esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Improved Learning for Salient Object Detection
###### Abstract  
Salient Object Detection (SOD) aims at identifying the most visually distinctive objects in a scene. However, learning a mapping directly from a raw image to its corresponding saliency map is still challenging. First, the binary annotations of SOD impede the model from learning the mapping smoothly. Second, the annotator's preference introduces noisy labeling in the SOD datasets. Motivated by these, we propose a novel learning framework which consists of the Self-Improvement Training (SIT) strategy and the Augmentation-based Consistent Learning (ACL) scheme. SIT aims at reducing the learning difficulty, which provides smooth labels and improves the SOD model in a momentum-updating manner. Meanwhile, ACL focuses on improving the robustness of models by regularizing the consistency between raw images and their corresponding augmented images. Extensive experiments on five challenging benchmark datasets demonstrate that the proposed framework can play a plug-and-play role in various existing state-of-the-art SOD methods and improve their performances on multiple benchmarks without any architecture modification.  
salient object detection; self-improved strategy +
Footnote †: journal: Applied  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-00000-00000-00000-00000-0000-00000-00000-00000-00000-00000-000000-00000-00000-00000-00000-00000-0  
part of a bicycle is labeled salient in the left but _not_ salient in the right. Moreover, both images with butterflies in Figure 1b share similar layouts (butterfly, flowers, and green background) but the flowers are marked as salient in the left image and not salient in the right. Therefore, SOD requires the model to be of high robustness to noisy labeling.  
To improve the robustness, we propose a novel **Aug**mentation-based **C**onsistent **L**earning (ACL) scheme. By simulating a cross-dataset validation scenario, ACL introduces salient-related data augmentation to images, aiming to enhance the consistency of predictions between raw and augmented images. In this way, ACL makes the prediction of a raw image be consistent to that of the augmented image. As a result, the SOD model is capable of accurately locating the most salient objects while disregarding irrelevant variations caused by augmentation, thus improving the robustness of the model.  
Our main contributions are summarized as follows.  
* We propose a novel Self-Improved Training strategy for SOD, which reduces the learning difficulty and effectively improves the performance.
* We present an Augmentation-based Consistent Learning scheme that regularizes the consistency between raw images and their corresponding augmented images at training time and improves the robustness of the models.
* The proposed method is model independent, which can be applied to existing prevalent methods without modifying the architecture to gain considerable improvements.
# Pockels Effect at the Interface between Water and Ti Electrode
## 3 Experimental Results
### Electroreflectance Spectra
#### 3.2.2 Electroreflectance Spectra under DC Electric Field  
Since the Pockels effect of interfacial water on the ITO electrode by electromodulation has been observed to depend on a DC bias voltage (unpublished), we studied whether there is a DC bias voltage dependence on the water-Ti electrode interface as well. See Figure 5 for comparison with the graph of Figure 3 without DC voltage: at DC +1.0 V, the magnitude of the signal increased about three times without change of the sign; at DC \(-\)1.0 V, the magnitude of the signal increased three times with the sign reversed.
# An Improved Target Network Model for Rail Surface Defect Detection
## 4 Experimental Results and Analysis
### Comparison with Classical Algorithms  
To further validate whether the proposed model is superior at detecting rail surface defects compared with classical algorithms, comparative experiments were conducted with mainstream object detection models such as faster RCNN, YOLOv5s, and YOLOv7 based on the same dataset (as shown in Table 2). This type of comparison helps to more naturally assess the detection performance among different methods. This study evaluated and compared the performance of precision, recall, F1 score, and mAP. An F1 score of 0.86 and mAP of 87.4% were achieved with the two-stage faster RCNN detection algorithm. On the other hand, YOLOv5s showed improved detection performance over faster RCNN, with mAP and F1 scores of 93.9% and 0.90, respectively; however, they were still lower than the proposed method. In comparison, our mAP increased by 1.3% and 4.9% compared with YOLOv5 and YOLOv7, respectively. Considering the superior comprehensive performance, the proposed model is more suitable for locating and detecting rail surface defects in  
Figure 7: Confusion matrix comparison. (**a**) P–R curve; (**b**) confusion matrix.  
real-world scenarios compared with other algorithms such as faster RCNN, YOLOv5s, and YOLOv7.
# Numerical Analysis on the Performance of a Radiant Cooling Panel with Serpentine-Based Design
## 3 Results and Discussions  
### Grid Independence Test  
A grid independence test was carried out on Design 1 (see Figure 1a) to determine the best number of elements required for the simulation. Table 6 shows the grid independence test result, where the number of elements is the variable, and the pressure drop and outlet temperature are taken for analysis.  
In the grid independence test analysis, the operating conditions and parameters were kept constant, while the number of meshing elements was varied. Based on the graph presented in Figures 4 and 5, the outlet temperature became constant as the number of elements increased, while the pressure drop did not seem to be affected greatly by the number of elements, since the result was showing a consistent value with a 0.0337 variance. Therefore, the total number of elements used in the simulation was chosen to be 313,492, since the outlet temperature began to converge into a steady value while there was negligible variation in the pressure drop at this number of elements. Choosing a larger number of elements would only increase the computing time, without any significant impact on the simulation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Elements** & **Outlet Temperature (K)** & **Pressure Drop (Pascal)** \\ \hline
275,090 & 291.9821 & 129.5508 \\
283,680 & 291.7517 & 129.4224 \\
313,492 & 291.6585 & 129.3006 \\
383,305 & 291.6650 & 129.6958 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of parameters and operating conditions for the simulation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Comparison 1**} & \multicolumn{3}{c}{**Comparison 2**} \\ \cline{2-6}  & \begin{tabular}{c} **Experimental.** \\ **Reproduced from** \\ **[27], Elsevier: 2020** \\ \end{tabular} & \begin{tabular}{c} **Conventional** \\ **Sperpenine** \\ **C** \\ \end{tabular} & \begin{tabular}{c} **Percentage** \\ **Error (\%)** \\ \end{tabular} & \begin{tabular}{c} **Experimental.** \\ **Reproduced from** \\ **[28], Elsevier: 2012** \\ \end{tabular} & \
\begin{tabular}{c} **Conventional** \\ **Sperpenine** \\ **Sperpenine** \\ \end{tabular} &
\begin{tabular}{c} **Percentage** \\ **Error (\%)** \\ \end{tabular} \\ \hline Inlet & 15.7 \({}^{\circ}\)C & 15.0 \({}^{\circ}\)C & 4.60 & 14.6 \({}^{\circ}\)C & 15.0 \({}^{\circ}\)C & 2.66 \\ Outlet & 17.8 \({}^{\circ}\)C & 18.6 \({}^{\circ}\)C & 4.30 & 18.4 \({}^{\circ}\)C & 18.6 \({}^{\circ}\)C & 1.07 \\ Surface & 21.3 \({}^{\circ}\)C & 22.5 \({}^{\circ}\)C & 5.33 & 21.5 \({}^{\circ}\)C & 22.5 \({}^{\circ}\)C & 4.44 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison with experimental results from other literatures.
###### Abstract  
Wireless sensor networks (WSNs) have been developed recently to support several applications, including environmental monitoring, traffic control, smart battlefield, home automation, etc. WSNs include numerous sensors that can be dispersed around a specific node to achieve the computing process. In WSNs, routing becomes a very significant task that should be managed prudently. The main purpose of a routing algorithm is to send data between sensor nodes (SNs) and base stations (BS) to accomplish communication. A good routing protocol should be adaptive and scalable to the variations in network topologies. Therefore, a scalable protocol has to execute well when the workload increases or the network grows larger. Many complexities in routing involve security, energy consumption, scalability, connectivity, node deployment, and coverage. This article introduces a wavelet mutation with Aquila optimization-based routing (WMAO-EAR) protocol for wireless communication. The presented WMAO-EAR technique aims to accomplish an energy-aware routing process in WSNs. To do this, the WMAO-EAR technique initially derives the WMAO algorithm for the integration of wavelet mutation with the Aquila optimization (AO) algorithm. A fitness function is derived using distinct constraints, such as delay, energy, distance, and security. By setting a mutation probability P, every individual next to the exploitation and exploration phase process has the probability of mutation using the wavelet mutation process. For demonstrating the enhanced performance of the WMAO-EAR technique, a comprehensive simulation analysis is made. The experimental outcomes establish the betterment of the WMAO-EAR method over other recent approaches.  
wireless communication; routing protocol; wavelet mutation; Aquila optimizer; wireless sensor networks +
Footnote †: journal: _Sensors_
## 3 Materials and Methods
### Data Pre-Processing and Filtering
Individually, even for moderate amounts of noise and blur, the accuracy of the network decreased significantly, and one can only assume that the combination of various of these image distortions can yield even poorer results. Therefore, in this paper, we implement a \"filtering\" or sample preselection process based on several traditional image processing metrics [30] to estimate whether an image can be considered for further processing or not, namely, the signal-to-noise ratio (SNR), the blurring index, and the variance of the image (for determining the contrast). Typically, the following parameters are correlated: an image with high levels of noise present and low contrast and low blurring. The images in the extended dataset were then first pre-processed using traditional image processing techniques. (1) We calculate the blurring index using a spectral blur estimation operator. (2) For estimating the contrast, we make use of the variance as a measure of the quality of the image, using the Michelson Contrast Ratio, as shown in Figure 8 for an example in a previous work in the context of medical imaging [31], filtering out images with high variance. (3) Additionally, we performed an SNR test using the methods proposed by [PERSON] and [PERSON] [30] to determine whether or not an image is well suited for the data augmentation and training phases. Once these tests have been carried out, we perform image restoration procedures for removing image distortion like blurring (using Wiener Convolution) and low contrast (using CLAHE, contrast limited adaptive histogram equalization) and repeating the SNR test to determine if the corrected images can be used for further processing. Figure 7: Examples of low-quality images deemed not useful for classification, which are automatically filtered during the pre-processing stage. This process was applied to the 18,345 images (divided in the eight classes in Table 2) yielding a reduced set of filtered images shown in the third row of Table 4. This set of images undergoes the data augmentation process, applying various geometric (flip, rotation), photometric (histogram equalization, changes in hue), and noise (saturation, blurring) transformations. The augmented dataset is comprised of 251,250 images. From the table, it can be observed that many instances of the non-extended classes were discarded. This is because of the fact that many of these images were corrupted by intense illumination (i.e., sun light) or were under-exposed; our data collection process was more careful, and thus the filtering for the D00, D10, D11, and D40 was less severe.
## 4 Parsimonious Network Based on Fuzzy Inference System (PANFIS)
### Network Architecture of PANFIS  
The unique property of PANFIS's network architecture is observed in the presence of the multivariate Gaussian function. The multivariate Gaussian function possesses a non-diagonal covariance matrix which triggers a non-axis-parallel ellipsoidal cluster [11]. Compared to the traditional Gaussian function with the standard product _t-norm_ operator, such a cluster offers better coverage to an irregular data distribution which does not span the main axes. As a result, it suppresses the fuzzy rule requirement to a lower level than that of the standard Gaussian rule. The multivariate Gaussian rule is, however, less transparent than the standard Gaussian rule since the atomic clause of the If-Then rule (AND-part) is omitted and thus does not have a fuzzy set representation. This calls for a transformation  
Figure 5: An architectural scheme of the PANFIS method.  
strategy which makes it possible to form accurate representation of a fuzzy set. The rule consequent of PANFIS is constructed under the first order TSK fuzzy rule, which is a linear function in the output space. The multivariate Gaussian function for the rule firing strength is expressed as follows:  
\[R_{i}=\exp(-(X-C_{i})\Sigma_{i}^{-1}(X-C_{i})^{T}) \tag{1}\]  
where \(X_{n}\in\Re^{1\times n}\) is the input feature and \(C_{i}\in\Re^{1\times u}\) is the center of the multivariate Gaussian function, while \(\Sigma_{i}^{-1}\in\Re^{u\times u}\) denotes the inverse covariance matrix which steers the orientation and size of the non-axis parallel ellipsoidal cluster. \(u\) is the input dimension. \(R_{i}\) stands for the firing strength of the fuzzy rule, which also indicates the degree of confidence of the fuzzy rule. To guarantee the partition of unity [11], the normalization of rule firing strength is performed as follows:  
\[\varphi_{i}=\frac{R_{i}}{\sum\limits_{i=1}^{C}R_{i}} \tag{2}\]  
where \(C\) is the number of fuzzy rules. The output of PANFIS results from the weighted average of the rule firing strength and the rule consequent, as follows:  
\[y=\sum\limits_{i=1}^{C}\Omega_{i}\varphi_{i}=\frac{\sum\limits_{i=1}^{C} \Omega_{i}R_{i}}{\sum\limits_{i=1}^{C}R_{i}} \tag{3}\]  
where \(\Omega_{i}=x_{e}W_{i}\) is the rule consequent of PANFIS, while \(x_{e}\in\Re^{1\times(n+1)}\) and \(W_{i}\in\Re^{(u+1)\times 1}\) are the extended input vector and the output weight vector of the \(i\)-th rule, respectively. The first-order TSK rule consequent is selected here because it offers a higher degree of freedom than the zero-order TSK rule consequent.
## 4 Methodology
### Pre-Processing of Speech Signals
#### 4.1.2 Speech Detection  
The speech detection and isolation algorithm is applied to the audio signal to remove the silence and extract the most beneficial information from the signal. In this stage, we use the MATLAB platform to detect speech functions. The detect speech function can be described in the block diagram as shown in Figure 2, with the following steps [23]:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Gender**} & \multirow{2}{*}{**Speaker**} & \multicolumn{2}{c}{**Utterance**} & \multirow{2}{*}{**Total for Each**} & \multirow{2}{*}{**Total Dataset**} \\ \cline{3-3} \cline{5-6}  & & **Enrolment** & & & **Verification** & **Speaker** \\ \hline
**Male** & 106 & 4 \(\times\) 24 phrases & 10 \(\times\) 4 & 136 utterances & 14,416 \\ \hline
**Female** & 32 & 4 \(\times\) 24 phrases & 10 \(\times\) 4 & 136 utterances & 4352 \\ \hline \hline \end{tabular}
\end{table}
Table 2: YOHO LDC speech dataset.  
Figure 2: Overview of the speech detection algorithm.  
1. The audio signal is converted to a time-frequency representation using a specified window and overlap length.
2. For each frame, the short-term energy and spectral spread are calculated.
3. Histograms are created for both short-term energy and spectral spread distributions.
4. The threshold is determined for each histogram.
5. The short-term energy and the spectral spread are smoothed by passing through successive five-element moving median filters across time.
6. Creating a mask by comparing the short-term energy and spectral spread with their respective thresholds. The feature must be above its threshold to declare a frame containing speech.
7. The masks are combined.
8. Merging the regions declared as speech.  
The result of the detected speech algorithm for the audio signal is illustrated in Figure 3.  
In addition, we scan and verify the dataset files of all 138 speakers before and after speech detection, as provided in Table 3.
## 4 Results
### Tropospheric VCD  
Figure 11 shows the tropospheric NO\({}_{2}\) VCD of OMI, EMI, and TROPOMI, respectively. On average, the tropospheric VCD of EMI (\(1.32\times 10^{16}\) molec cm\({}^{-2}\)) is 15% and 23% higher than that of OMI (\(1.14\times 10^{16}\) molec cm\({}^{-2}\)) and TROPOMI (\(1.07\times 10^{16}\) molec cm\({}^{-2}\)), respectively. These discrepancies could mainly result from the tropospheric AMF. The spatial distribution of EMI is in good agreement with OMI (r = 0.88) and TROPOMI (r = 0.89), which can be seen from the scatter plot of VCD from EMI vs. OMI and TROPOMI in the bottom panel of Figure 11.  
Figure 10: Distribution of the SCD deviations from box means for all valid pixels in August 2018. The black line shows a Gaussian function fitted to the histogram data. The width of the Gaussian (\(\sigma\)) corresponds to a slant column error of \(0.79\times 10^{15}\) molec cm\({}^{-2}\).  
Figure 9: Total VCD comparisons between EMI and (**a**) OMI and (**b**) TROPOMI over the North China Plain region on 22 January, 2019.
###### Abstract  
Monitoring the thermal behavior of distributed energy resources (DERs) network explores the dualism between thermal effects and electrical power flow. This paper proposes a design concept that monitors thermal conditions of DER grids, using ZigBee/GSM wireless sensor networks (WSNs) for real-time monitoring in rural and remote areas. The concept seeks to improve upon existing designs by integrating composite functions. The functions comprise temperature conditions monitoring, data acquisition, and wireless data transmission including data storage and abnormal conditions alert/notification for control solutions. Thus, the concept determines the thermal impact on the DERs integrated network. WSNs with temperature sensors LM35 are utilized to complement ZigBee and Global System for Mobile Communications (GSM) technologies as a communication assisted link. Temperatures are measured from solar Photovoltaic PV modules, wind turbine, distribution cables, protection control units, and energy storage facilities. The ATMEGA328P microcontroller is assigned for signal and control processing. The circuit performance is coordinated and displayed on an LCD screen for normal conditions, whereas abnormal scenarios communicate through an alert/notification by GSM Short Message Service (SMS) protocol. The development analysis was performed through algorithm and circuit simulations. Proteus software was used for circuit design. Both the algorithm and circuit analysis passed the assigned simulation stages.  
distributed energy resources; thermal condition monitoring; wireless sensor networks; ZigBee/GSM communications 2019 7 383; doi:10.3390/pr7060383
# A Review on Two-Phase Volumetric Expanders and Their Applications
## 2 General Principles of the Two-Phase Volumetric Expanders
### Flush Evaporation Fundamentals in Volumetric Two-Phase Expanders
They concluded that the numerical analysis of a flash expansion in a volumetric machine matches the experimental results when the employed numerical model considers that the liquid and vapor are isothermal. Conversely, they stated that the modelling is complicated when both phases cannot be regarded as isothermal, and this aspect still requires more attention. Consequently, these authors suggested that the technology of the expansion device affects the magnitude of the mixing between liquid and vapor phases, thus, appearing as a crucial aspect in the choice of the more appropriate numerical model.
# Solid-State Lithium Battery Cycle Life Prediction Using Machine Learning
## 5 Conclusions  
This paper focused on identifying degradation patterns and the _RUL_ prediction of solid-state lithium polymer batteries by using domain knowledge features. First, 11 physical quantities such as capacity, energy, and charge/discharge voltage differences were chosen as input features by analyzing degradation mechanisms, charging/discharging curves, and correlations between features and _RUL_. Then, the _RUL_ was estimated using a symbolic regression model, which showed that kurtosis and charge-specific capacity had strong correlations with _RUL_. Finally, the SR model showed a higher accuracy (R\({}^{2}\), 0.91) compared with the other three machine learning methods (SVR, GPR, and EN with R\({}^{2}\) as 0.81, 0.83, and 0.69, respectively). This improvement was attributed to the accurate tracking of battery aging by the proposed impedance evolution features and the successful optimization by the unique formula mining function of SR models. Solid-state lithium batteries are close to the gate of commercialization, and their _RUL_ prediction will become of vital importance. Improving the _RUL_ prediction model with higher accuracy, earlier estimation, and better interpretability is promising to further promote the large-scale applications of solid-state lithium batteries.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11104671/s1](https://www.mdpi.com/article/10.3390/app11104671/s1)]([https://www.mdpi.com/article/10.3390/app11104671/s1](https://www.mdpi.com/article/10.3390/app11104671/s1)), Table S1: 30 available features.  
[PERSON] and [PERSON] conceived and carried out the experiments. [PERSON] was in charge of the overall direction. [PERSON] provided the main datasets as the experimental foundation. [PERSON] classified and sorted the key technical patents and algorithms. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] carried out the energy storage conditions and life-span service performance investigation, and useful discussions in the final manuscript. All authors have read and agreed to the published version of the manuscript.  
This research and APC was funded by the National Natural Science Foundation of China, grant number 52077096, Hubei Provincial Science and Technology Major Project of China, grant number 2020 AEA011, the Key Research & Development Plan of Hubei Province of China under, grant number 2020 BAB100, and the excellent youthful project Scientific research of Hunan Provincial Department of Education, grant number 20B491.  
Not applicable.  
Data Availability Statement: The data presented in this study are openly available in [Zenodo] at [10.5281/zenodo.4743315], reference number [42].  
We thank S.T. for providing the battery data for carrying out this research. The authors declare no conflict of interest.