esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Tube Radius and Surface Tension on Capillary Rise Dynamics of Water/Butanol Mixtures
## 4 Conclusions  
The experimental correlation between the capillary tube radius and the liquid surface tension on the capillary rise dynamics was investigated. The conclusions of the present study are summarized as follows:  
1. The height of rise and the velocity of rise are dependent on the capillary tube radii. The larger the radius, the lower is the equilibrium height and the velocity of rise. The meniscus oscillation was observed only at the test case of \(r=0.85\).
2. The capillary rise process is divided into three characteristic stages: the inertial (Stage I), inertial + viscous (Stage II), and viscous (Stage III) flow region. The capillary rise was predominantly influenced by the pure inertia and hybrid region (inertia + viscous).
3. The capillary rise height decreased with an increase in the concentration of n-butanol and with a decrease in the surface tension of n-butanol/water mixtures. With respect to the velocity of rise, the higher the surface tension of the mixtures, the higher the velocity.
4. There were some correlations between the characteristic heights (\(h_{1}\), \(h_{2}\), and \(h_{\rm e}\)) and the capillary tube radius and liquid surface tension. Characteristic heights showed a linear decrease in the capillary tube radius and a linear decrease in the liquid surface tension. Consequentially, each characteristic height exhibited a linear function of the consolidated quantity of \(\sigma L/2\pi r^{2}\).  
Conceptualization, H.J.; methodology, S.P.; software, J.C.; investigation, S.B.; data curation, J.S.; writing--original draft preparation, S.J. and S.B.; writing--review and editing, S.B. and Y.S.; visualization, S.L.; project administration, Y.S.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.  
Figure 9: (**a**) Characteristic heights (\(h_{1}\), \(h_{2}\), and \(h_{\rm e}\)) as a function of the capillary tube radius (\(r\)). (**b**) Characteristic heights as a function of the surface tension of liquids (\(\sigma\)). (**c**) Characteristic heights as a function of the quantity in terms of \(r\) and \(\sigma\) (\(\sigma L/2\pi r^{2}\)). Solid lines represent linear fits between characteristic heights and each comparative parameter.  
**Funding:** This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1F1A1049268).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks
## 2 Recent Research Direction for D2D Communications
### Spectral Efficiency and Coverage Analysis  
The main advantages of enabling D2D communications in cellular networks beside relaxing the traffic load on the Macro BS/core network are enhancing the spectral efficiency (SE), reducing outage and increasing the coverage of the D2D users. Several studies were performed to evaluate these metrics in conjunction with D2D networks [1]. Towards that, tools from probability theory and stochastic geometry were extensively used to analyze the performance of D2D networks. Cognitive and energy harvesting D2D networks were modeled and analyzed using a tool from stochastic geometry. In addition, the Poisson cluster process was used to model the locations of the devices for coverage analysis of the clustered D2D networks. The coverage probability, the mean number of covered receivers and throughput of the multi-cast D2D transmission were also analyzed using tools from probability theory. Recently, analysis of the underlaid FD D2D network is provided interms of coverage probabilities and achievable sum-rates for both D2D users and CUs. To study the improvements in mmWave networks coming from enabling D2D links, a tool from stochastic geometry was used to analyze mmWave D2D networks concerning interference, coverage and data rate, especially for mmWave wearable. In addition, a fine-grained analysis of mmWave D2D networks using the Poisson bipolar model was given. Moreover, the locations of mmWave devices were modeled by Poisson cluster process to investigate the performance of mmWave clustered D2D network.
## 3 LRM/LRMM/LRRM Deviation
### LRMM Derivation  
Different from the LRM calibration shown in Table 1, in LRMM calibration, the asymmetrical match standards M\({}_{1}\) (R\({}_{21}\)) and M\({}_{2}\) (R\({}_{22}\)), with known reflection coefficients \(\Gamma_{\rm M1}\) and \(\Gamma_{\rm M2}\), are used to construct the T-matrices of error boxes. Similar to (31), the calibration Equation resulting from measurement of the two-port standard L is easily given as below:  
\[\left[\begin{array}{cc}0&1\\ 1&0\end{array}\right][T_{12}^{\rm L}]=z\left[\begin{array}{cc}1&1\\ \Gamma_{\rm R}&\Gamma_{\rm M1}\end{array}\right]\left[\begin{array}{cc}1&0 \\ 0&x\end{array}\right][W_{\rm L}]\left[\begin{array}{cc}1&0\\ 0&y\end{array}\right]^{-1}\left[\begin{array}{cc}1&1\\ \Gamma_{\rm R}&\Gamma_{\rm M2}\end{array}\right]^{-1}. \tag{43}\]  
The trace conservation cannot be used anymore, so similar to (22), (43) is expanded and the result is shown as follows:  
\[\left[\begin{array}{cc}w_{21}^{\rm L}&0&w_{11}^{\rm L}\\ w_{22}^{\prime}&-t_{21}^{\rm L}-t_{22}^{\rm L}\Gamma_{\rm M2}&w_{12}^{\rm L}\\ w_{21}^{\prime}\Gamma_{\rm M1}&0&w_{11}^{\prime}\Gamma_{\rm R}\\ w_{22}^{\rm L}\Gamma_{\rm M1}&-t_{11}^{\rm L}-t_{12}^{\rm L}\Gamma_{\rm M2}&w_{1 2}^{\rm L}\Gamma_{\rm R}\end{array}\right]\left[\begin{array}{c}xz\\ y\\ z\end{array}\right]=\left[\begin{array}{c}t_{21}^{\rm L}+t_{22}^{\rm L} \Gamma_{\rm R}\\ 0\\ t_{11}^{\rm L}+t_{12}^{\rm L}\Gamma_{\rm R}\\ 0\end{array}\right]. \tag{44}\]  
By solving the first three equations of (44), expressions for \(xz\), \(y\), and \(z\) in function of \(\Gamma_{\rm R}\) are obtained. These expressions are plugged into the last equation of (44) to obtain a quadratic equation about \(\Gamma_{\rm R}\) as shown below:  
\[d_{2}\Gamma_{\rm R}^{2}+d_{1}\Gamma_{\rm R}+d_{0}=0 \tag{45}\]  
where the coefficients are known and given by  
\[\left\{\begin{array}{ll}d_{2}=&w_{11}^{\rm L}w_{22}^{\rm L}t_{22}^{\rm L}(t_ {11}^{\rm L}-t_{21}^{\rm L}\Gamma_{\rm M1}+t_{12}^{\
# Induction Coil Design Considerations for High-Frequency Domestic Cooktops
## 4 The Proposal of High-Frequency Induction Coil Design
### Induction Coil Design Flowchart  
In this section, coil design steps in high-frequency induction cookers are examined. First, the required design parameters must be determined precisely. Basic design parameters are targeting inductance value, coil dimensions, total coil height, operating frequency, maximum coil current, and ambient temperature.  
Once these values are known, the number of turns and structure of the windings are designed. When designing the winding, current density and the strands of the litz wire are of great importance. Too high current density increases copper losses and creates additional thermal stress. By increasing the number of strands, the equivalent AC resistance is decreased. However, when deciding on these parameters, it is necessary to take into consideration the capabilities of the manufacturer because problems may arise during the production stages of induction coils with a very thin and high number of strands. As the number of conductors increases and the wire diameter decreases, the processing of litz wire becomes more difficult for the machine. Most of the machines that produce litz wire have a limited number of conductors. It is not possible to process conductors above this number. In addition, if the conductor diameter is too small, the wires will break during the process. This situation is directly related to the production capacity of the manufacturer. Considering these types of restrictions, the power and switching levels should be reconsidered. Planar structures can be preferred for very high-frequency and low-power applications.  
After deciding on the winding structure, a suitable magnetic core must be selected. When choosing a magnetic core, the saturation value and high-frequency characteristics of the core are important. The selected magnetic material must never be saturated at maximum ambient temperature and load current. Accordingly, the number of cores and their characteristics should be determined.  
After the number and configuration of magnetic cores have been determined, the air gap must be determined. The air gap is directly related to the power level desired to be transferred. To determine the air gap accurately, FEM analyses must be run with the pan. In analyses operated with a pan, the maximum magnetic field and core loss value to which the magnetic cells are exposed should be determined. Next, the air gap, formed by glass, mica, and spacers, needs to be determined. Since the glass and mica thicknesses are fixed here, the air gap can be adjusted with the help of the spacers to be used. Induction coil design flowchart is shown in Figure 11.
# Experimental Test of Continuous Wave Frequency Diverse Array Doppler Radar +
## 3 Data Collection II: Stationary Target Reflection
### Matched Filter Threshold and Receive Antenna Shielding  
The matched filter (MF) for our experimental setup is the convolution of the received signal with the Tx copy created using RF hardware (see Figure 1). Mathematically, this is represented as [47]  
\[s_{MF}(t)=\int_{-\infty}^{+\infty}F_{R,bb}(\tau)F_{Copy}(t-\tau)\,d\tau, \tag{9}\]  
where  
\[F_{Copy}(t)=\sum_{n=0}^{N-1}e^{2\pi n\Delta ft}. \tag{10}\]  
In this quasi-monostatic transmit/receive setup, we expect some level of sidelobe leakage from the transmit array to the receive antenna; therefore, we constructed a septum with an embedded aluminum sheet to shield the receive antenna. The MF threshold is then set by taking the MF response when the transmitters are on and no target is present.  
Ettus Research documentation states that the UBX-160 daughterboard has an experimentally determined minimum discernible signal (MDS) level of \(-135\) dBm in the HF band [48]. We empirically find the MDS level (\(-65\) dBm) inside the chamber by taking interference plus noise measurements with the transmitters on, then sampling input tones with decreasing power until the amplitude falls below the interference plus noise level. Assuming a lossless system, a \(0\) dBsm target yields a maximum detectable range \(R_{max}=15.9\) m found using the radar range equation  
\[R_{max}=\bigg{[}N\cdot\frac{P_{t}G_{t}G_{r}\lambda_{0}^{2}\cdot\sigma}{(4\pi )^{3}P_{r}}\bigg{]}^{1/4}, \tag{11}\]  
with number of elements \(N=3\), transmit power at each transmit antenna terminal \(P_{t}=14.7\) dBm, transmit and receive antenna gains \(G_{t}=G_{r}=6.5\) dB, RCS \(\sigma=0\) dBsm, and receive power \(P_{r}=-65\) dBm. Note that the preceding system parameters are given in a decibel (dB) scale but are converted to a linear scale when calculating \(R_{max}\). Thus, thenumber of elements \(N\) in Equation (11) accounts for the linear scaling of the transmit power and the cascaded transmit/receive gains that occur when \(N>1\).
# Exploring the Role of Emotions in Arabic Rumor Detection in Social Media
## 5 Results and Discussion
For example, anger and disgust hold minimal importance in news content, while surprise and negative emotions play a more significant role. This can be attributed to false news writers employing negative and surprising words to capture public attention. On the other hand, concerning user and content features, we observed a significant number of features in news posts that were correlated with class, such as follower count, list count, account verification status, URL status, and reply count. These results are consistent with the more informative features identified using RF in previous analyses. On the other hand, from the comments' perspective, features such as favorites count, account age, follower count, and screen name length were correlated with class. These findings revealed a marked difference in the prominence of features for each perspective, with certain features proving to be more effective in the news publisher context than in the replies context. Figure 3: RF feature importance scores for user-centric and content features in rumor detection. (**a**) Informative features on the news disseminators’ side. (**b**) Informative features on the commenters’ side. **Emotions' difference according to the news topic:** The intensity of emotions varies with respect to the topic being discussed. Figures 5 and 6 illustrate this; for example, we see in real news content that the intensity of positive sentiment in religious rumors is quite high, in contrast to true religious news. When it comes to health news, real news surpasses rumors in terms of positive and neutral sentiments and anticipation. Sad emotions are also strong in false news in all genres except religious rumors, where the author attempts to instill sadness in false information to attract attention. Additionally, trust and neutrality are similar in all types, with trust being higher in all kinds of rumors to deceive the reader into believing the news is real, while neutral sentiment is higher in all types of real news. On the other hand, the variation in intensity in replies to rumors is more noticeable than in the news. For example, we see a rise in trust emotions in religious and health rumors, whereas it is lower in political and social real news. In conclusion, the findings revealed a significant disparity in the density of emotions according to whether they occurred in news or comments, highlighting the necessity of incorporating textual contents via topic modeling or language models that can infer the news topic for accurate rumor detection. In summary, this study demonstrates the varying importance of emotions, user, and content features in detecting rumors from both the news publisher and commenter perspectives. Consequently, it is crucial to consider both aspects when identifying rumors to optimize the use of available information.
###### Abstract  
The monitoring of agronomic parameters like biomass, water stress, and plant health can benefit from synergistic use of all available remotely sensed information. Multispectral imagery has been used for this purpose for decades, largely with vegetation indices (VIs). Many multispectral VIs exist, typically relying on a single feature--the spectral red edge--for information. Where hyperspectral imagery is available, spectral mixture models can use the full VSWIR spectrum to yield further insight, simultaneously estimating area fractions of multiple materials within mixed pixels. Here we investigate the relationships between VIs and mixture models by comparing hyperspectral endmember fractions to six common multispectral VIs in California's diverse crops and soils. In so doing, we isolate spectral effects from sensor- and acquisition-specific variability associated with atmosphere, illumination, and view geometry. Specifically, we compare: (1) fractional area of photosynthetic vegetation (F\({}_{\rm v}\)) from 64,000,000 3-5 m resolution AVIRIS-ng reflectance spectra; and (2) six popular VIs (NDVI, NIRv, EVI, EVI2, SR, DVI) computed from simulated Planet SuperDove reflectance spectra derived from the AVIRIS-ng spectra. Hyperspectral F\({}_{\rm v}\) and multispectral VIs are compared using both parametric (Pearson correlation, \(\rho\)) and nonparametric (Mutual Information, MI) metrics. Four VIs (NIRv, DVI, EVI2) showed strong linear relationships with F\({}_{\rm v}\) (\(\rho>0.94\); MI \(>1.2\)). NIRv and DVI showed strong interrelation (\(\rho>0.99\), MI \(>2.4\)), but deviated from a 1:1 correspondence with F\({}_{\rm v}\). EVI and EVI2 were strongly interrelated (\(\rho>0.99\), MI \(>2.3\)) and more closely approximated a 1:1 relationship with F\({}_{\rm v}\). In contrast, NDVI and SR showed a weaker, nonlinear, heteroskedastic relation to F\({}_{\rm v}\) (\(p<0.84\), MI \(=0.69\)). NDVI exhibited both especially severe sensitivity to unvegetated background (\(\sim\)0.05 \(<\) NDVI \(<+0.6\)) and saturation (\(0.2<\) F\({}_{\rm v}<0.8\) for NDVI \(=0.7\)). The self-consistent atmospheric correction, radiometry, and sun-sensor geometry allows this simulation approach to be further applied to indices, sensors, and landscapes worldwide.  
v 2023  
1 Department of Geography, San Diego State University, San Diego, CA 92182, USA  
2 Lamont-Doherty Earth Observatory, Columbia University, New York, NY 10964, USA
# Ant Colony Optimization Task Scheduling Algorithm for SWIM Based on Load Balancing
## 4 Ant Colony Optimization Algorithm Based on Load Balancing
### Ant Colony Task Scheduling Algorithm Optimization Process  
The main idea is to select the best order of task scheduling through the ant colony algorithm, in which the scheduler can be regarded as an ant and the task scheduling process is compared with the ant foraging process. The pheromone is updated according to the hardware performance parameters of the system resource node and the system average load difference, and the expected time to complete all tasks is at a minimum. So, the expected completion time is used to calculate the heuristic function of the ant colony algorithm. Finally, a resource node with high pheromone concentration (high performance and low load) and minimum completion time is selected to handle the assigned task.  
The research content of SWIM task scheduling is to map each task to resource set \(S\) in task set \(T\), and to improve the task scheduling performance of the system on the premise of ensuring load balancing. The specific steps of the ant colony optimization task scheduling algorithm for SWIM based on load balancing are as follows. The flow chart of the algorithm is shown in Figure 2.  
Step 1: First, initialize the parameters in the algorithm. There are \(n\) tasks, \(m\) resources, the number of ants is \(q\), the maximum number of cycles is \(t_{\max}\), the initial time \(t\) is set to 0 (it can be assumed  
Figure 2: The flow chart of the ant colony optimization task scheduling algorithm based on load balancing.  
that there are \(n\) air flight planning missions and \(m\) navigation information resources in SWIM in this scenario).  
Step 2: Make \(t=t+1\). If \(t\leq t_{\text{max}}\), after initializing the search table, all ants search the path from the initial position and stop searching when all tasks enter the search table.  
Step 3: The hardware performance and load balance difference of the resource nodes are obtained by calculations, and the transferred probability \(P_{ij}^{k}(t)\) of each resource node is calculated by Equation (1), using \(P_{ij}^{k}(t)\) to select the perform task resource node.  
Step 4: Update the search table and add the scheduling task to the search table. Use _Tabu_ to record the set of tasks that ant \(k\) is currently passing through. Do not repeatedly select the path that has already passed as the next path.  
Step 5: Update the pheromone of the ant colony algorithm according to Equation (6) and save the selected ant information with the best scheduling result.  
Step 6: Repeat the above steps until \(t>t_{\text{max}}\), when the algorithm ends [19].
# Generalized Paxos Made Byzantine
## 2 Background and Overview
### Paxos and Its Variants
#### 2.1.3 Generalized Paxos  
Generalized Paxos addresses Fast Paxos' shortcomings regarding collisions. More precisely, it allows acceptors to accept different sequences of commands as long as non-commutative operations are totally ordered [13]. Non-commutativity between operations is generically represented as an interference relation. Generalized Paxos abstracts the traditional consensus problem of agreeing on a single value to the problem of agreeing on an increasing set of values. _C-structs_ provide this abstraction of an increasing set of values and allow us to define different consensus problems. If we define the sequence of learned commands of a learner \(l_{i}\) as a _c-struct learned\([l_{i}]\)_, then the consistency requirement for consensus can be defined as:  
* **Consistency--_learned\([l_{1}]\)_ and _learned\([l_{2}]\)_ are always compatible, for all learners \(l_{1}\) and \(l_{2}\).  
For two _c-structs_ to be compatible, they must have a _common upper bound_. This means that, for any two learned _c-structs_ such as _learned\([l_{1}]\)_ and _learned\([l_{2}]\)_, there must exist some _c-struct_ to which they are both prefixes. This prohibits non-commutative commands from being concurrently accepted because no subsequent _c-struct_ would extend them both since it would not have a total order of non-commutative operations. For instance, consider a set of commands \(\{A,B,C\}\) and an interference relation between commands \(A\) and \(B\) (i.e., they are non-commutative with respect to each other). If proposers propose \(A\) and \(C\) concurrently, some learners may learn one command before the other and the resulting _c-structs_ would be either \(C\bullet A\) or \(A\bullet C\). These are compatible because there are _c-structs_ that extend them, namely \(A\bullet C\bullet B\) and \(C\bullet A\bullet B\). These _c-structs_ that extend them are valid because the interfering commands are totally ordered. However, if two proposers propose \(A\) and \(B\), learners could learn either one in the first ballot and these _c-structs_ would not be compatible because no _c-struct_ extends them. Any _c-struct_ would start either by \(A\bullet B\) or \(B\bullet A\), which means that an interference relation would be violated. In the Generalized Paxos protocol, when such a collision occurs, no value is chosen and the leader intervenes by starting a new ballot and proposing a _c-struct_. Defining _c-structs_ as command histories enables acceptors to agree on different sequences of commands and still preserve consistency as long as dependence relationships are not violated. This means that commutative commands can be ordered differently regarding each other but interfering commands must preserve the same order across each sequence at any learner. This guarantees that solving the consensus problem for histories is enough to implement a state-machine replicated system.
## 4 Model and Calculation Principle
### Jarvis Model  
The Jarvis stomatal conductance model is based on the response of plant leaf stomatal conductance to a single environmental factor. Additionally, the comprehensive influence of multiple environmental factors on stomatal conductance when they change at the same time can be obtained via superposition. The specific form of the model is as follows:  
\[\mathrm{g_{s}}=\mathrm{g_{s}}(\mathrm{PAR})\mathrm{f}(\mathrm{VPD})\mathrm{f} (\psi)\mathrm{f}(\mathrm{T})\mathrm{f}(\mathrm{C_{a}}) \tag{7}\]  
where \(\mathrm{g_{s}}\) is stomatal conductance (\(\mathrm{\mu mol\cdot m^{-2}\cdot s^{-1}}\)); \(\mathrm{g_{s}}(\mathrm{PAR})\) is photosynthetically active radiation (\(\mathrm{\mu mol\cdot m^{-2}\cdot s^{-1}}\)), and \(\mathrm{f}(\mathrm{VPD})\), \(\mathrm{f}(\psi)\), \(\mathrm{f}(\mathrm{T})\), and \(\mathrm{f}(\mathrm{C_{a}})\) are the effects of saturated water vapor pressure difference (kPa), leaf water potential (kPa), temperature (\({}^{\circ}\)C), and \(\mathrm{CO_{2}}\) concentration (\(\mathrm{\mu mol\cdot mol^{-1}}\)) on stomatal conductance at leaf temperature, respectively.  
The influence function in the Jarvis model is:  
\[\mathrm{g_{s}}(\mathrm{PAR})=\frac{\mathrm{PAR}}{\mathrm{a}+\mathrm{PAR}} \tag{8}\]  
\[\mathrm{f}(\mathrm{VPD})=\frac{1}{\mathrm{b}+\mathrm{VPD}} \tag{9}\]  
\[\mathrm{f}(\mathrm{T})=\mathrm{cT}^{2} \tag{10}\]  
\[\mathrm{f}(\mathrm{C_{a}})=1-\mathrm{d}\mathrm{C_{a}} \tag{11}\]  
where \(\mathrm{f}(\psi)\) can be neglected [44].  
In Equations (8)-(11), \(\mathrm{a}\), \(\mathrm{b}\), \(\mathrm{c}\), and \(\mathrm{d}\) are model parameters with values of 134.165, \(-\)0.461, 0.002, and 0.002, respectively (the simulation results are better under this parameter) [45]. By substituting Equations (8)-(11) into Equation (7), the expression of the Jarvis model can be obtained as follows:  
\[\mathrm{g_{s}}=\frac{\mathrm{PAR\;cT}^{2}(1-\mathrm{d}\mathrm{C_{a}})}{( \mathrm{a}+\mathrm{PAR})(\mathrm{b}+\mathrm{VPD})} \tag{12}\]