esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Scope and Method of Research  
The competencies referred to in this study are the knowledge or skills needed for utilizing BIM in FAB construction projects. First, this study presents BIM competencies required for FAB construction projects through the analysis of typical work involved in FAB construction projects, and, for that purpose, a survey was conducted targeting the participants of three FAB construction projects (Paju, Tangjeong of Asan, and Cheongju), where BIM was being implemented.  
Considering that BIM in FAB construction projects was initially implemented to meet the needs of project owners and that the experience level of project participants on FAB construction was unequal since many of them were recruited from outside for their experience of prior BIM knowledge, the entire survey respondents were categorized into two groups, which are the owner group and the designer group when the comparative analysis of their competency status was conducted. Further, the comparative analysis was performed using SPSS Statistics(r) of IBM(r) to analyze the correlation between the career characteristics and the subdivided specific competencies.  
Taking both qualitative and quantitative research approaches, this study was conducted based on the following steps:  
Qualitative research approach:  
1. Analyzed the characteristics of BIM-based FAB construction projects, including the characteristics of FAB construction projects, cases of BIM implementations, reasons for the BIM implementation, involved tasks, etc. (expert interview)
2. Analyzed BIM-related competencies and performance standards specified by the National Competency Standards (NCS)
3. Defined and finalized BIM-FAB competencies based on adopting and modifying the competencies presented by the NCS to reflect the characteristics of FAB construction projects or by adding new competency items  
Quantitative research approach:  
1. Conducted survey on BIM-FAB competencies by soliciting survey participants from domestic FAB construction projects, which implemented and utilized BIM
2. Analyzed survey results to understand the current status of BIM competencies and the FAB construction project participants' awareness of required competencies
## 2 Materials and Methods
### Pyrosequencing and Data Processing  
Bacterial DNA sequencing was performed using the Illumina MiSeq(tm) sequencing system (Illumina Inc., San Diego, CA, USA) according to the manufacturer's instructions. Raw sequences of bacterial DNA were processed using Mothur pipeline (version 1.43.0, The University of Michigan, Ann Arbor, MI, USA) [24]. The forward and reverse reads obtained from Illumina platform were assembled, and sequences with the quality score of <20 and the ambiguous nucleotides were discarded before performing downstream analysis. The resulting sequences spanning the V3-V4 region were checked for the presence of chimera using the function chimera.uchime. Taxonomic classification was performed using the \"Greengenes reference database\" for bacteria. Greengenes was used as it was reported to provide the best combination of speed and quality [25]. The sequences were clustered into operational taxonomic units (OTUs) at 97% similarity level using distance-based greedy clustering method (DGC) in Mothur. OTUs with less than 10 sequences were discarded to reduce false diversity.
## 3 Mathematical Model of Power System with the HESS in the \(i\)th Control Area
The state feedback controller of power system in \(i\)th control area is designed as follows:  
\[u_{i}=K_{i}x_{i} \tag{9}\]  
Substituting Equation (9) into Equation (7), the corresponding expression can be obtained as follows:  
\[\left\{ \begin{array}{c}\dot{x}_{i}=(A_{i}+B_{i}K_{i})x_{i}+F_{i}w_{i}\\ z_{i}=(C_{1i}+D_{12i}K_{i})x_{i}\\ v_{i}=x_{i}\end{array} \right. \tag{10}\]  
Aiming at the problem about robust stability and disturbance suppression performance of control system, the key of state feedback controller design by using H\({}_{\infty}\) control theory lies in: solving appropriate state feedback controller promising the closed-loop system is stable and the H\({}_{\infty}\) norm of the closed-loop transfer function matrix is minimum or less than a given value. Let \(T_{zw}\) be the transfer function from input \(w\) to output \(z\). The goal of H\({}_{\infty}\) design is guaranteeing the H\({}_{\infty}\) norm of \(T_{zw}\) to be less than \(\gamma\) and it can be expressed as follows:  
\[\|T_{zw}\|=\|(C_{1i}+D_{12i}K_{i})[sI-(A_{i}+B_{2i}K_{i})^{-1}B_{1i}]\| \tag{11}\]  
The following three kinds of H\({}_{\infty}\) controller design problems can be differentiated according to the selection of \(\gamma\). If the state feedback controller \(K(s)\) can guarantee the closed-loop transfer function of the control system to be internally stable and the value of \(\|T_{zw}\|_{\infty}\) is minimum. That is, \(\|T_{zw}\|_{\infty}=\gamma_{0}\) and it is called H\({}_{\infty}\) controller optimal design. If \(K_{i}\) satisfies the equation \(\|T_{zw}\|_{\infty}=\gamma>\gamma_{0}\) based on satisfying the sufficient stability condition of \((A_{i},B_{i},C_{2i})\), then this kind of problem is H\({}_{\infty}\) controller suboptimal design problem. If \(\|T_{zw}\|_{\infty}=\gamma\leq 1\) exists, then the corresponding H\({}_{\infty}\) controller standard design problem [31; 32; 33]. Then, the following linear matrix inequality is established considering Equation (11) and standard design problem of H\({}_{\infty}\) controller design:  
\[\left[ \begin{array}{ccc}A_{i}X_{i}+B_{2i}W_{i}+\left(A_{i}X_{i}+B_{2i}W_{i} \right)^{T}&B_{1i}&\left(C_{1i}X_{i}+D_{12i}W_{i}\right)^{T}\\ B_{1i}^{T}&-I&0\\ C_{1i}X_{i}+D_{12i}W_{i}&0&-{\gamma_{i}}^{2}I\end{array} \right]<0 \tag{12}\]  
If there exists a set of feasible solutions \(X_{i}^{*}\) and \(W_{i}^{*}\) can ensure the above inequality holds true, then \(u_{i}=W_{i}^{*}(X_{i}^{*})^{-1}x_{i}\) is an H\({}_{\infty}\) state feedback controller of the system and the controller coefficient matrix is \(K_{i}=W_{i}^{*}(X_{i}^{*})^{-1}\). The control coefficient matrix \(K_{i}\) can be obtained by using the MATLAB LMI toolbox, feasp() or mincx() function to solve the linear matrix inequality in Equation (12).
# An Emerging Role of Long Noncoding RNAs as Novel Biomarkers for Breast Cancer Metastasis
## 5 Potential lncRNA-Based Biomarkers
### Tincr  
The terminal differentiation-induced noncoding RNA (TINCR) is a novel epithelial lncRNA associated with tumor proliferation and metastasis [71]. TINCR is highly expressed during and required for epidermal differentiation [72]. [PERSON] et al. examined the role of TINCR in BC. Their study showed that TINCR was correlated with poor prognosis and promoted tumor growth both in vitro and in vivo. An investigation of 120 BC samples revealed that the EGFR level was higher in tissues with high TINCR expression than in low-TINCR-expression tissues, with elevated levels in advanced LNM cases. Since the way lncRNA regulates EGFR expression remained unclear, further studies were performed. The suppression of TINCR caused a decrease in p-JAK2, STAT3, and p-STAT3 relative to their levels in control cells. TINCR is considered to be present in both the nucleus and the cytoplasm of BC cells. The cytoplasm TINCR behaves like ceRNAs and upregulates the EGFR expression by acting like a molecular sponge for miR-503-5p, whereas the nuclear TINCR can regulate the gene expression by functioning as the epigenetic modulators. This investigation also revealed that the regulation of the methylation of miR-503-5p loci and its transcription can be performed by TINCR-recruiting DNA (cytosine-5)-methyltransferasease 1 (DNMT1) [73].
## 3 MIMO UWB Antenna
### Antenna Array without MEBG  
Figure 4 presents the layout of the UWB MIMO antenna created with HFSSv15 software. Two UWB monopole antenna elements were joined to form the antenna. The initial patch antenna structure's dimensions were selected to achieve a resonance frequency of 6.85 GHz, which corresponds to the UWB's center frequency. The maximum allowable distance \(d\) between the MIMO antenna elements could be derived from the far-field expressions illustrated in Equations (11) and (12).  
\[v=f\lambda \tag{11}\]  
\[d\geq\frac{2D^{2}}{\lambda} \tag{12}\]  
where \(f\) denotes the resonant frequency, \(\lambda\) is the wavelength, and \(D\) represents the antenna's diagonal. At any distance above the far field, the MC in any MIMO antenna is at its minimum value. Due to the space constraints in UWB antenna design, the arrays are placed close together. Figure 5 illustrates the S-parameter of the MIMO antenna system without the MEBG decoupling structure. This demonstrates that for various values of distance \(d\) less than \(\frac{2D^{2}}{\lambda}\) far field, the MIMO antenna has an impedance bandwidth smaller than \(-10\) dB within the UWB frequency band. At a far field distance equal to some certain fraction \(\lambda\), the S-parameter (S\({}_{11}\) and S\({}_{12}\)) without decoupling structure is presented in Figure 5. This figure shows the variability of the S-parameter with the far field distances. Table 2 supplements the figure by showing the average MC effect when the decoupling structure has not been introduced into the array. As shown in Table 3, an increase in far field distance drastically improves the MC. Of course, this indicates the effect of increasing the separation between the array elements. In a closely packed array, the MC effect is more pronounced.  
Figure 3: The structures and dimensions (mm) of the M-EBG.  
Figure 4: UWB patch MIMO antenna without MEBG decoupling structure.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Distance (_d_) (mm)** & **Legend** & **MC (S\({}_{12}\))** \\ \hline
34 & & \(-12.40\) \\ \hline
44 & & \(-13.52\) \\ \hline
54 & & \(-14.59\) \\ \hline
64 & & \(-15.14\) \\ \hline
74 & & \(-16.62\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Variation of the MC with the far field distance for the MIMO UWB antenna arrays without EBG.  
Figure 5: S–parameter of UWB MIMO antenna without EBG considering variations in \(d\).
# An Integrated Adaptive Kalman Filter for High-Speed UAVs
## 3 Verification and Analysis
### Practical Data Based Verification  
#### 3.2.1 Experimental Environment  
In order to make it more convenient to compare the performance of four filtering algorithms, we first need to obtain the real-time data of sensors in the flight process of the UAV. Then the application of the algorithm is carried out in the post-processing. The experimental data was collected by a self-developed integrated system on UAV. The system consists of a digital processing board, a GNSS receiver (OEM615), an inertial measurement unit (ADIS16488) and other sensors. The core components of the digital processing board are a digital signal processor (DSP) (SM320C6748-HIREL) and field-programmable gate array (FPGA) (XC5 VLX30T), where they are connected by an external memory interface (EMIF). In order to ensure the parallel acquisition of sensor data, OEM615 and ADIS16488 are connected to the FPGA through their respective serial interface, and a RS-422 driver is connected to the FPGA as the external communication port of the system. Meantime, the FPGA also connects other digital sensors such as barometers, thermometers and so on. UART and RS-422 were chosen as the communicational protocol, and measurement data were saved in an SD card. Then we undertook the verification using MATLAB after the data were exported to the computer.
# Multi-Scale Flame Situation Detection Based on Pixel-Level Segmentation of Visual Images
###### Abstract  
The accurate analysis of multi-scale flame development plays a crucial role in improving firefighting decisions and facilitating smart city establishment. However, flames' non-rigid nature and blurred edges present challenges in achieving accurate segmentation. Consequently, little attention is paid to extracting further flame situation information through fire segmentation. To address this issue, we propose Flame-SeaFormer, a multi-scale flame situation detection model based on the pixel-level segmentation of visual images. Flame-SeaFormer comprises three key steps. Firstly, in the context branch, squeeze-enhanced axial attention (SEA attention) is applied to squeeze fire feature maps, capturing dependencies among flame pixels while reducing the computational complexity. Secondly, the fusion block in the spatial branch integrates high-level semantic information from the contextual branch with low-level spatial details, ensuring a global representation of flame features. Lastly, the light segmentation head conducts pixel-level segmentation on the flame features. Based on the flame segmentation results, static flame parameters (flame height, width, and area) and dynamic flame parameters (change rates of flame height, width, and area) are gained, thereby enabling the real-time perception of flame evolution behavior. Experimental results on two datasets demonstrate that Flame-SeaFormer achieves the best trade-off between segmentation accuracy and speed, surpassing existing fire segmentation methods. Flame-SeaFormer enables precise flame state acquisition and evolution exploration, supporting intelligent fire protection systems in urban environments.  
Keywords:axial attention mechanism; fire image segmentation; deep learning model; multi-scale feature fusion; flame situation detection +
Footnote †: journal: Medical Image and Vision Computing
# Multi-Player Tracking for Multi-View Sports Videos with Improved K-Shortest Path Algorithm
## 4 Experiments
### Results
#### 4.3.2 Players Tracking Performance
In Figure 10 and Table 1, 0%Color+0%Num represents that none of appearance features are considered in KSP-AF, which exactly corresponds the original KSP. 100%Color+100%Num means that the appearance feature of jersey color and jersey number can be available for all the players in all frames, which can lead to a deserved result (MOTA = 1.0, GMOTA = 1.0). Our KSP-AF tracking performance does not improve a lot, or even degrades to some extent at the beginning when the available features are very limited. Until the available proportions of jersey color and jersey number reach to 60% and 40% respectively, both the MOTA and GMOTA scores begin to outperform original KSP. After several repeated experiments, we can finally report that more than 70% color plus more than 50% jersey number over the whole sequence can ensure our KSP-AF tracking performance against original KSP. **(3) Tracking experiments with more sequences (STU dataset). **  
To further demonstrate the effectiveness of our method, the STU dataset collected by us is used; it includes 10 periods of sequences. We first use the original KSP to test, and then apply KSP-AF into the same sequences (with 70% jersey color and 50% jersey number). Figure 11 illustrates the comparison of MOTA and GMOTA scores among the ten periods of sequences, respectively. Numbers in brackets represent the frames length of each period, respectively. It is clear to see that, compared with the original KSP approach, both MOTA and GMOTA scores under KSP-AF obviously increase in each period. This means that our KSP-AF method with 70% of the jersey color and 50% of the jersey number can maintain the identity more steadily than the original KSP method. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Index** & \ \begin{tabular}{c} **Available Feature** \\ **proportion** \\ \end{tabular}  & \begin{tabular}{c} **Trajectories** \\ **Count** \\ \end{tabular}  & **TP** & **FN** & **FP** & **IDSW** & **GIDSW** & **MOTA** & **GMOTA** \\ \hline
1 & 0\%Color+0\%Num(KSP) & 14 & 14,982 & 18 & 18 & 252 & 4260 & 0.83 & 0.71 \\
2 & 50\%Color+20\%Num & 11 & 14,928 & 72 & 72 & 231 & 5270 & 0.84 & 0.64 \\
3 & 50\%Color+30\%Num & 13 & 14,932 & 68 & 68 & 201 & 4881 & 0.86 & 0.67 \\
4 & 60\%Color+30\%Num & 13 & 14,933 & 67 & 67
# A Novel Perceptual Hash Algorithm for Multispectral Image Authentication
## 4 Experiments and Discussion
### Security Analysis  
As described in Section 3, the performance of the security of the hash values is dependent on the security of the cryptographic encryption algorithm. The security of the chosen RC4 in this paper is widely researched and recognized, so that the security of our algorithm is guaranteed.
# A Review of Hybrid VLC/RF Networks: Features, Applications, and Future Directions
## 3 Hybrid VLC/RF Networks in Outdoor Scenarios with Harsh Conditions
The hybrid VLC/RF networks and their deployment unlock multiple possibilities for IoT applications in various outdoor scenarios [151]. The high-precision communication capabilities of VLC technology make it an effective solution for interfacing with smart sensors that require accurate real-time data transmission. At the same time, the RF network ensures ubiquitous connectivity and uninterrupted communication. Among the applications with potential for innovative systems are smart outdoor streetlights that autonomously adjust to changing traffic patterns, security cameras that detect movements with high precision, and traffic control systems that can effectively manage traffic congestion in real time. With the integration of VLC/RF networks, exciting new possibilities that expand the realms of IoT become a reality [152]. Several authors have started to investigate this topic, and the first steps in the design of hybrid VLC/RF systems in outdoor scenarios can already be found in the scientific literature [153; 154; 155]. In [153], an optimization problem was established to obtain the maximum possible information rate for users through the DC offset and AC peak amplitude of the VLC network access points along with the electrical power supplied to LEDS and relay relays. On the other hand, in [154] the energy efficiency of a Non-Orthogonal-Multiple-Access (NOMA) scheme of hybrid VLC/RF networks with imperfect hybrid channel state information was investigated. It was shown that a non-orthogonal multiple access scheme has higher performance compared to the VLC/RF network in an outdoor scenario where LOS availability is limited. The authors of [155] evaluated a Co-NOMA-based cooperative transmission scheme for hybrid VLC/RF systems in outdoor scenarios where users with weak VLC connections can connect to the network through users with strong VLC connections using the RF network. An increase in the sum rate of the system was demonstrated, as was increased user fairness. Recently, a hybrid FSO-VLC/RF system capable of providing communication services to emergency responders for an outdoor fronthaul network based on a machine learning algorithm was experimentally demonstrated in [156]. A study of outdoor VLC for vehicle-to-vehicle security applications was conducted in [157]. The authors introduce the privacy and security fields in the physical layer of VLC to improve the vehicle-to-vehicle power distribution.
# On the Intersection of Computational Geometry Algorithms with Mobile Robot Path Planning
## 2 Path Planning
### CG-Based Single-Robot Path Planning
Additional neighbors for connection and a rewiring strategy are needed for RRT to be converted into an optimal planner. This guarantees that the resulting roadmap has a tree structure. A general paradigm for the asymptotic analysis of sampling-based planners is described in a recent paper [22]. PRM constructs a roadmap in the configuration space \(C\) by randomly sampling configurations and attempting to connect them. Let \(G=(V,E)\) be a graph where \(V\) is the set of vertices (sampled configurations) and \(E\) is the set of edges (valid paths). 1. For \(i=1\) to \(n\), sample a configuration \(q_{\text{rand}}\) from \(C\);
2. If \(q_{\text{rand}}\) is collision-free, add it to \(V\);
3. For each \(q\in V\), attempt to connect \(q\) to \(k\) nearest neighbors in \(V\) that are collision-free;
4. Add valid connections to \(E\). After constructing the roadmap, search algorithms like A* can be used to find a path from \(q_{\text{start}}\) to \(q_{\text{goal}}\). On the other hand, RRT* is an optimization over the traditional RRT to ensure asymptotic optimality. Given a tree \(T\) rooted at \(q_{\text{start}}\):  
1. Sample a random configuration \(q_{\text{rand}}\) from \(C\);
2. Find the nearest node \(q_{\text{near}}\) in \(T\) to \(q_{\text{rand}}\);
3. Attempt to connect \(q_{\text{rand}}\) to \(T\) through \(q_{\text{near}}\), resulting in \(q_{\text{new}}\);
4. If the connection is valid, look for nodes in \(T\) within a radius \(r\) of \(q_{\text{new}}\) and attempt to rewire the tree to minimize the path cost. The rewiring ensures that \(T\) not only spans the configuration space, but also converges to an optimal solution as the number of samples increases. The framework takes advantage of a connection between such planners and conventional models of random geometric graphs, which have been thoroughly studied for many years and have many of their characteristics figured out by this point. Asymptotic features, or those with a high probability as the number of samples goes to infinity, are the focus of most analyses for sampling-based algorithms. For example, recent research [23] offers bounds on the likelihood of obtaining a PRM* solution that is close to optimal after a finite number of iterations. Additionally, we point out that the Fast Marching Technique (FMT*) [24] examines a setup comparable to this one for the algorithm, albeit for a specific situation of an obstacle-free workspace.
## 4 Performance Analysis
### Outage Probability
Substituting \(e\), \(f\), and \(x_{4}\) in (23) gives  
\[P_{c-w,p}^{i}=\frac{\gamma\left(\frac{\pi^{2}L}{16-\pi^{2}},\frac{2\pi}{16-\pi ^{2}}\sqrt{\frac{1}{\rho_{w,p}^{i}\big{(}k_{s}^{2}+k_{w}^{2}\big{)}}}\sqrt{ \frac{\rho_{w,p}^{i}}{\rho}}.\right)}{\Gamma\Big{(}\frac{\pi^{2}L}{16-\pi^{2}} \Big{)}} \tag{24}\]  
Using selection diversity, the WUE processes the signal received with minimum outage, which is given by  
\[P_{w}^{i}=min\Big{\{}P_{w,p^{\prime}}^{i},\ P_{c-w,p}^{i}\Big{\}} \tag{25}\]  
To evaluate the overall performance of the system, the average outage probability of all of the WUE for a frame duration \(T\) is estimated using  
\[P_{w}=\frac{2}{M}\sum_{i=1}^{M/2}P_{w}^{i}=\frac{2}{M}\sum_{i=1}^{M/2}min\Big{\{} P_{w,p^{\prime}}^{i},\ P_{c-w,p}^{i}\Big{\}} \tag{26}\]  
The outage probability of the SUE is dependent only on the direct transmission. Therefore, the average outage probability of the SUE for a frame duration \(T\) is estimated using  
\[P_{s}=\frac{2}{M}\sum_{i=1}^{M/2}P_{s,p}^{i} \tag{27}\]  
The average throughput of the proposed system can be calculated using [14]  
\[T=\bar{R}_{s,p}^{i}(1-P_{s})+\bar{R}_{w,p}^{i}\ (1-P_{w}) \tag{28}\]
# Design and Testing of Torveastro: An Outer Space Service Robot
## 2 Problems and Requirements
In addition, due to the limited availability of energy sources in orbital stations, energy consumption must be kept to an absolute minimum. The key to selecting and building a suitable structure for a space service robot, with an emphasis on environment adaptation and motion capabilities, is to attach the aforementioned issues with appropriate design and operation characteristics. In order to select and construct a practical structure for a space service robot, with an emphasis on environment response and locomotion capabilities, the aforementioned features, as well as appropriate design and operation considerations, are needed. The suggested system will work in one of three modes, allowing for the efficient performance of a variety of activities. In addition to autonomous tasks, the robot may be capable of assisting astronauts with building, maintenance, and monitoring.