esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this study, an actual PAI experimental setup is utilized to image steel samples with slit-type surface cracks and construct a dedicated PA dataset. On the constructed dataset, deep CNN models are utilized in conjunction with different machine learning regression models to estimate the length and width of the surface cracks on steel samples. A comparative analysis of the various combinations of CNN models for feature extraction and regression models for crack size estimations is presented. Furthermore, the acoustic response for various geometries with internal cracks were studied using numerical simulations, and the mutual differences among them were evaluated using the one-way ANOVA test. Several comparisons were performed to evaluate the effects of internal cracks with the same/different sizes and placed at different/same locations. The results of the study present small \(p\)-values (\(p\)-value \(<\) 0.0001) for all the comparisons, which proves that these groups' means have statistically significant differences. The results conclude that for crack size estimation, random forest regression performs the best among other regression models. The power of machine learning enables us to devise effective schemes to generate accurate results using low-resolution input data, saving precious time. Another inference from the results is that PAI is feasible for the detection of internal steel cracks. In the future, our research group plans to conduct actual PAI experimentation on steel samples with internal defects for the identification, classification, size, and location estimation of these internal defects.  
Conceptualization, M.Y.J.; data curation, J.Y.L.; formal analysis, A.A. and J.H.K.; funding acquisition, M.Y.J.; investigation, M.Y.J.; methodology, A.A.; project administration, J.Y.L., J.H.K. and M.Y.J.; resources, J.Y.L., J.H.K. and M.Y.J.; software, A.A.; supervision, M.Y.J.; validation, A.A. and J.H.K.; writing--original draft, A.A.; writing--review and editing, A.A. and M.Y.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) [grant number: 2022R1A2B5B01002377].  
Figure 19: ANOVA table presenting a \(p\)-value (\(p\)-value \(<\) 0.0001) that shows significant differences between the groups being compared; that is, samples with internal cracks of different sizes but placed at the same location.  
Figure 18: One-way ANOVA test showing that the means of both groups have a nonoverlapping interval when comparing samples with internal cracks of different sizes but placed at the same location.
# Correlation Analysis of Macro-Micro Parameters of Sandstone Based on PFC3D
## 2 Methodology
### Establishment of the PFC\({}^{3d}\)  
Using PFC\({}^{3d}\) 6.0 software, a three-dimensional discrete element numerical model of sandstone particles was created. This model featured a standard cylindrical specimen with a diameter of 50 mm and a height of 100 mm. The particle size ranged from 0.8 to 1.3 mm, and the model had a porosity of 0.35, consisting of 30,776 particles. Rigid loading plates were installed at both the top and bottom of the model to apply loads and record the stress-strain curve, as illustrated in Figure 2.  
According to the orthogonal experimental scheme, the inter-particle contact characteristic parameters were adjusted to sequentially conduct uniaxial compression and uniaxial  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline
**Number** & \(\overline{E}_{c}\) **(GPa)** & \(\mathbf{E_{c}}\)**/ \(  
tension numerical simulations, with the corresponding stress-strain curves recorded. Based on the simulation data, the macroscopic mechanical parameters of the sandstone specimen were determined as follows: \(\sigma_{t}\) and \(\sigma_{c}\) were taken as the peak stress values from the stress-strain curves in the tensile and compression tests, respectively, as shown in Figure 2(a),b. The value of \(E\) was determined by the slope of the stress-strain curve in the elastic stage.  
\[E=\frac{\sigma_{1}-\sigma_{2}}{\varepsilon_{1}-\varepsilon_{2}} \tag{1}\]  
In this context, \(\sigma_{1}\), \(\sigma_{2}\), \(\varepsilon_{1}\), and \(\varepsilon_{2}\) denote the stress and strain values at the two points during the elastic stage. \(v\) is the ratio of lateral strain to axial strain under uniaxial compression conditions.  
\[v=-\frac{\varepsilon_{1}}{\varepsilon_{2}} \tag{2}\]  
In the equation, \(\varepsilon_{1}\) and \(\varepsilon_{2}\) represent the lateral and axial strains of the specimen, respectively. The internal friction angle \(\varphi\) and cohesion \(c\) are determined by fitting the Mohr-Coulomb criterion to the triaxial compression test data.
# An Instrument Error Correlation Model for Global Navigation Satellite System Reflectometry
## 3 Results
### Dynamic Correlated Error Estimation and Impact of Tuning
We assume that correlated error due to misestimation of GPS transmit power is nearly constant for the timescales of interest. Figure 6: Generated untuned and tuned \(R_{mod}\) for two nearby tracks captured by two different CYGNSS receivers during the overlap period, each with three different representations. (**a**) Untuned \(R_{mod}\) as represented by sample index. The two tracks are concatenated in a vector, and the modeled error correlation is calculated for each combination of indices. The green vertical line represents an exemplar index (i = 129) along the track where the correlated error is estimated for every other observation in the neighborhood. The correlated error model allows for correlated errors across receivers. (**b**) Untuned \(R_{mod}\) for the same exemplar index as represented by the physical location of sample acquisition. (**c**) Untuned \(R_{mod}\) for the same exemplar index represented by the time of acquisition. The traces for the two different tracks are plotted in different colors. The error correlation along the same track is in blue, while the error correlation for the adjacent track is in red. (**d**) Tuned \(R_{mod}\) as represented by the sample index for the same two tracks as in (**a**). (**e**) Tuned \(R_{mod}\) for the same tracks represented by the physical location. (**f**) Tuned \(R_{mod}\) for the same exemplar index represented by the time of acquisition. Note that the tuning has virtually eliminated cross-track error correlation. As demonstrated in Figure 6, the cross-track correlation virtually disappears after tuning the model \(R_{mod}\). Taken with the data presented in Figure 5, there appears to be negligible cross-track error correlation, either when two tracks share a CYGNSS receiver or when two tracks share a GPS transmitter. This is a novel result but challenging to validate in practice, as the double-differencing may eliminate any shared error correlation between two tracks.
## 5 Experimental Results and Discussion
### Discussion of Results  
Table 12 compares the outcomes of the RNNBDL algorithm with those of other algorithms. An experimental study assessed the distinction between using five selected machine learning algorithms over three separate data sets and the Rotation Forest ensemble of these five different machine learning algorithms. Analysis of ten different results revealed that the Rotation Forest ensemble boosted the performance rate in all other results except the Naive Bayes algorithm. In addition to machine learning algorithms for software bug prediction, they were employed in deep learning algorithms to perform additional tests. The acquired results indicate that deep learning algorithms potentially detect extremely faulty modules with high accuracy. The final step of the study focused on developing an algorithm named RNNBDL and applied this algorithm to three separate data sets. Although the results of the RNNBDL algorithm were not promising, these results are still higher than the averages of the deep learning and machine learning algorithms. Figure 3 shows the success rates of ACC based average increments between base classifiers and ensemble classifiers. Figure 4 shows the success rates of the ACC-based average increments between DL and ML.  
Finally, the study developed an algorithm named RNNBDL and applied it to three separate data sets. Table 12 compares the ACC results from the RNNBDL deep learning algorithm with the ACC findings from all other tests. As depicted in Table 12, the RNNBDL algorithm outperformed other algorithms in terms of performance.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Data Set** & **ACC (\%)** & **AUC** & **FM** & **KE** \\ \hline SFP XP-TDD & 95.91 & 0.703 & 0.942 & 0.120 \\ Eclipse & 78.46 & 0.805 & 0.783 & 0.538 \\ Active MQ & 95.32 & 0.633 & 0.938 & 0.292 \\ \hline \hline \end{tabular}
\end{table}
Table 11: RNNBDL Algorithm Based Classification results for All Data set.  
Figure 5 illustrates the main effects plot for accuracy based on the methods and data sets. As depicted in Figure 5, the suggested algorithm, RNNDBL, achieved the highest average classification accuracy. However, the SVM base classifier algorithm generated the lowest average classification accuracy. Further details of the experimental results of DL and ML are presented in Figure 5.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Data set** & **SFP XP-TDD** & **Eclipse** & **Active MQ** \\ \hline ML (Avg) & Better & Better & Better \\ ML (Best) & Better & Better & Better \\ eML (Avg) & Better & Better & Better \\ eML (Best) & Better & Worst & Better \\ DL (Avg) & Better & Better & Better \\ DL (Best) & Same & Worst & Better \\ \hline \hline \end{tabular}
\end{table}
Table 12: Overall Evaluation of Experimental Results based on ACC.  
Figure 4: ACC based average increments between DL and ML.  
Figure 3: ACC-based average increments between base classifiers and ensemble classifiers.
## 5 Discussion  
The best summary of the literature analysis conducted will be a simple comparative example of selected numerical methods. For this purpose, the simplest structural element, i.e., a 1D rod made out of isotropic material with the parameters given in a Figure 4 has been chosen. This element has been modelled using three numerical methods--the classic FEM, FDSFEM and TDSFEM. The results of the numerical analysis are shown in Figure 5. In each of these cases, the rod has been excited by a 100 kHz carrier frequency signal. The excitation signal has been applied at one end of the rod, while the response has been recorded at the midpoint of the length of the element.  
Figure 4: Exemplary rod element, no model dispersion, 161 degrees of freedom.  
Thus, in the case of FEM, two options have been explored: a full inertia matrix and a diagonal one. As it can be noticed, the irresponsible use of FEM (the simplest two-node element) leads to significant errors. Those errors are the result of a numerical dispersion of the signal. In the response, the wave propagates either slower (in case of full inertia matrix) or faster (in case of diagonal inertia matrix). Naturally, these errors could be avoided by improving the mesh density or by using more advanced finite elements, or by using higher-order approximation functions. An interested reader is encouraged to follow some literature resources regarding this problem, like [82; 130; 131]. The second of the results discussed concerns the FDSFEM model. In this case, the correct time of signal propagation has been recorded, but no further reflections have occurred. This may be easily explained. In this method, a special throw-off element is applied at the opposite end to the excited one. This numerical activity eliminates the periodical feature of the Fourier transform, and, as its physical meaning is an energy conduit out of the system, there are no further internal boundary wave reflections. The third result presented concerns the application of TDSFEM. In this case, as a shape function, the Chebyshev polynomial of 6 th order has been applied. As it can be seen from the diagram, for the time of occurrence in Figure 5, even in the simplest case, the signal is reproduced without any dispersion and appears exactly in the appropriate time windows (marked in each diagram as a grey background behind the signal). Therefore, it can be concluded that TDSFEM seems to be the most appropriate method for modelling the phenomenon of elastic wave propagation.  
Figure 5: Errors resulting from numerical discretisation.
## 4 Fire detection algorithms
### Cloud and fire scars screening  
The reasonable selection of background pixels is the key to estimating the adaptive thresholds used in the contextual algorithm. If the background pixels selected are unrepresentative of the wider terrestrial background conditions, it may lead to significant offsets in the estimated background characteristics, and this will result in fire detection omission errors or commission errors. Thus, the contaminated pixels must be rejected before estimating the background characteristics and detecting fires. These noises mainly include cloud, water, sun glint, fire scars, etc.  
Daytime cloud contamination of MIR channel was the most common source of false detections. Being illuminated by sunlight, clouds can elevate the TOA MIR radiance due to the reflected sunlight and reduce the TOA TIR radiance due to their low temperatures [28]. Cloud detection was performed using a technique based on that used in the production of the IGBP AVHRR-derived Global Fire Product [29]. But due to the different spectral response for cloud, the thresholds used to cloud detection have been modified empirically for HJ-1B. If the following condition is satisfied, the pixel will be considered to be cloud-obscured:  
\[\rho_{1}+\rho_{{}_{2}}>0.8\;\;or\;\;T_{4}<265K\;\;or \tag{3}\]  
\[\rho_{1}+\rho_{{}_{2}}>0.6\;\;and\;\;T_{4}<285K\]  
Water detection is mainly based on the fact of that the reflectance of water in red and NIR channels are very low, and NDVI is less than zero [10]. A pixel is assumed to be water under the following conditions:  
\[\rho_{1}<0.1\;and\;\;\rho_{2}<0.1\;and\;\;\mathrm{NDVI}<0 \tag{4}\]Fire scars usually show higher temperatures than the background. They may cause overestimation of background temperatures. The NIR data provides very effective discrimination of post-fire surfaces (burn scars) since the reflectance difference between unburned and burned pixels is often maximized in this spectral region. So the NIR reflectance (due to lower reflectance of fire scar) can be used to eliminate the effect of fire scars. One pixel was contaminated by fire scars if it meets the condition given by: \(\rho_{2}<0.2\)[13].
# Bistatic Landmine and IED Detection Combining Vehicle and Drone Mounted GPR Sensors
## 3 Results
### SAR Image Comparison
#### 3.3.3 Effect of Inversion Path Length  
As aforementioned, the permittivity of the soil must be known or estimated in order to obtain an accurate SAR image. If the permittivity is not taken into account, the object is detected deeper and bigger than expected, as shown for the multimonostatic case in Figure 12a. Furthermore, if the permittivity or the angle of incidence is high, the resulting SAR image is considerably distorted, as shown for the multibistatic scenario in Figure 12b.
# A Multi-Service Adaptive Semi-Persistent LTE Uplink Scheduler for Low Power M2M Devices
## 3 Multi-Service Adaptive Semi-Persistent Scheduling Algorithm
### Intra-Class Time and Frequency Domain Multiplexing  
For any traffic class \(S\), if \(m_{S}=\frac{T_{SPS,S}}{T_{F}}>1\), the adaptive allocation of PRBs to the \(n_{S}\) subscribers (occurring in the same uplink subframe) are repeated every \(m_{S}\) number of LTE frames. So we can multiplex \(m_{S}\) instances of an uplink subframe (each serving \(n_{S}\) subscribers) in \(m_{S}\) different frames. Therefore, for intra-class scheduling, we can deploy frequency and time domain multiplexing simultaneously. Hence, the service capacity of the any traffic class \(S\) (for one particular uplink subframe) is given by the total number of supported adaptive SPS subscribers \(N_{S}^{\prime}\) as shown in Equation (10).  
\[N_{S}^{\prime}=n_{S}m_{S} \tag{10}\]  
In the example demonstrated in Figure 3, for \(T_{SPS,A}=20\,\mathrm{ms}\), \(n_{S}=3\) and \(m_{S}=2\), the traffic class A can support 6 subscribers in subframe 2. Similarly, for \(T_{SPS,B}=40\,\mathrm{ms}\), \(n_{S}=3\) and \(m_{S}=4\), the traffic class B can support 12 subscribers in subframe 3.  
Now, if traffic class \(S\) is assigned to \(C\) number of uplink subframes in any LTE frame, where \(1\leq C\leq 10\), the service capacity of class \(S\) in terms of supported adaptive SPS subscribers \(N_{S}\) would be expressed by Equation (11).  
\[N_{S}=CN_{S}^{\prime}=Cn_{S}m_{S} \tag{11}\]  
By replacing the expression for \(m_{S}\) from Equation (4), Equation (11) can be re-written as Equation (12).  
\[N_{S}=Cn_{S}\frac{T_{SPS,S}}{T_{F}} \tag{12}\]  
Figure 3: Time and frequency domain multiplexing for two example traffic classes.
## 3 Results
### Experiments
#### 3.1.2 Experiments on Notification of Accidents by Major Cities in Korea
If the resolution of CCTV is low, the condition of CCTV is unstable, or the viewing angle of CCTV is wide, the accuracy of AI CCTV's accident detection decreases. The model studied in this way proved through an experiment that the service is possible even in real road conditions. This study developed a vehicle accident detection model by learning only frontal collision type traffic accidents using YOLOV3. After that, it was developed so that a notification is sent to the application when detecting abnormalities by connecting it to the server, together with the obstacle model on the road provided in advance. When an abnormality is detected using the vehicle accident detection model created on the server, the notification that appears in the application sends information about abnormalities to FCM, and images of abnormalities to HTTP post within 5 s. In addition, when abnormalities are detected in CCTV that are related to the route or within 5 km of the current location in the initial web application state, a notification is displayed for 4 s. When a notification is sent to the application, the color of  
Figure 13: (**a**) Accident detected by CCTV at the Gundeulbawi Intersection in Daegu; (**b**) accident detected at Gwangcheon Bridge 1 in Gwangju; (**c**) accident detected on the Busan Jurye Ramp Overpass. the corresponding CCTV changes from black to red, and after the notification disappears, if you click the red CCTV to view the notification image, you can view the image again.
## 2 Materials and Methods
### Data Processing  
According to the process of THz spectral acquisition, the time-domain spectra need to be converted into the frequency-domain spectra by FFT first. Then, the THz optical parameters, such as the absorption coefficient and refractive index, are calculated based on the Fresnel formula. As an integral expression for continuous functions, theoretically, FFT assumes that the period of the time signal is infinite. However, only part of the data is intercepted in the actual analysis. Therefore, the basic concept of the data window or data block is adopted, and a limited amount of data is extracted from the continuous-time signal using the tool of window [29]. Finally, the extracted data in the window is calculated by FFT. The purpose of applying the window is to reduce the energy leakage and to improve the picket fence effect [30]. Applying the window in time-domain spectrum shows the characteristic of multiplication, and it shows the characteristic of convolution in the frequency-domain. Additionally, the smoothing process of convolution can be viewed as a set of filters with specific functions. As a result, the energy leakage is reduced because the energy of the original signal at a certain frequency band is displayed in combination with the shape of the filter. Because part of the energy of the original signal will be attenuated by applying the window function, a correction factor needs to be added to the final result. In linear spectrum analysis, the amplitude coefficient is generally used as the correction factor. The energy coefficient is generally used in the power spectrum [31; 32]. In this paper, eight window functions (apodization functions), including Boxcar, Triangular, Happ-Genzel, Blackman-Harris 3 terms, Blackman-Harris 4 terms, Norton-Beer Weak, Norton-Beer Medium and Norton-Beer Strong, were used to process the THz spectra.
## 5 Discussion
### Performance of the GSIP System  
In order to develop the gibberish speech impression prediction system, neural networks \(MLP_{profile+prosody}\) (responsible for predicting impression just from the profile information of volunteers and the acoustic prosody characteristics of a GS utterance) and \(GRU_{phones}\) (responsible for predicting human impression from the tokenized IPA phones of a GS utterance) were pre-trained using the obtained data set after the outlier impressions were removed. \(GRU_{phones}\) achieved and outstanding performance for predicting training and validation data, but for test data, the results seemed lackluster and mostly random, which shows a lack of generalization capability of the model. For \(MLP_{profile+prosody}\), the results were not as impressive for training and validation data, but it performed better than \(GRU_{phones}\) for test data, showing closer predictions for some of the test data.  
By using the pre-trained neural networks, we trained the \(GSIP\) system, which consisted of the average of both previously mentioned neural networks, which achieved a better performance for test data when compared to previous two neural network, but it showed a tendency of making more \"average\" estimates, since most utterances generated small emotional changes.  
The results were not satisfactory for test data, showing that even though the models could perform reasonably well for training and validation data, they could not properly learn how to generalize that knowledge for never-seen-before data.
###### Abstract  
SAGIN is formed by the fusion of ground networks and aircraft networks. It breaks through the limitation of communication, which cannot cover the whole world, bringing new opportunities for network communication in remote areas. However, many heterogeneous devices in SAGIN pose significant challenges in terms of end-to-end resource management, and the limited regional heterogeneous resources also threaten the QoS for users. In this regard, this paper proposes a hierarchical resource management structure for SAGIN, named SAGIN-MEC, based on a SDN, NFV, and MEC, aiming to facilitate the systematic management of heterogeneous network resources. Furthermore, to minimize the operator deployment costs while ensuring the QoS, this paper formulates a resource scheduling optimization model tailored to SAGIN scenarios to minimize energy consumption. Additionally, we propose a deployment algorithm, named DRL-G, which is based on heuristics and DRL, aiming to allocate heterogeneous network resources within SAGIN effectively. Experimental results showed that SAGIN-MEC can reduce the end-to-end delay by 6-15 ms compared to the terrestrial edge network, and compared to other algorithms, the DRL-G algorithm can improve the service request reception rate by up to 20%. In terms of energy consumption, it reduces the average energy consumption by 4.4% compared to the PG algorithm.  
space-air-ground integrated network; DRL; resource allocation; NFV +
Footnote †: journal: Future Internet