esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method for Underwater Wireless Data Transmission in a Hydroacoustic Channel under NLOS Conditions
## 5 Results
### Simulation Research  
The error in making a decision about the transmitted bit in the individual receive path can be caused by selective fading and/or noise. Selective fading is caused only by multipath propagation. In order to assess the impact of this phenomenon on the reception quality expressed in BER, a simulation using the Monte Carlo method and MFSK modulation was carried out. The mathematical model presented in Section 3 was used in this simulation. The influence of the number of replicas on the probability of a wrong decision was checked. In the simulation, the number of replicas was changed in the range of 2 to 100. In all cases, the total power of the replicas was the same and the amplitudes of the replicas were adopted according to the Weibull distribution. In Weibull distribution, the probability of occurrence of an amplitude of value 1 (maximum value) is very low. However, in received signals (Figure 10) there were always replicas with such amplitude. In order to reflect the real conditions in the simulated impulse response of the channel, before normalization, a signal with the maximum value equal to 1 was introduced so as to obtain the result as shown in the histogram presented in Figure 10. The simulation was performed without the presence of noise. The results of the above simulation are presented in Figure 14.  
A significant deterioration in the quality of reception is observed when increasing the number of replicas to a value of about 20. A further increase in the replica number does not cause a significant increase in BER. Taking into account the average number of replicas at individual measurement points (Figure 7), it can be concluded that the reception quality obtained by the simulation (Figure 14) is consistent with that measured in the laboratory conditions presented in Figure 12. For example, the average number of replicas at point No. 5 was about 100. At this point, the measured BER was about 0.02, which is a good agreement with the simulation result (Figure 14).
## 2 Materials and Methods
### IGC Determinations  
The adsorption and separation studies of \(n\)-paraffin mixtures were carried out by IGC. For this purpose, a Shimadzu chromatograph, model 14B, equipped with a flame ionization detector (FID) was used in a temperature range of 22-170 \({}^{\circ}\)C. Helium was used as a carrier gas using a flow rate of 1-5 cm\({}^{3}\)/min. The column, stainless steel with a length of 90 cm and an internal diameter of 0.3 cm, was packed with 5.0 g (6 cm\({}^{3}\)) of boehmite sample. The column was conditioned overnight at 150 \({}^{\circ}\)C under helium flow. The \(n\)-alkane hydrocarbons (analytical quality) in their vapor phases were injected in the smallest detectable quantity, in order to meet the requirement of adsorption to infinite dilution, which corresponds to zero coverage. To calculate the adsorbate retention time (\(t_{R}\)), the retention time (\(t_{0}\)) of the lower adsorbent marker (methane) was used as a reference. The corrected retention times were taken as the average value of three injections for each adsorbate. The net adsorbate retention volumes (Vn) were calculated from the Equation (3):  
\[Vn=JF(t_{R}-t_{0})(T_{C}/T_{A})(P_{0}-P_{W})/P_{0} \tag{3}\]  
where \(J\) is the James-Martin gas compressibility correction factor, \(F\) is the carrier gas flow rate at the flowmeter temperature (\(Tf\)), \(T_{C}\) is the column temperature, \(T_{A}\) is the room temperature (\(T_{A}=Tf\)), \(P_{0}\) is the outlet column pressure, and \(Pw\) is the vapor pressure of water at \(Tf\). The differential heat of adsorption (Q\({}_{d\beta}\)) was obtained from chromatographic measurements at infinite dilution (Henry zone) using the following expression and the corresponding linear dependence ln \(K_{S}\) vs. \(1/T_{C}\):  
\[Q_{dif}=-R[d(lnK_{S})/d(1000/Tc)] \tag{4}\]  
where \(R\) is the universal gas constant, \(K_{S}\) is the surface partition coefficient for solute (\(K_{S}=Vn/m\cdot Ss\)), defined as the net retention volume per unit of sample mass in the column (m) and the specific surface area of the adsorbent (\(Ss\)).  
The separation of the \(n\)-paraffin mixtures (C\({}_{5}\)H\({}_{12}\)-C\({}_{9}\)H\({}_{20}\)) was evaluated by the Bering and Serpinski's separation coefficient (\(K_{BS}\)). The temperature dependence of this coefficient for a mixture of two species is given by the following equation:  
\[ln\ K_{BS}=(Q_{dif2}-Q_{dif1})/RT \tag{5}\]where \(Q_{diff1}\) and \(Q_{diff2}\) are the differential heats of adsorption of the species 1 and 2 and \(T\) is the column temperature. When \(K_{BS}>1\) the total species separation is possible.
## 3 Materials and Methodology
### Construction of the Physical Analog Model
#### 3.1.2 Stress Monitoring Design  
Five pressure gauges with a 1 cm diameter were embedded in the model where stresses were to be determined. The pressure gauges were installed horizontally. The pressure gauges were installed at the following designated positions in the physical simulation model: two pressure gauges were buried above the two boundary coal pillars, namely 1# and 5#, positioned 25 cm from the edge of the model, and three pressure gauges were buried in the main roof for data measurements, namely 2#, 3#, and 4#, and successively positioned from the open-cutting face left to right at 45 cm, 95 cm, and 145 cm. Afterward, each pressure gauge was connected to a monitor via a wire for data acquisition. In order to study the additional mining pressure caused by mining and to avoid the influence of original rock stress, the micro-strain of the pressure gauges was set to zero before mining. Figure 4 depicts the photos of the pressure gauge monitoring layout. The monitoring indicators were as follows: (i) the stress change characteristics of the overburden strata during mining and filling above the goaf area of the backfill working face; (ii) the stress change characteristics above the two boundary coal pillars.
## 4 Linking the Two Approaches
### Application to the Multicopter Example
In the requirements of the multicopter, for instance, a certain rate of climbing is desired. This functional requirement can be verified by means of a simulation using a control model. Another requirement is that the main frame  
Figure 17: Design graph of the function model of the multicopter. needs be able to withstand the combined loads from the lifting forces of the four or more rotors. The fulfillment of the requirement can be evaluated with a finite element analysis (FEA) model. Other requirements can concern non-technical aspects such as economical aspects. For instance, a cost target may be defined in an early stage of the design phase. All components are defined in the automated design process, and for certain components that are not procured externally, manufacturing process are chosen. From the central design graph, the bill of materials (BOM) and the cost simulation are also created via appropriate interfaces. This simulation can evaluate whether the cost target can be achieved and can discard design variations that do not achieve these targets. Additionally, employing the integrated IF model it is feasible to identify which actors have achieved their intended functions and which have not. Additionally, based on simulation it is possible to determine whether the fulfilment of the desired function and behavior objectives is within the range of given design goals. In case a new product configuration is to be explored, this should enable the engineers to focus immediately on the necessary changes for complying with all given requirements. In this context, a multi-objective optimization process based on, e.g., pareto front analyses can lead to systems with an optimum fulfillment of a number of requirements. Currently, in many engineering disciplines, superstructure-based optimization and selection approaches are being investigated and applied [56; 57]. In these examples, alternative solutions are described in a superstructure representation and an optimal configuration is found by means of numerical simulation. In the presented research, the general topic of finding the optimum solution from alternatives generated by the GBDL was addressed, e.g., by means of pareto fronts. An optimization based on a superstructure representation could be an interesting addition and will be a topic for further research. Novel investigations of the function model are already possible in the given form, and this is discussed in the subsequent section.
## 6 Conclusions  
An improved method based on the traditional collocation method with FMU is proposed in this paper. This proposed method is validated by comparing the results to the simulation accomplished in PSOPT, and is proved to be highly efficient. At the same time, it is demonstrated that this method is available to solve the six-axis robotic manipulator problem, which was not solved with the traditional method before. By considering different loads in the six-axis case, there is an obvious impact on joints 4 and 5, which makes the input voltage of the actuator shift between the minimum and maximum values more frequently. A motors' life might therefore be affected. The experiments show that the OCP trajectory can stimulate the performance of the robotic manipulator by achieving a larger maximum of velocity, and extend the motors' life by controlling the currents under the rated maximum. Moreover, the calculation time increases exponentially when applying the LPM method with more nodes. Therefore, applying the switching structure, each stage of which uses the LPM method with fewer nodes, is more useful to eliminate the oscillating behavior of the controls.  
Figure 7: Experiment solutions of the OCP path with shorter DTT. In (**a,b**), the DDT of 0.903 s, 0.541 s and 0.451 s are refered to the red solid line, the black solid line and the gray dash line respectively. In (**c**), the DTT of the first column is 0.903 s, the DTT of the second column is 0.541 s, and the DTT of the third column is 0.451 s.  
For further research, we can study the effects of other different numerical integral methods besides LPM in the DCM on the solution, and study how to improve the proposed method so that it can be applied for real-time implementations. These studies may have great value.  
Conceptualization, Z.X. and J.D.; methodology, J.D. and Z.X.; software, Z.X.; validation, J.D. and Z.X.; formal analysis, Z.X.; investigation, Z.X.; resources, L.C. and J.D.; data curation, Z.X.; writing--original draft preparation, Z.X.; writing--review and editing, J.D.; visualization, Z.X.; supervision, L.C.; project administration, L.C. and J.D.; funding acquisition, L.C.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China under grant number 2018 YFB1701702 and by the National Key R&D Program of China under grant number 2019 YFB1706501.  
Not applicable.  
Data Availability Statement: Data are contained within the article. The data presented in this study can be requested from the authors.  
The authors declare no conflict of interest.
## 3 Methodology
### Image feature extraction
#### Intensity Features  
Gray-level intensity features used in this study are simply the _minimum, maximum, mean, and standard deviation_ of the gray-level intensity of every pixel in each segmented nodule and the same four values for every background pixel in the bounding box containing each segmented nodule. Another feature, _IntensityDifference_, is the absolute value of the difference between the mean of the gray-level intensity of the segmented nodule and the mean of the gray-level intensity of its background.
# On Self-Interference Cancellation and Non-Idealities Suppression in Full-Duplex Radio Transceivers
## 2 Proposed System Model and Techniques  
The architecture considered for performance analysis and result verification is a well-known transceiver model, i.e., DC transceiver, and is shown in Figure 1 along with SI cancellation techniques and transceiver component's non-idealities. The RF impairments, which the DC transceiver faces during practical realization and implementation, are IQ imbalance, phase noise, and frequency offset due to local oscillator (LO), non-linear behavior of PA and LNA, and DAC and ADC dynamic range.
## 3 Key Technologies in Trust Management for WSNs
### Trust Evaluation Methods
#### Dempster-Shafer
Then, the direct trust value is weighted and averaged with the integrated indirect trust value [14; 17; 18], as shown in Equations (13)-(15):  
\[rec\_Trust=\sum_{x=1}^{m}\varphi_{x}dir\_Trust_{rx}^{j} \tag{13}\]  
\[\varphi_{x}=\frac{dir\_Trust_{i}^{rx}}{\sum_{x=1}^{m}dir\_Trust_{i}^{rx}} \tag{14}\]  
\[int\_Trust=\alpha\cdot dir\_Trust+\beta\cdot rec\_Trust \tag{15}\]  
where \(m\) denotes the number of recommenders, \(dir\_Trust_{rx}^{j}\) represents the direct trust of node \(j\) evaluated by recommender \(rx\), and \(\varphi_{x}\) denotes the weight of the direct trust recommended by recommender \(rx\). \(dir\_Trust_{i}^{rx}\) represents the direct trust of recommender \(rx\) evaluated by node \(i\) and \(\alpha\) and \(\beta\) are the weights for the direct trust and recommended trust, respectively, with \(\alpha+\beta=1\). Additionally, [PERSON] et al. [32] used an improved density peak clustering (DPC) algorithm to identify and filter out nodes providing false recommendations before performing indirect trust evaluation using the weighted average method. _Outlier detection_  
Some models [10; 34; 45] use similar methods as those mentioned above but use outlier detection methods to calculate \(\varphi_{x}\). They assign weight based on the degree of difference between the trust value given by a particular recommender and the average trust value given by all recommenders, as shown in Equation (16):  
\[\varphi_{x}=1-\left|dir\_Trust_{rx}^{j}-\frac{\sum_{x=1}^{m}dir\_Trust_{rx}^{j }}{m}\right| \tag{16}\]  
[PERSON] and [PERSON] [30] used a Gaussian distribution outlier detection algorithm to calculate the reliability of each recommendation value. Recommendation values that deviate from the mean by more than twice the standard deviation are considered outliers and are excluded. The reliability of the recommendation values after excluding outliers is shown in Equations (17) and (18)  
\[f(x)=\frac{1}{\sigma\sqrt{2\pi}}e^{(-\frac{(x-p)^{2}}{2\sigma^{2}})} \tag{17}\]  
\[T=1-2\left|\int_{\mu}^{X}f(x)dx\right| \tag{18}\]  
where \(f(x)\) is the probability density function of a Gaussian distribution. Assuming the set of recommended trust values follows a Gaussian distribution, \(\mu\) is the mean of this set and \(\sigma\) is the standard deviation of this set. \(T\) is the reliability value of the recommended trust value \(X\).
# Visuo-Haptic Simulations to Understand the Dependence of Electric Forces on Distance
## 3 Methodology
### Design of Electric Force Visuo-Haptic Scenarios
#### 3.2.4 Force Calibration  
The force feedback of the visuo-haptic simulators was carefully calibrated to represent the theoretical behavior of the electric force for each charge distribution. For the point charge simulator, the force magnitude produced by the fixed charge on the test charge was adjusted to decrease as \(1/r^{2}\); for instance, when doubling the distance, the haptic device rendered a force four times smaller in all directions from the fixed point charge. This behavior was preserved for all allowed ranges of the magnitudes of the fixed charge distributions and the movable test charges. This was also considered when their signs were changed to produce attractive or repulsive forces. In a similar way, for the line charge, the force on the test point charge was properly calibrated to reproduce the theoretical \(1/r\) dependence for all radial distances to the line and allowed values for the linear charge density of the line or charge magnitude of the test charge, for both attractive and repulsive force cases. Finally, for the (infinite) plane charge simulator, the feedback force on the test point charge was set as a constant value regardless of its distance to the plane for given values and signs of the surface charge density and the charge magnitude of the test point charge.  
It is important to notice here that the guiding lines added to the visualization coupled with the simultaneous force feedback given by the haptic device provide the student with an integrated visual-tactile perception of the electric force, according to embodied cognition theory [1,29].
# Simulation-Based Performance Analysis for a Novel AVS/RS Technology with Movable Lifts
## 4 Analyses of the Novel AVS/RS/ML
### Details of the Simulation Model  
In this section, we detail the simulation models of the proposed designs. Figures 8 and 9 show the screenshots from the animated model of a warehouse aisle with 30 columns and 15 tiers of the storage rack.  
The AVS/RS/ML system design with single I/O point includes two different control policies for collision prevention for the cases of two or three AGVs in this system. If there are two AGVs in the warehouse aisle, the newly entering AGV first checks the location of AGV in the aisle. If an AGV is not standing on the way to the location of the prescribed storage column of that newly entering AGV, then that AGV enters the aisle and travels to the prescribed storage column to process the assigned transaction. The third AGV always waits at the I/O point, if there are two AGVs already in the aisle. There can be three AGVs in the warehouse aisle at the same time only in the case that one of them is traveling towards the I/O point at the ground level. Doing so, traffic collision control is realized in the simplest way.  
Figure 7: Top views of system designs with single and two input/output (I/O) points.  
The other considered assumptions are as following:  
* Arrivals of demand follow Poisson process with equal mean of storage and retrieval transactions.
* Random storage assignment policy is considered for arriving transactions.
* If the storage or retrieval sequence of the processed transaction is at the first tier of the storage rack, then ML will not be utilized.
* The maximum horizontal velocities of AGVs and MLs are 3 m/s and 1.5 m/s, respectively. The maximum vertical velocity of the AGV, when it is loaded on the ML, is 2 m/s.
* Accelerations and decelerations of AGVs and MLs are considered as 2 m/s\({}^{2}\) and 1 m/s\({}^{2}\), respectively.
* The distance between two adjacent bays is equal to 0.5 m. The distance between two adjacent tiers is assumed to be 0.35 m.
* Pick-up and drop-off times for totes equal to \(t_{\text{P}}\) = 3 s and \(t_{\text{S}}\) = 3 s.
* Gripping and ungripping times of the AGV equal \(t_{\text{g}}\) = 1 s and \(t_{\text{ug}}\) = 1 s.  
Figure 8: A screenshot from the animation of the simulation model with single I/O point.  
Figure 9: A screenshot from the animation of the simulation model with two I/O Points.
# Water Infusion on the Stability of Coal Specim under Different Static Stress Conditions
## 4 Discussion  
The axial stress and strain energy within the specimens are more sensitive to water saturation coefficient under a higher initial axial stress condition. With water infusion, three initial stress levels can be roughly classified for the investigated coal specimens as follows:  
1. When \(k_{s}\) is less than 0.45, i.e., the axial geo-stress is less than 40% of the UCS of rock pillar, water has limited effect on the stress and energy for coal at such a low stress level. The axial stress seems to slightly decrease with the increase of water saturation coefficient, indicating the coal pillar gradually degrades. However, the coal pillar keeps stable regardless of the water saturation coefficient.
2. When \(k_{s}\) ranges from 0.40 to 0.65, though the specimens still remain stable despite of increasing water saturation coefficient, the strain energy releasing rate began increasing rapidly. In deep coal mining, rockburst accompanying fast energy release commonly happens in such a geo-stress level [41; 42; 43; 44]. From Figure 10, water can release the stored energy within the specimen without triggering instability. Hence, water injection is often used for rockburst prevention [5; 11; 12].
3. When \(k_{s}\) exceeds 0.65, the stress drops abruptly. With the increase of \(k_{s}\), the critical instability water saturation coefficient decreases. This means that under such geo-stresses, the coal pillar is unstable and easier to be failed by water infusion.  
It should be noted that rock and coal with different mechanical properties may have different sensitivities to axial stress coefficient under water infusion, which leads to different threshold values for the classification.
# Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming
## 5 Methodology  
Since the STL syntax can be defined by means of context-free grammar (CFG), we rely on CFGGP [6], a grammar-based version of GP [24]. In CFGGP, candidate solutions are represented as derivation trees of a grammar \(\mathcal{G}=(N,T,s_{0},R)\), where \(N\) is the set of non-terminal symbols, \(T\) is the set of terminal symbols (with \(T\cap N=\emptyset\)), \(s_{0}\in N\) is the starting symbol, and \(R\) is the set of derivation rules. Each derivation rule describes how a non-terminal symbol may be replaced by a sequence of symbols, either terminal or non-terminal. A _derivation tree_ is a tree where nodes are symbols of the grammar: leaf nodes are terminal symbols, and non-leaf nodes are non-terminal symbols. The children of each node match one of the derivation rules for the corresponding non-terminal symbol. The string of the language defined by the grammar corresponding to a given derivation tree is the sequence of the leaves of the tree. Note that a derivation tree is not an abstract syntax tree: the former is in general deeper than the latter, for the same formula. Figure 1 shows an example of a derivation tree for the CFG of Figure 2.  
We use an improved version of CFGGP that promotes diversity in the population. The lack of diversity in the population may result in premature convergence towards a local optimum [25], in particular when the search space is discrete, as in CFGGP [26]. In this work, we promote diversity by simply enforcing the re-application of the genetic operator whenever a generated individual is already part of the population.