esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analyses and Results
### Results of Measurement Model Assessment
#### 5.1.2 Construct Reliability  
The construct reliability (CR) of each construct, calculated based on formula 1, should be greater than 0.7 to confirm the construct reliability. In this study, the CR was found to range from 0.803 to 0.942, as summarized in Table 3. The computed values of CR for the configuration (n = 7), composition (n = 4), extension (n = 4), integration (n = 7), modification (n = 5), and SaaS quality (n = 12) constructs were 0.892, 0.806, 0.839, 0.904, 0.929, and 0.937, respectively. Overall, the CR values for all six constructs were greater than 0.7 and subsequently all these constructs may be considered reliable.
# Computational Investigation of a Novel Box-Wing Aircraft Concept
###### Abstract
This is what led to the recent development of a novel box-wing aircraft concept, referred to as SmartLiner (SL) hereafter. Originally designed by the present third author, this novel concept (currently pending patent approval) comes as a triplane comprising backward and forward swept wings (see Figure 2). The main driver behind this concept relies on [PERSON]'s idea [6; 7] that a triplane should exhibit a lower  
Figure 1: Box-Wing Aircraft (BWA) design from Lockheed Martin (with NASA courtesy). induced drag than that of a biplane with identical lift (or wingspan) [41,42]. Compared to a typical (biplane) BWA, the present BWA/SL concept thus comprises an additional middle wing, which is connected to the other two. This novel configuration is expected to provide better aero-structural characteristics than a typical (two-wing) Prandlt plane, thanks to (i) its much stiffer closed frame structure, (ii) its further increased lifting areas, and (iii) its expected low-induced drag connected wingtips. Besides, thanks to its further increased lifting area, this BWA/SL concept could accommodate a reduced wingspan, thereby allowing a planform based on thin wings of high aspect ratio. In turn, this shall allow designing the wings using laminar flow airfoils, thereby further increasing their aerodynamic performance. This particular embodiment constitutes a new sub-class within the BWA family, along with those having a conventional wing-box, joined-wing, ring-wing, etc. Needless to say, however, such a disruptive BWA concept raises even more questions than a typical two-wing BWA, whether in terms of commercial exploitation, aerodynamic performance, structural efficiency, stability and control characteristics, etc. As a first step towards such an assessment of this BWA/SL aircraft concept, its aero-structural merits were here explored using computational means. The outcomes are summarized in the present article, which is organized as follows: Section 2 presents the computational approach that was used throughout the study, which relies on Computational Fluid Dynamics (CFD) and Fluid-Structure Interaction (FSI). In Section 3, the computational approach is first applied to a classical aircraft, namely the Common Research Model (CRM) configuration by NASA, thereby providing both a validation of the method and a reference solution against which to compare the BWA/SL aircraft concept. The latter is computationally explored and compared to its CRM counterpart in Section 4, before conclusions and perspectives are drawn in Section 5.
## 3 Materials and Methods
### Methods
#### 3.3.2 AEL Evaluation Index Model  
Analytic Hierarchy Process: The Analytic Hierarchy Process (AHP) is employed to analyze the data, systematically assigning weights to each element based on the decision problem to determine the optimal scheme, which is the one with the highest importance. The calculation formula is presented in Equation (1), where '_n_' signifies the count of hands.  
\[\omega_{Ai}=\frac{1}{n}\sum\limits_{j=1}^{n}\frac{a_{ij}}{\sum\limits_{k=1}^{n }a_{ij}}(j=1,2,\ldots,n) \tag{1}\]  
Entropy Weight Method: Entropy is employed to describe the uncertainty linked with a random phenomenon, evaluating the object's degree of randomness and disorder. Suppose there are '_i_' evaluation objects, each containing '_j_' evaluation indicators. The original matrix is \(X=(x_{ij})\) with \(x_{ij}\) denoting the value of the '_j_-th' evaluation index for the '_i_-th' evaluation object. The matrix is normalized to compute the normalized value \(p_{ij}\) for the _j_-th evaluation index of the _i_-th evaluation object as Equation (2).  
\[p_{ij}=\frac{x_{ij}^{\prime}}{\sum\limits_{i=1}^{n}x_{ij}} \tag{2}\]  
Sequentially, we calculated the information entropy \(e_{j}\) for each index as Equation (3) and derived the corresponding weight value on the calculated information entropy for each index as Equation (4).  
\[e_{j}=-\frac{1}{1n}\sum\limits_{i=1}^{n}p_{ij}\ln(p_{ij}),(0\leq e_{j}\leq 1) \tag{3}\]  
\[\omega_{Bj}=\frac{1-e_{j}}{\sum\limits_{i=1}^{n}a_{j}}(j=1,2,\ldots n) \tag{4}\]  
Lagrange Multiplier Method: The choice of the Lagrange algorithm in constructing the AEL machine learning model is driven by its adeptress at handling constrained optimization problems, making it a valuable tool for integrating various constraints and achieving the desired optimization outcomes in the context of determining comprehensive weights [30]. Utilizing the Lagrange multiplier method optimization to reconcile subjective and objective weights, the optimal combined consequences \(\omega_{CK}\) can be determined, mitigating the drawbacks of single assignments. \(\omega_{Ai}\) represents the weight subjectively determined for the _i_-th index, while \(\omega_{Bi}\) represents the weight objectively determined for the _j_-th index, which leads to the derivation of \(\omega_{CK}=[\omega_{CI},\omega_{C2},\ldots,\omega_{Cn}]\) as the ultimate optimal composite weight [30]. Equation (5) depicts the calculation formula.  
\[\omega_{CK}=\frac{\sqrt{\omega_{Ai}\omega_{Bj}}}{\sum\limits_{i=1}^{n}\sqrt{ \omega_{Ai}\omega_{Bj}}} \tag{5}\]
# Intelligent Space Object Detection Driven by Data from Space Objects
## 2 Dataset Construction and Labeling
### Dataset Construction
By adjusting this multiplier coefficient, it is achievable to obtain the simulated images with varied brightness. 3. Adjusting Positions: There are different viewpoints and lighting conditions because of the changes in the relative positions between the camera, light source, and satellite model. To capture the simulated images from all viewpoints, the position of light source relative to the camera is kept unchanged and the model remains stationary, while the camera is moved along the Earth's spherical surface centered on the satellite model. By means of latitude and longitude representation as a geographic method, sampling is conducted along the 0\({}^{\circ}\) to \(\pm\)90\({}^{\circ}\) latitude line, which leads to 210 sampling viewpoints. 4. Model Rendering Output: The output image resolution is set to 640 \(\times\) 480 pixels, and the image attribute is set to 256-bit pixels, with the contour information of the target retained effectively. After being rendered through 3D modeling software Blender3.4., these images are converted into a total of 6200 high-resolution spatial object images. Some of these images are shown in Figure 3.  
Figure 3: Based on the production method of the BUSSA-SID1.0 dataset, a total of 6900 datasets were simulated using Blender software. Herein, a portion of the satellite datasets are presented. Figure 2: Simulated imaging of satellite datasets. In addition, Figure 4 presents a small amount of real data obtained in this paper, as authorized for use by the original authors, and the simulation dataset collected by the authors of this paper from the website.
## 3 Experiments and Discussions  
### Experimental Setup  
Figure 7 shows a schematic of the fiber-based dual-detector chromatic confocal probe developed in the previous study by the authors [3]. A mode-locked femtosecond laser (C-Fiber, MenloSystems) with a repetition rate \(\
u_{\mathrm{rep}}\) of 100 MHz and a spectral range of 1480-1640 nm was employed as the light source. Single-mode step index fibers were employed for the light propagation and for the detectors together with an optical spectrum analyzer (AQ6370D, Yokogawa Electric Corp.). The laser beam from the laser source was transmitted through the single-mode step index fiber, and the output laser beam from the fiber was then collimated by a collimating lens to generate a collimated beam with a beam diameter of approximately 3.6 mm. The collimated laser beam was then made to pass through a polarized beam splitter (PBS) and a quarter wave plate (QWP). By utilizing a chromatic objective (Edmund Optics, #67-484), the collimated laser beam was focused on a plane mirror mounted on a one-axis piezoelectric (PZT) positioning stage. The combination of the PBS and the QWP made sure that the light source was effectively isolated from the laser beam reflected from the plane mirror. A capacitance displacement sensor, whose measurement axis was aligned to coincide with the laser axis, was located behind the plane mirror to measure the target position of the mirror along the Z-direction. The propagating direction of the reflected laser beam was then bent 90 degrees by the PBS and was divided into two sub-beams by a non-polarizing beam splitter (BS). By utilizing an achromatic lens L1 with a focal length \(f\) of 16.6 mm, one of the sub-beams (sub-beam1) was coupled into one end face of a single-mode step index fiber, having a core diameter of 8.2 \(\upmu\)m and a numerical aperture (NA) of 0.14. The other end face of the fiber was connected to the optical spectrum analyzer. Meanwhile, another sub-beam (sub-beam2) was coupled into one end face of another single-mode step index fiber, which was identical to that for the sub-beam1, by using another achromatic lens L2 identical to L1. It should be noted that the end face of the fiber for sub-beam2 was mounted on a precision manual stage system to give a defocus \(d\) to the end face of the fiber, with respect to the back focal plane of L2.
# A Dual-Channel End-to-End Speech Enhancement Method Using Complex Operations in the Time Domain
## 2 Algorithm Description
### CNAB Architecture  
The proposed CNABCFCN framework utilizes complex operations in all CNAB structures. As shown in the first part of Figure 1, the first layer in the CNAB structure consists of a complex LSTM layer, which we call complex shared-LSTM. The Hilbert transform (\(x_{c}^{a}[t]\)) of speech in each channel passes through this layer. Figure 2 demonstrates the rules of complex LSTM used in complex shared-LSTM. The complex operations inside complex LSTM are, explicitly,  
\[L_{r}=LSTM_{r}(Re(x_{c}^{a}[t]))-LSTM_{r}(Im(x_{c}^{a}[t])),\]  
\[L_{i}=LSTM_{i}(Re(x_{c}^{a}[t]))+LSTM_{i}(Re(x_{c}^{a}[t])),\]  
\[L_{out}=L_{r}+jL_{i}. \tag{10}\]  
The complex shared-LSTM in the CNAB includes two ordinary LSTM networks, \(LSTM_{r}\) and \(LSTM_{i}\), which process the real and imaginary parts of speech, respectively. The feature mappings of the real and imaginary parts are denoted by \(L_{r}\in\mathbb{R}^{N\times 1}\) and \(L_{i}\in\mathbb{R}^{N\times 1}\), respectively.  
The output of complex LSTM is a complex feature mapping, \(L_{out}\in\mathbb{C}^{N\times 1}\). After passing through the shared-LSTM layer, the output is fed into two complex LSTM layers, which correspond to the features of each channel. This process is called complex split-LSTM, and the complex operations are the same as in (10). The outputs of the split-LSTM layers are activated by complex linear functions and serve as the estimated beamforming filters, denoted by \(h_{c}^{a}[n]\), as shown in Figure 1. Finally, the original inputs are convolved with the estimated coefficients \(l_{c}^{a}[n]\) and summed together to produce the output of the CNAB. A detailed explanation of the CNAB will follow.
## 2 Problem Formulation
### 3 Case Studies Application  
#### 3.1 Description of Case Studies  
The hybrid electricity-natural gas energy systems shown in Figure 3 are composed by the revised IEEE 39-bus power system [35] and Belgian high-calorific 20-node gas system [3]. The IEEE 39-bus power network has 46 branches, five coal-fired units, three gas-fired units and two wind power units, where the capacity of wind power units accounts for 35% of the total installed capacity of 3903 MW. The Belgian high-calorific 20-node gas system has 24 pipelines, two gas wells, three gas storages and two compressors. The parameters of the power system are from References [35; 40] and the parameters of natural gas system are from Reference [3]. The revised parameters are shown in Tables 1 and 2 (inflow of gas storage is positive and outflow of gas storage is negative). Gas pressure limits of gas nodes are given in Table 3. Power demand and gas demand are given in Table 4. In addition, the theoretical predicted wind power output is given in Figure 4. The efficiency of P2G process is taken as 64% [6]. Wind curtailment cost is set as $100/MWh [47]. The short-term optimal dispatch for this hybrid energy system is studied to illustrate the behavior of the proposed model, the adopted algorithm and the proposed constraints handling methods in several case studies. These case studies are simulated with a low level of initial line pack (0.5 Mm\({}^{3}\)). In addition, all the case studies are implemented using MATLAB language programming.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Power Units** & \(P_{max}\)/MW & \(P_{min}\)/MW & **Ramp Up Rate/MW/h** & **Ramp Down Rate/MW/h** \\ \hline Coal-fired unit 1 & 470 & 150 & 80 & 80 \\ Coal-fired unit 2 & 470 & 135 & 80 & 80 \\ Coal-fired unit 3 & 340 & 73 & 80 & 80 \\ Coal-fired unit 4 & 300 & 60 & 50 & 50 \\ Coal-fired unit 5 & 243 & 73 & 50 & 50 \\ Gas-fired unit 1 & 260 & 0 & 260 & 260 \\ Gas-fired unit 2 & 230 & 0 & 230 & 230 \\ Gas-fired unit 3 & 220 & 0 & 220 & 220 \\ Wind power unit 1 & 750 & 0 & 750 & 750 \\ Wind power unit 2 & 620 & 0 & 620 & 620 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of power units.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c} \hline
**Node No.** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **
# Tunnel Traffic Evolution during Capacity Drop Based on High-Resolution Vehicle Trajectory Data
## 6 Conclusions and Discussion
In our research, we investigated the vehicle driving behaviors that cause the capacity drop in tunnel traffic. The vehicle movements can be coordinated via the cooperative connected and automated mobility (CCAM) technique to prevent the occurrence of capacity drop and congestion. Cooperative connected and automated mobility (CCAM) aims to achieve a safer, more efficient, and more comfortable mobility mode by coordinating vehicle motion states with sharing information [44]. CCAM mainly includes the technology of vehicular communications, automated driving, and cooperating transportation systems. Recently, numerical research proposed a series of innovative technology for CCAM, such as 5G-SDN-MPTCP-based communication systems [45], multi-tier orchestration platform architectures [46], safety-related assessment platforms [47], and a significant number of cooperative adaptive cruising control algorithms [48; 49]. There are also some evaluation and incentive systems related to the social and economic benefits of CCAM. State-of-the-art CCAM has been applied in practices in Europe, America, and Asia. For example, Europe conducted a project named H2020 5 GCroCo to define a successful path toward providing CCAM services along with cross-border scenarios and reduce the uncertainties of an actual 5G cross-border deployment [50]. A Plan for Europe is conducted to achieve 5G deployment along the main transport corridors for autonomous vehicles in European Union [51]. America has carried out a series of travel services by connected and automated vehicles, such as MILO in Texas, ARIBO in North Carolina, AAA, and Nevada [52]. Our research found that the disturbance caused by lane changes, especially when the disturbance reaches a certain degree, significantly impacts traffic stability and capacity drop. Thus, we can coordinate and control the vehicle lane change moment via the CCAM, to control the cumulative disturbance under the critical threshold. The proposed algorithm can help identify this threshold by analyzing the traffic flow and vehicle trajectory samples. Additionally, the algorithm should be slightly updated to identify more driving parameters for the CCAM system, such as headway, the gap for a lane change, acceleration/deceleration rate, etc. Conceptualization, L.Y. and Z.L. ; methodology, L.Y. ; validation, L.Y. and C.W. ; formal analysis, L.Y. ; writing--original draft preparation, L.Y. ; writing--review and editing, C.W. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The public can access the vehicle trajectory data used for this research on the website www.seutraffic.com. The authors declare no conflict of interest.
## 2 Materials and Methods
### Model Setup  
In the SW simulations, a fully spectral and instationary formulation was applied with logarithmic spectral discretization, distinguishing wind-sea and swell at an 8 s frequency [38]. The average water level (0.36 m) over the domain was initialized from tidal variations at boundaries. Wind forcing utilized ECMWF database data, and open boundary waves were modeled with ECMWF database information. Conditions, including diffraction, wave breaking, and bottom friction, were set to default. Energy transfer considered quadruplet wave-wave interactions due to varying depths. White capping for wind-dominated waves followed the MIKE21 recommendations.  
The SW results informed the HD runs. Higher-order time and space discretization (CFL 0.8) and an initial level (0.36 m) based on the mean sea level variation were employed. Coriolis forces, tidal components, and turbulence (Smagorinsky formulation, eddy viscosity 0.28) were applied. Bed resistance used [PERSON]'s number (50 [m\({}^{1/3}\)/s]) and a linearly varying wind friction factor.  
The SW and HD results were extracted for a smaller area with a finer mesh. In analyzing the hydrodynamics' effect on sediment transport, the MIKE21 HD and ST were run jointly. ST feedback recalculated currents and surface elevations. Models in the MIKE21 ST, combining wave and current effects, examined waves' influence on sediment transport.  
The sediment transport rate variation was simulated with a grain size of 0.105 mm, porosity of 0.4, and relative density of 2.645. The time step (300 s) recorded sediment transport rates and morphology every 5 min. Morphological model conditions included zero sediment flux gradient and bed level change at boundaries. Higher-order schemes were applied for space and time discretization. Bed resistance remained at the calibrated value (50 m\({}^{1/3}\)/s), updating every 5 min.
# Transmission Line Fault-Cause Identification Based on Hierarchical Multiview Feature Selection
## 2 Hierarchical Multiview Feature Selection (HMVFS)  
### Notation  
Sparsity-based multiview feature selection can be formulated as an optimization problem and denoted by loss functions and regularization items. Before introducing our formulation, the notation is stated.  
Matrices are denoted by boldface uppercase letters, and vectors are denoted by boldface lowercase letters. Given original feature matrix \(\mathbf{X}=[\mathbf{x}_{1},\mathbf{x}_{2},\dots,\mathbf{x}_{n}]^{\mathbf{T}} \in\mathbb{R}^{n\times d}\), each row of which corresponds to a fault instance, \(n\) is the total number and \(d\) denotes the size of features. \(\mathbf{X}^{(y)}\in\mathbb{R}^{n\times d^{(y)}}\) and \(\mathbf{x}_{i}^{(y)}\in\mathbb{R}^{d^{(y)}}\) denote a feature matrix and a vector in the \(v\)th view. There are two views in this paper; thus, \(\mathbf{X}=[\mathbf{X}^{(1)},\mathbf{X}^{(2)}]\). Suppose there are \(c\) categories, the label matrix will be represented as \(\mathbf{Y}=[\mathbf{y}_{1},\mathbf{y}_{2},\ldots,\mathbf{y}_{n}]^{\mathbf{T}} \in\{0,1\}^{n\times c}\). Weight matrix \(\mathbf{W}\) can be derived as \(\mathbf{W}=[\mathbf{W}^{(1)},\mathbf{W}^{(2)}]^{\mathbf{T}}=[\mathbf{w}_{1},\mathbf{w}_{2},\ldots,\mathbf{w}_{d}]^{\mathbf{T}}\in\mathbb{R}^{d\times c}\).
# The Impact of Gravity on Different Height Systems: A Case Study on Mt. Medvednica
## 6 Conclusions  
To determine different height systems based on geometric leveling measurements, there are two methods. One method involves transitioning to the system of geopotential heights and performing the adjustment in the geopotential height system, after which the desired height system is obtained by dividing the geopotential heights by the corresponding value of gravitational acceleration. The second method involves calculating the appropriate correction for the desired height system, which is then added to the measured height differences in the geometric leveling. This results in height differences in the desired height system, which can then be used for the adjustment to directly obtain the heights in the desired system. Using both methods will yield identical height values, meaning that it does not matter which method is used.  
However, for both methods of determining heights, it is essential to measure the gravitational acceleration at the points whose heights we wish to determine. This article presents an analysis of the impact of measured gravitational acceleration in different height systems, showing how the measured gravitational acceleration can influence the determined height by several centimeters. This means that for highly precise works, measuring gravity is not only recommended but necessary to maintain centimeter-level accuracy in height determination.  
Additionally, the article presents the relationship between orthometric and normal heights, specifically how these heights differ at certain elevations above sea level. While there is no difference at sea level, in mountainous regions, this difference is quite pronounced and can exceed values greater than 1 cm. These quantified results reinforce the theoretical understanding that gravity and elevation are intrinsically linked in height system realizations and provide a practical reference for future studies or operational implementations that aim to integrate gravimetric and leveling data in regions with significant elevation gradients.  
Conceptualization and general idea, M.P. and T.B.; methodology, M.P. and T.B.; software, T.B.; validation, A.B., L.B. and M.P.; formal analysis, T.B.; investigation, A.B., L.B. and T.B.; resources, M.P.; data curation, T.B.; writing--original draft preparation, T.B.; writing--review and editing, A.B., L.B. and M.P.; visualization, T.B.; supervision, M.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author due to Regulation on the Confidentiality of Defense Data of the Republic of Croatia, gravimetric data must not be disclosed without authorization.  
The authors would like to thank Geospace Ltd. for conducting gravity measurements for the purpose of this study.  
The authors declare no conflicts of interest.