esa-sceva/satcom-chunk-collection huggingface dataset:
# Human Activity Recognition Method Based on Edge Computing-Assisted and GRU Deep Learning Network
## 5 Results and Analysis
### Classification Performance  
To measure the effectiveness of the proposed OI-EleAttG-GRU (OEG) method, three effective algorithms are used in three human activity datasets, including Inception Neural Network HAR (IN), Support Vector Machine (SVM) and Deep Convolutional and LSTM Recurrent Neural Networks (DCL). The precision comparison in recognizing different human activity in percentages are listed in Table 3.  
Figure 9 illustrates the comparison of precision and recall on six human activity class sets. Table 3 and Figure 9 show that the proposed OEG is about 12.9%, 2.9%, and 3.5% higher than the other three methods in precision, respectively. In general, the performance of OEG is high, and it has high precision and recall rate.  
The experimental results show that the EleAttG-GRU structure achieves a significant recognition effect in the human activity classification model. For different datasets, we conducted multiple tests to adjust the number of neurons, the batch size, and the EleAttG-GRU layer to achieve better results.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Performance Evolution**} & \multirow{2}{*}{**OEG**} & \multirow{2}{*}{**SVM**} & \multirow{2}{*}{**IN**} & \multirow{2}{*}{**DCL**} & \multicolumn{3}{c}{**OI-EleAttG-GRU**} \\ \cline{5-8}  & & & & & **SVM** & **IN** & **DCL** \\ \hline Walking & 97.5 & 81.6 & 94.8 & 93.4 & 15.9 & 2.7 & 4.1 \\ Walking Upstairs & 94.2 & 74.6 & 91.2 & 87.3 & 19.6 & 3.0 & 6.9 \\ Walking Downstairs & 93.6 & 77.5 & 88.9 & 84.4 & 16.1 & 4.7 & 9.2 \\ Sitting & 99.1 & 90.5 & 96.3 & 98.6 & 8.6 & 2.8 & 0.5 \\ Standing & 97.6 & 92.4 & 98.7 & 97.3 & 5.2 & 1.1 & 0.3 \\ Running & 91.8 & 79.3 & 86.2 & 91.4 & 12.5 & 5.6 & 0.4 \\ Overall Precision & 95.6 & 82.7 & 92.7 & 92.1 & 12.9 & 2.9 & 3.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Precision Comparison of OI-EleAttG-GRU (OEG) and existing methods in recognizing different human activity in percentages (%).  
Figure 9: (**a**) Comparison of precision between OI-EleAttG-GRU and three methods on six human activity class sets. (**b**) Comparison of recall between OI-EleAttG-GRU and three methods on six human activity class sets.
# Improved Bilateral Filtering for a Gaussian Pyramid Structure-Based Image Enhancement Algorithm
## 5 Experiments and Results
### Subjective Analysis of Image Enhancement Algorithms  
To verify the effectiveness of our improved image enhancement technique, we conducted experiments comparing its performance to those of the SSR, MSRCR, NLR, and INF algorithms based on a database [28] with 4322 images, which is a real foggy test dataset provided by Gac Research Institute. The partial results of these experiments are shown in Figure 6, with Column 1 depicting original images, Column 2 depicting images processed using the SSR algorithm, Column 3 depicting images processed using the MSRCR algorithm, Column 4 depicting images processed using the NLR algorithm, Column 5 depicting images processed using the INF algorithm, and Column 6 depicting images processed using our improved image enhancement technique. There are degradation and dark regions in the original images in Figure 7, which are characteristic of uneven lighting, making them suitable for image enhancement. We note that with all five algorithms considered, most of the foreground details are preserved, and the color in the resulting images is enhanced following processing. However, with the SSR, MSRCR, and NLR algorithms, the \"halo\" effect can also be observed, as seen most obviously in Figure 7(a-2), Figure 7(d-3), Figure 7(e-3), Figure 7(f-3), and Figure 7(f-4). The MSRCR and INL algorithms darken the image and lose the details, as shown in Figure 7(a-3-f-3) and Figure 7(a-5-f-5). While details in the blurred areas of the original images can be enhanced using the other four algorithms, there is a deviation between the color in the input and output images. Hence, the color of the imaged object is not restored, and the visual representation of this object is thus distorted. In addition, the MSRCR and NLR algorithm was not effective in removing the fog from the foreground of the images, as shown in Figure 7(a-4-f-4). In contrast, the texture and color contrast of images processed using our technique are improved, and enhancement is effective for images in different low light situations. In addition, following processing, the deviation of color between the input and output images is small. As color fidelity is better, the overall visual representation of the image is retained. Finally, details in the image are more clearly identified, as can be seen from Figure 7(a-6-f-6). For a more quantitative representation of the effect of the different algorithms, histograms of the grayscale intensity of the images in Row (a) of Figure 6 are shown in Figure 8. From this, it can be seen that the distribution of pixel intensities is more uniform and the average grayscale intensity of the image is reduced. This is indicative of the fog removal effect obtained with our algorithm; fog is observed as a brighter area in an image, with a correspondingly large grayscale intensity. A reduction in the average grayscale intensity of an image thus suggests the removal of fog.
## 2 Materials and Methods
### Data and Data Processing
#### 2.2.1 Remote Sensing Data
The forest map was first aggregated to the MODIS resolution (500 m) and Landsat resolution (30 m). Then the forest map was used to extract the forest indicators from MODIS and Landsat datasets.
## 3 Results and Discussion
### Characterization of the PLA Sample
#### 3.1.3 FT-IR Analysis  
On the other hand, an FTIR analysis has been carried out for the chemical characterization of the PLA commonly used in 3D printing. PLA is composed of three main peaks: C-H, C=O and C-O. Spectra between 1050 and 1205 cm\({}^{-1}\) are attributed to asymmetric and symmetric stretching vibrations of the C-O carboxylic acid group. The stretching vibrations of the carbonyl C=O are observed at 1755 cm\({}^{-1}\), which presents a significant similarity to that obtained by [PERSON] and [PERSON] (2016). The peaks at 2992 cm\({}^{-1}\) and 1448 cm\({}^{-1}\) correspond to the stretching vibrations of the C-H alkane and the C-H bending alkane of the methyl group, respectively. When comparing the bands that appear in this spectrum with the FTIR corresponding to PLA in the literature (2016), it was found that this analyzed polymer is similar to L,D-PLA as shown in Figure 1.  
The results obtained in the TGA experiments consist of 200 points showing the variation in the PLA mass loss as a function of temperature at the heating rates 5, 10 and 15 \({}^{\circ}\)C min\({}^{-1}\), as shown in Figure 2. The initial weight of the sample for the case at 15 \({}^{\circ}\)C min\({}^{-1}\) was slightly higher.  
Figure 1: Thermogravimetric Analysis of the PLA sample.  
The DTG curves representing the derivative of the mass loss versus temperature are shown in Figure 3. At each heating, the maximum peak temperature can be seen where the greatest degradation of the sample occurs, resulting in 607, 624 and 631 K for 5, 10 and 15 \({}^{\circ}\)C min\({}^{-1}\), respectively, whose values are analogous to those presented by [PERSON] & [PERSON] in their study [10].
# Review of Automatic Estimation of Emotions in Speech
## 3 Acoustic Analysis of Speech
### Intonation Features  
Much research has noted that human listeners exploit the perceived intonation of speech to help distinguish emotions [48; 49]. However, most recent SER does not, preferring use of simpler Mel Spectra. To automatically estimate durations for detailed sequences is not easy, and often needs ASR, which may be unreliable. It is, however, simpler to estimate average speaking rate, which may be a good feature for SER.  
As for F0, there are many estimation algorithms [50]. However, integrating information about F0 into the SER methods is difficult, as intonation covers many speech frames. SER generally uses frame-based spectral amplitude data, exploiting information about VT resonances that are relevant over brief durations. While the use of both F0 and durations occurred in expert-system approaches for SER [49], most recent neural approaches do not directly use intonation. This lack may be a major reason for relatively weak SER accuracy levels.
## 3 Results and Discussions  
### Workability of RHA Recycled Aggregate Concrete  
The workability of fresh concrete mixtures can be reflected by the slump value [33; 34]. The impact of RHA with different replacement ratios on the workability of recycled aggregate concrete was investigated by means of a slump cone test, and the results are shown in Figure 4. It is noteworthy that 0% RHA and 10% RHA showed the same slump value, which may be due to the increase in the superplasticizer (Table 3), resulting in improved concrete workability. However, the improvement degree of the superplasticizer is similar to the reduction degree caused by the increase in RHA content. At the same time, it is due to the modification of the slump value, thus resulting in the same workability [35].  
With the gradual increase in the RHA substitution rate, the slump value of 20% RHA and 30% RHA decreased by 15.38% and 23.08%, respectively, compared with 0% RHA. This may be due to the increase in the RHA replacement ratio, leading to an increase in the porosity and total specific surface area of the cementitious material (Table 1). The system absorbs more water, thus causing a reduction in the free water within the paste, which will increase the mixtures' friction and discourage the flow, which in turn leads to the decrease in the concrete slump and reduced workability. This finding is similar to that of [PERSON] et al. [15], who reported that the slump of fresh concrete mixtures increased with increasing RHA content, and the mixtures with 30% RHA content had the worst workability.  
Figure 4: The impact of RHA replacement ratios on the workability of fresh concrete mixtures.
# Anomaly Detection in Financial Time Series by Principal Component Analysis and Neural Networks +
## 2 The PCA NN Anomaly Detection Approach
### Methodology Overview  
The anomaly detection model we propose is a two-step supervised learning approach. The contaminated time-series identification step aims to identify those with potential anomalies among the observed time series. The anomaly localization step consists of finding the location of the anomaly in each contaminated time series identified during the first step.  
The model used in the first step falls under the scope of a binary classification model, assigning to each time series a predicted label  
\[\widehat{A}^{i}=\begin{cases}1&\text{if $X^{i}$ is considered contaminated by the identification model.}\\ 0&\text{otherwise.}\end{cases}\]  
The second step uses a multi-classification model. For each contaminated time series identified in the first step, the model predicts a unique time stamp \(\widehat{L}^{i}\) at which the anomaly occurred. Formally, the observed value at the time stamp \(t_{\hat{\eta}}\) of the \(i\)-th contaminated time series is considered abnormal, meaning that the observation \(x^{i}_{t_{\hat{\eta}}}\) is abnormal. Thus,  
\[\widehat{L}^{i}=\hat{\eta}^{i}\quad\text{with}\quad\hat{\eta}^{i}\in\{1, \dots,p\}.\]  
Our two-step anomaly detection model localizes only one anomaly per run, if any. Several iterations allow for the removal of all anomalies. Indeed, as long as the time series is identified as contaminated in the first step of the method, the time series can proceed to the second step. Once all anomalies have been localized, the final stage of the approach is to remove the abnormal values and suggest imputation values (see Section 6.1).
## 4 Acquisition of Structural Surface Parameters  
The weak structural planes of engineered rock masses have a significant impact on rock mass stability. Their shape, mechanical properties, and spatial configurations largely control ground pressure activity and rock mass collapse processes in mines. Investigating the properties and characteristics of rock mass structural planes through field surveys and providing both qualitative and quantitative descriptions is of critical importance in the classification of rock mass stability [37].  
The ShapeMetrixX 3D system [38], developed in Austria, along with a self-developed recognition program, were applied to conduct a structural plane investigation and analysis in the Phase II mining area of the Jangfeng Iron Mine. The working principle is illustrated in Figure 10, and the structural plane identification results are shown in Figure 11. The system's accuracy is at the centimeter level.  
In this structural surface parameter survey, 14 measurement points were selected in the 115, 135, and 220 sublevels of Phase II. Due to underground environmental issues, 10 effective measurement points were synthesized. Six measurement points were synthesized in the 135 sublevel, and four measurement points were synthesized in the 220 sublevel. The distribution of the measurement points is shown in Figure 12.  
Figure 11: Structural surface recognition diagram.  
Figure 10: Schematic diagram of stereoscopic image synthesis.  
Due to space limitations, the structural surface orientation information for each measurement point will not be listed. The data from the 10 valid measurement points were summarized, and the stereographic projection diagrams of all structural planes for sublevels 135 and 220 were obtained, as shown in Figure 13. From Figure 13, it can be seen that there are three dominant structural surface sets in the 135 sublevel: the first set has a dip of 57\({}^{\circ}\) and a dip direction of 213\({}^{\circ}\); the second set has a dip of 78\({}^{\circ}\) and a dip direction of 120\({}^{\circ}\); the third set has a dip of 71\({}^{\circ}\) and a dip direction of 57\({}^{\circ}\). In the 220 sublevel, there are two dominant structural surface sets: the first set has a dip of 59\({}^{\circ}\) and a dip direction of 50\({}^{\circ}\); the second set has a dip of 75\({}^{\circ}\) and a dip direction of 228\({}^{\circ}\). The average trace length of the joints and fissures in the surrounding rock is 0.66 m for the 135 sublevel and 0.48 m for the 220 sublevel, indicating dense spacing. Additionally, the on-site structural surface investigation revealed that the structural surfaces in Phase II of the Jiangfeng Iron Mine are relatively rough, unopen, and highly weathered, with some structural surfaces showing obvious cementation when exposed to water.  
Figure 12: Location of underground structural surface measurement points in Phase II of Jiangfeng Iron Mine: (a) 135 m sublevel; (b) 135 m sublevel; (c) 220 m sublevel.
# Bar-Wave Calibration of Acoustic Emission Sensors
## 3 Results and Discussion
### AE Sensor Response
#### 3.2.2 Other Small-Diameter Broadband Sensors  
Two broadband sensors with a small sensing area were tested. These are Valpey-Fisher (now CTS-Valpey, Hopkinton, MA, USA) Pinducer and Digital Wave Corp. (DWC, Centennial, CO, USA) B1080-LD. The Pinducer was of VP-1093 design with the element diameter of 1.35 mm, but had a quarter-wave matching layer (140 ns delay time). Figure 10a gives its receiving sensitivities using the same color code as in Figure 8a. The sensitivity levels are 30-40 dB lower than those of KRN (with FET) sensors, but spectral flatness for the bar-wave sensitivity is improved by 20 dB or more (in terms of the range of sensitivity).  
As shown in Figure 10b, DWC B1080 sensor, which has an FET buffer, has sensitivity levels comparable to KRN sensors, but it has a nominally 3.2-mm element diameter, or about ten-times larger sensing area. Its normally incident velocity sensitivity is flat within \(\pm\)3 dB from 200 kHz to 1.5 MHz. This sensor also has sharply rising low-frequency bar-wave sensitivities.  
Figure 9: Effects of changes in L on the integrated signal intensity with two KRN sensors (#12053 and #12060). Red points: frequency range up to 0.5 MHz, Blue points: to 1 MHz, Green points: to 2 MHz.  
Here, it is worth noting that another miniature sensor design was developed with capacitive sensing principle. [PERSON] et al. [17] used a circular sensing electrode of 1.8 mm diameter, mounted inside a cylindrical housing of 12 mm diameter and 32 mm length. Although no sensitivity characteristics were given, they successfully recorded nanometer-level glass-capillary fracture signals.
# Axial Compression Behavior of Steel Angle-Corrugated Steel Plate-Confined Concrete Columns
## 6 Summary
### Proposed Design Model  
According to the principle of superposition, the ultimate bearing capacity of the SA-CS column could be calculated as Equation (24). In addition, early investigations presented that the compressive strength of confined concrete, which is under triaxial stress, is related to the effective confining stress (\(f_{\text{r}}\)), which can be expressed by Equation (25) [40].  
\[N_{\text{u}}=f_{\text{cc}}A_{\text{c}}+f_{\text{y2}}A_{\text{s2}} \tag{24}\]  
\[f_{\text{cc}}=f_{\text{c}}+5.3f_{\text{r}} \tag{25}\]  
As the cross-section of the SA-CS column is similar to a square, the arch effect exists in the column, which leads to only a portion of the core concrete being effectively confined. The core concrete is divided into the effectively confined area (\(A_{\text{ec}}\)) and the ineffectively confined area (\(A_{\text{ic}}\)). Figure 16 shows the division method. The effective confining coefficient (\(k_{\text{e}}\)) is determined as the ratio of the effective confined area to the total area and the calculated formulation is expressed as Equations (26) and (27).  
\[A_{\text{c}}=B^{2}+2B(h-t_{2}) \tag{26}\]  
\[k_{\text{e}}=\frac{A_{\text{ec}}}{A_{\text{c}}}=\frac{A_{\text{c}}-4A_{\text{ic }}}{A_{\text{c}}}=1-\frac{(\frac{\pi}{2}-1)B}{B+2(h-t_{2})} \tag{27}\]Based on the principle of the equal area of materials, the section of the SA-CS column can be converted to a square section, as shown in Figure 17. Equation (28) expresses the relationship between \(f_{\mathrm{r}}\) and the transverse stress (\(\sigma_{\mathrm{sh}}\)) according to the force equilibrium [41]:  
\[f_{\mathrm{r}}=\frac{2k_{\mathrm{e}}t^{\prime}\sigma_{\mathrm{sh}}}{B^{\prime}- 2t^{\prime}} \tag{28}\]  
where \(B^{\prime}\) is the width of the equivalent square and can be calculated as \(B^{\prime}=\sqrt{A_{\mathrm{c}}+A_{\mathrm{s1}}}\); \(t^{\prime}\) is the thickness of the equivalent square and can be calculated as \(t^{\prime}=\left(\sqrt{A_{\mathrm{c}}+A_{\mathrm{s1}}}-\sqrt{A_{\mathrm{c}}} \right)/2\).  
Referring to the paper by [PERSON] [19], the value of \(\sigma_{\mathrm{sh}}\) can be calculated by Equation (29), and Figure 18 illustrates the calculation results of the proposed design model. Compared with the existing codes, the proposed model can achieve results that are in good agreement with the FE results, whose deviation is less than 10%.  
\[\sigma_{\mathrm{sh}}=0.78e^{0.24-0.4\sqrt{5}}f_{\mathrm{y1}} \tag{29}\]  
Figure 16: Division of effectively and ineffectively areas.  
Figure 17: Equivalent conversion and force equilibrium.
# Simulation of the Infiltration of Fractured Rock in the Unsaturated Zone
## 2 Mathematical Model
### Governing Equations  
For simplicity, this description is restricted to immiscible, incompressible two-phase flow. No-slip boundary conditions are applied to the fracture walls, and no Brinkmann terms are used to couple fracture and matrix flow. The mass conservation equations for phase \(\alpha\in\{n,w\}\) read  
\[\phi\frac{\partial S_{\alpha}}{\partial t}=-\
abla\cdot v_{\alpha}+q_{\alpha} \tag{1}\]  
Here variable \(\phi\) is the porosity, \(S_{\alpha}\) is the phase saturation, and \(q_{\alpha}\) represents volumetric source/sink terms. Phase velocity \(v_{\alpha}\) is evaluated from phase pressure via extended Darcy's law  
\[v_{\alpha}=-\mathbf{k}\lambda_{\alpha}(\
abla p_{\alpha}-\rho_{\alpha}\mathbf{g}) \tag{2}\]  
where \(\mathbf{k}\) is the intrinsic permeability tensor, \(p_{\alpha}\) is the phase pressure, \(\rho_{\alpha}\) the phase density, and \(\mathbf{g}\) the gravity vector. Phase mobility \(\lambda_{\alpha}\) is defined as the ratio of phase relative permeability \(k_{\alpha}\) and phase dynamic viscosity \(\mu_{\alpha}\). Phase saturation must satisfy the constraint  
\[S_{w}+S_{n}=1 \tag{3}\]  
The phase pressure and phase saturation are coupled via capillary pressure  
\[p_{c}(S_{w})=p_{n}-p_{w} \tag{4}\]Capillary pressure is assumed to be a strictly monotonic function, increasing with non-wetting phase saturation.
## 5 Conclusions  
The present work fits into the stream of study and analysis of the friction impact on fracture parameters (SIFs and T-stresses) and crack propagation of CCBD specimens under diametric forces. Considering the frictional contact, the MFEM is developed to investigate the above effects. In addition, the following conclusions can be drawn from the simulation results of several examples:  
1. The friction has a significant effect on the mode II SIF after the crack is closed, but has no influence on the stress intensity factor of mode I and T-stress. When the crack is closed, the mode II SIF decreases obviously as increasing the friction.
2. There are significant effects on the crack propagation angle and crack propagation trajectory due to the change of the friction after the crack is closed with the appropriate relative crack length and loading angle.
3. When T-stress is positive, the influence of friction becomes obvious and the crack propagation angle increases with a lager friction coefficient.
4. After the crack is closed, as increasing the friction, the amount of the deviation increases and the curvature of the path decreases. Furthermore, the crack type is easier to change with the increase of friction.
5. The effects of friction on the mode II SIF and the propagation path are more obvious with the larger the relative crack length and loading angle.  
Conceptualization, J.H.; methodology, J.H.; validation, J.H., W.H. and S.D.; formal analysis, X.P. and J.L.; investigation, X.P. and J.L.; writing--original draft preparation, J.H.; writing--review and editing, S.D. and W.H. All authors have read and agreed to the published version of the manuscript.  
This paper received no external funding.  
Not applicable.  
The authors gratefully acknowledge the support from the National Natural Science Foundation of China (No. 11872042), the Sichuan Science and Technology Program (Nos. 2018)Y0024, 2019 YJ0156 and 2021 YJ0357), the China Postdoctoral Science Foundation (2019M653395) and the Sichuan University Postdoctoral Research Foundation (2019 SCU12049).
# Transparent CoAP Services to IoT Endpoints through ICN Operator Networks +
###### Abstract  
The Constrained Application Protocol (CoAP) is a specialized web transfer protocol which is intended to be used for constrained networks and devices. CoAP and its extensions (e.g., CoAP observe and group communication) provide the potential for developing novel applications in the Internet-of-Things (IoT). However, a full-fledged CoAP-based application may require significant computing capability, power, and storage capacity in IoT devices. To address these challenges, we present the design, implementation, and experimentation with the CoAP handler which provides transparent CoAP services through the ICN core network. In addition, we demonstrate how the CoAP traffic over an ICN network can unleash the full potential of the CoAP, shifting both overhead and complexity from the (constrained) endpoints to the ICN network. The experiments prove that the CoAP Handler helps to decrease the required computation complexity, communication overhead, and state management of the CoAP server.  
CoAP; IoT; ICN +
Footnote †: This paper is an extended version of our paper published in [PERSON]; [PERSON]; [PERSON] Observing IoT resources over ICN. In Proceedings of 2017 IFIP Networking Conference (IFIP Networking) and Workshops, Stockholm, Sweden, 12-16 June 2017.