esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Software-Defined Radio-Based Adaptable Packet Communication System for Small Satellites
## 3 Design and Implementation
### Design Aspects  
Feedback from the ground station transceiver is an essential part of this design, and thus both sides of the communication link are designed. However, the satellite transceiver is designed to support interoperability and compatibility with traditional systems. Feedback is achieved by exchanging signaling messages between the two ends of the system.  
The spaceborne transceiver can generate three types of signals to send AX25 packets: 2400 bps GMSK, 4800 bps GMSK and 9600 bps GMSK. It sends data in patches of packets. For LEO, the average time to pass over a given ground station is 10 min [11], and so the length of the packet is chosen so as not to exceed 10% of this value. Between patches of data, a signaling message is sent containing three packets of data, each at a different rate. While sending, the satellite is also listening in order to capture feedback. According to the feedback, the rate of downlink signal data transmission is selected. On the other hand, the ground station transceiver replies to the signaling message with its feedback, stating the fastest reliable transmission mode.  
The AX25 protocol is selected to ensure that the system will work smoothly with traditional versions of the communication subsystems.
# A Low Complexity Persistent Reconnaissance Algorithm for FANET
## 2 System Model and Problem Statement
### Problem Formulation
#### 2.2.3 Problem Construction  
Network connectivity is the guarantee of information interaction between UAVs. Drones can share data with each other only when they are connected to the FANET. The ACP also needs a connected network to control and adjust drone swarms. In addition, ensuring the safety of UAVs is a prerequisite for the regular operation of FANETs. Today, all kinds of jamming and hijacking functions are integrated into various mobile anti-reconnaissance equipment, threatening UAVs' security. Only when the safety of UAVs is guaranteed can drones cooperate to perform complex tasks.  
Considering the above two aspects, we define the overall performance function \(f\) as a weighted sum of network connectivity and FANET threat metric as shown in Equation (4),  
\[\begin{array}{rl}f\big{(}X_{V},\{\epsilon_{m}\}_{m\in M},\varphi(x),r_{thr},\rho\big{)}=&w^{C}f^{C}(X_{V},\rho)\\ &+w^{T}f^{T}(X_{R},\varphi(x),r_{thr}),\end{array} \tag{4}\]  
where \(w^{C}\) and \(w^{T}\) are the weights for network connectivity and FANET threat metric, respectively. Then, we can formulate the problem in the following way so that it can be solved by the TA&CRFO algorithm:  
\[\begin{array}{ll}\underset{x_{u}\in D}{\text{maximize}}&f\big{(}X_{V},\{ \epsilon_{m}\}_{m\in M},\varphi(x),r_{thr},\rho\big{)},\\ \epsilon_{m}\subseteq V,&\forall m\in M\end{array}\]  
\[\begin{array}{ll}s.t.&\|x_{i}-x_{j}\|\leq r^{C}&\forall(i,j)\in\rho(m)\end{array} \tag{6a}\]  
Figure 2: Example of threat density distribution in area of interest.  
\[\|x_{i}-x_{j}\|\geq r^{S}\ \ \forall(i,j)\in V,\ \ u\
eq v \tag{6b}\] \[\varepsilon_{m}\
eq\varepsilon_{n}\ \ \forall\varepsilon_{m}\in M, \forall\varepsilon_{n}\in M\] (6c) \[|\varepsilon_{m}|\geq 1\ \ \forall\varepsilon_{m}\in M\] (6d) \[w^{C}>0,\ \ w^{T}<0\] (6e) \[\varphi(x_{o})=\frac{\sum\varphi(x)}{|D|},\forall x\in D,x_{o}\
otin D \tag{6f}\]  
where \(r^{C}\), \(r^{S}\), and \(r_{thr}\) represent the maximum allowable link length for reliable direct communication between two nodes, the minimum safety distance to avoid collisions between UAVs, and the threat radius of each relay UAV that can be deployed in a given three-dimensional Euclidean space, \(D\in\mathbb{R}^{3}\), respectively. Constraint (6a) guarantees the UAVs' reliable end-to-end communication. A safe flight distance is produced by constraint (6b) to reduce the danger of UAV crashes. Each UAV can only undertake one mission at a time and at least one UAV is required to complete each task, according to constraints (6c) and (6d). Constraint (6e) means that the overall performance function \(f\) increases as the FANET's network connectivity increases and decreases as the FANET threat metric increases. Constraint (6f) describes the threat PDF definition for locations outside the simulation area.
## 3 Methodology and Models
### Data Collection and Preprocessing
#### 3.1.2 Data Preprocessing  
To ensure the consistency and usability of the data for machine learning purposes, several preprocessing steps were undertaken:  
* Column Renaming: Each dataset was originally named based on the station's reporting structure, which varied across locations. To harmonize the datasets, column names were standardized by appending the station name to each feature (e.g., 'Temperature_agxiados').
* Index Alignment: Time-based data from different stations had to be synchronized. This was done by unifying the date and time fields across all datasets, converting them into a single 'Date' index. This enabled the seamless merging of datasets from different stations.
* Data Resampling: Since the data were recorded at different intervals (hourly, daily, etc.), it was resampled to a daily frequency. This resampling was essential to align the meteorological data with the daily energy production data.
* Data Merging: The meteorological data from different stations were merged into a single dataset using the 'Date' index as a common key. This comprehensive dataset included all relevant features required for energy production modeling.
* Missing Data Handling: Missing values were handled in a two-step process:
* Columns with more than 65 missing values, which correspond to approximately 3.4% of the total 1900 time points (65/1900), were dropped to prevent excessive noise in the dataset and to maintain the integrity of the time series analysis.
* For the remaining missing values, imputation was performed using the mean of the nearest non-missing values within the same time window. This method  
Figure 2: Map of meteorological stations across Greece.  
preserves the local data structure and continuity, minimizing any potential bias introduced by gaps in the time series data, and ensuring that the overall temporal patterns remain intact.
* Data Dimension: The dataset used in this study consisted of daily meteorological data from 10 weather stations over 4 years (2015-2018). This resulted in the following:
* Number of records (rows): 1009 (after preprocessing, accounting for missing or incomplete data).
* Number of features (columns): Multiple meteorological variables, including wind intensity, humidity, temperature, and solar radiation. The number of the dataset's features was 49.
###### Abstract
Because it is hard to assign appropriate acceleration in advance, the method will also cause large angular velocity fluctuation. [PERSON] proposed a 4-3-4 polynomial splicing method [14], which guarantees C\({}^{2}\) continuity at connection points. However, only at the beginning and the end target points can velocities be specified. If angular velocities at the intermediate target points are specified, angular acceleration will have broken points and be discontinuous. [PERSON] et al. used seventh-order polynomials and ninth-order polynomials to carry out joint planning [15]. The joint rotation trajectory was smooth enough in high order and the joint velocity and acceleration were continuous. However, the computation of high-order polynomial was large, the velocity and acceleration were easy to fluctuate, and the peak values of acceleration and velocity were much higher. In summary, for PTP joint motion planning via multiple points with constrained velocities, if planning with low-order functions is adopted, angular accelerations at intermediate target points are discontinuous, which will bring additional impacts and vibrations. If planning with high-order functions is adopted [16; 17], the problem of acceleration discontinuity can be solved. However, it is necessary to give angular acceleration values at the target points in advance. Since it is hard to give appropriate angular accelerations in advance, angular velocities are likely to fluctuate largely, which is unfavorable to motion control. Moreover, the higher order of planning function, the more intense change of planning curves and the larger the peak value of velocity and acceleration. All these are unfavorable for the steady operation of the robot arm. In view of the problems in the plannings mentioned above, a modified Hermite interpolation is proposed in the paper. In the proposed planning, velocities at knots can be specified at will and C\({}^{2}\) continuity is guaranteed. Moreover, there will be no drastic fluctuations in angular velocity since the maximum order of the planning function is no more than three and acceleration preset is not needed.
## 3 Results  
Mean age of patients with open-angle glaucoma was 76.8 \(\pm\) 11.1 years (range: 35-88 years). 103 patients (50.2%) were female, and 102 patients (49.8%) male. One eye of each patient was included. During the whole period of 48 months, no repeat-surgery was needed in any included patient.  
Mean preoperative IOP in the study population of 205 patients was 24.5 \(\pm\) 4.3 mmHg (range 18 to 48 mmHg). Decimalised Snellen visual acuity (VA) increased from 0.46 \(\pm\) 0.27 preoperative to 0.68 \(\pm\) 0.27 postoperative. For all patients, the follow-up was 48 months. After 10 days, a slight increase of IOP was detected but was not statistically significant. Mean IOP after 48 months was 15.0 \(\pm\) 1.7 mmHg (range 10 to 20 mmHg). The IOP drop was statistically significant (\(p<0.001\)) at all measured postoperative intervals (Figure 6).  
Figure 5: (**a**) OCT image of the anterior chamber: Overview after HFDS procedure with a newly created pocket in the chamber angle (Visante OCT, Zeiss, Oberkochen, Germany). (**b**) OCT image of the anterior chamber of a normal angle anterior before HFDS surgery.  
Figure 6: Intraocular pressure (IOP) follow-up of operated eyes during 48 months.  
At month 48 after surgery, 54.7% of patients had an IOP \(<\) 15 mmHg, 77% had an IOP \(<\) 18 mmHg, and 84.9% had an IOP \(<\) 21 mmHg. Qualified success rate, defined as an IOP lower than 22 mmHg with medication, was 100% for all patients at 48 months (Figure 7).  
The average preoperative administration of pressure-reducing number of antihypertensive eye agents was 2.6 \(\pm\) 1.0. Following surgery, this value was decreased to 0.47 \(\pm\) 0.59 after 1 month, 0.40 \(\pm\) 0.54 after 3 months, 0.28 \(\pm\) 0.62 after 6 months, 0.31 \(\pm\) 0.49 after 12 months, 0.38 \(\pm\) 0.57 after 24 months, 0.42 \(\pm\) 0.83 after 36 months, and 0.48 \(\pm\) 0.97 after 48 months (Figure 8).  
There were no significant visual field changes (Octopus 900, Haag Streit, Switzerland) from baseline with a mean defect MD 7.06 \(\pm\) 6.54 and loss of variance LV 26.4 \(\pm\) 23.92. At 48 months, these values were MD 8.43 \(\pm\) 2.11 and LV 24.1 \(\pm\) 24.92 (\(p\) = 0.22 for MD, \(p\) = 0.58 for LV).  
The mean excavation preoperative was 0.63 \(\pm\) 0.22 and after 48 months 0.65 \(\pm\) 0.21, which is not statistically significant (\(p\) = 0.36).  
Figure 8: Progression of pressure-reducing eye agents pre-and postoperative during the 48 months, showing a marked and sustained postoperative reduction in numbers of agents needed.  
Figure 7: Qualified success rate after 48 months of follow-up.  
Temporary IOP elevation higher than 21 mmHg was observed in 18 of 205 eyes (8.7%). Four eyes (2%) showed transient fibrin formation. Fibrin was treated by topical dexamethasone and disappeared one day later.
## 3 Results
### The Impact of Factors on DB15 Adsorption on HNT  
#### 3.2.1 pH Value of Solution  
The adsorption limit of HNT relies upon the value of the pH solution. The pH value majorly influences the attributes of the adsorbent surface and the chemistry of the dye solution [22]. The value of pH, reflects the efficiency of the adsorbent. This boundary is critical when the interaction is scaled too higher. Technology readiness level (TRL) and the commercial stage [23]. As depicted in Figure 5a, the shape of the curve was consistent with the expected results already published in the literature. The primary components of HNT are the oxides of Al and Si. These oxides have various ionization features and surface charges because different zeta potential rates when remained in touch with water [24]. At pH > 1.5, the external surface of HNT has negative charges due to the presence of silica  
Figure 4: The deliberate mark of zero charges of HNT at the convergence.  
Figure 3: FTIR spectra of the samples under study.  
in its chemical structure (Figure 6). Conversely, at a lower pH the anionic dye exhibits a positive charge (see Figure 5a). Thus, adsorption at a higher level occurs in the pH range of 2-4. A rise is seen in the level of pH the amount of degree of protonation of NH\({}_{2}\) and OH groups of the dye decreases. Accordingly, the adsorption capacity of the substrate decreases sharply from pH 4 to 6. A sharp increase at pH > 10 is likely due to dye adsorption onto positively charged Al\({}^{3+}\) and Si\({}^{4+}\) ions. Moreover, the degradation of the dye at pH > 10, resulting in a decrease in absorbance of the experimental solution, cannot be ruled out.
# Effect of the Planetesimal Belt on the Dynamics of the Restricted Problem of 2 + 2 Bodies
## 4 Stability of Motion Near Equilibrium Points  
We found four paired equilibrium positions around each of \(L_{4}\) and \(L_{5}\). In this section, we analyze the stability of the infinitesimal body \(P_{1}\). Let \((x_{0},y_{0})\) be any equilibrium point of particle \(P_{1}\), and \(\delta\) and \(\zeta\) be small perturbations in the \(x\) and \(y\) directions, respectively, i.e., \(x_{1}=x_{0}+\delta\) and \(y_{1}=y_{0}+\zeta\),  
\[\ddot{\delta}-2n\dot{\zeta}=\frac{1}{\mu_{1}}U_{x_{1}}(x_{0}+ \delta,y_{0}+\zeta)=\frac{1}{\mu_{1}}(\delta U_{x_{1}x_{1}}^{0}+\zeta U_{x_{1}y _{1}}^{0}), \tag{21}\] \[\ddot{\zeta}+2n\dot{\delta}=\frac{1}{\mu_{1}}U_{y_{1}}(x_{0}+ \delta,y_{0}+\zeta)=\frac{1}{\mu_{1}}(\delta U_{y_{1}x_{1}}^{0}+\zeta U_{y_{1} y_{1}}^{0}). \tag{22}\]  
The equations can be written in matrix form as \(\dot{X}=AX\), i.e.,  
\[\begin{bmatrix}\dot{\delta}\\ \dot{\zeta}\\ \dot{\zeta}\\ \dot{\zeta}\\ \end{bmatrix}=\begin{bmatrix}0&0&1&0\\ 0&0&1\\ \frac{1}{\mu_{1}}U_{x_{1}x_{1}}^{0}&\frac{1}{\mu_{1}}U_{x_{1}y_{1}}^{0}&0&2n \\ \frac{1}{\mu_{1}}U_{x_{1}y_{1}}^{0}&\frac{1}{\mu_{1}}U_{y_{1}y_{1}}^{0}&-2n&0 \\ \end{bmatrix}\begin{bmatrix}\dot{\zeta}\\ \dot{\zeta}\\ \dot{\zeta}\\ \end{bmatrix}\]  
The characteristic equation of the matrix may be reduced to  
\[\lambda_{1}^{4}+(4n^{2}-\frac{1}{\mu_{1}}U_{x_{1}x_{1}}^{0}-\frac{1}{\mu_{1}}U _{y_{1}y_{1}}^{0})\lambda_{1}^{2}+\frac{1}{\mu_{1}^{2}}(U_{x_{1}x_{1}}^{0}U_{ y_{1}y_{1}}^{0}-{U_{y_{1}x_{1}}^{0}}^{2})=0. \tag{23}\]  
Solving Equation (23), we have,  
\[\lambda_{1,2}=\pm\Bigg{(}\frac{b-\sqrt{b^{2}-4c}}{2}\Bigg{)}^{ \frac{1}{2}}, \tag{24}\] \[\lambda_{3,4}=\pm\Bigg{(}\frac{b+\sqrt{b^{2}-4c}}{2}\Bigg{)}^{ \frac{1}{2}}, \tag{25}\]  
where, \(b=4n_{1}^{2}-\frac{1}{\mu_{1}}U_{x_{1}x_{1}}^{0}-\frac{1}{\mu_{1}}U_{y_{1}y_{ 1}}^{0}\) and \(c=\frac{1}{\mu_{1}^{2}}(U_{x_{1}x_{1}}^{0}U_{y_{1}y_{1}}^{0}-{U_{y_{1}x_{1}}^{ 0}}^{2})\).  
We note that Equations (24) and (25) are the eigenvalues of the characteristic Equation (23). With the help of these eigenvalues, we can find the stability of particle \(P_{1}\).
## 4 Conclusions
\begin{table}
\begin{tabular}{c c} \hline \hline
**Acronym** & **Description** \\ \hline IFR & Instrument Flight Rules \\ InIRA & Imaging Radar Altimeter \\ InSAR & Interferometric SAR \\ INSAT & Indian National Satellite \\ IPCC & Intergovernmental Panel for Climate Change \\ IRNSS & Indian Regional Navigation Satellite System \\ IS & Intensity-Space \\ LBP & Local Binary Pattern \\ LEO & Low Earth Orbit \\ LES & Leading Edge Slope \\ LiDAR & Light Detection and Ranging \\ LIFR & Low Instrument Flight Rules \\ LMP & Local Multiple Pattern \\ LOS & Line of Sight \\ LSWI & Land Surface Water Index \\ LUT & Look-Up Table \\ MANMAR & Manual of Marine Weather Observations \\ MCC & Maximum Cross-Correlation \\ METAR & Meteorological Aerodrome Report \\ MetOp & Meteorological Operational satellite \\ MGDFs & Multiscale Gaussian Differential Features \\ MIR & Mid Infrared \\ ML & Machine Learning \\ MLE4 & Maximum Likelihood Estimator \\ MNDWI & Modified Normalized Difference Water Index \\ MSAVI & Modified Soil-Adjusted Vegetation Index \\ MV & Minimum Variance \\ MVFR & Marginal Visual Flight Rules \\ NAIP & National Agriculture Imagery Program \\ NASA & National Aeronautics and Space Administration \\ NDBC & National Data Buoy Center \\ NDVI & Normalized Difference Vegetation Index \\ NDWI & Normalized Difference Water Index \\ NIR & Near-Infrared \\ NL & Newfoundland and Labrador \\ NOAA & National Oceanic and Atmospheric Administration \\ NRCS & Normalized Radar Cross Section \\ NRT & Near Real-Time \\ NOAA & National Oceanic and Atmospheric Administration \\ NSF & Nighttime Sea Fog \\ NWP & Numerical Weather Prediction \\ OC & Ocean Color \\ OOS & Ocean Oil Spill \\ OS & Ocean Salinity \\ OSC & Ocean Surface Current \\ OSCAR & Ocean Surface Current Analysis Real-time \\ OSW & Ocean Surface Wind \\ OT & Ocean Tide \\ OTL & Ocean Tidal Load \\ OTV & Optimum Threshold Value \\ OWH & Ocean Wave Height \\ PDF & Probability Density Function \\ PE & Polarimetric Entropy \\ PFT & Phase spectrum of Fourier Transform \\ POD & Probability Of Detection \\ PPP & Precise Point Positioning \\ PRF & Pulse Repetition Frequency \\ \(Pr_{Std}\) & Probability of Nighttime Sea Fog for each pixel obtained from the spatial uniformity analysis \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont.
## 4 Results and Analysis
### Measurement Model Assessment
\begin{table}
\begin{tabular}{l c c c c c} \hline
**Construct and Item Description** & **Convergent Validity** & **VIF** & **Composite Reliability** & \(\alpha\) & **AVE** \\ \hline ISM: Information Security & & & 0.890 & 0.814 & 0.729 \\ Management & & & & \\ ISM01 & 0.876 & 1.575 & & & \\ ISM02 & 0.865 & 2.030 & & & \\ ISM03 & 0.818 & 1.967 & & & \\ DIP: Digital Piracy & & 0.936 & 0.929 & 0.831 \\ DIP101 & 0.851 & 2.092 & & & \\ DIP103 & 0.948 & 4.006 & & & \\ DIP104 & 0.932 & 3.828 & & & \\ RES: Responsibility & & & 0.903 & 0.866 & 0.709 \\ RES01 & 0.743 & 1.772 & & & \\ RES02 & 0.887 & 2.348 & & & \\ RES03 & 0.891 & 2.508 & & & \\ RES04 & 0.839 & 1.984 & & & \\ COL: Collaboration & & 0.896 & 0.890 & 0.695 \\ COL01 & 0.737 & 1.851 & & & \\ COL02 & 0.850 & 2.400 & & & \\ COL03 & 0.872 & 2.941 & & & \\ COL04 & 0.876 & 3.531 & & & \\ COL05 & 0.827 & 3.302 & & & \\ DTE: Digital Transformation & & & 0.760 & 0.753 & 0.572 \\ Enablers & & & & & \\ DTE01 & 0.711 & 1.558 & & & \\ DTE02 & 0.728 & 1.741 & & & \\ DTE03 & 0.772 & 1.657 & & & \\ DTE04 & 0.810 & 1.316 & & & \\ IN: Innovation & & & 0.917 & 0.913 & 0.621 \\ IN01 & 0.768 & 2.063 & & & \\ IN02 & 0.802 & 2.183 & & & \\ IN03 & 0.810 & 2.330 & & & \\ IN04 & 0.820 & 2.721 & & & \\ IN05 & 0.823 & 2.640 & & & \\ IN06 & 0.791 & 2.171 & & & \\ IN07 & 0.765 & 2.091 & & & \\ IN08 & 0.722 & 1.934 & & & \\ PS: Problem-Solving & & & 0.893 & 0.851 & 0.626 \\ PS01 & 0.794 & 1.826 & & & \\ PS02 & 0.805 & 1.906 & & & \\ PS03 & 0.819 & 1.993 & & & \\ PS05 & 0.747 & 1.612 & & & \\ PS06 & 0.790 & 1.788 & & & \\ \hline \end{tabular} Source: Authors.  
\end{table} 
Table 1: _Cont._
## 3 The Proposed Approach
### Cause-Effect Graph
#### 3.2.1 Related Concepts  
(1) Boolean function  
A Boolean function describes how to determine the Boolean output based on some logical calculation of a Boolean input. A Boolean function (\(F\)) with \(n\) variables is shown in Equation (1):  
\[F:\{0,1\}^{n}\longrightarrow\{0,1\} \tag{1}\]  
The function has \(2^{n}\) different possible inputs, and the output is a Boolean value of True or False, represented by 1 or 0.  
Define new Boolean functions with \(n-1\) variables, as shown in Equations (2) and (3):  
\[F_{x_{1}}(x_{2},\ldots,x_{n})=F(1,x_{2},x_{3},\ldots,x_{n}) \tag{2}\]  
Figure 3: Graphical symbols of constraint relationships of the cause–effect graph: (**a**) E (mutually exclusive); (**b**) I (inclusive); (**c**) O (only); (**d**) R (required); (**e**) M (mask).  
\[F_{x_{1}^{\prime}}(x_{2},\ldots,x_{n})=F(0,x_{2},x_{3},\ldots,x_{n}) \tag{3}\]  
where \(F_{x_{1}}\) and \(F_{x_{1}^{\prime}}\) are the cofactors of \(F\). \(F_{x_{1}}\) is a positive factor and \(F_{x_{1}^{\prime}}\) is a negative factor;  
(2) Shannon decomposition  
The Shannon decomposition is a transformation of a Boolean function. It expresses any Boolean function as any one of the variables multiplied by a subfunction, plus the inverse of this variable multiplied by another subfunction, as shown in Equation (4):  
\[F(x_{1},x_{2},\ldots,x_{n})=x_{i}F_{x_{i}}+x_{i}^{\prime}F_{x_{i}^{\prime}} \tag{4}\]  
(3) If-then-else normal form  
An if-then-else normal form (INF) operator is denoted by \(x\longrightarrow y_{0},y_{1}\). An INF operator is defined as shown in Equation (5):  
\[x\longrightarrow y_{0},y_{1}=(x\wedge y_{0})\vee(\
eg x\wedge y_{1}) \tag{5}\]  
That is, the result is \(y_{0}\) if the value of \(x\) is 1; otherwise, the result is \(y_{1}\).  
It is straightforward to see that all the logical operators can be expressed using if-then-else operators and the constants 0 and 1 only. Furthermore, an if-then-else operator can be implemented in such a way that all tests are performed only on un-inverted variables. For example, \(\
eg x\) is \(x\to 0,1\). In this way, an INF paradigm can be constructed based on if-then-else operators.  
An INF paradigm is a Boolean expression constructed entirely from if-then-else operators and constants, so only tests on the variables need to be performed. For the Boolean expression (\(t\)), let \(t[0/x]\) denote the Boolean expression obtained by assigning the variable \(x\) in the \(t\) to 0, and Equation (6) can be obtained for the \(t\):  
\[t=x\longrightarrow t[1/x],t[0/x] \tag{6}\]  
Equation (6) is the Shannon expansion of the expression \(t\) for the variable \(x\). This simple equation has several useful applications. The first is that an INF can be generated from any expression (\(t\)). If the \(t\) does not contain any variables, then it is equal to 0 or 1 (i.e., an INF). Otherwise, a Shannon expansion can be generated for a variable (\(x\)) in the expression (\(t\)) where \(t[1/x]\) and \(t[0/x]\) have one less variable than the \(t\), and an INF for the \(t\) can thus be constructed recursively based on Equation (6).