esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
This study successfully validated the pull-back method for characterizing dynamic tensile strength in unidirectional reinforced concrete (UDRC) systems. The key findings are as follows:The one-dimensional stress assumption remains valid despite UDRC's heterogeneity. Finite element analysis showed that spall fractures initiate in regions maintaining stress triaxiality near \(-1/3\) and Lode parameters near \(-1\), confirming uniaxial stress conditions in the bulk concrete.  
The presence and type of reinforcement had a negligible influence on the measured pull-back velocity and the resulting dynamic tensile (spall) strength, which is governed by the concrete matrix. However, the reinforcement was found to be critical for post-fracture integrity, preventing fragment separation and providing residual capacity.  
These findings enable confident application of the pull-back method to reinforced concrete systems and suggest that plain concrete properties provide conservative estimates for spall resistance in protective structure design. The matrix-dominated behavior allows reinforcement selection based on other design considerations. These conclusions are based on a configuration with a single, central reinforcing bar; future work should explore different reinforcement ratios, multiple bar configurations, and higher strain rate regimes to broaden the method's validated application range.  
Conceptualization, X.Y.; methodology, X.Y.; software, X.Y.; validation, J.Z., J.G.; writing--original draft preparation, X.Y.; writing--review and editing, X.Y.; visualization, J.G.; supervision, X.Y.; project administration, X.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (no. 11390361).  
Not applicable.  
All the data will be provided upon request.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
UDRCUnidirectional reinforced concrete  
DICDigital image correlation
## 4 Case Study and Experimental Implementation
### Hyperparameters Optimization
A range of clusters between 2 and 10 has been considered for the analysis process, with a view to selecting the optimal number of clusters. In accordance with the validation results presented in Figure 8, which indicate a DBI value of 0.47, the clustering model incorporates four clusters, representing the four failure classes. Figure 8: Optimization of clusters number selection. Figure 7: Hyperparameters optimization results. (**a**)Validation accuracy with different kernel sizes. (**b**) Validation accuracy with different filter sizes. (**c**) Validation accuracy with different learning rates. (**d**) Validation accuracy with different number of layers.
## 2 Architectures
### End-to-End LF-MMI ASR Applying Domain Adversarial Training  
Different acoustic conditions of TV shows can have a negative impact on the PyChain-based baseline's performance. To reduce this effect, we explored the integration of DAT [24], trying to improve the PyChain-based baseline system. More specifically, in this approach, we aimed to make acoustic representations invariant to the domain of the TV show characteristics by using a Domain Adversarial Neural Network (DANN).  
For this adversarial architecture, a training dataset denoted as \(\{x_{i},y_{i},z_{i}\}_{N}^{i=1}\) is composed of \(x_{i}\), which are the acoustic features, and \(y_{i}\), \(z_{i}\), which are the posteriors of the senones and the type of TV show, respectively.  
Different from the PyChain-based baseline system training, in which the acoustic representation is trained so as to minimize the LF-MMI loss function, in DAT, the acoustic representations are learned adversarially against the secondary task (i.e., TV show classification). In this way, the domain-dependent information is suppressed in the representation, as it is irrelevant for the primary task (i.e., posterior classifier).  
As can be seen in Figure 1, the parameters of our adversarial architecture consist of three parts, \(\theta=\left\{\theta_{x},\theta_{y},\theta_{z}\right\}\), where \(\theta_{x}\) denotes the parameters of the first layers of the TDNN used as the feature extractor and \(\theta_{y}\) and \(\theta_{z}\) denote the parameters of the pdf posteriors and the TV show classifier sub-networks, respectively.  
Between the feature extractor and the TV show classifier, a Gradient Reversal Layer (GRL) [24] was implemented. In the forward propagation, the GRL keeps the input unchanged and reverses the gradient by multiplying it by a negative coefficient during the backpropagation.  
According to [24], for this adversarial training, the objective function for the TV show classifier \(L_{dom}\) is defined as:\[L_{dom}(\theta_{x},\theta_{z})=-\sum_{i=i}^{N}\log P(z_{i}|x_{i};\theta_{x},\theta _{z}) \tag{2}\]  
The DNN acoustic model and the adversarial branch were jointly trained to optimize the following:  
\[\min_{\theta_{x},\theta_{y}}\max_{\theta_{z}}\ L_{MMI}(\theta_{x},\theta_{y})- \lambda L_{dom}(\theta_{x},\theta_{z}), \tag{3}\]  
where \(\lambda\) is a trade-off parameter between the pdf classification loss \(L_{MMI}\), which corresponds to the LF-MMI loss defined in Equation (1), and the domain loss \(L_{dom}\), related to the TV show classification task, which aims to make deep acoustic features invariant to the domain of the TV show characteristics.
## 3 Message Transmission and Notification
### Aws
Initially, it validates parameters by retrieving the _organizationId_ and _checkKey_ from the event headers and comparing them against values stored in DynamoDB. If validation fails, it returns a 400 status code. The function then parses the incoming message and checks if the deviceld is valid and the message status is as expected. If these checks are successful, it retrieves the mapping from _deviceldFrom_ to _deviceldTo_ and stores the message details in DynamoDB. Figure 9 shows the setup for the API with Lambda. Following the message validation and storage, the function interacts with the SWARM API. It first logs in to obtain an authentication token, then sends an acknowledgment for the received message, and finally prepares and transmits a new message to the SWARM API. The message data are encoded in base64 to ensure proper handling. After these operations, the function logs out from the SWARM API to complete the session. Finally, the function sends a notification email using the _send-email_ function to inform about the received message. The email contains details about the message and the target device. The Lambda function concludes by returning a 200 status code, indicating that the message was processed successfully. This setup leverages AWS services for a scalable and serverless solution while integrating with external APIs to manage and process messages effectively. We also implemented functionality to prepare and send new messages to the SWARM. This involves encoding the message data in base64 and ensuring proper authentication by logging in and out of the HIVE account at the beginning and end of the session. Our Lambda function is connected to the API Gateway, which facilitates the transition of notifications and responses. As depicted in Figure 9, the API Gateway is configured to work with SWARM Webhook, utilizing REST API endpoints. In DynamoDB, we maintain a record of previous messages and store email addresses for notifications. Additionally, we use Amazon S3 (Simple Storage Service) for data storage, performance optimization, and backup, ensuring that our data handling is both efficient and secure.
# Tracking the Rhythm: Pansori Rhythm Segmentation and Classification Methods and Datasets
## 1 Introduction
Two novel Passori rhythm datasets are proposed in this work and define a baseline of the proposed dataset, which provide inspiration for subsequent research. We introduce a novel benchmark of Passori rhythm segmentation using the data-driven deep learning method. We trained the HR network [17] and DeepLabV3+ network [18] for segmentation in an end-to-end manner. A modified HR network and a novel GlocalMuseNet are trained for rhythm classification and later used for both segmentation and classification purposes. The GlocalMuseNet outperforms the other classifier for the classification and segmentation of Passori rhythm. The DeepLab V3+ network shows the best performance in the semantic segmentation of rhythm. Overall, semantic segmentation outperformed classification-based segmentation, despite a time-consuming data annotation process that required expert supervision. The major contributions of this work are summarized as follows:  
1. We developed two supervised datasets for rhythm classification and segmentation. 2. We propose GlocalMuseNet, a DNN, which was found to be efficient at capturing the acoustic information and preserving the resolution in feature maps. The network outperforms other DNNs for the classification of Passori rhythm. 3. A novel segmentation model (modified HR) is proposed for the semantic segmentation of music rhythm that is found to be superior to a vanilla HR network. 4. We compared the classification and semantic segmentation-based methods for the structural analysis of music. This paper is structured as follows. Section 2 describes the proposed dataset for rhythm classification and segmentation. The methods in Section 3 introduce the Passori rhythm classification and segmentation datasets, music preprocessing methods, and neural network architectures used in the study. The experiments and results in Section 4 discuss the classification and segmentation results and analyze the performance of the trained DNNs. Finally, we present the conclusions and potential areas for further research in Section 5.
## 3 PL Prediction and DWN-ANN Design
### RT Data Pre-Correction  
Due to the complex composition of farmland types, which includes a mixture of crops, earth, and water, it is challenging to accurately reconstruct them for RT simulations. Consequently, the obtained RT simulation data often exhibit significant discrepancies with the actual results, leading to the lower training accuracy of the PTM in DWN-ANN. In light of this, we designed the RT pre-correction module, which utilizes the measurement data to invert the real conductivity and relative dielectric constant of the ground material during RT simulation, so as to improve the accuracy of the obtained RT simulation data.  
The measured farmland can be further divided into different sub-scenarios, and a preliminary 3D map reconstruction of the farmland is carried out, where the conductivity and relative dielectric constant of the ground material are initially set to default values of the grassland. Then, RT simulations are performed on the farmland scenario to calculate the NLoS path reflection points corresponding to different UAV locations. Furthermore, by combining the actual GPS coordinates of the UAV with the calculated reflection point positions, the measurement data generated by reflections on the ground in different sub-scenarios are divided into different groups. Finally, the measurement data from different sub-scenarios are compared with the corresponding RT data, and the ground material parameters for each sub-scenario are continuously adjusted until the generated RT data match the measurement data.  
After RT pre-correction, more accurate RT simulation data can be obtained in the farmland scenario, improving the accuracy of the PTM in DWN-ANN and further reducing the amount of measurement data required by the OTM.
## 2 The Proposed Methods
### Extension Neural Network (ENN)
#### 2.3.1 ENN Architecture  
Figure 3 is the architecture of ENN, including the input layer and output layer. Neurons in the input layer are the features of the samples, as they connect the weights of the input neurons and output neurons, including the maximum weight value and the minimum weight value. These connections are the \(j^{th}\) input neuron and \(k^{th}\) output neuron, respectively, and are denoted by \(W_{kj}\) and \(U_{kj}\). The weight center \(Z_{kj}\) is calculated according to \(W_{kj}\) and \(U_{kj}\). After adding the concept of extension distance to the weight value in the artificial neural network, the convergence speed of various features trained by the output layer can be strengthened, in order to achieve the purpose of shortening the learning time.
# Analysis of Information Availability for Seismic and Volcanic Monitoring Systems: A Review
## 5 Conclusions
The goal is to find information gaps in frames using sequence file characterization algorithms and to develop data recovery mechanisms by evaluating enabled transmission media and redundant links to monitoring points (stations). Subsequently, the proposed model should be tested in an isolated environment from a real seismic-volcanic monitoring network through secondary nodes and a central synchronization node with the acquisition systems of the primary data center. Finally, data recovery and the improvement in the availability of information from the data centers should be evaluated, as well as the contribution to early warnings in seismic-volcanic monitoring. On the other hand, as a perspective to the proposed information retrieval method results, techniques based on artificial intelligence could be applied to automate these processes in the future. With the analysis of this study's results, it may be possible to develop automatic recovery functions through machine learning algorithms and validation methods that reduce the response time of early warnings to seismic and volcanic events. Conceptualization, S.A.; methodology, S.A., L.U.-A. and C.T.-B. ; investigation, S.A., L.U.-A. and C.T.-B. ; writing--original draft preparation, S.A.; writing--review and editing, S.A., L.U.-A. and C.T.-B. ; supervision, L.U.-A. and C.T.-B. All authors have read and agreed to the published version of the manuscript. The APC was funded by Vicerrectorado de Investigacion, Innnovacion y Vinculacion from Escuela Politecnica Nacional. Not applicable. [PERSON] gratefully acknowledge the support of Instituto Geofisico from Escuela Politecnica Nacional (IGEPN) in this work to improve the seismic and volcanic monitoring systems. [PERSON] thanks the support of PROFAPI project PRO-A8-033. The authors declare no conflict of interest. The following abbreviations are used in this manuscript:  
SDC seismological data center  
EEW earthquake early warning  
WSN wireless sensor network  
SEED Standard for the Exchange of Earthquake Data  
MEED SEED including raw waveform data  
SEG-Y Society of Exploration Geophysicists format  
RTPD REFTEK Protocol (RTP) server  
JSON JavaScript Object Notation  
XMLExtensible Markup Language  
USGS United States Geological Survey  
IRIS Incorporated Research Institutions for Seismology  
IASPEI International Association of Seismology and Physics of the Earth's Interior  
CTBTO Comprehensive Nuclear Test Ban Treaty Organization  
FDSN International Federation of Digital Seismograph Networks  
IG-EPN Instituto Geofisico, Escuela Politecnica Nacional, Ecuador  
SGC Servicio Geologico Colombiano  
IGP Instituto Geofisico de Peru  
CSN Centro Sismologico Nacional, Chile
## 5 Numerical Study
### The CGA in the Inventory Policy Optimization
#### 5.2.1 Small Network (N1)  
Figure 6 illustrates the topology of network N1. The network consisted of two external sources (grey circles) and three controlled nodes (dashed circles). The numbers displayed at the arrows indicate the SF and LTD, respectively. For instance, node 3 acquired 20% of goods from node 1 with a delay of 7 periods. The base holding cost obtained in the initial simulation run for the RSLs chosen according to (13) equaled \(1.03\times 10^{5}\) units. Although a non-sophisticated topology was considered with a granularity \(\delta=1\) unit, the full search approach required exploring a space of over \(10^{9}\) potential solutions. While being time-consuming and inefficient for larger constructs, it formed the baseline for the CGA performance evaluation in the analyzed case of a small-sized network.  
The plot presented in Figure 7 illustrates the relationship between the holding cost and the customer satisfaction level (quantified through the fill rate) for different fitness function coefficients \(\gamma\) and \(\varphi\). As expected, setting a bigger \(\gamma\) allowed one to reduce the holding costs, while a larger \(\varphi\) improved the customer satisfaction level. The simultaneous increase of both coefficients brought the system to the desirable conditions of full customer satisfaction that was obtained with minimal holding costs. Hence, the largest density of points in Figure 7 was for the fill rate between 95-100%. The detailed data regarding the N1 optimization are grouped in Table 8. The presented data confirmed the analytical implications of (16) in that the networked policy indeed allowed one to reduce the costs for a given fill rate objective. Using the graph or the table, the inventory manager can easily adjust the problem parameters according to the cost limitations for a given service level to be attained. The RSL vector will be found automatically using the CGA.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Fitness Function Coefficients**} & \multicolumn{2}{c}{\
\begin{tabular}{c} **Order-Up-To** \\ **(OUT) Policy** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Networked OUT** \\ **(NOUT) Policy** \\ \end{tabular} } \\ \hline \(\gamma\) & \(\varphi\) & **Holding Cost** & **Fill Rate** & **Holding Cost** & **Fill Rate** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 8: Policy performance in network N1.  
Figure 6: Goods distribution network N1.  
The fitness improvement toward the optimal solution established by the full-search method (dashed) for the objective function parametrized by \(\gamma=1\) and \(\varphi=20\) is illustrated in Figure 8. The CGA quickly steered the RSL allocation for either of the investigated policies, where 98% of the reference value was reached within 36 iterations. In the latter phase, the convergence rate was faster for the centralized, i.e., NOUT, policy, which was attributed to a more accurate view of the system structure being used in the goods distribution decisions.
## 3 Results and Discussion
### Model Fitting Accuracy Check  
The predictive capability of the dual principal component model for SSCFC performance metrics was assessed through coefficient of determination (R\({}^{2}\)) analysis (Table 15). The model demonstrates optimal predictive accuracy for flowability (R\({}^{2}\) = 88.8%), revealing that the W/C ratio, SS substitution rate, and ceramsite substitution rate collectively capture the evolution of rheological behavior in cementitious systems. Comparatively, compressive strength models exhibit relatively lower predictive capacity (7d: 65.3%; 14d: 65.0%; 28d: 65.0%), which is attributable to multi-scale mechanisms encompassing hydration product crystallinity and pore network evolution that remain unaccounted for in the current modeling framework.  
The superior model fidelity for flowability originates from its linear dependence on material constituents, whereas the intricate nature of compressive strength necessitates integration of microstructural descriptors (e.g., hydration product morphology) or nonlinear modeling techniques (e.g., machine learning algorithms) to improve predictive accuracy.
## 3 Dynamic Link Establishment of Mega-Constellations
### Constraints on Establishing Dynamic Links
\begin{bmatrix}\alpha_{1,1}(t)&\alpha_{1,2}(t)&\dots&\alpha_{1,1584}(t) \\ \alpha_{2,1}(t)&\alpha_{2,2}(t)&\dots&\alpha_{2,1584}(t)\\ \vdots&\vdots&\ddots&\vdots\\ \alpha_{1584,1}(t)&\alpha_{1584,2}(t)&\dots&\alpha_{1584,1584}(t)\end{bmatrix}  \tag{7}\]  
In order to facilitate the implementation and administration of dynamic link formation, it is essential to document at each discrete time interval the set of satellites with which every individual satellite is capable of establishing a dynamic temporary link. The matrix \(\alpha(t)\) is symmetric and initialized with zeros, signifying no initial connections between satellites. Non-zero elements in \(\alpha(t)\) denote the duration during which corresponding satellites can maintain a connection, subject to specified constraints. If a satellite fails to meet these constraints, the corresponding element in \(\alpha(t)\) is set to zero. Once the entire orbital cycle's matrix is constructed, the optimization process involves determining the best matching satellite for each satellite combination at the current time.
# Comparison of Heuristic Algorithms in Discrete Search and Surveillance Tasks Using Aerial Swarms
## 1 Introduction
### Organization of This Paper  
This paper is organized as follows: In Section 2, the problem is presented, defining the search area and its grid, the agent's dynamics and the measurement of the algorithms' performance. Furthermore, the four different types of scenarios used to compare the algorithms are shown; in Section 3, the proposed algorithm based on a set of behaviors is explained in detail, as well as how it is configured for any scenario. In Section 5, the algorithms are compared measuring their performance for the four scenarios types; also, the communications needed by each algorithm and their adaptation to surveillance missions are analyzed. Finally, in Section 6, the conclusions of this work are depicted, as well as the proposed future developments.  
The main contributions of this papers are: (i) the proposal of a behavioral network made up by six behaviors; (ii) systematical comparison with six search patterns in four different scenario types, considering the wide variations of the parameters that define each scenario; (iii) a performance model for this type of mission.