esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Theoretical Analysis of the Water Dissociation Reaction Rate
Comparing the two pathways that obstruct the reaction, a schematic of the reaction mechanism of IPA/water is presented in Figure 3b. The IPA blocks the reaction by its interaction with the metal center, requiring a lower pressure to occupy all metal centers in the unit cell [42]. On the contrary He/water reaction mechanism presented in Figure 3d illustrates how adding guest molecules physically prevent the formation of organized water clusters, hence reducing water dissociation reaction at higher pressures. Thus, the water dissociation reaction can be simply controlled by assisting or hampering cluster formation by a guest molecule such as methanol or isopropanol, therefore systematically tuning the effective reaction barriers [14]. For both pure water and alcohol/water mixtures, a central aspect of MOF-74 is the ability to adsorb several molecules within each pore at relatively low external pressures (\(\sim\)few Torr), thanks to van der Waals attractive forces. This is only possible in the confined space of a nanoporous material. In the confined environment of MOF-74, several water and alcohol molecules can coexist within each pore, forming nano-structures, which generally lowers the dissociation barriers and stabilizes the dissociated state. Consequently, the formation of water or alcohol/water networks occurs at convenient pressures, and these networks can remain stable even after the cell is pumped out. Such networks cannot be realized on flat oxide surfaces because the external pressure would need to be prohibitively high and because entropy would prevent structured networks to develop for lack of a supporting nanostructured framework around [11; 12].
## 4 Multiple Metrics Implementation
### Implementation Overview and Description
#### 4.1.5 Multi-Criteria Decision Making Technique  
The AHP technique is the main component of the multiple metrics algorithm, which takes the RLMTWs and RLMVs to make a routing decision. The procedure of the multiple metrics for deciding a good quality link involves the following: (1) link metric weighing; (2) assigning priority weights RLMTWs; and (3) making a route decision to select a path to destination.
# A Traceable Spectral Radiation Model of Radiation Thermometry
## 2 The Direct (Physical) Model of Radiation Thermometry
### Final Spectral Data of the Physical Model  
A theoretical example of spectral behaviour within the direct model (14) is presented in Figure 8. A spectral emissivity characteristic was randomly generated for this research, which is intentionally non-homogeneous across the entire sensitivity spectrum of the sensor to emphasize the effect of non-homogeneous spectral emissivity, as can occur with real objects.  
Figure 6: The total radiance of a black body source at different temperatures and the actual detected radiance at the sensor, forming the sensor characteristic function. VOx sensor sensitivity from Figure 5 was used for this plot.  
Figure 8a presents the raw spectral radiances \(M_{\text{BB,obj}}\Big{(}T_{obj},\lambda\Big{)}\), \(M_{\text{env}}(\lambda)\) and \(M_{\text{BB,atm}}(T_{atm},\lambda)\) of the three contributing sources of radiation. Figure 8b presents the influence parameters \(\varepsilon(\lambda)\), \(\tau(\lambda)\) and \(S(\lambda)\). Figure 8c presents the detected spectral radiance of the three contributing sources, as expressed in Equation (15) prior to integration, and the sum of the three, as expressed in Equation (14) prior to integration.  
Figure 7: The direct (physical) model of radiation thermometry, describing the physical process of thermal emission, propagation, absorption and measurement of object (spectral) radiance. The rectangles represent the black body emissions, while the square and triangular operators represent the scaling and summation for the (spectral) radiance. The arrows with the double lines represent the spectral radiance, while the single-line arrows represent the radiance (the total radiant power without spectral information).
## 2 Background
### Basics of NAND Flash Memory
Note that if we can prevent the page buffer from reading the data in a flash cell or inhibit the data in the page buffer from transferring out of the flash chip via a data-out path, its effect will be equivalent to destroying the stored page in the selected WL. **Multi-level Cell Flash Memory**  
In order to reduce the cost per bit value of flash memory, multi-leveling techniques are widely used. The multi-level cell (MLC) technology was initially developed for storing 2 bits per cell, but it was extended to support TLC (triple-level cell, 3 bits/cell) and QLC (quadruple-level cell, 4 bits/cell). Figure 2 illustrates \(V_{th}\) distributions for 2\({}^{m}\)-state NAND flash memory, which stores \(m\) bits within a single flash cell by using 2\({}^{m}\) distinct \(V_{th}\) states (i.e., \(m\) is 2 and 3 for MLC and TLC, respectively). As \(m\) is increased to store more bits within a flash cell, more \(V_{th}\) states should be put into the limited \(V_{th}\) window, which is fixed at the flash design time. Therefore, a more careful management is required for multi-level flash memory to form finer \(V_{th}\) states, as \(m\) increases. Furthermore, as \(m\) increases, a \(V_{th}\) margin (i.e., a gap between two neighboring \(V_{th}\) states) inevitably becomes narrower as shown in Figure 2a,b. When the \(V_{th}\) margin becomes smaller, two neighboring \(V_{th}\) states are more likely to be overlapped under various noise conditions, thus degrading the reliability of the NAND flash memory. For example, the MLC flash memory can tolerate up to 3000 program and erase (P/E) cycles, while the TLC flash memory can tolerate only 1000 P/E cycles. As a result, as \(m\) increases, many optimization techniques that were effective for smaller \(m\) become less effective or inapplicable because of the worsened flash reliability.
## 2 Related Works  
Recent years have seen a boom in the number of articles published on the THz band, including several tutorials and review papers, as well as technical works. With an abundance of spectrum resources between 0.1 THz and 10 THz, THz-frequency transmission has emerged as a viable short-distance, high-speed, and reliable communication technique for LoS connections in UAV-aided communication networks. Losses due to free-space spreading and molecule absorption are all that LoS channel models need to include. The potential of THz communications to enable mobile communications is notable [21]. UAV communication via the THz channel has been set up in few papers [22; 23]. The authors of [22] analyzed the orientation and location estimation capabilities of the THz band between two UAVs based on both position and orientation error boundaries. Using their methodology, they determined that millimeter-level positional accuracy, which is necessary for dispersed sensing, is possible provided that the distance between the receiver and transmitter is sufficiently small. Additionally, their simulations showed that there is no significant improvement in location accuracy beyond a certain point when the bandwidth is increased. The authors of [23] looked at the difficulties in using infrastructure-free, high-data-rate, and low-latency wireless UAV systems in THz communications.  
In the literature, a number of stochastic as well as deterministic models addressing the THz channel have been put forward. Refs. [24; 25] examined free-space path loss, in particular, diffraction as well as absorption losses. Ref. [26] offered a condensed method of simulating molecule absorption as a function of frequency and atmospheric circumstances. Gamma-gamma, log-normal, and Weibull distributions were used as traditional channel models by the authors of [27] to quantify the impact of pointing errors. The authors of [28] modeled antenna gains with misalignment as a random variable. A more thorough misalignment model was considered in another study [29] that was developed for free-space optical communications and took air turbulence, receiver detector size, and displacement jitter into consideration. This model was also used by [30; 31]. The effectiveness of THz systems is measured in terms of outage probability and connection capacity in all of the models listed above. To the best of the authors' knowledge, none of the available literature has addressed the performance of digital modulation techniques in LoS THz communication channels.  
Despite the significance of channel fading in UAV communications, the literature has relatively little research on this topic. The development of UAV networks, for instance, was the subject of [32], but the communication and, in particular, control of UAVs are the main topics of this study. Although the characteristics and needs of aerial networking for civil applications were addressed in [33], the focus of this assessment was on the communication features of UAVs, specifically, network layer designs. On channel modeling, Refs. [32; 33] hardly make a mention.
## 2 Data Transmission in a Wireless Seizure Detection System
### Transmission of Entire Raw EEG  
At every sampling period, the EEG signals are sampled and transmitted to the data server without any preprocessing. Depending on the data size of one time sample of the EEG signals and the maximum payload size of a data packet, the transmission of the EEG signals may be broken up into multiple data packets.
## 4 Application
### Visualization of Traffic Stream  
The pre-processed AIS data for the simulation area and the trajectories of these ships are shown in Figure 9a. The voyage data of the vessel were used to measure the similarity between voyages with DTW. Subsequently, the data were reduced to a multidimensional scale for clustering analysis and clustered using the DBSCAN algorithm. Consequently, three clustering results were obtained, as shown in Figure 9b-d. Additionally, curve fitting  
Figure 8: Comparison of ship direction arrow as per grid cell interval in area “A”: (**a**) Ship AIS data preprocessing and trajectory refinement. (**b**) Distribution of AIS data on grid level 5. (**c**) Distribution of AIS data on grid level 6. (**d**) Distribution of AIS data on grid level 7. (**e**) Result of direction arrow on grid level 5. (**f**) Result of direction arrow on grid level 6. (**g**) Result of direction arrow on grid level 7.  
was applied to extract representative routes for the identified traffic flows, as shown in Figure 9e-g. These three representative routes extracted from the simulated waters were merged into one and plotted, as shown in Figure 9h, and the latitude and longitude of the waypoints of the representative routes are shown in Table 2.  
Figure 9: The process of extracting traffic flows and representative routes. (**a**) Preprocessed ship trajectory data and DBSCAN cluster analysis results for ship trajectory similarity for identifying the traffic stream. (**b**) First cluster of DBSCAN cluster analysis results. (**c**) Second cluster of DBSCAN cluster analysis results. (**d**) Third cluster of DBSCAN cluster analysis results. (**e–g**) Applying the curve fitting for identifying routes. (**h**) Integrated representative routes in the simulation area.
## 4 Computational Complexity Analysis  
The computational complexity is analyzed in view of additions and multiplications [28]. The computational complexities of different structures are shown in Table 1, where \(M\), \(N\), and \(L\) are the length of the control filters \(W_{f}(z)\), \(W_{b}(z)\), and \(W_{c}(z)\), respectively. Moreover, \(H\) represents the length of the estimated secondary path \(\hat{S}(z)\). In Table 1, the \"tra structure\" is defined as the traditional structure in Reference [10]. The \"sim structure\" is the simplified structure using the FxLMS algorithm in the feedback control filter. The \"tsim structure\" represents the simplified structure using the traditional leaky FxLMS algorithm in the feedback control filter [19]. The \"nsim structure\" expresses the simplified structure using the new leaky FxLMS algorithm in the feedback control filter. Finally, the \"mod structure\" means the modified structure.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Structure** & **Additions** & **Multiplies** & **Complexity** \\ \hline tra structure [10] & 2(M + N + L) + 4H & 2(M + N + L) + 4H + 5 & 4(M + N + L) + 8H + 5 \\ sim structure & 2(M + N + L) + 3H & 2(M + N + L) + 3H + 5 & 4(M + N + L) + 6H + 5 \\ tsim structure [19] & 2(M + N + L) + 3H + 1 & 2(M + L) + 3(H + N) + 6 & 4(M + L) + 5N + 6H + 7 \\ nsim structure & 2(M + N + L) + 3H + 1 & 2(M + L) + 3(H + N) + 7 & 4(M + L) + 5N + 6H + 8 \\ mod structure & 2(M + N + L) + 5H & 2(M + N + L) + 5H + 5 & 4(M + N + L) + 10H + 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computational complexities of the proposed structures compared to traditional structure.  
It is obvious from Table 1 that the \"sim structure\" has the lowest computational cost in different control structures, and the computational cost of the \"mod structure\" is slightly increased compared to the \"tra structure\". The computational complexity of the \"mod structure\" is only \(2H\) more than that of the \"tra structure\". In the simplified structure, the leaky FxLMS algorithm and new leaky FxLMS algorithm have more computational load than the FxLMS algorithm. The computational load of the \"tsim structure\" is \(N+2\) more than that of the \"sim structure\". The computational load of the \"nsim structure\" is \(N+3\) more than that of the \"sim structure\". The computational load of the \"nsim structure\" is only one more than that of the \"tsim structure\". Therefore, the simplified structure and the modified structure have better performance than the traditional structure. Meanwhile, the performance of the simplified structure can be improved by the use of the new leaky FxLMS algorithm in the feedback control filter.  
On the basis of the digital signal processor TMS320C6748, the computing time of different structures is shown in Table 2, where \(M,N,\) and \(L\) are 512, and \(H\) is 4. The computing time is acquired when the weight coefficients of the control filters are updated one at a time.
# Broadband Flexible Microstrip Antenna Array with Conformal Load-Bearing Structure
## 2 Antenna Design
### Antenna Array  
As shown in Figure 3, the composition of the antenna array includes four elements designed above, and a four-way Wilkinson power divider with equal amplitude and phase. The proposed four antenna elements are configured with 2 \(\times\) 2 in equal spacing \(d_{1}\), and the four-way Wilkinson power divider is printed on the lower surface of the middle substrate, connecting with the microstrip feedings of the proposed four antenna elements. Due to the independence of the feeding network, the Wilkinson power divider can be designed separately, which makes the antenna array easy to extended.
## 3 Applications of Game Theory in WSN
### Game Theory in Applications of WSN  
#### 3.4.1 Target Tracking  
A method for target tracking based on multi-agent and game theory was proposed in [109, 110]. When a target appears in the sensing field of WSN, sensor nodes begin to form coalition dynamically and then they start to negotiate with game theory. Coalition is formed to track it with the target moving. Utilizing multi-agent method and game theory in WSN enables nodes to perform tasks coordinately to achieve some desired objectives.
## 3 Results
### Optical Properties  
The measured optical properties of the fabricated coatings and neat glass are presented in Figure 5. The transmittance of the MOD1, MOD2, MOD3, and MOD4 samples was similar to the transmittance of glass in the tested range (Figure 5a). The differences in values were about 2%. The reference sample had a lower transmittance than the other samples. The reflectance of the measured samples was similar and ranged from 6% to 9% over the tested range (Figure 5b). The reference sample had a broad absorptance band of about 1000 nm. The other measured samples had an absorptance not greater than 4% within the tested range (Figure 5c).
# Research and Application of an Improved Sparrow Search Algorithm
## 4 Solving Multi-Objective Association Rule Mining with the AWSSA
### Fitness Function Selection  
In this paper, the quality of association rules is considered in terms of support, confidence, and Lift [34] for discovering rules with high generality, more reliability, and strong association. Support is defined as follows:  
\[\text{Sup}(A\to B)=\frac{\text{Sup}(BA)}{|D|} \tag{12}\]  
where \(|D|\) is the total number of transactions, and \((BA)|\) represents the number of transactions containing both \(A\) and \(B\). Support can be thought of as the frequency of occurrence  
\begin{table}
\begin{tabular}{c c c c|c c c c} \hline \hline \(0\) & \(1\) & \(2\) & \(3\) & \(4\) & \(0\) & \(1\) & \(2\) & \(3\) & \(4\) \\ \hline \(-1  
of a rule in the transaction set, which is used to characterize the degree of prevalence of a rule. If the support is too low, it means that the resulting rule is quite rare, which may not be meaningful to the decision maker. The second objective function is the confidence, which is usually used to measure the credibility of a rule to help determine the strength of the association rule, i.e., the likelihood that itemset \(B\) will occur under the condition that itemset \(A\) occurs. Rules with high confidence are usually more reliable because their occurrence in the data is well-founded. The confidence level is defined as follows:  
\[\text{Conf}(A\to B)=\frac{\text{Sup}(BA)}{\text{Sup}(A)} \tag{13}\]  
In ARM, sometimes there are misleading associations that appear to be related on the surface but are actually caused by chance events, and lift can help us determine the authenticity of such associations. The lift tells us whether the itemsets included in the rule are more related than if they had appeared separately, and it helps to identify non-independent relationships between the itemsets; a lift of less than 1 indicates that \(A\) plays no role in the likelihood of \(B\)'s occurrence, whereas a larger lift indicates that \(A\) is more related to \(B\).  
\[\text{Lift}(A\to B)=\frac{\text{Sup}(BA)}{\text{Sup}(A)\cdot\text{Sup}(B)} \tag{14}\]  
The workflow of the AWSA in MOARM is shown in Figure 14.  
Figure 14: Flowchart of the proposed multi-objective AWSA.
## 4 Applications  
The interest in developing ultrafast powerful laser sources lies in the possibility they offer to explore highly non-linear regimes of light-matter interaction, namely the strong-field regime and temporally-resolved phase transitions.  
Strong-field phenomena are activated in matter undergoing interaction with an external electric field on the same strength-scale of the internal binding fields. This kind of phenomena thus involves a change in the structural properties of matter, mainly induced by the activation of electron excitation processes. The strong-field light-matter interaction regime is suitable for studying electron dynamics, as well as the corresponding structural evolution in atoms, molecules and solid-state systems. Up to now, the main experimental techniques used for accessing strong-field phenomena are: High-order Harmonic Generation (HHG) spectroscopy, photoelectron spectroscopy and pump-probe spectroscopy based on attosecond pulses.  
Electron and nuclear dynamics occur on an ultrafast time-scale, ranging from hundreds of femtoseconds down to few attoseconds. Strong-field processes are thus deeply affected by the temporal properties of the exciting pulses, namely duration, field frequency (wavelength) and CEP.  
In this sense, the use of CEP-stable few-cycle IR pulses leads to a number of significant advantages [54]. In particular, an electron interacting with an external field experiences a ponderomotive energy gain. This energy gain scales towards higher values when longer optical wavelengths (in the near-IR and  
Figure 9: (**a**) Scheme of the Frequency-domain Optical Parametric Amplification (FOPA) system; (**b**) example of the amplified spectrum in the FOPA [53]. The black curve corresponds to the seed spectrum. The spectral ranges of the four BBO crystals used for the amplification in the Fourier plane are marked by vertical lines. The colored spectra correspond to amplification for different pump levels. The blue curve corresponds to the experimental gain curve. Adapted with permission from [53], Copyright NPG, 2014.  
mid-IR) are used. Higher electron energy corresponds to shorter De Broglie wavelength, which is useful for imaging small targets and for exciting transitions from deeply bound states.  
Moreover, CEP-locked few-cycle pulses provide reproducible waveforms and can potentially be used to perform a fine control of ultrafast processes with a sub-cycle precision. High energy tunable few-cycle parametric sources in the IR spectral region allow one to fully exploit these advantages.  
In this section, the main achievements in strong-field physics obtained by exploiting high-energy few-cycle near-IR and mid-IR sources will be discussed. In particular, attention is paid to reviewing very recent advances in HHG spectroscopy, photoelectron spectroscopy and attosecond science.
## 2 Equations of Motion
### Occurrence Condition of Relative Motion  
Relative motion of the superstructure and base structure occurs under conditions of excitation greater than friction in the equilibrium state. The equilibrium state of friction and inertia forces becomes \(\theta_{r(l)}=\theta_{o}\) and \(y_{r(l)}=0\) from Equations (1) and (2), and \(D_{r(l)}=g/2\) can be obtained from Equation (10). Substituting these values into Equation (9) yields the following equation for equilibrium with excitation:  
\[\mu g=-\ddot{X}. \tag{14}\]  
Given the excitation of the sine wave function of the frequency \(f\) and amplitude \(X_{o}\), the minimum excitation frequency causing the relative motion from Equation (14) can be obtained as follows:  
\[f_{s}=\frac{1}{2\pi}\sqrt{\frac{\mu g}{X_{o}}}. \tag{15}\]  
In Equation (15), \(f_{s}\) denotes the frequency at which relative motion begins to occur, so we refer to it as the starting frequency. Moreover, as \(f_{s}\) is a function of the excitation amplitude and friction coefficient (friction force), the desired \(f_{s}\) value can be adjusted by determining the friction coefficient in accordance with the excitation amplitude condition. Figure 6 illustrates the changes in \(f_{s}\) according to the amplitude and any friction coefficients. These data are very useful for initial design.
## 2 Model and Method
### Revolution and Self-Rotation Speed  
When the bearing is in motion, the outer ring is stationary, and the inner ring rotates, while the balls have both revolution and self-rotating speed. The oil supply hole speed is the same as the inner ring speed, while the main flow region and ball revolution speed are the same as the cage speed. The cage speed is as follows [24]:  
\[n_{m}=\frac{n_{i}}{2}\left(1-\frac{D\cos\alpha}{d_{m}}\right) \tag{9}\]  
The rotating speed of the cage relative to the inner ring:  
\[n_{mi}=n_{m}-n_{i} \tag{10}\]Assuming that there is no serious sliding at the contact between the inner ring raceway and the ball, the linear velocity of the ball at the contact point should be equal to the linear velocity of the raceway:  
\[v_{m}=\frac{1}{2}\omega_{m}d_{m}(1-\frac{D\cos\alpha}{d_{m}})=\frac{1}{2}\omega_ {r}D \tag{11}\]  
Since \(n\) is proportional to \(\omega\), and the Formula (10) is substituted into:  
\[n_{r}=(n_{m}-n_{i})\frac{d_{m}}{D}(1-\frac{D\cos\alpha}{d_{m}}) \tag{12}\]  
Substituting Formula (9) into Formula (12), the self-rotation speed of the balls is as follows:  
\[n_{r}=\frac{d_{m}n_{i}}{2D}\left[1-\left(\frac{D\cos\alpha}{d_{m}}\right)^{2}\right] \tag{13}\]  
where \(n_{m}\) is the cage speed; \(n_{i}\) is the inner ring speed; \(D\) is the diameter of the ball; \(\alpha\) is the contact angle; \(d_{m}\) is the pitch diameter; \(n_{r}\) is the ball's self-rotation speed; \(n_{mi}\) is the rotating speed of cage relative to inner ring; \(v_{m}\) is the cage linear velocity; \(\omega_{m}\) is the cage angular velocity; \(\omega_{r}\) is the ball angular velocity.