esa-sceva/satcom-chunk-collection huggingface dataset:
# Messy Broadcasting in Grid
## 2 Related Work
There are other similar list-based models; one example is the _Fully Adaptive_ model, which is studied in [30]. One might think of a more generalized version, in which instead of one-level knowledge (only neighbors), each vertex has \(k\)-level knowledge. Informally, in most cases of concern, replacing an edge with a path of length \(k\) creates graphs with similar difficulties to the original graph with one-level knowledge. Accordingly, the knowledge gained is not particularly useful. In [31], this difference was better analyzed, albeit the model there is slightly different than ours. In the classical model, Ref. [32] computes the broadcast time and an optimum scheme for the two-dimensional grid. It is easy to show that for a \(d\)-dimensional grid \(G=[n_{0}\times n_{1}\times\ldots\times n_{d-1}]\), the broadcast time is \(\sum_{i=0}^{d-1}(n_{i}-1)\). However, the broadcast time for any vertex in \(G\) remains undetermined; Ref. [1] provides bounds for these vertices. To our knowledge, messy broadcasting has not been extensively studied in grids, except in the context of \(d\)-dimensional hypercubes \(Q_{d}\). For \(Q_{d}\), Ref. [7] calculates \(t_{3}(Q_{d})\) and \(t_{2}(Q_{d})\) and provides a bound for \(t_{1}(Q_{d})\). In the classical model, Ref. [32] takes a slightly different approach to study an infinite two-dimensional grid. Let \(f(n,t)\) be the maximum number of vertices that can be informed after \(t\) time-units in an infinite two-dimensional grid. They proved that \(f(2,t)\leq 2t^{2}-6t+8\) for \(t\geq 2\) and conjectured equality. Inspired by this conjecture, Refs. [33; 34; 35] improved the results to  
\[f(n,t)\leq\frac{2^{n}}{n!}t^{n}+\frac{2-n2^{n}}{(n-1)!}t^{n-1}\\ +\frac{1}{(n-2)!}\bigg{(}4-n-\frac{1}{3}(2^{n+1}-n2^{n-2})+n^{2}2^ {n-1}\bigg{)}t^{n-2}+O(t^{n-3}).\]  
Infinite two-dimensional hexagonal arrays or triangular arrays, along with more conjectures and special case proofs have also been reported; more detailed information and references can be found in [3]. No prior work has addressed the messy models before this paper.
# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 3 Materials and Methods
### Machine Learning Models
#### 3.4.3 Random Forest  
Like the other mentioned models, random forest models can handle nonlinear data and be utilized in regression tasks. Using random forest to build large numbers of decision trees to solve a regression problem was a fit for our use case and prediction problem.  
Compared to a decision tree model, the ability of random forest to merge multiple decision trees produces a more accurate prediction. Because random forest is a bootstrap resampling technique that works best with higher dimensions and volume, the result was not exceptional with the study's volume of data. Separate models were created for each scenario. Each utilized a grid search for a range of values to optimize hyperparameters for each experiments' scenarios and set of features.
## 2 Materials and Methods  
Improved In Situ Online Measurement Principle of Soil Electrical Conductivity with Four-Terminal Electrodes  
The resistivity method, also referred to as the four-terminal or four-electrode method, employs sensors designed with four electrodes or probes typically arranged in a linear configuration. In this setup, the outer electrodes (F+ and F\(-\)) serve as the excitation terminals, while the inner electrodes (S+ and S\(-\)) act as the measurement terminals, as illustrated in Figure 1.  
A constant current I is applied to the excitation terminal in the conventional four-terminal method, and the conductivity \(ECa\) of the soil is converted by measuring the voltage \(V\) across \(S+\) and \(S-\), as shown in Equation (1):  
\[EC_{a}=\frac{1}{V}I\frac{\frac{1}{L_{F+S+}}-\frac{1}{L_{S+S-}}-\frac{1}{L_{F+S-} }+\frac{1}{L_{S-F-}}}{2\pi} \tag{1}\]  
where L\({}_{F+S+}\), L\({}_{S+S-}\), L\({}_{F+S-}\), and L\({}_{S-F-}\) represent the respective distances between the electrodes. V represents the potential difference measured across the electrodes S+ and S-, and I represents the constant current output by the excitation electrodes F+ and F-.  
When the four electrodes are arranged in a straight line and equidistant, it can be expressed as L\({}_{F+S+}\) = L\({}_{S+S-}\) = L\({}_{S-F-}\) = d. This electrode configuration constitutes the Wenner configuration [14], and the above formula can be simplified to the following:  
\[EC_{a}=\frac{1}{V}I\frac{1}{2\pi d} \tag{2}\]  
From the above equation, it is evident that when the current remains constant, the electrical conductivity (\(EC_{a}\)) is inversely proportional to the potential difference across the measuring electrodes S+ and S-. Consequently, this relationship forms the basis for determining soil electrical conductivity using this method.  
The conventional \"four-terminal method\" for detecting soil electrical conductivity necessitates a constant excitation current (I). This method employs a detection circuit comprising discrete, low-integration components, which are susceptible to systematic errors and contribute to increased unreliability due to the interactions among these components. If the excitation current (I) is not constant, Equation (2) changes to the following:  
\[EC_{a}=\frac{1}{\frac{V}{T}}\cdot\frac{1}{2\pi d}=\frac{1}{R}\cdot\frac{1}{2\pi d} \tag{3}\]  
where R denotes the probe impedance.  
It is evident that the soil electrical conductivity exhibits a linear relationship with the reciprocal of the probe impedance. Consequently, soil electrical conductivity can be determined by measuring the probe impedance. Equation (3) offers a theoretical foundation for the \"four-terminal method\" of soil electrical conductivity detection using a non-constant current source design.
# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
## 6 Issues and Challenges
### Programmability  
In cloud computing, users program their code and deploy them on the cloud, where the cloud provider is in charge to decide where the computing is conducted. The program is usually written in a certain programing language and compiled for a certain platform. However, in edge computing, computation is offloaded from the cloud, and the edge nodes are heterogeneous in most cases [28]. In the case of vehicular environments, edge nodes are associated with the existing or preplanned road infrastructure. Therefore, application designers can overcome the programmability challenge by identifying edge nodes in advance, with the objective of providing applications that may be deployed in the MEC paradigm according to the road network infrastructure.
## 4 Measurement and Analysis of PD  
### PD Detection Experiment Platform  
PD detection experiments were conducted in a high-voltage switchgear unit to assess the actual PD detection capability of the built-in antenna sensor. The experiment circuit is shown in Figure 12. The built-in antenna sensor is tightly attached to the inner wall of the high-voltage switchgear. An external UHF antenna sensor is simultaneously adopted to verify the superiority of the built-in antenna sensor for PD detection. The external UHF antenna sensor is a single-band microstrip patch  
Figure 11: The measured VSWR of the antenna.  
Figure 10: Physical map of the designed antenna.  
antenna [21] (center frequency: 390 MHz, frequency band: 350-450 MHz), which is placed at the door gap of the switchgear. In order to collect PD signals better and avoid the influence of the sensor position on experimental results, the external UHF antenna sensor remains stationary and as close to the insulation defects as possible. The 10 kV three-phase high-voltage switchgear shown in Figure 13 is adopted in this experiment. It has five isolation spaces: bus, circuit breaker, cable, secondary instrument and standby isolation space. The experiments are conducted in a cable isolation room because of its relatively high fault rate in actual operation. An AC testing transformer (YDTW-25/100, Tang Longsheng Electric Co., LTD, Wuhan, China) is used as experiment power source, and a high-speed oscilloscope (LeCroy Oscilloscope, New York, NY, USA; its highest sampling rate is 20 GHz) is used to record the waveform of PDs. The needle-to-plate model, with 10 mm separation distance, is used as the insulation defect to generate PD, and the voltage applied to the needle-to-plate is evenly boosted with a gradient of 0.5 kV.
# A Complete Pipeline for Heart Rate Extraction from Infant ECGs
## 2 Materials and Results
### The ECG Pipeline
#### 2.2.3 ECG Peak Detection  
R-peak detection is the process by which an algorithm finds the R-peak within the QRS complex for all QRS complexes in an ECG signal. By testing all the pre-existing methods, it was clear that Neurokit2 was one of the best suited to these datasets (Figures 4 and 5) and worked well with the proposed preprocessing approaches of 15 Hz HPF or EMD filtering (Figures 7 and 8). Neurokit2 peak detection was used in the proposed ECG pipeline without alteration in this specific step.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Study** & **Method** & **Key Findings** \\ \hline \multirow{6}{*}{[PERSON] and [PERSON](2001)} & & The results of their research state that reference projects should be carefully selected to extract the maximum possible information from the decision maker. It also presents the results of simulations based on utility functions, including the interdependence between criteria, and shows that UTA manages this problem effectively by adjusting its coefficients. [63] \\ \hline \multirow{2}{*}{[PERSON] and [PERSON] (2012)} & \multirow{2}{*}{CSW weights in the UTA-STAR method.} & They interpret the decision maker’s preferences in the UTA-STAR method in a new way by using the common set of weights (CSW) in DEA [53]. \\ \hline \multirow{6}{*}{[PERSON] et al. (2012).} & Based on an MCDA approach, where the UTA-STAR method is used to aggregate the marginal performance of KPIs. & Their results can help the organization evaluate and revise its strategy and adopt modern management approaches in daily practice [69]. \\ \hline \multirow{2}{*}{[PERSON] and [PERSON] (2016)} & Regression method & An extreme ranking analysis is implemented, calculating each alternative’s best and worst possible position in the ranking [68]. \\ \hline \multirow{2}{*}{[PERSON] et al. (2018)} & \multirow{2}{*}{Questionnaire} & Their results showed that risk allocation affects the contractor’s role behavior through the contractor’s sense of trust, but not the contractor’s trust in the owner [44]. \\ \hline \multirow{2}{*}{[PERSON] and [PERSON] (2019)} & Ratings for the relative values provided by strategies via a survey. & Owners and contractors identify distinct sets of CSU solution strategies as high value and low effort, with some overlap [48]. \\ \hline \multirow{6}{*}{[PERSON] and [PERSON] (2020)} & Using Sustainable Balanced Scorecard (SBSC), Analyzing Networks Process (ANP) and estimating the utility function of reference projects according to the results created and using the UTA-STAR method. & The results of this research, in addition to identifying the key criteria of sustainable development and classifying them in the form of sustainable, balanced scorecard funds, addressed achieving a prioritization pattern and choosing the optimal project for current and future projects [64]. \\ \hline \multirow{2}{*}{[PERSON] (2020)} & Descriptive and analytical & Providing a model in which owners and contractors are jointly committed to the project [49]. \\ \hline \multirow{6}{*}{Our study} & \multirow{6}{*}{UTA-STAR Method In uncertainty} & In this research, we considered the actual cost to complete the project as a continuous variable (calculation of the expected utility of the employer and the contractor for each deviation from the target cost) and, unlike previous studies, determined the win-win ratio through mathematical reasoning. We used a multi-criteria utility function. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Literature review from aspects of both theory and application.
## 3 Experiment and Results
### Impactor's Performance  
Furthermore, to verify the separation performance of the cascade impactor, we observe the particulate matter collected on the surface of the SAWR through a microscope, as shown in Figure 17. The SAW sensor on the LCP stage's impaction plate collected fewer particles, but the particle size was relatively large; the second stage collected the particle's size mainly in the range of 2.5 to 10 \(\upmu\)m; the third stage collected the most significant number of particles, most of them were smaller than 2.5 \(\upmu\)m. Therefore, the collected particles on the surface of the SAWR on the impaction plate of the three different impactors were in line with the design expectations.
## 2 Numerical Modelling Using Plaxis 2D
### Geometry Configuration of Each Model Case  
Modelling the entire bridge design for the numerical analysis may be too complex and time consuming. Therefore in this study, only the right-hand side of the bridge and abutment are considered. This method of modelling the free-standing geometry of the bridge abutment was also done by [PERSON] et al. (2019, 2020), [PERSON] et al. (2015, 2018), and [PERSON] (2020). As a result, the right-hand side model of each bridge is depicted in Figures 4-6. To validate the method of modelling the free-standing geometry of the bridge abutment using the Plaxis 2D version, numerical analyses were conducted with reference data from [PERSON] et al. (2015) and the results showed a good agreement with very minimal difference.  
Considering the right-hand side of the bridge and abutment, the 0.90-m thick integrated approach backfill and the 0.90-m thick RC bridge girder are removed and converted into its equivalent vertical loads. Thus, the height of the abutment is left at 6.3 m from the base. The models are designed with enough horizontal and vertical distance from the right-end and bottom boundaries, respectively. The horizontal distance from the right-end to the wall facing is more than three times the height of the wall. The vertical distance from the base of wall to the bottom boundary is almost twice the height of the wall. The front-end is 7.5 m from the wall facing because this is the half-point of the bridge (with a clear width of 15 m). Consequently, the boundary conditions are modelled wherein the vertical sides of the models at \(X=0\) and \(X=30\) are fixed on the \(X\)-direction, while the bottom boundary at \(Y=-12\) is fixed on both the \(X\) and \(Y\)-directions. In other words, a fixed boundary condition is applied at the bottom of the model while a roller boundary condition is applied on both sides of the model. The FE models of the bridges are simulated in Plaxis 2D based on the example procedures in the Plaxis 2D Reference and Tutorial Manuals [23,24] as well as on a few related literature studies including [PERSON] et al. [19,20], [PERSON] et al. [15,21], [PERSON] et al. [13], [PERSON] et al. [9], and [PERSON] et al. [14].  
For the mesh condition, all models used a plane strain model with 15-noded triangular elements, which are the default setting in Plaxis 2D. After the mesh generation, the Case 1 numerical model (GRS-IB) had a total of 49,687 nodes and 5441 soil elements with an average element size of 0.3176 m. The Case 2 numerical model (GRS-IBS) had a total of 56,730 nodes and 6284 soil elements with an average element size of 0.2956 m. Lastly, the Case 3 numerical model (IB) had a total of 32,762 nodes and 3915 soil elements with an average element size of 0.3745 m. The number of nodes and elements differ from each numerical model because the mesh depends on the geometry of the numerical model.
## 2 Materials and Methods
### Batch Adsorption Experiments  
Batch equilibration technique was used for fluoride sorption experiments. A weighed quantity of each form of modified vermiculite (HDTMA-VMT\({}_{0.2}\), HDTMA-VMT\({}_{0.3}\), and HDTMA-VMT\({}_{0.4}\)) was agitated with 10 mg/L fluoride solution at room temperature and 200 rpm, thus, the modification with the highest fluoride removal was used for the rest of analysis (optimisation). The optimisation was carried out in 250 mL shaking bottles containing 50 mL of 8 mg/L fluoride solution and a weighed quantity of the adsorbent. The effect of pH on the agitating mixture was investigated by varying from 2 to 10 while noting the pH with the most favourable fluoride removal. Other parameters which were investigated include: adsorbent dosage (0.1-2 g), contact time (5-70 min), adsorbate concentration (5-100 mg/L), and temperature (25 \({}^{\circ}\)C-55 \({}^{\circ}\)C). After each experiment, the test solution was centrifuged at 5000 rpm for 10 min and the supernatant decanted to a 100 mL beaker. To 10 mL of the decanted solution, 1 mL of TISAB (III) was added (10:1) to complex with any cation present and the residual fluoride was determined using an ion-selective electrode. The percentage fluoride removal was calculated using Equation (1).  
\[\%\ Fluoride=\frac{C_{o}-C_{e}}{C_{o}}\times 100 \tag{1}\]  
where \(C_{o}\) and \(C_{e}\) are the initial and equilibrium concentrations of the adsorbate (mg/L), respectively. The amount of fluoride (\(q_{e}\)) adsorbed in milligram per gram of the adsorbent was calculated using Equation (2).  
\[q_{e}=\frac{C_{o}-C_{e}}{m}\times v \tag{2}\]  
where \(m\) is the mass (g) of adsorbent and \(v\) is the volume of the solution in litres (L).  
The adsorption isotherm was determined using the data obtained from optimisation of adsorbate concentration. The adsorption kinetics and mechanism-based model were, however, determined using data obtained from optimisation of contact time.
## 1 Introduction: Sources of Sugars for Biofuel Production  
Ethanol and biodiesel have been industrially produced from biomass by fermentation and chemical trans-esterification of plant oils, respectively. For example, sugarcane-derived sugars (sucrose) have been used for ethanol fermentation in Brazil, and corn-derived starches (glucose) have been the major feedstock in the USA. Since consumption of these feedstocks for biofuel production competes with demands for animal feeds and human consumption [1], lignocellulosic biomass (LCB) has been suggested as an alternative and sustainable feedstock for biofuel industries. In using biomass for microbial fermentation, both non-LCB and LCB require pretreatment and hydrolysis of raw feedstock to release fermentable sugars from biomass consisting of complex and polymeric structures (Figure 1). The hydrolysis process of non-LCB such as corn starch has been well established in existing fermentation industries, but deconstruction of LCB has been limited due to the resistance of LCB against chemical and enzymatic treatment [1]. Moreover, hydrolysates of LCB include a mixture of pentose and hexose, inhibitory compounds (e.g., furfural, phenols) and toxic solvents produced during pretreatment, which all make downstream microbial fermentation difficult. Therefore, there have been studies to establish microbial hosts that co-utilize pentose and hexose, and to engineer tolerance of the microbial hosts against the above-mentioned toxic components [1].  
Beyond native fermentation pathways or natural biodiesel resources such as vegetable oils, advanced biofuel molecules are now synthesized in microbial hosts where heterologous or synthetic metabolic pathways are reconstructed in fermentative hosts. In this review, we will summarize recent achievements and progress in microbial biofuel production with updates on biofuel production from LCB-derived sugars. Various pathways and hosts for ethanol and advanced biofuel production will be discussed, with a particular emphasis on metabolic engineering strategies to improve the microbial conversion bioprocess.  
Figure 1: Overall scheme of biofuel production from biomass.
# NBTI and Power Reduction Using an Input Vector Control and Supply Voltage Assignment Method
## 3 Methodology
### ILP Formulation for NBTI Mitigation and Leakage Reduction Only
#### 3.1.2 ILP Formulation for Leakage Reduction  
The ILP formulation for leakage reduction is similar to that for NBTI mitigation. The same linear constrain sets in Table 2 can be exploited to represent logic functionality of the circuit. Different from the path-based ILP formulation for NBTI mitigation, the objective function in the ILP formulation for leakage reduction is generated by accumulating all of the leakage powers of the gates. Firstly, the leakage power of each gate is described as a linear function of the variables, which represent the input state of the gate, as shown in Table 3.  
where \(P_{ab}\) is leakage power of the gate when the input signal value is \(ab\). Then, we can sum all of the leakage powers of the gates and obtain the objective function in ILP formulation as follows:  
\[\text{min}\ P_{\text{IBg}}=\sum_{k=1}^{G}P_{\text{IBg}}(g_{k}) \tag{17}\]  
where \(G\) is the total number of gates in the circuit.
# Characteristics of Removal of Lead, Cadmium and Chromium from Soil Using Biosorbent and Biochar
## 2 Materials and Methods
### Removal of Heavy Metals from the Soil  
The removal of heavy metals--lead (Pb), cadmium (Cd), and chromium (Cr)--was conducted using three different sorbents: aspen wood sawdust, biochar, and activated carbon. For each metal, tests were performed at three different sorbent concentrations (Cs): 2%, 5%, and 10%. Control samples were prepared without any sorbent. For the soil enriched with the three heavy metals, three samples were prepared, each containing one type of sorbent at a concentration of 10%. The sorption process lasted for 15 days for Series I and 36 days for Series II.  
The process of heavy metal removal was conducted in PP containers, where 30 g of model soil was weighed into each container. The appropriate amounts of sorbents were then added to the prepared soil, and the mixture was thoroughly stirred. The samples were gently mixed every three days to ensure uniform contact between the materials and the soil. Additionally, during the sorption process, the moisture content of the soil was monitored and adjusted to 60% using deionized water.  
After the specified time interval, the materials were separated from the soil (only the coarser fraction of the material was used, and the samples were mixed in a way to avoid damage). The samples were then dried in a laboratory dryer at 50 \({}^{\circ}\)C for 24 h. The prepared samples were subsequently subjected to further analysis to characterize the sorbents and model soil, with a particular focus on the content of heavy metals.