esa-sceva/satcom-chunk-collection huggingface dataset:
## 10 Results
### Mesh Latency Results  
The average latency results for the tunnel as well as the stope are shown in Figure 12. The latency values were obtained using the OpenWrt ping utility. A total of 2000 samples were recorded for each latency test case. Each test case was also performed three times, after which the average latency was calculated. The results show that the double hop experiments had higher latency values as compared to a single hop; however, this was expected due to the additional signal processing that occurs across each wireless hop. The results also show that the best performance for both protocols were in the stope. This can be attributed to the waveguide effect provided by the stope. The 5 GHz experiments also gave the best results, and  
20/40/80 Kbps data transmission rates, whilst the Wi-Fi6 power evaluations were recorded at 1/3 Mbps as well as 80 Kbps data transmission rates.  
Figure 12: Average latency.  
Figure 13: Wi-Fi6 latency samples.  
The results show that both protocols performed best whilst transmitting at close to their peak transmission rates with no packets loss. When trying to send at rates faster than their peak transmission, they suffered from packet loss. The Wi-Fi6 device demonstrated a maximum transmission rate of 3.6 Mbps, whereas the OpenThread device peaked at 86 Kbps. From the results, it can be seen that Wi-Fi6 was the most power-efficient protocol with best transmission power consumption of 3.7 times less than that of OpenThread for the transmitter and 3.9 for the receiver. Despite Wi-Fi6 exhibiting higher instantaneous transmission power compared to OpenThread, its overall power consumption is significantly lower due to its shorter transmission times. For the idle power consumption evaluation, both OpenThread and Wi-Fi6 nodes operated as zperf servers; Wi-Fi6 exhibited an idle power consumption of 26.62 mW, whilst OpenThread recorded 22.61 mW, exhibiting a similar performance. Additionally, the results illustrate that at low transmission rates, specifically when both protocols transmit the same data amount at 80 Kbps, OpenThread consumed 8.3 times less power than Wi-Fi6. This can be attributed to the increased overhead requirements of Wi-Fi6. However, the viability of Wi-Fi6 is still better for mine applications.
## 4 Plasmon Dispersion
Coupled plasmon modes of these systems can be determined from the following equation when the retardation effect is ignored [15]:  
\[\frac{1}{\varepsilon_{2}}\frac{\varepsilon_{1}+\varepsilon_{\mathrm{sub}}\tanh qd _{\mathrm{sub}}}{\Gamma+\tanh qd_{2}}+\frac{1}{\varepsilon_{\mathrm{sub}}} \frac{\varepsilon_{\mathrm{sub}}+\varepsilon_{\mathrm{l}}\tanh qd_{\mathrm{ sub}}}{1+\Gamma\tanh qd_{2}}=0 \tag{15}\]  
where \(d_{\mathrm{sub}}\) is the thickness of the substrate that has the permittivity \(\varepsilon_{\mathrm{sub}}\), \(d_{2}\) is the thickness of the spacer between the graphene layer and the substrate, \(\Gamma=\left(\varepsilon_{0}+\mathrm{i}\sigma q/\omega\right)/\varepsilon_{2}\), and \(\varepsilon_{1}\), \(\varepsilon_{2}\), \(\varepsilon_{0}\)  are respectively the permittivity of the medium below the substrate, the permittivity of the spacer, and the permittivity of free space above the graphene layer, as shown in Figure 3b,c. In the case of double-layer graphene, as shown in Figure 3d, Equation (15) is reduced by setting \(d_{1}\to 0\). In the following, we thoroughly study the coupled plasmon dispersion of these systems while considering the existence of a finite scattering rate of carriers in graphene. Figure 3: Structures considered in this paper: (**a**) graphene on a semi-infinite substrate of a constant permittivity; (**b**) graphene on a polar substrate; (**c**) graphene on a metal substrate; and (**d**) double-layer graphene. The substrate is characterized by a permittivity of \(\varepsilon_{\mathrm{sub}}\) and a thickness of \(d_{\mathrm{sub}}\). The spacer between the graphene layer and the substrate is characterized by a permittivity of \(\varepsilon_{2}\) and a thickness of \(d_{2}\). Below the substrate is a semi-infinite dielectric medium that has a permittivity of \(\varepsilon_{1}\).
# Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case
## 5 Experimental Results
### Testbed Description  
Figure 10 shows a simplified block scheme of the MEC testbed.  
The LTE radio interface was implemented using commercial small cells operating in the B40 frequency band (2300-2400 MHz). In the tests, the Huawei tablets MediaPad M3, compatible with such bands, represented the end-user devices, provided with ad hoc pre-programmed SIM cards to register to the testbed radio network [43].  
To implement the ME system NFVI, we made use of a portable INTEL-based server (GOMA FlexPAC Industrial portable workstation), of small dimensions to reduce space occupancy (43.4 \(\times\) 34.8 \(\times\) 22.9, in cm) and equipped with an Intel Xeon E5-2630v3 2.4 GHz, 8 Core CPU, and 64 GB DDR4 RAM. The GPU-accelerated version of the i-EVS ME app runs on this GOMA server, equipped with one NVIDIA Quadro M4000 GPU.  
To realize a complete emulation of an LTE network, an Evolved Packet Core (EPC) running on a physically separated server was also used [43].  
In the testbed, a Video Traffic Generator can connect to the SGi-LBO interface of the SGWLBO data plane. In this way, we could simulate loading conditions on the system, both to characterize its performance, and to verify its functionalities under stress conditions.  
Connectivity to the management and monitoring functions, running on an external physical system, is provided through the interface indicated as Ext MWC in Figure 10. The management and monitoring block includes the Open Baton orchestrator, combined with the ME functions described in Section 3. Moreover, this block includes Zabbix [44], an open source solution for network and application monitoring.  
Figure 10: Simplified block scheme of the implemented MEC testbed.  
The testbed creates a physical area in which consumers, through their mobile devices, can access the immersive video services offered by the i-EVS ME app running on the local ME system NFVI.  
End users can move in the testbed area creating and sharing media contents using the end-user app.
## 4 Discussion
### Possible Mechanisms of Drought on Sugarcane  
We also obtain a deeper understanding of the influencing mechanisms of drought on sugarcane by using the spatiotemporal evolutions of fPAR, SIFpar and SIFyield, and find that the responses of sugarcane SIF induced by drought can be attributed to these related factors to a certain extent. During the early stage of the 2009 drought, especially on DOY 241 (which corresponds with the second half of August), both SIFpar and SIFyield experienced anomalous losses compared with the multiyear mean, while fPAR remained relatively constant. We also found that the SIF, SIFpar and SIFyield of sugarcane decreased earlier than the fPAR and EVI. These results indicate that the LUE of sugarcane decreased significantly but the sugarcane remained green in most parts of the study area during the early stage of the 2009 drought. This finding is consistent with previous studies [27; 51]. For example, [PERSON], [PERSON] [27] found that PAR-normalized SIF decreased rapidly compared with VIs during the heat stress stage of winter wheat. In addition, sugarcane is a temperature-loving and light-loving crop, and the increased PAR and unchanged fPAR may have a positive impact on the EVI with a certain lag time on DOY 225 and DOY241; thus, the EVI showed a positive change percent on DOY 257 (Figure 8a,b). These results coincide with the results of previous studies [24; 80] and may explain why the sugarcane EVI is not able to capture the early stage of drought stress.  
During the later stage of the 2009 drought, fPAR decreased with the largest magnitude along with a slight decreasing trend of the EVI in the central parts in September 2009. The spatial distribution of decreased fPAR is highly consistent with that of the decreased EVI across the entire study region from October to November 2009. The response of fPAR to drought is consistent with greenness but with a certain lag time, and this finding also agrees with the results of previous studies [81; 82]. In particular, the larger negative anomalies of SIF, SIFyield, and SIFpar than EVI and fPAR indicate that both the photosynthesis capacities and greenness are negatively influenced by drought stress but with a larger decreased magnitude in photosynthesis capacities. The decreased SIF can be mainly attributed to the reduction in both sugarcane photosynthesis capacities and greenness and finally caused a decrease in sugarcane yield. Therefore, the more pronounced responses of SIF are the results of both physiological and structural variations induced by drought stress.
## 4 Conclusions  
Through the study on the preparation and performance of sintered bricks made of polyaluminum chloride slag and glass powder, the following conclusions can be drawn:  
(1) It is feasible to prepare sintered bricks by using polyaluminum chloride slag and glass powder, and products with good properties can be obtained. When the mass ratio of polymeric aluminum chloride slag to glass powder to quicklime is 100:10:2.5, molding moisture is 20%, molding pressure is 25 MPa, heating speed is 10\({}^{\circ}\)C/min, sintering temperature is 1100 \({}^{\circ}\)C and insulation time is 2 h at 1100 \({}^{\circ}\)C, we can obtain a bulk density of 1.64 g/cm\({}^{3}\), sintering shrinkage of 5.14%, water absorption of 18.62% and compressive strength of 19.6 MPa of the fired brick. The polymeric aluminum chloride slag contains 2.59% chloride salt, and after high-temperature sintering, the leaching concentration of the chloride ions of the sintered brick with polyaluminum chloride slag is 7.1 mg/L. The pH value and heavy metals meet the discharge requirements of the Comprehensive Discharge Standard for Wastewater (GB8978-1996).  
(2) With the increase of glass powder content, molding pressure, molding moisture and sintering temperature, the compressive strength of sintered bricks can be improved and the water absorption can be reduced.  
(3) XRD and SEM results show that the phase of sintered brick at 1100 \({}^{\circ}\)C is mainly composed of quartz, spinel, albite and other phases. With the increase of temperature, more liquid-filled pores are formed inside the sintered brick, which makes the structure more compact. The compact internal structure and reasonable crystal phase composition make the sintered brick have excellent performance.  
Writing--original draft preparation, F.H.; writing--review and editing, F.X.; visualization, J.O.; supervision, G.Z.; project administration, Q.L.; funding acquisition, Q.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [The key scientific research project of the Education Bureau of Hunan Province] grant number [21A0286] And [The Hunan Provincial University Innovation Platform Development Fund Project] grant number [20K111].  
Not applicable.  
The authors thank for the test instruments and equipment provided by Hunan Provincial Key Laboratory of High Performance Special Concrete.  
The authors declare no conflict of interest.  
Figure 11: SEM spectrum of sintered bricks at different temperatures (3 \(\upmu\)m).
# ICT Adoption and Stock Market Development: Empirical Evidence Using a Panel of African Countries
###### Abstract  
The aim of this study was to examine the impact of adopting information and communication technologies (ICT) on the development of African stock exchanges. The study examined a panel of 11 African stock exchanges for the period 2008-2017 and employed the generalised method of moments (GMM) to estimate the results. The results of the study documented that ICT adoption had a positive impact on stock market development in African countries. Firstly, it was found that the stock market traded volume and mobile-telephone user variables were positively related. Secondly, a positive relationship was also proven between the stock market traded volume and the broadband user variable. Thirdly, a positive relationship was documented between the stock market capitalisation variable and the fixed telephone user variable. Fourthly, the research findings confirmed a positive relationship between the stock market turnover ratio and the fixed telephone user variable. The findings of this study imply that policymakers should be more resolute when formulating ICT policies. ICT adoption can spur stock market development which in turn can propel economic growth, resulting in the economic prosperity of the African countries. Moreover, ICT adoption could enhance the integration of African stock exchanges, further buttressing the drive towards the common market areas in various regions.  
stock exchange; ICT adoption; stock market development; economic growth; Africa 2022
## 3 Results  
Tables 2-4 show, for each shade and each illuminant, the average CIELAB coordinates for the three measured samples with their corresponding standard deviations (in brackets). The three ceramics studied showed similar color gamut sizes for all illuminants, as reflected by the narrow range of the MCDM: 4.1-4.4 for VST (Table 2), 4.0-4.2 for NSP (Table 3), and 4.3-4.6 for IEC (Table 4).  
Figure 4 shows, as an example, the average color shifts (or color inconstancies) in terms of the relative displacements of the centers of gravity of the color gamuts with respect to the D65 illuminant, differentiating the \(a^{*}\)\(b^{*}\), \(a^{*}\)\(L^{*}\), and \(b^{*}\)\(L^{*}\) planes for shade A1 (similar results can be found for the remaining shades). In terms of the D65 illuminant color shifts, it can be observed that (1) all LED illuminants studied produced \(a^{*}\)\(b^{*}\) shifts in several different directions of the CIELAB diagram; (2) in general, shifts in \(b^{*}\) were larger than those in \(a^{*}\); and (3) the \(L^{*}\) coordinate showed the lowest shifts for the nine LED illuminants, producing in all cases increases below 0.8 units.  
Figure 5 shows total color differences (color inconstancies) between chromaticity coordinates obtained with each LED illuminant and chromaticity coordinates obtained with the D65 illuminant. Differences in lightness, chroma, and hue components of the total CIEDE2000 color difference are shown. It can be noted that in general for all illuminants, differences in hue made the greatest contribution to the total color difference with respect to illuminant D65 (Figure 5), in particular for LED B5 and LED RGB1, independently of the dental ceramic analyzed. However, in the case of LED B3 for all ceramics and LED B2 for IEC ceramic, chroma differences for all shades analyzed were mainly found. Lightness differences were almost negligible with respect to D65, due to the fact that all the illuminants tested had Y = 100 [22].  
Most interesting, for all tested ceramics and shades, the CIEDE2000 color differences representative of color inconstancies were higher than 50:50% PT\({}_{00}\) and, in general, lower than 50:50% AT\({}_{00}\), only for LEDs B2, B3, B4, and V2.  
Figure 4: Projections of the color shifts produced by each one of the nine LED illuminants on the \(a^{\rm s}\)\(b^{\rm s}\), \(a^{\rm L}\)\({}^{\rm s}\), and \(b^{\rm s}\)\(L^{\rm s}\) planes, using as common origin the coordinates under illuminant D65 for shade A1. Results for the three dental ceramics analyzed are distinguished (left: VST; center: NSP; right: IEC).  
Figure 5: Color inconstancy for each shade and ceramic material when the illuminant changes from D65 standard illuminant to each of the LED illuminants considered. (First: VST; second: NSP; third: IEC). Perceptibility (PT\({}_{00}\))-(dashed line) and acceptability (AT\({}_{00}\))-(solid line) color threshold values are indicated.
## 3 Network Handover Algorithm Based on Location Prediction
### Movement Prediction
This ensures that the state transfer probability does not drop to zero, even if the state residence time is extended, as follows:  
\[a_{ij}^{\prime}= \begin{cases}\qquad\varepsilon_{a},i=j\\ (1-\varepsilon_{a})a_{ij},j\
eq i\end{cases}  \tag{19}\]By introducing \(\varepsilon_{a}\), \(a_{ij}\) can obtain suitable non-zero values when continuously in the same cluster. In this way, the model can perform effective state transfer even in the case of consecutive identical cluster sequences, avoiding the problem of prediction failure. After obtaining the trained HMM, we use the Viterbi algorithm for location prediction. We return the obtained maximum hidden state sequence to the grid cells within the cluster, which is the location where the user is most likely to move.
## 7 Results and Discussion
### Comparison with Baselines of Alternate Classification Techniques  
In the next section, the baseline models with alternate text classification techniques are compared with the proposed U-IntentCapsNet in terms of the accuracy and F1 scores with respect to the intent detection.  
It is clearly evident from the results presented in Table 10 that the proposed model demonstrated a significant improvement from the classifiers using TFIDF-based as well as neural models for text classification. Compared with the best baseline, i.e., the Baseline V, in which an n-gram convolution layer was used with max pooling, the proposed model had a 0.144 improvement in the F1 score. In comparison with the Baseline VII, which only leveraged from the BLSTM without capsule networks, the proposed U-IntentCapsNet model showed an improvement of almost 5% in accuracy. This highlights the effectiveness of using a capsule network-based architecture for intent detection. The proposed model performed significantly well against the feature engineered, machine learning (ML)-based, classification techniques. When compared with the best performing ML-based baselines, the Baselines I/II, having TFIDF as features and SVM/NB as the classifier, the proposed model had a 7.6% improvement in accuracy. It is evident from Table 10 that the proposed model demonstrated novelty and effectiveness in modelling text for intent detection and that it improved upon the baselines by a significant margin.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Baseline** & **Model** & **Prec.** & **Recall** & **F1** & **Acc.** \\ \hline I & SVM & 0.83 & 0.82 & 0.82 & 0.82 \\ II & NB & 0.83 & 0.81 & 0.82 & 0.82 \\ III & MLP & 0.82 & 0.81 & 0.81 & 0.80 \\ IV & LR & 0.81 & 0.79 & 0.79 & 0.78 \\ \hline V & CNN & 0.875 & 0.700 & 0.764 & 0.880 \\ VI & LSTM & 0.740 & 0.775 & 0.754 & 0.843 \\ VII & BLSTM & 0.747 & 0.788 & 0.765 & 0.859 \\ VIII & CLSTM & 0.572 & 0.367 & 0.352 & 0.773 \\ \hline \multicolumn{5}{c}{**U-IntentCapsNet**} & **0.911** & **0.911** & **0.908** & **0.908** \\ \hline \hline \end{tabular}
\end{table}
Table 10: Precision, recall, F1 scores and accuracy of intent detection for baseline and the proposed U-IntentCapsNet model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Intent** & **Prec.** & **Recall** & **F1** & **Acc.** \\ \hline INFO & 0.9357 & 0.9523 & 0.9439 & 0.9523 \\ NAV & 0.7622 & 0.7485 & 0.7553 & 0.7485 \\ TRAN & 0.7907 & 0.7234 & 0.7555 & 0.7234 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Intent detection results with respect to the web query’s intent.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **INFO** & **NAV** & **TRAN** \\ \hline INFO & **1260** & 17 & 22 \\ NAV & 37 & **122** & 8 \\ TRAN & 57 & 10 & **168** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Confusion matrix for the proposed U-IntentCapsNet model.
## 5 Pollutant Source Localization
### Inclusion of the Heuristic  
According to Figure 7, the heuristic \(h(t_{k})\) influences the calculation of the next position \(p_{k+1}\), when the UAV losses the direction of the increasing pollutant concentration and \(\lambda\geq\Lambda\). For this  
Figure 7: Pollutant source localization algorithm.  
condition, the current direction of the vehicle \(\theta_{k}\) is influenced by the direction to the heuristic position \(h(t_{k})\) as follows:  
\[p_{k+1}=p_{k}+\theta_{h(t_{k})}\frac{1}{[\psi_{k}]}\gamma+\beta \tag{27}\]  
where:  
\[p_{k},p_{k+1} =\text{current and calculated positions of the vehicle;}\] \[\gamma =\text{a real number that means the waypoints resolution, defined according to the UAV capacity and the environment dimension. High values of \(\gamma\) remains at large distances among the calculated waypoints \(p_{k}\). Conditions of a large targeted environment and low GPS resolution of the UAV will require high \(\gamma\) values; \[\beta =\text{a real random vector that means the deviation parameter, which allows small deviations, from the calculated waypoint \(p_{k}\), to compensate the stochastic behavior of the system. Low values of \(\beta\) are recommended; \[\theta_{h(t_{k})} =\text{a real vector that means the direction of the current position of the UAV to the most probable region of the source location, obtained from the LMLS; and \[\left[\psi_{k}\right] =\text{a real number that represents the average of historical pollutant information captured from $p_{k-1}$ to $p_{k}$. This parameter helps to establish smaller steps of movements of the UAV in regions with high concentrations of the pollutant.  
To complete the algorithm, if the UAV found a direction of increasing pollutant concentration or \(\lambda<\Lambda\), the vehicle decides to follow this direction, and the position is calculated as follows:  
\[p_{k+1}=p_{k}+\theta_{k}\frac{1}{[\psi_{k}]}\gamma+\beta \tag{28}\]  
where \(\theta_{k}\) is the vector of the current direction of the UAV, and as in the previous case, the average of the measurements in \([\psi_{k}]\) avoids abandoning the areas with high concentrations of pollutant.
## 5 Simulation Results
In such way, conventional measurements as data loss ratio and PSNR are equally not sufficient for assessment of the monitoring quality when employing the proposed relaying approaches. After simulations and initial verifications, we expect that the proposed energy-efficient relaying mechanism performs better than traditional relaying paradigms and optimizations based only on local relevance. The energy savings are significant and losses on monitory quality are concentrated in visual data originated from lower relevant source nodes. This is different from the Data-relevance relaying approach, where all images are lower-bounded along the time. In this context, we expect the SR-DWT-based relaying approach as the best optimization solution for visual sensor networks, assuring high monitoring quality but still bringing significant energy savings. As a final comment, we have not assessed the impact in storage and processing when employing the proposed approaches on resource-constrained nodes. The values of SR and DR in data packets will be considered when forwarding packets and such processing should be accounted when estimating the overall performance. In fact, conventional intermediate nodes in wireless sensor networks will employ some routing protocol that will process information in data packets and thus packet processing in such nodes is already expected. We are proposing only slight additional processing in nodes that implement the proposed approaches, which is the comparison of the values of SR and DR according to predefined energy thresholds and dropping of packets according to the current energy level. In such way, we believe that this additional processing can be tolerable when energy saving is achieved over the network. In general words, we expect that wireless visual sensor networks will be composed of sensor motes with limited resources, depending on the network budget and the application requirements [39, 40]. In such way, different performance may be perceived depending on the deployed sensor motes, turning difficult to achieve a cost function for all types of sensors. Nevertheless, future works will be concerned with assessment of processing and storage costs when employing the proposed relaying approaches.