esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Multi-Criteria Ordinal Classification Based on an Interval Outranking Approach
The method requires the definition of interval outranking and preference relations between actions and the limiting boundary, as follows:  
**Definition 2. **  
(i) \(xS(\beta,\lambda)B\) iff there is a \(w\in B\) such that \(xS(\beta,\lambda)w\) and there is no \(y\in B\) with \(yP_{r}(\beta,\lambda)x\);  
(ii) \(BP_{r}(\beta,\lambda)x\) iff there is a \(w\in B\) such that \(wP_{r}(\beta,\lambda)x\) and there is no \(y\in B\) with \(xP_{r}(\beta,\lambda)y\). The \"pessimistic\" and \"optimistic\" assignment rules are:  
**Definition 3 (\"pessimistic\" rule). **_Suppose Set \(B\) satisfies the conditions on the limiting profiles. Step 1: Compare \(x\) to \(B\); Step 2: If \(xS(\beta,\lambda)B\), then assign \(x\) to class \(C_{2}\); Step 3: If not(\(xS(\beta,\lambda)B\)), then assign \(x\) to \(C_{1}\)._  
**Definition 4 (\"optimistic\" rule). **_Suppose Set \(B\) satisfies the conditions on the limiting profiles. Step 1: Compare \(x\) to \(B\); Step 2: If \(BP_{r}(\beta,\lambda)x\), then assign \(x\) to class \(C_{1}\); Step 3: If not(\(BP_{r}(\beta,\lambda)x\)), then assign \(x\) to \(C_{2}\)._  
It is easy to prove that the \"optimistic\" rule always suggests an assignment to a class not worse than the one suggested by the \"pessimistic\" procedure. For a detailed description of INTERCLASS-nB, the reader is referred to [21].
## 3 Results and Discussion
### Bioactive Properties
#### 3.4.3 Antioxidant Activity Against ABTS and DPPH  
The highest antioxidant activity against both ABTS and DPPH was found for fresh fruit and was 448.62 \(\pm\) 4.23 \(\upmu\)M Trolox/100 g DM and 328.85 \(\pm\) 16.98 \(\upmu\)M Trolox/100 g DM, respectively (Table 3). The freeze-dried samples had a high antioxidant activity of 364.51 \(\pm\) 9.12 \(\upmu\)M Trolox/100 g DM against ABTS and 258.78 \(\pm\) 5.16 \(\upmu\)M Trolox/100 g DM against DPPH. Indeed, convection-dried fruit had the lowest free radical scavenging capacity. Drying temperature also played a significant role, as quince dried at 60 \({}^{\circ}\)C had the lowest activity against ABTS (232.12 \(\pm\) 11.47 \(\upmu\)M Trolox/100 g DM) and DPPH (144.24 \(\pm\) 5.83 \(\upmu\)M Trolox/100 g DM).  
Quince fruits are a good source of natural phenolic antioxidants. These antioxidants play an essential protective role in the plant against ultraviolet radiation, act as a defence against pathogens and predators, act as an attractant in fruit spreading. Quince is a natural and inexpensive source of potent antioxidants, such as phenolic acids and flavonoids. However, the antioxidant capacity is attributed not only to the content of phenolic and organic acids but also to the action of numerous other compounds present in quince and possible synergistic and antagonistic effects [1]. However, when exposed to elevated temperatures and oxygen, many bioactive compounds responsible for antioxidant activity are degraded [42,43]. Therefore, the present study showed a loss of free radical scavenging capacity due to convection drying. Similar correlations, but for dried raspberry fruit, were reported in a study by [PERSON] et al. [42], which found lower radical scavenging capacity in convection-dried samples compared to fresh or freeze-dried. To preserve the high antioxidant activity, freeze drying quince fruit appears to be a good processing method. Another study by [PERSON] et al. [44] proved that such fruits can then be used to produce functional foods. The researchers developed a cookie recipe with freeze-dried Japanese quince fruit. The cookies had 2-3.5 times higher antioxidant activity and fewer secondary lipid oxidation products than the control cookies. In addition, the product enriched with natural antioxidants showed microbiological stability for as long as 16 weeks of storage.
## 4 Fuzzy Logic Coding and Results  
To model the fuzzy logic MATLAB codes, rules were set according to the parameters that were used. The parameters of EMI, cross correlation, and curing time were used to analyze the regular pattern of concrete strength development. Here, the temperature effects on strength developments can be observed from the graph, as the specimen 1 was placed in 5 \({}^{\circ}\)C, so the very initial strength growth was observed slowly, the specimen 2 was placed in 20 \({}^{\circ}\)C, so the initial growth was comparatively  
Figure 6: Cross correlation (CC) of impedance data observed by five channels for 48 h.  
Figure 7: CC of the impedance data observed by five channels for seven days.  
faster than the 1 st specimen, similarly for specimen 3, the growth was faster in initial hours. Figure 8 show the strength development patterns for specimens 1, 2, and 3.  
In Figure 8, the solid line shows the data of specimen 1; this experiment was conducted at 5 \({}^{\circ}\)C. It can be seen that the concrete strength establishment started late in the first hour. Continuous and steady growth was observed from this point up to a curing time of 25 or 26 h. However, after 26 h, the strength-gaining process became more rapid; this continued until 60 h, enabling the concrete to develop the majority of its strength. In the third day, growth was observed to slow and become less steady. After the 80 th hour of curing, the strengthening process was observed to slow further and remain constant until the 28 th day of aging; this is the duration in which a concrete gain its estimated strength.  
The data collected from specimen 2 are shown by dotted line in Figure 8. Here, the graph shows that the concrete strength establishment was started similarly to specimen 1. In the earlier hours, it was observed to be relatively slow when compared to the curing of the second day. After 28 and 29 h of curing (on the second day), the strength-gaining process appears to begin, rising rapidly until the end of the second day. After the second day, the strength development was slower, and the same ratio was observed until 142 h of curing. After this point, the strength development slowed further and was constant until the 28 th day of curing.  
Similarly, the results for specimen 3 are presented by dashed line in Figure 8. Here, the data indicate that the strength development was started in the initial ages, between the first and second hours; this had been happened because this specimen was placed at a temperature of 40 \({}^{\circ}\)C. The graph further shows that the strength development was fairly slow until the eighth or 10 th hours; after 10 h the strength was observed to rise a bit faster until the end of the first day. On the second day, the strength grew faster until the 45 th or 46 th hour of curing, and then on the third day it was observed to be slower when compared to the second day (but still rapid). After the 115 th hour, it became constant and the strength development growth was slower until the 28 th day of curing.  
Figure 8: Predicted data for concrete strength development of specimen 1, 2, and 3.
# An Efficient Routing Protocol for Quantum Key Distribution Networks
## 2 The Proposed QOLSR Protocol
### The Proposed QOLSR Protocol
#### 2.3.1 Efficient Link-State Awareness Mechanism for QKD Networks
Based on the above scheme, there will be WARN-MIN quantum secure keys that can be used to provide communication services during path switching, which can enhance quantum secure key utilization in QKD networks.
# Quantitative Retrieval of Soil Salinity Using Landsat 8 OLI Imagery
## 2 Materials and Methods  
### Study Area  
The Songnen Plain is located in the southwestern part of Heilongjiang Province, China, between the Greater Khingan Mountains, Lesser Khingan Mountains, and Changbai Mountains (\(119^{\circ}45^{\prime}\)\(\sim\)\(129^{\circ}36^{\prime}\) E, \(42^{\circ}50^{\prime}\)\(\sim\)\(49^{\circ}18^{\prime}\) N) and covers an area of about \(23.75\times 10^{4}\) km\({}^{2}\) (Figure 1). The study area is located in a semi-arid sub-humid zone and has a temperate continental climate. The annual average precipitation is 400-500 mm, and the annual average evaporation is 1250-1650 mm. According to the field survey conducted by the ministry of water resources and agriculture of China, the area of saline-alkali land in the Songnen Plain was about 2.40 million hm\({}^{2}\) in 1950. It was dominated by a slightly salinized reed marsh with almost no alkaline soil. The area of saline-alkali land reached 3.20 million hm\({}^{2}\) in 1990 and 3.94 million hm\({}^{2}\) in 2006 [19]. Most of the saline-alkaline land is located at altitudes of 150 m or less (primarily at 130-140 m) and near rivers [20]. Land salinization in the Songnen Plain is caused by several factors. Plate tectonics affected the structure and properties of the Earth's crust, influencing material sources and transport paths [21]. The groundwater acted as the solvent and carrier for the dissolved salts. Since the groundwater level is close to the surface, the salt was deposited on the surface. [22; 23]. An arid or semi-arid climate promotes the rise of the water and salt in the soil profile [24; 25]. In the last several decades, land salinization has significantly increased and threatened the local food security because grain is an important commodity in Heilongjiang Province.
# Controlling Remotely Operated Vehicles with Deterministic Artificial Intelligence
## Appendix B C++ Code
state_estimator.cpp_  
#include <state_estimator/state_estimator.h>  
////////////////////////////////////////////////////////////////////////  
namespace { template<size_t N> std::map<State_estimator_defs::State, int> Get_state_idxs(conststd::array< State_estimator_defs::State, N> states) { std::map<State_estimator_defs::State, int> state_idxs; for(size_ts=0;s<states.size();s++) { state_idxs[states[s]]=s; } returnstate_idxs; } template<size_t N> Eigen::Matrix<double, N, 1> Get_polar_correct(conststd::array<State_estimator_defs:: State, N> states) { Eigen::Matrix<double, N, 1> polar_correct; for(size_ts=0;s<states.size();s++) { polar_correct(s)=State_estimator_defs::Is_polar(states[s]); } returnpolar_correct; } } // Anonymous namespaceState_estimator::State_estimator() : m_pmodel(std::make_shared<Model_type>(0)), m_state_idxs(Get_state_idxs(m_pmodel->Get_states())), m_kf(m_pmodel.get(), &Model_type::A, &Model_type::B, &Model_type::Q, decltype(m_kf)::MatrixN1::Zero(), decltype(m_kf)::MatrixN1::Zero(), Get_polar_correct(m_pmodel->Get_states())), m_first(true) { }  
Kf_type::MatrixNNP=m_kf.Get_estimate_covariance(); std::array<State_estimator_defs::State,Model_type::Num_states()>states=m_pmodel->Get_states(); std::map<State_estimator_defs::State,double>state_estimates; //KF_type is templated on the size of model, so they will both have the same //dimensionality for(decltype(states)::size_types=0;s<states.size();s++) { state_estimates[states[s]]=P(s,s); } returnstate_estimates; }
```
_Appendix B.13. state_estimator_defs.h_
```
#ifndefSTATE_ESTIMATOR_DEFS
#defineSTATE_ESTIMATOR_DEFS
#include<string>
#include<sno/so_exception.h> namespaceState_estimator_defs { /**
* @brief Enumerated states estimated by this state estimator */ enum State { X, Y, Z, X_dot, Y_dot, Z_dot, X_dot_bias, Y_dot_bias, Z_dot_bias, X_accel, Y_accel, Z_accel, X_accel_bias, Y_accel_bias, Z_accel_bias, Phi, Theta, Psi, Phi_dot, Theta_dot, Psi_dot, Phi  
Theta_dot_bias, Psi_dot_bias, Phi_accel, Theta_accel, Psi_accel, Phi_accel_bias, Theta_accel_bias, Psi_accel_bias ];  
/**  
* @brief Enumerated referenceframes wemightcareabout
*/ enumReference_frame
* Sensor, //< Arbitrarysensorframe Body, //< Vehicle ''body'' frame Body_NED, //< Horizontal components of bodyframe NED, //< Worldframe, north/east/down ];  
/**
* @brief Seeif astaterepresentsavaluethatrequirespolarcorrection
* @params State
* @returnTrueifthestaterequirespolarcorrection
* @throwsso::Invalid_argumentifsdoesnotrepresentaknownstate
*/ boolIs_polar(constStates);  
/**
* @brief Get astringrepresentationofastate
* @params Statetogetthestringrepresentationof
* @returnStringrepresentationof s
* @throwsso::Invalid_argumentifsdoesnotrepresentaknownstate
*/ std::stringState_to_string(constStates);  
#endif  
_Appendix B.14.
## 4 Experimental Evaluation
### DBLP Dataset  
Figure 8 illustrates the accuracy of all embedding methods and similarity measures with the DBLP dataset and Table 8 shows the precision values for all methods.  
As observed in the figure, DeepWalk shows the _best_ accuracy among all embedding methods, while NetMF shows the worst one in terms of MAP, precision, recall, PRES, and F-score. Among similarity measures, JPRank shows the _best_ accuracy and it is _close_ to that of JacSim, while SimRank shows the worst accuracy in terms of all metrics. Now, by comparing the accuracy of DeepWalk with that of JPRank, it is observed that JPRank _outperforms_ DeepWalk by 64.89%, 50.34%, 51.70%, 52.71%, and 51.11% in terms of MAP, precision, recall, PRES, and F-score, respectively; Table 9 shows the percentage of improvements in accuracy obtained by JPRank over all other methods with the DBLP dataset.  
As observed in the figure, DeepWalk and NetMF show the best accuracy among all embedding methods in terms of MAP, precision, recall, PRES, and F-score. However, their accuracies are _not_ tangible, where NetMF outperforms DeepWalk in terms of precision, PRES, and F-score, while DeepWalk shows better accuracy in terms of MAP and recall. NERD shows the worst accuracy among all embedding methods. SimRank* shows the _best_ accuracy among all similarity measures, while SimRank shows the worst one in terms of all metrics. Now, by comparing the accuracy of NetMF with that of SimRank*, it is observed that they show very _close_ accuracy; SimRank* outperforms NetMF in terms of precision, recall, and F-score, while NetMF outperforms SimRank* in terms of MAP and PRES. Table 11 shows the percentage of improvements in accuracy obtained by SimRank* over all other methods with the TREC dataset.
## 4 Optimal Design Problem Formulation
### Economic Criteria: Total Life-Cycle Cost (TPC)  
The objective function of the economic criterion is formulated as a cost minimization problem of the net present value of the total life-cycle costs (TPC) of all system components alongside the implementation of the flexibility requirements under different system scenarios. The decision variable of the optimization problem is the capacity of the WT (\(C_{WT}\)), PV (\(C_{PV}\)) and ESS (\(C_{ESS}\)).  
\[\text{minimize }TPC=\sum_{z=1}^{Z}\left\{CI_{z}+\sum_{n=1}^{n=N}\frac{(O \&M_{z}+RP_{z}-RV_{z})}{(1+r)^{n}}\right\}\times C_{z} \tag{14}\]  
where \(z\) indexes the \(z_{th}\) component and \(C_{z}\) is the decision variables that represent the optimum component capacities of each of the system components (PV, ESS, and WT). The \(TPC\) components are the capital costs (\(CI_{z}\)), yearly operation and maintenance costs (\(O\&M_{z}\)), replacement costs (\(RP_{z}\)) and the salvage value \(RV_{z}\), \(N\) is the project lifetime, n is the time step in the project life, i.e., a year and \(r\) is the discount rate. The system components have a yearly operation and maintenances cost over the project lifetime.
## 4 Analysis of the Results
### Discussion on the Research Questions  
This section presents the responses to the research questions raised at the beginning of this research, after the analysis of the results was carried out.  
* RQ1: The transition in assessment methods due to COVID-19 has been substantial, as educators adapted to unfamiliar approaches to meet new challenges in evaluating the students' academic performance.
* RQ2: There is no significant difference in the question types of assessment used between STEM and non-STEM instructors. It could be noted that the only differences observed are in the use of Cloze quizzes and images.
* RQ3: The introduction of FTP has significantly transformed the use of questions. Prior to the training and implementation of FTP, the GIFT format predominated, restricting question types to Multiple-Choice, True/False, Matching, Short Answer, Numerical and Essay. With FTP, the ease of generating questions via XML dramatically expanded the available options in Moodle, motivating educators to explore and use a broader range of standard question types.
* RQ4: After using FTP, educators use images in their quizzes more frequently. FTP facilitates the insertion of images, which previously may have been perceived as more complex, and many educators had not considered using images before.  
These insights illustrate how FTP has positively impacted educators' assessment practices, expanding their capabilities and options within Moodle. With FTP, feedback can be easily given to the student, which can help them improve their learning. This can help the teacher configure and adapt the assessment questions to each student. In addition, using images makes the assessment questions more attractive to students, and therefore, they will show more interest.
# Effect of Pole Head Profile on Torque Performance of a Disc-Type Multipole Magnetorheological Brake
## 5 Results
### Effect of Coil/Shank Shape and Radius  
Brake performance is directly affected by the resistance of the coil based on parameters such as the number of wire turns, applied current and coil/shank dimensions. Increasing the number of wire turns and amount of input current influences the yield stress, and the saturation limits depend on the magnitude of the yield's shear stress. An approximate number of wire turns is anticipated by the quotient of the coil area and the axial width of the coil. The greater axial width of the coil yields a greater number of turns and thus achieves a higher braking torque. The wire is to be wound precisely so that each strand effectively occupies a total cross-sectional area, which improves braking torque. Torque increment is extremely low once the selected coil material reaches the magnetic saturation point.  
Ellipse pole head geometry was designed with an elliptical shank to gain higher yield stress, and for all other geometries, a circular coil was used for analysis. Shank radii varied from 6.5 mm to 8.5 mm, and pole head radii based on the saturation condition from 7.0 to 8.0 mm were preferred; the number of wire turns was calculated based on the axial width of the coil; the gauge of the wire was 0.57 mm at a constant applied current of 2 amperes. From Figure 9, we can observe three magnetic conditions namely, fixed concentration flux, fixed dispersive flux, and fixed total flux, which relate to electric and magnetic fields acting on elliptical and circular coils that are important for understanding the behavior of electromagnetic fields. In Figure 9, (i) represents the fixed concentration flux, where the applied current is 2 A and the lower number of turns results in a lower amount of flux flow; (ii) represents the fixed dispersive flux condition, where the coil current is 2A and the average number of turns, which results in equally distributed flux flow through all the sides of pole head profiles; (iii) is the saturated condition of a coil with 2A coil current and a maximum number of turns, which shows an impact on the flux flow. Higher braking performance was obtained at an optimum coil saturation condition using an 8.0 mm shank radius for the square, trapezoid, circle, pentagon and hexagonal pole heads and using a 6.5 mm shank radius for the ellipse pole head.  
Figure 8: Magnetic flux flow in different pole head profiles: (**a**) trapezoid, (**b**) ellipse, (**c**) pentagon, (**d**) circle, (**e**) hexagon, (**f**) square.
# A Method for the Rapid Propagation of Emergency Event Notifications in a Long Vehicle Convoy
## 3 Simulation Environment and the Convoy VANET System
### Convoy VANET System  
The purpose of the convoy VANET system is to quickly and efficiently disseminate Safety Messages (SM) to all vehicles in the convoy. An SM can be any highly time-sensitive piece of information, but here, we considered it to be a hard braking maneuver. To accomplish this goal, the Convoy VANET system included a few independent components. The most important component is the routing choice method. In essence, the routing system is a combination of traditional ad hoc routing schemes and the methods proposed by [PERSON] and [PERSON]. When a node decides to (re)transmit an SM it will decide upon a preferred retransmitter (PRTX). This choice of PRTX will be listed in the SM and read by all that receive the message. If the designated PRTX receives the SM, it will retransmit the SM immediately. Every other node that receives the SM will decide on a retransmission delay that is based on its physical distance to the designated PRTX (if it is known). If a node hears that the SM has been retransmitted by a rearward peer, then it cancels its queued SM retransmissions to reduce the number of unnecessary retransmissions. After the initial \"wave front\" of transmissions, the nodes will be listening for any peers that did not receive the SM and then attempt to rectify the situation. The goal of this type of system is for the SM to have the opportunity to move as quickly as possible down the convoy, even if it is not received by every node on the first pass. The remaining details will be described in the following subsections.
# A Method for the Rapid Propagation of Emergency Event Notifications in a Long Vehicle Convoy
## 3 Simulation Environment and the Convoy VANET System
### Simulated Wireless Channel  
We used ns3's included YANS Wifi Physical and YANS Wifi Channel to simulate the physical layer of the channel. The propagation loss model was the combination of the log-distance, Nakagami, and range cutoff values. Range cutoff was added to eliminate extremely low probability events from affecting the results. In addition, ns3's included constant speed propagation delay model was used, though this choice has little effect on the simulation's fidelity. The simulated wireless channel was designed to be less reliable compared to the findings listed in Section 1.3. This was done for two reasons. First, we wanted to show the performance of our system in a degraded communication environment. Second, we wanted to reduce the number of simulated vehicles, as it would otherwise require a longer transmission distance to reach the desired reliability degradation. The issue of string stability still exists whether each \"entity\" is one vehicle or multiple all moving in unison due to being within the high-reliability transmission region of the leader. The approximate reception reliability vs. distance for the default parameter set is listed in Table 1.