esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Conceptual Model and Theoretical Framework  
Scholars give a great importance to theory or framework development. In spite of its great importance within information discipline, the refinement of existing frameworks/models, theories, and development of new theory or theoretical frameworks have been neglected by the researchers [44; 84]. It is confirmed by the literature, a limited number of studies are available on theory development in the IS context and especially for the BIS adoption process. By rigorous systematic literature review, we have found just a few studies with theories and frameworks for the BIS adoption. A good quality theory or framework is considered a significant element to improve the knowledge of scholars and practitioners within the theoretical domain [28; 103].  
[PERSON] has suggested various methods to develop theory or framework in [44]. He also stated; theory or framework might make novel contributions to a discipline:  
* If the focal phenomena of a specific theory had not been discovered by previous theories.
* An existing and well-known theory or framework is considered to be novel if its focal phenomena modify in new ways. it might enhance the novelty of theory.
* A theory might be considered novel, if some significant changes brings to a well-established theory -possibly deleting or adding associations and constructs, specifying the existing constructs and associations more comprehensively.  
The proposed theoretical framework in Figure 8; consists of four contexts; individual context, technological context, organizational context, and environmental context. The purpose of this framework is to develop a conceptual BIS adoption model with concise prediction and clear explanation of the main constructs and determinants. The framework is developed with the blend of most potential models and theories such as TOE, TAM, DOI, and institutional theory. The earlier published literature has confirmed that the TOE framework has been integrated frequently with other theories to examine the adoption of innovations [86]. Initially, the TOE does not provide a tactile model with determined factors that influence the firms' decision for technology adoption [104]. It provides only taxonomy for categorization the determinants within their respective context. For example, TOE has no determinants in technological and environmental context [96]. Therefore, it always integrates the DOI theory and institutional theory in these contexts, the frequent integration of both theories is considered now an integral part of TOE [56; 61]. On the other hand, TOE has no specific constructs for innovation acceptance at individual level. As a result, TAM is integrated to fill this gap. TAM is considered as one of the most parsimonious and famous models for evaluating individuals' IT acceptance. The major contribution of TOE and TAM is to provoke and encourage the practitioners and researchers to adopt the innovation at firm and individual level [71; 102].  
Figure 7: Tag cloud visualization of the potential determinants that influence the BIS adoption and acceptance.
# Improving Real-Time Position Estimation Using Correlated Noise Models +
## 1 Introduction
However, the OU noise model did not reach the same performance with altitude inference and scored worse. We note that we carried out all modelling on a desktop computer, hence the algorithm in Appendix A in its current state is only suitable for a similar implementation. The computational cost of this algorithm is minimal, of the order of \(O(d_{x}^{2}d_{t})\) where \(d_{t}\) is determined by a design choice and can be as low as unity, while \(d_{x}\) is the dimension of the augmented state-vector; hence, it can be of the order of 10. One of the challenges one may face with in implementing the algorithm in hardware is the parameter's potential dependence on geographic location. Investigating this potentially weak dependence is left for future research. The structure of the paper is as follows: In Section 2, we analyse the GPS signals, fitting noise model parameters while observing their variation with respect to global position and coordinate type (latitude, longitude and altitude). The well-characterised noise allows us to construct well-informed models and prior distributions for the variables used in KF algorithms in Section 3. In Section 4,we formulate the scoring rules to evaluate algorithm performance. In Section 5, we present the results of the inferences and uncertainty quantification. We conclude in Section 6.
# Low-Temperature and Low-Pressure Silicon Nitride Deposition by ECR-PECVD for Optical Waveguides
## 3 Characterization and Results  
### Refractive Index and Thickness  
We used a combination of variable-angle spectroscopic ellipsometry (VASE) and prism coupling to determine the refractive index and thickness of the SiO\({}_{x}\)N\({}_{y}\):H\({}_{z}\) films on the silicon and thermally oxidized silicon pieces, respectively. The results of the refractive index and thickness measurements are displayed in Figure 1. The anticipated relationship was observed between the N2/Ar flow rate and refractive index, with films having a higher refractive index at decreased N2/Ar:SiH4/Ar flow rate ratios due to increasing silicon content. An expected trend is observed in the deposition rate which increases from 1.43 to 2.11 nm/min for Film 1 and Film 5, respectively. This is correlated to the increase in N2/Ar flow rate providing a higher concentration of N2 in the chamber and increased pressures during film growth. The films show the expected behaviour of decreased thickness after annealing, which can be attributed to hydrogen removal and film densification. Additionally, an increased refractive index is observed after annealing, which is consistent with film densification as has been seen in [28].  
The films were grown from 300 to 450 nm thick to ensure a planar waveguide mode was supported at 1550 nm, without the need for a top cladding layer. This allowed for the refractive index, thickness and loss to be measured using prism coupling at longer wavelengths, including 1550 and 1640 nm. However, at these thicknesses, we observed the annealed films lifting off the edges of 80% of the samples which were deposited on large SiO\({}_{2}\)-Si substrates, which is due to the known tensile stress in Si\({}_{x}\)N\({}_{y}\) films. We measured film refractive indices in the range of 1.81-2.07 before annealing across the N\({}_{2}\)/Ar:SiH\({}_{4}\)/Ar flow ratios shown in Figure 1. Film 5 with the highest N\({}_{2}\)/Ar flow showed a drastic thickness decrease of \(\sim\)100 nm after annealing, while Film 1 with the lowest N\({}_{2}\)/Ar flow showed a drastic increase in the index of \(\sim\)0.33. This is anticipated to be a cause of Film 1 and Film 5 being, respectively, overly silicon- and nitrogen-rich and therefore in different dominant regimes for Si-H and N-H bonding. Film 5, moreover, is likely in a regime beyond the stoichiometric film ratio [2], causing a difference in bond reorganization in comparison to the other films after annealing.
# Computer Modelling of Heliostat Fields by Ray-Tracing Techniques: Simulating the Sun
## 3 Sun Radiation
### Earth-Sun Distance  
The Earth-Sun distance \(D\) is continuously changing (see Figure 6), from a minimum of 1.470983 \(\times\) 10\({}^{8}\) km at Perigee to a maximum of 1.520982 \(\times\) 10\({}^{8}\) km at Apogee [28]. Sun rays are thus slightly more parallel at Apogee than at Perigee. Distance \(D\) can be calculated if the  
Figure 4: A Sun ray emitted randomly from the (yellow) spherical surface illuminating Earth can be associated with a polar angle \(\theta\) (ranging from 0 to 90\({}^{\circ}\)) which in turn allows to calculate the non-parallel angle \(\alpha\) observed at Earth.  
Figure 5: The initial Sun vector \(\overset{\rightarrow}{K}_{i}\) is scrambled to \(\overset{\rightarrow}{K}_{f}\) as a result of the two random angles \(\alpha\), \(\phi\) to describe non-parallel rays resulting from the non-negligible diameter of the Sun disk.  
true anomaly \(\
u\) is known for the date and time required, a side product of the astronomy algorithm described above to calculate Sun's position [22]:  
\[D=\frac{a_{0}\big{(}1-e^{2}\big{)}}{1+e\text{cos}\,\
u} \tag{17}\]  
where \(a_{0}=1.495985\)\(\times\)\(10^{8}\) km is the length of the semi-major axis of the Earth-Sun elliptical orbit and \(e=0.016708\) is the eccentricity of the Earth-Sun orbit (for standard epoch J2000 [22]).
# Deceptive Jamming Detection for SAR Based on Cross-Track Interferometry
## 2 Principle of Deceptive Jamming Detection
### Signal Model  
According to [21], the jammer transmits the deceptive jamming signal with a time delay to cover useful information, while the true target reflects echo according to the radar cross section (RCS). As can be seen, the slant range histories for the jamming signal and the real echo are obtained in different ways. After echo analysis and imaging, the final SAR imagery of the deceptive jamming part in the master image and slave image are, respectively, represented by [20]  
\[\begin{array}{l}J_{M,out}(t_{r},t_{a})\\ =GA\sigma(x,y)\sin c\left(t_{r}-\frac{2R_{M}}{c}\right)\sin c(t_{a}-t_{0})\exp \left(-j\frac{4\pi}{\lambda}R_{M}\right)\end{array} \tag{5}\]  
\[\begin{array}{l}J_{S,out}(t_{r},t_{a})\approx&G\Delta\sigma(x,y)\sin c\left( t_{r}-\frac{R_{M}+R_{S}}{c}\right)\sin c(t_{a}-t_{0})\\ &\cdot\exp\left(-j\frac{4\pi}{\lambda}R_{M}\right)\exp\left(j\frac{2\pi}{ \lambda}\left(R_{M|0}-R_{S|0}\right)\right)\end{array} \tag{6}\]  
with  
\[R_{M}=\sqrt{\left(X_{s}-x\right)^{2}+\left(Z_{s}-z\right)^{2}} \tag{7}\]  
\[R_{M|0}=\sqrt{{X_{s}}^{2}+y^{2}+{Z_{s}}^{2}} \tag{8}\]  
\[R_{S|0}=\sqrt{\left(X_{s}+B\cos(\alpha)\right)^{2}+y^{2}+\left(Z_{s}+B\sin( \alpha)\right)^{2}} \tag{9}\]  
where \(t_{r}\) is the fast (range) time, \(G\) is the jammer gain factor, \(A\) represents the compression gain in 2-D matched filtering, \(\sigma(x,y)\) is the backscattering coefficient of the false point target, \(R_{M}\) is the minimum slant range between the master antenna and the target, \(c\) is the speed of light, \(t_{0}\) is the time corresponding to the nearest distance between the antenna and the point target, \(\lambda\) is the wavelength, and \(R_{S}\) is the minimum slant range between the slave antenna and the target. \(R_{M|0}\) denotes the  
Figure 1: InSAR and jammer geometry.  
distance between the master antenna and the jammer when the slant range between master antenna and target is \(R_{M}\). \(R_{SJ0}\) is the distance between the slave antenna and the jammer when the slant range between slave antenna and target is \(R_{S}\).  
For the imaging output of the real target, the master image and slave image can be expressed as [22]  
\[\begin{array}{ll}S_{M\_out}(t_{r},t_{a})=&A\sigma(x,y)\sin c\Big{(}t_{r}- \frac{2R_{M}}{c}\Big{)}\\ &\cdot\sin c\big{(}t_{a}-t_{0}\big{)}\exp\Bigl{(}-i\frac{4\pi}{\lambda}R_{M} \Bigr{)}\end{array} \tag{10}\]  
\[\begin{array}{ll}S_{S\_out}(t_{r},t_{a})=&A\sigma(x,y)\sin c\Big{(}t_{r}- \frac{R_{M}+R_{S}}{c}\Big{)}\\ &\cdot\sin c\big{(}t_{a}-t_{0}\big{)}\exp\Bigl{(}-i\frac{2\pi(R_{M}+R_{S})}{ \lambda}\Bigr{)}\end{array} \tag{11}\]
## 4 Controller Design  
Designing a stable controller is a critical challenge in the practical implementation of control systems. While the previous section established sufficient stability conditions for ensuring the stability of the closed-loop system, the task of designing an effective and implementable controller remains a significant objective. In this section, we address this challenge by formulating the controller design problem as an optimization problem, which is a well-established methodology in control theory. This approach provides both flexibility and robustness, allowing for the systematic incorporation of various performance criteria into the design process. Moreover, the optimization framework ensures the derivation of a smooth and computationally efficient controller that meets the desired stability and performance requirements, making it practical for real-world implementation.  
We propose the following optimization problem to achieve the optimal output controller:  
\[\text{Minimize }\ u \tag{48}\] \[\text{subject to: Stability Condition}\]  
In the optimization problem (48), the control input \(u\) is defined as \(u=Kx\), where \(K\) represents the controller gain matrix. This optimization problem leverages the stability condition derived from Theorem 2, specifically using Equation (29), which converts the optimization into a nonlinear problem. The formulation ensures that the controller adheres to stability constraints while optimizing system performance. Furthermore, the controller gain matrix \(K\) is selected to ensure the asymptotic stabilization of the system.  
The utilization of optimization in the design process not only addresses the inherent complexities in FO nonlinear systems but also ensures that the controller remains stable in the presence of input saturation and time delays. The method we present provides a systematic approach to balancing the trade-off between stability and performance, thereby offering a comprehensive solution to controller design in FOSs subject to nonlinear dynamics and practical constraints.
## 3 Results and Discussion
### Experiment Setup
#### 3.3.2 Number of Beehives  
The studies included in this review used different numbers of beehives. Some studies (11.5%) used the audio of a limited number of bees for a specific purpose. In [75], the authors used the audio of 104 bees (forager bees which were caught at feeders placed nearby the hives) to investigate hissing patterns in _Apis mellifera_. In addition, in [86], 60 bees from 3 species of bumblebees, _Bombus pascuorum_, _B. humtilis_, and _B. hypnorum_, were used to monitor daily foraging activity and distinguish between their arrivals and departures. The authors in [99] studied similar variables using groups (n = 105) of _B. hortorum_, _B. lupidarius_, _B. pascuorum_, _B. pratorum_, _B. terrestris_, and _B. lucorum_ to determine the similarity of vibrations among different species of bees and examine the extent to which vibrations generated from buzz pollination differed from those produced in a defensive context. While the majority (46.4 %) of the studies did not mention the number of beehives, the most common setups in the 54.6% of the remaining studies included between one and six beehives. A limited number of studies used a larger number of beehives [49; 84; 85]. For example, in [84; 85], 46 wooden domiciles were deployed to collect data from _Bombus_ in 2018 and 2019. Out of 46 domiciles, only 6 and 11 were occupied by different bumble bee species in 2018 and 2019, respectively. Moreover, the work in [49] recorded audio of 26 beehives which provided a very large number of audio samples and \"hive-independent\" models (predicting the beehive state in hives different to the ones where the model was trained on) were used to predict hive strength.
## 2 Materials and Methods
### Samples Used for Measurements
& & & & & & \\  & Tokyo, Japan) & & & & & & \\ \hline \multirow{2}{*}{B-5} & Hollow glass beads (Q-CEL\({}^{\text{\textregistered}}\) 5020 FPS) (Potters-Ballotini Co., Ltd., Ibaraki, Japan) & & & & & & \\  & & & & & & & \\ \hline \multirow{2}{*}{C-1} & Micro glass beads (EMB-10) (Potters-Ballotini Co., Ltd., Ibaraki, Japan) & 0.0066 & 0.8820 & \(5.78\times 10^{-4}\) & \(2.79\times 10^{-3}\) & 0.661 & \(\blacktriangle\) \\  & & & & & & & \\ \hline \multirow{2}{*}{A2-2} & Calcium silicate (Tobermorite powder (TJ)) (Japan Insulation Co., Ltd., Osaka, Japan) & 0.0181 & 0.0805 & \(1.46\times 10^{-4}\) & \(8.29\times 10^{-3}\) & N/A \({}^{*}\) & - \\  & & & & & & & \\ \hline \multirow{2}{*}{A2-3} & Calcium silicate (Tobermorite powder (TJ)) (Japan Insulation Co., Ltd., Osaka, Japan) & 0.0183 & 0.0868 & \(1.59\times 10^{-4}\) & \(7.09\times 10^{-3}\) & N/A \({}^{*}\) & \(\bullet\) \\  & & & & & & & \\ \hline \multirow{2}{*}{A2-4} & Calcium silicate (Tobermorite powder (TJ)) (Japan Insulation Co., Ltd., Osaka, Japan) & 0.0159 & 0.0737 & \(1.17\times 10^{-4}\) & \(4.24\times 10^{-3}\) & N/A \({}^{*}\) & \(\bullet\) \\  & Co., Ltd., Osaka, Japan) & & & & & & \\ \hline \multirow{2}{*}{B-1} & Tak (Featherfield Co., & 0.00450 & 0.5126 & \(2.31\times 10^{-4}\) & \(3.67\times 10^{-3}\) & 0.810 & \(\times\) \\  & Ltd., Hiroshima, Japan) & & & & & & \\ \hline \multirow{2}{*}{B-2} & Hollow glass beads (Sphericl\({}^{\text{\textregistered}}\) (110P8)) & & & & & & \\  & (Potters-Ballotini Co., & 0.0102 & 0.3642 & \(3.70\times 10^{-4}\) & \(4.30\times 10^{-3}\) & N/A \({}^{*}\) & \(\times\) \\  & Ltd., Ibaraki, Japan) & & & & & \\ \hline \multirow{2}{*}{B-3} & Crystalline cellulose (Ceolus\({}^{\text{\textregistered}}\) TG-F20) (Asahi & 0.0110 & 0.2961 & \(3.25\times 10^{-4}\) & \(8.94\times 10^{-3}\) & N/A \({}^{*}\) & \(\times\) \\  & Kasei Corporation. & & & & & & \\ \hline \multirow{2}{*}{B-4} & Hollow glass beads (Cell spheres) (Taheiyo & 0.0053 & 0.1319 & \(6.99\times 10^{-5}\) & \(2.33\times 10^{-3}\) & N/A \({}^{*}\) & \(\times\) \\  & Cement Corporation. & & & & & & \\ \hline \multirowThe normal incident sound absorption coefficient was measured as follows. Lightweight powder layers absorb sound when a sound wave excites the longitudinal vibration modes of the powder. The elastic wave velocity in the powder layer is related to the first peak frequency of the sound absorption coefficient as follows [12]:  
\[c_{p}=4l_{p}f_{peak} \tag{5}\]  
The elastic wave velocity is also related to the longitudinal elastic modulus of the powder layer as follows [12]:  
\[c_{p}=\sqrt{\frac{E}{\rho}} \tag{6}\]
## 6 Conclusions  
This contribution demonstrates the capability of an analytical soft sensor to determine compressible volumetric flow rates without an actual flow rate sensor and only two pressure signals in a pipe. It begins with an introduction to current advances in the computation of transient flow rates, followed by a section on the fundamentals of fluid mechanics and the Laplace techniques to solve the governing equations to obtain an analytical model. Subsequently, the analytical model is presented, explaining the assumptions made and the inverse Laplace transformation. This transformation converts the solved equation from the Laplace domain to the time domain, enabling its use for flow rate computation. The developed model is investigated in the Laplace domain to obtain a deeper understanding, especially regarding its modeling of compressibility phenomena.  
The findings of this research present a significant advancement in soft sensors for transient flow rate measurement. The soft sensor accurately computed the volumetric flow rate and was compared to numerically obtained results from the 1-D hydraulic simulation software DSHplus. Four test cases were investigated: sine waves, sums of sine waves, sawtooth waves, and a step function to validate the soft sensor. The soft sensor agreed well with each test case's numerically computed values. The soft sensor was able to determine even high-frequency flow rates of up to 1000 Hz and capture the compression phenomena visible in the step case.  
**Author Contributions:** Conceptualization, F.B.-P., T.K., E.G.P. and K.S.; Data curation, F.B.-P., T.K. and E.G.P.; Formal analysis, F.B.-P., T.K. and E.G.P.; Funding acquisition, F.B.-P. and K.S.; Investigation, F.B.-P., T.K. and E.G.P.; Methodology, F.B.-P., T.K. and E.G.P.; Project administration, F.B.-P.; Resources, F.B.-P., T.K., E.G.P. and K.S.; Software, F.B.-P. and T.K.; Supervision, F.B.-P., E.G.P. and K.S.; Validation, F.B.-P., T.K. and E.G.P.; Visualization, F.B.-P. and T.K.; Writing--original draft, F.B.-P. and T.K.; Writing--review and editing, F.B.-P., T.K., E.G.P. and K.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The IGF research project 21475 N/1 of the research association Forschungszuratorium Maschinenbau e. V. (FKM), Lyoner Strasse 18, 60528 Frankfurt am Main was supported by the budget of the Federal Ministry of Economic Affairs and Climate Action through the AiF within the scope of a program to support industrial community research and development (IGF) based on the decision of the German Bundestag.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# UAV Formation Trajectory Planning Algorithms: A Review
## 7 Future Research Focus and Direction
### Communication Networking  
The confrontation between UAVs has become more and more information-based. When the UAV formation is performing tasks, it is necessary to ensure that the UAVs can communicate and share information normally and at the same time deal with external communication interference. Although some scholars have noticed related problems, the problem of how to solve communication interference in UAV formation trajectory planning is still a difficult problem.
###### Abstract  
In the deep well drilling process in the Fukang Depression of the Eastern Junggar Basin, rock fracturing issues and low rate of penetration (ROP) have posed significant challenges to drilling efficiency. Accurate predictions of ROP prior to drilling are of considerable value in this context. Precise predictions enable on-site engineering teams to proactively identify drilling difficulties and anticipate potential complex scenarios, facilitating them in designing preventive measures in advance, such as selecting appropriate drill bits, adjusting drilling parameters, or employing specific drilling techniques to address these issues. This, in turn, enhances drilling efficiency and greatly reduces drilling risks. Traditional mechanical-specific energy drilling rate models, despite their widespread use, exhibit significant disparities with actual results when predicting ROP. These models only consider the influence of drill bits, drilling tools, and some drilling parameters on ROP, failing to adequately account for the variations caused by engineering factors and failing to capture the interrelationships between various parameters, especially when dealing with complex subsurface formations in the Fukang Depression. Random forest is a non-parametric algorithm in the field of machine learning that is suitable for analyzing and predicting ROP affected by various complex and non-linear drilling parameters. This paper establishes a Random forest model based on a dataset containing multiple variables of logging parameters and the actual ROP. The model ranks and assesses the important feature parameters of ROP to reveal their impact. Additionally, the model uses bootstrap sampling and feature random selection to construct multiple decision trees, reducing the risk of overfitting and endowing the model with a high generalization capability. Evaluation metrics indicate that the model exhibits a high prediction accuracy and performs well, significantly improving the accuracy of mechanical drilling rate predictions in the deep wells of the Fukang Depression. This model provides robust support and serves as a positive demonstration for addressing mechanical drilling rate issues in complex subsurface formations in the future.  
Fukang Sag; ROP; mechanical-specific energy; data-driven; random forest Article