esa-sceva/satcom-chunk-collection huggingface dataset:
# Voice Communication Systems Impact on Navigating Officers
## 3 Results
### UHF Routine Radio Call  
A routine UHF call is a non-emergency radio call from a crew member to an OOW on the navigational bridge via ship's portable handheld UHF radio. The collected data (statistical sample) have the following parameters: Range = 238 min (2-240 min), Mean = 32.28, Median = 15, Standard deviation = 51.88 and 90% confidence interval for the mean value [20.23, 44.33]. The data are congruent to a lognormal distribution. By rejection of outliers, beyond the three standard deviations, the entry  
Figure 5: Time between two incoming calls via ship’s internal phone system [min].  
parameters are as follows \(\mu_{l}=32.76\) and \(\sigma_{l}=57.95\) (Figure 6). The lognormal distribution is given by the following expressions:  
\[\mu=\ln\left(\frac{\mu_{l}^{2}}{\sqrt{\sigma_{l}^{2}+\mu_{l}^{2}}}\right)=\ln \left(\frac{32.76^{2}}{\sqrt{57.95^{2}+32.76^{2}}}\right)=2.78 \tag{11}\]  
\[\sigma=\sqrt{\ln\left[\frac{(\sigma_{l}^{2}+\mu_{l}^{2})}{\mu_{l}^{2}}\right]} =\sqrt{\ln\left[\frac{57.95^{2}+32.76^{2}}{32.76^{2}}\right]}=1.19 \tag{12}\]  
\[f(x)=\frac{1}{ox\sqrt{2\pi}}e^{-\left(\ln\left(x\right)-\mu\right)^{2}/\left( 2\sigma^{2}\right)}\ =\frac{1}{1.19x\sqrt{2\pi}}e^{-\left(\ln\left(x\right)-278\right)^{2}/284} \tag{13}\]  
\[x>0;\ \mu_{l},\ \sigma_{l}\ \epsilon\ R^{+}\]  
The average time between two consecutive UHF radio calls is 34 min, giving an average frequency of seven calls per watch. It should be emphasized that the majority of the interviewed participants have stated that, on more complex ships, the average time between UHF calls might be 10 min or less, giving the frequency of approximately 24 calls per watch.  
The interviewed participants have responded that UHF radio calls were usually rather short, usually lasting up to 1 min, the same as in the case of internal phone calls. The reason is the same, asking or providing one or a smaller set of information related to the ship's routine operations. However, the major difference is that officers and crew members on duty usually make handheld UHF radio calls with an assigned radio, whereas crew members who do not have an assigned or nearby radio use the ship's internal phone system.  
Considering the frequency of incoming calls via UHF radio and their duration, it is estimated that during one day, officers of the watch spend approximately 42.8 min (or 7 min during one watch) talking on the UHF radio.  
Considering all four types of communication systems, it can be estimated that during one watch an OOW has to answer approximately 14 different incoming calls, which last 16.19 min altogether or 6.7% of the watch keeping time.  
Figure 6: Time between two handheld UHF radio routine calls [min].
# Spatial Shape-Aware Network for Elongated Target Detection
## 4 Loss Functions  
### Regression Loss Function Design  
The regression task for this study focuses on predicting the precise locations of target objects. To ensure scale invariance and position invariance, thereby providing consistent training feedback for samples of different sizes and locations, we adopt a distance vector \(\Delta\ =\ (d_{x},d_{y},d_{w},d_{h},d_{\theta})\) approach as follows:  
\[d_{x}=(t_{x}-b_{x})/b_{w},\quad d_{y}=(t_{y}-b_{y})/b_{h} \tag{7}\]  
\[d_{w}=log(t_{w}/b_{w}),\quad d_{h}=log(t_{h}/b_{h}) \tag{8}\]  
\[d_{\theta}=tan(t_{\theta})-tan(b_{\theta}) \tag{9}\]  
Here, the ground truth bounding box and the predicted bounding box are represented by b and g, respectively. \(t_{x},t_{y},t_{w},t_{h},t_{\theta}\) represent the ground truth values for the center coordinates, width, height, and angle. \(b_{x},b_{y},b_{w},b_{lt},b_{\theta}\) are predicted values for the corresponding parameters. The regression loss \(L_{\text{reg}}\) is calculated as the sum of the Smooth \(L_{1}\) losses for these residuals across the five parameters in \(\Delta\), given by  
\[L_{\text{reg}}=\sum_{i=1}^{5}L_{\text{smooth}}(\Delta g-\Delta p_{i}) \tag{10}\]  
The \(L_{\text{smooth}}\) is defined as  
\[L_{\text{smooth}}(x)=\begin{cases}0.5x^{2}&\text{if}|x|<1\\ |x|-0.5&\text{otherwise}\end{cases} \tag{11}\]  
where \(\Delta p_{i}\) represents the distance vector of the predicted box while \(\Delta g\) represents the distance vector of the ground truth box. The discrepancy between ground truth boxes and predicted bounding boxes is measured using Smooth \(L_{1}\) loss, ensuring numerical stability and robustness against outliers.  
By leveraging the Smooth \(L_{1}\) loss, the model achieves balanced optimization, where large residuals are penalized less severely than by \(L_{2}\) loss, and small residuals are effectively minimized. This design facilitates more precise boundary regression.
# Error Dynamics Based Dual Heuristic Dynamic Programming for Self-Learning Flight Control
## 5 Conclusions  
This article presents the ED-DHP method for online nonlinear flight attitude control. ED-DHP defines the augmented system state, which contains tracking error and reference trajectory, to solve the tracking problem. This can make the actor neural network learn the feedforward and feedback control terms simultaneously without requiring complete knowledge of the vehicle dynamics and the reference trajectory dynamics. An RLS method is used to identify not only the input dynamics but also the total uncertainty based on the error dynamics in real-time.  
This proposed method is applied in two numerical experiments based on a morphing air vehicle model: sinusoidal signal tracking at a fixed operating point and guidance command tracking with morphing process at variable operating points. The results indicate the robustness and adaptiveness of ED-DHP. They also reveal that ED-DHP can learn the multichannel attitude control policy online without offline training.  
In the meantime, there are some questions worth pondering:  
1. Augmented state increases the dimension of the system state space, which may affect the convergence speed of the algorithm without offline training;
2. The systems with input constraints can be further considered by designing the new cost function to make this method better apply to real vehicle systems;
3. ED-DHP is suitable for the control of fast loop subsystems. It is necessary to optimize the error dynamic and RLS method to expand the application scenarios of ED-DHP.  
Conceptualization, X.H. and J.L.; methodology, X.H., H.Z. and Y.P.; software, X.H.; validation, Y.Z., J.L. and Z.W. formal analysis, X.H., Y.Z. and J.L.; writing\(-\)original draft preparation, X.H.; writing\(-\)review and editing, Y.Z., J.L., H.Z., Z.W. and Y.P.; visualization, Y.Z.; supervision, J.L. and 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, grant number U21B2028.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 11: Weights updating of actor NN and critic NN.
## 3 Methods
### Time Series-Based Methods
#### 3.3.2 Bi-Directional Long Short-Term Memory  
Bi-LSTM, unlike traditional LSTM models, considers bidirectional dependencies within the temporal structure of input data, allowing it to capture intricate relationships and patterns present in sequential data. By processing both past and future information simultaneously, Bi-LSTM gains a holistic understanding of the context within time series data, enhancing its predictive accuracy and feature extraction capabilities. Consequently,Bi-LSTM emerges as a powerful tool across various domains such as natural language processing, time series forecasting, and sequential data analysis [29].  
The bidirectional nature of Bi-LSTM enables it to analyze sequences in both forward and backward directions, facilitating a comprehensive comprehension of temporal dynamics and intricate dependencies within the data. This dual perspective empowers Bi-LSTM to uncover nuanced patterns and correlations that might be missed by unidirectional models, thereby improving its ability to model complex sequential data and make accurate predictions. As a result, Bi-LSTM stands out as a versatile and effective solution for tasks requiring nuanced understanding and prediction of sequential patterns [30].
## 2 Study Area and Data Sources
### Remote Sensing Data  
#### 2.3.1 Sentinel-1 SSM Product  
Sentinel-1 is a C-band synthetic aperture radar constellation composed of two satellites S1A and S1B launched in 2014 and 2016, respectively, by the European Space Agency (ESA).  
The operational imaging mode IW provides dual acquisitions in VV and VH polarizations. S1A and S1B scan the world in 175 orbits with a revisit time of six days [48]. The S1 products were downloaded from Data Hub website and processed using SNAP and OTB (Orfeo ToolBox) as described in [17].  
SSM was retrieved based on the method described in [17] relying on the inversion of the water cloud model using the backscattering coefficient and the interferometric coherence derived from S1 data. The above-ground biomass derived from the interferometric coherence is used as vegetation descriptor in the model and hence, the SSM is derived from radar data using a brute-force method. The approach was validated using a large in situ database including the drip fields D1 from Morocco and other fields in Tunisia. Reasonable metrics were obtained with R = 0.74, RMSE = 0.06 m\({}^{3}\)/m\({}^{3}\) and bias = 0.01 m\({}^{3}\)/m\({}^{3}\). For more details about the approach, the reader is referred to the original paper [17]. Among the two incidence angles available on D1 (35.2\({}^{\circ}\) and 45.6\({}^{\circ}\)), SSM used in this study is derived from descending orbit #52 (35.2\({}^{\circ}\) incidence angle) since the lower incidence angles are more suitable for SSM retrieval [17; 49]. D2 is not considered because the special growing conditions affected the geometry of the field and therefore affect the modeling of the radar signal leading to inaccuracy in the SSM retrieval (see [17] for more details). SSM for F2 and F3 is retrieved from orbit #45 (-40\({}^{\circ}\) of incidence angle) and from orbit #154 at 40\({}^{\circ}\) of incidence angle for R1. The VV polarization is chosen for all fields as it is less impacted by the volume diffusion within the canopy than VH [17; 36; 50]. Please note that S1 derived SSM is available every 6 days for D1 and R6 seasons 2016-2017 and 2017-2018 while it is available every 12 days for F2 and F3 during the season 2015-2016 as only S1A was in orbit. Obviously, remote sensing products were not available for F1 as the field was monitored during 2003.
## 4 Review: Slant Links Rain Fade Models
### Limitations
#### 4.1.6 Karasawa Model  
In the Karasawa model [88], the rain area size parameter acts as a function of the rainfall rate for 0.01% of the time to minimize the prediction error. The model considers a \"single volume\" for falling rain and does not account for the two types of rainfall, convective and stratified rain. The model's drawbacks are that it applies only to mid-latitude regions, at relatively high elevation angles, and supports frequency bands between 10-20 GHz. The detailed steps of the Karasawa model are given in Algorithm 6.  
```
Calculate \(k\) and \(\alpha\)
1 Calculate specific attenuation \(\gamma\gets kR^{\alpha}{}_{0.01}\)
2 if\(\varphi<36^{\circ}\)then
3\(H_{R}\gets 4\)
4 else
5\(H_{R}\gets 4-0.075(\varphi-36^{\circ})\)
6 if\(\theta<10^{\circ}\)then
7\(L_{S}\leftarrow\) Follow CCIR 564-2 /* CCIR 564-2 [106] */
8 else
9\(L_{S}\leftarrow(H_{R}-H_{S})/\sin\theta\)
10 Calculate, \(L_{0}\)*\(a\exp(-bR_{0.01})\) /* a, b are constants. */
11 Calculate, \(A_{0.01}\leftarrow\gamma\cdot L_{S}\)
12 Calculate for p%, \(A_{p}\gets 0.12A_{0.01}p^{-(0.546+0.043\log p)}\) /* \({}_{0.001\leq p\leq 1}\) */
13 Return \(A_{p}\)
```  
**Algorithm 6**Karasawa Model [88]  
Advantages  
The model uses vertical and horizontal path length \"reduction factors.\" It also includes a rain cell size parameter that depends on the rainfall rate (0.01%). This parameter can reduce the prediction error (e.g., eliminate the weakness of the former CCIR methods). Another beneficial aspect of this model is that, except for rainfall rate, the model considers the \"probabilities of occurrence\" and \"mean rainfalls\" for both cell and debris rain.  
Limitations  
This model considers uniform rainfall intensity (mm/h); in real life, rainfall is not normally uniform in a rain cell. Another drawback is that the model considers a single volume for falling rain and does not account for the difference between the cell and debris rain.
## 3 Method
### Improved MEAG-YOLO Model Structure
#### 3.2.1 Multi-Scale Convolutional Attention  
Considering the requirements for precise and efficient information extraction from complex backgrounds and multi-object detection, this study integrates MSCA into the backbone network. MSCA efficiently aggregates features from various convolutional layers. It captures extensive contextual information and incorporates feature maps of various scales. Additionally, this method improves object detection's accuracy and reliability by utilizing data from various feature layers. The core theory of MSCA is to integrate features across various model layers effectively. This mechanism excels in scenarios with significant variations in target sizes by aggregating feature maps from different scales to boost the model's expressive capacity. The MSCA module allows the model to learn features independently at each scale and improve scene understanding through cross-scale fusion. This fusion strategy greatly enhances detection accuracy, particularly for targets that vary in size, are occluded, or are in complex environments. Additionally, the spatial attention mechanism introduced by MSCA dynamically focuses on different image regions, optimizing feature representation and processing workflows. The inclusion of spatial attention helps the model concentrate on crucial areas that significantly influence the task. Overall, the MSCA module improves the model's ability to detect multi-scale targets in  
Figure 2: MEAG-YOLO model diagram.  
complex settings and enhances its applicability in resource-limited environments. The structure of the MSCA is shown in Figure 3.  
The MSCA module is a novel approach that combines multi-scale features and spatial attention information through a convolutional attention network. It efficiently encodes contextual information using lightweight convolution and activates spatial attention by performing element-wise multiplication on the multi-scale features. During the decoding phase, MSCA aggregates multi-level features from different stages, allowing for the acquisition of multi-scale contextual information and adaptation across both spatial and channel dimensions. The mathematical framework of the MSCA module is described in detail in Equations (1) and (2):  
\[\text{Att}=\text{Conv}_{1\times 1}(\sum_{i=0}^{3}\text{Scale}_{i}(\text{DW}- \text{Conv}(F))) \tag{1}\]  
\[\text{Out}=\text{Att}\otimes F \tag{2}\]  
\(F\) signifies input features, Att represents the attention map, Out refers to the output, \(\otimes\) denotes matrix multiplication, \(\text{DW}-\text{Conv}\) indicates depthwise convolution, and \(\text{Scale}_{i}\) identifies concatenation. The module comprises three branches, each substituting traditional depth convolutions with two depthwise strip convolutions. This modification efficiently extracts the target features.
## 2 Materials and Methods  
### Primary Processing  
E21 alloy and E21-B4C (1.5 and 2.5 wt.%) nanocomposites were synthesized utilizing disintegrated melt deposition technique (DMD) [15]. E21 alloy procured in the form of chunks from Luxfer MEL Technologies (formerly known as Magnesium Elektron) and B4C nanoparticles of \(\sim\)50 nm size procured from Nabond, Hong Kong, were added in sandwich fashion and superheated to 750 \({}^{\circ}\)C, under an inert argon gas atmosphere, within a graphite crucible using a resistance heating furnace. The crucible was equipped with a plug and nozzle for bottom pouring. Upon reaching the temperature (750 \({}^{\circ}\)C), the melt was stirred at 450 rpm for 5 min utilizing a stirrer coated with ZIRTEX 25 to avoid iron contamination. After stirring, the plug of the crucible was pulled, and the molten metal was down poured into the mold. Before entering the mold, the molten metal was disintegrated by two jets of argon gas, with a flow rate maintained at 25 lpm. Following solidification, an ingot of 40 mm diameter was obtained. Three materials were cast in total: (i) Elektron 21 alloy; (ii) Elektron 21 with B4C nanoparticles of approximately 50 nm added at 1.5 wt.%; and (iii) 2.5 wt.%, respectively, using this method. The composition of the final materials is given in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Material (wt.)** & **Nd** & **Gd** & **Zn** & **Zr** & **B4C** & **Mg** \\ \hline E21 Alloy & 2.8 & 1.4 & 0.3 & 0.5 & – & Bal. \\ E21-1.5B4C Nanocomposite & 2.8 & 1.4 & 0.3 & 0.5 & 1.5 & Bal. \\ E21-2.5B4C Nanocomposite & 2.8 & 1.4 & 0.3 & 0.5 & 2.5 & Bal. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nominal elemental composition of the materials in the study supplied by Luxfer MEL Technologies.
# Networked Microgrids: A Review on Configuration, Operation, and Control Strategies
## 3 Networked Microgrids' Control
### Communication
#### 3.1.2 Protocols  
In the context of a networked microgrid system, effective and reliable operation relies on adherence to specific rules and conventions collectively known as communication protocols. These protocols play a crucial role in facilitating the exchange of information by establishing a common language and structure for communication. Three fundamental elements characterize communication protocols: syntax, semantics, and timing. Syntax pertains to the format and structure of exchanged data, including details such as data encoding, bit order, and framing. Semantics involves interpreting the meaning of exchanged data, encompassing control signals, commands, or responses conveying specific actions. Timing governs synchronization and coordination, addressing factors like communication rates and data transmission sequencing. These elements collectively define the rules and conventions necessary for accurate and reliable data exchange in diverse domains. Various communication protocols, including Modbus, MQTT, DNP3, and the IEC 61850 series standards [101], are utilized in networked microgrids [100]. However, a standardized protocol is currently lacking across the communication system of networked microgrids [102].
## 3 Results  
Out of 21 TMD patients assessed for eligibility, six were excluded (four did not meet inclusion criteria, two declined to participate). Fifteen patients were enrolled after providing informed consent and were randomized to rESWT therapy plus physical therapy (\(n=8\)) or physical therapy alone (\(n=7\)), as depicted in Figure 2.  
There were no differences between groups in terms of sociodemographic, clinical characteristics and pain (Table 1).  
Figure 2: Study flow-chart.  
We demonstrated a statistically significant difference between the groups at T1 (Right side: \(p\) = 0.040; RBC = \(-\)0.46 [\(-\)0.85, 0.23]; Left side: \(p\) = 0.033; RBC = \(-\)0.64 [\(-\)0.91, \(-\)0.02]) and T3 (Right side: \(p\) = 0.031; RBC = \(-\)0.61 [\(-\)0.89, 0.04]; Left side: \(p\) = 0.032; RBC= \(-\)0.68 [\(-\)0.92, \(-\)0.09]) (see Table 2 for further details). Moreover, our findings showed a significant VAS reduction in group A (Right side: T0 = 4.71 \(\pm\) 2.56 vs. T1 = 1.71 \(\pm\) 1.89, \(p\) = 0.02, mean difference (MD) = 3.00, standard error (SE) = 0.58; Left side: T0 = 4.86 \(\pm  
Concerning the secondary outcome, Group A patients showed a statistically significant reduction in BAR (at T2: Group A with 63.9 \(\pm\) 14.78 versus Group B with 81 \(\pm\) 11.74, \(p\) = 0.04, RBC = \(-\)15.29 [\(-\)44.12, \(-\)8.05]; at T3: Group A with 67.5 \(\pm\) 22.83 versus Group B with 84.4 \(\pm\) 4.88, \(p\) = 0.03, RBC = \(-\)37.05 [\(-\)51.18, \(-\)11.62]). Moreover, the real rESWT group reported a significant increase in total standardized muscle activity (IMP, IMPACT score) compared to the sham group (\(\Delta\)T0-T4: Group A: MD of 15.00 \(\pm\) 19.89 versus Group B: MD of 9.30 \(\pm\) 25.09, \(p\) = 0.03) and a significant POC in masseter muscles at T1 (RBC = \(-\)11.60 [\(-\)11.40, \(-\)1.34], \(p\) = 0.04). Furthermore, there were significantly between-group differences in muscle activity in sEMG indexes (POC MM, BAR, IMP, ASIM) at specific time-points (see Table 3 for further details).
###### Abstract  
Spatial-temporal prediction is an important part of a great number of applications, such as urban traffic control, urban traffic management, and urban traffic planning. However, real-world spatial-temporal data often have complex patterns, so it is still challenging to predict them accurately. Most existing spatial-temporal prediction models fail to aggregate the spatial features in a suitable neighborhood during fixed spatial dependencies extraction and lack adequately comprehensive time series analysis for intricate temporal dependencies. This paper proposes a novel model named multi-scale spatial-temporal transformer network (MSSTTN) to deal with intricate spatial-temporal patterns. Firstly, we develop an improved graph wavelet neural network, which learns how to pass the spatial graph signals of different frequency scales to adjust the neighborhood of feature aggregation adaptively. Then, we propose decomposing the time series into local trend-cyclical parts of various scales during time series analysis, making the model capture more reliable temporal dependencies. The proposed model has been evaluated on publicly available real-world datasets. The experimental findings indicate that the proposed model exhibits superior performance compared to conventional techniques including, spatial-temporal transformer (STTNs), GraphWaveNet, and others.  
spatial-temporal prediction; multi-scale; graph wavelet neural network; multi-scale series-decomposition +
Footnote †: journal: Applied sciences
## 3 Results and Analysis
### Analysis of the Water and Salt Migration Results for Saline Soil under Freeze Thaw Cycles
#### 3.2.2 Analysis of Water--Salt Changes
When the freeze--thaw cycle temperature increased above the freezing temperature, the ice crystals in the soil pores gradually thawed, the salt crystals dissolved, and the liquid water content increased. However, multiple freeze--thaw cycles led to an increasing trend in the liquid water content, confirming the existence of water vapor in the coarse-grained soil. After migrating to the cold end, the water vapor exhibited a vapor--liquid phase transition,  
Figure 11: 5 Variation curves of the water and salt contents along the height of the saline soil column under freeze—thaw cycles. Figure 12: Migration height of fluorescein-labeled liquid water. resulting in an increase in the liquid water content. In the nonfrozen zone at H = 28.2 cm (Figure 13b), the temperature remained higher than the freezing temperature, and no ice crystals were produced during the freeze--thaw cycles. The liquid water content reached its maximum within the first 80 h and then stabilized, which occurred synchronously with the changes in external water replenishment. The increase in the liquid water content during the first 80 h was mostly due to the unsaturated characteristics of the coarse-grained saline soil. The high matric suction of the soil caused the migration of a large amount of water to H = 28.2 cm, and the fluctuation in the liquid water content during the following 400 h could be attributed to the consumption-release of liquid water due to salt crystallization-dissolution caused by temperature changes, as well as the increase-decrease in matric suction caused by temperature variations, leading to changes in the liquid water content at H = 28.2 cm in the nonfrozen zone.