esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The paper has proposed a novel approach to identifying pipeline ponding volumes, by combining the percussive detection method and a CNN. The proposed approach is low-cost but user-friendly and effective. The experiment was performed based on the proposed method and the experimental results show the effectivity and high accuracy of the proposed recognition model. The major findings of the proposed approach can be summarized as follows:  
* The way of processing percussion-caused audio signal by converting to Mel spectrogram can be considered as a novel and cost-effective approach in detecting pipeline ponding volume. It presents a simple but very effective acoustic signal processing method;
* The actual output of the CNN is basically consistent with the theoretical output during the proposed approach. The results demonstrate that the CNN recognition accuracy reaches 98.34% and can be effectively adopted to pipeline ponding detection;  
Figure 8: Comparison of recognition accuracy of three models.  
* The proposed method is suitable for the detection of ponding volume in pipelines of different specifications, and the output performance of the six pipelines in the CNN models had an accuracy rate of 90.9-100%, a recall rate of 90-100%, and an F1-Measure of 94.7-100%;
* The recognition accuracy of CNN falls between 98.33% and 99.44%, which indicates that this recognition model has a more stable and superior performance than the DTM recognition model and the SVM recognition model. Therefore, it can be concluded that the method combining the percussive detection method and the CNN proposed in this paper has better application prospects in pipeline ponding detection.  
The research in this paper demonstrates the feasibility and effectiveness of the proposed pipeline ponding detection method. The essence and mechanism of the proposed method is identifying underlying dynamical characteristics of percussion-caused audio signals of pipeline ponding. However, this paper also has its shortcomings: the length and diameter of the six different pipelines selected were too singular to determine the effective detection distance of the proposed percussion detection method. In follow-up research, designing corresponding experiments to detect the effective distance of the percussive detection method in pipeline health detection will become our research focus.  
Conceptualization, D.Y.; Data curation, M.X.; Funding acquisition, G.L.; Investigation, M.X.; Methodology, D.Y.; Project administration, M.X.; Resources, D.Y.; Software, M.X.; Supervision, D.Y. and T.W.; Validation, M.X.; Writing--original draft preparation, M.X.; Writing--review and editing, T.W. and G.L. 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.: 51808417).  
Not applicable.  
Due to the nature of this research, participants of this study did not agree for their data to be shared publicly and only available upon reasonable request.  
The authors declare no conflict of interest.
# Error Recovery in the Time-Triggered Paradigm with FTT-CAN
## 5 Resource Optimization Process
### Server Interference
#### 5.3.2 Direct Interference  
In this scenario, we consider that the WCRT of a given message occurs when that message suffers the maximum indirect interference from the error server, assigned with highest priority, and one error hits the message itself. To reach this conclusion, just consider the following scenario for an arbitrary message \(m_{i}\):  
1. Once ready, \(m_{i}\) suffers the maximum possible indirect interference (both from high-priority messages and from the error server), being scheduled for transmission in EC \(k\);
2. In EC \(k\): 1. there are no errors; thus, \(m_{i}\) is transmitted at EC \(k\); 2. message(s) other than \(m_{i}\) are affected by errors; thus, \(m_{i}\) is still transmitted in EC \(k\) (note that errors in EC \(k\) are handled in EC \(k+1\)); 3. \(m_{i}\) is affected by an error; thus, \(m_{i}\) and its replicas are scheduled for the following EC. The WCRT of \(m_{i}\) is then \(k+1\).  
Therefore, to assess the schedulability of the message set and obtain the WCRT of the messages considering direct interference, we use the procedure described in Algorithm 4.  
Firstly, we execute the Algorithm 3 with the parameter _Cut_errors_ set to 1, because firstly the interference due to indirect errors must be computed with maximum errors minus one, to account for the error directly affecting the message under analysis (Line 1). Then, for each message we assess the impact of the direct error (Lines 3 and 4), and finally we verify if the deadline is violated (Line 5). If the message set is schedulable, the response time of all messages is returned (Line 6).
```
1:Run Algorithm Ind_Interf(M, LEC, LSW, RepLevel, Cut_errors = 1), obtaining Schedulable and RespTime
2:if Schedulable = FALSE then return Schedulable = FALSE end_if
3:for each \(m\) in M do
4:\(\textit{RespTime\_Direct(i)}\) = RespTime(i) + 1
5:if RespTime_Direct(i) > D then return Schedulable=FALSE end_if end_for
6:return Schedulable=TRUE, RespTime_Direct
```
**Algorithm 4**_Direct_Interf_Response Time considering errors - Direct and Indirect Interference
# Investigation of the Opposite-Electrode Effect on the Planar Solid-State Pulse-Forming Line
## 3 Simulation and Verification  
Using the FEM, a simplified cross-sectional model of the forming line (as shown in Figure 2) was created, with the thickness of the dielectric plate (_h_) set to 4 mm, the relative dielectric constant of the dielectric material (\(\epsilon_{r}\)) set to 320, and the environmental medium set to air. In this study, the electrostatic simulation module in COMSOL Multiphysics was employed, and by refining the mesh according to the physical field parameters, the simulation error can be maintained below 5%. In this simulation, the Maxwell capacitance of the model was determined by applying a voltage to the high-voltage electrode while grounding the reference electrode. Electrostatic simulations were conducted for different widths of the high-voltage electrode \(w\) and grounding electrode width \(w+d\), and the simulation results are shown in Figure 3.  
The simulation results verified the correctness of the theoretical model presented in Section 2, showing that the unit length capacitance of the PFL increases with the increase in the width of the ground electrode \(w+d\) and approaches a fixed value gradually.  
To analyze the electric field at the edge of the electrodes when the widths of the high-voltage electrode and the ground electrode are not consistent, a finite element simulation software program was used to simulate the static electric field of the simplified cross-section model of the forming line shown in Figure 2. Furthermore, the simulation used the electrostatic simulation module in COMSOL Multiphysics, with the same mesh setting as described above; the simulation results are shown in Figure 4. In this simulation, the thickness of the dielectric plate (\(h\)) was set to 10 mm, the relative dielectric constant (\(\varepsilon_{r}\)) was set to 320, the high-voltage electrode was designated as electrode A, and the ground electrode was designated as electrode B. To determine the normalized electric field at the edge of the electrode, we applied a voltage of 1 V to electrode A while grounding electrode B. Subsequently, the electric field monitoring lines were placed 0.1 mm beneath electrode A and 0.1 mm above electrode B on the dielectric plate. Ultimately, the widths of electrodes A and B were varied, and the maximum electric field along the monitoring line was statistically analyzed for each width. The correlation between electrode width and electric field strength is illustrated in Figure 4.  
The simulation results show that when the widths of the high-voltage electrode and the ground electrode are the same, it is beneficial to reduce the maximum field strength on the field monitoring plane. In cases where the widths of the high-voltage electrode and the ground electrode are not consistent, the side with the smaller electrode width will experience an additional enhancement of the electric field.  
Figure 3: The simulation results of capacitance per unit length.
###### Abstract  
Distributed antenna systems (DASs) are known to be effective to enhance coverage, spectral efficiency, and reliability in mobile communication systems. Because multiple antennas are physically separated in space, DASs benefit from both micro- and macro-diversity, which makes DASs significantly more robust compared to conventional co-located antenna systems in fading channels. However, when multiple antennas are not dispersed enough, there exists a certain degree of correlation in large-scale fading (shadowing), which degrades the macro-diversity gain. In practice, various measurements indicate a high degree of correlation of shadowing in DASs. However, most of the previous studies on DASs have not considered the correlated shadowing and its corresponding performance loss. Motivated by this limitation, we analyze the impact of the correlated shadowing to better evaluate DAS-based schemes with dual diversity transmitters. Assuming that shadowing correlation is an exponentially decreasing function of the inter-element separation, we derive the outage probability of DAS in composite Rayleigh-lognormal shadowing channels. Also, we present numerical and simulation results, which indicate there exists an optimal inter-separation between antennas that minimizes the outage rate to balance a trade-off between macro-diversity and path loss.  
distributed antenna systems (DASs); cooperative transmission (CT); correlated shadowing; exponentially decaying correlation +
Footnote †: journal:  
_Aticticle_
## 4 Practical Implementation: Identification and Separation of Leukemic Cells from Healthy Ones
### Leukemic Cell Detection: Biomedical Rationale  
Acute myeloid leukaemia is a malignant neoplasm of the bone marrow accounting for 10% of all haematological disorders. Although in recent years new therapeutic approaches have been devised, the overall survival of patients is less than 30%. This is mostly due to the high rate of disease relapse that is observed in patients treated with standard targeted chemotherapy. After treatment, the disease is often not completely eradicated, and leukemic cells still persist although in numbers that are below detectable levels by standard means. A microfluidic device that can easily distinguish malignant from healthy cells, therefore, would be extremely useful. Most common microfluidics separation techniques, however, work with cells of different sizes, but, in the present case, this would not work since malignant and healthy cells are approximately the same size.  
The proposed solution takes advantage of the fact that malignant cells are more flexible than healthy ones. A microchannel is lined with several hair-like flexible structures that, in the rest of the  
Figure 5: Mutual relations between ‘forces’ and discrete mechanics, ‘interactions’ and discrete multiphysics, and ‘information’ and deep multiphysics.  
paper, are called cilia. When cells move along the channel, the cilia bend to allow the passage of the cells. When cilia bend, stresses are generated at their base. The idea of the microfluidic separator is to distinguish between rigid or flexible cells by looking at the stress patterns occurring during their passage. In a real device, these stresses could be measured by using a piezoelectric material at the base of the cilia. AI is used to associate specific stress patterns with the stiffness of the cell.  
The geometry of the microfluidic device is shown in Figure 6: the length of the channel is 200 \(\upmu\)m, and the width is 50 \(\upmu\)m. In the channel, there are 11 flexible cilia: 6 on the upper wall and 5 on the lower wall. The distance between two contiguous cilia is 20 \(\upmu\)m. At the right end of the channel, there are two gates, one that connects the channel with a chamber where soft cells are collected and another that connects with a chamber where rigid cells are collected. In a real device, a magnet can be used to open or close these gates. However, this study focuses on the modelling aspects, and I do not deal here with the more technical aspects of building the microchannel.  
In Section 4.2, the DMP model of the device is introduced. In Section 4.3, the model is coupled with an ANN to obtain a deep multiphysics model.
## 3 Research Methodology
### Inverse S-Equation  
[PERSON] et al. proposed an inverse S-shaped function in 2015 to fit all cities in the sample using nonlinear least squares fitting using impervious surface data from 28 cities in China [62]. Some scholars also used other different types of data to fit the inverse S function, which also verified the inverse S law proposed by [PERSON] et al. [63]. [PERSON] proposed the compactness index to describe the compactness of the spatial distribution of urban elements based on the inverse S equation, which proved the feasibility of the method [64]. Therefore, this study uses this inverse S equation to fit and analyze the circular density distribution of each city in Shandong Province. The equation is as follows:  
\[f(r)=\frac{1-c}{1+e^{\alpha((2r/D)-1)}}+c \tag{3}\]  
where f(r) denotes the density of urban elements, r denotes the distance of urban elements to the city center, e is a natural constant, and c denotes the density of urban elements at the edge of the city, where \(\alpha\) and D represent the characteristics of the decay curve of urban elements from the center outward, \(\alpha\) denotes the parameter that responds to the slope of the inverse S-shaped function curve, and D is the distance from the edge of the city to the center.  
The urban areas are divided according to the fitted to-be-determined coefficients, with area \(r_{1}\) denoting the central area, area \(\rm r_{1}\)-\(\rm r_{2}\) denoting the urban core, area \(\rm r_{2}\)-\(\rm r_{3}\) denoting the urban near-urban area, and area beyond \(\rm r_{3}\) denoting the urban outer-urban area.  
\[r_{1}=\frac{D}{2}(\frac{-1.316957}{\alpha}+1)\]\[r_{2}=\frac{D}{2} \tag{4}\]  
\[r_{3}=\frac{D}{2}\left(\frac{1.316957}{\alpha}+1\right)\]  
Deriving the compactness indicator K from the inverse S-shaped function characterizes the degree of compactness of the urban space from the city center to the city periphery.  
\[K=1-\frac{1}{2}(1-\frac{r_{3}-r_{2}}{D}) \tag{5}\]
## 3 Methods
### Data Collection and Pre-Processing
#### 3.2.2 Deep Learning and Image Processing Data Collection and Pre-Processing  
The study's primary purpose is to realize the identification and measurement of dangerous points and abnormal point cracks in the subway and the engineering facilities along the subway line subsidence. Engineering facilities include roads, buildings, bridges, etc., and their materials are not the same. Therefore, the dataset we used for model training should contain as many materials as possible, such as asphalt, concrete, bricks, etc. In order to obtain a large and diverse set of high-quality crack images, the data sources for the study consisted of the following 4 sources:  
1. Using search engines such as www.baidu.com (accessed on 5 March 2021) and www.bing.com (accessed on 12 March 2021) to retrieve and collect crack images posted on the Internet;
2. Handheld cameras in Shenzhen perform manual shooting;
3. Use of DJI's 4 RTK drone to shoot high-rise and hidden places;
4. Public data sets in existing research.  
After collecting the data, the images need to be preprocessed by being cropping into a fixed size. The images are then labeled to distinguish whether they contain cracks or not. Finally, the crack information is highlighted by grayscaling and image enhancement.  
Image Cropping and Labeling  
When deep learning performs image classification, the input image needs to be cropped to a uniform size. In the data collected in this experiment, the pixels of the images taken by the camera are 3024 \(\times\) 4032, the pixels taken by the drone are 4864 \(\times\) 3648,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Project** & **Parameter** \\ \hline Satellite & SENTINEL-1 \\ Country/Organization & ESA \\ Imaging mode & Interference wide mode \\ Wavelength & C-band (5 cm) \\ Return to cycle & 12 days \\ Incident angle & 29–46\({}^{\circ}\) \\ Spatial resolution & 5 m \(\times\) 20 m \\ Polarization mode & Dual polarization: HH + HV, VV + VH, Single polarization: HH, VV \\ Scan width & 250 KM \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter table of sentinel No. 1.  
the public data set is 227 \(\times\) 227, and the sizes of the images downloaded from the Internet vary. However, the training of deep learning models requires uniform-size image data. At the same time, in order to avoid the impact of image compression and distortion on the model training effect, this study cuts the oversized image into 227 \(\times\) 227 image samples, and filters out the images that do not contain concrete and crack information. Finally, a dataset with 8026 images was obtained. In this dataset, there are 1294 images taken manually, 912 images taken by drones, 2000 images downloaded from the Internet, and the public data set contains 4000, with positive and negative factors each accounting for 50% (see Table 3 for details). Finally, according to the set-aside cross-validation method, the data were divided into training, validation and test sets in the ratio of 8:1:1. As shown in Table 4, the test set contains 6556 images, the validation set contains 820 images, and the test set contains 820 images.
###### Abstract  
Thermally synthesized graphitic carbon nitride (g-C\({}_{3}\)N\({}_{4}\)) over pulsed laser deposition (PLD) produced urchin-like iron oxide (FeOx) thin films were fabricated via in situ and ex situ processes. Materials characterisation revealed the formation of the graphitic allotrope of C\({}_{3}\)N\({}_{4}\) and a bandgap E\({}_{\rm g}\) for the combined FeO\({}_{7}\)/g-C\({}_{3}\)N\({}_{4}\) of 1.87 and 1.95 eV for each of the different fabrication strategies. The in situ method permitted to develop a novel petal-like morphology, whereas for the ex situ method, a morphological mixture between FeOx bulk and g-C3N4 was observed. Given the improved optical and morphological properties of the in situ film, it was employed as a proof of concept for the direct photocatalysis and photo-Fenton removal of ciprofloxacin antibiotic (CIP) under visible light irradiation. Improved photocatalytic activity (rate constant k = 8.28 \(\times\) 10\({}^{-4}\) min\({}^{-1}\)) was observed, with further enhancement under photo-Fenton conditions (k = 2.6 \(\times\) 10\({}^{-3}\) min\({}^{-1}\)), in comparison with FeO\({}_{7}\)/g-C\({}_{3}\)N\({}_{4}\) of 1.87 and 1.95 eV for each of the different fabrication effects demonstrate the in situ methodology as a viable route to obtain working heterojunctions for solar photocatalysis in thin-film materials, rather than the more common powder materials.  
FeOx/g-C\({}_{3}\)N\({}_{4}\) thin films; petal-like morphologies; Z-scheme; visible light photocatalysis; ciprofloxacin degradation
## 4 Mapping YANG to OPC UA
### YANG Data Definition Mapping
* Grouping statement mapping: the grouping statement defines a set of nodes that can be assembled into reusable collections and instantiated with the uses statement. In OPC UA, grouping statements are mapped to a custom object type and instantiated using the object declaration of the type. * List statement: list contains one or more child data definition statements, including a grouping of leaf or container entries, with each uniquely identified with one or more key leaves. The list statements are mapped to an ObjectsFolder reference by custom object types. Also, the holder object is created in the objects folder so that the list entries can be added programmatically at runtime. * Typedef statement mapping: typedef statement allows for the creation of a new derived data type by redefining the base type. The base type can be the YANG built-in type or a custom type. Typedef statements are mapped to DataType definitions in OPC UA. * RPC statement mapping: the rpc statement defines a YANG RPC operation. It contains a block of sub-statements that holds detailed input and output nodes. The operation name, input, and output parameters are modeled using YANG data definition statements. The RPC statement is mapped to the method node in the OPC UA model design. The notification statement defines a NETCONF notification. YANG data definition statements model the notification content. The notification is mapped to Custom Event Type objects in the OPC UA model design.
###### Abstract  
Existing route-clustering methods often fail to identify abnormal sections or similarities between routes, mainly when working with large or long datasets. While sub-route clustering can detect regional patterns, it struggles to accurately capture the overall route structure. The present study proposes a new ship route-clustering method that enhances computational efficiency and noise recognition while addressing these limitations. We refined Automatic Identification System data via four data-cleaning processes and applied a statistical distance measurement to assess ship trajectory similarity. Dimensionality reduction was then used to facilitate clustering. The clustering of ship route similarities is non-parametric and can be applied to datasets not separated based on density to find clusters of various densities. Density-Based Spatial Clustering of Applications (DBSCA) applies to many research fields; using the DBSCA with Noise (DBSCAN) algorithm, we propose an improved DBSCAN algorithm that automatically determines the parameters Epsilon and MinPts. In this study, as a core ship route-clustering process, we propose a sub-route clustering process by setting the distance and density of data points to clear standards for re-analysis and completion. The proposed approach demonstrates markedly enhanced clustering performance, offering a more sophisticated and efficient basis for ship route decision-making.  
ship route clustering; ship trajectory similarity; sub-route clustering; DBSCAN; automated parameter tuning; MASS technology +
Footnote †: journal: Applied sciences  
[PERSON]  
1 Graduate School of Maritime Transportation System, Mokpo National Maritime University,  
Mokpo 58628, Republic of Korea; [EMAIL_ADDRESS]  
2 Division of Navigation Science, Mokpo National Maritime University, Mokpo 58628, Republic of Korea  
2 [EMAIL_ADDRESS]; 1-82-61-240-7193