esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Results
### Experiment 2: Good/Faulty Assessment  
The good and faulty fingerprint image samples were entered into the proposed Power Spectrum + Prewitt Filter algorithm, the conventional Power Spectrum algorithm, and SSIM, after which the results were recorded. In the experimental results of the proposed algorithm, the box and whisker plot and line chart (Figure 8) showed that the results of the Power Spectrum + Prewitt algorithm had a clear difference between the two groups of data, and this finding was consistent with the trend wherein the FIQMs of the good samples were higher than those of faulty samples. On the other hand, the conventional algorithm could not identify the quality of fingerprint images; with the results even good fingerprints had lower FIQMs. With regard to SSIM, the charts reveal that it could not distinguish good samples from faulty samples, when we used a good fingerprint sample for reference. For comparison, the differences in FIQMs and positive/negative aspects between the proposed and conventional algorithm accuracy are summarized in Tables 1 and 2.  
Figure 7: Experimental results of Sharpness Assessment: (**a**) box and whisker plot and (**b**) line chart.
## 3 Methodology
modes of vibration appear as horizontal lines (constant frequency) over many kilometres where the vehicle speed is high enough to excite them. Bridges, switches, tunnels and other instantaneous changes in track stiffness appear as vertical lines in the spectrogram because they excite all the frequencies in a very short section of the track. Wheel defects, such as out-of-roundness, wheel corrugation and wheel flats, are wavelength-constant effects, as is rail corrugation. Therefore, they appear in the frequency domain spectrogram as excitations with the same shape as the vehicle speed, since:  
\[f=\frac{V}{\lambda} \tag{2}\]  
where \(f\) is the frequency, \(V\) is the vehicle speed and \(\lambda\) is the wavelength. To distinguish between wheel defects and rail corrugation, it is important to assess for how long the excitation lasts: rail corrugation is expected to be found for a few hundred metres, while wheel defects last for the entire acceleration acquisition. The last step of the methodology is to formulate a hypothesis about the excitation in the wavelength range of the rail corrugation, that cannot be attributed to other causes. The hypothesis can be supported by multibody dynamics (MBD) simulations, which can also be useful to evaluate the vehicle elastic modes of vibration and compare them with the horizontal lines in the spectrogram. In the present work, an MBD model (in SIMPACK) of a commuter train, whose parameters are confidential information, was equipped with an elastic wheelset to analyse the influence of these short wavelength excitations. In the simulation model a co-running track model is implemented. The track model has two layers--under rail pad and under sleeper pad--and is parameterised as a mid-stiffness-like track. The vehicle has a standard S1002 wheel profile, and the rail profile is 60E1 with a standard 1:40 rail cart. The operating speed of the train on the given network is about 160 km/h and, considering the required excitation wavelength of about 30 mm to 300 mm, this means that the elastic model of the wheelset should cover high frequencies, approximately up to 3000 Hz. The finite element model of the wheelset was built from first and second order solid elements and reduced using component mode synthesis (CMS-Method) [26] and incorporated into the vehicle model (Figure 2). The general rail measurement data could not cover the required wavelength range for these investigations, so the excitations were synthetised from axle box accelerations. As not only the rail can have such short wavelength defects, but also the wheel can have corrugation and OOR defects, the question arises as to whether it is possible to distinguish between them. In order to answer this question, several simulation scenarios were carried out. Firstly, rail corrugations only, then the wheel corrugation/OOR only and then both defects combined. Figure 2: (**a**) Finite element model of the given wheelset symbolically depicted with a second order bending of the LHS wheel disc; (**b**) MBD model of the train with the reduced model built in.
## 3 Materials and Methods
### Mathematical Model Formulation  
The PDP-TW was formulated as a MILP model based on the adaptation of the model proposed by [PERSON] and [PERSON] [6], while excluding the transshipment component. This modification is justified by the fact that the company under study does not utilize transshipment points in its logistics operations, making it inappropriate to retain this characteristic in the model. Therefore, its removal was essential to ensure the model aligns with the operational reality being analyzed. The notations are defined as follows. Let \(R\) be the set of requests. Let \(G=(V,\ A)\) be a directed graph, where \(V\) and \(A\) are the set of vertices and arcs, respectively. The set of vertices \(V\) is defined as \(V=O\cup O^{\prime}\cup P\cup D\), where \(O\) represents the origin node, \(O^{\prime}\) represents the destination node, \(P=\{p_{r}:r=1,\ \dots,|R|\}\) denotes the pickup nodes, and \(D=\{d_{r}:r=1,\ \dots,\ |R|\}\) denotes the delivery nodes. The set of arcs is defined as \(A=\{(i,j)|i,j\in V,\ i\
eq j\}\). Each request \(r\in R\) is characterized by a quantity of baggage, a pickup location, and a delivery location, denoted by \(q_{r}\), \(p_{r}\) and \(d_{r}\), respectively. The PDP-TW aims to seek a set of routes that minimize the total distance traveled by the set of vehicles \(K\), and satisfy the following constraints: (i) each pickup node \(p_{r}\in P\) and delivery node \(d_{r}\in D\) must be visited exactly once, (ii) each request \(r\in R\) must be assigned to a single vehicle \(k\in K\), (iii) each request \(r\in R\) must be pickup and delivery within its respective time windows, and (iv) the number of requests \(r\in R\) served on a route must not exceed the homogeneous vehicle capacity \(C\). To illustrate the PDP-TW problem, Figure 1 provides an example instance. Figure 1(a) shows the problem setup with \(|R|=5\), including the depot (serving as both \(O\) and \(O^{\prime}\) for each vehicle), pickup locations (\(P\)), and delivery locations (\(D\)). The depot is represented by a triangle, pickup locations by squares, and delivery locations by circles. Each vehicle has a luggage capacity of 5 units, and the baggage quantities associated with the requests are given by \(q_{r}=\{2,\ 2,\ 2,\ 2,\ 1\}\). Additionally, each location has a specific time window within which it must be visited, indicated above each node. Figure 1(b) illustrates a feasible solution, with the routes represented by dashed lines. Above each dashed line the travel time of the arc is presented in minutes. Given the previous definitions and information in Table 1, the PDP-TW is formulated as follows.
# Missing Data Imputation in the Internet of Things Sensor Networks
## 1 Introduction
Multiple imputation however is a model- based technique that is further classified into two: Discriminative (such as Multiple Imputation by Chain Equations (MICE) [15], matrix completion [16] and random forest imputation (missforest) [17]) and Generative multiple imputation method consists of Deep Learning (DL) techniques such as Neural Networks (NN) [18], Generative Adversarial Networks (GAN) [19], and Variational Auto Encoders (VAE) [20]. In this study, we evaluate the consequence of incomplete/missing values on data generated from Low-cost environmental sensors and propose a Best Fit Missing Value Imputation (BFMVI) model based on data clustering for the missing value imputation process. We compare the effects of our suggested technique on sensor calibration to state-of-the-art techniques. The rest of the paper is laid out as follows: In Section 2, we present our motivation and the contribution of this paper. Section 3 identifies recent research that has been carried out with regards to missing data imputation for sensor calibration. Section 4 presents our experiments and results, clearly describing the dataset used for simulations and our proposed approach. Imputation techniques and their effects on sensor calibration are discussed in Section 5. Lastly, Section 6 concludes the paper and suggests some areas for further research.
## 4 Numerical Examples
### Example 3--MBB beam
Thus, it is important for designers to consider uncertainties in the design realization to balance minimizing costs and maximizing reliability.
## 3 Results
### Signal Spectral Analysis  
Spectral analysis of the signals at the different zones of consideration is presented in Figure 4b. It is a representation of the power spectral density of the GPR signals at the different areas of consideration. It describes the power present in the signal as a function of frequency, per unit frequency [46]. The magnitude of the lower spectral was higher at the suspected high matric potential, while the reverse was the case at the high-frequency spectral.  
Time-frequency plots for the GPR signal at various soil conditions are shown in Figure 5. Before water infiltration, low-frequency spectral evolved longer to the time ~22 ns at the suspected high matric potential. In contrast, the same frequency spectral was short-lived to ~11 ns at the suspected low matric potential (Figure 5a). This pointed out the difference in the dielectric properties at the different zones, which may have been influenced by the variation in degree of water saturation. However, with water infiltration, the time-frequency plots show a significant influence of the water presence at the suspected low-potential zones than the high-potential zones (Figure 5b,c).  
Figure 5: Time-frequency plots from the GPR datasets with corresponding signal traces. (**a**) Before water infiltration; (**b**) after 25 L water infiltration; and (**c**) after 50 L water infiltration.  
The centroid frequency plot (Figure 6) also corroborated the delineation of matric potential variations within the considered zones in the subsurface. Essentially, the centroid frequency is a spectral decomposition technique that allows the location of the center mass of the signal spectrum as a function of time with distance [47]. It is computed using the weighted mean of the frequencies present in the signal. It is determined using S-transform, with their magnitudes as the weights [48] to represent changes in the propagation condition of the signals. Thus, the value of centroid frequency decreases/increases as the signal enters a high/low attenuation area. In the present scenario, the centroid frequency decreased with depth at the suspected high matric potential (dotted rectangle labeled as High MP in Figure 6) and increased with depth to a depth of about 0.6 m at the low matric potential.  
The centroid frequency computation allows a glance at the state variable of the media through which the pulse EM energy has propagated, and its integration with other forms of data analysis enhances the fidelity of the resulting outcome.
## 3 Advanced Control Functionalities for Power-Frequency Regulation
### Droop Controllers Proposed to Be Incorporated in the Smart-Transformer  
The following droop controllers are proposed to be implemented in the ST control structure, aiming to act on the reference active power of the local ESS (incorporated in the ST's LV DC bus) and in the reference voltages and frequency of the hybrid AC/DC network. The presented droop controllers follow the general functional relation represented in Figure 12 and expressed according to Equation (20).
## 4 Numerical Results and Discussion  
In this section, computer simulations are carried out to verify the correctness of the theoretical derivation over an AWGN channel. Moreover, in order to verify the feasibility of the system over a UWA channel, the proposed system is simulated on the Watermark platform [18], then the field experiments are carried out in pool. By comparing with the MSSS system, the BER performance of the MSSS-I/Q-IM system with different parameters is analyzed.
## 5 Results and Discussions
Therefore, the prediction results of STM can be effectively improved by improving the accuracy of the similarity measurement of traditional STM. Moreover, in the case of only using monitoring data, MP-SAX-STM can also achieve life prediction, and the prediction results were better than the traditional STM. Besides, for Set #4, the improvement effect of MP-SAX-STM was also approximately enhanced with the increase of the know data length. To further illustrate the life prediction results of the MP-SAX-STM and STM, one set was randomly selected from the 30 sets of life cycle data from Set #2 and Set #4 to predict the remaining useful life (RUL) under different lengths as shown in Figure 10 respectively. And the RUL error was shown in Figure 11.  
Compared with STM, as shown in Figure 10, both the MP-SAX-STM RUL prediction results of Set #2 and Set #4 were closer to the actual RUL. Therefore, under multiple operating conditions, the life prediction can be effectively realized by MP-SAX-STM even if the degradation model was not extracted and only the monitoring data which was sensitive to the operating condition was used. In addition, as shown in Figure 11, for Set #2, the RUL error of MP-SAX-STM fluctuated around 0, while the error of STM was much less than 0 and gradually approached 0. For Set #4, although the overall trend of the error of MP-SAX-STM passed through 0, the error of STM was always greater than 0. Therefore, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indicators** & **1/2** & **2/3** & **3/4** & **4/5** \\ \hline MAE & 9.30\% & 13.69\% & 13.51\% & 16.70\% \\ RMSE & 8.65\% & 13.88\% & 11.49\% & 12.36\% \\ MAE & 8.67\% & 13.45\% & 12.82\% & 13.30\% \\ MASE & 9.30\% & 13.69\% & 13.51\% & 16.70\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Relative reduction of each indicator of MP-SAX-STM compared with STM of Set #4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indicators** & **1/2** & **2/3** & **3/4** & **4/5** \\ \hline MAE & 23.71\% & 24.48\% & 22.28\% & 21.74\% \\ RMSE & 22.35\% & 22.32\% & 23.20\% & 21.78\% \\ MAPE & 25.21\% & 25.13\% & 22.50\% & 22.63\% \\ MASE & 23.71\% & 24.48\% & 22.28\% & 21.74\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Relative reduction of each indicator of MP-SAX-STM compared with STM of Set #2. Altogether, the prediction result of STM can be effectively improved by improving the similarity measurement accuracy of STM. In addition, in the scenario of multiple operating conditions, MP-SAX-STM can effectively solve the life prediction problem that the degradation model of monitoring data cannot be accurately obtained, and effectively realized the life prediction.
# An Efficient Blockchain Transaction Retrieval System
## 3 System Implementation
### Retrieval Structure Generation and Analysis
#### 3.3.1 BB+ Tree  
The B+ tree is a very classic retrieval structure with the advantages of efficient retrieval speed and supporting range retrieval. In the blockchain retrieval scenario, account addresses and block hashes are treated as key-value pairs and managed through the B+ tree. During blockchain transaction retrieval, the account address is retrieved as the key value in the B+ tree to obtain the block hash and the corresponding block for the complete transaction. However, with the rapid growth of the account address, the B+ tree is directly used to manage the account address retrieval items, resulting in the too-large tree structure and too-large retrieval space, directly affecting the retrieval time.  
Given the deficiency of the B+ tree, we propose a BB+ tree. The BB+ tree has two core elements: Bloom filter and B+ tree. Then, we will introduce the BB+ tree in detail. We use the Bloom filter to optimize the B+ tree, mainly because the Bloom filter uses less space and has fast calculation speed. It can have better scalability and retrieval efficiency when facing a large number of account address retrieval items.  
As shown in Figure 4, the BB+ tree is composed of a two-level structure, where the first level is the Bloom filter, and the second level is the B+ tree. The first level structure is used to quickly classify key values and reduce the search space: when a new pair of key-value enters, the key value is first input into the Bloom filter for calculation. The Bloom filter will generate an m-bit array. According to the bit array, the key-value pairs are managed by the subB+ tree corresponding to the input bit array. The second level structure is that after receiving the key-value pair of the first level structure, the BB+ tree will construct the subB+ tree according to the key value of the key-value pair to facilitate the key-value retrieval item. Similar to the B+ tree, the front and back pointers in the BB+ tree are set between leaf nodes to support range retrieval.  
The following is the pseudo-code analysis of the BB+ tree when inserting key-value pairs in Algorithm 1 and retrieving the key value in Algorithm 2:
## 4 Experiment Results  
Cracks, incomplete fusion, incomplete penetration, porosity, slag inclusion, undercut, spatter, and blowhole are the eight common types of defect characteristics. The maintenance staff according to the engine maintenance manual, must have a NDT Inspector Engineer with FAA, CAA, and EASR certifications to complete the maintenance for aeronautics engines [29]. Our model learns to identify defect categories through image recognition. After the model is compiled, the program is lightweight and can be installed in the Jetson(tm) TX2 embedded computer. The computer can automatically detect the characteristic values of structural defects, such as engine components, weld beads, and blade bodies.  
The test process can identify the type of defect even if the original input X-ray image does not require any preliminary training. The customized DNN and the overall neural network model is evaluated in terms of the mAP (mean average precision). The results confirmed that the model reached the technical standards of a qualified human inspector. The average accuracy (mAP) obtained by the experimental test was 0.82 if the image did not contain a defect and was 0.8 if the picture had certain defects. Figure 11 illustrates an inspection result of an X-ray image cracks and blowhole, etc., and Figure 12 depicts the training performance.  
X-ray photograph image checks are used because it is difficult to find a slight line with hidden cracks that have the same length and narrow shapes. This is different from other common wide cracks. We consider that the AE-RTISNet provided the best image region results when compared with the more traditional multiple Fast R-CNN approach.  
Figure 10: Image file labeling.  
To overcome this issue, we employed a deep learning paradigm of transfer learning tackling both single and multiple detection. We also compared with the original Mask-RCNN, YOLO-V2, and YOLO-V3. Although the test results are listed in Table 1, the accuracy rate of YOLO was slightly higher than that of Fast RCNN, and the Loss score of Fast RCNN was higher than that of YOLO. However, the average elapsed times of the one-stage methods were much less than the two-stage methods.  
A field comparison of human visual inspection and the proposed AI-based inspection is shown in Table 2. The experiment verified that the augmentation of an AI-based inspection system for engine diagnosis is feasible and can be an effective tool in quality assurance. The results imply that the maintenance cycle can be made shorter. A preliminary estimate of the reduction in working hours for each engine is more than 60 h, which is equivalent to the savings of a minimum of USD 3 million.  
Figure 11: Inspection result of an X-ray image cracks and blowhole etc.  
Figure 12: Visualization of the training process.
# Numerical Simulation of Non-Isothermal Mixing Flow Characteristics with ELES Method
## 3 Simulations with the Numerical Method ELES
### Validation of the Numerical Method ELES
#### 3.3.2 Temperature and Velocity Distributions Downstream along Vertical Lines  
Spatial distributions of the axial velocity component \(V_{z}\), the temperature, and their fluctuation intensities along the vertical lines at position \(Z=0.5\) and \(1\)\(D_{m}\) in the W-ref case were compared and are shown in Figure 9 (vertical lines named 'line-I' and 'line-II' are shown in Figure 1).  
The calculated distributions were in good agreement with those in the experiment. The fluctuation intensities were well-simulated to obtain peak values at positions where the physical quantity had a sudden change along the vertical direction. The distribution trends of temperature were similar to that of the axial velocity component, and both their obvious features appeared at the position around \(Y=-0.2\)\(D_{m}\). The velocity fluctuation intensities were slightly underestimated within ELES simulations, and it was considered that the fluctuations were actually enhanced in the experiments due to the dense arrangement of quite a number of thermocouples.  
In addition, the peak values of the temperature fluctuation intensity were overestimated in ELES analyses, especially at the downstream position \(Z=1\)\(D_{m}\) (the same in AQUA analyses performed by [PERSON] et al. [5]), which indicated that it became thermal mixing in the actual flow likely because of the enhancement of the velocity field disturbance downstream during the experiment. In addition, it obtained more appropriate results near the wall with ELES, especially for the capture of the velocity fluctuation intensity, than that in AQUA analyses performed by [PERSON] et al. [5].  
Figure 9: Temperature and velocity distributions along vertical lines in the W-ref case: (a) Line–I, \(=0.5\)\(D_{m}\); (b) Line–II, \(Z=1\)\(D_{m}\)
# A Single Simulation Platform for Hybrid Photoacoustic and RF-Acoustic Computed Tomography
## 3 Results
### Photoacoustic Computed Tomography (PACT) of the Homogeneous Phantom  
The optical excitation for the first phantom uses a wavelength of 800 nm. This specific wavelength was chosen as it is often used in photoacoustics, due to the absorption spectrum of the absorbers of interest, such as hemoglobin. With the homogeneous phantom parameters, the simulated optical fluence is given in Figure 2.  
The position of the absorbers is easily approximated by the nulls in the fluence distribution. The approximate shapes can be identified, which can be used for more stable reconstruction of the material parameters. The 8 mm spacing is large enough to identify two distinct absorbers, with the ultimate resolution governed by the reconstruction of the fluence that would be done in practice. The initial pressure induced by this fluence distribution, as well as the original and reconstructed absorption coefficient images are provided in Figure 3.  
The fluence is not constant across the domain, and so the two absorbers induce slightly different initial pressures. The larger object is reconstructed with less error, since it is approximately constant over a larger area. Non-idealities in the reconstructed background pressure are suppressed by using the fluence, and so the reconstructed objects are clearly separated from the background.