esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Problem Formulation
### Optimization Objective  
The blockchain collaborative storage optimization problem in SIoV is formally defined as follows: The system consists of multiple RSUs with limited available storage resources, where each RSU \(r_{i}\) serves a set of vehicles \(V^{i}\), and the service areas of different RSUs do not overlap. The challenge is to partition RSUs into \(L\) cluster \(CLU\), and distribute all blocks in the current blockchain ledger \(B\) across \(N_{l}\) RSUs within each cluster \(C_{l}\) while respecting the storage constraints of RSUs. The goal is to minimize the total block access cost incurred by all vehicles in the system when performing reputation verification. The optimization objective is formulated as follows:  
\[\min\sum\limits_{C_{l}\in CLU}\sum\limits_{r_{i}\in C_{l}}\sum\limits_{v_{m} \in V^{i}}C_{m} \tag{10}\]  
\[s.t.\sum\limits_{r_{i}\in C_{l}}rc_{i}\geq\eta\sum\limits_{b_{k}\in B}s_{k}, \eta\geq 1 \tag{11}\]  
\[\sum\limits_{r_{i}\in C_{l}}x_{ik}\geq 1,\forall b_{k}\in B \tag{12}\]  
\[\sum\limits_{b_{k}\in B}x_{ik}\cdot s_{k}\leq rc_{i},\forall r_{i}\in C_{l} \tag{13}\]  
Equation (11) regulates the formation of clusters, ensuring that the total storage capacity of all RSUs within a cluster is at least equal to the total size of the blockchain, thereby guaranteeing that the formed cluster can store a complete copy of the blockchain. Equations (12) and (13) govern the block allocation process within the cluster, ensuring that each block in the blockchain is stored at least once within the cluster to meet the demand of any vehicle served by the RSUs in the cluster, while also ensuring that the number of blocks allocated to a single RSU does not exceed its own storage capacity.
## 2 Results and Discussion
### Particle Size and Number Density of Particles  
Figures 2 and 3 show the change of number-average particle diameter (\(D_{n}\)) and \(N_{p}\) with \(X\), respectively. Figure 4 includes micrographs from final latexes prepared using both dosing rates. To estimate \(N_{p}\) the following equation was used:  
\[N_{p}=\frac{6C_{p}}{\pi\rho_{part}D_{n}^{3}} \tag{3}\]  
where \(C_{p}\) is polymer concentration in g/mL water and \(\rho_{part}\) is the density of porous particle in g/mL. This density was calculated using the following equation, developed by considering 1 gram hypothetical porous particle (\(P\)) with porosity (\(\phi\)) in cm\({}^{3}\)/g:  
\[\rho_{part}=\frac{P}{P\phi+\frac{P}{\rho_{p}}}=\frac{1}{\phi+\frac{1}{\rho_{p}}} \tag{4}\]  
In this equation \(\rho_{p}\) is the polymer density, for which all the polymer is considered as polystyrene with a density of 1.05 g/mL [22]. Figure 2 depicts a very similar \(D_{n}\) behavior for both polymerizations, with slightly larger sizes for the polymerization carried out at the higher dosing rate up to 60%-65% global conversion. However, the final \(D_{n}\) value was close to 28 nm in both cases. The absence of dependence of final particle size on monomer dosing rate in this study contrasts with the known direct relationship between both these variables in semicontinuous heterophase polymerization under monomer starved conditions [16; 17; 18; 21].  
Figure 1: Evolution of instantaneous, \(x_{i}\) (—) and global, \(X\) (—) conversions with relative time in polymerizations at 0.05 (**a**) and 0.15 (**b**) g/min in organic phase dosing rate. Runs M ( •); runs MR (\(\blacktriangle\)).  
The evolution of \(N_{p}\) shown in Figure 3 is also similar for both polymerizations, with higher values up to 60%-66% global conversion for the polymerizations carried out at the lower dosing rate, though the difference between both polymerizations is more pronounced than that in Figure 2 as a consequence of the dependence of \(N_{p}\) on \(D_{n}{}^{-3}\) (Equation (3)). However, in the last part of the polymerizations, the values of \(N_{p}\) for both polymerizations show a slight decrease, which is more evident in the polymerization at the lower dosing rate in such way that the final \(N_{p}\) values are higher for the higher dosing rate. This fact suggests the occurrence of a certain degree of particle coalescence at the end of the polymerizations.  
Figure 3: Particle number density as a function of global conversion in polymerizations at 0.05 (**a**) and 0.15 (**b**) g/min in organic dosing rate. Runs M (\(\blacktriangle\)); runs MR (\(\blacktriangle\)).  
Figure 2: Number average diameter measured by STEM as a function of global conversion in polymerizations at \(F_{o}\) 0.05 (**a**) and 0.15 (**b**) g/min. Runs M (\(\blacklozenge\)); runs MR (\(\blacktriangle\)).
# An Approach to Aid Decision-Making by Solving Complex Optimization Problems Using SQL Queries
## 3 Experimental Design and Results
### First Experiment: Performance of Using SQL Statements  
This experiment used SQL queries to solve different optimization problems. It was developed in two phases. The first phase evaluates exclusively the performance of solving the 3 CG in planar graphs using SQL. The use of planar graphs is motivated due to their application in the solution of the channel assignment problem in an overlapped Basic Service Sets environment [31], which can be modeled through planar graphs. As a result, even though the approach has an exponential growth in time, it is observed that cases with even dozens of nodes are solved in around ten seconds. The second phase extends the performance evaluation on the other revised problems. The PSP, MCP, and 3 CG are solved using SQL statements and compared in performance against naive brute force strategies under a small set of random instances. The results show competitiveness in using SQL queries over brute force algorithms. The remainder of this section details better each of the phases of this experiment and their results.
###### Abstract  
Pnasor measurement units (PMUs) are deployed at power grid nodes around the transmission grid, determining precise power system monitoring conditions. In real life, it is not realistic to place a PMU at every power grid node; thus, the lowest PMU number is optimally selected for the full observation of the entire network. In this study, the PMU placement model is reconsidered, taking into account single- and multi-capacity placement models rather than the well-studied PMU placement model with an unrestricted number of channels. A restricted number of channels per monitoring device is used, instead of supposing that a PMU is able to observe all incident buses through the transmission connectivity lines. The optimization models are declared closely to the power dominating set and minimum edge cover problem in graph theory. These discrete optimization problems are directly related with the minimum set covering problem. Initially, the allocation model is declared as a constrained mixed-integer linear program implemented by mathematical and stochastic algorithms. Then, the 0/1 integer linear problem is reformulated into a non-convex constraint program to find optimality. The mathematical models are solved either in binary form or in the continuous domain using specialized optimization libraries, and are all implemented in YALMIP software in conjunction with MATLAB. Mixed-integer linear solvers, nonlinear programming solvers, and heuristic algorithms are utilized in the aforementioned software packages to locate the global solution for each instance solved in this application, which considers the transformation of the existing power grids to smart grids.  
Keywords:phasor measurement unit (PMU); optimization; optimal PMU placement (OPP); channel limit capacity; multi-channel PMUs; observability; binary-integer programming; nonlinear programming; algorithms; sufficient conditions for optimality +
Footnote †: journal: _Algorithms_
## 2 Materials and Methods
### Bacterial Inoculation and DNA Extraction  
To obtain 20-80 colony forming units (CFU) per plate, untreated (UWW) and treated wastewater (TWW) samples were decimally diluted with saline water and passed through a cellulose filter (pore diameter of 0.45 \(\upmu\)M, Millipore, Merck KGaA, Darmstadt, Germany). Greater accuracy was achieved by plating in triplicates. The total number of bacteria and bacteria resistant to \(\upbeta\)-lactams (amoxicillin, cefotaxime) and tetracyclines (oxytetracycline, doxycycline), as well as the total number of _Escherichia coli_ and _Escherichia coli_ resistant to the same drugs were determined on plates with TSA medium (Oxoid, Thermo Fisher Scientific, Carlsbad, USA) and the mFc Agar medium (Merck, Merck KGaA, Darmstadt, Germany) with/without antibiotic supplementation, respectively. The plates were incubated at 30 \({}^{\circ}\)C for 48 h. _E. coli_ were cultured at 44.5 \(\pm\) 0.2 \({}^{\circ}\)C for 24 h. The filters were then transferred to sterile screw cap tubes (50 mL), and 30 mL of 1 \(\times\) PBS was added to the tubes. They were shaken (200 rpm/min, three hours) at room temperature. The entire precipitate was then transferred to 2.0 mL Eppendorf tubes and centrifuged (9000 rpm/min, 15 min). DNA extraction from a bacterial pellet was then performed using isolation kits (Genomic Mini A&A Biotechnology kit, Gdynia, Poland), according to the manufacturer's instructions. The concentration and quality of the extracted DNA was determined by microspectrophotometry (NanoDrop(r) ND-1000, Nano Drop Technologies, Willmington, DE, USA). The DNA was stored at \(-\)20 \({}^{\circ}\)C for further analysis.
## 2 Literature Review  
First, this study provides an overview of the research available in the literature on site selection for renewable energy. Systematic review studies show that renewable energy selection studies are primarily directed towards a specific energy type dominated by solar and wind [8; 9]. According to Table 1, MCDM methods are used in a variety, combined, and extended with fuzzy sets in the problem of location selection. However, site selection studies for integrated renewable energy are lacking. One of the related studies was site selection for a hybrid wind and solar energy system by [PERSON] et al. [13], in which the analytic hierarchy process (AHP) method was applied to select the location of integrated renewable energy in China. In 2017, [PERSON] et al. used the AHP method in combination with geographic information system (GIS) to identify suitable locations for an integrated wind-wave energy project [14]. Recently, a study on the selection of wind and wave integrated energy locations in China's Hainan province was published by [PERSON] et al. [15]. This expert-based assessment is performed using a triangular fuzzy extension of the AHP method. In addition, several DEA-MCDM combinations for renewable energy site selection problems were found [16; 17]. Besides the DEA method, different MCDM methods could be helpful.  
For example, [PERSON] et al. used the Game Theory to assess the construction site [18]. According to [PERSON] et al., the selection of project implementation sites highly depends on the available project implementation team [19] and available contractors [20], and feasible technologies [21]. Moreover, the alternative that is suitable to determine the local and international scopes and aims in a dynamically changing business environment [22] must protect nature and cultural heritage [23], which are requirements to turn to green energy. However, quantitative assessments that consider the psychological behavior of decision makers have not been found in the literature for this problem.  
In practical terms, Vietnam is one of the leading countries in wind energy development in Southeast Asia [1]. At the same time, Vietnam has a coastline along the country. Therefore, Vietnam has great potential for offshore energy projects that take advantage of the inexhaustible energy sources of both wind and waves. However, no study on offshore wind and wave integrated energy has been found for the case of Vietnam. From the above review, it can be seen that there is a lack of studies on location selection for mixed renewable energy for Vietnam that apply both qualitative and quantitative analysis, including the psychological behavior of decision makers.
# Combining Machine Learning and Logical Reasoning to Improve Requirements Traceability Recovery
## 2 Related Work  
### IR-Based Methods  
Software artifacts contain a lot of textual information. The classic method is using IR techniques for traceability link recovery. This kind of method considers that software artifacts with high text similarity probably have traceability links. [PERSON] et al. propose using VSM, LSI and other IR methods to calculate the textual similarity between artifacts directly, obtaining candidate links by setting a threshold or selecting the first \(k\) links [11; 12; 13; 14]. [PERSON] et al. improve traceability recovery by using a smoothing filter [13].  
[PERSON] et al. apply a variety of different IR methods, namely JS (Jenson-Shannon) [15], VSM [7], LSI [8] and LDA (Latent Dirichlet Allocation) [16] for traceability recovery to improve the accuracy of independent methods [17]. The results show that VSM, JS and LSI are equivalent, while the precision of LDA is lower than other IR methods. However, LDA can capture unique dimensions and provide orthogonal similarity measures. The combination of LDA and other IR technologies can improve the precision of traceability recovery.  
[PERSON] et al. propose a relational topic model (RTM) [18] based on LDA, which can extract and analyze topics and the relationships between them from software artifacts [2]. Case studies show that when RTM is combined with VSM and JS, the results of traceability recovery are improved. They proposed the RTM is orthogonal to VSM and JS [2].  
[PERSON] et al. propose a simple method based on IR which only considers nouns [5; 19]. The proposed method extracts the nouns from software artifacts and indexes them to define the semantics of the software artifacts. They propose that the basic principle of the method is that the language used in the software documents can be classified as sectoral language, in which nouns provide more indication of the semantics of the document [5]. The proposed method is applied to JS and LSI models, improving the precision.  
[PERSON] et al. propose an automatic technology that combines traceability recovery with LDA topic modeling [1]. It automatically records traceability links during software development and learns probabilistic topic models on artifacts. The learned model allows semantic classification of artifacts and topic visualization of software systems. At the same time, they propose a prospective method which creates links between artifacts based on the operation of developers. They implement several tools to validate their method.  
This type of method can create and maintain the traceability links automatically and reduce the cost of traceability recovery. However, it treats the contents of software artifacts as simple text, which will be interfered with by a lot of irrelevant redundant information, and returns a large number of false-positive links, resulting in low precision.
###### Abstract  
The antitumor drug 3-amino-1,2,4-benzotriazine-1,4-dioxide (tirapazamine, TPZ (**1**)) along with a number of newly synthesized tirapazamine derivatives (TPZs) bearing substitutions at the 3-amine position of TPZ (**1**) were estimated for their antibacterial activity against representative Gram-negative _Escherichia coli_ (ATCC 25922) and _Salmonella enterica_ (SL 5676), as well as Gram-positive _Staphylococcus aureus_ (ATCC 25923) bacterial strains. Their activities in terms of minimum inhibitory concentrations (MICs) varied in the range of 1.1 \(\upmu\)M (0.25 \(\upmu\)g/mL)-413 \(\upmu\)M (128 \(\upmu\)g/mL). Amongst the most potent derivatives (**1-6**), acetyl- and methoxycarbonyl-substituted TPZs (**2** and **4**) were the strongest agents, which exhibited approximately 4-30 fold greater activities compared to those of TPZ (**1**) along with the reference drugs chloramphenicol (CAM) and nitrofurantoin (NFT). The inhibitory activities of the compounds were highly impacted by their structural features. No reliable relationships were established between activities and the electron-accepting potencies of the whole set of studied compounds, while the activities of TPZ drug (**1**) and the structurally uniform set of molecules (**2-6**) were found to increase with an increase in their electron-accepting potencies obtained by means of density functional theory (DFT) computation. A greater steric, lipophilic and polar nature of the substituents led to a lower activity of the compounds. The combined antibacterial _in vitro_ trial gave clear evidence that TPZs coupled with the commonly utilized antibiotics ciprofloxacin (Cipro) and nitrofurantoin (NFT) could generate enhanced (suggestive of partial and virtually complete synergistic) and additive effects. The strongest effects were defined for TPZs-NFT combinations, which resulted in a notable reduction in the MICs of di-\(N\)-oxides. These preliminary findings suggest that the synthesized novel di-\(N\)-oxides might be used as sole agents or applied as antibiotic complements.  
heterocyclic di-\(N\)-oxides; 1,2,4-benzotriazine di-\(N\)-oxides; tirapazamine (TPZ); antibacterial activity; minimum inhibitory concentrations (MICs); chloramphenicol; ciprofloxacin; nitrofurantoin; QSAR; drug combination cooperative behavior
# Methodology for CubeSat Debris Collision Avoidance Based on Its Active ADCS System
###### Abstract
The difference in horizontal and vertical position of the satellite in a Local Vertical, Local Horizontal (LVLH) reference system at TCA is compared with threshold miss distances of Conjunction Data Messages (CDMs) to verify if a maneuver with ADCS could be useful. Also, a control algorithm will be implemented in the laboratory on a clean chamber setup and on an inert table, and with an Engineering Qualification Model (EQM) of a CubeSat (LUME). This research introduces a novel application of the Extended Kalman Filter specifically tailored for the control of a reaction wheel in a CubeSat, designed addressing the specific constraints of CubeSats, such as limited computational resources, power constraints, and weight. The algorithm was tested in the lab to validate its effectiveness, confirming that this approach performed as expected under realistic conditions. It is a computationally efficient methodology and frugal in terms of power consumption, critical aspect of Cubesats that usually operate under limited resources.
## 3 Results
### Internal Forces of the Main Structural Components  
Figures 19-21 contain the internal forces (axial forces and bending moments) for each of the following models: probabilistic, deterministic and design models.  
This type of diagram representation has been chosen in order to be easily read and understood, while also being as complete as possible. The following notes explain how to read the diagrams:  
* All internal forces diagrams are divided in two as follows:
* the upper half is for the circular pier section;
* the bottom half is for the bored piles.
* The height and depth values are measured from the upper side of the pile cap.
* Due to large differences in values of the two halves of the diagrams, each of them uses a different representation scale (check the top/bottom of the diagram).  
Figure 19: Bending moment M3 (longitudinal) diagram for pier and bored piles.  
Figure 21: Axial force N diagram for pier and bored piles.  
Figure 20: Bending moment M2 (transverse) diagram for pier and bored piles.  
Table 5 sums up the differences in internal forces between the three models as follows:  
For the circular piers, when comparing with the probabilistic analysis, the design model overestimates the bending moment for the X direction (bridge longitudinal axis) and is pretty close for the Y direction (bridge transverse direction). It is interesting to note that the difference in bending moments for the X direction (a ratio of 1.70) is a lot smaller than the difference of acceleration at superstructure level, where the ratio was 3.50. This means that a part of the acceleration difference does not generate bending in the piers, which is a completely different behavior than the traditional, simplified design concepts. For the axial forces, the probabilistic approach gives the strongest response with an increase of 20% over the typical design model. Still, this is not very problematic as it will only have a minor effect on the design of the pier.  
The deterministic SSI model is close to the mean value of the probabilistic analysis for all internal forces of the circular piers, but it underestimates the response by 20-30% when comparing to the design values of the 80% non-exceedance probability internal forces.  
The biggest differences in behavior are found below the soil surface as follows: the internal forces generated by transverse actions on the bored piles are underestimated by the design model by 25-35%. This actually means that the structure does not reach the demanded safety level. The axial force in the piles from the design model is larger than the one from the probabilistic analysis by approx. 10%; the axial force is the main factor for choosing the pile length and an overestimation will generate an important increase in the cost of the structure.  
Overall, the smaller transverse forces and larger axial force in the bored piles point to a stiffer overall foundation model that is not subjected to the actual forces that would be generated by the ground motion during a seismic event.
# Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning
## 4 Clinical Dataset
### Missing Data
#### 4.3.2 Multiple Imputation by Chained Equations (MICE)  
This method involves imputing missing values in a dataset by modeling each incomplete variable conditional on the observed values of the other variables. The imputation equations are created for each variable with missing data. The general idea is to perform imputation in a series of steps, with each step updating one variable at a time. This process is iterated until convergence. The imputation models can vary depending on the nature of the variables (continuous, categorical, etc.) and the assumptions you make about the data [33].  
Multiple Imputation by Chained Equations (MICE) is an iterative imputation method that imputes missing values in a dataset by modeling each variable with missing data as a function of other variables. The method is based on the idea of imputing missing values multiple times, creating multiple datasets with plausible imputed values. These datasets are then analyzed separately, and the results are combined to account for the uncertainty introduced by the imputation process [34].  
The general equation for imputing a missing value in MICE can be described as follows:  
\[Y_{i}^{m}=\mu_{i}+\epsilon_{i}^{m}\]  
where \(Y_{i}^{m}\) represents the imputed value for the missing data in a variable, \(i\), that \(m\)-th imputed dataset; \(\mu_{i}\) is the predicted value for the missing data; and \(\epsilon_{i}^{m}\) is a random error term sampled from the distribution of the residuals of the predictive model [35].  
The MICE algorithm proceeds through multiple cycles, where each cycle involves updating the imputed values for each variable based on the imputed values of other variables. This process is repeated until convergence, and the final imputed values are obtained by combining the results from all imputed datasets [36].  
In this study, we used IterativeImputer from the scikit-learn library, which implements MICE for imputing missing data. This method employs a Bayesian Ridge Regression model to predict missing values iteratively. In each iteration, the model imputes missing values for each feature in the dataset, taking into account the correlation between features. By default, it uses 10 iterations to converge, though this can be adjusted. The imputed data are then combined, ensuring that the variability introduced by the imputation process is incorporated into the final analysis.