esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### sEMG Differences across Muscles  
Table 2 shows the mean and SD values of the normalised sEMG muscle response (%MVIC), as well as the ICC\({}_{2,1}\) and 95% confidence interval (CI) values for all analysed core muscles across the different testing conditions. For _neutral_, no significant differences on sEMG muscle activity were observed across all abdominal musculature (RA-LEO, \(p\) = 0.541; RA-REO, \(p\) = 0.264; RA- LIO, \(p\) = 0.381; RA-RIO, \(p\) = 0.737; LEO-REO, \(p\) = 0.656; LEO-LIO, \(p\) = 0.828; LEO-RIO, \(p\) = 0.768; REO-LIO, \(p\) = 0.809; REO-RIO, \(p\) = 0.433; LIO-RIO, \(p\) = 0.588). However, compared to each abdominal muscle, the sEMG response of the back-muscle LES was significantly lower (LES compared to the RA, REO, LEO, LIO and RIO: \(p<0.001\)).  
Similarly, for _extension_, no differences on the sEMG muscle activation were detected across all evaluated abdominal muscles (RA-LEO, \(p\) = 0.752; RA-REO, \(p\) = 0.551; RA- LIO, \(p\) = 0.640; RA-RIO, \(p\) = 0.646; LEO-REO, \(p\) = 0.806; LEO-LIO, \(p\) = 0.449; LEO-RIO, \(p\) = 0.454; REO-LIO, \(p\) = 0.288; REO-RIO, \(p\) = 0.292; LIO-RIO, \(p\) = 0.994). However, the LES showed significantly lower sEMG activation compared to each abdominal muscle (LES compared to the rest of abdominal musculature: \(p<0.001\)).  
However, statistical differences on sEMG muscle activity across all abdominal muscles were found for _flexion_. Particularly, _flexion_ elicited a higher sEMG activation of the RA compared to both EO (RA-LEO, \(p\) = 0.044; RA-REO, \(p\) = 0.002) but not in comparison to both IO (RA-LIO, \(p\) = 0.524; RA-RIO, \(p\) = 0.645). _Flexion_ also triggered a higher sEMG response in both IO when compared to the REO (LIO-REO, \(p\) = 0.013; RIO-REO, \(p\) = 0.008) but not to the LEO (LIO-LEO, \(p\) = 0.154; RIO-LEO, \(p\) = 0.112). No significant differences were observed when comparing the both sides of external and internal obliques, (REO-LEO, \(p\) = 0.352;RIO-LIO, \(p\) = 0.860). Finally, the sEMG activation on the LES during _flexion_ remained significantly lower in comparison to each one of the analysed abdominal muscles (LES-RA, \(p\) < 0.001; LES-LEO, \(p\) < 0.001; LES-REO, \(p\) = 0.001; LES-LIO, \(p\) < 0.001; LES-RIO, \(p\) < 0.001).
# Listeners' Spectral Reallocation Preferences for Speech in Noise
## 3 Expt. 1: Spectral Reallocation Preferences for Full-Bandwidth Speech
### Stimuli
#### 3.2.1 Spectral Tilt Adjustment  
Spectral tilt adjustment made use of pre-emphasis or de-emphasis to increase or decrease tilt, respectively, implemented in the time domain using digital filtering with the transfer function \(1-\lambda z^{-1}\) for pre-emphasis, and its reciprocal for de-emphasis. The parameter \(\lambda\) controls the degree of emphasis/de-emphasis and was drawn linearly from the interval [0.2, 1] to produce tilts in the range [\(-10.85\), \(0.59\)] dB/octave. Some 23 steps were used, the central one having the original (unmodified) tilt value of \(-5.25\) dB/octave, along with 11 having a progressively flatter tilt than the original, and 11 with a progressively steeper tilt. Spectral tilt adjustments were implemented using the filter function in Matlab 2016b.
# A Deep Learning Approach for Short-Term Electricity Demand Forecasting: Analysis of Thailand Data
## 6 Results and Analysis
### Parameters Tuning for Scenario 1  
The electricity demand for the year 2018 is forecasted utilizing the refined parameters illustrated in Table 4 with a dataset size of 105,216 and a test size of 17,520. Compared to the other two RNN-LSTM and RNN models, the RNN-GRU model performed better. We achieved the lowest minimum loss of validation (MAE) of 213.36 MW compared to 221.79 MW and 242.35 MW using the RNN-GRU simulation setup.  
In Scenario 1, as stated in Table 4, we were able to attain the following outcomes:  
* The LSTM model with nodes = 128, layers = 1, dropout = 0.15, lookback period = 8 days, and epochs = 36 produced the minimal validation loss of 221.79 MW.
* The GRU model with nodes = 64, layers = 1, dropout = 0.15, lookback period = 5 days, and epochs = 42 obtained the minimal validation loss of 213.36 MW, which was the lowest loss across all three models.
* The RNN model with nodes = 64, layers = 3, dropout = 0.15, lookback period = 8 days, and epochs = 38 achieved the minimal validation loss of 242.35 MW, which was the highest loss across all three models. When optimized hyperparameters are used, Table 5 shows that the RNN-GRU model achieved its best performance in terms of both performance metrics (MAE and MAPE) with MAE = 214.79 MW and MAPE = 2.08%.
## 3 Results and Discussion
### Water Demand Projection
This could be attained through operational actions, many of which require predictive models and instruments to be applied [54]. Therefore, the developed model could be integrated with adaptive cropping patterns to enhance the water allocation through the proper supply of the desired needed water in the scheme, thereby avoiding over-supply in the period of low demand and under-supply in the period of high demand. The projected daily available discharges for irrigation supply, actual water demand, and recommended irrigation supply in response to the ensemble of 10 climate models with the worst-case scenario (RCP 8.5) from 1 January to 31 December 2020 is shown in Figure 13. It can be noted that the irrigation system was unable to satisfy the high water demand particularly in January, March-May in the dry-season and July, September-November in the wet-season due to shortage of water in the main canal. As a result, many farmers at the downstream do not usually receive supplies of irrigation in their fields. Noticeably, while shortages of water are experienced in some periods along the season, there are excess water in other periods, which run as waste. A study by [55] found that rice production and agricultural irrigation water use would be affected by potential risks on the future climate change. The highest environmental and agricultural sustainability index values are related to the incorporation of the change in crop pattern with improvement of the overall irrigation efficiency [56]. Climate change requires new approaches combining adaptation and mitigation strategies to realize the goal of sustainable development [57]. To assess water resource utilization many concepts have been applied for example, water footprint [55], a conception method by which indices of risk vis-a-vis drinking water was assessed, on the basis of a probability estimation methodology [58]. Furthermore, [57] analyzes the motivations driving a case study at water authority level using Information and Communication Technologies (ICTs) to support strategic management decisions involving risky choices. A storage reservoir was recently built in the scheme, however, its capacity is inadequate. It is therefore necessary to construct reservoir(s) to store more water for future use. The storage capacity would be based on the projected discharge at the supply intake taking into account the streamflow from the water source. The excess water could be stored in the reservoir and use during the period of high demand. Finally, a proper assessment on future changes in rice production for the scheme is recommended, through investigations of how climate change impacts irrigation water planning and management for rice.
## 1 Introduction
They found that the spectra of near-fault ground motions typically have two distinct local peaks that are representatives of the high- and low-frequency components [15]. [PERSON] et al. proposed an approach to identify the pulse-like motions in earthquake recordings, based on the congruence relationship between the response spectrum and the dimensionless \(\Pi\)-response spectrum [16]. However, only one channel of the recorded signals was considered in this approach. [PERSON] proposed a wavelet-based approach to quantitatively classify near-fault earthquake records. Pulse-related parameters can be extracted based on the one pulse waveform with the largest pulse energy [1]. This approach is quite useful at identifying whether the one set near-fault earthquake motion is pulse-like or not. However, the pulse component extracted by this method is composed of one pulse waveform, which is not an adequate representation of the interested pulse feature of earthquake motion records if the multi-pulse waveform is of concern. For an earthquake event with highly non-uniform slip distribution, such as the Chi-Chi event in Taiwan (1999-September-20 17:47:16 UTC, Mw = 7.6), the type of pulse sequence observed depends on the instrument's distance relative to the asperities. These factors contribute to the existence of the multi-pulse near-fault earthquake motion [17]. The number of dominant pulses in the velocity time-history might be related to the number of finite asperities in a fault. However, it is difficult to estimate the slip distribution pattern in a destructive fault a priori [18]. Many attempts have been made to investigate the characteristics of the near-fault earthquake motion by identifying or extracting the multi-pulse components in the recorded seismic signal. For example, a sum of two and three velocity pulses was utilized to create pulse representations for two records [19]. However, only one component from strike-normal or strike-parallel was considered. The strike-normal or strike-parallel direction may not be the one containing the largest pulse energy,the unfavorable horizontal direction with the largest pulse energy should be first obtained before analyzing the corresponding characteristic of multi-pulse components [1]. M&P [20] and DB4 [14], are adopted as the mother wavelet to transform near-fault earthquake motions with variant profiles, respectively. This manually selected mother wavelet cannot fit the variant characteristic of near-fault earthquake motions. Furthermore, the most unfavorable horizontal direction is not considered in Reference [20]. In order to identify and extract the most unfavorable multi-pulse components embedded in the near-fault earthquake motion, we propose a novel adaptive wavelet transform algorithm. Instead of using one fixed mother wavelet for all seismic signals, an iterative algorithm was proposed to obtain the optimum mother wavelet for each potential pulse. Thus, the resulting multi-pulse component could be represented in higher quality.
## 3 Results and Discussion
### The Electrocatalytic Reduction and Detection of 2,4-Dinitrotoluene
#### 3.3.4 Interference Study  
Good selectivity is very vital and challenging for electrochemical sensors. The selectivity of the modified sensor was evaluated using chronoamperometry as shown in Figure 7.  
The selectivity of the modified electrode was assessed towards potential interfering species like hydrazine, thiourea, glucose, ascorbic acid, ethanol, urea, ammonia and KI in the presence of 0.01 mM 2,4-DNT. It was found that no amperometry response was obtained when 0.1 mM solution of each ion was introduced into 0.01 M PBS. However, the amperometric response was observed when 0.01 mM of 2,4-DNT was added to the solution which indicated good selectivity of the Zn-MOF-8@Ag modified Au electrode towards 2,4-DNT in practical applications.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Type of Electrode** & **Linear Range (\#M)** & **Sensitivity (\(\mu\)A\({}^{-1}\)\(\mu\)M\({}^{-1}\) cm\({}^{-2}\))** & **Detection Limit (\(\mu\)M)** & **References** \\ \hline GC/G-Li-CO\({}_{3}\) & 0–0.20 & –0.552 & 0.035 & [28] \\ GC/FG0/SrTiO3 & 0.5–0.8 & 71.66 & 0.128 & [29] \\ GF/Zn-MOF-8@Ag & 0.002–0.9 & –0.238 & 0.041 & Our work \\ \hline \end{tabular}
\end{table}
Table 1: Summary of linear range, detection limit and sensitivity to 2,4-DNT in previously reported literature.  
Figure 7: The amperometric anti-interference ability of a Zn–MOF-8@AgQDs modified Au electrode by addition of 10 mM 2,4-DNT and 80 mM of each interfering reagent (hydrazine, thiourea, glucose, ascorbic acid, ethanol, urea, ammonium, KI) followed by addition of 10 mM 2,4-DNT in PBS at 0.05 V applied potential.
## 3 Reliability Sensitivity Analysis of Emergence Extension
### Sensitivity Analysis
#### 3.2.1 Sensitivity Analysis of Torques  
Sensitivity is defined by the ratio of change (or gradient) of failure probability to the change of random variables. According to the reliability index of the NLG emergency extension mechanism in Equation (3) and partial derivative, the sensitivity of the \(i\)th torque is  
\[\frac{\partial\beta}{\partial M_{i}}=\frac{\sum\limits_{i=1}^{9}C_{i}^{2}M_{i} ^{2}-C_{i}^{2}M_{i}\cdot\sum\limits_{i=1}^{9}M_{i}}{\left(\sqrt{\sum\limits_{ i=1}^{9}C_{i}^{2}M_{i}^{2}}\right)^{3}} \tag{9}\]  
in which \(\frac{\partial\beta}{\partial M_{i}}\) reflects the influence levels of the \(i\)th torque on the failure probability of emergency extension. The larger the variation of \(\frac{\partial\beta}{\partial M_{i}}\) is, the larger the influence of the corresponding torque on reliability index and failure probability is.  
By substituting the torque and the corresponding variable coefficient into Equation (9), the sensitivities of each torque to start reliability are obtained as shown in Figure 4. When the landing gear angle is 2.66\({}^{\circ}\), the corresponding unlocking is completed, and then M\({}_{9}\) disappears.Therefore, the sensitivity of M\({}_{9}\) only exists in [0\({}^{\circ}\), 2.66\({}^{\circ}\)].  
Figure 4: Sensitivities degree of nine torques to strut rotation angle: (**a**) Sensitivity degree of M\({}_{1}\); (**b**) Sensitivity degree of M\({}_{2}\); (**c**) Sensitivity degree of M\({}_{3}\); (**d**) Sensitivity degree of M\({}_{4}\); (**e**) Sensitivity degree of M\({}_{5}\); (**f**) Sensitivity degree of M\({}_{6}\); (**g**) Sensitivity degree of M\({}_{7}\); (**h**)Sensitivity degree of M\({}_{8}\); (**i**) Sensitivity degree of M\({}_{9}\).  
As displayed in Figure 4, the sensitivities of nine torques have different change trends irregularly with strut rotation angle. Therefore, it is difficult to specifically identify which torques are the main factors influencing the failure probability of emergency extension with the variation of strut rotation angle.
## 3 Results  
Before thermocycling (w), average roughness values (Ra) showed significant differences between polished and glazed samples for L (_p_ = 0.035) and H (_p_ = 0.033). Glazing increased roughness values in L, C, and S samples and decrease them in E and H. The roughness values increased as follows: Sp \(<\) Ep \(<\) Lp \(<\) Hp \(<\) Cp, Hg \(<\) Eg \(<\) Sg \(<\) Cg \(<\) Lg. Differences were significant between Cp and Sp (_p_ = 0.003), Ep (_p_ = 0.014), and Lp (_p_ = 0.029), respective Lg and Hg (_p_ = 0.036).  
Related to different surface finishing methods, after hydrothermal aging polished surfaces had significantly lower roughness than glazed ones for C (_p_ = 0.007) and H (_p_ = 0.001). The values increased as follows: Hp \(<\) Sp \(<\) Cp \(<\) Ep \(<\) Lp, Hg \(<\) Sg \(<\) Eg \(<\) Cg \(<\) Hg. Related to thermocycling, Ra values increased significant for Ep, Lp, and Sp, respective Eg, Sg, and Hg (Figure 1).  
Related to surface processing, TP and OP values increased and CR decreased for glazed samples, with results being significant before thermocycling and insignificant after.  
Mean calculated optical parameters ranged between 17.01 and 24.79 for TP, 4.40 and 8.05 for OP, and 0.46 and 0.60 for CR (Figures 2-4, Tables 2-4). TP, OP and CR values were insignificantly changed by thermocycling.  
Figure 1: Mean Ra roughness values and SD values of the samples, before and after thermal aging.  
Related to materials, for \(p\) samples before thermocycling, the values decreased significantly as follows: Ep > Hp > Lp > Cp > Sp, except from Hp to Lp. For g samples they decreased as follows: Lg > Hg > Eg > Sg > Cg, being significant only between Sg and Cg.  
After thermocycling, values decreased as follows: Ep > Lp > Hp > Cp > Sp, being insignificant between Lp and Hp, and Cp and Sp. For glazed samples they decreased as follows: Eg > Hg> Lg > Cg > Sg, being insignificant between Hg and Lg.
###### Abstract  
The Advanced Metering Infrastructure (AMI) is one of the Smart Grid (SG) applications that used to upgrade the current power system by proposing a two-way communication system to connect the smart meter devices at homes with the electric control company. The design and deployment of an efficient routing protocol solution for AMI systems are considered to be a critical challenge due to the constrained resources of the smart meter nodes. IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) was recently standardized by the IETF and originally designed to satisfy the routing requirements of lossy and low power networks like wireless sensors (WSN). We have two kinds of AMI applications, on one hand AMI based WSN and on the other hand AMI based PLC communication. In this paper, we proposed a real and simulated implementation of RPL behavior with proper modifications to support the AMI based WSN routing requirements. We evaluate RPL performance using 140 nodes from the wireless sensor testbed (IoT-LAB) and 1000 nodes using Cooja simulator measure RPL performance within medium and high-density networks. We adopted two routing metrics for path selection: First one is HOP Count (HC) and the second is Expected Transmission Unit (ETX) to evaluate RPL performance in terms of packet delivery ratio; network latency; control traffic overhead; and power consumption. Our results illustrate that routes with ETX calculations in low and medium network densities outperform routes using HC and the performance decreases as the network becomes dense. However, Cooja implementation results provides an average reasonable performance for AMI with high-density networks; still many RPL nodes suffering from high packet loss rates, network congestion and many retransmissions due to the selection of optimal paths with highly unreliable links.  
s
mart grid; routing protocols; objective function; RPL performance; testbed +
Footnote †: journal:  
Article
## 2 Concept, System Setups, and Methods
The modulation bandwidth of the VLC system is obtained by a vector network analyzer (N5227A, Keysight) which connects with the transmitter and the receiver side. BERT is used to transmit and receive the signals in order to measure BER at different data rates.
# Flow Duration Curves from Surface Reflectance in the Near Infrared Band
## 2 Materials and Methods
### Evaluation of the Results  
The performance of the procedure is quantified through the statistical test of Kolmogorov-Smirnov [26] (k-s), which is a nonparametric hypothesis test that evaluates the difference between the cumulative distribution functions of two sample datasets (i.e., two-sample test; simulated and observed discharge series, in this case). If the two series are from the same continuous distribution, the test fails to reject the null hypothesis at a certain significance level (10% in this study).  
Further comparisons are carried out in terms of discharges, and the performances are evaluated by the Pearson correlation coefficient, \(R_{p}\), and the Spearman (rank) correlation coefficient, \(R_{s}\). The first evaluates the linear relationship between two datasets, the second their monotonic relationship (whether linear or not). To evaluate the goodness of the simulated time series of river discharge in representing the ground-observed variable, four performance indices are selected:  
* Root mean square error, \(RMSE\), the second sample moment of the residuals (or differences) between predicted and observed values. It ranges from 0 (perfect fit) to +\(\infty\) (low performances).
* Relative \(RMSE\), \(rRMSE\), defined as: \[rRMSE\ (\%)=\frac{RMSE}{\overline{Q}}\cdot 100,\] (1) where \(\overline{Q}\) is the mean value of the observed time series. \(rRMSE\) varies in the range from 0 (perfect fit) to +\(\infty\) (low performances) and it is expressed in a percentage.
* Normalized \(RMSE\), \(NRMSE\), defined as: \[NRMSE\ (\%)=\frac{RMSE}{Q_{max}-Q_{min}}\cdot 100,\] (2) where \(Q_{max}\) and \(Q_{min}\) are the maximum and the minimum values of the observed time series, respectively. \(NRMSE\) varies in the range from 0 (perfect fit) to +\(\infty\) (low performances) and it is expressed in a percentage.
* The Nash-Sutcliffe efficiency [27], \(NSE\), defined as: \[NSE\ (\%)=1-\frac{\sum_{t=1}^{T}(Q_{obs}^{t}-Q_{sim}^{t})^{2}}{\sum_{t=1}^{T}(Q_{ obs}^{t}-\overline{Q})^{2}}\cdot 100,\] (3) where \(Q_{obs}^{t}\) is the ground-observed discharge value and \(Q_{sim}^{t}\) is the discharge value estimated by with the proposed approach. NSE is specifically used in hydrological applications because it is sensitive to extreme values. It ranges from \(-\infty\) to 1, with 1 corresponding to a perfect match of simulated discharge to the observed discharge and 0 indicating that the simulated predictions are as accurate as the mean of the observed discharge. For values less than 0, the observed mean is a better predictor than the model used.
## 6 How to Evaluate Defense Systems
### Data Collection  
[PERSON] et al. [38] propose a refinement to traditional IDS to be more efficient in a Cloud environment. To test their system, they use three sets of data. The first represents legitimate actions. In the second, they altered the services and their usage frequency to simulate anomalies. The last set simulates policy violations. In order to evaluate the event auditor that monitors the requests received and the responses sent on a node, they chose to examine the communication elements, since log data present little variations, making attacks difficult to detect. A feed-forward neural network is used for the behavior-based technique, and the simulation includes five legitimate users and five intruders. Their scenario simulates ten days of usage. Although the results yielded a high number of false negatives and positives, its performance improved when the training period of the neural network was prolonged. To evaluate their behavior-based system, they looked for the number of false positives and negatives. They show that their system consistently has more false negatives than false positives. With still a high level of uncertainty, the false alarms disappeared within 16 days of simulation training. With longer training periods, they noticed even lower false positives, but also the very non-deterministic nature of neural networks, since false positives were not stable after several iterations. To evaluate the Knowledge-Based System, they used data from both the logs and the communication system. They created a series of rules to build a security policy that should be respected. They conclude that their system could allow real time analyses, provided the number of rules per action remains low.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Experiment** & **Efficiency** \\ \hline
**Design** & **Impact of defense system** \\ Data collection & Overhead bandwith \\ Simulation & Processing time \\ Testbed & Detection attack \\  & False alarm \\  & False negative \\ \hline
**Parameters** & **Mitigation** \\ Attack Rate & Filtration \\ Attack Duration & Limitation \\  & Quality of Service (QoS) \\  & Migration Virtual Machine (VM) \\  & Traceback \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of Evaluation Defense System in DDoS Attacks.