataset includes data such as the fishing country, the year and month of fishing, the fished species, and the catch production (in tons). Since the Antarctic krill fishery starts on December 1 st of each year and ends in November of the same year, in this study, the fishing season of a certain year is defined as starting from December 1 st of the previous year and ending on November 30 th of that year. For example, the fishing season of 2021 was from 1 December 2020 to 30 November 2021.  
For the collinearity analysis of environmental factors, correlations among environmental variables are commonly present in models. Collinearity may lead to instability of parameter estimation and biases in inferential statistics [17]. By screening out environmental variables with weak correlations through correlation analysis, it is possible to optimize efficiency, reduce overfitting, and effectively improve the accuracy of model predictions. Therefore, in this paper, a Pearson correlation test was conducted on these six environmental factors (Figure 1). If the Pearson correlation coefficient is less than \(\,\)10.7\(\,\), it is considered that there is no significant correlation [18]. Thus, in this study, six environmental factors, namely SIC, GLD, SSS, SST, SSH, and CHL, were selected for modeling and analysis of the factors influencing the habitat distribution of Antarctic krill.