esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Proposed Segmentation Model  
We designed a convolutional network based on the U-Net semantic segmentation architecture [25] which is a fully convolutional network that combines an encoder network that generates a dense feature map, followed by a decoder network that generates pixel-wise classification predictions. Figure 2 is a visual description of the model developed in this work and Figure 3 is flowchart describing the entire YeastNet platform. By zero-padding the tensor for every convolution operation, the output prediction tensor maintains the same size dimensions as the input image.  
The network is composed of repeated motifs that consist of two 3 \(\times\) 3 convolutional + ReLU layers and a resolution changing layer. In the encoder part of the network, the resolution changing layer in each motif is a 2 \(\times\) 2 max-pooling layer. Four repeats of this down-sampling motif make up the encoder network. The decoder follows with four repeats of an up-sampling motif whose final layer is a transpose convolution. The final set of operations consists of two 3 \(\times\) 3 convolutional + ReLU layers and a 1 \(\times\) 1 convolutional layer which predicts the class probability for each pixel.
## 4 Results and Discussions
### Effect of Surcharge Magnitudes
#### 4.2.2 Bending Moment  
Figure 15 demonstrates the non-dimensional bending moment profiles of piles under different surcharge magnitudes, increasing first and then decreasing as the depth increases. There are significant positive relationships between the bending moment and surcharge magnitude. The maximum non-dimensional bending moment increases from 0.229 \(\times\) 10\({}^{-3}\) to 2.865 \(\times\) 10\({}^{-3}\) (from 0.40 \(\times\) 10\({}^{-3}\) to 4.41 \(\times\) 10\({}^{-3}\)) in the case of \(\beta\) = \(-20^{\circ}\) (\(\beta\) = \(-10^{\circ}\)) while the surcharge load varies from 34.375 to 184.375 kPa. In addition, the location of the maximum bending moments of the negative battered piles (\(\beta\) = \(-20^{\circ}\) and \(-10^{\circ}\)) for the case of \(p\) = 34.375 kPa appears near \(z_{\alpha}\) = 0.3. However, it would appear in the middle of somewhere between \(z_{\alpha}\) = 1 and \(z_{\alpha}\) = 0.8 for different surcharges (\(p\) = 84.375, 134.375, and 184.375 kPa). In addition, the locations of the maximum bending moments in the case of \(\beta\) = \(-20^{\circ}\) are deeper than those in the case of \(\beta\) = \(-10^{\circ}\), indicating that the negative battered pile of greater inclination can transfer the earth pressure to a deeper depth.  
Figure 14: Variations of non-dimensional pile deflections with depth under different surcharge magnitudes: (**a**) \(\beta\) = \(-20^{\circ}\); (**b**) \(\beta\) = \(-10^{\circ}\).
# Underwater Radiated Noise Prediction Method of Cabin Structures under Hybrid Excitation
## 4 Experiments  
### Experimental Conditions  
The test object is a submarine cabin shrinkage model, the cabin model is a double-shell structure, and the pressure hull is an outer frame form. The length of the main double-shell section \(L=2300\) mm, the light shell radius \(R_{1}=2000\) mm, the pressure shell radius \(R_{2}=1600\) mm, ballast devices are arranged at both ends, and four solid ribs and ten ring ribs are distributed at equal intervals between the inner and outer shells. The test objects are shown in Figure 15a, while the finite element model is shown in Figure 15b.  
Figure 14: Radiation noise predicted results at different angles: (**a**)100 Hz; (**b**) 630 Hz; (**c**) 1000 Hz.  
Figure 13: Radiated noise prediction results with different SNRs: (**a**) Far-field radiation noise spectrum. (**b**) Far-field radiation noise one third octave in-band mean error.  
A shaker and air sound source were arranged in the cabin model as the excitation equipment. The sound source excitation was a sound box, 13 cm long, 11 cm wide, 18 cm high, and with 8 cm diameter of the circular radiant surface. The shaker model was JZ-50, the frequency band was 50-3000 Hz, and the maximum excitation force was 500 N. The excitation signals of the two excitation devices were single-frequency signals (1/3 octave point within 100 Hz-1 kHz). A column coordinate system was established with the center of the left end face of the cabin model as the origin of the coordinates. The coordinate of the acoustic excitation source was \((0,0,L/2)\), while the coordinate of the force excitation source was \((R_{2}/2,0,L/2)\). The collector model was B&K3660-D, and the front section of the collector was set with a high-pass digital filter with an upper frequency of 7 Hz. The sampling rate of the collector was \(12.8\times 2.56\) kHz. The hydrophone type was B&K8103, the vibration sensor type was PCB M352C68, and the microphone type was MP201.  
Inside the cabin, the measuring points were numbered in a clockwise sequence (from the watertight door end to the cabin), and a microphone and vibration sensor were arranged at each measuring point. The positions of the measuring points on different sections of the shell were represented by the letters A-E. The positions of different measuring points on the same section were represented by numbers. There were 40 microphones and 40 vibration sensors arranged in the cabin, as shown in Figure 16 ab. Four far-field investigation points were set in the underwater acoustic field, as shown in Figure 16c. The coordinates of investigation points 1-4 are \((6,0,L/2)\), \((11,0,L/2)\), \((6,0,L/2-3)\), and \((6,0,L/2+3)\), respectively.
# Explainable Artificial Intelligence for State of Charge Estimation of Lithium-Ion Batteries
## 4 Experiments and Results
### E1: SoC Estimation Using Different NN Architectures
This is further expanded upon in Section 4.3. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Train**} & \multicolumn{2}{c}{**Validation**} & \multicolumn{2}{c}{**Test**} \\ \cline{2-7}  & **MSE** & **MAE** & **MSE** & **MAE** & **MSE** & **MAE** \\ \hline
**LSTM** & \(3.4\times 10^{-4}\) & \(11.8\times 10^{-3}\) & \(5.6\times 10^{-4}\) & \(14.7\times 10^{-3}\) & \(2.9\times 10^{-4}\) & \(12.2\times 10^{-3}\) \\
**GRU** & \(3.8\times 10^{-4}\) & \(12.6\times 10^{-3}\) & \(6.2\times 10^{-4}\) & \(15.7\times 10^{-3}\) & \(3.5\times 10^{-4}\) & \(13.3\times 10^{-3}\) \\
**CNN-LSTM** & \(4.6\times 10^{-4}\) & \(14.6\times 10^{-3}\) & \(7.3\times 10^{-4}\) & \(18.5\times 10^{-3}\) & \(4.5\times 10^{-4}\) & \(15.4\times 10^{-3}\) \\
**Informer** & \(1.9\times 10^{-3}\) & \(33.6\times 10^{-3}\) & \(2.2\times 10^{-3}\) & \(35.8\times 10^{-3}\) & \(1.9\times 10^{-3}\) & \(33.5\times 10^{-3}\) \\
**ResNet** & \(19.4\times 10^{-3}\) & \(12.1\times 10^{-2}\) & \(18.2\times 10^{-3}\) & \(11.5\times 10^{-2}\) & \(20.4\times 10^{-3}\) & \(12.5\times 10^{-2}\) \\
**ResNet-LSTM** & \(4.0\times 10^{-3}\) & \(50.4\times 10^{-3}\) & \(3.8\times 10^{-3}\) & \(49.9\times 10^{-3}\) & \(3.9\times 10^{-3}\) & \(49.4\times 10^{-3}\) \\
**InceptionTime** & \(41.2\times 10^{-2}\) & \(57.8\times 10^{-2}\) & \(37.0\times 10^{-2}\) & \(53.9\times 10^{-2}\) & \(41.5\times 10^{-2}\) & \(58.3\times 10^{-2}\) \\
**Fusion Hybrid** & \(3.5\times 10^{-4}\) & \(13.4\times 10^{-3}\) & \(3.1\times 10^{-4}\) & \(13.2\times 10^{-3}\) & \(3\times 10^{-4}\) & \(12.8\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: E1’s NNs and E2’s Fusion Hybrid Model performance results for train, validation, and test sets according to the evaluation metrics. Figure 4: CNN-LSTM SoC prediction: SoC predictions (blue) versus real SoC (orange). (**a**) EI’s result. (**b**) E3’s result, trained only with CURRENT and VOLTAGE. (**c**) E3’s result, trained only with CURRENT and VOLTAGE as well as a reduced window size. There is no stark visual difference between subfigures (**a–c**).
# Coaxial Fibres Incorporated with Phase Change Materials for Thermoregulation Applications
## 2 Materials and Methods
### Fibres' Physical, Chemical, Thermal, and Mechanical Characterization  
#### 2.4.1 Bright-Field Microscopy  
The morphology of the fibres will be presented herein. In addition, this test will give indications of the presence of a coaxial system. This analysis was carried out by bright-field microscopy using a Leica DM IL LED inverted microscope (Leica Microsystems, Weetzlar, Germany). Images were taken at 5\(\times\) magnification, and the average fibre diameter was determined using ImageJ(r) software (version 1.53, National Institutes of Health, Bethesda, MD, USA).
## 3 Model Interpretation for Signal Classifier  
As we mentioned in Section 2, model interpretation works have been proposed by many researchers. In the medical domain, most researchers focus on a model interpretation given image data, but few works deal with it given time series data. Specifically, in the medical domain, for diagnosis given time series data such as electroencephalogram (EEG) and electromyography (EMG), physicians consider not only periodic patterns, but also frequency bands [27; 28; 29], whereas existing methods take account of periodic patterns [24; 25]. Therefore, we aim to develop an improved model interpretation given time series data. We simply define our problem as follows.  
**Problem definition:** Given a black box classifier \(f\), which is inaccessible model parameters, and to classify an input data \(x\), which is a time series, our goal is to provide an interpretable representation \(\xi(x)\) of the input.  
In the rest of this section, we introduce our explanation method and the building blocks for our approach.
# Research on Collision Warning Method for Ship-Bridge Based on Safety Potential Field
## 6 Discussion  
This paper focuses on the problem of ship collision avoidance and risk warning in inland river bridge areas. The established safety potential field model can be used for the dynamic simulation of inland river bridge area. The risk assessment of ship navigation in bridge-area waters can facilitate the management of ships in the bridge area and the delineation of virtual hazardous areas, which is conducive to emergency planning [37]. Meanwhile, this helps to assess the safe speed and distance of vessels in complex bridge area environments [38] and improve water traffic management [39; 40].  
Compared with the existing research on collision warnings for ships and bridges in inland bridge areas, the main innovation of this paper is the overall analysis of collision warning for inland ships and bridges in static and dynamic traffic scenarios based on navigational risk using the safety potential field method. Firstly, the safety potential field model proposed in this paper is different from the previous models [41]. The kinetic field model in this study combines the effects of wind and current when ships navigate in the bridge area. The state of the ship after being subjected to wind and current is predicted by Kalman filtering and the predicted values are used to calculate the kinetic energy field, which is then coupled into the safety potential field model, which is not covered in previous studies. By updating the kinetic energy field at each step, the safety potential field values are also updated, which makes the model more realistic and accurately reflects the changes of the risk in the bridge area. Secondly, this paper applies a risk study to inland ship navigation, which is different from many previous bridge area warning methods [42]. Compared with the radar detection method and with video surveillance and image processing methods, the warning range of the safety potential field-based ship-bridge warning method proposed in this paper includes the entire bridge area, which is broad and computationally small, and can be updated in real time. In addition, the model uses a three-dimensional isopotential map, which can visualise the risk status of the bridge area.  
The proposed model can be used for bridge collision avoidance in inland rivers. In terms of risk warning, the model can be used as an early warning system for navigational risks. In addition, the model can improve the management of navigation traffic in the vicinity of bridge areas [7]. For example, the safe distance between inland waterway vessels and the channel bank wall can be quantified and regulated using the proposed model. For bridges, the model can be integrated with shipboard systems or shore-based control systems to guide ships in inland waterways.
## 3 Results
### TMAD Analysis  
Considering the speckle-tracking derived TMAD of tricuspid valve, all measured TMAD indices (TMADlat, TMADsept, TMADmid and TMADm%) were significantly decreased in the non-worsening group as well as in the worsening group in comparison with the control group. Additionally, in all three groups, we found a descendent distribution of the TMAD values from the lateral to mid and to the septal point (Figure 4).  
Figure 3: Receiver-operator characteristic analysis. ROC curves result from all the variables (echocardiography, BNP level and 6 MWD) that showed significant difference between controls and worsening PAH group.  
Furthermore, we compared the TMAD indices between the two PAH groups. Although all were lower in the worsening group compared to the non-worsening group, only the TMADm% was significantly decreased in worsening PAH children (\(p\) = 0.010) (Table 2, Figure 5).  
We have performed ROC curve analysis for TMADm% and BNP level that showed significant difference between non-worsening and worsening PAH groups. We found a cut-off value of 16.15% for TMADm% that can predict worsening in PAH children, with a sensitivity and specificity of 88.90% and 72.70%, respectively (Figure 6).  
Additionally, we found a cut-off value of 34.35 pg/mL for BNP level that can predict worsening in PAH children, with a sensitivity and specificity of 77.80% and 72.70%, respectively (Figure 7).  
Figure 4: Comparison between worsening, non-worsening PAH children and controls regarding TMAD of tricuspid valve values in three points: lateral, midpoint and septal. * worsening or non-worsening PAH vs. control group, \(p\) < 0.05.  
Figure 5: Comparison between worsening, non-worsening PAH children and controls regarding TMADm%; * non-worsening or worsening PAH vs. controls; ** worsening vs. non-worsening PAH group; \(p<0.05\).  
Figure 6: Receiver-operator characteristic analysis. ROC curves result from TMADm% that showed significant difference between non-worsening and worsening PAH groups. The optimum cut-off value for TMADm% to predict clinical worsening was 16.15%; the sensitivity and specificity were 88.90% and 72.70%, respectively.  
Discussion. This Study Emphasizes the Prognostic Value of TMAD of the Tricuspid Valve in PAH Children
## 3 Results and Discussion
### Optimization of Digestion Temperature  
Since the amount of energy used for the digestion of sodium monofluophosphate in the toothpaste sample is an important factor, five temperature levels (100, 120, 140, 160 and 180 \({}^{\circ}\)C) were evaluated. It is worth indicating that sample mass was kept at 0.05 g and time at 60 min. As can be observed in Figure 2, low agreements (50-67%) were obtained when toothpaste samples were subjected to lower temperatures of 100, 120 and 140 \({}^{\circ}\)C. These observations suggest that temperatures less than or equal to 140 \({}^{\circ}\)C are insufficient to break the covalent bond between phosphorus and fluoride in monofluorophosphate (Figure 3). Therefore, an increase in temperature to 180 \({}^{\circ}\)C drastically increased agreement from 50% to 100%. Thus, the optimum digestion temperature was 180 \({}^{\circ}\)C, as agreed in other studies in the literature [26; 27]. Therefore, temperature was directly proportional to the digestion efficiency of toothpaste samples using deionized water.  
Figure 1: Influence of sample weight on fluoride determination in toothpaste samples using F-ISE after WB-MAD. Digestion conditions: digestion temperature, 180 \({}^{\circ}\)C; digestion time, 60 min; water volume, 10 mL and \(n\) = 3.
# Evaluation of Rice Straw Ash as a Pozzolanic Addition in Cementitious Mixtures
## 3 Results
### Analysis of RSA Reactivity
#### 3.2.3 Thermogravimetry Analysis
The fixed CH was calculated according to the following equation (Equation (4)) [36]:  
\[Fixed\ CH\ (\%)=\frac{CHc*\%C-CHp}{CHc*\%C}\times 100 \tag{4}\]  
where:  
\(CH_{C}\) is the amount of portlandite present in the control paste for time t  
\(CH_{p}\) is the amount of portlandite present in the paste with RSA for time t  
%C is the proportion of RSA present in the paste (e.g., 0.7 is for a PC/RSA paste with a 7/3 mass ratio  
The fixed CH values are summarized in Table 5. The expected trend was generally observed: a rising fixed CH percentage for the same paste with curing time. After 28 curing days, the fixed CH for 7/3 paste was 81.5%, and this parameter decreased as the replacing percentage lowered. This behavior is common for reactive pozzolanic materials: for 7/3 paste, the amount of portlandite formed in the hydration of PC was the lowest among the tested pastes, because it had the lowest proportion of PC; additionally, this paste had the highest percentage of RSA, which means that a higher amount of reactive material can be pozzolanically combined with portlandite. Then, the proportion of the fixed CH was highest for the 7/3 paste; this parameter decreased sequentially for pastes 7.5/2.5, 8/2 and 8.5/1.5, for which the fixed CH percentages were 69.5%, 61.1% and 49.8% respectively. It can be concluded that RSA pozzolanic reactivity was good, and agreed with the electrical conductivity and pH results in aqueous suspension  
When comparing RSA reactivity with the reported RHA values, the reactivity found in these pastes for RSA was greater than for RHA. [PERSON] et al. [37] obtained fixed CH a percentage of 32% at 28 days for a 15% replacement paste for RHA, and other authors have obtained similar values [38]. Therefore, we conclude that despite having a lower silica content, RSA yielded good pozzolanic behavior.
## 1 Introduction  
Wireless Sensor Networks (WSNs) have attracted widespread curiosity regarding various applications such as industrial monitoring, environmental monitoring, etc. With the massive proliferation of WSNs, the operational, deployment cost, and sensor lifetime management of the WSN nodes in a dynamic and critical infrastructure is a stringent task [1, 2, 3]. In consideration of the aforementioned issues, the Software Defined Network (SDN) to assist the WSN was designed using Software Defined Wireless Sensor Networks(SDWSN) [4; 5], which is composed of three planes: the sensor plane, the forwarding plane, and the control plane. The sensor plane is composed of wireless sensor nodes, the forwarding plane is composed of OpenFlow SDN switches that forward the data from the sensor plane, and the control plane is composed of single/multiple SDN controllers for the global management of the SDWSN environment. The adoption of the SDWSN provides flexibility to manage, deploy, secure, and monitor the whole network [6; 7].  
As the SDWSN is still in the early stages of research, energy consumption, standardization, monitoring, security, temperature, electromagnetic noise, and radiation are of major concern. Normally, wireless sensor nodes are highly prone to these issues [8; 9; 10; 11; 12]. Prior research has focused on the clustering of such nodes. However, the prior clustering methods were highly prone to sparsity and mobility issues [13]. Other issues associated with these nodes are secure routing and scheduling, which are highly prone to link failure and security issues [14]. For that, much research adopts sleep scheduling and trust-based routing in the SDWSN environment [15]. On the other hand, the routing among SDN switches also faces severe security issues by imposing attacks on weak links and compromising switches [16]. These attacks were held in the existing environment due to the static nature of detection and prevention techniques. Furthermore, the SDWSN environments were highly prone to several types of routing attacks such as selective forwarding attacks, which refuse to forward messages from selected nodes, reducing traffic and increasing data loss. On the aspect of scheduling, the existing works relied on scheduling in one tier (i.e., schedules the overall nodes in one tier), which also creates interference and complexity when the nodes possess mobility [17].  
Generally, the SDWSN environment needs full-fledged research on security, energy consumption, and interference issues. The proposed work overcomes the aforementioned by performing a complexity-aware and dynamic secure model using intellectual techniques.
## 3 Method
### System Architecture  
The new architecture implemented in this study is described as follows: By selecting appropriate channel ratios, the task numbers and answer features are concatenated to form input feature maps, thereby optimizing the task's input channel and feature structure. This design improves the efficiency and accuracy of data processing, ensuring the stability and reliability of subsequent analysis processes. Next, the improved DenseNet proposed in this study is used as the classifier for the task. The improved DenseNet is based on the pre-trained DenseNet201 model, which was experimentally selected as a suitable backbone architecture for this task, with a new fully connected layer added for feature extraction and classification. Softmax is chosen over tanh as the activation function to further enhance the model's performance and stability. Additionally, the task is designed as a multi-class classification to optimize the structure of the output vector, reducing the complexity of the output vector and improving accuracy. Moreover, to achieve the goal of clinical application, an AI scoring system application software with a user interface was implemented. The model's prediction results are imported into a scoring rule decoding system, which decodes and identifies errors in the answers based on a set of rules. According to the rules in the scoring manual, all results are summed and the raw scores are calculated. As an example, the rule for question 2 is that the horizontal line should be more than half and the vertical line should not exceed 30\({}^{\circ}\). Figure 3 uses question 2 as an example to show answers that satisfy and do not satisfy the rule. Subsequently, the system performs norm referencing to convert the raw scores into standard scores and achievement levels and finally displays the detailed results and performance of the children in the VMI scoring test. This entire process ensures the accuracy and fairness of the scoring, making the final results reliable and valuable for reference. The following section provides a detailed description of how the architecture is integrated through the experimental content of each stage. Figure 4 is the new architecture proposed in this paper.  
Figure 4: Proposed architecture.  
Figure 3: (a) Question 2 prompt (b) satisfies the rule and (c) does not satisfy the rule.