esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Setup
### Analysis of Variance  
Analysis of variance (ANOVA), also known as the F-test method, is a fundamental statistical technique for detecting significant differences among multiple group means. The methodology decomposes total data variability into systematic effects and stochastic errors, establishing hypothesis verification via F-statistic construction. Specifically, the F-value is mathematically expressed as the ratio of the between-group mean square to the within-group mean square. When the derived F-value exceeds the critical threshold at a specified significance level, the null hypothesis of equal group means is rejected, confirming statistically significant inter-treatment differences [53]. This method establishes a rigorous analytical framework for multi-group comparisons by quantifying contributions of controlled factors and random variations.  
\[SS=\sum\limits_{i=1}^{n}(x_{i}-\overline{x})^{2} \tag{7}\]  
\[f=m-1 \tag{8}\]  
\[f_{e}=n-m \tag{9}\]  
\[\overline{x}=\frac{1}{n}\sum\limits_{i=1}^{n}x_{i} \tag{10}\]  
\[SS_{T}=\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}(x_{ij}-\overline{x})^{2} \tag{11}\]  
Figure 7: Compressive strength test device.  
\[SS_{A}=\sum\limits_{i=1}^{a}\sum\limits_{j=1}^{n_{1}}(x_{i}-\overline{x})^{2} \tag{12}\]  
\[SS_{E}=\sum\limits_{i=1}^{a}\sum\limits_{j=1}^{n_{1}}(x_{ij}-\overline{x_{i}})^{2} \tag{13}\]  
\[F=\frac{MS\dot{A}}{MSE}=\frac{\frac{SS_{A}}{\overline{x}-\overline{x}}}{ \frac{SS_{E}}{\overline{x}-\overline{x}}} \tag{14}\]  
where \(SS\) denotes the error sum of squares; \(SS_{T}\) indicates the total sum of squares; \(SS_{A}\) signifies the between-group sum of squares; \(SS_{E}\) refers to the within-group sum of squares; MST represents the total mean square; MSA denotes the between-group mean square; MSE indicates the within-group mean square;\(f\) represents the degrees of freedom for factors; and \(f_{e}\) denotes the degrees of freedom for error.  
Critical \(F\)-values at different significance levels can be found in the F-test critical value table (F\(\alpha\)). For instance, at \(\alpha\) = 0.05 with factor degrees of freedom (f) = 2 and error degrees of freedom (fe) = 6, the critical value F0.05(2,6) equals 5.14. By comparing the calculated F-value with tabulated critical values, statistical significance can be determined. Larger calculated F-values indicate more significant variance and better model fit.
## 2 LiCSAR and LiCSBAS
### Time Series Analysis
#### 2.4.5 Step 1-5: Mask Noisy Pixels in the Time Series  
Time series can be obtained for all pixels with a sufficient number of available unwrapped data. However, the quality of a stack of unwrapped data may differ significantly from pixel to pixel. Some pixels may be very unreliable due to factors including remaining gaps and unwrapping errors.  
To address this problem, Step 1-5 generates a mask for the time series to differentiate between \"good\" and \"bad\" pixels using several noise indices derived during the previous steps (Figure 3 and Table 1). The pixel is masked if any of the values of the noise indices for a pixel is worse (larger or smaller) than a specified threshold. The threshold for each noise index can be manually adjusted to obtain a better (i.e., harsher or gentler) mask for the time series and velocity.
# Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
## 2 Related Work
### Signcryption  
Encryption and digital signatures are two encryption tools that can ensure confidentiality, integrity, and non-repudiation. Until 1997, cryptographic systems used separate components to provide these security functions. In public key schemes, the traditional scheme is to digitally sign the message and then perform encryption (signature-then-encryption). However, there are two problems: the operation efficiency is low and the cost is high. To solve this signcryption was proposed In 1997 [PERSON] [15] proposed the first signcryption scheme. Signcryption simultaneously performs digital signature and encryption. Signcryption compared to the traditional signature-then-encryption scheme, can effectively improve computational efficiency, by reducing computational cost and communication overhead. In addition, many other signcryption schemes have been proposed throughout the years, each of them having its problems and limitations while offering different levels of security and computational cost [16; 17].
###### Abstract  
The PI3K/AKT/MTOR signalling pathway plays an important role in the growth and proliferation of tumour cells. \(N\)-((3S,4S)\)-4-(3,4-Difluorophenyl)piperidin-3-yl)-2-fluoro-4-(1-methyl-1H-pyrazol-5-yl)benzamide (Hu7691) is a new-generation selective AKT inhibitor developed at Zhejiang University. In this study, we developed an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) for the measurement of Hu7691 in dog plasma. Plasma was precipitated with acetonitrile and then separated on a trifunctionally bonded alkyl column. Excellent separation efficiency and selectivity were achieved by adjusting the mobile phase ratio, with a total running time of only 5 min. The linear dynamic range of the calibration curve was 5-1000 ng/mL. The method was fully validated, and all performance metrics met the criteria. The validated method was used for the pharmacokinetic monitoring and bioavailability assessment of Hu7691 in dogs. The results showed that the area under the curve and peak plasma concentration of Hu7691 increased with increasing dose (oral 5, 10, 20 mg/kg, intravenous 10 mg/kg), and oral bioavailability series were 86.7%, 50.8%, and 50.5%, respectively, indicating a high bioavailability of Hu7691 in dogs. This provides a test basis for the clinical application of the compound.  
Hu7691; UPLC-MS/MS; dog plasma; pharmacokinetics +
Footnote †: journal: Applied Sciences
# ABPCaps: A Novel Capsule Network-Based Method for the Prediction of Antibacterial Peptides
## 2 Materials and Methods  
### Dataset Preparation  
ABPs were retrieved from GRAMPA, a comprehensive public database containing a large collection of ABPs against different bacteria and their activity levels [38]. After retrieving the ABP sequences from GRAMPA, we performed a pre-processing step to eliminate redundant records and to retain only those peptides with a length between 10 and 150 amino acids. The resulting ABPs dataset, which was used as positive samples for the study, contained 6017 non-replicated peptides targeting different bacteria. For accurate identification of ABPs in a wide range of sequences, we also collected peptides with antimicrobial functions but without antibacterial activity (non-ABPs) as well as normal peptides without antimicrobial activity (non-AMPs) as negative samples. The non-ABP data sets were obtained from various general AMP databases such as dbAMP [8], DRAMP [7], DBAASP [39], AVPdb [40], AntiCP 2.0 [41], and AntiFP [42]. To build the negative dataset, the researchers excluded entries with UniProt keywords such as \"antimicrobial\", \"antibiotic\", \"antiviral\", \"antifungal\", \"secreted\", \"exrected\", or \"effector\". The peptides were then selected at random to create a negative set of samples with the same number of sequences as the positive set. The length distribution of the non-AMPs was matched to that of the ABPs to ensure unbiased results. We ended up using 80% of the positive and negative datasets to generate training sets, while the remaining 20% was used to generate independent test sets.
# Consolidating ICESat-2 Ocean Wave Characteristics with CryoSat-2 during the CRYO2 ICE Campaign
## 3 Methods
### CryoSat-2  
Functioning as a conventional radar altimeter system, CryoSat-2 measures the leading edge of the return waveform to assess the SWH; here, the SWH is averaged over 14 km along the ground track [6]. As the reflected signal hitting the wave crest is returned earlier than the reflected signal hitting the wave trough, the height of the wave can be inferred from the time difference between the earliest and latest returns of the reflected signal. Frequently, when using altimetry measurements, multiple geophysical corrections are applied; as only the wave heights are of interest in this study (corresponding to a height difference), no corrections other than the corrections discussed in the previous section have been used.
###### Abstract  
In this paper, we investigate the performance of a detector recently proposed by us that is applied in the relay station receiving signals from two terminals concurrently exchanging data in the two-way relaying system. This is one of the potential configurations to save resources in fifth-generation systems, similar to non-orthogonal multiple access, which is also considered for such systems. Two-way relaying can be implemented using physical network coding. This technique originates from the network coding idea, in which network nodes can perform some mathematical operations. The idea of the investigated detector lies in the application of tentative decisions about weaker signals in the detection of stronger ones and then, after improved detection of stronger user signals, achieving more reliable decisions about the weaker ones. We compare the performance of the proposed detector with the performance of a detector in which the relay makes decisions on the data symbols received from the stations participating in two-way relaying on a symbol-by-symbol basis. Simulation results performed for two-way relaying with physical network coding reported in this paper confirm the superiority of the proposed detector when compared with the standard physical network coding solution applied in the relay node.  
multi-carrier transmission; physical network coding; successive interference cancellation; two-way relaying +
Footnote †: journal: 2021  
Application of an Interference Cancellation Detector in a Two-Way Relaying System with Physical Network Coding  
15 April 2021  
Accepted: 26 May 2021  
Submitted: 28 May 2021  
**Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.**  
**Copyright:** 2021 by the authors. License MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).
## 2 Materials and Methods
### Numerical Modeling  
#### 2.2.1 Liquid Water and Water Vapor Flow  
The governing equation for one-dimensional flow of liquid water and water vapor in a variably saturated rigid porous medium is given by the following mass conservation equation [1]:  
\[\frac{\partial\theta}{\partial t}=\frac{\partial}{\partial z}\left(K_{lh} \frac{\partial h}{\partial z}+K_{lh}+K_{lT}\frac{\partial T}{\partial z}+K_{ ch}\frac{\partial h}{\partial z}+K_{vT}\frac{\partial T}{\partial z}\right) \tag{1}\]  
where \(\theta\) is the total volumetric water content (cm\({}^{3}\) cm\({}^{-3}\)), \(T\) is time (s), \(z\) is the spatial coordinate positive upward (cm), \(h\) is the matric potential (cm), \(T\) is the temperature (\({}^{\circ}\)C), \(K_{\rm lb}\) (cm d\({}^{-1}\)) and \(K_{\rm lT}\) (cm\({}^{2}\) K\({}^{-1}\) d\({}^{-1}\)) are the isothermal and thermal hydraulic conductivities for liquid phase fluxes due to gradients in \(h\) and \(T\), respectively. \(K_{\rm vh}\) (cm d\({}^{-1}\)) and \(K_{\rm vT}\) (cm\({}^{2}\) K\({}^{-1}\) d\({}^{-1}\)) are the isothermal and thermal vapor hydraulic conductivities, respectively. The source sink term is not considered because there is no vegetation on the soil surface.  
Soil hydraulic properties are described by the modified Mualem-van Genuchten model [44; 45]:  
\[K_{lh}(h)=K_{g}S_{e}^{l}\left[1-\left(1-S_{e}^{\frac{1}{h}}\right)^{m}\right] ^{2} \tag{2}\]  
Figure 1: **(a)** The 1.2 m soil column profile. Sensors are installed at depths of 5 cm, 10 cm, 20 cm, 50 cm, and 100 cm to measure soil water content and temperature. **(b)** Meteorological data during the study period, including daily precipitation, surface temperature, and net radiation.  
\[\theta(t)=\left\{\begin{array}{l}\theta_{r}+\frac{\theta_{s}-\theta_{s}}{\left(1+ \left|\lambda h\right|^{n}\right)^{n}}\;,h<h_{s}\\ \theta_{s}\;,h\geq h_{s}\end{array}\right. \tag{3}\]  
where \(S_{\rm e}\) is the effective water content, \(\theta_{\rm r}\) and \(\theta_{s}\) denote the residual and saturated water content, respectively (cm\({}^{3}\) cm\({}^{-3}\)), \(\alpha\) (cm\({}^{-1}\)) and \(n\) (\(-\)) are parameters that define the shape of the water retention function, \(K_{\rm s}\) represents saturated hydraulic conductivity (cm\({}^{3}\) d\({}^{-1}\)).
# Study on Noise Reduction and Data Generation for sEMG Spectrogram Based User Recognition
## 3 Proposed sEMG Spectrogram-Based User Identification
### Data Increase Using Matching Pursuit  
For the sEMG signals processed through noise and resting state signal removal, the amount of data was increased by generating sEMG signals. The generative adversarial  
Figure 5: Partitioned one motion cycle signals.  
Figure 6: Removal of resting state signals.  
network (GAN) is the most widely used technique for data generation. GAN comprises a generator that generates data and a discriminator that assesses the generated data; during training, the generator and the discriminator compete against each other to improve the performance. The GAN requires a substantial amount of data in advance to generate data [13]. However, it is difficult to apply the GAN for generating sEMG signals as the sEMG database is acquired using a small number of motion repetitions, i.e., the amount of data obtained is insufficient. Hence, matching pursuit, which can generate signals using a small amount of data and enables quick data generation using a relatively simple formula compared with other data generation techniques, was employed in this study. The matching pursuit algorithm was first introduced by [PERSON] and [PERSON] [16]. The basic idea of matching pursuit is to first select atoms individually to identify the atom with the highest inner product using the current signal after expressing signals with approximation, subtracting an approximation that uses only that one atom from the signal, and repeating the process until the residual signal is decomposed. The approximate decomposition of the matching pursuit algorithm can be expressed as shown in Equation (1) below, where \(\mathcal{R}^{(m)}\) denotes the residual signal and, based on the number of repetitions \((m)\), the index \(\
u_{i}\) is obtained and \(\alpha_{r}\) is derived.  
\[\mathcal{S}=\sum_{i=1}^{m}\alpha_{r_{i}}\mathcal{D}_{r_{i}}+\mathcal{R}^{(m)} \tag{1}\]  
Accordingly, a signal that is similar to the current signal can be generated by applying matching pursuit to the sEMG signals. Signals can be generated by changing the similarity based on the number of repetitions. The signals were generated to exhibit cross-correlation similarity between 90% and 99%. In this study, preprocessing was performed as follows. First, noise and resting state signals in the signals were removed using the band-pass and band-stop filters mentioned earlier. Subsequently, sEMG data signals were generated using matching pursuit. All the preprocessed one motion cycle EMG signals were convenient to visually check muscle activation and are combined into 12-channel signals in the temporal domain, as shown in Figure 7.
## 4 Stability Analysis Model of Filled-Slope Reinforced by Frame with Prestressed Anchor-Plates
### Stability Analysis in the Case of the First Type of Slip Surface
#### 4.2.2 Search for Models of Slip Surfaces  
(1) The center coordinates and radius of the slip surface  
The search model of the first kind of slip surface is shown in Figure 12. Although the established model is a three-dimensional model, the slip surface always changes in the _xoy_ plane, so it is simplified to two-dimensional coordinates. In the Cartesian coordinate system, \(P(x_{c},y_{c})\), \(C(e,H)\), \(O(0,\,0)\), the angle between the tangent of point \(O\) and the horizontal is \(\theta\), the slope of the tangent is \(k^{\prime},k^{\prime}=tan\theta\).  
Then the slip surface equation is:  
\[(x-x_{c})^{2}+(y-y_{c})^{2}=R^{2} \tag{10}\]  
And satisfies:  
\[\left.\begin{array}{l}x_{c}{}^{2}+y_{c}{}^{2}=R^{2}\\ (e-x_{c})^{2}+(H-y_{c})^{2}=R^{2}\\ x_{c}=-k^{\prime}y_{c}\end{array}\right\} \tag{11}\]  
Figure 12: Search model of the first kind of slip surface.  
Combining Equations (10) and (11) yields:  
\[\begin{array}{l}x_{c}=\frac{-k^{\prime}(H^{2}+e^{2})}{2H^{2}-2k^{\prime}e}\\ y_{c}=\frac{H^{2}+e^{2}}{2H-2k^{\prime}e}\\ R=\frac{\sqrt{1+k^{2}}(H^{2}+e^{2})}{2H-2k^{\prime}e}\end{array} \tag{12}\]  
Therefore, the slip surface is controlled by \(\theta\) and \(e\). The value range of \(\theta\) and \(e\) is determined, and the appropriate step size is set respectively, and the search of slip surface can be realized by constantly changing.  
The angle between the tangent of any point on the arc and the horizontal plane  
At any point \(M(x_{k},y_{k})\) on the arc, the angle between the tangent and the horizontal plane is \(\alpha_{k}\), which can be obtained from the geometric relationship:  
\[\sin\alpha_{k}=\frac{x_{c}-x_{k}}{R} \tag{13}\]  
\[\cos\alpha_{k}=\frac{y_{c}-y_{k}}{R} \tag{14}\]  
The self-weight of the soil block \(i\)  
According to Figure 9, the coordinate of the soil strip \(i\) at the slip surface is (\(x_{i}\),\(y_{i}\)), and when the upper end of the soil strip is on \(OB\), the upper coordinate is (\(x_{i1}\),\(y_{i1}\)), so the formula for calculating the self-weight of the soil strip \(i\) is as follows:  
\[W_{i}=\left\{\begin{array}{l}\gamma b_{i}(H-y_{i})\ x_{i}\geq\frac{H}{\tan \beta}\\ \gamma b_{i}(y_{i1}-y_{i})\ 0\leq x_{i}\leq\frac{H}{\tan\beta}\end{array}\right. \tag{15}\]  
where \(\gamma\) is the weight of fill soil.
# Integrating Machine Learning Algorithms: A Hybrid Model for Lung Cancer Outcome Improvement
## 2 Methodology
### Applied Algorithms for Predicting Lung Cancer
#### 2.1.1 Support Vector Machine  
A support vector machine (SVM) is a computational model employed for categorization and prediction tasks. In simpler terms, decision trees ask questions, a Random Forest obtains opinions from many trees, and a support vector machine draws a smart line to make sense of things [32].  
\[f(x)=\mathbf{W}\cdot\mathbf{x}+b \tag{1}\]  
Here, \(\mathbf{W}\) is the weight vector, and \(\mathbf{x}\) is the input feature vector. \(b\) is the biased term.
## 5 Modelling of Lane-Changing Behaviours (LCB)
### Development of LCB Model
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multirow{2}{*}{**Def.**} & \multicolumn{4}{c}{**Scenario-1**} & \multicolumn{4}{c}{**Scenario-2**} & \multicolumn{4}{c}{**Scenario-3**} \\ \cline{3-14}  & & **Obs.** & **Pre.** & **Diff.** & \(p\) & **Obs.** & **Pre.** & **Diff.** & \(p\) & **Obs.** & **Pre.** & **Diff.** & \(p\) \\  & & **Yes** & **Yes** & **Diff.** & \(p\) & **Yes** & **Yes** & **Diff.** & \(p\) & **Obs.** & **Pre.** & **Diff.** & \(p\) \\ \hline Driving & Yes & 0.41 & 0.33 & 0.08 & 0.000 \({}^{\star}\) & 0.45 & 0.38 & 0.07 & 0.000 & 0.52 & 0.53 & 0.01 & 0.000 \({}^{\star}\) \\ Familiar & No & 0.46 & 0.40 & 0.06 & 0.000 \({}^{\star}\) & 0.48 & 0.43 & 0.05 & 0.000 & 0.60 & 0.56 & 0.04 & 0.000 \({}^{\star}\) \\ \hline \multirow{2}{*}{Gender} & Male & 0.45 & 0.36 & 0.09 & 0.002 \({}^{\star}\) & 0.50 & 0.42 & 0.08 & 0.000 & 0.55 & 0.53 & 0.02 & 0.000 \({}^{\star}\) \\  & Female & 0.62 & 0.56 & 0.06 & 0.000 \({}^{\star}\) & 0.55 & 0.44 & 0.11 & 0.000 & 0.65 & 0.60 & 0.05 & 0.000 \({}^{\star}\) \\ \hline Driving Simulator & Yes & 0.48 & 0.42 & 0.06 & 0.002 \({}^{\star}\) & 0.40 & 0.46 & 0.06 & 0.000 & 0.70 & 0.63 & 0.07 & 0.000 \({}^{\star}\) \\ Exper. & No & 0.29 & 0.23 & 0.06 & 0.041 \({}^{\star}\) & 0.35 & 0.41 & 0.06 & 0.038 \({}^{\star}\) & 0.62 & 0.56 & 0.06 & 0.044 \({}^{\star}\) \\ \hline \multirow{3}{*}{Driving} & Beginner & 0.52 & 0.64 & 0.12 & 0.002 \({}^{\star}\) & 0.72 & 0.61 & 0.11 & 0.002 & 0.69 & 0.61 & 0.08 & 0.001 \({}^{\star}\) \\ Driving & Medium & 0.40 & 0.49 & 0.09 & 0.001 \({}^{\star}\) & 0.51 & 0.43 & 0.08 & 0.013 & 0.59 & 0.52 & 0.07 & 0.000 \({}^{\star}\) \\ Skill & Good & 0.30 & 0.35 & 0.05 & 0.000 \({}^{\star}\) & 0.49 & 0.42 & 0.07 & 0.000 & 0.56 & 0.51 & 0.05 & 0.000 \({}^{\star}\) \\  & V. Good & 0.28 & 0.34 & 0.06 & 0.000 \({}^{\star}\) & 0.46 & 0.39 & 0.07 & 0.000 & 0.66 & 0.61 & 0.05 & 0.000 \({}^{\star}\) \\ \hline \multirow{3}{*}{Driver} & Aggress. & 0.68 & 0.63 & 0.05 & 0.000 \({}^{\star}\) & 0.55 & 0.46 & 0.09 & 0.000 & 0.56 & 0.57 & 0.01 & 0.000 \({}^{\star}\) \\ Profile & Medium & 0.41 & 0.34 & 0.07 & 0.000 \({}^{\star}\) & 0.51 & 0.39 & 0.12 & 0.000 & 0.52 & 0.57 & 0.05 & 0.000 \({}^{\star}\) \\ Calm & 0.43 & 0.34 & 0.09 & 0.000 \({}^{\star}\) & 0.52 & 0.43 & 0.09 & 0.000 & 0.45 & 0.51 & 0.06 & 0.000 \({}^{\star}\) \\ \hline \hline \end{tabular} Note: \({}^{\star}\) Significant at 0.01 level, \({}^{\star\star}\) Significant at 0.05 level.  
\end{table} 
Table 10: Observed and predicted lane-changing behaviour of drivers in the examined tunnel scenarios. Figure 10: Predicted Lane-Changing Behaviour (LCB) probabilities for continuous parameters: (**a**) speed, (**b**) age and (**c**) driving simulator experience.
# DYNASTI--Dynamic Multiple RPL Instances for Multiple IoT Applications in Smart City
## 5 Performance Evaluation and Results
### Results
#### 5.5.2 Varying the Number of Applications
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Application** & **Instance** & \begin{tabular}{c} **Traffic** \\ **Pattern** \\ \end{tabular} & \begin{tabular}{c} **Traffic** \\ **Type** \\ \end{tabular} & \
\begin{tabular}{c} **Messages** \\ **Interval** \\ \end{tabular} &
\begin{tabular}{c} **Scheduling** \\ \end{tabular} \\ \hline
1 & 10 & Regular & Normal & 7 & 7,8 \\
2 & 20 & Sporadic & Critical & 3 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Scheduling of DYNASTI with two applications. Figure 10(a) compares the delay of the DYNASTI solution with the _Static Instances_ approach. Comparing the approaches in a scenario with three applications, there is a large difference between _Static Instances_ and DYNASTI, approximately 76 ms. _Static Instances_ increases the delay for critical application as the number of applications increases. Comparing the approaches in scenario with two applications, _Static Instances_ achieves slightly shorter delay than DYNASTI, and the difference between them was approximately of 7 ms. This demonstrates that the DYNASTI solution was able to achieve the shortest delay with the highest number of applications because DYNASTI can achieve an optimized messages control of the multiple instances. Comparing the scenarios of two and three applications in _Static Instances_, it is possible to observe that there was a significant increase in the delay of approximately 76 ms as the number of applications increases, whereas DYNASTI reaches a very similar delay for both scenarios (two and three applications). This can be explained by the fact that DYNASTI interrupts control and application messages, while _Static Instances_ does not. Figure 10(b) shows the energy consumption for the DYNASTI and _Static Instances_ approaches. In the scenario with two applications, a significant level of energy savings was achieved when using DYNASTI (31.01% more than _Static Instances_). In the scenario with three applications, this difference was bigger, with energy savings of 44.10% when using DYNASTI. The results demonstrated that the DYNASTI solution enables dealing with more applications through the flexible management of multiple instances that allows for reducing the number of control  
Figure 11. Average end-to-end delay and average energy consumption. messages, lost packets, and delay. In addition, DYNASTI is able to reduce power consumption, allowing longer device lifetimes.