esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Ignition Delay  
Ignition delay (ID) is defined as the time elapsed from the start of injection (SOI) to the start of combustion. Figure 5 shows the ignition delay comparison obtained from schlieren technique and from OH* chemiluminescence imaging. It can be seen that ID values for both techniques are almost identical, therefore, any ID value used in the current work is valid. Hereafter, the represented ID values correspond to those from the schlieren technique.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Current Work** & \begin{tabular}{c} **CMT** \\ **(2012)** \\ \end{tabular} & \begin{tabular}{c} **SNL** \\ **(2017)** \\ \end{tabular} & \begin{tabular}{c} **TU/e** \\ **(2012)** \\ \end{tabular} & \
\begin{tabular}{c} **IFPEn** \\ **(2012)** \\ \end{tabular} &
\begin{tabular}{c} **CAT** \\ **(2010)** \\ \end{tabular} \\ \hline LL [mm] & 10.0 & 10.8 & 9.60 & - & 11 & 8.67 \\ ID [ms] & 0.405 & 0.435 & 0.428 & 0.41 & 0.4 & - \\ LOL [mm] & 18.89 & 17.73 & 17.66 & 15.8 & 14.5 & 16.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of main spray parameters between database available in the ECN [20] and current work for dodecane under Spray A conditions (900 K, 1500 bar and 15% O\({}_{2}\)).  
Figure 4: Liquid length for 15% O\({}_{2}\) and 1500 bar of injection pressure.  
In Figure 6, ID for dodecane, HVO, OME\({}_{\rm x}\) and OME\({}_{1}\) have been compared against diesel. All the operation conditions have been presented with their standard deviation. The results attend to the trend found in previous studies [23; 35]. When the air temperature, injection pressure, and ambient oxygen concentration are increased, the ID values decrease. It is a common behavior for all the tested fuels.  
Furthermore, Figure 6 shows that dodecane, HVO, and OME\({}_{\rm x}\) ignite before diesel does. The three fuels have a higher cetane number than diesel (see Table 1), and the results confirm the relevance of this parameter on autoignition. Based on this, one could expect OME\({}_{\rm x}\) to ignite later than HVO and dodecane. However, its molecular structure has high oxygen content, which makes it more reactive. This explains that OME\({}_{\rm x}\) ignites earlier than HVO and dodecane, despite its lower cetane number. For OME\({}_{1}\), although its molecular structure also contains oxygen, its cetane number is much lower when compared to the other fuels tested. Thus, it is the last one to ignite as shown in Figure 6. It is important to mention that the OME\({}_{1}\) at 800 K and 15% of O\({}_{2}\) does not ignite and at 21% of O\({}_{2}\), the ignition occurs very late (around 2400 \(\upmu\)s).  
Figure 5: Comparison between ignition delay measured from schlieren and from OH\({}^{\star}\) chemiluminescence images.  
Figure 6: Comparison between ignition delay of diesel and the ignition delay for the other fuels tested.
## 3 Second Step of One-Bounce Scattering Signal Identification Algorithm  
After the first step of the identification algorithm, all scattering signals can be divided into multiple scattering signal combination sets, and each scattering signal combination set corresponds to a source scatterer, from which each scattering signal comes BS, respectively. The scattering signals of the scattering signal combination set can be used for the localization of the corresponding source scatterer. However, due to measurement error, some scattering signals may be mistakenly identified as scattering signals from the other scatterer, or some scattering signals' measurements of TOA or AOA may have large errors, these all would result in large MS localization error if these scattering signals are used for localization of scatterer and then scatterer is used as a virtual station for MS localization. In order to improve the MS localization accuracy, we have to delete these scattering signals by the second step of the identification algorithm. To simplify the analysis, we take the identification of scattering signals from one scatterer as an example of derivation in the following analysis, and this scatterer is referred to as target scatterer S.
# Research on Fleet Size of Demand Response Shuttle Bus Based on Minimum Cost Method
## 4 Numerical Simulation
### Impact of Different Fleet Sizes on Operation
#### 4.3.1 Traveling Distance  
The influence of fleet size and demand density on travelling distance is shown in Table 6. For a given fleet size, the DRC traveling distance increases as the demand increases [44]. For a given level of demand, an increase in DRC fleet size results in a decrease in the overall diversions distance as well, and a decrease in the distance traveled by vehicles. As the number of vehicles operating increases from one to two vehicles/hour, the vehicle distance traveled decreases significantly, while the trend of decreasing distance traveled becomes flatter when the fleet size is larger than two vehicles/hour.
## 4 Discussion  
Our study was undertaken to assess the safety and efficacy of intra-articular autologous MAT in treating symptomatic non-elite athletes with ankle OA who were experiencing limitations in the activities of daily living and/or had been managed by a conservative approach. Although several works have evaluated the effectiveness of intra-articular MAT in treating ankle pain [17; 29; 30], to the best of our knowledge, none have examined its efficacy in athletes. In a recent study, the effect of autologous MAT on ankle OA was examined after arthroscopic debridement; the authors reported that the procedure was safe and that it had favorable effects on patients with advanced ankle OA, even though they stressed that the procedure may be more effective in patients with moderate OA [30]. The most important finding of our work is that autologous MAT proved to be a useful approach to treat pain, inflammation and associated dysfunction in athletes with mild-to-moderate ankle OA. Clinical evaluation documented a significant or marked improvement in the clinical scores throughout the three-year follow-up period. Although the clinical measures showed that the benefits peaked at two years and the scores subsequently declined during the third year, the difference with the baseline scores was still significant. None of the patients experienced complications related to the procedure.  
According to a review [31] and a systematic review of the literature [32], treatment with autologous MAT is safe in patients with orthopedic conditions and usually has favorable clinical outcomes. The rationale of the effects of autologous MAT injection is well-documented. Its actions depend on paracrine effectors contained in the adipose tissue, which possess anti-inflammatory and pro-regenerative properties [33; 34]. These chemical mediators support cell viability and proliferation, as well as extracellular matrix deposition, by downregulating the markers of inflammation and matrix degradation in several cell types, including cartilage cells and synovioocytes [35]. In vivo, MAT has been documented to exert a chondrocyte action in a rabbit OA model by enhancing the repair of cartilage defects [36; 37]. In fact, both in vitro and in vivo studies have reported that stem cells are characterized by anti-inflammatory and regenerative properties through growth factors and cytokines [38]. Therefore, autologous MAT may provide an ideal approach to treat ankle OA in younger patients, especially athletes, even enabling surgical procedures to be postponed.  
Our data indicate that the treatment was increasingly effective up to 24 months, and that its benefits gradually declined over the final 12 months, similar to viscos supplementation [23]. A possible explanation for this decline is joint overuse by the athlete [39]. Nonetheless, the benefits accruing over the first 24 months are a clear indication of treatment success, and the subsequent worsening of clinical scores did not involve their return to baseline.  
Figure 1: AOFAS and FADI scores at 36 months follow-up.
## 6 Conclusions  
By drastically reducing the uncertainties in the choice of the resource to use for the next string of messages, we showed that we can significantly reduce the message collisions in the C-V2X sidelink Mode 4. We proposed the use of the \"lookahead\", that contains the next starting resource location in the time-frequency plane. By exchanging the lookahead information piggybacked on the periodic safety message, we can eliminate most message collisions arising from the ignorance of other UE's internal decisions. The only required cost to implement our lookahead scheme is the inclusion of the lookahead in the control part of the packet. Currently, the SCI Format 1 for Mode 4 communication is too small to accommodate the three numbers that describe the location and the size of the resource planned for the next semi-persistent burst of packets. Considering the stringent reliability requirement in future C-V2X applications such as autonomous driving, we may well weigh the possibility of including the lookahead information in the next version of the SCI to improve the SPS performance in the next generation of C-V2X communication.  
**Author Contributions:** H.K. conceived and designed the experiments; Y.J. and S.K. performed the experiments and analyzed the data; H.K. wrote the paper.  
**Funding:** This research was supported by a grant (18 CTAP-C133064-02) from Technology Advancement Research Program (TARP) funded by Ministry of Land, Infrastructure and Transport of Korean government.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Deep Attention Model for Action Recognition from Skeleton Data
## 5 Discussion  
This paper proposes a new IndRNN-based deep attention model for skeleton-based action recognition. The proposed model employs a deep network to obtain the attention instead of just one or two layers in the conventional attention models, and a deep IndRNN network for classification. Moreover, a new loss function is proposed based on the triplet loss to regulate the attention model among different action categories. The new loss function explicitly enforces the intra-class attention distance to be no larger than the inter-class attention distance. Experiments have demonstrated that the proposed model can attain more robust attention weights and better performance than the existing methods.  
The proposed method takes the simple joint coordinates as input, and advanced features such as the geometric features can be considered. In addition, spatial feature extraction techniques such as the graph convolution are worth investigating with the proposed method in the future.  
Conceptualization, Y.G., C.L., S.L., M.Y. and H.Y.; methodology, Y.G., C.L., S.L. and X.C.; software, Y.G. and C.L.; investigation, Y.G., C.L., S.L., X.C., M.Y. and H.Y.; writing, Y.G., C.L. and S.L. All authors have read and agreed to the published version of the manuscript.  
This research was partly funded by National Key R&D Program of China (2018 YFE0203900), the National Natural Science Foundation of China (No. 61901083, 62001092 and 62101512), Fundamental Research Program of Shanxi Province (20210302124031), the open project program of state key laboratory of virtual reality technology and systems, Beihang University (VRLAB2021A01) and SDU QILU Young Scholars program.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# RGB-D Camera for 3D Laser Point Cloud Hole Repair in Mine Access Shaft Roadway
## 5 Repair of Point Cloud
### Point Cloud Patching of SVD + ICP Algorithm  
The singular value decomposition (SVD) algorithm is a matrix decomposition algorithm based on the sum of squares of minimum error to solve the objective function. It can reduce the dimension of the target feature space, thereby obtaining the feature description of the target and improving the noise resistance of the algorithm. After the rough registration of the target point cloud and source point cloud based on the SVD algorithm, the ICP algorithm is used to further refine the registration of target point cloud and source point cloud to successfully repair the source point cloud. The RGB-D roadway point cloud and the 3D laser scanning point cloud were repaired by SVD + ICP algorithm; the RMSE value was 22.3248 mm and the repair results are shown in Figure 16a,b.
# MALDI-TOF/MS Analysis of Extracellular Vesicles Released by Cancer Cells
## 2 Applications
### Melanoma  
Melanoma is a frequent, aggressive, and deadly skin cancer, primarily affecting young and middle-aged people, and is mainly related to UV-radiation exposure, the number of nevi, and genetic susceptibility [32]. Its incidence and mortality have constantly increased especially in the Caucasian population. Nevertheless, immunotherapies and targeted therapies have markedly improved survival rates compared with the use of chemotherapy. The MALDI analysis of exosomes derived from melanoma cells can provide great help in deciphering the complex mechanisms underlying its onset.  
Cancer cells are known to use EVs for several functions including throwing out harmful molecules (for instance, differentiation-inducing proteins such as histone H1.0, or antitumor drugs) and bringing molecules such as mRNA, microRNA, and proteins to other cells that can be functional in that new environment. [PERSON] et al. reported evidence that EVs released from A375 melanoma cells contain both a modified form of H1.0 histone and the corresponding mRNA [33]. Moreover, the specific search for RNA binding proteins, which have a recognized role in oncogenesis, led the authors to demonstrate the presence of the transcription factor MYEF2 (a DNA-binding repressor of the gene encoding the myelin basic protein) by MALDI-TOF analysis coupled to an affinity chromatography approach. In a further study, [PERSON] et al. [34] rapidly obtained MALDI fingerprints of intact exosomes shed by cell lines derived from different melanoma stages (SBCI2, WM115, and WM239), permitting their classification through mathematical analysis. In addition, it was proven that the approach allowed the tracking of specific proteins through their detection in both cells and exosomes. Well-known biomarkers for Melanoma were also individuated in the exosome mass spectral fingerprints. The method may represent a valuable option for the early diagnosis and follow-up of the disease via liquid biopsies and could be extended to other cancer types. The MALDI profiling of intact melanoma cells and related exosomes was also recently carried out by [PERSON] et al. [35]. Melanoma cell lines (LCP and SK-Mel28) with a different metastatic tendency toward the bone were targeted and compared to obtain the relevant lipid profiles and detect potential distinctive lipid markers for each line. The study provided useful information on the lipid content of melanoma, which could be exploited to clarify the role played by exosomes in cancer and to develop new therapies. Specifically, saturated fatty acids with shorter chains were observed more in LCP cells than in SK-Mel28; sphingomyelin, lysophosphatidylcholine, and phosphatidic acid levels were higher in exosome membranes than in parental cells, and bis(monoacylglycerollycero)phosphate was proposed as a specific lipid marker of exosomes.
# Calculation of Three-dimensional Energy Product for Isotropic Nd\({}_{2}\)Fe\({}_{14}\)B Magnet
## 4 Conclusions  
In summary, we have explored magnetization and the demagnetizing field via micromagnetic simulations to clarify the energy product in a multidomain reversal mode. More specifically, the energy product calculated by the product of the mean value of the B-field and the H-field was compared with the energy product calculated faithfully to their essence while considering the three-dimensional components of magnetization and the demagnetizing field. The simulation results show the obvious degradation of the energy product by comparing the one-dimensional energy product calculated by integration because the distributions of the magnetizations and the demagnetizing fields are different in the multidomain reversal mode. However, the energy product considering three-dimensional components is 28% larger than the energy product calculated in the conventional way. Therefore, to obtain the correct energy product, the magnetic alignment, the demagnetizing factor determined by the shape of the magnet, and the reversal mode should be considered.  
Conceptualization, N.K.; methodology, N.K. and H.-S.H.; software, H.-S.H.; validation, J.P. and K.-S.L.; formal analysis, N.K.; investigation, N.K.; resources, N.K.; data curation, N.K.; writing--original draft preparation, N.K.; writing--review and editing, K.-S.L. and J.P.; visualization, N.K.; supervision, C.-J.C.; project administration, J.P.; funding acquisition, C.-J.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Future Materials Discovery Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (2016M3 DIA1027835) and by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Ministry of Trade, Industry and Energy (MOTIE) of Korea (20192010106850, Development of magnetic materials for IE4 class motor).  
Not applicable.  
Figure 5: Distribution of the demagnetization factors (-\(H_{\mathrm{d}}/M_{\mathrm{z}}\)) of each element at saturation (red) and in the remanent state (blue). The black dashed lines indicate the 10% truncated means.
# A Novel Protocol Using Captive Portals for FIDO2 Network Authentication +
## 2 Related Work
### Captive Portal Authentication  
Despite the numerous security problems of captive portals [18], there have been improvements in this field. The RFC 8910 [19] published in 2020 adds a DHCP code to improve the captive-portal detection. In wireless networks, the appearance of WPA3, a new Wi-Fi Alliance standard, adds encryption to open Wi-Fi networks with the Opportunistic Wireless Encryption (OWE) protocol [20]. This feature mitigates eavesdropping attacks on open wireless networks and, therefore, makes them more secure to use a captive portal on them.  
Captive-portal authentication systems rely on web authentication technology. Apart from the well-known passwords, there are other identity verification factors that can be used in the web. Multi-factor authentication (MFA) implies the use of more than one method to verify the identity of a user. Online systems typically use a knowledge-based factor such as a password or a PIN. Different strategies include possession-based authentication [21]. For example, authentication based on digital certificates stored on smart cards or USB keys can verify the identity of users in online accounts. Other methods, usually used in MFA, are One-Time Password (OTP) codes. These can be generated by a specific device, generated via a software application, sent via SMS or via push notifications to the user.  
In this work, we integrate captive portals with a trending technology for web authentication, based on asymmetric cryptography: FIDO2 credentials. This method can be classified as possession-based authentication, as FIDO2 credentials are stored within a device that the user owns. This can be a personal computer, a smartphone or a FIDO2 security token.
## 4 Results and Discussion
Finally, based on the discussion above, the expression of the empirical correlation is defined as:  
Figure 4.1  
It is ascertained that \(b=6.138\) from the linear fitting of \(\log(Nu)\) and \(\log(PR)\) with a coefficient determination of \(R^{2}=0.9798\), as shown in Figure 4.2 Following this, \(\log(Nu/PR^{6.138})\) and \(\log(Re_{\max})\) are linearly fitted with a slope of \(m=0.153\) and a coefficient determination of \(R^{2}=0.8158\). Next, by completing \(\log(Nu/PR^{6.138}/Re_{\max}{}^{0.153})\) and \(\log(Va)\) linear fitting with \(n=0.504\) and \(R^{2}=0.8296\), the coefficient \(c\) is determined as \(-1.137\) and \(a\) is calculated from the intercept, producing a value of \(1.021\). Figures 14-17 show the determination of all the coefficients. Figure 4.3 Determination of coefficient \(b\). Figure 4.4 Heat transfer rate variation with \(A_{r}\), \(Va=250\). As a result, within the ranges used in the experiments, i.e., \(200<Re_{\max}<1200\), \(100<Va<350\), \(1.1<PR<1.3\), and \(8.3<l/d_{h}<20\), the space-cycle average Nusselt number of oscillating-flow heat transfer in the finned heat exchanger with parallel plates, can be predicted using the following correlation:  
Figure 4.5  
To prove the self-consistency of the empirical correlation, the predicted results are compared to the corresponding experimental results, as shown in Figure 4.6 Out of the total 298 tested data points, 98.6% of the predicted points lie within adeviation of \(\pm 20\%\), and 83.9% of the predicted points lie within adeviation of \(\pm 10\%\), indicating that the comprehensive empirical correlation is able to provide acritical guidance for the design of finned heat exchangers in regenerative thermal engines. Figure 4.7 Determination of coefficient \(n\). Figure 4.8 Determination of coefficients \(c\) and \(a\). Figure 4.9 Determination of coefficient \(m\).
# Recognizing Urban Functional Zones by GF-7 Satellite Stereo Imagery and POI Data
## 2 Study Area and Data
### Data
The OSM road network data are used to divide urban functional units, downloaded on 24 March 2021.
# Machine Learning-Powered Rainfall-Based Landslide Predictions in Hong Kong--An Exploratory Study
## 3 The Pilot Study--ML-Powered Natural Terrain Landslide Susceptibility Analysis
### Algorithm Selection
\begin{table}
\begin{tabular}{p{42.7 pt} p{284.5 pt}} \hline \hline
**ML** & \multicolumn{1}{c}{**Brief Descriptions**} \\
**Algorithms** & & \\ \hline \hline \multirow{4}{*}{Decision Tree} & It is known as one of the most commonly used algorithms in studies of a similar nature. It works by recursive partitioning of data (i.e., splitting of tree) based on splitting criteria that yields the maximum information gain as assessed based on Gini impurity value. Despite this, it is a less robust algorithm and sensitive to the predictive data. It has been adopted for its computational efficiency and high interpretability to facilitate an understanding of the other two tree-based algorithms. \\ \hline \multirow{4}{*}{Random forest} & It is a tree-based ensemble learning algorithm, of which performance is greatly enhanced in terms of robustness and generalizability compared with Decision Tree. Random Forest adopts the bagging method which lowers variance and avoids overfitting of the models by injecting randomness into the model though feature selection. The collection of independent tree-structured predictors formed from different subsets of randomly selected features are combined giving the majority vote cast as the final prediction for classification problems. \\ \hline \multirow{4}{*}{XGBoost} & It is also a tree-based ensemble learning algorithm. XGBoost adopts a scalable gradient tree boosting (GTB) system in which outputs from many weak tree-based learners are ensembled in a stage-wise manner. A weak learner learns from the errors of the previous learner and improves the model at each stage under this method. While Gradient Boosting has recently become popular, it is less routinely used in landslide susceptibility studies, but it improves the accuracies of ML models according to [PERSON] et al. (2020). In fact, the performance of XGBoost is widely recognized in several ML and data mining challenges. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Brief description on the selected machine learning algorithms.