esa-sceva/satcom-chunk-collection huggingface dataset:
# Operator and Graph Theoretic Techniques for Distinguishing Quantum States via One-Way LOCC
## 3 Operator Structures and One-Way LOCC
### Application: Sets of Indistinguishable Lattice States  
We have also been able to use the 'operator structure road map' outlined above to find sets of lattice states [8; 9; 25] that are indistinguishable under one-way LOCC. The following is taken from [12].  
Recall the two-qubit Bell states \(|\Phi_{0}\rangle,|\Phi_{1}\rangle\) defined above. The rest of the Bell basis is given by:  
\[|\Phi_{2}\rangle=\frac{|01\rangle-|10\rangle}{\sqrt{2}}\qquad|\Phi_{3}\rangle= \frac{|00\rangle-|11\rangle}{\sqrt{2}}.\]  
These states can be naturally identified with the Pauli matrices by \(|\Phi_{i}\rangle=(I\otimes\sigma_{i})|\Phi_{0}\rangle\) and where we write \(I=\sigma_{0}\), \(X=\sigma_{1}\), \(Y=\sigma_{2}\), \(Z=\sigma_{3}\).  
The lattice states are a generalization of the Bell states, which are very useful in their own right.  
For \(n\geq 1\), the class of lattice states \(\mathcal{L}_{n}\) is given by \(n\)-tensors of the Bell states,  
\[\mathcal{L}_{n}=\{|\Phi_{i}\rangle:i\in\{0,1,2,3\}\}^{\otimes n}\subseteq \mathbb{C}^{2^{n}}\otimes\mathbb{C}^{2^{n}}.\]  
States in \(\mathcal{L}_{n}\) can be identified with elements of the Pauli group \(\mathcal{P}_{n}=\{\otimes_{k=1}^{n}\sigma_{i_{k}}\}\), using an extension of the Bell state identification above.  
**Theorem 5**.: _For every \(n>1\) and \(d=2^{n}\), there exist sets of \(m\) lattice states in \(\mathbb{C}^{d}\otimes\mathbb{C}^{d}\) that are not distinguishable with one-way LOCC, where_  
\[m=\left\{\begin{array}{ll}2\sqrt{2d}-1&\text{ if $n$ is odd}\\ 3\sqrt{d}-1&\text{ if $n$ is even.}\end{array}\right.\]  
**Remark 3**.: _As discussed further in [12], this result is new and can be extended to the so-called 'generalized Pauli states', where a new proof is given of an established result, and which leads to an improvement for a studied subclass of states [26]. The following example illustrates the approach._  
**Example 2**.: _For an example in \(\mathcal{L}_{n}\), with n fixed, we can set_  
\[S_{1} = \{I^{\otimes i}\otimes Z\otimes I^{\otimes n-i-1}\}_{i=0}^{k-1}\] \[S_{2} = \{I^{\otimes i}\otimes Z\otimes I^{\otimes n-i-1}\}_{i=k}^{n-1} \cup\{X^{\otimes n}\}.\]  
_It is easy to check that the algebra generated by \(S_{1}\) has dimension \(2^{k}\); the algebra generated by \(S_{2}\) has dimension \(2^{n+1-k}\); and the algebra generated by \(S_{1}\cup S_{2}\) has dimension \(2^{n+1}\). This gives us:_  
\[S=\left(\{I,Z\}^{\otimes k}\otimes I^{\otimes(n-k)}\right) \cup \left(I^{\otimes k}\otimes\{I,Z\}^{\otimes n-k}\right)\] \[\cup \left(X^{\otimes k}\otimes\{X,Y\}^{\otimes n-k}\right)\]  
_with \(|S|=2^{k}+2^{n-k+1}-1\), which achieves its minimum when \(k=\lfloor\frac{n}{2}+1\rfloor\) and \(|S|\in\{2\sqrt{2d}-1,3\sqrt{d}-1\}\)._
###### Abstract  
The aim of this work is to assess the effectiveness of biomass gasification power plants in Russia (Irkutsk region) and compare them with other types of electricity and heat cogeneration systems. Biomass, which is waste from logging and wood processing, is considered as fuel for gasification plants. As a criterion, the cost of energy is used. Analytical relations are obtained for the cost of electric energy at a given cost of thermal energy and vice versa, thermal energy at a given cost of electric energy. These relationships are applied to assess the economic efficiency and compare small-power plants (up to 200-500 kW) such as mini-combined heat and power (CHP) on fuel chips and fuel pellets, coal-fired CHP and gas and liquid fuel power plants (gas-piston and diesel power plants). The latter are equipped with heat recovery boilers and supply consumers with heat and the electric power simultaneously. The calculation results show that the cost of electricity when using wood fuel is significantly less than the cost of electricity from a diesel power plant due to the use of cheaper fuel. In this regard, for autonomous energy systems of small power, especially near logging points, energy supply from biomass gasification power plants is a preferable solution than the use of diesel power plants. Wood fired energy cogeneration systems (mini-CHP) can also successfully compete with coal and gas power plants if they have cheap wood fuel at their location. With the introduction of carbon dioxide emissions charges, the use of not only wood chips, but also pellets becomes competitive in comparison with coal and gas.  
wood waste; energy use; cogeneration; wood gasification mini-CHP; economic efficiency; cost of energy +
Footnote †: journal: Applied Sciences
## 3 System Model and Assumptions
### Proposed Slice Allocation System
#### 3.2.1 Static Slice Allocation System (SSA)  
The SSA system is an adaptation of the schemes proposed in [14,15], but it differs in that it considers the combined admission control and resource allocation for the three slices in the 5G C-RAN. In addition, we evaluate the SSA system using different performance metrics from the schemes in [14,15]. We use the SSA system as a reference to compare it with the proposed ASA system.  
The SSA system divides the total BBUs pool resources (in terms of RBs) into three parts: the first part for URLLC slice traffic, the second for eMBB slice traffic, and the third for mMTC slice traffic. Figure 3 shows the SSA system model. From a logical point of view, the system behaves like three separate systems: each slice request has its own queue and reserved resources.  
The total resources reserved for uRLLC:  
\[R_{RSVD}^{1}=P_{uRLLC}\times R \tag{10}\]  
The total resources reserved for eMBB:  
\[R_{RSVD}^{2}=P_{eMBB}\times R \tag{11}\]  
The total resources reserved for mMTC:  
\[R_{RSVD}^{3}=P_{mMTC}\times R \tag{12}\]The total number of RBs currently used by all connected requests of slice i is limited by \(R^{i}_{RSVD}\), such that:  
\[\sum_{i=1}^{i=3}R^{i}_{RSVD}\leq\text{R} \tag{13}\]  
The arrival slice's request \(i\) is admitted if the sum of the slice's currently utilized RBs and the increment load of this arrived request are less than or equal to the total reserved resource of this slice. Therefore, the admission criterion for the new slice's request \(i\) can be expressed as:  
\[R^{i}_{c}+\Delta R_{i}\leq R^{i}_{RSVD} \tag{14}\]  
If the total RB partition of slice \(i\) requests is consumed, then the newly arriving slice's request \(i\) will be buffered in its corresponding queue and serviced according to the First In, First Out (FIFO) policy. If the queue for slice \(i\) is full, the incoming slice's request \(i\) will be rejected. Moreover, if a queued request exceeds the queuing time limit (\(MTD_{i}\)) before receiving service, it will be dropped from its queue. Each resource partition \(R^{i}_{RSVD}\) is dedicated to serving requests for slice \(i\) and cannot be utilized for other slices, even if it is available. Therefore, the system can guarantee the QoS requirements of each network slice. However, the system resources are underutilized due to a lack of resource sharing between the slice partitions.
## Theoretical Modeling  
### Co-rotation Based Geometrically Nonlinear Structural Model  
In the co-rotational formulation, beams are assumed to be linearly elastic, undergoing large translations and rotations but small strains. The rigid-body motions are separated from the strain-producing deformations at the local element level. It is assumed that the internal element behavior is linear, whereas nonlinearity is introduced via the co-rotational technique.  
The kinematics and statics of 3D co-rotational beams were presented in the previous work of the authors [35; 36], where the local and global degrees of freedom, nodal and elemental reference systems, tangential transformation matrix \(\mathbf{T}\), material stiffness matrix, geometric stiffness matrix \(\mathbf{K}_{t\sigma}\) and static tangent stiffness matrix \(\mathbf{K}_{t,stat}\) are defined.
## 3 Proposed Architecture
### IFS-CMR Parameters
Layer Selection Decision (LSD): The output parameter values, which are always between 0 and 1, denote three decisions--the interval [0, 0.3] indicates that _the vehicle_ can use the edge layer resources, the values in the interval (0.3, 0.7) specify that the layer to be used is the fog layer, whereas the values in [0.7, 1] specify the cloud layer as the most appropriate layer to run their applications.
## 5 Conclusions  
This study sheds light on how CKD patients might be diagnosed and treated early on in the disease's progression. A total of 400 patients contributed to the dataset, which included 24 distinct characteristics. After the estimation of missing values using the mean and mode methods, KNN imputation is employed to fill in the missing values. Later, the RFE process is applied to select the desirable attribute of CKD, thus enhancing the data quality in the preprocessing stages. To classify features, KNN, SVM, RF, DT, LR, and XGBoost were used. All of the classifiers' parameters were tweaked to achieve the best classification results, and all of the algorithms produced promising results. The proposed model also demonstrated decreased entropy by showing very less impurity measure. All other algorithms were outperformed by the XGBoost technique, which achieved the best precision, recall, F1-score, and accuracy of 98.90%, 98.90%, 98.90%, and 98.00%, respectively. Future work is planned to include the deep learning method, especially to deal with raw CKD informative image datasets.  
Conceptualization, M.T.R., V.K.V. and R.T.; methodology, M.T.R. and M.H.; software, M.H.; validation, V.K.V. and I.L; formal analysis, V.K.V.; investigation, M.T.R.; resources, I.I.; data curation, M.H.; writing--original draft preparation, M.T.R.; writing--review and editing, M.T.R., V.K.V. and I.I.; visualization, V.K.V.; supervision, R.T.; project administration, I.I.; funding acquisition, I.I. All authors have read and agreed to the published version of the manuscript.  
The National Research Foundation of Ukraine funded this research under project number 2021.01/0103.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Study and Year** & **Sampling Strategy** & **Accuracy** \\ \hline [PERSON] et al. (2021) [32] & 70–30\% training-testing & 97.00\% \\ [PERSON] et al. (2020) [33] & 10-fold cross-validation & 97.12\% \\ [PERSON] et al. (2020) [34] & 10-fold cross-validation & 90.40\% \\ [PERSON] et al. (2018) [35] & 70–30\% training-testing & 95.0\% \\ Proposed model & 75–25\% training-testing & 98.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the outcomes of this study with other studies found in the literature.  
Figure 11: Accuracy of different ML techniques in classification of CKD.
# The Car-Following Model and Its Applications in the V2X Environment: A Historical Review
## 4 Discussion
### The Existing Shortcomings
Desttitution of Large-scale Open-source Data Set for the V2X Environment  
It is obvious that large-scale open-source data sets such as the NGSIM play a great role in promoting research on car-following behavior and even traffic flow theory as a whole; although, it is easier to collect large-scale and high-precision data sets with the penetration of V2X technology in transportation systems. Most of the previous works were carried out based on numerical simulation without using field data or were only based on small-scale data sets collected in exclusive experiment roads by the corresponding researchersthemselves. It is certain that the results of these studies contribute much to understanding the car-following behavior in the V2X environment. However, there are some limitations in this way. On the one hand, due to the limitations of exclusive experiment roads and the experiment scale, there are deviations between the data collected in the experiment roads and the normal roads in transportation systems, which will negatively impact the performance of models in fitting actual car-following behavior. On the other hand, compared with works based on the open-source data set, the comparability and portability of those studies based on the exclusive data set are poor. It is no exaggeration to say that the lack of data sets such as the NGSIM in the V2X environment has restricted the further development of car-following behavior research to a considerable extent. The key issue for researchers and traffic management institutions is how to organize and effectively collect large-scale and high-precision data sets corresponding to different development stages of the V2X environment and formulate the unified data standards and evaluation methods. Adaptation of the Estimation Methods of Energy Consumption and Emission for Future Transportation Systems  
It is hard to measure the energy consumption and especially the emission of vehicle(s) when driving. Thus, the estimation methods, along with the numerical simulation based on the car-following models, has been widely employed to assess the energy consumption and emission affected by various sources of information in the V2X environment. With the development of V2X technology, there is also the upgrading of the vehicle and energy industry. Driven by national policies, the energy consumption and emission performance of newly manufactured vehicles, whether using non-fossil fuels or not, have been effectively improved by technological updating. The energy industry is also continuously improving the environmental performance of its products such as gas, which means less emissions caused by per unit energy consumption. Due to these factors, there is a certain deviation between the output of the previous widely used estimation method and the actual values of vehicle(s) energy consumption and emission. Thus, the estimation method needs to be updated and then calibrated based on the instrument vehicle driving experiments at a large scale under various traffic conditions.
# Improving Particle Swarm Optimization Analysis Using Differential Models
## 3 Numerical Algorithm Analysis of Standard PSO
### Differential Equation Model of Standard PSO  
Set \(\varphi_{1}=c_{1}r_{1}\), \(\varphi_{2}=c_{2}r_{2}\), \(\varphi=\varphi_{1}+\varphi_{2}\), then the standard PSO can be rewritten as:  
\[\left\{\begin{array}{l}v_{jk}(t+1)=\omega v_{jk}(t)-\varphi x_{jk}(t)+\left( \varphi_{1}p_{jk}(t)+\varphi_{2}p_{gk}(t)\right)\\ x_{jk}(t+1)=\omega v_{jk}(t)+(1-\varphi)x_{jk}(t)+\left(\varphi_{1}p_{jk}(t)+ \varphi_{2}p_{gk}(t)\right)\end{array}\right. \tag{25}\]  
The evolution of Equation (25) is a difference model, and its corresponding differential model can be obtained by using the unified model (Equation (3)) to obtain Equation (26).  
\[\left\{\begin{array}{l}\frac{dv_{jk}(t)}{dt}=(\omega-1)v_{jk}(t)-\varphi x_{ jk}(t)+\left(\varphi_{1}p_{jk}(t)+\varphi_{2}p_{gk}(t)\right)\\ \frac{dx_{jk}(t)}{dt}=\omega v_{jk}(t)-\varphi x_{jk}(t)+\left(\varphi_{1}p_{ jk}(t)+\varphi_{2}p_{gk}(t)\right)\end{array}\right. \tag{26}\]  
in:  
\[\frac{dv_{jk}(t)}{dt}=v_{jk}(t+1)-v_{jk}(t),\ \frac{dx_{jk}(t)}{dt}=x_{jk}(t+1)-x_ {jk}(t)\]  
The differential model (Equation (26)) can obtain the standard PSO algorithm by using [PERSON]'s method with a step size of 1.  
The differential model (Equation (26)) is then solved using existing numerical methods for solving systems of differential equations. For example, for the system of differential Equation (26), taking the step size as \(h\), [PERSON]'s method can be used to obtain:  
\[\left\{\begin{array}{l}v_{jk}(t+1)=[1+h(\omega-1)]v_{jk}(t)-h\varphi x_{jk}+ h\left(\varphi_{1}p_{jk}+\varphi_{2}p_{gk}\right)\\ x_{jk}(t+1)=h\omega v_{jk}+(1-h\varphi)x_{jk}+h\left(\varphi_{1}p_{jk}+ \varphi_{2}p_{gk}\right)\end{array}\right. \tag{27}\]  
The biggest difference between Equations (25) and (27) is the introduction of the step size \(h\). Therefore, when using the evolution of Equation (27) to solve the problem, four parameters need to be determined, namely the inertia weight \(\omega\), the cognitive coefficient \(c_{1}\), the social coefficient \(c_{2}\), and step size \(h\).
## 3 Results
### Surface Wind Analysis with ASCAT  
The ASCAT scatterometer onboard the MetOp-C satellite made an overpass over the Ionian Sea at 7:11 p.m. UTC 17 September 2020 as Medicare Ianos was starting to impact the Western Greek islands. Its observed 10 m sea surface wind field is shown in Figure 11 alongside those from the model simulation outputs at 7:00 p.m. UTC, horizontally averaged to approximate the sensor resolution. The statistics were calculated by averaging the four model grid points nearest to every observation grid point contained within the area of 36-41\({}^{\circ}\)N and 16-22\({}^{\circ}\)E, totaling a sampling size of \(N=1471\). All experiments overestimated  
Figure 10: Instantaneous surface precipitation rate (mm-h\({}^{-1}\)) at 10:30 p.m. UTC 17 September 2020 for all simulations (**a–h**) and as observed by the GPM-CO DPR overpass at 10:31 p.m. UTC (**i**) at 2 km above the sea level.  
the maximum observed wind speed in the eyewall, while they underestimated the local minimum in the eye. It has to be highlighted that ASCAT, because of its low spatial resolution, could present difficulties in capturing the small eye features of lanos. It is also true that the simulations were able to represent the low-level vortex with great precision and alignment in relation to that of the observations, as indicated by the high correlation coefficients (\(r\geq 0.7\)) in the top right corner of every experiment panel. Nevertheless, the GFS experiments predicted stronger winds in the western quadrants of the TLC than their ECMWF counterparts, with RMSE values ranging between 4.3 and 5.1 m\(\cdot\)s\({}^{-1}\) and between 3.4 and 3.6 m\(\cdot\)s\({}^{-1}\), respectively. Furthermore, the simulations with ECMWF showed an \(r\) value of 0.79-0.81 compared with 0.70-0.76 for GFS simulations.  
Among the different microphysics schemes, Morrison appeared to be the best in both global model groups considering \(r\) and RMSE. Within the ECMWF group, the WDM6 and Thompson experiments were those that more accurately placed the bulk of the surface wind peak area in the southwestern quadrant of the medicane, similarly to the ASCAT observations.  
Figure 11: The 10 m wind direction (barbs in kt) and speed (contours in m\(\cdot\)s\({}^{-1}\)) at 7:00 p.m. UTC 17 September 2020 for all simulations (**a**–**h**) and as observed by the ASCAT MetOp-C overpass at 7:11 p.m. UTC (i). The simulated wind fields were averaged every four grid points in each horizontal direction to result in roughly the same horizontal resolution as the satellite product.
## 4 Implementation Procedure
### Implementation of Proposed Model  
The implementation is performed in a cloud computing environment using Python v.3.10; the data fragmentation using the RFO-based SVM initializes by loading the dataset and splitting it into features (x) and target labels (y). The dataset is preprocessed, and the feature learning phase is performed to extract relevant data from the features to enhance the quality and usefulness of the dataset for subsequent analysis.
# Effect of Fuel Composition on Carbon Black Formation Pathways
## 3 Results and Discussion
### Carbon Black Formation--Dependence upon Fuel Compositional Differences
#### 3.5.1 CT-CB  
Coal tar is a high aromatic feedstock. As summarized earlier, aromatics can bypass the normal sequence of fuel pyrolysis, acetylene production, and benzene formation [29; 35]. Forming resonance stabilized radicals at lower temperatures by reactions not restricted to H-atom abstraction [5; 6], particle inception and growth can begin earlier and at lower temperatures than for a feedstock requiring such a reaction sequence [20; 26]. With origin either from the feedstock or via a molecular breakdown/buildup reaction sequence, by a combination of physical and reactive condensation, aromatics readily form CB seed nuclei [36; 37]. Further growth can immediately continue via coalescence of inception particles and by reactive aromatic condensation [5; 6]. Rates for both processes are accelerated by the locally high concentration of seed nuclei and aromatics--as occurs when they originate from the feedstock. With inception beginning by rapid coalescence and occurring at lower temperatures, the particle core structure is poorly developed and will appear to have multiple nucleation centers--reflecting nuclei clustering--as shown in Figure 2, or disordered cores as in Figure 3. Additionally, feedstock aromatics will impart a higher H/C content by alkyl and/or methylated substituents, these being partially preserved at the initially lower temperature--netting a higher sp\({}^{3}\) content. With the rapid depletion of heavy aromatics,  
Figure 5: Carbon black formation pathway to illustrate the difference in the formation of CT- and FCC-CB.  
further CB growth occurs via fuel pyrolysis products. The induction time required for these pyrolysis reactions allows mixing with surrounding combustion gases, elevating the temperature field while diluting the growth species concentration. Surface lamellae growth occurs under a very different reaction environment: smaller growth species, e.g., acetylene, step-wise radical creation/addition mechanism, e.g., HACA, and higher temperature. These factors and particularly the latter favor development of extended lamellae with more order [28; 38]--consistent with the observed radial variation in nanostructure to an ordered shell. The greater length and stacking of perimeter lamellae are indicators that the feed-derived growth species and temperature field are different for this stage of particle growth compared to earlier particle inception and initial growth. As inferred from the radial nanostructure of the CB particles, growth occurs under locally different chemical/temperature conditions. Different fuel components contribute to different stages of early versus latter particle growth--with aromatic content accelerating early inception and growth.
## 4 Results
### Landslide Hazard
#### 4.1.2 Results of the RF Model  
To ensure that the RF model has good predictive performance, it is necessary to determine the number of decision trees first. For the RF model, the more decision trees there are, the better the predictive performance will be, but the calculation time will also be longer. However, if there are too few decision trees, the predictive performance of the model will be poor [36]. Thus, using the MATLAB R2022a software, we obtained the optimal number of decision trees through the analysis of the out-of-bag error [62]. The optimal number of decision trees was determined to be 100, with a min_samples_leaf value of 5. Then, the LHIs were calculated using the established model. Finally, the gained LSI results were loaded into the GIS software to generate the landslide hazard maps.  
In the landslide hazard map generated by the FR-RF model (Figure 7c), the zones with very low (0-0.12), low (0.12-0.26), moderate (0.26-0.45), high (0.45-0.70), and very high (0.7-1) landslide hazards represent 34.99%, 27.72%, 20.56%, 9.97%, and 6.76% of the entire study area, respectively. However, according to the DFR-RF model (Figure 7d), the zones with very low (0-0.13), low (0.13-0.30), moderate (0.30-0.52), high (0.52-0.77), and very high (0.77-1) landslide hazards account for 36.48%, 29.17%, 14.16%, 12.51%, and 7.68%, respectively. Additionally, compared with the FR-RF model, the DFR-RF model predicts significantly fewer zones with moderate landslide hazards. Moreover, Figure 7 shows that the zones with very high and high landslide hazards predicted by the RF model are also mainly located between Aoyitake and Bulunkou.