esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Incremental Computation: Approach
The second statement ensures that every request for the introduction of a time step is immediately satisfied. The third statement ensures that all conditions of the form \(enabled(T)\) are satisfied whenever \(T\) represents a time step. We denote the single-shot version of a program \(\Pi\) by \(\sigma(\Pi)\). The soundness and completeness of Algorithm _Incremental_ is thus given by the following theorem. Theorem 4.1: _Given a program \(\Pi\), set \(p\) of pre-determined time steps, and set \(O\) of observations, the trajectory encoded by \(Incremental(p,O)\) coincides with the trajectory encoded by the answer set of \(clingo(\sigma(\Pi)\cup p\cup O)\)._  
Proof: Recall that the action descriptions considered are deterministic and strongly consistent, which means that \(\text{clingo}(\sigma(\Pi)\cup p\cup O)\) is guaranteed to return a single answer set. The claim can be proven by induction. For the base case, one can note that, if the while loop of _Incremental_ is never executed, then the computation carried out by _Incremental_ is essentially equivalent to that carried out by \(\text{clingo}(\sigma(\Pi)\cup p\cup O)\) and thus the trajectories coincide. Along the same lines, if the set produced by \(\text{clingo}(\sigma(\Pi)\cup p\cup O)\) does not contain any literal formed by relation _newtime_, then the while loop of _Incremental_ is never executed and, once again, the trajectories encoded by the results of the two computations coincide. For the inductive case, let us assume that the trajectories produced by the two algorithms share a (non-empty) prefix \(\pi\) and let us show that the next state in both trajectories (a) is associated with the same time step in both trajectories and (b) is the same in both cases. Let us begin with (a). Let \(t_{i}\) and \(t_{c}\) denote the time steps associated with the state that follows \(\pi\), respectively, in the trajectory produced by _Incremental_ and in that produced by clingo. Note that there must exist an iteration of the while loop of _Incremental_ in which \(m=t_{i}\) at step 7. By contradiction, suppose that \(t_{c}<t_{i}\). If that were the case, it would mean that the bodies of \(\Pi\) were satisfied at one or more time steps from \(\pi\), and caused clingo to conclude \(newtime(t,t_{c})\) for some \(t\). Due to the fact that \(\pi\) is the shared prefix, then the bodies of same rules would have been satisfied during one of _Incremental_'s _solve_ calls, yielding \(newtime(t,t_{c})\) as well. However, if that were the case, \(t_{i}\) would coincide with \(t_{c}\), which causes contradiction. Reasoning in a similar way, we can conclude that it is impossible that \(t_{c}>t_{i}\). Hence, \(t_{c}=t_{i}\). Furthermore, because the two time steps coincide, it is easy to see that the corresponding states must also be the same.
## 2 Materials and Methods
### Liposome Membranes  
Monolayer liposomes were produced by the sonication of DPPC and EYL in distilled water using an ultrasonic disintegrator (UD-20; Techpan, Warszawa, Poland). The sonication time for a single 2 mL sample was 6 min. The process consisted of 6 cycles involving 30 s of sonication and 30 s of cooling. The concentration of lecithin in the sample was 40 \(\rm{\upmu M}\). After sonication, the aqueous dispersion of the liposomes was divided into two sections containing TEMPO and 16 DOXYL spin probes, respectively. The concentration of the probes relative to lecithin (DPPC or EYL) was 500 ppm. The samples containing liposomes and probes were shaken for 10 min and allowed to stabilize for 15 min. The liposomes were supplemented with the appropriate vanadium complex, shaken again for 5 min, and measured in an EPR spectrometer. EPR measurements were performed at constant temperature (22 \({}^{\circ}\)C) using an X-band spectrometer (SE/X28 Wroclaw University of Technology). The spectra were recorded with the following instrument settings: time constant--0.3 s, modulation amplitude--0.8 \(\times\) 10\({}^{-1}\) mT, scan time--128 s, and sweep range--8 mT. The process was repeated several times, each time with a higher concentration of the additive. The additive concentrations were measured relative to the lecithin in the sample. The EPR spectra (Figure 4a,b) provide information on the fluidity of the membranes in different regions. The TEMPO probe dissolves in both aqueous and lipid environments, indirectly providing information on the fluidity of the surface layer acting as a barrier to the penetration of the lipid environment. The 16 DOXYL probe located in the centre of the lipid bilayer provides information on the fluidity of the inner part of the membrane (Figure 5). The EPR spectrum of the TEMPO probe was used to calculate the spectroscopic partition parameter (F) showing how the probe is split between the membrane and its environment. F is defined as the ratio of the amplitudes of the high-field line in the EPR spectrum of the probe dissolved in water (P) to the amplitude of the low-field line (H) of the probe dissolved in the lipid environment (Figure 4a). The F value is related to the fluidity of the membrane surface layer [38; 43]. The spectrum of the 16-DOXYL probe was used to determine the \(\tau\) parameter. Its value depends on the degree of fluidity of the membrane in the middle layer, and it increases with the rise of stiffness (ordering) of the probe environment [38; 44; 45]. For an isotropic environment, \(\tau\) is the probe rotational correlation time (Figure 4b). The relative measurement errors were as follows: 2.5% for the F parameter and 3% for the \(\tau\) parameter.  
Figure 4: ESR spectra of spin probes placed in the liposome membrane. (**a**) TEMPO probe and spectroscopic partition parameter (\(F\)); (**b**) 16-DOXYLesteracid methyl ester probe and rotational correlation time parameter (\(\tau\)); \(\tau\) = 5.95 \(\cdot\)\(\Delta\)H(\(\rm{I_{0}}/1+1\))\({}^{1/2}\) + (\(\rm{I_{0}}/1\)–1)\({}^{1/2}\) – 2)10\({}^{-10}\) [s].
## 4 Evolutionary Algorithms for Task Scheduling Problem
### Modified Particle Swarm Optimization
#### 4.1.1 Particle: Position and Velocity  
The representation step plays an important role in the successful design of PSO algorithm, which aims at finding an appropriate mapping between problem solution and PSO particle. In our method, binary representation is used, each particle is expressed as a \(m\times n\) matrix, called position matrix, in which \(m\) is the number of available nodes including Fog nodes and Cloud nodes and \(n\) is the number of tasks. \(X_{k}\), which is the position matrix of \(t\)th particle in the swarm, has two main characteristics:  
\(i.\)\(X_{k}(i,j)\in\left\{0,1\right\}\left(\forall i,j\right),i\in\left\{1,2, \cdots,m\right\},j\in\left\{1,2,\cdots,n\right\}\)  
\(ii.\)\(\sum_{i=1}^{m}X_{k}(i,j)=1(\forall j),j\in\left\{1,2,\cdots,n\right\}\)  
In position matrix, each element is either 0 or 1. If \(X_{k}(i,j)=1\), then task \(T_{j}\) is executed in node \(N_{i}\). The second condition ensures that there is exactly one element with value 1 in each column, which means each task is performed on only one node.  
For example, a set of 10 tasks is executed in a Cloud-Fog system of the nodes; one solution of task scheduling can be:  
\(NodeTasks=\left\{T_{1}^{3},T_{2}^{1},T_{3}^{3},T_{4}^{2},T_{5}^{1},T_{6}^{2},T_{7}^{2},T_{8}^{3},T_{9}^{1},T_{10}^{3}\right\}\)  
An example of the position matrix of particle corresponding to the above solution is shown in Figure 2.  
Each particle moves to a new position based on its velocity. The velocity matrix dimensions are exactly the same as the position matrix, i.e., \(m\times n\). Its elements are real numbers in the range \([-V_{max},V_{max}]\). In other words, the \(k\)th particle has velocity \(V_{k}\) satisfying the condition:
## 5 Smart Community
### Smart Environment Management
#### 5.1.2 Noise Monitoring  
Noise pollution is a major concern for the public, as it disrupts daily routines and causes discomfort such as headaches, leading to a decline in quality of life [66; 68]. As a result, smart cities have begun implementing noise monitoring IoT systems to continuously monitor environmental acoustic parameters at various locations [66]. [PERSON] et al. [60] introduced an IoT system with delay tolerance units to monitor noise levels in the city center of Dublin. Based on the noise data collected in Dublin, [PERSON] et al. [68] proposed a big data framework, MapReduce algorithm, to analyze noise data. [PERSON] et al. [62] and [PERSON] et al. [61] both focus on New York's IoT noise monitoring system, Sounds Of New York City (SONYC). [PERSON] et al. [62] introduced the basic information of SONYC. [PERSON] et al. [61] updated the SONYC by using a real-time Convolutional Neural Network(CNN)-based embedding model, disconnecting from power or network infrastructure support and dynamically choosing good network frequencies.  
To monitor urban acoustic environments, people may collect audio data with a microphone [60; 61; 62; 68], remote sonometer (e.g., Model Cesva TA120) [66; 69], sound pressure sensors (e.g., MP34 DT01) [44]. [PERSON] et al. [61] used TDK InvenSense ICS-43434 as a microphone in the Sounds of New York City IoT system. The processor was an Intel XScale 255 [60], Cypress PSoC4 BLE [64].  
[PERSON] et al. [66] used the k-means algorithm to cluster behavior patterns of sound pressure levels geographically and temporally, which is effective for analyzing noise data captured by acoustic sensors in Barcelona. Another research work by [PERSON] and [PERSON] [69] proved that artificial neural networks can predict long-term noise patterns at a location based on short-term measurements. They also emphasized that increasing the number of hidden layers and collecting an hour of data every hour during the 14:00-22:00 time interval can improve the performance of artificial neural networks.  
Since low-cost acoustic pressure sensors are not accurate and measurements are highly variable, [PERSON] et al. [70] introduced RaveGuard, an unmanned noise monitoring platform that utilizes artificial intelligence strategies to improve the accuracy of low-cost devices. They first deployed RaveGuard along with professional sound intensity meters in the center of Bologna, Italy, for more than two months to collect a large amount of accurate noise pollution data. They then applied supervised learning algorithms (with Random Forest achieving the best performance) to train these data into an IoT platform called InspectNoise for use with low-cost microphones to improve the accuracy of their noise detection.
## 6 Platform Evaluation
### Third Cycle--Mobile Application With Cloud Backend  
During the third Build-Measure-Learn cycle, this research developed a more elaborate version of the cloud-based mobile platform and conducted an evaluation of it, consulting an expert panel. This panel was composed of seven professionals in the architecture, engineering and construction sectors and included experts in construction management, sustainability, and architecture.  
This evaluation allowed the panel to interact mainly with the mobile application, simulating a situation in which a project team could use both the microblogging and LL platforms on-site. They tested both the online and the offline mode. Finally, they tested the consistency of the information entered through the mobile application with what was shown to them in the Web application.  
Table 5 shows the scores resulting from the first and second parts of the questionnaire. Regarding the open questions, all respondents indicated that the platform has the potential to improve the LL process in construction companies because it provides systematization of a process that has not yet been formalized in SMEs. They also identified some threats to the correct implementation and adoption of this cloud-based mobile platform. They were mainly concerned about the resistance to change present in the construction companies' organizational culture. Despite this concern, they were optimistic and suggested possible ways to address such issues, for instance, to financially quantify the benefits of having each lesson (or the extra expenses of not having it earlier), to provide incentives to those who use the platform regularly, and/or to allow this platform to communicate with other systems that have already been implemented within the company (e.g., Office 360 or BIMx).  
They also saw great potential in the proposed data-processing features because, in their opinion, they turned the platform into an active and useful agent within the project team rather than a burden imposed by headquarters. They even suggested improvements to the machine learning features. For instance, one of the experts who specializes in management suggested automatically generating \"quick start bundles\" of lessons for projects that are just starting, using project profiles as well as user profiles in the recommendation system.
## 2 Material and Methods  
This review was carried out following the guidelines of the PRISMA Statement (Preferred Reporting Items for Systematic Review and Meta-Analysis) [36, 37]. A systematic electronic search was performed in MEDLINE/Pubmed and Scopus databases using the keywords \"(eugenol) AND (bond strength)\" (no restriction was applied for language and publication dates). Eligible studies were those that assessed immediate bond strength of adhesive systems to coronal dentin after contact with eugenol-containing materials by testing specimens with a reduced size (micro-shear bond strength (\(\mu\)SBS) or micro-tensile bond strength (\(\mu\)TBS)). Inclusion criteria comprised the presence of a comparison of bond strength values with and without (control group) eugenol, with mean and standard deviation values presented in megapascal (MPa).  
The searched titles and abstracts were checked carefully, and noneligible articles were excluded. Subsequently, full texts of the selected studies were obtained, and data were extracted to gather information about (1) method, (2) teeth type, (3) adhesive system, (4) eugenol-based material, (5) eugenol contact time, (6) storage time of samples prior the bond strength test, (7) sample size, (8) mean bond strength, and (9) standard deviation.  
To assess the individual risk of bias of each study, six methodological items were analyzed: (1) screening for caries and cracks on teeth, (2) blindness of the researcher, (3) teeth randomization, (4) use of materials according to manufacturers' instructions, (5) time of storage before debonding, and (6) presence of pretest failure that was included in statistical analyses. Such studies were classified s having high, moderate, or low risk of bias: studies with high risk failed to report four items or more, those with moderate risk failed to report 3 or 2 items, and those with low risk failed to report one item or less. The bias risk of the articles was evaluated by plotting the effect by the inverse of its standard error. The symmetry of the resulting funnel plot was visually assessed.  
Meta-analysis was conducted using the \"Review Manager Software (RMS)\" (version 5.3) from the Cochrane Collaboration (Copenhagen, Denmark), and the mean difference with 95% confidence interval (CI) was calculated for mean bond strength of all groups of the studies included using the inverse variance method. The p-value for significance was set at \(p<0.05\). Statistical homogeneity (I\({}^{2}\)) of the treatment effect was assessed by the modified chi-square test (Cochran's Q). The assessment indicated that there was statistical homogeneity when \(p>0.10\). Global and subgroup analyses were carried out (subgroups were arranged in ER or SE adhesive system and \(\mu\)SBS or \(\mu\)TBS).
## 3 Prediction Model of Pile Axial Stress
### Hyper-Parameter Tuning  
Hyper-parameters constitute the intrinsic parameters of an algorithm that can be fine-tuned to optimize the performance of a prediction model. As each algorithm has its unique set of hyper-parameters, modifying them inevitably impacts the predictive model's performance. The hyper-parameters for the aforementioned algorithms are listed in Table 5 for reference.  
A description of the hyper-parameters considered for RF, XGB, and LightGBM can be found in the following references: [28], [29], and [30]. Hyper-parameter tuning involves optimizing the parameters to achieve the best possible performance of the predictive model  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Case 1** & **Case 2** & **Case 3** & **Case 4** \\ \hline LR & 763.97 & 769.57 & 744.39 & 754.37 \\ L1 & 767.49 & 772.81 & 744.37 & 754.34 \\ L2 & 769.18 & 774.53 & 744.61 & 754.87 \\ MLP & 997.53 & 1100.50 & 1074.01 & 994.62 \\ SVR & 1352.94 & 1352.64 & 1352.45 & 1352.22 \\ RF & 207.17 & 289.75 & 204.55 & 291.34 \\ XGB & 187.29 & 276.95 & 185.06 & 275.98 \\ LightGBM & 184.28 & 244.13 & 186.97 & 241.69 \\ \hline \hline \end{tabular}
\end{table}
Table 4: RMSE of the training of each algorithm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **RF** & **XGB** & **LightGBM** \\ \hline n\_estimators & 426 & n\_estimators & 96 & n\_estimators & 140 \\ max\_depth & 7 & max\_depth & 8 & max\_depth & 14 \\ min\_samples\_split & 10 & learning\_rate & 0.15 & learning\_rate & 0.06 \\ max\_leaf\_nodes & 20 & colsample\_bytree & 0.93 & colsample\_bytree & 0.95 \\ random\_state & 21 & subsample & 0.78 & subsample & 0.07 \\  & reg\_alpha & 41.21 & reg\_alpha & 47.2 \\  & reg\_lambda & 35.17 & reg\_lambda & 11.06 \\  & gamma & 0.05 & random\_state & 23 \\  & random\_state & 16 & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Hyper-parameters of each algorithm.  
applied with the algorithm. In this study, the Optuna framework was employed to search for the optimal value by adjusting the hyper-parameters in each trial within the specified range or set list. Hyper-parameter tuning was carried out using the validation data from the previously divided data in the algorithm selection phase. The results of the hyper-parameter tuning and the resulting predictive models for each case are summarized in Table 6.  
After tuning the hyper-parameters using the validation data, LightGBM showed the best performance.
# Research on the Jiamusi Area's Shallow Groundwater Recharge Using Remote Sensing and the SWAT Model
## 4 Results  
From 2008 to 2016, Jiamusi's Groundwater Recharge Was Calculated Using the Remote Sensing Water Balance Method  
The groundwater recharge of the whole Jiamusi area from 2008 to 2016 was calculated by remote sensing, and the groundwater recoverable amount was calculated by using the average mining coefficient of different hydrogeological zones of Jiamusi City (\(\rho=0.45\)), as shown in Table 7. The annual precipitation and evapotranspiration were obtained by TRMM and MOD16, and the detailed data are shown in Figures 5 and 6: The average annual runoff depth was estimated by the GLDAS model, and the soil water storage in the research calculation area was \(=-1.5\) mm. The relationship between groundwater recharge and precipitation evapotranspiration is shown in Figure 7.
# A Novel Network Protocol Syntax Extracting Method for Grammar-Based Fuzzing
## 4 Design and Implementation
### Main-Parsing Module
#### 4.2.4 Expanding and Concatenating  
The basic path set of the protocol dissector file obtained in the previous step is a multi-level nested list structure containing all basic blocks. Take Figure 6 sample code's basic path set as an example, as shown in Figure 7, each letter represents a basic block, and the colors identify different types of basic blocks. Arrows indicate that a basic block contains sub-basic blocks, and the numbers indicate the three basic paths obtained. For example, basic block A contains sub-basic blocks C, D, and E. The multi-level nested structure is then expanded to generate each basic path that will eventually generate each packet type. Each basic block has the following three expansion methods according to its type:  
* Branch basic block: Select each of its sub-basic blocks to concatenate with the previous and next basic blocks.
* End Basic Block: Concatenate the basic block itself with the preceding and following basic blocks.
* Other basic blocks: Concatenate all sub-basic blocks with the basic blocks before and after them in sequence.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Value** & **Basic Block** & **Push** & **Pop** \\ \hline \multirow{2}{*}{Ordinary Basic Block} & \multirow{2}{*}{0} & ordinary & 1 & 1 \\  & & for & 1 & 1 \\  & & while/do while & 1 & 1 \\ \hline \multirow{2}{*}{Branch Basic Block} & \multirow{2}{*}{1} & (if/else if/else) & 2 & - \\  & & switch & 1 & 1 \\ \hline \multirow{4}{*}{Branch Item Basic Block} & \multirow{2}{*}{2} & function & 1 & 1 \\  & & if & - & 1 \\ \cline{1-1}  & & else if & 1 & 1 \\ \cline{1-1}  & & case & 1 & 1 \\ \cline{1-1}  & & default & 1 & 1 \\ \cline{1-1}  & 3 & else & 1 & 2 \\ \hline \multirow{2}{*}{End Basic Block} & \multirow{2}{*}{4} & return & - & - \\  & & 5 & function\_end & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed division of basic blocks and corresponding stack operations.  
Figure 6: Basic path set structure.  
Figure 7: Expanding and concatenating of basic blocks.  
For each expanded basic path, delete all basic blocks between the return basic block and the first function-ending basic block after it, this is because return represents an exit of the function and may be located at some place other than the end of the function body, then the field information acquired between this return statement and the end of the function body is invalid information for the basic path that contains this return statement, so these invalid information should be deleted. This process can be seen in the first path in Figure 7, basic blocks between \"P\" and \"J\" (P, M, J), and basic blocks between \"E\" and \"B\" (E, B) are deleted.  
Repeating this process until each basic path contains only ordinary basic blocks. Each ordinary basic block stores the component field information of the current packet type. The elements of all ordinary basic blocks in each basic path are merged and only the field information is retained, and finally, the complete packet type is obtained.
# Enhancing Machine Learning Models Through PCA, SMOTE-ENN, and Stochastic Weighted Averaging
## 2 Materials and Methods
### Methodology
#### 2.2.1 Feature Engineering and Principal Component Analysis (PCA)
The transformation is achieved through an orthogonal linear transformation, where the data matrix \(X\) is transformed into a new matrix \(Z\), representing the principal components. The principal components are calculated as  
\[Z=XW\]  
Here, \(X\) is the original data matrix, \(W\) is the matrix of eigenvectors (principal components), and \(Z\) is the transformed data matrix (projected data). The eigenvectors correspond to the directions of maximum variance in the data, while the eigenvalues indicate the amount of variance explained by each principal component. The principal components are ordered such that  
\[\lambda_{1}\geq\lambda_{2}\geq\cdots\geq\lambda_{n}\]  
where \(\lambda_{1}\) represents the largest variance, \(\lambda_{2}\) the second largest, and so on. By selecting the first few principal components, PCA reduces the dimensionality of the dataset while retaining the most important variance. This transformation simplifies the model, reduces computation time by lowering the number of features, and improves model performance by minimizing overfitting and enhancing generalization.