esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method for Enterprise Architecture Model Slicing
## 3 Research Method  
There are two main paradigms in IS research: behavioral science and design science [27; 28]. The behavioral science paradigm seeks to develop and justify theories that explain or predict human or organizational behavior. In contrast, the design science paradigm is more problem-solving and aims to create artifacts to extend the boundaries of human or organizational capabilities. For the proposed research question, we pursue a solution contributing to reusing EA models and employ the **design science** method.  
According to [29], **design** can be both a process (set of activities) and a product (artifact). As identified by [30], four types of design **artifacts**/products (constructs, models, methods, and instantiations) can be produced by design science research in IS. Among them, a **method** defines processes and guides how to solve problems. We aim to construct a method as the design artifact to describe how to extract and reuse existing data in an EAR. Design **processes**/activities include building and evaluating artifacts. For design science research, it is crucial to find a proper balance between rigor and relevance [27]. While rigor is usually achieved by applying existing foundations and methodologies appropriately, framing research activities to address business needs could assure research relevance.  
When **building** our proposed method, we selectively applied existing foundations and methodologies to achieve rigor [27]. We examined and adapted the existing PS techniques. We analyzed and found commonalities between the two areas (e.g., program reuse and EAreuse) concerning the problem to be solved/goal, solution, criteria pattern, and algorithm premise. The commonalities support us in applying the PS method to the EA domain. We then adapted the PS method to derive our EAS based on the EAS's unique challenges. We applied pseudo-codes, case studies, and testing to achieve rigor to **validate** our method. We used a case study to provide an in-depth observational evaluation [27] in a business environment. We used pseudo-codes and executed reference implementations to provide structural and functional testing [27]. The official case study hosted by The Open Group was selectively chosen to ensure the relevance of our approach to appropriate business problems. In this case, the EAR was modeled in ArchiMate modeling language, which conforms to the TOGAF EAF. Both TOGAF and ArchiMate are the most used international standards in the EA field. More details about the case are provided in Sections 4 and 5.
# A Comprehensive Study of the Use of LoRa in the Development of Smart Cities
## 4 Results of the Analysis of the Selected Works
### Analysis of the Application Layer
#### 4.1.2 Healthcare  
IoT applications in healthcare are focused on the monitoring of physiological and environmental aspects by the user to determine his/her health status [31]. The ubiquitous characteristic of IoT allows healthcare solutions for the follow-up of patients, especially those in critical conditions such as people with mental disorders [32]. IoT in healthcare also allows the development of systems that support people with physical limitations and those who have chronic diseases [33]. In the field of health, the monitoring process is intended to be the least invasive for people. To this aim, the use of wearables is considered [34]. The data generated by the different components can be processed in Fog computing [35]. The use of Cloud computing is considered in healthcare applications for processingand storage, but in some cases, privacy issues have risen, since the information managed by the systems is very sensible.
## 6 Conclusion  
WSAN is an area still in its infancy, despite some recent progress. It is anticipated that WSANs will evolve rapidly and become pervasive in the near future, much in the same way as the Internet came to the desktop before. Lessons should be taken from Internet that WSANs have to be designed with QoS support in mind. This paper has discussed the requirements and challenges for supporting QoS in WSANs. Some interesting open research topics have been identified, though the spectrum of research in this field can be much broader. The challenges are formidable and extensive research from multiple disciplines is needed before QoS-enabled WSANs become reality.
# MRAS Using Lyapunov Theory with Sliding Modes for a Fixed-Wing MAV
## 2 Fixed-Wing MAV Mathematical Model
### Directional-Lateral Dynamic  
The lateral dynamics generate the roll motion and, simultaneously, induce a yaw motion (and vice versa); then, a natural coupling exists between the rotations about the roll and yaw axes [24]. It is considered a decoupling of the yaw and roll angles [25]. Thus, each angle can be seen as a pure motion and controlled independently. Generally, the effects of the engine thrust are also ignored [24]. In Figure 2, the yaw angle (directional dynamics) is represented, described by the following equations:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(\rho\) & \(1.05\) kg/m\({}^{3}\) \\ \(S\) & \(0.09\) m\({}^{2}\) \\ \(\bar{c}\) & \(0.14\) m \\ \(b\) & \(0.914\) m \\ \(I_{xx}\) & \(0.16\) kg\(\cdot\)m\({}^{2}\) \\ \(I_{yy}\) & \(0.17\) kg\(\cdot\)m\({}^{2}\) \\ \(I_{zz}\) & \(0.02\) kg\(\cdot\)m\({}^{2}\) \\ \(C_{m_{t}}\) & \(-50\) \\ \(C_{m_{x}}\) & \(0.25\) \\ \(C_{n_{r}}\) & \(-0.01\) \\ \(C_{n_{r}}\) & \(0.0005\) \\ \(C_{lp}\) & \(-0.15\) \\ \(C_{l_{yz}}\) & \(0.005\) \\ \(V^{\prime}\) & \(60\) km/h \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fixed-wing MAV parameters.  
Figure 1: Pitch angle.  
\[\dot{\psi} = r \tag{8}\] \[\dot{r} = \frac{\rho VSb^{2}}{4I_{zz}}C_{n}r+\frac{\rho V^{2}Sb}{2I_{zz}}C_{n_{ yz}}\dot{\sigma}_{\tau} \tag{9}\]  
where \(\psi\) represents the yaw angle and \(r\) denotes the yaw rate concerning the center of gravity of the fixed-wing MAV. \(\delta_{r}\) is the rudder deviation. \(C_{n_{r}}\) and \(C_{n_{yz}}\) are the dimensionless coefficients for the yaw angle, and the values of these constant values are in Table 1. The equations that define the lateral dynamics or roll angle are:  
\[\dot{\phi} = p \tag{10}\] \[\dot{p} = \frac{\rho VSb^{2}}{4I_{xx}}C_{l_{p}}p+\frac{\rho V^{2}Sb}{2I_{xx} }C_{l_{a_{2}}}\dot{\delta}_{a} \tag{11}\]  
where \(p\) denotes the roll rate, \(\phi\) describes the roll angle, and \(\delta_{a}\) represents the deflection of the ailerons. \(C_{l_{p}}\) and \(C_{l_{a_{2}}}\) are the dimensionless coefficients for the roll angle. The constant values are in Table 1. Figure 3 presents the roll motion variables.  
**Remark 1**.: _The mathematical uncoupled model represents the Euler angles and the angular rates because in real-time flight tests it is possible to achieve the desired attitude and altitude of the fixed-wing MAV (see [26; 27; 28]). To develop the adaptive controller presented in this work requires knowledge of such physical variables. Table 1 contains the parameters essential parameters. The Solid Works (2020) software obtained some of these values, and the aerodynamic coefficients were obtained experimentally._
# Superpixel Segmentation of Hyperspectral Images Based on Entropy and Mutual Information
## 4 Experiments and Analyses
### Comparison Experiments  
In this section, the performance of the proposed IM-CHD algorithm was compared with similar existing algorithms. At first, the IM-CHD was compared with the conventional image color visualization method [21] based on information measures (CV-CHD). Then the proposed IM-CHD was compared to six other combined algorithms. Specifically, the conventional first principal component (FPC) extraction algorithm [33] and principal component (PC) weighted false color composition (FCC) algorithm [19] were combined with the conventional SLIC algorithm [16], new LSC algorithm [34], and the CHD superpixel segmentation algorithm [19] to obtain the 6 combined algorithms, i.e., FPC based SLIC (FPC-SLIC), FPC based LSC (FPC-LSC), FPC based CHD SS (FPC-CHD), FCC based SLIC (FCC-SLIC), FCC based LSC (FCC-LSC), FCC based CHD (FCC-CHD). In the experiments, the number of iterations, Niter, was set to 10, the PC threshold was set to \(\mathrm{T}=0.99\), and the distance weight coefficient was set to \(\mu=1\). In IM-CHD method, the parameters were set as follows: \(n=1\), \(m=20\) and \(\sigma=0.05\).
## 3 Results
### Water Use Efficiency  
Due to increases in both evapotranspiration and GPP under climate change scenarios, we evaluated water use efficiency (defined as GPP per unit water uptake) to explore the environmental sustainability of _Pinus densiflora_ (Figure 7). For the case of precipitation change only (Figure 7a), water use efficiency was not considerably influenced by increases in precipitation. This finding indicated that water stress was not one of the limiting factors to prevent the growth of _Pinus densiflora_ at the study site. When _Pinus densiflora_ were exposed to an increasing water deficit caused by heat-induced drought stress, decreases in water use efficiency of 2.84, 3.07, 2.83, and 2.88% were modeled under RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5, respectively (Figure 7b). For the case of CO\({}_{2}\) change alone, water use efficiency almost linearly increased with rising atmospheric CO\({}_{2}\) concentration (Figure 7c). We found an increase in water use efficiency of 6.08 gC g\({}^{-1}\) H\({}_{2}\)O per ppm increase of CO\({}_{2}\). When all of the climate change variables were considered together, the benefits of CO\({}_{2}\)-induced improvements in water use efficiency were lowered to 2.57, 6.77, 8.70, 13.68% under RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5, respectively, compared to baseline (Figure 7d).  
Figure 6: Projected (**a**) evaporation and (**b**) transpiration over the 50-year study period for the scenario of precipitation change alone (first column), temperature change alone (second column), CO\({}_{2}\) change alone (third column), and all together (fourth column) under RCP 2.6 (blue), 4.5 (red), 6.0 (green), and 8.5 (orange) for the 2050s (from 2036 to 2065). Error bars are standard deviations. The variables at baseline, climate conditions based on the 2010s (from 1986 to 2015), are shown in dashed lines.
# Towards a Multi-Perspective Time of Flight Laser Ranging Device Based on Mirrors and Prisms
## 3 Results
### Reflecting from a Prism  
Measurements with the AR-coated prism behaved quite differently than the mirrors. Figure 10a shows the compensated measurement error for the entire suite of measurements, from 5 cm to 55 cm distance between the ranging device and the prism. Figure 10b shows the compensated measurement data for the same dataset. Measurements for all but the first configuration (blue, 5 cm) plateau at some distance.
# An Analysis of the Altitude Impact on Roots Compressor Operation for a Fuel Cell System
## 3 Results
### Altitude Impact
On the contrary, once the net power exceeds 135 kW, the optimal balance-of-plant operation is found for nearly identical levels of pressure in the cathode regardless of the working altitude. This is related to the fact that it is in these conditions where the pressure has the highest impact on the fuel cell stack performance, as can also be observed in Figure 8B. This, together with the relatively low variability of the compressor efficiency once the corrected mass flow exceeds 0.15 kg/s, makes the system optimization swing towards the sweet spot of the fuel cell stack. Figure 7: Compressor working point as a function of altitude: (**A**) pressure ratio vs. corrected mass flow; (**B**) efficiency vs. corrected mass flow.
## 3 Analysis of Factors Affecting Shear Behavior of Reinforced Concrete Beams
### Summary  
In this section, the shear behavior of strengthened beams under the influence of factors such as the amount of FRP grid reinforcement, the elastic modulus of the FRP grid, the shear span ratio of the beam, the concrete strength, and the shear reinforcement ratio is studied in detail. An increase in the FRP grid thickness or a decrease in the grid spacing will increase the reinforcement amount, leading to an increase in the shear behavior capacity of the strengthened beam and a decrease in the effective strain of the FRP grid, but the influencing effect will gradually weaken. When the reinforcement amount is constant, the combination of small thickness and small spacing is more effective in increasing the shear behavior capacity. As the elastic modulus of the FRP grid increases, the overall stiffness of the beam increases, the ductility decreases, the ultimate load increases slightly, and the effective strain of the FRP grid decreases significantly. As the shear span ratio increases, the stiffness of the beam decreases, and both the shear behavior capacity and the effective strain of the FRP grid decrease. An increase in the concrete strength and the shear reinforcement ratio can both increase the shear behavior capacity and the effective strain of the FRP grid, but the influencing effect of the latter is relatively small.  
Based on the analysis of the influencing effects of the above factors, it is clear that they have significant impacts on the shear behavior capacity of the strengthened beam and the effective strain of the FRP grid, which provides an important basis for deriving the formula for the effective strain of the FRP grid and the calculation formula for the shear behavior capacity of the beam.
## 6 Conclusions  
1. Our high-precision molybdenite weighted average Re-Os model age of 118 \(\pm\) 1.7 Ma (2\(\sigma\)) first obtained for the stockwork- and disseminated-style gold-bearing ores indicates that a discrete gold event occurred in Xiawolong deposit, ca. 4 m.y. older than that of the other gold mineralization in ca. 114 Ma in eastern Jiaodong.
2. The fine-grained granite hosting the gold-bearing ores, dated using the LA-ICP-MS zircon U-Pb method to be 118 \(\pm\) 2 to 117 \(\pm\) 2 Ma (2\(\sigma\)), is the crystallization product of granitic magma, related to the Sanfoshan batholith, in the late stage.
3. The Xiawolong gold deposit is genetically related to the Sanfoshan granitic magmatism, which has high fO\({}_{2}\) and is rich in Au at the magmatic-hydrothermal transition stage, and the change in fO\({}_{2}\) is most likely to make a significant contribution to the precipitation of Au.
4. The gold exploration in the ore districts formed an extensional tectonic regime that should not only focus on the fault-hosted Au but also on the fine-grained granite-hosted Au around the apical portions of the small granitic intrusions with high fO\({}_{2}\).
5. The fine-grained granite crystallized from the late-stage granitic magma with high fO\({}_{2}\) is also rich in Au, except for the W-Mo-Cu-Zn-U-Be-Li-Nb-Ta-Sn-Bi-elements, and the high-precision magnetometric survey could be used to find the fine-grained granite-hosted Au.  
**Author Contributions:** Conceptualization, M.W. and Z.W.; Data curation, M.W. and Z.W.; Formal analysis, M.W. and P.L.; Funding acquisition, Z.W.; Investigation, M.W., Z.W. and P.L.; Methodology, M.W.; Project administration, Z.W.; Resources, M.W., Z.W. and P.L.; Software, M.W., Z.W. and P.L.; Supervision, Z.W.; Validation, M.W.; Visualization, M.W. and Z.W.; Writing--original draft, M.W. and Z.W.; Writing--review and editing, M.W. and Z.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was financially supported by the National Key Research and Development Program of China (2022 YFC2903602) and the National Natural Science Foundation of China (Grant No. 41772061).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Acknowledgments:** The authors thank the team members at China University of Geosciences (Beijing) and Shandong Provincial No. 6 Exploration Institute of Geology and Mineral Resources for their field support.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 INS/GNSS Concepts
### Extended Kalman Filter (EKF)
#### 2.4.1 Direct Mode (State Model)
.\] (10) Also, define the following: \[\mathbf{Q}_{k-1} =\mathbb{E}[\mathbf{w}_{k-1}\mathbf{w}_{k-1}^{T}],\] \[\mathbf{R}_{k} =\mathbb{E}[\mathbf{v}_{k}\mathbf{v}_{k}^{T}],\] \[\mathbf{P}_{k-1}^{+} =\mathbb{E}[(\hat{\mathbf{x}}_{k-1}^{+}-\mathbf{x}_{k-1})(\hat{ \mathbf{x}}_{k-1}^{+}-\mathbf{x}_{k-1})^{T}],\] (11) \[\mathbf{P}_{k}^{-} =\mathbb{E}[(\hat{\mathbf{x}}_{k}^{-}-\mathbf{x}_{k})(\hat{ \mathbf{x}}_{k}^{-}-\mathbf{x}_{k})^{T}],\] \[\mathbf{P}_{k}^{+} =\mathbb{E}[(\hat{\mathbf{x}}_{k}^{+}-\mathbf{x}_{k})(\hat{ \mathbf{x}}_{k}^{+}-\mathbf{x}_{k})^{T}],\] where \(\mathbb{E}[\cdot]\) is the expected value operator, and \(\mathbf{Q}\) and \(\mathbf{R}\) represent the covariance matrices of the process noise and measurement noise, respectively, \(\mathbf{P}\) is error covariance matrix, the superscript (\(-\)) denotes the a priori estimate, and superscript (\(+\)) denotes the a posteriori estimate. Once the process and measurement models are defined by the previous equations, the DEFK procedure can be summarized as follows: 1. Prediction--Estimates the states for the next time step using the accelerations and current angular velocity combined with the states produced by the DEKF for the previous time step: \[\hat{\mathbf{x}}_{k}^{-}=\mathbf{\Phi}_{k-1}\hat{\mathbf{x}}_{k-1}^{+},\] (12) \[\mathbf{P}_{k}^{-}=\mathbf{\Phi}_{k-1}\mathbf{P}_{k-1}^{+}\mathbf{\Phi}_{k-1 }^{T}+\mathbf{Q}_{k-1}.\] (13) 2. Kalman Gain--Finds the Kalman gain \(\mathbf{K}\) based on the estimation produced in the Prediction step: \[\mathbf{K}_{k}=\mathbf{P}_{k}^{-}\mathbf{H}_{k}^{T}(\mathbf{H}_{k}\mathbf{P}_ {k}^{-}\mathbf{H}_{k}^{T}+\mathbf{R}_{k})^{-1}.\] (14) 3. Update--Uses measurements at time step \(k\) and the Kalman gain to correct the estimation produced in the Prediction step: \[\hat{\mathbf{x}}_{k}^{+}=\hat{\mathbf{x}}_{k}^{-}+\mathbf{K}_{k} (\mathbf{z}_{k}-\mathbf{H}_{k}\hat{\mathbf{x}}_{k}^{-}),\] (15) \[\mathbf{P}_{k}^{+}=(\mathbf{I}-\mathbf{K}_{k}\mathbf{H}_{k})\mathbf{P}_{k}^{ -}.\] (16)
# Detection of Quasi-Static Trapped Human Being Using Mono-Static UWB Life-Detection Radar
## 3 Experimental Results
### Experimental Results for Scanning Operating Mode  
To comprehensively compare the improvement of the measurement sensitivity and specificity with that of previous studies, two laboratory experiments (named as the type-I and type-II experiments) and two practical experiments were carried out. Two Golay-coded radars were used to verify the proposed scanning operating mode. For simplicity, the two radars are labeled as No. 1 and No. 2 radars, respectively.
# Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction
## 1 Introduction
Furthermore, maintaining a minimal complexity overhead of any proposed fast CU size prediction is a compelling challenge for HEVC intra-prediction, due to its lower overall encoding complexity compared to HEVC inter-prediction. In this context, the recent literature is composed of numerous statistical inference-based methods [6; 9], texture properties [10; 11], and machine learning-based algorithms [12; 13; 14] that focus on reducing the encoding complexity of HEVC intra-predicted frames. However, rigid decision trees and pre-trained static prediction models make these algorithms less adaptable to the dynamic changes of the video content characteristics. Therefore, it is highly beneficial to investigate implementation of friendly and flexible encoding algorithms that can effectively trade-off the encoding efficiency to the computational complexity while maintaining the user's Quality of Experience (QoE) on the visual quality of the video content intact. To this end, this paper proposes a content-adaptive CU size selection algorithm for HEVC intra-prediction utilising weighted Support Vector Machine (SVM) models. The proposed algorithm performs an online model formation and training during the encoding process to determine the CU sizes for a given content early on. The experimental results reveal that the proposed algorithm achieves a significant encoding time reduction compared to HM16.1 [15] implementation and state-of-the-art algorithms, with minimal impact on coding efficiency and perceived visual quality of the video content. The remainder of this paper is organised as follows. Section 2 provides an overview of the HEVC block partitioning, CU size selection process, and the existing work in the literature on computational complexity reduction for HEVC intra-prediction. The CU split likelihood modelling using Weighted SVMs and the proposed fast CU size selection algorithm are presented in Sections 3 and 4, respectively. Next, the experimental results are discussed in Section 5 and, finally, Section 6 concludes with potential for future improvements.
## 3 Experiment and Results
### Real-Vehicle Testing
#### 3.2.2 Statistical Testing of Real-Vehicle Data  
To ensure the robustness and validity of the dataset, a comprehensive statistical verification protocol was implemented. As summarized in Table 4, the dataset exhibits a right-skewed distribution (\(\gamma_{1}\) = +0.65), with amplitude values concentrated in the 2000-2100 range and a pronounced tail extending toward higher amplitudes (up to 2944). Despite this asymmetry, the skewness magnitude remains within the acceptable threshold (<0.8), suggesting minimal bias in central tendency estimates. The platykurtic kurtosis (\(\gamma_{2}\) = \(-\)0.42) further indicates a flatter distribution compared to normality, characterized by lighter tails and reduced sensitivity to extreme values--a critical feature for applications requiring stability in high-variance environments. Outlier contamination was well-controlled, with only \(\sim\)4.5% of data points exceeding the IQR-based bounds (1343-2879), below the 5% acceptability limit. However, the statistically significant result obtained for the Shapiro-Wilk test (\(p\) = 0.028 < 0.05) rejects the normality assumption, necessitating the use of non-parametric methods (e.g., Mann-Whitney U test) for a robust hypothesis testing. In conclusion, the dataset demonstrates high reliability for amplitude-dependent analyses, validated by three key attributes: controlled asymmetry (\(\gamma_{1}\) = +0.65) within empirical tolerance limits; stable dispersion, with 68% of values distributed within \(\mu\) \(\pm\)\(\sigma\) (1794-2345); and low outlier influence (<5%), ensuring minimal distortion in analytical outcomes.  
These findings confirm the dataset's suitability for applications such as signal processing, anomaly detection, and empirical modeling, provided that the methodologies account for its non-normal distribution through robust statistical techniques (e.g., rank-based tests or data transformations).