esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Detailed DoA Testing Method  
To examine DoA estimation accuracy, measured calibration planes were imported to Matlab, where 10 snapshots of sinusoidal test signal were generated for all considered test directions \((\varphi_{t},\theta_{t})\) of impinging RF signals. During each test, as snapshots have to be received at the antenna's output [19; 20; 23; 33], all sinusoidal test signals were multiplied by two-row ESPAR antenna radiation patterns values measured at appropriate test directions. Then, additive white Gaussian noise (AWGN) was added to obtain a required signal-to-noise ratio (SNR) and RSS value was calculated. Thus, the SNR value has not to be measured as it is determined by setting the appropriate value of spectral power density of numerically added AWGN. This reflects the measurement procedure presented in [20]. In result, for every considered test direction \((\varphi_{t},\theta_{t})\) an RSS value has been obtained for all considered two-row ESPAR antenna radiation patterns.  
Figure 10: Anechoic chamber measurement setup used for the calibration phase of the proposed generalized PPCC-MCP algorithm for two-row ESPAR antenna.  
To compare the results with those already available in the literature, the test signal directions have to be set with discrete angular steps equal to \(\Delta\varphi_{t}=10^{\circ}\) and \(\Delta\theta_{t}=10^{\circ}\) in horizontal and elevation directions, respectively [24; 28; 33]. In consequence, one obtains 36 test horizontal directions \(\varphi_{t}\in\{0^{\circ},\,10^{\circ},\,\ldots,350^{\circ}\}\) for 9 elevation angles \(\theta_{t}\in\{90^{\circ},\,80^{\circ},\,\ldots,10^{\circ}\}\), which results in 324 testing points. Then, for every elevation angle, PPCC-MCP algorithm is used and, based on obtained 36 estimated direction of arrival angle \(\tilde{\varphi}\) values, root-mean-square error (RMSE) is calculated together with precision being the maximal DoA estimation error value within the plane. However, it has been observed in [43] that when \(\Delta\varphi_{t}=5^{\circ}\) and \(\Delta\theta_{t}=5^{\circ}\) is used, more accurate test results can be provided. Therefore, additional new testing directions, especially in elevation, reveal lack of accuracy of DoA estimation using ESPAR antenna together with PPCC-MCP algorithm for low \(\theta\) angles when \(M=9\) calibration planes are used. As it was overlooked in [24], to verify DoA estimation performance in a detailed way for the proposed two-row ESPAR antenna and generalized PPCC-MCP algorithm, the number of testing points has to be further increased. To this end, we propose to use detailed DoA testing method that involve \(\Delta\varphi_{t}=1^{\circ}\) and \(\Delta\theta_{t}=1^{\circ}\) in horizontal and elevation directions correspondingly. In result, 360 test horizontal directions \(\varphi_{t}\in\{0^{\circ},\,1^{\circ},\,\ldots,359^{\circ}\}\) for 90 elevation angles \(\theta_{t}\in\{90^{\circ},\,89^{\circ},\,\ldots,1^{\circ}\}\) are created and, consequently, 90 RMSE and 90 precision values will be available for each plane.
## 3 Results  
### Single Modality  
The first set of experiments was performed under a single-modality scenario, i.e., only using image information from DE MRI. The segmentation architecture was the traditional UNet [16], adding batch normalization layers.  
The performance of the proposed data augmentation method was assessed in its fixed and adaptive version. Table 1 presents the segmentation results evaluated through the Dice score and its standard deviation.  
The results show that the augmentation method increases the Dice score for the myocardium as more training data become available, finding its peak value at 200% (when two MI infarction cases are created from each normal image), and its standard deviation also decreases. For larger augmentation rates (>200% DA), the segmentation performance was either maintained or decreased.  
It is worth mentioning that the adaptive data augmentation also achieves good results that are even better than its fixed counterpart (as in the case of 50% DA), and it uses much less data since the number of cases to augment based on the model feedback varies from 20 to 25.  
When doing a case-by-case analysis, it was found that most of the time, the algorithm was able to notoriously improve the segmentation on the structure of interest (the myocardium), as shown in the top part of Figure 7. However, the segmentation performance dropped slightly in a few cases, as shown in the bottom part of Figure 7.
# Automated Destination Renewal Process for Location-Based Robot Erands
## 3 Methods
### Data Collection of Indoor Space
#### 3.1.3 Room Segmentation  
Next, delineating boundaries within the space is crucial for the performance of errand services. This is because the effective organization of unexplored areas or the execution of LBSs relies on distinct spatial boundaries. Within the process, the morphological segmentation algorithm is employed to divide the space at points where the distance between occupied spaces in the grid map is close. To achieve this, all occupied spaces are eroded by a threshold distance, converting them into free space (see Figure 3a,b). During this erosion, some cells in adjacent regions touch each other, occurring when the distance between occupied spaces is closer than the erosion threshold. This phenomenon is more common in narrow passages than in wide spaces. In other words, while non-eroded cells remain at the center of each space, the boundaries are divided by eroded cells. Therefore, the segmented free space indicates different spaces (see Figure 3c). To demonstrate that the spaces remain distinct even after restoring eroded cells, the bundles of free space are labeled with different IDs. Once labeling is complete, cells with different IDs label adjacent eroded cells (see Figure 3d). This process is repeated until all eroded cells are labeled, extending until occupied space or other labeled cells are encountered. Upon completion of this stage, the grid map is labeled with different IDs for each space.
## 3 Results and Discussion
### Compressive Properties
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Simulation/Test Values** & \begin{tabular}{c} **Mass** \\ **(g)** \\ \end{tabular} & \
\begin{tabular}{c} **Maximum** \\ **Compressive Load (N)** \\ \end{tabular} &
\begin{tabular}{c} **LWN-C** \\ **(N/g)** \\ \end{tabular} \\ \hline BS-1 & Simulation values & 103.42 & 32,100 & 310.38 \\ BS-2 & Simulation values & 103.42 & 31,875 & 308.21 \\ BS-3 & Simulation values & 103.42 & 32,059 & 309.99 \\ BS-4 & Simulation values & 103.42 & 32,128 & 310.66 \\ BS-5 & Simulation values & 103.42 & 32,097 & 310.36 \\ BS-6 & Simulation values & 103.42 & 32,106 & 310.44 \\  & Test sample \#1 & 103.44 & 32,317.29 & 312.43 \\ BS-7 & Test sample \#2 & 103.43 & 31,380.76 & 303.40 \\  & Test sample \#3 & 103.43 & 31,754.47 & 307.01 \\  & Simulation values & 103.42 & 32,113 & 310.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between compression data from experiments and simulations. Figure 3: Compression finite element model of BS-7. It can be observed in Figure 4b that when the displacement is close to 2 mm, the compression force of all BS theoretical models increases with the increase of displacement, and then the failure phenomenon occurs successively. It is obvious that BS-2 is the first to be damaged. Compared with other BSs, the damage caused by BS-2 is the most serious with the increase of placement, while the damage caused by other BSs is not much different. In particular, Figure 4c clearly illustrates the compression force corresponding to the failure of each BS. It can be found that the difference between the maximum bearing compression force of each BSs is very small, and according to Table 2, the LWN-C value of each BSs is basically the same, all of which are maintained at about 310 N/g. In addition, compared with other structures, BS-4, BS-6, and BS-7 have relatively large maximum bearing capacity, and the maximum bearing capacity of BS-4 can reach 32,128 N, while the minimum bearing capacity of BS-2 is 31,875 N. The reason is that compared with BS-2, BS-4, BS-6, and BS-7 has multilayer quadrilateral or hexagonal composite structures, which makes their compression performance superior. It can be observed from Figure 5 that BS-7 has a relatively stable deformation, and a big fold is generated after compression. Figure 4: Compressive properties of different BSs. (**a**) Comparison between experimental results and simulation results for BS-7; (**b**) load-displacement simulation curves for different BSs; (**c**) the maximum compressive load of BSs; (**d**) compression test.
# Online Efficiency Optimization and Speed Sensorless Control of Single-Phase Induction Motors
## 4 Energy Efficiency Optimization
### Gradient Descent Algorithm  
In order to minimize the SPIM loss, taking into account motor parameters variations, the corrective parameter \(K\) is defined to update \(K_{s}\). \(K\) is calculated online using an optimization algorithm called Gradient Descent [25]. The algorithm is described as follows:  
1. The initial value of \(K\) is set to 1, which is the case where no correction is yet made to \(K_{s}\). The input power (\(P_{in}\)) is measured for this case. Then, the initial value of \(K\) variation (\(\Delta K\)) is set to an arbitrary value, the iteration index (\(i\)) is set to 1, the convergence criterion is \(\varepsilon\) and the convergence factor is \(\lambda\).
2. The values of \(K\) are updated via following equation: \[K^{(i+1)}=K^{(i)}+\Delta K\] (29)
3. After a specified time, which takes into account the time required before the changes made in \(K_{s}\) appear in the motor operation and motor reaches to a new stable state, the new input power (\(P_{in}\)) is measured.
4. Inasmuch as after passing through the transient state, the motor speed will be regulated and reaches its set value; the load power, therefore will be equal to its previous value. As a result, changes in input power will be equal to changes in loss. Therefore, the algorithm is expected to work equally by using the input power instead. In other words:  
\[p_{in}^{(i+1)}-P_{in}^{(i)}=[P_{out}^{(i+1)}+P_{loss}^{(i+1)}]-[P_{out}^{(i)}+P_{ loss}^{(i)}]=P_{loss}^{(i+1)}-P_{loss}^{(i)} \tag{30}\]  
It is important to note that for a reliable performance of the optimization module, the time delay between consecutive iterations shall be long enough to let the speed transients diminish adequately.  
The approximated power loss gradient is calculated as follows:  
\[\
abla P_{loss}=\frac{dp_{loss}}{dk}\cong\frac{\Delta P_{loss}}{\Delta K}= \frac{P_{in}^{(i+1)}-P_{in}^{(in)}}{K^{(i+1)}-K^{(i)}} \tag{31}\]  
In order to approach the minimum point of power loss, \(K\) must change in the opposite direction of power loss gradient i.e.,  
\[\Delta K=-\lambda\
abla P_{loss} \tag{32}\]  
If \(\Delta K\) is smaller than \(\varepsilon\), the algorithm will be stopped. Otherwise, the iteration index (i) is increased by one and the algorithm will return to step 2.  
It is noteworthy that this algorithm has a deficiency of getting trapped in local minima. In our case, however, as there is only one minimum to be found [21], such deficiency is dismissed.
# Multi-Source Information Fusion Fault Diagnosis for Gearboxes Based on SDP and VGG
### Multi-Source Information Fusion Based on DS Evidence Theory  
Multi-source information fusion technology can result in greater comprehensiveness of the data sources for diagnostic results, which may help to improve the accuracy of the diagnostic results. It can be divided in terms of hierarchy into data-level fusion, feature-level fusion, and decision-level fusion, where decision-level fusion is located at the highest level. The classification results of each sensor for each state input into the decision-level theory as a body of evidence. Figure 6 shows the technical route of decision-level fusion.  
The images obtained using the SDP method were used to perform bearing health diagnosis on the basis of the data acquired by each sensor using the VGG network structure. Ultimately, the decision-level fusion of the local diagnostic results of each sensor obtained by the VGG network was conducted using DS evidence theory. Before fusion, a corresponding recognition framework was established for each sensor. In the presence of three fault states where the conditions were normal, 0.007\", and 0.021\", the corresponding identification frame was \(\Theta=\{\text{A},\text{B},\text{C}\}\). Since the fused bodies of evidence were acquired from two sensors, the body of evidence E = {E1, E2}, where E1 represents the bearing base end sensor (DE sensor) and E2 represents the bearing fan end sensor (FE sensor). Then, the fusion rules were used to fuse the bodies of evidence of the two sensors at the decision level. Ultimately, in the present study, fault diagnosis with multi-source information fusion of bearings was realized.  
The FE sensor data were extracted in the same way as the data from the DE sensor, and the optimal parameters of the FE sensor were obtained as a time lag coefficient of 4, an angular amplification factor of 30\({}^{\circ}\), and a deflection angle of 120\({}^{\circ}\). When the deflection angles were both 120\({}^{\circ}\), the DE sensors with different values were fused with the FE sensor to provide multi-source information, and the bearing accuracy was obtained before and after the fusion of the two sensors, as shown in Figure 7.  
According to Figure 7a, the accuracy of the FE sensor was 99.99% before fusion, the accuracy of the DE sensor was 99.98%, and the accuracy after fusion was 100%. Since the same parameter was used in FE sensor for the purposes of comparison, the accuracy remained the same, while, as can be seen in Figure 7b, the accuracy of the DE sensor before fusion was 99.97%, and the accuracy after fusion was 100%. In Figure 7c, it can be seen that the accuracy of the DE sensor before fusion was 99.98%, and the accuracy after fusion was 100%. Therefore, according to the fusion results, it can be concluded that after using DS fusion technology, the accuracy of failure detection of the bearings was improved.  
Figure 6: DS evidence theory technical route.
## 5 Industry Applications and Manufacturers
### Key Industry Applications  
One of the most important advantages of using autonomous vehicles is that they are not subject to human control. Firstly, humans may become tired or fatigued during long working hours. Furthermore, other factors such as losing focus while driving or talking on the phone are not present in autonomous vehicles, and therefore, the use of autonomous vehicles can be considered much safer than human-controlled vehicles.  
As will be explained in further detail below in Section 4, partially autonomous vehicles with varying levels of automation are currently accessible worldwide to provide humanity with a more confident and safer way of delivering this service.  
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Manufacturer** & **Fleet/Series** & **Type of Sensor** \\ \hline K-MATIC & Various forklift models & LiDAR, cameras, ultrasonic sensors \\ \hline Seegrid & Palion series, GP8 & LiDAR, stereo cameras \\ \hline Crown Equipment & TSP Series, RM Series & LiDAR, RFID, ultrasonic sensors \\ \hline OTTO Motors & OTTO 100, OTTO 1500 & LiDAR, 3D cameras \\ \hline Vecna Robotics & Pallet Jack, Tugger & LiDAR, computer vision, ultrasonic sensors \\ \hline Balyo & R-MATIC, VNA-MATIC & LiDAR, cameras, gyroscopes \\ \hline Hyster Robotic CB & Hyster Robotic counterbalance forklift & LiDAR, vision systems, proximity sensors \\ \hline \hline \end{tabular}
\end{table}
Table 12: Forklift manufacturers and types of sensors used for navigation.  
Figure 20: The Combi-AGT autonomous-guided forklift truck by combilitt.  
Many expected advantages will be available if autonomous vehicles are used. The first will be a reduction in accidents as a result of minimizing the proportion of human interference with the vehicle while driving. Secondly, the exertion of driving would be alleviated for vehicle drivers, allowing them to perform other tasks or enjoy a moment of respite. Regarding vehicles used for transporting goods, it will be possible to operate those vehicles for longer hours than vehicles driven by humans.
## 4 Axial Compression Bearing Capacity
### Extended Parameter Analysis
#### 4.1.2 Parameter Extended Analysis  
The influencing parameters of the axial compression bearing capacity for the strengthened specimens included the thickness and strength of the SSAWC, self-stress, and the thickness and concrete strength of the LCSS. The influence of the cross-sectional area of the filled concrete on the bearing capacity was considered in accordance with the code in [24]. Due to the limitations of the prefabricated mold used for the segments, the cross-sectional area of the LCSS basically remained invariable. Therefore, the influencing parameters were mainly self-stress, the strength of the SSAWC, and the concrete strength of the LCSS. Among them, the strength of filled concrete was set to three grades: C35, C40, C50; the longitudinal stress values were set to 0.8 MPa, 1.0 MPa, and 1.2 MPa; and the concrete strength of the LCSS was set to three grades: C35, C40, C50. As such, 9 extended numerical models were built to study the influence of these parameters, with Table 8 showing the specific parameter settings and peak loads.  
Figure 12: Finite element model. (**a**) Model of specimen P1; (**b**) Model of specimen S1-S.  
Figure 13: Load–displacement curves of specimens acquired using the numerical model and tests.  
1. The influence of self-stress on the bearing capacity: Tables 7 and 8 show that when the self-stress value was between 0 and 1.2 MPa, the peak load of the strengthened specimens increased with the increase in the self-stress. When the self-stress increased by 0.2 MPa, the bearing capacity increased by about 100 kN. However, as the self-stress value increased, the growth rate of the peak load decreased. The reason for this was that after the stress increased, the LCSS was easily damaged and the restraint effect on column was reduced, resulting in the decrease in the bearing capacity.
2. The influence of the strength of the SSAWC on bearing capacity: It can be seen from Tables 7 and 8 that the peak load of the strengthened specimens increased with the increase in strength of the filled concrete. When the concrete strength increased by 1 MPa, the bearing capacity of the strengthened specimens increased by 35 to 45 kN. However, the higher the strength of the filled concrete, the lower the growth rate of the bearing capacity of the strengthened specimens. This was because after the strength of the filled concrete increased, the LCSS failed first, resulting in a reduction of the increase in the bearing capacity.
3. The influence of the concrete strength of the LCSS on the bearing capacity: Tables 7 and 8 show that the peak load of the specimens increased with the increase in concrete strength of the segment. When the concrete strength of the LCSS increased by 1 MPa, the bearing capacity of the strengthened increased by 35 to 60 kN. However, the higher the strength of the segment concrete, the smaller the growth rate of the peak load. The reason was similar to that of the strength of the filled concrete.
## 5 Conclusions  
The FQS algorithm improves on the quick search (QS) algorithm, by applying the bad character rule, aided with a statistically maximal expected shift value introduced in this work and a pre-testing stage before full pattern matching. Unlike previous approaches that blindly tested the first and last symbols in the pattern [20,21], our pre-testing stage is performed by computing the statistical maximal expected shift position. We have compared FQS against three other competitive QS variants: the QS itself, FJS and the Horspool algorithm. A range of text files were searched, including randomly generated text files with different alphabet sizes (\(2\leq|\Sigma|\leq 256\)), and practical benchmark text files, namely _E. coli_, Bible and World192, from the Canterbury Corpus. The pattern lengths were varied from 10 to 1,000 with 19 varieties. We find that, statistically, FQS has the overall best performance (practical running time, number of symbol comparisons and number of pattern shifts) over all of the other three algorithms, mostly especially for text files with alphabet sizes less than 128. The results suggest that FQS could have important applications in practice, especially for genomic data sets, such as DNA or RNA sequences with four symbols or protein sequences with 20 symbols.
###### Abstract  
Recently, demand for handwriting recognition, such as automation of mail sorting, license plate recognition, and electronic memo pads, has exponentially increased in various industrial fields. In addition, in the image recognition field, methods using artificial convolutional neural networks, which show outstanding performance, have been applied to handwriting recognition. However, owing to the diversity of recognition application fields, the number of dimensions in the learning and reasoning processes is increasing. To solve this problem, a principal component analysis (PCA) technique is used for dimensionality reduction. However, PCA is likely to increase the accuracy loss due to data compression. Therefore, in this paper, we propose a line-segment feature analysis (LFA) algorithm for input dimensionality reduction in handwritten text recognition. This proposed algorithm extracts the line segment information, constituting the image of input data, and assigns a unique value to each segment using \(3\times 3\) and \(5\times 5\) filters. Using the unique values to identify the number of line segments and adding them up, a 1-D vector with a size of 512 is created. This vector is used as input to machine-learning. For the performance evaluation of the method, the Extending Modified National Institute of Standards and Technology (EMNIST) database was used. In the evaluation, PCA showed 96.6% and 93.86% accuracy with k-nearest neighbors (KNN) and support vector machine (SVM), respectively, while LFA showed 97.5% and 98.9% accuracy with KNN and SVM, respectively.  
feature extraction; dimensionality reduction; line-segment features; \(k\)-nearest neighbor; support vector machine
# A Novel Deep Learning Approach for Machinery Prognostics Based on Time Windows
## 3 Experiment and Analysis
### Experimental Setup  
#### 3.2.1 Data Pre-Processing  
In the CNN, the characteristics of relatively smaller values will be weakened, and the characteristics of relatively larger values will be amplified. To eliminate the problems caused by different numerical ranges, the data need to be standardized. The zero-mean normalization method was used in this paper, which is denoted as:  
\[TRAIN_{j}^{i}=\frac{train_{j}^{i}-\mu_{train}^{i}}{\sigma_{train}^{i}}, \tag{4}\]  
where \(TRAIN_{ij}\) represents the normalized value of the \(j\)th data point from the \(i\)th sensor in a training sample, \(train_{ij}\) represents the true value of the \(j\)th data point from the \(i\)th sensor, \(\mu_{train}^{i}\) represents the mean of the \(i\)th sensor and \(\sigma_{train}^{i}\) represents the standard deviation.  
At the same time, the standardization of data in the testing set should be based on the mean and standard deviation of the training set, so that the impact of future data on the current model can be avoided.