esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Model Construction
### Specim Generation
As shown in Figure 5b, the specimen volume consists of three parts: Part I is a cone composed of the center point \(O\) and the wall at the lower edge of the upper wall; Part II is a cone composed of the center point \(O\) and the wall at the upper edge of the lower wall; and Part III is a tetrahedron composed of the center point \(O\) and the nodes on the shell unit, which is shown by the enlarged schematic diagram of Figure 5b. The bottom of the tetrahedron is a triangle formed by the nodes on the shell unit, which forms a tetrahedron with the center point \(O\) of the specimen [24]. The volume of a cone can be calculated directly via the following formula. In the tetrahedron volume calculation, the coordinates of the four vertices are set to \(O(x_{0},y_{0},z_{0})\), \(M(x_{1},y_{1},z_{1})\), \(N(x_{2},y_{2},z_{2})\), and \(R(x_{3},y_{3},z_{3})\), and the coordinates of the tetrahedron centroid, point \(O\), can be expressed as:  
\[O(x_{0},y_{0},z_{0}) \begin{cases}x_{0}=\frac{x_{1}+x_{3}+x_{3}}{3}\\ y_{0}=\frac{y_{1}+y_{2}+y_{3}}{3}\\ z_{0}=\frac{z_{1}+z_{2}+z_{3}}{3}\end{cases}  \tag{10}\]  
Figure 5: Schematic diagram of the numerical model: (**a**) numerical model; (**b**) model volumetric strain calculation. The area of the base \(S_{\text{A}}\) in tetrahedron \(V_{\text{i}}\) is calculated using the following equation:  
\[\overset{\rightarrow}{m} =(x_{1}-x_{0},y_{1}-y_{0},z_{1}-z_{0}) \tag{11}\] \[\underline{n} =(x_{2}-x_{0},y_{2}-y_{0},z_{2}-z_{0})\] (12) \[S_{A} =\frac{1}{2}|\overset{\rightarrow}{m}\times\overset{ \rightarrow}{n}| \tag{13}\]  
The stress applied to tetrahedron \(V_{\text{i}}\) can be expressed as:  
\[\sum\sigma=\sigma_{3}\times\sum S_{A} \tag{14}\]  
where \(\sigma_{3}\) is the confining pressure and \(\sum S_{A}\) is the total area of the boundary surface of the shell membrane. The specimen volume was calculated by the following formula:  
\[V=V_{1}+V_{2}+\sum_{1}^{n}V_{i}=\frac{1}{3}\pi r^{2}h+\sum_{1}^{n}V_{i},\ i=1,2,3, \cdots,n \tag{15}\]  
wherein  
\[V_{i}=\frac{1}{6} \begin{vmatrix}x_{1}^{i}-x_{0}\ y_{1}^{i}-y_{0}\ z_{1}^{i}-z_ {0}\\ x_{2}^{i}-x_{0}\ y_{2}^{i}-y_{0}\ z_{2}^{i}-z_{0}\\ x_{3}^{i}-x_{0}\ y_{3}^{i}-y_{0}\ z_{3}^{i}-z_{0}\end{vmatrix} =\frac{1}{6} \begin{vmatrix}x_{0}\ y_{0}\ z_{0}\ 1\\ x_{1}^{i}\ y_{1}^{i}\ z_{1}^{i}\ 1\\ x_{2}^{i}\ y_{2}^{i}\ z_{2}^{i}\ 1\\ x_{3}^{i}\ y_{3}^{i}\ z_{3}^{i}\ 1\end{vmatrix}  \tag{16}\]  
where \(n\) is the number of shell unit wall triangles.
# Construction 4.0: A Systematic Review of Its Application in Developing Countries
## 6 Research Gaps to Be Filled  
The lack of research in the field of Construction 4.0 in countries such as India, Bangladesh, Indonesia, Thailand, and Vietnam poses problems and possibilities for the digitalisation of the construction sector. Although there has been notable advancement in comprehending and using Construction 4.0 principles in industrial sectors, the building industry is falling behind in fully utilising digital technology. A significant research gap exists in the insufficient comprehension (awareness and understanding) of how Construction 4.0 concepts and technology may be efficiently implemented in developing nations such as Nepal, India, Bangladesh, and Indonesia, where building activities have a vital impact on economic growth.  
Furthermore, there is a dearth of thorough research investigating the preparedness of the construction industry in these nations to adopt Construction 4.0 projects. The implementation of digital technologies in construction processes is significantly hindered by infrastructural restrictions, such as insufficient technology infrastructure, low levels of digital literacy, and a lack of worker skills. Moreover, the fragmented structure of the construction sector, which heavily depends on small and medium-sized firms (SMEs) and subcontractors, adds extra complexity to the adoption of Construction 4.0 solutions.  
Another area of study that must be addressed is the socio-economic impact of Construction 4.0 in these nations. Although the potential advantages of adopting Construction 4.0, such as enhanced productivity, efficiency, and sustainability, are increasingly acknowledged, there is a lack of empirical research about how this adoption affects job creation, labour practices, and income inequality in the construction industry. Comprehending the socio-economic consequences of Construction 4.0 is crucial for policy makers and industry stakeholders to develop comprehensive plans that foster both economic expansion and social fairness.  
In addition, there is a lack of research that specifically examines the legislative frameworks and regulatory settings that support the implementation of Construction 4.0 in these nations. Robust laws and regulations are essential for establishing a conducive atmosphere that fosters innovation, investment, and cooperation among all parties involved. Nevertheless, current rules may be deficient in terms of precision or in their capacity to tackle obstacles encountered by the construction sector, such as the establishment of common standards for the exchange of information, safeguarding data privacy, and protecting intellectual property rights pertaining to digital resources.
## 4 Performance Evaluations  
This section provides a detailed explanation of our performance evaluation environments and results. Ideally, the performance evaluation should be conducted in a mobile environment. However, this approach poses several challenges, including the extensive time required to evaluate a large dataset containing 510 sign language classes, the difficulty for sign language performers to master and perform all gestures accurately, and the variability in performance capabilities across different mobile devices. For these reasons, our dynamic sampling rate adjustment method was evaluated in a server environment rather than on a mobile platform. Although the performance evaluation was conducted in a server environment, it was performed with varying fps (frames per second) to account for differences in computing performance across different mobile environments, specifically by adjusting the number of image frames extracted during sign language utterances.  
We compared the performance of our dynamic sampling rate adjustment method against the fixed sampling rate method proposed in previous research [17], as well as a baseline with no sampling adjustment. Table 4 shows the hardware and software configuration for the experimental environments.
# Beamforming Based on a SSS Angle Estimation Algorithm for 5G NR Networks
## 1 Introduction
### Previous Works on Beam-Pair Selection Enhancement
The proposed approach can predict the optimal beam pair for any point in the current cell and can further work for a new environment with the same type of building distribution. Moreover, a BM strategy based on a conjunction of beam-orientation information retrieved from on-board sensors at the UE with RSRP measurements, was proposed in [28] to improve beam determination accuracy and achieve reduced power transmission values, allowing a considerable higher UE battery life. In this algorithm, the source of side-information is at the UE, and thus, it requires no additional signaling overhead to be used for the optimal transmit and receive beam pair predictions. In [29], a beam pair selection algorithm was presented that deploys BM procedures at both next-generation node B (gNB) and UE ends of a NR system to acquire a set of beam pair links using SSBs transmitted as a burst in the downlink (DL) direction. In this algorithm, a set of beamformed SSBs is swept over both azimuth and elevation directions to be further transmitted over a spatial-scattering channel and consequently processed over multiple receive-end beams. Afterwards, the RSRP values for each correspondent transmit-receive beam pair combination are measured, and the beam pair link with the maximum RSRP value is chosen as the most suitable one for establishing the connection. Most of the approaches referred to above consider that only few directions over wide beams are established, and then the beams are refined until we get the best beam pair. This can result in a delay in achieving good communication, or even losing communication before it is properly established. Other approaches are too complex, which can be a strong limitation for real-time systems. Therefore, to avoid both high computational complexity and taking a very long time to find the best pair of beams, we exploit the information obtained from all received SSBs, instead of selecting the beam corresponding to the best RSRP followed by the refinement process. In this way, it is possible to optimize the receiver-side beam by aligning it with the already gNB selected beam, leading to better performance results for the IBE stage.
# The Hyperloop System and Stakeholders: A Review and Future Directions
## 4 Discussion
### Future Directions
#### 4.1.8 Communication System  
Creating a communication system capable of operating with high capacity within an automated system at ultra-high speeds and a tube-based environment is very challenging. Optical fiber and wireless communication between antennas with radio waves is considered an effective option for data transmission and should be further investigated. Due to certain limitations of the GSM-R and the rapid growth of railway services, the LTE-R (Long-Term Evolution-Railway) shall be considered the next generation communication system, providing capabilities for data transmission and passenger services such as Internet access and high-quality voice or mobile video broadcasting [130]. However, due to speed limitations of 500 km/h, the current railway communication system, based on LTE, is not compatible with the expected hyperloop speeds. A leaky waveguide solution with a centralized, cooperative, Cloud Radio Access Network (C-RAN) has shown promising results for the bidirectional communication link between the train and the ground [127]. New generation 802.11 networks (WiFi 6) and 5G NR could also be considered as an option; however, network configuration and optimization are required for further evaluation [124].
# Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data
## 3 Methods  
In this section, we formally describe our data quality measures. We focus on multi-class classification tasks where each data point is associated with a class label out of \(c\) categories (\(c\geq 2\)). Our measures are created by adapting ideas from Fisher's LDA [4]. Fisher's LDA is a dimensionality reduction technique, finding a projection matrix that maximizes the between-class variance and minimizes the within-class variance at the same time. Motivated by the idea, we propose two types of data quality measures, class separability \(M_{sep}\) and in-class variability \(M_{var}\) and \(M_{var_{i}}\). For efficient handling of large-scale high-dimensional data, we also propose techniques to reduce both computation and memory requirements taking advantage of statistical methods, bootstrapping [6] and random projection [5].
## 6 Simulation Results
### Performance Evaluation Using Different Projection Orders  
We analyzed also the second case of combination by differentiating the MISO filters according to the projection orders, which is very important since the combination of APA-based filters with different projection orders is able to provide diversity to the combined scheme [1; 16]. In particular, we chose \(K_{1}=1\) for the first MISO filter, which turns out to be a multichannel NLMSfilter, and \(K_{2}=4\) for the second MISO filter. The parameter setting for the adaptive combination was the same as used for the first case. Different selections have been chosen for the step-size value, which was the same for both the individual MISO filters. We compared the theoretical values of the EMSE of (59) for the combined architecture with the measured steady-state EMSEs of (63) over 50 independent runs. We also compared the measured EMSEs of the two individual MISO filters. Results are shown in Figure 4 and prove the validity of (59), as again, the measured EMSE values approached the theoretical ones.  
Even for this case, we evaluated the convergence performance of the combined MISO scheme. The scenario was the same as in the first case, and the input signal length was \(L=24,\)000. Again, we considered an abrupt change at time instant \(n=L/2\). The combined MISO filter was composed of two individual MISO filters having different projection orders, \(K_{1}=1\) and \(K_{2}=4\), and same step sizes, \(\mu_{1}=\mu_{2}=0.01\). Performance was evaluated in terms of the measured EMSE (63), averaged over 10,000 independent runs. We compared the adaptive combined MISO filter with the corresponding individual filters. The results are depicted in Figure 5, where it is possible to notice that even in this case, the  
Figure 4: Steady-state theoretical and measured EMSE for a combined MISO filter using different projection orders.  
Figure 3: Convergence performance of a combined MISO filter using different step-size values with the related evolution of the mixing parameter \(\lambda_{0}\left[n\right]\).  
combined MISO filter was capable of exploiting the advantages of both the individual MISO filters, as also highlighted by the evolution of the mixing parameter on the current projection (i.e., \(\lambda_{0}\left[n\right]\)).
# New-Generation ASiR-V for Dose Reduction While Maintaining Image Quality in CT: A Phantom Study
###### Abstract  
Over the last few decades, the need to reduce and optimize patient medical radiation exposure has prompted the introduction of novel reconstruction algorithms in computed tomography (CT). Against this backdrop, the present study aimed to assess whether reduced radiation dose CT images reconstructed with the new-generation adaptive statistical iterative reconstruction (ASiR-V) maintain the same image quality as that of routine image reconstruction. In addition, the optimization of image quality parameters for the ASiR-V algorithm (e.g., an optimal combination of blending percentage and noise index (NI)) was investigated. An abdominal reference phantom was imaged using the routine clinical protocol (fixed noise index of 18 and 40% ASiR reconstruction). Reduced radiation dose CT scans were performed with varying NI (22, 24, and 30) and using the ASiR-V reconstruction algorithm. Quantitative and qualitative analyses of image noise, contrast, and resolution were performed against NI and reconstruction blending percentages. Our results confirm the ability of the ASiR-V algorithm to provide images of high diagnostic quality while reducing the patient dose. All the parameters were improved in ASiR-V images as compared to ASiR. Both quantitative and qualitative analyses showed that the best agreement was obtained for the images reconstructed using ASiR-V with NI24 and a high percentage of blending (70-100%). This preliminary study results show that ASiR-V allows for a significant reduction in patient dose (about 40%) while maintaining a good overall image quality when appropriate NI (i.e., 24) is used.  
X-ray computed tomography; ASiR; interventional radiology; iterative reconstruction
# Active Control of Broadband Noise Inside a Car Using a Causal Optimal Controller
###### Abstract  
This paper investigates active broadband noise control inside vehicles with a multichannel controller. The noncausal inversion of a practical nonminimum-phase secondary path is formulated, and its influence on noise-reduction performance is analyzed. Based on multiple coherence between reference signals and undesired noise, a novel formulation for identifying primary paths with correlated excitation signals is presented and a causal optimal controller is proposed. Meanwhile, the proposed controller can be used as an accurate predictor to estimate the maximal achievable noise reduction and provide a reference to improve the control systems. The robustness of the proposed algorithm is examined by varying the uncertainty of primary paths. Finally, the performance of the proposed causal optimal controller is validated using the data measured in a car. The results show that the proposed algorithm outperforms traditional algorithms and achieves a significant broadband noise reduction in time-invariant systems.  
active noise control; broadband noise; nonminimum-phase secondary path
# Reverse Engineering and Topology Optimization for Weight-Reduction of a Bell-Crank
## 2 Theory of Weight Reduction and Stiffness Matrices
### Vibration and Eigenvalue Problems  
The eigenvalues of vibration and responses to an excitation mechanism play an important role in the design process. Likewise, the efficiency and accuracy of the FE analysis that solves the eigenvalue problem plays a crucial role in the success of present topology optimization of continuum structures [21]. Consider the linearized equation of motion for a vibration excitation in a discrete undamped structure [15; 22; 23], in our case the bell crank, can be expressed in the form:  
\[M\ddot{\mathbf{q}}+\mathbf{Kq}=\mathbf{F} \tag{10}\]  
with \(\mathbf{M}\) and \(\mathbf{K}\) the global mass and stiffness matrices of the structure, \(\mathbf{F}\) is the force vector, and \(\mathbf{q}\) is the vector of unknown displacement DOF.  
The solution of Equation (10) results in an eigenvalue problem, with \(\mathbf{\omega}\) are the eigenvalues and \(\mathbf{\phi^{N}}\) the associated eigenvectors, which can be expressed as [23]:  
\[\left(-\mathbf{\omega}^{2}M^{MN}+\mathbf{K}^{MN}\right)\mathbf{\phi}^{N}=0 \tag{11}\]  
where \(\mathbf{M}^{MN}\) is the mass matrix, \(\mathbf{K}^{MN}\) is the stiffness matrix, and \(\mathbf{M}\) and \(\mathbf{N}\) are the DOFs. The eigenvalues of Equation (11) are usually solved using the Lanczos or subspace iteration method.
## 2 Materials and Methods
### Mono-Culture Experiments on Cyanobacterial Strains in Presence of Aminolay (AC)  
_Microcyts_ sp. KW (denoted as M.S.) and _Anabaena_ sp. KVF57 (denoted as A.S.) were obtained from the Korea Research Institute of Bioscience and Biotechnology (KRIBB, Daejeon, Korea). Two cyanobacterial seeds were cultured with 300 mL of a BG-11 medium (original recipe) in a 500 mL glass flask, along with 2.0% CO\({}_{2}\) (\(v/v\)) bubbling (flow rate, 150 mL/min; light intensity, 100 \(\upmu\)mol/(m\({}^{2}\) x s), and 125 rpm mixing). For MgAC-treated cultivation, M.S. and A.S. cultures (with a cell density of 5 \(\times\) 10\({}^{5}\) cells/mL) were grown in a BG-11 medium and subjected to the above conditions, but with the addition of AC in different concentrations (0, 0.01, 0.02, 0.05, 0.1, 0.5, and 1.0 g/L). The supernatant in samples was taken periodically to monitor the optical density (OD) values. The sampling was selected from the middle site along with the glass flask.
# Cross-View Attention Interaction Fusion Algorithm for Stereo Super-Resolution
## 3 Experimental Results
### Ablation Analysis  
This section studies the impact of different network parameters and network structure on stereo super-resolution performance through ablation experiments, mainly including two parts: attention stereo fusion module and enhanced cross-view interaction strategy.
# Integrating Environmental Data for Mental Health Monitoring: A Data-Driven IoT-Based Approach
## 5 Discussion
### Limitations  
Several limitations of this study must be acknowledged. Firstly, the dataset was specific to tropical cities, which may limit the generalisability of the findings to other climates and populations. Expanding the dataset to include a broader range of environmental conditions and user demographics could improve the robustness and applicability of the models.  
Secondly, while the models effectively classified stress levels, relying solely on temperature and humidity as predictors may oversimplify the complex nature of stress. Incorporating additional variables, such as air quality, noise levels, or physiological data (e.g., heart rate), could provide a more holistic understanding of emotional states.  
Lastly, labelling stress levels based on predefined thresholds for temperature and humidity may not account for individual differences in stress responses. Future work should explore personalised models that adapt to users' unique sensitivities to environmental factors, enhancing the system's relevance and accuracy.
# Large Eddy Simulation of Film Cooling with Bulk Flow Pulsation: Comparative Study of LES and RANS
## 4 Results and Discussion  
### Mesh Sensitivity Test  
The plots of centerline effectiveness of the mesh sensitivity test at 0 Hz for the blowing ratio M = 0.5, as obtained using the LES Smagorinsky-Lilly model, are shown in Figure 2a. Five different meshes were tested, and the specifications of each of these meshes are listed in Table 4. The third mesh, which contained 1.77 million cells, exhibited almost the same centerline effectiveness as the two finer grids of the fourth and the fifth meshes. Therefore, the mesh with 1.77 million cells was selected to use in the CFD simulations.  
Figure 2: Adiabatic film cooling effectiveness at the centerline at 0 Hz (steady state) in (**a**) the mesh sensitivity test based on large eddy simulation (LES) calculations; (**b**) centerline effectiveness obtained in various Reynolds-averaged Navier-Stokes simulations (RANSs).
## 3 Cost Model and Problem Description
### Problem Description  
In Section 3.1, we discuss the cost models involved in the operating cost of computing equipment in CDC, and they are host energy consumption cost \(C_{H}\), the VM migration cost \(C_{mig}\), and SLAV penalty cost \(C_{SLAV}\). In this paper, our goal is to minimize the associated operating cost \(C\) of CDC. Based on above models, we have:  
\[MIN\quad C=C_{H}^{\prime}+\sum_{k=1}^{L}(C_{k}^{mig}+C_{k}^{mig,net})+C_{ SLAV}. \tag{27}\]  
We name this problem as the Minimizing Computing Resources Cost problem (MCRC problem) in server consolidation. The constraints of MCRC problem are:  
\[\sum_{i=1}^{N}\beta_{i,j,k}=1,\forall j,\forall k, \tag{28}\]  
\[\sum_{i^{\prime}=1}^{N}\gamma_{j,i,i^{\prime},k}=1,\quad i\
eq i^{\prime}, \forall j,\forall k, \tag{29}\]  
\[\sum_{j=1}^{M}\beta_{i,j,k}\times s_{j}^{cpu}\leq s_{i}^{cpu},\forall i, \forall k, \tag{30}\]  
\[\sum_{j=1}^{M}\beta_{i,j,k}\times s_{j}^{mem}\leq s_{i}^{mem},\forall i, \forall k, \tag{31}\]  
\[\sum_{j=1}^{M}\beta_{i,j,k}\times s_{j}^{disk}\leq s_{i}^{disk},\forall i, \forall k, \tag{32}\]  
and  
\[\sum_{j=1}^{M}\beta_{i,j,k}\times s_{j}^{net}\leq s_{i}^{net},\forall i, \forall k. \tag{33}\]  
Equation (28) indicates that any VM can only run on a unique host in any given time segment. Equation (29) indicates that the destination host of any VM migration exists and is unique. In Equations (30)-(33) indicate that the CPU, memory, disk and network resources provided by each host to the VMs cannot exceed its own resource limits, respectively.  
Now we analyze the complexity of the MCRC problem. Considering a simple case that satisfies the following conditions. If the hosts in the CDC are homogeneous, the resource requirements of any VM \(v_{j}\) in any time segment \(t_{k}\) are fixed values and satisfy In Equations (30)-(33), then the VM migration cost and SLAV penalty cost are both 0, and the objective function of the MCRC problem is:  
\[MIN\quad C=C_{H}. \tag{34}\]  
Apparently, the MCRC problem in this simple case can be reduced to the Bin-Packing problem. Since Bin-Packing problem is NP-hard, the MCRC problem is also NP-hard.