esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Hybrid Storage System Simulation  
In this section, the above described battery and the flywheel models are joined to compose a hybrid storage system consisting of two 47.7 battery ranks (see Table 1) and a flywheel unit having the characteristics reported in Tables 2 and 3.  
Figure 12: Simulation results related to the application of a test reference power; **(a)**: output power reference and FWR speed; **(b)**: error between reference and actual output power; **(c)**: EM internal temperature; **(d)**: EM losses.  
By means of a low-pass filter, the low frequency components of the required power are supplied by the batteries whereas the high frequency ones are supplied by the flywheel. Obviously, the lower the filter cut frequency, the lower the stress induced on the batteries. On the other hand, the filter cut frequency depends on the flywheel maximum power. For this hybrid system, the chosen filter cut frequency is equal to 1 mHz. When the high frequency components of the power exceed the maximum flywheel power, the battery supplies this gap. Figure 13 shows the Simulink scheme to manage the power between the two storage systems.  
Figure 14 shows how the total amount of the required power is shared between the batteries and the flywheel.  
In Figure 14b it is possible to see the effect of the power low-pass filter, which smooths the power that the battery has to supply.  
It is worth considering that in this simulation, when the SoC of the flywheel is equal to zero, the flywheel recharges by absorbing power from the grid. In real grid frequency regulation operations, the flywheel should be rated so that its capacity is sufficient to support the primary frequency regulation, whose duration is about 15-25 s, before the flywheel SoC falls to zero. Only when the frequency of network returns within its tolerance band should the flywheel recharge.  
By exploiting the developed models, 1424 accelerated aging cycles are simulated and the residual battery capacity for the battery model and for the hybrid one are compared. As it is possible to see in Figure 15, the battery model capacity at the end of the aging simulation is 9% less than the hybrid storage system one.  
Figure 14: Hybrid system aging test simulation: (**a**) Total power; (**b**) battery power; (**c**) flywheel power.  
Figure 13: Power control of the hybrid storage system.  
On the basis of the implemented models, it is interesting to determine the maximum number of aging cycles that each system can tolerate before the battery capacity decreases by 20%. For the considered module, the 20% decrease in the capacity corresponds to a residual capacity of 48 Ah. Figures 16 and 17 show the maximum number of cycles for the only-battery model and for the hybrid one, respectively. As it possible to see, in the hybrid system, the batteries can sustain 22.63% more aging cycles.  
Figure 16: Only-battery storage system: maximum number of aging cycles.  
Figure 17: Hybrid storage system: maximum number of aging cycles.  
Figure 15: Comparison between the battery residual capacity after 1424 aging cycles for the two systems.
## 4 System Design and Goals
### Message Sequence
#### 4.2.2 LISTEN State  
The LISTEN state or period can be used for the following:  
* To compute network traffic by listening to the ongoing HI and TC messages broadcast. Network traffic computation can be used to determine the duration of the next LISTEN state or the next DB_CHORE state. Such states can be increased if the network traffic is heavy or set to default if the network traffic is light.
* To create or update the Link and Neighbor Sets of the node from received HI and TC messages. This state can be quickly exited if:
* A HELLO broadcast is heard. Nodes in the LISTEN state can immediately move to the next state (DB_NEIGHBORS_RCVD) if a HELLO broadcast is received. The state is summarized in Figure 4 below.  
Figure 3: Group of nodes with sample Local Set entries.
# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 3 Case Studies and Issues for Conventional CVR
### Case Studies: Non-Optimal Results  
Many service providers have implemented CVR mechanisms since the 1970's in order to study the effect of this system optimization. A few of these case studies are summarized in Table 1 and discussed in following sections. In the CVR test cases and studies considered here, a non-optimal or non-existent feedback loop was used. The savings can be optimized using real-time feedback and machine-learning algorithms to improve system efficiency.
## 5 Experimental Results
### Dataset
#### 5.2.2 Began  
For the BEGAN model proposed by [23], the author did not share their dataset, so we implemented their proposed method and tried to generate computer-generated images based on the CelebA dataset as previously described in Section 4.3. The BEGAN is a generative adversarial network (GAN) which applies an auto-encoder as the discriminator. The authors also used an equilibrium method for balancing adversarial networks which helped the network converged to diverse and visually pleasing images.  
For the balance between D and G--gamma is set to 0.4, the total number of iterations is 200,000, the batch size is 16, the BEGAN's learning rate is set to 0.0001, while the learning rate of k is set to 0.001, the learning rate will be decayed after 3000 iterations.  
Figure 5 describes the results after different iterations. It is easy to notice that at the beginning of the training process (iteration 5000), the generated images are blurry; however, at the end of the training process (iteration 200,000), the generated images become more realistic.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Prediction**} \\ \cline{3-4}  & & **Normal** & **CG** \\ \hline \multirow{2}{*}{**Actual**} & Normal & TP (True positive) & FN (False negative) \\ \cline{2-4}  & CG & FP (False positive) & TN (True negative) \\ \hline \hline \end{tabular}
\end{table}
Table 2: A confusion matrix for summarizing the classification results.  
The total training time is approximately 6 h; each epoch takes an average of 9 s. Then the trained model was used to generate 5000 computer-generated images of size 64 \(\times\) 64. Figure 6 shows some samples of the dataset. Some of the generated images look realistic, such as the images in row 8, column 8 and row 7, column 2.
# Influence of Cracking on Oxygen Transport in UHPFRC Using Stainless Steel Sensors
###### Abstract  
Reinforced concrete elements frequently suffer small cracks that are not relevant from the mechanical point of view, but they can be an entrance point for aggressive agents, such as oxygen, which could initiate the degradation processes. Fiber-Reinforced Concrete and especially Ultra High Performance Concrete increase the multi-cracking behavior, reducing the crack width and spacing. In this work, the oxygen availability of three types of concrete was compared at similar strain levels to evaluate the benefit of multi-cracking in the transport of oxygen. The types of concrete studied include traditional, High-Performance, and Ultra-High-Performance Fiber-Reinforced Concrete with and without nanofibers. To this purpose, reinforced concrete beams sized \(150\times 100\times 750\) mm\({}^{3}\) were prepared with embedded stainless steel sensors that were located at three heights, which have also been validated through this work. These beams were pre-cracked in bending up to fixed strain levels. The results indicate that the sensors used were able to detect oxygen availability due to the presence of cracks and the detected differences between the studied concretes. Ultra High Performance Concrete in the cracked state displayed lower oxygen availability than the uncracked High Performance Concrete, demonstrating its potential higher durability, even when working in cracked state, thanks to the increased multi-cracking response.  
UHPFRC; fibers; multi-cracking; air permeability; oxygen; stainless steel sensor; voltammetry
# Explaining Deep Learning Models for Tabular Data Using Layer-Wise Relevance Propagation
## 3 Proposed Approach
### First Phase
#### 3.1.1 Datasets  
Telecom Customer Churn Prediction Dataset (TCCPD)  
Companies want to be able to predict if a customer is at risk of leaving. Retaining an existing customer is better than getting a new customer. There are two types of customer churn--voluntary and involuntary churn. Voluntary churn is of most interest for the company as it is the individual customer's decision to switch to another company or service provider. Understanding the factors/features that are associated with the customer leaving is important. Each record in the data [52] has initially 19 features, which when converted from categorical values to non-categorical, becomes 28 features associated with the customer, to be used for training a customer churn prediction model. These features have meaningful feature names, allowing us to interpret explanations with domain level judgement.  
Credit Card Fraud Detection Dataset (CCFDD)  
Financial companies dealing with credit cards have a vested interest in detecting fraudulent transactions. This highly imbalanced dataset has transactions carried out by European cardholders during September 2013. As shown in Table 1, the dataset is highlighted as imbalanced. Each record contains 30 features out of which 28 are converted by Principal Component Analysis (PCA) and then labelled as V1, V2...V28. The remaining two features (time and amount) are in their original form. Each record is labelled as either 0 (normal (\(-\)ive)) or 1 (fraudulent (+ive)). Further details about this dataset are available at the Kaggle competition [53].
# Wavelet-Based Optimum Identification of Vehicle Axles Using Bridge Measurements
## 8 Conclusions  
For beam-and-slab bridges, FAD sensors mounted under the slab are used to determine the vehicle configuration (vehicle speed, axle number, and axle spacing), which is required information for BWIM systems and some aspects of SHM systems. However, field tests have shown that FAD signals sometimes fail to clearly identify vehicle axles. This can be due to bridge vibration, multiple vehicle presence, or deviation of vehicles transversely in their lane. This paper proposes a wavelet-based approach, with Shannon entropy and a correlation factor, to enhance axle detection from FAD signals. The mother wavelet with maximum correlation, combined with the scale of least Shannon entropy, is used to automatically select an optimum transform. The fidelity of the proposed approach is investigated including measurement noise, multiple presence, different vehicle velocities, different types of vehicle and even in real traffic flow conditions. In this paper, only heavy trucks are used in the two field tests. Investigating the feasibility of axle identification for lightweight vehicles using the proposed algorithm is planned as future work.  
Conceptualization, C.T. and H.Z.; methodology, C.T.; software, C.T.; validation, E.J.O., N.U. and B.Z.; formal analysis, C.T.; investigation, N.U.; resources, H.Z.; data curation, B.Z.; writing--original draft preparation, C.T.; writing--review and editing, E.J.O.; visualization, C.T.; supervision, H.Z. and E.J.O.; project administration, C.T. and H.Z.; funding acquisition, C.T. and H.Z. Please turn to the CRediT taxonomy for the term explanation. Authors must be limited to those who have contributed substantially to the work reported. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (No. 52008162) and the Key Research and Development Program of Hunan Province (No. 2019 SK2172).  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Characteristics of the Soft Plasma Jet  
The soft plasma jet exhibited output characteristics of typical nonthermal atmospheric pressure plasma. Figure 1b shows the typical voltage (peak voltage, 1.34 kV; rms voltage, 0.61 kV) and current (peak current, 2.76 \(A\); rms current, 110 mA) waveforms of the plasma discharge. The period of the applied voltage/current was 12 us, and the duty ratio of the pulse-on and pulse-off times was 19%. The soft plasma jet could transfer 3.3 J/s to the sample. The optical emission spectrum of the soft plasma jet (Figure 1c) exhibited emission lines corresponding to \(N_{2}\), atomic nitrogen, atomic oxygen, and the hydroxyl radical. Emission bands generated by radiative transitions of the second positive and first negative system of the excited molecular nitrogen were observed at 310 to 440 nm. The emission lines at 777 and 822 nm can be attributed to atomic oxygen and atomic nitrogen, respectively [17; 18; 19; 20]. The emission at 777 nm was produced by the oxygen atom transition \(O\big{(}2s^{2}2p^{3}\big{(}4^{5}\big{)}3s\to 2s^{2}2p^{3}\big{(}4^{5} \big{)}3p\big{)}\)[18; 19], and the emission at 822 nm was produced by the nitrogen atom transition \(N\big{(}2s^{2}2p^{3}\big{(}3^{P}\big{)}3s\to 2s^{2}2p^{3}\big{(}3^{P} \big{)}3p\big{)}\)[18; 20]. These results are due to the experimental conditions of our plasma device operating at atmospheric pressure using air as the working gas. Because oxygen molecules received sufficient numbers of electrons from the plasma, they could be transformed into reactive oxygen intermediates through the following \(O_{2}-\)reduction pathway: \(O_{2}\to O_{2}^{-}\to H_{2}O_{2}\to OH\to H_{2}O\)[21; 22].
## 4 Experimental Results and Analysis
### Ablation Experiment  
In order to verify the effectiveness of the module proposed in this paper, a model ablation experiment is conducted. In this section, four groups of control experiments are set up to compare the segmentation accuracy and detection speed of different groups. Experiment 1 is based on the basic U-Net. Experiment 2 replaces the encoder convolution block with the LMRC block, and the decoder remains unchanged. Experiment 3 keeps the encoder unchanged and adds a lightweight deep fusion module to the decoder. In experiment 4, the encoder and decoder are replaced by LMRC blocks and lightweight deep fusion modules, respectively, but the loss function does not use the dynamic loss function. Finally, it is compared with the EDPNet proposed in this paper. The experimental results are shown in Table 1.  
As can be seen from the table, in Experiment 2, the encoder's convolutional block was redesigned into an LMRC block, and the number of convolutional channels in each layer was halved, which greatly reduced the number of parameters and increased the inference speed of a single image by six times. However, the segmentation accuracy mIoU and F1-Score were both reduced. The results of Experiment 3 prove that the decoder's lightweight deep fusion module effectively compensates for the loss of accuracy caused by halving the convolution channel, and the segmentation accuracy has increased. Experiment  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **mIoU/\%** & **F1-Score/\%** & **Parameter/M** & **Time/ms** \\ \hline Exp1 & 90.6 & 95.0 & 4.32 & 564 \\ Exp2 & 89.9 & 94.6 & 0.59 & 94 \\ Exp3 & 91.3 & 95.4 & 1.09 & 330 \\ Exp4 & 92.7 & 96.1 & 0.60 & 96 \\
**EDPNet** & **93.4** & **96.5** & **0.60** & **96** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model ablation experiment results.  
4 tests the LMRC block and the LDFM; the accuracy mIoU is improved by 2.1 percentage points compared with the basic network, and the F1-Score is improved by 1.1%. In addition, we also calculated the 95% confidence intervals and standard deviations of mIoU and F1-Score for EDPNet. The standard deviation of mIoU is 0.38, with confidence intervals of (92.49, 93.42). The standard deviation of F1-Score is 0.19, and the confidence intervals are (96.06, 96.53). This further proves the stability and reliability of the model. Finally, the EDPNet proposed in this paper achieved the highest segmentation accuracy, and the inference speed was further improved to 96 ms. Compared with Experiment 4, it proves that the dynamic loss function can refine the ice segmentation image.
# Wireless Sensor Network Aided Assembly Line Monitoring According to Expectations of Industry 4.0
## 3 Results
### Test Results and the Comparison of the Result Accuracy in On-Line Mode  
For testing purposes, the on-line gathered data was analyzed using both the neural networks and the WkNN method to determine the position of the wireless nodes. In this chapter, the localization results for test and industrial laboratories are compared having in mind both the off-line and on-line measurements. The results for the test laboratory were obtained earlier [28], but for some measurements, the results are shown together with the results obtained for the industrial laboratory, for easier and more quality comparison.
## 4 Experiments
### Comparison of Improvement Methods and Results
#### 4.3.1 BackBone Improvement Experiment  
The BackBone network is improved by adding the channel attention mechanism SE module, the abbreviation is GAM for Global Attention Mechanism module [32], the abbreviation is SelfAttention for Self-attention Mechanism module [33], and the abbreviation is CBAM for the region- and channel-based attention mechanism module [34], respectively, to the 10 th layer of the BackBone network (starting from layer 0) of the YOLO v8n model.  
Their performance comparisons are shown in Table 1; adding different types of attention mechanisms can improve the detection accuracy of the model to different degrees under the same experimental conditions, the addition of the channel attention mechanism SE module brings the highest detection benefit. The channel attention mechanism SE module is used to improve the detection accuracy by calculating the weight coefficients of each channel and scaling and fusion of the features; its [EMAIL_ADDRESS] increased by 3.2%, but providing accurate feature representations while making the network computationally intensive, and its computation increased by 3.9 GFLOPs.
# An Automatic Fault Diagnosis Method for the Reciprocating Compressor Based on HMT and ANN
## 2 The Proposed Method and Its Theories
### Hit-or-Miss Transform (HMT)  
The sectionalized curves were saved as digital images before feature extraction. A digital image comprises pixels, which is the minimal unit with a particular location and value. By convention, the top left pixel is defined as the origin of a digital image. Therefore, digital images are matrices in essential. Taking the binary image as an example, the value of the elements in matrix is either 1 or 0, and 1 means white, 0 means black. Since the binary image only have two kinds of pixels, the pixels in binary images were divided into  
Figure 1: A \(p\)–\(V\) diagram.  
foreground and background pixels. In this study, the white pixels were foreground pixels, in contrast, the black pixel were background pixels [17].  
Hit-or-miss transform (HMT) is a morphological image processing method to detect specific shape in binary images [17]. The HMT of image \(I\) is defined as:  
\[\begin{array}{rcl}I\mathbin{\text{\textcent{\text{\text{\text@underline{ \char 3}}}}}}B_{1,2}&=&(A\ominus B_{1})\cap(A^{c}\ominus B_{2})\\ &=&\{z|(B_{1})_{z}\subseteq A\}\cap\{z|(B_{2})\subseteq A_{c}\}\end{array} \tag{2}\]  
where \(B_{1}\) is structuring element to detect shapes in the foreground, \(B_{2}\) is structuring element to detect shapes in the background, \(A\) is the set of the foreground pixels, and \(A^{c}\) is the set of the background pixels. The equation means that \(B_{1}\) finds a match in \(A\), and simultaneously, \(B_{2}\) finds a match in \(A^{c}\) in the same position.  
In this article, three pairs of structuring elements: \(B_{1,2}\)1, \(B_{1,2}\)2 and \(B_{1,2}\)3, were designed to extract features of compression curve and expansion curve. The \(B_{2}\)1, \(B_{2}\)2 and \(B_{2}\)3 are all empty sets. The \(B_{1}\)1, \(B_{1}\)2 and \(B_{1}\)3 is defined as:  
\[B_{1}\ =\ \left(\begin{array}{cc}1&0\\ 0&1\end{array}\right) \tag{3}\]  
\[B_{1}\ ^{2}\ =\ \left(\begin{array}{cc}1\\ 1\end{array}\right) \tag{4}\]  
\[B_{1}\ ^{3}\ =\ \left(\begin{array}{cc}1&1\end{array}\right) \tag{5}\]  
The three structures were selected is due to the reason that, from the perspective of digital images, the thinned compression curve and the expansion curve only comprise the three pixel structures. After the HMT was used, the number of the three pixel structures are counted, respectively, which are \(N^{1}\), \(N^{2}\) and \(N^{3}\). Then, a comprehensive gradient \(\beta\) was calculated based on the numbers of the pixel structures:  
\[\beta\ =\ \arctan(\frac{N^{1}+N^{2}}{N^{1}+N^{3}}) \tag{6}\]  
The gradient can imply the inclination of the curve. In this article, the gradient of the compression curve \(\beta_{c}\) and the expansion curve \(\beta_{e}\) were extracted.
# Experimental and Theoretical Study on Anchorage Loss of Prestressed CFRP-Reinforced Concrete Beams
## 4 Conclusions  
The anchorage loss mechanism of strengthening reinforced concrete beams with externally prestressed CFRP tendons was studied. FEM simulations were conducted, and the TCS was established to calculate the anchorage loss of the prestressed tendon-main beam connection system. Field tests on actual bridges were then performed to obtain the actual anchorage loss and the proportion of the total prestress loss, validating the reliability of the FEM simulations and correcting the TCS. The influencing factors of anchorage loss were also clarified. The conclusions are as follows:  
1. The FEM simulations revealed that the deformation of the connecting screw and the front end of the steel reaction frame accounts for approximately 95% of the total anchorage loss in a CFRP tendon-main beam connection system. The TCS was established based on the stress forms of these components, and the calculated results are consistent with the FEM analysis, indicating a high level of reliability.
2. In the control group experiment, a CFRP tendon with a length of 12 m and a diameter of 12 mm, subjected to a prestress of 950 MPa, showed an anchorage loss of approximately 1.5% of the set prestress value, corresponding to an anchorage deformation of about 1 mm. Further studies indicated that the deformation magnitude is influenced by the prestress level, the dimensions of the steel reaction frame's front end, the connecting screw length, and the thread gap values.
3. The experimental results show relative discrepancies with both the FEM and theoretical results, attributed to gaps values \(\omega_{3}\) between the threaded connections. A 2 mm gap at each threaded connection was identified, and this can be reduced by tightening the threads with external force. However, the influencing pattern of these gaps requires further investigation. After adjusting the calculation formulas, the error was controlled within approximately 5%, demonstrating higher precision.
4. This study provides a comprehensive framework for accurately predicting and mitigating anchorage loss in externally prestressed CFRP tendons, with significant implications for future engineering applications.  
Software, G.T.; Formal analysis, H.J.; Investigation, Q.L., H.J., G.T. and P.Z.; Resources, Q.L., H.J., G.T. and P.Z.; Data curation, G.T.; Writing--original draft, Q.L. and G.T.; Writing--review and editing, Q.L. and H.J.; Visualization, H.J.; Supervision, P.Z.; Funding acquisition, P.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (grant No. 52078256) and Zhejiang province public welfare projects (grant No. LGF22E080023).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.