esa-sceva/satcom-chunk-collection huggingface dataset:
# ReSQoV: A Scalable Resource Allocation Model for QoS-Satisfied Cloud Services
## 5 Experiment Results
### Threats to Validity  
Experiments are conducted in the virtualized environment and simulator. Although appropriate model assumptions are made based on experiment design and prior literature,  
\begin{table}
\begin{tabular}{c l l l l l l} \hline \hline S \# & & **Df** & **Sum Sq** & **Mean Sq** & **F value** & **Pr (\textbackslash{P})** & \\ \hline
**1** & **System** & 1 & 61.6 & 61.6 & 1236.281 & \textbackslash{}<2\times 10^{-16}\) & *** \\
**2** & **Response Time** & 1 & 0.1 & 0.1 & 2.396 & 0.123 & \\
**3** & **Throughput** & 1 & 515.5 & 515.5 & 10,345.388 & \textbackslash{}<2\times 10^{-16}\) & *** \\
**4** & **System:Response Time** & 1 & 0 & 0 & 0.076 & 0.783 & \\
**5** & **System:Throughput** & 1 & 1.7 & 1.7 & 34.05 & \(2.252\times 10^{-8}\) & *** \\
**6** & **Response Time:Throughput** & 1 & 0.1 & 0.1 & 1.135 & 0.288 & \\
**7** & **System:Res\_Time:Throughput** & 1 & 0.8 & 0.8 & 16.572 & \(6.84\times 10^{-5}\) & *** \\  & **Residuals** & 192 & 9.6 & 0 & & & \\ \hline \hline \end{tabular} SD: Sources of Deviation, DF: Degree of Freedom, SS: Sum of Squares; Sig: Significance codes: 0 *** 0.001 *** 0.01 *** 0.01 * 0.05 * 0.05 * 0.1 * 1.  
\end{table}
Table 6: Result of Statistical Analysis of Variance.  
Figure 8: Resource Utilization.  
we can only offer conjecture on the causes of any variability that we detect. We considered only medium instance types in this study. A follow-up study is needed to determine whether the outcomes of variability and detectability improve with bigger instance size and more resourceful cloud infrastructure. Another threat to our study's internal validity is that we chose to execute all experiments in 20 iterations, which may be regarded as a low number of iterations. As the public clouds act as a black box whereby we have no control over resource orchestration, we cannot generalize the results to the public cloud. Other QoS measures, such as memory use, power consumption, or cost, should be investigated in the future. Furthermore, any performance research conducted on cloud infrastructure is essentially aimed at a moving target. We believe the fundamental conclusions and consequences of our work will stay steady as long as virtualization and resource allocation in the private environment are handled.
## 5 Experiments
### Tuning the Parameters of HGA  
The time performance of the proposed algorithm varies depending on two main parameters: population size and maximum generation number. Besides, execution time linearly increases in proportion to the maximum generation number since it is a stopping criterion in the algorithm. According to the practice in the QAP literature, however, it is common to use maximum time limit for fair evaluation of the performance. Thus, to properly tune parameters, we set maximum generation number to infinite, fixing maximum time limit to 30 min. In Figure 6, for the problem of tai60a, we provide boxplots of 30 repeated percentage deviations from the published best-known solution with increasing population size ranging from 10 to 100. The reason we choose tai60a for tuning parameters is that it is a fairly hard problem of a moderate size.  
We observe performance is not substantially different along with population size, meaning that population size has little effect on the performance of the proposed algorithm. Therefore, we simply decide to use population size 20, which has the minimum sample average under the infinite maximum-generation number. Instead, we use a 2-h maximum time limit for a practical purpose to test the proposed HGA on all the following comparative experiments. Settings of parameters are summarized in Table 7.  
\begin{table}
\begin{tabular}{l l} \hline
**Parameters** & **Value** \\ \hline Population size & 20 \\ Maximum generation number & Infinite \\ Maximum time limit & 2 h \\ Tabu list sizes & \([0.9n,\,1.1n]\) \\ Aspiration & \(10\times n^{2}\) \\ Number of iterations & 2000 \\ \hline \end{tabular}
\end{table}
Table 7: Parameter setting for the proposed hybrid genetic algorithm (HGA).  
Figure 6: Boxplot of percentage deviations with increasing population size.
## 3 Results and Discussion
### Hardened Concrete Properties
#### 3.2.2 Compressive Strength  
The obtained compressive strength and its variation compared to the control concrete versus different BPW percentages are shown in Figure 6.  
Figure 4: Slump test results.  
Figure 5: Density values of cast concrete after 28 days of curing.  
The compressive strength of the concrete containing all types of BPW was lower than the control mix, and the higher the BPW percentage, the lower the compressive strength. This phenomenon can be attributed to the lower compressive strength of BPW compared to other ingredients and also weaker cohesion and friction at ITZ of BPW and the cement paste compared to the natural aggregate (Figure 7) [19; 38; 65; 66]. It is also evident that hard and hybrid BPW reduced the compressive strength less significantly compared to the soft BPW. This difference can be seen clearly in Figure 6, where the reduction in compressive strength is linearly related to the BPW percentage (PP).  
The relationship between the compressive strength ratios versus the density ratios of BPW-containing concrete to control concrete is shown in Figure 8.  
Figure 6: The compressive strength ratio of BPW-containing concrete to control concrete versus BPW percentages.  
Figure 7: SEM micrographs of the interfacial zone between the natural aggregate (**a**,**b**), BPW (**c**,**d**), and the cement matrix.  
It can be seen that a decrease in compressive strength is accompanied by a decrease in density (D). It is also notable that the relation of compressive strength and density is different for the three BPW types of addition.
## 3 Survey of Techniques for Updating  
WSN software update research is described here based on the three categories identified in [1]: the protocols for _disseminating_ the update, _reducing the traffic_ required for the disseminated updates, and the sensor node _execution environment_, along with an additional category: _fault detection and recovery_.  
Common issues addressed by all of these are power efficiency, performance, security, and dependability. In this survey we only consider work directly related to software updating in WSNs--noting, however, that much of this work is built on results from distributed systems, mobile systems, embedded systems, broadcast mechanisms, mobile agents, middleware, routing protocols, data dissemination and collection protocols, security, and software updating for other classes of computer systems. Note that fault detection is only considered when it is integral to the technique being surveyed, as opposed to an underlying, operating system feature.
# SNR Analysis for Quantitative Comparison of Line Detection Methods
## 4 Method--SNR of SGAD for Line Detection  
In this section, a line detection method, based on the sum of gradient angle differences [6], is introduced. Then, its performance is investigated based on its SNR derivations. Subsequently, the SGAD-based line detection can be compared with the SD-based line detection in terms of their SNRs.
## 7 Current Challenges and Problems
### Suitable Power and Class Amplifier  
The power amplifier plays an important role in low and high frequency bands to amplify the signal to a certain coupling coil which transmits the power and data to the implantable devices. Thus, an implant system needs a highly efficient power amplifier. The suitable class of amplifier is very important to transmit a stable RF signal with high efficiency compared to these conventional amplifiers such as class A, B, C [92]. In general, class E and F amplifiers have the same properties and are widely used in implantable devices and telemetry systems. The E class amplifier requires only one active device within kHz to several MHz, in which shifting resonance frequency decreases the output power transmission. Thus, an automatic frequency correction circuit is needed for high rates of data transmission. However, F class amplifiers require at least two active devices, which increases the power consumption and size. The frequency is limited in low frequencies due to its switching losses. Thus, higher frequencies and inductively coupled system are suitable for better response and comparatively efficient even in the case of frequency shifting.
## 4 The Joint Spatial-Code Clustered IA Scheme  
The conventional IA can achieve low interference leakage and high throughput when the number of users satisfies the IA feasibility condition. However, in large-scale networks with limited antennas, the IA feasibility condition cannot be met, and the interference leakage may become intense, giving rise to significant degradation of the QoS. Since the interference from the unintended users cannot be eliminated only through the spatial domain, extra dimensions have to be introduced to address this problem. In this section, we jointly explore the spatial as well as the code dimensions, and propose a novel joint spatial-code clustered IA scheme to mitigate the interference that the conventional IA cannot suppress effectively. Moreover, most of the existing works mainly concentrate on IA from the perspectives of sum rate and DoFs, without further analyzing the error performance. However, in the practical communication systems, BER serves as an important measurement of the QoS. Therefore, we formulate the BER expressions of the proposed JSCC-IA scheme in the cases of identical and different propagation delays, respectively.
## 2 Materials and Methods
### Hardware
#### 2.2.2 Wireless Communication  
The data are wirelessly transferred using radio waves, eliminating the need for poles or cables. An antenna and radio device were installed at both ends of a point-to-point wireless connection. Multiple technologies are employed in point-to-point wireless communication systems. Numerous parties have produced wireless components specific to various applications. These components are typically classified based on their capabilities, features, and uses. An nRF24L01 wireless transceiver module was selected for the monitoring system. Designed to enable ultralow-power wireless applications, the nRF24L01 module is a 2.4 GHz single-chip transceiver.
## 3 Results and Discussion
### Profiles of VOCs during Different Paocai Fermentations
As shown in Figure 5B, all Q\({}_{2}\) values on the left were lower than the original point on the right, and the regression line of point Q\({}_{2}\) intersected with the vertical axis below zero, and the intercept of \(-\)0.306 was less than 0.05, indicating that this PLS-DA model did not exhibit over-fitting and had good representativeness. A total of 13 compounds were screened out as the difference substances and included compounds like (\(-\))-4-terpineol, \(\alpha\)-terpineol, nerol, 4-isopropylbenzoylyl alcohol, carved, \(\alpha\)-cadinol, terpinolene, isobutyl phenylacetate, 1-isothiocyanatobutane, dimethyl disulfide, 1,4-dithiane, dimethyl trisulfide and 2,4-dittert-butylphenol. Further to explore the correlation between microorganisms and VOCs, a Spearman correlation analysis was carried out with a relative abundance exceeding 5% and the nine biomarkers previously identified. It can be seen from Figure 5C that the microorganisms have different effects on the VOCs of paocai fermented by two kinds of vegetables. Nerol was also significantly correlated with six kinds of microorganisms in the red radish paocai to different degrees. In the cabbage paocai, the correlations between nerol, \(\alpha\)-cadinol, isobutyl phenylacetate and the microorganisms were consistent, that is, they have a significant positive correlation with seven species and a significant negative correlation with three species at the same time, indicating that these 13 microorganisms have the same regulatory effect on the changes of these three substances. In addition, 1-isothiocyanatobutane, 1,4-dithiane and dimethyl trisulfide, which account for a high proportion of compounds in the red radish paocai, were related to six kinds of microorganisms, especially _L. paraplantarum_ species. However, 2,4-dittert-butylphenol in the cabbage paocai and \(\alpha\)-cadinol in the red radish paocai were not related to microorganisms. From the perspective of the relationship between microorganisms and VOCs, _F. siligitis, L. parafarraginis, L. namurensis, L. zymae_ and _S. paracollinoides_ were found to be the key microorganisms for distinguishing the two kinds of paocai, and 4-isopropylbenzoylyl alcohol, \(\alpha\)-cadinol, terpinolene and isobutyl phenylacetate were the key VOCs for distinguishing the two kinds of paocai made from red radish and cabbage.
# Adaptive Packet Coding for Reliable Underwater Acoustic Communications
## 6 Proposed Two-Step DP Approach  
In each transmission, we separately define the system state for the \(k\)-th block and the \((k+1)\)-th block. For the \(k\)-th block, the system state is \(\mathbf{c}_{i}^{(k)}=(q_{i-1}^{(k)}\), \(h_{i-1}^{(k)}\), \(q_{i-2}^{(k+1)}\), \(h_{i-2}^{(k+1)}\)\(B_{i}^{(k)}\)), where \(B_{i}^{(k)}\) is the maximum number of transmitted packets for the \(k\)-th block. For the \(i\)-th transmission of the \((k+1)\)-th block, the sender already knows the transmission result of the \((i-1)\)-th transmission. Thus, the system state is \(\mathbf{c}_{i}^{(k+1)}=(q_{i-1}^{(k)},\,h_{i-1}^{(k)},\,q_{i-1}^{(k+1)},\,h_{i-1}^{( k+1)},\,B_{i}^{(k+1)})\).  
As analyzed above, the problem for the RLPC-J-ARQ scheme is also a sequential decision-making problem. Thus, it could also be solved based on the DP approach. The main difference is that the \(B_{i}^{(k)}\) and \(B_{i}^{(k+1)}\) depend on the \(N_{i-1}^{(k+1)}\) and \(N_{i}^{(k)}\), as given in (32) and (33), respectively. In other words, the state of the \(i\)-th transmission is not only related to the \((i-1)\)-th transmission, but also to \(N_{i}^{(k)}\).  
Based on the idea of DP, we propose a two-step DP approach which computes the cost in two steps for each transmission. The proposed method still adopts recursive computing based on the Bellman equation. It is similar to the RLPC-ARQ scheme; we first calculate the cost of the \(M\)-th transmission, the \(i\)-th transmission and finally the first transmission. In each transmission, we first calculate the cost of the \((k+1)\)-th block. Then, the overall cost of the current transmission is calculated.
## Case Studies
### Non-Gaussian Statistical Analysis  
First, the histogram plot Var1, Var2, Var3 with a probabilistic distribution function (PDF) fitting is frequently carried out to detect if the data are impacted by non-Gaussian noises or subject to human interventions. It is often used to measure the heavy-tailedness underlying process during assessment and frequently the a suitable moment to call for further insight. Histogram fitting is significant since some parts of the process can be more impacted by the disturbances or can be more exposed to the process nonstationarity, can be cross-correlated or might be subject to human interventions. In real situations it is a challenge to have similar properties for all the loops.  
The etching process requires uniform temperature behaviors from batch to batch, therefore Var1 is the major concern in the current MIMO system. As introduced in Section 2.3, the \(\alpha\)-stable distribution is performed to detect whether the distributions are Gaussian, Levy or else. In Figure 4, time series Var1 of the MIMO system has significant fat tail properties. Therefore, it can be inferred that there are non-Gaussian noises coming to the system.  
Var2 is the variable of errors from flow loop. The higher flow rate can exchange heat efficiently whereas more turbulence could be introduced at the same time. Therefore, the flow loop is interacted with the temperature loop. From the Figures 4 and 5, we could see the clear correlations between Var1 and Var2.  
Figure 3: Output errors of three loops: Var1, Var2, Var3 with first tuning parameters in blue; Var1’, Var2’, Var3’ with second tuning parameters in red.  
Figure 2: Control structure of the MIMO system.  
The histogram of the Var3 explicitly shows very poor or even not working control. The histogram is highly scattered, almost uniform. The loop is uncontrolled with possible oscillations and operating on the edge of the stability region or manually operated.
## 3 Experimental Work
### Split Hopkinson Pressure Bar Tests and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Specimen** & \begin{tabular}{c} **Length/** \\ **Diameter** \\ **Ratio** \\ \end{tabular} & \begin{tabular}{c} **Pressure** \\ **of Test** \\ **(MPa)** \\ \end{tabular} & \begin{tabular}{c} **Impact** \\ **Velocity** \\ **(MPa)** \\ \end{tabular} & \begin{tabular}{c} **Dynamic** \\ **Peak Stress** \\ \end{tabular} & \begin{tabular}{c} **Dynamic** \\ **Strain at** \\ **Reak Stress** \\ \end{tabular} & \
\begin{tabular}{c} **Peak** \\ **Reak Stress** \\ \end{tabular} &
\begin{tabular}{c} **Diff (Dynamic** \\ **Increase Factor)** \\ **Rate 1/s** \\ \end{tabular} \\ \hline UHSC-1 & 0.56 & 0.12 & 8.46 & 146 & 0.027 & 1109 & 1.08 \\ UHSC-2 & 0.56 & 0.15 & 7.92 & 162 & 0.02 & 963 & 1.20 \\ UHSC-3 & 0.57 & 0.12 & 8.53 & 143 & 0.048 & 1288 & 1.06 \\ UHSC-4 & 0.57 & 0.15 & 11.12 & 231 & 0.01 & 549 & 1.71 \\ UHSC-5 & 0.80 & 0.27 & 12.64 & 253 & 0.012 & 753 & 1.87 \\ UHSC-6 & 0.81 & 0.30 & 13.48 & 210 & 0.007 & 593 & 1.56 \\ UHSC-7 & 0.81 & 0.20 & 11.10 & 150 & 0.017 & 1257 & 1.11 \\ UHSC-8 & 0.82 & 0.23 & 11.86 & 223 & 0.009 & 545 & 1.65 \\ UHSC-9 & 0.82 & 0.20 & 11.14 & 163 & 0.011 & 584 & 1.21 \\ UHSC-10 & 0.82 & 0.17 & 9.99 & 209 & 0.011 & 531 & 1.55 \\ UHSC-11 & 0.82 & 0.30 & 13.38 & 180 & 0.015 & 950 & 1.33 \\ UHSC-12 & 0.83 & 0.20 & 10.97 & 195 & 0.009 & 523 & 1.44 \\ UHSC-13 & 1.06 & 0.20 & 11.04 & 154 & 0.027 & 814 & 1.14 \\ UHSC-14 & 1.08 & 0.27 & 12.68 & 158 & 0.019 & 843 & 1.17 \\ UHSC-15 & 1.08 & 0.10 & 7.84 & 198 & 0.027 & 602 & 1.33 \\ UHSC-16 & 1.09 & 0.30 & 13.35 & 191 & 0.02 & 960 & 1.41 \\ UHSC-17 & 1.10 & 0.23 & 11.77 & 166 & 0.013 & 696 & 1.23 \\ UHSC-18 & 1.15 & 0.30 & 13.45 & 166 & 0.017 & 835 & 1.23 \\ UHSC-19 & 1.16 & 0.20 & 10.94 & 170 & 0.008 & 461 & 1.26 \\ UHSC-20 & 1.17 & 0.20 & 11.12 & 163 & 0.012 & 515 & 1.21 \\ UHSC-21 & 1.18 & 0.10 & 7.89 & 164 & 0.011 & 353 & 1.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Compressive strength values of the ultra-high-strength concrete. Figure 10: Strain rate and length/diameter ratio relations. In order to observe the effects of test pressure on the dynamic response the test pressure was varied in the range of 0.1 to 0.3 MPa. The variation of the strain rate with the test pressure is shown in Figure 11. As it is seen in the Figure 11, the strain rate increases almost linearly as the test pressure increases. The measured dynamic compressive strength values are given in the fifth column in Table 3. As expected, the peak stress values show some variations due to the brittle nature of the concrete and the variations of the constituent ratios in each specimen. Nevertheless, the peak values remained within a range of 143 to 253 MPa. The last column in Table 3 contains the percentage increase in the strength of the UHSC with respect to the maximum quasi-static strength of 135 MPa.
## 4 Discussion  
This study analyzed a game-based AR learning environment for ESD. A total of 217 pre-service teachers participated in four different intervention groups: A (AR + GAME), B (AR + non-GAME), C (non-AR + GAME), and D (non-AR + non-GAME). Aimed to investigate the potential of both AR and GAME, the participants' K and CSE development over time was assessed.  
Various studies reported AR improving knowledge acquisition [11; 50; 51; 52; 53] and self-efficacy beliefs [45; 48; 54; 58; 71]. Therefore, we expected that Kpre would positively relate to Kpost (H1) and CSEpre would positively relate to CSEpost (H1), both with significantly better scores in the AR+GAME group (in our case, A as compared to B, C, and D).
## 4 Results and Discussion
### Evolution Characteristics in the Three-Phase Mixing Process  
The velocity distributions under non-earated and aerated conditions are shown in Figure 7. The fluid around the impeller has a large velocity value. A local circulating flow is formed under the paddle rotation and baffle action (Figure 7a). When the mixed fluid fields are inflated in Figure 7b, the mixed fluid is lifted from the bottom by the rotation of the blade and sent through the blade by strong shear forces at high speed. The impeller rotation effect causes a portion of the fluid to descend and then rise again, creating a circulating flow in the lower portion.  
From the figure, the loop flows in the left and right parts are different. Under the action of the baffle, many local turbulent vortices are formed in the mixed flow field in the aerated state, which improves the evolution disorder of flow patterns. The above phenomenon shows that the gas filling control directly affects the three-phase mixing course and can increase the chaotic evolution trend to enhance mass-transfer efficiencies and material mixing qualities.  
The vorticity cloud diagrams are obtained to investigate the influences of inflation conditions on three-phase mixed courses, as shown in Figure 8. As the mixing container is inflated, the vorticity distribution becomes complex, as shown in Figure 8a. In impeller  
Figure 6: Particle energy curves in the three-phase mixing process. (**a**) Initial state. (**b**) Particle rise. (**c**) Particle dispersion. (**d**) Particle mixing.  
rotation and baffle obstruction, the vorticity of turbulent vortices accumulates on both sides of the mixing vessel, as shown in Figure 8b,c. In the upper part of the impeller, the vorticity value is small, and the fluid field appears to have a lower vorticity zone and presents an asymmetric state (Figure 8c). In Figure 8d, the impeller has a larger vorticity value. When the inflation rate of the mixing vessel is large, the turbulent field has higher upper impulse energy, and the vorticity value in the inflation region is the largest, which produces intensive disturbances to mixing systems. The above phenomena illustrate that the dynamic control method of aeration increases the material mixing process of the turbulent field and causes complex polymerization and dissipation phenomenon of local turbulent vortices. It improves the quality and efficiency of the three-phase mixing process to a certain extent.  
Figure 8: Vorticity distributions of flow field under aeration conditions. (**a**) Initial state. (**b**) Particle rise. (**c**) Particle dispersion. (**d**) Particle mixing.  
Figure 7: Velocity distributions of mixed flow field under non-aerated and aerated conditions. (**a**) Non-aerated. (**b**) Aerated.