esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment Evaluations
### Romo_SODsda Effectiveness
#### 4.1.3 Experiment Results  
To evaluate the effectiveness of RMO_SODsDA, we considered preprocessing results _UA_, _CR_, and the \(k\)-\(n\) RSSOD constraints as inputs, and repeatedly implemented the experiments 20 times. The experiment results are shown in Tables 7-10, where \(E_{k\text{-}n\text{ RSSOD}}\) represents the set of RSSOD constraints, and \(C_{t\text{-}m\text{ SMER}}\) represents the set of SMER constraints.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **|U|** & **|P|** & **|CR|** & **Execution Time (s)** \\ \hline Americas-large & 3485 & 10127 & 423 & 78.78 \\ Americas-small & 3477 & 1587 & 213 & 6.31 \\ Apj & 2044 & 1164 & 456 & 5.60 \\ Customer & 10961 & 284 & 276 & 4.66 \\ Domino & 79 & 231 & 20 & 0.01 \\ Emea & 35 & 3046 & 34 & 0.02 \\ Firewall1 & 365 & 709 & 69 & 0.11 \\ Firewall2 & 325 & 590 & 10 & 0.15 \\ Healthcare & 46 & 46 & 15 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Datasets.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**|\(|\)\(E_{22\text{ RSSOD}}\)\(|\)\(=\) 30**} & \multicolumn{3}{c}{**|\(|\)\(E_{2\text{-}2\text{ RSSOD}}\)\(|\)\(=\) 50**} \\ \cline{2-7}  & \(\mathbf{C_{2\text{-}2\text{ SMER}}}\) & \(\mathbf{C_{t\text{-}t\text{ SMER}}}\) & \(\mathbf{C_{t\text{-}m\text{ SMER}}}\) & \(\mathbf{C_{2\text{-}2\text{ SMER}}}\) & \(\mathbf{C_{t\text{-}t\text{ SMER}}}\) & \(\mathbf{C_{t\text{-}m\text{ SMER}}}\) \\ \hline Americas-large & 984(95\%) & 984(95\%) & 982(96\%) & 2461(92\%) & 2568(92\%) & 2574(93\%) \\ Americas-small & 441(80\%) & 439(80\%) & 436(80\%) & 1279(84\%) & 1272(84\%) & 1285(84\%) \\ Apj & 40(80\%) & 39(80\%) & 37(80\%) & 124(84\%) & 124(84\%) & 124(84\%) \\ Customer & 19(95\%) & 15(80\%) & 11(80\%) & 48(96\%) & 36(78\%) & 33(76\%) \\ Emea & 57(80\%) & 54(80\%) & 51(80\%) & 142(84\%) & 138(84\%) & 135(83\%) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of SMER constructed from 2–2 RSSOD.  
Firstly, by analyzing the results in Tables 7-10, it was observed that there was a certain number of RSSOD constraints that could not be enforced when we constructed 2-2 SMER, \(t\)-\(t\) SMER, or \(t\)-\(m\) SMER constraints. The less that efficient RSSOD constraints were enforced (in other words, the less the percentage in the bracket), the fewer the constructing SMER constraints.  
Second, Tables 7-10 show that the process of constructing 2-2 SMER constraints is more difficult than the other two processes, because the 2-2 SMER was more restricted than the others. We take the Customer dataset as an example. In Table 9, percentages of successfully constructing 2-2 SMER constraints are 17% with 30 RSSOD and 14  
constructing 2-2 SMER constraints was 139, 366, 505, 1203, 2022, 5762, 6164, and 14,667 s, respectively. Similar to analyses for the number of constructing constraints, detailed discussions of execution time were omitted in this paper owing to limited space.
# A Novel Connectivity-Based LEACH-MEEC Routing Protocol for Mobile Wireless Sensor Network
## Proposed Framework
### Energy Model
\[E_{mov}(N_{i})=q_{Mob}d_{Mob}(N_{i}) \tag{10}\]  
where \(d_{Mob}(N_{i})\) shows the mobility of node \(N_{i}\) that is calculated using Euclidean distance after \(T_{m}\) seconds. \(q_{Mob}\) shows the coefficient of the energy consumption rate. Hence, Equation (6) is derived by merging Equations (7) and (10). \[E_{Wt\_rd}(N_{i})=E_{Mob}(N_{i})+E_{Tran}(N_{i})+E_{Recv}(N_{i}), \tag{11}\]  
\[E_{Rem}(N_{i})=E_{Ini}(N_{i})-E_{Wt\_rd}(N_{i})(N_{i}), \tag{12}\]  
where \(E_{Wt\_rd}\) is the energy weight, which is assumed as the energy cost of every node after every single round. Hence, \(E_{Rem}(N_{i})\) is calculated based on Equation (12). \(E_{Ini}(N_{i})\) is the initial energy of Node(\(N_{i}\)) and \(E_{Wt\_rd}(i)\) is the energy weight of node \(N_{i}\).
## 2 Computational Model
### Constitutive Model and Parameters
#### 2.2.1 Soil-Water Characteristic Curve  
The soil-water characteristic curve reflects the relationship between water saturation and matric suction (Figure 1b). A large number of scholars choose the van Genuchten numerical model to describe the relationship between water saturation and matric suction because many researchers have proved that this model is applicable to many soil types. In this research, the matric suction in the soil is defined as \(p_{c}\equiv p_{g}-p_{w}\) and the van Genuchten model can be written as follows [18]:  
\[p_{c}=-p_{0}[(\frac{S_{r}-S_{rw}}{1-S_{rw}})^{-\frac{1}{m}}-1]^{1-m} \tag{3}\]  
where \(p_{c}\) is the matric suction, \(p_{o}\) is the air-entry pressure, \(S_{rw}\) is the residual degree of water saturation, and \(m\) is a model parameter related to the material.
## 1 Introduction  
The use of thermal energy storage (TES) systems in applications in which either energy supply is intermittent or energy demand has large fluctuations is a key aspect of any energy system, able to reduce energy consumption or costs, besides enhancing the flexibility of the system and reducing the dependence on the energy source availability [1]. The importance of the implementation of TES systems to support the integration of renewable energies in buildings was made evident by different initiatives of the European Commission aimed at achieving a considerable reduction of energy consumption in buildings by increasing self-consumption [2].  
TES technologies have been developed for applications that cover a wide temperature range, from low-temperature applications such as ice storage or the conservation and transport of temperature-sensitive materials [3; 4], going through medium temperature applications such as space heating and cooling in buildings [5; 6], and domestic hot water (DHW) generation [7; 8; 9], up to high-temperature applications such as concentrated solar power (CSP) plants [10; 11].  
Among the different TES technologies available or under investigation, latent heat storage using solid-liquid phase change materials (PCM) has some advantages over sensible heat storage because it has higher heat storage capacity and a more stable thermal behavior during both charging and discharging processes. There are many studies available in the literature regarding the main aspects of the use of PCM as TES material in different applications [12; 13; 14].  
Since the PCM selection procedure is an important step to achieve proper and reliable operation of the TES system, further efforts are needed to ensure that this step is done correctly. For this purpose, most studies rely on the main PCM thermophysical properties such as melting temperature, enthalpy, density, and conductivity. However, other properties such as health hazards, thermal cycling, and stability should also be taken into account to complement the most common selection criteria [15; 16]. The PCM selection methodology proposed by [PERSON] et al. [15] and [PERSON] et al. [16] requires a comprehensive study of different properties and aspects regarding the potential PCM candidates, which should include a detailed experimental characterization. Nevertheless, when the number of PCM candidates is quite large, a preliminary selection based on information provided by the PCM manufacturers or available in the literature is deemed necessary before conducting a more thorough experimental analysis of the most suitable PCM candidates.  
The objective of this paper is to present the methodology and the results obtained for the initial step of an overall evaluation and materials selection process of the most suitable PCM to be used in two innovative systems that implement a compact hybrid electrical and thermal energy storage, intended to provide heating, cooling, and DHW in residential buildings in cold and warm weather conditions.
# First Request First Service Entanglement Routing Scheme for Quantum Networks
## 2 Preliminaries  
In order to evaluate the link performance between arbitrary paired communication users ([PERSON] and [PERSON]), a brief introduction about the bi-photon entanglement distribution knowledge is given in this section. Here, we assume that the polarized continuous-wave pumped entangled photon source is performed for [PERSON] and [PERSON]. The overall efficiency between the source and [PERSON] ([PERSON]) is \(\eta_{A}\) (\(\eta_{B}\)). The brightness of the photon source is \(B\). Thus, the measured single photon counts for [PERSON] and [PERSON] can be calculated by [53]  
\[S_{\rm A}^{\rm m}=B\eta_{\rm A}+\rm DC_{A}\ \rm and\ S_{\rm B}^{\rm m}=B\eta_{ \rm B}+\rm DC_{B}, \tag{1}\]  
where \(\rm DC_{A}\) and \(\rm DC_{B}\) are the dark counts.  
The total measured coincidences count between [PERSON] and [PERSON] \(\rm CC^{\rm m}\) is defined as  
\[\rm CC^{\rm m}=\eta^{t_{\rm CC}}\rm CC^{t}+\rm CC^{\rm acc}, \tag{2}\]  
where \(t_{\rm CC}\) is the coincidence window, \(\eta^{t_{\rm CC}}\) is the coincidence-window dependent detection efficiency. \(\rm CC^{t}\) is the true coincident counts, which represents the rate of the events that two photons of a pair must be detected to observe their polarization correlation. \(\rm CC^{\rm acc}\) is the accidental coincidence count.  
The true coincidence count rate is calculated as  
\[\rm CC^{t}=B\eta_{A}\eta_{B}. \tag{3}\]  
The count rate of accidental coincidences in window \(t_{\rm cc}\) is given by  
\[\rm CC^{\rm acc}=S_{A}^{\rm m}S_{B}^{\rm m}t_{\rm CC}. \tag{4}\]  
The count rate of the erroneous coincidences \(\rm CC^{err}\) is calculated as  
\[\rm CC^{err}=\eta^{t_{\rm CC}}\rm CC^{t}e^{pol}+\frac{1}{2}\rm CC^{\rm acc}, \tag{5}\]  
where \(e^{pol}\) is the system polarization error probability.  
Therefore, the quantum bit error rate (QBER \(ER\)) can be expressed by  
\[ER=\frac{\rm CC^{\rm err}}{\rm CC^{\rm m}}=\frac{\eta^{t_{\rm CC}}\rm CC^{t}e ^{pol}+\frac{1}{2}\rm CC^{\rm acc}}{\eta^{t_{\rm CC}}\rm CC^{t}+\rm CC^{\rm acc }}. \tag{6}\]In this paper, we assume that [PERSON] and [PERSON] perform the BBM92 protocol to generate the information-theoretical-secure keys. Since the noise parameters are independent of measurement settings, we assume that the phase error equals to \(ER\). Thus, the secure key rate (\(R^{\mathrm{s}}\)) can be expressed as [54]  
\[R^{\mathrm{s}}=\frac{1}{2}\mathrm{CC}^{\mathrm{m}}[1-f(ER)\mathrm{H}_{2}(ER)- \mathrm{H}_{2}(ER)], \tag{7}\]  
where \(f(ER)\) is the information reconciliation efficiency and \(\mathrm{H}_{2}(x)\) is the binary Shannon entropy, which is defined as:  
\[\mathrm{H}_{2}(x)=-x\log_{2}(x)-(1-x)\log_{2}(1-x). \tag{8}\]
## 3 Theoretical Models and Results
### Wear Model  
In compressive crushing, rock particles of various sizes are squeezed and crushed against the liner of the crushing chamber. The geometry of the crushing chamber is changed due to the wear of crusher liner, which is related with the types of rock material and the pressure distribution on the liner. In order to indicate the worn crusher liner influence on the product size distribution and shape change, it is necessary to know the previous research on wear model.  
[PERSON] and [PERSON] [15] developed a wear model to predict the worn geometry of cone crushers. In the model, it is proposed that wear is proportional to the maximum average pressure  
Figure 4: Multi-material crushing pressure model.  
which occurs during the crushing event. The effect of shear forces along the crushing surfaces was implemented in the model. The wear is computed according to Equation (6).  
\[\Delta\omega=\frac{F_{n}+QF_{s}}{W}, \tag{6}\]  
\(F_{n}\) represents normal pressure at the surface, \(F_{s}\) represents shear-stress at the surface, here \(Q\) is a model parameter that scales the effect of the shear force when there is no slip, \(W\) is the wear resistance coefficient depending on the type of liner material and rock, and \(\Delta\omega\) is expressed in mm, pressure in MPa.  
However, Equation (6) does not contain a time variable. Actually the liner wear is affected by some other factors, such as working time and the pressure of crushing zones. The liner wear not only concentrates near the point of the CSS but also distributes the surface of the liner.  
In order to solve this problem, a new model of liner wear is proposed, as shown in Equation (7):  
\[\Delta\omega_{i}(t)=\frac{F_{n,i}+QF_{s,i}}{F_{n,p}+QF_{s,p}}*(\mu*\int\limits _{0}^{t}m(t)dt), \tag{7}\]  
where \(\Delta\omega_{i}(t)\) represents the wear loss of crushing zone \(i\) in \(t\) hours and its unit is mm, \(t\) represents the working time and its unit is hour, \(F_{n,i}\) represents the normal pressure of crushing zone \(i\), \(F_{s,i}\) represents the shear-stress of crushing zone \(i\), \(F_{n,p}\) represents the normal pressure near the point of the CSS, \(F_{s,p}\) represents the shear-stress near the point of the CSS, \(\mu\) represents the wear rate depending on the amount of crushed material per hour, \(m(t)\) represents the crushed product and its unit is kg, pressure in MPa.  
The Equation (7) is obtained combining the empirical model for predicting liner wear with the time parameter. The new model of liner wear contains the productivity variable which is a function of time variable. Therefore, this model can calculate liner wear with the crusher working, which provides a theoretical model for a significant improvement of the prediction of cone crusher performance considering liner wear.
# Research on Equalization Strategy of Lithium Battery Pack Based on Multi-Layer Circuit
## 4 Multi-Layer Balancing Strategy
### Equilibrium Strategy Analysis  
Under the above equalization circuit, each battery in battery module \(\mathrm{B}_{\mathrm{m,in,1}}\) has the same equalization current. Battery module \(\mathrm{B}_{\mathrm{m,in,1}}\) can be regarded as a single battery, and only single battery equalization can be considered.  
\[\mathrm{SOC}_{\mathrm{c,m,i}_{\mathrm{m,1}}}(\mathrm{k}+1)=\mathrm{SOC}_{ \mathrm{c,m,i}_{\mathrm{m,1}}}(\mathrm{k})+\mathrm{aI}_{\mathrm{ex}}(\mathrm{ k})+\mathrm{d}\mathrm{I}_{\mathrm{1,i_{1}}}(\mathrm{k})+\mathrm{d}\mathrm{I}_{ \mathrm{2,i_{2}}}(\mathrm{k})+\ldots\ldots\mathrm{.}+\mathrm{d}\mathrm{I} _{\mathrm{m,in}}(\mathrm{k})\]  
\[\mathrm{SOC}_{\mathrm{c,m,i}_{\mathrm{m,r}}}(\mathrm{k}+1)=\mathrm{SOC}_{ \mathrm{c,m,i}_{\mathrm{m,r}}}(\mathrm{k})+\mathrm{aI}_{\mathrm{ex}}(\mathrm{ k})+\mathrm{d}\mathrm{I}_{\mathrm{1,i_{1}}}(\mathrm{k})+\mathrm{d}\mathrm{k} _{\mathrm{2,i_{2}}}(\mathrm{k})+\ldots\ldots\mathrm{.}+\mathrm{d}\mathrm{I} _{\mathrm{m,in}}(\mathrm{k}) \tag{14}\]where \(\rm SOC_{c,m,j_{m},l}(k)\) is the average SOC of the battery module \(\rm B_{m,j_{m},l}\) on the left side of an equalizer on layer m; and \(\rm I_{m,j_{m}}(k)\) represents the equalization current of layer m.  
According to Equations (13) and (14), the following optimization model can be obtained:  
\[\rm minimize_{I_{m,j_{m}}(k)}\;J(SOC_{c,m,j_{m}}(k+1),I_{m,j_{m}}(k))\]  
subject to  
\[\rm SOC_{c,m,j_{m}}(k+1)=SOC_{c,m,j_{m}}(k)+al_{ex}(k)+dkI_{l,j_{1}}(k)+dkI_{2 j_{2}}(k)+.......+dkI_{m,j_{m}}(k)\]  
\[SOC_{c,min}\leq SOC_{c,m,j_{m}}(k)\leq SOC_{i,max}\]  
\[\rm I_{-max}\leq I_{c,m,j_{m},c}(k)\leq I_{+max}\]  
\[\rm I_{-max}\leq I_{c,m,j_{m},d}(k)\leq I_{+max}\]  
\[0\leq I_{m,j_{m}}\leq I_{m,max}\]  
\[\left\{\begin{array}{l}I_{c,m,j_{m},c}(k)=I_{ex}+kI_{l,j_{1}}+kI_{2j_{2}}(k) +\ldots+kI_{m,j_{m}}(k)\;if(I_{m,j_{m}}\geq 0)\\ I_{c,m,j_{m},d}(k)=I_{ex}+kI_{l,j_{1}}+kI_{2j_{2}}(k)+\ldots+kI_{m,j_{m}}(k)\;if (I_{m,j_{m}}\leq 0)\end{array}\right.\]  
where \(\rm I_{c,m,j_{m},c}(k)\) represents the maximum charging current flowing through battery modules \(\rm B_{m,j_{m},l}\) and \(\rm B_{m,j_{m},r}\); \(\rm I_{c,m,j_{m},d}(k)\) represents the maximum discharge current flowing through battery modules \(\rm B_{m,j_{m},l}\) and \(\rm B_{m,j_{m},r}\); and \(\rm I_{m,j_{m}}\) represents the equalization current of layer m.  
Using the PS0 to solve the above model, the optimal equilibrium current of the m-layer can be calculated.  
It can be seen from the above analysis that \(\rm n^{2n}\) dimensional matrix must be solved before layering; the \(m\)-th layer should be solved for \(\rm m\) times after layering, and the entire module should be solved for \(\rm 2^{n}-1\) times. After stratification, the calculating difficulty is lessened, making it more practical to use.
###### Abstract  
In the paper, we statistically analysed data on the average hourly wind speed obtained from the meteorological station Poprad (located at the Poprad-Tatry airport, the Presov region, Northern Slovakia) for the period 2005-2021. High altitude and rough mountainous terrain influence the weather conditions considerably and are a source of occasional weather risks. Finding an appropriate wind speed distribution for modelling the wind speed data is therefore important to determine the wind profile at this particular location. In addition to the commonly used two- and three-parameter Weibull distribution, a more flexible exponentiated Weibull (EW) distribution was applied to model the wind speed. Based on the results of the goodness-of-fit criteria (the Kolmogorov-Smirnov test, the Anderson-Darling test, [PERSON]'s and Bayesian information criteria, the root mean square error, and the coefficient of determination), the EW distribution obtained a significantly better fit to seasonal and monthly wind speed data, especially around the peaks of the data. The EW distribution also proved to be a good model for data with high positive skewness. Therefore, we can recommend the EW distribution as a flexible distribution for modelling a dataset with extremely strong winds or outliers in the direction of the right tail. Alongside the wind speed analysis, we also provided the wind direction analysis, finding out that the most prevailing direction was west (W)--with an occurrence rate of 34.99%, and a mean wind speed of 3.91 m/s, whereas the northern (N) direction featured the lowest occurrence rate of only 4.45% and the mean wind speed of 1.99 m/s.  
wind speed; two-parameter Weibull distribution; three-parameter Weibull distribution; exponentiated Weibull distribution; goodness-of-fit criteria +
Footnote †: journal: Applied Sciences
# Adaptive Trajectory Tracking Control for
## 1 Introduction
Motivated by the above-mentioned observations, an adaptive trajectory tracking control strategy for engineering implementation, which is performed by using first-order sliding surface, second-order sliding surface and neural network minimum learning parameter method, is designed for an underactuated USV subject to unknown dynamics and time-varing external disturbances. The contributions of this paper can be summarized as follows:  
(1) A novel control approach for an underactuated USV to achieve trajectory tracking by stabilizing surge velocity and sway velocity through a first-order sliding surface and a second-order sliding surface, respectively, is proposed. (2) Compared with the multi-layer neural network, neural network minimum learning parameter method has a small amount of computation. It is used to compensate unknown dynamics and time-varing disturbances, which can not only reduce the calculation burden of the controller, but also reduce the chattering phenomenon of the sliding mode control algorithm. The rest of this paper is organized as follows. In Section 2, problem formulation and preliminaries are introduced. Trajectory tracking control laws (first-order sliding surface for surge control law and second-order sliding surface for yaw control law) are introduced in Section 3. The stability proof is given in Section 4. In Section 5, numerical simulations are given to prove the correctness of the proposed control scheme. Finally, conclusions of this paper are summarized in Section 6.
## 1 Introduction
### Motivation  
Although many blockchain-based e-voting solutions exist, the specific challenges faced in Palestine make it difficult to apply these systems directly. One of the main issues is that they are not flexible enough to accommodate the unique political, social, and technological environment in Palestine, particularly the difficulties of conducting elections in divided regions, the West Bank and Gaza. A more tailored system, such as VoteChain, is necessary to address the country's distinct electoral processes, legal structures, and security needs.  
Furthermore, existing systems often fall short in terms of protecting privacy and anonymity, which are crucial in places where political retaliation is a real threat. Some systems do not fully secure voter identities or prevent unauthorized tracking of votes, putting voters' privacy and safety at risk. VoteChain, on the other hand, incorporates advanced cryptographic techniques, such as homomorphic encryption, and plans to implement privacy-preserving technologies, such as zero-knowledge proofs (ZKP), to ensure that voter identities remain completely protected while still allowing for transparent and verifiable elections.  
Another challenge is scalability, especially in large elections where infrastructure is limited. Many systems struggle with high computational demands and energy use, leading to delays and inefficiencies. VoteChain addresses this by using private blockchains and off-chain solutions to improve efficiency, reduce costs, and manage large numbers of voters, making it more appropriate for national elections in Palestine.  
Trust and decentralization are both equally important in Palestine, where central control can easily lead to manipulation. Many systems depend on centralized authorities for key voting processes, which poses a risk. VoteChain, however, uses a decentralized governance model, distributing control across multiple nodes. This enhances transparency and limits the chances of corruption or manipulation.  
Additionally, VoteChain is designed to comply with Palestinian legal and regulatory requirements, unlike many other systems that do not easily integrate with the Central Election Commission (CEC) or local voter registration systems. This compliance is crucial for seamless integration into the existing election framework. Finally, infrastructure limitations in Palestine, particularly in rural areas with unreliable internet, make many blockchain-based systems impractical. VoteChain is built to function in low-tech environments, offering offline voting features and low-resource computing options, ensuring that even voters in underserved areas can participate.
## 5 Results and Analysis
### Sell-P  
SURAA outperforms the SELL-P scheme, overall, by fairly large margins (average speedup: 40\(\times\), max: 562\(\times\)). The high speedup of SURAA against SELLP is particularly attributed to the poor performance of SELLP for two matrices, _ASIC_680k_ (speedup, 562\(\times\)) and _FullChip_ (speedup, 378\(\times\)); see Figure 7. SELL-P mostly has produced very low throughput for high _npr variance_ matrices such as _ASIC_680k_, _FullChip_, _rail4284_, and others (see Figures 6 and 7). This behavior will be investigated further in Section 5.4. SURAA achieves a higher throughput against SELL-P for all the 26 matrices except four matrices, _thermal2_ (0.71\(\times\)), _amazon0312_ (0.72\(\times\)), _Andrews_ (0.79\(\times\)), and _Thermonech_TK_ (0.70\(\times\)). The first two of these matrices have been mentioned already while discussing the HYB performance. The reason for this low performance, as before, is that SURAA did not invoke dynamic parallel kernel for these four matrices because these matrices have low average nonzero per row (see Table 3 and Figure 6). We have mentioned this earlier that SURAA does not invoke dynamic kernel for matrices which have average _npr_ value less than 32. A possible modification in the future to improve the SURAA performance for such matrices is either to optimize our CSR scalar kernel or replace just the SURAA CSR scalar kernel with the cuSPARSE CSR kernel, which is very likely to make SURAA faster than all the techniques for all the matrices. Another possibility is to tune the parameter that invokes the dynamic kernel to an optimum value or to embed further intelligence into the kernel invocation process.