esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Method
### Generating an Initial Solution
#### 4.1.2 Decoding-Method Example  
An example value of a scheduling problem with 10 jobs and 3 machines is provided in Table 2.  
\(P_{im}\) is the processing time of job \(i\) on machine \(m\), and \(E_{im}\) is the energy consumed to produce job \(i\) on machine \(m\). \(D_{i}\) is the due date of job \(i\) and \(a_{j}\) is penalty cost of job \(j\).  
1. [label=Step 1]
2. Separately apply ROV to the job and machine tracks from the smallest to the largest value; results are shown in Table 3.
3. Apply Jobs 7, 3, and 10 to Machines B, C, and A, respectively.
4. Assign Job 8 to B, but it is not allowed to produce Job 8 on Machine B; thus, assign Job 8 to Machine C instead of Machine B. Since it has the lowest total processing time, Jobs 9, 8, 5, 6, 3, and 7 are assigned to Machines A, B, A, C, B, and B, respectively. The results of this assignment are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Job \(j\)** & \(m\) & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline \multirow{3}{*}{\(P_{jm}\)} & A & 22 & 15 & 30 & 16 & 29 & 25 & 26 & 18 & 26 & 19 \\ \cline{2-11}  & B & 24 & N/A & 20 & 29 & 25 & 22 & 29 & N/A & 16 & 16 \\ \cline{2-11}  & C & 24 & 16 & 18 & 22 & 18 & N/A & 17 & 17 & 18 & 27 \\ \hline \multirow{3}{*}{\(E_{jm}\)} & A & 26 & 17 & 29 & 19 & 27 & 25 & 26 & 23 & 22 & 29 \\ \cline{2-11}  & B & 21 & N/A & 30 & 18 & 25 & 23 & 30 & N/A & 28 & 26 \\ \cline{2-11}  & C & 24 & 29 & 19 & 30 & 17 & N/A & 19 & 18 & 28 & 24 \\ \hline \multirow{3}{*}{\(D_{i}\)} & D\({}_{j}\) & 30 & 45 & 80 & 85 & 120 & 45 & 150 & 30 & 50 & 200 \\ \cline{2-11}  & a\({}_{j}\) & 483 & 421 & 338 & 449 & 438 & 480 & 383 & 321 & 389 & 418 \\ \hline \hline \end{tabular} Remarks: N/A is a job-machine restriction in which the job could not be produced on that machine.  
\end{table}
Table 2: Values of \(P_{jm}\) and \(E_{jm}\) for 10 jobs and 3 machines.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline Track & **Job/Machine** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **A** & **B** & **C** \\ \cline{2-11}  & Value in position & 0.48 & 0.7 & 0.22 & 0.43 & 0.75 & 0.91 & 0.04 & 0.41 & 0.48 & 0.34 & 0.95 & 0.31 & 0.65 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Track after \\ ROV \\ \end{tabular} } & **Job/Machine** & 7 & **3** & **10** & **8** & **4** & **1** & **9** & **2** & **5** & **6** & **B** & **C** & **A** \\ \cline{2-11}  & Value in position & 0.04 & 0.22 & 0.34 & 0.41 & 0.43 & 0.48 & 0.48 & 0.7 & 0.75 & 0.91 & 0.31 & 0.65 & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Track 1 before and after applying rank order value (ROV).  
Table 5 illustrates that the makespan equals 98 m. The minimum labor cost was assumed to be THB 100 per m, production-overhead cost per working unit was THB 9800, and the number of tardy jobs was 5, so the penalty cost was THB 2094. Therefore, the total cost for this assignment was THB 247 + THB 9800 + THB 2094 = THB 12,141.
## 4 Conclusions  
In this work, we described synthesis and caracterization of nickel-\(\beta\)-diimine complexes immobilized on \(\beta\)-zeolite, [Si]-MCM-41 and [Si,Al]-MCM-41 modified with an ionic liquid. These catalytic systems were used in ethylene oligomerization reactions and compared with the same reactions in homogeneous media. Catalytic properties of synthesized materials were tested under different conditions, varying Al/Ni molar ratio, temperature, pressure and catalyst loading.  
The results obtained in oligomerization reactions demonstrate the efficiency of heterogenized catalytic system. The techniques of characterization of the different complexes supported on \(\beta\)-zeolite or mesoporous materials of the MCM type corroborate that immobilization of the complex did not alter the structure of the supports of Si and Al.  
Comparison between the heterogeneous systems indicates that differences in pore sizes of the support materials did not influence the activity, but the presence and strength of acid sites, occurring in \(\beta\)-zeolite and Al-MCM-41, affects reaction activity. We suggest that the effect of acid sites of the support on the metal site is to increase the strength of ethylene coordination to the metal, causing the system to be more active.  
Comparison between homogeneous and heterogeneous systems containing the \(\beta\)-diimine-nickel complex showed that heterogenized systems are more selective in the formation of 1-butene. This higher selectivity indicates a positive relationship between porosity and confinement of the catalyst within channels of the support materials.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data and wrote the paper.  
The authors thank CNPq for their financial support in the form of grants.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Entry**} & \multirow{2}{*}{**Al/Ni**} & \multirow{2}{*}{**TOF (s\({}^{-1}\))**} & \multicolumn{3}{c}{**Selectivity (\%)**} \\ \cline{3-5}  & & & **C\({}_{4}\) (1-C\({}_{4}\))** & **C\({}_{6}\)** & **C\({}_{\geq 8}\)** \\ \hline
17 & 25 & 1.0 \(\pm\) 0.1 & 100 (94) & - & - \\
18 & 50 & 1.4 \(\pm\) 0.1 & 100 (96) & - & - \\
19 & 100 & 1.0 \(\pm\) 0.1 & 100 (93) & - & - \\
20 \({}^{*}\) & 100 & 2.3 \(\pm\) 0.1 & 100 (90) & - & - \\ \hline \hline \end{tabular}  
* NiILAl-MCM-41. Reactive conditions: solvent = toluene, cocatalyst = EASC, \(n_{\text{cataly}}\) = 5 \(\upmu\)mol, temperature = 10 \({}^{\circ}\)C, pressure = 10 atm.  
\end{table}
Table 6: Results of catalytic tests of the complex supported on MCM-41 and Al-MCM-41 modified with ionic liquid.
# Effects of Introducing Rest Intervals in Functional Fitness Training
## 1 Introduction
Another interesting focus of study would be to examine what happens when maintaining the same multimodal exercises in two FFT programs while modifying rest intervals, in which participants carry out the same number of repetitions with the same workload (same absolute training volume and absolute load but different density of training due to the different rest intervals). This could give rise to two situations: (1) Relative work intensities could differ as an absolute workload in one individual could represent different relative intensities depending on the velocity reached [13; 14; 15; 16]. That is, with the introduction rest periods, repetitions could be performed at a higher velocity, representing a lower relative intensity for that absolute load. (2) Consequently, this could lead to different extents of effort and fatigue between the two FFTs, as rest intervals could translate to the recovery of predominant energy pathways in this type of exercise. In high-intensity intermittent exercise, high-energy phosphagen metabolism is a predominant system [17]. Among the mechanisms that give rise to fatigue is the inhibition of muscle contraction due to the build-up of final metabolic products [18] such as hydrogen ions, which reduce the pH, generating metabolic acidosis and inhibition of the enzyme phosphofructokinase (PFK). This reduction in PFK is accompanied by a decrease in the resynthesis of phosphocreatine [19] and the inhibition of glycolysis [20], with negative effects on muscle contraction. Thus, the introduction of rest intervals could increase the time of phosphocreatine reserve depletion and decrease metabolic acidosis. Phosphocreatine resynthesis involves a rapid first stage in which 70% of reserves are recovered in the first 30 s, and a second longer stage that lasts for around 3-5 min [21]. On the other hand, technically demanding exercises such as the squat, snatch, and clean and jerk create tension and overloading of joints in the shoulder and lumbar region [22]. Since there is evidence relating fatigue levels with modified movement biomechanics [23], introducing rest periods in FFT workouts could help reduce muscular fatigue and avoid biomechanical alterations that could result in injury. Therefore, the objective of the present study was to determine whether introducing rest intervals in a FFT workout could reduce the variables rate of perceived exertion (RPE), blood lactate concentration, heart rate (HR), and muscular fatigue compared to a standard \"rounds for time (RFT)\" FFT with no preset rest intervals. Our hypothesis is that there will be a reduction in the variables of RPE, HR, blood lactate concentration, and muscular fatigue in the FFT where mandatory recovery times are introduced.
# Gill Histopathology as a Biomarker for Discriminating Seasonal Variations in Water Quality
## 1 Introduction  
Increased anthropogenic activity in terms of rising industrial, mining and agricultural activity over the last few decades has caused increased water pollution. The most common methods for monitoring water quality in aquatic ecosystems are measurements of pollutant concentrations in water and sediment [1]. Since these methods are not cost-efficient and they do not provide information on biological reactions of aquatic organisms, a vast array of biomarkers has been developed in order to qualitatively and quantitatively assess biological reactions of aquatic organisms to water pollution [2].  
Histopathological alterations in various fish organs have a pronounced value in aquatic toxicology [3] and are widely used in the monitoring of aquatic pollution [4, 5, 6]. Among them, gills are particularly useful in environmental monitoring given that they have a large surface area, thin epithelium, are in direct contact with water and are generally considered to be among most affected organs by waterborne compounds [7, 8]. So far,many studies have assessed gill structural changes in response to waterborne pollutants and environmental pollution, and have recognized them as valuable biomarkers of water pollution [3; 5; 9; 10; 11].  
Histopathological alterations are determined as reliable biomarkers in differentiating levels of pollution between different aquatic ecosystems [4; 5; 12; 13]. However, there are very few studies which account for seasonal differences in gill reactions and which analyze the differences in gill reaction to seasonal variations in water quality. The aim of the present study was to evaluate whether histopathological alterations in bream gills can discriminate seasonal variations in water quality within the same aquatic ecosystem. The present study was a part of a much larger monitoring study conducted on the Tamis River; therefore, it did not aim to make a strong correlation between specific water quality parameters or specific pollutants and gill alterations, but rather to assess whether gill histopathological analysis can be utilized for the monitoring of seasonal differences in water quality. As a study organism we used freshwater bream, _Abramis brana_, an indigenous freshwater fish species which is commonly found in the Danube River basin [14]. Key features that make this species a good bioindicator are its bottom-dwelling nature, as it is exposed to pollution from both water and sediment during feeding on benthic animals [14], as well as its stationary nature, as it exhibits high site fidelity [15; 16], and thus alterations observed would most likely be a depiction of local environmental conditions. Therefore, an additional aim was to evaluate if freshwater bream could be a good study organism for pollution monitoring. In achieving the mentioned aims, we can further expand our knowledge on biological responses of aquatic organisms to variations in environmental conditions and can apply this knowledge to environmental monitoring.
## 6 Conclusions and Future Work  
The article designs a new PM\({}_{2.5}\) concentration prediction framework (FPHFA) for short-term and long-term PM\({}_{2.5}\) prediction is proposed. FPHFA is a hybrid deep learning model based on the attention mechanism. The FPHFA model consists of three components: multi-channel 1D CNNs, Bi LSTM, and an attention mechanism. Based on the above experimental results, the proposed FPHFA model yields better performance than classical deep learning and other hybrid deep learning models. From historical data on pollutant concentration and meteorology, FPHFA can more clearly handle temporal correlation characteristics and can capture spatial features from surrounding sites, enabling more accurate predictions of PM\({}_{2.5}\) concentration. The following are this paper's main contributions:  
1. This paper was the first attempt to combine multi-channel 1D CNNs, Bi LSTM, and attention mechanisms for hybrid fusion learning of PM\({}_{2.5}\)-related time series data, yielding a model which can capture spatial-temporal dependent features.
2. The attention mechanism in the FPHFA model was used to focus on information that is more useful for prediction for different instants, thus improving the final prediction outcomes.
3. We proved the effectiveness of FPHFA by conducting experiments on the Beijing historical air pollution dataset, and the experimental outcomes show that our model has excellent prediction capability.  
Furthermore, a number of factors have an impact on PM\({}_{2.5}\) concentration, such as traffic, buildings, and population, but this work did not consider these factors, which are left for future work.  
Conceptualization, D.L.; methodology, D.L.; software, D.L.; validation, D.L.; formal analysis, Y.Z.; investigation, J.L.; resources, J.L.; data curation, D.L.; writing--original draft preparation, D.L.; writing--review and editing, D.L. and Y.Z.; visualization, Y.Z.; supervision, J.L.; project administration, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Lanzhou Jiaotong University (grant no. EP 201806).
## 3 Results and Discussions
### X-Ray Diffraction Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Phase Composition**} & \multicolumn{2}{c}{**Quantitative Phase Composition**} & \multirow{2}{*}{**Crystallite Mean Size (nm)**} \\  & & **(wt\%)–**Rietveld Analysis** & \\ \hline \multirow{2}{*}{AZ63\_5m} & MgAl\({}_{2}\)O\({}_{4}\) & 82.2 \(\pm\) 0.4 & 35.9 \(\pm\) 1.6 \\  & MgO & 7.5 \(\pm\) 0.4 & NA \\  & gamma-Al\({}_{0-667}\)O & 10.3 \(\pm\) 1.2 & NA \\ \hline \multirow{2}{*}{AZ63\_10m} & MgAl\({}_{2}\)O\({}_{4}\) & 89.2 \(\pm\) 0.3 & 33.3 \(\pm\) 0.3 \\  & MgO & 7.1 \(\pm\) 0.4 & NA \\  & gamma-Al\({}_{0-667}\)O & 3.7 \(\pm\) 0.5 & NA \\ \hline \multirow{2}{*}{AZ63\_20m} & MgAl\({}_{2}\)O\({}_{4}\) & 91.2 \(\pm\) 0.2 & 35.0 \(\pm\) 0.3 \\  & MgO & 4.9 \(\pm\) 0.2 & NA \\ \cline{1-1}  & gamma-Al\({}_{0-667}\)O & 3.8 \(\pm\) 0.2 & NA \\ \hline \end{tabular}
\end{table} 
Table 6: Quantitative phase analysis and microstructural parameters of the PEO coatings detached from the substrate (in powder form). Figure 10: XRD patterns of the detached PEO coatings (inset: zoom on the 40\({}^{\circ}\)–70\({}^{\circ}\) 20 range). for magnesium oxide and the supposed gamma-alumina, given the quality and number of XRD line profiles associated with those crystalline phases. The reaction mechanism for magnesium oxide and magnesium aluminate spinel formation are explained in detail in a 2017 [PERSON] et al. paper [14]. The presence of aluminum-rich areas can be due to:  
* Decomposition of the electrolyte under the influence of the strong discharges that penetrate the layer, which leads to the presence of electrolyte species on the surface of the pancakes and at the coating/substrate interface after a single discharge [5]. Thus, aluminum ions from the electrolyte and oxygen species present in the pores and discharge channel forms aluminum oxide allotropic forms;
* Ejecting process of molten magnesium and alloying elements (approx. 6% aluminum), followed by an oxidation process in the discharge channel, and finally quenching and deposition. The formation of \(\gamma\)-alumina is associated with a fast cooling rate [43]. It was shown [26] that the alloying elements of the AZ series can be simultaneously oxidized along with Mg, and the data obtained from the SEM-EDS cross-section analysis support the importance of alloying elements in the formation of the PEO coatings
# Self-Optimizing Traffic Steering for 5G mmWave Heterogeneous Networks
## 3 Self-Optimizing Traffic Steering
### Use Cases  
Based on the proposed framework, self-optimizing traffic steering can be performed flexibly to provide further benefits for both networks and users. To analyze the potential advantages, several use cases are further discussed in this subsection.
# Optimizing Faulting Prediction for Rigid Pavements Using a Hybrid SHAP-TPE-CatBoost Model
## 2 Materials and Methods
### SHAP Method for Results Interpretation  
Interpretability is crucial in machine learning models, and in this study, we adopted the SHAP method proposed by [PERSON] et al. (2016) for the tree models. The SHAP analysis is derived from Shapley values in cooperative game theory. Shapley values are used to measure the impact of each feature on the prediction's outcome. Specifically, [PERSON] defined the value of a feature, f, as the average marginal contribution of the f over all possible feature subsets and gave a formula for the Shapley value. In practice, the Shapley value is solved by approximation, while SHAP is an optimization algorithm for estimating the Shapley value.  
The SHAP method represents the explanation model based on Shapley values in the form of additive feature attribution, shown as follows.  
\[g(z^{\prime})=\psi_{0}+\sum_{j=1}^{M}\psi_{j}z^{\prime}_{j} \tag{2}\]  
The function \(g(z^{\prime})\) represents the interpretation function of \(z^{\prime}\), where \(z^{\prime}\) is a 0/1 vector of length \(M\) on the M-dimensional space, with 1 indicating the presence of a feature and 0 indicating the absence of it. \(M\) corresponds to the number of features in the model. The constant term \(\psi_{0}\) represents the average prediction value across all samples, and \(\psi_{j}\) represents the Shapley value of feature \(j\).  
\[\psi_{j}=\sum_{S\subseteq\{x_{1},\ldots,x_{p}\}\backslash\{x_{j}\}}\frac{|S|! (p\!-\!|S|\!-\!1)!}{p!}(f_{x}(S\cup\{x_{j}\})-f_{x}(S)) \tag{3}\]  
where \(\{x_{1},\ldots,x_{p}\}\) represents the set of all input features, \(p\) represents the number of inputs features, \(\{x_{1},\ldots,x_{p}\}\backslash\{x_{j}\}\) represents all possible subsets of input features that do not include feature \(\{x_{j}\}\), and \(f_{x}(S)\) represents the predicted value for the feature subset \(S\).  
The SHAP method is a widely recognized attribution approach in the field of interpretable machine learning in recent years. Compared to other methods, it has the advantage of being model-agnostic ([PERSON] et al., 2016), theoretically allowing it to explain any machine learning model. This provides SHAP with greater flexibility and scalability, enabling it to interpret machine learning algorithms from different perspectives ([PERSON] et al., 2016).
## 3 Results and Discussion
### Characterization of Constituent Materials
#### 3.1.3 XRD and SEM/EDX results of FA  
Moreover, the different minerals present in the FA powder were determined by XRD crystallographic analysis [34]. As shown in Figure 9, mullite, quartz, hematite, lime and calcium carbonate are present [9].  
Lastly, 20 spectra were acquired via the SEM/EDX carried out on a FA sample, five of which are indicatively shown in Table 5. The presence of sulphur which has been reported to increase the ettrinigite formation during cement hydration was confirmed [35].  
Step 2: Testing and Step 3: Selecting best Performing Pastes of the Binary and Ternary Cemetitious Matrices  
After Step 1 was presented in Section 2.3, in which the concept behind the designing of cementitious matrices was unfolded, in this section the results of sample testing and selecting best performing pastes is analyzed. Apart from the pure CEMI paste (B1), nine additional Portland limestone cement initial formulations (B2-10) were designed, containing different proportions of CEMI and limestone or CEMI, limestone and fly ash, as already discussed. Best performing pastes were judged according to the delivered compressive strength and embodied carbon dioxide content. As shown below, best relations between strength and embodied CO\({}_{2}\) were obtained for B7: PC60 LS20 FA20 and B8: PC45 LS20 FA35, which delivering 80 MPa/t/t and 110 MPa/t, respectively. These two formulations were selected to be further optimized, later, forming the quaternary cementitious matrices. Thermal analyses  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**FA—All Results in Atomic \% Normalized to 100\%**} \\ \hline
**Spectrum** & **C** & **O** & **Na** & **Mg** & **Al** & **Si** & **P** & **S** & **Cl** & **K** & **Ca** & **Ti** & **Fe** \\ (1,1) & 16.73 & 55.58 & 0.49 & 0.74 & 8.84 & 13.62 & 0.00 & 0.14 & 0.04 & 0.82 & 1.06 & 0.28 & 1.66 \\ (2,1) & 18.86 & 52.35 & 0.06 & 0.72 & 12.23 & 13.3  
were also performed on all initial cementitious matrices (B1-B10). All results are presented in the following sections.
# Business Impact Analysis of AMM Data: A Case Study
## 5 Results
### Risk Assessment by BIA Guidelines by Asset
#### 5.2.6 Accounting System (AS)  
This is a system in which everything is accounted for and contractually evaluated, and all personal data about customers are stored here. This is why it is one of the most sensitive systems. _Legal and contractual obligations_: If there is an AS outage, it will not be possible to subsequently bill customers for their electricity consumption, transmit billing data to EMO, handle disconnections and claims, or handle complaints. Such an outage will affect the vast majority of activities and may result in a violation of Decree 359/2020 Coll. because the distributor could not meet its obligations to transmit billing data promptly. Furthermore, Decree No. 540/2005 Coll. may be violated in almost all respects, as well as Law No. 458/2000 Coll. and Decree No. 408/2015 Coll. Again, however, this depends on the length of the outage and the status of the requirements in the US. However, if there is a system outage for an extended period, this could have a high impact on the company in terms of claims for financial damages. _Management and operation of the organization_: If there is a widespread outage of the AS, it could also halt the ability to take readings in the field, as readings requests would stop coming in, and there would be nowhere to send billing data. Most of the systems connected to the AS could stop working until everything is back up and running. Therefore, I would rate the risk associated with an outage as critical. _Loss of credibility_: There is a high risk of loss of credibility in a large-scale AS outage, as the outage would affect the operations of a large part of the business as well as customers; for this reason, the impact is rated as high. _Financial loss/Disruption to operations_: The financial loss does not need to be quantified here, as it could be astronomical in the extreme case of compensation payments; therefore, the the risk of impact is critical. A comprehensive assessment of the risks and parameters of data availability and maturity is presented in Table 18.