esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Reinforcement Learning-Based Joint Scheduling of 5G and TSN in Industrial Networks
## 2 Related Work
### 5G-TSN Integration Architecture  
In order to support TSN protocols smoothly, several function entities are defined in 5G systems in both control and data planes, and the whole 5 GS is exposed as a TSN bridge, which is indicated in Figure 2. In this article, we delve into a 5G-TSN joint scheduling algorithm under this 5G TSN bridge architecture.  
Centralized User Configuration (CUC) and Centralized Network Configuration (CNC) are two key elements in the TSN domain for service management and network controlling. CUC is responsible for service requirement collection and TSN capability discovery for end stations, while CNC acts as a network controller, which takes responsibility for network topology discovery, transmission path selection and GCL orchestration.  
In order to keep signaling interworking with CNC, a new TSN Application Function (TSN-AF) is proposed in the 5G control plane. TSN-AF follows IEEE 802.1 Qcc, reports information of the 5G TSN bridge to the CNC; meanwhile, it receives the service information (period, flow direction, packet, etc.) and QoS requirements of time-triggered applications from CNC. Then TSN-AF delivers QoS-related information received from the TSN domain to the Policy Control Function (PCF) in the 5G core network to make appropriate QoS profiles in 5 GSs.  
In the data plane, a Network-side TSN Translator (NW-TT) and a Device-side TSN Translator (DS-TT) act as 5G-TSN gateways to execute protocol translation and data forwarding between TSN and 5G domains. NW-TT and DS-TT monitor the time-triggered application status and report to the TSN-AF; meanwhile, they also receive configuration information from the CNC delivered through TSN-AF. In NW-TT and DS-TT, TAS is adopted for flow management and resource scheduling. As a result, data forwarding from NW-TT or DS-TT is based on a precise time, which can eliminate large delay jitter for time-triggered applications.
# Constrained Optimization of FPGA Design for Spaceborne InSAR Processing
## 4 System Analysis under Double Constraints
### Design Method Based on Gradient Descent Theory
Setting the point \(T_{k}=MIN\) (\(T_{j}\)) (\(1\leq k\leq n\) and \(k\in Z\)) as the initial state and setting the \(T_{j}\) (\(1\leq j\leq n\), \(j\in Z\) and \(j\
eq k\)) as the next state, then \(\
abla G\) between (\(R_{k}\),\(T_{k}\)) and (\(R_{j}\),\(T_{j}\)) is calculated. 3. Finding the maximum value of \(\
abla G\) through the following operation: \(\
abla G\) (\(k\),\(J\)) = \(MAX\) {\(\
abla G\)(\(k\),\(j\))} (\(1\leq j\leq n\), \(j\)\(\in\)\(Z\) and \(j\
eq k\)). 4. When \(\exists l\in Z\), \(1\leq l\leq n\), \(l\
eq k\) and \(\
abla G\) (\(k\),\(J\)) \(\geq G_{gate}\), (\(R_{j}\),\(T_{j}\)) is defined as the balanced scheme. Otherwise, there is no balanced scheme. 5. If there is a balanced scheme in step (4), the point (\(R_{k}\),\(T_{k}\)) is deleted to form a new feasible zone. The value of \(T_{l}\) replaces the original value of \(T_{l}\) (i.e., \(T_{k}=T_{l}\)), and steps (2), (3), and (4) are repeated until there is no balanced scheme. \(G_{gate}\) is the threshold set by developers according to the requirement of the system. \(G_{gate}\) was set as 3 in this paper to achieve high system performance. The following section presents a detailed analysis of the InSAR system based on what we have proposed above.
## 5 Conclusions  
The objective of the present study was to investigate the ultimate strength and collapse behavior of a simply supported curved plate under longitudinal compressive load as well as complement the numerical results of the previous study. For this purpose, a series of buckling collapse experiment was carried out by considering flank angle, slenderness ratio, and aspect ratio of curved plates. In order to verify the effectiveness of the experimental results, an elastoplastic large deflection analysis was performed. Finally, new empirical design formulae were derived based on the experimental results for predicting the ultimate strength of curved plates. The inferences from the present study are summarized below.  
Figure 15: Correlation between the empirical formula with the ultimate strength of curved plates.  
The ultimate strength of a simply supported curved plate increases with the increase in the flank angle and thickness. In addition, the ultimate strength of a curved plate is significantly low when the slenderness ratio is 2.01 because of special phenomenon such as secondary buckling.
* As the flank angle increases, an additional membrane stress, which is generated in the longitudinal direction at the central part of the curved plate, can restrain the lateral deflection and improve the strength of the curved plate.
* As the flank angle increases, the buckling mode of a simply supported curved plate sequentially changes in the order of two half-wave, three half-wave, and one half-wave under longitudinal compressive loads when aspect ratio is 2. The buckling mode are similar to those of the flat plates as the aspect ratio decreases.
* The difference in the ultimate strengths obtained from the FE analysis and experiment increased with the increase in flank angle, as well as with the decrease in slenderness ratio.
* Improved empirical formulae were derived based on the quantitative experimental database for predicting the ultimate strength of a curved plate. In addition, the result of correlation shows good agreement between the experimental results and the new design formulae. The results can be helpful as a reference in the design of curved plates.  
Conceptualization and project administration, J.-M.L.; test and writing, J.-H.K. and D.-H.P.; experimental review and editing, S.-K.K.; numerical simulation and editing, M.-S.K. All authors have read and agreed to the published version of the manuscript.  
This research was a part of the project titled 'Development of the safety standards for marine hydrogen storage vessels and fuel supply systems', funded by the Ministry of Oceans and Fisheries, Korea. This work was supported by the R&D Platform Establishment of Eco-Friendly Hydrogen Propulsion Ship Program (No. 20006644) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea).  
Not applicable.  
The data presented in this study are available on request from corresponding author.  
The authors declare no conflict of interest.
# Using Vocal-Based Emotions as a Human Error Prevention System with Convolutional Neural Networks
## 2 Literature Review
### Voice-Based Emotion Detection  
The extraction of voice features and emotions represents a challenging and complex research area, influenced by various factors, such as the individual's physical condition, gender, mental state, and surrounding noise [10], with the extracted features of a female voice also differing from a male voice and the voice of a child. [PERSON] [11] also explained that it is possible to employ emotional detection systems in various areas of life, such as polygraph systems, contact centers, car systems, robots, and smart applications [11]. As emotional states affect speech features [12], using software similar to the Phonetic and Acoustic Analysis Toolkit (PRAAT) can help in a manual analysis of focal features to detect emotions from speech. Also, it is possible to detect the eight types of human emotions (such as sadness, happiness, anger, etc.) in detail through a system that follows three stages: First, the features are extracted from the database through Librosa [13], which is a Python package that extracts and analyzes important features of simple sound and music. Second, sample training is carried out to match the previously extracted features. Thirdly, the testing and classification of emotional sound samples is unknown [14]. Through the application of a number of automated learning algorithms, including the trees algorithm and K-nearest neighbors (KNN) algorithm, we can obtain the most efficient algorithm between them. The preference emerged after comparing three algorithms to the additional tree work manic algorithm with a resolution of up to 99% [10]. On another hand, based on the accuracy of the results of a previous study, a low frequency produces more accurate emotional information with a high acceptance rate when using the Jitter method [14; 15]. Moreover, the experimental results of using an SVM classifier to classify emotion categories for several different languages showed the following accuracies: 78.57%, 79.3, 81.43%, 82.8%, and 89.23% [16]. In ref. [17], the authors proposed building a CNN model for an emotion distinction task with a 71% accuracy. Their model was evaluated using a dataset for a speech emotion recognition system using speech samples, with characteristics being extracted from these speech samples using the Librosa [13] package. Classification performance is based on the extracted characteristics. Thus, we can determine the emotion of the speech signals [18]. Table 1 provides a summary of previous works on voice-based emotion detection.
# NPP-VIIRS DNB Daily Data in Natural Disaster Assessment: Evidence from Selected Case Studies
## 2 Materials and Methods
### Methods
#### 2.3.2 PNL Image  
The NPP-VIIRS DNB data one month prior to and 10 days after the natural disaster (excluding images with poor data quality) were averaged to create pre- and post-disaster composite datasets, respectively. This method was necessary for two reasons. Firstly, most missing data caused by clouds could be removed by averaging the images over the selected period. Secondly, the intensity of city lights observed by satellites can fluctuate due to many factors such as atmospheric scattering and absorption [39] and differences in scan angle [65]. By averaging the data, these fluctuations could be smoothed and the data would reflect the real light. We chose a relatively longer pre-disaster period (one month) to smooth the normal time fluctuations and reflect the real light during normal conditions. A shorter 10-day post-disaster period was chosen, as this is the critical period that reflects the changing conditions before recovery work occurs. Seasonal effects of NTL data caused by changes in NDVI, snow, and albedo [71] were negligible due to the minimal change in these factors during this short time period. A PNL image was used to compare the DNB images pre- and post-disasters. PNL was calculated for each pixel by dividing the post-disaster composite data by the pre-disaster composite data. This approach follows previous work by [PERSON] et al. in assessing power outages [36], which is calculated as:  
\[PNL=100\times\left(\frac{Rad_{post}}{Rad_{pre}}\right) \tag{2}\]  
where \(Rad_{post}\) is the post-disaster composite DNB radiance and \(Rad_{pre}\) is the pre-disaster composite DNB radiance. PNL can reflect changes of lights before and after the disaster, where a PNL > 1 means more light after the disaster, PNL < 1 means less light after the disaster, and PNL = 1 means no changes.  
The areas where \(\text{PNL}<1\) were viewed as the disaster-affected areas. However, when the \(Rad_{pre}\) and/or \(Rad_{post}\) values are very small, any small change in DNB radiance due to background noises in an areas [10] can result in an extremely high or low PNL value using Equation (2), which would be unacceptable. To avoid this situation, we reset all \(Rad_{pre}\) and \(Rad_{post}\) values to 0.3 nW\(\cdot\)cm\({}^{-2}\cdot\)sr\({}^{-1}\) when the actual value was lower than this threshold. This threshold was chosen empirically, whereas either lower or higher than this threshold resulted in large anomalies in the PNL.
## 2 Basic Concepts of DQ0-Based Models
### Example--Modeling a Network Based on DQ0 Quantities
* If \(\mathrm{det}(A)\leq 0\), there is a complex conjugate pair of poles on the imaginary axis, and additional analysis in simulation reveals that the system is unstable. Why is the system unstable with zero resistance and capacitance? One explanation is that the power source on the right has no local energy storage device. In this example, if the capacitor \(C\) is present and is sufficiently large, then the system is stable. The conditions for stability are fully analyzed in [33]. For instance, assuming \(0<P<\frac{\lvert V\rvert^{2}}{2\lvert Z\rvert^{2}}\) the condition for stability is  
\[C>C_{\mathrm{min}}=\frac{P}{\omega_{s}\lvert V\rvert^{2}\sqrt{1+\left(\frac{R }{2\omega_{s}L}\right)^{2}}}. \tag{70}\]
## 4 Numerical Examples
### Case 2
Lastly, the initial fault current levels and the fault current levels after the application of the six countermeasures are illustrated in Figure 10. Notice that all resulting fault current levels were reduced and are now within the maximum CB breaking duties. From an average initial fault current level excess of 5%, applying the six countermeasures resulted to a fault current level average of 12.85% reduction below CB limits in the twenty-six FLC buses. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reactance** & **Intermediate Value** & **Discretized Value** & **Countermeasure** \\ \hline \(X_{\text{BS6}}\) & 7.08526 & 10.0000 & bus split \\ \(X_{\text{BS1}}\) & 3.54256 & 10.0000 & bus split \\ \(X_{\text{CLR10}}\) & 0.07066 & 0.10000 & install CLR \\ \(X_{\text{CLR1}}\) & 0.02877 & 0.10000 & install CLR \\ \(X_{\text{CLR2}}\) & 0.01835 & 0.10000 & install CLR \\ \(X_{\text{CLR9}}\) & 0.01423 & 0.10000 & install CLR \\ \(X_{\text{CLR11}}\) & 0.01059 & 0.10000 & none \\ \(X_{\text{CLR7}}\) & 0.01023 & 0.10000 & none \\ \hline \hline \end{tabular}
\end{table} 
Table 10: \(X\) variables of the bus splitting and CLR candidates (Case 2). Figure 9: Countermeasures and their corresponding least membership function values.
## 2 Traditional and Smart Measurement Devices  
The penetration of DGs, EVs, and variable loads bring complex interaction characteristics with high uncertainties that makes the operation, monitoring, and management of DNs more challenging [50; 51]. Control and Data Acquisition (SCADA) measurement systems are currently applied to monitor and control DNs [52]. The information from Remote Terminal Units (RTUs), which are conventional measurement devices, are installed at various locations of DNs, which are recorded through SCADA [53]. Measurement data are unsynchronized voltage and branch current magnitudes, real and reactive power injections, and flows within a lowest refresh interval time [54]. These measurements, which are available using SCADA systems, fail to capture the dynamic behavior of ADNs [55]; therefore, PMUs are introduced to facilitate a suitable and reliable monitoring of ADNs in terms of higher precision and speed [56]. Phasor Measurement Units (PMUs) are advanced measurement devices in power grids that provide an accurate and synchronized voltage bus and branch current phasor measurement [57]. PMUs have been widely used for wide-area monitoring, protection, and the control of TNs [58]. However, PMUs have not been deployed widely in ADNs due to economic constraints as well as structure of the DNs [59; 60]. DN monitoring will be enhanced by incorporating more PMUs in an ongoing modernization effort for ADNs due to the following reasons [61; 62]:  
* The linear mathematical SE calculation will be formulated, since state variables could be measured by PMUs.
* PMU measurements are available with a high precision (0.1% and 0.01 rad for magnitudes and phase angle, respectively); thus SE computations can be performed using PMU measurements with high-speed processing and accuracy.
* Tracking the dynamic behaviors of ADNs could be captured using high-resolution PMU data in DSSE calculations.  
SE results will be enhanced by including different measurement data from multiple measuring instruments due to redundancy [60]. However, measuring information integration from different sources is not straightforward for two main reasons [63]:  
1. The absence of unique coordination time among different measurements leads to time-skew problems, and datasets might not be established in an exact time that is representative in a control center. Time inconsistency might also result from the effect of the communication delays of monitoring devices [64].
2. The precision weights of measured quantities from SCADA and PMU measurements are different; therefore, current SE software should be altered or corrected to make all measurement values applicable in SE algorithms [65].  
The SCADA and PMU measurements, as well as their refresh samples, are shown in Figure 1a,b respectively.
# A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation
## 4 Trajectory Feature Extraction and Restoration Model Construction  
Ensuring the accuracy and reliability of trajectory data is a key part of trajectory analysis, although the interpolation method can be used to repair sparse trajectory restoration by simply completing the missing values and repairing abnormal data, which improves the quality of trajectory data to a certain extent. However, due to the continuous multiple missing points in sparse trajectories, the interpolation method cannot completely and accurately reflect the trajectory state changes. Therefore, to guarantee restoration accuracy, it is still necessary to carry out sparse trajectory restoration with the help of neural networks. With its powerful learning ability and pattern recognition ability, neural networks can capture the complex relationships and nonlinear rules in depth in the trajectory data. It further optimizes the repaired data to improve the completeness and continuity of the trajectory data. Through the secondary repair of the neural network, it is more effective in filling in missing data and correcting abnormal data and finally obtains more reliable trajectory data. The AIS data contain a large amount of multi-dimensional information about the vessel, and if all the data are directly input into the model without selecting the filtering, it leads to an increase in the redundant information of the input features and reduces the restoration effect. Therefore, to improve the accuracy and efficiency of trajectory restoration, it is crucial to use the correlation method to analyze the intrinsic relationship between different features of the data. By analyzing the correlation of each feature in the AIS data, the features that are closely related to the trajectory restoration task are identified to avoid the redundancy of the input data, which in turn improves the effectiveness and computational efficiency of the model.  
The overall framework of this study is shown in Figure 4. Firstly, the preliminary restoration trajectory data are divided so that the trajectory data meets the input requirements of the model. Then, a comprehensive indicator system of feature relevance is constructed to determine the dimensions of the input features. Then, based on the correlation coefficient, the Akaike information criteria (AIC) and Bayesian information criteria (BIC) are used to select the appropriate input features step. Finally, the neural network model is built to train the data and output the restored trajectory, and the accuracy of the model and trajectory restoration is measured using evaluation metrics.
# Lightweight Transformer with Adaptive Rotational Convolutions for Aerial Object Detection
## 3 Proposed Methodology
### Hybrid Vision Transformer Architecture
#### 3.1.3. Self-Attention with Rotational Invariance  
The core of the ViTs architecture is the self-attention mechanism, which computes the relationships between different patches. In the RASST model, the self-attention mechanism is modified to account for rotational invariance.  
The standard self-attention is computed as:  
\[Attention(Q,\ K,\ V)=softmax\biggl{(}\frac{QK^{\intercal}}{\sqrt{d_{k}}}\biggr{)}V \tag{5}\]  
where \(Q=W_{Q^{ip}}\), \(K=W_{K^{ip}}\), \(V=W_{V^{ip}}\) are the query, key, and value matrices, \(d_{k}\) is the dimension of the key vectors.  
In our modified version, we introduce rotational alignment between patches by incorporating rotational encoding directly into the self-attention computation:  
\[\text{R}-Attention\ (Q,\ K,\ V,R)=softmax\left(\frac{Q(K+R)^{\intercal}}{\sqrt{d_{k}}} \right)V \tag{6}\]  
where R is the rotational adjustment matrix derived from the rotational encoding \(\text{RE}\left(\theta_{p}\right)\). The rotational adjustment ensures that patches with similar orientations have stronger attention weights. This modification enhances the model ability to focus on relevant patches that are similarly oriented, thereby improving the detection of objects that appear at arbitrary angles.
## 3 Experimental Results and Analysis  
### Streamer Analysis  
To explore the impact of nano-particles on transformer oil, we measured and compared the streamer morphology of pure transformer oil and nano-liquids. Considering the poor translucency under higher volume fractions, TiO\({}_{2}\) nano-liquids and Al\({}_{2}\)O\({}_{3}\) nano-liquids were prepared with volume fraction of 0.002%.  
As shown in Figure 4a1-a5, b1-b5, c1-c5 are the streamer morphology of pure transformer oil, TiO\({}_{2}\) nano-liquids, and Al\({}_{2}\)O\({}_{3}\) nano-liquids shot by high-speed camera, respectively. Note that each liquid here was tested 20 times and we randomly selected five sets of typical streamers for each liquid.  
Figure 3: Typical breakdown waveforms obtained by the oscilloscope. The typical voltage-rising duration is approximately 10 us.  
Figure 2: Schematic diagram of the microsecond pulse breakdown strength test platform.  
As we can see, there is a conspicuous difference between the pure transformer oil and nano-liquid in streamer morphology.  
In order to facilitate the analysis and discussion, we plotted the schematic diagram of streamer morphology in Figure 5. To distinguish the branches of the streamer, we defined the bright channel from needle electrode to plate electrode as the main streamer, the branches on main streamer as the primary streamer branch, and the other branches on primary streamer branch as the secondary streamer branch.  
Figure 4: The streamer morphology of pure transformer oil and nano-liquids shot by high-speed camera. (**a1–a5**) pure transformer oil, (**b1–b5**) TiO\({}_{2}\) nano-liquids, (**c1–c5**) Al\({}_{2}\)O\({}_{3}\) nano-liquids.  
Figure 5: The schematic diagram of streamers.  
As we can see in Figure 4a1-a5, the streamers in pure transformer oil exhibit thick bush-like qualities. Certain nodes in the main streamer bifurcate and develop into primary streamer branches, then additional nodes are updated in primary streamer branches with more dispersed secondary streamer branches. While in the case of Figure 4b1-b5,c1-c5, the streamers in TiO\({}_{2}\) nano-liquids and Al\({}_{2}\)O\({}_{3}\) nano-liquids exhibit tree-like qualities. The bifurcation nodes are mainly distributed in the head of the main streamer with a few primary streamer branches. Moreover, the propagation length of primary streamer branches is much shorter than that in pure transformer oil, which is accompanied by less secondary streamer branches. In consequence, the overall morphology of streamers in nano-liquids is more sparse than that in pure transformer oil. This suggests that the addition of TiO\({}_{2}\) nano-particles and Al\({}_{2}\)O\({}_{3}\) nano-particles significantly affects the streamer morphology.