esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Characterization of BC Surfaces after LPArP Treatment  
#### 2.4.1 Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (ATR-FTIR)  
After the Ar plasma modification of the BC, the chemical structure of the surfaces of BC was analyzed using ATR-FTIR. The analysis was carried out using an ALPHA II FTIR spectrophotometer (Bruker Daltonic, Bremen Germany) with a diamond ATR adapter. The spectra were collected in the wavenumber range of 3800-400 cm\({}^{-1}\) with a resolution of 2 cm\({}^{-1}\) (32 scans). The processing of collected ATR-FTIR spectra was performed using SpectraGryph 1.2 and OriginPro2021 software. The IR indices of the BC crystallinity, such as the total crystallinity index (TCI), lateral order index (LOI), and hydrogen bond intensity (HBI), were calculated from the band's absorbance ratios of A1371 cm\({}^{-1}\)/A2900 cm\({}^{-1}\), A1429 cm\({}^{-1}\)/A897 cm\({}^{-1}\), and A3400 cm\({}^{-1}\)/A1320 cm\({}^{-1}\), respectively. The allomorph I\({}_{\alpha}\) content was calculated using Equation (1):  
\[I_{\alpha}\alpha=2.55-\left(\frac{\Delta 750}{(\Delta 710+\Delta 750)}\right)-0.32 \tag{1}\]  
where A750 is a band intensity of 750 cm\({}^{-1}\) for \(I_{\alpha}\), and A710 is a band intensity of 710 cm\({}^{-1}\) for \(I_{\beta}\).
# Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications
## 4 Results and Discussion
### Fixed Subset Size Simulations  
In this trial, the cost function of proposed satellite selection algorithm is constructed by the sum of accuracy and integrity impacts instead of driven by the performance requirement, so that it can be used for fixed number satellite selection.  
\[\delta P_{i}=\frac{2*Slope_{i,i}}{2 drns_{0}}+\frac{\delta Slope_{i}}{Slope_{ \max,0}} \tag{19}\]  
The accuracy and HPL of the subset selected by each satellite selection algorithm during the simulation are shown in Figure 6a,b separately and the consumed time of each algorithm is shown in Figure 6c. The average 2 drms, HPL and consumed time of each algorithm are listed in Table 4.  
Figure 5: Sky plot view of the satellite geometry at the beginning of the trial.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Requirement** & **Parameter** \\ \hline Accuracy (95\%) & 32 m \\ Missed Alert Probability & 0.001 \\ False Alert Probability & 3.33 \(\times\) 10\({}^{-7}\) per sample \\ HAL & 0.1 NM \\ Mask angle & 5 degrees \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance requirements for the LNAV.  
All the three algorithms can obtain good accuracy performance which is well below 28 m when 12 satellites are selected in the dual-constellation navigation system. The down-date algorithm can obtain a little smaller 2 drms than the geometric algorithm as the large slope satellites which have great accuracy impacts are chosen as a priority. Additionally, the proposed algorithm can reach a bit better accuracy performance than the downdate algorithm and is close to the optimal 2 drms value. As for the integrity performance, the geometric algorithm and the downdate algorithm reach similar HPLs, with an average of about 48 to 52 m, but the HPL of the geometric algorithm fluctuates more strongly. While the proposed algorithm performs best among the three with an average HPL value of about 48 to 52 m, the HPL of the geometric algorithm is still better than the downdate algorithm.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Algorithms** & **2 drms (m)** & **HPL (m)** & **Consumed Time (s)** \\ \hline Geometric algorithm & 13.9855 & 51.6916 & 0.0024 \\ Downdate algorithm & 13.1456 & 48.0024 & 0.0014 \\ Proposed algorithm & 11.4943 & 33.0684 & 0.0123 \\ Brute force algorithm & 10.2619 & 24.4370 & 30.4349 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average 2 drms, HPL and consumed time of each algorithm in the fixed subset size trial.  
Figure 6: 2 drms and HPL of the subset and consumed time of each satellite selection algorithm in the fixed subset size trial. (a) 2 drms value; (b) HPL value; (c) Consumed time.  
33 m, as the integrity impacts of alternative satellites are taken into consideration. The consumed times of the geometric algorithm and the downdate algorithm are well below 0.01 s and the proposed algorithm takes a little longer time with an average of about 0.012 s. However, all of them are significantly efficient than the brute force algorithm.
## 3 Results and Discussion
### Positive Pressure Range  
Positive pressure on the windward area of low-aspect-ratio cylinders, such as oil tanks, is critical for buckling destruction [42; 43]. The zero-pressure angle, which is the angle at which the wind pressure coefficient equals 0, is an important parameter representing the positive pressure range over the windward area [44; 45]. The positive pressure range is the sum of the zero-pressure angles on the left and right sides of the cylinder. The fluctuation of the positive pressure range with \(Re\) at various heights of models is shown in Figure 9. In general, the positive pressure range decreased with \(Re\), and the value above M2 was greater than M1. For M1, the positive pressure range increased at heights below 0.70 H of the model, but before \(Re\) = 2.2 \(\times\) 10\({}^{5}\), the value at 0.91 H was the smallest, then tended to be the same as 0.70 H. For M2, the variation of the positive pressure range with \(Re\) was similar to M1, and the maximum and minimum values were detected at 0.70 H and 0.91 H, respectively.  
The influence of wind fields on the positive pressure range is presented in Figure 10. It was observed that the positive pressure range for M2 increased with turbulence, and similar results emerged for M1, except for the T1 wind field. The results also reveal that \(AR\) is the main factor influencing the positive pressure range, which is consistent with the research presented by [PERSON] [44] and [PERSON] [45].  
Figure 8: The variation of the base pressure coefficient with \(Re\) under different wind fields.  
Figure 9: The variation of positive pressure range with \(Re\) at different height of models.
## 2 Interval Type-2 Fuzzy Sets
### Footprint of Uncertainty and Membership Functions  
According to [12], the uncertainty of primary pertinence functions in type-2 fuzzy sets is represented by the footprint of uncertainty (FOU). If this aspect is not present, the type-2 fuzzy set in question becomes an ordinary fuzzy set. Figure 3 illustrates the FOU of triangular interval type-2 fuzzy set. Its height should be consistent with the type of problem under analysis as well as with the desired degree of uncertainty [10].  
The FOU provides an additional degree of freedom for a type-2 fuzzy set so as to directly handle system uncertainties. Thus, it is worth mentioning that FOU is able to model uncertainties in systems making use of type-2 fuzzy sets [15; 16]. The FOU of an IT2 TFN set is illustrated by Figure 3.  
Concerning the most usual FOU values, [17] demonstrate the analytical structure variation in values of FOU (\(\theta\)) ranging from 0 to 0.5. In order to discuss other values of FOU, [18] reported the differences between FOU values and used PI controllers through interval type-2 fuzzy numbers. It is worth mentioning that the value of the FOU depends  
Figure 3: Interval type-2 triangular fuzzy number and the footprint of uncertainty and \(\mathrm{H}\Big{(}\widetilde{A}^{\mathrm{L}}\Big{)}\). Adapted from [12].  
on the type of application and what is to be controlled. Table 1 summarizes the objectives according to FOU values.  
Thus, it can be observed how FOU can make systems more flexible, in addition to allowing an increase in the speed of response and/or stabilize data in order to avoid decision-making errors.  
With regard to SPC, [19] describe that the aim of process monitoring is to detect shifts in statistical parameters. In this case, when interval type-2 fuzzy sets are used, the value of the FOU can increase this capacity for detecting statistical parameters, when greater response speed is required (rigorous inspection), or decrease the capacity for detecting these parameters, reducing unnecessary stops on the production line (moderate inspection).
# Sensor-Based Solid Waste Handling Systems: A Survey
###### Abstract  
As a consequence of swiftly growing populations in the urban areas, larger quantities of solid waste also form rapidly. Since urban local bodies are found to be unable to manage this perilous situation effectively, there is a high probability of risks relative to the environment and public health. A sudden change is indispensable in the existing systems that are developed for the collection, transportation, and disposal of solid waste, which are entangled in turmoil. However, Smart sensors and wireless technology enable cyber-physical systems to automate solid waste management, which will revolutionize the industry. This work presents a comprehensive study on the evolution of automation approaches in solid waste management systems. This study is enhanced by dissecting the available literature in solid waste management with Radio Frequency Identification (RFID), Wireless Sensor Networks (WSN), and Internet of Things (IoT)-based approaches and analyzing each category with a typical architecture, respectively. In addition, various communication technologies adopted in the aforementioned categories are critically analyzed to identify the best choice for the deployment of trash bins. From the survey, it is inferred that IoT-based systems are superior to other design approaches, and LoRaWAN is identified as the preferred communication protocol for the automation of solid waste handling systems in urban areas. Furthermore, the critical open research issues on state-of-the-art solid waste handling systems are identified and future directions to address the same topic are suggested.  
Internet of Things; solid waste management; trash bin; LoRaWAN; Wi-Fi; smart city; remote monitoring 2022 22 22340
# Communication Protocol Design for IoT-Enabled Energy Management in a Smart Microgrid
## 5 Numerical Simulation Results
### Impact of Channel Gains  
In this section, the impact of channel gains between different HEMSs is illustrated for the error probabilities at individual HEMSs and the average error performance. Figure 4 shows the BER sensitivity index at HEMS 1 and 6, when the channel gain between HEMSs 3 and 4 dropped from 100 to 50 for a number of different channel gains between other HEMSs. The sensitivity index is computed using the ratio of the change in BER and the change in the channel gain. For example, if a 50% change in channel gain results into a 5% change in BER, then, the sensitivity index will be 0.1. A negative sensitivity index indicates that the BER with \(v_{34}=50\) is low compared to the case when all channel gains are set as equal. Thus, it can be seen that the sensitivity index is negative when all other channel gains are less than 50, and then, it becomes positive. That is, when the other channel gains are more than 50, dropping \(v_{34}\) to 50 worsens the error performance. It can be seen that the sensitivity index increases with the channel gain between other HEMSs. So, as the channel gain increases, the impact of changing \(v_{34}\) increases. However, with 20 dB SNR, the impact becomes less strong after a certain channel gain. Also, it can be observed that with low SNR, the sensitivity is high when the channel gain of the other HEMSs is low, and it becomes less strong when the channel gains are high. On the other hand, when the SNR is high, the sensitivity becomes low, with a smaller channel gain for the other HEMSs, and it increases when the channel gain is high.  
Figure 5 shows the the average BER of the overall microgrid and the relative impact of the channel gain between different HEMSs. The channel gain in one of the links that have been set to 50 while the other channel gains remain 100. It can be seen that the drop in channel gain between HEMSs 1 and 2, as well as HEMSs 5 and 6, does not significantly  
Figure 4: BER sensitivity at HEMS 1 and 6 with different channel conditions and different SNR.  
degrade the average BER performance. As the channel gain drops for intermediate links such as the one between HEMSs 2 and 3 or HEMSs 4 and 5, there is a moderate increase in average BER. The channel between the middle HEMSs (HEMSs 3 and 4) has a greater influence on the average BER, which is similar to the worst case error probability results, as shown in Figure 3a. Note that there is a constant SNR penalty between the different scenarios, and the average BER decreases at the same rate with an increase in the SNR. There is a 2 dB SNR penalty between the best possible scenario (when the channel gain degrades for the HEMSs at the edge) and worst possible scenario (when the channel gain degrades for the HEMSs in the middle).
## 3 Experimental Results
### Comparison of Multi-Focus Image (i.e., Desk)
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Methods** & \(\mathbf{Q^{AB/F}}\) & \(\mathbf{L^{AB/F}}\) & \(\mathbf{N^{AB/F}}\) \\ \hline DCT + Average & 0.5187 & 0.4782 & **0.0063** \\ DCT + Variance & 0.7165 & 0.2612 & 0.0478 \\ DCT + Contrast & 0.6212 & 0.2554 & 0.3629 \\ DWT & 0.6302 & 0.2552 & 0.3362 \\ SIDWT & 0.6694 & 0.2764 & 0.1564 \\ DCHWT & 0.6529 & 0.314 & 0.0789 \\ DCT + SML & 0.6774 & 0.3074 & 0.0324 \\ DCT + Eng\_Corr & 0.7288 & 0.253 & 0.0391 \\ DCT + SF & 0.7213 & 0.26 & 0.0415 \\ DCT + VOL & 0.7285 & 0.2519 & 0.0421 \\ DCT + EOL & 0.728 & 0.2522 & 0.0425 \\ DCT + AC\_Max & 0.6763 & 0.291 & 0.0696 \\ DCT + Corr & 0.7246 & 0.2541 & 0.0456 \\ Proposed Method & **0.9871** & **0.0071** & 0.0117 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: The outcomes for the desk image and comparisons with existing techniques in the literature ([26]). \begin{table}
\begin{tabular}{l c c} \hline \hline
**Methods** & \(\mathbf{Q^{AB/F}}\) \\ \hline [PERSON] et al.—with SR-4 & 0.89 \\ [PERSON] et al. & 0.72 \\ [PERSON] et al. & 0.73 \\ [PERSON] et al. & X \\ [PERSON] et al.—with SR-2 & 0.87 \\ [PERSON] et al. & 0.73 \\ [PERSON] et al. & 0.73 \\ [PERSON] et al. & X \\ [PERSON] et al. & 0.68 \\ [PERSON] et al. & X \\ [PERSON] et al. & 0.71 \\ [PERSON] et al. & X \\ [PERSON] et al. & X \\ [PERSON] et al. & X \\ [PERSON] et al. & 0.88 \\ [PERSON] et al. & X \\ [PERSON] et al.—with SR-3 & 0.88 \\ [PERSON] et al. & 0.71 \\ Proposed Method & **0.9871** \\ \hline \hline \end{tabular}
\end{table} 
Table 14: The outcomes for the desk image and comparisons with existing techniques in the literature ([27]).
## 3 Discussion
Compared with the published proteins, _CpTPS4_ had the highest similarity with S- +-linalool synthase of _Cinnamomum microratum_ (94.44%, Accession RWR97874.1), and _CpTPS1_ had the highest similarity with geraniol synthase of _Cinnamomum microratum_ (96.55%,Accession RWR98053.1). _CpTPS3_, _CpTPS5_ and _CpTPS9_ had the highest similarity with alpha thujene synthase/sabinene synthase of _Litsea cubeba_ (Accession AEJ91556.1), and the similarities were 90%, 90.21% and 90.69% respectively. Sequence similarity alone can not predict the specific biochemical function of a single TPS family member, because only a few amino acids can cause dramatic changes in the terpenoid structure of a given TPS enzyme [49; 50]. In addition, many TPS are prolific enzymes, which can generally produce mixtures of different proportions of the same compounds [51]. In the study of amino acid sequence similarity of limonene synthases in different species, it was also displayed that sequence similarity was more closely related to genetic relationship than function [24; 25]. Therefore, it is difficult to judge the specific functions of genes from the similarity of sequences. The specific functions of these five genes need to be further verified. The future functional verification and utilization of these monoterrepenoids should focus on these genes.
## 2 Materials and Methods
### Dataset  
Some requirements were considered in the dataset selection for this study. A dataset containing information about the defect incidence in software modules from multiple projects based on distinct programming languages in the same organization was required to support the achievement of the paper's goals. Moreover, a well-known and widely used dataset in the literature of the present research domain was necessary because of the established references and benchmarks to support future comparisons. Finally, since the present study's results are compared to the previous one, using datasets from the same organization was highly desired. NASA opened 14 datasets regarding distinct software development projects to support research on software module defect prediction [58]. The datasets cover 14 distinct softwaredevelopment projects based mainly on three programming languages: C, C++, and Java. The nature of those software systems from the same organization was also different, probably with distinct architectures and design patterns, since some were related to, for example, spacecraft instruments (CM1), a storage management system for receiving and processing ground data (KC1, KC2, and KC3), a combustion experiment of a space shuttle (MC1), a video guidance system (MC2), a zero-gravity experiment related to combustion (MW1), flight software from an earth-orbiting satellite (PC1, PC2, and PC4), s dynamic simulator for attitude control systems (PC2), and a cockpit security increase system (PC5) [59; 60]. Finally, the original study used a dataset from a single NASA project, which the present one seeks to extend. Therefore, the NASA MDP datasets were a natural choice since they meet all the study's requirements. Each instance of NASA MDP datasets corresponds to a software module's diverse static source-code metrics (features) and a class indicating whether the module was found to be defective or not. Those source-code metrics characterize code features associated with software quality: distinct lines of code measures, McCabe metrics, Halstead's base, derived measures, and branch count metrics [61; 62; 63; 64]. The number of features in each dataset varies slightly, with some having additional source-code metrics compared to others. Moreover, each dataset's number of instances is distinct because of each project's different number of modules. Since NASA MDP datasets became popular, slightly different versions have been available in distinct repositories. The pre-cleaned version [39] was used in the present study. Table 1 shows each NASA dataset's characteristics.
# Constraint Preserving Mixers for the Quantum Approximate Optimization Algorithm
## 3 Construction of Constraint Preserving Mixers
### Trotterizations
For each item in the list S that the algorithm produces, all Pauli-strings commute. We can illustrate the difference between Algorithms 2 and 3 for \(B=\{|01\rangle,|10\rangle\}\) and \(T_{1\leftrightarrow 2}\). With Algorithm 2, we have \(P_{1,2}=\frac{1}{4}(X-iY)(X+iY)\) and \(P_{2,1}=\frac{1}{4}(X+iY)(X-iY)\), which can be simplified to \(S=P_{1,2}+P_{2,1}=\frac{1}{2}(XX+YY)\). With Algorithm 3, we have \(A_{1}=X,B_{1}=Y\) and \(S=A_{2}=\frac{1}{2}(A_{1}X+B_{1}Y)=\frac{1}{2}(XX+YY)\) without the need to simplify the expression. ```
Data: Feasible states \(B\), transition matrix T (zero diagonal) fulfilling Theorem 1 Result: Non-vanishing coefficients \(c_{i_{1},\cdots,i_{n}}\) of Pauli-strings, Equation (31) S=[ ] for all\(1\leq j<k\leq|J|\), \(s.t.\) (\(T\))\({}_{jk}\
eq 0\)do  Recursively compute \(A_{n}\) and \(B_{n}\) for \(|w\rangle=\left|x_{I_{j}}\right\rangle\) and \(|z\rangle=x_{I_{k}}\) via Equation (39) Append (\(T\))\({}_{jk}A_{n}\) to S  end for check for cancellations and return S
```  
**Algorithm 3**Decompose \(H_{M}\) given by Equation (10) into Pauli-strings directly  
As shown above, Trotterizations can also lead to missing transitions. It is suggested in [2] that it is useful to repeat mixers within one mixing step, which corresponds to \(r>1\) in Equation (6). However, as we see in Figure 6, there can be more efficient ways to obtain mixers that provide transitions between all pairs of feasible states. One way to do so is to construct an exact Trotterization (restricted to the feasible subspace) as described in [19]. However, the ultimate goal is not to avoid Trotterization errors, but rather to provide transitions between all pairs of feasible states. We will revisit the topic of Trotterizations in Section 5 in more detail for each case and show that there are more efficient ways to do so.
## 2 Computational Simulation Details
### Force Field  
The choice of force field type directly impacts the precision of molecular dynamics simulations. The combined empirical force field approach (ClayFF + CVFF) has been extensively used, and has proven effective for studying the interfacial properties of cementitious materials with other organic or inorganic materials [16; 17]. To analyze the mechanism of interfacial water content on the bonding performance of the C-S-H/SiO\({}_{2}\) system, the combined empirical force field approach was utilized in the present investigation.  
Figure 1: Diagrammatic sketch of structures: (**a**) C-S-H gel; (**b**) SiO\({}_{2}\); (**c**) 5 Å thickness water; (**d–g**) C-S-H/SiO\({}_{2}\) system featuring a varying interfacial water-layer thickness (0 Å, 1 Å, 3 Å, 5 Å). (The O atoms, Si atoms, Ca atoms, and H atoms are represented by red balls, yellow balls, green balls, and white balls, respectively).  
Both the C-S-H and interface water molecules were given the ClayFF force field. The ClayFF is a reliable general empirical force field, which can simulate the structural performance and dynamical characteristics of the C-S-H and its interfacial properties with precision and efficiency. This force field was applied successfully to the simulation of cementitious materials, and their interatomic interactions at the liquid-phase interface [18; 19]. Furthermore, this force field was also employed to simulate the connection between water molecules, along with clay mineral surfaces [20; 21].  
The SiO\({}_{2}\) aggregates were simulated, utilizing the CVFF force field. This force field's potential functions consist of bond and non-bond interactions, and it mainly contains the calculations of bond length, bond angles, torsion angles, and dihedral angles. In short, simulating the C-S-H/SiO\({}_{2}\) system interaction utilizing the combined empirical force field (ClayFF + CVFF) approach was successful.