esa-sceva/satcom-chunk-collection huggingface dataset:
# Early Signatures of Brain Injury in the Preterm Neonatal EEG
## 4 Discussion
### Implications and Future Direction  
This study demonstrates that micro-scale EEG sharp-wave patterns, previously reported by our team in the recordings from preterm fetal sheep after HI, also exist with similar morphology and characteristics in the early hours of post-birth clinical data of preterm neonates. This cohort was selected to include only infants whose recordings started within 6 h of birth. Future studies will examine the evolution of the EEG after 6 h of life. We demonstrated that the morphology of micro-scale sharp waves observed in the initial hours of post-birth neonatal EEG in extremely preterm newborns and post-HI-fetal sheep EEG data are notably consistent (with an average overall correlation of r = 0.94 across all subjects from the two groups). The reader should note that preterm brain injury is multifactorial and commonly involves hypoxia-ischemia (HI) and exposure to infection, alone or in combination [49, 50]. To make matters worse, the preterm infant may be injured in utero, survive, and remain unborn with developing brain injury. However, serial EEG recordings support the concept that EEG abnormalities are most often closely related to the time of birth (-53.8%), with some injury occurring well before birth (19.2%); while postnatal injury occurs, it is less common (23%) [51]. Although most interventions are most effective if they are started in the latent phase, there is now preclinical evidence in preterm fetal sheep that anti-inflammatory therapy delayed until 72 h after HI can attenuate surprisingly severe delayed white matter injury [14].  
One important factor to consider is that the pattern of evolution of sharp-waves after HI can be markedly affected by the severity of neuronal injury within the latent phase, and therefore early and continuous monitoring can play a pivotal role in the early assessment of the injury. This important factor must be carefully considered in future studies, as being consistent with maturation of neural connectivity, EEG characteristics and the associated patterns' morphology (including seizures) are highly influenced by age and severity of the injury [52, 53, 2]. Thus, there is a significant need to design more focused research into the early assessment of EEG patterns (along with other electrophysiological data) to precisely determine timing of the injury in preterm infants with signs of disturbed brain function. Through such investigations, a deeper understanding of the early neurological processes in extremely preterm infants could emerge, thereby potentially paving the way for more targeted interventions and enhanced care strategies in neonatal healthcare.
## 2 Experimental Study on Adhesion Properties of Raw Coal
### Test Plan and Method  
1. Test plan The test plan considered the significant impact of moisture content on the flow and cohesion properties of raw coal. Additionally, the height of coal storage in a bunker, which affects the compaction force on the raw coal, has a significant influence on the adhesion characteristics of raw coal. Finally, the adhesion properties between raw coal and bunker walls are important parameters for studying the mechanism of sticky blockage in coal bunkers. Therefore, the experiment focused on the factors of raw coal moisture content (\(w\)), compaction force (\(p\)), and the two interfaces of coal-coal and coal-wall. Through shear testing of raw coal, the study aimed to understand the variation in adhesion parameters under different influencing factors and shear interfaces. Based on the measured raw coal parameters, its initial moisture content is approximately 3%. To investigate the effect of moisture content on the adhesion properties of the coal material, we prepared raw coal samples with four moisture contents (\(w\) = 0%,  
Figure 2: Raw coal shear test system.  
1%, 3%, 5%) using drying and humidification methods. Three levels of compaction force (\(p\) = 300 kPa, 400 kPa, 500 kPa) were selected, with three sets of normal stresses set for each level of compaction force. In total, 72 sets of experiments were designed for the two interfaces (coal-coal interface and coal-wall interface), with the influencing factors and their levels detailed in Table 2. The correspondence between compaction force and normal stress is shown in Table 3.
2. Test method The procedure for operating the raw coal shear testing device is as follows: 1 Filling: First, the required raw coal mass for the test was determined to be 120 g based on the volume of the shear box. Subsequently, coal particles within each particle size range were weighed according to the particle size distribution ratio, mixed uniformly, and placed into the shear box, ensuring a continuous and uniform filling process to prevent the formation of easily shearable layers within the coal sample due to interrupted filling. 2 Compaction: After filling, place the normal load application plate on top and apply a controlled stress to reach the set compaction force, maintaining stability. 3 Pre-shearing: Use a displacement-controlled loading mode to shear the coal material in the shear box at a rate of 0.24 mm/min. Stop shearing when the shear stress stabilizes. 4 Shearing: Change the normal stress to the predetermined value and resume shearing in displacement-controlled loading mode at a rate of 0.24 mm/min. Stop the test and record the shear stress data when the shear stress reaches its maximum or a stable state.
## 7 Security Analysis
### Protocol Security Analysis
#### 7.1.1 The Analysis Using Security Properties of Set 1  
* Secrecy: This is achieved since rand_a and rand_b are never revealed to the attacker. It also covers confidentiality and privacy of the protocol's data.
* Aliveness: \(UE_{A}\) and \(UE_{B}\) obtain the aliveness of each other during \(PublMsg\) and \(LookUp\) messages exchange. \(UE_{A}\) gets non-injective agreement on rand_a with \(UE_{B}\) and \(UE_{B}\) with \(UE_{A}\) on rand_b.
* Weak Agreement: This is achieved when both entities obtain non-injective agreement on rand_a, rand_b, respectively, as \(UE_{B}\) ends running the protocol with \(UE_{A}\) guaranteed that \(UE_{B}\) has been talking to \(UE_{B}\).
* Non-injective Agreement: \(UE_{A}\) obtains non-injective agreement on \(PK_{UE_{A}}\) and \(PK_{UE_{B}}\) with \(UE_{B}\), respectively, and holds in ProVerif with event(endUEB(A_107, B_108, K, rand_a_109, rand_b_110)) ==> event(beginUEA(A_107, B_108, K, rand_a_109, rand_b_110)).
* Injective Agreement: It is central to the protocol's purpose; injective agreement between \(UE_{A}\) and \(UE_{B}\) on \(PK_{UE_{A}}\) and \(PK_{UE_{B}}\) assures each other that they are known. \(UE_{A}\) to \(UE_{B}\) and \(UE_{B}\) to \(UE_{A}\) holds with inj-event (endUEB(A_116, B_117, K_118, rand_a_119, rand_b_120)) ==> inj-event (beginUEA(A_116, B_117, K_118, rand_a_119, rand_b_120)).
# Recent Progress on Vegetation Remote Sensing Using Spaceborne GNSS-Reflectometry
## 4 Model Simulations and Discussion
### Differences in Coherent and Incoherent Scattering Characteristics  
Figure 4 shows the comparison between coherent scattering (\(\theta_{\rm i}=30^{\circ}\), \(\theta_{\rm s}=30^{\circ}\), \(\varphi_{\rm s}=0^{\circ}\sim 180^{\circ}\)) and noncoherent scattering (\(\theta_{\rm i}=10^{\circ}\), \(\theta_{\rm s}=50^{\circ}\), \(\varphi_{\rm s}=0^{\circ}\sim 180^{\circ}\)). The simulation analysis shows that the energy of coherent scattering is much larger than that of incoherent scattering at various polarizations. At present, when using CYGNSS data to study vegetation parameters, it is basically considered that the energy of the receiver is coherent scattering energy. In the later application process, how to effectively extract  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Stand** & **Soil** & **Trunk** & **Branch** \\ \hline Aspen & 5.99 + 0.99 i & 14.49 + 4.76 i & 10.19 + 3.36 i \\ \hline \hline \end{tabular}
\end{table}
Table 3: The permittivity of soil and canopy layers.  
Figure 3: Specular (**a**) and non-specular (**b**) scattering characteristics of various polarizations.  
incoherent scattering energy and use this part of the information to obtain more vegetation information will be a direction of future development.
# Magnetic Localization Method for Vehicles Based on Transformer
## 3 Methods
### Transformer Model Structure  
Figure 4 shows the proposed model structure, which consists of two parts: an encoder and a decoder. The encoder is responsible for extracting magnetic features from sequences. The decoder aims to predict the current location by exploring the deep temporal position features with the help of the features extracted from the encoder.  
The magnetic field sequence has a size of [\(B\),\(W\),\(3\)], where \(B\) and \(W\) represent the numbers of batches and points contained in the input sequences, respectively, and 3 represents the three axes of the magnetic field. After executing the encoder, deep magnetic features with shapes of [\(B\),\(W\),\(D\)] are extracted, where \(D\) is the dimensionality of the deep features.  
The decoder is calculated \(W\) times. At each step \(i\), the previous positions, along with the deep magnetic features extracted by the encoder, are fed into the decoder. In this way, the final position is recursively predicted. Since we focus only on the current position, which is the last point within the sequence, once \(i=W\), the last position is pushed out as the final result. Next, we describe the encoder and decoder in detail.  
Figure 3: The architecture of our proposed magnetic localization method.
## 1 Introduction
### Decoding CRF Layer  
The Conditional Random Field (CRF) is a model widely applied to sequence labeling tasks [28]. In the field of natural language processing, particularly in Named Entity Recognition (NER) tasks, the introduction of a CRF layer can significantly enhance the model's performance. This improvement is attributed to CRF's ability to incorporate additional constraints in sequence prediction, aiding the model in more accurately capturing the dependencies among labels. By incorporating a CRF layer on top of the BiGRU-Attention model, a robust framework for Named Entity Recognition can be established [29]. While the BiGRU-Attention model effectively addresses the problem of long-distance dependencies by focusing on important information through the attention mechanism, it does not inherently enforce constraints on the rationality among labels. At this juncture, the role of the CRF layer becomes particularly critical: it not only considers the best prediction for individual labels but also optimizes the joint probability of the entire label sequence, ensuring that the final predicted sequence is globally optimal.  
For example, in the recognition of named entities within the Chinese marine coral reef ecosystem, the CRF can utilize constraints to ensure the correct identification of entityboundaries. It can force the model to follow specific annotation rules, such as a \"B-\" tag for the beginning of an entity not being directly followed by an \"I-\" tag for the inside of an unrelated entity. In this manner, the CRF layer helps the model correctly predict the relationships among adjacent tags, avoiding label sequences that do not conform to actual language patterns.  
Positioning the CRF layer as the output layer of the BiGRU-Attention model [30] implies that in determining the label for each word, the model considers not only the features and contextual information of the word itself but also the constraints of its adjacent labels. This design significantly improves the accuracy and robustness of the model in complex text data, especially in the task of Named Entity Recognition within Chinese marine coral reef ecosystem texts. The core function of CRF is to model the dependencies among labels through a transition score matrix, thereby outputting a globally optimal and reasonable label sequence. Receives an observation sequence \(\{X_{1},X_{2},\dots,X_{n}\}\), and outputs a state sequence \(\{Y_{1},Y_{2},\dots,Y_{n}\}\) through probability calculation. The calculation method involves computing the sentence label score based on the state score (emission score) outputted by the BiGRU-Attention model and the transition score. The calculation Formula (8) is as follows:  
\[\text{score}(x,y)=\sum_{i=1}^{n}P_{i,y_{i}}+\sum_{i=1}^{n}A_{y_{i},y_{i+1}} \tag{8}\]  
\(P_{i,y_{i}}\) represents the score of predicting the \(i\)th character as the \(y_{i}\)th label; \(A_{y_{i},y_{i+1}}\) represents the score of transitioning from the \(y_{i}\)th label to the \(y_{i+1}\)th label.
## 3 Results and Discussion
### Effect of Winding Angle

Table 5: Test data of NOL ring samples with varying tension. The volume content of NOL ring fibers with a winding angle of 0 \({}^{\circ}\) was calculated according to Formulas (5) and (6) [26]. The NOL ring resin mass content is known to be 19.89%. \[\frac{W_{2}}{W_{1}+W_{2}}\times 100\%=19.89\%, \tag{5}\]  
\[V_{1}=\frac{\frac{W_{1}}{\rho_{1}}}{\frac{W_{1}}{\rho_{1}}+\frac{W_{2}}{\rho_{2 }}}\times 100\%=71\%, \tag{6}\]  
where \(W_{1}\) and \(W_{2}\) are fiber mass and resin mass, respectively, and \(\rho_{1}\) and \(\rho_{2}\) are fiber density and resin density, respectively. \(\rho_{1}=1.8\) g/cm\({}^{3}\), \(\rho_{2}=1.1\) g/cm\({}^{3}\). The fiber tensile strength after winding is calculated as follows:  
\[\sigma_{1}=\frac{\sigma_{t}}{V_{1}}=3484\,\text{Mpa}, \tag{7}\]  
where \(\sigma_{1}\) is the tensile strength of fiber after winding and \(\sigma_{t}\) is the tensile failure strength of NOL. The tensile strength of a single bundle of T700-grade carbon fibers is 4900 MPa. Ignoring the contribution of resin strength to the strength of the composite, the tensile strength of a purely circumferentially wound NOL ring is reduced by 30% during the process compared with a single bundle of fibers. By calculation, the tensile strength of NOL rings with 45 and 30\({}^{\circ}\) winding angles is reduced by 20-25% compared with that of rings with pure circular winding. Theoretically, the tangential stress equivalent values of NOL rings with 45 and 30\({}^{\circ}\) winding angles are 90 and 95.5% of pure annular winding, respectively, but the measured values are smaller than the equivalent values. This is because, in the process of preparing NOL ring samples, the fibers in the spiral direction will be cut off, the tension exerted on the fibers will be relaxed, and the fibers will shrink in the composite material. The interfacial integrity between the fibers and the matrix is disrupted. It affects the uniformity of force transfer between the spiral wound layer fibers and the substrate, the interlayer bonding, and the overall mechanical properties of the material. The mechanical properties of 45\({}^{\circ}\)-wound NOL rings and 30\({}^{\circ}\)-wound NOL rings are reduced by 17.76 and 16.24%, respectively. A similar law can be obtained from the interlaminar shear strength.
# An Efficient Opportunistic Routing Based on Prediction for Nautical Wireless Ad Hoc Networks
## 3 The Proposed Algorithm
### The Description of WRSP  
#### 3.2.1 Transmission Quality Prediction  
In dynamic nautical wireless ad hoc networks, the distances between communication nodes are constantly changing as the nodes move. According to the marine wireless propagation model introduced in [14], the signal strength of the receiver is also changing. Furthermore, ambient noise also makes the received signal strength change frequently, even if the distance between communication nodes is the same. The signal power at the receiver can be calculated by:  
\[Pr(d)=P_{tr}-P_{pl}(d_{0})-10\phi log_{10}(\frac{d}{d_{0}})-\gamma \tag{2}\]  
where \(P_{tr}\) is the transmitting power, \(P_{pl}(d_{0})\) denotes the average path loss at a reference distance \(d_{0}\), \(\phi\) is the path loss constant, and \(\gamma\) is the Gaussian white noise, with \(\gamma\sim(\mu,\delta^{2})\).  
The received signal power has a direct influence on the packet reception ratio (PRR) for the receiver. We utilized the PRR to evaluate the transmission quality between two networking nodes. The PRR given by a theoretical model [15] is:  
\[P_{prr}=(1-\frac{1}{2}exp^{-\frac{Pr(d)-P_{R}}{2}})^{8C_{f}} \tag{3}\]  
where \(Pr(d)\) denotes the signal power at the receiver for a distance \(d\), and \(P_{n}\) and \(C_{f}\) are the noise power and carrier frequency, respectively.
## 2 Material and Methods
### Characterization of Fragment Size and Shape Distributions
#### 2.4.2 Fractal Model  
Fractal theory can quantitatively describe any complex morphology, such as irregular shape, fracture characteristics and distribution features of fragments [29; 41; 42]. Consequently, the fractal method has been used to investigate the breakage characteristics of coal and rock fragmentation, such as rock burst, blasting, impact loading and uniaxial compression [43; 44; 45; 46; 47; 48].  
The fractal relation of the particle size distribution is generally defined using the relationship between the cumulative numbers of the fragment particles and the grain size in a statistically self-similar system [49]. It can be described using the following equation:  
\[N(d>d_{i})={k{d_{i}}^{-D}} \tag{5}\]  
where \(N(d>d_{i})\) is the cumulative number of fragments larger than a certain size \(d_{i}\); \(k\) is the number of elements at a unit-length scale; and \(D\) is the fractal dimension. The higher the fractal dimension is, the higher the breakage degree of the coal.  
However, it is very difficult to estimate the quantities of all fragments and the sizes of each particle. To avoid this problem, [PERSON] [50] put forward a fractal model of particle size distribution based on the relationship between the cumulative mass and particle size of fragments:  
\[M(d<d_{i})/M_{T}={(d_{i}/d_{m})}^{3-D} \tag{6}\]  
Figure 6: Fitting curves of the cumulative probability of coal fragments based on the GEV distribution function and Weibull distribution function (impact velocity = 253 m/s). (**a**) The fitting curves of all the particle sizes, (**b**) and (**c**) is the partial magnification of fitting curves for the large and small particle sizes, respectively.  
where \(M(d<d_{i})\) is the cumulative mass of fragments smaller than \(d_{i}\); \(M_{T}\) is the total mass of coal fragments; and \(d_{m}\) is the particle size of the largest fragment.  
To better obtain the fractal dimension of coal fragments, a natural logarithm transformation for Equation (6) is made as follows:  
\[\ln[M(d<d_{i})/M_{T}]=(3-D)\ln(d_{i}/d_{m}) \tag{7}\]  
The fitting line can be displayed in the coordinate system of \(\ln[M(d<d_{i})/M_{T}]\sim\ln(d_{i}/d_{m})\), according to an experimental statistical fragment analysis. The value of (\(3-D\)) in Equation (7) represents the slope of the fitting line, and then, the fractal dimension D can be calculated.
# Fed-Hetero: A Self-Evaluating Federated Learning Framework for Data Heterogeneity
## 3 Notations and Preliminaries
### Metrics to Measure Data Heterogeneity
#### 3.3.2 Jensen-Shannon Divergence (JSD)  
Jensen-Shannon Divergence (JSD) [24; 25] is a statistical measure used to compare similarity between two probability distributions. This helps to find how similar class distributions are between a pair of distributions. The Jensen-Shannon Divergence between two probability distributions \(S\) and \(R\) is defined as follows:  
\[\text{JSD}(S\ \|\ R)=\frac{1}{2}KL_{\text{D}}(S\ \|\ M)+\frac{1}{2}KL_{\text{D}}(R\ \|\ M) \tag{5}\]  
where:  
* \(M=\frac{1}{2}(S+R)\) is the average of the two distributions.
* \(KL_{D}(S||M)\) is the Kullback-Leibler divergence from \(S\) to \(M\): \[KL_{D}(S||M)=\sum_{x}S(x)\log\biggl{(}\frac{S(x)}{M(x)}\biggr{)}\] (6)
* \(KL_{\text{D}}(R||M)\) is the Kullback-Leibler divergence from \(R\) to \(M\): \[KL_{D}(R||M)=\sum_{x}R(x)\log\biggl{(}\frac{R(x)}{M(x)}\biggr{)}\] (7)  
JSD range values are displayed in Table 2.  
A JSD value near zero indicates similar distributions, and as the value moves toward 1, it indicates dissimilar distributions. The choice of the upper-limit threshold is specific to the dataset used in the study. The details of the threshold are given in the Experiment Setup section in Section 5.
# Parallel-Structure Deep Learning for Prediction of Remaining Time of Process Instances
## 3 Proposed Method
### Data Preprocessing  
Data preprocessing is a step that must be fulfilled before the data are ready to be used by the model during training. As shown in Figure 1, our proposed method required two types of data as input: the data features forwarded to the Embedding layer, which we call a set of prefixes (SOP), and chart image data, which will be forwarded to the CNN part.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Author** & **Method** & **Objective** \\ \hline [PERSON] et al. (2014) [2] & Decision Trees & \begin{tabular}{c} Predicting the Linear \\ Temporal Logic rules \\ \end{tabular} \\ \hline [PERSON] et al. (2015) [8] & HMM and Random Forest & Predicting the outcome \\ \hline [PERSON] et al. (2015) [9] & Queueing theory & Predicting the delays \\ \hline [PERSON] et al. (2016) & \begin{tabular}{c} Clustering and \\ Hyperparameters \\ Optimization \\ \end{tabular} & \begin{tabular}{c} Predicting the Linear \\ Temporal Logic rules \\ \end{tabular} \\ \hline [PERSON], [PERSON] and & \begin{tabular}{c} Deep learning, LSTM \\ \end{tabular} & Predicting the next event \\ \hline [PERSON] et al. (2017) [13] & Deep learning, LSTM & \begin{tabular}{c} Predicting the remaining time, \\ the outcome, and the \\ next event \\ \end{tabular} \\ \hline [PERSON] et al. (2017) [15] & \
\begin{tabular}{c} Deep learning, \\ Data-Aware LSTM \\ \end{tabular} & Predicting the remaining time \\ \hline [PERSON] et al. (2017) [16] & Feature Engineering & Predicting the remaining time \\ \hline [PERSON] &
\begin{tabular}{c} Optimizing the \\ hyperparameters \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._Both inputs were originally generated from event logs, as shown in Figure 2. The details of each data generation will be discussed in the following subsections.
# Chaotic Communication System with Symmetry-Based Modulation
###### Abstract  
Communication systems based on chaotic synchronization are gaining interest in the area of secure and covert data transmission. In this paper, a novel digital communication technique based on a coherent chaotic data transmission approach is proposed. In general, this technique resembles the well-known approach based on the modulation of nonlinearity parameters. The key idea of this study is to modulate a signal by varying not the system parameter but the symmetry coefficient in discrete chaotic models obtained by the special numerical integration method. For this purpose, the self-adjoint semi-implicit integration method of order 2 is used to obtain discrete master and slave models of the considered chaotic oscillator. The experimental results explicitly show that, like during parameter modulation, transmitting and receiving oscillators may completely synchronize only if the symmetry coefficients are equal in both systems. The architecture of the communication system based on the proposed modulation is presented. The practical applicability of the approach is confirmed by transmitting a test binary sequence between the transmitter and receiver models and preliminary benchmarking of the obtained communication system. Since the symmetry coefficient modulation does not significantly impact the chaotic behavior of the transmitting digital system, its better suitability for covert messaging was experimentally confirmed by comparing it with the parameter modulation technique.  
chaotic communication system; symmetric integration; variable symmetry; covert communication 2021113698. [[https://doi.org/10.3390/app11083698](https://doi.org/10.3390/app11083698)]([https://doi.org/10.3390/app11083698](https://doi.org/10.3390/app11083698))