esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Cruise Control for Cut-In Scenarios Based on Model Predictive Control Algorithm
## 3 The Cut-In Vehicle Safety Distance Model
### The Safe Distance Model of the Cut-In Vehicle for Curve Lane
#### 3.2.1 The Minimum Longitudinal Safety Distance between Cut-In Vehicle L and Preceding Vehicle P  
As shown in Figure 17 below, cut-in vehicle L changes lanes from the outer lane to the inner lane. The dashed-line vehicles are the positions of the two vehicles at the initial time, and the solid-line vehicles are the positions of the two vehicles at the possible collision time. The arc length from cut-in vehicle L along the outer lane to preceding vehicle P at the initial moment is \(l_{1}(0)\). The arc length from preceding vehicle P along the inner lane to cut-in vehicle L at the initial moment is \(l_{2}(0)\). The minimum safe distance between the host vehicle and the preceding vehicle at the initial moment is assumed to be \(MSD(L,P)\).  
In order to make it so that cut-in vehicle L does not collide with preceding vehicle P during the lane change, the following equation needs to be satisfied:  
\[S_{r}(t)=\left(\begin{array}{c}l_{2}(0)+\int_{0}^{t}\int_{0}^{\lambda}(a_{p }(\tau)-a_{L}(\tau))d\tau d\lambda+\\ (\varphi_{P}(0)-\varphi_{L}(0))\times t-l_{0}\end{array}\right)>0\quad\forall t \in\left[t_{c}+t_{adj},T\right] \tag{21}\]  
According to the geometric relationship, \(l_{1}(0)\) and \(l_{2}(0)\) satisfy the following formula:  
\[l_{2}(0)=\frac{l_{1}(0)}{R}(R-D) \tag{22}\]  
The minimum safe distance between cut-in vehicle L and the preceding vehicle along the inner lane during the lane change is:  
\[l_{2}(0)=Max\left[\int_{0}^{t}\int_{0}^{\lambda}\Delta a_{L-P}d\tau d\lambda +\Delta\varphi_{L-P}(0)\times t+l_{0}\right] \tag{23}\]  
According to the law of cosines, the minimum safe distance, \(MSD(L,P)\), between cut-in vehicle L and preceding vehicle P at the initial moment of lane change is:  
\[MSD(L,P)=\begin{cases}\left(R^{2}+(R-D)^{2}-2R(R-D)\cos\varphi\right)^{1/2},& l_{2}(0)>0\\ -\left(R^{2}+(R-D)^{2}-2R(R-D)\cos\varphi\right)^{1/2},&l_{2}(0)<0\end{cases} \tag{24}\]  
where \(\varphi\) is the central angle corresponding to \(l_{2}(0)\), which is obtained as:  
\[\varphi=\frac{l_{2}(0)}{R-D} \tag{25}\]  
Figure 17: The positions of cut-in vehicle L and preceding vehicle P.
# Clustering Analysis of Wind Turbine Alarm Sequences Based on Domain Knowledge-Fused Word2 vec
## 4 The Proposed Method  
This paper proposes a clustering analysis of alarm sequences using the K-means algorithm, as illustrated in Figure 5. The overall process involves preprocessing alarm data to obtain alarm sequences. Subsequently, the domain knowledge-fused Word2 vec (DK-Word2 vec) method is employed to represent alarms in vector form. Lastly, K-means clustering is applied to group alarm sequences, using WMD and WRD as distance metrics. In order to demonstrate the effectiveness of the proposed method, the labeled alarm sequences are used to evaluate the clustering performance. The proposed process allows us to cluster alarm sequences with the same fault type together, assisting on-site wind farm operators in promptly identifying the causes of alarms.
# Feature-Level Camera Style Transfer for Person Re-Identification
## 4 Using Transferred Feature for Person Re-ID
### Generative Model Training Stage  
The generative model training stage is to train a GAN to accomplish camera style transfer. The network structure is introduced in Section 3.4. The training process is illustrated in Figure 3. The dotted boxes and lines in the figure mean that during the generative model training stage, all parameters in feature extractors \(E_{I}\) and \(E_{C}\) stop updating, and gradient back-propagation is stopped before it reaches input features \(f^{I}\) and \(f^{C}\). \(E_{I}\) and \(E_{C}\) only provide input data for GAN in this stage.  
The identity feature \(f^{I}\) and camera style feature \(f^{C}\) are input into GAN in pairs through a feature reassemble algorithm. To adequately train the CST model, each identity feature should be paired with camera style features of different camera labels. \(f^{I}\) and \(f^{C}\) share the same batch size \(N\), and the number of different camera labels \(M\) is much less than \(N\). The input image batch can be properly arranged so that each \(N\) of camera style feature \(f^{C}\) in a same batch cover all \(M\) different camera labels. Then, for each of the \(N\) identity features \(f^{I}_{k}\) in a batch, one camera style feature \(f^{C}_{j}\) of each camera label is chosen to form a feature pair \((f^{I}_{k}|_{k=1}^{N},f^{C}_{j}|_{j=1}^{M})\). Thus, the batch size of feature pairs is \(N\times M\).  
The loss functions use in this stage are introduced in Section 3.2. Through this training stage, the generator \(G\) is trained to produce feature \(f^{\prime}|y_{i},c_{j}=G(f^{I}_{i}|y_{i},f^{C}_{j}|c_{j})\). The generated feature \(f^{\prime}\) maintains the same identity label \(y_{i}\) as the input source feature \(f^{I}_{i}\) while the camera label is transferred to \(c_{j}\), which is the same as target feature \(f^{C}_{j}\).
# Search results that are considered mostly satisfying after screening are marked with *
### Paper Selection  
The paper selection process is detailed in the flowchart shown in Figure 3. Initially, 730 papers were identified. Of these, 68 were duplicates, 4 were not written in English, and 25 were inaccessible, leaving 633 papers for further review. Titles and keywords were screened for relevance, resulting in the exclusion of 459 papers. This step reduced the pool to 174 papers, which were then evaluated based on their outlines and research designs, leading to the exclusion of 96 additional papers.  
Figure 2: Flowchart of network-based paper searching.  
The full text of the remaining 78 papers was then reviewed. Papers were excluded if they (a) failed to address at least one of the following five key aspects: interface design, stimulation parameters, sensory verification results, applications to health challenges, or potential side effects in human subjects; (b) were not original research studies or high-quality reviews; or (c) relied on chemical methods rather than physical approaches for interface design and stimulation parameters.  
After applying these criteria, 59 papers were excluded, resulting in 19 papers that met the review objectives. This rigorous selection process ensured that the included studies were highly relevant and reliable, forming a robust basis for meaningful conclusions.
## 3 Analysis and Results
### Effect of Wear Ring Structure Size on Sealing Performance
It can be seen that, under the same working pressure, the contact stress of the three sealing surfaces of I showed no obvious change with the wear ring width L. The value of contact force F increased with the increase in width L. Compared with L = 3.25 mm, the value of contact force F increased by 21.16% when L was 3.75 mm. Therefore, the chamfer, thickness, and width of the wear ring should be selected to be within a certain range to ensure sealing and reduce friction when the working pressure and compression ratio of the O-ring are constant.
# Relating Sentinel-1 Interferometric Coherence to Moving Events on Grasslands
## 2 Materials and Methods
### Processing  
Processing of the Sentinel-1 images was performed with the SNAP tool (version 2.0.0) provided by the European Space Agency. The SNAP Graph Processing Tool (GPT) was used together with Python 3 to automate the processing workflow. The Sentinel-1 precise orbit files were applied to the scenes. Coregistration to sub-pixel accuracy was done using the S1 TOPS Coregistration function called S1 Back Geocoding. After the coregistration, coherence images were calculated for each image pair according to the procedure outlined in Section 2.5, including the removal of the flat Earth phase term. Terrain correction was done using SRTM 3 sec data and bilinear interpolation, resulting in 4 m \(\times\) 4 m pixel spacing raster in the European Petroleum Survey Group (EPSG): 3301 coordinate system. Sentinel-1 ground range resolution ranged from 4.87 to 5.05 m within the study area. The 4 m pixel spacing was chosen for the output of the coherence raster, in order to use all of the spatial variation recorded by the sensor. Additionally, backscattering coefficients for VH and VV polarizations were calculated for each of the Sentinel-1 images using the GPT Calibration function.  
The field measurements of the six weekly inspected grasslands were averaged to an 80 m \(\times\) 250 m area surrounding the measurement transect (see Section 2.1). For all field measurement areas involved in the study, the following parameters were calculated from the images over an area defined by the spatial extent of the moving event for each acquisition date: (1) average VV and VH coherence; and (2) average backscatter values for VV and VH. Additionally, time separation in days between an event and the first acquisition in an interferometric pair was calculated. All of these parameters were recorded for three acquisitions before and three after a moving event for each geometry. Moving events' outlines were buffered inside based on the size of the averaging window. This was done in order to exclude pixels that are outside the outline from the calculations. The work flow was automated using the ArcGIS Python package _arcpy_.
## 4 Discussion  
### Different Performance of VIs in GPP Estimation  
SIF has the strongest linear correlation with flux tower GPP estimates among the three VIs. When using individual vegetation indices to construct GPP models, the performance of the SIF-based model is higher than that of the NIR\({}_{v}\)- and LAI-based models. Unlike NIRv and LAI, SIF responds to changes in canopy structure and photosynthetically active radiation absorbed by chlorophyll [69]. In addition, SIF is sensitive to vegetation with slight interannual variation in greenness and can be retrieved under some clouds and aerosols [70]. Several studies have shown that SIF measurements are superior for detecting photosynthetic activity in evergreen forests [34; 71; 72]. [PERSON] et al. [73] found that SIF is more accurate than NIR\({}_{v}\) and EVI in monitoring the phenology of drylands since it is not contaminated by soil background. Another study found that SIF is superior to traditional VIs and the new vegetation index NIR\({}_{v}\) in estimating large-scale GPP [74]. However, the  
Figure 6: Importance of variables for optimal performance in random forest models. (**a,c,e**) represent PFT-specific models. (**b,d,f**) represent universal models. Rows one through three represent the models constructed based on SIF, NIR\({}_{v}\), and LAI, respectively. Variable importance scores were normalized using the minimum–maximum method. The explanatory variables are marked with ”px” at the end of the abbreviation, indicating the climate factors for the previous x months (e.g., Tminp2 represents the minimum air temperature two months ago).  
shortcomings of SIF are apparent. There is no satellite designed explicitly for SIF, and SIF retrievals have a relatively short time span and coarse spatial resolution, limiting long-term global GPP estimation and prediction [75].  
Nevertheless, when combined with vegetation's biophysical properties and environmental and climate factors, the NIR\({}_{\rm v}\)-based models outperformed SIF-based and LAI-based models. NIR\({}_{\rm v}\) is a spectral reflectance-based vegetation index, and LAI is a vegetation characteristic parameter with actual physical significance, both of which can capture structural changes in vegetation canopies. LAI and leaf level CO\({}_{2}\) are almost equally crucial for GPP according to [PERSON] et al. [76]. SLA is a critical trait that reflects the photosynthetic capacity of plants, which is related to leaf-scale CO\({}_{2}\) uptake and can manifest the growth and reproduction strategy under certain environmental conditions. With the combined influence of CO\({}_{2}\) and SLA, NIR\({}_{\rm v}\) can provide more universal predictions of GPP than LAI. Furthermore, NIR\({}_{\rm v}\) captures the fraction of sunlit and shaded leaves in vegetation canopies, while LAI ignores the difference in photosynthetic capacity between these two types of leaves [77; 78]. Our findings suggest that NIR\({}_{\rm v}\) can better predict GPP when combined with other predictors and compensate for the spatial-temporal limitations of SIF measurements.
## 1 Introduction
### Objectives and Main Contributions  
This article proposes the integration of VR into STEM subjects, to enhance students' motivation and performance. The primary focus is on creating an educational metaverse, providing students with a virtual space to design and propose robotic systems for improving industrial processes. The ultimate goal is to familiarize students with the application of current technology in their degree subjects and future professional endeavors, enabling them to present their proposals in a more natural and interactive manner.  
The article extensively describes the educational metaverse's design and introduces a work methodology that leverages version control repositories, facilitating the development of proposals across various STEM disciplines.  
To validate the effectiveness of the educational metaverse for presentations, the article presents results from students enrolled in the \"Projects RII 1: Organization and Scenarios\" course at the Escuela Politecnica Superior de Alocy of the Technical University of Valencia (Spain) during the academic year 2022/2023.  
The key contributions of this work are as follows:  
* Motivating students in STEM courses, through project-based learning, employing VR tools tailored to their specific subjects.
* Designing an educational metaverse prototype, granting students individual spaces to develop their projects and present them interactively.
* Proposing a work methodology centered around version control repositories, enabling STEM teachers to seamlessly integrate the educational metaverse innovation into their classes.  
The sections of this article are as follows. First, the proposed metaverse and methodology are thoroughly described in Section 2. Subsequently, Section 3 provides insights into the usability of the interface and covers other relevant aspects. Towards the end of the article, Section 3.2 offers a comprehensive discussion, while Section 4 presents concluding remarks on the findings.
## 3 Fuzzy Logic in WSNs  
Since its introduction by [PERSON] in the 1960s, Fuzzy Logic (FL) has gained much attention due to its ease of implementation and simplicity [27]. The applications of FL have grown significantly.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Mobility** & **Localization** & **Localization** & **Area** & **Anchor** & **Movement** & **Movement** & **Area** & **Accuracy** \\
**Protocol** & **Method** & **Processing** & **Type** & **Path** & **Constraints** & **Covered** & **Accuracy** \\ \hline SCAN & Range-free & Distributed & 2-D & Mobile & Static & No & Yes & Low \\ Hilbert & Range-free & Distributed & 2-D & Mobile & Static & No & Yes & Low \\ CIRCLES & Range-free & Distributed & 2-D & Mobile & Static & No & No & Low \\ MAALRH & Range-free & Distributed & 2-D & Mobile & Static & No & No & Low \\ Z-Curve & Range-free & Distributed & 2-D & Mobile & Static & No & Yes & High \\ LMAT & Range-free & Distributed & 2-D & Mobile & Static & No & Yes & High \\ H-Curves & Range-free & Distributed & 2-D & Mobile & Static & No & Yes & High \\ DPMB & Range-free & Distributed & 2-D & Mobile & Dynamic & No & No & Weak \\ NLA\_MB & Range-free & Distributed & 2-D & Mobile & Dynamic & Yes & Differs & High \\ \hline \hline \end{tabular}
\end{table}
Table 1: General Comparison of Mobility-Assisted Movement schemes in WSNs.  
FL has been the main tool in many protocols and models in WSNs. For example, FL was used to enhance the networks lifetime as in [28; 29; 30]. Another usage of FL in WSNs was introduced by [PERSON] et al., in [31], to propose a novel scheme, the Fuzzy Logic Cluster Formation Protocol (FLCFP), that uses Fuzzy Logic Inference System (FIS) in the cluster formation process. The work succeeded in choosing a balanced cluster head (CH) in each cluster and provides a competitive lifetime in comparison to other cluster formation models such as LEACH. Other similar works using FL are presented in [32; 33; 34; 35]. A novel model of implementing FL to dynamically control the traffic lights using WSNs is proposed in [36]. It employs multiple fuzzy-logic controllers for a real-time traffic monitoring using WSNs. Moreover, FL was used in several data routing protocols in WSNs including those in [37; 38; 39; 40]. However, to our knowledge, no study of using FL in path planning for mobility-assisted localization in WSNs has been proposed. Therefore, we introduce our FLPPL model.  
Simply, the concept behind it is to smooth the classical Boolean logic of True (1 or yes) and False (0 or No) to a partial value located between these values coming in a term of the degree of membership [31]. Such degree is determined based on multiple inputs, a decision function and one or more output functions. The set of inputs will be tested and evaluated together according to the applied set of rules. Rules works as a medium for the logical relationship between the inputs and the outputs. Thus, the results of the rules are combined and defuzzified in order to produce the degree of the final output [27]. Figure 1 depicts the generic structure of the Fuzzy-logic system as illustrated in [27].
## 3 Simulation Analysis
### Solid \(r_{0}\) with Variable \(\beta\)  
In this part, the PDF of the CE is discussed in the condition of variable \(\beta\) when \(D\) and \(r_{0}\) stay the same. The parameters are all listed in Table 4.  
Figure 5a-d show the situation whereby the PDF of the CE changes with different \(\beta\) when \(r_{0}\) equals to 0.5 for the same receiving device. The result contains the case that \(\beta\) equals to 11/3, which means the Kolmogorov turbulence model is used. The curves in these pictures show that the distribution of the CE differs from each other. Figure 6 gives a clearer tendency of the changing PDF.  
When the power law \(\beta\) has a slight change, the average value of the CE does not change much, but the distribution of the CE varies. This means that the variance of the wavefronts is different for each case, which will lead to different power fluctuations of communication terminals. In the free-space optical communication link though the atmosphere, non-Kolmogorov turbulence is improved by widely exiting, where \(\beta\) is not equal to 11/3 in all conditions. Kolmogorov theory could not describe turbulence at all conditions. It is not accurate to use one distribution to describe the fluctuation of the CE in different turbulence conditions. The statistical model of the coupling efficiency based on the non-Kolmogorov model is more accurate and necessary to evaluate the performance of the receiving device.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Value** \\ \hline Wavelength (\(\lambda\)) & 1550 nm \\ Pupil aperture (\(D\)) & 1 m \\ Focal length of coupling system (\(F\)) & 4.17 m \\ MFD of single-mode fiber (\(d\)) & 5.5 μm \\ Atmospheric coherence length (\(r_{0}\)) & 0.5 m \\ \(\beta\) & 2.35–3.76 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Analysis parameters at condition of solid \(r_{0}\) with variable \(\beta\).  
The simulation in this section indicates that the statistical model built for the CE has the robustness for both weak and strong turbulence. With the non-Kolmogorov turbulence applied, it allows the power law \(\beta\) to be alterable so that the theoretical distribution of the CE could change, which leads to an expendable application.
# Effect of Nylon Fiber Addition on the Performance of Recycled Aggregate Concrete
###### Abstract  
The adhered mortars in recycled aggregates (RA) may lower the performance of the concrete, by for instance reducing its strength and durability, and by cracking. In the present study, the effect of nylon fiber (NF) on the permeability as well as on the mechanical properties of concrete incorporating 100% RA was experimentally investigated. Concrete was produced by adding 0, 0.6 and 1.2 kg/m\({}^{3}\) of NF and then cured in water for a predetermined period. Measurements of compressive and split tensile strengths, ultrasonic pulse velocity and total charge passed through concrete were carried out, and the corresponding test results were compared to those of concrete incorporating crushed stone aggregate (CA). In addition, the microstructures of 28-day concretes were examined by using the FE-SEM technique. The test results indicated that recycled coarse aggregate concrete (RAC) showed a lower performance than crushed stone aggregate concrete (CAC) because of the adhered mortars in RA. However, it was obvious that the addition of NF in RAC mixes was much more effective in enhancing the performance of the concretes due to the crack bridging effect from NF. In particular, a high content of NF (1.2 kg/m\({}^{3}\)) led to a beneficial effect on concrete properties compared to a low content of NF (0.6 kg/m\({}^{3}\)) with respect to mechanical properties and permeability, especially for RAC mixes.  
recycled coarse aggregate concrete; nylon fiber; mechanical properties; permeability; microstructure +
Footnote †: journal: Applied Sciences
# Expansion Lemma--Variations and Applications to Polynomial-Time Preprocessing
## 3 Crown Decomposition and Applications
### Vertex Cover  
We now describe a summary of a kernel of \(3k\) vertices for Vertex Cover (defined in Box 1) provided by Chor et al. [5] where the earliest application of Lemma 1 can be seen. The isolated vertices of the input of graph \(G\) can be safely removed. Then, they invoke the algorithm in Lemma 1, which either gives a matching of size \(k+1\) in \(G\) or a crown decomposition of \(G\) if \(G\) has more than \(3k\) vertices. In the former case, it can be concluded that the input instance is a non-instance as any vertex cover of \(G\) needs at least \(k+1\) vertices to cover the edges of the matching.  
**Box 1.** Vertex Cover Problem.  
Vertex Cover (VC)  
**Input:**_An undirected graph \(G=(V,E)\) and an integer \(k\)._  
**Parameter:**_k._  
**Question:**_Is there a set \(S\subseteq V(G)\) of, at most, \(k\) vertices for every \(uv\in E(G)\), \(u\in S\), or \(v\in S\)?_  
Hence, they assume that the algorithm returns a crown decomposition of \(G\) with crown \(C\) and head \(H\). Let \(M\) be the matching of \(H\) into \(C\) that saturates \(H\). Any vertex cover that covers the edges in \(G[H\cup C]\) has to contain at least \(|M|=|H|\) many vertices. Since \(M\) saturates \(H\) and \(C\) is an independent set, the set \(H\) covers all of the edges in \(G[H\cup C]\). Hence, we can safely reduce the problem instance to \((G\setminus(H\cup C),k-|H|)\). Hence, we conclude that we can reduce the vertex cover instance as long as \(|V(G)|>3k\). Hence, vertex cover has a kernel of \(3k\) vertices. This bound has been improved to a kernel with \(2k-\log k\) vertices by Lampis et al. [22] using linear programming techniques. See Li and Zhu [23] for another \(2k\)-vertex kernel for Vertex Cover.