esa-sceva/satcom-chunk-collection huggingface dataset:
# Time Series Clustering Model based on DTW for Classifying Car Parks
## 2 Data and methodology
### Framework for Classifying Car Parks  
In order to classify the car parks, we develop a framework containing four aspects, including preprocessing, distance measurement, clustering, and evaluation, which is given in Figure 2.  
The framework in Figure 2 can be described as follows:  
Firstly, we employ the weekly periodicity of the data to fill in the missing values, and then convert the number of parking cars into the occupancy rate by Equation (1). It is obvious that the occupancy rate of each car park changes in one week's periodicity, which means that the training data could be reduced to one week.  
\[occupancy\ rate=occupancy/capacity \tag{1}\]  
where \(occupancy\) is the number of parking cars, and \(capacity\) is the maximum number of cars that can be parked in the car park.  
Secondly, according to the characteristics of the time series data, the DTW distance is employed as the distance measurement for the time series data of the car parks.  
Thirdly, density-based partition around medoids (DBPAM) clustering method is proposed to obtain the robust clustering results, which contains multiple steps, including calculating distance radius, obtaining density of the neighbor area, initializing \(k\) centers, and clustering.  
The last but not the least is to evaluate the performance of the proposed model.  
Figure 2: Framework of car park clustering.
# Ultrasonic Power and Data Transfer through Multiple Curved Layers Applied to Pipe Instrumentation
## 2 Power and Data Acoustic Channel System Overview
### Power and Data Communication Method  
The sensors and the complementary electronics that are located on the inside block are completely powered from the outside block through ultrasonic waves that reach the inside block. These acoustic waves are generated in the transmitting transducer, outside the PZT (shown in Figure 1), by a continuous wave (CW) voltage that is applied at the outside the PZT's terminals (the signal generation and RF amplifier in Figure 1). The waves then propagate and impinge the face of the receiving transducer, inside the PZT (shown in Figure 1), in the form of vibration. Some energy is attenuated in the acoustic channel because of material losses [18; 21] and reflection due to an acoustic impedance mismatch between the layers across the channel; however, sufficient power reaches the inside block to supply the electronic components. The transducer in the inside block then converts the vibration to voltage. The electric energy is stored in the inside block in order to power all the necessary electronics and sensors.  
The data communication principle is based on the impedance variation [15; 16; 17; 18]. The digital information from the sensors is read by a microcontroller unit (MCU), which encodes it in two logic levels, and sends it to one of its pins. This pin is connected to the metal oxide semiconductor field effect transistor (MOSFET) transistor that short-circuits the terminals of the inside piezoelectric transducer to ground, as shown in Figure 1. By switching the transducer terminals between open and shorted,  
Figure 1: Power and data acoustic channel (PDAC) overall system diagram.  
the acoustic impedance of the channel changes because the electrical terminal of the PZT has its boundary condition altered. Consequently, the equivalent electric impedance seen in the terminals of the outside PZT also, slightly, changes, which consequently varies the CW signal amplitude in the outside PZT's terminal. Therefore, an amplitude modulation scheme is achieved, and it can be demodulated by the outside block, using a simple envelope detector followed by a digital signal processor in order to interpret the sensors' data. Therefore, power is sent from the outside block to the inside block and data is sent from the inside block to the outside block using a single channel and carrier wave.
## 5 Experimental Evaluation
### Research Questions  
The evaluation of our approach is accompanied by a set of research questions that mainly focus on the improvement achieved by our proposed method against jGenProg. In this section, we are going to explain the importance of the research questions that we have included and their role in the experiment findings.  
* RQ1 [Validation Time]: To what extent is our proposed approach effective in reducing test execution time (cost) compared with jGenProg? In particular, we discuss the performance difference between jGenProg and MPPEngine in terms of validation time reduction. Here, we measure the average time taken by each technique to validate one patch. We tabulate Min, Median, Max, SD, and Average time taken by the approaches. Finally, we compare the results and prove our approach is more effective than the existing approach.
* RQ2 [Repair Efficiency]: Does MPPEngine produce more patches for bugs compared to jGenProg? With the implementation of MPTPS, our approach should validate more program variants in the given time and produce more plausible patches compared to jGenProg because MPTPS helps in early detection of incorrect patches and reduces validation time and validates more patches in the remaining time. In RQ2, we investigate the repair efficiency of each technique and prove our MPPEngine approach is equally or more efficient than jGenProg.
* RQ3 [Repair Effectiveness]: Is there any new patch generated for bugs by MPPEngine for which jGenProg does not produce any solution? We have implemented a technique that includes fault-recorded test prioritization and sampling to identify failing test cases in the early stage of validation. And based on the results, we calculate a new fitness function. Fitness function is an important factor for selecting the best candidates among incorrect patches for re-repairing. There is a chance that APR tools produce test suite adequate patches after re-modification. So, under this research question, we will be experimenting with the repair effectiveness of our approach by monitoring patch generation for each bug.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Subjects** & **No. of Buggy Programs** & **KLoc * & **Test KLoc * & **Tests** \\ \hline JFreeChart & 26 & 96 & 50 & 2205 \\ Commons Lang & 65 & 22 & 6 & 2245 \\ Commons Math & 106 & 85 & 19 & 3602 \\ Joda Time & 27 & 28 & 53 & 4130 \\ \hline \hline \end{tabular}  
* KLoc for the most recent version, as reported by SLOC Count.  
\end{table}
Table 1: Defects4j benchmark for experiments [14].
## 4 Application of the Concepts: Design of an In-Line Measuring Machine
### Analysis of Current Gauging Process and Target Specifications
Another consideration is that the inspection area of the measurement system must cover the overall dimensions of the workpiece (450 mm \(\times\) 450 mm). The tolerance range for the features from the casting operation is 0.8 mm and the target reliability using a gauge repeatability and reproducibility (R&R) study is 10% or less in each measurement point [46]. Therefore, as a starting point to select components, the overall target precision of the measuring system was specified as 0.040 mm. The new system has to perform measurements for the same part, and under the same environmental conditions as the current system. Among the expected upgrades are the capacity to analyze, store and transport data, and factors such as added flexibility to handle other parts were considered a desirable goal.
## Materials and Methods
### Finite Element (FE) Simulation  
The Explicit solver of commercial software ABAQUS 2019 is used for FE simulation of the architected metamaterial under quasi-static compression. The dimension of the model is the same as that of 3D-printed specimen. The model is placed between two rigid-body plates which are established to simulate the top and bottom compression platforms during the experiment. The bottom plate is completely fixed underneath the model with an encastre boundary condition, whereas the top plate is set to move along the Z direction at the velocity of 5 mm/min. The boundary conditions are illustrated in Figure 2. The out-of-plane deformation is constrained. The General Contact algorithm is applied as the interaction property of the model with both tangential and normal behavior. A penalty friction coefficient of 0.2 as well as a hard pressure-overclosure contact are defined. The mesh of the model is generated using eight-node linear reduced-integration element(C3D8R) with an average element size of 0.25 mm. The model geometry is large when compared to the element size. The element quality analysis tool is applied which indicates no errors or warning elements. The displacement and reaction force of the top plate are recorded to generate stress-strain graphs. A combination of ductile damage and shear damage is employed to simulate damage evolution of any element. The prime properties of the selected material poly lactic acid (PLA+) are summarized in Table 2.  
Figure 2: Boundary conditions used for FE simulations.
# Two-Photon Excitation of Azobenzene Photoswitches for Synthetic Optogenetics
## 5 Two-Photon Compatible MAG-Based Photoswitches
The most recent development in MAG photochemistry includes a compromise between the _-cis_' thermal stability and 2P-absorptivity by addition of a strong inductive electron withdrawing group in the _ortho_ position, in conjunction with an asymmetric azo-core (Figure 3). This new design has been based on previous _ortho_ substitutions made onto azobenzene molecules that can affect the steric or electronic barrier for isomerization, not to mention to robustly slow down its thermal relaxation rates [68; 74]. Examples of this include the symmetrical azo-core photoswitch denoted toCl-MAG1 (Figure 3); highly decorated with tetra-_ortho_-chloro substitutions, exhibiting a very slow thermal relaxation (\(\tau_{\text{cis}}\) = 3.5 h at 37 \({}^{\circ}\)C in PBS, pH = 7.0) [68], not to mention red-shifting its peak 1P absorption to 470 nm (the most rightward shift seen in PTLs) (Figure 3). When computationally modeled onto the azo-core of MAG0 and MAG2p, these substitutions yielded new photoswitches, designated \(MAG_{2p,F}^{slow}\) (Figure 3) [67]. Surprisingly, these substitutions did not lead to a major shift in the 1P absorption of the photoswitch (\(\lambda_{\text{peak}}\) = -360 nm), however they significantly increased the thermal stability of the _-cis_ variant when compared to previous MAG2p (MAG2p, \(\tau_{\text{cis}}\) = 118 ms in 80% PBS:20% DMSO [65]; \(MAG_{2p,F}^{slow}\), \(\tau_{\text{cis}}\) = 10 min in 99% PBS:1% DMSO [67]). Modeling also showed that the photoswitch should exhibit a slightly increased 2P-absoprtion cross section (MAG2p, \(\sigma\)2 = 56 GM; \(MAG_{2p,F}^{slow}\), \(\sigma\)2 = 69 GM), therefore potentially resolving the problem of insufficient accumulation of opened channels during illumination. Although the authors do not show comparison of 1P vs. 2P-mediated current size (which should differ), they do however extensively characterize the responses obtained by a fluorescent calcium activity reporter (RCaMP2) and show that equivalent responses are obtained when using optimal 1P-(405 nm) or 2P-excitation (780 nm) (Table 1). These results are in-line with our own, showing the compatibility of this photoswitch, and technique, for all-optical interrogation of cells.
# New Approach for Brain Tumor Segmentation Based on Gabor Convolution and Attention Mechanism
## 4 Conclusions  
In this paper, a new Ga-U-Net model is proposed to combine Gabor convolution and an attention mechanism with an overall 3D U-Net encoder-decoder architecture. It contains both GA modules and CBAM modules. Since the Gabor convolution kernel, as a visual sensory field, has certain advantages in multi-scale and multi-directional feature extraction, Gabor convolution is very effective for texture feature analysis and extraction, which can assist on the capturing of local structures and features in the image and often has advantages for tasks that need to focus on local information. The dataset used for experiments is three-dimensional and the tumors in the dataset are generally small and complex. Theoretically, improved segmentation accuracy can be achieved by the introduction of Gabor convolution. Experimental results have shown that this method is indeed effective. In addition, because of the applicability and generality of the CBAM attention mechanism, it is added to the encoder part in the hope that the input features can be better extracted. Finally, experimental results show that the introduction of the attention mechanism can improve the performance of the model.  
Figure 10: Loss curves obtained for the validation set.  
The model proposed in this paper also has some disadvantages. For example, the computation of 3D Gabor convolution requires relatively more time. In addition, some parameters of the filter, such as scale and direction, need to be selected reasonably. Improper selection may lead to performance degradation. The training cost becomes even higher due to the introduction of the attention mechanism. In addition, the advantages of Gabor convolution can only be realized in specific scenarios. In the future, improvements will continue to be made to this network in the hope that it can be adapted to other medical segmentation tasks for improved accuracy.  
Conceptualization, Y.C. and Y.S.; methodology, Y.C. and Y.S.; software, Y.C.; validation, Y.C. and Y.S.; formal analysis, Y.C.; investigation, Y.C.; resources, Y.C.; data curation, Y.C.; writing--original draft preparation, Y.C.; writing--review and editing, Y.S.; visualization, Y.C.; supervision, Y.S.; project administration, Y.S.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Jiangsu University of Science and Technology, grant number 1132921208.  
Not applicable.  
The data in this research can be obtained at the following link: [[http://braintumorsegmentation.org/](http://braintumorsegmentation.org/)]([http://braintumorsegmentation.org/](http://braintumorsegmentation.org/)) (accessed on 4 August 2023). The source code of an implementation of the proposed approach is available at [[https://github.com/cy253/Gabor-u-net.git](https://github.com/cy253/Gabor-u-net.git)]([https://github.com/cy253/Gabor-u-net.git](https://github.com/cy253/Gabor-u-net.git)) (accessed on 10 May 2024).  
The authors declare no conflicts of interest.
# Fault Diagnosis Method of Rolling Bearing Based on CBAM_ResNet and ACON Activation Function
## 3 Experimental Results and Discussion
### Diagnostic Results under Noise Interference
By using improved residual blocks with embedding CBAM attention mechanism and introducing Meta ACON-C activation function, the model network maximizes the learning of sample features, mitigates the interference of noise, improves the transmission capability and robustness of the model, and finally obtains good fault diagnosis accuracy. Further, we plotted two confusion matrices of the fault diagnostic results of two models which close performance in the experiments with an SNR of 4 dB, as shown in Figure 12. The evaluation metrics of the proposed model in a single experiment (4 dB), which can reveal the diagnostic performance of the model for a certain fault type, are shown in Table 7. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fault Type Label** & **Precision (\%)** & **Recall (\%)** & **F1 Score (\%)** \\ \hline
0 & 98.76 & 100 & 99.38 \\
1 & 100 & 100 & 100 \\
2 & 100 & 95 & 97.43 \\
3 & 100 & 100 & 100 \\
4 & 84.21 & 100 & 91.43 \\
5 & 91.95 & 100 & 95.81 \\
6 & 100 & 75 & 85.71 \\
7 & 100 & 100 & 100 \\
8 & 98.76 & 100 & 99.38 \\
9 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Evaluation metrics of the proposed model. Figure 11: Fault diagnosis results of different models under variable noise. From Figures 11 and 12, with SNR of 4 dB, the average accuracy of the WDCNN model which with wide kernel in the first convolutional layer, is better than that of 1 DCNN and MSCNN in fault diagnosis, but the samples of four fault type label are misdiagnosed under noise interference, for example, 48 samples with a fault label-0 were diagnosed with a fault label-4. In addition, only two types of misdiagnosis occur in the proposed model and the number of misdiagnoses is very small, its biggest mistake is to diagnose 15 samples with a fault label-6 as being a fault label-4. Relatively speaking, it performs more undisturbed than the models in these three different articles.
# Role of Blockchain Technology in Combating COVID-19 Crisis
## 5 Blockchain-COVID 19 Ongoing Research
### Telemedicine and Remote Healthcare Systems  
Remote healthcare is an example of prevention means that concerns the spread of predatory use of Telemedicine. Medicalchain, a platform that was implemented on an Ethereum and Hyperledger base, has been applied to perform remote assistance that is related to medical consultancy credentials. The system ensures that patient-doctor data that are processed through the consultancy process is protected and stored correctly. After filling the agreed-upon consent mold for marketplace requests, the system allows the patient to confidentially share the information with researchers as a third party according to the filled form [52].  
Another proposed solution, HealPoint, suggests the most relevant doctors to a user (patient) according to specific determinants such as geographical region, knowledge, and skills. It also permits cases to take another medical opinion remotely [53].
# High-Performance Computing and ABMS for High-Resolution COVID-19 Spreading Simulation
## 6 Use-Cases: Modeling the Emilia-Romagna and Lombardy Regions
### Results for the Lombardy Region  
For the Lombardy region case study, there were no high-resolution data for municipality, rather it was only available at the province level. For this reason, we extended all the features already validated for Emilia-Romagna using the 10 provinces of Lombardy. Moreover, according to [6], we decided to simulate for this case study the first pandemic wave between the 20 February 2020 and the 30 April 2020. We implemented the commuting algorithm considering each province as a large and single urban center. As previously reported, during the first pandemic period, real data about new infections were affected by the small capacity of doing a large number of COVID-19 tests among people. Thus, real data were underestimated and were about eight times higher, as proven by the serological analysis led by Italian institutions in July 2020 [6]. In light of this, we considered the real  
Figure 12: Positives’ daily representation—Emilia Romagna region.  
Figure 13: Positives’ incremental representation—Emilia Romagna region.  
data contagion incremental curve and a curve representing a serological data projection representing a rough estimate of the neglected and never traced positives from the COVID-19 tracking activity. In Figure 14, it is possible to see how the commuting curve (in black) is very close to our estimation (in green), even though there is no social structure anymore that supports the agents' meeting activity. Moreover, for completeness, we also reported our previous results (in red); this data was obtained using a power-law social network structure. Qualitative data comparison is shown in Table 1. Finally, to validate our model, we compared the simulation data with the official data provided by the Italian Government (Protezione Civile), named \"Real Data\". Considering that the official data have been recognized as seriously underestimated in the first wave of the pandemic due to the lack of molecular tests, we decided to compare with the results provided by the serological investigation led by the Italian authorities over the Lombardy population in July 2020 to understand the real impact of the spreading in the previous months (serological data).  
From the data qualitative analysis, it is clear that even the simulation that points the foundations of its social network on commuting mechanisms are plausible and acceptable, proving the robustness of the implemented ABMS model.
# Effect of DEM Used for Terrain Correction on Forest Windthrow Detection Using COSMO SkyMed Data
## 2 Materials and Methods
### Methods
#### 2.2.2 Terrain Correction  
Other than the calibration itself, SAR data needs to be corrected for distortions that are mainly related to side-looking geometry. Thus, terrain corrections aim at ensuring that such distortions will be corrected and the resulting image will be as close as possible to the real world. Even though methods can be found in literature that can help to reduce the above-mentioned distortions [1; 2; 3; 24; 25], they are all based on similar principles. Given this, in this work we decided to make use of one of the most used methods in literature, being the one offered by ESA on their SNAP platform for processing Sentinel data (SNAP--ESA Sentinel Application Platform v8.0, [[http://step.esa.int](http://step.esa.int)]([http://step.esa.int](http://step.esa.int)), accessed on 15 November 2024). The terrain correction step is made up of two main steps: (i) radiometric terrain correction (TF) and (ii) geometric terrain correction (TC). In this study, both steps were applied using state-of-the-art methods implemented in the software SNAP. Inside the SNAP modules, when requested, a bilinear interpolation was used.  
The radiometric terrain correction was done using the terrain flattening module of SNAP that is implementing the radiometric terrain correction algorithm proposed by [PERSON] [26]. The algorithm using a DEM, resampled to a pixel spacing equal to or better than  
Figure 2: Architecture of the processing chain adopted in this study.  
the one of the SAR data, normalises the backscatter with respect to the local incidence angle, effectively reducing the influence of slope and topographic effects on the measured signal.  
The geometric terrain correction was done using the range-doppler terrain correction module of SNAP that applies the method proposed in [27] for geocoding SAR images from single 2D raster radar geometry. The algorithm uses a DEM to accurately model and compensate for distortions due to topography, ensuring that each pixel in the SAR image is correctly geolocated. The module applies the range-Doppler equation, which relates the sensor's range and Doppler history to the ground coordinates, in combination with the DEM to project the slant-range image into a ground-range, map-projected coordinate system.  
Additionally, in these two steps, during the terrain correction, it is also important to map and mask the layover/shadow areas. In this study, this was done using the algorithm presented in [27] that we implemented in an R [28] script. This algorithm uses as input the DEM, the SAR incidence angle with respect to the ellipsoid, and the SAR local incidence angle. These last two products were obtained as output of the range-doppler terrain correction module.