esa-sceva/satcom-chunk-collection huggingface dataset:
# A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
## 4 Mathematical Model  
Two mathematical models for assigning time slot and energy consumption are discussed below. This section explains the time slot that is required by a source node for forwarding the data packets to sink node. At first iteration, initially, \(t_{0}\,s\) is taken as a specific time slot. Source node sends hello packet and waits for \(t_{0}\,s\) for taking acknowledgment from other nodes. Because multiple receivers reply in that time; therefore, average time is calculated. This average time is used for the time slot allocation of next iteration.  
\[t_{1}=\frac{\sum NRT}{NN} \tag{24}\]  
where, \(\sum NRT\) represents the summation of all nodes'reply time and \(NN\) is number of nodes that are replied. Similarly in the next iteration, multiple nodes become source nodes and create a reliablelink. In Equations (25)-(28), there are two summation symbols used: the first symbol is used to add all receivers' reply time while in the second symbol limit is assigned from 1 to \(n\), where \(n\) represents the total number of source nodes found in the previous iteration and outer summation sign acting as a loop.  
\[t_{2}=\sum_{i=1}^{n}\frac{\sum NRT_{i}}{NN_{i}} \tag{25}\]  
Average time required by source nodes for \(n\) iterations is calculated as:  
\[t_{n}=\sum_{i=1}^{n}\frac{\sum NRT_{i}}{NN_{i}} \tag{26}\]  
Summation of average time for \(n\) iterations is written as:  
\[t_{t}=t_{1}+t_{2}+...t_{n} \tag{27}\]  
All corresponding values are placed inside the Equation (28)  
\[t_{t}=\frac{\sum NRT}{NN}+\sum_{i=1}^{n}\frac{\sum NRT_{i}}{NN_{i}}+...\sum_{i =1}^{n}\frac{\sum NRT_{i}}{NN_{i}} \tag{28}\]  
\(m\) represents the number of nodes in network.  
\[t_{t}=\frac{\sum NRT}{NN}+\sum_{k=1}^{m}\left[\sum_{i=1}^{n}\frac{\sum NRT_{i }}{NN_{i}}\right] \tag{29}\]  
The results of \(t_{1}\) can also be calculated through next term, which is the summation of average node reply time. Hence, the average time that is required by a source node to reach at any sink node is calculated through Equation (30).  
\[t_{t}=\sum_{k=1}^{m}\left[\sum_{i=1}^{n}\frac{\sum NRT_{i}}{NN_{i}}\right] \tag{30}\]
# Multi-Granularity Semantic Collaborative Reasoning Network for Visual Dialog
## 5 Experimental Results and Analysis
### Error Case Analysis  
In this section, we illustrated some of the zero-scoring examples in terms of the R@10 metric. These can be categorized into three groups based on the reasons for the errors. The first is common-sense reasoning. That is, when faced with the question, \"What material is the table?\" or \"How tall is the man?\" (Figure 6a,b), common-sense reasoning is needed. However, our model was confused even after accurately locating the target visual objects. The second was related to the image not containing the target visual objects. Because \"traffic lights\" did not exist in the corresponding image (Figure 6c), our model picked the best candidate, which led to incorrect answers. The third type relates to keyword matching. When modeling the intersections between the question and dialog history, our model memorized the keywords from the dialog history (e.g., \"eyes\"), thus ranking answers with keywords at the top. For example, \"No, his eyes are open\" and \"No, it's eyes are open\" (Figure 6d). However, these answers are sometimes inaccurate.  
Figure 5: Example results from the validation set of VisDial v1.0 using the SgSR, TgSR, and MGSCRN models. The scores of the top 10 ranked answers are shown in brackets indicating the degree of relevance of the candidate answer options (out of 100) to the question. Here, \(R_{GT}\) denotes the rank of GT answer, NDCG is a score ranking of all relevant answers for the question, and \(N_{rel}\) is the number of candidate answer options with non-zero relevance.
## 1 Introduction  
The capacity to accurately predict channel state information (CSI) is a key enabler of proactive resource allocation strategies, which are central to many visions for efficient and low-latency communications in 6G and beyond (see, e.g., [1]). The problem of channel prediction is relatively straightforward in the presence of known channel statistics. In fact, under the common assumption that multi-antenna frequency-selective channels follow stationary complex Gaussian processes, optimal channel predictors can be obtained via linear minimum mean squared error (LMMSE) estimators such as the Wiener filter [2]. However, in practice, the channel statistics are not known, and predictors need to be optimized based on training data obtained through the transmission of pilot signals [3, 4, 5, 6, 7, 8]. The problem addressed by this paper concerns the design of data-efficient channel predictors for multi-antenna frequency-selective channels.
# Clustering Moving Object Trajectories: Integration in CROSS-CPP Analytic Toolbox
## 4 Experiments  
### DataSet Description  
The experiments presented in this section were performed using the Kaggle dataset of taxi trajectorie [[https://www.kaggle.com/crailtap/taxi-trajectory](https://www.kaggle.com/crailtap/taxi-trajectory)]([https://www.kaggle.com/crailtap/taxi-trajectory](https://www.kaggle.com/crailtap/taxi-trajectory)), (accessed date 17 April 2021). both It contains trajectory information from 1 July 2013 to 30 June 2014 for 442 taxis running in the city of Porto, in Portugal. In total, 1,704,757 trajectories are available.  
Each register contains 9 features, described as follows:  
* TRIP_ID: (string) It contains an unique identifier for each trip.
* A: if the trip was dispatched from the central.
- B: If the trip was demanded on a specific stand.
- C: Otherwise; i.e., a trip was demanded on a random street.
* ORIGINCALL (integer)
* TAXI_ID (integer):
* TIMESTAMP (integer) Unix Timestamp (in seconds). It identifies the trip's start;
* DAYTYPE (char)
* MISSING_DATA (Boolean)
* POLYLINE (String): It contains a list of GPS coordinates (i.e., WGS84 format) mapped as a string. The beginning and the end of the string are identified with brackets (i.e., [ and ], respectively). Each pair of coordinates is also identified by the same brackets as [LONGITUDE, LATITUDE]. This list contains one pair of coordinates for each 15 s of trip. The last list item corresponds to the trip's destination, while the first one represents its start. Some trips have missing data points, in which the MISSING_DATA column is TRUE.  
For this paper, the TIMESTAMP and POLYLINE features were used.
## 6 Conclusions  
Deep learning-based identification of coseismic deformation using InSAR has emerged as a pivotal research focus in earthquake monitoring and surface deformation analysis. However, existing studies are limited by dataset size, sample diversity, and data processing efficiency, which constrain the generalizability and real-world applicability of deep learning models. To overcome these challenges, this study presents an automated approach for constructing InSAR-based coseismic datasets specifically designed for deep learning applications. A high-quality, manually labeled dataset was developed, encompassing 62 moderate-to-large earthquakes that occurred globally between 2015 and 2024. From 353 raw interferograms, 1773 standardized coseismic interferograms were generated through a preprocessing pipeline and subsequently expanded to 14,000 samples using data augmentation techniques. The resulting dataset demonstrates substantial diversity in earthquake magnitude, focal depth, fault mechanism, and temporal distribution, thereby ensuring broad representativeness. Compared to existing datasets, it provides greater volume, richer deformation features, and improved internal consistency. The dataset construction methodology proposed in this study is systematic, reproducible, and scalable, enabling continuous expansion as new InSAR coseismic observations become available. This work delivers a large-scale, diverse, and high-quality dataset that offers a robust foundation for advancing deep learning research in InSAR-based coseismic deformation detection and analysis.  
Conceptualization, X.L., Z.W. and Y.Z.; methodology, X.L., Z.W. and Y.Z.; resources, X.L., Z.W., Y.Z. and X.S.; investigation, X.L., Y.Z. and Z.L.; data curation, X.L., Z.W., Y.Z., X.S. and Z.L.; writing--original draft preparation, X.L., Z.W., Y.Z., X.S. and Z.L.; writing--review and editing, Y.Z. and Z.W.; visualization, X.L., Y.Z. and Z.L.; supervision, Y.Z., Z.W. and X.S.; project administration, Y.Z. and Z.W.; funding acquisition, Z.W. 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 (Nos. 42476063, 42176068).  
The dataset generated in this study has been publicly released through Zenodo. The full InSAR dataset, consisting of 14,000 Sentinel-I based image patches, can be accessed at [[https://zenodo.org/records/15382562](https://zenodo.org/records/15382562)]([https://zenodo.org/records/15382562](https://zenodo.org/records/15382562)), accessed on 11 April 2025.  
The Sentinel-I SAR data were freely provided by COMET LiCSAR. LiCSAR contains modified Copernicus Sentinel data 2015-2024 analyzed by the Centre for the Observationand Modelling of Earthquakes, Volcanoes and Tectonics (COMET). LiCSAR uses JASMIN, the UK's collaborative data analysis environment ([[http://jasmin.ac.uk](http://jasmin.ac.uk)]([http://jasmin.ac.uk](http://jasmin.ac.uk))). Some maps were generated using the General Map Tool (GMT) version 6 [48].  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# A Survey of Structure of Atmospheric Turbulence in Atmosphere and Related Turbulent Effects
###### Abstract  
The Earth's atmosphere is the living environment in which we live and cannot escape. Atmospheric turbulence is a typical random inhomogeneous medium, which causes random fluctuations of both the amplitude and phase of optical wave propagating through it. Currently, it is widely accepted that there exists two kinds of turbulence in the aerospace: one is Kolmogorov turbulence, and the other is non-Kolmogorov turbulence, which have been confirmed by both increasing experimental evidence and theoretical investigations. The results of atmospheric measurements have shown that the structure of atmospheric turbulence in the Earth's atmosphere is composed of Kolmogorov turbulence at lower levels and non-Kolmogorov turbulence at higher levels. Since the time of [PERSON], people began to study optical wave propagation in atmospheric turbulence. In the early stage, optical wave propagation in Kolmogorov atmospheric turbulence was mainly studied and then optical wave propagation in non-Kolmogorov atmospheric turbulence was also studied. After more than half a century of efforts, the study of optical wave propagation in atmospheric turbulence has made great progress, and the theoretical results are also used to guide practical applications. On this basis, we summarize the development status and latest progress of propagation theory in atmospheric turbulence, mainly including propagation theory in conventional Kolmogorov turbulence and one in non-Kolmogorov atmospheric turbulence. In addition, the combined influence of Kolmogorov and non-Kolmogorov turbulence in Earth's atmosphere on optical wave propagation is also summarized. This timely summary is very necessary and is of great significance for various applications and development in the aerospace field, where the Earth's atmosphere is one part of many links.  
atmospheric turbulence; non-Kolmogorov turbulence; Kolmogorov turbulence; intensity scintillation; angle of arrival fluctuation; beam wander; beam spread; temporal power spectrum Article  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON] Survey of Structure of Atmospheric Turbulence in Atmosphere and Related Turbulent Effects  
15 October 2021  
26 November 2021  
1
## 2 Method Introduction and Data Processing
### Standardized Processing
#### 2.3.2 Min-Max Standardization  
The purpose of deviation standardization is to make the value range of the new data [0,1]. The calculation formula of this method is shown in Equation (3).  
\[x_{j}^{\prime}=\frac{x_{j}-min}{max-min} \tag{3}\]  
In the formula, \(x_{j}\) for \(j=1,2,\ldots,m\) represents the j-th sample data, \(x_{j}^{\prime}\) represents the new data of the j-th sample data after normalization, \(j=1,2,\ldots,m\), \(m\) represents the amount of sample data, min represents the minimum value in the sample data, and max represents the maximum value in the sample data.
## 1 Introduction
Using this method, the adaptive optical system has a strong ability to adapt to the environment and enhance the correction accuracy, which increased its application potential, but this method did not consider the impact of the network model's size on the correction accuracy. Therefore, this study focused on the automatic selection of a suitably size network to improve the accuracy of the neural network model. [PERSON] [23] investigated the effects of the size of a convolutional neural network on the prediction accuracy of the Zernike coefficient prediction accuracy. When the convolutional neural network model was too small, the network was poorly fitted; when the model was too large, the performance degraded due to over-fitting and gradient disappearance. The manual design of high-precision convolutional neural networks requires professional experience to adjust the network's hyperparameters multiple times [24; 25]. Therefore, this method required a great deal of computational and human resources to increase the network's accuracy and could not guarantee improvements in the network's accuracy and efficiency [26; 27; 28]. To resolve the above problems, an adaptive optical closed-loop control algorithm, based on the hyperparametric optimization of convolutional neural networks, was proposed. In this paper, a set of hyperparameter optimization frameworks was designed that prioritized the automation of the model design and its accuracy, resulting in a model (Auto-U-net) with higher prediction accuracy. In addition, the model (Auto-U-net) directly predicted and controlled the correction voltage values without calculating additional data calculations. The manually designed convolutional neural networks, such as ResNet and DenseNet, were built as the convolutional neural network contrast groups. The hyperparameter optimization strategy was first applied to adaptive optics. Subsequently, a set of appropriate hyperparameter convolutional neural networks was automatically selected. Moreover, the convolutional neural network model (U-net) with the optimal accuracy and minimal parameters was iterated in order to improve the accuracy and efficiency of adaptive optics systems.
## 3 Numerical Simulations Using the ANN Flow Law  
Now that the flow stress models have been defined, trained and the results analyzed in terms of their relative performance in reproducing the experimental behavior recorded during compression tests on Gleeble, we will now numerically implement these models in Abaqus as a user routine in Fortran in order to perform numerical simulations. Following training, the optimized internal parameters of the ANNs are saved in HDF5 [37] files. Subsequently, a Python program is responsible for reading these files and generating the Fortran 77 subroutine for Abaqus.  
The implementation of a user flow law in Abaqus FE code, specifically using the Explicit version, involves programming the computation of the stress tensor \(\mathbf{\sigma}_{1}\) based on the stress tensor at the beginning of the increment \(\mathbf{\sigma}_{0}\) and the strain increment \(\Delta\varepsilon\). A predictor/corrector algorithm, such as the radial-return integration scheme [7], is typically employed. For detailed implementations, [PERSON] et al. [12] discusses the Safe Newton integration scheme, and [PERSON] et al. [13] focuses on an application related to the Arrhenius flow law. During the corrector phase, the flow stress \(\sigma\) must be evaluated at the current integration point as a function of \(\varepsilon\), \(\dot{\varepsilon}\), and \(T\). This process involves solving a non-linear equation defining the plastic corrector expression and computing three derivatives of the flow stress: \(\partial\sigma/\partial\varepsilon\), \(\partial\sigma/\partial\dot{\varepsilon}\), and \(\partial\sigma/\partial T\). Typically, the subroutine VUHARD in the Abaqus explicit is responsible for computing these quantities, and its implementation depends on the structure and activation functions of the ANN.
## 4 Discussion
Approximate entropy (ApEn) is a measure of system complexity initially applied to cardiovascular physiological data. It measures how similar two sequences of a specific length in the next point of a TS are. A new entropy algorithm was developed to reduce the bias of ApEn, i.e., counting self-matched sequences. Sample entropy (SampEn) does not count self-matches and has more relative consistency than ApEn [17]. Another technique, multiscale entropy (MSE), proposed to use SampEn to calculate entropy in different time scales. It can be applied to investigate physiological deficits at each time scale that can be related to postural fluctuations [48]. The aforementioned entropy families have been used to measure fluctuations in sequences of a TS but are not able to infer causality between these sequences of points or two different sources of TS. Based on Information Theory, TE is used to measure dependency between two TS and is capable to detect effective connectivity based on interaction delays between the signals [15, 20]. Additionally, our methodology with GLMM was able to show elements in favor, for the first time, the dynamics of the TE between the two Fz components of the platforms with a causal relationship from the right to the left side. The time (duration and repetition) of stimulation also shows an effect over the TE, showing that our specific protocol of HD-tDCS over the TP is able to promote changes in WBA. Future studies are necessary to explore random effects related to personal characteristics to promote a broader knowledge involving causality on dynamic entropy data. Moreover, each person shows a particular baseline towards the central (spatial) force-displacement, and a more complex statistical structure can be accommodated, such as spatial-domain in this data-driven modeling [49]. As a natural evolution of this study, we shall explore spatial dynamic responses, which will shed light on the postural control task, and stimulation timing. This can be added in further statistical modeling and can be incorporated as latent effect information. This can be explained by the upright quiet standing postural control being an unstable condition involving the body counteracting the force of gravity. In order to keep the center of mass within the base of support, the individuals depend on somatosensory information that comes from body segments organization, muscular activity, body orientation, and vertical perception. Therefore, postural control is a complex system being affected differently by each individual and population under investigation [50].
# Enabling Role-Based Orchestration for Cloud Applications
## 3 TOSCA Model-Based Approach to Cloud Application Orchestration
### Architectural Design of a Topology Model-Based Orchestration System
#### 3.1.1 TOSCA Modeling
For example, the _dcc.operational.autoScaling_ node type in Listing 4 is converted to the OAM trait instance _AutoScaling_. Its properties' value will be assigned to _AutoScaling spec_ fields with the same name. The value of the property _maxReplicaCount_ will be assigned to the field _spec.maxReplicaCount_, and the value of the property _promQuery_ will be assigned to the field _spec.promQuery_. ```
1#TOSCAtopologytemplate
2 topology_template:
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4 node_template:
5...
6 auto-scaling:
7 type:dcc.operational.autoScaling
8 properties:
9 maxReplicaCount: 5
10 minReplicaCount: 1
11 promQuery:sum(rate(http_request[2m]))
12 promThreshold: 3
13 requirements:
14-attach-to:wordpress-comp
15 relationship:attachesTo
16...
17
18
19#OAMapplicationconfiguration
20 apiVersion:core.oam.dev/vialpha2
21 kind:ApplicationConfiguration
22 metadata:
23 name:WordPress-app
24 spec:
25 components:
26...
27 traits:
28-trait:
29 apiVersion:extend.dcc/vialpha1
30 kind:AutoScaling
31 spec:
32 maxReplicaCount: 5
33 minReplicaCount: 1
34 promQuery:sum(rate(http_request[2m]))
35 promThreshold: 3
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For two nodes connected by an _AttachesTo_ Relationship type in TOSCA, their OAM entities will be organized into an Application Configuration. Specifically, a Trait mapped from an operational capability type is made from part of a Component mapped from a workload capability node. Figure 7 depicts this kind of conversion from TOSCA Template Topology containing the _AttachesTo_ relationship to OAM Application Configuration. Another important relationship is a dependency between two workloads. As illustrated in our motivational scenario, the connection between WordPress server and MongoDB database is modeled as such a dependency relationship. Different from relationships between a workload and an operational capability, dependency between workloads has no counterpart concept in OAM specification. To overcome this deficiency, we leverage a data transfer mechanism in OAM Kubernetes runtime that permits data passing among components within an Application Configuration. The idea is to create a custom relationship type _workloads_connection_ with two end points. One is the field path to which the target node (producer) writes data as output, and the second is the field path of field from which the source node (consumer) reads input data. The conversion engine sets the data input and output in the OAM components converted from two workload Node templates connected by the _workloads_connection_ relationship template. Figure 8 illustrates this conversion from TOSCA Topology Template containing _workloads_connection_ relationship to OAM Application Configuration.
# Upscaling Customer Access Network Using Spectrum Conversion-Slicing-Duplication Technique
## 1 Introduction
### Hybrid System--10g-Tdm-Ocdma-Pon  
The proposed 10G-TDM-OCDMA-PON hybrid system is based on the PON code interleaving TDM and 2D-MD (P/W) bits for the OCDMA stage. A downstream FTTH network of 16 houses and 4 high-speed TDM channels is simulated. In the OLT, the time division multiplexer for channel N has N number of users, where each time slot will be assigned to a particular ONU, the same multiplexer will be used with all TDM groups, and the only difference between them is the OCDMA encoder, where each TDM group has a different OCDMA code with different uses of wavelength and polarization. The TDM signal will modulate through a light wave encoded by the MZM, and then the modulated signal is grouped together by a power combine. On the receiver side, a power divider and a polarization reducer have been used to separate the polarized living signal. FBG is used as a diffusion compensator and OCDMA decoder to pass only the light signal assigned to a specific OTDMA group. A power divider is used to split the light signal for all the houses  
Figure 3: Hybrid system—OCDMA.  
and then an optical time division demultiplexer is placed inside each ONU to recover the information sent to this ONU [16].